CONSTRUCTION COST ESTIMATING INCORPORATING BUILDING ... · CONSTRUCTION COST ESTIMATING...

CONSTRUCTION COST ESTIMATING INCORPORATING BUILDING

INFORMATION MODELLING (BIM) IN THE MALAYSIAN CONSTRUCTION

INDUSTRY

Noor Akmal Adillah Ismail B.Sc Quantity Surveying (Hons) (UM), M.Sc Construction Management (UTM)

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

Science and Engineering Faculty

Queensland University of Technology

2017

Construction Cost Estimating Incorporating Building Information Modelling (BIM) In The Malaysian Construction Industry

© 2017 Noor Akmal Adillah Ismail Page i

Keywords

Building Information Modelling (BIM), Cost estimating, Malaysian construction industry,

quantity surveying, Quantity Surveyor


© 2017 Noor Akmal Adillah Ismail Page ii

Abstract

Construction cost estimating is one of the main activities performed by Quantity Surveyors.

Various factors influence the reliability of a cost estimate, but Quantity Surveyors’

understanding and knowledge of project information provide the most vital input towards the

accuracy of estimates. While Building Information Modelling (BIM) has been acknowledged

as contributing to more efficient and effective construction industry working practices; it

also provides a significant advantage in the preparation of cost estimates. The adoption of

BIM technology and processes is currently accelerating worldwide. However, compared to

other countries, Malaysia has made slower progress, although it has urged Quantity

Surveyors to take appropriate actions in evaluating the influence of BIM in the country,

especially for their practices. With the recent national agenda of Construction Industry

Transformation Programme (CITP) (2016-2020), overcoming the limitation of BIM usage

becomes a priority to be cultivated by all stakeholders in increasing the productivity of the

construction industry. Therefore, the aim of this research is to build a framework to guide the

Quantity Surveyors in Malaysia to use BIM to achieve more dependable results in their cost

estimating practices. A questionnaire survey was used to explore these and other factors

leading towards BIM technology adoption. Structural Equation Modelling (SEM) was

applied to examine causal effect relationships between the elements. This study then

employed further focus group interviews to better understand the surveyed outcomes. Most

of the surveyed respondents observed or forecast better understanding of input information

through the use of BIM capabilities, including data visualisation, a reliable database and data

coordination. Also, they perceived the benefits of accomplishing their tasks more quickly

through using those BIM capabilities. The focus group discussions subsequently validated

those factors’ interconnections and delineated guidelines for incorporating cost estimating

within BIM. Amongst the key findings highlighted are the strategic actions underlining the

process and the functions of people to act accordingly towards the BIM technology

capabilities in improving the reliability of cost estimates. The overall analysis contributes to

the establishment of a framework of BIM adoption in cost estimating practices in Malaysia.

It produces a strategy to promote the BIM innovation endeavour amongst Quantity

Surveyors.


© 2017 Noor Akmal Adillah Ismail Page iii

Table of Contents

Keywords .......................................................................................................................................... i Abstract ............................................................................................................................................ ii Table of Contents............................................................................................................................. iii List of Figures ................................................................................................................................. vi List of Tables ................................................................................................................................. viii List of Abbreviations ........................................................................................................................ x Statement of Original Authorship .................................................................................................... xii Acknowledgements........................................................................................................................ xiii CHAPTER 1: INTRODUCTION .................................................................................................. 1 1.1 Research Background............................................................................................................. 1 1.2 Statement of Research Problem .............................................................................................. 3 1.3 Research Questions ................................................................................................................ 4 1.4 Research Aim and Objectives ................................................................................................. 5 1.5 Research Significance ............................................................................................................ 6 1.6 Overview of Research Methodology ....................................................................................... 7 1.7 Research Scope and Limitations ............................................................................................. 9 1.8 Thesis Structure ..................................................................................................................... 9 1.9 Chapter Summary ................................................................................................................ 11 CHAPTER 2: LITERATURE REVIEW .................................................................................... 12 2.1 Building Information Modelling (BIM) in the Construction Industry ..................................... 13

2.1.1 BIM Definitions ........................................................................................................ 16 2.1.2 BIM Characteristics and Benefits ............................................................................... 17

2.2 The Reliability of Cost Estimates ......................................................................................... 19 2.2.1 Cost Estimating Practice by the Quantity Surveyors ................................................... 21 2.2.2 Factors Influencing the Reliability in Cost Estimates .................................................. 24 2.2.3 BIM Influence on Cost Estimating Factors ................................................................. 28

2.3 Cost Estimating Incorporating BIM ...................................................................................... 29 2.3.1 BIM and Quantity Surveying Practice ........................................................................ 32 2.3.2 BIM Impact on the Reliability of Cost Estimates ........................................................ 35 2.3.3 The Relationships between BIM Improved Information and the Estimator .................. 35 2.3.3.1 Data Visualisation, Reliable Database and Data Coordination .................................... 36 2.3.3.2 Input Information, Understanding and Knowledge ..................................................... 41 2.3.4 The Challenges of Using BIM in the Cost Estimating Process .................................... 42

2.4 The Adoption of BIM Technology........................................................................................ 43 2.4.1 Overview of Technology Adoption Theories .............................................................. 43 2.4.2 Perceived Benefits towards BIM Adoption ................................................................ 46 2.4.3 BIM Models and Frameworks ................................................................................... 47 2.4.4 BIM Maturity Models................................................................................................ 48

2.5 The Implementation of BIM in Malaysia .............................................................................. 50 2.5.1 Timeline Analysis of BIM Development .................................................................... 51 2.5.2 Construction Industry Transformation Programme (CITP) 2016-2020 ........................ 53 2.5.3 BIM Research for Malaysian Construction Industry Context ...................................... 56

2.6 Conceptual Framework and Hypotheses ............................................................................... 60


© 2017 Noor Akmal Adillah Ismail Page iv

2.7 Chapter Summary ................................................................................................................ 63 CHAPTER 3: RESEARCH DESIGN AND METHODOLOGY ................................................ 64 3.1 Research Approach .............................................................................................................. 65 3.2 Research Process.................................................................................................................. 69

3.2.1 Literature Review ...................................................................................................... 70 3.2.2 Expert Interviews ...................................................................................................... 71 3.2.3 Survey Questionnaire ................................................................................................ 77 3.2.4 Ethical Consideration ................................................................................................ 83 3.2.5 Data Analysis Approach ............................................................................................ 84 3.2.6 Framework Validation ............................................................................................... 86

3.3 Chapter Summary ................................................................................................................ 88 CHAPTER 4: ANALYSIS ON CURRENT BIM PRACTICE BY MALAYSIAN QUANTITY SURVEYORS ............................................................................................................................... 89 4.1 Preliminary Data Analysis .................................................................................................... 90

4.1.1 Response Rate ........................................................................................................... 90 4.1.2 Coding and Data Entry .............................................................................................. 91 4.1.3 Screening and Cleaning Data for Missing Values ....................................................... 91 4.1.4 Treating Outliers ....................................................................................................... 92 4.1.5 Normality of Distribution .......................................................................................... 92 4.1.6 Validity and Reliability ............................................................................................. 92

4.2 Respondents’ Profile ............................................................................................................ 93 4.2.1 Years of Experience in Construction Cost Estimating ................................................. 94 4.2.2 Professional Background ........................................................................................... 94 4.2.3 Current Roles in Organisations .................................................................................. 95 4.2.4 Nature of Current Organisations’ Business ................................................................. 96

4.3 Respondents’ BIM Adoption in the Malaysian Construction Industry .................................... 96 4.3.1 Awareness of BIM Usage .......................................................................................... 97 4.3.2 BIM Software Usage and Its Application in Project Stages ......................................... 97 4.3.3 Knowledge in BIM ...................................................................................................100 4.3.4 The Importance of BIM in Current Roles ..................................................................101 4.3.5 Planning of Future BIM Usage .................................................................................103 4.3.6 Overall BIM Awareness and Usage towards BIM Knowledge, Importance and Future

Planning ...................................................................................................................104 4.4 Chapter Summary ...............................................................................................................109 CHAPTER 5: STRUCTURAL EQUATION MODELLING (SEM) ANALYSIS .................... 111 5.1 Overview of Structural Equation Modelling (SEM) .............................................................112

5.1.1 Uni-dimensionality ...................................................................................................112 5.1.2 Validity and Reliability ............................................................................................112 5.1.3 Fitness Index ............................................................................................................113

5.2 Modelling the SEM Measurement Models ...........................................................................114 5.3 SEM Measurement Models Validation by Confirmatory Factor Analysis (CFA) ..................115

5.3.1 Improved Information Measurement Model Fit .........................................................116 5.3.2 Perceived Benefits Measurement Model Fit ..............................................................119 5.3.3 Cost Estimates Reliability Measurement Model Fit ...................................................122 5.3.4 BIM Adoption Measurement Model Fit ....................................................................125

5.4 Analysis of SEM Structural Model ......................................................................................128 5.4.1 The Assessment of Normality of Data .......................................................................128 5.4.2 SEM Structural Model ..............................................................................................129

5.5 Inter-Relationships amongst Constructs ...............................................................................131 5.5.1 Causal Effects between Improved Information, Perceived Benefits, Cost Estimates

Reliability and BIM Adoption ..................................................................................131 5.5.2 Mediating Effects of Perceived Benefits and Cost Estimates Reliability towards

Improved Information and BIM Adoption .................................................................133


© 2017 Noor Akmal Adillah Ismail Page v

5.6 Chapter Summary ...............................................................................................................140 CHAPTER 6: FOCUS GROUP DISCUSSION AND VALIDATION OF FRAMEWORK .... 142 6.1 Overview of Focus Group Practice ......................................................................................143 6.2 Statistical Findings towards Framework Establishment ........................................................144 6.3 Focus Group Processes........................................................................................................146 6.4 Framework Validation by Focus Group ...............................................................................148

6.4.1 Selection Criteria for Focus Group Panellists ............................................................148 6.4.2 Focus Group Insights towards Framework ................................................................151 6.4.2.1 Improved Information through Data Visualisation, Reliable Database and Data

Coordination ............................................................................................................151 6.4.2.2 The Reliability of Cost Estimates through Input Information, Understanding and

Knowledge ...............................................................................................................154 6.4.2.3 Perceived Benefits in Adopting BIM in Practice .......................................................158 6.4.2.4 The BIM Technology Adoption for the Reliability in Cost Estimates .........................160 6.4.2.5 Summary of Insights between BIM and Non-BIM Users ...........................................161 6.4.3 Evaluation for Framework Validity and Reliability ...................................................167

6.5 Final Framework .................................................................................................................170 6.6 Chapter Summary ...............................................................................................................172 CHAPTER 7: CONCLUSION AND RECOMMENDATIONS ................................................ 176 7.1 Overview of Research Objectives ........................................................................................177 7.2 Conclusions on the Research Objectives ..............................................................................178

7.2.1 Research Objective 1 ................................................................................................178 7.2.2 Research Objective 2 ................................................................................................180 7.2.3 Research Objective 3 ................................................................................................183

7.3 Research Contributions .......................................................................................................188 7.3.1 Theoretical Contributions to Body of Knowledge ......................................................188 7.3.2 Practical Contributions to Industry ............................................................................190

7.4 Research Limitations ...........................................................................................................193 7.5 Recommendations for Future Research ................................................................................194 BIBLIOGRAPHY ....................................................................................................................... 195 APPENDICES ............................................................................................................................ 210 Appendix A: Questionnaire ............................................................................................................210 Appendix B: Preliminary Data Analysis .........................................................................................217 Appendix C: SEM Analysis ...........................................................................................................241 Appendix D: Focus Group Kit........................................................................................................243 Appendix E: List of Publications ....................................................................................................256


© 2017 Noor Akmal Adillah Ismail Page vi

List of Figures

Figure 1.1: Research methodology framework ................................................................................... 8 Figure 2.1: Status of BIM adoption globally .................................................................................... 13 Figure 2.2: Percentage of BIM implementation levels among contractors in various countries .......... 14 Figure 2.3: Forecasting error over time/information release ............................................................. 20 Figure 2.4: Estimating accuracy through the design stage ................................................................ 21 Figure 2.5: Estimating work process ................................................................................................ 22 Figure 2.6: Estimating cycle ............................................................................................................ 22 Figure 2.7: Hierarchy of estimating data .......................................................................................... 23 Figure 2.8: Types of estimates ......................................................................................................... 23 Figure 2.9: A model of stages in the innovation-decision process ..................................................... 44 Figure 2.10: Theory of Reasoned Action (TRA) model .................................................................... 44 Figure 2.11: Theory of Planned Behaviour (TPB) model ................................................................. 45 Figure 2.12: Technology Acceptance Model (TAM) ........................................................................ 45 Figure 2.13: Unified Theory of Acceptance and Use of Technology (UTAUT) model ...................... 46 Figure 2.14: UK BIM maturity model ............................................................................................. 49 Figure 2.15: BIM maturity model by Bilal Succar............................................................................ 50 Figure 2.16: BIM percentage level of adoption in Malaysia ............................................................. 55 Figure 2.17: Stages of Malaysia’s BIM adoption ............................................................................. 55 Figure 2.18: Conceptual framework of BIM improved information and cost estimates reliability

towards adoption .................................................................................................................... 60 Figure 3.1: The research ‘onion’...................................................................................................... 65 Figure 3.2: Mixed method sequential explanatory design ................................................................. 67 Figure 3.3: Research process flow ................................................................................................... 70 Figure 3.4: Literature review process............................................................................................... 71 Figure 3.5: Expert interview process ............................................................................................... 72 Figure 3.6: Survey process by questionnaire .................................................................................... 78 Figure 3.7: Example of answer interface for the QUT Key Survey tool ............................................ 78 Figure 3.8: Sampling design process ............................................................................................... 83 Figure 3.9: Conventional approach to Structural Equation Modelling ............................................... 85 Figure 3.10: Steps of Confirmatory Factor Analysis (CFA) .............................................................. 86 Figure 4.1: Percentage of BIM software types used by respondents .................................................. 99 Figure 4.2: Percentage of respondents’ BIM usage by project stages ...............................................100 Figure 4.3: Percentage of respondents’ knowledge in BIM .............................................................101 Figure 4.4: Percentage of BIM importance in respondents’ current roles .........................................102 Figure 4.5: Percentage of respondents’ future planning on BIM usage ............................................103 Figure 4.6: BIM Awareness VS BIM Usage ...................................................................................104 Figure 4.7: BIM Awareness and Usage VS BIM Knowledge ..........................................................105


© 2017 Noor Akmal Adillah Ismail Page vii

Figure 4.8: BIM Awareness and Usage VS BIM Importance ..........................................................106 Figure 4.9: BIM Awareness and Usage VS BIM Future Usage .......................................................107 Figure 4.10: Overall BIM Knowledge, Importance & Future Usage based on BIM Awareness &

Usage ....................................................................................................................................108 Figure 5.1: The preliminary model and relationships between constructs .........................................115 Figure 5.2: Initial measurement model for Improved Information construct.....................................116 Figure 5.3: Final measurement model for Improved Information construct ......................................118 Figure 5.4: Initial measurement model for Perceived Benefits construct ..........................................119 Figure 5.5: Final measurement model for Perceived Benefits construct ...........................................121 Figure 5.6: Initial measurement model for Cost Estimate Reliability construct ................................123 Figure 5.7: Final measurement model for Cost Estimate Reliability construct .................................124 Figure 5.8: Initial measurement model for BIM Adoption construct ................................................125 Figure 5.9: Final measurement model for BIM Adoption construct .................................................127 Figure 5.10: SEM structural model .................................................................................................129 Figure 5.11: Direct effects and indirect effects in the construct relationship .....................................133 Figure 5.12: The mediation test procedure for Improved Information-Perceived Benefits-BIM

Adoption relationship ............................................................................................................135 Figure 5.13: The mediation test procedure for Improved Information-Cost Estimates Reliability-BIM

Adoption relationship ............................................................................................................136 Figure 5.14: The mediation test procedure for Improved Information-Cost Estimate Reliability-

Perceived Benefits relationship ..............................................................................................138 Figure 6.1: Framework of BIM adoption in cost estimating practice ................................................144 Figure 6.2: Steps in the design and use of focus groups ...................................................................146 Figure 6.3: Strategy of incorporating cost estimating practice within BIM.......................................171


© 2017 Noor Akmal Adillah Ismail Page viii

List of Tables

Table 2.1: BIM characteristics and benefits ..................................................................................... 19 Table 2.2: Classification of cost estimates ....................................................................................... 24 Table 2.3: Cost estimating factors ................................................................................................... 26 Table 2.4: Research on BIM-supported cost estimation .................................................................... 31 Table 2.5: Research on BIM-impact towards quantity surveying practice ......................................... 33 Table 2.6: Summary of research establishing BIM frameworks or models ........................................ 47 Table 2.7: Timeline analysis of BIM implementation in Malaysia .................................................... 51 Table 2.8: Research on BIM in the Malaysian construction industry context .................................... 56 Table 3.1: Overall research strategy ................................................................................................ 66 Table 3.2: Overall research approach ............................................................................................... 69 Table 3.3: Key summary points of In-depth Interviews .................................................................... 75 Table 3.4: Respondents’ background information for Preliminary Exploratory Study ....................... 76 Table 3.5: Key summary points of Preliminary Exploratory Study ................................................... 77 Table 3.6: Questionnaire content ..................................................................................................... 79 Table 3.7: Justifications on focus group selection ............................................................................ 87 Table 4.1: Total number and percentage of overall responses ........................................................... 90 Table 4.2: Summary of reliability test of overall responses .............................................................. 93 Table 4.3: Distribution of respondents based on construction cost estimating experience .................. 94 Table 4.4: Distribution of respondents based on professional background ........................................ 95 Table 4.5: Distribution of respondents based on current roles in organisations.................................. 95 Table 4.6: Distribution of respondents based on nature of current organisations’ business ................ 96 Table 4.7: Distribution of respondents based on awareness on BIM usage ........................................ 97 Table 4.8: Statistics of BIM software types used by respondents ...................................................... 98 Table 4.9: Statistics of respondents’ BIM usage by project stages ...................................................100 Table 4.10: Distribution of respondents based on knowledge in BIM ..............................................101 Table 4.11: Statistics of BIM importance in respondents’ current roles ...........................................102 Table 4.12: Statistics of planning on future BIM usage by respondents ...........................................103 Table 5.1: AVE and CR formula ....................................................................................................113 Table 5.2: Categories of Fitness Indexes and Level of Acceptance ..................................................114 Table 5.3: Research hypotheses for constructs ................................................................................114 Table 5.4: Indicators for the initial measurement model for Improved Information construct ...........117 Table 5.5: Indicators for the final measurement model for Improved Information construct .............118 Table 5.6: Validity and reliability assessment for Improved Information measurement model .........119 Table 5.7: Indicators for the initial measurement model for Perceived Benefits construct ................120 Table 5.8: Indicators for the final measurement model for Perceived Benefits construct ..................121 Table 5.9: Validity and reliability assessment for Perceived Benefits measurement model ...............122


© 2017 Noor Akmal Adillah Ismail Page ix

Table 5.10: Indicators for the initial measurement model for Cost Estimate Reliability construct .....123 Table 5.11: Indicators for the final measurement model for Cost Estimate Reliability construct .......124 Table 5.12: Validity and reliability assessment for Cost Estimates Reliability measurement model ..125 Table 5.13: Indicators for the initial measurement model for BIM Adoption construct ....................126 Table 5.14: Indicators for the final measurement model for BIM Adoption construct ......................127 Table 5.15: Validity and reliability assessment for BIM adoption measurement model ....................127 Table 5.16: The assessment of normality distribution for items in constructs ...................................128 Table 5.17: The Fitness Index assessment for SEM structural model ...............................................130 Table 5.18: Indicators for final SEM structural model .....................................................................130 Table 5.19: The Regression Path Coefficient between the constructs and its significance ................132 Table 5.20: The result of hypothesis testing for constructs (H1 to H6) .............................................132 Table 5.21: The standardised regression weight and its significance for each path for Improved

Information-Perceived Benefits-BIM Adoption relationship ...................................................134 Table 5.22: The result of mediation test for Improved Information-Perceived Benefits-BIM Adoption

relationship ...........................................................................................................................135 Table 5.23: The standardised regression weight and its significance for each path for Improved

Information-Cost Estimate Reliability-BIM Adoption relationship .........................................136 Table 5.24: The result of the mediation test for Improved Information-Cost Estimate Reliability-BIM

Adoption relationship ............................................................................................................137 Table 5.25: The standardised regression weight and its significance for each path for Improved

Information-Cost Estimate Reliability-Perceived Benefits relationship ...................................137 Table 5.26: The result of mediation test for Improved Information-Cost Estimate Reliability-Perceived

Benefits relationship ..............................................................................................................138 Table 5.27: The result of Mediation Test (H7, H8 and H9) .............................................................139 Table 6.1: Selection criteria of focus group participants ..................................................................149 Table 6.2: Panellists for Focus Group 1 (BIM users) .......................................................................149 Table 6.3: Panellists for Focus Group 2 (Non-BIM users) ...............................................................150 Table 6.4: Summary of insights from focus group participants ........................................................162 Table 6.5: Framework evaluation based on CIPP model .................................................................167 Table 6.6: Strategy of incorporating cost estimating practice within BIM ........................................171 Table 6.7: Overall insight similarities between focus groups ...........................................................174


© 2017 Noor Akmal Adillah Ismail Page x

List of Abbreviations

AEC Architecture, Engineering and the Construction

AGFI Adjusted Goodness of Fit Indices

AMOS Analysis of Moment Structures

AVE Average Variance Extracted

BCA Building and Construction Authority

BCIS Building Cost Information Service

BIM Building Information Modelling

BQ Bill of Quantities

CAD Computer-Aided Design

CAE Computer-Aided Engineering

CAM Computer-Aided Manufacture

CFA Confirmatory Factor Analysis

CFI Comparative Fit Index

CEO Chief Executive Officer

CIDB Construction Industry Development Board

CIPP Context Input Process Product

CITP Construction Industry Transformation Programme

ChiSq/DF Chi Square or X² value and associated DF

Chi/Sq Chi Square or X²

CORENET Construction & Real Estate Network

CR Composite Reliability

CR Critical Region

CRC Cooperative Research Centre

CREAM Construction Research Institute of Malaysia

CSF Critical Success Factors

DF Degree of freedom

GDP Gross Domestic Product

GFI Goodness-of-Fit Index

GSA General Services Administration

IDT Innovation Diffusion Theory

IFC Industry Foundation Classes

IPD Integrated Project Delivery

IT Information Technology

JKR Jabatan Kerja Raya (Public Work Deparment)


© 2017 Noor Akmal Adillah Ismail Page xi

LOD Level of Development

MAPS Management and Assessment of Project Safety

MEP Mechanical Electrical Plumbing

MI Modification Indices

MOW Ministry of Works

M&E Mechanical & Electrical

NBIMS National BIM Standard

NBS National BIM Survey

NFI Normed Fit Index

NIBS National Institute of Building Sciences

PR1MA Perbadanan Perumahan Rakyat 1Malaysia (Malaysian Housing Programme)

PWD Public Work Department

QS Quantity Surveyor

QTO Quantity Take-Off

QUT Queensland University of Technology

RICS Royal Institution of Chartered Surveyors

RISM Royal Institution of Surveyors Malaysia

RMK11 Rancangan Malaysia Ke-11 (the 11th Malaysian Plan)

RMSEA Root Mean Square Error of Approximation

ROI Return on Investment

SEM Structural Equation Modelling

SME Small and Medium Enterprises

SMM Standard Method of Measurement

SPSS Statistical Package for the Social Sciences

TAM Technology Acceptance Model

TLI Tucker-Lewis Index

TPB Theory of Planned Behaviour

TRA Theory of Reasoned Action

UiTM Universiti Teknologi MARA

UK United Kingdom

US United States

UTAUT Unified Theory of Acceptance and Use of Technology

VO Variation Order

2D Two-dimensional

3D Three-dimensional

4D Four-dimensional

5D Five-dimensional


© 2017 Noor Akmal Adillah Ismail Page xii

Statement of Original Authorship

The work contained in this thesis has not been previously submitted to meet

requirements for an award at this or any other higher education institution. To the best of my

knowledge and belief, the thesis contains no material previously published or written by another

person except where due reference is made.

Signature: QUT Verified Signature

Date: November 2017


© 2017 Noor Akmal Adillah Ismail Page xiii

Acknowledgements

Bismillah, in the name of Allah S.W.T.,

Alhamdulillah, all the praises be to Allah. First and foremost I thank Allah for the strengths and

all of His blessings given to me throughout this tough yet wonderful PhD journey.

Special appreciation goes to both of my supervisors, Associate Professor Robert Owen and

Professor Robin Drogemuller for their continuous support throughout my PhD journey.

Thank you to my family for their true understanding and motivation.

Thank you to my friends for their never-ending kindness and help.

Thank you all for your endless love and encouragement.

Thank you for the memories.

Last but not least, I would like to thank Universiti Teknologi MARA (UiTM) and Ministry of

Higher Education (MOHE) Malaysia for sponsoring this study.

Thank you.

I also acknowledge and thank Professional Editor, Diane Kolomeitz, who has provided copyediting and proofreading services according to the guidelines laid out in the University-

endorsed national policy guidelines for editing theses.

Chapter 1: Introduction

© 2017 Noor Akmal Adillah Ismail Page 1

1Chapter 1: Introduction

1.1 RESEARCH BACKGROUND

Cost estimating is one of the main tasks to be performed by Quantity Surveyors in

their practices. It becomes part of the crucial responsibilities of Quantity Surveyors as

estimators (Gee, 2010) to ensure that proposed construction projects are within the budget

set by the clients. A traditional cost estimating process entails using various methods (Ahuja

& Campbell, 1988; Schuette & Liska, 1994; Ashworth & Skitmore,1999; Peurifoy &

Oberlender, 2002; Brook, 2008; Greenhalgh, 2013), which are evolved throughout the

construction phases, starting from the inception until the completion of a project. Providing a

reliable and accurate cost estimate is a very challenging task, as it is not only used as a tender

basis but also functions as a reference for future project management (Samphaongoen,

2010). Hence, the right choice of cost estimating methods & improvement to its process and

procedure are essential to improve the accuracy of cost estimates (Azman et al., 2013).

There are numerous factors that affect the accuracy of cost estimates, ranging from project

information and its characteristics, project team, clients, contractual matters, and also other

external influences (Akintoye, 2000; Trost & Oberlender, 2003; Serpell, 2004; Tas &

Yaman, 2005; Elhag et al., 2005; Enshassi et al., 2005; Chan & Park, 2005; Liu & Zhu,

2007; Stoy et al., 2008; Aibinu & Pasco, 2008; Koleola & Henry, 2008; Olatunji & Sher,

2010; Azman et al., 2013; Cheng, 2014). Those relevant factors will be significantly

considered by Quantity Surveyors before they start estimating the project costs to obtain

clients’ optimum satisfaction towards their profit.

Building Information Modelling (BIM) as a strategy is gaining increasing attention in the

Architecture, Engineering and the Construction (AEC) fields (Takim et al., 2013). It is

emerging as a prerequisite for many projects and is increasingly demanded by clients (Eadie

et al., 2013). While conventional practices are still poorly integrated, the implications of

BIM to the construction industry are significant (Olatunji, 2011). BIM had its origins in the

1970s, was developed in the 1980s-90s, and today increasingly functions to produce data-

rich models of buildings and structures for information exchange between stakeholders

(Egbu & Coates, 2012; Wong et al., 2011). The application of BIM in construction can make

the industry more efficient, effective, flexible and innovative (Takim et al., 2013; Sebastian

& van Berlo, 2010) and is believed to reduce many building industry problems (Jensen &

Jóhannesson, 2013).



BIM has increased profitability, reduced costs, cultivated better time management and

improved relationships, and encouraged integration between stakeholders in a construction

project (Azhar et al., 2009). Based on research by Egbu & Coates (2012), BIM can alleviate

some challenges of remote construction projects such as effective communication,

procurement, building accurate schedules and quantity take-off, and overall, creates a mutual

understanding between stakeholders. Additionally, BIM has the potential to restructure the

quantity surveying profession (Olatunji et al., 2010) due to its capability in generating

automated take-offs and measurements from the model (Lee et al., 2013; Cho et al., 2011).

BIM technology that allows semi-automated measurements and cost estimates from the

models has offered new skills and knowledge to the QS profession, in advising the project

team (RICS, 2014).

Today, construction industry practices in most countries all over the world widely employ

BIM, and its implementation is increasing from year to year (NBS, 2016; RICS, 2014;

McGraw Hill Construction, 2014a). USA, Finland, Denmark, Norway, Netherlands, UK,

Australia, Hong Kong & Singapore are among the countries heading towards BIM (Quek,

2012). Malaysia, where the growth of BIM has been driven mainly by the private sector

since 2009 (CIDB, 2014), has made slow progress in developing BIM systems (Quek,

2012); the level of implementation falling on 0 and 1 scale of the BIM UK maturity model

(Zahrizan et al., 2013), indicates the need for a solution to these issues. The Malaysian

government proposed to implement BIM for their construction projects in 2016 (CIDB,

2013). Thereby CIDB, as the professional body that has been given the mandate for

managing BIM uptake in Malaysia, has urged BIM implementation amongst the construction

players in their practice to produce more effective construction projects (CIDB, 2013;

Bernama, 2014). Subsequently, in September 2015, CIDB in collaboration with the Ministry

of Works Malaysia released the Construction Industry Transformation Programme (CITP)

2016-2020 to promote the adoption of BIM technology to improve the productivity of the

Malaysian construction industry (CIDB, 2015).

This research aims to build a framework to guide the Quantity Surveyors in Malaysia to use

BIM to achieve more dependable results in their cost estimating practices. The objectives of

this research include identifying the factors that influence cost estimating practice and

investigating the potential BIM drivers that lead to more accurate cost estimates amongst

Malaysian Quantity Surveyors. This research will also analyse the significant relationships

between the cost-estimating factors and BIM drivers in promoting better accuracy for

construction cost estimates. Overall, this research will establish a framework to provide



guidelines towards cost-estimating practice, incorporating BIM in the Malaysian

construction industry.

1.2 STATEMENT OF RESEARCH PROBLEM

The Malaysian government has a goal to implement BIM initially by the year 2016

(CIDB, 2013; Bernama, 2014) In this regard, CIDB has urged the industry players to better

understand and use BIM (CIDB, 2013; Bernama, 2014). The direction continued to evolve

from 2016 towards 2020, with the new agenda of the Construction Industry Transformation

Programme (CITP) 2016-2020 (CIDB, 2015). The essence was to drive the industry towards

sustainable construction, and contribute to long term affordability, quality and efficiency. It

has been highlighted in the CITP that the low productivity level of the Malaysian

construction industry is due to the slow uptake of technology and modern practices.

Amongst the issues addressed was the limited adoption of BIM technology to modernise the

construction processes towards improving its productivity. Despite the efforts to encourage

BIM usage amongst managerial and operational teams in the Malaysian construction

industry, CIDB (2013) has reported that the slow level of transformation towards BIM could

be due to the lack of standardised BIM processes and the absence of guidelines for its

implementation. Hence, the CIDB has emphasised that BIM guidelines be provided to the

construction industry users, for better communication and work collaboration (CIDB, 2013).

Apart from the CIDB emphasising the provision of BIM guidelines for the industry users,

Quek (2012) has specifically suggested the Royal Institution of Surveyors Malaysia (RISM),

especially the Quantity Surveying (QS) Division, to initiate measured actions in evaluating

the influence of BIM.

One of the significant tasks provided by the Quantity Surveyors is cost estimating.

Estimating practice and procedures are continuously hit by errors, inaccuracies, omissions

and ethical flaws, though BIM could provide significant solutions to these challenges

(Olatunji & Sher, 2010). However, some requirements of the BIM process need to be

acknowledged before implementing it for cost estimation, especially the necessary

information for the model (Kang et al., 2012). The information provided by BIM models

should sufficiently represent the exact proposed building (Monteiro & Martins, 2013), so

that Quantity Surveyors, as estimators, can appropriately understand the actual construction

process on site (Kim et al., 2013). It is necessary for BIM modellers to provide frameworks

for model data, which recognise estimating variables (Olatunji & Sher, 2010) and serve as an

interoperability platform for BIM. BIM-supported cost estimating entails a similar process to

that of traditional estimating, including extracting quantities and pricing the project

(Meerveld et al., 2009). Therefore, the same influential factors or variables considered in a



conventional estimating process should also be examined when BIM-supported estimating is

adapted, to ensure that BIM works at its full potential.

Extensive research has been conducted on cost estimating incorporating BIM (Shen & Issa,

2010; Cho et al., 2011; Olatunji, 2011b; Cheung et al., 2012; Chen, 2013; Kim et al., 2013;

Lee et al., 2013; Ma et al., 2013; Monteiro & Martins, 2013; Wu et al., 2014; Choi et al.,

2015). Most of those researchers have evaluated BIM benefits over traditional methods, as

well as having developed new approaches and models to help BIM users to easily adopt the

technology in their estimating practices. Nevertheless, as intensive as these studies are, the

relationships between cost estimating factors and BIM drivers contributing to accuracy in

cost estimates are not being investigated. There is a gap in analysing those variables that

could potentially lead to cost estimate effectiveness, and which ultimately yield more

efficient construction projects.

1.3 RESEARCH QUESTIONS

The main aim of this research is to build a framework to guide Quantity Surveyors in

Malaysia to use BIM to achieve more dependable results in their cost estimating practices.

Based on the research background and research problem discussed previously, three research

questions are demonstrated in achieving the research aim, as follows:

a) What is the current BIM implementation status of Quantity Surveyors in Malaysia?

Before further investigating the impact of BIM technology towards the quantity

surveying practice in Malaysia, it is necessary for this research to delve into the level of

BIM implementation amongst practitioners. Apart from adding new input in the extant

literature, it is important to recognise the current status of the technology adoption. Thus,

the research conducted becomes more dependable. Due to limited sources available from

the literature regarding the Quantity Surveyors adopting BIM in Malaysia, this part

constitutes more perspectives about the issues.

b) To what extent could improved information from BIM affect the reliability of cost

estimates?

Previous related research has shown that various BIM factors have influenced the

reliability of construction cost estimates. Reliable cost estimates produced are closely

associated with the retrieval of information by Quantity Surveyors as estimators.

Therefore, the identification of BIM-improved information characteristics and the

aspects contributing towards the accuracy of cost estimates should be further explored.



The significant relationships between the factors involved also need to be further

analysed.

c) How can increased BIM adoption be promoted amongst Quantity Surveyors in

Malaysia?

The strategy framework proposed in this research should be able to identify areas of

improvement in quantity surveying practice in the use of BIM tools. It will assist

Malaysian Quantity Surveyors to look into the BIM capabilities in improving project

information that potentially enhances their cost estimating tasks. Strategic actions to

effectively attain the capabilities provide a mechanism that should facilitate the

practitioners to promote BIM technology adoption in their practice.

1.4 RESEARCH AIM AND OBJECTIVES

To answer the research questions mentioned above, hence accomplishing the

research aim, three research objectives are formulated. The following are the research

objectives:

a) To determine the current BIM implementation status of Quantity Surveyors in Malaysia

The identification of current status of BIM employment by Malaysian Quantity

Surveyors provides the context of BIM usage related to types of BIM software and

project stages in which BIM is applied. Other than that, BIM awareness of respondents

pertinent to their BIM knowledge, its importance in the current practice and the

possibility of BIM usage in the future, is also explored.

b) To investigate the effects of BIM-improved information on increasing the reliability of

cost estimates in quantity surveying practice

This research studies the capabilities of BIM-improved information concerning data

visualisation, reliable database and data coordination. While the reliability of cost

estimates elements are the project input information, the understanding and knowledge

of Quantity Surveyors as estimators. The causal effects between all factors within both

aspects are investigated in searching for the possible relationships that exist to impact the

quantity surveying practice significantly.

c) To produce a strategy for incorporating construction cost estimates within BIM to

promote the adoption of BIM technology in the quantity surveying practice in Malaysia



Through the different perspectives of BIM and non-BIM users, relevant approaches

regarding BIM capabilities to suit the cost estimating practice by the Quantity Surveyors

are framed. The critical areas of improvement on process and people are strategised in

parallel with the capabilities of the BIM technology outlined. Ultimately, this research

establishes a strategy framework that consists of the technology, process and people

functions as a guideline towards producing more reliable cost estimates with the

employment of BIM. Hence, it further promotes the development of BIM innovation in

quantity surveying practice.

1.5 RESEARCH SIGNIFICANCE

The findings of this research develop a strategy framework to assist the Malaysian

Quantity Surveyors to estimate construction costs by incorporating BIM. The strategy

demonstrates the possible relationship between cost estimating and BIM factors to produce

more accurate cost estimates. Those outcomes lead them to a clearer understanding of the

values in cost estimate accuracy, thus making their cost estimating practice more effective.

This research provides new insights to other researchers, construction players and the

construction industry as a whole.

To researchers, apart from gaining more information on the research design process and

methodology, this research is beneficial, pertaining to adding more insights to the collective

knowledge of BIM, especially in cost estimating practices. These include benefits in terms of

recognising possible BIM capabilities that might affect the cost estimating practice, as well

as the relationship between the variables. Theoretically, this research contributes to empirical

research on BIM potential affecting the reliability of cost estimates. It highlights the

establishment of people approaches on capabilities towards knowledge in BIM

implementation for the quantity surveying practice. The study identifies the strategy

concerning people, process and technology aspects in developing BIM innovation in the

practice of producing more reliable cost estimates for construction projects. Moreover, this

research provides recommendations for further research, which will broaden the studies in

cost estimating using BIM that can be undertaken by other researchers in the future.

To construction players, this research is focusing only on Quantity Surveyors’ practices;

however, the developed framework for this research can be employed by other estimators in

the construction industry. This strategy framework accommodates a better method of

preparing more accurate cost estimates by relating BIM key drivers before estimating the

costs. In providing such an appropriate and useful guidance, it potentially leads them to

fewer underestimates or overestimates in costing. Other than that, the study initiates

awareness amongst the practitioners in the construction industry, specifically the Quantity



Surveyors on the BIM capabilities in improving their current cost estimating execution. The

strategy provided through the framework in this research recommends the potential areas of

improvement for quantity surveying practice pertaining to their cost estimating tasks.

Eventually, the development of the strategy demonstrating the BIM deliverables towards the

reliability of cost estimates could promote the BIM adoption within the quantity surveying

practice.

Above all, this research contributes to the recent Malaysian government initiative as outlined

in the Construction Industry Transformation Programme (CITP) 2016-2020 agenda to

promote technology advantage throughout the construction project life cycle. With the

framework established in this research, it possibly raises awareness of BIM capabilities in

improving the quality of construction projects, especially related to the cost decisions

managed by the Quantity Surveyors. The dissemination strategy for the research outcomes

supports the roles of key stakeholders highlighted in the CITP agenda, such as CIDB, JKR

and the likes, in evaluating the BIM uptake in Malaysia.

1.6 OVERVIEW OF RESEARCH METHODOLOGY

Primarily based on the research ‘onion’ adapted from Saunders et al. (2012), the

overall research is philosophically pragmatic, an involved inductive and deductive approach.

This research applied a mixed-method approach, combining the questionnaire survey and

focus group discussions. Prior to conducting these survey and focus group methods, a series

of expert interviews was employed to better support the initial information obtained from the

literature review. The quantitative survey results were analysed with SEM analysis, while the

qualitative focus group discussion was interpreted using content analysis.

Apart from gaining more robust measures, this mixed method design type was chosen due to

the better understanding that could be gained from both qualitative and quantitative means.

The qualitative focus group discussions could reflect what the participants meant by their

quantitative answers from the earlier survey. The discussions provided a more engaging

experience between the researcher and the participants, in figuring out the reasons why the

surveyed results have occurred. The selection of SEM analysis in this research as compared

to the common regression analyses, allows complex hypotheses involving various constructs

to be tested concurrently in one system. The focus group approach overcomes the time and

cost limitation of doing separate interviews for the study purpose, yet brings together the

experts at one time to discuss the pertaining topics. Hence, it provides fast feedback and

broader information for the study.



The type of sequential explanatory design of mixed method (Creswell, 2009; Creswell,

2012) was utilised for the research. It started with the data collected quantitatively using a

questionnaire survey. A set of closed-ended questionnaires was prepared based on the

information gathered from the previous literature review and expert interviews. The drafted

questionnaire was then pre-tested before distributing to the actual respondents to check for

its clarity so that any potential problems could be identified and eliminated. The final

questionnaire after a few series of pre-testing was then released to the sampled respondents.

This questionnaire survey was conducted for this research to determine the current BIM

practice amongst the respondents and also to investigate the effects of BIM on the reliability

of their cost estimates, which leads towards the adoption of the technology. Subsequently, an

SEM model produced from the survey results was then translated into an appropriate

framework and verified through focus group discussions. The focus group interviews were

attended by both BIM and non-BIM users in separate sessions, to discuss further the

relationships and dependencies between the factors shown in the framework. Based on the

final findings, an appropriate strategy for implementing BIM in the quantity surveying

practice pertaining to the reliability of cost estimates was then formulated as a guideline. The

research methodology framework is simply described in Figure 1.1 below. The framework is

developed in this section to briefly portray the research methodology processes rigorously

explained in Chapter 2.

Figure 1.1: Research methodology framework



1.7 RESEARCH SCOPE AND LIMITATIONS

This research has some limitations in the construction industry in terms of its scope.

The scope of this research confines the BIM capabilities concerning its data visualisation,

reliable database and data coordination to impact the input information, understanding and

knowledge of Quantity Surveyors as estimators towards developing a reliable cost estimate.

This research aims to promote and increase BIM adoption within the quantity surveying

practice in Malaysia, by formulating a strategy through a framework as a guideline for the

practice to incorporate BIM. Accordingly, the promotion of the technology adoption in

practice merely measures related people’s capacities to acknowledge BIM potentials along

with the inevitable process involved throughout the implementation.

This research focuses on the quantity surveying practice in incorporating the BIM

technology in construction projects. It discloses more on the Quantity Surveyors’ roles and

responsibilities based on their work routines as professional consultants. It excludes the

individual functions of other disciplines in the project team, although there might be some

interrelated and collaborative tasks within the team. As this research concerns the reliability

of cost estimates as its subject, the BIM perspectives communicated by the research

participants are relative to their cost estimating tasks. As such, it restricts the views for other

activities in their roles. It also does not represent other disciplines in the same project team

due to the different practices conducted amongst them.

This research only involves the practitioners from Malaysia. Thus, the viewpoints from the

respondents accorded to the study are restricted within the country only. Should the same

study be carried out with the similar samples and design, the results might vary due to the

different environments and backgrounds encountered. Either equivalent or unique

perspectives might be received from the respondents; this is influenced by the economic,

cultural, law and political issues of the nation.

1.8 THESIS STRUCTURE

Overall, this thesis consists of seven chapters. The following describes briefly, each

of the chapters provided for the research.

Chapter 1 introduces the research by providing the research background; hence the research

problems are identified. In parallel with that, the research questions and objectives are

formulated based on the main aim to be achieved at the end of the study. This section

outlines the research significance, briefly explains the methodology conducted for the study,

and frames the scope and limitations throughout the carrying out of the research.



Chapter 2 reflects the detailed literature review done for the research. It includes two main

topics, which are Building Information Modelling (BIM) and construction cost estimates.

Apart from the general overview of its development in the construction industry, the BIM

topic explains the characteristics and benefits of the technology in general. The topic

regarding the cost estimates reviews the practice by Quantity Surveyors towards establishing

the reliability in cost estimates. This section also grasps the issues on the capabilities of the

BIM technology in impacting the cost estimating practice. Since the research promotes the

adoption of BIM technology in Malaysia, some innovation theories are discussed, along with

the current development of BIM in the Malaysian construction industry. This chapter,

overall, identifies the research gaps and establishes the needs of a strategy framework

concerning people, process and technology to incorporate BIM within the cost estimating

practice.

Chapter 3 describes the research methodology applied to conduct the research. It portrays the

philosophy on which the research is based. The research that is characterised as pragmatic

combines both quantitative and qualitative approaches. The combined approach is known as

sequential explanatory mixed method design. Apart from the literature review and a

preliminary study conducted for data gathering, this chapter explains the primary methods

selected for the research, which are the questionnaire survey and focus group interviews.

Both techniques are justified appropriately to investigate the research questions, hence

achieving the research aim and objectives. Furthermore, this section also describes the data

analysis approach to analyse results from both methods.

Chapter 4 analyses the results from the first part of the questionnaire survey pertaining to the

current BIM practice of Malaysian Quantity Surveyors. This section is designed in response

with Objective 1 of the research. The preliminary analysis in this chapter covers the response

rate, coding and data entry, data cleaning, outliers’ treatment, checking of data normality,

and also the validity and reliability data assessment. The following section then reports on

the respondents’ demographic information containing years of experience in related fields,

professional background, current roles and nature of their organisations’ business.

Subsequently, the respondents’ level of BIM adoption is described through their awareness

and usage of the technology. It covers the BIM software used and its application in

construction project stages, the respondents’ knowledge in BIM, its importance in their

current roles, and their plan for using BIM in their future practice.

Chapter 5 presents the results from the remaining sections of the questionnaire. This chapter

demonstrates the Structural Equation Modelling (SEM) analysis that is applied to evaluate

the surveyed outcomes in this research. Initially, it overviews the SEM method involving the



terms of uni-dimensionality, validity and reliability, and the fitness index that needs to be

complied with before finalising the SEM model. Next, it explains the measurement and

structural models produced and validated by confirmatory factor analysis (CFA). The

relationships amongst constructs in the final model are described in line with the hypotheses

set up earlier for this research.

Chapter 6 validates the final model generated in Chapter 5 through focus group discussions.

Firstly, the chapter overviews and translates the SEM model results into an appropriate

framework for the focus group interview purpose. The focus group processes are briefly

described and followed by the actual validation part that explains the validation panellists’

criteria and their insights towards the framework. The insights are then summarised between

the different groups of participants of BIM and non-BIM users.

Chapter 7 concludes the findings, concerning research aim, research questions and research

objectives. Some research contributions that are divided into theoretical and practical are

highlighted in this chapter. This section also outlines the research limitations as well as the

recommendations for future research.

1.9 CHAPTER SUMMARY

This chapter specifies the research background and the problem statement with

regards to the issue of cost-estimating practice in implementing BIM. The relationships

between improving project information through technology and the Quantity Surveyors’

competence were highlighted in contemplating the people capabilities approach as a solution

towards adopting BIM effectively in the quantity surveying practice. The research questions

and objectives were developed to tackle the raised issues. This chapter then described the

significance upon conducting the study. It was followed by the brief explanation of the

research methodology, the scope and limitations of the research and finally, the chapters that

structure the thesis.

Chapter 2: Literature review


2Chapter 2: Literature review

This chapter mainly reviews the literature regarding the cost estimate reliability and

its relationships with BIM-improved information towards the adoption of BIM technology.

The report consists of six main sections; Building Information Modelling (BIM), the

reliability of cost estimates, cost estimating incorporating BIM, the adoption of BIM

technology, the implementation of BIM in Malaysia, and the development of framework and

hypotheses based on sections described previously.

Initially, this chapter introduces BIM as a technology that has been practised in the

construction industry worldwide. Apart from defining BIM from many perspectives, the

general BIM characteristics are featured in this section, indicating the BIM benefits that

positively impact the construction industry activities. Next, the chapter describes the cost

estimating practice that is usually conducted by the Quantity Surveyors. Factors influencing

the reliability of the cost estimates are discussed accordingly. This chapter then focuses on

how the BIM technology could improve the cost estimating practice. Those benefits

highlighted are BIM-improved information values, which contain three elements of data

visualisation, reliable database and data coordination. The values are then related to the input

information, understanding and knowledge as significant factors that Quantity Surveyors as

estimators should possess in contributing to more reliable cost estimates.

Subsequently, this chapter examines BIM adoption and its relationships with the aspects of

people, process and technology. As the scope of this research is within the construction

industry in Malaysia, this chapter also presents an overview of BIM implementation in the

country to signify the adoption level of BIM technology, concerning the quantity surveying

practice. A framework is further developed, and hypotheses are generated based on the

reviewed literature and where gaps are highlighted. The framework and hypotheses are to be

tested in Chapter 5.



2.1 BUILDING INFORMATION MODELLING (BIM) IN THE CONSTRUCTION INDUSTRY

BIM technology has been grown rapidly throughout the world in recent years,

mostly reported in international survey series such as SmartMarket Reports, National BIM

Surveys (NBS) and Masterspec. For example, a survey by McGraw Hill Construction in the

United Kingdom (UK) in 2010 recorded that the BIM adoption rate amongst construction

professionals was 35%, primarily by the architects (60%), engineers (39%) and contractors

(23%) respectively (McGraw Hill Construction, 2010).

In a National BIM Survey conducted in New Zealand at the end of 2011, result

showed that 34% of the respondents were aware and currently using BIM, 12% of them were

neither aware nor using BIM and 54% were just aware of BIM (Masterspec, 2012).

Significantly, the same survey conducted the next year reported that the result of respondents

being aware and currently using BIM had increased to 57%, and also that the category of

neither aware nor using BIM had reduced to 2% (Masterspec, 2013; NBS, 2013b). Recently,

the International BIM Report 2016 surveyed the BIM implementation of five countries,

namely, The UK, Canada, Denmark, Czech Republic and Japan; results showed that both the

usage and awareness amongst their construction players have increased (NBS, 2016a).

Figure 2.1: Status of BIM adoption globally (RICS, 2014)



Figure 2.2: Percentage of BIM implementation levels among contractors in various countries (McGraw Hill Construction, 2014a)

The BIM benefits that drive increasing implementation of BIM in the construction industry

have encouraged the construction players to practice BIM in most of their building projects.

BIM use is continuing in development internationally; consistent with the emerging

awareness amongst builders that BIM can bring betterment to their organisations. Figure 2.1

represents the global adoption of BIM in the US, UK, Europe, Middle East, India, China and

Australia. Figure 2.2 also shows that the BIM implementation level is increased amongst

contractors, by countries, from 2013 to 2015.

The UK took its official steps in BIM when the Government BIM Task Group formally

launched the BIM Policy in May 2011, in which the Government-advanced goal was to

adopt collaborative 3D BIM as a minimum by 2016 (RICS, 2014; buildingSMART

Australasia, 2012). Through the National BIM Survey (NBS) series, increased awareness

and use of BIM in the UK has been reported from year 2011 to 2013, where positive

feedback was given by the respondents, stating the many benefits they gained from BIM

technology (NBS, 2011; 2012; 2013). From the pattern of the surveys conducted by years, it

was concluded that the UK construction industry had received BIM positively and

recognised all the good values BIM had offered; hence this brought them to lead the world in

employing BIM (NBS, 2014). BIM adoption in UK is expanding from year to year, thus

BIM has been projected to become universal and routine amongst the construction players in

the future (NBS, 2016b).



The USA started to research interest in BIM in 1988 through the National Institute of

Building Sciences (NIBS) initiative and its Facility Information Council (RICS, 2014).

However, only in 2003, the GSA (General Services Administration), which is responsible for

4D and BIM technologies uptake in US federally-funded building, set up the National 3D-

4D-BIM Program. The US continued to expand their BIM implementation with the

establishment of the National BIM Standard (NBIMS-US) in 2007, undertaking government

support at the national level (RICS, 2014). The NBIMS-US series has continued to expand,

with the latest version launched in 2014 (McGraw Hill Construction, 2014a).

Building and Construction Authority (BCA) under the Singapore Government has so far

established the first BIM roadmap (2010-2015) and the second BIM roadmap (2015-2020);

in which incentives are offered to those amongst the construction players who will contribute

to increasing BIM adoption in the industry (BuildingSMART Australasia, 2012; Lijun et al.,

2016). It was in 2000 when a fully government-funded program called CORENET

(Construction & Real Estate Network) was firstly initiated to drive the use of information

technology, and it continuously monitors BIM progress in Singapore (BuildingSMART

Australasia, 2012). In 2008, Singapore developed an E-submission system (one BIM

incorporating all information needed for project submission) guided by the Building and

Construction Authority (BCA); it was progressively applied by agencies in 2010 and 2011

(McGraw Hill Construction, 2014a).

The BIM was introduced to South Korea in the first BIM conference held in the US in 2005,

attended by people from construction industries from various countries. This led to the first

BIM conference in South Korea in 2008. It was followed by the formation of The Korea

Institute of BIM and Korea IT Convergence Institute, as well as online BIM communities to

support BIM implementation in South Korea. It has been documented in the SmartMarket

Report that 9 out of 10 BIM users in South Korea (92%) obtained BIM advantages and by

having positive views towards BIM values, they believed they achieved more from their

continuous usage (McGraw Hill Construction, 2012).

