BEng, MEng - QUT ePrints · BEng, MEng Submitted in fulfilment of the requirements for the degree...

IMPROVING COST ESTIMATION

PERFORMANCE: AN INVESTIGATION OF

PREDICTION TECHNIQUE AND PERSON-

ENVIRONMENT INTERACTION

Bo Xiong

BEng, MEng

Submitted in fulfilment of the requirements for the degree of

Doctor of Philosophy

School of Civil Engineering and Built Environment

Faculty of Science and Engineering

Queensland University of Technology

2016

Improving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment

Interaction i

Abstract

Job performance of construction cost engineers is critical to the successful

operation of projects and organisations. From a review of previous studies, two

approaches — exploring efficient estimation techniques and examining person-

environment interactions — are found to be valuable for improving cost estimation.

Following this logic, this thesis by publications makes contributions to both

objectives.

A hybrid approach based on Akaike information criterion (AIC) and principal

component regression (PCR) is firstly proposed to solve overfitting and collinearity

problems which are common in cost estimation. Although there have been many

studies of estimation and model fitting in construction, few have focused on

addressing the overfitting and collinearity problems that frequently occur in

developing predictive models. In an application of estimating the cost of construction

project preliminaries, the AIC-PCR approach is demonstrated in comparison with

alternative regression models and three data mining techniques of artificial neural

networks, case based reasoning and support vector machines. In addition to reducing

the risks of overfitting and collinearity, experimental results show that the AIC-PCR

approach presents a good predictive accuracy. In addition to the new approach,

effects of early cost drivers on determination of contingencies are also examined.

Besides of skills on technical tools, Job performance is influenced by

interactions between people and their environment. To identify such factors, a

literature review is firstly conducted. Building on the person-environment (P-E) fit

theory and the stimulus-organism-response (S-O-R) paradigm, a conceptual model to

understand the job performance of construction professionals is developed.

Psychological reactions such as work stress and job satisfaction need to be specially

emphasised for their mediating role in linking environmental factors and individual

differences on job performance. Structural equation modelling (SEM) is used as the

principal statistical method to explore interactions between people and their

environment. However, previous applications of SEM in construction management

area are not very satisfactory. Therefore, this research critically reviews extent SEM

iiImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

applications for solving problems related to construction management. Some

common drawbacks (such as comparatively small sample size, questionable

construct validity, and low GOF) of these applications are pointed out, with

suggestions for improvement.

Work stress is explicitly examined, since cost engineers face a high level of

uncertainty and much responsibility. The perceived stress questionnaire (PSQ) is

used to measure cost professionals’ work stress. Principal component analysis and

confirmatory factor analysis are utilised to test the dimensions of occupational stress;

this area has mostly been overlooked in previous research on stress in the

construction context. Analysis of the results identifies one stressor — demand — and

three secondary emotional reactions to the work situation — worry, tension and lack

of joy.

Job satisfaction, another indicator of person-environment fit, is also important

to employee performance. Recent decades have seen an increasing number of

theoretical explorations and empirical demonstrations of the nexus between job

satisfaction and job performance. Some argue that “happier workers produce more”,

while others insist that workers with better performance achieve satisfaction through

greater rewards. This study conducts a fine-grained analysis to propose a new

conceptual model on satisfaction-performance (S-P) nexus. Job satisfaction is

subdivided into economic satisfaction and noneconomic satisfaction. This

assumption is validated in this study by a principal component analysis of empirical

evidence from a questionnaire survey of construction cost engineers. Additionally,

this model is tested with construction project participants, using survey data from

construction companies about their most recent project experiences.


Interaction iii

Keywords

Akaike Information Criterion

Collinearity diagnosis

Construction cost estimation

Cost drivers

Cost engineers

Job performance,

Job satisfaction

Multiple linear regression

Overfitting diagnosis

Person-environment fit

Principal component analysis

Principal component regression

Stimulus-organism-response

Stimulus-reaction-performance

Psychological reaction

Structural equation modelling

Work stress

ivImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

List of Publications by Candidate

Journal papers published

Bo Xiong*, Martin Skitmore, Bo Xia. A critical review of structural equation

modelling applications in construction research. Automation in Construction,

49, 59-70, 2015. (ERA:A) (Chapter 3, Section 3.2)

Bo Xiong*, Martin Skitmore, Bo Xia. Exploring and validating the internal

dimensions of occupational stress: Evidence from construction cost

estimators in China. Construction Management and Economics, 33(5-6), 495-

507, 2015. (ERA:A) (Chapter 4)

Bo Xiong*, Martin Skitmore, Bo Xia, Md Asrul Masrom, Kunhui Ye, Adrian

Bridge. Examining the influence of participant performance factors on

contractor satisfaction: A structural equation model. International Journal of

Project Management. 32(3), 482-491, 2014. (ERA: A) (Chapter 5, Section

5.2)

Bo Xiong*, Weisheng Lu, Martin Skitmore, K.W. Chau, & Meng Ye,

Virtuous nexus between corporate social performance and financial

performance: A study of construction enterprises in China, Journal of Cleaner

Production, 129, 223-233, 2016. (ERA: A)

Bo Xia, Bo Xiong, Martin Skitmore, Peng Wu, Fang Hu, Investigating the

impact of project definition clarity on project performance: a Structural

Equation Modelling (SEM) Study, Journal of Management in Engineering,

32(1), 04015022,2016. (ERA: A*)

Xiaolong Gan, Jian Zuo, Kunhui Ye, Martin Skitmore, Bo Xiong, Why

sustainable construction? Why not? An owner's perspective, Habitat

International, 47, 61-68, 2015. (ERA:A)

Brendon Lim, Madhav Nepal, Martin Skitmore, Bo Xiong, Drivers of the

accuracy of developers’ early stage cost estimates in residential construction,

Journal of Financial Management of Property and Construction, 21(1), 4-20.

(ERA:C)


Interaction v

Journal manuscripts

Bo Xiong*, Martin Skitmore, Bo Xia, Sidney Newton, A hybrid approach for

reducing overfitting and collinearity: an application in construction cost

estimation. Submitted to Journal of Civil Engineering and Management

(Chapter 2, Section 2.1)

Bo Xiong*, Martin Skitmore, Md Asrul Masrom, Bo Xia, A fine-grained

analysis of contractor satisfaction in promoting project management

performance. Submitted to Project Management Journal (Chapter 5, Section

5.3)

International conference papers

Bo Xiong (Oral presenter), Exploring dimensions of job satisfaction and

relationships with performance: evidences from construction professionals,

CIB World Building Congress 2016, Tampere, Finland, May 30 – June 3,

2016. (Chapter 5, Section 5.1)

Bo Xiong (Oral presenter), Bo Xia, Examining the impacts of early cost

drivers on contingencies with path analyses, 2014 ASCE Construction

Research Congress, Atlanta, USA, May 19-21, 2014, pp. 1518-1527.

(Chapter 2, Section 2.2)

Bo Xiong (Oral presenter), Martin Skitmore, Bo Xia, Exploring the internal

dimensions of work stress: Evidence from construction cost estimators, 2014

30th ARCOM, UK, Sep 1-3, 2014, pp 321-329.

Bo Xiong (Oral presenter), Will lean construction be paid off: lessons learnt

from BREEAM buildings, 2015 IGLC Summer School, Perth, Australia,

August 1-2 2015, pp 51-55.

Bo Xiong (Oral presenter), The role of person-environment fit in promoting

job performance: towards a conceptual model and a research agenda, EPPM

2015, Gold Coast, Australia, September 1-3, 2015, pp 476-484.

viImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

Table of Contents

Abstract .................................................................................................................................................... i

Keywords .............................................................................................................................................. iii

List of Publications by Candidate .......................................................................................................... iv

Table of Contents ................................................................................................................................... vi

List of Figures ..................................................................................................................................... viii

List of Tables ......................................................................................................................................... ix

List of Abbreviations ............................................................................................................................. xi

Acknowledgements ............................................................................................................................... xv

Statement of Original Authorship ....................................................................................................... xvii

CHAPTER 1: INTRODUCTION ....................................................................................................... 1

1.1 Background .................................................................................................................................. 1 1.1.1 Improving construction estimation by technique innovation ............................................ 1 1.1.2 Improving performance by considering person-environment interactions ....................... 2

1.2 LITERATURE REVIEW ............................................................................................................ 3 1.2.1 Literature review on driving factors and techniques of cost estimation ........................... 3 1.2.2 Literature review on job performance .............................................................................. 9

1.3 RESEARCH QUESTIONS AND OBJECTIVES ..................................................................... 13

1.4 Thesis Outline ............................................................................................................................ 14 1.4.1 Chapter 2: Construction cost estimation techniques ....................................................... 14 1.4.2 Chapter 3: Conceptual framework and structural equation modelling ........................... 16 1.4.3 Chapter 4: Work stress ................................................................................................... 17 1.4.4 Chapter 5: Job satisfaction .............................................................................................. 18 1.4.5 Chapter 6: Conclusions ................................................................................................... 20

CHAPTER 2: CONSTRUCTION COST ESTIMATION TECHNIQUES ................................... 21

2.1 A new cost estimation approach ................................................................................................ 21 Statement of contribution ........................................................................................................... 21 2.1.1 Introduction .................................................................................................................... 23 2.1.2 Literature review ............................................................................................................ 25 2.1.3 A hybrid approach .......................................................................................................... 27 2.1.4 Application in construction cost estimation .................................................................... 31 2.1.5 Conclusions .................................................................................................................... 36

2.2 Impacts of early cost drivers ...................................................................................................... 38 Statement of contribution ........................................................................................................... 38 2.2.1 Introduction .................................................................................................................... 40 2.2.2 Early cost drivers ............................................................................................................ 41 2.2.3 Research method ............................................................................................................ 42 2.2.4 Path analysis modelling .................................................................................................. 45 2.2.5 Findings and discussions ................................................................................................ 47 2.2.6 Conclusion ...................................................................................................................... 48

CHAPTER 3: CONCEPTUAL FRAMEWORK AND STRUCTURAL EQUATION

MODELLING 51

3.1 Towards a conceptual framework of job performance .............................................................. 51 3.1.1 Introduction .................................................................................................................... 51 3.1.2 Development of the conceptual framework .................................................................... 52 3.1.3 Discussion ...................................................................................................................... 57 3.1.4 Conclusions .................................................................................................................... 59


Interaction vii

3.2 Structural equation modelling .................................................................................................... 60 Statement of contribution ........................................................................................................... 60 3.2.1 Introduction .................................................................................................................... 62 3.2.2 Methodology ................................................................................................................... 63 3.2.3 Critical issues in the application of SEM ........................................................................ 69 3.2.4 Discussion and recommendations ................................................................................... 83 3.2.5 Conclusions .................................................................................................................... 85

CHAPTER 4: WORK STRESS ........................................................................................................ 87

Statement of contribution ...................................................................................................................... 87

4.1 Introduction ................................................................................................................................ 89

4.2 Literature review ........................................................................................................................ 90 4.2.1 Occupational stress and its effects .................................................................................. 90 4.2.2 Stressors and coping strategies ....................................................................................... 93 4.2.3 Measures of occupation stress and divisibility ............................................................... 94

4.3 Research method ........................................................................................................................ 95 4.3.1 Perceived stress questionnaire ........................................................................................ 96 4.3.2 Translation and back translation ..................................................................................... 97 4.3.3 Data collection and demographics .................................................................................. 98 4.3.4 Data reliability ................................................................................................................ 99

4.4 Data analysis and discussion .................................................................................................... 100 4.4.1 Principal component analysis ....................................................................................... 100 4.4.2 Discussion-PCA results ................................................................................................ 101 4.4.3 Validation with SEM .................................................................................................... 103 4.4.4 Validation with SEM Discussion of the CFA and SEM results .................................... 106

4.5 Conclusion ............................................................................................................................... 107

CHAPTER 5: JOB SATISFACTION ............................................................................................. 109

5.1 The nexus between job satisfaction and job performance of construction cost engineers ....... 109 5.1.1 Introduction .................................................................................................................. 109 5.1.2 Literature review ........................................................................................................... 110 5.1.3 Research method ........................................................................................................... 113 5.1.4 Results .......................................................................................................................... 114 5.1.5 Discussion and conclusions .......................................................................................... 116

5.2 Examining the influence of participant performance factors on contractor satisfaction: A

structural equation model .................................................................................................................... 118 Statement of contribution ......................................................................................................... 118 5.2.1 Introduction .................................................................................................................. 120 5.2.2 Introduction .................................................................................................................. 121 5.2.3 Research method ........................................................................................................... 122 5.2.4 Results .......................................................................................................................... 130 5.2.5 Findings and discussion ................................................................................................ 134 5.2.6 Conclusions .................................................................................................................. 137

5.3 The nexus between contractor satisfaction and project Management performance ................. 140 Statement of contribution ......................................................................................................... 140 5.3.1 Introduction .................................................................................................................. 142 5.3.2 Theoretical background and hypotheses development ................................................. 143 5.3.3 Methodology ................................................................................................................. 145 5.3.4 Results .......................................................................................................................... 152 5.3.5 Discussion ..................................................................................................................... 159 5.3.6 Conclusions .................................................................................................................. 160

CHAPTER 6: CONCLUSIONS ...................................................................................................... 163

6.1 Summary and discussion .......................................................................................................... 163

6.2 Limitations and recommendations ........................................................................................... 165

BIBLIOGRAPHY ............................................................................................................................. 169

viiiImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

List of Figures

Figure 1.1 Cost estimate accuracy range change curves with project progress ....................................... 4

Figure 1.2 Number of related articles by journal .................................................................................. 11

Figure 2.1 Collinearity diagnostics for Model 3 ................................................................................... 35

Figure 2.2 The initial model .................................................................................................................. 42

Figure 2.3 The next to last path analysis model .................................................................................... 45

Figure 2.4 Final path analysis model .................................................................................................... 46

Figure 3.1 Main P-E fits and psychological reactions ........................................................................... 54

Figure 3.2 Proposed conceptual framework .......................................................................................... 55

Figure 3.3 Schematic diagram of a structural equation model .............................................................. 64

Figure 3.4 Article selection ................................................................................................................... 67

Figure 3.5 Number of SEM-based articles by journals and year ........................................................... 69

Figure 4.1 Effects of dimensions of stress on organizational commitment ......................................... 106

Figure 5.1 Main conceptual models of the S-P nexus ......................................................................... 112

Figure 5.2 Proposed conceptual model for this study ......................................................................... 113

Figure 5.3 Model evaluations by regression analysis .......................................................................... 115

Figure 5.4 Structural component ......................................................................................................... 126

Figure 5.5 Measurement component ................................................................................................... 131

Figure 5.6 Final SEM model results ................................................................................................... 133

Figure 5.7 Conceptual model 1 ........................................................................................................... 147

Figure 5.8 Conceptual model 2 ........................................................................................................... 148

Figure 5.9 Model 1A testing H2A: COS causes CPMP ...................................................................... 154

Figure 5.10 Model 1B testing H2B: CPMP causes COS .................................................................... 154

Figure 5.11 Model 2 ............................................................................................................................ 157


Interaction ix

List of Tables

Table 2.1 Sample descriptions - part 1 .................................................................................................. 32

Table 2.2 Sample descriptions - part 2 .................................................................................................. 33

Table 2.3 Elemental cost items framework ........................................................................................... 33

Table 2.4 Developed regression models ................................................................................................ 34

Table 2.5 Comparison of results for Application 1, price estimating .................................................... 36

Table 2.6 Description of variables ........................................................................................................ 44

Table 2.7 Model fit indices ................................................................................................................... 46

Table 2.8 Standardized direct, indirect and total effects of variables .................................................... 47

Table 3.1 Issues related to research design ........................................................................................... 75

Table 3.2 Issues related to model development ..................................................................................... 78

Table 3.3 GOF evaluation criteria and practical results ........................................................................ 81

Table 3.4 Description of reported GOF indices .................................................................................... 82

Table 3.5 Recommendations for selected issues in SEM ...................................................................... 84

Table 4.1 Perceived stress questionnaire ............................................................................................... 96

Table 4.2 Translation and back translations .......................................................................................... 97

Table 4.3 PCA with varimax rotation ................................................................................................. 100

Table 4.4 Standardized regression weights ......................................................................................... 104

Table 4.5 Goodness of fit .................................................................................................................... 105

Table 5.1 Measures of Job satisfaction ............................................................................................... 113

Table 5.2 Principal component analysis with varimax rotation .......................................................... 114

Table 5.3 Correlations between factors ............................................................................................... 115

Table 5.4 forms of effects ................................................................................................................... 116

Table 5.5 Constructs and measurement of SEM ................................................................................. 128

Table 5.6 Details of respondents ......................................................................................................... 129

Table 5.7 Reliability test of the questionnaire responses .................................................................... 130

Table 5.8 Standardized regression weights and SMCs ....................................................................... 131

Table 5.9 Results of goodness of fit (Adapted from Ong and Musa (2012)) ...................................... 132

Table 5.10 P values and indirect effects (Sobel test) ........................................................................... 134

Table 5.11 Description of projects ...................................................................................................... 148

Table 5.12 Measurement constructs and items .................................................................................... 149

Table 5.13 Reliability test ................................................................................................................... 150

Table 5.14 Validity test results ............................................................................................................ 153

Table 5.15 Results of hypothesis tests ................................................................................................. 155

Table 5.16 Standardized regression weights and SMCs ...................................................................... 155

Table 5.17 Goodness of fit .................................................................................................................. 157

Table 5.18 Hypothesis direct effects ................................................................................................... 157

xImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

Table 5.19 Standardized direct/indirect/total effects ........................................................................... 158


Interaction xi

List of Abbreviations

AACE: American Association of Cost Engineering

ADF: Asymptotically distribution-free

AGFI: Adjusted goodness-of-fit index

AHP: Analytic hierarchy process

ANN: Artificial neural networks

AVE: Average variance extracted

AUTCON: Automation in Construction

AIC: Akaike information criterion

ASCE: American Society of Civil Engineers

B&E: Building and Environment

BCIS: Building Cost Information Service

BREEAM: Building Research Establishment Environmental Assessment

Methodology

CBR: Case based reasoning

CFA: Confirmatory factor analysis

CFI: Comparative fit index

CI: Condition index

CIOB: Chartered Institute of Building

CME: Construction Management and Economics

CNY: Chinese yuan

CR: Composite reliability

CV-SEM: Covariance-based structural equation modelling

CVP: Coefficient variance proportion

CWB: counterproductive work behaviour

xiiImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

D-A: Demands-abilities

ECAM: Engineering, Construction and Architectural Management

ES: Economic satisfaction

EW: Equal weights

GA: Genetic algorithm

GDM: Gradient descent method

GFI: Goodness-of-fit index

GLS: Generalized least square

GFA: Gross floor area

GOF: Goodness of fit

IFI: Incremental fit index

IJPM: International Journal of Project Management

JME: Journal of Management in Engineering

JCEM: Journal of Construction Engineering and Management

KNN: K-nearest neighbour

LEED: Leadership in Energy and Environmental Design

LOOCV: Leave one out cross validation

LV: Latent variable

MAE: Mean absolute error

MAER: Mean absolute error rate

MAPE: Mean absolute percentage error

ML: Maximum likelihood

MLR: Multiple linear regression

MSE: Mean squared error

MV: Manifest variable

N-S: Needs-supplies


Interaction xiii

OCB: Organizational citizen behaviour

PA: Path analysis

PCA: Principal component analysis

PCFI: parsimony comparative fit index

PCR: Principal component regression

PSQ: perceived stress questionnaire

P-E: Person-environment

PLS: Partial least squares

PLS-SEM: partial least squares path modelling

PNFI: Parsimony normed-fit index

PS: Production-related/ noneconomic satisfaction

RICS: Royal Institution of Chartered Surveyors

RMR: Root mean square residual

RMSE: Root mean squared error

RMSEA: Root mean square error of approximation

RR: Ridge regression

RSS: Residual sum of squares

SEM: Structural equation modelling

SMC: Squared multiple correlation

S-O-R Stimulus-organism-response

S-P: Satisfaction-performance

SRMR: Standardized root mean square residual

S-R-P: Stimulus-reaction-performance

SSE: Sum of squares error

SVM: Supportive vector machine

SVR: Supportive vector regression

xivImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

TLI: Tucker-Lewis Index

TP: Task performance

TSS: Total sum of squares

ULS: unweighted least squares

UK: United Kingdom

US: United States of America

VIF: Variance inflation factor

WHI: Work–home Interference


Interaction xv

Acknowledgements

This thesis would not be possible without the help of many people. Firstly, I

would like to thank my supervisor Professor Martin Skitmore, who has been a

fabulous mentor and supporter of my research and career development. I would like

to thank Martin for planning reasonable goals and encouraging me to complete tasks

in a timely manner. Thanks to Martin’s example, I understand that hard work, time

management and integrity are important for scholarly success.

I would like to thank my associate supervisor Dr Bo Xia, research project

leader Associate Professor Sidney Newton and external supervisor Dr Pablo

Ballesteros-Pérez for providing invaluable advice on research and career

development whenever I was in need of their help. In particular, I would like to thank

Bo for helping me to refine my research topic, guiding me in the transition to post-

doctoral studies and giving me the opportunity to assist with several courses.

I am thankful to Professor Kunhui Ye, the supervisor of my master by research

program, for providing advice on many issues such as research planning,

questionnaire distribution, grant applications and my academic career in general.

My gratitude extends to thesis committee members for encouraging my study

and giving constructive feedback in key completion stages of this thesis: Professor

Stephen Kajewski, Professor Hannes Zacher, Professor Simon Washington and

Professor Laurie Buys. I would also like to thank Dr Le Chen for always sharing her

opinions on academic development.

I wish to acknowledge valuable support from academics in the discipline of

Construction and Project Management. Special thanks go to Professor Jay Yang,

Associate Professor Adrian Bridge, Associate Professor Karen Manley, Dr Madhav

Nepal, Dr Fiona Lamari, Dr Melissa Teo and Dr Carol Hon. I would also like to

thank my senior Dr Md Asrul Masrom for letting me access his data, and Dr Mei Li,

Dr Yulin Liu, Miss Hao Zhang and Mr Xin Hu for their help with questionnaire

development and discussions.

I would like to thank Professor Abdol R. Chini for hosting and directing my

short-term visiting research in the M.E. Rinker, Sr. School of Construction

xviImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

Management at the University of Florida. I thank Professor Charles J. Kibert for

sharing the inspiring conference proceeding and providing me a workplace in the

Powell Centre for Construction and Environment.

I would like to thank Professor Jack Goulding for inviting me to engage in

short-term visiting research in the Centre for Sustainable Development at the

University of Central Lancashire, UK, for supervising my visiting research project

and providing advice on academic life. There, suggestions from Professor Akintola

Akintoye and Dr Farzad Pour Rahimian were inspiring and sincerely appreciated.

I would like to thank Professor Andrew Baldwin for inviting me to conduct

short-term visiting research in the School of Civil and Building Engineering at

Loughborough University, UK; I also thank Dr Mohammed Osmani for admirably

constructed supervision and many suggestions for career development.

I would like to thank Professor K W Chau for inviting me to conduct short-

term visiting research in the Department of Real Estate and Construction at the

University of Hong Kong; I also thank Associate Professor Wilson Lu for his kind

directions, inspiring revisions and timely encouragements.

Besides the academic advisors mentioned, I made many friends in visited

universities and when I attended conferences. I would like to express my special

thanks to them as well as all my friends in Brisbane for their fellowship and support.

I am grateful to the School of Civil Engineering and Built Environment for

providing wonderful facilities and services. I would also like to thank Professor Paul

Burnett and Ms Linda Clay for inviting me to serve as a member of QUT Research

Student Center User Advisory Group in 2015, which turned out to be a precious

experience. Supports from QUT SEF HDR team are also sincerely appreciated. I

thank Dr Christina Houen for editing substantial parts of my thesis to the standards

and guidelines of the Institute of Professional Editors (IPEd).

Lastly, and most importantly, I wish to thank my family for their support and

encouragement. My parents have always given me selfless love, generous support

and timely encouragement throughout my life. They are always happy to hear me tell

stories of my experiences. And I need to thank them in Chinese now:

感谢父母、亲友们一直的关爱与支持，让我心无旁骛的完成了博士阶段的学习。我一定

继续努力，不辜负你们的期望！


Interaction xvii

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:

Date: _______1/07/2016_________

QUT Verified Signature

xviiiImproving Cost Estimation Performance: An Investigation of Prediction Technique and Person-Environment Interaction

Chapter 1: Introduction 1

Chapter 1: Introduction

1.1 BACKGROUND

It is common to find that the final costs of projects greatly exceed estimates

(Williams, Lakshminarayanan, & Sackrowitz, 2005). An analysis of 258

transportation infrastructure projects worth US$90 billion reveal that nine out of ten

cost overruns are caused by inaccurate estimation in the early stages (Flyvbjerg,

Bruzelius, & Rothengatter, 2003). Similarly, 74% of cost growth in projects

undertaken by chemical, oil, and mineral industries in North America are caused by

underestimation in the early stages (Merrow, Chapel, & Worthing, 1979). Therefore,

accurate cost estimation is critical to project success (Lowe, Emsley, & Harding,

2006; Skitmore, Stradling, Tuohy, & Mkwezalamba, 1990).

For organizations like government authorities or real estate developers,

inaccurate early estimates will cause low efficiency in the use of money, missed

development opportunities and unsuccessful project management (Oberlender &

Trost, 2001). The difficult task of cost estimation is always assumed by construction

cost engineers (quantity surveyors in the UK). Quantity surveyors advise clients on

the likely tender price of proposed projects, and assist in setting a budget accordingly

(Lowe, et al., 2006). In addition to estimation techniques, the estimation performance

of these professionals is affected by psychological reactions like work stress (Leung,

Zhang, & Skitmore, 2008). Therefore, this thesis by thesis explores both aspects of

this challenging task.

1.1.1 Improving construction estimation by technique innovation

Cost estimation in the early stages of construction, with limited information

and unclear scope definition, is a complicated and stressful work. Estimators are

involved in many subjective decisions because of the complexity and uncertainty of

construction work. Skitmore (1985) attributes the ability to make good subjective

judgements to estimating expertise. Through a pioneering series of experiments to

measure the early stage estimating abilities of quantity surveyors, Skitmore (1985)

confirms the role of expertise in achieving accuracy, and finds that experts are more

2 Chapter 1: Introduction

relaxed and confident. This expertise is further linked with experience, that is, an

experienced quantity surveyor can give a more accurate estimate (Skitmore et al.,

1990). However, becoming an expert by increasing one’s experience consumes a lot

of resources and time. Millions of dollars may be at risk through poor and inefficient

estimation practices, and bigger mistakes may occur during an estimator’s early

years. Additionally, distrust and job ambiguity can exacerbate estimators’ stress

(Leung, Ng, Skitmore, & Cheung, 2005). This dilemma calls for understanding how

to build cost drivers in the early stages, and developing some inexpensive, quick and

reasonably accurate estimating techniques; these objectives motivate this research.

Analysing historical data with statistical methods can help to improve

prediction accuracy. This is consistent with Skitmore’s (1985) finding that experts

are able to recall the estimating details of previous projects and are good at adjusting

to new requirements. Some techniques like activity based cost estimates and

estimates of elements such as floor area are criticised for unsuitability or inaccuracy

in the early stages. To date, three methods usually recommended for forecasting

early estimates are multiple linear regression (MLR), artificial neural networks

(ANN) and case based reasoning (CBR) (Kim, An, & Kang, 2004). Although MLR

is a comparative way that is widely used, practitioners face overfitting and

collinearity problems in modelling. ANN needs much training time for each use.

Given the comparative inaccuracy of MLR and stiffness of ANN to add new case,

CBR is more flexible in adding new cases for continuous improvement. However,

current applications of CBR in early estimates (An, Kim, & Kang, 2007; Kim, et al.,

2004; Kim & Kim, 2010; Kim, Choi, Kim, & Kang, 2005) are defective in

determining similarity weights and adapting the model to new cases.

A hybrid approach based on Akaike information criterion and principal

component regression is proposed to address these problems and validated in this

thesis. In addition, this approach can help to improve the general MLR method (see

Chapter 2). Effects of cost drivers are discussed in Chapter 1 and Chapter 2.

1.1.2 Improving performance by considering person-environment interactions

Cost engineers are important professionals in construction who work mainly in

offices. Therefore, their job performance, including task performance and


organisational citizen behaviour (OCB), are influenced by environmental factors

such as organisational support and organisational politics, and psychological

reactions to job satisfaction, work stress and so on.

However, the interaction of these factors in the work performance of cost

construction professionals has been little explored. Leung, Zhang et al. (2008)

examined the effects of organisational support on stress via mediation of some

stressors (such as unfair rewards) among cost engineers in Hong Kong. Cost

estimation is an experience based task (Skitmore, 1985); experiential learning is very

important in improving cost estimating abilities (Lowe & Skitmore, 1994).

Therefore, the organisational learning climate is critical to the job performance of

cost engineers. This link was not found to be significant in the study of Lowe and

Skitmore (2007), which may be attributed to the researchers’ focus on specific

measurement of task performance and not taking mediating variables into account.

Many studies (such as Egan, Yang, and Bartlett, 2004) acknowledge that a better

learning climate would enhance the transfer of knowledge in organisations. On the

other hand, Bergeron, Shipp, Rosen, and Furst (2011) point out that the task

performance of the individual may not necessarily improve significantly by spending

extra time on OCB. However, organisational performance should benefit from

organisational citizen behaviours (Mikkelsen & Grønhaug, 1999). Bearing these

challenges in mind, it is worth investigating whether job performance can be

improved through further exploration of person-environment interactions.

Therefore, a thorough literature review of studies of driving factors that are key

to job performance published in managerial and psychological journals is presented,

and a comprehensive framework is proposed in Chapter 3, based on the theory of

Person-Environment (P-E) fit. Psychological reactions including work stress and job

satisfaction are explored in Chapters 4 and Chapter 5 respectively.

1.2 LITERATURE REVIEW

1.2.1 Literature review on driving factors and techniques of cost estimation

Influencing cost drivers

Estimation in the early stage is mostly inaccurate, yielding limited and vague

information. In this study, early stage refers to the pre-design stage, including a


feasibility study. Barnes (1974) proposes the accuracy of estimation at this stage is

around -40% to +20%, as shown in Figure 1.1 (Barnes, 1974; Skitmore, 1987b). It is

reported that inaccuracy of these estimates is around 30% in Germany, and this

inaccuracy is mainly caused by simply multiplying the floor area with an indicator,

which is inaccurately measured in certain projects because of uncertain cost drivers

(Stoy & Schalcher, 2007). These findings are consistent with AACE International’s

general cost estimate classification system across stages including concept screening,

feasibility, budget authorization, control and bid/tender (Christensen & Dysert,

2005). For example, the expected accuracy ranges for feasibility are -15% to -30%

(low) and +20% to +50% (high). Skitmore (1987b) states that during the pre-tender

stage, the accuracy of forecasting can improve as the design progresses for more

gradual release of information. Despite the risks of inaccuracy, an inexpensive,

quick, and comparatively accurate pre-design estimation is important for its effects

on decision making and feasibility studies (Li, Shen, & Love, 2005). To achieve this,

a review of previous studies on building cost relevant drivers at the early stage

should be conducted first, and the impacts of these drivers on building cost formation

should also be examined, but this is rarely considered in previous research.

Figure 1.1 Cost estimate accuracy range change curves with project progress

Skitmore (1987a) proposes that building prices can be seen as a result of a

series of interdependent causal mechanisms, and emphasises the market effect on the

formation of building prices. Primary cost drivers include building type, size,

complexity and quality, type of client, contractor selection, contractual arrangements,

location, and the economic and legal environment of the project location. Skitmore


and Ng (2003) pinpointed the effect of the contractor period on construction costs by

establishing a time-cost simultaneous model with details of 93 Australian projects.

They found that the errors in predicted actual construction costs become smaller as

the contract period increases. Similarly, Gunner and Skitmore (1999) found that three

variables, floor area, number of storeys above ground and contract period, have

comparatively high correlations with contract sums after conducting correlation

analysis for Singapore data.

Li et al. (2005) constructed two early estimate regression models for reinforced

concrete(RC) office buildings and steel office buildings in Hong Kong, China. They

selected seven variables: average floor area, total floor area, average storey height,

number of above-ground storeys, total building height, number of basements and

completion year, and found that total floor area, total building height, and average

floor area are important ones for modelling. While total building height is hard to

know from the beginning, the height of a floor is associated with design

specifications and some situations such as with or without air conditioning systems

(Stoy & Schalcher, 2007). An inherent limitation with this study is that the total

sample size and the two sub-sets used to develop the two models are small (37, 12, 7

respectively) and their representativeness of office buildings in Hong Kong is not

clear.

Elhag, Boussabaine, and Ballal (2005) conducted a questionnaire survey to

separate 67 factors into six categories. Although six ranking lists by importance are

proposed, impacts of these variables are still unknown and most factors are not

available in the pre-design stage. But it is interesting to find that complexity of

building services ranks as the top two in the project characteristics category. This is

consistent with the fact that building projects have become more complex nowadays,

and complexity is a critical characteristic to project success (Xia & Chan, 2012).

From the contractors’ perspective, a tender price is the sum of total costs

(including direct and indirect costs) and mark up (expected profit) (Runeson &

Skitmore, 1999; Yuan, 2011). Factors that influence a contractor’s mark-up

percentage should be mentioned here. Li, Shen, and Love (1999) conducted a study

to predict contractors’ mark-up percentage based on ten independent variables,

including project size, location, market conditions, number of competitors, project

type, project complexity and four other contractor-specific characteristics. Similarly,


Liu and Ling (2003) used three variables, market condition, project complexity and

project size, to estimate mark-up percentage. Tendering theory has been developed

for more than 50 years since the first proposal by Lawrence Friedman in 1956 and

many versions have evolved from that (Runeson & Skitmore, 1999).

Building costs can also represented by contract price/winner’s bid price, since

it is the cost for a client of completing a project. The actual contractor cost is always

ambiguous in the market place. Although variation in cost from a good detailed

estimate should be small (within 5%), Park and Chapin (1992) found that the actual

cost can vary almost ±20% from the estimated costs (Park & Chapin, 1992).

Therefore, using contractors’ expected cost is possibly unreliable because of its

inaccuracy.

In summary, understanding these building cost drivers will help clients and

quantity surveyors to avoid vagueness in scope, which is a major cause of cost

overrun (Akinci & Fischer, 1998). Soetanto and Proverbs’ (2002) study in the UK

indicates that contractor satisfaction increases with the perception that clients do not

know what they want. On the other hand, Xiong et al. (2014) found that this

conclusion is not applicable to Malaysian cases, where contractor satisfaction

increases with the client’s clarity of objectives. Despite the differences between these

findings, the importance of clear scope should be emphasised for achieving project

success (Xia, Xiong, Skitmore, Wu, & Hu, 2015). The above literature review seeks

to support this objective.

Building cost modelling techniques

The term “building cost modelling” was formally mentioned in the Building

Cost Research Conference held in 1982 (Newton, 1991). Cost models are the basis

for cost forecasting, and understanding their properties is vital to effective control

and development of future techniques (Skitmore & Marston, 1999a). Newton (1991)

proposed an agenda for this area of research, and classified 56 relevant published

works from 1960-1988 by nine dimensions. In terms of technique, the usage of

expert systems and networking was less than 5% (Newton, 1991). However, case-

based reasoning (CBR) as an expert system and artificial neural networks (ANN)

have improved greatly in efficiency with the aid of developing computer techniques

in the last two decades (Chou & Tseng, 2011).


Traditional estimation methods at early stage include element based floor area

models, probabilistic models and regression models (Raftery, 1987). The problem of

the first method is that the floor area is not the only factor affecting cost. According

to Stoy and Schalcher (2007), inaccuracy of these estimates by simply multiplying

the floor area by a certain indicator is around 30% in Germany. Acceptability of the

probabilistic model for cost estimating, usually in the form of a Monte Carlo

simulation, is questionable (Chau, 1997; Fellows, 1996; Li, et al., 2005).

The multiple linear regression (MLR) method has been regarded as a powerful

tool in early estimates for many years (Li et al., 2005; Skitmore & Patchell, 1990). Li

et al (2005) established regression cost equations by seven basic variables in early

stages for two types of office buildings in Hong Kong. But this research is limited by

the use of small samples. Aiming at optimal predictive ability with the current

sample, regression has a principle of parsimony (step-wise regression). This

character reduces the possibility of inputting a large number of predictors, and

reduces regression’s ability to explain changes (Kim, et al., 2004).

The artificial neural networks model (ANN) simulates the learning process of

the human brain by forming thousands of simulated neurons, and is widely used for

its predicting ability in many fields (Kim et al., 2004; Kim et al., 2005). Kim et al.