It can be seen from the earlier evidence and the few examples above that the trend of BIM

adoption is increasingly dominating the world. Most of the development of BIM in those

countries was complemented with policies, standards, guidelines and other approaches to

improve BIM usage among the users and also to grow BIM awareness within the non-users.

Nevertheless, the BIM benefits sought should be explored comprehensively in exploring

effective BIM projects.



2.1.1 BIM Definitions

A comprehensive definition of BIM combines three aspects of the model itself, the process

to develop the model and the use of the model (RICS, 2014). Similarly, Crotty (2012)

detailed BIM as having three approaches: a model as reference, standard formats to

exchange data and protocol for data interchange.

However, Succar (2009) defined BIM as a combination of policies, processes and

technologies that created a methodology to digitally control construction project design and

its data. Egbu & Coates (2012) termed BIM as a methodology accommodating relationships

between building objects in forms of digital data for construction activities. In addition,

Kuiper & Holzer (2013) simply described BIM as information management process of the

building and infrastructure throughout its life cycle.

A BIM object is described in the NBS National BIM Report (2014) as comprising of data

that specifies the product; geometry that physically serves as the product’s characteristic;

visualisation data that demonstrates the object as a significant appearance; and functional

data in setting up the object to the right position (NBS, 2014).

Many are attempting to define or redefine BIM with varying degrees of effectiveness

(Kymmell, 2008; Azhar et al., 2009; Epstein, 2012; Demian & Walters, 2013; Abbasnejad &

Moud, 2013). Despite many interpretations, conclusively BIM are broadly defined, whether

as a process or methodology, a model or software, containing the all-encompassing data of

digital technology. For the context of this research, the definition of BIM has been adopted

from Eastman et al. (2011) as follows;

“…a modelling technology and associated set of processes to produce,

communicate, and analyse building model. Building models are characterised

building components that are represented with digital representations (objects)…,

components that include data that describe how they behave…, consistent and non-

redundant data such that changes to components data…, coordinated data such

that all views of a model are represented in a coordinated way.”

Eastman et al. (2011) also interpreted the modelling terms that are not related to BIM

technology, so as to avoid confusion amongst the practitioners. Those which are not

categorised as BIM models include 3D models without or with only a few object attributes;

models that define objects but have no behaviour support; multiple 2D models that have to

be combined to define buildings; and models that disallow various views of dimensions and

restrict the reflection of those views.



2.1.2 BIM Characteristics and Benefits

The implementation of BIM technology could potentially be driven by various

characteristics ranging from the government’s pressure, clients’ demand, project’s

requirement, competencies amongst organisations, and also from the individual awareness

on evolution of modern technology. Apart from those characteristics, there are various

benefits that have motivated implementers towards BIM adoption in their practices. These

benefits are significant in determining substantial values that could alternatively substitute

certain conventional approaches for better outcomes.

In Australia, various surveys have been conducted in establishing key drivers for

implementing BIM. Through a focus group discussion of 24 Australian construction industry

practitioners, Olatunji (2011b) had found that training, hardware, software, management,

recruitment, marketing, services and maintenance are the drivers that led them to practise

BIM. A questionnaire survey, carried out by Newton & Chileshe (2012) to 29 construction

organisations in Australia, identified that the BIM benefits that inspire those organisations

the most: improve constructability, improve visualisation, improve productivity, reduce

clashes, improve quality & accuracy, improve client satisfaction, increase competitiveness,

improve information sharing and improve sustainability. Whereas Aibinu & Venkatesh

(2013), in their web survey to 180 Australian quantity surveying firms, concluded that the

drivers that led firms to initiate BIM included ease of use, client requirement, quality

information in models, case studies, industry wide use, cost benefit analysis, integration with

current software and also scenario training.

In the United Kingdom, Kassem et al. (2012), via a web-based questionnaire to 52

consultants and 46 contractors, identified drivers for BIM to be established. Those are:

incorporating integrated project delivery or new contract types of Design & Build,

professional accreditation, research on key performance, reduced insurance premium, formal

education & training, execution plans, government or legislation forces, forces by clients and

the transformational change management approach. Eadie et al. (2013) submitted an online

questionnaire to the top 100 UK construction contractors, which developed the BIM drivers

of: clash detection, government pressure, competitive pressure, accurate construction

sequencing, cost savings, client pressure, improved built output quality, time saving,

improved design quality, improved communication, improved capacity, desire for

innovation, design health & safety, automation of schedule, facilitation of increased pre-

fabrication, streamlined design activities, cost savings and facilitated facilities management

activities. Also, Thurairajah & Goucher (2013), through a questionnaire sent to several

companies in the UK, summarised that the BIM benefits are encompassing cost consultant’s



attitude, improved project collaboration, easier sharing & obtaining information, increased

cost estimates accuracy, improved cost estimates reliability, supporting early supply chain

involvement, early schedule information, quicker predicted cost impact of design changes,

improved visualisation and also clash detection.

A survey has been done by Mcauley et al. (2013) of both public and private sectors in

Ireland, which indicated that BIM adoption must be driven by cultural change. The changes

to be made include political landscape, education & training, future-proofing BIM for

business and changing attitudes. While in India, as reviewed by Sawhney & Singhal (2013),

the drivers to enable BIM in the country are slightly different between the interview and

online survey. From the semi-structured interview with 16 professionals it was found that

technological advancement, current work system improvement, client demands, information

accuracy, 3D visualisation, and collaboration with foreign consultants are the drivers that

have guaranteed successful BIM implementation. However, the online survey reported

adoption is driven by client demands, BIM usage by other consultants, BIM usage by

competitors, natural technological development, top management and improvement in

current system. As in the focus group discussion conducted by CIDB (2014) in Malaysia, the

BIM drivers generated by the group are incorporating company image, repeat client

business, reduced conflict, cost control, design interpretation, reduced work, improved

quality, new business, requirements, visualisation, productivity and communication

improvement.

Recently, an online survey has been executed by McGraw Hill Construction (2014) with 727

contractors in 10 countries, including Australia, Brazil, Canada, France, Germany, Japan,

New Zealand, South Korea, the United Kingdom and United States. The BIM benefits

extracted from the survey were divided into internal, project and process, inclusive of

reduced errors & omission, collaboration, enhanced image, reduced rework, reduce cost &

time, better cost control, marketing new business, offering new services, increased profits,

maintain repeat business, reduced cycle time of workflows, faster client approval cycles,

improved safety, and faster regulatory approval cycles. Table 2.1 below summarises the

various characteristics and benefits offered by BIM as the drivers of implementation,

outlined by several authors in their research.



Table 2.1: BIM characteristics and benefits

BIM characteristics & benefits Authors & Years

Cost & time saving AlabdulQader et al. (2013); Zhang et al. (2014); Liu et al. (2010); Becerik-Gerber & Rice (2010); Yan & Damian (2008)

Reduced human resource Yan & Damian (2008)

Quality & performance improvement

AlabdulQader et al. (2013); Liu et al. (2010); Becerik-Gerber & Rice (2010); Yan & Damian (2008); Zhang et al. (2014)

Clash detection AlabdulQader et al. (2013); Aranda-mena et al. (2008)

Improved accuracy Liu et al. (2010); Becerik-Gerber & Rice (2010); Zhang & Gao (2013)

Increased profitability Liu et al. (2010); Becerik-Gerber & Rice (2010)

Enhanced collaboration & communication

AlabdulQader et al. (2013); Zhang et al. (2014); Zhang & Gao (2013); Aranda-mena et al. (2008); Liu et al. (2010)

Better presentation & documentation process

AlabdulQader et al. (2013); Zhang et al. (2014); Becerik-Gerber & Rice (2010); Zhang & Gao (2013)

Improved planning & design AlabdulQader et al. (2013); Zhang et al. (2014); Zhang & Gao (2013)

Better visualisation Zhang & Gao (2013); Becerik-Gerber & Rice (2010)

Improved information Liu et al. (2010); Aranda-mena et al. (2008)

2.2 THE RELIABILITY OF COST ESTIMATES

Generally, reliability is defined as a probability of success (Bazovsky, 1961). It is further

described that the closer the estimate will come to true probability, the more confidence is

rested in the estimate. Serpell ( 2004) added that the estimate’s reliability is measured by the

confidence level associated with the accuracy range. Accuracy is a very common term used

in describing a cost estimate in a construction project. Any cost estimates in any construction

projects can never be fully accurate. Depending on the quality and detail of information

supplied, the required level of accuracy is varied throughout the project stages, ranging from

rough estimates at the beginning of the project to much more reliable and accurate estimates

towards the construction stage (Greenhalgh, 2013). Thereby, the reliability of cost estimates

is highly affected by the nature and quality of available information on project elements

(Olatunji & Sher, 2010). To reflect the reality, the cost data probably used to estimate costs

should be reliable and updated (Tas & Yaman, 2005).

In whatever estimated cost, the accuracy is indicated by its closeness to the actual value

(Ashworth, 2013). In other words, an accurate cost estimate is referred to as an estimate



without any errors or mistakes (Azman et al., 2013; Flanagan & Norman, 2006; Serpell,

2004), whereas, the estimating error is defined as the difference of value range obtained

between estimate and actual cost (Serpell, 2004). Thus, the smaller the error, the higher the

accuracy (Flanagan & Norman, 1983). Theoretically, it is the one that most reflects the

actual or tendered price of a construction project (Azman et al., 2013; Serpell, 2004;

Barzandeh, 2011). On the other hand, An et al. (2011) highlighted the relationship between

reliability and accuracy through the quality measurement of conceptual cost estimates. It is

further mentioned that estimate reliability is determined by the equality between expected

and required accuracy range. Additionally, expected estimates’ accuracy and reliability

define the quality for the decision maker, in which the quality of the estimates affect the

capability of the estimators in estimating the future project costs (Serpell, 2004). Instead of

data availability to estimate costs, the skills and judgement of estimators will determine the

cost estimates’ reliability (Flanagan & Norman, 1983).

Skitmore (1988) explained that different types of information have different levels of effect

on accuracy. There is a positive correlation between cost estimation accuracy and the

supplied project information. It was further described that the spread of errors is lessened

over the time and information release (refer Figure 2.3).

Figure 2.3: Forecasting error over time/information release (Skitmore, 1988)

Similarly, according to Greenhalgh (2013), the level of accuracy in cost estimates is

increased as the detail of information progressed from inception to the design development

phases. This is shown by Figure 2.4, on how the error range of cost estimate is refined from

inception (±30%) to the scheme design (±20%) and detailed design (±10%). However, this

range is slightly deviated from what has been described by Ahuja & Campbell (1988). The

ranges as have been outlined are initial stage (±25%), design stage (between ±15 % to ±10%)

and tender & construction stage (±5%). Whilst, Butcher (2003) has emphasised that even the



accuracy of an estimate is certainly influenced by many factors, it is commonly predicted to

be (±5%) of the average bid.

Figure 2.4: Estimating accuracy through the design stage (Greenhalgh, 2013)

Nevertheless, it can be concluded that, for a cost estimate to be accurate, there must be some

factors that influence its accuracy. It is crucial to identify those factors and its impact

towards cost estimate reliability (An et al., 2011). The related factors vary from inception to

the final stage of a construction project.

2.2.1 Cost Estimating Practice by the Quantity Surveyors

Cost estimating is being acknowledged as the most influential part in every construction

project (Barzandeh, 2011; Samphaongoen, 2010; Meerveld et al., 2009; Butcher, 2003;

Akintoye & Fitzgerald, 2000). The practice normally includes defining scope of work,

determining project basis and deciding on the suitable estimating methods to be used. Cost

estimates are generally developed through the project stages, incorporating conceptual,

design development and execution, depending on the details attained from the project.

The traditional cost estimating process described by Peurifoy & Oberlender (2002) starts

with the kick-off meeting, and is followed by work plan establishment, estimate preparation

and estimate documentation. After some reviews and adjustments, the estimates are ready for

project execution. In this continuous cycle process, feedback from completed projects is

being considered for the next improvement. The whole process is shown in Figure 2.5.



Figure 2.5: Estimating work process (Peurifoy & Oberlender, 2002)

The continuous cycle of the estimating process has also been illustrated by Ahuja &

Campbell (1988) as in Figure 2.6. The use of different estimating techniques for different

project phases has been outlined in this estimating cycle. Estimated percentage of accuracy is

given for each technique as well as potential variables that affect cost estimating process.

Figure 2.6: Estimating cycle (Ahuja & Campbell, 1988)

Subsequently, the hierarchy of estimating data by Greenhalgh (2013) has featured the

variation of cost estimating methods based on construction project phases. In the hierarchy

of estimating data as shown in Figure 2.7, the developed information gained from the early

stages of inception to the detailed design will specify the most appropriate method to be

used.



Figure 2.7: Hierarchy of estimating data (Greenhalgh, 2013)

Furthermore, Schuette & Liska (1994) explained types of estimates to be used in each phase

of a construction project. As depicted in Figure 2.8, the estimates begin with feasibility

estimates in the first stage of project conception by an owner. This is followed by conceptual

estimates after a construction manager is engaged and before preparing the contract

documents. Detailed estimates are produced by contractors in accomplishing the project

tender. After letting the project to the winning contractor, change estimates and progress

estimates are established throughout the project completion.

Figure 2.8: Types of estimates (Schuette & Liska, 1994)

Overall, there have been classifications made by several authors in terming cost estimates as

types and methods. Despite any terms used by these authors, the estimates adopt the same



purpose in accordance with the project stages. The classification of cost estimates by these

authors is summarised in Table 2.2 below. Regardless of what types or methods of estimates

are applied, in this context of research, the most vital in a construction project is to produce

reliable cost estimates. The reliability of a cost estimate is necessarily dependent on many

factors involved for the whole of the project phases. Whereby, these various factors are

originated from distinctive estimates evolved throughout the different project stages.

Table 2.2: Classification of cost estimates

Sources Classification of cost estimates

Ahuja & Campbell (1988)

Preliminary estimate, elemental analysis estimate, unit price estimate & detailed estimate

Schuette & Liska (1994) Feasibility estimates, conceptual estimates, detailed estimates, change order estimates & progress estimates

Smith (1995) Approximate, elemental, analytical & operational

Peurifoy & Oberlender (2002) Approximate estimates & detailed estimates

Ashworth & Skitmore (1999)

Traditional estimating - approximate, analytical or operational Computer-aided estimating - computerised traditional methods & computerised statistical methods

Brook (2008) Single-rate approximate estimating, multiple-rate approximate estimating, approximate quantities, analytical estimating & operational estimating

Greenhalgh (2013) Design estimates, tender or bid estimates & control estimates

2.2.2 Factors Influencing the Reliability in Cost Estimates

There are many essential factors that should be considered to establish more accurate cost

estimates. Assessment of all factors involved, before conducting any cost estimates

exercises, should be done in the early stages of planning for construction projects. These

factors would provide Quantity Surveyors as estimators some information to benefit them in

obtaining more accurate values while they estimate the cost. Below is the elaboration on the

factors influencing the reliability in cost estimates, as discussed in Ismail et al. (2015).

Updated information of a project, the essential element in estimating cost, would definitely

help the estimators to produce more reliable cost estimates (Tas & Yaman, 2005; Olatunji &

Sher, 2010). Aibinu & Pasco (2008) examined the factors of estimate accuracy related to

project information, which are project value, gross floor area, number of storeys, project

location, procurement route, project type, structural material used and price intensity.

Through multiple regression analysis, they found that the estimate accuracy of the projects

are most influenced by project size, whereas the estimates of smaller projects are more



biased than the estimates of larger projects. Additionally, Stoy et al. (2008) have integrated

the relevant cost drivers regarding project information for their regression model, to improve

accuracy of prediction for residential buildings. The model incorporates variables of

compactness of the building, number of elevators, absolute size of the project, construction

duration, proportion of openings and region. Some of the factors affecting accuracy are

considered part of the project information, according to Azman et al. (2013), including,

project value & project size, price intensity theory, number of bidders, location (state), types

of school and contract period.

Apart from project information, researchers have also outlined other factors related to

characteristics of the project: clients, contracting matters, estimator and also external

influence. A comparative study conducted by Akintoye (2000) identified that the relevant

principal factors influencing project cost estimating practice are project complexity,

technological requirements, project information, project team requirement, contract

requirements, project duration and market requirement. Similarly, Enshassi et al. (2005) had

grouped factors which are project complexity, project information, technological

requirements, contract’s efficiency, market requirements, project’s duration and project’s

risk. While Serpell (2004) classified the main factors affecting the accuracy of conceptual

estimates as scope quality, information quality, uncertainty level, estimator performance and

quality of estimating procedure.

Elhag et al. (2005), assessed and ranked cost-influencing factors of construction projects at

the pre-tender stage for building projects in the UK. From the sixty-seven variables involved,

the factors have been classified into six main categories which are client characteristics,

consultant & design parameters, contractor attributes, project characteristics, contract

procedures & procurement methods and external factors & market conditions. It was found

that the architects and consultants contribute more significant effects to project costs than the

contractors, suggesting that the project costs are more determined at the early design stages

rather than later construction stages. However, factors affecting the accuracy of a pre-tender

cost estimate in Nigeria are slightly different, as addressed by Koleola & Henry (2008).

There, the factors are related to: expertise of consultants, quality of information & flow

requirements, project team’s experience of construction type, tender period & market

condition, extent of completion of pre-contract design and complexity of design &

construction.

A framework identifying the controlled critical factors for effective cost estimation by Liu

& Zhu (2007) has outlined two main types of factors, which are control factors and

idiosyncratic factors. Control factors are further classified into input, behavioural and output



control categories. Control factors are the factors that can be controlled by estimators, while

idiosyncratic factors are the factors outside the control of the estimator, such as market

condition, weather, and site constraint. Factors influencing construction projects have been

consolidated by Cheng (2014) into four categories, which are environmental &

circumstances influence, scope of contract, projects risks and management & technique.

By factor analysis and multivariate regression performed on 45 elements from 67 completed

construction projects, Trost & Oberlender (2003) had ranked five factors contributing a

significant impact on estimate accuracy. The factors are incorporating basic process design,

team experience & cost information, time allowed to prepare estimate, site requirements and

bidding & labour climate. While using principal component technique to construct a

predictive project cost model, Chan & Park (2005) had found the determinants influencing

the project cost. The determinant variables were categorised into three main groups,

including project design, complexity & time, professional level of the project team and

contractor’s competency.

Ismail et al. (2015), through interviews with some Malaysian Quantity Surveyors, primarily

found out that accurate project information is vital in producing reliable cost estimates. Other

than that, the skills and knowledge of the estimators also becomes the main contributor

towards accuracy of a cost estimate. Based on the factors influencing cost estimates

discussed above, in summary, the cost estimating factors are re-grouped by Ismail et al.

(2015) into several main factors. The summary list of main factors identified is depicted in

Table 2.3 below.

Table 2.3: Cost estimating factors

Authors (Year) Main factors

Aki

ntoy

e (2

000)

Tros

t & O

berle

nder

(200

3)

Serp

ell (

2004

)

Ensh

assi

et a

l. (2

005)

Cha

n &

Par

k (2

005)

(Elh

ag e

t al.

(200

5)

Liu

& Z

hu (2

007)

Aib

inu

& P

asco

(200

8)

Stoy

et a

l. (2

008)

Kol

eola

& H

enry

(200

8)

Che

ng (2

014)

Azm

an e

t al.

(201

3)

Ism

ail e

t al.

(201

5)

Project information

Project value √ √

Project size/floor area √ √ √ √ √

Price intensity √ √

Project location √ √ √ √ √ √ √

Project type √ √ √ √

Project duration √ √ √

Storey/compactness/volume/opening √ √ √ √



Project characteristics

Design/construction (drawing/scope/process) √ √ √ √ √ √ √ √ √

Information (flow/availability/quality) √ √ √ √ √ √ √

Project complexity (design/construction) √ √ √ √

Project team requirements

Experience/expertise/professional level √ √ √ √ √ √ √ √ √

Team alignment/capacity/communication √ √ √

Estimation design/ process/procedure √ √ √ √

Management & technique (time/cost control) √ √ √

Client requirements

Client’s budget/financial status √ √ √

Return profit /money issues √ √

Client characteristic/type √ √

Time/quality requirements √ √

Contract requirements

Scope of contract √ √

Tender/contract period √ √ √

Tender selection method √

Procurement route/contractual arrangement √ √ √ √

Type of contract/standard √ √

Pre-contract (design/construction) √ √

External influences

Site requirements √

Bidding/contractor attributes √ √ √ √ √

Market conditions (rates/inflation/fluctuation) √ √ √ √ √ √ √

Technology requirements √ √ √ √

Uncertainties (contingencies/variations) √ √ √ √ √

Political situation √ √

Environmental (climate/geology/disaster) √

Disputes (contract/regulations/payment) √

Overall, the key influencing factors tabulated indicate that the differences of opinions

between those researchers are due to some probable reasons. This includes the geographical

region of surveyed experts and also the effects of policies and working culture in the practice

of their related organisations. Nevertheless, by mapping those factors as a benchmark within

the quantity taking off using digital models, the Quantity Surveyors as cost estimators could

provide a more reliable source for better cost decisions amongst the BIM stakeholders. It



might serve at least as minimum criteria in improving the reliability and accuracy of cost

estimates. Therefore it determines the appropriateness of BIM parameters embedded in BIM

tools, in meeting the requirements specified by those cost estimating factors.

Comprehensively, although the developed categories are non-definitive, BIM-supported cost

estimation should be approached from the standpoint addressing those factors. Relatively, it

helps in attaining a uniform level of quality towards producing more accurate and reliable

cost estimates.

2.2.3 BIM Influence on Cost Estimating Factors

The main cost-estimating factors have been highlighted previously and are categorised into

project information, project characteristics, project team requirements, client requirements,

contract requirements and external influences. The impact of BIM towards establishing more

reliable cost estimates should be approached by considering the effects of technology on

those factors. The following explains some of the BIM effects on the factors.

As compared to other factors, the existence of the BIM innovation has much more impact on

the project information development throughout the construction stages. The advantage of

additional data, possibly visualised and retrieved from the BIM models, provides more

reliable and accurate information for cost advice (Goucher, 2012). This could lead to fewer

assumptions made by the cost consultants to reflect the project cost estimates. A BIM digital

database, using intelligent objects to symbolise the building elements, has the ability to show

much more information on objects associated with each other simultaneously, rather than

applying a series of separate drawings (Kumanayake & Bandara, 2012).

For project characteristics, the BIM usage requires the designers to incorporate more

information earlier in the design rather than in the traditional approach (Leicht & Messner,

1997). The complete building information attached to the digital objects representing the

building elements facilitates the designers to have more time identifying and coordinating

the conflicts in the design using the BIM models, instead of checking the errors manually

through the traditional documents. BIM adds value to the project characteristics by

demonstrating the productive activities of detecting building system clashes by deploying

BIM model checking and visualisation application to resolve conflicts, hence improving the

collaborative process within the team (Kumanayake & Bandara, 2012).

With regards to the project team requirements, Olatunji et al. (2010) confirm that BIM

assures improvements on collaboration and integration amongst the disciplines, especially in

communicating project information. This perhaps eliminates the limitation of the manual and

conventional CAD applications, which are disassociated and work individually. Instead,



BIM creates an accessible platform of project data by using one centralised model for more

effective information exchange and interoperability within the project team (Goucher, 2012).

In relation to client requirements, the potential of time saving from BIM , due to the

automated quantity take-off for the estimates, accelerates the validation process by the

clients as the models clearly display information for quicker decision making (Leicht &

Messner, 1997). Through manipulating the BIM coordinated information, it permits fast

model checking for any conflicts in building components. It further solves most of the

constructability issues, by radically deflating load of change orders, thus accelerating build

process and time to market (Zhang & Gao, 2013).

The effects of BIM on the contract requirement as proposed by Gardezi et al. (2013) could

be on BIM acting as conflict resolution tool to solve major issues amongst the stakeholders

in the construction team. BIM is far better than adopting traditional approach in defining the

roles and relationships of building stakeholders through its integrated procurement and

contractual delivery method (Sebastian, 2011). BIM documents such as standards, protocols

and guides provided for systematic BIM implementation in the construction projects, reflect

the holistic view of the BIM procurement process with clarity of responsibilities for each

role in the construction team (Sacks & Gurevich, 2016).

Concerning the effects of BIM on other external factors, for example, the technology could

act as a living and historical database containing all materials, components and systems that

are connected to each other. It delivers information on the design, construction and lifecycle

assessment of the building itself to be used for the operation of service and maintenance

along with energy usage evaluation, towards projecting more intelligent building strategic

planning (Kumanayake & Bandara, 2012). Simulations via BIM 3D models assist the

builders to provide analysis on the building energy performance, authoring the stakeholders

to assess many features such as building orientation for maximising solar gains, insulating

material options for the building envelope, choices of HVAC systems, among others

(Rajendran et al., 2014).

2.3 COST ESTIMATING INCORPORATING BIM

The BIM-supported estimating undergoes the processes of analysing, quantity extraction,

pricing and finalising estimates, which are much similar to the traditional estimating method

(Meerveld et al., 2009). The difference is only in the additional application of 3D models,

instead of traditional 2D drawings as the primary source for estimation. Moreover, the cost

estimating process adopting BIM is highly reliant upon accurate and complete 3D model in

achieving reliable cost estimates (Sylvester & Dietrich, 2010).



BIM brings significant benefits for estimators to predict costs for construction projects,

compared to using conventional methods. Comprehensively, automated processes in BIM

allow the estimators to extract quantities from 3D models to estimate construction costs

quickly and accurately (Meerveld et al., 2009), although it should be noted that a different

form of care is needed in ensuring the correct selection of objects from digital models versus

drawings (CRC Construction Innovation, 2009).

The advantage of model-based estimating processes by using BIM is that it reduces the time

of manually calculating the costs from 2D drawings (Bylund & Magnusson, 2011). Other

benefits of BIM-supported estimates drawn out by Monteiro & Martins (2013) are a direct

connection between model and planning software, availability of levels of detail in

measurement for different project stages, and also possibility of data extraction other than

typical calculation, in the traditional method. There are several researchers who have

previously studied the impact of BIM technology on cost estimating practice, as summarised

in Table 2.4 below.

Conclusively, most of the authors highlighted that the potential of a BIM-supported

estimating approach overcomes the shortcomings of traditional methods, leading to

improving the reliability and accuracy of the cost estimates. For example, Shen & Issa

(2010) featured the capability of BIM visualisation to improve the performance of entry-

level users, especially to facilitate them envisioning more complex estimating tasks. Cheung

et al. (2012) highlighted the ability of the BIM database to concurrently evaluate and update

changes in building construction as the design develops, and further automate the

measurement of building quantities. Chen (2013) gained high estimate accuracy by using

BIM application as compared to a conventional approach, in estimating the quantity of

reinforced concrete as case studies. It demonstrated the effectiveness of using a BIM setting

to reduce human errors, due to omissions and miscalculations (Chen, 2013; Lee et al., 2013;

Ma et al., 2013). Similarly, Monteiro & Martins (2013), Wu et al. (2014) and Choi et al.

(2015) reviewed the key benefits of using a BIM platform in estimating construction project

costs, that evolve into solutions towards inefficiencies of traditional process.



Table 2.4: Research on BIM-supported cost estimation

Authors & Years Research approaches Research findings

Shen & Issa (2010)

Evaluate performance of BIM-assisted estimating versus traditional estimating method in quantity take-offs of cases with different levels of estimates

Better performance in BIM-assisted estimates compared to traditional method, in which the more complicated levels of estimate, the clearer benefits of using BIM-assisted estimating tool

Cheung et al. (2012)

Propose cost estimation module that enables quick and intuitive exploration of early stage design in 3D modelling environment

The tool is able to update cost estimates concurrently with the design development and assess the changes implication in building to reflect the quantities and cost respectively

Chen (2013)

Differentiate construction quantity estimates made by BIM & conventional method through two case studies

Estimates made by BIM have high accuracy compared to conventional estimates, which lead to omissions & errors

Lee et al. (2013)

Establish automation search using BIM data to find most appropriate work item for buildings; conduct case study to demonstrate proposed approach

A validated ontological approach to assist cost estimators in using BIM more easily, hence reduce possible errors in judgement

Ma et al. (2013)

Compare cost estimation of tendering building projects (TBP) using CAD drawings & traditional software, with estimates using IFC data & BIM-estimate design model

BIM-estimate can critically reduce workload & errors, therefore, improve TBP cost estimates accuracy & efficiency

Monteiro & Martins (2013)

Explore case studies investigating BIM input & output dynamics for quantity take-off

BIM potentials & benefits are going beyond traditional process, regarding estimates accuracy; planning; information extracting

Wu et al. (2014)

Review of BIM-based cost estimating in UK QS practice by evaluating the ability of existing BIM technology to support

BIM’s capability of automation measurement is the key benefit that speeds up traditional process; more efficient operational solution in linking quantities and cost information, and updating changes

Choi et al. (2015)

Introduce QTO process by using open BIM to improve low reliability of estimation in early design stage

QTO results improves the schematic estimation task and improves the reliability of cost estimates



2.3.1 BIM and Quantity Surveying Practice

BIM has been widely used in quantity surveying practice all over the world, particularly in assisting Quantity Surveyors to develop project cost estimates. A survey conducted by the Royal Institution of Chartered Surveyors (RICS) showed that 10% of its 156 Quantity Surveyors members were actively using BIM application, mostly in the design and construction phases (BCIS, 2011). It was observed in Tse et al. (2009) that BIM-based projects in Hong Kong provide Quantity Surveyors a significant involvement, specifically in materials take-off, as compared to their traditional practice. Although BIM automates building quantities for the measurement take-off, it was convinced that the mechanism of measurement still requires the knowledge of Quantity Surveyors to produce more accurate cost estimates from the BIM model. Arguing that the BIM execution does not challenge the roles of the Quantity Surveyors, Olatunji et al. (2010) posit that the technology instead has a limitation to be overcome. Despite employing BIM, the function of Quantity Surveyors as cost advisors towards project decision-making throughout the construction development is still significant (Nagalingam et al., 2013).

In New Zealand, a series of research have been conducted regarding the employment of BIM

application amongst the Quantity Surveyors in the country (Boon & Prigg, 2012; Stanley & Thurnell, 2014; Harrison & Thurnell, 2015). Boon & Prigg (2012) revealed that the ability of the Quantity Surveyors in using the digital software determines the effectiveness of extracting quantities from the BIM model, thereby making the estimating process more efficient. Stanley & Thurnell (2014) and Harrison & Thurnell (2015) respectively emphasised on the advantage of using 5D-BIM over traditional 2D drawings, especially to increase visualisation of the building details, which leads to improved efficiency in identifying potential risks and changes. Other main BIM benefits for quantity surveying practice featured by other authors are cost checking for decision making (Matipa et al., 2010), automation of quantity take-off (Wijayakumar & Jayasena, 2013), improved project performance by time, cost and quality aspects (Wong et al., 2014), and value-added to services (Crowley, 2013). Other than that, in implementing BIM for quantity surveying practice, the barriers were also outlined by other authors. Some of them include cost, lack of awareness and training (Zhou et al., 2012), collaboration amongst disciplines (Stanley & Thurnell, 2014), and lack of integrity and standard (Aibinu & Venkatesh, 2013). Table 2.5 summarises the overall existing research on BIM impact towards the quantity surveying practice, covering mostly the BIM benefits and barriers, the development of models or frameworks, and the review of the BIM capabilities towards the Quantity Surveyors performance.



Table 2.5: Research on BIM-impact towards quantity surveying practice

Authors (Years) Issues Highlights

Matipa et al. ( 2010) Impact of new measurement rules on building information model schema for QS practice

Survey results indicated that the use of cost checking mechanism is comprehensive within the sector & need not be overemphasised

Olatunji et al. (2010) Relationship between BIM system & roles of QS

Exploration on BIM opportunities & challenges to overcome flaws in the traditional QS roles

Quek (2012) Strategies/frameworks to adopt BIM for QS

A framework for assessing BIM impact on the QS profession initiated by the BIM Technical Committee of the RISM

Boon & Prigg (2012) Investigation on current state of QS evolution on BIM usage in New Zealand

The main difference between cost modelling in BIM environment & QS practice in a non-BIM environment is the ability to use software to extract quantities

Zhou et al. (2012) Investigation on readiness of the UK QS firms in implementing BIM

Respondents acknowledged the BIM benefits, however the cost aspect becomes the main barrier, other than lack of awareness & project team training

Monteiro & Martins (2013)

A survey on modelling guidelines for quantity take-off-oriented BIM-based design

Although it is possible to use specifications of the manual-based measurements for BIM model to extract quantities, adjustments for the specifications needed to be revised according to BIM features

Mitchell (2013) 5D QS views on procurement & cost saving

Benefits of BIM in reducing duplication, rework & dispute leading to the determination of BIM values in the future to encourage BIM adoption

Aibinu & Venkatesh (2013)

Understanding of BIM experience by QS firms in Australia

BIM implementation barriers were due to model integrity, incomplete information by model, lack of knowledge, lack of demand by clients, cost & learning time to adopt BIM

Kulasekara et al. (2013)

Exploration of BIM influence in QS practice & comparison with conventional methods

BIM could assist QS to automate quantity take-off however limited in terms of pricing that requires expertise to analyse related pricing components



Wijayakumar & Jayasena (2013)

A review on the potential of BIM tools to automate QTO of QS requirements

Overview of usages of BIM QTO tools & their capabilities in order for QS to truly receive benefits from BIM

Nagalingam et al. (2013)

A review on the QS key roles & responsibilities in local building procurement & future expectations in BIM-based project delivery

Cost management function of QS was identified, in which their changing roles depends on their capabilities to remain as financial advisors towards project decision making

Crowley (2013) Opportunities of QS profession in promoting BIM through education & training

Majority of surveyed QS professionals in Ireland agreed on BIM changed practice towards improving productivity, efficiencies & added values to QS services

Wong et al. (2014) Identification of BIM capability in QS practice towards project performance

Questionnaire & interviews on BIM capabilities related to time, cost & quality aspects of project performance

Stanley & Thurnell (2014)

Benefits & barriers of 5D BIM for QS in New Zealand

Findings suggest that 5D BIM provides advantages over QS traditional method but to increase efficiency, increasing visualisation of construction details & earlier risk identification

Harrison & Thurnell (2015)

BIM implementation by QS practice in New Zealand

Main perceived benefits of 5D BIM were: increased visualisation of building, bulk checking for manual measurement, efficient data extraction for estimating, schedules of quantities development, rapid identification & costing of design changes, & commercial advantage over competitors.

Osman et al. (2015) Proposed BIM adoption model for QS firms

A developed model (conceptual framework) by integrating Diffusion of Innovation (DOI) Theory, Institutional Theory and fit into Technology-Organisation- Environment (TOE) framework in order to assist QS organisations in making decisions towards adopting BIM

Nadeem et al. (2015) BQ with 3D views using BIM

The BIM perceived benefits of Hong Kong respondents on visualisation of process, time reductions, efficiency & accuracy of BQ preparation process

Ismail et al. (2016) BIM capabilities for quantity surveying practice

The categories of BIM capabilities towards QS practice are data visualisation, reliable database & data coordination



2.3.2 BIM Impact on the Reliability of Cost Estimates

A reliable cost estimate demands accurate building information to minimise estimation errors

especially in the early phase process (Choi et al., 2015). As described by Monteiro &

Martins (2013), using 2D measurements and 2D designs could lead to errors and omissions

throughout the various construction stages, which ultimately cause inaccurate estimation for

budgeting. Yet, BIM tools serve more detailed and accurate cost estimates for construction

projects with the availability of geometric properties for building elements in producing

spatial quantities, such as area and volume in text form (Monteiro & Martins, 2013). In a

pilot study conducted by Shen & Issa (2010), it was proved that a significant improvement

has been achieved through the application of BIM tools to increase the efficiency and

accuracy of cost estimates compared to the traditional estimating method, and it was

sufficient even by only using the function of 3D visualisation. Additionally, Choi et al.

(2015) outlined specific processes on how open BIM is used in quantity take-off for

schematic estimation in the early design stage, to improve the cost estimates reliability. The

process stages include verification of elements’ physical quality to increase the accuracy of

calculated quantity and also the verification of input data for estimation. These critical

processes become the solution of low reliability in cost estimates, which are performed

manually. It is deduced that cost estimates reliability could be improved through the

implementation of BIM technology. Unhesitatingly, improved information being the

influential input in estimating construction costs is among the beneficial assets BIM could

deliver.

2.3.3 The Relationships between BIM Improved Information and the Estimator

BIM has much to offer when it comes to improving cost estimating reliability, which

includes early schedule information, enhanced speed in predicting cost impact of design

changes, better understanding through improved visualisation as well as the possibility to

access additional information for documentation (Thurairajah & Goucher, 2013). By BIM

3D as-built models with rich data replacing traditional 2D methods, it creates information

integration with a seamless flow of information from the initial phase to the final phase of

the life cycle (Goedert & Meadati, 2008). The integrated information is granted by the BIM

models controlling the information in a single repository to establish consistent, accurate and

accessible data (CRC Construction Innovation, 2007).

All additional information provided by BIM will possibly help the estimators to improve the

reliability of their cost estimates, as they can have a better understanding through BIM 3D

visualisation (Thurairajah & Goucher, 2013). The concepts of information, knowledge and



understanding have been intensely discussed (Zeleny, 1987; Ackoff, 1989; Briggs et al.,

2002; Hey, 2004; Chen, 2005; Rowley, 2007; Ahsan & Shah, 2006). It is highlighted that

people with knowledge are usually able to structure sets of information they have gained,

and interpret the relationships among that information. Only when they understand the cause

and consequences of information relationships, can they manage to make good judgements

and take wise action. By understanding a system, it potentially leads to beliefs in changing

future outcomes. Understanding objects, events or ideas means we are able to grasp its

significance for past, present and future activity, and then be capable of translating that

understanding into a new experience (Johnson, 2015). In other words, Johnson argued that

understanding is not just operations of concepts, ideas or representations, but is a process of

experiential simulation within complex, interconnected activities. Understanding why things

have happened needs more than beliefs; rather, it requires reasons and a grasp of

relationships between the propositions involved (Hills, 2015).

2.3.3.1 Data Visualisation, Reliable Database and Data Coordination

In general, BIM technology potentially permits a better production quality of data by

automation of information documentation (CRC Construction Innovation, 2007; Furneaux

& Kivvits, 2008; Bryde et al., 2013; Kalinichuk & Tomek, 2013; Abbasnejad & Moud,

2013; Nagalingam et al., 2013). However, there are relatively no previous studies addressing

specific concerns on how information could be improved through data visualisation, reliable

database and data coordination. However, Marrero (2007) explains the application of

visualisation that could carry information more effectively, rather than only reading raw

data. Perhaps, people can understand things better through better information delivery with

effective visualisation. When there is a significant interaction between visualisation, data,

task and user (Zhu, 2007), the database functions as an important resource centre of a system

to effectively store and retrieve data, as well as maintaining the correctness of the data (Paul

& Jain, 2008). Paul & Jain (2008) stressed that in allowing multiple users to access, create,

update and retrieve data from the database, there is a need to provide a reliable database with

fault-tolerance for most systems. In the essence of construction projects, the database with

complete information becomes a centre in managing shared resources amongst the

construction team members. Data coordination has emerged as an important function in

manipulating these shared resources, and it has been recognised as one of the contributors to

project success. While there is a definite relationship between data and information (Rowley,

2007), DeLone & McLean (1992) have assigned six major categories in measuring

information system success. The six categories are system quality, information quality, use,

user satisfaction, individual impact and organisational impact.



(i) Data Visualisation

The concern of an effective project management is not only emphasising supplying

adequate and extensive information about the related project, but also to equip the

project with diversified visualisation tools to facilitate information delivery (Chuang et

al., 2011).

Dahl et al. (2001) refer to visualisation as visual mental images used by designers during

the design process, where it allows the generation, interpretation and manipulation of

information through spatial representation. In engineering education, visualisation means

forming a picture, a model or a scheme of an object in the mind (Kolari & Savander-

ranne, 2004). This visual mental image formed will then help the student visually to

interpret the meaning of concepts or processes, or explain abstractions, thus becoming an

efficient tool for understanding things. In claiming that visualisation is only a medium to

help humans understand things better, Valle (2013) highlights that the purpose of

visualisation is to gain “insight” and not to create pretty pictures, which brings

understanding in assisting people to identify relationships between objects or actions to

resolve new problems. Where the visualisation tasks are divided into three groups of

information retrieval, information analysis and information dissemination, the analysing

information part is the most valuable in obtaining insight from data (Chen & Floridi,

2013).

Extensively, visualisation assists team members in construction projects to understand

better what has been constructed through 3D model views (Ali et al., 2014), in which the

visualisation models are not only representing building detailed properties but also

simulating their interaction and movement (CRC Construction Innovation, 2009). The

BIM models that can be used for simulation and visualisation at any stage (Sheth &

Malsane, 2014) enable construction sequence visualisation (Eadie et al., 2013) and allow

the construction team to visualise better what they intend to build (Boon, 2009). In fact,

as claimed by Furneaux & Kivvits (2008), the visualisation process, demonstrating

project stages from the beginning to the end of the construction projects could help

especially non-technically-inclined people to understand and issue any identification. As

disclosed in the Rajendran et al. (2014) study, the respondents agreed that in 2D drawing

as a traditional method, it is difficult to demonstrate clear visualisation, and this has led

to numerous design problems in the construction industry. However, BIM becomes the

solution in rectifying the drawback by providing a clear building visualisation setting for

a better understanding of the building design. With rich 3D design visualisation content

in BIM modelling and analysis to help the team understand better (Kim, 2012), BIM



upgrades over 2D drafting by allowing the team-viewing building and its contents from

all angles and at the same time, detects any related issues at earlier stages for amendment

to restrict costly changes (InfoComm International, 2011). Additionally, Leicht &

Messner (1997) differentiate both BIM and the traditional method by describing

multiple, dynamic and better 3D views of building offered in BIM models compared to

only predefined 2D views in traditional documents.

(ii) Reliable Database

The Building Information Model is a database that supplies information such as

manufacturing data, pricing, physical data (weight, size and material finish) and also

data for many other elements incorporated in the building (InfoComm International,

2011). The digital database provides an archive to all physical and functional

characteristics of output rather than a series of separated drawings, in which all related

information is formed into intelligent objects. The BIM projects then adopt these

intelligent objects to automatically symbolise the project elements in any plan, elevation,

section or detail, which can be adjusted parametrically as the design changes

(Kumanayake & Bandara, 2012). Giving more than ordinary drawings could offer, BIM

merges building design, construction and maintenance properties in one beneficial model

as a data repository that could be shared by all the team members, therefore, furnish the

project with more efficiency and accuracy in information, as compared to traditional 2D

CAD drawings (InfoComm International, 2011). As a database is driven, BIM software

is claimed by Boshoff (2014) to be a powerful tool that integrates engineering and

architectural design elements. It not only acts as an intelligent model represented by

digital objects, but the object can also be counted and ordered for any relationships

depending on data amount attributed to the model to establish more understanding and

probably connect more information existing behind the objects. All architectural

elements used in the BIM model are stored in the libraries, where the complete building

model and all of its representations are incorporated in a single BIM file. Consequently,

any changes made to the BIM model will influence all related drawings in the file. Thus,

it authorises the users to not only generate but also update project documents

automatically with all building information data attached to the elements (Kim, 2012;

Kumanayake & Bandara, 2012).

Nagalingam et al. (2013) point out that BIM technology could assist the Quantity

Surveyors with their practice. This can be achieved through the information of the whole

building represented in BIM along with the complete design documents related that have

been stored in an integrated database. This integrated environment allows all parametric



information to be interconnected, and with that will acknowledge any changes affecting

the assembly and construction process. This makes exact quantities and specific

materials in a form of electronic format available for design documents for the Quantity

Surveyors to produce their taking-offs and cost estimates. A robust database is

established through the parametric elements, in which the information gathered from the

database contributes to keeping everyone more efficient, hence enhancing the

communication between the design and construction teams (InfoComm International,

2011). Furthermore, by the BIM database being developed based on its proprietary

classification system, the one that has been acknowledged to be compatible with the

system is the Industry Foundation Classes (IFC) format (Monteiro & Martins, 2013).

The IFC format that captures both geometry and properties of intelligent building objects

associated with their relationships within the BIM models, allow information sharing

across the team (InfoComm International, 2011). The IFC file, where data is arranged

based on its classes and properties, supports data exchange among BIM application

platforms such as Autodesk Revit in exporting and importing construction information

data files required (Ma & Liu, 2014).

In line with what project costing needs most is adequate information that the BIM

models could potentially better generate an improvement in the reliability of cost

estimates. When using BIM, the reference that is typically used along with it is the Level

of Development (LOD) Specification that allows BIM users in the AEC industry to

designate the content of project information based on stages of design and construction

process. The LOD Specification applied in BIM to define its reliable database is

described as a detailed interpretation of a schema developed by the American Institute of

Architects (AIA) to characterise model elements of different building systems at

different levels of development (BIMForum, 2016). It is further explained that the LOD

is a reliable output, to which the elements’ geometry are attached with the relevant

information that a project team can depend on, especially in decision-making when

operating the models. Additionally, NATSPEC (2013) interprets LOD as a conceptual

framework parallel with the industry standard, underlining relative development of

model elements from conception to completion of a project. It is a metric of progressive

scale, indicating project deliverables to be communicated amongst the project

stakeholders. The fundamental LOD definitions range from LOD100 up to LOD500,

depending on the degree of project information to be progressed. However, composing

the appropriate details in LOD could become a consistent issue due to its direct effect on

the information provided within the project (RICS, 2014b). Therefore, in furnishing

sufficient information for BIM automated, cost estimate preparation, the project team



should agree on certain principles in confirming the required LOD, so that the modelling

process will match and be compatible with the estimating rules. The approach on

tackling the cost uncertainties issues should be prudently delivered to make the costing

process using 3D modelling easier and reliable (Mitchell, 2012), without any insufficient

or excessive data having been supplied.

(iii) Data Coordination

Boshoff (2014) defines BIM as a generation of coordinated 3D models that integrate the

representation of 3D objects across synchronised 2D drawings. In comparison with the

paper-based drawings that appear to be difficult to integrate and coordinate among each

other, BIM models become a solution to this problem to effectively communicate the

vital information between the people in the construction field (Kumanayake & Bandara,

2012). This is done by the user developing 3D components in the software and

congregating them in a single model, which shows multiple views of the building model.

Where multiple views are possible, it, therefore, allows any notification throughout the

reflected views if any changes happen in the model. Additionally, the information of

plans, sections and elevations that have been developed from the same data has been

coordinated; this improves the coordination of information exchange (Demian &

Walters, 2013) and hence translates better coordination among design, engineering and

construction disciplines (Alp & Manning, 2014). This coordinated information delivery

further improves the coordination of construction documentation, consequently, reduce

errors and reworks (Gerrard et al., 2009; Sawhney & Singhal, 2013; Aranda-mena et al.,

2008; Popov et al., 2010; NBS, 2014; Mccartney 2010).

All combining models of design and construction that have been developed using BIM

are located in a BIM database. These models can be viewed simultaneously to spot any

clashes between architectural, structural, mechanical, electrical and plumbing systems

(Kumanayake & Bandara, 2012). The systems can be coordinated, where all equipment,

fixtures, pipes, ducts, conduits, structural members and other building components are

analysed with “clash detection” tools to check any conflicts before the installation of the

system (Campbell, 2007). This would benefit the designers and other team members in

monitoring what area each is developing, to ensure that they have sufficient information

as well as diminishing unwanted conflicts and reworks among them (Leicht & Messner,

1997). The BIM model has details on each building component, which can be retrieved

from its modelled elements and allow the team members to access the information

easily. With all information being made available for everyone in the team, it alerts of



any design changes and their consequences to whomever is responsible, thereupon

improving coordination among the team members (InfoComm International, 2011).

2.3.3.2 Input Information, Understanding and Knowledge

Apart from the general cost estimating factors presented in the previous section, it is an

estimator that plays a significant role to consider all the factors involved, in producing more

reliable and accurate estimates. Cost estimators have become the subject of many research

works, highlighting their imperative function in estimating tasks towards achieving cost

estimate accuracy (Flanagan & Norman, 1983; Ogunlana & Thorpe, 1991; Uher, 1996;

Morrison, 2006; Cheung et al., 2008; Alroomi et al., 2011). For estimators to effectively

estimate a cost, what they need most is the reliable input information they receive to

calculate or estimate the price of a desired building as an output. To fully utilise the

information they have in hand, they must understand the information that is generally

translated by the knowledge that they possess as experienced cost estimators.

(i) Input Information

Reliable sources of cost information are an organised database that are accurate, up-to-

date and well-kept records (Mohammed Abdullah Eben Saleh, 1999). Through a project

data case study, past studies and survey questionnaire, Ling & Boo (2001) reported that

the important method in preparing estimates is to have good quality and a sufficient

quantity of design information. The essentials of having good quality of information and

information flow is also enlisted by Serpell (2004), Akintoye (2000) and Enshassi et al.

(2005). To increase the estimator’s ability to dealing with project information and

maximising efficient time of preparing estimates, the completeness information

produced from other professionals working in the same project is highly imperative

(Alroomi et al., 2011). Instead of the importance of data availability and its required

level of information (An et al., 2011), this information should be organised in a

computerized system to improve the effectiveness and accuracy of the cost estimating

practice (Enshassi et al., 2005).

(ii) Knowledge and Understanding

Knowledge and understanding are the two meaningful elements that interpret the root

term of an experience (Ackoff, 1989; Briggs et al., 2002; Hey, 2004; Chen, 2005; Ahsan

& Shah, 2006). Human factors related to an estimator’s experience level in preparing

estimates present a significant role in influencing the cost estimates’ quality (Trost &

Oberlender, 2003). Thus, as cost estimation is an experience-based process, the

understanding of cost determinants will enhance the estimator’s competence in



forecasting, thus delivering more reliable and accurate cost estimates (Elhag et al.,

2005). Alroomi et al. (2011) claimed that the typical experience needed in a construction

project encompasses familiarity with the construction process, different execution

methodologies, different site conditions and construction process requirements; the

ability to communicate this cost information is highly affected by the estimator’s

experience. Added by Uher (1996), in the current practice, the knowledge and

experience of estimators have a balanced priority when it comes to a subjective cost data

extraction and subjective identification and response to risk.

2.3.4 The Challenges of Using BIM in the Cost Estimating Process

Besides BIM giving uncounted benefits towards improving the cost estimating practice, the

technology also has some challenges to overcome. The challenges are usually associated

with the data quality of the BIM model, incompatibility between BIM modelling process and

the QS cost estimating rules, and some data exchange issues.

As expounded by Kang et al. (2012), a BIM 3D model for cost estimation is a great help for

construction professionals as the quantity of objects can be calculated automatically, making

it easy and quick to estimate the total building costs. However, Kang et al. (2012) also stated

some limitations in orchestrating the process smoothly. The limitation lies in the project

detail drawings and specifications that need to be supplied to create a 3D model with

sufficient information attached. With lack of essential data stored and more assumptions

induced to build up the costs, this will affect the quality of the BIM model in establishing

more accurate cost estimates. Although there are applications available towards entangling

this issue, they are often not easily compatible with the BIM functions, due to the contrary

approach of manipulating 3D models.

Additionally, Kraus et al. (2007) articulated that the unstandardised procedures and diversity

of work practised amongst consultants frequently make the process of incorporating their

designated BIM operation with the cost estimating rules incompatible and more complicated.

Thus, the lack of defined procedures on BIM-cost estimating standardisation within the

project team would lead to discrepancies in the digital models that somehow repudiate the

initial purpose of integrating and sharing BIM platform to precipitate the existing process.

Furthermore, as emphasised by Sabol (2008), enrooting project information peculiarly at

project early stages is crucial in developing preliminary cost estimates within the BIM

environment. However progress demands that all project consultants deliver their most

serious efforts in determining appropriate BIM models to communicate data exchanges

between them. They need to certify that the data exchange between BIM and costing



applications can be accomplished and the interoperability of the information is supported and

integrated with the assigned standard.

Elsewhere, Wu et al. (2014) also highlighted the BIM-based estimating challenges, despite

its advanced technology towards contributing many benefits in mitigating industry flaws.

Similarly, the issues implicate the substandard BIM models and inadequate information,

conflicts related to data exchange, and lack of standardisation and inappropriate pricing

format. Those issues, if not resolved, could lead towards improbable project planning and

imprecise decision making. Obviously, what makes the inefficiencies towards time, cost and

quality in cost estimating practice is the lack of modelling standards on BIM-supported cost

estimating (Smith, 2016). It further necessitates that Quantity Surveyors, as cost estimators,

adapt various approaches in costing the project, leading to unreliable cost assumptions and

inaccurate estimates. Through demonstrating two case studies, Firat et al. (2010) conclude

that the largest issue in measuring building quantities via a model-based system is the lack of

design control and modelling guidelines; this should then be tackled accordingly.