(2004) point out that many previous researchers have proved that the accuracy of

applying ANN is higher than that of the regression method in forecasting cost, and

they confirm that opinion by analysing 530 historical costs in Korea. Besides having

a mysterious process, another problem of ANN is that adding a new case means

retesting models with all the data again. This is time-consuming and does not support

sustainable improvement. In regard to forecasting ability, Kim et al. (2004) compare

Mean Absolute Error Rate (MAER) between ANN and CBR, and find that CBR’s

MAER (=4.81%) is less than the average of 75 ANN models (=5.65%), although

higher than the best ANN model (2.97%). Kim et al. (2005) find that ANN’s mean

error (6.66%) is almost twice CBR’s mean error (3.68%), in a study of 540 apartment

buildings in South Korea. Thus the accuracy of ANN is not greater than that of CBR,

especially considering the randomness in determining weights in Kim et al.’s study

(2004).

Case based reasoning (CBR) is a method of solving a new case by using

previous experience (Aamodt & Plaza, 1994; Xu, 1994). The inherent logic of CBR


is consistent with Skitmore’s (1985) finding that experts are good at recalling

estimating details of previous projects and then adjusting to new requirements

(Skitmore, 1985). After a new solution is achieved, the new case can be used to

enrich the current database, which means that CBR is a method that suports

sustainable improvement. Xu (1994) compares CBR with rule based expert systems

and finds that CBR is superior in (1) getting a solution with partial understanding; (2)

providing a closer match to actual human reasoning; (3) providing more explanation

capability (Xu, 1994).

CBR is a widely used tool, and there have been many studies on solving

construction related problems during the last decade (Kim & Kim, 2010). Despite

CBR’s suitability, there are only a few studies on using CBR to conduct an early

estimate (An et al., 2007; Kim et al., 2004; Kim & Kim, 2010; Kim et al., 2005).

Kim et al. (2005) used 540 apartment buildings’ cost data to compare the quality of

estimates of ANN and CBR, and found that ANN’s error rate was twice that of CBR.

Kim et al. (2004) examined the estimating capability of different methods by using

MLR, ANN and CBR separately with 530 historical cases, and found that CBR was

better than MLR and the average accuracy of 75 ANN models. Although CBR’s

error rate is bigger than the best ANN model, Kim et al. (2004) pointed out the

limitation of ANN models for updating new cases. The difference between these two

studies also indicates the importance of methods for determining variable importance

weights when calculating similarity indexes.

Kim et al. (2004) use the gradient descent method (GDM) available in

ESTEEM, software developed from CBR. An et al. (2007) compare estimate quality

of three weight deciding methods: equal weights (EW), GDM, and the analytic

hierarchy process (AHP) for 580 residential buildings’ cost data in Korea, and find

that AHP-CBR is more accurate. AHP is time consuming, and carries the risk of

subjectivity. In a study of cost estimation for 216 pre-stressed concrete beam bridges

completed in Korea, Kim and Kim (2010) propose connecting CBR with a genetic

algorithm (GA) to avoid the subjectivity that can occur with AHP. The validity of

this method is unknown without comparing it to other methods (EW, GDM), and

subjectivity in the adaptation process is another problem. Only length and width are

chosen as the basis for a ratio to adjust cost, and the importance of the width’s weight

is 0, as calculated by using GA. Kim et al. (2005) compare the performance of the


GDM and regression methods and find that using regression is better. It is reasonable

to assume that regression performs better because the weights are not decided by

intuition or randomly, but by sampling the data statistically. Regression’s parsimony

principle is also reflected in Kim et al.’s (2005) study in three factors: storey, unit per

storey and finishing grades do not have weights.

1.2.2 Literature review on job performance

Conceptualisation of job performance

Job performance is the central construct in occupational psychology

(Viswesvaran & Ones, 2000) and even the ultimate goal in organisational

management practices (Judge, Thoresen, Bono, & Patton, 2001). Theories of job

performance can be traced back to Taylor’s Scientific Management, providing

techniques such as synthesis and standardisation to improve efficiency of the

production process and productivity of workers. “Fordism”, a further application of

“Taylorism”, is well known for high productivity generated by machines, but higher

wages provided to attract workers to do “boring” works on assembly lines. In the era

of “post-Fordism”, non-technical factors such as organisational culture are believed

to be critical to achieving success (Bonanno & Constance, 2001). Review of previous

research into job performance indicates the three main kinds of job performance to

be task performance, organisational citizen behaviour and counterproductive work

behaviour (Viswesvaran & Ones, 2000).

Early studies measuring job performance focused on task performance,

indicating the extent to which employees complete the professional duties specified

in their work descriptions. Task performance is defined as

the proficiency with which incumbents perform activities that are formally

recognized as part of their jobs; activities that contribute to the organization’s

technical core either directly by implementing a part of its technological

process, or indirectly by providing it with needed materials or services.

(Borman & Motowidlo, 1993b, p73)

For example, task performance was used in the Hawthorne studies exploring

the linkages between job satisfaction and the task performance of workers.

Organisational citizen behaviour (OCB) — assuming job responsibilities and

innovation for the benefit of an organisation without reward expectations


(Eisenberger, Fasolo, & Davis-LaMastro, 1990) — has been increasingly emphasised

in many organisational studies. It has been proposed that OCB should comprise five

dimensions of altruism, conscientiousness, sportsmanship, courtesy and civic virtue

(LePine, Erez, & Johnson, 2002; Organ, 1988a). Following this typology, Podsakoff,

Ahearne, and MacKenzie (1997) conducted a study measuring performance in terms

of the quantity and quality of 218 people working in a paper factory, and found that

altruism and sportsmanship led to better performance. However, such OCB

dimensions are not significantly discriminating (LePine et al., 2002). Smith, Organ,

and Near (1983) identify two main kinds of OCB behaviours, including generalised

compliance (indicating conscientious self-disciplined behaviours) and altruism

(indicating a willingness to help others). A positive relationship between

organisational support and OCB was found by Eisenberger et al. (1990).

Counterproductive work behaviour (CWB) is behaviour conducted to

intentionally harm corporate legitimate interests (Dalal, 2005). Such behaviours

include property/equipment sabotage, substance abuse (Sackett & Wanek, 1996), and

behaviours reducing the effectiveness of employees (Fox, Spector, & Miles, 2001).

CWB is assumed to share similar antecedents with OCB and task performance

(Dalal, 2005). For example, Fox et al. (2001) found that job stressors including

organisational constraints, interpersonal conflict and perceived injustice result in

CWB via the mediation of negative emotion. Additionally, the relationships between

job stressors and CWB are stronger for individuals with higher level negative

affectivity (Penney & Spector, 2005) or low in conscientiousness (Bowling &

Eschleman, 2010).

Studies of job performance of construction professionals

Maloney and McFillen in an early (1983) study argued that no validated model

of worker performance existed for the construction industry, although the importance

of organisational constraints, job satisfaction, and motivation had been increasingly

acknowledged outside the industry. To understand research progress into the job

performance of construction professionals, a literature review was carried out. The

keyword “job performance” for article selection was applied to a group of high

impact construction journals, comprising: the Journal of Construction Engineering

and Management (JCEM) and Journal of Management in Engineering (JME) from

the ASCE library; the International Journal of Project Management (IJPM),


Automation in Construction (AUTCON) and Building and Environment (B&E) from

Elsevier; Construction Management and Economics from Taylor and Francis; and

Engineering, Construction and Architectural Management (ECAM) from Emerald.

227 search records were initially found. These records were browsed to identify

articles where the job performance of construction professionals is the key theme,

and 13 articles were selected for detailed review. The studies cover construction

professionals, including architects and engineers, quantity surveyors and project

managers. The number of articles by journal is presented in Figure 2.1.

Figure 1.2 Number of related articles by journal

Environmental and individual factors are significant determinants of job

performance. Aiming to improve the work performance of construction project

managers, Pheng and Chuan (2006) point out the importance of the working

environment, and conducted a study to explore job related, project related and

organisational related factors. The differences in these factors between contractor and

consultant project managers are explored by Pheng and Chuan (2006). In addition to

environmental factors, Carr, De La Garza, and Vorster (2002) point to the necessity

of linking individual personality traits to job performance for engineering and

architectural professionals providing project design services. For example, a person

with a personality preference for “judging” performs better in preparing contract

documentation than others with a preference for “perception” (Carr et al., 2002).

Building on motivation theory, Tuuli and Rowlinson (2009) explore the relationships

between the psychological empowerment and job performance of project

0

1

2

3

4

5

AUTCON B&E ECAM IJPM C&E JME JCEM


management staff, finding that empowered employees have better a job performance.

In addition to the significant contribution of knowledge of job techniques, time

management abilities, problem solving and relationship management are also critical

predictors of the performance of project managers in mass housing building projects

(Ahadzie et al., 2008a).

Work stress and job satisfaction are popular topics. Leung et al. (2005), for

example, examine the impact of stress on the estimation performance of professional

cost engineers in Hong Kong, finding that stress negatively affects overall

performance in both linear and inverted U-shaped forms. In addition to the effects of

stress, Leung et al. (2006) also explore the effects of stress-coping behaviours on

estimation performance to show that, for instance, both preparatory action and

support seeking actions can improve estimation performance. Although not testing

the significance of the mediation effect of career commitment, Leung, Yu, and

Chong (2015) further demonstrated the negative effect of stress on career

commitment, and the positive effect of career commitment on cost estimation

accuracy. These findings indicate a proactive personality is also important for

improving job performance (Leung, Shan Isabelle Chan, & Dongyu, 2011). For

construction project managers, Leung et al. (2011) explore the nexus between stress

and performance through structural equation modelling (SEM) to demonstrate the

negative effect of job stress on task performance. Job satisfaction is another

psychological factor that affects the performance of construction professionals. As

pointed out by Ling and Loo (2013), job characteristics (such as work autonomy) and

personality characteristics (such as work knowledge and skills) affect the satisfaction

of construction project managers and their work performance.

Other studies focus on conceptualising job performance. In an attempt to

develop competency-based performance measures for construction project managers,

for example, Ahadzie, Proverbs, and Olomolaiye (2008b) draw on empirical

evidence from Ghana to point out the necessity of distinguishing between task

performance behaviours and contextual performance behaviours. Liu and Fellows

(2008), on the other hand, investigated the OCB of quantity surveyors in Hong Kong,

and found that an individualistic orientation was negatively correlated with OCB,

whereas collectivism is positively correlated with OCB. In an another study, Dainty

et al. (2005), aiming to assist human resource management decisions by a better


understanding of behavioural competencies, identified the core competencies of

construction project managers and developed a model to predict performance. They

found that self-control and team leadership are critical predictors of project

management performance.

In reviewing previous studies, it is apparent that there are some limitations.

Firstly, there is no consideration of mediators or moderators. Regression analysis is

usually applied in a one-shot approach, with predictors on one side of the equation

and a dependent variable on the other, without considering the interactions between

the predictors. Recently developed statistical methods such as SEM can be helpful in

this situation (Xiong, Skitmore, & Xia, 2015a). Another problem is the definition of

concepts. For example, Ling (2002) identified that both hard attributes (such as job

knowledge) and soft attributes (such as commitment) affect the performance of

architects and engineers in design-build projects. However, these attributes seem to

be a mixed combination of predictors of performance and measures of performance.

For example, Ling (2002) found that performance can be predicted by the attribute of

the speed of producing design drawings, which is really a measure of performance

rather than a predictor. Another problem is “scope-matching”, in that items should be

measured at the same level or cross level analyses are needed. In the study by Ling

and Loo (2013), for instance, performance was measured at the project level, while

satisfaction was measured at the individual level.

1.3 RESEARCH QUESTIONS AND OBJECTIVES

Based on the research background and literature review, this thesis therefore

seeks to examine two main research questions:

• What can be done to improve prediction technique by dealing with overfitting

and multicollinearity problems frequently occurred in construction research?

• What is role of psychological reactions on job performance of cost engineers?

By addressing these questions, this study contributes greatly to improving

construction cost estimation by technique innovation and understanding the

organisation-individual interactions of construction cost engineers. Primary research

objectives are thus developed as:


• To develop a hybrid approach based on Akaike information criterion (AIC)

and principal component regression (PCR) to deal with overfitting and

multicollinearity problems

• To evaluate the efficiency of the AIC-PCR approach with an application of

construction cost estimation.

• To examine the role of psychological reactions in promoting job performance

by developing a comprehensive framework.

• To examine job satisfaction and work stress, and their relationships with job

performance with empirical evidences from construction cost engineers.

1.4 THESIS OUTLINE

This thesis is presented by publication. Besides of the introduction and

conclusion chapters, several papers/manuscripts comprising the main content of the

dissertation are presented.

1.4.1 Chapter 2: Construction cost estimation techniques

In this chapter, a new estimation approach is firstly proposed to deal with

problems of overfitting and collinearity; the improved predictability of this approach

is compared with three widely used methods including artificial neural network

(ANN), case-based reasoning (CBR), and supportive vector regression (SVR). An

early version of this study was presented in PhD Student Poster Session of the 2014

Construction Research Congress in Atlanta, US. A conference paper exploring the

cost drivers of contingencies is also presented in Section 2.2.

Bo Xiong*, Martin Skitmore, Bo Xia, Sidney Newton. A hybrid approach for

reducing overfitting and collinearity: an application in construction cost

estimation. Submitted to Journal of Civil Engineering and Management.

Paper Abstract


Although many estimation and modelling studies have been conducted, little

research has focused on addressing the overfitting and collinearity problems that

frequently occur in developed predicative models in construction. This study

concerns itself with providing a hybrid approach based on Akaike information

criterion (AIC) and principal component regression (PCR) for those problems. An

application of estimating the preliminaries of construction projects demonstrates the

method and to test its effectiveness in comparison with competing models including

alternative regression models and three data mining techniques of artificial neural

networks, case based reasoning and support vector machines. The experimental

results show that the AIC-PCR approach presents a good predictive accuracy.

Therefore, the hybrid model is a promising alternative for avoidance of overfitting

and collinearity. An abstract should be a brief summary of significant items of the

main paper. An abstract should give concise information about the content of the

core idea of the paper and clearly describe methods and the major findings reported

in the manuscript.

Bo Xiong*, Bo Xia. Examining the impacts of early cost drivers on

contingencies with path analyses. 2014 ASCE Construction Research

Congress, Atlanta, USA, May 19-21, 2014, pp. 1518-1527.

Paper Abstract

The accuracy of early cost estimates is critical to the success of construction

projects. In previous research, the selected tender price (clients' building cost) is seen

as a holistic dependent variable when examining early stage estimates. Unlike other

components of construction cost, the amount of contingencies is decided by

clients/consultants with consideration of early project information. Cost drivers of

contingencies estimates are associated with uncertainty and complexity, and include

project size, schedule, ground condition, construction site access, market conditions,

and so on.

A path analysis of 133 UK school building contracts was conducted to identify

the impacts of nine major cost drivers on the determination of contingencies by

different clients/cost engineers. This research finds that gross floor area (GFA),

schedule, and requirements for air conditioning have statistically significant impacts


on contingency determination. The mediating role of schedule between gross floor

area and contingencies (GFAScheduleContingencies) was confirmed with the

Soble test. The total effects of the three variables on contingencies estimates were

obtained with the consideration of this indirect effect. The squared multiple

correlation (SMC) of contingencies (=0.624) indicates that the identified three

variables can explain 62.4% variance of contingencies, which is comparatively

satisfactory considering the heterogeneity of different estimators, unknown

estimating techniques and different projects.

1.4.2 Chapter 3: Conceptual framework and structural equation modelling

This chapter covers two sections before introductions of detailed studies on job

satisfaction and work stress. The first section reviews previous studies on the job

performance of construction professionals and develops a conceptual framework

based on the person-environment fit theory to reveal the role of psychological

reactions in promoting job performance. An early version of this section was

partially presented at the 6th International Conference on Engineering, Project, and

Production Management (EPPM, 2015) held in Gold Coast, Australia.

Section 3.2 presents a critical review of structural equation modelling (SEM),

since SEM is the main data analysis approach in followed studies in examining

psychological reactions on job performance of construction cost engineers.

Bo Xiong,* Martin Skitmore, Bo Xia. A critical review of structural equation

modelling applications in construction research. Automation in Construction,

(ERA: A, IF=1.822). Published January 2015, 49, 59-70.

Paper Abstract

Structural equation modelling (SEM) is a versatile multivariate statistical

technique, and applications have been increasing since its introduction in the 1980s.

This paper provides a critical review of 84 articles involving the use of SEM to

address construction related problems over the period 1998-2012 including, but not

limited to, seven top construction research journals. After conducting a yearly

publication trend analysis, it was found that SEM applications have been accelerating


over time. However, there are inconsistencies in the various recorded applications

and several recurring problems exist. The important issues that need to be considered

are examined in research design, model development and model evaluation and are

discussed in detail with reference to current applications. A particularly important

issue concerns the construct validity. Relevant topics for efficient research design

also include longitudinal or cross-sectional studies, mediation and moderation effects,

sample size issues and software selection. A guideline framework is provided to help

future researchers in construction SEM applications.

1.4.3 Chapter 4: Work stress

In this chapter, sub-dimensions of work stress are revealed by utilising the

perceived stress questionnaire (PSQ) with cost professionals. These findings benefit

model development and future research.


dimensions of occupational stress: Evidence from construction cost

estimators in China, Construction Management and Economics (ERA: A).

33(5-6), pp. 495-507.

Paper Abstract

A recurring feature of modern practice is occupational stress among project

professionals, which has debilitating effects on the people concerned and indirectly

affects project success. Previous research outside the construction industry has

involved the use of a psychology perceived stress questionnaire (PSQ) to measure

occupational stress, resulting in the identification of one stressor – demand – and

three sub-dimensional emotional reactions in terms of worry, tension and joy. The

PSQ is translated into Chinese with a back translation technique and used in a survey

of young construction cost professionals in China. Principal component analysis and

confirmatory factor analysis are used to test the divisibility of occupational stress,

which is little mentioned in previous research on stress in the construction context. In

addition, structural equation modelling is used to assess nomological validity by

testing the effects of the three dimensions on organisational commitment; the main

finding is that lack of joy is the sole significant effect. The three-dimensional


measurement framework facilitates the standardising measurement of occupational

stress. Further research will establish if the findings are also applicable in other

settings and explore the relations between stress dimensions and other managerial

concepts.

1.4.4 Chapter 5: Job satisfaction

In this chapter, relationships between satisfaction and performance are

explored. The first section explores the nexus at an individual level as represented by

construction cost engineers. The second section examines the performance of other

project participants on two dimensions of contractor satisfaction. The third section

explores the nexus of construction contractors. Three studies demonstrated the fine-

grained model proposed for explaining S-P nexus.

Bo Xiong. Exploring dimensions of job satisfaction and relationships with

performance: Evidence from construction professionals. CIB World Building

Congress 2016 (ERA: A), Tampere, Finland, May 30–June 3, 2016.