2.4 THE ADOPTION OF BIM TECHNOLOGY

Understanding why an individual chooses to adopt a technology is essential to a

successful implementation technology. It is highly dependent on the individual’s adoption

patterns, although the decision to adopt is determined by higher level management (Straub,

2009). Straub (2009) emphasised that individuals should concentrate on understanding,

adopting and learning new technology by themselves instead of focusing only in the formal

organisation. There are several adoption theories related to the individual adoption of

technology, namely Roger’s Innovation Diffusion Theory (IDT), Theory of Reasoned Action

(TRA), Theory of Planned Behaviour (TPB), Technology Acceptance Model (TAM), and

Unified Theory of Acceptance and Use of Technology (UTAUT).

2.4.1 Overview of Technology Adoption Theories

Rogers in 1962 initiated the adoption technology theory through the Innovation Diffusion

Theory (IDT). IDT introduced five characteristics of innovations that described different

adoption rate among the users. The five components outlined that successful adoption is

dependent on the perceived innovations being more advantageous, compatible, easily tried,

highly observable, and not difficult to use (Rogers, 1995).



Figure 2.9: A model of stages in the innovation-decision process (Rogers, 1995)

The next adoption theory is the Theory of Reasoned Action (TRA) that was introduced by

Fishbein and Ajzen in 1975 (Ajzen & Fishbein, 1980). TRA emphasised the relationships of

individual beliefs, attitudes, norms, intentions and behaviour, in using or rejecting a

technology.

Figure 2.10: Theory of Reasoned Action (TRA) model (Sadeghi & Farokhian, 2011)

In 1991, Ajzen proposed another Theory of Planned Behaviour (TPB) that was designed to

describe human behaviour in a particular context (Ajzen, 1991). TPB added perceived

behavioural construct, rather than only attitude and subjective norm elements in portraying

intention and behaviour to adopt a technology.



Figure 2.11: Theory of Planned Behaviour (TPB) model (Ajzen, 1991)

The Technology Acceptance Model (TAM), was suggested by Davis (1989). TAM

highlighted a perceived ease of use and perceived usefulness to determine whether

individuals have the intention to use, and what leads them to use technology.

Figure 2.12: Technology Acceptance Model (TAM) (Davis, 1989)

Venkatesh et al. (2003) recommended the Unified Theory of Acceptance and Use of

Technology (UTAUT) which featured the approach of performance expectancy, effort

expectancy, social influence, and facilitating conditions; together with gender, age,

experience, and voluntariness of use, towards users’ behavioural intention and usage

behaviour in accepting a technology.



Figure 2.13: Unified Theory of Acceptance and Use of Technology (UTAUT) model (Venkatesh et al., 2003)

2.4.2 Perceived Benefits towards BIM Adoption

In respect of BIM adoption, Lu et al. (2012) remarked that the diffusion of BIM adoption is

predominantly determined by the industry perceived benefits towards the technology.

Accordingly, for this research, the main focal point is the antecedent of improved

information in BIM, as a perceived benefit towards the technology adoption. Further,

perceived benefits are entailed as one of the underpinning measures in most of the

technology adoption theories mentioned earlier. Roger’s IDT has outlined the relative

advantages as one of the aspects that impact technology adoption (Rogers, 1995). Related to

the perceived benefits, it is the perception or beliefs of individuals that the innovation is

better than the traditional way when they see the innovation as being advantageous for them

(Straub, 2009). It has been theorised that the more the given perceived relative advantage,

the better the adoption rate will be. Along the same lines as perceived benefit, TAM termed

perceived usefulness to describe how the individual’s beliefs that technology could help

them perform their job better may lead to the rejection or acceptance of the technology

(Davis, 1989). Similarly, in UTAUT, they combine the constructs of relative advantages of

IDT and perceived usefulness in TAM to form the measure of performance expectancy in

evaluating technology adoption (Venkatesh et al., 2003). They define the performance

expectancy as individuals’ beliefs that using the technology at some point will assist them to

increase job performance. Meanwhile, beliefs function as behavioural determinants to

adopting a technology (Davis, 1989) and TPB reiterates that it is to be determined by a

person’s intentions and actions. Accordingly in TRA, Ajzen (2012) added that to understand



what factors contribute to people’s behaviour to adopt or not to adopt a technology is by

assessing their beliefs, the grounds that affect their attitudes towards behaviour.

2.4.3 BIM Models and Frameworks

In facilitating the existing users and increasing the level of BIM adoption amongst the non-

users, efforts were made through several research studies that attempted to develop BIM

models or frameworks to suit the requirements of the stakeholders in the construction

industry. The summary of the related research is shown in Table 2.6 below. Most of the

research established a conceptual model/framework pertaining to certain issues (Cao et al.,

2015; Chen et al., 2015), adoption model/framework for organisations (Son et al., 2015;

Osman et al., 2015), capabilities’ relationship framework (Wong et al., 2015) education

framework (Ali et al., 2016), adoption framework for BIM codes & standards (Quek, 2012),

and application models/frameworks in employing BIM projects (Succar, 2009; Cerovsek,

2011; Jung & Joo, 2011; Porwal & Hewage, 2013; Xu et al., 2015; Lu et al., 2016; Xu et al.,

2016; Soon et al., 2016).

Table 2.6: Summary of research establishing BIM frameworks or models

Authors (Years) Issues Types of model/framework

Succar (2009) BIM framework for a research & delivery foundation for industry stakeholders

Deliverables framework

Cerovsek (2011) A critical review of BIM for technological development

Methodological framework

Jung & Joo (2011) BIM for practical implementation Implementation framework

Quek (2012) Strategies and frameworks of BIM for Quantity Surveyors

Adoption framework

Porwal & Hewage (2013)

BIM partnering for public construction projects

Procurement framework

Cao et al. (2015) Practices & effectiveness of BIM Conceptual model

Chen et al. (2015) Bridging BIM & building Conceptual framework

Son et al. (2015) Antecedents affecting BIM adoption in design organisations

Adoption framework

Wong et al. (2015) BIM capabilities in QS practice & project performance

Relationship framework

Osman et al. (2015) BIM for QS firms Adoption model



Xu et al. (2015) Incorporating expert reasoning into BIM-based cost estimating process

Cost estimation model

Lu et al. (2016) 5D BIM for construction projects Financial decision making framework

Xu et al. (2016) Integrating application of costing for computer-aided professional practice in the construction industry

Construction cost estimation framework

Soon et al. (2016) QS firm in managing the constraints of BIM implementation

Change model

Ali et al. (2016) BIM for QS students in Malaysia Education framework

This research focuses on the cost estimating practice by the Malaysian Quantity Surveyors that

incorporates BIM technology. Amongst the above listed research, the studies from Quek (2012),

Wong et al. (2015), Osman et al. (2015), Xu et al. (2015), Lu et al. (2016), Xu et al. (2016), Soon

et al. (2016), and Ali et al. (2016) are much affiliated towards the scope of this research. Quek

(2012) made known the strategies that the Quantity Surveyors in Malaysia should take to assess

the BIM impact on the need to create more awareness and understanding towards technology

capabilities in facilitating their practice. It has been emphasised that an independent approach

should be initiated by the QS professionals I n highlighting their roles as project cost advisors

within BIM implementation. Meanwhile, the existing research articulated the BIM and QS

practice issues more to an adoption of BIM code and standards (Quek, 2012), general BIM

capabilities towards QS performance (Wong et al., 2015), theoretical adoption aspects (Osman et

al., 2015), challenges attributes (Soon et al., 2016), education facets (Ali et al., 2016), and

technical features of BIM applications in cost estimating (Xu et al., 2015; Xu et al., 2016; Lu et

al., 2016). Since previous research was fragmented in delivering the relevance of the QS cost

estimating practice using BIM, more research considering the necessary process should be

studied in depth.

2.4.4 BIM Maturity Models

In measuring the adoption level of BIM usage amongst the stakeholders in the construction

industry, two common BIM maturity models are regarded significant for this research. They are

the UK BIM maturity model developed by Bew and Richard in 2008 (Bew & Underwood, 2009)

and BIM maturity model by Bilal Succar (Succar, 2010).

The UK BIM maturity model diagram as portrayed in Figure 2.14 has evolved through three

levels of BIM implementation. The definition for each level as explained by Bew & Underwood



(2009) denotes the BIM progress accomplished throughout the evolution of overall levels. Level

0 represents the application of unmanaged 2D such as CAD, in which most paper drawings are

used. Level 1 demonstrates more manageable CAD whether in the 2D or 3D environment with

some collaboration tools operated, but with no integration between the data packages. Level 2

provides a managed 3D platform worked in separate discipline tools although with attached data,

making it nearly approaching the integration part. The UK BIM task group, through the PAS

1192-5:2015 document, however, updated the definition for Level 3, which has been described as

having an integrated electronic information with full automated connectivity and being web-

stored (BSI, 2015). The detailed definitions of the different stages in Level 3 are consequently

explained by Digital Built Britain, as summarised below (HM Government, 2015);

• Level 3A – enables improvements in the Level 2 model

• Level 3B – enables new technologies and systems

• Level 3C – enables the development of new business models

• Level 3D – capitalises on world leadership

Figure 2.14: UK BIM maturity model (Source: Bew & Underwood, 2009)

The BIM maturity model by Succar as described in Figure 2.15 is based on three stages

concerning the technology, process and policy components. As explained in Succar (2010), the

stages consist of object-based modelling (BIM Stage 1), model-based collaboration (BIM Stage

2), and network-based integration (BIM Stage 3). There are a further four steps identified along

those stages; (1) A Steps starting from pre-BIM leading to BIM Stage 1, which involve manual,

2D or 3D CAD environment; (2) B Steps initiating object-based modelling from Stage 1 leading

to Stage 2; (3) C Steps introducing model-based collaboration from Stage 2 leading to Stage 3;



and (4) D Steps commencing network-based integration in Stage 3 leading towards Integrated

Project Delivery (IPD).

Nevertheless, both maturity models will not be thoroughly discussed throughout this research,

but are the benchmarks of measuring the level of BIM adoption concerning the related issues in

this research.

Figure 2.15: BIM maturity model by Bilal Succar (Source: Succar, 2010)

2.5 THE IMPLEMENTATION OF BIM IN MALAYSIA

BIM has been promoted in the Malaysian Construction Industry since 2011 where

Construction Industry Development Board (CIDB) Malaysia has been given the mandate to

lead the BIM uptake amongst the construction industry players (Bernama, 2014). However,

the first BIM term was mentioned by the CEO of the Construction Industry Development

Board Malaysia (CIDB), Datuk Seri Prof. Ir. Dr Judin Abdul Karim, during the first BIM

conference of Malaysia in 2009, encouraging BIM to be implemented in the industry to gain

more effective construction projects (CIDB, 2013). Since then, CIDB has undertaken some

initiatives to expand BIM use in the Malaysian Construction Industry, including setting up a

national BIM project for reference, assigning BIM committees to monitor BIM activities as

well as conducting BIM seminars and workshops. CIDB has claimed to spend about RM1.5

million for the BIM programmes, starting early 2014 (Bernama, 2014).

However, BIM implementation in Malaysia was earlier known to be driven by private

sectors since 2009. This has been acknowledged by Khor Wei Moon, Technical Design

Director of Sunway Group (a property development & construction company), even without

support from the government in engaging BIM (McGraw Hill Construction, 2014a). Even

though 80% of the architecture firms in Malaysia are aware of the BIM benefits offered to

them, unfortunately only 20% of them are currently using BIM (Mohd-Nor & Grant, 2014).

As a result of low implementation of BIM in Malaysia, the BIM level remains at 0 to 1,



considered as the lowest level in the scale of the BIM UK maturity model (Zahrizan et al.,

2013). In realising the scenario of slow BIM adoption in the industry, the CEO of CIDB

during International BIM Day 2014 again urged the construction players to jointly encourage

BIM in their projects to produce more BIM professionals (Bernama, 2014).

2.5.1 Timeline Analysis of BIM Development

To detail the BIM growth in Malaysia, Table 2.7 shows its chronology by timeline analysis.

Looking at the current progress of BIM implementation in the Malaysian construction

industry, competent actions should be taken by construction players. Other than benefiting

from BIM for better construction project planning and management, adoption will contribute

to more BIM professionals being produced in the industry.

Table 2.7: Timeline analysis of BIM implementation in Malaysia (Sources: CIDB, 2013; buildingSMART Malaysia, 2015; CIDB, 2015)

Year Progress

2007

BIM has been a new internal issue in the Malaysian Public Work Department (PWD) or Jabatan Kerja Raya Malaysia (JKR). In fact, there were BIM initiatives by JKR and JKR occupied BIM Departments as well, but limited to JKR projects only.

(Malaysian Public Works Department (PWD) or Jabatan Kerja Raya Malaysia (JKR) is the federal government department in Malaysia under Ministry of Works Malaysia (MOW), which is responsible for construction and maintenance of public infrastructure in Malaysia)

2009

The progress of BIM was mainly driven by private sectors from 2009 in Malaysia.

In August 2009, the BIM term was first used in the Malaysian construction industry during the Conference of Infrastructure & Construction Asia’s Building Information Modelling & Sustainable Architecture 2009. The CEO of the Construction Industry Development Board Malaysia (CIDB), Datuk Seri Prof. Ir. Dr Judin Abdul Karim, in his keynote speech, urged construction companies to adopt ICT and BIM in obtaining effective workflow for project development and implementation.

(CIDB is a professional board which one of its functions is to promote and stimulate the development, improvement and expansion of the Malaysian construction industry)

2010

The first national BIM project in Malaysia, the National Cancer Institute Putrajaya was announced and launched as the first government project using BIM methodology.

A survey on adoption of BIM was conducted and reported that the BIM usage in Malaysia was about 2%.

http://en.wikipedia.org/wiki/Government_of_Malaysia

http://en.wikipedia.org/wiki/Malaysia

http://en.wikipedia.org/wiki/Ministry_of_Works_%28Malaysia%29



2011

CIDB had taken up a BIM initiative for the construction industry in Malaysia as a whole, whilst JKR had limited BIM usage only for internal JKR projects. However, CIDB in collaboration with JKR had conducted BIM programs in Malaysia throughout the years.

2013

The national BIM project, National Cancer Institute Putrajaya, completed two weeks earlier than the stipulated completion date, due to the implementation of BIM.

In April 2013, JKR conducted a survey, which showed an increase of 20% on BIM usage in Malaysia.

On 24th July 2013, CIDB and JKR set up a National BIM Steering Committee, chaired by JKR, and with committee members consisting of government agencies, professional bodies, private sectors and academia.

On 19th June 2013, CIDB in collaboration with the Construction Research Institute of Malaysia (CREAM) and Universiti Teknologi MARA (UiTM) organised a seminar and workshop, entitled “Issues and Challenges in Implementing Building Information Modelling (BIM) by SMEs in the Construction Industry”. It was attended by the SMEs of various sectors and sub-sectors, academia, private developers, government agencies and consultants. This seminar and workshop aimed to discuss issues and challenges faced by SMEs in adopting BIM.

The Workshop Report (Series 2) was published following the outcomes of BIM Steering Committee meetings and a workshop conducted with regards to BIM Roadmap implementation for Malaysian Construction Industry.

2014

A workshop on the “Contractor’s Acceptance of Building Information Modelling (BIM) Towards Improvement of Project Performance and Profitability” was held on 25th of February 2014.

On 22nd September 2014, International BIM Day was organised by CIDB. Panel speakers were from local & international BIM professionals. It aimed to expose public & private sectors to BIM approaches in improving processes & management of construction projects.

CIDB, JKR and industry players developed a BIM Roadmap 2015 and BIM Guide for the Malaysian construction industry. BIM’s definition in the Malaysian construction industry context by the BIM Steering Committee was defined in BIM Roadmap 2015.

BIM initiatives are currently being led by the industry's BIM expert and certified trainer of BIM applications. The Malaysian AEC industry is moving forward to adopt BIM at a different level of maturity. Currently, as recorded by CIDB, there are more than 20 projects which have been utilising the concept of BIM.

2015

Perbadanan PR1MA Malaysia (affordable housing provider throughout Malaysia) under the BIM Unit of Cost Strategy Department has established a Building Information Modelling Guide to assist industry practitioners in managing BIM requirements in their project delivery.



2016

The government of Malaysia has decided to implement BIM for their projects by year 2016. Hence, it is foreseen that industry players are required to understand and be able to use BIM in the future as BIM becomes more important in government projects.

2016 -2020

CIDB in collaboration with the Ministry of Works Malaysia has launched the Construction Industry Tranformation Programme (CITP) 2016-2020 in September 2015. This document contains a national agenda to transform the Malaysian construction industry to become highly productive, environmentally sustainable and globally competitive. One of the four strategic thrusts outlined is on improving the productivity through adoption of technology. The CITP promotes BIM as an advantage and the issues of limited BIM adoption should be tackled accordingly.

In line with the CITP agenda, the CIDB recently urged the developers in Malaysia to use

BIM for government projects that cost more than RM100 million by 2020 in order to

optimise the project cost (Bernama, 2016a;Bernama, 2016b). Along with that, a BIM centre

of excellence is to be set up in Kuala Lumpur by March 2017 to enable stakeholders to better

understand BIM. Collaboration with several universities in Malaysia on BIM usage for

government projects has also been done to continuously accelerate the adoption within the

construction industry. Other than the completed National Cancer Institute Putrajaya & the

Selangor Malaysian Anti-Corruption Commission Building in Shah Alam, which was still

under construction, the Government’s future planning is to apply a BIM system to build

hospitals in selected several states in Malaysia.

2.5.2 Construction Industry Transformation Programme (CITP) 2016-2020

The construction industry is crucial for the Malaysia economy and is positively correlated with

the economic development measured by Gross Domestic Product (GDP) per capita. To become a

developed nation, Malaysia requires more energy-efficient & higher quality buildings,

infrastructure & cities. Thus, the national agenda of CITP was aimed to transform the Malaysian

construction industry based on four strategic thrusts, which are; (1) raising overall productivity

level of the industry; (2) incorporating environmental sustainability in the design, construction &

maintenance of building & infrastructure; (3) improving competitiveness in the capability &

capacity of the industry players internationally; (4) improving overall quality, safety &

professionalism of the industry.

One of the CITP four strategic thrusts is to address the issue of productivity of the construction

industry in Malaysia. The industry productivity level was perceived as one of the lowest in the

economy, with the slow uptake of technology and modern practices being identified. The CITP

aimed to improve productivity to match with higher wages. Productivity drives the growth of the

Malaysian economy, in which the requirement to adopt modern technologies and practices will



reduce the dependencies on low-skilled construction workers. Therefore, there is a need to

increase the usage of technology & modern methods that are capable to improve the productivity

of the construction industry.

One of the solutions towards the case is adopting the technology named Building Information

Modelling (BIM). The CITP encouraged the adoption of construction IT such as BIM towards

increasing the productivity, leading to more cost-efficient & faster delivery of construction

projects, hence achieving higher earnings as part of the economy contribution. BIM technology

benefits the construction projects to reduce reworks & redundancies that lead to cost savings,

hence directly impact on the improvement of the construction industry productivity. It was

formerly recognised as an important focus & strategy under the 11th Malaysian Plan (RMK11)

(EPU, 2015) in which unlocking the productivity towards accelerating economic growth

becomes one of its main goals. In achieving the target, RMK11 articulated the requirement of

leveraging data to improve lower costs and escalate open data amongst the construction industry

stakeholders. In regard to that, BIM tools were perceived to provide a platform towards

integrating the data in one resource centre for one particular project. The CITP aspiration was

then to become the next goal in the RMK11 service delivery enhancement mission.

However, through the CIDB Survey 2014, the adoption of BIM in Malaysia was reported as low,

at 10% as compared to other countries such as the US, Singapore and the UK (refer Figure 2.16).

The survey used a minimum of Stage 1 BIM maturity as a base of the BIM adoption percentage.

The CITP has identified several challenges of BIM implementation amongst the construction

players in Malaysia. Those are the lack of experts to prepare & apply BIM plans; the high costs

invested in BIM software & upgrading of hardware to make it compatible; the lack of standards

and guidelines; the requirement of mindset changes to the current way of working especially in

using new technologies; the lack of readiness of local authorities & regulators to accept BIM

mainly in training the local staff; the high need of collaboration & integration amongst various

disciplines towards successful BIM adoption.



Figure 2.16: BIM percentage level of adoption in Malaysia (Source: CIDB, 2015)

The limited adoption of BIM to improve the productivity in the Malaysian construction industry

demanded the CITP to include some initiatives to roll out the technology advantage across

construction project life-cycle under productivity thrust of P4. The P4 thrust underlined the

initiative to facilitate BIM adoption in the construction industry via regulation (P4a), establish a

reference centre to support the development & adoption of BIM (P4b), and implement a

competency & learning management system (P4c). Through this P4 initiative, CITP suggested

that Malaysia upgrade its current BIM implementation of Stage 1 to Stage 2. Stage 1 adoption

characterises the BIM usage upon object-based software within one discipline only, while Stage

2 adoption features two disciplines or more applying the digital model to collaborate in a project.

The stages of BIM implementation in Malaysia was actually referred to in the UK BIM Maturity

Level guide as a benchmark. The four BIM maturity levels of adoption as implied in the CITP

document are shown in Figure 2.17 below.

Figure 2.17: Stages of Malaysia’s BIM adoption (Source: CIDB, 2015)

It was recommended in the CITP that Malaysia should produce the minimum

implementation rate of 40% within BIM Stage 2 for the public projects above RM100

million. Another aim was that adequate training in implementing BIM should be supported



for all the construction players in Malaysia. The CITP agenda comprehensively delivered the

significance of the BIM platform as a driven-technology towards achieving the improvement

in the productivity of the Malaysian construction industry. Therefore, all the industry

stakeholders must work together and take appropriate actions towards accomplishing the

CITP aspiration. As for researchers specifically, more studies need to be conducted in

conveying all of those issues pertaining to challenges in implementing the BIM technology

in the country.

2.5.3 BIM Research for Malaysian Construction Industry Context

In realising the connotation of BIM towards driving the Malaysian construction industry, several

research works particularly studying the Malaysian BIM context have been developed by several

authors. The topics varied from the implementation status, critical success factors, benefits and

barriers, impacts, tools application, initiatives, and so on. Table 2.8 encapsulates the studies as

follows;

Table 2.8: Research on BIM in the Malaysian construction industry context

Authors (Years) Issues Highlights

Enegbuma & Ali (2011)

A preliminary study on BIM implementation

Assessing the state of present BIM policies, technology know-how, level of usage, barriers & suggestions on the state-of-the-art of BIM in the Malaysian construction industry

Enegbuma & Ali (2011)

Critical Success Factors (CSFs) analysis of BIM implementation

Several CSFs of BIM from literature & validation of preliminary findings of interviews with industry top management

Ali et al. (2013) BIM awareness & readiness among QSs & QS firms

Infancy rate of awareness & readiness level due to concerns on workforce training supports, legal & integration issues

Gardezi et al. (2013) Prospect of BIM as conflict resolution tool

Proposed BIM model/framework to potentially reduce adverse effects of construction problems such as delays, cost overrun, productivity, quality, etc.

Latiffi et al. (2013) BIM application Exploring BIM implementation based on a literature review on previous BIM studies pertaining to definitions, history, construction issues, tools & benefits

Zahrizan et al. (2013) Exploration of BIM adoption by qualitative approach

Interviews with organisation experiencing BIM revealed the difficulties of implementation due to lack of guidelines, lack of government involvement & resistance to change among people



Ali et al. (2014) A preliminary study on the barriers & driving factors in implementing BIM

Barriers identified were lack of knowledge, reluctance & insistence; while driving factors were government support & enforcement, promotion of training programs & initiative of senior management

Enegbuma et al. (2014)

A preliminary study impact of BIM use

People perception had the highest effect on collaborative processes in which business process re-engineering had the highest effect on BIM adoption


Measurement of theoretical relationships in BIM adoption

In the BIM model that resulted the dimensions are people, process , technology, strategic IT planning & collaborative planning, which aimed at improving BIM adoption


BIM penetration Linking paths to factors of users perceptions of people, process & technology on how they react in strategic IT implementation & collaborative environment to fully support extensive BIM penetration

Harris et al. (2014) Prioritising BIM initiatives

Four-step process to identify the priority initiatives for BIM implementation which are overseas review benchmarking, engagement with locals, prioritisation & focus group validation

Latiffi et al. (2014) BIM roles Interviews with BIM government project consultants revealed the effects & potential improvement of BIM

Latiffi et al. (2014) Government’s initiative in using BIM for construction projects

An interview revealed several initiatives by Public Works Department (PWD) and other bodies

Latiffi et al. (2015) Application of BIM in the first government project

Interviews revealed the necessities & benefits of BIM to increase quality in completing the project successfully

Yusuf et al. (2015) BIM potential for effective building industry practice

An integration strategy to facilitate adequate smooth adoption of BIM in the building industry to overcome shortcomings in traditional processes

Rogers et al. (2015) BIM adoption from the perspectives of engineering consulting services firms

Main barriers of adoption were lack of well-trained personnel, guidance & governmental support; while main drivers were market demands & competitive advantage




Effects of perceptions on BIM adoption

The high correlation between people, process & technology in which process significantly affected BIM adoption

Ismail et al. (2015) Perspectives of QS on cost estimating incorporating BIM

Potential drivers to implement BIM are client, innovation, speed, time & cost, improvement & self-awareness, efficiency, and competition

Hussain et al. (2015) A pilot study in unlocking the potential value of BIM implementation

Measuring the potential of value of BIM implementation from the perspective of client organisation, from which resulted that Malaysia is on the right track to implement BIM like other developed countries

Lan & Omran (2015) Evaluating the understanding of industry towards BIM technology

A survey amongst architects revealed the level of BIM acceptance amongst them is still low

Lan et al. (2015) BIM level of understanding & implementation among civil and structural engineers in Penang

Identifying understanding & main factors of organisational change towards enhancing BIM implementation

Yaakob et al. (2016) Critical Success Factors (CSFs) of implementing BIM

Identification of CSFs of BIM implementation from past research

Zainon et al. (2016) The Rise of BIM & its impact towards quantity surveying practices

Reviewing challenges & opportunities of BIM in QS profession

Mamter & Abdul Rashid (2016)

Holistic BIM adoption and diffusion

Reviewing existing literature on BIM adoption & diffusion theory/model/framework

Most of the studies listed above explored the general BIM adoption, reviewing and assessing the

issues such as the level of usage, benefits and barriers of employing BIM (Enegbuma & Ali,

2011; Latiffi et al., 2013; Zahrizan et al., 2013; Ali et al., 2014; Latiffi et al., 2015). A series of

research by Enegbuma and colleagues in 2014 and 2015 specifically emphasised the

relationships of people, process and technology aspects for the strategic implementation of BIM.

Other than that, other researchers took other approaches in evaluating BIM in Malaysia such as

identifying the critical success factors in employing BIM (Enegbuma & Ali, 2011; Yaakob et al.,

2016), framework-related (Gardezi et al., 2013; Mamter & Abdul Rashid, 2016), BIM potential

values (Yusuf et al., 2015; Hussain et al., 2015; Latiffi et al., 2014), BIM initiatives (Harris et al.,

2014; Latiffi et al., 2014).



By professions, Rogers et al. (2015) delved into the BIM implementation amongst the

engineering consultation services firms in Malaysia. The study led to main BIM barriers of well-

trained personnel, guidance and governmental supports identified. However the market demands

and competitive advantage became the key drivers that motivated those firms to continue using

BIM in the future. Whereas, Lan & Omran (2015) revealed that the low level of BIM adoption

amongst the Malaysian architects was due to the professionals who were not yet to accept the

technology in their practice. They suggested that setting a training plan was the best way to

overcome the BIM adoption challenges, other than recommending government initiative in

accelerating BIM development in the construction industry practice. Additionally, Lan et al.

(2015) reported that amongst the civil and structural engineers in Penang (one of states in

Malaysia), the level of BIM understanding was quite high, although the implementation was

still at the introductory stage.

In focusing on the QS profession in Malaysia, a few research works have been recognised

particularly in evaluating the BIM evolution in practice. In assessing the BIM awareness and

readiness amongst the Quantity Surveyors and quantity surveying firms, Ali et al. (2013)

suggested that the infancy rate level as the outcome, was mainly due to training support, legal and

integration issues. Whilst specifically focused on the cost estimating practice by the Quantity

Surveyors, Ismail et al. (2015) acknowledged the potential BIM implementation drivers from the

perspective of the interviewees. The drivers include the client, innovation in practice, speed in

tasks, time and cost, improvement and self-awareness, efficiency and competitiveness. Other

than that, Zainon et al. (2016) analysed the challenges and opportunities of BIM in the QS

profession, specifically in Malaysia. The challenges were classified as teamwork and

collaboration, legal issue, the use of information and changes in practice, implementation issue,

financial and time considerations, process change, human resource and organisational issue,

professional support, technical challenges, and management. The opportunities incorporated

transformation in job scope, re-branding of Quantity Surveyors, globalisation, and beyond limit.

As far as those research concerns, lack of research discussed the BIM operation within the cost

estimating practice by the Quantity Surveyors. The study by Ali et al. (2013) was specifically

designed to evaluate the awareness and readiness of the Quantity Surveyors and quantity

surveying firms in Malaysia. However, the insufficient responses obtained from the study could

not make generalisations for the overall population, yet were treated as exploratory. The cost

estimating task is the major activity in quantity surveying practice, in which establishing the most

accurate and reliable cost estimates as discussed in the previous section, is critical towards

achieving more successful construction projects. Thus, more studies should be actioned,



especially in assessing the explicit BIM capabilities in contributing towards improving the cost

estimating practice.

2.6 CONCEPTUAL FRAMEWORK AND HYPOTHESES

In closing the gaps identified from the previous literature, this research tends to

develop a model in incorporating the cost estimating practice by the Quantity Surveyors

within the BIM environment. Based on the literature reviewed previously, this section links

the relationship of BIM improved information on cost estimate reliability towards the

relationship of perceived benefits of BIM adoption. The relationship results then lead to the

conceptual framework development and hypotheses representing the framework are

established. This conceptual framework is developed based on the literature discussion in

this chapter and will be finalised as a final framework in the data analysis chapter (Chapter

5). Based on the literature described above, the conceptual framework is presented, as in

Figure 2.18 below.

Figure 2.18: Conceptual framework of BIM improved information and cost estimates

reliability towards adoption

The first relationship significantly links the BIM-improved information and cost estimate

reliability antecedents. BIM-improved information through its data visualisation, reliable

database and data coordination, is justified to have a significant impact on the cost estimate

reliability. BIM, which allows 3D visualisation at any construction stage, generates better

understanding for users (Ali et al., 2014; Sheth & Malsane, 2014; Eadie et al., 2013; Kim,

2012; Furneaux & Kivvits, 2008); overcoming the difficulties of comprehending

construction processes by using only 2D views in traditional method (Rajendran et al., 2014;



InfoComm International, 2011; Leicht & Messner, 1997). Thus, if relates to estimators’ task

to produce cost estimates, BIM through its better visualisation helps the estimator to

understand the estimating process better to improve its reliability (Thurairajah & Goucher,

2013).

Additionally, BIM that creates a reliable database supplies digital information and

potentially establishes more understanding with additional information obtained from the

building model (Kumanayake & Bandara, 2012; InfoComm International, 2011). The data

repository could be shared amongst team members in the project, with any information being

updated when there are any changes that occur; hence generating more efficiency and

accuracy in the information as compared to traditional drawings (Kim, 2012; Kumanayake &

Bandara, 2012; InfoComm International, 2011). Coordinated data in the BIM model also

synchronises the information and identifies any clashes between building elements, in which

it improves the exchange of information amongst the team members’ systems (Alp &

Manning, 2014; Demian & Walters, 2013; Kumanayake & Bandara, 2012; Campbell, 2007).

This practice, therefore, reduces errors and reworks (Gerrard et al., 2009; Sawhney &

Singhal, 2013; Aranda-mena et al., 2008; Popov et al., 2010; NBS, 2014; Mccartney 2010)

of the estimation process to produce more reliable cost estimates. The hypothesis that could

be generated in describing this relationship is as follows.

H1: Improved Information has a positive and significant effect on cost estimates reliability

It has been rationalised earlier how data visualisation, reliable database and data coordination

as essential features of BIM technology could improve the information in systems.

According to Leicht & Messner (1997), the components that distinguish BIM and traditional

documentation are a visualisation of geometric information, data availability for further

analysis, and the existence of information. BIM offers numerous benefits of its

implementation, including that it improves information received to be manipulated for

design and construction utilisation throughout the project stages. Obviously, the BIM

application is capable of automatically extracting visual information such as floor plans,

elevations, 2D and 3D sections, detailed sections, text-based information, model analysis and

simulation results. Exchange information is executed smoothly via proprietary format within

the database without losing data, and the interaction between applications is often ensured

through IFC (Monteiro & Martins, 2013). The IFC that allows information to be shared

across the team (InfoComm International, 2011) next permits data coordination within the

same shared resources. This research speculates that improved information is regarded as

perceived benefit towards a human intention to use BIM technology. Its dimensions of data

visualisation, reliable database and data coordination, reflect as motivation factors for the



non-users in adopting BIM. The interests contained within those dimensions will possibly

create beliefs that the BIM technology will assist them to improve their job performance,

later leading to accepting and adopting the technology (Straub, 2009; Davis, 1989;

Venkatesh et al., 2003). Therefore, this research hypothesises that

H2: Improved Information has a positive and significant effect on perceived benefits

H3: Perceived benefits has a positive and significant effect on BIM adoption

H4: Improved Information has a positive and significant effect on BIM adoption.

There is a lack of research precisely explaining the cost estimate reliability impact on the

intention of individuals to adopt BIM technology. However, producing a reliable cost

estimate is a goal that an estimator desires when dealing with pricing construction projects.

Revisiting adoption technology theory, it is acknowledged that relative advantage (Rogers,

1995), perceived usefulness (Davis, 1989), and performance expectancy (Venkatesh et al.,

2003) towards the technology are among the factors that lead people to adopt it. As this

research highlights, cost estimate reliability could be improved through BIM

implementation; initially it should be pointed out that cost estimate reliability could be the

reason why estimators adopt BIM. With what BIM might offer in establishing more accurate

and reliable cost estimates, it will be whether the estimators’ perceived benefits influence

their decision in using BIM in their practice. Since cost estimate reliability is theoretically

impacted by the improved information elements as deduced in this research, it also becomes

the reason for the adoption of BIM amongst estimators. Thus, the following hypotheses in

this research are

H5: Cost estimates reliability has a positive and significant effect on perceived benefits

H6: Cost estimates reliability has a positive and significant effect on BIM adoption.

The direct relationships amongst improved information, cost estimate reliability, and the

perceived benefits described previously, might also imply possible mediation effects within

those antecedents. As for that, this research also tests the additional hypotheses, as below.

H7: Perceived benefits mediates the relationship between improved information and BIM

adoption

H8: Cost estimates reliability mediates the relationship between improved information and

BIM adoption

H9: Cost estimates reliability mediates the relationship between improved information and

perceived benefits



2.7 CHAPTER SUMMARY

This chapter has presented the research literature and provided a background and

status of BIM implementation in the Malaysian construction industry. It is found that

Malaysia has a low adoption of BIM technology even though it has been claimed that the

country started their BIM journey in 2007. Also, BIM is currently dominated by the private

sectors with government projects by PWD only being conducted internally as pilot projects,

mostly used for research development. The CIDB is the organisation that manages BIM

uptake in Malaysia. Some initiatives have been planned to encourage more industry players

to use BIM in their practices to establish more effective and efficient construction projects. It

is suggested that more guidelines and a roadmap are set up to help the industry stakeholders

to start implementing BIM.

This research focused on the cost estimating practice used by estimators (mostly Quantity

Surveyors), in which the practice must ensure that it establishes reliable cost estimates for

construction projects it involves. BIM technology is highlighted to be one of the great

solutions to improve the cost estimating practice in the construction industry. To enhance the

level of BIM adoption in the Malaysian construction industry, this research has developed a

conceptual framework that demonstrated the relationships between BIM-improved

information towards cost estimates reliability that impacted the adoption of BIM technology

among users. Improved information in BIM that generates better data visualisation, reliable

database and data coordination are hypothesised to impact the cost estimate reliability. It

potentially affects input information received by estimators, as well as their understanding

towards the estimating process, leading to employing the knowledge they obtained from the

process to produce better cost estimates. More reliable cost estimates resulting from BIM-

improved information is then associated with the perceived benefits of estimators towards

adopting BIM technology.

The next chapter describes the detailed methodology adopted in this research, based on the

reviewed literature and developed framework in this chapter.

Chapter 3: Research design and methodology


3Chapter 3: Research design and methodology

The previous chapter reviewed the literature relevant to the relationships between

BIM improved information and cost estimate reliability elements towards the adoption of

BIM technology. This chapter next discusses the research methodology applied to justify

these relationships, pertaining to the research questions and objectives set in this research.

The overall research strategies employed for this research are explained in attaining the

expected research outcomes. Adapting the research ‘onion’ from Saunders et al. (2012), this

research applies mixed method strategies, which include literature reviews, expert

interviews, questionnaire surveys and focus group discussion.

The methodology that has three main phases starts with the initial information

gathering, involving literature review and expert interviews in the first phase. It is followed

by the second phase of main data collection by using a questionnaire. The survey data is later

analysed in the next phase by adopting the statistical approach of the Statistical Package for

the Social Sciences (SPSS) that includes Structural Equation Modelling (SEM). Finally, the

third phase of this research involves the focus group discussion method for validation of

framework established in the previous survey analysis. Additionally, the results from expert

interviews conducted in the first phase are briefly explained in this chapter to provide initial

information pertaining to the main context highlighted in this research.



3.1 RESEARCH APPROACH

According to Saunders et al. (2012), a research strategy is a methodological link

between the research philosophy and methods chosen by the researcher for data collection

and data analysis. Hence, the research approach must align the proper choice of methods

with the philosophy adopted for the research. Accordingly, this research exploits the research

‘onion’ adapted from Saunders et al. (2012) to demonstrate the research strategy (refer

Figure 3.1).

To relate the research ‘onion’ with this research, it is useful to overview initially the

approach applied for this research. The overall strategy for this research is designed to

coordinate research questions, research objectives and expected research outcomes with data

collection and analysis methods respectively, as shown in Table 3.1.

Figure 3.1: The research ‘onion’ (Saunders et al., 2012)

Based on the research strategy tabulated in Table 3.1, this research is seen as using a mixed

method approach as it combines the questionnaire survey (quantitative approach) and focus

group discussion (qualitative approach) as primary methods (Saunders et al., 2012; Creswell,

2009). Prior to these survey and focus group methods, the exploratory means by interviewing

experts is employed for this research to gain more insights and to support information

obtained from the literature review (Saunders et al., 2012).



Table 3.1: Overall research strategy

No Research Questions

Research Objectives

Data Collection Method

Data Analysis Method

Expected Research Outcomes

1

What are the most influenced cost estimates reliability and BIM factors amongst Quantity Surveyors in Malaysia?

Information gathering

Preliminary exploratory study

Literature review

Expert interviews

Content analysis

Preliminary framework

Preliminary exploratory results

2

What are the current BIM implementation statuses of Quantity Surveyors in Malaysia?

To determine the current BIM implementation statuses of Quantity Surveyors in Malaysia

Questionnaire survey

Descriptive analysis by SPSS

BIM adoption level including software usage, knowledge, importance and future planning

3

To what extent could improved information from BIM affect the reliability of cost estimates?

To investigate the effects of BIM-improved information on increasing the reliability of cost estimates in Quantity Surveying practice

Questionnaire survey

Focus group discussion

Structural Equation Modelling (SEM) Framework validation

Significant relationships between cost estimates reliability & BIM factors towards BIM adoption

4

How can increased BIM adoption be promoted amongst Quantity Surveyors in Malaysia?

To produce a strategy for incorporating construction cost estimates within BIM to promote the adoption of BIM technology in the quantity surveying practice in Malaysia

Focus group discussion

Framework validation

A framework to assist Malaysian Quantity Surveyors in adopting BIM for their cost estimating practice



Data collected from both literature review and interviews are used as inputs to the survey

questionnaire in establishing relationships between variables in cost estimate reliability and

BIM factors, which carry the explanatory means (Saunders et al., 2012). Both the

exploratory and explanatory means involved makes this research a mixed method.

Since this research mainly used the survey to collect data and applied a focus group for

framework validation established from the survey, it is specifically addressed as a sequential

mixed method. The sequential mixed methods begin, whether with the qualitative method

and followed by a quantitative method, or vice versa (Creswell, 2009). This research

collected the primary quantitative data through survey and followed up by qualitatively

validating the framework through focus groups, to explain the quantitative results. Thus, it is

called an explanatory sequential mixed methods design (Creswell, 2012). The explanatory

sequential design for this research is illustrated in Figure 3.2, adapted from Creswell (2009).

Figure 3.2: Mixed method sequential explanatory design (adapted from Creswell, 2009)

In inductive research, theories are developed from research data analysis (Babbie, 2011). For

this research, a framework is developed as the main data collected and analysed from the

survey. As such, this research is data-driven and for that reason, this approach is considered

as inductive (Saunders et al., 2012; Cavana et al., 2001). However, as regarded by Creswell

(2009), since this research is on a mixed-method basis, it may include theory deductively,

involving framework verification derived from a quantitative approach, which is a survey.

This research survey is conducted for a particular phenomenon at a particular time; in

referring to that basis, it is a cross-sectional study (Saunders et al., 2012).



A mixed-methods approach is further related to pragmatism philosophy as it comprises both

qualitative and quantitative data (Creswell, 2009). Pragmatism philosophy integrates

different perspectives in interpreting data, which could be addressed by qualitative and

quantitative methods reliant upon the research question’s design (Saunders et al., 2012).

Pragmatism philosophy needs a rationale on why a mixed method (combining both of

qualitative and quantitative data) is applied in this research (Creswell, 2009). The rationale

of using a mixed method is described based on the process employed in this research, as

follows.

The intent of this research is to develop a framework for Malaysian Quantity Surveyors to

critically identify factors of cost estimate reliability and BIM uptake for their practices. It

becomes necessary for this research to gather information initially from expert interviews to

confirm the factors extracted from the literature review. It then provides rigorous input for

the next survey, the main data collection for this research. Relationships between those

factors are derived from quantitative analysis to further develop a framework. The

framework has to be verified by the experts amongst industry players to clarify the elements

included in the framework, giving the reason why this research demands focus group

validation.

There are other possibilities by which other methods or approaches could be used for this

particular study. Nevertheless, due to the uncertainties the study has in terms of background

and status of implementation, with limited information of previous literature, it has been

designed in the manner in which it has been executed. The preliminary interviews and

questionnaire survey needed to explore and clarify the most influential factors or aspects

pertaining to issues studied, before further analysing those in more detailed focus group

discussions. This kind of mixed-method approach produced more robust measures, in which

more understanding could be gained from both quantitative and qualitative approaches.

Other than allowing fast responses amongst assembled experts, the qualitative focus groups

provided more engaging feedback, which reflected the outcomes given quantitatively by the

survey participants. Alternatively, more detailed information could be obtained through in-

depth individual interviews. However, due to the limitation of cost and time allocated, the

focus group method suited this cross-sectional study, furnishing this research with broader

information similar to other comparison methods.

Conclusively, the overall approach taken for this research can be summarised based on the

research ‘onion’ adapted from Saunders et al. (2012). It is represented by Table 3.2 below.



Table 3.2: Overall research approach

Elements in research ‘onion’ Selection of approach Philosophy Pragmatism

Approach Induction & deduction

Methodological choice Mixed method simple (sequential)

Strategies Mixed methods research (questionnaire & focus group)

Time horizon Cross-sectional

Data collection & data analysis Questionnaire & SEM analysis

Focus group discussion & content analysis

3.2 RESEARCH PROCESS

This part of the research process gives a detailed explanation of the research methods

within the research methodology phase, namely: literature review, expert interviews,

questionnaire and focus group. This research is divided into three main phases commenced

consecutively. The phases start with information gathering (Phase 1), followed by main data

collection (Phase 2) and end with the development and validation of a framework (Phase 3).

The whole process of this research is described, as in Figure 3.3.

Briefly, the information gathering phase (Phase 1) constitutes clarifications of research

problems, aim and objectives of the research through literature review, and expert

interviews. At this stage, the preliminary research framework has already been described so

as to give a picture of research outcomes. The variables obtained from literature and

interviews consequently benefit the next process in Phase 2, which is the data collection.

From the data gathered from Phase 1, the survey (Phase 2) is executed by using a

questionnaire method. The process is then progressed to Phase 3, where the results of the

survey are statistically analysed by using a Statistical Package for Social Science (SPSS) and

also confirmatory factor analysis (CFA) of the Structural Equation Method (SEM). The

relationships between the variables are traced from the SEM to develop the research

framework. Expert panel validation by a focus group next verifies and refines the

framework. The finalisation of the framework is the ultimate process for Phase 3, leading to

the conclusions to be drawn and recommendations to be made from this research.



Figure 3.3: Research process flow

3.2.1 Literature Review

Among the uses of literature for a qualitative study are the determination of the research

problem of the study, as part of the review section of literature in research, which becomes a

basis for comparing findings for the qualitative study (Creswell, 2009). Apart from the

literature review being developed as a research section, it enhances the research problem,

where the research aims and objectives are being established concurrently. The literature

review section in this research has been divided into two main research themes of “Cost

Estimating Practice” and “Building Information Modelling (BIM)”. The process

encompassed in the literature review can be described, as in Figure 3.4. Instead of the

literature review being developed as a research section, it enhances the research problem,

where the research aims and objectives are being established concurrently. Additionally, cost

estimate reliability and BIM factors drawn out from the extensive literature are correlated

with the responses from the expert interviews. Both data gathered from literature and

interviews are employed to build the questionnaire for a survey in the next phase.



Figure 3.4: Literature review process

3.2.2 Expert Interviews

Interviewing qualitatively is a process where an interviewer observes interviewees and asks

them related questions (Babbie, 2011) to obtain their thoughts & feelings (Tharenou et al.,

2007) about a topic. It is conducted face-to-face (Tharenou et al., 2007) where the questions

can be structured, semi-structured or unstructured forms (Fellows & Liu, 2008). Figure 3.5

shows the expert interview process developed for this research.

This research is a semi-structured interview type, where the researcher has set the general

guide to the questions, but it is flexible to allow any issues raised along the conversations

(Babbie, 2011). Audio recordings are employed aside from the researcher taking notes, to

help the process of transcribing and analysing the information afterwards (Fellows & Liu,

2008; Dawson, 2009). The information recorded from the interviews is analysed by using

content analysis (Tharenou et al., 2007). This analysis gives coding to the identified cost

estimate reliability and BIM factors by accommodating a mind-map concept to get the data

visibly apparent (Babbie, 2011). Interviews conducted for this research are mainly done to

compile information for questionnaire use in the next survey. Specific respondents to supply

sufficient information have to be chosen for the interviews (Tharenou et al., 2007).



Therefore, for this research purpose, experienced Quantity Surveyors in both cost estimating

and BIM, have to be classified in advance before carrying out the interviews.

As for this research, the expert interviews were conducted using in-depth interviews and

preliminary exploratory study. The in-depth interviews were intended to confirm the

extracted information from the literature review as well as to get comprehensive information

from actual practice. The preliminary exploratory study was carried out to refine the cost

estimate reliability and BIM factors derived from both literature review and in-depth

interviews were formerly employed. The final data gained was manipulated to develop a

questionnaire for the next survey. To draw different perspectives for this research, the

interviews were expected to obtain a variety of information regardless of whether those

interviewed respondents were BIM-users or non-BIM users.

Figure 3.5: Expert interview process

3.2.2.1 IN-DEPTH INTERVIEWS

In-depth interviews were conducted in August 2014 by adopting a face-to-face interview

method. The interviews took place around Selangor, Malaysia, and engaged four Quantity

Surveyors specialised in construction cost estimating with more than 20 years of working

experience. The maximum one-hour interview for each session was audio-recorded with

permission of the interviewees, and Ethics Consent Forms were signed. The recorded

conversations were then transcribed to text documents (Microsoft Word). The main



interview questions (semi-structured and open-ended) are basically pertaining to; (1) the

current method used in estimating the costs for construction projects; (2) the factors

influencing cost estimates; (3) the strategies to improve current practice of cost estimating;

and (3) the potential drivers in encouraging their company in implementing BIM for their

cost estimating practice.

Answers showed that the respondents are still exercising the traditional way to estimate cost

with the help of the Standard Method of Measurement (SMM) (Packer, 2014). Microsoft

Excel computer assistance is being used actively but without specific cost estimating

software. Single rates and approximate quantities types are commonly used to estimate the

cost of a construction project. Most of the respondents have emphasised that the information

of a project such as type, location, size and other factors are vital to producing more accurate

estimates. The Quantity Surveyors’ skills and knowledge also becomes a major factor

contributing to the estimate’s accuracy. There were several strategies that have been grouped

into four, namely, training & knowledge, software, sharing & networking, and database of

information. A respondent mentioned that the Quantity Surveyors must have regular

estimating cost training to upgrade their skills as the skills will come along with knowledge

and experience. As for software usage, even though the respondents had not been using any

particular cost estimating software recently, they agreed that it could bring benefits such as

reducing human errors, proper data saving and also easy sharing with others. The

respondents also suggested the importance of having networking with other companies, to

help them in acquiring the information they did not have in their database. Additionally, a

systematic, updated and informative database keeping all the historical data should be

incorporated in each company, so data from previous projects can be used in future projects.

Despite the respondents not practising BIM technology in their companies, a question was

designed to gain perspectives towards BIM drivers in implementing BIM amongst the non-

users. The potential BIM drivers that might encourage the respondents to adopt BIM include

client demands; innovation to conventional methods; speed in estimating costs; reduced time

& cost of project delivery; improvement to practice; efficiency in projects; and competition

from other companies. It can be concluded that the responses given were not extensive and

showed their very basic knowledge of BIM. If they were exposed widely to BIM technology,

they would undoubtedly find many more benefits from BIM, thus leading to the possible

implementation of the technology for their companies.

Acknowledging that Malaysia is still in the low level of BIM implementation, it is difficult

to get the Quantity Surveyors as estimators that fully utilise BIM in their practice. However,

in Australia, BIM adoption is far better than in Malaysia. Therefore, to get more real insights



of BIM usage in cost-estimating practice, another face-to-face interview was held in October

2014. The interview took place in and around Brisbane, Australia, and involved a 5D

Quantity Surveyor with extensive BIM experience. The company where the respondent

worked is a world leading BIM and 5D quantity surveying firm with more than 20 years’

experience. Instead of overviewing the BIM concept, the interview tracked the benefits

earned by the respondent when employing BIM for their cost-estimating practice. In giving

an answer on BIM concept, the respondent personally defined BIM as a collaborative

approach to build a building, from conception to asset management. It is essentially a central

store of information that everyone in the project puts information into, and coordinates it to

transparently collaborate with other team members, for them to understand projects

effectively.

BIM drivers captured from the literature were listed in the interview to be elaborated by the

respondent. The respondent recognised that predominantly, (1) BIM has saved time by the

quick check of any changes in the BIM model so that the team could work faster; (2) they

were able to give valuable feedback to clients via BIM models with building function

analysis and value management, which made their work more efficient and hence improved

their performance; (3) they had detected a lot of clashes and design errors in most of their

construction projects by prototyping buildings through BIM models; (4) BIM made building

measurement more accurate, consistent, and less ambiguous, as it supplied more information

from a reliable database compared by using manual 2D drawings; (5) BIM enhanced

collaboration among the team members as they could communicate and brainstorm project

information through the BIM models to get the best solution for the project; (6) BIM helped

in improving project presentation and documentation by showing the growth dimensions of

the project processes and methods; (7) BIM has provided better visualisation for the client

instead of direct translation from 2D drawings; and (8) BIM has improved information for

the project by contributing lots of information from the start which catered to better

understanding among the team members.

The key summary points that can be made based on in-depth interviews are shown in Table

3.3 below.



Table 3.3: Key summary points of In-depth Interviews

Most influencing factors towards more reliable estimates

• Project information (type, location, size, etc.) • Estimator’s skills and knowledge

Strategies for improving cost estimates

• Training and knowledge • Software usage • Sharing and networking of information • Database of information

Potential BIM Drivers to adopt BIM (non-BIM user)

• Client demands • Innovation to conventional methods • Speed in estimating costs • Reduced time and cost of project delivery • Improvement to practice • Efficiency in projects • Competition from other companies

Benefits of using BIM (BIM user)

• Save time by the quick check of any changes in the BIM model

• Give valuable feedback to client via BIM models with building function analysis and value management

• Detect clashes and design errors in projects by prototyping buildings through BIM models

• Building measurement is more accurate, consistent, and has fewer ambiguities as it supplies more information from a reliable database

• Enhance collaboration among the team members as they could communicate and brainstorm project information through the BIM models

• Improve project presentation and documentation by showing the growth dimensions of the project processes and methods

• Provide better visualisation for the client via BIM models • Improve information for the project for better

understanding among the team members

3.2.2.2 PRELIMINARY EXPLORATORY STUDY

This preliminary exploratory study was completed during July and August 2015 in Malaysia.