Paper Abstract

Theoretical explorations and empirical demonstrations of the nexus between

job satisfaction and job performance have never ceased. Some argue “happier

workers produce more”, while some insist that workers with better performance

achieve satisfaction through bigger chances of rewards. In a review of previous

studies, weak empirical evidence may be attributed to changing definitions of

concepts. This study conducts a fine-grained analysis to propose a new conceptual

model based on the S-P nexus. Firstly, job satisfaction is divided into economic

satisfaction (ES) and production-related/noneconomic satisfaction (PS). This

assumption is validated in this study by principal component analysis of empirical

evidence from a questionnaire survey of construction professionals in China. It is

found that the effects of ES and PS on job performance are different and warrant

further study. The proposed model will be helpful to both academics and

practitioners when investigating the nature of the satisfaction-performance nexus and

making strategic decisions on personnel management.


Bo Xiong,* Martin Skitmore, Bo Xia, Md Asrul Masrom, Kunhui Ye, Adrian

Bridge. Examining the influence of participant performance factors on

contractor satisfaction: A structural equation model. International Journal of

Project Management, 32(3), 482-491, 2014.

Paper Abstract

Participant performance is critical to the success of projects. At the same time,

enhancing the satisfaction of participants not only helps with problem solving but

also improves their motivation and cooperation. However, previous research related

to participant satisfaction is primarily concerned with clients and customers and

relatively little attention has been paid to contractors.

This paper investigates how the performance of project participants affects

contractor project satisfaction in terms of the client's clarity of objectives (OC) and

promptness of payments (PP), designer carefulness (DC), construction risk

management (RM), the effectiveness of their contribution (EW) and mutual respect

and trust (RT). With 125 valid responses from contractors in Malaysia, a contractor

satisfaction model is developed based on structural equation modelling.

The results demonstrate the necessity for dividing abstract satisfaction into

two dimensions, comprising economic-related satisfaction (ES) and production-

related satisfaction (PS), with DC, OC, PP and RM having significant effects on ES,

while DC, OC, EW and RM influence PS. In addition, the model tests the indirect

effects of these performance variables on ES and PS. In particular, OC indirectly

affects ES and PS through mediation of RM and DC respectively. The results also

provide opportunities for improving contractor satisfaction and supplementing the

contractor selection criteria for clients.

Bo Xiong,* Martin Skitmore, Md Asrul Masrom, Bo Xia. A fine-grained

analysis of contractor satisfaction in promoting project management

performance. Submitted to Project Management Journal.

Paper Abstract


Despite the fast growth of project-based companies and industries, studies on

the satisfaction-performance (S-P) nexus of project participants are lacking. This

study aims to explore the role of contractor satisfaction in affecting contractor project

management performance along with considering external effects from other key

participants. Fine-grained hypothesized models are developed by using two broad

dimensions of satisfaction toward noneconomic factors and economic factors.

Structural equation modelling techniques are applied with data collected from 117

projects. Modelling results show that it is insufficient to simply conclude that

contractor satisfaction influences project managerial performance and the vice versa,

and the satisfaction disaggregation is necessity. Additionally, it is found that

noneconomic satisfaction contributes to performance, which in turn contributes to

economic satisfaction. The theoretical and practical implications are further

discussed.

1.4.5 Chapter 6: Conclusions

In this chapter, the objectives of the thesis are reviewed through describing the

contributions of the studies conducted in the process of this research. Implications for

future research are discussed in detail.

Chapter 2: Construction cost estimation techniques 21

Chapter 2: Construction cost estimation

techniques

2.1 A NEW COST ESTIMATION APPROACH

Statement of contribution

The authors listed below have certified that:

1. They meet the criteria for authorship in that they have participated in the

conception, execution, or interpretation, of at least that part of the publication in their

field of expertise;

2. They take public responsibility for their part of the publication, except for

the responsible author who accepts overall responsibility for the publication;

3. There are no other authors of the publication according to these criteria;

4. Potential conflicts of interest have been disclosed to (a) granting bodies, (b)

the editor or publisher of journals or other publications, and (c) the head of the

responsible academic unit, and

5. They agree to the use of the publication in the student’s thesis and its

publication on the Australasian Research Online database consistent with any

limitations set by publisher requirements.

In the case of this chapter:

A new cost estimation approach

Bo Xiong*, Martin Skitmore, Bo Xia, Sidney Newton, A hybrid approach for

reducing overfitting and collinearity: an application in construction cost estimation,

Submitted to Journal of Civil Engineering and Management.

Contributor Statement of contribution

Bo Xiong

Conducted a literature review, designed the research, wrote the

manuscript and acted as the corresponding author.

27/06/2016

Martin Skitmore Directed and guided this study, and proofread the manuscript.

Bo Xia Directed and guided this study.

Sidney Newton Directed and guided this study, and proofread the manuscript.

22 Chapter 2: Construction cost estimation techniques

Principal Supervisor Confirmation

I have sighted email or other correspondence from all Co-authors confirming their

certifying authorship.

Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


2.1.1 Introduction

Estimates such as of scope, cost and schedule are needed for most projects and

many papers have demonstrated the use of estimation methods such as multiple

linear regression (MLR) for this purpose (Cheung & Skitmore, 2006; Li, et al., 2005;

Skitmore, et al., 1990). Two main problems are collinearity and overfitting, as the

existence of either can produce significantly biased results. Overfitting occurs when

too many independent variables are incorporated into the developed (training) model.

An extreme example is where there are as many variables as cases so that, although a

perfect fit is obtained with the sample data, the model has little chance of

representing the population and predicting accurately. Collinearity occurs when the

independence assumption is violated. That is, when the independent variables are

highly correlated and can be largely represented by other variables. These problems

make some researchers seek other methods such as artificial neural networks (ANN),

case-based reasoning (CBR) and support vector machines (SVM) for solutions.

Unlike these black box or indirect approaches, MLR produces the desired parameter

estimates directly and accurately if collinearity and overfitting are dealt with

properly.

The collinearity problem in ordinary least squares (OLS) regression was

recognised several decades ago (Skitmore & Marston, 1999a) and many treatments

have been developed. Although it is possible to improve a model by simply deleting

one or more predictors with a high 𝑅𝑖2 (see Eq. (8)) (O’brien, 2007), keeping or

removing a variable should depend on the theoretical underpinning involved

(Andersen & Bro, 2010). Ridge regression (RR), partial least squares regression

(PLS) and principal component regression (PCR) (see Liu, Kuang, Gong, and Hou

(2003)) are three popular methods developed to deal with collinearity and avoid the

loss of information when deleting variables (Næs & Martens, 1988; Vigneau,

Devaux, Qannari, & Robert, 1997). Although these methods are comparable in

predictive ability, RR and PLS still produce biased estimates of the regression

coefficients of the predictor variables (O’brien, 2007). PCR, on the other hand, is

more consistent with stepwise MLR and collinearity diagnostics. In addition,

although PCR does not necessarily lead to improved predictions relative to OLS,

such improvements do nevertheless occur quite often in practice (Næs & Martens,

1988). The currently recommended method of overcoming collinearity problems is


therefore to use the PCR procedure to correct parameter estimates (Liu, et al., 2003).

This involves the application of the default stepwise regression approach in selecting

predictor variables by simultaneously minimizing the sum of squares error (SSE) and

maximizing adjusted R2 in principal component selection.

To deal with overfitting problems, the principal of parsimony needs to be

considered in variable selection (Andersen & Bro, 2010). Including too many

variables leads to a high variance in parameter estimates and an overfit model with

weak generalizability, while too few variables leads to a lack of necessary

information and decreased model fit (Johnson & Omland, 2004). For overfitting

problems, the Akaike Information Criterion (AIC), an asymptotically unbiased

estimator of the expected relative Kullback-Leibler information quantity (Kullback &

Leibler, 1951), has been recommended for choosing suitable predictor variables

(Akaike, 1974). This statistic represents the amount of information lost in the model

fit when adding predictor variables to help avoid overfitting with a comparatively

small sample size (Posada & Buckley, 2004) and is given by:

AIC = -2ι + 2K (2.1)

with maximized log-likelihood (𝜄) and 𝐾 estimable parameters. Despite the

“superficial” form of the AIC formula, it is well founded in information theory and

with a non-arbitrary “penalty term” 2𝐾 (Burnham & Anderson, 2002).

This paper aims to provide a solution collinearity and overfitting in estimation

exist simultaneously. As pointed out by Xu (1994), using traditional PCR to counter

multicollinearity problems increases the risk of overfitting. Using the SSE and

adjusted R2 criteria can result in some irrelevant variables being input to the OLS

regression model (Xu, 1994). The Akaike information criterion (AIC) is the most

commonly used information theoretic approach to measuring how much information

is lost between a selected model and the true model. It has been used widely as an

effective model selection method in many scientific fields, including ecology

(Johnson & Omland, 2004) and phylogenetics (Sullivan & Joyce, 2005). Compared

with the use of adjusted R2 to evaluate the model solely on fit, AIC also takes model

complexity into account (Johnson & Omland, 2004). In addition, AIC has several

important advantages over the likelihood ratio test (Posada & Buckley, 2004).


Acknowledging the effectiveness of AIC in model selection, this paper

presents a hybrid Akaike information criterion-Principal component regression (AIC-

PCR) approach to deal with the problems of overfitting and collinearity frequently

occurring in OLS regression. Additionally, comparisons with other estimation

methods using an artificial neural network (ANN), case based reasoning (CBR) and

support vector machines (SVM) are conducted within an objectively quantitative

source dataset and a Likert-scaled dataset for validation.

2.1.2 Literature review

Construction cost estimation

The term “building cost modelling” was formally introduced in the Building

Cost Research Conference held in 1982 (Newton, 1991). The accuracy of early stage

construction cost estimates is very important (Lowe et al., 2006; Skitmore et al.,

1990). Understanding the properties of a cost model is therefore vital for the

effective control and development of future techniques (Skitmore and Marston,

1999). Although the accuracy of cost estimating is expected to improve as more

information is released as design evolves (Skitmore, 1987), clients still require

accurate cost advice before design work to assist in assessing the feasibility of

different development proposals. For organizations such as government authorities

and real estate developers, inaccurate early estimates can result in the inefficient use

of money, missed development opportunities and unsuccessful project management

(Oberlender and Trost, 2001). Furthermore, it has become increasingly common for

the final cost of projects to exceed the estimated costs and by an increasing margin

(Williams et al., 2005). For example, Flyvbjerg et al. (2003) analysed 258

transportation infrastructure projects worth US$90 billion and found that 9 out of 10

cost overrun projects are a direct result of inaccurate estimation in the early project

stages. Similarly, Merrow et al. (1979) found that 74% of the cost growth of projects

undertaken by the chemical, oil, and minerals industries in North America is also

caused by underestimation in the early project stages.

Applications of MLR, CBR, ANN and SVM

The regression method has been used as an effective tool in estimation of

project performance for decades. For example, Williams (2003) uses regression


models developed using data from five transportation agencies in the US to predict

the final cost of highway projects. Li, et al. (2005) construct regression estimate

models for office buildings in Hong Kong. To optimize predicative ability within the

sample, the stepwise regression approach can be applied to meet the principle of

parsimony. For example, Masrom et al. (2013) apply forward and backward stepwise

regression to identify key items from 95 possible factors of contraction satisfaction

and Guerrero, Villacampa, and Montoyo (2014) use stepwise regression modelling to

predict the construction time of 168 Spanish building projects. Despite its

applicability in many situations, the regression method are taken for granted as all

variable input approach other than the stepwise one, which makes the method

unfairly weak in comparisons. For example, Son, Kim, and Kim (2012) use the full

variable input MLR rather than stepwise regression when comparing with the SVM

method in the dataset with severe collinearity.

Applications of Artificial neural networks (ANN) and case-based reasoning

(CBR) methods accounted for less than 5% of 56 publications related to construction

cost estimation during 1960-1988 (Newton, 1991), but have developed rapidly since

then with the aid of improved computer techniques (Chou & Tseng, 2011). ANN

simulates the learning process of the human brain by representing variables as input-

output nodes in a weighted network trained on data, and has been used to make

predictions in a variety of fields (Kim, et al., 2004; Kim, et al., 2005). Kim, et al.

(2004) develop an ANN for cost estimation using data from 530 projects in Korea

and show the accuracy to be slightly higher than that provided by regression.

Cheung, Wong, Fung, and Coffey (2006) use ANN to predict project performance

based on information available at the bidding stage from the Hong Kong Housing

Authority. However, ANN is a “black box” method and suffers the potential

drawback of having to retrain the model completely with all data whenever a new

case is added. Additionally, ANN studies have difficulties of generalization because

of overfitting nature (Min and Lee, 2005).

CBR is a method that uses previous experience to solve new cases (Aamodt &

Plaza, 1994; Xu, 1994). It is particularly suited to: (1) obtaining a solution with

partial understanding; (2) providing a reasonably close match to actual human

reasoning; and (3) providing more explanation of its working (Xu, 1994). The

inherent logic of CBR is consistent with Skitmore (1985)’s finding that construction


experts predict by recalling the estimating details of previous projects and then

adjusting these to suit new requirements. The number of publications applying CBR

to construction related problems during the last decade is also increasing (Kim &

Kim, 2010). For example, Kim, et al. (2005) use both ANN and CBR to model the

construction cost of 540 Korean apartment buildings, finding that CBR performs

particularly well. Kim, et al. (2004) examine the estimating capabilities of MLR,

ANN and CBR using data from 530 projects to find that CBR outperforms both

MLR and an average of 75 alternative ANN models.

Support vector machines (SVM) are developed mainly by Vapnik (2000) based

on structural risk minimization, and have been shown to ensure good generalization

(Movahedian Attar et al., 2013). An et al. (2007b) apply SVM to classify the

accuracy of cost estimations for 62 Korean building projects and for regression

purposes. Attar, Khanzadi, Dabirian, and Kalhor (2013) use support vector

regression (SVR) to forecast how far construction costs deviate from client

expectations during contractor prequalification, and find that SVR performs better

than ANN. Son, Kim, et al. (2012) use PCA-SVR, a SVM approach aided by

principal component analysis to reduce dimensions, to predict the construction costs

of 84 building projects. However, they ignore the severe collinearity of the 64

predicting variables used in their dataset and the overfitting that results from using so

many variables/principal components given such a relatively small sample size.

However, there has been little study of how effectively the methods handle data

where the risk of overfitting and collinearity is significant in construction research.

2.1.3 A hybrid approach

The treatment of modelling problems is usually considered in terms of

detection and correction (Farrar & Glauber, 1967). Various diagnoses are possible

and several need to be considered before an appropriate approach can be proposed to

solve collinearity and overfitting.

Collinearity diagnosis

When several variables/predictors in a multivariate regression model are highly

correlated, one variable can be linearly and largely explained by the other variables.

The coefficient estimates of a multiple regression with this problem may change


erratically in response to small changes in the model or the data, rendering the

coefficient estimates unreliable. The variance inflation factor (VIF) is widely used as

a standard way to detect collinearity, with larger VIF values indicating more severe

correlation. In the ideal situation, when the predictors are not correlated, all 𝑅𝑖2 = 0

and the VIF values of all variables have a minimum value of 1. A larger 𝑅𝑖2

(dependency on other predictors) leads to a larger VIF. A VIF larger than 10 is

usually used to indicate significant collinearity (Neter et al., 1989). However, high

VIF values do not necessarily worsen the regression analysis and the influence of

other factors on the variance of regression coefficients should also be considered

(O’brien, 2007).

Two questions are particularly important in collinearity diagnostics: (1) how

many dimensions in the predictor space are nearly collinear; and (2) which predictors

are most strongly implicated in each of those dimensions (Friendly & Kwan, 2009).

To address these questions, Belsley, Kuh, and Welsch (2005) propose a strategy

involving principal component analysis, known as Belsley collinearity diagnostics.

The strategy seeks to identify collinearity by introducing two statistics: a condition

index (CI) and coefficient variance proportion (CVP). CI is defined as CIk =

√λ1/λk where is the Eigenvalue in collinearity diagnostics. Belsley, et al.

(2005) recommend to be cautious with 𝐶𝐼>10. Friendly and Kwan (2009) regard

𝐶𝐼k<5 as “ok”, 5<𝐶𝐼k<10 as “warning” and 𝐶𝐼k> 10 as “danger”. CVP indicates the

proportion of variance of each variable associated with each principal component as

a decomposition of the coefficient variance for each dimension (Belsley, et al., 2005;

Friendly & Kwan, 2009) Caution is needed with two or more 𝐶𝑉𝑃k>0.5.

Overfitting diagnosis

Leave one out cross validation (LOOCV) is a commonly used method in model

selection to detect overfitting and compare predictive ability (Xu, 1994). Compared

with the insufficient data utilization and unreliability of the traditional separate

holdout-set, in-out sample performance, LOOCV does not waste data and has better

reliability in model predictive ability comparisons (Moore, 2001). LOOCV is easy to

understand in that the 𝑖 th model is developed by training the remaining dataset

without the 𝑖th case and then using this model to predict the 𝑖th case, and calculating

the mean error after repeating this exercise N (i.e. sample size) times with

replacement. Although widely applied in model development and selection in many


other scientific areas (such as chemistry) this technique is comparatively new to the

construction management and economics field. For example, Cheung and Skitmore

(2006) used the technique to compare the efficiency of the storey enclosure area

method and four other traditional methods in very early design stage cost forecasting.

Despite having wide application and well known predictive properties,

however, LOOCV is often criticized for being time-consuming and performs

comparatively poorly in selecting linear models when compared with more classical

statistical methods (Rivals & Personnaz, 1999). For this reason LOOCV is only used

here to compare regression models developed by other statistical linear model

development criteria (i.e. SSE, adjusted R2, and AIC).

AIC-PCR procedure and formulas

If a MLR has overfitting problems and the model with lowest AIC still suffers

from collinearity problems, then the AIC-PCR procedure may be useful. AIC-PCR is

described in eight steps, as follows:

Step 1: Proceed with the AIC criterion stepwise regression, with a column

comprising the actual values of the dependent variable Y and a matrix X comprising

all independent variables to obtain a model with the lowest AIC. The logic is to add

the one variable that most helps to reduce the AIC of the model, and then repeat

adding another variable or removing an existing variable. Whichever most helps

reduce AIC is actioned until the lowest AIC with k predictors is achieved. Software

such as MATLAB has an automatic command for this task. Note that, whilst SPSS

can provide the AIC values of a stepwise regression model using Syntax

programming, these models are still selected according to the SSE criterion.

Step 2: Proceed to obtain collinearity diagnostics including VIF, CI and 𝐶𝑉𝑃.