By combining information from reviewed literature and in-depth interviews, the cost

estimate reliability and BIM factors were outlined to form closed-ended questions in the next

preliminary exploratory study. As excessive cost estimate reliability and BIM factors were

derived from the literature and previous in-depth interviews, the purpose of this preliminary

exploratory study is to narrow down cost estimate reliability and BIM factors so as to

develop the next conceptual framework for this research. Thirty respondents (Quantity

Surveyors) from various organisation businesses were approached. They were reached either



through face-face-to-face interviews or emailed interviews. Their range of experiences in

cost estimating is varied from one to five years and up to more than 20 years. For

background information on BIM usage, the respondents were also asked about their

involvement in any BIM projects, awareness of BIM usage in the construction industry and

knowledge of BIM. Table 3.4 below presents the summary of respondents’ background

information.

Table 3.4: Respondents’ background information for Preliminary Exploratory Study

Experience in estimating cost for construction projects Number of respondents 1-5 years 10 6-10 years 12 11-15 years 1 16-20 years 1 >20 years 6

Total 30 Nature of current organisation’s business Number of respondents Client/Developer 1 Quantity Surveying firm 19 Contractor firm 4 Authority/Government 5 Others 1 Total 30

Involvement in BIM projects Number of respondents Yes 4 No 26 Total 30 Awareness of BIM usage in the construction industry Number of respondents Aware, use it 4 Aware, have not used it, interested in future 22 Aware, have not used it, not interested in future 1 Not aware, not use it 3 Total 30 Knowledge of BIM Number of respondents Quite knowledgeable 4 A little knowledgeable 18 Not knowledgeable 7 Not sure 1 Total 30

The results indicate that there are three groups of respondents involved in this survey

interview that can be categorised as junior (1-5 years of experience); senior (6-10 years of

experience); and experienced (more than 10 years of experience). The balance in the division

of the respondents’ group is relevant in establishing varied responses. Most of the



respondents are currently working in quantity surveying firms. Only four of them were

previously involved in BIM projects. The majority of respondents were aware of BIM usage

in the construction industry; even if they have not ever used it, they were all interested in

future adoption. In rating BIM knowledge amongst respondents, the majority of the

respondents claimed to have a little knowledge of BIM.

From the interview results, the majority of the respondents highlighted that the most

influencing factor when estimating cost for better reliability is the input information received

to estimate cost; and also the knowledge and understanding of the Quantity Surveyor as

estimators themselves. The greatest BIM benefit that could drive them in adopting the

technology is the improved information through its 3D model visualisation, reliable

database, and coordinated data. The key summary points highlighted in this preliminary

exploratory study are outlined as follows (Table 3.5);

Table 3.5: Key summary points of Preliminary Exploratory Study

Cost estimating factors towards reliability in cost estimates

BIM drivers towards BIM adoption

Input information • Availability of project information

such as type, location and size of project

• Completeness of information supplied from design and construction drawings for the project

Visualisation • Simulation of building construction • Better understanding on construction

processes

Database • Better production of information • Higher quality with more accurate

information Estimator’s requirement • Knowledge of general cost estimating

process • Understanding of operational

procedure in estimating cost

Coordination • Improved project planning and design • Better information received amongst

construction teams

3.2.3 Survey Questionnaire

A survey by questionnaire is typically conducted by choosing respondent samples and

distributing a set of questions (Babbie, 2011). This research performs a closed-ended

questionnaire type, in which the questions outline a set of responses determined by the

researcher in advance (Fellows & Liu, 2008; Babbie, 2011; Tharenou et al., 2007). This kind

of survey can be administered by hand, email or online (Fellows & Liu, 2008; Tharenou et

al., 2007). This research employed an online survey through the QUT Key Survey, as it is

cheaper and claims to have comparable response rates to other approaches (Babbie, 2011).

Figure 3.6 simply illustrates the process of how the survey by questionnaire was done for



this research. While, Figure 3.7 shows the example of the answer interface for the QUT Key

Survey tool.

Figure 3.6: Survey process by questionnaire

Figure 3.7: Example of answer interface for the QUT Key Survey tool



Amongst the advantages of using this survey method is that it is easier to collect data from a

large population, rather than through direct observation. Also, standardised responses are

easier to process (Babbie, 2011). Furthermore, the data from a survey can be managed to

develop models of any particular relationships between the variables studied (Saunders et al.,

2012). In this vein, the end results of this research survey have shaped the possible

relationship between the main research constructs, subsequently building a framework for

the research.

3.2.3.1 QUESTIONNAIRE DEVELOPMENT

The information gathered from the literature review, and earlier expert interviews, were used

to develop the main survey questions. As summarised in Table 3.6, the questionnaire

contains five sections (namely Sections A, B, C, D and E) with a total of 54 items. Section A

(10 items) aims at obtaining the respondents’ background in the construction field. Section A

involves clarification of respondents’ experience in terms of years in the construction cost

estimating practice specifically. It also requests information on respondents’ professional

background, current role and the nature of their current organisation’s business. Apart from

that, BIM fundamental questions on awareness, knowledge, importance, and future planning

are designed to gain data on respondents’ current state of BIM implementation. Types of

BIM software and their application by construction stages are exclusively developed for

respondents who have already used BIM in their practice. This section is essential to screen

the targeted respondents and ensuring that they are eligible and qualified to complete the

questionnaire so that more reliable information is gathered from the survey. Moreover,

Section A enables respondents to be grouped according to different subjects (BIM user and

non-user), which they have indicated in the questionnaire. This approach could shape the

conclusions made for the findings.

Table 3.6: Questionnaire content

Section Description No. of items Question type Objective A Respondent’s Background Information To provide

background information and the context of BIM implementation amongst respondents

Years of involvement in the construction cost estimating

1 Multiple choice questions (one possible answer only)

Professional background, current role, nature of current organisation’s business

3

BIM awareness 1 Types of BIM software used; Application of BIM by stages

2 Multiple choice questions (can tick more than one answer)



BIM knowledge, BIM importance, BIM future planning

3 Likert scale questions (strongly disagree to strongly agree – 1 to 10 rating)

Total items in Section A 10 B Improved Information Likert scale questions

(strongly disagree to strongly agree – 1 to 10 rating)

To identify factors that possibly influence respondents’ cost estimating practice

Data visualisation 5 Reliable database 4 Data coordination 4

Total items in Section B 13 C Perceived Benefits Likert scale questions


To investigate respondents perceived benefits towards BIM adoption

Task accomplishment 3 Estimates accuracy 3 Useful in role 3 Employment prospect 3

Total items in Section C 12 D Cost Estimates Reliability & BIM Adoption Likert scale questions


To analyse the significant relationships between the cost estimates reliability and BIM factors in promoting BIM adoption

Factors of cost estimates reliability

9

Considerations of BIM adoption towards cost estimates reliability

9

Total items in Section D 18 E Other comments 1 Open-ended question To have other

comments from respondents which are significant to the research project

Total items in Section E 1 Total overall items in questionnaire 54

Sections B, C and D consist of factors of cost estimate reliability and BIM towards the

technology adoption, which contribute mainly to the development of the research

framework. Sections B, C and D, are the critical elements in forming the research

framework. Section B collects BIM improved information factors that influence respondents’

cost estimating practice, consisting of data visualisation, reliable database and data

coordination. Section C assembles respondents’ perceived benefits connecting to those

factors, towards adopting BIM. While Section D interrelates all the factors towards



producing more reliable cost estimates, this ultimately affects the considerations of adopting

BIM.

Section E invites the respondents to give other comments that may be significant to the

research project. Although Section E serves as additional information to be collected in this

survey, it could make an important contribution to the research project. Section E in the

questionnaire provides space for the respondents to give comments that they consider

relevant to the research. This section is necessary, given that some significant variables

might not be covered in the questionnaire, particularly in the main sections B, C and D.

Consequently, Section E adds more input to the development of the research framework.

3.2.3.2 SCALES OF MEASUREMENT

Scales of measurement are response options to the items designed in the questionnaire that

measure variables in the research study (Creswell, 2012). To determine measurement scales

in research, the researchers must firstly identify what type of data they are dealing with,

namely categorical and numerical data. Categorical data refers to data, the value of which

cannot be measured numerically but is classified based on variable’s characteristics; while

numerical data refers to a data value that can be counted as quantities (Saunders et al., 2009).

The categorical data is further described as nominal and ordinal data; and numerical data is

alternatively termed as interval and ratio data. Scale as a tool or mechanism to measure these

data is termed based on these types of data, known as nominal scale, ordinal scale, interval

scale and ratio scale (Sekaran, 2003). This research mostly used nominal, ordinal and

interval scales in the questionnaire to provide response options to respondents. In relation to

Creswell (2012)’s explanation, nominal scales are applied in this research to check

respondents’ working experience, current role and organisation; as well as the types of

software and BIM application employed by respondents. On the other hand, ordinal scales

are used to describe the rank of knowledge, awareness, importance and continuation of usage

in BIM by respondents. Finally, interval scales are adopted for obtaining the level of

agreement towards the item descriptions in the questionnaire. The 10-Likert scale (“strongly

agree” to “strongly disagree”) is used in this research to illustrate the equal intervals among

the responses.

3.2.3.3 QUESTIONNAIRE PRE-TESTING

Pre-testing a questionnaire before it is distributed is important to identify and eliminate

potential problems that respondents might face when answering the questionnaire (Frazer &

Lawley, 2000; Cooper & Schindler, 2003; Zikmund et al., 2007). There are some options of

pre-testing to refine the instrument that varied from informal reviews by colleagues to setting



up a situation similar to an actual study (Cooper & Schindler, 2003). As recommended by

Frazer & Lawley (2000), the questionnaire designed for this research has been pre-tested

with academicians, research fellows, and a few respondents from previous interviews so as

to provide feedback about whether it facilitates the research purpose. Overall, instead of pre-

testing being carried out only to test the whole questionnaire and survey procedure in

general, it is also specifically to get feedback on individual items in the questionnaire (Czaja

et al., 2014). Pre-testing involves the processes of questionnaire screening to check for any

wording difficulties in the questions, leading question problems, and bias caused by question

order (Zikmund et al., 2007). After having a series of thorough pre-testing processes and

making revisions to the tested questionnaire and its procedures, the final questionnaire is

then decided upon, to be released to the sample respondents. A good pre-testing will ensure

that data collection process meets the requirements of answering research questions and

achieving their objectives.

3.2.3.4 SURVEY SAMPLE AND DESIGN

In a targeted survey, the samples are selected from a well-defined population (Czaja et al.,

2014). The concept of sampling implies drawing a small portion of the population to be

observed to get information and generalise findings rather than to study the entire population

(Ary et al., 2010). However, in concluding the findings, researchers must be confident that

the samples genuinely represent the population to avoid a biased sample. There are two

major types of sampling procedures: probability sampling and non-probability sampling.

Based on Czaja et al. (2014), the probability sampling is also known as random sampling,

which applies random process to select sample population, and the possibility of samples to

be chosen is solely depending on chances. The non-probability sampling does not use

random process, but selects samples based on judgement, convenience or quota. The

probability sampling is further divided into simple random sampling, systematic sampling,

and stratified sampling, while the nonprobability sampling is classified into convenience

sampling and quota sampling. In deciding which method of sampling to consider, this

research applies the guideline of selecting a probability sample from Saunders et al. (2009).

In accordance with the guideline, this research has selected the systematic random sampling

from probability sampling options. Quantity surveyors as estimators in Malaysia are the

targeted population in this research survey. The lists of Quantity Surveyors that are

registered under the Royal Institution of Surveyors Malaysia (RISM) have been chosen as

for the sampling frame. By using systematic random sampling technique, the samples from

respondents are determined for every nth in the list of registered members (Creswell, 2012).

The sample size for this research is established on the Krejcie & Morgan (1970) formula,

based on population available in the RISM list of members. Actual sampling units that have



been finalised are then the samples of survey respondents, in which an online survey using

the QUT Key Survey tool is applied. Figure 3.8 below summarises the whole sampling

design process conducted for the research, adapted from Zikmund (2003).

Figure 3.8: Sampling design process (adapted from Zikmund, 2003)

3.2.4 Ethical Consideration

Ethics is described as a code of conduct while doing research and it applies to both the

researchers that undertake the research and the respondents that contribute the research data

(Sekaran, 2003). As a very critical aspect of any research project, it provides the researchers

with principles and procedures of what is and what is not considered ethical (Saunders et al.,

2009). It is simply understood as an action of seeking permission before commencing any

research especially collecting data dealing with humans. An Ethics Application for this

research has been approved on 25th of July 2014 under the Human-Low Risk Ethics

Category, with an approval number of 1400000464. This ethics approval is valid until 25th of

July 2017, subject to receipt of satisfactory progress reports. A variation request has to be

submitted for the ethics to update any changes or additions in the research process. Health &

Safety approval has been applied for online through the QUT system of MAPS



(Management and Assessment of Project Safety) under the Engineering Faculty. This MAPS

system is to clarify the research processes and all risks associated with each process.

3.2.5 Data Analysis Approach

To analyse the previous survey results, this research works with two types of analyses,

namely descriptive analysis and inferential analysis. Descriptive analysis deals with basic

data analysis, transforming raw data into a form that can be easily understood and

interpreted; whereas inferential analysis involves analysis beyond descriptive statistics,

allowing statistical significance of hypotheses to be evaluated (Zikmund, 2003). This

research firstly adopts the Statistical Package for the Social Sciences (SPSS) method, the

descriptive analysis for Chapter 4. It then undergoes the Structural Equation Modelling

(SEM) technique, the inferential analysis for Chapter 5. Prior to conducting a descriptive

analysis of survey data, a preliminary data analysis is done. It incorporates the processes of

data coding and data entry; data screening and cleaning; treating extreme data values;

checking the normality of data distribution, and also assessing the validity and reliability of

survey data. While the descriptive analysis is used to provide the context of BIM

implementation amongst the respondents, the inferential analysis is employed to describe the

relationships between the variables tested in this research by adopting the SEM method.

3.2.5.1 DESCRIPTIVE ANALYSIS

The descriptive analysis starts once no errors or no out-of-range values are identified in the

survey data. Among the uses of descriptive statistics are to describe a characteristic of

research samples and to address specific research questions (Pallant, 2013). For this research,

a descriptive analysis chapter (Chapter 4) provides the context of BIM practice amongst the

respondents, in which it mainly addresses one of the research questions in the study. The

characteristics of respondents are specified regarding their construction cost estimating

experience by years, professional background, current roles in organisations including the

organisations’ nature of business, and that pertaining to BIM adoption in their practice.

Descriptive statistics analyse data by merely describing and summarising it in a meaningful

way, so that specific patterns can emerge from the data. This research employs tables, charts,

graphs and figures to represent the distribution of respondents based on the characteristics

previously outlined.

3.2.5.2 INFERENTIAL ANALYSIS

The inferential analysis is univariate statistics that involve testing of statistical hypotheses. A

hypothesis is defined as a statement of the assumption that theoretically explains certain

phenomena, which needs to be confirmed by empirical evidence (Zikmund, 2003).



Figure 3.9: Conventional approach to Structural Equation Modelling (Kaplan, 2009)

This research has developed hypotheses in line with the literature and interviews conducted

earlier. The hypotheses describe the relationships between the variables established in the

research. For the inferential analysis, the SEM method is used to model relationships

between variables (Hoyle, 2012). The conventional approach of the SEM method has been

described by Kaplan (2009), as in Figure 3.9. The variables for this research are referring to

the cost estimate reliability and BIM factors. As this research intends to develop a

relationship framework between those factors, the SEM method is considered as the

appropriate method to be employed for this research. The SEM method is commonly

comprised of processes, including model specification to fit the theory; gathering samples

and measures of variables; estimation of the model to obtain parameters; modification to fit

the assessment done; and lastly the interpretation and discussion of results reported (Kaplan,

2009; Hoyle, 2012). The SEM approach requires confirmatory factor analysis (CFA) to be

run prior to establishing a final model, and this research applies the steps suggested by

Awang (2015). The steps start with the analysis run by SPSS-AMOS software to obtain the

indexes of fitness for the measured model. In the case of the index values not achieving the

standard requirement of model fitness, any items with low factor loading must be deleted

first. If the model is still not fit after removing any of those items, the Modification Indices

(MI), indicating redundant items, need to be further examined. Any redundant items then



have to be eliminated or otherwise set as “free estimates” to achieve the required fitness

index for the model. The next step after the model is considered as fit, is to evaluate the

Composite Reliability (CR) and Average Variance Extracted (AVE) for model reliability and

validity purpose. The final step is to assess the normality of data distribution for the

remaining items in the measured model. Figure 3.10 further illustrates the steps for the above

mentioned CFA process.

Figure 3.10: Steps of Confirmatory Factor Analysis (CFA) (adapted from Awang, 2015)

3.2.6 Framework Validation

This research selects a focus group method to validate the framework composed through the

survey process and SEM analysis. The focus group method is also known as a pooled

interview and normally involves five-to-fifteen people brought together to discuss related

subject matter in a private and comfortable place (Babbie, 2011). The focus group interview

is commonly carried out to discuss people’s opinions (Fowler, 2009) and the interviewers

can concurrently ask the participants the questions pertaining to the discussion (Babbie,

2011). The selection of participants for a focus group is not sampling method-oriented and

does not serve any statistical population (Babbie, 2011). This research requires a validation

of the framework on the relationships between cost estimating and BIM factors. Hence, to



seek opinions for the research framework, volunteered Quantity Surveyors, the experts in

both cost estimating and BIM practices are chosen amongst participants from past surveys,

to join the focus group discussion. The justifications on why a focus group approach is

selected to validate the research framework are outlined based on the advantages offered, as

in Table 3.7 below.

Table 3.7: Justifications on focus group selection

Advantages of focus group approach Justifications

Fast feedback (Stewart et al., 2007; Morgan, 1997; Krueger, 1988)

As this research has limited time in which to complete, it is better to have a focus group discussion bringing together the experts at one time to get their feedback on the developed framework quickly, rather than having the interviews separately.

Broader information (Stewart et al., 2007; Morgan, 1997)

In focus groups, the participants are able to discuss with each other pertaining to the framework while the session could possibly broaden ideas amongst them, hence leading to various ways to improvise the framework.

Save cost & time (Stewart et al., 2007; Krueger, 1988)

Focus groups allow several experts to have discussions at one time. The researcher has no need to arrange different appointments with different participants or to have separate meetings with them, which obviously saves time. It also saves cost in terms of transportation, for the researcher does not have to go to different places to interview the different participants.



3.3 CHAPTER SUMMARY

For this research, the primary processes sequentially include; literature review,

expert interviews, questionnaire survey, data analysis through SEM and framework

validation by focus group discussion. Every process is specially designed for this research to

ensure that ultimately, it could answer the research questions and meet its research

objectives. As outlined previously, the literature review and expert interview processes were

conducted as to give input for the survey by questionnaire, as well as producing a

preliminary framework for the research. Data collected through the survey was analysed

using descriptive and inferential statistical methods of SPSS and SEM. While descriptive

analysis provides the context set in this research, the inferential analysis used the survey data

to confirm the hypotheses developed, hence established a final research framework. The

research framework was then validated by focus group technique, by assigning a few expert

panellists from the desired field. At the end of this research process, along with the final

validated framework, the conclusions were drawn, and recommendations were made

accordingly.

Chapter 4: Analysis on current BIM practice by Malaysian Quantity Surveyors


4Chapter 4: Analysis on current BIM practice by Malaysian Quantity Surveyors

This chapter describes the analysis of data from the first section of the questionnaire. It

mainly consists of respondents’ demographic background and their BIM adoption in current

practice. Demographically, the respondents’ profiles were specifically assessed based on their

years of experience in construction cost estimating, professional background, current role in the

organisation, and also the nature of their organisations’ business. For the BIM adoption profiles,

they were evaluated on their awareness of BIM usage in the construction industry, knowledge in

BIM, BIM software usage amongst the BIM users and its application in project stages, the

importance of BIM in their current roles, as well as their planning on BIM usage in the future.

Pertaining to the results, this chapter achieves Objective 1 addressed in this research, which is to

explore the current BIM practice amongst Malaysian Quantity Surveyors by providing the

context of its implementation.

The analysis in this section involves the preliminary data being examined, to ensure the

validity and reliability of the questionnaire used in this research is statistically validated. Prior to

performing the analysis, the data have to be coded, screened and cleaned. These processes

include treating the outliers of the data and checking the normality of their distribution.

Subsequently, the measurement of validity and reliability determines that the research instrument

(questionnaire) precisely measures what has been set out in this research. Descriptive analysis

was applied to analyse the data for this chapter, in which frequencies or percentages of samples’

distribution were described by using tables, charts and figures. The descriptive analysis is

important to statistically interpret the data. Ultimately, the patterns that emerged from the data

analysis could be translated in a more meaningful way.



4.1 PRELIMINARY DATA ANALYSIS

Data results were analysed as a preliminary measure and this was followed up by a

subsequent analysis. Prior to data being preliminarily analysed returned questionnaires need to be

checked to ascertain that only usable data is included for the statistical analysis. The usable

questionnaires are then finalised for coding and data entry in the statistical analysis software,

SPSS. Also by using the SPSS software, screening missing values, examining outliers, and

assessing normality for the data distribution were able to be conducted for Section B, C and D of

the questionnaire survey. Lastly, the validity and reliability of the data are evaluated in ensuring

that only valid and reliable responses are used for the next analysis.

4.1.1 Response Rate

In this research, a questionnaire survey was distributed to Malaysian Quantity Surveyors

registered with the Royal Institution of Surveyors Malaysia (RISM). There are about 1140

Quantity Surveyors (QS) that registered with RISM on the list, yet only 295 were randomly

chosen for the research samples (calculated based on the Krejcie & Morgan (1970) table (see

Appendix B). This research initially obtained 205 responses from the survey conducted.

However, from the total responses received, three were identified as incomplete responses to

be analysed, and therefore unusable. Table 4.1 shows the total number and percentage of

overall responses in this research.

Table 4.1: Total number and percentage of overall responses

Sample Number of responses Percentage (%)

Registered QS with RISM 295 205 69.49%

Unusable questionnaires 295 3 1.01%

Usable questionnaires 295 202 68.47%

Overall response rate 68.47%

Baruch (1999) emphasised that for research integrity purposes, usable responses have to be

reported as the appropriate response rate as it explicitly refers to number and percentage of

usable questionnaires. Hence, by deducting the unusable responses from the sample, the

overall response rate becomes 68.47%. Johnson and Wislar (2012) noted that even though

there is no proof showing the minimum acceptable response rate for any research, 60% is

commonly used as the threshold. However, Baruch (1999), through comparative analysis for

different academic studies, found that the average response rate was 55.6%. As this research

has approximately 68.5% response rate, it is considered satisfactory for related data analyses

to rely on the received responses.



4.1.2 Coding and Data Entry

The finalised 202 responses retrieved from the survey were then treated as raw data. They

needed to be coded as data entry in the SPSS software to be statistically analysed (Pallant,

2013). Data coding was done by assigning numerical and character symbols to the

questionnaire items. Once all items in the questionnaire were coded, the data were then

manually entered into the computer, in which statistical software known as SPSS Version 21

was used for further analysis. Prior to entering the data into the software, variables

specifying required information were defined to align with the earlier coding set. These

variables that make up the data files should have been given the name and value labels

appropriately. The survey has, overall, four sections (A, B, C and D) to be evaluated, in

which every item in each section is necessarily represented by one unique code (see details

in Appendix B).

4.1.3 Screening and Cleaning Data for Missing Values

Screening and cleaning data were carried out to ensure that only valid and reliable data was

used in interpreting this research. These processes include handling any missing values in the

dataset to improve analysis results. Bannon (2015) refers to missing data in a data set as the

absence of one or more values of the variables studied. The possible reasons why missing

values occurred in the data set might be due to participants choosing not to answer some

items in the questionnaire, incorrectly answering the items or because of data entry errors.

Since the errors could affect analyses, Pallant (2013) recommends two steps in the data-

screening process: checking for errors for variables with the out-of-range scores, and finding

and correcting or deleting the error values in the data file. The analysis showed that the data

set has the total percentage of missing values, approximately 5% (refer Appendix B). Due to

the small percentage of missing values, it is considered to have had little additional impact

towards the sample size (Raaijmakers, 1999). Hence, the data is acceptable for further

analysis without the need of exclusion of any response employed in this survey (Bannon,

2015). There are a few methods of treating missing values in a dataset (Raymond, 1986;

Raaijmakers, 1999; Burke, 2001; Pallant, 2013). With the limitation and caution of other

methods, this research applies missing data dichotomy or “dummy” variables for the missing

data. Raymond (1986), by adopting the strategy from Cohen and Cohen (1983), proposes a

method that involves the replacement of the missing values with “dummy” values to indicate

the presence of missing data within the variables. This missing data dichotomy procedure

suggests simply coding all the missing values as 999, to increase freedom in the error term.



4.1.4 Treating Outliers

Outliers are defined as the extreme values in a data set that are located well above or well

below in comparing with other values, far away from the mean (Pallant, 2013; Ghosh &

Vogt, 2012; Berenson et al., 2009). This extreme observation may occur due to errors in

measurement, errors in recording or transcription of data, and also from valid measurement

items with very long tail distribution or different population (Blischke et al., 2011). Ghosh

and Vogt (2012) suggest that outliers in data should be treated in some ways; whether to

keep and treat like other values in data, modify to suit other values in samples, or to

eliminate the values from the sample. Regardless of any good reason, Burke (2001) implies

that extreme values should not be removed from the dataset based only on statistical

grounds. With the judgement using the overall graphical plots of entire data, any extreme

values existing in the dataset should remain in the dataset, considering the data is normally

distributed by passing the normality test (Burke, 2001; Ghosh and Vogt, 2012). For that

principle, this research assumed that the data set used has no extreme values that affect the

diversion of data distribution. It can be referred to as the dissemination of outliers in the data

showing by the Q-Q plot and Boxplot from SPSS output (see Appendix B). The data values

are still in the normal range of distribution. Therefore, no items are considered to be deleted

for the further analysis.

4.1.5 Normality of Distribution

A normal distribution in a set of data is described by the symmetrical and bell-shaped curved

distribution with highest frequency scores in the middle, and smaller frequency scores

towards the extremes (Pallant, 2013; Zikmund et al., 2007). The Test of Normality by

Kolmogorov-Smirnov and Shapiro-Wilk was used to assess the normality of distribution of

the data set (Pallant, 2013). Appendix B presents the overall results of normality for the

dataset distribution (only items of Sections B, C and D in the questionnaire). Based on

Pallant (2013), the Sig. values of more than 0.05 indicate that the data is normally

distributed. However, larger samples may violate the results, hence suggesting that the Sig.

value is accepted at 0.000. As this research has samples of more than 200 (large samples),

the results signify that the data were normally distributed, within the acceptable range of the

test. The normality of the data distribution is also portrayed by using histograms, generated

from the SPSS output (see Appendix B).

4.1.6 Validity and Reliability

Validity is concerned with the relevance and significance of research elements, whether they

measure what they designate to measure (Drost, 2011). Validity measures the performance

of a survey instrument in terms of face, content, criterion and construct validity (Litwin,



1995; Cooper & Schindler, 2003; Zikmund et al., 2007; Fellows & Liu, 2008; Pallant, 2013).

In this research, the validity of the survey instrument (questionnaire) was evaluated through

pre-testing it to a group of sample respondents. Required revisions for the questionnaire were

made pertaining to received feedback to improve its clarity and content. Whereas, reliability

is concerned with the accuracy of data measurement, in which it is free of random error

when estimated (Cooper & Schindler, 2003; Zikmund et al., 2007; Pallant, 2013). Reliability

statistically measures the survey instrument’s data reproducibility (Litwin, 1995), in ensuring

that it is consistently assessed (Fellows & Liu, 2008; Babbie, 2011). The internal consistency

of the survey instrument relies on how well a set of items in the questionnaire measures

particular characteristics, based on average intercorrelations among the items within the test

(Drost, 2011). The reliability of a dataset involving this internal consistency can be measured

by using Coefficient alpha (referred as Cronbach’s alpha). The coefficient of internal

consistency is increased as the number of items are increased, in which the range of values is

varied from 0 to 1, where the higher values indicate the greater reliability (Pallant, 2013).

This research follows the rules of thumb by George and Mallery (2003) that provide the

Cronbach’s alpha values for reliability test: “_>0.9=Excellent; _>0.8=Good;

_>0.7=Acceptable; _>0.6=Questionable; _>0.5=Poor; _<0.5=Unacceptable”. The summary

of the reliability test for the dataset is presented in Table 4.2 below; with all items in the

questionnaire sections having a high reliability. Appendix B shows the details of the

reliability test for each item in the questionnaire.

Table 4.2: Summary of reliability test of overall responses

Cronbach’s alpha Reliability No. of Items

Section B: Improved Information 0.975 High 13

Section C: Perceived Benefits 0.978 High 12

Section D: Cost Estimates Reliability & BIM Adoption

0.985 High 18

4.2 RESPONDENTS’ PROFILE

This section describes the background information of the respondents regarding their

years of experience in construction cost estimating, their professional background, current

roles in their organisation, and also the nature of business of their current organisation. This

information, to some extent, explains the characteristics of respondents for this survey,

which has a significant role in determining that reliable outcomes are established for this



research. The results are presented by showing the distribution of responses using tables, pie

charts, graphs and appropriate figures.

4.2.1 Years of Experience in Construction Cost Estimating

The respondents have been divided into three main groups, having 1 to 5 years, 6 to 10

years, and more than 10 years of experience in construction cost estimating. Table 4.3

represents the distribution, with the majority of respondents (37.1%) having more than 10

years in estimating construction costs. Nonetheless, the distribution of percentage is not

significantly different amongst the groups. A total of 32.7% of respondents have between 1

to 5 years of experience, and respondents ranging from 6 to 10 years of experience shared

30.2% of the overall percentage. The fair distribution amongst the group positively conveys

a greater range of BIM context in terms of awareness, knowledge and application amongst

the respondents.

Table 4.3: Distribution of respondents based on construction cost estimating experience

Experience Frequency Percentage (%)

1-5 years 66 32.7

6-10 years 61 30.2

> 10 years 75 37.1

Total 202 100.0

4.2.2 Professional Background

The respondents’ professional background was also investigated, for it also indirectly

reflects the pattern of responses given for this survey. This survey was aimed at Quantity

Surveyors that have a quantity surveying background, considering that most of their practice

involves construction cost estimating works. The survey results indicate that almost all of

respondents were from a quantity surveying background, taking 94.6% from the overall

percentage (refer Table 4.4). The remaining portion was averaged from project management

(1.5%), building construction (0.5%), civil engineering (1.0%) and the (2.5%) professional

background of others. Therefore, the results portray reliable and valid sources from the

respondents to later provide adequate and useful findings for the research.



Table 4.4: Distribution of respondents based on professional background

Background Frequency Percentage (%)

Quantity Surveying

191 94.6

Project Management

3 1.5

Building Construction

1 0.5

Civil Engineering

2 1.0

Others 5 2.5

Total 202 100.0

4.2.3 Current Roles in Organisations

It does not necessarily mean that when respondents have quantity surveying backgrounds,

they also have roles as Quantity Surveyors doing cost estimates in the companies that they

are working with. In certain cases, they might be assigned to roles as project managers, civil

engineers, contractors or others. To confirm whether the respondents have actively served as

Quantity Surveyors in their organisations, a survey question was designed concerning this

subject. Table 4.5 demonstrates that the majority of respondents are Quantity Surveyors

(83.7%), while the rest act as Project Manager (3.0%), Construction Manager (0.5%),

Contractor (0.5%), Civil Engineer (0.5%) and others (11.9%). With the Quantity Surveyors

being the majority of respondents in the survey, it met the research requirements in acquiring

feedback from the appropriate respondents to generalise the findings.

Table 4.5: Distribution of respondents based on current roles in organisations

Roles Frequency Percentage (%)

Quantity Surveyor

169 83.7

Project Manager

6 3.0

Construction Manager

1 0.5

Contractor 1 0.5

Civil Engineer 1 0.5

Others 24 11.9 Total 202 100.0



4.2.4 Nature of Current Organisations’ Business

To have various views across cost estimating practice amongst the Quantity Surveyors, the

respondents were also asked about the nature of their current organisations’ business. This is

due to other than being employed by quantity surveying firms, Quantity Surveyors are

potentially working with clients or developers, contractor firms, authority or government

agencies, or a variety of academic institutions. The survey results showed that about half of

the respondents were occupied by quantity surveying firms (52.0%), as indicated in Table

4.6 below. These are followed by the respondents involved with clients or developers

(15.8%), contractor firms (12.4%), authorities or government agencies (7.4%), academic

institutions (5.9%) and others not listed (6.4%).

Table 4.6: Distribution of respondents based on nature of current organisations’ business

Nature of Business Frequency Percentage (%)

Client/Developer 32 15.8

Quantity Surveying firm 105 52.0

Contractor firm 25 12.4

Authority/ Government Agency

15 7.4

Academic Institution 12 5.9

Others 13 6.4

Total 202 100.0

4.3 RESPONDENTS’ BIM ADOPTION IN THE MALAYSIAN CONSTRUCTION INDUSTRY

The background profile developed from the survey results has defined the

characteristics of the entire group of respondents, thus verifying the appropriate responses to

be analysed. This section further examines the context of BIM adoption in the construction

industry, specifically in Malaysia, from the respondents’ perspectives. The context includes

the respondents’ awareness of BIM usage and their existing knowledge in BIM. The BIM

users amongst the respondents have further detailed the software used by them and its

applications in their construction project stages. To view the prospect of BIM

implementation in their practice, the respondents that were at least aware of BIM technology



were directed to the survey questions on BIM importance in their current roles and also their

expectation in using BIM for future planning.

4.3.1 Awareness of BIM Usage

The level of BIM adoption amongst the Malaysian Quantity Surveyors was initially assessed

based on their awareness of the BIM usage in the construction industry. It was shown in

Table 4.7 that 68.3% of overall respondents in this survey were aware of BIM usage but

have not used it in their practice. Only 12.9% of them were aware and currently using BIM

when estimating costs. Whereas 9.4% were aware and have used BIM except for cost

estimating, and 9.4% of the respondents were unaware of BIM usage within the construction

industry. As a whole, it can be concluded that BIM awareness amongst the respondents is

high regardless of whether they are applying any BIM technology in their practice or not.

Even though the revolution of BIM has been globally diffused in the construction industry in

recent years, BIM low implementation and slow development in Malaysia might be the

cause of unawareness amongst those respondents.

Table 4.7: Distribution of respondents based on awareness on BIM usage

Awareness Frequency Percentage (%)

Aware & currently using BIM in cost estimating

26 12.9

Aware and have used BIM (but not in cost estimating)

19 9.4

Aware of BIM but have not used it

138 68.3

Not aware of BIM 19 9.4

Total 202 100.0

4.3.2 BIM Software Usage and Its Application in Project Stages

The surveyed respondents that were aware and implemented BIM in their practice were

questioned on the types of BIM software they adopt and in what project stages they usually

use the software. These two categories of questions were designed as multi-responses, to

which the respondents can choose more than one answer, whichever are related to their

practice. Table 4.8 and Figure 4.1 depict the statistic of BIM software types utilised by



respondents. Most of the respondents used other than listed software (total of 39%). From

what they have specified in the survey, the Glodon software is surprisingly the most

commonly adopted (27.1%). Glodon was not underlined in the survey main list as it was not

generally reported as one of BIM software by the previous literature. Apparently, Glodon

software is specially designed for a quick modelling and BIM-supported quantity take-off

(www.glodon.com), to suit the practice of Quantity Surveyors to perform building cost

estimating accurately and efficiently. Other than that, the respondents have selected Revit

and CostX (both with 23.7% respectively) as the second highest types of software usage.

They are then followed by Archicad (8.5%), Navisworks and Ripac (3.4%) and then the rest

(1.7% respectively).

Table 4.8: Statistics of BIM software types used by respondents

Types of BIM Software Responses

N Percent

Revit 14 23.7% Tekla 1 1.7% Archicad 5 8.5% Navisworks 2 3.4% CostX 14

16 2 1 1 1 1 1

23.7% 27.1% 3.4% 1.7% 1.7% 1.7% 1.7%

1.7%

Glodon* Ripac* Cubit* Binalink* Buildspace* DimensionX* Concep*

Total 59 100.0%

*Others = 39% in total



Figure 4.1: Percentage of BIM software types used by respondents

For the application based on construction project stages, the majority of respondents used the

BIM software for the project estimating phase (28.7%). This phase typically consists of

taking off quantities and estimating the construction costs for the desired projects. It could be

seen that the respondents used the BIM software in this phase, for it is the most important

stage in their practice. Estimating is one of the most crucial tasks in the quantity surveying

field. The Quantity Surveyors play a significant role in this phase in ensuring that they

produce reliable cost estimates for the project. Table 4.9 and Figure 4.2 detail the statistic

distribution of BIM usage by stages in respondents’ projects. Instead of only using BIM

software for estimating, the respondents also employed the technology in other stages such

as design (14.8%), bidding (13.9%), planning (13%), construction (13%), scheduling (4.6%),

site analysis (3.7%), operation management (1.9%), facilities management (1.9%) and others

(4.6%).

0 5 10 15 20 25 30

Revit

Tekla

Archicad

Navisworks

CostX

Glodon

Ripac

Cubit

Binalink

Buildspace

DimensionX

Concep

23.7

1.7

8.5

3.4

23.7

27.1

3.4

1.7

1.7

1.7

1.7

1.7

Percentage (%)

Type

s of S

oftw

are



Table 4.9: Statistics of respondents’ BIM usage by project stages

BIM Usage by Project Stages Responses

N Percent

Planning 14 13.0% Bidding 15 13.9% Design 16 14.8% Scheduling 5 4.6% Estimating 31 28.7% Site Analysis 4 3.7% Construction 14 13.0% Operation Management 2 1.9% Facilities Management 2 1.9% Others 5 4.6%

Total 108 100.0%

Figure 4.2: Percentage of respondents’ BIM usage by project stages

4.3.3 Knowledge in BIM

Amongst the respondents that were at least aware of BIM development, they also had to rate

their knowledge in BIM technology from 1 to 10 of the scale (from not at all knowledgeable

to very knowledgeable rating). Rather than the total of 202 respondents in this survey, 181

were considered as at least aware of BIM, excluding two missing values. The distribution

detail on their BIM knowledge rating is tabulated in Table 4.10. Figure 4.3 below shows the

level of respondents’ knowledge on a scale from 1 to 10 by percentage. From the figure, it

0 5 10 15 20 25 30

Planning

Bidding

Design

Scheduling

Estimating

Site Analysis

Construction

Operation…

Facilities…

Others

13

13.9

14.8

4.6

28.7

3.7

13

1.9

1.9

4.6

Percentage (%)

Proj

ect S

tage

s



can be interpreted that most of the respondents tend to weight their knowledge in BIM as

moderate. This result is aligned with the rate of awareness on BIM usage amongst those

surveyed. They are, in the majority, aware of BIM regardless of using it. If they were to

apply even moderate knowledge actively in their practice, their BIM knowledge might be

higher rated.

Table 4.10: Distribution of respondents based on knowledge in BIM

Knowledge Rating Frequency Percentage (%) Not At All Knowledgeable 4 2.2 - 22 12.2 - 39 21.5 - 20 11.0 - 29 16.0 - 28 15.5 - 19 10.5 - 18 9.9 Very Knowledgeable 2 1.1

Total 181 100.0

Figure 4.3: Percentage of respondents’ knowledge in BIM

4.3.4 The Importance of BIM in Current Roles

The level of BIM importance in respondents’ current roles was investigated for those who

were at least aware of BIM. The statistical details of frequency and percentage for BIM

importance distribution amongst respondents are shown in the following Table 4.11.



Table 4.11: Statistics of BIM importance in respondents’ current roles

BIM Importance Frequency Percentage (%) Not At All Important 7 3.8 - 8 4.4 - 17 9.3 - 10 5.5 - 31 17.0 - 21 11.5 - 21 11.5 - 38 20.9 - 13 7.1 Very Important 16 8.8

Total 182 100.0

Figure 4.4 additionally illustrates the distribution of the percentage through bar chart and

radar profile. The survey outcome reveals that respondents have rated BIM to be of a high

level of importance in their current roles.

Figure 4.4: Percentage of BIM importance in respondents’ current roles

The results might be significantly contributed from the respondents that have been using

BIM in their practice, especially for estimating cost. The after-effects of using BIM in their

projects might have improved the traditional processes, hence making BIM relevant to be

applied in their consequent projects. Their roles as Quantity Surveyors that are dealing with

mostly cost estimating indicates that BIM might have furnished their practice with benefits

that assist them to establish more reliable and accurate cost estimates.



4.3.5 Planning of Future BIM Usage

To perceive the BIM prospective, the respondents that were at least aware of BIM in this

survey were examined regarding their future planning in using BIM.

Table 4.12: Statistics of planning on future BIM usage by respondents

Future BIM Usage Frequency Percentage (%) Least Likely 3 1.6 - 2 1.1 - 8 4.4 - 8 4.4 - 15 8.2 - 15 8.2 - 31 17.0 - 46 25.3 - 21 11.5 Most Likely 33 18.1

Total 182 100.0

Figure 4.5: Percentage of respondents’ future planning on BIM usage

It illustrates in Table 4.12, and also in Figure 4.5 that the respondents were most likely

planning to adopt BIM in their future practice. Given the previous high rating on BIM

importance in their current roles, it potentially affects the respondents’ future BIM planning.

Indeed, it implies that the respondents were highly expected to use BIM in the future. They

might potentially believe that BIM would continuously bring improvement in their existing

practice; therefore its usage should be extended for future projects.



4.3.6 Overall BIM Awareness and Usage towards BIM Knowledge, Importance and Future Planning

This section presents overall BIM awareness and usage amongst the respondents, which

explains BIM knowledge, importance and future planning in their practice. The respondents

were divided into three groups of whether they are currently using BIM, have used BIM but

not for cost estimating, and have not used BIM at all. These three groups were either aware

or not aware of BIM existence in the construction industry.

Figure 4.6 shows the distribution of respondents based on their awareness and usage of BIM

technology. Overall, the majority (90.6%) of the respondents are aware of BIM, while the

rest (9.4%) are not aware and not using BIM at all. However, from the 90.6% of respondents

that are aware of BIM, only 22.3% use BIM (12.9% currently using BIM in cost estimating;

9.4% have used BIM but not in cost estimating). The majority of respondents (68.3%) do not

use BIM in their practice even though they are aware of the technology.

BIM knowledge, its importance and future planning of its usage, are next assessed in line

with the level of awareness and usage of BIM amongst the respondents as described in the

following Figures 4.7, 4.8, 4.9 and 4.10.

Figure 4.6: BIM Awareness VS BIM Usage

Figure 4.7 interprets that most of the respondents who are aware and use BIM in their

practice have rated themselves as somewhat knowledgeable about BIM technology (highest

mark of 6 and 7 of rating scale). However, those respondents who are aware but do not use

BIM considered themselves as barely knowledgeable about BIM technology (highest mark

of 3 of rating scale). It becomes apparent that the BIM users are expected to have more BIM

knowledge as compared to non-users, since they are practically using the technology, giving

12.9%9.4%

9.4%

68.3%

Not aware of BIM Aware of BIM0

102030405060708090

100

BIM Awareness VS BIM Usage

Do not use BIM at all

Have used BIM (but not incost estimating)

Currently use BIM in costestimating



them more hands-on technical experience, rather than only learning through reading

materials and so on.

Figure 4.7: BIM Awareness and Usage VS BIM Knowledge

Meanwhile, in evaluating BIM importance in their practice, the respondents that are aware

and use BIM often, rated the technology as mostly important (at level 8 on the rating scale)

in assisting them in their current roles. While those who are just aware but not using BIM in

their practice, mostly rated the BIM technology as somewhat important (at level 5 on the

rating scale). The different perception towards BIM importance between these two groups is

predictable given that the actual use of BIM technology has the high possibility to improve

users’ performance with its many benefits, hence increases the importance level of its usage.

In contrast, the limited BIM knowledge without the actual application of the technology

restricts the evaluation of BIM significance in the respondents’ practice, leading the non-

BIM users to moderately rate BIM as important. Figure 4.8 depicts the results of BIM

importance perceptions amongst the BIM and non-BIM users.

0%10%20%30%40%50%60%70%80%90%

100%

Aware & currentlyusing BIM in cost

estimating

Aware & have usedBIM (but not in cost

estimating)

Aware of BIM buthave not used it

BIM Awareness and Usage VS BIM Knowledge

10. Very knowledgeable

9

8

7

6

5

4

3

2

1. Not at all knowledgeable



Figure 4.8: BIM Awareness and Usage VS BIM Importance

Other than BIM knowledge and BIM importance being determined amongst respondents that

are aware of BIM, they are also examined on their planning of using BIM for their future

practice (refer Figure 4.9). Figure 4.9 portrays those respondents that are aware and currently

using BIM their cost estimating practice have a very high probability of using BIM in the

future. It is evident that BIM technology has significantly assisted them in establishing more

reliable cost estimates, leading them to continuously use BIM for future projects. In the

meantime, respondents that are aware and have used BIM but not in cost estimating are most

probably planning to use BIM in their next practice. By not specifically adopting BIM

technology for current cost-estimating practice, it can be said that many undiscovered

benefits towards cost estimate reliability and accuracy are not yet explored by the

respondents. Subsequently, this affects judgements towards the planning of BIM usage

onwards. Likewise, the respondents that are aware of BIM but have not used it provide

similar results of being more likely to use BIM for their future practice. Even though without

any experience of using BIM technically, they might envisage BIM furnishing benefits

towards their practice from various sources, such as reading materials or other experiences.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


estimating


estimating)


BIM Awareness and Usage VS BIM Importance

10. Very important

9

8

7

6

5

4

3

2

1. Not at all important



Figure 4.9: BIM Awareness and Usage VS BIM Future Usage

The majority of respondents are aware of BIM existence in the construction industry

regardless of their level of awareness and usage of the technology. As previously discussed,

BIM awareness and usage amongst the respondents contribute to their BIM knowledge, their

perceptions of BIM importance towards their current roles, and BIM prospective for their

onwards planning. Figure 4.10 thereby illustrates these three characteristics, linking directly

to the respondents’ awareness and usage of BIM. The results can be interpreted and

summarised as follows;

(1) The respondents who are aware and currently using BIM in cost estimating have fair

BIM knowledge. They rated BIM as influential in their current practice. Therefore, they

are very likely to implement BIM in their next projects. The results reflect that the BIM

technology they are currently adopting is beneficial for their cost-estimating practice. By

that, they regarded BIM as important in developing more reliable cost estimates and are

looking forward to using the technology continuously to constantly improve their cost-

estimating practice.

(2) The respondents who are aware and have used BIM (but not in cost estimating) have

fair BIM knowledge. They rated BIM as significant in their current practice and may

adopt BIM in their future projects. With BIM being employed not specifically for

estimating costs, to a certain extent this has provided different perspectives towards

future implementation of the technology. The respondents may possibly not have gained

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%


estimating


estimating)


BIM Awareness and Usage VS BIM Future Usage

10. Most likely

9

8

7

6

5

4

3

2

1. Least likely



all the BIM benefits specifically related to their cost estimating practice, hence leading to

undecided future planning for BIM utilisation.

(3) The respondents that are aware of BIM but have not used it have limited BIM

knowledge. They rated BIM as quite important towards their current practice, and they

might adopt BIM in the future. The results indicate that despite not using any BIM

applications in their projects, they credit BIM with being able to assist them to perform

better to improve their current practice, therefore achieving more successful projects.

Figure 4.10: Overall BIM Knowledge, Importance & Future Usage based on BIM Awareness & Usage

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%

Overall BIM Knowledge, Importance & Future Usage based on BIM Awareness & Usage

1 2 3 4 5 6 7 8 9 10

Aware &

currently using BIM

in cost estimating

Aware &

have used BIM

(but not in cost estimating)

Aware of BIM

but have not used it

Scale Low High



4.4 CHAPTER SUMMARY

This chapter has presented the descriptive analysis for the survey, mainly providing

the context of BIM implementation amongst the Quantity Surveyors in the Malaysian

construction industry. In confirming the data set is valid and reliable for analysis, it was

initially checked for its missing values, outliers, and the normality of its distribution. Then, a

reliability test was carried out further to examine validity and reliability of the data. Besides

approximately 5% of missing values found, no extreme values as outliers, and considered as

having normality in the data distribution, the data set also has a high reliability (with

Cronbach’s Alpha more than 0.9) when conducting the reliability test.

For the background’s profile, the survey results outlining that respondents are having

between 1 to 5 year, 6 to 10 years and more than 10 years of estimating construction costs,

were fairly distributed. This range of years provides diversity in a way of getting different

contexts from various perspectives of respondents. The majority of respondents were

Quantity Surveyors with quantity surveying background and currently attached with quantity

surveying firms. This profile has met the requirement for this research that seeks qualified

Quantity Surveyors that are actively practising estimating (quantity take-off and cost

estimates) in their work, to establish valid and reliable findings.

In providing context for BIM adoption amongst Malaysian Quantity Surveyors, the

respondents were required to rate their awareness and usage of BIM in their current practice.

It resulted that the majority of respondents were aware but had not used BIM in their

practice. However, BIM users amongst the respondents were quite favourable, with more

than 20% currently or having used BIM regardless of using it in cost estimating or not. They

commonly used software other than that listed in the survey, mostly Glodon, other than

CostX and Revit; they mostly employed these in cost-estimating practice. The respondents

that were at least aware have mostly rated themselves as having moderate knowledge in

BIM. For them, BIM plays a major role in their practice. Thus they predicted that they would

be using BIM for their future planning.

Based on the overall descriptive analysis presented amongst respondents, it showed

the high BIM awareness with average knowledge, but that less than one-quarter employing

the technology in their practice at present were still unsatisfactory. Thus, it can be interpreted

that the implementation status of BIM amongst the Quantity Surveyors in Malaysia is still

low. The usage of Glodon software is the main option by most of the respondents, rather

than other globally used software such as Revit and CostX. This translates that the software

was chosen due to its suitability and was core in the respondents’ main task of conducting

measurement for taking-off building quantities, the most crucial responsibility in their



practice. It is apparently in line with the survey statistics of BIM usage by project stages that

portrays that the usage of the technology amongst the respondents is most focussed at the

project estimating phase instead of other project phases. It indirectly indicates that the

adoption of BIM in the respondents’ practice is bounded by the moderate knowledge they

have, specifically in project costing incorporating a BIM mechanism. Ultimately, this

scenario might further explain the low level of BIM employment amongst the Malaysian

Quantity Surveyors. Nevertheless, it is possible that the number of BIM users amongst the

QS professionals in Malaysia will be increased in the future, due to the statistics that signify

the importance and interests of frequent non-users using BIM in their forthcoming projects.

This will shed some light on the greater expansion of BIM growth within the quantity

surveying practice in Malaysia, hence, bringing technology usage in the practice to the

higher level.

Overall, the descriptive analysis results give a satisfactory overview about the

respondents in this survey, which in turn, provides a valid and reliable consideration for

further analysis in the next chapter.

Chapter 5: Structural Equation Modelling (SEM) analysis


5Chapter 5: Structural Equation Modelling (SEM) analysis

The previous chapter provides the context of current BIM practice amongst the

Quantity Surveyors in the Malaysian construction industry. The chapter mainly analysed the

respondents’ background and BIM adoption amongst them. Despite background profiles

such as experience, roles, and organisations, BIM adoption amongst the respondents was

investigated pertaining to their awareness, usage including the software and project stages,

knowledge and importance, as well as future planning. Providing the context of BIM

adoption amongst the respondents in Chapter 4, has contributed to achieve Objective 1 in

this research. From the descriptive analysis, it resulted that the respondents’ BIM awareness

was high, regardless of whether the technology has been or has not been applied in their

practice. The respondents that were at least aware of BIM have rated themselves as having

moderate knowledge in BIM, in which they conceived that BIM was important in their

practice and they anticipated to use BIM in the future. Chapter 4 yields valid and reliable

results that enable further analysis to be conducted in Chapter 5.

This chapter presents the outcomes from the SEM analysis for the remaining

sections (B, C and D) in the survey. The analysis was conducted in line with Objective 2 in

this research, to examine the causal effect relationships of improved information in BIM

towards improving the reliability of Quantity Surveyors’ cost estimates, leading to the

adoption of the technology. Prior to this analysis, measurement models were firstly modelled

and validated by Confirmatory Factor Analysis (CFA) to establish the Fitness Index, in

ensuring that the models were fit for the SEM analysis. Once the SEM structural model was

fit, the causal effect relationships between the variables were examined, leading to the

establishment of a final model for the research. The final model would be developed as the

main framework for cost estimate reliability incorporating BIM adoption framework. The

framework will be validated by experts in focus group discussions, of which the details are

available in the next chapter.



5.1 OVERVIEW OF STRUCTURAL EQUATION MODELLING (SEM)

Structural Equation Modelling (SEM) is a statistical method that is used for

modelling and estimating relationships between variables in a dataset. Prior to developing

SEM models, assumptions represented by research hypotheses in interpreting the research’s

theory, need to be established. Hypotheses are designed to describe the causal effect

relationships amongst the variables measured in the research. SEM models are divided into

two parts, namely a measurement model and a structural model; in these, the measurement

model is also known as Confirmatory Factor Analysis (CFA) model (Hoyle, 2012). Based on

set-up hypotheses, SEM analysis begins with the specifications of measurement models, to

be evaluated by the CFA. The CFA validation for the models involves assessing the uni-

dimensionality, validity and reliability, and fitness of the measurement models. After the

measurement models have undergone the CFA analysis, the models are then considered fit to

be further analysed in the finalised SEM structural model.

5.1.1 Uni-dimensionality

A set of items is considered to be uni-dimensional when there is a variable that makes it

possible to explain correlations between the items measured (Hattie, 1985; Falissard, 1999).

By referring to Awang (2015), uni-dimensionality in an assessed model is achieved when all

items measured in the model have satisfactory values of factor loadings. Factor loadings

symbolise how much the underlying items explain the observed variable. Any items that

have low factor loadings should be deleted from the model. The value range of factor

loadings for established items is at least 0.60 or higher (≥0.60), while for newly developed

items, the values should be more than 0.50 (>0.50). The items having lower factor loading

values than mentioned above should be removed or deleted from the model.

5.1.2 Validity and Reliability

Validity is a statistical term describing the items that are supposed to measure its intended

construct accurately (Litwin, 1995; Zikmund et al., 2007; Babbie, 2011; Pallant, 2013; Hair

et al., 2014). The validity of SEM models can be assessed through their convergent validity,

construct validity and discriminant validity (Awang, 2015). The items in the measurement

models need to be significant to achieve the convergent validity. The significance of the

items is evaluated by Average Variance Extracted (AVE), in which the AVE value of 0.50

above should be obtained. To acquire construct validity that examines how fit the items

measure the construct in the models, the constructs need to achieve the required level of

Fitness Index (to be explained in the next section). The discriminant validity of the models is

assessed through the Modification Indices (MI). The high values of MI (more than 15) show



that the models have redundant items, hence one of the paired items needs to be deleted or

set as “free parameter estimates”.

Reliability means the ability of items measuring their intended construct to gain consistent

results with free-from-random errors (Litwin, 1995; Fellows & Liu, 2008; Babbie, 2011;

Cooper & Schindler, 2003; Zikmund et al., 2007; Pallant, 2013). Higher reliability values

represent greater relationships between the items as indicators and the constructs (Hair et al.,

2014). Reliability in the SEM models is measured using the criteria of Internal Reliability,

Composite Reliability (CR) and Average Variance Extracted (AVE) (Awang, 2015). The

internal reliability of the measurement models shows how strong the items are holding

together to measure their intended construct. This type of reliability is achieved when all the

items obtain the values of Cronbach’s alpha more than 0.70 through the Reliability Test

using SPSS (as described in previous Chapter 4). The composite reliability that evaluates the

internal consistency of the constructs in the models is measured through CR values that must

at least be 0.60 or beyond (≥0.60). In indicating the variation percentage for items in the

constructs, the Average Variance Extracted (AVE) is calculated, in which the AVE value of

0.50 or more is required (≥0.50). The calculation for AVE and CR values is based on the

given formula, as shown in Table 5.1 below;

Table 5.1: AVE and CR formula

AVE = ∑K²/n K = factor loading of every item n = number of items in the model CR = ( ∑K² ) / [( ∑K² ) + ( ∑1− K2)]

5.1.3 Fitness Index

Fitness Indexes reflect how fit the model is to the data (Awang, 2015). Fitness Indexes in

SEM models are assessed through three model fit categories namely Absolute Fit,

Incremental Fit and Parsimonious Fit. This research applies the Fitness Indexes’ values listed

by Awang (2015), as tabulated in the following Table 5.2.



Table 5.2: Categories of Fitness Indexes and Level of Acceptance (Source: Awang, 2015)

Name of Category Name of Index Index Full Name Level of Acceptance

Absolute fit

Chi-Square Discrepancy Chi Square

P-value > 0.05 (not applicable for large sample size more than 200)

RMSEA Root Mean Square of Error Approximation

RMSEA < 0.08

GFI Goodness of Fit Index GFI > 0.90

Incremental fit

AGFI Adjusted Goodness of Fit

AGFI > 0.90

CFI Comparative Fit Index CFI > 0.90

TLI Tucker-Lewis Index TLI > 0.90 NFI Normed Fit Index NFI > 0.90

Parsimonious fit Chisq/df Chi Square/Degrees of Freedom

Chi-Square/df < 3.0

5.2 MODELLING THE SEM MEASUREMENT MODELS

The use of Structural Equation Modelling (SEM) is firstly started with a theory set in

this research, in which the constructs and relationships involved in the theory are modelled

and tested. The SEM model consists of measurement models and a structural model.

Initially, measurement models are developed, and their constructs are validated with

Confirmatory Factor Analysis (CFA). The four main constructs that are involved in this

research theory are Improved Information, Perceived Benefits, Cost Estimates Reliability

and BIM Adoption. The research hypotheses deduced from these constructs’ relationships,

and to be tested in this chapter, are as in Table 5.3.

Table 5.3: Research hypotheses for constructs

H1 Improved Information has a positive and significant effect on Cost Estimates Reliability

H2 Improved Information has a positive and significant effect on Perceived Benefits

H3 Perceived Benefits has a positive and significant effect on BIM Adoption

H4 Cost Estimates Reliability has a positive and significant effect on BIM Adoption

H5 Improved Information has a positive and significant effect on BIM Adoption

H6 Cost Estimates Reliability has a positive and significant effect on Perceived Benefits

H7 Perceived Benefits mediates the relationship between Improved Information and BIM Adoption

H8 Cost Estimates Reliability mediates the relationship between Improved Information and BIM Adoption

H9 Cost Estimates Reliability mediates the relationship between Improved Information and Perceived Benefits



The hypothesised relationships between the constructs are simply described in Figure 5.1

below.

Figure 5.1: The preliminary model and relationships between constructs

Each construct in Figure 5.1 is measured by some indicators (items in the questionnaire).

The Improved Information construct is measured by three main sub-constructs, which are

data visualisation (5 items), reliable database (4 items) and data coordination (4 items),

bringing to a total 13 items measured for this construct. The Perceived Benefits construct is

examined based on BIM usage expectation towards practice, which is indicated by 12 items.

The Cost Estimate Reliability construct is evaluated by three main sub-constructs, namely

input information (3 items), understanding (3 items), and knowledge (3 items), while the

BIM Adoption construct is investigated through 9 items. SEM measurement models are then

developed, and Confirmatory Factor Analysis (CFA) is conducted for each of these

constructs. The model fitness is assessed before further analysis on the SEM structural

model.

5.3 SEM MEASUREMENT MODELS VALIDATION BY CONFIRMATORY FACTOR ANALYSIS (CFA)

Prior to modelling the measurement models to the structural model, it is essential

that the models are validated by the CFA process to confirm its fitness. The validation

processes examine the constructs’ uni-dimensionality, validity and reliability, and fitness of

the measurement models. The uni-dimensionality is achieved when measured items have an

acceptable range of factor loadings towards respective constructs. The validity and reliability

of the items are measured through specified Average Variance Extracted (AVE),

Modification Indices (MI), Fitness Indexes and Composite Reliability (CR). The overall

values in Fitness Index indicated by AMOS shows whether the measurement models are fit

to be included in the structural model for SEM further analysis. These processes are carried



out one by one to describe every measurement model fit for every construct of Improved

Information, Perceived Benefits, Cost Estimate Reliability and BIM Adoption, as specified

previously. The defined measurement models fit are explained in the following subsections.

5.3.1 Improved Information Measurement Model Fit

The Improved Information construct consists of three sub-constructs, namely data

visualisation, reliable database and data coordination. Each of the sub-constructs is measured

by some indicators. The indicators are from the 13 items observed in Section B of the

survey’s questionnaire. Five items measure the data visualisation sub-construct, and four

items measure the reliable database and data coordination constructs respectively. Figure 5.2

presents the initial measurement model for the improved information construct derived from

AMOS analysis. Table 5.4 lists the indicators for the initial measurement model for

Improved Information construct.

Figure 5.2: Initial measurement model for Improved Information construct



Table 5.4: Indicators for the initial measurement model for Improved Information construct

Constructs Codes Indicators

Improved Information

dv1 Data visualisation helps to get accurate data

dv2 Data visualisation helps to understand information better

dv3 Interpreting information accurately with data visualisation

dv4 Data visualisation helps to make decisions with confidence

dv5 Data visualisation assists in achieving company’s goals

rd1 A reliable database improves information system reliability

rd2 A reliable database provides complete information

rd3 A reliable database generates better task performance

rd4 A reliable database assists in achieving company’s goals

dc1 Data coordination improves information system reliability

dc2 Data coordination provides complete information

dc3 Data coordination generates better task performance

dc4 Data coordination assists in achieving company’s goals

Figure 5.2 shows CFA results of fitness indexes and factor loading for every item. As shown

in Figure 5.2, most of the fitness indexes have achieved the acceptable range of respective

indexes; (ChiSq/df=1.927<3.00), (RMSEA=0.068<0.08), (GFI, CFI, TLI and NFI >0.90);

except for (AGFI=0.873<0.90). Additionally, some of the evaluated items have negative or

less than 0.5-factor loadings (dv4, dv5, rd4 and dc3), whereby the model has failed the uni-

dimensionality assessment. To make sure of the uni-dimensionality of the model, the low

factor loadings need to be deleted. Figure 5.3 demonstrates the final measurement model for

an Improved Information construct that fits the acceptable range of index after the

undervalued items are deleted. High MI values are treated, in which one of the paired

redundant items is eliminated to obtain discriminant validity. The final measurement model

shows that all the error covariance values are less than 15, which is within the acceptable

range for MI (see Appendix C). The construct validity is achieved, in which the Fitness

Indexes for the construct (as shown in Figure 5.3) are within the required level. The values

of Fitness Indexes for final measurement model are: P-value=0.052, RMSEA=0.062,

GFI=0.974, AGFI=0.935, CFI=0.995, TLI=0.990, NFI=0.988 and ChiSq/df= 1.777. Table

5.5 shows the Indicators for the final measurement model for Improved Information

construct.



Figure 5.3: Final measurement model for Improved Information construct

Table 5.5: Indicators for the final measurement model for Improved Information construct










The model has passed the reliability assessment, in which the value of CR ≥0.6 is achieved.

The convergent validity of the sub-constructs is significant with a value of AVE ≥0.5. The

results of AVE and CR, which assess the convergent validity and composite reliability of the

improved information measurement model, are reported as shown in Table 5.6. With all the

acceptable values described, therefore, this final measurement model for the Improved

Information construct is fit for inclusion in the SEM structural model for further analysis.



Table 5.6: Validity and reliability assessment for Improved Information measurement model

Constructs Items Factor Loading (≥0.6) AVE (≥0.5) CR (≥0.6)

Data visualisation

dv1 0.94

0.877 0.955 dv2 0.94 dv3 0.93 dv4 deleted dv5 deleted

Reliable database

rd1 deleted

0.893 0.944 rd2 0.93 rd3 0.96 rd4 deleted

Data coordination

dc1 0.93

0.785 0.879 dc2 deleted dc3 deleted dc4 0.84

5.3.2 Perceived Benefits Measurement Model Fit

The Perceived Benefits construct contains 12 indicators to be assessed in CFA analysis.

These 12 indicators are derived from 12 items in Section C of the questionnaire. Figure 5.4

illustrates the initial measurement model for the BIM perception construct. Table 5.7 lists the

indicators for the initial measurement model for Perceived Benefits construct.

Figure 5.4: Initial measurement model for Perceived Benefits construct



Table 5.7: Indicators for the initial measurement model for Perceived Benefits construct


Perceived Benefits

per1 Data visualisation enables accomplishing tasks more quickly

per2 Data visualisation increases estimates accuracy

per3 Data visualisation is useful in role

per4 Data visualisation improves employment prospects

per5 A reliable database enables accomplishing tasks more quickly

per6 A reliable database increases estimates accuracy

per7 A reliable database is useful in role

per8 A reliable database improves employment prospects

per9 Data coordination enables accomplishing tasks more quickly

per10 Data coordination increases estimates accuracy

per11 Data coordination is useful in role

per12 Data coordination improves employment prospects

It shows in Figure 5.4 that the initial measurement model for the Perceived Benefits

construct does not have a good fit. The values of Fitness Indexes are of an unacceptable

range: (ChiSq/df=11.316>3.00), (GFI, AGFI, CFI, TLI and NFI <0.90), and

(RMSEA=0.227>0.08). In this case, this measurement model needs an improvement to fit

the index specified. Items with low factor loadings (less than 0.5) need to be deleted to

achieve uni-dimensionality in the model. Due to high MI values existing in the model, paired

items with lower factor loadings are chosen to be eliminated, as such the discriminant

validity that can be obtained from the model (refer Appendix C). The revised measurement

model (final measurement model) is presented in Figure 5.5. From the results, it can be

shown that the Fitness Indexes achieve the construct validity with the value of: P-

value=0.527, RMSEA=0.000, GFI=0.992, AGFI=0.975, CFI=1.000, TLI=1.001, NFI=0.997

and ChiSq/df= 0.832. Table 5.8 shows the indicators for the final measurement model for

Perceived Benefits construct.



Figure 5.5: Final measurement model for Perceived Benefits construct

Table 5.8: Indicators for the final measurement model for Perceived Benefits construct


Perceived Benefits






Additionally, the convergent validity of the model is achieved by AVE value of 0.851 (≥0.5).

The model composite reliability assessment is confirmed by the significant value of

CR=0.966 (≥0.6) obtained from the model. Table 5.9 below presents the results of the

validity and reliability evaluation for the Perceived Benefits measurement model. With the

required level of Fitness Indexes, uni-dimensionality, validity and reliability attained for the

model, the final measurement model for Perceived Benefits construct is qualified to be

included in the next SEM structural model assessment.



Table 5.9: Validity and reliability assessment for Perceived Benefits measurement model


Perceived Benefits

per1 .895

0.851

0.966

per2 deleted per3 deleted per4 deleted per5 .942 per6 .952 per7 .887 per8 deleted per9 .934 per10 deleted per11 deleted per12 deleted

5.3.3 Cost Estimates Reliability Measurement Model Fit

The Cost Estimates Reliability construct has three sub-constructs, namely, input information,

understanding and knowledge. Each of the sub-constructs consists of three indicators

measuring items in Section D of the questionnaire. This makes this construct evaluate a total

of nine items in this measurement model (three items measure input information sub-

construct, three items measure understanding sub-construct, and three items measure

knowledge sub-construct). Figure 5.6 shows the initial measurement model for the Cost

Estimate Reliability construct. Table 5.10 lists the indicators for the initial measurement

model for Cost Estimate Reliability construct.

The results in Figure 5.6 indicate that the measurement model does not have a good fit. The

Fitness Index values for the initial measurement model are: (ChiSq/df=3.157>3.00),

(AGFI=0.858<0.90), and (RMSEA=0.104>0.08). This model has an item with low factor

loading (kno2=-0.03). Therefore, the item needs to be deleted to achieve the uni-

dimensionality for the model. The discriminant validity of the model is accomplished when

some of the paired items with lower factor loadings are deleted (MI values should be less

than 15 for every item (see Appendix C). The final measurement model for the Cost

Estimate Reliability construct is presented in Figure 5.7. The Fitness Index values (Figure

5.7) that confirm the construct validity are: P-value=0.098, RMSEA=0.062, GFI=0.983,

AGFI=0.941, CFI=0.997, TLI=0.993, NFI=0.994 and ChiSq/df= 1.781. Table 5.11 shows

the indicators for the final measurement model for Cost Estimate Reliability construct.



Figure 5.6: Initial measurement model for Cost Estimate Reliability construct

Table 5.10: Indicators for the initial measurement model for Cost Estimate Reliability construct


Cost Estimates Reliability

info1 Data visualisation used to better understand input information

info2 A reliable database used to better understand input information

info3 Coordinated data needed to better understand input information

und1 Data visualisation assists to better understand to prepare estimates

und2 A reliable database assists to better understand to prepare estimates

und3 Coordinated data assists to better understand to prepare estimates

kno1 Data visualisation helps to improve knowledge of estimating process

kno2 A reliable database helps to improve knowledge of estimating process

kno3 Coordinated data helps to improve knowledge of estimating process



Figure 5.7: Final measurement model for Cost Estimate Reliability construct

Table 5.11: Indicators for the final measurement model for Cost Estimate Reliability construct









For the convergent validity and composite reliability assessment, the constructs in the final

measurement model of Cost Estimate Reliability achieves the acceptable values of AVE

(≥0.5) and CR (≥0.6). It is reported in the following Table 5.12. The initial measurement

model has been improved, and the final measurement model for the cost estimate reliability

construct is found fit to be further analysed with other measurement models in the whole

SEM structural model.



Table 5.12: Validity and reliability assessment for Cost Estimates Reliability measurement model

Constructs Items Factor Loading (≥0.6)

AVE (≥0.5) CR (≥0.6)

Input Information info1 0.98

0.941 0.970 info2 deleted info3 0.96

Understanding und1 deleted

0.931 0.964 und2 0.97 und3 0.96

Knowledge kno1 0.96

0.912 0.954 kno2 deleted kno3 0.95

5.3.4 BIM Adoption Measurement Model Fit

The BIM Adoption construct incorporates nine indicators in its measurement model,

representing nine items measured in Section D of the questionnaire. The following Figure

5.8 presents the initial measurement model for the BIM Adoption construct. Table 5.13 lists

the indicators for the initial measurement model for BIM Adoption construct.

Figure 5.8: Initial measurement model for BIM Adoption construct



Table 5.13: Indicators for the initial measurement model for BIM Adoption construct


BIM Adoption

adop1 BIM with data visualisation application provides better input information

adop2 BIM with a reliable database provides better input information

adop3 BIM with coordinated data provides better input information

adop4 BIM with data visualisation application helps to understand project better

adop5 BIM with a reliable database helps to understand project better

adop6 BIM with coordinated data helps to understand project better

adop7 BIM data visualisation application improves costing expertise

adop8 BIM with a reliable database improves costing expertise

adop9 BIM with coordinated data improves costing expertise

The Fitness Index, as shown in Figure 5.8, highlights the poor fit of the measurement model

and the values of specified indexes are (ChiSq/df=25.496>3.00), (GFI, AGFI, CFI, TLI and

NFI <0.90), and (RMSEA=0.349>0.08). Therefore, the model needs some improvement to

achieve the acceptable index. To gain uni-dimensionality for the model, an item with low

factor loading less than 0.5 is deleted. Since the Fitness Index is still not meeting the

required value after item deletion, the MI values for the model are checked for possible

redundant items. Some high MI values are detected (more than 15); hence paired items with

lower factor loadings are deleted to obtain the discriminant validity of the model (see

Appendix C). The final measurement model for the BIM Adoption construct, after

conducting item deletion, is portrayed in Figure 5.9. The model achieves the construct

validity with the acceptable Fitness Index of: P-value=0.589, RMSEA=0.000, GFI=0.997,

AGFI=0.987, CFI=1.000, TLI=1.002, NFI=0.999 and ChiSq/df= 0.530. Table 5.14 shows

the indicators for the final measurement model for BIM Adoption construct.



Figure 5.9: Final measurement model for BIM Adoption construct

Table 5.14: Indicators for the final measurement model for BIM Adoption construct


BIM Adoption





The convergent validity and composite reliability for the model are also achieved with the

AVE value is 0.884 (≥0.5), and CR value is 0.968 (≥0.6). Table 5.15 shows the details of the

validity and reliability assessment for the model. From the overall satisfied values of Fitness

Index, uni-dimensionality, validity and reliability for the measurement model, the model is

then accepted for inclusion in the next assessment of the whole structural model.

Table 5.15: Validity and reliability assessment for BIM adoption measurement model


BIM adoption

adop1 0.97

0.884 0.968

adop2 0.99 adop3 0.97 adop4 deleted adop5 deleted adop6 deleted adop7 deleted adop8 0.82 adop9 deleted



5.4 ANALYSIS OF SEM STRUCTURAL MODEL

After the fitness indexes have been achieved for all measurement models for the

constructs, the models are sufficiently fit to be analysed for the whole SEM structural model.

However, prior to the structural model analysis, the normality of final data included in the

measurement models needs to be assessed. This is to ensure that the data is fairly distributed,

and no non-normality issues occur. Otherwise, the data need to be treated properly before the

analysis in the structural model. Consequently, the constructs in final measurement models

are ready to be further modelled and analysed in the SEM structural model.

5.4.1 The Assessment of Normality of Data

To examine the normality of data, the skewness measuring every item in the final

measurement models need to be evaluated. The normality assessment detail for the data is

presented in Table 5.16 below. Data that is normally distributed is indicated by the absolute

value of skewness of 1.0 or lower, and the Critical Region (CR) for the skewness does not

exceed 8.0. Table 5.16 shows that the skewness values for every item are less than 1 with CR

values less than 8.0. Therefore, the final data for measurement models is assumed to be

normally distributed.

Table 5.16: The assessment of normality distribution for items in constructs

Variable min max skewness CR kurtosis CR kno1 4.000 10.000 -.444 -2.576 -.620 -1.798 kno3 3.000 10.000 -.568 -3.294 -.382 -1.109 und2 4.000 10.000 -.597 -3.464 -.298 -.864 und3 4.000 10.000 -.520 -3.020 -.354 -1.028 info1 1.000 10.000 -.916 -5.315 .907 2.631 info3 1.000 10.000 -.821 -4.761 1.026 2.976 adop8 1.000 10.000 -.919 -5.333 .648 1.880 adop3 4.000 10.000 -.675 -3.918 -.350 -1.015 adop2 4.000 10.000 -.809 -4.695 .005 .014 adop1 3.000 10.000 -.878 -5.095 .128 .372 per7 2.000 10.000 -1.077 -6.250 .934 2.709 per6 2.000 10.000 -1.182 -6.861 1.206 3.498 per9 2.000 10.000 -1.153 -6.692 1.455 4.220 per5 2.000 10.000 -1.144 -6.636 1.027 2.978 per1 2.000 10.000 -1.192 -6.918 1.301 3.774 dc1 2.000 10.000 -.816 -4.736 .305 .885 dc4 2.000 10.000 -.517 -3.000 -.269 -.780 rd1 4.000 10.000 -.495 -2.871 -.597 -1.731 rd2 3.000 10.000 -.540 -3.133 -.411 -1.193 rd3 3.000 10.000 -.602 -3.493 -.391 -1.135



dv1 3.000 10.000 -.627 -3.639 -.089 -.259 dv2 3.000 10.000 -.763 -4.429 .090 .260 dv3 3.000 10.000 -.703 -4.079 .020 .059 Multivariate 419.978 88.008

5.4.2 SEM Structural Model

The structural model for SEM is developed according to the measurement models described

in Section 5.3 previously. Measurement models are established for every construct measured

(Improved Information, Perceived Benefits, Cost Estimates Reliability and BIM Adoption

constructs). To generate the SEM structural model, the final model is expanded, with all final

measurement models in the previous section 5.3 being combined and linked based on the

hypotheses relationships shown in Figure 5.1. Hence, the SEM structural model with its

specified Fitness Indexes is formulated as presented in Figure 5.10 below.

Figure 5.10: SEM structural model

The overall fitness measures for the structural model as shown in Figure 5.10 are: P-

value=0.000, RMSEA=0.079, GFI=0.824, AGFI=0.777, CFI=0.962, TLI=0.956, NFI=0.935

and ChiSq/df= 2.259. All values achieve the acceptable level of desired Fitness Index, except



for GFI =0.824<0.90 and AGFI =0.777<0.90. The majority of the values obtained fulfil the

common index categories reported in the literature, except for the GFI value due to its

sensitivity towards sample size (Hooper et al., 2008; Sharma et al., 2005). Therefore, this

final SEM structural model is considered as fit for the research. All the indicators (items

measured for respective constructs) in the structural model have high factor loadings (>0.50).

This is due to all negative or factor loadings having been excluded for measurement models

during CFA analyses that were conducted earlier. Table 5.17 summarises the results of the

Fitness Index assessment for final SEM structural model.

Table 5.17: The Fitness Index assessment for SEM structural model

Category of Index Name of Index Index Value Comments

Absolute fit RMSEA 0.079 The required level is achieved

GFI 0.824 Acceptable, the value is within range

Incremental fit CFI 0.962 The required level is achieved

Parsimonious fit ChiSq/df 2.259 The required level is achieved

The final indicators (items in the questionnaire) measuring the constructs involved in the

final SEM structural model are described and tabulated in Table 5.18 below.

Table 5.18: Indicators for final SEM structural model






rd1 A reliable database improves information system reliability





Perceived Benefits









Cost

Estimates Reliability






BIM Adoption





5.5 INTER-RELATIONSHIPS AMONGST CONSTRUCTS

As described previously, the model has four primary constructs, namely Improved

Information, Perceived Benefits, Cost Estimate Reliability and BIM Adoption. All these

constructs have been measured through some indicators, namely, a set of items in the

questionnaire. Earlier, the CFA procedure has been conducted for the measurement models

for each construct. Subsequently, a structural model combining all measurement models for

the construct has been established as a final SEM model for the research. This section

examines the relationships between the constructs studied, which confirm the model

hypotheses described in Section 5.2 previously. The inter-relationships include the causal

effects and mediating effects amongst the improved information, perceived benefits, cost

estimate reliability and BIM adoption.

5.5.1 Causal Effects between Improved Information, Perceived Benefits, Cost Estimates Reliability and BIM Adoption

The causal effect relationships between the constructs are analysed to confirm the

hypotheses H1, H2, H3, H4, H5 and H6 in the model (refer Table 5.1). The final SEM

structural model (Figure 5.10) indicates that the value of R² for the model is 0.84. The value

represents 84% of the BIM Adoption construct that could be estimated by three other

constructs: Perceived Benefits, Improved Information and Cost Estimate Reliability. At the

same time, 62% of the Perceived Benefits construct (R²=0.62) could be measured through

Improved Information and Cost Estimate Reliability constructs, while 74% (R²=0.74) of the

Cost Estimate Reliability constructs could be measured by Improved Information construct.

Figure 5.10 also shows the values of regression weight indicating the effect of a construct on

its corresponding construct. Table 5.19 presents the details of the regression path coefficient

and its significance based on p-value (<0.05). The path details can be seen in the previous

Figure 5.1.



Table 5.19: The Regression Path Coefficient between the constructs and its significance

Construct Path Construct Estimate S.E C.R. P-value Result

Cost Estimate Reliability

<--- Improved Information 0.862 0.067 12.582 *** Significant

Perceived Benefits <--- Improved

Information 0.434 0.117 4.041 *** Significant

Perceived Benefits <---


0.382 0.117 3.627 *** Significant

BIM Adoption

<--- Perceived Benefits 0.149 0.054 2.824 0.005 Significant

BIM Adoption <---


0.601 0.088 7.685 *** Significant

BIM Adoption

<--- Improved Information 0.219 0.084 2.873 0.004 Significant

*** indicate high significance at < 0.001

Table 5.19 shows the paths and its coefficient (in bold) which indicate the effects of every

construct on its respective measuring constructs. For example, the path coefficient of

Improved Information to Cost Estimate Reliability is 0.872. This value indicates that for

every one unit increase in Improved Information, its effects would contribute 0.872 units of

increase in Cost Estimate Reliability. The effects of Improved Information to Cost Estimate

Reliability are significant with P-value of less than 0.05. Thus, that the hypothesis H1:

Improved Information has a positive and significant effect on Cost Estimate Reliability is

supported. The result of every hypothesis is tabulated in the following Table 5.20.

Table 5.20: The result of hypothesis testing for constructs (H1 to H6)

Hypotheses Statements Estimate P-value Results on Hypotheses

H1: Improved Information has a positive and significant effect on Cost Estimate Reliability

0.862 <0.001 Supported

H2: Improved Information has a positive and significant effect on Perceived Benefits


H3: Perceived Benefits has a positive and significant effect on BIM Adoption 0.149 0.005 Supported

H4: Cost Estimate Reliability has a positive and significant effect on BIM Adoption




H5: Improved Information has a positive and significant effect on BIM Adoption

0.219 0.004 Supported

H6: Cost Estimate Reliability has a positive and significant effect on Perceived Benefits


5.5.2 Mediating Effects of Perceived Benefits and Cost Estimates Reliability towards Improved Information and BIM Adoption

The mediating effects between the constructs are analysed to confirm the hypotheses H7, H8

and H9 in the model (refer Table 5.1). In analysing a mediator amongst the constructs

involved, there are two effects incorporated in the relationships; direct effect and indirect

effect. Direct effect exists directly between the independent construct and dependent

construct, while, the indirect effect comes from the independent construct to the dependent

construct but goes indirectly through the mediating construct. Figure 5.11 illustrates the

direct effects and indirect effects developed from the constructs’ relationships, with their

respective regression weights derived previously from the final SEM structural model

(Figure 5.10). The standardised regression weights and their significance for each path can

be referred to in the previous Table 5.19.

Figure 5.11: Direct effects and indirect effects in the construct relationship

In this model, the mediating effects are tested using the Sobel Mediation Test. According to

this test, to confirm whether mediating effects exist in the tested relationship, the value of

total indirect effects must be higher that the direct effect. This is due to when the mediator

enters the relationship and has an effect indirectly on the relationship; the direct effect will

be reduced. Some of the direct effects have shifted to the mediator. The mediating effect acts

as ‘partial mediation’ if the value of direct effect is still significant, even though it is

reduced. Instead, if the value of direct effect is no longer significant, it is called ‘complete

mediation’. However, no mediation occurs if the direct effect value is larger than the total of



indirect effects when calculated. There are two mediators analysed in the model; Perceived

Benefits construct and Cost Estimates Reliability construct. The Perceived Benefits construct

potentially gives mediating effect in the direct relationship between Improved Information as

an independent construct and BIM Adoption as a dependent construct (H7). The Cost

Estimate Reliability construct may influence two direct relationships between Improved

Information and BIM Adoption constructs (H8), and between Improved Information and

Perceived Benefits constructs (H8). The diagram above (Figure 5.11) illustrates the direct

effect of the Improved Information construct on the BIM Adoption construct.

The first mediator to be tested is the Perceived Benefits construct as a mediator towards the

direct relationship of Improved Information and BIM Adoption constructs (H7). The main

hypothesis of this relationship is that Perceived Benefits mediates the relationship between

Improved Information and BIM Adoption. Before conducting the mediation test, the

standardised regression weights and probability values are obtained to indicate the

significance for the paths involved in the relationship. The required information is given in

Table 5.21 below.

Table 5.21: The standardised regression weight and its significance for each path for Improved Information-Perceived Benefits-BIM Adoption relationship

Construct Path Construct Estimate P-value Result

BIM adoption <--- Improved Information

0.22 0.004 Significant

Perceived Benefits

<--- Improved Information

0.43 <0.001 Significant

BIM adoption <--- Perceived Benefits


Based on values and relationships in Table 5.21, Figure 5.12 below describes the procedure

for testing the mediating effect of Perceived Benefits towards the relationship.



Figure 5.12: The mediation test procedure for Improved Information-Perceived Benefits-BIM Adoption relationship

Table 5.22: The result of mediation test for Improved Information-Perceived Benefits-BIM Adoption relationship

Hypotheses Statements for Path Analysis (3 sub-hypotheses)

Estimate P-value Results on Hypotheses

H7a: Improved Information has significant effect on Perceived Benefits 0.43 <0.001 Supported

H7b: Perceived Benefits has significant effect on BIM Adoption


H7c: Improved Information has significant effect on BIM Adoption


As presented in Figure 5.12, the mediation test for the Improved Information-Perceived

Benefits-BIM Adoption shows that no mediation occurs in the relationship because the total

value of indirect effect (0.06) is lower than the value of direct effect (0.22). The overall

result of the mediation test is presented in Table 5.22. The tabulated results show the effects

on paths involved are significant; hence, the mediation test is valid.

The second mediator to be tested in the model relationship is the Cost Estimate Reliability

construct. This construct potentially mediated two direct relationships; the Improved

Information-Cost Estimates Reliability-BIM Adoption relationship and the Improved

Information-Cost Estimates Reliability-Perceived Benefits relationship. Firstly the Cost

Estimates Reliability construct is tested as a mediator of the Improved Information-Cost

Estimates Reliability-BIM Adoption relationship (H8). The standardised regression weights

and their probability values are presented in Table 5.23 to indicate the significance for the



paths involved in the relationship. The results presented in Table 5.23 shows that all

respective paths in the relationship are significant.

Table 5.23: The standardised regression weight and its significance for each path for Improved Information-Cost Estimate Reliability-BIM Adoption relationship


BIM adoption






BIM adoption

<--- Cost Estimates Reliability


Figure 5.13: The mediation test procedure for Improved Information-Cost Estimates Reliability-BIM Adoption relationship

Figure 5.13 represents the mediation test for the relationship involving the direct and indirect

effects in the paths. The mediation test for the Improved Information-Cost Estimate

Reliability-BIM Adoption confirms that mediation occurs in the relationship. The total value

of indirect effect (0.51) is higher than the value of direct effect (0.22). The type of mediation

involved in this relationship is partial mediation, since the effect in the relationship

(Improved Information to BIM Adoption) is still significant (refer Table 5.23) after the

mediator (Cost Estimates Reliability) is included in the model.



Table 5.24: The result of the mediation test for Improved Information-Cost Estimate Reliability-BIM Adoption relationship



H8a: Improved Information has significant effect on Cost Estimates Reliability


H8b: Cost Estimates Reliability has significant effect on BIM Adoption


H8c: Improved Information has significant effect on BIM Adoption


The overall result of the mediation test for the Improved Information-Cost Estimates

Reliability-BIM Adoption relationship is described in Table 5.24 above. The mediation test

for the relationship is valid as all effect paths involved, as shown in the Table 5.24, are

significant.

Next, the mediating effect of the Cost Estimate Reliability construct towards the direct

relationship of Improved Information and Perceived Benefits (H9) is tested. The hypothesis

tested is that Cost Estimate Reliability mediates the relationship between Improved

Information and Perceived Benefits. The standardised regression weights and their

probability values indicating the paths’ significance are obtained, as shown in Table 5.25

below.

Table 5.25: The standardised regression weight and its significance for each path for Improved Information-Cost Estimate Reliability-Perceived Benefits relationship


Perceived Benefits



Perceived Benefits

<--- Cost Estimates Reliability



<--- Improved Information 0.86 <0.001 Significant

Based on the values and relationships in Table 5.25, Figure 5.14 below describes the

procedure for testing the mediating effect of Cost Estimate Reliability towards the

relationship.



Figure 5.14: The mediation test procedure for Improved Information-Cost Estimate Reliability-Perceived Benefits relationship

As presented in Figure 5.14 above, the mediation test for the Improved Information-Cost

Estimate Reliability-Perceived Benefits shows that no mediation occurs in the relationship.

The total value of indirect effect (0.33) is lower than the value of direct effect (0.43). The

overall result of the mediation test is presented in Table 5.26. The presented results show that

the paths describing the indirect and direct effects in the relationship are significant. Thus,

the mediation test is valid.

Table 5.26: The result of mediation test for Improved Information-Cost Estimate Reliability-Perceived Benefits relationship



H9a: Improved Information has significant effect on Cost Estimate Reliability


H9b: Cost Estimate Reliability has significant effect on Perceived Benefits


H9c: Improved Information has significant effect on Perceived Benefits


The mediation test conducted on potential mediators in the model, which are Perceived

Benefits and Cost Estimate Reliability, is verified. From the mediating relationships

developed in the model, only Cost Estimate Reliability construct has a role as partial

mediator in the direct relationship between Improved Information and BIM Adoption

constructs. The mediation test confirms the hypotheses H7, H8 and H9 as described earlier in

table 5.1. The result of the mediation test is summarised as in Table 5.27 below.



Table 5.27: The result of Mediation Test (H7, H8 and H9)

Hypotheses Statements Types of Mediation Results on Hypotheses

H7: Perceived Benefits mediates the relationship between Improved Information and BIM Adoption

No mediation Not supported

H8: Cost Estimate Reliability mediates the relationship between Improved Information and BIM Adoption

Partial mediation Supported

H9: Cost Estimate Reliability mediates the relationship between Improved Information and Perceived Benefits

No mediation Not supported



5.6 CHAPTER SUMMARY

This chapter employed SEM using AMOS application to examine the relationships

between the main constructs in the model namely Improved Information, Perceived Benefits,

Cost Estimate Reliability and BIM Adoption. There are two types of relationships evaluated

based on the final SEM structural model developed, which are the causal effects and

mediating effects amongst the constructs. The causal effects’ relationships are tested on six

hypotheses (H1 to H6) while mediating effects examine three relationships (H7, H8 and H9).

Prior to testing the hypotheses in the final model, a measurement model is created for each of

the main constructs involved in the prescribed model. Every measurement model needs to

have a good fit, measurements being based on particular Fitness Index, before further

analysis in the structural model. The CFA analysis towards the whole model is conducted,

with items with low factor loadings deleted. Also, in treating redundant items, paired items

with lower factor loadings are eliminated.

The overall model fit measures are: P-value=0.000, RMSEA=0.079<0.08,

GFI=0.824, CFI=0.962>0.90, TLI=0.956>0.90, NFI=0.935>0.90 and ChiSq/df=2.259.

Overall, the index values achieve the required level and within the range of absolute fit,

incremental fit and parsimonious fit reported in most of the literature. Regarding

relationships amongst the constructs tested in this final model, the model confirms that all

causal effect relationships that exist (H1 to H6) are positive and significant, while, from the

Mediation Test conducted on H7, H8 and H9, results show that only H8 is supported. The

mediation test result on hypotheses confirms that Cost Estimates Reliability mediates the

relationship between Improved Information and BIM Adoption with partial mediation. The

other two hypotheses (H7 and H9) failed the test, indicating that no mediation occurs in the

respective relationships in both hypotheses.

Based on the findings presented in this chapter, the third objective in the research is

addressed, which is to examine the effects of improved information in cost estimate

reliability towards BIM adoption amongst Malaysian Quantity Surveyors. BIM improved

information was evaluated through the capabilities of the technology on its data

visualisation, a reliable database, and data coordination. It was justified via the survey that

BIM data visualisation should provide accurate data, better understanding and accurate

information interpretation; whereas, a BIM reliable database should improve information by

inaugurating complete data, therefore promote better task performance. Meanwhile, BIM

coordinated data should improve information by enhancing the reliability of the information

system, subsequently achieving the company’s goal. In summary, it can be explicated from

the surveyed results that the implication of those three BIM capabilities is presumed to



provide a better approach in understanding project information, to prepare cost estimates

afterwards. In the longer effect, it might potentially improve the estimator’s knowledge in

the entire estimating process towards establishing more reliable cost estimates. Better

visualisation of the project information, through BIM 3D models, obviously unfolds the

restriction of data captured from traditional drawings. While BIM models provide a reliable

database that should be developed with sufficient information as the project progresses, the

coordinated embedded data in the models, build up more effective and integrated

communication amongst the disciplines in a project team. Nonetheless, there might be some

limitations that exist in assimilating the BIM operation, in manipulating those three BIM

capabilities in the existing conventional method of cost estimating to improve project

information. Therefore, a focus group discussion should be executed to verify the interpreted

surveyed outcomes contributed by the respondents.

The next chapter validates the final model established in this chapter, through a

focus group, therefore validating the final framework for the research.

Chapter 6: Focus group discussion and validation of framework


6Chapter 6: Focus group discussion and validation of framework

Chapters 4 and 5 previously presented the results derived from the survey data. The data

were analysed quantitatively using SPSS and SEM statistical methods. Chapter 4 provides the

context of BIM implementation amongst the Quantity Surveyors in Malaysia. Chapter 5 tested

the hypotheses that developed the framework established in this research. The final SEM model

is presented in Chapter 5 with the confirmed relationships amongst the variables tested in the

framework.

This research employs mixed-method design involving both quantitative and qualitative

approaches. The survey conducted represents a quantitative method, the data of which were

analysed in Chapters 4 and 5. This chapter uses a qualitative approach, in which it validates the

final framework generated in the previous chapter. The validation of the framework applies a

focus group discussion method. There are two focus groups formed (BIM and non-BIM users),

in which each group consists of five and six expert validation panellists respectively. The

panellists are selected based on their working experience in cost estimating, BIM experience,

professional background, current roles in organisations, and also the nature of their organisations’

business.

The first section of Chapter 6 overviews the focus group practices in general. The second

section revisits the final framework hence summarises the findings from the framework. The next

section discusses the processes on how the focus group validations are conducted. The following

section reviews the focus group discussion content from both designated groups based on the

validated framework. Where content is more for validating the framework, their suggestions and

recommendations are considered to improve the framework. Additional information gained from

the discussion is presented to supplement the existing findings available in the framework.

Lastly, the chapter summary section concludes the results from the focus group validation and

highlights the significant key findings to be further discussed next, in Chapter 7.



6.1 OVERVIEW OF FOCUS GROUP PRACTICE

The focus group practice is characterised as an interactive discussion about specific

issues; conducted with a preselected group of people (Hennink & Leavy, 2014). There are

different thoughts from several authors on how many participants would be a suitable fit for

every focus group administered. However, this research applies the approach from Babbie

(2011) that specifies minimally five to maximally fifteen people for each designated focus

group discussion. Additionally, depending on the research purpose, focus group discussions

may be executed with different sub-groups of the assigned population; contributing further to

the different explanation phenomenon (Hennink & Leavy, 2014). In the case of this

particular research, cost estimating practice with BIM judgement is based on viewpoints

from two groups of BIM and non-BIM users. Therefore, the research gains distinctive views

regarding the studied context and attains more understanding towards the different aspects

issued.

The focus group method is applicable for both exploratory and explanatory research; this can

even be employed for mixed-method research design. This research is a mixed-method

study, in which quantitative means (questionnaire survey) is applied in the first stage,

subsequently followed by qualitative means (focus group discussions). Focus group method

is used as an explanatory tool to explain and clarify the findings derived from the preceding

quantitative survey (Hennink & Leavy, 2014). To be specific, the focus group discussions

are adopted in this research to purposely review and validate the framework established in

the previous stage of the research, the questionnaire survey. The analysed survey has derived

some output that generates the framework, thus needs to be further evaluated by expert

panels from the focus group. Consequently, the focus group discussions confirm the

findings, yet may also provide additional outcomes for the research.

Amongst the advantages of conducting a focus group method in research as compared to

individual interviews are (Stewart et al., 2007); (1) data are provided more quickly from a

group of people rather than separate individuals; (2) large and rich amount of data with

deeper meanings are possible to obtain through open discussion among participants; (3)

collaborative effect with participants reacting to responses of other focus group members

might produce uncovered information or suggestions; and (4) flexibility in focus group

design that allows a wide range of topics from a variety of people, with different settings, to

be analysed.



6.2 STATISTICAL FINDINGS TOWARDS FRAMEWORK ESTABLISHMENT

The previous chapter analysed and interpreted the results from the survey data. The

chapter then sequentially assembled the data into an essential output for the framework. The

statistical findings for the framework were obtained from the examined hypotheses earlier

set for the research. It was found from the SEM analysis results that the “Cost Estimates

Reliability” construct has become the partial mediator between “Improved Information” and

“BIM Adoption” constructs. It indicates that apart from achieving improved information for

their projects, the aspect of how the improved information affects cost estimate reliability

becomes of interest to them in adopting BIM technology. Cost estimate reliability mediates

the relationship between having information improved for more successful projects towards

BIM adoption in their practice. Figure 6.1 ultimately illustrates the whole findings derived

from the analysis, hence generates the final framework for the research to be validated in the

next focus group discussions.

Figure 6.1: Framework of BIM adoption in cost estimating practice

Comprehensively, it is described in the framework that in adopting BIM technology for cost

estimating practice, there are two most important aspects involved. The first one is the BIM

capabilities of data visualisation, reliable database and data coordination to improve

information to establish more reliable cost estimates. The second one is the cost estimate

reliability built upon estimator’s values, which are the received input information they have



to comprehend, the understanding they should possess to prepare cost estimates, and the

knowledge they must have to improve the process of estimating. While BIM improved

information impacts an estimator’s values in establishing cost estimate reliability, the

relationship between both directly affects the adoption of the BIM technology. Also, in

accepting the technology, the perceived benefits from BIM implementation are mainly on the

quick task accomplishment in estimating practice, other than gaining estimate accuracy and

BIM is valuable for current role usage. The framework highlighted key points, which are

summarised below.

(a) Improved Information

• Data visualisation – Data visualisation could improve information by providing

accurate data, better understanding and accurate interpretation of the information.

• Reliable database – Reliable database could improve information by establishing

complete information and improving task performance.

• Data coordination – Data coordination could improve information by improving the

reliability of information system and achieving company’s goal.

(b) Cost Estimates Reliability

• Input information –The use of data visualisation and coordinated data could improve

understanding of input information.

• Understanding – The use of reliable database and coordinated data could improve

understanding in preparing cost estimates.

• Knowledge – The use of data visualisation and coordinated data could improve

knowledge of estimating process.

(c) Perceived Benefits

• Task could be accomplished quickly through data visualisation, reliable database and

data coordination.

• Cost estimate accuracy could be increased through the reliable database.

• The reliable database could become useful in estimators’ roles.

(d) BIM Technology Adoption

• Estimators are motivated to adopt BIM if the technology can give better

understanding towards input information through data visualisation, reliable

database and data coordination.

• Estimators are motivated to adopt BIM if the technology could improve their cost

expertise.



Prior to evaluating and validating the above framework, the following section demonstrates

the processes executed for this research in conducting the focus group discussions. The

processes initially started with the selection of participants and the appointment of the

moderator, later to determine the number and size of focus groups.

6.3 FOCUS GROUP PROCESSES

In conducting focus group discussions to validate the framework, this research

applies the steps suggested by Stewart et al. (2007), as presented in Figure 6.2.

Figure 6.2: Steps in the design and use of focus groups (Stewart et al., 2007)

Primarily, the purpose of the conducted focus group is defined by referring to the research

question (see Chapter 3). It is to evaluate a developed framework in explaining the

relationship between cost estimate reliability and BIM factors to better support the BIM

adoption in quantity surveying practice.

After identifying the clear agenda of conducting a focus group, the sampling frame is

determined. The sampling frame is defined as a list of people representing the larger

population of research interest (Stewart et al., 2007). As per research setting, eligible

members of the population are to be included in the sample for the focus group where

Problem definition/Formulation of research question

Identification of sampling frame

Identification of moderator

Generation & pretesting of interview guide

Recruiting the sample

Conducting the group

Analysis & interpretation of data

Writing the report

Decision making & action



purposive sampling is further applied to choose the participants (Morgan, 2008). In

purposive sampling, the potential focus group participants are narrowed down from the

samples of the previous survey. Only surveyed respondents that stated their interest in

continuation focus groups were contacted and selected as participants. However, due to poor

feedback from previously surveyed respondents for focus group participation, snowball

sampling was also carried out for this research. Additional participants were recruited with

assistance from the earlier participants of the focus groups (Morgan, 2008).

The moderator is usually assigned concurrent with designing the interview guide and

recruiting the participants (Stewart et al., 2007). However, based on Fern (2001), the

moderator that plays a role as the controller during the discussion, is not necessarily

appointed for the focus group due to certain reasons, depending on the research setting.

While the recruitment process was conducted with surveyed respondents, the set in the

sampling frame were contacted through emails and telephones. They were asked about their

willingness to participate in the focus group discussions at a particular time and place, with

all requirements for the discussions being briefly explained. Apart from that, generating and

pre-testing the interview guide was done with related questions regarding the established

framework developed to assist the focus group sessions amongst the participants (refer

Appendix D for the interview guide).

Eventually, the intended focus groups were executed in accordance with a designated setting.

The discussions were held in an environment set so that all the participants could interact

with each other effectively; providing balanced seating arrangement and maximising eye

contact. Instead of being facilitated by audio-recording, the focus group sessions also

employed note-taking as an alternative backup for acquiring information from the

discussions. Every session began with a simple presentation, aimed at introducing the

developed framework, before any further discussion. The focus group sessions initially

began with an opening speech, continued with the intended discussion, and ended with the

debriefing for the session.

The processes of the focus group method are finalised with raw data from the discussions

that are analysed and interpreted accordingly. Hence a final report is produced to conclude

the results, in which decisions are made towards establishing the final framework for the

research.



6.4 FRAMEWORK VALIDATION BY FOCUS GROUP

The established framework from surveyed results is validated qualitatively by using

a focus group method. In ensuring that the focus group process is run smoothly, thorough

preparation needs to be done to ensure smooth sessions of the focus group. Apart from the

selection of participants, a focus group also requires moderator and note taker to assist

throughout the discussions. A moderator is usually appointed to facilitate and control the

focus group discussion in such a way that the participants keep track of discussing the

proposed issues. As a reference, the interview guide (refer Appendix D) designated based on

the survey findings, is used by the moderator. A note taker, on the other hand, assists in

writing down the important points highlighted by the participants regarding the issues. The

conversations are usually audio-recorded during the sessions. However, note taking is

necessary to back up the information retrieved in case of malfunction possibilities of

recording equipment. The other consideration in conducting focus group discussions is the

selection of location. Instead of having set a suitable date and time for the event, it must be

ensured that the location set is accessible for all participants, to avoid any absences for the

intended focus group. Every session of the focus group is expected to last for one to two

hours. The following section further explains the procedure of the expert panellists’ selection

for the focus group and also the discussion agenda of the interview sessions.

6.4.1 Selection Criteria for Focus Group Panellists

Before conducting the focus group discussions, expert panellists as focus group participants

must be carefully selected. To meet the research purpose, the selection criteria of the focus

group participants are based on the requirements as stated in Table 6.1 below. The

participants are divided into two types of focus group, namely BIM users and non-BIM

users. The criteria required between those groups of participants in terms of the working

experience in cost estimating are slightly different. The previously surveyed results showed

that a number of the BIM users are amongst the respondents that have 1 to 5 years of

experience in estimating construction cost. It is justified that, apart from having difficulties

to get participants with more than ten years working experience in cost estimating and using

BIM, this research should allow some leeway towards the selection criteria of the

participants to fit the research validation purpose. Other than that, participants for both

groups need to have their professional background, current roles in organisations, and nature

of their current organisations’ business to be closely related to the construction industry

environment.



Table 6.1: Selection criteria of focus group participants

Selection Criteria Group 1 (BIM users) Group 2 (non-BIM users)

Working experience in construction cost estimating

More than three years (with BIM experience) More than ten years

Professional background Quantity Surveying, Project Management, Building Construction, Civil Engineering, etc.

Current roles in organisations Quantity Surveyor, Project Manager, Construction Manager, Contractor, Civil Engineer, Architect, etc.

Nature of current organisations’ business

Quantity Surveying firms, Contractor firms, Government agencies, etc.

After identifying the criteria of expert panellists required, two series of focus group

discussions (BIM users and non-BIM users) were conducted in August 2016. Tables 6.2 and

6.3 summarise the criteria of selected panellists for both the BIM and non-BIM focus groups.

Table 6.2: Panellists for Focus Group 1 (BIM users)

Focus Group

Panellists 1

Selection criteria

Working experience in

cost estimating

Professional background

Current roles in organisations


P1 >10 years Civil Engineering

Training Engineer

Quantity Surveying firm

P2 3 years Quantity Surveying

Quantity Surveyor Quantity Surveying firm


Quantity Surveyor



Quantity Surveyor

Authority/Government Agency


Quantity Surveyor




Table 6.3: Panellists for Focus Group 2 (Non-BIM users)

Focus Group

Panellists 2

Selection criteria

Working experience in

cost estimating

Professional background

Current roles in organisations



Quantity Surveyor


P7 >10 years Quantity Surveying

Quantity Surveyor

Client/Developer

P8 >10 years Quantity Surveying

Quantity Surveyor



Quantity Surveyor



Quantity Surveyor Quantity Surveying firm


Quantity Surveyor


The focus group discussions consisted of five participants for Group 1 (BIM users) and six

participants for Group 2 (non-BIM users), fulfilling all the criteria designated for the

research, as tabulated in Table 6.1. Every expert panellist involved was provided with a

consent form, a sign-in sheet, and a framework evaluation form to fill in, as well as an A4-

printed framework for their reference throughout the discussion. Apart from note-taking,

each discussion was audio-recorded to capture opinions, with the permission of expert

panellists who participated in the focus group. The discussion was organised in a way that

every panellist in the focus group freely responded to each other’s answers without any

particular order. In addition, they did not necessarily agree with other people’s views.