The variance inflation factor (VIF) of the 𝑖 th predictor variable, indicating its

collinearity with other predictors is given by:

Xi = α + β1X1 + ⋯ + βnXn + error (Xi is excluded in the right side) (2.2)

Total sum of squares (TSS) = ∑ (Xi-Xi)2n

1 (2.3)

Explained sum of squares (ESS) = ∑ (Xi-Xi)2n

1 (2.4)

Residual sum of squares (RSS) = ∑ (Xi-Xi)2n

1 (2.5)


𝑇𝑆𝑆𝑖 = 𝐸𝑆𝑆𝑖 + 𝑅𝑆𝑆𝑖 (2.6)

𝑅𝑖2 = 𝐸𝑆𝑆𝑖/𝑇𝑆𝑆𝑖 (2.7)

𝑉𝐼𝐹 =1

1−𝑅𝑖2 = 𝑇𝑆𝑆𝑖/𝑅𝑆𝑆𝑖 (2.8)

where, 𝑋i is the mean value of 𝑋i, and ��i is the estimated value of 𝑋i. TSS is

the sum of the squared differences between each observation and the overall mean;

ESS is the sum of the squared deviations between the estimated values and mean

values of each variable; and RSS is the sum of the squared residuals. CI and CVP can

be determined by applying the collinearity diagnostics command in software such as

MATLAB and SPSS, or calculated using equations 2.2-2.8 as provided.

Step 3: Proceed with the principal component analysis (PCA) with software

such as MATLAB and SPSS to transform the k correlated variables to a set of

uncorrelated principal components, 𝐶i. All components should be extracted at this

stage, and they should account for 100% of the variance.

Step 4: Compute the standardized dependent variable, the 𝑝 standardized

independent variables and the values of the 𝑝 principal components respectively in

preparation for establishing 𝑝 standardized principal component regression

equations:

𝑌′ = (𝑌 − ��)/𝑆𝑌 (2.9)

Xi' =

Xi-Xi

SXi

(i = 1, … . , k) (2.10)

Cj = a1jX1' + a2jX2

' + ⋯ + akjXk' (i = 1, … . , k; j = 1, … . , p) (2.11)

where, 𝑌′ denotes the standardized dependent variable; 𝑌 the dependent

variable; 𝑆Y the standard deviation of the dependent variable; �� the mean of the

dependent variable; 𝑋i′ the 𝑖 th standardized independent variable; 𝑋i the 𝑖 th

independent variable; 𝑋i the mean of the 𝑖th independent variable; 𝑆Xithe standard

deviation of the 𝑖th independent variable, 𝐶j the 𝑗th principal component and 𝑎ij the

coefficient of the principal component matrix (the matrix consists of 𝐶j and 𝑋i).


Step 5: Proceed with the AIC criterion stepwise regression of principal

components to estimate 𝑌′ in MATLAB and if not all principal components are

significant, select the lowest AIC regression equation, as in:

y' = ∑ Bj' Cj (j = 1, … , q ≤ p) (2.12)

Step 6: Transform Eq. (12) with Eq. (11) to obtain:

y' = ∑ bi' Xi

' (i = 1, … k) (2.13)

where ��′ is the standardized estimate of the linear regression equation and 𝑏i′

the 𝑖 th standardized regression coefficient of the standardized linear regression

equation (Liu, et al., 2003).

Step 7: Calculate the regression coefficients and constant, and transform the

standardized linear regression equation into a general linear regression equation

𝑏𝑖 = 𝑏𝑖′(

𝐿𝑦𝑦

𝐿𝑥𝑖𝑥𝑖

)1/2 (2.14)

b0 = Y- ∑ bi Xi (i = 1, … , k) (2.15)

y = b0 + ∑ bi Xi (i = 1, … , k) (2.16)

where, 𝑏i is the regression coefficient of 𝑖th variable; 𝐿yy the sum of squares of

dependent variable Y; 𝐿xixi the sum of squares of the ith independent variable 𝑋i;and

𝑏0 is the constant of the new linear model.

Step 8: Calculate the mean squared error (MSE) of the final model from

MSE =1

n∑ (Yi-Yi)

2n1 (2.17)

2.1.4 Application in construction cost estimation

Preliminaries

According to the standard elemental cost categorization used by the Building

Cost Information Service (BCIS), construction costs comprise eight components:

substructure, superstructure, internal finishes, fittings, services, external works,

preliminaries, and contingencies. The quantities of the items involved in most of

these cost components are relatively straight-forward to determine given the level of


information typically available at an early project stage, and the value of

contingencies is decided by the clients/consultants prior to tendering. However, the

preliminaries component contains an allocation of budget to general overheads, site

overheads, risk contingency and profit, and this is often a key competitive

component not determined until the tendering stage. The actual allocation is

influenced by a complex combination of past and recent experience on the part of the

contractor, the current workload of the contractor, market conditions, and project

characteristics often determined by the contractor, such as contract duration

(Akintoye, 2000; Tah et al., 1994). The cost of subcontractor rework is another

expenditure component generally included as a component of the preliminaries

(Love and Li, 2000). The mark up strategy for preliminaries is also different to that

of other cost components and can be used to achieve an unbalanced tender that

significantly improves the cash flow of a contractor (Kaka, 1996).

Sample projects

The sample cases comprise 204 UK school building projects completed during

2000-2012, and selected at random from a large commercial cost database. Project

differences due to geographical location, construction year and rate of inflation are

addressed by rebasing all prices to the same date (Fourth quarter, 2012) and location

(Greater London district), using the BCIS Construction Price Index. Some important

characteristics of the sample cases are presented in Tables 2.1 and 2.2. The left hand

column provides the categories, or cost drivers. These are blank in places/for some

projects due to a lack of complete information on such features as building height

and contract type.

Table 2.1 Sample descriptions - part 1

Category Type Frequency Percentage

Building

function

Primary schools 86 42.16%

Secondary schools 64 31.37%

Nursery schools 29 14.22%

Special schools 13 6.37%

Sixth form/tertiary colleges 12 5.88%

Structure

Brick construction 70 34.31%

Steel framed 115 56.37%

Timber framed 15 7.35%

Concrete framed 3 1.47%

Unspecified 1 0.49%

Selection of Selected competition 164 80.39%


contractor Open competition 10 4.90%

Design and build - competitive 12 5.88%

Two stage tendering 12 5.88%

Unspecified 6 2.94%

Table 2.2 Sample descriptions - part 2

Category Description

Gross floor area (m2) Range from 64 to 19670; M=1471.64; SD=2209.57

Stories Range from 1 to 4; M=1.41; SD=0.60

Schedule (months) Unspecified: 87

Remaining: range from 5 to 32; M=10.83; SD=4.27

Ground condition Unspecified: 23

Bad(1)-Moderate(3)-Good(5); M=3.74; SD=1.42

Work space Unspecified: 21

Highly restricted(=1)-Restricted(=3)-Unrestricted(=5); M=3.92; SD=1.24

Site access Unspecified: 17

Highly restricted(1)-Restricted(3)-Unrestricted(5); M=3.71; SD=1.20

Market condition

Unspecified: 49

Low competitive(1)-Less competitive(2)-Average(3)-Competitive(4)-Highly

competitive(5); M=3.99; SD=0.79

Air Conditioning Yes=1; No=0; 26 cases are 1; 178 cases are 0

Preliminaries (£) Range from 0 (1 case) to 3,391,713; M=318,448; SD=443,345

Preliminaries/GFA Range from 0 (1 case) to 702; M=251; SD=116

Elemental cost items framework and sample descriptions

The first edition of an Elemental Standard Form of Cost Analysis was released

in 1961 by the Royal Institution of Chartered Surveyors (RICS). The 2012 edition of

this standard is used as the reference to construct the analysis framework. Elemental

cost items in the first column of Table 2.3 refer to variables used for model

development in the MLR process, and those in the second code column refer to the

selected variables in the PCR process.

Table 2.3 Elemental cost items framework

Elemental cost items Code Elemental cost items Code

1 Substructure x1 5F Space heating and air treatment x19

2A Frame x2 5G Ventilating systems x20

2B Upper floors x3 5H Electrical installations x21

2C Roof x4 5I Gas installations x22

2D Stairs x5 5J Lift and conveyor installations x23

2E External walls x6 5K Protective installations x24

2F Windows and external

doors

x7 5L Communications installations x25


2G Internal walls and

partitions

x8 5M Special installations x26

2H Internal doors x9 5N Builder's work in connection x27

2 Superstructure 5O Builder's profit and attendance x28

3A Wall finishes x10 5 Services

3B Floor finishes x11 6A Site works x29

3C Ceiling finishes x12 6B Drainage x30

3 Internal finishes 6C External services x31

4 Fittings x13 6D Minor building works x32

5A Sanitary appliances x14 6 External works

5B Services equipment x15 7 Contingencies x33

5C Disposal installations x16 8 Preliminaries y

5D Water installations x17

5E Heat source x18

Experimental results

In the model development phase four models are developed by stepwise

regression modelling under the criteria SSE (model 1), adjusted R square (model 2),

AIC (model 3) and by directly entering all predictor variables (model 4), as presented

in Table 2.4. The diagnoses for overfitting and collinearity follow.

In this case, using LOOCV to detect overfitting involves developing

204x4=816 sub-models to evaluate their predictive ability. The MSELOOCV values

for each model are presented in Table 2.4, in parentheses. Although all four models

are comparable in MSE values, the model overfitting varies greatly. For example,

Model 2 has the lowest MSE and highest adjusted R2 but its predictive ability is

weaker. Model 3, developed under the AIC, has the lowest MSELOOCV. As the

collinearity diagnoses for the four models indicate, collinearity is a common problem

- with more than one VIF larger than 10, CI larger than 10 and CVP larger than 0.5.

That is to say, even the overfitting-reduced Model 3 suffers collinearity. Although

both MATLAB and SPSS can help handle such diagnoses, MATLAB programming

is selected for its ability to visualize the collinearity diagnostics (Friendly and Kwan,

2009). Figure 2.1 provides a visual illustration of the diagnostics for Model 3. For

clarity, only the principal components with CI values larger than five are shown.

Table 2.4 Developed regression models Models Equations MSE (1010)


1

(SSE) �� = 25220.000 + 0.396x1 − 0.373x6 − 0.915x8 − 1.381x10 + 2.391x12

+ 1.069x14 + 2.621x17 + 0.604x19 + 0.760x21

− 4.892x22 + 0.690x24 + 1.497x25 − 1.586x27

− 1.299x31 + 0.135x32

1.410

(2.160)

2 �� = 27296.000 + 0.322x1 + 0.276x2 + 0.103x4 − 0.390x6 − 0.920x8

− 0.602x9 − 1.558x10 + 0.292x11 + 2.413x12

+ 0.837x14 − 0.511x15 + 3.050x17 + 0.528x19

+ 0.725x21 − 7.454x22 + 0.382x24 + 1.336x25

− 1.692x27 − 3.903x28 + 0.082x29 − 1.354x31

+ 0.118x32 + 0.186x33

1.355

(2.643)

3 �� = 22722.000 + 0.418x1 − 0.385x6 − 0.885x8 − 1.364x10 + 2.407x12

+ 0.937x14 + 2.748x17 + 0.576x19 + 0.738x21

− 5.893x22 + 0.568x24 + 1.453x25 − 1.665x27

− 1.396x31 + 0.136x32 + 0.200x33

1.391

(2.118)

4 �� = 36079.000 + 0.239x1 + 0.313x2 + 0.397x3 + 0.132x4 − 0.148x5

− 0.402x6 − 0.069x7 − 0.943x8 − 0.587x9 − 1.468x10

+ 0.245x11 + 2.210x12 − 0.051x13 + 1.151x14

− 0.613x15 + 0.088x16 + 2.908x17 − 0.440x18

+ 0.539x19 + 0.205x20 + 0.743x21 − 7.191x22

+ 0.665x23 + 0.375x24 + 1.239x25 + 0.186x26

− 1.129x27 − 4.189x28 + 0.081x29 + 0.015x30

− 1.400x31 + 0.119x32 + 0.159x33

1.414

(4.090)

PCR �� = 28152.762 + 0.366x1 − 0.390x6 − 0.804x8 − 1.531x10 + 2.439x12

+ 1.320x14 + 2.463x17 + 0.607x18 + 0.753x21

− 4.395x22 + 0.444x24 + 1.612x25 − 1.764x27

− 1.241x31 + 0.110x32

1.324

AIC-

PCR �� = 26801.134 + 0.430 x1 − 0.387 x6 − 0.857 x8 − 1.314 x10 + 2.426 x12

+ 1.010 x14 + 2.643 x17 + 0.587 x19 + 0.718 x21

− 6.337 x22 + 0.473 x24 + 1.700 x25 − 1.881 X27

− 1.419 x31 + 0.102 x32 + 0.132 x33

1.301

Figure 2.1 Collinearity diagnostics for Model 3

According to the diagnoses of overfitting and collinearity, this dataset is

suitable for testing the AIC-PCR approach as it displays both features. Following the

AIC-PCR approach, Model 3 with the lowest overfitting and best predictability is

obtained by applying the PCR under AIC. Table 2.4 gives the MSE results of four

models for comparisons with Model 1 representing: a stepwise regression under SSE;

PCR, representing the traditional PCR approach developed under SSE; Model 3


representing stepwise regression under AIC; and AIC-PCR representing the proposed

approach. It is found that the AIC-PCR approach not only avoids overfitting (by

applying the AIC criterion) and collinearity (by applying PCR) but also improves

predictability (with 7.73% less MSE than the default stepwise regression model) and

is more accurate (with 1.74% less MSE than the traditional PCR approach using the

SSE criteria).

Comparisons with other methods

The AIC-PCR approach is compared with other data mining techniques of

ANN, PCA-SVR and K-Nearest Neighbour (KNN) as a basic type of CBR, to see

how well it performs. Two absolute evaluation criteria are the root mean squared

error (RMSE) and mean absolute error (MAE). A scaled criterion of mean absolute

percentage error (MAPE) is also used, where:

RMSE = √1

n∑ (Yi-Yi)

2n1 (2.18)

MAE =1

n∑ | Yi-Yi

n1 | (2.19)

MAPE =1

n∑ |

Yi-Y

Yi|n

1 (2.20)

Table 2.5 Comparison of results for Application 1, price estimating

Models RMSE MAE MAPE

AIC-PCR 114061.725 74954.314 0.419

PCR 115064.347 76398.030 0.425

ANN 194183.576 89780.485 0.623

KNN 260417.245 128660.219 0.419

PCA-SVR 211449.878 103757.670 0.621

The comparison of results presented in Table 2.5 confirms the effectiveness of

AIC-PCR, its error rate being the same or lower than the other four methods

whichever criteria are used.

2.1.5 Conclusions

The main aim of this study was to build an alternative approach to deal with

the ubiquitous overfitting and collinearity problems that occur in construction

research. A hybrid AIC-PCR method is developed and tested using construction cost


data on 204 construction projects. The study has found that the hybrid approach not

only reduces the risk of overfitting and collinearity, but also results in better

predictability compared with the commonly used stepwise regression models and

traditional PCR approach under the SSE criterion. The study also validates its

applicability by comparison with other conventional methods including ANN, CBR

and SVM. The approach is a promising alternative to be recommended for equivalent

situations where overfitting and collinearity can be problematic especially when the

linear form is approximate to describe relationships between independent variables

and dependent variables..

Some limitations should be acknowledged. Firstly, the sample tested in the

study is from construction projects. Additionally, there is not yet a standard cut-off

study on determining a certain linear level for applying the proposed approach or

other approaches like SVM. These two limitations are sufficient to prevent this

approach from global generalization. However, the new model offers fertile ground

for further research and practice. Whilst the experimental application presented

should be taken cautiously in wider generalization, it does demonstrate the capability

of the hybrid approach in avoiding overfitting and collinearity problems and gaining

accurate estimates. Future studies could benefit from testing it applicability in other

contexts and improving the proposed procedure.


2.2 IMPACTS OF EARLY COST DRIVERS





field of expertise;











Impacts of early cost drivers

Bo Xiong*, Bo Xia, Examining the impacts of early cost drivers on contingencies

with path analyses, 2014 ASCE Construction Research Congress, Atlanta, USA,

May 19-21, 2014, pp. 1518-1527.


Bo Xiong


manuscript and acted as the oral presenter.

07/03/2016

Bo Xia Assisted with manuscript revision.





Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


2.2.1 Introduction

An accurate cost estimate cannot be achieved without a clear scope by the

client, a completed design by the designer and a sound effort estimate by the cost

engineer. However, time, money and professional skills limit the performance of all

the stakeholders - a reality that motivates the setting of contract contingencies, which

aim to provide sufficient reserve money to cover the cost of mistakes and risks in

future. Other than flexibility in specification, float schedule, and some other

arrangements, the contingencies discussed in this research specifically refer to the

money put aside by the client for “known” “unknown” risks and “unknown”

“unknown” risks.

The determination of suitable contingencies is challenging to the client or

consultant. If the amount of contingencies is set too high, the unused money will be

wasted. If this amount is set too low, it will be insufficient for compensating

potential risks and may even hinder construction progress. The ideal amount of

contingencies should be close to the amount of project cost overrun. To obtain

accurate estimates of typical contingency values, a number of estimating techniques

such as the floor area method, percentage method, regression, artificial neural

networks, and Monte Carlo simulation are available (Mak and Picken 2000; Idrus et

al. 2011). Many early cost drivers can be considered in these methods to generate

representative values. However, the inconsistency of these “input” variables

undermines the possibility of understanding their impact.

This research aims to identify the early cost drivers of contingency values and

explore their impact via path analysis modelling. 133 UK school building contracts

with contingency values are used as empirical cases. Gross floor area (GFA),

proposed schedule and presence of air conditioning (AC) are used as independent

variables. The Soble test shows the proposed schedule have a mediating effect. The

total effects of GFA, schedule and AC are then calculated. This squared multiple

correlation (SMC) is 0.624, indicating that the identified three variables explain

62.4% variance of contingencies - a comparatively satisfactory result considering the

unknown estimating techniques and the different projects involved - and suggests

that contingency setters to be quite homogeneous.


2.2.2 Early cost drivers

Estimating in early pre-design stage is often inaccurate due to the limited and

vague information available. It is estimated that inaccuracy at this time is around -

40% to +20% (Barnes 1974; Skitmore 1987). Flyvbjerg et al (2003) analysed data

from 258 transportation infrastructure projects worth US$90 billion and found that

cost overrun in nine out of ten projects were caused by inaccurate estimation in the

early stage. Similarly, it is reported that inaccuracy of such estimates is around 30%

in Germany, and it is mainly caused by simply multiplying floor area with an certain

ratio, neglecting other cost drivers (Stoy and Schalcher 2007). A quick and accurate

early estimate is important to the decision-making of clients. To achieve this, a

review of previous studies on relevant building cost drivers at early stage is

necessary. The impacts of these drivers on building cost components should be also

examined, which motivates this research. Contingencies are selected as the

dependent variable in this research for its sensitivity to early project information.