Initially, the discussions started with an introductory session containing ice breaking, a brief

explanation on evaluating the established research framework and also some ground rules of

the focus group discussion. The session was then continued with the participants delivering

their general views about the framework and discussing the related key questions asked of

them. The discussion ended with the most important issues highlighted by the participants

about the framework and summaries were made based on the issues raised during the

discussion. A debriefing session ended the conversation in which final words pertaining to

the topic discussed from the participants were requested in order to close the discussion. The



detailed guide on how the focus group interview was conducted can be referred to in

Appendix D.

6.4.2 Focus Group Insights towards Framework

The respondents’ insights from the focus group interviews were analysed based on the key

points highlighted previously in the framework. This includes the analysis of the (1)

improved information through data visualisation, reliable database and data coordination; (2)

the reliability of the cost estimates through input information, understanding and knowledge;

(3) perceived benefits of adopting BIM in practice and; (4) the BIM technology adoption for

the reliability of cost estimates. The insights were obtained and examined separately between

two different groups of BIM and non-BIM users.

6.4.2.1 Improved Information through Data Visualisation, Reliable Database and Data Coordination

The previous survey results showed that most of the respondents consider that data

visualisation improves information by providing accurate data, better understanding and

accurate interpretation of the information. It also described the opinion of the respondents

that a reliable database could improve information by establishing complete information and

improving task performance. The responses further portrayed that data coordination could

improve information by improving the reliability of information systems and achieving

company’s goal. The below explanation interprets the insights from the focus group

participants regarding the surveyed results as mentioned above.

(i) Data visualisation could improve information by providing accurate data, better

understanding and accurate interpretation of the information

Amongst the BIM users, they claimed that they gained more comprehensive

information when they used a 3D model for taking-off and estimates, as it gives more

details when compared to using manual 2D drawings. Even though 2D drawings also

enable visualisation, the data is not incorporated as in visualisation through a 3D model.

The data contained in a 3D model becomes more accurate as more details are gradually

provided together with the complete specification for the particular project. Moreover,

the 3D model assists in detecting errors and changes in ensuring that only correct data

are inserted into the model. Data visualisation through a 3D model demonstrates better

understanding in contrast with the limited imagination of traditional drawings. By

manipulating the 3D model, the user can view the design and construction of buildings

without relying overly on a designer’s cross-section drawings. A 3D model allows

verification, based on the construction technology, of the unparalleled elements in the



building. Direct views shown by the model resembles the actual building. Hence, it

helps the taker-off to gain a greater confidence level in measuring the building elements.

Although the same traditional method of measurement is applied to 3D-model

computation, the difference is the in the precise shape of the physical building that can

possibly be displayed in the 3D views. By virtue of this, it also enables the users of the

3D model to better foresee complex construction such as infrastructural works,

basements, underground structures, and so on. Therefore, it contributes to better

interpreting the allocation of the preliminary value for the projects.

While non-BIM users emphasised that it is valuable if they have a 3D model that allows

visualisation on the difficulty of the construction, the complexity of the procurement,

and so on, other than providing the cost plan. For them, constructing with the 3D

modelling is a better way rather than envisioning through traditional drawings.

Visualising the complicated cross-sections especially for the mechanical, electrical and

plumbing (MEP) works with the 3D model that could not be performed with 2D

drawings is very useful. It may help in integrating the individual layouts of MEP,

architectural and structural components for connection details especially within the

elements of beams and columns. The 3D virtual drawings should provide visual

information that enables a digital visit throughout the building. Hence, it provides a

better understanding that should be interpreted in the form of cost outcomes. In this

case, other than a cost database, visualisation is also required. The 3D model improves

on the lack of 3D views in 2D drawings, in which it provides an opportunity to see and

view the building from inside. Additionally, quantities measurement based on 3D

drawings could expedite the process as it simplifies the taking-off and squaring

procedures. However, despite the possibility and easier way of taking-off quantities

through viewing the 3D model, the process of estimating the cost of the building

requires more than that. There are lots of other factors to be considered rather than just

viewing the 3D model alone. Therefore, 3D modelling is only applicable to accelerate

the process of taking-off quantities, instead of estimating the building costs.

(ii) Reliable database could improve information by establishing complete information

and improving task performance

As BIM users, the participants view the complete information for the more reliable

database as the information that could be developed from the initial stage of the project

until the asset management phase. The information included in the model as a database,

is based on the requirement from the model users. As for Quantity Surveyors’ usage, it

is not necessary for the information to be integrated up to the asset management level.



The most important is that the database can provide sufficient information for a taking-

off purpose, regardless of developing and receiving the other information later on.

Albeit the information is embedded in the model based on usage, it relies upon the roles

of the model authors or designers in preparing adequate information. The designers

need to provide sufficient information to make the database reliable so that the Quantity

Surveyors can extract the data effectively for their usage. Due to this, there are some

classifications or levels of development (LOD) in defining the building information

from the 3D model that the designers need to accomplish accordingly throughout the

specific projects. Based on the 3D model, information of different building elements

can be retrieved concurrently, in which it is beyond what traditional drawings can

execute. However, the effectiveness of the 3D model performance still depends on the

type of software used.

For non-BIM users, a reliable database is closely related to the received information in

facilitating Quantity Surveyors to estimate the building costs. The more information

they obtain, the more accurate is the estimate. Thus, less assumption should be made to

build up the cost. Additionally, the data must be updated to be useful and consistently

reliable. Constant updating of the data is crucial concerning the changes in market

conditions and other pricing factors. It involves the Quantity Surveyors’ judgement to

also sense the market condition while foreseeing the difficulty in the building design.

Based on their skills and experience, it helps them in making decisions by

acknowledging all those related factors. For them, if BIM could provide a better

indicator to improve their attitude towards their experience, it could improve their

practice. Moreover, a reliable database also eases their work by reducing mistakes,

therefore facilitates them in giving better cost advice towards the projects.

(iii) Data coordination could improve information by improving the reliability of the

information system and achieving the company’s goal

From the perspective of BIM users, coordinated data of the 3D model requires each

discipline in the project team to be responsible to initiate, excerpt and get prepared in

communicating any information for the project. It is a two-way communication amongst

the disciplines in detecting errors and notifying changes in the model, leading to a more

reliable information system obtained for the project. Getting clash-free information at

the end is definitely worth the effort of longer time taken in analysing the model. It

should also be a collaborative effort between the client and all consultants, in which the

client plays a significant role in encouraging a collaborative BIM environment amongst

the disciplines. BIM collaboration provides a platform for sharing and coordinating the



information, enabling effective communication amongst the team disciplines.

Concurrent viewing of data through the model is gathered from all designers to detect

unusual elements so that the data becomes coordinated, hence more accurate. The

essential process involves coordinating data between architectural, structural and

mechanical & electrical (M&E) elements. Subsequently, the team reach a certain level

of detail and clash-free information, upon which the Quantity Surveyors can depend for

their cost estimating task. In addition, the appointment of a coordinator in the project

team to organise coordinated data makes the process of detecting, analysing, reporting

and rectifying the clashes more efficient.

For non-BIM users, they define project team coordination as the integration amongst the

disciplines. In relation to producing coordinated data, it requires all the disciplines to be

competent towards designing at a very early stage of the project. Each discipline should

know their responsibility towards other disciplines, to achieve effective teamwork

towards a successful project. This co-operative environment might reduce mistakes

throughout the process, therefore increase accuracy in their task. It becomes their

guiding principle as professionals to, even if not at 100% in minimising the errors, at

least guarantee the accuracy of their construction project at a satisfactory level. Working

in isolation only makes each discipline in the project team becoming too protective

towards their individual data; therefore what they acquire is only self-satisfaction, but

not the satisfaction of others. Similarly, in the case of using 3D models, if the drawings,

estimating and measurement tasks are isolated, their usage becomes limited. If the

measurement task and visualising from the digital drawings could be done concurrently,

it generates better understanding, leading to holistic information produced for the cost

estimates.

6.4.2.2 The Reliability of Cost Estimates through Input Information, Understanding and Knowledge

Previously surveyed results demonstrate the effects of improved information through data

visualisation, reliable database and data coordination, on the reliability of cost estimates

concerning the input information received towards understanding and knowledge of the

estimator. The results highlighted that the use of data visualisation and coordinated data

could improve understanding of input information. Also, a reliable database and coordinated

data could improve understanding of the estimators in preparing cost estimates. Other than

that, it resulted that the use of data visualisation and coordinated data could improve the

estimator’s knowledge of the estimating process. The feedback from the focus group

participants, based on claims resulted from the survey, are described as follows;



(i) The use of data visualisation and coordinated data could improve understanding of

input information

Amongst the BIM users, they acknowledge that visualising through a 3D model assists

in generating automated quantities for measurement purposes only. Hence, estimating

the building cost requires additional built-up rates considering other pricing factors.

While there are other factors contributing to the cost, the 3D model only accommodates

automated measurement of the building components and not the operation that makes

up the components. Extracting information from the model is based on the level of

details available. Specifically, the input details embedded in the model determine the

received information for the users’ intended usage. Although some automated basic

parameters are provided in the model, filling in of the intended field, especially non-

geometric elements, is still required to obtain the desired information. Yet, it is partly up

to human effort to contribute to the development of the information in the model. Due

to this model setting, the reliability of the model to establish building costs depends on

the specified information in the model. It literally relies upon to what extent the

designers could complete the data in the model and the Quantity Surveyors as

estimators could manipulate the received information from the model. It is unfeasible

for the model to be too reliable as a database. Therefore, the integrity of the model

needs to be analysed prior to extracting information for further usage. The data attached

should be in parallel with the model, in which any discrepancies need to be reported for

further rectification. As the quality of the 3D model determines the quality of the output

information, the designers should enhance their model for the Quantity Surveyors to

receive high-quality information. The Quantity Surveyors must also be capable of using

the software pertaining to receiving files from the 3D model so that they are able to do

the quantity measurements. It makes the process of information delivery more effective,

leading to more reliable cost estimates produced.

Prior to receiving any information for their cost estimating purpose, the non-BIM users

emphasised that it is critical to have detailed information. It is imperative for them to

gain as much information as possible to lessen the assumptions made towards the

project due to insufficient data supplied. It is the sole responsibilities of the designers

for the project to provide complete information. Despite that the information varies

based on the different specifications and standards, accurate data attached to the project

depends on the sources from where the information is obtained. Acquiring accurate data

is vital as it accomplishes more values for the project. Frequently, the Quantity

Surveyors as cost estimators are assigned from the very beginning of the project so that



they can establish an early estimate that will be used as a cost indicator towards the final

account of the project. This initial estimate assists the Quantity Surveyors to commit to

any financial issues that might occur throughout the completion of the project. In coping

with this direction, data visualisation with a 3D model facilitates establishing a more

accurate estimate for more successful project outcomes. Visualising and extracting data

from the 3D model somehow overcoming the limitation of data derived from previous

projects. The historical data from past projects is rather individualistic, incapable of

being shared, and discourages interaction amongst the disciplines in the project team. In

relation to the traditional method, obtaining data for the desired project requires a longer

time to make it complete as a cost data reference to estimate the project costs. Overall,

the accuracy of the data certainly depends on the sources concerning where the data is

obtained and who supplies the data.

(ii) The use of reliable database and coordinated data could improve understanding in

preparing cost estimates

The BIM users opined that understanding the building can be established through a

reliable database; due to the fact that any assigned materials to construct the building

can be rechecked, on their changes and discrepancies. The 3D model gives more merit

to new estimators amongst the Quantity Surveyors pertaining to interpretation of the

building elements. Although they know how to read the drawings and do the

measurement, users’ interpretation capability towards the drawings is very limited. With

the 3D models benefiting them to directly view the design and construction of the

building, they can understand the information better, hence this improves their

interpretation towards performing the measurement. However, before applying the 3D

modelling, they firstly need to understand how to occupy traditional methods, so that

extracting information from the model becomes easier and faster. The 3D model permits

coordinated data in terms of detecting the clashes within information supplied by all

disciplines. Apart from encouraging better understanding amongst the project team

towards the building construction, the data coordination also complies with better

understanding and satisfaction of the client. Training is crucial for the Quantity

Surveyors as cost estimators to use this platform of BIM technology for an effective

information extraction from the 3D model. The more the technology is used, the more

understanding can be gained towards how it operates. The standard procedure of an

operation (SOP) for the practice is required and needs to be understood to ease the

process of obtaining input and output of the data.



Non-BIM users rely on the level of the Quantity Surveyors’ capability to improve their

understanding in preparing the estimates. Digital information probably may facilitate

the non-experienced estimators in interpreting the design and construction. It is more

difficult for them to visualise the plans, elevations, sections, and so on in the traditional

drawings, as they lack experience in their practice. By having 3D information, it is

easier for them to understand the difficulty of measuring buildings, especially with

complicated shapes, which greatly affects the building costs. While a 3D model with

complete information improves the understanding of non-experienced Quantity

Surveyors in estimating cost, it accelerates tasks of the experienced ones to do the

measurement. By directly viewing the building through the 3D environment, they can

take-off quantities quickly, and at the same time detect and report any unusual

construction while doing the taking-off. The 3D model also establishes coordinated data

from improved views on complex mechanical, electrical, and plumbing works (MEP),

therefore enhances the understanding of the users to measure and provide the cost

estimates for those works. However, the non-BIM users point out that a 3D model is

inadequate in providing accurate data, although it might improve understanding of

Quantity Surveyors as estimators.

(iii) The use of data visualisation and coordinated data could improve knowledge of

estimating process

For BIM users, to be a cost-estimating expert, Quantity Surveyors not only have to

know how to derive data and rely upon it to deliver information but also need to have

the foundations of measurements to build up the data when it is unavailable. It is then

based on their experience and rules-of-thumb in making decisions for the most reliable

building cost. When there is insufficient information provided for the project, their

experience comes into play, to make assumptions as queries for the designers. In this

case, 3D models can help them in dealing with unclear drawings received and therefore

having to assume the measured quantities. Knowledge is established through the

improvement of the details. More details can be obtained through a 3D model as

compared to schematic drawings, leading to more precise interpretation towards

estimation and its impact on cost. Standard methodology on how to conduct cost

estimates also leads to the better input of information. The standard procedures in

extracting quantities from the 3D model should be standardised amongst the estimators,

as achieving the integrated understanding is required within the organisation. They

indicate that experience alone does not take up 100% of what is needed to deliver a

competent Quantity Surveyor as a cost estimator. Knowledge and technology must be



well-balanced to become a better estimator. All that is needed is for practitioners to

learn the process to produce a more reliable cost.

The non-BIM users acknowledge that the 3D model could simplify the measurement by

computerisation without any longer using the traditional scale rule. However, they

consider that Quantity Surveyors as estimators need to understand the method used to

measure so that the knowledge they have is dependable and establishes reliability in

their cost estimates. Their experience is demanded in putting up assumptions towards

the measurement for the project that has inadequate data. The 3D model might benefit

in extracting the quantities for the cost-estimating purpose, except for the price factors

that need to be reimbursed, based on the experience of the Quantity Surveyors. In the

end, knowledge and experience of Quantity Surveyors as estimators certainly

determines the usefulness of any information derived from the project. For them, there

might be a situation that BIM does not have certain data for the project. In that situation,

the person that does not learn from the traditional method of measurement, but solely

relies on the 3D model, will not be able to solve problems or making decisions

effectively, as compared to the person that previously learnt from 2D drawings. The

process is not practical if the 3D model exists, but the user neglects the cost-estimating

procedures including all the design variables involved to make up the costs. To suit the

current situation in the construction industry, the knowledge of using digital information

must be integrated with the knowledge in measurement. The composite knowledge

between both aspects makes the usage of the 3D model for measurement more valuable.

After all, the effectiveness of BIM is built upon the information it could furnish for its

user.

6.4.2.3 Perceived Benefits in Adopting BIM in Practice

The previous survey resulted that the respondents perceived the benefits of accomplishing

their task quickly through data visualisation, reliable database and data coordination if they

were to adopt BIM. Other than that, they are also concerned with increasing the cost estimate

accuracy through the BIM reliable database. Also, the reliable database could become useful

in their roles. Further explanation below portrays the opinions from the participants of the

focus group, regarding the benefits perceived by the previously surveyed respondents.

From the views of BIM users, they have been using a BIM model for most of their projects

and only apply traditional methods for certain building elements. BIM model is used as it

makes the measurement process faster, due to building construction being viewed directly

through 3D visualisation. The visualisation also facilitates the Quantity Surveyors to monitor

the interim payment for the project, in which it would be easier to view the progress to the



client for the approval of the payment. Moreover, a 3D model through the automated

quantities regenerated allows easier operation of adding or omitting any elements as

compared to manual drawings. Hence, it initiates faster decision making on the changes

made for the project. In objectifying the company’s vision, for them, it is satisfactory if the

production process could be lessened. More projects could be accomplished and more profits

could be generated, leading to further promoting the time and cost saving as well. However,

a 3D model sometimes reviews unnecessary and excessive data that could confuse the users

in matching with their quantification of quantities. Thus, accurate information is required to

avoid too much information to choose from the model. The information needs to be

completed by the designers sufficiently, so that the Quantity Surveyors could reduce

assumptions made towards the project. The available data in the model is also inflexible in

that it disallows any adjustment made, resulting in some of the measurements being

conducted manually. The data produced by the BIM model is sometimes based on

information plugged in by the users. Thus it aborts the checking of information that has been

embedded in the model. It is useful to use 3D models for extracting quantities, but the level

of detail needs to be improved from time to time. Therefore, better task performance can be

achieved when the details in the model can be manipulated towards the end of the project.

Non-BIM users anticipate that a 3D model is less significant in establishing costs because it

considers other pricing factors for the project. Nonetheless, the model could be employed to

effectively generate quantities of the building components. The quantity measurement partly

contributes towards pricing the project costs. Hence, the information for the measurement

also needs to be substantial to ultimately make the produced cost more reliable. If BIM can

contribute towards establishing more accurate quantities, at least one of the components

(quantities and rates) for the project cost is resolved, in which the quantities are assured

beforehand. With computerisation and its tools like software, it should expedite and simplify

the process, including reducing people to do the measurement, as compared to more labour-

intensive processes in the conventional method. Likewise, a 3D model should reduce the

errors that frequently occur by applying traditional drawings. It should diminish re-works,

especially in decreasing revisions of tender to construction drawings. Their goal is to

depreciate mistakes during the estimating process, to the degree that they could produce less

variation order (VO), leading to more accurate estimates generated. It also could help in

accelerating the time allocated in preparing Bill of Quantities (BQ) for tender documents. To

prove to their clients their capabilities, as professionals, they must ensure that the cost they

estimate for any project is accurate. They need interaction with the project team since they

lack information to use for their estimating purpose. Regarding using BIM, they consider

that the record keeping should be better in the virtual model, rather than keeping the



traditional drawings manually. It is easier to make reference and extract information through

the virtual system, plus it is sustainable for any construction projects. However,

implementing BIM in any project needs a thorough plan in compromising with discrepancies

in the construction stage. Hence the data for the project could remain until the maintenance

stage as well. The model needs to be flexible for the users to include or exclude any design

information needed. Ultimately, BIM is very useful to Quantity Surveyors if it is possible to

provide clearer information and shorten the time for estimating process.

6.4.2.4 The BIM Technology Adoption for the Reliability in Cost Estimates

Overall, the respondents in the previous survey revealed that they are motivated to adopt

BIM if the technology could give better understanding towards project input information

through data visualisation, reliable database and data coordination. Ultimately, in adopting

BIM, they prefer it if the technology can improve their cost expertise. Below, the explanation

discusses the views from the focus group participants with regard to those previously

surveyed results.

The BIM users recommended that prior to adopting the BIM technology, the Quantity

Surveyors should be aware of traditional 2D before they extend to the 3D modelling. The

process that involves technology comes with human factors and policy as well. Apart from

investment in the hardware and software regardless of return on profit (ROI) gained,

investing in people’s training on skills is required. It demands trust and patience throughout

developing the skills of the people. There might be disappointments throughout the BIM

delivery if it does not meet the high expectation of the users. However, more experience

could be developed over the lessons learned throughout the process. Researching BIM and

understanding its capabilities should be conducted in establishing its values towards

achieving success in the construction project. BIM is operated based on integrated project

delivery, in which none of the individuals in the team works in silos. Each of them needs to

continuously improve and update their skills in parallel with the technology adoption in the

competitive environment of the construction industry. The traditional method might become

less in usage to the extent that the technology grows into a requirement for every project in

the future. The acceptance of BIM in the industry is still at a low level. However, the usage

is continuous and keeps expanding at various levels of the construction project. The

consultants might ignore BIM, yet the enforcement of its employment by certain projects

might lead them to the technology adoption. BIM is satisfactory in contributing its benefits

towards effective and efficient projects. Nevertheless, dealing with the technology requires

lots of effort, especially in the collaboration amongst the disciplines in the construction team.



In adopting the BIM technology, the non-BIM users give priority to their objectives in

providing services to the construction industry. There is a possibility of adopting, if the

technology can assist them in delivering satisfactory services. For them, the survival of a

business depends on providing excellent services consistently. If better service is delivered, a

better opportunity in terms of projects will be gained. The fact is the people’s changes

towards BIM technology uptake could not be forced. Still, it really depends on how the

company operates their business to secure more prospective jobs and clients. It necessitates

every discipline to change their mindset, in that the technology is a valuable investment that

could benefit the construction industry in solving many problems. Based on organisation-

driven requirements, having the technology in an organisation is teamwork demanding that

all the disciplines in the project team collaborate. Although the requirement of using BIM is

minimal impact, the technology would undoubtedly improve the quantity surveying practice,

specifically in the measurement tasks. BIM is a tool that helps the Quantity Surveyors to stay

competitive and reliable so that their existence in the construction industry can remain.

Regardless of the time and cost constraints, BIM technology requires skills of the Quantity

Surveyors in embedding the data into the model. In this case, they need to have substantial

knowledge in the basic traditional measurement, rather than only relying on the technology.

For the technology to be useful, the knowledge should be integrated with the ability to

estimate cost, excel in computerisation and know how to operate drawings for measurement

purposes. Quantity Surveyors must have a requisite foundation for that knowledge to

produce reliable and accurate data using a BIM platform. In other words, for the BIM to give

appropriate interpretation towards the design and process, it depends on the capability of the

users. Hence, some criteria need to be established in certifying the reliability of BIM in

contributing more values and increasing its integrity as a system. Last but not least,

imparting the BIM technology towards improving the traditional way of measurement is not

a theoretical phenomenon but needs to work in close collaboration with its development in

the local industry. The other issues of BIM highlighted by non-users are the limitation of

using BIM only for particular projects, the continuity of expertise in the company, and also

the copyright towards the model created.

6.4.2.5 Summary of Insights between BIM and Non-BIM Users

The previous sections have thoroughly reported the point of views on formerly surveyed

results from focus group participants involving both BIM and non-BIM users. The

discussion is centred on the four primary subject matter concerning; (1) the improved

information through data visualisation, reliable database and data coordination; (2) the

reliability of cost estimates through input information, understanding and knowledge; (3) the

perceived benefits towards adopting BIM in practice and; (4) the BIM technology adoption



for the reliability of cost estimates. Table 6.4 below summarises the insights from both

groups regarding the four topics mentioned above.

Table 6.4: Summary of insights from focus group participants

Descriptions Group 1 (BIM users) Group 2 (Non-BIM users)


Data visualisation could improve information by providing accurate data, better understanding and accurate interpretation of information

• 3D visualisation supplies more details

• 3D visualisation assists in detecting errors and changes

• 3D visualisation views actual design and construction of buildings

• 3D visualisation allows verification of unparalleled building elements based on construction technology

• 3D visualisation enables better forecasting of complicated construction

• 3D visualisation provides opportunity to see and view building from inside

• 3D visualisation envisions complicated cross-sections of drawings

• 3D visualisation integrates the individual layouts of drawings for connection details

• 3D visualisation foresees the difficulty of construction and complexity of procurement

• 3D visualisation expedites taking-off process; however, estimating costs needs additional pricing factors

Reliable database could improve information by establishing complete information and improving task performance

• A reliable database develops information based on users requirement

• A reliable database provides sufficient information for taking-off purpose

• A reliable database relies upon the designers to prepare adequate information based on LOD

• A reliable database retrieves concurrent information for different building elements from 3D model

• A reliable database receives an accurate information to estimate building costs

• A reliable database leads to fewer assumptions to build up costs

• A reliable database has constant updates of the data

• A reliable database eases tasks by reducing mistakes

• A reliable database facilitates in giving better cost advice

• A reliable database still depends on estimator skills and experience in making decision on costs

Data coordination could improve information by improving the reliability of information system and achieving

• Data coordination relies on each discipline’s responsibility to communicate information

• Data coordination involves collaborative efforts between client and consultants

• Data coordination provides platform for sharing and

• Data coordination allows integration amongst the disciplines in project team

• Data coordination requires all disciplines to be competent and responsible towards design in early stage

• Data coordination provides co-



company’s goal coordinating information • Data coordination allows

concurrent viewing of data gathered from all designers to detect clashes

• Data coordination permits estimator to depend on information for their cost estimating task

• Data coordination needs a coordinator to organise process

operative environment that might reduce mistakes and increase accuracy

• Data coordination avoids working in isolation and too protective towards individual data

• Data coordination integrates measurement task and visualising from digital drawings to produce holistic information

Reliability of Cost Estimates

The use of data visualisation and coordinated data could improve understanding of input information

• 3D visualisation generates quantities for measurement purpose only; cost estimating requires additional built-up rates

• Understanding the extracted information from model is based on the level of details available

• Understanding demands human efforts to contribute to the development of information in the model

• Improved understanding requires integrity of model to be analysed before using information

• Designers should enhance model for high-quality information for better understanding

• Estimator must be capable of using software to receive information effectively for their better understanding

• It is critical to have detailed information towards better understanding to lessen assumptions

• It involves the responsibilities of designers to provide complete information to improve understanding

• Better understanding needs accurate data that depends on the sources where the information is obtained

• Accurate data gives more values towards project understanding to establish an early estimate as a cost indicator

• 3D model facilitates to improve understanding to establish more accurate estimates

• Visualisation and extraction of information in 3D overcome limitation of historical data that restricts understanding

The use of reliable database and coordinated data could improve understanding in preparing cost estimates

• Model allows checking on changes and discrepancies in building

• Merits to non-experienced estimator to interpret building elements

• Need to prior understand how to occupy traditional methods for easier and faster information extraction from model

• 3D model permits clashes detection of data from all

• Understanding relies upon cost estimator’s capability

• Digital information may facilitate the non-experienced estimators to interpret building design and construction

• 3D model with complete information accelerates tasks of experienced estimator to do measurement

• 3D views establish coordinated data from complicated MEP works



disciplines • Better understanding and

satisfaction of client • Needs standard procedure to

ease input and output data process

• 3D model is inadequate to provide accurate data for cost estimation although it might improve understanding

The use of data visualisation and coordinated data could improve knowledge of estimating process

• A cost estimating expert must know how to derive and rely upon data and to have foundation of measurement to build up data when it is unavailable

• Experience is needed to make assumptions when there is insufficient data

• 3D helps to establish knowledge through the improvement of details

• Standard methodology to conduct estimates leads to better input of information

• Knowledge and technology must be well-balanced to be a competent estimator

• Estimator needs to understand the method to measure so that the knowledge they have is dependable to establish reliable cost estimates

• Experience is important to make assumption when data is inadequate

• 3D model might benefit in terms of extracting quantities but not for price factors that require experience

• Knowledge and experience of estimator determine the usefulness of information derived

• The knowledge of using digital information must be integrated with knowledge of measurement

Perceived Benefits

Task could be accomplished quickly through data visualisation, reliable database and data coordination

• 3D visualisation accelerates measurement process due to building construction is viewed directly

• 3D visualisation facilitates in monitoring progress payment for client’s approval in easier way

• Automated quantities generated through 3D model allows easier operation of adding and omitting building elements

• Initiates faster decision making on changes made through 3D model

• Lessens production process with more projects could be accomplished and more profits could be generated

• Accurate information supplied to model is required to avoid excessive and unnecessary data

• The data produced by BIM

• 3D model is less significant to establish costs that need other pricing factors

• Model could be employed to generate quantities for measurement effectively

• Information for measurement needs to be substantial, if BIM could contribute accurate quantities, one part is resolved

• Computerisation and its tools should expedite and simplify process, reducing people to do measurement

• 3D model should reduce errors and diminish reworks

• Could help in accelerating time for preparing BQ for tender documents

• Needs interaction with project team to have more information for more accurate cost estimates

• Record keeping should be better in virtual model for

Cost estimates accuracy could be increased through the reliable database

The reliable database could become useful in estimator roles



model is inflexible that disallow adjustment

• Aborts checking for plugged-in information by users

• 3D model is useful to extract quantities but to improve level of details

easier reference to extract information

• Data should remain until maintenance stage

• Model needs to be flexible to include and exclude design information

BIM Technology Adoption

If BIM could give better understanding towards input information through data visualisation, reliable database and data coordination

• Estimator should be aware of traditional 2D before extending to 3D modelling

• People’s training on skills is required to develop experience

• Researching BIM and understanding its capabilities

• Continuous improvement and updated skills is required in the competitive environment of construction industry

• Requirement of using the technology in project lessens the usage of traditional method

• Enforcement might lead to adoption

• Adoption demands lots of effort on collaboration amongst disciplines

• Adoption a technology prioritises on providing satisfactory services for business survival

• Changes towards technology cannot be forced, depends on how the company operates business to secure jobs and clients

• Adoption of technology relies upon mindset changing on BIM valuable investment amongst disciplines in the team

• BIM requirement in the organisation demands collaboration and teamwork

• Although requirement of using BIM in QS practice is with minimal impact, the technology undoubtedly improves measurement task

• With the technology estimator might stay competitive and reliable to remain in the industry

• BIM adoption requires integrated knowledge in both traditional method and technology

If BIM could improve the cost expertise

The summary outlines some significant remarks between the insights of BIM and non-BIM

users towards the issues discussed in the focus group sessions. In term of improved

information, it highlights that data visualisation through 3D views probably benefits

Quantity Surveyors significantly in their measurement task. Actual building design and

construction that could be directly examined through 3D perspectives facilitates, particularly

in visualising complicated building features. Therefore, it provides more accurate data for

the users to better understand and interpret the information received. However, the non-BIM

users argued that digital visualisation could only accelerate taking-off quantities in the



measurement process. To fully develop the overall costs for the project, it requires other

pricing factors, not solely data visualisation. Both groups agreed that a reliable database

should be able to establish sufficient information in developing the project costs. BIM users

rely upon the designers to prepare information based on LOD for the BIM model. Similarly,

the non-BIM users need constant updating of the data to lessen assumptions in the

measurements made throughout the project. Non-BIM users ruled out that a 3D model might

reduce errors and ease their task in measurement. However, in making decisions on costs for

the project, the Quantity Surveyors’ judgement based on their skills and experience is still

involved. However, there is no doubt that 3D model visualisation might facilitate them in

giving better cost advice. For data coordination, both groups acknowledged that

collaboration amongst disciplines in the team is vital in communicating and sharing the

project information. Non-BIM users emphasised the integration between traditional

measurement task and digital visualisation to develop holistic information. BIM users

recommended a coordinator role in organising data coordination within the team.

With regards to the reliability of cost estimates, both groups recognised that the data

visualisation could improve understanding of input information to develop more accurate

estimates. However, this depends on the details produced by the designers during the whole

of the project. The non-BIM users observed the possibility of a 3D model to resolve the

restricted historical data as project information. Nonetheless, the BIM users highlighted the

limitation of a 3D model that is capable of automating quantities for the measurement but not

of building up rates for the cost-estimating purpose. Additionally, Quantity Surveyors as

estimators must be competent in using the BIM software to effectively receive information

for their estimating task. Both groups agreed that a reliable database and coordinated data in

digital modelling facilitate most of the non-experienced estimators amongst the Quantity

Surveyors. A 3D model could improve their understanding in cost estimate preparation by

enhancing their interpretation of the building design and construction. Anyhow, the non-BIM

users believed that a 3D model alone is still inadequate to provide accurate data for cost

estimation. They strongly relied on the cost estimator’s capability to better understand the

formation of a cost estimate. Likewise, the BIM users indicated that they needed to have the

ability to operate the traditional method of measurement prior to extracting information from

the 3D model. Moreover, in improving their knowledge of the estimating process, the two

groups highlighted that they still required experience to make assumptions over insufficient

data provided for the project. The BIM users considered that a 3D model helps in

establishing knowledge through the development of details. However, the non-BIM users

queried the capability of the model to price the building costs that depend on other pricing

factors involving those that are experience-based. They believed that in these cases, the 3D



model only benefits in extracting quantities for the building measurement. Ultimately, both

groups agreed that a Quantity Surveyor needs to understand the mechanism in building

measurement by integrating both measurement and technology knowledge, so that their cost

estimates are dependable.

Both groups of BIM and non-BIM users perceived the benefits of 3D visualisation that

accelerates and simplifies the measurement process leading to reduced errors and reworks.

They also expected that the 3D model would be adequately flexible for them to include and

exclude information. Overall, in adopting the BIM technology, both groups have similar

opinions regarding the capabilities of Quantity Surveyors, which are that Quantity Surveyors

as estimators need to have competent skills and knowledge to stay reliable and competitive

in their field of practice. It requires both traditional and digitalised approaches to eventually

improve their cost expertise. Other than that, they both agreed that the technology adoption

demands efforts in collaboration amongst all disciplines in every project team.

6.4.3 Evaluation for Framework Validity and Reliability

Before finishing the focus group discussions, the participants were required to complete a

framework evaluation form that was developed using a context, input, process, product

(CIPP) evaluation model (Stufflebeam, 2003). The purpose of completing the evaluation

form is to confirm the validity and reliability of the focus group method in assessing the

established framework. Table 6.5 below encapsulates the comments from the focus group

participants of both BIM and non-BIM users, evaluating the aspects of context, input,

process and product as embodied in the CIPP model.

Table 6.5: Framework evaluation based on CIPP model

Aspects in CIPP model Group 1 (BIM users) Group 2 (Non-BIM users)

CONTEXT EVALUATION

Is the framework assessing the quantity surveying practice needs?

Yes. The framework assesses the quantity surveying practice needs

Yes. It assesses quantity surveying practice needs especially on time and accuracy factors.

Is the framework relevant to quantity surveying practice needs?

Yes. The framework is not only relevant to quantity surveying practice needs, but also benefits the designer teams.

Yes. It perceives better understanding of design in 3D models, in which it adds value to accurate estimation in order to close gaps of missing information from 2D drawings.



Is the framework sufficiently responsive to quantity surveying practice needs?

Yes. It has information towards quantity surveying practice.

Yes. It is responsive in terms of assessing the current methods effectively; in ensuring the accuracy of data information. It does include the essence of time, cost and quality factors mainly considered in the construction industry. However, it still needs further input on human resources.

INPUT EVALUATION

Does the framework clearly define its strategies to improve quantity surveying practice?

Yes. The information/data collected for the framework clearly define the strategies. It shows that BIM is just not a tool; it is more a process of manipulating information for project needs.

Yes. It helps to identify an area of improvement for quantity surveying practice. Yet, the framework does not cover all activities in the Quantity Surveyors’ roles. The strategies are still depending on project team direction in any construction projects.

Are the strategies consistent with the goals of quantity surveying practice?

Yes. It is consistent towards quantity surveying practice goals in terms of assisting in the production of reliable estimates. The limitation is only on time taken to achieve the goal.

Yes. It has to be relevant, in which it is important to be adaptive to change.

Are the framework strategies appropriate for the position of Quantity Surveyors within the industry?

Yes. With the addition of the needs to develop BIM basic knowledge and competencies.

Yes. The very important strategy is it needs to be effective; especially to Quantity Surveyors who are comfortable with IT. It is appropriate within the construction industry, especially for government agencies.

PROCESS EVALUATION

Are there any problems with the framework to deliver the needs of quantity surveying practice?

It is necessary to add-on the needs to develop BIM basic knowledge and competencies for Quantity Surveyors to better understanding what BIM can offer. It is also to highlight the collaboration amongst the building teams. Additionally, to incorporate skills and development

The needs are mainly for reliable data and expertise to produce accurate cost estimates. It needs to establish human capital input as well in the framework.



programme within the framework. Most importantly, the information received from the designers should be fit and acceptable to go through BIM process.

Does the framework implementation process need adjustment or revision for necessary improvement?

In general, the elaboration of the framework is good enough for both layman and BIM experts. The updates of information in the framework are required parallel with Quantity Surveyors’ skills. Needs to include in the framework the role of every discipline in any project teams.

The implementation process in the framework needs to be clearly defined. The framework needs an improvement especially on the models for cost estimates. The framework requires minimum adjustment on human factors.

PRODUCT EVALUATION

What are the merits and worth of the framework in improving the quantity surveying practice?

The framework proposed can be used to explain the expected BIM deliverables for the Quantity Surveyors. It can be applied to show the relevant process and factors in using a BIM model in preparing reliable estimates. With the relevant process shown in the framework, BIM potentially will improve the Quantity Surveyors practice. Anyhow, the framework gives further understanding of BIM.

The business objective will have to be complemented in the framework. The framework is beneficial towards producing better and accurate cost estimates and costing. It is very useful in increasing the quality of quantity surveying practice. The framework describes the benefits of having a 3D image and coordination of design in order to understand and detect missing information, design, etc.

What directions should the framework take in the future?

The framework can be adapted and suitable to be inclusive in the BIM syllabus development at the education institutions level, as it highlights the scope of cost-estimate preparation. The framework is possible to improve the process in previous projects; hence upgrading the skills of Quantity Surveyors in using BIM. It needs to put emphasis within the framework on the role of each discipline in a project team. The framework will be beneficial to the

The framework needs the additional input on training in the usage and merits of BIM. The framework requires integration between the various professionals. Also, to integrate all designers’ perspectives in the framework. The framework needs to show practicality in the implementation of BIM. The framework should take into consideration the skills of the users. The framework needs to highlight more on how it improves design visualisation by adding the value of cost estimation.



construction industry. The framework requires the quantity surveying practice to move forward and necessitates further updates.

6.5 FINAL FRAMEWORK

With the focus group discussion validating the survey results, the findings eventually

embellished the former framework generated for this research. Figure 6.3 below illustrates

the final framework and Table 6.6 presents the strategy information concerning the

framework. Conclusively, based on the findings, the framework captured three elements of

people, process and technology that made up the overall strategy in improving the cost

estimating practice by the Quantity Surveyors in using BIM. The people aspect requires the

ability of the BIM users to understand the traditional method of measurement as the principle

of fathoming BIM for their construction projects; possess the knowledge and skills of

technology to efficiently use BIM; and establish their construction practice experience to be

adopted along with BIM application. The process aspect includes the BIM procedure that

involves the roles and responsibilities of every discipline to acknowledge when practising

BIM in a construction project; the integrated information to be provided to be used by all

related stakeholders; and the standard methodology to be administered in the project system

that employs BIM. The technology aspect comprises of the BIM model that caters an

effective visualisation, a reliable database and coordinated data to be sufficiently exploited

by the Quantity Surveyors for their cost estimates purposes.



Figure 6.3: Strategy of incorporating cost estimating practice within BIM

Table 6.6: Strategy of incorporating cost estimating practice within BIM

BIM IMPROVED

INFORMATION

BIM 3D MODEL (TECHNOLOGY)

BIM PROCEDURE (PROCESS)

BIM USERS (PEOPLE)

Capabilities Potential outcomes Strategic actions

Data Visualisation

• Present effective data visualisation through 3D views

• Provide sufficient project details in the 3D model

• Demonstrate actual building design and construction through 3D views

• Integrate 3D views from architectural, structural and MEP

• Envision complicated elements and connections within building through 3D views

• Detect errors and changes made to project design and construction

• Expedite taking-off process of building measurement

• Accommodate more accurate and detailed data for users

• Provide opportunity to view and see building from inside

• Improve users’ understanding towards building design and construction

• Facilitate progress payment for client’s approval

• Cultivate better interpretation of users to forecast difficulty in more complicated buildings

• Aid in verifying and rectifying unparalleled elements in the building

Roles & responsibilities • Understand extracted

information based on LOD available

• Involve responsibilities of designers to provide complete information

• Supply accurate information to model to avoid excessive and unnecessary data

• Analyse integrity of model before further usage

• Enhance model to receive high-quality information for better understanding

Integrated information • Contribute human

efforts to the development of details

• Depend on reliable sources to obtain accurate information

• Allow checking and changes and discrepancies in

Understanding methods of measurement • Aware of traditional 2D

before extending to 3D modelling

• Need prior understanding on basic conventional measurement before occupying 3D model

• Understand methods of measurement so that existing knowledge is dependable to develop reliable cost estimates

• Know how to derive and rely upon data and to have foundation of measurement to build up data when it is unavailable

Knowledge and skills in technology • Capable of using

software to receive information effectively for better understanding

• Establish knowledge through the

Reliable Database

• Develop information based on users’ requirement

• Provide sufficient

• Sustain accurate information to estimate building costs

• Overcome limitation of



information for taking-off purpose

• Produce adequate information based on LOD

• Serve constant updates of data

• Retrieve concurrent information for different building elements from 3D model

historical data that restricts understanding

• Lead to fewer assumptions made to build up project costs

• Improve task performance by reducing mistakes due to complete information available

• Facilitate in giving better cost advice

building • Practice effective

interaction within project team to generate more information

• Induce efforts on collaboration amongst disciplines in project team

Standard methodology • Incorporate standard

operation to ease input and output of data

• Authorise standard methodology to conduct cost estimates

• Allow flexibility for data adjustments to model

• Permit operation of adding and omitting building elements in the model

• Prioritise on providing satisfactory services for business survival

• Consider business operation to secure jobs and clients

• Conduct requirement of using BIM to lessen the usage of traditional methods

improvement of details in the model

• Research BIM and understand its capabilities

• Change mindset on BIM valuable investment

• Stay competitive and reliable to remain in the construction industry

Established experience • Require experience to

make assumptions towards insufficient data

• Integrate the knowledge of digital information and knowledge of building measurement

• Well-balanced knowledge and technology to be a competent estimator

• Develop training on skills to evolve experience

• Update skills and their continuous improvement for a competitive environment of construction industry

Data Coordination

• Rely on each discipline’s responsibility to communicate project information

• Avoid working in silos and overly protective towards individual data

• Involve collaboration efforts between client and consultants

• Provide platform for sharing and coordinating project information

• Permit concurrent viewing of data gathered from all designers

• Require a coordinator to organise process of data coordination within team

• Allow information integration amongst disciplines in project team

• Create competence and liability of all disciplines towards design in early stage of project

• Consummate co-operative environment that is able to reduce errors and increase accuracy

• Achieve company’s goal to produce holistic information for the project

• Improve the reliability of information system for the project

6.6 CHAPTER SUMMARY

This chapter presented the analysed focus group interviews from both BIM and non-

BIM users. It validated the quantitative results obtained from the previous survey. The

validated findings discussed the BIM capability impacts of data visualisation, reliable

database and data coordination towards the reliability of cost estimates concerning the input

information, understanding and knowledge. Other than that, the perceived benefits of using

BIM and motivation factors towards adopting the technology amongst the focus group

participants were also reviewed, pertaining to the survey results.

It is found that a BIM model that employs 3D visualisation could improve information by

providing accurate data, better understanding and precise interpretation of information. The

3D model could benefit in terms of demonstrating more details, detecting errors and changes

and viewing the actual design and the construction of a building for a better judgement,

especially towards more complicated buildings. Despite the capability of the 3D model to

generate automated take-off quantities for the measurement part, it excludes the built-up

rates for the project costs that require other pricing factors. The BIM database could be

reliable to improve information for the project depending on the details provided by the

designers. For a Quantity Surveyor, information received from the 3D model would be



reliable if it could cater sufficient information for measurement. Thus it improves task

performance by the fewer assumptions made and mistakes reduced for better decision

making on the overall building costs. Besides, data coordination in BIM could improve

information by having a better information system for the project. Coordinated data within

the project team could be accomplished with all the disciplines working in a collaborative

environment to communicate and share information with each other. It further leads to

achieve the company’s goal towards developing more reliable cost estimates.

In contributing towards the reliability of cost estimates, it has been highlighted that the use

of data visualisation and coordinated data could improve understanding of project input

information by Quantity Surveyors as cost estimators. However, it builds upon the human

effort to develop and manipulate the details in the model to produce high-quality information

and perform a better assessment towards the project costs. The use of a reliable database and

coordinated data indeed promote a better understanding in preparing cost estimates,

particularly for the non-experienced estimators amongst the Quantity Surveyors. Yet,

understanding the traditional method of measurement is crucial before using the 3D model to

assist Quantity Surveyors in taking off the quantities for the building measurement purpose.

The data visualisation and coordination from the BIM model could improve the knowledge

of estimating process provided that Quantity Surveyors adapt both skills of traditional

measurement and the ability to use the technology.

The respondents perceived that they could accomplish their tasks more quickly through data

visualisation, a reliable database and data coordination. The merits of a 3D model in

supplying reliable data could decrease errors and reworks resulting in more accurate

measurement produced for further usage of estimating the project costs. A reliable database

promoted by the BIM implementation is supposed to become useful in a Quantity Surveyor’s

role, as it could expedite the preparation of the Bill of Quantities (BQ) for the tender

documents. In adopting the BIM technology specifically for the cost estimation, a 3D model

through its data visualisation, a reliable database and coordinated data, is capable of

delivering more understanding towards the project information. However, to make the

understanding mechanism effective, it requires that Quantity Surveyors as estimators to be

competent enough in integrating both knowledge of the traditional method and the

technology used. Only then, the information derived from the 3D model could be fully

utilised, hence leading to improving their cost expertise.

Overall similarities between groups are tabulated in the Table 6.7 below.



Table 6.7: Overall insight similarities between focus groups

Descriptions Similarities of insights


• Data visualisation through 3D model views actual design & construction of buildings, hence providing more accurate data, better understanding & interpretation

• A reliable database provides sufficient information for fewer assumptions in taking-off, hence improving task performance

• Coordinated data allows shared information amongst disciplines in the project team, hence achieving a collaborative goal in the company

Reliability of cost estimates

• Understanding of project information requires more details provided by the designers

• 3D views through a BIM model give more merits to non-experienced estimators to interpret building constructions

• BIM model could improve knowledge of estimating process provided that the estimators integrate both knowledge of traditional measurement and knowledge of using digital information

Perceived benefits • Timeliness of measurement tasks in which BIM model

facilitates to accelerate BQ preparation by automating building quantities for any additions or omissions

BIM technology adoption

• If BIM capabilities could give better understanding towards project input information leading to improving cost expertise, that requires the estimators to possess integrated knowledge of traditional method and BIM technology to establish holistic information for the project

Meanwhile, both focus groups also highlighted different insights of the BIM capabilities for

their practice. For the BIM users, they criticised the BIM model that often supplies excessive

and unnecessary data for their measurement tasks. It makes the process of preserving and

eliminating specific data beforehand for taking-off and estimating purposes consumes more

time. Therefore it defeats the purpose of having automated quantities from the model to

accelerate BQ preparation. Further, the non-BIM users opposed the BIM capabilities to fully

facilitate cost estimation. Estimating costs demands that Quantity Surveyors not only take off

quantities from the drawings or models but also to consider additional pricing factors to

make up the overall costs for the desired projects. Thus, a BIM model might assist in

automating building quantities, however Quantity Surveyors as estimators need to apply

their skills and experience to build up the price.



The next chapter presents the conclusions, answering all research questions developed at the

beginning of the research. The chapter also summarises the overall research based on the research

problems issued, as well as demonstrating the implications and contributions towards the

knowledge and the current construction industry practice. Along with that, some worthy

recommendations are suggested to be employed in any further research.

Chapter 7: Conclusion and recommendations

© 2017 Noor Akmal Adillah Ismail 176

7Chapter 7: Conclusion and recommendations

Overall, this thesis has seven chapters. Chapter 1 identified problems to further

develop the research questions and research objectives. Chapter 2 reviewed the literature on

the capabilities of BIM-improved information with its data visualisation, reliable database

and data coordination. It clarified the possible impacts of the capabilities on the estimator’s

received input information, understanding and knowledge towards the reliability of project

cost estimates. The potential relationships amongst the factors further led to the hypotheses

generated towards BIM technology adoption for the reliability of cost estimates in quantity

surveying practice. As this research was focusing on the BIM adoption amongst Quantity

Surveyors in Malaysia, the literature review chapter also reported on current BIM

implementation in the Malaysian construction industry to better examine the significance of

the research to be conducted. Chapter 3 discussed the design and methodology developed to

carry out the research. It mainly involved the mixed-method approach containing a

quantitative survey by using questionnaire and qualitative focus group interviews to validate

the survey results. The results chapters for both methods used were established in Chapter 4,

5 and 6, based on the research objectives. Chapter 4 presented the current status of BIM

implementation amongst the Malaysian Quantity Surveyors through descriptive analysis

using SPSS. Chapter 5 demonstrated the SEM analysis results explaining the hypothesised

factor relationships formed in the previous literature review chapter. Chapter 6 further

examined the surveyed results through the focus group interview discussion. Ultimately,

Chapter 7 concludes the overall outcomes from the survey and focus group, leading to the

establishment of the final framework for this research. In a nutshell, all research questions

were answered and the research objectives were concluded as achieved through this chapter.

The research contributions towards knowledge and industry practice are outlined, as well as

the limitations throughout the conduct of this research. Recommendations for future research

are also made in this chapter.



7.1 OVERVIEW OF RESEARCH OBJECTIVES

The aim of the research is to build a framework to guide Quantity Surveyors in

Malaysia to use BIM to achieve more dependable results in their cost estimating practices.

The focused respondents for this research are the Malaysian Quantity Surveyors that are

usually assigned to produce cost estimates for construction projects. To achieve the research

aim, the research objectives were developed based on the literature as presented in Chapter

2. Before the objectives development, the research questions were generated about the issues

and gaps found in the literature. There are three research objectives that were built upon

three research questions, as follows;

(i) Research Question 1: What is the current BIM implementation status of Quantity

Surveyors in Malaysia?

Research Objective 1: To determine the current BIM implementation status of

Quantity Surveyors in Malaysia

(ii) Research Question 2: To what extent could improved information from BIM affect

the reliability of cost estimates?

Research Objective 2: To investigate the effects of BIM-improved information on

increasing the reliability of cost estimates in quantity surveying practice

(iii) Research Question 3: How can increased BIM adoption be promoted amongst

Quantity Surveyors in Malaysia?

Research Objective 3: To produce a strategy for incorporating construction cost

estimates within BIM to promote the adoption of BIM technology in the quantity

surveying practice in Malaysia

Specific research methods employing both quantitative and qualitative approaches were

designed to conduct the research to answer the research questions and achieve the research

objectives. The quantitative method applied a survey questionnaire, while the qualitative

method adopted focus group interviews that validated the previous survey results. SPSS was

used for the descriptive analysis, and SEM-AMOS was used for the inferential analysis. The

SEM model produced a framework that was further validated by the focus group discussions

from the distinctive panellists of BIM and non-BIM users. Conclusions and

recommendations were made based on the outcomes from both methods, leading to the

refinement of the final framework.



7.2 CONCLUSIONS ON THE RESEARCH OBJECTIVES

There are three primary objectives set for the research as outlined in section 7.1. The

first objective was to determine the current BIM implementation status of Quantity

Surveyors in Malaysia. Through a survey by questionnaire, the respondents were asked some

related questions concerning the BIM adoption in their current practice. The results were

then descriptively analysed using SPSS. The second objective was to investigate the effects

of BIM-improved information on increasing the reliability of cost estimates in quantity

surveying practice. By adapting the Likert scale of 1 to 10 in the questionnaire, the

respondents were required to give their feedback pertaining to the questions constructed for

Objective 2. SEM-AMOS was used to analyse the results of this section, leading to the

development of a structural model that shaped the main framework for the research. The last

objective was to produce a strategy for incorporating construction cost estimates within BIM

to promote the adoption of BIM technology in the quantity surveying practice in Malaysia.

Based on the previous SEM model, which made the framework, focus group interviews were

conducted. The content of the discussion derived from the focus group was analysed

qualitatively to verify the essence in the framework formerly generated. The following

sections discuss in detail how the research objectives were accomplished, leading to

responding to the research questions, eventually achieving the primary research aim.

7.2.1 Research Objective 1

RO1: To determine the current BIM implementation status of Quantity Surveyors in

Malaysia

This first objective was intended to answer the question of “What is the current BIM

implementation status of Quantity Surveyors in Malaysia?”. Section A in the questionnaire

survey was mainly designed concerning some related aspects of BIM implementation status

amongst the respondents. In this particular study, the status of BIM adoption has been

explored in achieving this objective; Chapter 2 has presented descriptive results on the

current BIM practice by Malaysian Quantity Surveyors.

Broadly, it could be seen that the Quantity Surveyors in Malaysia are, in the majority, aware

(90.6%) of BIM existence in the construction industry, despite not implementing the

technology in their current practice. Amongst 100% of those who are aware of BIM,

approximately only 25% used BIM, predominantly for cost estimating. The only previous

research that recorded the BIM implementation status by Malaysian Quantity Surveyors or

quantity surveying firms was by Ali et al. (2013). Although the study has addressed the



infancy rate of BIM awareness and readiness amongst those professionals, the report was not

considered thorough due to the very limited and insufficient responses the study has

obtained. Thus, no obvious difference could be compared between the previous research and

this current study, in terms of adoption status, whether it is increased or decreased. It could

be concluded that the awareness of BIM is widely dispersed within the quantity surveying

practice in Malaysia. Yet, the implementation of the technology is not extensive and still in

the low level of adoption. In comparing it with other studies assessing the BIM adoption

level amongst other disciplines in the Malaysian construction industry, this study provides

the most likely similar outcomes. Lan & Omran (2015) reported on the low level of BIM

employment amongst Malaysian architects, while Lan et al. (2015) recorded the infancy rate

of BIM usage amongst civil and structural engineers in one of the states in Malaysia,

although their levels of understanding towards BIM technology were quite high.

In terms of the software application, Glodon is mostly used amongst the Malaysian Quantity

Surveyors that employed BIM in their practice. It is significant as the software is specifically

designed for BIM-supported measurement for the taking-off quantities purpose, the most

crucial tasks for their cost estimating practice. However, as stated in the Yaakob et al. (2016)

study, Glodon is not one of the software that has been suggested by the government to be

employed by the Malaysian construction industry players. Rather, the type of software is not

commonly used by the Quantity Surveyors in their practice in Malaysia or any other

countries, as reported by related studies in this research. Nevertheless, it was revealed

through some verbal discussion with focus group participants, that Glodon was chosen

because of its cheaper cost as compared to other cost-estimating software applications.

Therefore, it somehow suits the usage of the quantity surveying practice, where cost was

often acknowledged as one of the implementation barriers for adopting BIM technology

(Zhou et al., 2012; Aibinu & Venkatesh, 2013; Ismail et al., 2015). BIM was highlighted as

mainly used in the estimating stage throughout the construction projects handled by the

Quantity Surveyors in Malaysia. It reflects the estimating stage that emerges as a critical

function in quantity surveying practice to produce reliable and accurate costs for desired

projects (Cheung et al., 2012; Chen, 2013; Ma et al., 2013; Monteiro & Martins, 2013; Choi

et al., 2015).

Overall, the BIM users have moderate knowledge in BIM while non-BIM users have

restricted knowledge in BIM. Although both groups classified the different level of BIM

knowledge that they have, they all viewed BIM technology as influential for their roles as

Quantity Surveyors. Mostly, BIM could facilitate their measurement tasks in producing more



accurate quantities and more reliable costs. Different groups have unique views of what BIM

could guarantee for their practice improvement. However, all were agreed that BIM could

help them to achieve better outcomes and deliver more successful projects. Due to this, both

groups are very much alike in advanced planning of practising BIM for their prospective

projects. The results tally with the recent BIM survey report by NBS (NBS, 2016). From the

five countries assessed in the survey, it is becoming the norm that awareness has overstepped

the practical usage of BIM. In foreseeing themselves becoming professional in practice,

regardless of using BIM or not, or the limited understanding towards BIM that they have, the

positive intentions towards using BIM in the future were significantly shown through the

survey.


RO2: To investigate the effects of BIM-improved information on increasing the reliability of

cost estimates in quantity surveying practice

The second objective was set up to respond towards the question of “To what extent could

improved information from BIM affect the reliability of cost estimates?” Sections B, C and

D, were developed in the surveyed questionnaire to achieve this objective. The survey

responses were then analysed using the SEM analysis and the results were presented in

Chapter 5. Through the SEM analysis, a final SEM structural model was established, and

further represents the main framework for the research. It was found that there were

significant causal effect relationships that existed between the primary constructs studied in

this research. The four constructs involved were improved information, perceived benefits,

cost-estimate reliability, and BIM adoption. Six main hypotheses connected each construct to

another and were all supported, with all contributing positive and significant effects. Besides,

there were another three hypotheses testing mediation effects amongst the constructs for this

research. However, it resulted that only the relationship between improved information and

BIM adoption is mediated by the reliability of cost estimates. The reliability of cost estimates

becomes a partial mediator in the supported causal effects relationship between those

constructs. In validating the results of the survey, focus group interviews were conducted,

and the results were discussed in Chapter 6.

The questionnaire survey outlined the three BIM capabilities of data visualisation, a reliable

database and data coordination, which are evident to improve information. Through the SEM

analysis, it was discovered that effective data visualisation should provide accurate data,

better understanding and accurate interpretation of information. Regardless of BIM or non-



BIM users, the participants from the focus group interviews agreed that 3D visualisation is

capable of accommodating more accurate data for the users. Data visualisation from the 3D

model can furnish the users with more project details. The 3D views of actual building

design and construction enable envisioning of its complicated elements and connections. The

integrated views from architectural, structural and MEP 3D models, therefore, facilitate in

detecting errors and verifying unparalleled elements in the building. Using this data

visualisation through a 3D model, potentially improves the user’s understanding.

Subsequently, it cultivates better interpretation for the users to forecast the difficulty of more

complex building construction.

The surveyed results also revealed that a reliable database should improve information by

establishing complete information and improving task performance. Both groups of BIM and

non-BIM users validated that a reliable database is a database that is capable of providing

sufficient and accurate information for the measurement purpose to further estimate the

building costs. A reliable database should develop information based on a user’s

requirement, to lead to fewer assumptions made in building up the costs. Accordingly, it

depends upon the designers to serve adequate information and to continually update the data

available for the desired project. In improving a Quantity Surveyor’s task performance, a

reliable database should ease their estimating task by reducing mistakes in the building

measurement. In using the 3D model, a reliable database should be able to retrieve

concurrent information for different building elements. Thus, it facilitates Quantity

Surveyors as estimators to give better cost advice.

Also, the survey results showed that data coordination should improve information by

improving the reliability of an information system and achieving the company’s goal. Both

focus groups agreed that coordinated data is built upon integration amongst all disciplines in

the project team. Data coordination avoids working in silos, which promotes vigilance

towards individual data. A platform for sharing and coordinating data should be organised to

communicate every project’s information. It is the responsibility of every discipline to be

competent and liable towards design in the early stage of the project. It also involves a

collaborative effort between the consultants and client. Data coordination that converges data

from all disciplines to detect clashes set up a co-operative environment reduces errors and

increases accuracy in both design and construction. Later, the coordinated data will compose

information that becomes dependable for Quantity Surveyors to carry on with their

estimating tasks. The role of coordinator could be a merit for the project team to organise the

process of data coordination amongst the disciplines efficiently.



In impacting the reliability of cost estimates, from the surveyed views, the use of data

visualisation and coordinated data are supposed to provide a better understanding of the

project input information. However, focus group panellists emphasised that in improving

understanding of project input information for the reliability of cost estimates, it depends on

the sources and the level of details provided for the project. In this case, the designers should

take responsibility to prepare sufficient information so that this could lessen assumptions

made towards the project. A 3D model could facilitate the users to better understand the

project, provided that it is enhanced by the designers to produce high-quality information

better. Understanding information demands human efforts, not only for the designers to

provide complete data, but for the Quantity Surveyors as estimators to analyse the model

integrity before using the model. The Quantity Surveyors themselves must be able to use any

software related to the model to retrieve information efficiently.

Meanwhile, the surveyed outcomes also highlighted that the utilisation of a reliable database

and coordinated data could improve understanding in preparing cost estimates. From the

perspectives of focus group discussion, a 3D model facilitates the non-experienced

estimators amongst the Quantity Surveyors to interpret building design and construction

more effectively for their projects. When the model is capable of establishing coordinated

data from complicated arrangements such as in MEP, it allows checking on the discrepancies

and changes occurred within the building elements. The 3D model authorises clash

detections of data given by all disciplines. With this capability, it also helps the experienced

estimators to obtain adequate information and accelerates their measurement tasks. However,

before deploying the 3D models, both experienced and non-experienced estimators should

firstly understand the traditional measurement method for easier and faster information

extraction from the model. Due to this, understanding in preparing cost estimates highly

relies on the Quantity Surveyors’ capability to master traditional and BIM methods

altogether.

The use of data visualisation and coordinated data was further claimed by surveyed

respondents to improve knowledge of the estimating process. Based on focus group

opinions, 3D model guides towards establishing knowledge through the improvement of

details for extracting building quantities. Nonetheless, the knowledge and experience of

estimators determine the usefulness of the information derived from the model. This

mechanism requires the knowledge of using digital information to be integrated with the

knowledge of building measurement. To be an expert, Quantity Surveyors as cost estimators

need to be adept at extracting and engaging with the 3D data, and in the meantime to



establish a foundation of building measurement. It is crucial for Quantity Surveyors as

estimators to have a measurement basis to build up data when the information is unavailable.

In this case, a Quantity Surveyor’s experience is required to make assumptions towards

insufficient information from the model. The Quantity Surveyors need to comprehend the

method to measure the building for the knowledge they have that is dependable to produce

reliable cost estimates. Principles towards a methodology of executing cost estimates would

lead to better input and output information.

Conclusively, BIM that serves improved information via its data visualisation, reliable

database and data coordination, potentially enhances the reliability of cost estimates, as

discussed earlier. Visualisation and extraction information from the 3D model certainly

solves the issues of limited data from the traditional drawings. Detailed data is essential to

give more values in understanding the project better, especially in establishing early

estimates as cost indicators for the project. However, the 3D model is insufficient to provide

precise data for cost estimation, although it might improve the understanding of the Quantity

Surveyors in preparing the estimates. The 3D visualisation evidently expedites the

measurement process as it automates take-off quantities from the model. Still, cost

estimating demands additional built-up rates to include other pricing factors into the overall

project costs. Similarly, Kulasekara et al. (2013) addressed the same issue of BIM that could

help in generating automated building quantities from the digital model, yet is limited in

terms of costing for the project that requires additional pricing components. Even with the

availability of a reliable database, determining the external price factors urges Quantity

Surveyors’ skills and experience as estimators towards making a decision on final costs. In

being a competent cost estimator, knowledge and technology must be well-balanced. In line

with what has been emphasised by Boon & Prigg (2012), the main difference of cost

estimating practice between BIM and non-BIM environments is the ability of the estimators

to use related software in extracting building quantities for the measurement tasks.

Coordinated data, integrating common measurement tasks and visualising from digital

drawings, should produce holistic information for the project. A feasible standard procedure

to simplify input and output data processes should nurture better understanding and

satisfaction towards a successful preparation of cost estimates.


RO3: To produce a strategy for incorporating construction cost estimates within BIM to

promote the adoption of BIM technology in the quantity surveying practice in Malaysia



The third objective was initiated to justify the question of “How can increased BIM adoption

be promoted amongst Quantity Surveyors in Malaysia?” This objective is attained by

associating the results from the survey and focus group discussion, concerning their

perceived benefits and motivational aspects in adopting the BIM technology. Previously in

the survey and upon justification through focus group discussion, the respondents remarked

on the benefits of accomplishing their task quickly through the BIM capabilities in

improving information, namely data visualisation, a reliable database and data coordination.

Furthermore, they anticipated that the reliable database could increase the estimates’

accuracy for their projects and also become useful in their roles as estimators. They would

adopt the technology if BIM could give a better understanding towards project input

information through those capabilities. Additionally, they might adopt BIM if its reliable

database could improve their cost expertise as Quantity Surveyors. Conclusively, the

capability of the 3D model that accelerates measurement tasks summed up all the quicker

processes that occurred in cost-estimating practice, ultimately leading to faster decision

making. The automated database generated through the model additionally reduces errors,

thus accomplishing more accurate and reliable cost estimates. However, in achieving that,

the project information attached with the model needs to be substantial, demanding

interaction and communication amongst the disciplines in the project team.

Regarding adopting the BIM technology for improving understanding and cost expertise

towards the reliability of cost estimates, the focus group discussion was more focused on the

people’s capabilities, especially Quantity Surveyors as cost estimators. Generally, BIM

technology adoption could be a possibility for the quantity surveying practice, provided it

could deliver and prioritise satisfactory services for its business survival. Despite the fact

that mandated enforcement of the technology might lead to adoption, changes towards the

technology literally cannot be forced. It positively depends on how the company operates a

business to secure jobs and clients. Rather, BIM adoption relies on changing the mindset of

people involved in the team in appreciating the BIM valuables, if it is to be invested in the

project. Using this, BIM requirement in the organisation insists on collaboration efforts and

teamwork amongst the disciplines.

In incorporating cost estimates within BIM, the requirement of using BIM in quantity

surveying practice was claimed to give minimum impact. However, the technology is

undeniably improving the measurement task of Quantity Surveyors as cost estimators. The

BIM technology could be used effectively and benefit the cost estimating practice, provided

that the Quantity Surveyors as cost estimators themselves take responsibility for their self-



improvement. BIM adoption in quantity surveying practice requires integrated knowledge

between both traditional method and the current technology. Having said this, Quantity

Surveyors as cost estimators should be aware of traditional 2D covering essential building

measurement before extending to 3D modelling. Afterwards, people’s training on skills of

using the technology would be occupied in developing experience of exploring BIM and

understanding its capabilities. Nowadays, the requirement of using technology in

construction projects gradually lessens the usage of the conventional approach. Therefore,

the Quantity Surveyors as cost estimators need to stay competitive and reliable by

stimulating continuous improvement and updating essential skills, to remain in the industry.

In order to promote the adoption of BIM technology within the construction cost estimating

practice by the Quantity Surveyors in Malaysia, a set of strategies demonstrating the

capabilities, potential outcomes and strategic actions of using BIM is recommended in this

research. These strategies were developed from the findings of the focus group discussion

that validated the previous survey results. Ultimately the established strategies produced a

final framework for the overall strategy of construction cost-estimating practice within BIM

technology. Previously in Chapter 6, Figure 6.3 illustrates the established framework for the

strategy of construction cost-estimating practice within BIM technology, and Table 6.6

presents the information concerning the strategy framework. The strategic actions

underlining the process and the functions of people to act accordingly towards the

technology capabilities are highlighted. The key points of those strategic actions are featured

and summarised as below;

• Information

The procedure must consider the sufficiency and availability of data through the level of

development (LOD) in the model that should be evolved throughout the design and

construction of the project. The model has to be analysed in terms of integrity and

enhanced to receive high-quality information for further usage. The estimator as the

model user must be capable to use related software to effectively extract information

from the model.

• Roles and responsibility

The process administered within the model must allow flexibility for data adjustments

amongst the disciplines in the project team. The designers should adequately provide

information demanded for the building measurement and cost-estimating process by

avoiding including unnecessary and excessive data. The estimator needs to know how to

derive and rely upon the data supplied, for easier and faster information extraction.



• Skills and knowledge

The process should authorise standard methodology and operation within the project

team for better understanding towards cost-estimate preparation skills. The model needs

to have sufficient information so that the estimators could establish knowledge

throughout the improvement of details. The estimator needs to be able to integrate the

skills of using digital information and the knowledge of building measurement in the

case of insufficient data occurred in the model.

• Collaboration and integration

The procedure should practise effective interaction amongst the disciplines in the project

team to efficiently communicate the information required for the project. The efforts of

collaboration should be initiated by all disciplines with no more individuals working in

silo. While all the project information should be integrated within the BIM platform, the

estimators should integrate their knowledge and skills to effectively capture the

information derived from those disciplines.

• Mindset changing and competitiveness

The procedure should consider business operation and survival, prioritising on providing

satisfactory services towards securing jobs and clients. It should conduct the requirement

of using technology to improve the usage of traditional methods. The estimators require

mindset changing that the technology will give valuable investment. They need to stay

competitive and reliable to remain in the industry, hence need to update skills and create

continuous improvement in their practice.

Impliedly, those key points of strategy become the BIM drivers for Malaysian Quantity

Surveyors in adopting the technology successfully in their practice. Those drivers of

implementing BIM could be correlated with the previous studies researched in other

countries. For example to highlight, Mcauley et al. (2013) made known the similar factor of

the business survival and changing attitudes as the main drivers of BIM technology adoption.

The related factors of changes and business competitiveness are also cited as one of the

several adoption drivers in other studies (McGraw Hill Construction, 2014; Sawhney &

Singhal, 2013; Eadie et al., 2013; Newton & Chileshe, 2012; Olatunji; 2011b). In

contradiction, although the factor of government enforcement towards BIM adoption

becomes favoured in some studies (Eadie et al., 2013; Kassem et al., 2012), this study

however emphasized that changes towards using new technologies indisputably could not be

forced. It mainly depends on how the individuals change their mindsets towards self-

improvement and how the organisations operate their business to survive and maintain

services within the competitive industry environment.



Conclusively, in accomplishing and promoting the outlined strategy framework, further

dissemination plans and engagements with the related organisations need to be executed. The

dissemination approach in evaluating BIM uptake requires the channels or tools such as

publications, research reports, programmes, events, or syllabus. Targeted audiences could be

from the research community in the same field, implementing team from the construction

industry disciplines, and also associated policy makers. The summary of annunciating the

strategy is encapsulated as follows;

• Publications of research findings in related journals such as Automation in Construction,

IT in Construction, etc.

• Commercialisation of strategy framework in collaboration with the Royal Institution of

Surveyors Malaysia (RISM) to promote BIM adoption within quantity surveying

practice

• Potential collaboration with the Construction Industry Development Board (CIDB)

Malaysia in line with the development of national guidelines

• Research & Development (R & D) programmes through seminars or workshops

focusing on the area of BIM with Construction Research Institute of Malaysia (CREAM)

• Projects with universities and other related institutions to provide information as part of

syllabus for quantity surveying courses

Chapter 7: Conclusion and recommendations Page 188


7.3 RESEARCH CONTRIBUTIONS

This study conveys two different categories of contributions, namely theoretical

contributions to the body of knowledge and practical contributions towards the industry.

Both contributions are delivered in a different context as explained below.

7.3.1 Theoretical Contributions to Body of Knowledge

Theoretically to the body of knowledge, this research has made several contributions to fill

the gap in the existing literature. The underlying aim of the research is to improve the

accuracy and timeliness of the cost-estimating practice within the Malaysian construction

industry. Through this research, the extent to which the capabilities of BIM impacting

quantity surveying practice, specifically cost estimation, has been identified. The

significance of this research is delivered through BIM-improved information capabilities in

supporting the reliability of cost estimates by Quantity Surveyors. This research has

extended the BIM capabilities’ literature by highlighting the people performance in

promoting BIM technology adoption in the quantity surveying practice. It provides more

theoretical insights in the quantity surveying practice, into how the people’s competence

could influence the effectiveness of the new technology. The value-added contributions

towards theory development are discussed in detail as follows;

(i) Empirical research on BIM potentials affecting the reliability of cost estimates in

quantity surveying practice

BIM has benefited the construction industry in various ways for better improvement

of the construction project conducts. Likewise, the technology also gave favourable

effects towards the quantity surveying practice, especially in accelerating the

measurement tasks for the cost-estimation exercise. This research enriches the

existing studies in the literature by focusing the roles of Quantity Surveyors as cost

estimators on affecting the BIM potentials of establishing more reliable estimates.

Methodologically, apart from reviewing the literature, this study was based on data

collected through the participants in the preliminary exploratory study, questionnaire

survey and focus group interviews. Through preliminary exploratory study, it

classified the possible BIM capabilities contained in the existing literature, which

affect the reliability of cost estimates. BIM capabilities to improve information were

accentuated on its data visualisation, reliable database and data coordination. The

aspects influencing the reliability of cost estimates were asserted on the information,

understanding and knowledge values. The factors featured were tested and the

relationships were hypothesised through a survey. Surveyed results about these



factors were then analysed applying SEM, and justified through focus group

interviews. The focus group discussions revealed that BIM could improve

information for the reliability of cost estimates through its capabilities of data

visualisation, reliable database and data coordination. However, Quantity Surveyors

as cost estimators need to be competent in both the building measurement and BIM

technology knowledge. Therefore, this study will accord the existing empirical

record with regards to the people capabilities leveraging the BIM performance;

particularly in the cost estimating practice of the Quantity Surveyors.

(ii) Establishment of people approaches on capabilities towards knowledge in BIM

implementation for quantity surveying practice

This research has established that BIM employment in the quantity surveying

practice is feasible and useful provided that the users are capable of manipulating the

traditional measurement method and the BIM model altogether. They should possess

an understanding of basic conventional 2D, prior to extending to 3D modelling. To

adapt with BIM practice, the Quantity Surveyors must develop the skills and

experience of not only using the BIM model but also in mastering the foundation of

building measurement. Through this, they can build the composite knowledge

combining those two aspects that would consequently lead to produce more reliable

cost estimates for the desired project. The knowledge would facilitate decision

making and give cost advice for the project. It has been demonstrated in this study

that the human factors play the most important roles, despite the BIM offering

various advantages and the strategic actions initiated towards implementing the

technology. The study emphasises that the practical usage of the BIM technology

highly depends on the initiatives taken by the Quantity Surveyors to grasp holistic

information from both traditional and digitalised methods in preparing their cost

estimates. By doing this, they would probably make the cost dependable as an

indicator throughout the project. In such a way, it leads to continuously improving

the accuracy and timeliness of their cost estimating practice.

(iii) Identification of strategy concerning people, process and technology in

implementing BIM for the reliability of cost estimates in quantity surveying practice

This research also remarks on the concept of people, process and technology from

the perspectives of the Quantity Surveyors towards BIM innovation in their practice.

It has been discussed extensively in the literature, not only in the construction



management field, but also in other areas such as educational, business, psychology,

and many others. Through the synthesis of all qualitative and quantitative methods

involved, this research has evolved a strategy framework concerning this concept in

utilising BIM for the Quantity Surveyors’ cost estimates. The developed framework

has addressed the concerns in the BIM model, its procedures and its users. All of the

issues should be prioritised before undergoing the technology to assist Quantity

Surveyors in producing more reliable cost estimates. First, the strategy framework

outlines the BIM-improved information capabilities, namely data visualisation,

reliable database and data coordination. Along with that, through the BIM 3D model

that the technology used, the potential outcomes from the capabilities proposed are

framed. Next, the framework underlines the strategic actions from the BIM

procedure as the related process to conduct, and the BIM users as the people who

operate the technology. The tabulated strategy is illustrated accordingly to highlight

the essential elements contained in each aspect of technology, process and people,

for better accomplishment of BIM innovation towards cost-estimating practice.

From the theoretical perspective, this research overall speculates that holistic input from all

aspects of people, process and technology are essential. It ensures that BIM could be applied

effectively in quantity surveying practice specifically for cost-estimating purposes. Overall,

people’s competencies, as the operators of the BIM system, comprise the uppermost factor to

guarantee that both technology and process aspects can be executed efficiently. This study

provides, through its strategy framework, the traits that Quantity Surveyors should acquire as

BIM users, in conjunction with the BIM mechanisms and its potential outcomes.

7.3.2 Practical Contributions to Industry

Other than the theoretical contribution towards the body of knowledge, this study also

assigns the contribution within the construction industry practices. Most of all, this study

potentially constitutes better accuracy and timeliness towards cost-estimate preparation by

Quantity Surveyors. This research indicates the practical implication in widening the

understanding of Quantity Surveyors in effectively adopting BIM in their practice.

Specifically, this research could create awareness amongst Quantity Surveyors on what BIM

technology would positively contribute towards their practice development particularly in

cost-estimating exercises. This study then identifies the areas of improvement in quantity

surveying practice through the benefits offered by the technology. Sequentially, all the

possible improvements are strategised into a framework that could become a guideline for

Quantity Surveyors in adopting BIM. The framework consists of strategy demonstrating the

BIM deliverables towards improving the reliability of cost estimates. Hence, it promotes



BIM adoption in the quantity surveying practice in Malaysia. The detailed discussion on the

practical contribution is as follows

(i) Awareness of practitioners on BIM capabilities towards their practice

This study finding is not only creating awareness amongst the Quantity Surveyors

but also other practitioners, especially the designers’ team. It enlightens the potential

of BIM capabilities in advancing and sustaining their practice to survive in the

increasingly challenging construction industry. The findings that are developed into

a strategy framework present the BIM capabilities of improving the project

information with the potential outcomes of execution. By researching and analysing

the strategy, the practitioners might recognise the lack of procedures and also

knowledge and skills insufficiencies in implementing BIM in their practice.

Therefore, they would take the further actions of planning for learning and training

to encourage mindset and attitude change amongst members of the project team in

accepting worthy BIM technology. Ultimately, the continuous process of

exemplifying the transition process might lead to more reliable outcomes being

sustained in the future.

(ii) Identification of areas of improvement for quantity surveying practice

This study recommends the assessment of quantity surveying practice needs, in

terms of time and accuracy factors related to their cost-estimating exercise. The

strategy framework provided is relevant, for it observes better understanding of 3D-

model design in adding values towards accurate estimation, and closing the gaps of

missing information from 2D drawings. It is responsive in terms of assessing current

methods in ensuring the accuracy of information effectively, in particular with the

essence of time, cost and quality. The strategy is consistent with the goals of

quantity surveying practice to have BIM to assist in the production of reliable

estimates. The information in the strategy clearly defines the necessary actions of the

users regarding applying the BIM capabilities. It shows that BIM is not merely a

tool; nonetheless it is more about the transformation of the process than

manipulating the project information more efficiently. It also demonstrates that

despite expediting the measurement tasks, the BIM model could produce more

quality information for further estimating reliable costs for the project. Perhaps, the

identification of critical areas in the strategy allows the Quantity Surveyors to devise

appropriate planning in adapting BIM for their practice.



(iii) Development of strategy demonstrating BIM deliverables towards the reliability of

cost estimates to promote BIM adoption in quantity surveying practice

This study resulted in the establishment of a strategy framework demonstrating the

BIM deliverables in aiding Quantity Surveyors in producing reliable cost estimates.

The framework provides a set of guidelines in procuring the BIM capabilities, with

strategic actions on the procedures and the operators of the BIM model. It also

outlines the potential effects that could be gained if effective methods were

conducted throughout the process. Apart from the proposed framework used to

explain expected BIM deliverables, it can be applied to clarify the relevant process

and determinants of using BIM in the preparation of reliable cost estimates. The

framework is very useful in describing BIM advantages that induce further

understanding amongst Quantity Surveyors to increase the quality of their cost

estimates. Eventually, this strategy framework approach to the people, process and

technology aspects could be initiated to promote BIM adoption for quantity

surveying practice endeavours.

Based on the practical implications described, it can be concluded that this research mainly

contributes to the formation of a BIM strategy that should be accepted by Quantity

Surveyors to assist their practice properly. By identifying the areas of improvement, it gives

further understanding on how the BIM technology could facilitate their cost-estimating tasks.

It outlines the BIM capabilities and their potential outcomes, as well as the strategic actions

towards executing the procedures and equipping the users with the skills and knowledge

required. This strategy framework sheds some light on the effective application of BIM

within the quantity surveying practice towards achieving more reliable estimates.



7.4 RESEARCH LIMITATIONS

Apart from the contributions highlighted, this research also has some limitations.

This research might be limited in terms of the following.

• The scope of the study involves only participants from Malaysia. The results and

findings from this research derived from the perspectives of the Malaysian Quantity

Surveyors. Therefore, the strategy framework established in this study is only

applicable to the particular usage of this country. It might not generalise the practice

of quantity surveyors in a broad context globally. Different countries might react

equivalently or distinctively (refer section 7.2.3), should the same study be

conducted with the same targeted population, as applied for the respective countries.

Nevertheless, the information from this study could contribute to the body of

references and become a reliable source for any similar research administered in

other countries.

• The focus of the study is concerned only with the quantity surveying practice. The

participants of this study were amongst the Quantity Surveyors without any

connection with other disciplines related to the construction industry. Thus, the

results and findings of the research imply limited context on the overall

implementation of BIM technology in Malaysia. The research does not postulate the

viewpoints of all construction players in the country. The attitudes and behaviour

responses towards this research content might be different amongst the stakeholders,

depending on their practice’s goals and business aims. However, this study could be

a good benchmark in evaluating BIM performance and its impacts towards other

disciplines by using the same research approach.

• The strategy framework established does not cover all activities within the Quantity

Surveyor’s roles. It only represents one of their most prominent tasks, to estimate a

dependable cost as an indicator throughout the desired construction project. The

variables studied in this research are associated with the BIM data visualisation,

reliable database and data coordination aspects only. Those characters have been

identified as most impacting the cost-estimating exercise in quantity surveying

practice. Hence, those limited issues impacting only the roles of the Quantity

Surveyors in cost-estimating tasks restricts this research, to generalise the

appropriateness of the strategy framework to be applied in other scopes as stated.



7.5 RECOMMENDATIONS FOR FUTURE RESEARCH

This research is further providing more opportunities for future research to be

explored, especially on the people capabilities to venture on BIM potentials towards

improving their practices. Below are recommendations that might assist upcoming research.

• In this study, the BIM capabilities towards developing information in construction

projects have focused on the three aspects of data visualisation, reliable database and

data coordination impacting the reliability of cost estimates. Given that the other

factors could intervene between the relationships of other BIM capabilities and

Quantity Surveyor roles in a broader context, future research should expand the

study approach to promoting more possibilities of BIM adoption within the practice.

• This research identified the potential of people competencies, specifically on their

effort in providing holistic knowledge of building measurement and technology

towards adopting BIM in their practice. Therefore, future research should develop

more information to specifically address this aspect of knowledge and its impact in

improving BIM performance within the quantity surveying practice. The human

factor should be thoroughly studied to examine its genuine effects in contributing to

the other factors of process and technology in employing BIM.

• The challenges of implementing BIM in quantity surveying practice, specifically in

the preparation of cost estimates, should be further explored. Although this research

provides the strategy of implementing BIM effectively for cost estimate tasks, it is

valuable to evaluate the possible obstacles faced by Quantity Surveyors despite the

implementation. There might be some appropriate considerations to take into

account to resolve the challenges occurred. Thus, all challenges and ways to

overcome them, need to be contemplated in the future research.

Bibliography Page 195


8Bibliography

Abbasnejad, B., & Moud, H. I. (2013). BIM and basic challenges associated with its definitions, interpretations and expectations. International Journal of Engineering Research and Application, 3(2), 287–294.

Ackoff, R. L. (1989). From data to wisdom. Journal of Applied Systems Analysis, 16(1), 3–9. Ahsan, S., & Shah, A. (2006). Data, information, knowledge, wisdom: A doubly linked

chain? In International Conference on Information and Knowledge Engineering (IKE’06) (pp. 270–278).

Ahuja, H., & Campbell, W. (1988). Estimating (1st ed.). Englewood Cliffs, N.J.: Prentice-Hall.

Aibinu, A. A., & Pasco, T. (2008). The accuracy of pre-tender building cost estimates in Australia. Construction Management and Economics, 26(12), 1257–1269.

Aibinu, A., & Venkatesh, S. (2013). Status of BIM adoption and the BIM experience of cost consultants in Australia. Journal of Professional Issues in Engineering Education & Practice.

Ajzen, I. (1991). The Theory of Planned Behavior. Organisation Behavior and Human Decision Process, 179–211.

Ajzen, I. (2012). Martin Fishbein’s legacy: The Reasoned Action Approach. The ANNALS of the American Academy of Political and Social Science, 640(1), 11–27.

Ajzen, I., & Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, N.J.: Prentice-Hall.

Akintoye, A. (2000). Analysis of factors influencing project cost estimating practice. Construction Management and Economics, 18(1), 77–89.

Akintoye, A., & Fitzgerald, E. (2000). A survey of current cost estimating practices in the UK. Construction Management & Economics, 18(2), 161–172.

AlabdulQader, A., Panuwatwanich, K., & Doh, J. (2013). Current use of Building Information Modelling within Australian AEC industry. In Hokkaido University Collection of Scholarly and Academic Papers : HUSCAP.

Ali, K. N., Al Jamalullail, S. N. S. I., & Boon, T. C. (2013). Building Information Modelling Awareness and Readiness. Universiti Teknologi Malaysia-Royal Institution of Surveyors Malaysia.

Ali, K. N., Mustaffa, N. E., Keat, Q. J., & Enegbuma, W. I. (2016). Building Information Modelling (BIM) educational framework for quantity surveying students: The Malaysian perspective. Journal of Information Technology in Construction, 21(November 2015), 140–151.

Ali, M., Haron, T., & Marshall-ponting, A. (2014). Exploring the barriers and driving factors in implementing Building Information Modelling (BIM) in the Malaysian construction industry : A preliminary study. Journal of The Institution of Engineers, Malaysia, 75(1), 1–10.

Alp, N., & Manning, C. (2014). Creating a plan for Building Information Modeling. In Proceedings of PICMET ’14: Infrastructure and Service Integration (pp. 2732–2734).

Alroomi, A., Hyung, D., Jeong, S., Asce, M., & Oberlender, G. D. (2011). Analysis of cost-



estimating competencies using criticality matrix and factor analysis. Journal of Construction Engineering and Management, 138(11), 1270–1280.

An, S., Cho, H., & Lee, U. (2011). Reliabilty assessment of conceptual cost estimates for building construction projects. International Journal of Civil Engineering, 9(1), 9–16.

Aranda-mena, G., Chevez, A., Crawford, J., Wakefield, R., Froese, T., Frazer, J., … Nielsen, D. (2008). Business Drivers for Building Information Modelling. Brisbane, Australia.

Ary, D., Jacobs, L. C., & Sorensen, C. (2010). Introduction to research in education (8th ed.). Belmont, CA: Wadsworth Cengage Learning.

Ashworth, A. (2013). Cost studies of buildings (5th ed.). New York: Routledge. Ashworth, A., & Skitmore, M. (1999). Accuracy in estimating. In M. Skitmore & V.

Marston (Eds.), Cost modelling (1st ed.). London: E & FN Spon. Awang, Z. (2015). SEM made simple: A gentle approach to learning Structural Equation

Modelling. Selangor, Malaysia: MPWS Rich Publication Sdn. Bhd. Azhar, S., Khalfan, M., & Maqsood, T. (2009). Building Information Modeling (BIM): Now

and beyond. Australasian Journal of Construction Economics and Building, 12(4), 15–28.

Azman, M. A., Abdul-Samad, Z., & Ismail, S. (2013). The accuracy of preliminary cost estimates in Public Works Department (PWD) of Peninsular Malaysia. International Journal of Project Management, 31(7), 994–1005.

Babbie, E. R. (2011). Introduction to social research (5th ed.). Belmont, Calif: Wadsworth. Bannon, W. (2015). Missing data within a quantitative research study: How to assess it, treat

it, and why you should care. Journal of the American Association of Nurse Practitioners, 27, 230–232.

Baruch, Y. (1999). Response rate in academic studies - A comparative analysis. Human Relations, 52(4), 421–438.

Barzandeh, M. (2011). Accuracy of estimating techniques for predicting residential construction costs – A case study of an Auckland residential construction company.

Bazovsky, I. (1961). Reliability theory and practice. Englewood Cliffs, N.J.: Prentice-Hall. BCIS. (2011). RICS 2011 Building Information Modelling Survey Report. Building Cost

Information Service (BCIS) of Royal Institution of Chartered Surveyors (RICS). London.

Becerik-Gerber, B., & Rice, S. (2010). The perceived value of Building Information Modeling in the U.S. building industry. Journal of Information Technology in Construction, 15, 185–201.

Berenson, M. L., Levine, D. M., & Krehbiel, T. C. (2009). Business statistics: concepts and applications (10th ed.). NSW: Pearson Education Australia.

Bernama. (2014, September 23). Adopt BIM technology, CIDB urges industry players. Daily Express. Kuala Lumpur.

Bernama. (2016, November 17). Developers to be required to use BIM on govt projects of RM100mil and up. The Star Online. Shah Alam.

Bew, M., & Underwood, J. (2009). Delivering BIM to the UK market. In J. Underwood & U. Isikdag (Eds.), Handbook of Research on Building Information Modeling and Construction Informatics (pp. 30–64). IGI Global.

BIMForum. (2016). Level of Development Specification. BIM Forum. Blischke, W. R., Karim, M. R., & Murthy, D. N. P. (2011). Warranty data collection and

analysis. Springer Series in Reliability Engineering. New York: Springer.



Boon, J. (2009). Preparing for the BIM revolution. In 13th Pacific Association of Quantity Surveyors Congress (PAQS 2009) (pp. 33–40).

Boon, J., & Prigg, C. (2012). Evolution of quantity surveying practice in the use of BIM – the New Zealand experience. In Proceedings of the CIB International Conference on Management and Innovation for a Sustainable Built Environment. (pp. 84–98).

Boshoff, J. (2014). Building Information Modelling (BIM). Civil Engineering, (March), 56. Briggs, R. O., De Vreede, G.-J., Nunamaker Jr, J. and, & Sprague, R. (2002). Decision-

making and a hierarchy of understanding. Journal of Management Information Systems, 18(4), 5–9.

Brook, M. (2008). Estimating and tendering for construction work (4th ed.). Amsterdam: Elsevier Butterworth-Heinemann.

Bryde, D., Broquetas, M., & Volm, J. M. (2013). The project benefits of Building Information Modelling (BIM). International Journal of Project Management, 31, 971–980.

BSI. (2015). PAS 1192-5-2015_Specification for security-minded building information modelling, digital built environments and smart asset management. British Standards Institution (BSI).

BuildingSMART Australasia. (2012). National Building Information Modelling initiative - Volume 1: Strategy. Sydney.

buildingSMART Malaysia. (2015). buildingSMART Malaysia. Retrieved November 18, 2016, from http://mybuildingsmart.org.my/

Burke, S. (2001). Missing values, outliers, robust statistics & non-parametric methods. LC-GC Europe Online Supplement, Statistics & Data Analysis 2.0, 19–24.

Butcher, N. (2003). Cost estimating simplified. California. Bylund, C., & Magnusson, A. (2011). Model based cost estimations - An international

comparison. Lund University. Campbell, D. A. (2007). Building Information Modeling: The web 3D application for AEC.

In Web3D 2007 (pp. 173–177). Perugia, Italy. Cao, D., Wang, G., Li, H., Skitmore, M., Huang, T., & Zhang, W. (2015). Practices and

effectiveness of Building Information Modelling in construction projects in China. Automation in Construction, 49, 113–122.

Cavana, R., Delahaye, B., & Sekaran, U. (2001). Applied business research (1st ed.). Milton, Qld: Wiley.

Cerovsek, T. (2011). A review and outlook for a “Building Information Model” (BIM): A multi-standpoint framework for technological development. Advanced Engineering Informatics, 25(2), 224–244.

Chan, S. L., & Park, M. (2005). Project cost estimation using principal component regression. Construction Management and Economics, 23(3), 295–304.

Chen, C. (2005). Top 10 unsolved information visualization problems. IEEE Computer Graphics and Applications, 25(4), 12–16.

Chen, J. (2013). Application of BIM on quantity estimate for reinforced concrete. Applied Mechanics and Materials, 357-360(2013), 2402–2405.

Chen, K., Lu, W., Peng, Y., Rowlinson, S., & Huang, G. Q. (2015). Bridging BIM and building: From a literature review to an integrated conceptual framework. International Journal of Project Management, 33(6), 1405–1416.

Chen, M., & Floridi, L. (2013). An analysis of information visualization. Syntheses, 190,



3421–3438. Cheng, Y.-M. (2014). An exploration into cost-influencing factors on construction projects.

International Journal of Project Management, 32(850-860). Cheung, F. K. T., Rihan, J., Tah, J., Duce, D., & Kurul, E. (2012). Early stage multi-level

cost estimation for schematic BIM models. Automation in Construction, 27, 67–77. Cheung, F., Wong, M., & Skitmore, M. (2008). A study of clients’ and estimators' tolerance

towards estimating errors. Construction Management and Economics, 26(4), 349–362. Cho, J. H., Son, B. S., & Chun, J. Y. (2011). Application of OLAP information model to

parametric cost estimate and BIM. Journal of Asian Architecture and Building Engineering, (November), 319–326.

Choi, J., Kim, H., & Kim, I. (2015). Open BIM-based quantity take-off system for schematic estimation of building frame in early design stage. Journal of Computational Design and Engineering, 2(1), 16–25.

Chuang, T.-H., Lee, B.-C., & Wu, I.-C. (2011). Applying cloud computing technology to BIM visualization and manipulation. In 28th International Symposium on Automation and Robotics in Construction (pp. 144–149).

CIDB. (2013). BIM in Malaysia. Retrieved January 1, 2014, from www.bimcenter.com.my CIDB. (2014). Issues and challenges in implementating BIM for SME’s in the construction

industry. Kuala Lumpur. CIDB. (2015). Construction Industry Transformation Programme (CITP) 2016-2020. Kuala

Lumpur: Construction Industry Development Board (CIDB) Malaysia. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the

behavioral sciences. Hillsdale, NJ: Erlbaum. Cooper, D. R., & Schindler, P. S. (2003). Business research methods (8th ed.). New York:

McGraw-Hill. CRC Construction Innovation. (2007). Adopting BIM for facilities management: Solutions

for managing the Sydney Opera House. Cooperative Research Centre for Construction Innovation, Australia.

CRC Construction Innovation. (2009). National guidelines for digital modelling. Cooperative Research Centre for Construction Innovation, Australia.

Creswell, J. (2009). Research design (3rd ed.). Thousand Oaks, Calif: Sage Publications. Creswell, J. W. (2012). Education research: Planning, conducting and evaluating

quantitative and qualitative research (4th ed.). Boston: Pearson. Crowley, C. (2013). Identifying opportunities for Quantity Surveyors to enhance and expand

the traditional quantity modelling . In CITA BIM Gathering 2013 (pp. 1–7). Czaja, R., Blair, J., & Blair, E. (2014). Designing surveys: A guide to decisions &

procedures (3rd ed.). Thousand Oaks, CA: Sage Publications. Dahl, D. W., Chattopadhyay, A., & Gorn, G. J. (2001). The importance of visualisation in

concept design. Design Studies, 22(1), 5–26. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of

information technology. MIS Quarterly, 13(3), 319–340. Dawson, C. (2009). Introduction to research methods: A practical guide for anyone

undertaking a research project (4th ed.). Oxford: How To Books. DeLone, W. H., & McLean, E. R. (1992). Information systems success: The quest for the

dependent variable. Information Systems Research, 3(1), 60–95. Demian, P., & Walters, D. (2013). The advantages of information management through



building information modelling. Construction Management and Economics, 1–13. Drost, E. A. (2011). Validity and reliability in social science research. Educational Research

and Perspectives, 38(1), 105–123. Eadie, R., Odeyinka, H., Browne, M., Mckeown, C., & Yohanis, M. (2013). An analysis of

the drivers for adopting Building Information Modelling. Journal of Information Technology in Construction, 18, 338–352.

Eastman, C., Teicholz, P., Sacks, R., & Liston, K. (2011). BIM handbook (1st ed.). Hoboken, NJ: Wiley.

Egbu, C., & Coates, P. (2012). Building Information Modelling (BIM) implementation and remote construction projects: Issues, challenges and critiques. Journal of Information Technology in Construction, 17(May), 75–92.

Elhag, T. M. S., Boussabaine, a. H., & Ballal, T. M. a. (2005). Critical determinants of construction tendering costs: Quantity surveyors’ standpoint. International Journal of Project Management, 23(7), 538–545.

Enegbuma, W. I., & Ali, K. N. (2011). A preliminary critical success factor (CSFs) analysis of Building Information Modelling (BIM) implementation in Malaysia. In Proceedings of the Asian Conference on Real Estate (ACRE 2011): Sustainable Growth, Management Challenges. Johor Bahru, Malaysia.

Enegbuma, W. I., & Ali, K. N. (2011). A preliminary study on Building Information Modeling (BIM) implementation in Malaysia. In 3 rd International Post Graduate Conference in Engineering (IPCIE2011) Hong Kong (Vol. 1, pp. 399–407).

Enegbuma, W. I., Aliagha, G. U., & Ali, K. N. (2015). Effects of perceptions on BIM adoption in Malaysian construction industry, 15, 69–75.

Enegbuma, W. I., Aliagha, U. G., & Ali, K. N. (2014). Measurement of theoretical relationships in Building Information Modelling adoption in Malaysia. In The 31st International Symposium on Automation and Robotics in Construction and Mining (ISARC 2014).

Enegbuma, W. I., Dodo, Y. A., & Ali, K. N. (2014). Building Information Modelling penetration factors in Malaysia. International Journal of Advances in Applied Sciences (IJAAS), 3(1), 47–56.

Enegbuma, W. I., Ologbo, A. C., Aliagha, U. G., & Ali, K. N. (2014). Preliminary study impact of Building Information Modelling use in Malaysia. Product Lifecycle Management for a Global Market, 442, 51–62.

Enshassi, A., Mohamed, S., & Madi, I. (2005). Factors affecting accuracy of cost estimation of building contracts in the Gaza Strip. Journal of Financial Management of Property and Construction, 10(2), 115–125.

Epstein, E. (2012). Implementing successful building information modeling (1st ed.). Boston: Artech House.

EPU. (2015). Eleventh Malaysia Plan 2016-2020: Anchoring growth on people. Economic Planning Unit (EPU). Malaysia: Prime Minister’s Department.

Falissard, B. (1999). The unidimensionality of a psychiatric scale: a statistical point of view. International Journal of Methods in Psychiatric Research, 8 (3), 162–167.

Fellows, R., & Liu, A. (2008). Research methods for construction. Oxford: Wiley-Blackwell. Fern, E. F. (2001). Advanced focus group research. Thousand Oaks, CA: SAGE

Publications. Firat, C. E., Arditi, D., Hämäläinen, J.-P., Stenstrand, J., & Kiiras, J. (2010). QUANTITY



TAKE-OFF IN MODEL-BASED SYSTEMS. In Proceedings of the CIB W78 2010: 27th International Conference –Cairo, Egypt (pp. 16–18).

Flanagan, R., & Norman, G. (1983). The accuracy and monitoring of Quantity Surveyors’ price forecasting for building work. Construction Management and Economics, 1:2, 157–180.

Foo, D. E. L. (2016, November). Developers to use BIM system on Government projects by 2020. PropertyGuru.com.my.

Fowler, F. J. J. (2009). Evaluating survey questions and instruments in survey research methods. Thousand Oaks, Calif: Sage Publications.

Frazer, L., & Lawley, M. (2000). Questionnaire design & administration. Milton, Qld: John Wiley & Sons Australia.

Furneaux, C., & Kivvits, R. (2008). BIM – Implications for Government. CRC for Construction Innovation, Brisbane.

Gardezi, S. S. S., Shafiq, N., & Khamidi, M. F. B. (2013). Prospects of Building Information Modeling (BIM) in Malaysian construction industry as conflict resolution tool. Journal of Energy Technologies and Policy, 3(11), 346–350.

Gee, C. (2010). The influence of Building Information Modelling on the quantity surveying profession.

George, D., & Mallery, P. (2003). SPSS for Windows step by step: a simple guide and reference 11.0 update (4th ed.). Boston: Allyn and Bacon.

Gerrard, A., Zuo, J., Zillante, G., & Skitmore, M. (2009). Building Information Modeling in the Australian architecture engineering and construction industry. In J. Underwood & U. Isikdag (Eds.), Handbook of research on Building Information Modeling and construction informatics (pp. 521–544). IGI Global.

Ghosh, D., & Vogt, A. (2012). Outliers: An Evaluation of Methodologies. In Joint Statistical Meetings (pp. 3455–3460). San Diego, CA: American Statistical Association.

Goedert, J. D., & Meadati, P. (2008). Integrating construction process documentation into Building Information Modeling. Journal of Construction Engineering and Management, 134, 509–516.

Goucher, D. (2012). Usability and Impact of BIM on Early Estimation Practices : Cost Consultants Perspective. In Proc., CIB MCrp, Management of Construction: Research to Practice (pp. 555–569).

Greenhalgh, B. (2013). Introduction to estimating for construction (1st ed.). Abingdon, Oxon: Routledge.

Harris, M., Irfan, A., Ani, C., Haron, A. T., Preece, C., & Husain, A. H. (2014). Prioritizing Building Information Modeling (BIM) initiatives for Malaysia construction industry. In XXV FIG Congress: Engaging the challenges, enhancing the relevance. Kuala Lumpur.

Harrison, C., & Thurnell, D. (2015). BIM implementation in a New Zealand consulting quantity surveying practice. International Journal of Construction Supply Chain Management, 5(1), 1–15.

Hattie, J. A. (1985). Methodology review: Assessing unidimensionality of tests and items. Applied Psychological Measurement, 9(2), 139–164.

Hennink, M. M., & Leavy, P. (2014). Focus group discussions. New York: Oxford University Press.

Hey, J. (2004). The data, information, knowledge, wisdom chain: The metaphorical link. Intergovernmental Oceanographic Commission, (26), 18.



Hills, A. (2015). Understanding why. Noûs, 1–28. HM Government. (2015). 3-Digital Built Britain Level 3 Building Information Modelling -

Strategic Plan. UK Government. Hooper, D., Coughlan, J., & Mullen, M. R. (2008). Structural equation modelling:

Guidelines for determining model fit. Electronic Journal of Business Research Methods, 6(1), 53–60.

Hoyle, R. H. (2012). Handbook of structural equation modeling. New York: Guilford Press. Hussain, A. H., Khairi, M., Husain, A., Irfan, A., Ani, C., Irza, N., & Ali, Z. M. (2015).

Unlocking the potential value of BIM implementation in Malaysia : A pilot study. European Journal of Advances in Engineering and Technology, 2(12), 11–20.

InfoComm International. (2011). Building Information Modeling (BIM) Guide. InfoComm International.

Ismail, N. A. A., Drogemuller, R., Beazley, S., & Owen, R. (2016). A review of BIM capabilities for quantity surveying practice. In MATEC Web of Conferences (Vol. 66, p. 00042). EDP Sciences.

Ismail, N. A. A., Owen, R., & Drogemuller, R. (2015). Cost estimating practice incorporating building information modelling (BIM): Malaysian quantity surveyors perspectives. In The 7th International Conference of Sustainable Development in Building and Environment (SuDBE) 2015. Reading, UK.

Ismail, N. A. A., Utiome, E., Owen, R., & Drogemuller, R. (2015). Exploring accuracy factors in cost estimating practice towards implementing Building Information Modelling (BIM). In Proceedings of the 6th International Conference On Engineering, Project, and Production Management (EPPM2015) (pp. 364–373).

Jensen, P. A., & Jóhannesson, E. I. (2013). Building information modelling in Denmark and Iceland. Engineering, Construction and Architectural Management, 20(1), 99–110.

Johnson, M. (2015). Embodied understanding. Frontiers in Psychology, 6(June), 1–8. Johnson, T. P., & Wislar, J. S. (2012). Response rates and nonresponse errors in surveys.

JAMA: The Journal of the American Medical Association, 307(17), 1805–1806. Joseph F. Hair Jr., Black, W. C., Babin, B. J., & Anderson, R. E. (2014). Multivariate data

analysis (7th ed.). Pearson Education Limited. Jung, Y., & Joo, M. (2011). Building information modelling (BIM) framework for practical

implementation. Automation in Construction, 20(2), 126–133. Kalinichuk, S., & Tomek, A. (2013). Construction industry products diversification by

implementation of BIM. International Journal of Engineering and Technology Innovation, 3(4), 251–258.

Kang, J., Ryoo, B., & Faghihi, V. (2012). Five challenges you need to know for successful BIM application in developing countries. In Third International Conference on Construction in Developing Countries (ICCIDC–III). Bangkok, Thailand.

Kaplan, D. (2009). Structural equation modeling: foundations and extensions (2nd ed.). Sage Publications.

Kassem, M., Brogden, T., & Dawood, N. (2012). BIM and 4D planning : a holistic study of the barriers and drivers to widespread adoption. KICEM Journal of Construction Engineering and Project Management.

Kim, G., Park, H., & Shin, J. (2013). An assessment of the accuracy of cost estimation using Building Information Modeling in design process. Applied Mechanics and Materials, 291-294, 2822–2825.



Kim, J. (2012). Use of BIM for effective visualization teaching approach in construction education. Journal of Professional Issues in Engineering Education and Practice, 138(3), 214–223.