Skitmore (1987) proposed that building prices should be seen as a result of a

series of interdependent causal mechanisms and identified building type, size,

complexity and quality, type of client, contractor selection, contractual arrangements,

location, and economic, legal environment of project location as primary cost

drivers. By analysing empirical cases from Singapore, Gunner and Skitmore (1999)

found three variables, i.e. floor area, number of stories above ground and contract

period, to have comparatively high correlations with contract sums. Li et al. (2005)

constructed regression models to estimate the building cost for office buildings in

Hong Kong using seven variables in modelling, i.e. average floor area, total floor

area, average storey height, number of above-ground stories, total building height,

number of basements and completion year. Total floor area, total building height, and

average floor area were found to be the most important ones. For many cases where

it is hard to know building heights at the early stage, Stoy and Schalcher (2007)

recommended the situation with or without air conditioning systems is an indicator

(Stoy and Schalcher 2007). Client type (public or private) also significantly affects

bid decisions of contractors according to a wide questionnaire survey conducted in

China (Ye et al.).

According to the findings from literature review and information accessible at

early project stage, nine early cost drivers are used to establish the initial model as


presented in Figure 2.2. Differences due to location, contract period and inflation rate

are compensated by rebasing all 133 cases with corresponding cost indices to make

them comparable.

Figure 2.2 The initial model

2.2.3 Research method

This section describes the advantages, application and measurement indices of

path analysis modelling. Then descriptions of selected cost drivers and sample data

are presented.

Path analysis

Path analysis (PA) is the original structural equation modelling (SEM)

technique, which is widely used to explore and test causal relationships in social

science, such as in psychology, education and health (Kline 2010). SEM normally

describes the relationships between two kinds of variables, i.e. latent and observed.

Latent variables cannot be observed directly due to their abstract character. In

contrast, observed variables contain objective facts or use an item rating scale in a

questionnaire. Several observed variables can reflect one latent variable. Compared

with other multivariate analysis methods, SEM has the ability to estimate multiple


and interrelated relationships and define a model to explain these relationships well

(Kline 2010; Xiong et al. 2013).

PA is a comparatively simple SEM form for analysing structural models with

observed variables only. Due to resource limitations and situation constraints, it is

not always possible for several variables to reflect one and this is why PA is still

widely used (Kline 2010). For example, path analysis accounts for 25% of around

500 applications of SEM published in 16 psychology journals from 1993-1997

(MacCallum and Austin 2000). There are also increasing uses of path analysis to

explore construction-related issues. Brown et al (2007) construct a path analysis

model to explore the relationship between human capital and time performance in

project management and found that performance improves with increase investment

in human capital (Brown et al. 2007). Zhang and Fang (2013) apply path analysis to

explore the cognitive reasons of Chinese scaffolders’ unwillingness to use harnesses

in work and built a path analysis model to explain data collected from questionnaire.

The proposed cost drivers in this paper can be observed directly or transformed in

understandable ways. Therefore, path analysis is suitable to explore the impact of

cost drivers on contingency values. The software AMOS is used to do the modelling.

An ideal model should be theoretically sensible and fit the sample data well.

Measuring goodness of fit is an essential task preceding path analyses. Many criteria

have been generated for this purpose. The overall measurement is the probability

level of the Chi-square test, if the p value is 0.05 or less, the departure of the data

from the proposed model is significant at the 0.05 level, i.e. the proposed model is

not significantly consistent with the observed data. However, the Chi-square test has

some severe flaws, such as sensitivity to violations of the assumption of multivariate

normality, model complexity and sample size (Finney and DiStefano 2006; Lei and

Wu 2007). Other fit indices thus have been developed to judge models from other

three perspectives. These include absolute fit, incremental fit (comparative fit) and

parsimonious fit (Xiong et al. 2013). The commonly used indices are presented in

Table 6.10. It is worth mentioning that there are no commonly agreed thresholds for

the listed parsimony indices, which are used mainly to choose the most parsimonious

of several acceptable, but similar, models. For this research, they are used to choose

between the next to last model (Figure 6.2) and the final model (Figure 6.3).


Variables and Data

Ten variables (see Table 2.6) are used in the analysis. Due to the lack of

information at in the early design stage, Schedules are those stipulated in invitations-

to-bid by clients. These may be different from schedules proposed by contractors

and agreed schedules in contracts. Ground condition, work space and site access

reflect the quality of site conditions. Market condition describes the demand and

supply condition or market competition of the local construction industry and at a

certain time. Public/Private refers to the client type. To be comparable, the

contingencies of 133 cases are all rebased to a common level by applying cost

indices. All the variables except air conditioning and client type (public/private) are

zero-mean normalized (Z score) to be comparable in path analysis (Brown et al.

2007). For the sample size, there is a rule of thumb to measure that the ratio of

sample size to number of items tested should be more than 5, and the higher the

better in the range 5-20 (Kline 2010; Lei and Wu 2007). Therefore the sample size is

satisfactory. The contractor selection method of selected cases is limited to the open

competition/selected competition other than negotiation, since contract selection was

identified as a factor affecting construction time and cost (Skitmore and Ng 2003).

Table 2.6 Description of variables

Gross floor area (m2) Range from 105-13835; mean=1522.29; SD=1980.83

Schedule (months) Range from 4-32; mean=11.00; SD=4.40

Stories Range from 1-4; mean=1.47; SD=0.61

Ground condition

Bad (=1), moderate (=3), good (=5); Mean=3.59;

SD=1.37

Work space

Highly restricted (=1), restricted (=3), unrestricted (=5);

mean=3.84; SD=1.26

Site access

Highly restricted (=1), restricted(=3), unrestricted(=5);

mean=3.67; SD=1.27

Market condition

Low competitive (=1), less competitive (=2), average

(=3), competitive (=4), highly competitive (=5);

mean=4.14; SD=0.68

Air Conditioning Yes=1; No=0; 115 cases are 0; 19 cases are 1

Public/Private Public=1; Private=0; 48 cases are 0; 85 cases are 1

Contingencies (£)

Range from 5605-530415; mean=91136.69;

SD=98833.71


2.2.4 Path analysis modelling

An initial path model is developed as shown in Figure 6.1 and the model fit is

presented in Table 2.7. This model performs badly as many insignificant variables

are incorporated and some necessary relationships are ignored. After a series of

corrections, the next to last model is achieved (see Figure 2.4). In this model the

coefficient ( -0.015) of air conditioning (AC) schedule is not significant (at the

0.05 level). This relationship is thus deleted to obtain a more parsimonious model

and improvements are reflected in parsimony indices between the next to last model

(Figure 2.3) and the final model (Figure 26.4) as presented in Table 2.7. The

coefficients shown in Figure 2.2 and Figure 2.3 are the standardized estimates. **

means the p value of a highlighted coefficient is smaller than 0.01; *** means the p

value of a highlighted coefficient is smaller than 0.001. In the final model, the

squared multiple correlations (SMC) of contingencies (0.624) indicates the identified

three variables and relationships can explain 62.4% variance of contingencies, which

is regarded as satisfactory considering that all those settings of contingencies are

generated by different sources with unknown estimate techniques in different project

situations.

Figure 2.3 The next to last path analysis model


Figure 2.4 Final path analysis model

Goodness of fit

In Table 2.7, both of the last two models show acceptable goodness of fit

according to the Chi-square test, absolute fit indices and incremental fit indices. The

parsimonious fit indices are the used for the final selection. Although all the three

indices used for testing parsimony do not have commonly agreed cutoffs, higher

PNFI, PGFI and smaller CAIC reflect better parsimony.

Table 2.7 Model fit indices

Goodness of fit

measure Criteria Initial model

The next to

last model

Final

model

Chi-square test

Probability level >0.05 0.000 0.628 0.862

Absolute fit

GFI >0.9 0.758 0.999 0.999

RMSEA <0.08 0.211 0.000 0.000

SRMR <0.05 0.185 0.019 0.023

Incremental fit

CFI >0.9 0.369 1.000 1.000

TLI >0.9 0.211 1.022 1.024

NFI >0.9 0.349 0.999 0.999

Parsimonious fit

PNFI Higher 0.279 0.166 0.333

PGFI Higher 0.496 0.100 0.200

CAIC Smaller 360.969 53.248 47.421


Direct effects, indirect effects and total effects

As seen from Figure 2.3 and Figure 2.4, the GFA has an indirect impact on

contingencies through schedule (GFAScheduleContingencies). In order to test

whether the mediating role of schedule is significant, the Sobel test is used to verify

the significance of mediation effects (Sobel 1982; Xiong et al. 2013).

The Sobel test statistic is 3.172 (p=0.002), which means the mediation effect of

GFAScheduleContingencies is significant at 0.05 level. The standardized direct,

indirect and total effects are shown in Table 2.8.

Table 2.8 Standardized direct, indirect and total effects of variables

Effects on Contingencies Gross floor area Schedule Air condition

Direct effects 0.579 0.250 0.150

Indirect effects 0.000 0.177 0.000

Total effects 0.579 0.427 0.150

2.2.5 Findings and discussions

The main research finding of the path analysis is that gross floor area,

schedule, air conditioning are the three most influential variables that can be used to

predict contingency values, and their total effects are presented in Table 2.8.

Gross floor area (GFA)

GFA is the most influential cost driver on determination of contingencies with

the total effect of 0.756. It is interesting to see that GFA has both a direct effect

(=0.579) and an indirect effect (=0.177) via schedule on contingencies. GFA also has

a major impact (=0.705) on schedule determination. The powerful role of GFA is

consistent with previous research findings and the wide use of GFA method in cost

estimation (Gunner and Skitmore 1999; Skitmore 1987; Stoy and Schalcher 2007).

The defect of using GFA ratio method can also be identified in this research.

The SMC of schedule is 0.498, which indicates GFA can explain 49.8% variance of

schedule. The unexplained variance part of schedule makes it inaccurate to use GFA

solely to predict schedule. Additionally, the unexplained variance of contingencies

and the existence of air conditioning inevitably lead to inaccurate prediction when

using GFA as the solo predictor to estimate contingencies.


Schedule

Schedule has a direct effect (=0.250) on contingencies. The schedule also acts

as a mediator between GFA and contingencies as confirmed by the Sobel test. Cost

estimation and schedule estimation are sometimes highly correlated and may be

determined by similar factors (Skitmore and Ng 2003). For example, the GFA is a

common factor for both schedule and contingencies. It needs to be mentioned that

the schedule used in this research refers to the proposed schedule by the client at pre-

tender stage.

Air conditioning (AC)

The effect of air conditioning has been little explored in previous research.

Installing air treatment systems requires high ceiling superstructures with

comparatively high median floor height (Stoy and Schalcher 2007). By investigating

290 properties, Stoy and Schalcher (2007) found that the average height of projects

with AC was 0.11m higher than that of projects without AC. Considering the general

mild climate in UK, installing AC possibly also indicates higher requirement for

quality and higher degree of risk. The use of air conditioning in mild climates is an

interesting socioeconomic issue in its own right.

2.2.6 Conclusion

Of the variables path analysed, the three most influential in their effects on

contingency values of 133 UK school building contracts are GFA, Schedule and AC,

with Schedule acting in a mediating role. In explaining 62.4% of the variance, it is

demonstrated that consultants involved are quite homogeneous in their contingency

valuations.

Some limitations of this analysis should be mentioned. First, the sample cases

are school buildings in UK, thus it is not possible at this stage to generalize the

conclusions to other types of buildings or other countries. Another limitation is that,

although the analysis provides some insights into the considerations taken into

account in setting contingency values, the lack of data of project cost overruns

disallows any examination of the accuracy of the contingency values involved. That

is, we have gained some impression of how contingency values of obtained but,

although the indications are that this quite common among contingency setters, this


does not shed any light on the extent to which the values are appropriate (cover the

unexpected additional building costs involved). Of course, it can be argued with

some justification that assigning higher values to contingencies may well attract

higher extra costs due to contractors knowing the amount of money been set aside

and therefore being more vociferous in claiming the extra costs, etc.

Future research would benefit from examining more closely the effects of the

personal characteristics of the contingency setters and incorporate information on

cost overruns for measuring the appropriateness of the contingency values made.

This may reveal some statistical patterns that provide insight into the underlying

phenomena at work.

Chapter 3: Conceptual framework and structural equation modelling 51

Chapter 3: Conceptual framework and

structural equation modelling

3.1 TOWARDS A CONCEPTUAL FRAMEWORK OF JOB

PERFORMANCE

3.1.1 Introduction

With individual expertise critical to the success of projects and the company,

the performance of construction professionals is a key concern for both academics

and practitioners alike (Ahadzie, Proverbs, & Olomolaiye, 2008a; Leung,

Olomolaiye, Chong, & Lam, 2005), since construction projects have become

increasingly complex in recent decades (Xia & Chan, 2012). Factors such as working

environment, individual personality, job knowledge, working experience and

psychological reactions have been identified as predictors of job performance in a

few explorative studies (Leung, Liu, & Wong, 2006; Pheng & Chuan, 2006).

However, a conceptual model to reveal the mechanism of job performance is needed

to link these developed concepts with theoretical foundations.

In the area of human resource management and organisational psychology, the

person-environment (P-E) fit is a concept widely used (Schneider, 2001). In

personnel selection, managers favour candidates who share similar values to those of

the company and have the specific skills needed to fit in well (Greguras &

Diefendorff, 2009). For current employees, a P-E misfit may result in increased staff

turnover (Westerman & Cyr, 2004). Although a few studies (such as Xiong,

Skitmore, and Xia, 2015b) point out that employee behaviour is affected by

psychological reactions encountered in specific situations, P-E fit theory has been

little used in research into the job performance of construction professionals. This

could be attributed to the criticism that directly measuring the discrepancy between

the commensurate constructs of P and E is not easy and faces several conceptual

barriers (Kristof, 1996; Schneider, 2001). After a thorough review of studies on P-E

fit (Caplan, 1987; Chuang, Hsu, Wang, & Judge, 2015), this paper applies indirect

measures of P-E fit comprising job satisfaction, work performance and organisational

commitment as extrinsic assessments.

52 Chapter 3: Conceptual framework and structural equation modelling

Being associated with the stimulus-organism-response (S-O-R) paradigm, the

two psychological reactions in terms of P-E assessments are used to connect

environmental stimulus and individual characteristics with job performance. The

model augments previous conceptual frameworks that link environmental factors or

psychological reactions with job performance without mediators. The mediation role

of P-E fit assessments identified in this model contributes to knowledge of the job

outcomes of construction professionals. Therefore, the proposed framework aims to

reveal a bigger picture for job performance and its antecedents, thereby further

increasing the effectiveness of human resource management practices. A further

objective is to propose a research agenda based on the proposed model for the benefit

of future studies.

3.1.2 Development of the conceptual framework

Many theories, such as human resource management, personality, competency,

motivation, self-determination, work adjustment and P-E fit, have been used to

explain the job performance of employees (Greguras & Diefendorff, 2009;

Schneider, 2001). This section intends to develop a framework for examining

organisational behaviour and human resource management by adjusting assessments

of person-environment fit within the stimulus-organism-response (S-O-R) paradigm

of human behaviour.

Person-environment fit theory

Behaviour was early considered as a function of person and environment by

Lewin (1935). P-E fit has been the dominant theory used to address various issues in

psychology, such as personnel selection, vocational psychology and social

psychology (Schneider, 2001). With the increasing awareness of psychological

illness caused by work stress in the 1980s, P-E fit theory was used by many

researchers (Caplan 1987); Edwards, 1996) to derive findings in related studies

(Edwards, Caplan, & Van Harrison, 1998; Xiong, et al., 2015b). Fit is also

emphasised in personnel selection, in that the knowledge, skills, ability and

personality of an individual should match the criteria of a specific job. As Bretz and

Judge (1994) point out, P-E fit is a direct predictor of career success, and recruiting

individuals with a better fit leads to a more satisfied work force. In addition to the


effect on willingness to join organisations, perceptions of P-E fit are found to be

critical to turnover intentions through the mediation of attitudes such as job

satisfaction and organisational commitment (Westerman & Cyr, 2004). Work

performance and organisational outcomes are two long-term outcomes attributed to

P-E fit (Kristof, 1996)

Several distinctions are proposed to explore the multi-dimensional P-E fit

concept. Muchinsky and Monahan (1987), for example, propose the notion of

supplementary and complementary fit. Supplementary fit means that an individual

fits the environment by sharing similar characteristics with those that exist in the

environment. Based on the psychological paradigm of similarity-attraction, it is

common for a person to assess the similarity of his/her values and attitudes with the

organizational climate and values of a company (Kristof, 1996). Some sub-themes of

P-E fit, such as person-person fit and person-group fit, focus on supplementary fit

(Chuang et al., 2015). Complementary fit, in contrast, is developed based on the

psychological paradigm of needs-fulfilment, which occurs when a person can

provide something needed by the environment (e.g. organisation) or vice versa

(Kristof, 1996; Muchinsky & Monahan, 1987). “Current themes of PE fit that follow

the concept of complementary fit include demands-abilities (D-A) fit and needs-

supplies (N-S) fit” (Chuang et al., 2015, p. 482). As indicated in Kristof’s (1996)

literature review, empirical studies of work performance are mostly linked to

complementary fit. Therefore, the complementary fit is used to represent the P-E fit

in the development of the conceptual framework.

The measurement constructs for P-E fit have been a key concern, since

findings vary for different methods (Spokane, 1987). In the theory of work

adjustment, it is argued that independent commensurate measures of people and

environment are desirable (Spokane, 1987). Difference scores, such as the algebraic

form (X-Y), absolute form (|X-Y|) and squared differences (X-Y)2 are used as direct

measures in many studies, with the assumption that a lower discrepancy between P

and D results in better outcomes (Kristof, 1996; Rounds, Dawis, & Lofquist, 1987).

However, direct measures face several criticisms. One is that the independent effects

of P and E are hard to explore when two constructs are confounded (Kristof, 1996).

Direct measures are believed to be a violation of the Lewinian conceptualisation of

“constellation”, in the definition of behaviour as a constellation of person and


environment, and also to be inappropriate for anthropomorphising environments

(Schneider, 2001). Therefore, alternative measures of assessing the person and

environment simultaneously across multiple dimensions of P-E fit are necessary. As

pointed out by Kristof (1996), the perception of P-E fit in organisational situations

may have a stronger influence on variables such as stress, satisfaction and

commitment, than does fit itself. Since P-E fit is the implicit key to understanding

human behaviour (Schneider, 2001), it is important to identify extrinsic measures of

P-E fit.

Psychological reactions including job satisfaction and work stress are used as

measurable assessments of two P-E fit types —needs-supplies (N-S) fit, demands-

abilities (D-A) fit— as presented in Figure 3.1 (Giauque, Resenterra, and Siggen,

2014; Kristof, 1996). Job satisfaction is a response to the discrepancy between ‘How

much is there?’ and ‘How much should there be?' (Nerkar, McGrath, & MacMillan,

1996; Wanous & Lawler, 1972a), and is demonstrated to be an assessment of "needs-

supplies" fit (Pervin, 1987; Rounds, et al., 1987). Work stress is a reaction to the

deviation between the requirements and actual abilities of employees in fulfilling job

tasks (Tennant, 2001) and therefore an assessment of “demands-abilities” fit. As

demonstrated by Edwards (1996), D-A fit is critically linked to tension and N-S fit to

satisfaction. Similarly, Leung, Chan, and Yuen (2010) used items of D-A

discrepancy of construction works to measure the level of work stress.

Figure 3.1 Main P-E fits and psychological reactions


Conceptual framework

Unlike the Stimulus-Response mechanism dominating most animals, human

behaviours applying judgement and analytical ability are usually regarded as

following the mechanism of Stimulus-Organism-Response (S-O-R) (Mehrabian &

Russell, 1974). Although some studies on the job performance of construction

professionals explore the direct effects of environmental factors, few use P-E fit

assessments as mediators. Based on the S-O-R paradigm and P-E fit theory, an

adapted conceptual model described as Stimulus-Reactions-Performance is

developed for studying employee behaviour, as shown in Figure 2.2. The postulation

here is that environmental factors and individual factors affect job performance

(fully/partially) as mediated by P-E fit assessments. In the time perspective, it is

reasonable to assume job performance may affect environmental factors and P-E fit

assessments in future. As noted by Spokane (1987), reciprocal relationships may

exist between reinforcers in the work environment and the needs of the individual.

Figure 3.2 Proposed conceptual framework

Psychological reactions as the P-E fit assessments

As pointed out by Caplan (1987), there are two basic assessments of P-E fit

when exploring influences: “one involving the fit between environmental supplies

and personal motives, goals and values and the other involving the fit between

environmental demands and personal skills and abilities” (Caplan, 1987, pp. 295-


296). Job satisfaction and work stress can be used to assess these two kinds of P-E

fit.

The assumption that “happier workers produce more” can be dated back to the

Hawthorne studies and the human relations movement of the 1930s (Brayfield &

Crockett, 1955), whereas work stress received little attention until there was

increased recognition and study of mental disorders in the 1980s (Tennant, 2001).

Since then, the nexus between work stress and employee behaviour has been

increasingly studied. Following the Hawthorne studies of job satisfaction among

employees, research into the possible connections between job satisfaction and job

performance comprises an appreciable portion of behaviour research in management

(Organ, 1988b). Three mainstream hypotheses of the job S-P linkage include: (1) job

satisfaction causes job performance; (2) job performance causes job satisfaction; (3)

another complex relationship exists that includes moderators, mediators or

antecedent variables. For potential antecedents, job satisfaction is positively related

to organisational learning climate (Egan, et al., 2004). As noted by Tett and Meyer

(1993), job satisfaction is a strong predictor of organisational commitment and

employee turnover. A meta-analysis of 55 studies of OCB supports job attitudes and

job satisfaction as robust predictors of OCB (Organ & Ryan, 1995).

Work stress, indicating the deviation between requirements and actual abilities

of people in fulfilling job tasks, has become an important concept in organisational

management for the prevalence of psychological disorders (Tennant, 2001). In

addition to health issues related to work stress such as diastolic blood pressure under

stressful working conditions (Matthews, Cottington, Talbott, Kuller, and Siegel

(1987), exploring the antecedents and influences of work stress in the managerial

context has practical and theoretical implications. For example, according to an

online survey of 306 nurses, including 263 American hospital nurses and 40 non-

American nurses, social support from co-workers decreases job stress and improves

job performance (AbuAlRub, 2004). Additionally, in another survey of 305 Chinese

employees in 48 service organisations, co-worker support is found to be a significant

moderator for the nexus between job stress and performance, in that higher stress

results in better performance if the level of co-worker support is high (Hon, 2013).

Leung, et al. (2005) use work stress to predict the estimated performance (task

performance) of construction cost engineers in Hong Kong.


3.1.3 Discussion

The proposed framework links psychological reactions and job performance

developed in the S-O-R paradigm and P-E fit theory. This model provides a rich

framework for understanding the ‘big picture’ of employee behaviour research. The

framework could also be useful for identifying corrective approaches such as

developing organisational support to improve employee performance.

Consistent with the tradition of focusing on individual differences in the

selection of employees (Schneider, 2001), individual characteristics — especially

knowledge, skills and ability — are emphasised. For example, Hunter (1986)

reviewed hundreds of papers measuring relationships between general cognitive

ability and job performance in various jobs, and found that cognitive ability affects

job performance through daily-used job knowledge and skills. Wade and Parent

(2002) found that a deficiency of job skills leads to lower job performance of

webmasters, in their analyses of a worldwide survey with 232 responses. Dilchert,

Ones, Davis, and Rostow (2007), in an analysis of 3,021 applicants for the police

force in the US, found that individual cognitive ability negatively affects

counterproductive work behaviour (CWB) and that workers with higher cognitive

ability consider before engaging in counterproductive activities. Meier and Spector’s

(2013) longitudinal study of 663 employees in the US over an eight month period

found a reciprocal nexus between stressful working conditions and CWB.

Based on the organisational psychology tradition, especially of the Stimulus-

Organism-Response (S-O-R) paradigm (Mehrabian & Russell, 1974; Schneider,

2001), the proposed model includes the effects of environmental factors. As

Schneider (2001) points out, many studies of P-E fit are narrowly focused on

identifying commensurate measures for P and E. By using three assessments as

indirect measures to reflect P-E fit, therefore, the proposed model is able to examine

effects of organisational factors such as organisational support, organisational

politics and organisational learning climate. For instance, Smith et al. (1983) have

identified a positive relationship between job satisfaction and altruistic behaviour in a

survey of 422 employees and their supervisors in two banks in the US. Eisenberger et

al. (1986) incorporated commitment items into a US Survey of Perceived

Organizational Support, and their analysis of 361 responses found individual


absenteeism to be negatively correlated with organisational support. Rhoades and

Eisenberger’s (2002) meta-analysis of 70 studies related to organisational support

shows that employees care for beneficial organisational supports such as fairness,

supervisor support, organisational rewards and enjoyable job conditions. Further, and

consistent with the norm of reciprocity, employees are likely to establish a long term

approach to social exchanges by paying back with hard work and job loyalty

(Wayne, Shore, & Liden, 1997).

According to Ferris and Kacmar (1992), the political nature of the working

environment is not a concept but a fact of life. A business company is a political

coalition where decisions are not totally decided by the market but also by bargaining

processes (March, 1962). Perceptions of organisational politics are caused by the

employees’ tendency to assign humanlike characteristics to organisations

(Eisenberger, Huntington, Hutchison, & Sowa, 1986; Rhoades & Eisenberger, 2002).

Although more political behaviour happens in the higher levels of organisations

(Ferris & Kacmar, 1992), lower level employees perceive more impact from their

lack of control of organisational processes, which in turn decreases their job

satisfaction (Gandz & Murray, 1980).

Consistent with the general definition of organisational climate (Hellriegel &

Slocum, 1974), organisational learning climate (OLC) can be regarded as involving a

set of attributes related to the learning of members in an organisation. The effects of

OLC on organisational performance have been largely acknowledged by both

academics and practitioners (Mikkelsen & Grønhaug, 1999). Additionally, OLC is

believed to improve organisational learning when an individual or group of

individuals in an organisation face problems and need help from ‘the organisation’

(Argyris & Schön, 1978). Egan et al.’s (2004) examination of the relationships

among OLC, job satisfaction and organisational performance also found that OLC is

positively related to job satisfaction and intentions to transfer knowledge among

employees, while turnover intention is negatively influenced by OLC and job

satisfaction.

In addition to the factors discussed above, potential moderators need to be

considered when solving complex and unsettled problems (Xiong et al., 2015a).

Other variables to be included in the model include gender, age, industry, country,


culture, job alternatives in the market, reward contingency and individual learning

style.

Building on previous studies, the proposed model assumes that psychological

reactions act as variables in which the effects of environmental and individual factors

on job performance are fully or partially mediated. The relationships between

psychological reactions and job performance are major concerns in this thesis. Two

specific propositions are proposed: (1) job satisfaction, an indicator of N-S fit,

positively affects job performance; and (2) work stress, an indicator of D-A fit,

negatively affects job performance. The exact relationship might be in the form of a

reverse U shape.

3.1.4 Conclusions

The job performance of construction professionals is a product of the

interactions between person and environment. In addition to objective environmental

factors and individual differences, psychological reactions (defined as P-E fit

assessments in this study) are also critical. From a cross-sectional perspective, the

study assumes that stimulus factors in the environment and individual differences

affect job performance via the mediation effects of P-E fit assessments. When time

lags (seasons, years) are considered, job performance may influence future

perceptions of external stimulus factors and P-E fit assessments.

The proposed conceptual framework can be used both to understand previous

studies and to underpin future studies. Based on P-E fit theory and the S-O-R

paradigm, the framework can be used as a reference in avoiding pseudo-causation

conclusions. Although further refinements are inevitable, the proposed framework

also contributes to: expansion to other outcomes, such as turnover intention, and

adaptation with new concepts; identifying potential moderator in these relations..


3.2 STRUCTURAL EQUATION MODELLING





field of expertise;











Bo Xiong*, Martin Skitmore, Bo Xia. A critical review of structural equation

modeling applications in construction research, Automation in Construction, 2015,

49 (Part A), 59-70.


Bo Xiong Searched previous studies which applied SEM in construction

research, conducted a critical review, made suggestions for future

research, wrote the manuscript and acted as the corresponding

author.

07/03/2016


Bo Xia Directed and guided this study.





Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


3.2.1 Introduction

Since Bentler's appeal to apply the technique to handle latent variables (i.e.

unobserved variables) in psychological science Bentler (1980), structural equation

modelling (SEM) has become a quasi-routine and even indispensable statistical

analysis approach in the social sciences. Computer programs designed for

conducting SEM analyses have emerged and enabled the technique to be used in

even wider applications (Baumgartner & Homburg, 1996). Newly developed

graphical user interfaces have also made much easier for researchers and

practitioners to use (Kline, 1998).

On one hand, the utility of SEM in approximating reasonable results in

measurement and structural analyses has been widely acknowledged (Bagozzi & Yi,

2012; Bentler, 1980; Byrne, 2001; Hair, 2006; Jöreskog & Sörbom, 1996). On the

other hand, SEM has been criticized for generating implausible conclusions due to its

indiscriminate use (Baumgartner & Homburg, 1996). Some results obtained through

SEM are of doubtful authenticity, especially when both researchers and reviewers

have little experience with the method. The overall quality of SEM applications in

construction research is similarly affected. Many mistakes exist in current

publications and basic principles are often violated or ignored.

Despite the special care needed in SEM applications, no explicit body of

knowledge has been developed for their use in construction research to assess the

proposed models, and errors continue to be made over assumptions and

interpretations. The purpose of this paper, therefore, is to provide a comprehensive

and critical review of SEM applications in construction research to date, through the

evaluation of previous applications of SEM to solving related research problems

including, but not limited to, papers published in leading construction journals. The

review focuses on the practical use of the SEM technique and analyses the

applications in terms of model design, model development and model evaluation

issues for the benefit of future research.


3.2.2 Methodology

Introduction to SEM

The emergence and development of SEM was regarded as an important

statistical development in social sciences in recent decades and this “second

generation” multivariate analysis method has been widely applied in theoretical

explorations and empirical validations in many disciplines (Fornell & Larcker,

1981a; Kline, 2005). Compared with other statistical tools such as factor analysis and

multivariate regression, SEM carries out factor analysis and path analysis

simultaneously (Xiong, Skitmore, Xia, et al., 2014), since it can (1) measure and

accommodate errors of manifest variables (i.e. observed variables); (2) represent

ambiguous constructs in the form of latent variables (i.e. unobserved variables) by

using several manifest variables; and (3) simultaneously estimate both causal

relationships among latent variables and manifest variables (Kline, 2005; Xiong,

Skitmore, Xia, et al., 2014). In addition, SEM can also provide group comparisons

with a holistic model, resulting in much more vivid impressions than traditional

ANOVA. SEM can also handle longitudinal designs when time lag variables are

involved (Gollob & Reichardt, 1987; MacCallum & Austin, 2000).

As introduced above, SEM describes and tests relationships between two kinds

of variables - latent variables (LVs) and manifest variables (MVs). Latent variables

cannot be observed directly due to their abstract character. In contrast, observed

variables contain objective facts and easier to measure. Several observed variables

can reflect one latent variable (Byrne, 2001). As presented in Fig.1, a structural

equation model usually consists of two main components, a structural model and

several measurement models. A simple measurement model includes a latent

variable, a few associated observed variables and their corresponding measurement

errors. The structural model consists of all LVs and their interrelationships. For

model development purposes, some researches aim to validate their assumptions of a

dimensional framework of one or several discriminant LVs (e.g. Ding and Ng

(2007)), while others aim to elicit the causal relationship between the LVs.

Confirmatory factor analysis (CFA) with correlating latent variables satisfies the

former purpose, while these correlations need to be replaced by directional

relationships for the latter (Kline, 2005; Xiong, Skitmore, Xia, et al., 2014).


Figure 3.3 provides a simple example of a structural equation model

investigating the effect of LV Y1 on LV Y2, and where several MVs are used to

represent the LVs. The MVs are shown in rectangles, the LVs in ellipses,

measurement errors in circles and with arrows indicating the direction of the effects.

If directional arrow between Y1 and Y2 is replaced by a correlation two-way arrow,

the model is a CFA and its purpose is to test whether MVs can represent LVs well

(i.e. convergent validity) and whether Y1 and Y2 are different (i.e. discriminant

validity). The basic concepts and principles of SEM are now well established with

the help of early explorations by researchers in the 1980s (e.g. (Bagozzi & Yi, 1988;

Baron & Kenny, 1986; Bentler, 1980; Bentler & Chou, 1987; Fornell & Larcker,

1981a; Mulaik et al., 1989)), structured textbooks (e.g. Byrne (2001); Kline (2005)),

well developed soft programs (e.g. LISREL by Jöreskog (1970), EQS by Bentler

(1989) and AMOS by Arbuckle (1994)), and Structural Equation Modeling, the first

ranked journal for mathematical methods, in publication since 1994 (Golob, 2003).

These are rich sources for beginners to acquire the basic knowledge needed before

applying SEM.

Figure 3.3 Schematic diagram of a structural equation model

The use of SEM in construction research is relatively new, with the early work

by Sarkar et al, published in the Journal of International Management (Sarkar,

Aulakh, & Cavusgil, 1998), in their examination of the mediation effects of relational

bonding between variables such as role clarity and the collaborative behavioural

processes of global construction firms. Another early work is Molenaar et al's

examination of the effects of a range of factors on contract disputes between owners

and contractors (Molenaar, Washington, & Diekmann, 2000), published in the


Journal of Construction Engineering and Management. In both cases, SEM helped to

deepen the understanding of traditional research topics. SEM has also proved to be a

helpful tool in some emerging research areas. Lee and Yu, for example use SEM to

examine the effects of three antecedent variables on the intention to use the Project

Management Information System and user satisfaction, and the effect on construction

management efficiency (Lee & Yu, 2012), while Yang et al apply SEM to assess the

impact of information technology on project success, finding that project

performance is not affected directly but through the mediation role of knowledge

management (Yang, Chen, & Wang, 2012). Son, Park, Kim, and Chou (2012)

applied SEM to measure the acceptance and usage of mobile computing devices

among construction professionals in South Korea and Park et al. investigated the

effects of selected antecedent variables such as organizational support for

construction professionals' acceptance of web-based training (Park, Son, & Kim,

2012).

Article selection

Many previous review papers (e.g.Baumgartner and Homburg (1996);

(MacCallum & Austin, 2000; Sunindijo & Zou, 2012)) focus on analysing

publications in leading journals in their specific research fields, such as marketing.

However, research in construction can be seen as a combination of multiple

disciplines covering both technical and managerial topics. Therefore, this review

provides a comprehensive search of quality SEM applications for solving problems

in construction. Although it is an obvious option to use academic databases, none of

these is fully inclusive. Elsevier’s Scopus, for example, while they publish

AUTCON, IJPM and B&E, JCEM and JME are from the ASCE library, CME from

Taylor& Francis, and ECAM from Emerald.

To achieve a comprehensive search, the Google Scholar was used as the first

stage. According to a recently published analysis in Science, Nicolás Robinson-

Garcia, a bibliographer at the University of Granada in Spain said that “Google

Scholar's compendium of articles is at least as comprehensive as the leading

commercial academic search databases Thomson Reuters’ Web of Science and

Elsevier's Scopus - and for many disciplines in the social sciences and humanities,

even better.” (Bohannon, 2014). Additionally, Harzing conducted a longitudinal

study of Google Scholar coverage between 2012 and 2013 of four disciplines in


Chemistry and Physics concluded that Google Scholar has become suitable for

bibliometric research (Harzing, 2014). The oversell impression is that all leading

construction journals are included in a Google Scholar search.

Firstly, two key phrases “structural equation model” and “construction

industry” were used to search in Google Scholar. Admittedly, while the use of

“construction industry” rather than “construction” may exclude a few relevant

publications, the abstract and multiple meanings of “construction” make the search

results too broad. To reduce the risk of missing relevant publications, a series of

“research” searches without using the “construction industry” key phrase was

conducted directly in 31 journals. 532 records were initially found on 4 April 2013.

Each of these records were examined to identify articles where SEM was applied as

the main statistical tool, the problems targeted are construction related or involve

related subjects such as professionals/companies in the industry, and are from peer

reviewed journals to assure selection quality. The source journals of the articles

selected in this way were then searched directly.

Path analysis (PA) models are special cases of the SEM technique for

analysing structural models just with observed variables (Xiong & Xia, 2014).

Despite its comparatively simple form, PA still accounts for 25% of the roughly 500

applications of SEM published in 16 psychology journals between 1993 and 1997

(MacCallum & Austin, 2000). Partial least square path modelling, known as PLS-

SEM in some publications, is a “soft” and component-based modelling technique in

theoretical exploration involving less strict inherent model assumptions and biased

parameter estimates compared with traditional SEM (i.e. covariance-based SEM).