Kolari, S., & Savander-ranne, C. (2004). Visualisation promotes apprehension and comprehension. Engineering and Technology, 20(3), 484–493.

Koleola, T., & Henry, N. (2008). Factors affecting the accuracy of a pre-tender cost estimate in Nigeria. Cost Engineering.

Kraus, W., Watt, S., & Larson, P. (2007). Challenges in estimating costs using building information modeling. In AACE International Transactions (pp. 1–4).

Krejcie, R. V, & Morgan, D. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30, 607–610.

Krueger, R. A. (1988). Focus groups. Thousand Oaks, CA: Sage Publications. Kuiper, I., & Holzer, D. (2013). Rethinking the contractual context for Building Information

Modelling (BIM) in the Australian built environment industry. Australasian Journal of Construction Economics and Building, 13(14), 1–17.

Kulasekara, G., Jayasena, H. S., & Ranadewa, K. A. T. O. (2013). Comparative effectiveness of quantity surveying in a Building Information Modelling. In The Second World Construction Symposium 2013: Socio-Economic Sustainability in Construction (pp. 101–107).

Kumanayake, R. P., & Bandara, R. M. P. S. (2012). Building Information Modelling (BIM); How it improves building performance. In International Symposium on Ensuring National Security Through Reconciliation & Sustainable Development (pp. 357–365).

Kymmell, W. (2008). Building information modeling (1st ed.). New York: McGraw-Hill. Lan, H., & Abdelnaser Omran. (2015). Evaluating the Understanding of Industry towards

BIM Technology in Malaysia. ACTA TEHNICA CORVINIENSIS- Bulletin of Engineering, (Fascicule 2 (April-June)), 133–136.

Lan, H., Abdelnaser Omran, Hanafi, M. H., Muhamad Khalid, S. S., Syed Zainee, S. N., & Lai Boon Hooi. (2015). Building Information Modelling (BIM): Level of understanding and implementation among Civil and Structural Engineers in Penang. ANNALS of Faculty Engineering Hunedoara – International Journal of Engineering, (Fascicule 3 (August)), 169–175.

Latiffi, A. A., Brahim, J., Mohd, S., & Fathi, M. S. (2014). The Malaysian Government’s initiave in using Building Information Modelling (BIM) in construction projects. In ASEA-SEC -2, Bangkok, Nov. 3-7, 2014.

Latiffi, A. A., Mohd, S., & Brahim, J. (2014). Building Information Modelling (BIM) roles in the Malaysian construction industry. In A. Chantawarangul, K., Suanpaga, W., Yazdani, S., Vimonsatit, V., and Singh (Ed.), Sustainable Solutions in Structural Engineering and Construction (pp. 749–754). USA: ISEC Press.

Latiffi, A. A., Mohd, S., & Brahim, J. (2015). Application of Building Information Modeling (BIM) in the Malaysian construction industry: A story of the first Government project. Applied Mechanics and Materials, 773-774(March), 943–948.

Latiffi, A. A., Mohd, S., Kasim, N., & Fathi, M. S. (2013). Building Information Modeling (BIM) application in Malaysian construction industry. International Journal of Construction Engineering and Management, 2(4(A)), 1–6.

Lee, S., Kim, K., & Yu, J. (2013). BIM and ontology-based approach for building cost estimation. Automation in Construction.



Leicht, R. M., & Messner, J. I. (1997). Comparing traditional schematic design documentation to a schematic building information model. In Proceedings of CIB W78 24th International Conference on Information Technology in Construction (pp. 39–46).

Lijun, S., Edirisinghe, R., & Goh, Y. M. (2016). An investigation of BIM readiness of owners and facility managers in Singapore : Institutional case study. CIB World Building Congress 2016, 259–270.

Ling, Y. Y., & Boo, J. H. S. (2001). Improving the accuracy of approximate. Building Research & Information, 29(4), 312–318.

Litwin, M. S. (1995). How to measure survey reliability and validity (Vol 7.). Sage Publications.

Liu, L., & Zhu, K. (2007). Improving cost estimates of construction projects using phased cost factors. Journal of Construction Engineering and Management, 133(1), 91–95.

Liu, R., Issa, R. R. A., & Olbina, S. (2010). Factors influencing the adoption of Building Information Modeling in the AEC Industry. In Proceedings of the International Conference on Computing in Civil and Building Engineering (ICCBE ) 2010. Nottingham University Press.

Lu, Q., Won, J., & Cheng, J. C. P. (2016). A financial decision making framework for construction projects based on 5D Building Information Modeling (BIM). International Journal of Project Management, 34(1), 3–21.

Lu, W., Peng, Y., Shen, Q., & Li, H. (2012). A generic model for measuring benefits of BIM as a learning tool in construction tasks. Journal of Construction Engineering and Management, (139), 195–203.

Ma, Z., & Liu, Z. (2014). BIM-based intelligent acquisition of construction information for cost estimation of building projects. Procedia Engineering, 85, 358–367.

Ma, Z., Wei, Z., & Zhang, X. (2013). Semi-automatic and specification-compliant cost estimation for tendering of building projects based on IFC data of design model. Automation in Construction, 30, 126–135.

Mamter, S., & Abdul Rashid, A. A. (2016). Holistic BIM adoption and diffusion in Malaysia. In MATEC Web of Conferences (p. 00094). EDP Sciences.

Marrero, N. (2007). Visualization metrics: An overview. Visualization, 1, 1–3. Masterspec. (2012). New Zealand National BIM Survey 2012. Masterspec. (2013). New Zealand National BIM Survey 2013. Matipa, W. M., Cunningham, P., & Naik, B. (2010). Assessing the impact of new rules of

cost planning on Building Information Model (BIM) pertinent to quantity surveying practice. In Procs 26th Annual ARCOM Conference 2010 (pp. 625–632).

Mcauley, B., Hore, A. V, & Deeney, J. (2013). Public/private BIM: An Irish perspective. In Proceedings of the CITA BIM Gathering 2013 (pp. 25–34). Dublin, Ireland.

McCartney, C. (2010). Factors effecting the uptake of Building Information Modeling (BIM) in the Auckland Architecture, Engineering, & Construction (AEC) industry. New Zealand.

McGraw Hill Construction. (2010). SmartMarket report: The business value of BIM in Europe.

McGraw Hill Construction. (2012). SmartMarket report: The business value of BIM in South Korea.

McGraw Hill Construction. (2014a). SmartMarket report: The business value of BIM for construction in major global markets.



McGraw Hill Construction. (2014b). SmartMarket report: The business value of BIM for owners.

Meerveld, H. Van, Hartmann, T., Adriaanse, A. M., & Vermeij, C. (2009). Reflections on estimating - The effects of project complexity and the use of BIM on the estimating process. Netherlands.

Mitchell, D. (2012). The 5D QS: Today’s Methodology in Cost. Royal Institute of Chartered Surveyors (RICS) COBRA.

Mitchell, D. (2013). BIM implementation: A 5D QS’s view on procurement and cost savings. In RICS COBRA 2013.

Mohammed Abdullah Eben Saleh. (1999). The automation of cost estimate: An improvement in construction project information under design , tendering and execution. Architectural Science Review, 42:4, 253–264.

Mohd-Nor, M. F. ., & Grant, M. P. (2014). Building Information Modelling (BIM) in the Malaysian architecture industry. WSEAS Transaction on Environment and Development, 10, 264–273.

Monteiro, A., & Martins, J. P. (2013). A survey on modeling guidelines for quantity takeoff-oriented BIM-based design. Automation in Construction, 35, 238–253.

Morgan, D. L. (1997). Focus groups as qualitative research (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Morgan, D. L. (2008). The SAGE encyclopedia of qualitative research methods. (L. M. Given, Ed.). Thousand Oaks, CA: SAGE Publications.

Morrison, N. (2006). The accuracy of quantity surveyors’ cost estimating. Construction Management & Economics, 2:1(1984), 57–75.

Nadeem, A., Wong, A. K. D., & Wong, F. K. W. (2015). Bill of Quantities with 3D views using Building Information Modeling. Arabian Journal for Science and Engineering, 40(9), 2465–2477.

Nagalingam, G., Jayasena, H. S., & Ranadewa, K. A. T. O. (2013). Building Information Modelling and future Quantity Surveyor’s practice in Sri Lankan construction industry. In The Second World Construction Symposium 2013: Socio-Economic Sustainability in Construction (pp. 81–92).

NATSPEC. (2013). BIM and LOD Building Information Modelling and Level of Development. NATSPEC BIM Paper.

NBS. (2011). Building Information Modelling Report March 2011. NBS. (2012). National BIM Report 2012. NBS. (2013a). National BIM Report 2013. NBS. (2013b). NBS International BIM Report 2013. NBS. (2014). NBS National BIM Report 2014. NBS. (2016a). International BIM Report 2016. NBS. NBS. (2016b). National BIM Report 2016. NBS. Newton, K., & Chileshe, N. (2012). Awareness, usage and benefits of Building Information

Modelling (BIM) adoption – The case of the South Australian construction organisations. In Procs 28th Annual ARCOM Conference (pp. 3–12).

Ogunlana, S., & Thorpe, A. (1991). The nature of estimating accuracy: Developing correct Associations. Building and Environment, 26(2), 77–86.

Olatunji, O. A. (2011a). Modelling organizations’ structural adjustment to BIM adoption: a pilot study on estimating organizations. Journal of Information Technology in



Construction (ITcon), 16, 653–668. Olatunji, O. A. (2011b). Modelling the costs of corporate implementation of building

information modelling. Journal of Financial Management of Property and Construction, 16(3), 211–231.

Olatunji, O. A., & Sher, W. D. (2010). A comparative analysis of 2D computer-aided Estimating (CAE) and BIM estimating procedures. In Handbook of Research on Building Information Modeling and Construction Informatics: Concepts and Technologies (pp. 170–189). IGI Global.

Olatunji, O. A., Sher, W., & Gu, N. (2010). Building Information Modeling and quantity surveying practice. Emirates Journal for Engineering Research, 15(1), 67–70.

Olatunji, O. A., Sher, W., & Ogunsemi, D. R. (2010). The impact of Building Information Modelling on Construction Cost estimation. In W055-Special Track 18th CIB World Building Congress May 2010 Salford, United Kingdom (Vol. 2308).

Osman, J., Mazlina, S., Khuzzan, S., & Razaksapian, A. (2015). Building Information Modelling: Proposed adoption model for quantity surveying firms. In Proceeding of IC-ITS 2015, International Conference on Information Technology & Society (pp. 151–165). Kuala Lumpur, Malaysia.

Packer, A. D. (2014). Building measurement. New York: Routledge. Pallant, J. (2013). SPSS survival manual: a step by step guide to data analysis using IBM

SPSS (5th ed.). Crows Nest, N.S.W: Allen & Unwin. Paul, S., & Jain, R. (2008). Database systems performance evaluation techniques. St. Louis,

MI, USA. Peurifoy, R., & Oberlender, G. (2002). Estimating construction costs (5th ed.). New York:

McGraw-Hill. Popov, V., Juocevicius, V., Migilinskas, D., Ustinovichius, L., & Mikalauskas, S. (2010).

The use of a virtual building design and construction model for developing an effective project concept in 5D environment. Automation in Construction, 19(3), 357–367.

Porwal, A., & Hewage, K. N. (2013). Building Information Modeling (BIM) partnering framework for public construction projects. Automation in Construction, 31, 204–214.

Quek, J. K. (2012). Strategies and frameworks for adopting Building Information Modelling (BIM) for Quantity Surveyors. Applied Mechanics and Materials, 174-177, 3404–3419.

Raaijmakers, Q. A. W. (1999). Effectiveness of different missing data treatments in surveys with Likert-type data: Introducing the relative mean substitution approach. Educational and Psychological Measurement, 59(5), 725–748.

Rajendran, P., Seow, T., & Goh, K. (2014). Bulding Information Modeling (BIM) in design stage to assist in time, cost and quality in construction innovation. International Journal of Conceptions on Management and Social Sciences, 2(3), 52–55.

Raymond, M. R. (1986). Missing data in evaluation research. Evaluation & The Health Professions, 9(4), 392–420.

RICS. (2014a). International BIM implementation guide. London, UK. RICS. (2014b). Overview of a 5D BIM project : RICS information paper, UK 1st edition. Rogers, E. M. (1995). Diffusion of innovations (4th ed.). New York: Free Press. Rogers, J., Chong, H.-Y., & Preece, C. (2015). Adoption of Building Information Modelling

technology (BIM): Perspectives from Malaysian engineering consulting services firms. Engineering, Construction and Architectural Management, 22(4), 424–445.



Rowley, J. (2007). The wisdom hierarchy: representations of the DIKW hierarchy. Journal of Information Science, 33(2), 163–180.

Sabol, L. (2008). Challenges in Cost Estimating with Building Information Modeling. IFMA World Workplace.

Sacks, R., & Gurevich, U. (2016). a Review of Building Information Modeling Protocols, Guides and Standards for Large Construction Clients. Journal of Information Technology in Construction (ITcon), 21(21), 479–503.

Sadeghi, T., & Farokhian, S. (2011). Services quality model for online banking services by behavioral adoption theories and comparative study. African Journal of Business Management, 5(11), 4490–4499.

Samphaongoen, P. (2010). A visual approach to construction cost estimating. Marquette University.

Saunders, M., Lewis, P., & Thornhill, A. (2009). Research methods for business students (5th ed.). Harlow, England: Pearson Education Limited.

Saunders, M., Lewis, P., & Thornhill, A. (2012). Research methods for business students (6th ed.). Harlow, England: Pearson.

Sawhney, A., & Singhal, P. (2013). Drivers and barriers to the use of Building Information Modelling in India. International Journal of 3-D Information Modeling, 2(3), 46–63.

Schuette, S., & Liska, R. (1994). Building construction estimating (1st ed.). New York: McGraw-Hill.

Sebastian, R. (2011). Changing roles of the clients, architects and contractors through BIM. Engineering, Construction and Architectural Management, 18(2), 176–187.

Sebastian, R., & van Berlo, L. (2010). Tool for benchmarking BIM performance of design, engineering and construction firms in the Netherlands. Architectural Engineering and Design Management, 6(4), 254–263.

Sekaran, U. (2003). Research method for business: A skill building approach (4th ed.). New York: John Wiley & Sons, Inc.

Serpell, A. F. (2004). Towards a knowledge-based assessment of conceptual cost estimates. Building Research & Information, 32(2), 157–164.

Sharma, S., Mukherjee, S., Kumar, A., & Dillon, W. R. (2005). A simulation study to investigate the use of cutoff values for assessing model fit in covariance structure models. Journal of Business Research, 58(7), 935–943.

Shen, Z., & Issa, R. R. A. (2010). Quantitative evaluation of the BIM-assisted construction detailed cost estimates. Journal of Information Technology in Construction (ITcon), 15, 234–257.

Sheth, A. Z., & Malsane, S. M. (2014). Building Information Modelling, a tool for green built environment. In All India Seminar on Innovation in Green Building Technology, Green Build 2014. Nagpur, India.

Skitmore, R. M. (1988). Factors affecting estimating accuracy. Cost Engineering, 30(12), 12–17.

Smith, A. (1995). Estimating, tendering, and bidding for construction (1st ed.). London: Macmillan.

Smith, P. (2016). Project Cost Management with 5D BIM. Procedia - Social and Behavioral Sciences, 226(October 2015), 193–200.

Son, H., Lee, S., & Kim, C. (2015). What drives the adoption of building information modeling in design organizations? An empirical investigation of the antecedents



affecting architects’ behavioral intentions. Automation in Construction, 49(PA), 92–99. Soon, L. T., Hassan, H., & Zainul Abidin, N. (2016). Quantity surveying firm change model

in managing the constraints of BIM implementation. Research Journal of Fisheries and Hydrobiology, 11(3), 105–110.

Stanley, R., & Thurnell, D. (2014). The benefits of, and barriers to, implementation of 5D BIM for quantity surveying in New Zealand. Australian Journal of Construction Economics and Building, 14(1), 105–117.

Stewart, D. W., Shamdasani, P. N., & Rook., D. W. (2007). Focus groups: Theory and practice (2nd ed.). Thousand Oaks, CA: SAGE Publications.

Stoy, C., Pollalis, S., & Schalcher, H. (2008). Drivers for cost estimating in early design: Case study of residential construction. Journal of Construction Engineering and Management, 134(1), 32–39.

Straub, E. T. (2009). Understanding technology adoption: Theory and future directions for informal learning. Review of Educational Research, 79(2), 625–649.

Stufflebeam, D. L. (2003). The CIPP model for evaluation. In International handbook of educational evaluation (pp. 31–62). Netherlands: Springer.

Succar, B. (2009). Building information modelling framework: A research and delivery foundation for industry stakeholders. Automation in Construction, 18(3), 357–375.

Succar, B. (2010). Building Information Modelling Maturity Matrix. In Handbook of research on building information modelling and construction informatics: Concepts and technologies (pp. 65–103). IGI Global.

Sylvester, K. E., & Dietrich, C. (2010). Evaluation of Building Information Modeling (BIM) estimating methods in construction education. In 46th ASC Annual International Conference Proceedings Associated Schools of Construction Boston, MA.

Takim, R., Harris, M., & Nawawi, A. H. (2013). Building Information Modeling (BIM): A new paradigm for quality of life within Architectural, Engineering and Construction (AEC) industry. Procedia - Social and Behavioral Sciences, 101, 23–32.

Tas, E., & Yaman, H. (2005). A building cost estimation model based on cost significant work packages. Engineering, Construction and Architectural Management, 12(3), 251–263.

Tharenou, P., Donohue, R., & Cooper, B. (2007). Management research methods. Cambridge, England: Cambridge University Press.

Thurairajah, N., & Goucher, D. (2013). Advantages and challenges of using BIM: A cost consultant’s perspective. In 49th ASC Annual International Conference Proceedings, California Polytechnic State University (Cal Poly). San Luis Obispo, California.

Trost, S. M., & Oberlender, G. D. (2003). Predicting accuracy of early cost estimates using factor analysis and multivariate regression. Journal of Construction Engineering and Management, 129(2), 198–204.

Tse, K. T., Wong, A. K., & Wong, F. K. (2009). Building information modelling in material take-off in a Hong Kong project. In G. Q. Shen, P. Brandon, & A. N. Baldwin (Eds.), Collaborative Construction Information Management (1st ed., pp. 186–197). New York: Spon Press.

Uher, T. E. (1996). Cost estimating practices in Australian construction. Engineering, Construction and Architectural Management, 3(1/2), 83–95.

Valle, M. (2013). Visualization: A cognition amplifier. International Journal of Quantum Chemistry, 113(17), 2040–2052.



Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425–478.

Wijayakumar, M., & Jayasena, H. S. (2013). Automation of BIM quantity take-off to suit QS’s requirements. In The Second World Construction Symposium 2013: Socio-Economic Sustainability in Construction (pp. 70–80).

William G. Zikmund, Ward, S., Lowe, B., & Winzar, H. (2007). Marketing research (Asia Pacif.). Victoria, Australia: CENGAGE Learning.

Wong, A. K. D., Wong, F. K. W., & Nadeem, A. (2011). Government roles in implementing building information modelling systems: Comparison between Hong Kong and the United States. Construction Innovation: Information, Process, Management, 11(1), 61–76.

Wong, P. F., Salleh, H., & Rahim, F. A. (2014). The relationship of Building Information Modeling (BIM) capability in quantity surveying practice and project performance. International Journal of Civil, Environmental, Structural, Construction and Architectural Engineering, 8(10), 1031–1036.

Wong, P. F., Salleh, H., & Rahim, F. A. (2015). The relationship of Building Information Modeling (BIM) capability in quantity surveying practice and project performance. Informes de La Construcción, 67(540, e119).

Wu, S., Wood, G., Ginige, K., & Jong, S. W. (2014). A technical review of BIM based cost estimating in UK quantity surveying practice, standards and tools. Journal of Information Technology in Construction (ITcon), 19(31), 534–562.

www.glodon.com. (n.d.). Glodon Takeoff for Architecture and Structure. Xu, S., Liu, K., & Tang, L. C. (2015). Incorporation of expert reasoning into the BIM-based

cost estimating process. In A. B. Raidén & E. Aboagye-Nimo (Eds.), Procs 31st Annual ARCOM Conference (pp. 651–660). Lincoln, UK: Association of Researchers in Construction Management.

Xu, S., Liu, K., Tang, L. C. M., & Li, W. (2016). A framework for integrating syntax, semantics and pragmatics for computer-aided professional practice: With application of costing in construction industry. Computers in Industry, 83, 28–45.

Yaakob, M., Wan Ali, W. A., & Radzuan, K. (2016). Identifying critical success factors (CSFs) of implementing building information modeling (BIM) in Malaysian construction industry. In IProceedings of the International Conference on Applied Science and Technology 2016 (ICAST’16) (Vol. 19, pp. 20105–15095). AIP Publishing.

Yan, H., & Damian, P. (2008). Benefits and barriers of Building Information Modelling. In 12th International Conference on Computing in Civil and Building Engineering 2008.

Yusuf, B. Y., Ali, K. N., & Embi, M. R. (2015). Building Information Modelling (BIM): A potential for effective building industry practice in Malaysia. Jurnal Teknologi, 77(15), 55–61.

Zahrizan, Z., Ali, N. M., Haron, A. T., Marshall-ponting, A., & Abd, Z. (2013). Exploring the adoption of Building Information Modelling (BIM) in the Malaysian construction industry: A qualitative approach. IJRET: International Journal of Research in Engineering and Technology, 2(8), 384–395.

Zainon, N., Mohd-rahim, F. A., & Salleh, H. (2016). The rise of BIM in Malaysia and its impact towards quantity surveying practices. In MATEC Web of Conferences (Vol. 66, p. 00060). EDP Sciences.



Zeleny, M. (1987). Management support systems: Towards integrated knowledge management. Human Systems Management, 7, 59–70.

Zhang, D., & Gao, Z. (2013). Project time and cost control using Building Information Modeling. In ICCREM 2013: Construction and Operation in the Context of Sustainability (pp. 545–554).

Zhang, L., Wang, G., Chen, T., & He, G. (2014). Survey of BIM application status and characteristics in China. In J. Wang, Z. Ding, L. Zou, & J. Zuo (Eds.), Proceedings of the 17th International Symposium on Advancement of Construction Management and Real Estate (pp. 969–979). Berlin, Heidelberg: Springer Berlin Heidelberg.

Zhou, L., Perera, S., Udeaja, C., & Paul, C. (2012). SME readiness of BIM: A case study of a quantity surveying organisation. In First UK Academic Conference on BIM, Northumbria University. Newcastle-upon-Tyne, UK.

Zhu, Y. (2007). Measuring effective data visualization. In International Symposium on Visual Computing (pp. 652–661). Springer Berlin Heidelberg.

Zikmund, W. G. (2003). Business research method (7th ed.). USA: Thomson South-Western.

Zikmund, W. G., Ward, S., Lowe, B., & Winzar, H. (2007). Marketing research. Victoria, Australia: CENGAGE Learning.

Appendices Page 210


9Appendices

Appendix A: Questionnaire

Appendices Page 211


Appendices Page 212


Appendices Page 213


Appendices Page 214


Appendices Page 215


Appendices Page 216


Appendices Page 217


Appendix B: Preliminary Data Analysis

KREJCIE & MORGAN (1970) TABLE

Appendices Page 218


CODING OF QUESTIONNAIRE DATA

SECTION A: Respondents’ Background Information

QUESTION NO.

CODING IN SPSS QUESTIONS

1 Experience How long have you been involved in construction cost estimating?

2 Background What is your professional background?

3 Role What is your current role?

4 Nature What is the nature of your current organisation’s business?

5 Aware Are you aware of Building Information Modelling (BIM) usage in the construction industry?

6 Knowledge Based on a scale of 1 to 10, how would you rate your knowledge of Building Information Modelling (BIM)?

7 Software What type(s) of BIM software have you used? (Select all that apply)

8 Stage At what stage(s) do you usually apply BIM for construction projects? (Select all that apply)

9 Importance Based on a scale of 1 to 10, how would you rate the importance of BIM in your current role?

10 Future Based on a scale of 1 to 10, how would you rate the likelihood of using BIM in the future?

SECTION B: Improved Information

QUESTION NO.


B1 dv1 Data visualisation helps me to get accurate data.

B2 dv2 Data visualisation helps me to understand information better.

B3 dv3 I can interpret information accurately with data visualisation.

B4 dv4 Data visualisation helps me to make decisions with confidence.

B5 dv5 Data visualisation assists in achieving my company’s goals.

B6 rd1 A reliable database improves information system reliability.

B7 rd2 A reliable database provides me complete information.

B8 rd3 A reliable database generates better task performance.

B9 rd4 A reliable database assists in achieving my company’s goals.

B10 dc1 Data coordination improves information system reliability.

B11 dc2 Data coordination provides me complete information.

B12 dc3 Data coordination generates better task performance.

B13 dc4 Data coordination assists in achieving my company’s goals.

Appendices Page 219


SECTION C: Perceived Benefits

QUESTION NO.

CODING IN SPSS

QUESTIONS

C1 per1 It would enable me to accomplish tasks more quickly.

C2 per2 It would increase my estimates accuracy.

C3 per3 It is useful in my role.

C4 per4 It would improve my employment prospects.









SECTION D: Cost Estimates Reliability & BIM Adoption

QUESTION NO.


D1 info1 I use data visualisation to better understand input information.

D2 info2 I use reliable database to better understand input information.

D3 info3 I need coordinated data to better understand input information.

D4 und1 Data visualisation could assist me to better understand to prepare estimates.

D5 und2 Reliable database could assist me to better understand to prepare estimates.

D6 und3 Coordinated data could assist me to better understand to prepare estimates.

D7 kno1 Data visualisation will help me to improve my knowledge of estimating process.

D8 kno2 Reliable database will help me to improve my knowledge of estimating process.

D9 kno3 Coordinated data will help me to improve my knowledge of estimating process.

D10 adop1 BIM with data visualisation application provides better input information.

D11 adop2 BIM with reliable database provides better input information.

D12 adop3 BIM with coordinated data provides better input information.

D13 adop4 BIM with data visualisation application helps me to understand my project better.

D14 adop5 BIM with reliable database helps me to understand my project better.

D15 adop6 BIM with coordinated data helps me to understand my project better.

D16 adop7 BIM with data visualisation application improves my costing expertise.

D17 adop8 BIM with reliable database improves my costing expertise.

D18 adop9 BIM with coordinated data improves my costing expertise.

Appendices Page 220


MISSING VALUES IN DATA

Only for Sections B, C & D in questionnaire


Univariate Statistics

N Mean Std. Deviation

Missing No. of Extremesa

Count Percent Low High

dv1 202 7.7376 1.71487 0 .0 8 0

dv2 202 7.9158 1.69220 0 .0 8 0

dv3 202 7.6881 1.69188 0 .0 10 0

dv4 201 7.6667 1.70978 1 .5 8 0

dv5 201 7.2985 1.85484 1 .5 0 0

rd1 202 7.9406 1.61351 0 .0 4 0

rd2 202 7.7327 1.69219 0 .0 8 0

rd3 202 7.8416 1.68831 0 .0 8 0

rd4 201 7.5224 1.68248 1 .5 0 0

dc1 202 7.9406 1.65912 0 .0 6 0

dc2 202 7.8119 1.67340 0 .0 8 0

dc3 201 7.8458 1.63129 1 .5 5 0

dc4 202 7.5248 1.74546 0 .0 0 0

a. Number of cases outside the range (Q1 - 1.5*IQR, Q3 + 1.5*IQR).






per1 202 8.2079 1.70348 0 .0 6 0

per2 202 8.2475 1.80053 0 .0 21 0

per3 201 8.0945 1.85095 1 .5 10 0

per4 202 7.9208 1.87512 0 .0 11 0

per5 202 8.2673 1.65052 0 .0 5 0

per6 202 8.3020 1.67596 0 .0 1 0

per7 202 8.1287 1.70841 0 .0 7 0

per8 200 7.9500 1.85600 2 1.0 11 0

per9 202 8.3465 1.56094 0 .0 13 0

per10 202 8.3515 1.64218 0 .0 15 0

per11 201 8.1542 1.68852 1 .5 9 0

per12 202 7.9752 1.81875 0 .0 11 0


Appendices Page 221







info1 202 7.8168 1.72518 0 .0 8 0

info2 202 7.8069 1.70377 0 .0 7 0

info3 202 7.8218 1.65656 0 .0 6 0

und1 202 7.8911 1.59228 0 .0 6 0

und2 202 7.9455 1.55240 0 .0 5 0

und3 202 7.9059 1.51479 0 .0 4 0

kno1 202 7.7723 1.56695 0 .0 4 0

kno2 201 7.7960 1.56307 1 .5 5 0

kno3 202 7.7624 1.60316 0 .0 5 0

adop1 202 8.1881 1.60666 0 .0 5 0

adop2 202 8.1980 1.53253 0 .0 4 0

adop3 202 8.1188 1.57901 0 .0 4 0

adop4 201 8.1642 1.61800 1 .5 5 0

adop5 202 8.1238 1.64197 0 .0 5 0

adop6 202 8.1089 1.60473 0 .0 5 0

adop7 202 8.0000 1.64513 0 .0 5 0

adop8 202 7.9802 1.70735 0 .0 6 0

adop9 202 7.9455 1.66975 0 .0 6 0


Appendices Page 222


OUTLIERS IN DATA



Descriptives


Skewness Kurtosis

Statistic Std. Error Statistic Std. Error

dv1 202 7.7376 1.71487 -.632 .171 -.061 .341

dv2 202 7.9158 1.69220 -.769 .171 .122 .341

dv3 202 7.6881 1.69188 -.708 .171 .051 .341

dv4 201 7.6667 1.70978 -.603 .172 -.046 .341

dv5 201 7.2985 1.85484 -.478 .172 -.365 .341

rd1 202 7.9406 1.61351 -.498 .171 -.581 .341

rd2 202 7.7327 1.69219 -.544 .171 -.391 .341

rd3 202 7.8416 1.68831 -.607 .171 -.371 .341

rd4 201 7.5224 1.68248 -.364 .172 -.492 .341

dc1 202 7.9406 1.65912 -.822 .171 .343 .341

dc2 202 7.8119 1.67340 -.709 .171 .007 .341

dc3 201 7.8458 1.63129 -.663 .172 .011 .341

dc4 202 7.5248 1.74546 -.521 .171 -.245 .341

Outliers showing by Boxplot

Respondents ID

dv1 27, 41, 117, 171

dv2 27, 41, 117

dv3 27, 41,105, 117

dv4 41, 105, 162, 204

dv5 No outliers shown

rd1 No outliers shown

rd2 105, 136

rd3 105

rd4 No outliers shown

dc1 27,171

dc2 27

dc3 27

dc4 No outliers shown

Appendices Page 223


Normal Q-Q Plot

Appendices Page 224


Boxplot

Appendices Page 225



Descriptives


Skewness Kurtosis


per1 202 8.2079 1.70348 -1.201 .171 1.364 .341

per2 202 8.2475 1.80053 -1.321 .171 1.455 .341

per3 201 8.0945 1.85095 -1.215 .172 1.207 .341

per4 202 7.9208 1.87512 -1.073 .171 .754 .341

per5 202 8.2673 1.65052 -1.152 .171 1.083 .341

per6 202 8.3020 1.67596 -1.191 .171 1.266 .341

per7 202 8.1287 1.70841 -1.085 .171 .988 .341

per8 200 7.9500 1.85600 -1.079 .172 .818 .342

per9 202 8.3465 1.56094 -1.162 .171 1.522 .341

per10 202 8.3515 1.64218 -1.266 .171 1.549 .341

per11 201 8.1542 1.68852 -1.120 .172 1.161 .341

per12 202 7.9752 1.81875 -1.040 .171 .800 .341


Respondents ID

per1 27, 50, 117, 165

per2 27, 50, 95, 99, 117, 118, 141, 165, 171

per3 5, 27, 50, 62, 165

per4 27, 62, 88, 95, 165

per5 27, 117, 165

per6 27

per7 27, 62, 165

per8 27, 88, 95, 165, 171

per9 27, 99, 117, 118, 165

per10 27, 95, 99, 117, 118, 165, 171

per11 27, 62

per12 27, 62, 88, 95

Appendices Page 226


Normal Q-Q Plot

Appendices Page 227


Boxplot

Appendices Page 228





Skewness Kurtosis


info1 202 7.8168 1.72518 -.923 .171 .960 .341

info2 202 7.8069 1.70377 -.938 .171 1.020 .341

info3 202 7.8218 1.65656 -.827 .171 1.082 .341

und1 202 7.8911 1.59228 -.679 .171 -.071 .341

und2 202 7.9455 1.55240 -.601 .171 -.275 .341

und3 202 7.9059 1.51479 -.524 .171 -.333 .341

kno1 202 7.7723 1.56695 -.447 .171 -.605 .341

kno2 201 7.7960 1.56307 -.545 .172 -.382 .341

kno3 202 7.7624 1.60316 -.572 .171 -.361 .341

adop1 202 8.1881 1.60666 -.885 .171 .162 .341

adop2 202 8.1980 1.53253 -.815 .171 .035 .341

adop3 202 8.1188 1.57901 -.680 .171 -.329 .341

adop4 201 8.1642 1.61800 -.777 .172 -.216 .341

adop5 202 8.1238 1.64197 -.711 .171 -.387 .341

adop6 202 8.1089 1.60473 -.741 .171 -.213 .341

adop7 202 8.0000 1.64513 -.907 .171 .896 .341

adop8 202 7.9802 1.70735 -.926 .171 .695 .341

adop9 202 7.9455 1.66975 -.943 .171 .888 .341


Respondents ID

Respondents ID

info1 82, 171, 172, 176 adop1 95

info2 82, 171, 172, 176 adop2 No outliers shown

info3 82, 172 adop3 No outliers shown

und1 171, 176 adop4 No outliers shown

und2 No outliers shown adop5 No outliers shown

und3 No outliers shown adop6 No outliers shown

kno1 176 adop7 102

kno2 176 adop8 102, 121

kno3 145, 176 adop9 102, 121

Appendices Page 229


Normal Q-Q Plot

Appendices Page 230


Boxplot

Appendices Page 231


Appendices Page 232


NORMALITY OF DISTRIBUTION IN DATA



Tests of Normality

Kolmogorov-Smirnova Shapiro-Wilk

Statistic df Sig. Statistic df Sig.

dv1 .185 202 .000 .920 202 .000

dv2 .183 202 .000 .907 202 .000

dv3 .202 202 .000 .920 202 .000

dv4 .189 201 .000 .926 201 .000

dv5 .180 201 .000 .940 201 .000

rd1 .163 202 .000 .917 202 .000

rd2 .167 202 .000 .928 202 .000

rd3 .186 202 .000 .918 202 .000

rd4 .179 201 .000 .937 201 .000

dc1 .192 202 .000 .907 202 .000

dc2 .203 202 .000 .916 202 .000

dc3 .199 201 .000 .919 201 .000

dc4 .177 202 .000 .933 202 .000

a. Lilliefors Significance Correction


Tests of Normality



per1 .209 202 .000 .863 202 .000

per2 .221 202 .000 .838 202 .000

per3 .215 201 .000 .857 201 .000

per4 .198 202 .000 .876 202 .000

per5 .236 202 .000 .862 202 .000

per6 .226 202 .000 .858 202 .000

per7 .210 202 .000 .877 202 .000

per8 .196 200 .000 .879 200 .000

per9 .197 202 .000 .868 202 .000

per10 .218 202 .000 .852 202 .000

per11 .199 201 .000 .875 201 .000

per12 .184 202 .000 .884 202 .000


Appendices Page 233



Tests of Normality



info1 .176 202 .000 .909 202 .000

info2 .213 202 .000 .906 202 .000

info3 .162 202 .000 .918 202 .000

und1 .181 202 .000 .917 202 .000

und2 .177 202 .000 .918 202 .000

und3 .163 202 .000 .926 202 .000

kno1 .191 202 .000 .926 202 .000

kno2 .204 201 .000 .921 201 .000

kno3 .198 202 .000 .923 202 .000

adop1 .208 202 .000 .886 202 .000

adop2 .205 202 .000 .892 202 .000

adop3 .192 202 .000 .902 202 .000

adop4 .205 201 .000 .889 201 .000

adop5 .193 202 .000 .892 202 .000

adop6 .186 202 .000 .897 202 .000

adop7 .183 202 .000 .901 202 .000

adop8 .195 202 .000 .896 202 .000

adop9 .186 202 .000 .901 202 .000


Appendices Page 234


SECTION B: Improved Information (Histogram)

Appendices Page 235


SECTION C: Perceived Benefits (Histogram)

Appendices Page 236


SECTION D: Cost Estimates Reliability (Histogram)

Appendices Page 237


Appendices Page 238


RELIABILITY TEST



Reliability Statistics

Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.975 .976 13

Item-Total Statistics

Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha

if Item Deleted

dv1 92.8838 321.393 .855 .840 .974

dv2 92.7020 321.632 .864 .873 .973

dv3 92.9343 321.899 .859 .873 .973

dv4 92.9394 322.149 .851 .848 .974

dv5 93.3283 322.821 .759 .837 .976

rd1 92.6717 324.425 .868 .872 .973

rd2 92.8838 321.047 .878 .895 .973

rd3 92.7727 319.852 .906 .894 .972

rd4 93.0859 323.866 .835 .899 .974

dc1 92.6717 322.211 .878 .890 .973

dc2 92.8030 320.849 .893 .931 .973

dc3 92.7525 323.233 .880 .906 .973

dc4 93.0859 321.937 .831 .904 .974

Appendices Page 239




Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.978 .979 12


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha

if Item Deleted

per1 89.8232 299.781 .863 .884 .977

per2 89.7879 296.320 .868 .922 .977

per3 89.9495 294.698 .870 .905 .977

per4 90.1212 294.544 .858 .918 .977

per5 89.7576 299.240 .903 .926 .976

per6 89.7273 298.067 .912 .960 .976

per7 89.8990 296.558 .913 .967 .976

per8 90.0758 295.075 .860 .959 .977

per9 89.6818 302.066 .897 .932 .976

per10 89.6768 299.377 .898 .953 .976

per11 89.8788 298.341 .892 .964 .976

per12 90.0657 297.168 .843 .951 .977

Appendices Page 240




Cronbach's Alpha

Cronbach's Alpha

Based on

Standardized

Items N of Items

.985 .986 18


Scale Mean if

Item Deleted

Scale Variance if

Item Deleted

Corrected Item-

Total Correlation

Squared Multiple

Correlation

Cronbach's Alpha

if Item Deleted

info1 135.6800 604.822 .838 .944 .985

info2 135.6800 606.430 .832 .937 .985

info3 135.6700 609.338 .818 .913 .985

und1 135.6050 604.893 .913 .926 .984

und2 135.5500 605.405 .931 .947 .984

und3 135.5900 608.123 .917 .926 .984

kno1 135.7200 609.770 .863 .922 .985

kno2 135.7050 608.832 .879 .945 .984

kno3 135.7300 608.138 .865 .923 .985

adop1 135.2900 603.423 .934 .954 .984

adop2 135.2850 607.019 .931 .953 .984

adop3 135.3650 604.977 .929 .959 .984

adop4 135.3250 604.562 .908 .945 .984

adop5 135.3600 603.156 .910 .953 .984

adop6 135.3900 603.184 .929 .951 .984

adop7 135.4950 609.005 .828 .910 .985

adop8 135.5150 605.688 .840 .957 .985

adop9 135.5450 608.340 .826 .950 .985

Appendices Page 241


Appendix C: SEM Analysis MODIFICATION INDICES (MI)

Improved Information Covariances: (Group number 1 - Default model) Paired Items M.I. Par Change

e11 <--> e13 6.937 .095 e10 <--> R3 5.711 -.079 e10 <--> e13 8.453 -.122 e8 <--> e13 10.008 .129 e8 <--> e11 5.527 -.058 e7 <--> e11 24.177 .131 e7 <--> e10 16.647 -.122 e6 <--> R1 4.910 .093 e6 <--> e13 9.851 -.139 e6 <--> e11 15.188 -.105 e6 <--> e10 25.464 .153 e2 <--> e13 7.546 -.125 e2 <--> e10 9.040 .093 e2 <--> e7 7.043 -.085 e2 <--> e6 15.272 .126 Perceived Benefits Covariances: (Group number 1 - Default model) Paired Items M.I. Par Change

e10 <--> e12 92.245 .888 e8 <--> e12 10.465 .215 e8 <--> e10 13.667 .243 e6 <--> e12 7.781 .147 e6 <--> e8 19.908 -.166 e5 <--> e6 16.458 .105 e4 <--> e12 9.013 -.200 e4 <--> e10 5.657 .156 e4 <--> e8 9.126 -.142 e4 <--> e6 10.165 .119 e4 <--> e5 37.575 .201 e3 <--> e10 18.686 -.207 e3 <--> e6 11.222 .091 e3 <--> e5 14.411 -.090 e3 <--> e4 33.060 -.198 e2 <--> e12 9.170 -.149 e2 <--> e10 6.863 -.127 e2 <--> e8 7.995 .098 e2 <--> e6 18.462 -.118 e2 <--> e4 22.340 -.164 e2 <--> e3 30.163 .139 e1 <--> e12 13.612 -.237

Appendices Page 242


Paired Items M.I. Par Change e1 <--> e6 14.965 -.139 e1 <--> e5 12.404 -.112 e1 <--> e4 15.814 .181 e1 <--> e2 19.654 .149

BIM Adoption

Covariances: (Group number 1 - Default model)

M.I. Par Change e8 <--> e9 162.679 .757 e7 <--> e9 123.977 .673 e7 <--> e8 135.917 .709 e5 <--> e6 73.685 .175 e3 <--> e9 12.227 -.092 e3 <--> e8 22.219 -.124 e3 <--> e7 20.195 -.121 e2 <--> e9 7.707 -.070 e2 <--> e8 5.077 -.057 e2 <--> e7 4.443 -.055 e2 <--> e6 11.239 -.046 e2 <--> e5 8.011 -.044 e2 <--> e3 6.450 .027 e1 <--> e6 6.385 -.042 e1 <--> e5 18.355 -.080 e1 <--> e2 30.821 .070 Cost Estimates Reliability

Covariances: (Group number 1 - Default model)

M.I. Par Change e6 <--> e9 9.861 .073 e6 <--> e7 5.277 -.050 e5 <--> R2 5.168 .036 e5 <--> R1 6.076 -.061 e4 <--> R2 4.242 -.044 e4 <--> R1 6.462 .082 e4 <--> e9 7.829 -.066 e4 <--> e7 9.602 .069 e4 <--> e6 8.905 -.059 e3 <--> e6 4.827 .044 e3 <--> e4 6.968 -.055 e2 <--> e9 14.647 .085 e2 <--> e7 12.050 -.072 e1 <--> e9 4.489 -.046 e1 <--> e4 17.054 .078

Appendices Page 243


Appendix D: Focus Group Kit

CONSENT FORM

Appendices Page 244


Appendices Page 245


FOCUS GROUP SIGN-IN SHEET

PARTICIPANT’S BACKGROUND INFORMATION

How long have you been involved in

construction cost estimating?

o Less than 5 years

o 5 to 10 years

o More than 10 years

Please state: _______years

What is your professional background?

o Quantity Surveying

o Project Management

o Construction Management

o Building Construction

o Civil Engineering

o Others

(please specify):________________

What is your current role?

o Quantity Surveyor

o Project Manager

o Construction Manager

o Contractor

o Civil Engineer

o Architect

o Others

(please specify):______________

What is the nature of your current organisation’s

business?

o Client/Developer

o Quantity Surveying firm

o Contractor firm

o Engineering firm

o Architectural firm

o Authority/Government Agency

o Academic Institution

o Others

(please specify):________________

Are you aware of Building Information Modelling (BIM) usage in the construction industry?

o Aware and currently using BIM in cost estimating

o Aware and have used BIM (but not in cost estimating)

o Aware of BIM but have not used it

o Not aware of BIM

Appendices Page 246


FRAMEWORK EVALUATION FORM

Aspects Comments

CONTEXT EVALUATION

Is the framework assessing the quantity surveying practice needs?

Is the framework relevant to quantity surveying practice needs?

Is the framework sufficiently responsive to quantity surveying practice needs?

INPUT EVALUATION

Does the framework clearly define its strategies to improve quantity surveying practice?

Are the strategies consistent with the goals of quantity surveying practice?

Are the framework strategies appropriate for the position of quantity surveyors within the industry?

PROCESS EVALUATION

Are there any problems with the framework to deliver the needs of quantity surveying practice?

Does the framework implementation process need adjustment or revision for necessary improvement?

PRODUCT EVALUATION

What are the merits and worth of the framework in improving the quantity surveying practice?

What directions should the framework take in the future?

Appendices Page 247


FOCUS GROUP INTERVIEW GUIDE

Date:

Location:

Moderator:

Note Taker:

Time Focus Group Started: Ended:

Number of participants:

Type of Group: BIM users / Non-BIM users

Purpose: To evaluate a developed framework in explaining the relationship between cost estimates reliability and BIM factors to better support the BIM adoption in quantity surveying practice.

Agenda Allocation of time

A. Introduction

- Firstly, we would like to thank all of you for your time to participate in this focus group discussion. My name is Noor, and also with us today we have our moderator _______ and note taker _______.

- The purpose of conducting this focus group is to evaluate a developed framework for PhD research as shown in the A4-printed paper distributed to you. This framework explains the relationship between BIM capabilities and estimator values towards BIM technology adoption, in improving cost estimates reliability for QS practice.

- Therefore, we need your input and want all of you to share your

valuable thoughts with us regarding the framework.

- Before we start, let’s do a quick round of ice breaking. May all participants briefly introduce their names and organisations?

5 minutes

B. Agreements

- As to begin, I would like to shortly explain some of the ground rules for this focus group discussion.

- You need to fill in the given Sign-In sheet with a few demographic questions and Consent Form as to indicate your willingness to participate in this focus group session.

- We would like to audio-record and taking note this discussion to make sure that we can capture opinions or ideas we hear from the group.

5 minutes

Appendices Page 248


- However, the information you give us is strictly confidential in which no names will be associated with anything you say in the conversation. No specific statements will be linked to individual subjects in the transcribed notes.

- If there are any questions that you do not wish to answer, you do not have to do so; however please try to be as involved as possible. You do not have to speak in any particular order.

- Feel free to respond to each other. Remember that there is no right or

wrong answer, and you do not have to agree with other people’s views.

- I would appreciate if all participants would refrain themselves from discussing the comments of other group members outside the focus group.

- Any questions before we proceed with the discussion? (Assigning following questions to the moderator)

C. Opening question

- I am going to give you a couple of minutes to look at the framework and give your views. What do you think about this framework?

10 minutes

D. Key questions

Theme – BIM capabilities

1) What is your opinion about data visualisation can give you accurate data for cost estimating?

2) How do you achieve a better understanding of information through data visualisation?

3) How can you interpret information accurately through data visualisation when estimating the costs?

4) How does a reliable database give you complete information for your cost estimates?

5) How does a reliable database help you to perform your cost estimating task better?

6) What do you think about coordinated data can assist to achieve information system reliability in your task?

7) How coordinated data amongst your team members can help to achieve your company’s goal?

Theme – Estimator’s values

1) How do you think that the use of data visualisation helps the cost estimators to understand better the input information used for their cost estimates?

2) What about the use of coordinated data amongst the team members, how it also helps the cost estimators to understand better the input information used for their cost estimates?

3) How the use of reliable database helps the estimators to understand better to prepare cost estimates?

4) How about the use of coordinated data amongst the team members helps the estimators to understand better how to prepare cost estimates?

15 minutes

15 minutes

Appendices Page 249


5) What is your opinion about data visualisation improves your knowledge of estimating process?

6) How coordinated data among the team members improves your knowledge of estimating process?

Theme – Perceived benefits

1) What are your thoughts about the benefits of quick task accomplishment, increased estimates accuracy and useful in your role could lead you to adopt the technology?

2) How do you think that data visualisation, reliable database and data coordination help you to accomplish your cost estimating task quickly?

3) What about the using a reliable database that helps you to achieve accurate cost estimates?

4) How do you relate that a reliable database can be useful in your role of estimating costs?

Theme – Technology adoption

1) Overall, what is your opinion that you possibly adopt a BIM technology that can give you a better understanding of your cost estimating through its data visualisation?

2) Also, give your opinion about you possibly adopt a BIM technology that can give you a better understanding of your cost estimating through its reliable database?

3) Finally, give your opinion about you possibly adopt a BIM technology that can give you a better understanding of your cost estimating through its coordinated data?

15 minutes

15 minutes

E. Ending question

- Based on what we have discussed today, in your opinion, what is the most important issue that you would like to highlight about the framework?

5 minutes

F. Summary question

- Moderator to summarise the opinions from the key questions and the main ideas issued during the discussion. To ask the participants whether the summary is appropriate with what they have raised in the conversation.

5 minutes

G. Final question

- Moderator briefly explains the purpose of the research and evaluated framework. To ask participants any final advice they would like to give.

5 minutes

H. Debriefing

- Before we end this session, we would like to thank all of you again for participating in this focus group and your opinions will be valuable for the research.

5 minutes

Appendices Page 250


- We hope that you have found the discussion interesting. However, if there is anything that you feel unhappy and wish to complain about the discussion, please do not hesitate to contact me or speak to me later.

- Before you leave, please submit your completed sign-in sheets, consent

forms, and framework evaluation sheets.

- This focus group has been a very fruitful discussion and thank you again for your cooperation.

Materials for Focus Group:

o Sign-in sheets (background information) o Consent forms o Framework evaluation forms o A4-printed framework for each participant o Name tags for participants o Notepads and pens for each participant o Focus group guide for moderator o Note-taking form for note-taker o Recording equipment o List of participants o Gifts for participants o Refreshments

Appendices Page 251


FOCUS GROUP NOTE TAKING FORM

Date:

Location:

Moderator:

Note Taker:

Time Focus Group Started: Ended:

Number of participants:

Type of Group: BIM users / Non-BIM users

RESPONSES TO QUESTIONS:

Opening question: What do you think about this framework? Brief Summary/Key Points Notable Quotes

Key questions: Theme – BIM capabilities

8) What is your opinion about data visualisation can give you accurate data for cost estimating? Brief Summary/Key Points Notable Quotes

9) How do you achieve a better understanding of information through data visualisation? Brief Summary/Key Points Notable Quotes

Appendices Page 252


10) How can you interpret information accurately through data visualisation when estimating the costs?

Brief Summary/Key Points Notable Quotes

11) How does a reliable database give you complete information for your cost estimates?


12) How does a reliable database help you to perform your cost estimating task better? Brief Summary/Key Points Notable Quotes

13) What do you think about coordinated data can assist to achieve information system reliability in your task?


14) How coordinated data amongst your team members can help to achieve your company’s goal?


Appendices Page 253


Key questions: Theme – Estimator’s values

7) How do you think that the use of data visualisation helps the cost estimators to understand better the input information used for their cost estimates?


8) What about the use of coordinated data amongst the team members, how it also helps the cost estimators to understand better the input information used for their cost estimates?


9) How the use of reliable database helps the estimators to understand better to prepare cost

estimates? Brief Summary/Key Points Notable Quotes

10) How about the use of coordinated data amongst the team members helps the estimators to understand better how to prepare cost estimates?


11) What is your opinion about data visualisation improves your knowledge of estimating process?


Appendices Page 254


12) How coordinated data among the team members improves your knowledge of estimating process?


Key questions: Theme – Perceived benefits

5) What are your thoughts about the benefits of quick task accomplishment, increased estimates accuracy and useful in your role could lead you to adopt the technology?


6) How do you think that data visualisation, reliable database and data coordination help you to

accomplish your cost estimating task quickly? Brief Summary/Key Points Notable Quotes

7) What about the using a reliable database that helps you to achieve accurate cost estimates?


8) How do you relate that a reliable database can be useful in your role of estimating costs?


Appendices Page 255


Key questions: Theme – Technology adoption

4) Overall, what is your opinion that you possibly adopt a BIM technology that can give you a better understanding of your cost estimating through its data visualisation?


5) Also, give your opinion about you possibly adopt a BIM technology that can give you a better

understanding of your cost estimating through its reliable database? Brief Summary/Key Points Notable Quotes

6) Finally, give your opinion about you possibly adopt a BIM technology that can give you a better understanding of your cost estimating through its coordinated data?


Ending question: Based on what we have discussed today, in your opinion, what is the most important issue that you would like to highlight about the framework?


Summary/final questions: Brief Summary/Key Points Notable Quotes

Appendices Page 256


Appendix E: List of Publications

Appendices Page 257


Appendices Page 258


CONSTRUCTION COST ESTIMATING INCORPORATING BUILDING ... · CONSTRUCTION COST ESTIMATING...

Documents

Transcript of CONSTRUCTION COST ESTIMATING INCORPORATING BUILDING ... · CONSTRUCTION COST ESTIMATING...