Their differences are similar to those of principal component analysis and factor

analysis. However, PLS path modelling is an appealing technique due to its

predictability with small sample sizes and non-normal data (Hair, Sarstedt, Pieper, &

Ringle, 2012). Although PA and PLS have their own uses as introduced above, the

traditional covariance-based and latent variables that contained SEM has had wide

applications and methodological advances over more than 30 years of development

(Ringle, Sarstedt, & Straub, 2012). Articles using PA and PLS are excluded in this

review - a common practice in similar reviews in other fields (e.g. Baumgartner and

Homburg (1996); (Hair, Sarstedt, et al., 2012)). Finally, 84 suitable articles published


during 1998-2012 were identified as satisfying the selection criteria. The selection

process is illustrated in Figure 3.4.

Outline the research design (e.g., quantitative, qualitative). If quantitative, spell

out the independent, dependent and classificatory variables (and sometimes

formulate an operational statement of the research hypothesis in null form so as to set

the stage for an appropriate research design permitting statistical inferences). If

qualitative, explain and support the approach taken and briefly discuss the data

gathering procedures that were [will be] used (observations, interviews, etc.)

Figure 3.4 Article selection

Unit of analysis

In the situation where several models are presented in one article, the models

selected for analyses were based on similar criteria to those of Shah and Goldstein.


That is: (1) when the initial model and other alternative models are evaluated

simultaneously, only the final model is included in the analysis; (2) when a single

model is evaluated by splitting a sample, only the model tested with the verification

sample is included (Shah & Goldstein, 2006); and (3) when parallel constructs are

evaluated separately as confirmatory factor analyses, only the model with best

goodness of fit is included. In this way, only one model was selected for analysis

from each article. This process resulted in 84 models, of which 7 are Confirmatory

Factor Analysis (CFA) models and 77 are SEM models. The CFA models were

mainly used for validation of existing or newly developed frameworks, while the

SEM models were mainly used for exploring the interrelationships among latent

variables. If the objective and main contributions of one article is validation with

CFA, only the final CFA model was selected for analysis, as is the case with Ding

and Ng, for example, in their testing of the reliability and validity of the Chinese

version of McAllister's trust scale (Ding & Ng, 2007).

Overview and trend

7 of the 31 journals are regarded as key journals in this review and specially

marked in Figure 3.5, which shows the increase in the frequency of SEM application-

based articles in 3-year periods. To assess the growth of SEM applications, the

number of construction management articles were regressed on an index of

publication years (yearly from 1998), considering both the linear and quadratic

effects of time. The regression model is highly significant (F(2,12) = 34.6,

p=1.04*10-5<0.0001) and, with R2 =0.852, explains 85.2% of the variance of SEM

applications. The linear trend (t= -2.61, p=0.02) and quadratic effect (t=2.62, p=0.02)

are both significant, simultaneously growing more negative linearly and accelerating

positively over time. In comparison, SEM applications in marketing and psychology

grew linearly over time without acceleration (Baumgartner & Homburg, 1996;

Hershberger, 2003), while applications in operations management did not grow

linearly but accelerated over time. This research aims to enhance the suitability of

future applications by taking a critical review of current applications.


Figure 3.5 Number of SEM-based articles by journals and year

3.2.3 Critical issues in the application of SEM

Issues relating to research design

Research design: cross-sectional studies and longitudinal studies

An SEM cross-sectional study involves a system of variables and constructs at

a certain time point, while a longitudinal study is concerned with the

interrelationships between constructs over time (MacCallum & Austin, 2000). Cross-

sectional designs are common with SEM applications in psychology research

(MacCallum & Austin, 2000). Cross-sectional studies are often focused on

identifying directional relationships among variables. However, these “causal”

models may be not appropriate in situations where the variables involved are

continually changing, since they omit the values of the variables at prior times, the

effects of variables on themselves over time and time interval for these causal

relationships (Gollob & Reichardt, 1987). In such cases, therefore, it is necessary to

consider time lags in the research design. In other words, a longitudinal component is

needed.


As MacCallum and Austin point out, there are two commonly applied

longitudinal designs in SEM with repeated data of the same observed variables. The

first type is sequential design, where different variables are measured on successive

occasions to explicate the interrelationships among variables over time. The second

type comprises what are known as ‘growth curve models’, where the interest is in

changes in the same variables over time. These two types of design are not mutually

exclusive (MacCallum & Austin, 2000).

Opportunities exist, therefore, for construction management SEM designs to be

enriched by the consideration of time lags. Longitudinal designs are also preferred to

cross-sectional designs in strict causal modelling in order to avoid potential halo

effects caused by neglected autoregressive influences. For example, the effects of

variable B at time 1 on itself at time 2 should be considered in investigating the

effect of variable A at time 1 on variable B at time 2 (Gollob & Reichardt, 1987).

83 of the 84 articles reviewed are cross-sectional designs. For example, Leung,

Zhang, et al. (2008), used a cross-sectional design in examining the effects of

organizational supports in cost estimation while Ahuja, Yang, Skitmore, and Shankar

(2010) used a cross-sectional design in examining the relationships between the

factors affecting the adoption of information communication technologies by small

and medium enterprises. 76 of the 83 cross-sectional studies reviewed are focused on

identifying directional relationships among variables. One article uses a combined

longitudinal design in describing the development of trust between cross-functional,

geographically distributed co-workers (Zolin, Hinds, Fruchter, & Levitt, 2004).

Model specifications: constructs, indicators and identification

An important and controversial issue that needs to be considered early in model

specification is the construct type of measurement models (Bagozzi & Yi, 2012).

There are two possible relationships between latent variables (LVs) and manifest

variables (MVs) in terms of reflective constructs and formative constructs in

measurement models. However, some studies have specification problems in that,

instead of correctly using formative constructs, they apply only reflective constructs

without considering any possible distinction between two model structures. For

example, Jarvis et al’s review of articles published in top-tier marketing journals

found 28% of constructs to be incorrectly specified. The main features of reflective

constructs are:


1. the causal directions are from latent variables to manifest variables

2. changes in latent variables lead to changes in manifest variables

3. manifest variables can be exchanged or deleted without affecting theoretical

meaning of corresponding latent variables for covering same themes.

Formative constructs, however, have the corresponding features of:

1. the causal directions are from manifest variables to latent variables

2. changes in manifest variables lead to changes in latent variables

3. manifest variables cannot be exchanged or deleted without affecting

theoretical meaning of corresponding latent variables and are not necessary to share

common themes (Jarvis, MacKenzie, & Podsakoff, 2003).

Therefore, care is needed in specifying the constructs, since current covariance-

based SEM software such as LISREL, AMOS and EQS can only handle reflective

constructs. For dealing with formative constructs, a method such as partial least

square structural modelling is necessary (Hair, Sarstedt, et al., 2012).

Another issue, which concerns the research framework or questionnaire design

in some situations, is which manifest variables should be allocated to reflect a latent

variable. Allocating more manifest variables per latent variable leads to more distinct

sample moments for model identification but also more parameters to estimate,

increasing the required sample size. It is not necessary to have a larger MV:LV ratio

to achieve a better model fit. Adding more variables is inappropriate in some

situations, as less data for each variable leads to worse parameter estimates and away

from the “true model” (Posada & Buckley, 2004). Therefore, variable selection needs

to take into consideration the information available and the principle of parsimony. A

measurement model can only be identified with three or more manifest variables, and

Keline proposes a three-variable principle, where three manifest variables are used to

reflect a latent variable (Kline, 2005). However, many papers contain models with an

MV:LV ratio of less than 3. Shah and Goldstein’s review of operations management

applications found this to be the case for 33.6% (38 of 113) of the models

encountered (Shah & Goldstein, 2006).

Single indicator constructs using only one manifest variable to represent one

latent variable are only suitable when a manifest variable can perfectly represent a


latent concept. As Ringle, et al. (2012) pointed out, using a single indicator is a risky

choice as it performs worse than multi-item scales in most situations. Model

identification is also important for successful modelling. An obvious inherent feature

of identification is that there must always be a positive difference between the

number of known equations and the number of parameter estimates needed. The

degree of freedom (d.f.) is a function of this difference. If the number of MVs is p,

the known equations representing the total number for variance-covariance matrix to

be analysed is the sum of variances of each MVs (=p) and covariance between MVs

(=p(p-1)/2) (Byrne, 2001). Therefore, d.f. = p(p+1)/2-q. where q is the number of

free parameters to estimate in the proposed model (Rigdon, 1994). Model

identification is a complex problem that cannot be explained thoroughly in one

paragraph, but low degrees of freedom generally indicate unreliable results. In

addition to the indication of model identification, larger values of degree of freedom

also indicate that a smaller sample size can be tolerated for a similar model fit

(MacCallum, Browne, & Sugawara, 1996).

In our review, 25% (21 of 84) models have a general MV:LV ratio of less than

3 and 55.4% (46 of 83, one unreported) models contain at least one measurement

model with less than 3 manifest variables. In many cases also, the identification

problems involved in some or all of the measurement components are not explained,

nor is any consideration made of adding additional constraints. 13.3% of the models

(11 of 83) contain at least one single indicator construct. However, many

applications do not meet the mentioned requirements of applying single indicator

constructs. For example, one article (Cheung, Chow, & Yiu, 2009) uses a single item

in asking if “the negotiating parties were forced to articulate and clarify their

positions” to reflect the latent variable “position clarification”, but the factor loading

is only 0.45 which means only 20.25% variance of the latent variable is explained by

the selected single item and 79.75% variance is explained by the error. Only 52.4%

(44 of 84) articles provided d.f. values, while some articles presented Chi square test

results with degree of freedom ratios but not the d.f. values.

Mediators and moderators

There are two important classifications of (latent) variables in SEM. The first

divides variables into endogenous variables (i.e. dependent variables in regression

models) and exogenous variables (i.e. independent variables). The second


categorization is based on the “positions” of these variables, with antecedents,

dependent variables, mediators and moderators. Mediators and moderators are often

necessary in research design, especially for solving complex and unsettled problems

in theory development. Identifying and quantifying the mediation (moderation)

effects of variables is useful in making contributions to the body of knowledge and

both variables are the focus of research design in many situations (Baron & Kenny,

1986). Even mediated moderation and moderated mediation are necessary in more

complex situations (Muller, Judd, & Yzerbyt, 2005).

In our review, all the applications are restricted to covering only simple

mediation or moderation effects. 11.9% of the (10 of 84) articles examined mediation

effects, but few tested their significance. For example, Mostert et al compare

mediated models and alterative models and confirm the mediating effects of negative

WHI (Work–home Interference) in the relationship between job demands/job

resources and burnout, and the mediating effect of positive WHI in the relationship

between job resources and work engagement (Mostert, Peeters, & Rost, 2011). 3.6%

of the (3 of 84) articles examined the effects of moderators in detail. Yang et al tested

the moderating effect of team relationships and team size separately by conducting a

two-way ANOVA when examining the relationship between knowledge

management and project performance (Yang, et al., 2012). Such analyses rare

however.

Sample size issues

Establishing the sample size is enough for testing the proposed model is

another critical decision to be made before data collection and analysis. Bagozzi and

Yi (2012) advise having a sample size of at least 100 for the results to be reasonably

reliable and suggest 200 to be more appropriate since less than this increases the risk

of sample non-normality and hence the accuracy of results. Compared with the

arbitrary threshold values of sample size, another rule of thumb is to have a

minimum number of parameters to estimate ratio of 5:1, although a 10:1 ratio is also

recommended for assuring the distribution of variables (Bentler & Chou, 1987).

Kline also recommends bootstrapping analysis as a method of improving the

reliability of SEM results obtained from comparatively small samples (Kline, 2005).

Another caution for sample size is that if the aim is to identify differences

among different respondent groups (i.e. multiple group analysis is necessary), each


group needs to have a large enough sample size. One advantage of using SEM is that

it is powerful in testing hypotheses across samples. The multiple group analyses

allows many interesting tests, such as identifying factor loadings across groups, path

coefficients between latent variables across groups and the means of factors across

groups (Bagozzi & Yi, 2012)

In the papers reviewed, 31.0% (26 of 84) of models are derived from sample

sizes less than 100, 77.4% (65 of 84) have a sample size less than 200, 10.8% (7 of

65) have a sample size of less than 200 after applying bootstrapping, 85.7% (72 of

84) have a sample size to free parameters ratio less than 5, and 94.0% (79 of 84) have

a sample size to free parameters with a ratio of less than 10. Three studies conducted

multiple group analysis - across gender (M. Goldenhar*, Williams, & G. Swanson,

2003), country (Mohamed, 2003) and parental status, job type and race (Mostert, et

al., 2011).

Software programs

SEM was popularized by the launch of the linear structural relationships

(LISREL) computer program as the first SEM program developed by Jöreskog

(1970), resulting in SEM being regarded as the same as LISREL for a few years

(Golob, 2003). Two other popular software programs are EQS by Bentler (1989) and

AMOS by Arbuckle (Arbuckle, 1994). Apart from the very early versions of

LISREL, all of these programs provide a graphical user interface platform as a

replacement or complement of previous programming platforms, which makes SEM

easier for researchers and practitioners to use. Kline’s detailed comparison of these

three programs, found them to be similarly powerful in analysing structural equation

models and that the choice should be based on user preference (Kline, 1998). For

example, AMOS has a very user friendly user interface platform and is good at

handling incomplete data. EQS, on the other hand, does well in data screening and

dealing with non-normal data, while LISREL has advantages in dealing with very

complex situations, such as where nonlinear constraints are needed. When the

correlation matrix is only available as the input matrix rather than the covariance

matrix and raw data, EQS and LISREL are recommended since current AMOS

versions cannot handle the correlation matrix (Shah & Goldstein, 2006). In our

review, 55.4% (46 of 83, one unknown) models were built in AMOS, 31.3% (26 of

83) models in LISREL and 13.3% (11 of 83) in EQS.


Table 3.1 Issues related to research design Categories Tested items Total CFA (=7) SEM (=77)

Research

design

Cross-sectional designs 83 7 76

Longitudinal designs 1 0 1

Model

specification

Models with control variables 4 0 4

With second order CFA structure in SEM 8 / 8

Multi group analysis 3 0 3

Mediation effect tested 10 / 10

Moderator effect tested 3 / 3

Bootstrap 7 0 7

Latent variables N=84 N=7 N=77

Mean (SD) 7.13 (3.63) 5.71 (3.25) 7.25 (3.65)

Median 6 5 6

Range (2,28) (2,11) (3, 28)

Structural model relations N=83 N=7 N=76

Mean (SD) 9.84 (9.05) 6.71 (4.75) 10.13 (9.31)

Median 8 6 8

Range (1, 72) (1, 15) (2,72)

MVs in the smallest construct N=83 N=7 N=76

<3 46 (55.4%) 3 (42.9%) 43 (56.6%)

Single indicator construct 11 (13.3%) 1 (14.3%) 10 (13.2%)

Mean (SD) 2.63 (1.23) 2.57 (0.98) 2.63 (1.25)

Median 2 3 2

Range (1,6) (1, 4) (1,6)

Number of manifest variables N=84 N=7 N=77

Mean (SD) 28.65 (17.58) 17 (5.13) 29.7 (17.9)

Median 24 19 24

Range (8, 108) (8, 23) -8,108

MV: LV ratio N=84 N=7 N=77

<3 21 (25%) 2 (28.6%) 19 (24.7%)

Mean (SD) 4.19 (2.04) 3.41 (1.00) 4.26 (2.10)

Median 3.5 3.2 3.5

Range (1.9, 13.8) (2.1, 4.8) (1.9, 13.8)

Sample size

(N=84)

<100 26 (31.0%) 2 (28.6%) 24

Between 100 to 200 39 (46.4%) 2 (28.6%) 37

>200 19 (22.6%) 3 (42.8%) 16

Mean (SD) 162.4(122.6) 165.3 (76.1) 162.1 (126.3)

Median 125.5 196 116

Range (32, 831) (32, 232) (36, 831)

Sample size/

parameter

ratio (N=84)

<5 72 4 68

<10 79 6 73

Mean (SD) 3.13 (3.00) 5.09 (4.37) 2.95 (2.82)

Median 1.99 3.70 1.94

Range (0.4,14.3) (0.9, 13.6) (0.4, 14.3)

Software

programs

applied

(N=84)

AMOS 46 7 39

LISREL 26 0 26

EQS 11 0 11

Unknown 1 0 1

Issues relating to model development

Model development issues after collecting data comprise data screening,

reliability tests and validity tests of constructs. The normality of data should be

considered when choosing estimation methods in SEM. Many articles present the


validity of constructs and model evaluation at the same step, but it is common for

models to have poor goodness of fit (GOF), often caused by the inadequate validity

of constructs. Additionally, the validity of constructs is critical for approximating

“true” models, which is the core of SEM design but can be questionable in practice.

Research design: cross-sectional studies and longitudinal studies

Before SEM model building, it is important to test the characteristics of the

data. Multivariate normality of data is an important assumption made when applying

the default estimation method of maximum likelihood in SEM. Violation of this

assumption, especially with small samples, may inflate the GOF statistic and

underestimate the standard errors (MacCallum, Roznowski, & Necowitz, 1992). The

normality of the data can usually be evaluated by observing the skewness and

kurtosis statistics. Skewness is the standardized third moment of the data and

measures the extent to which a variable’s distribution is asymmetrical (towards right

or left). Kurtosis is the standardised fourth moment of the data and measures a

distribution’s peakedness (narrow/heavy tailed) (Hair, 2014). Both statistics are

asymptotically zero for the normal distribution and values more extreme than ±1 are

often taken to indicate non-normality.

When dealing with non-normal data, the choice of suitable estimation methods

is important for achieving reliable SEM results. There are many estimation methods

available for model development, such as the commonly used maximum likelihood

(ML), generalized least square (GLS), unweighted least squares (ULS) and

asymptotically distribution-free (ADF) methods. While ML is comparatively robust

to moderate violations of normality, and some distribution-free methods such as ULS

and ADF can also be helpful in these situations, distribution-free methods are

generally less powerful (Shah & Goldstein, 2006). It is also recommended to use the

robust methodology available in EQS to handle non-normality issues (Kline, 1998).

Special care is needed in research design, data collection and related factors

affecting missing values (Bagozzi & Yi, 2012). Some traditional considerations such

as dealing with missing values, identifying suspicious responses and outliers are also

necessary. Since these are quite common problems, not specific to SEM but

mentioned in only a few of the articles reviewed, some suggestions for missing

values are: (1) mean value replacement is not a good option when there are more

than 5% missing values per indicator as this decreases the variability of data (Hair,


2014); (2) a returned questionnaire with more than 15% missing values should be

treated as an invalid response (Hair, 2014); and (3) the full information maximum

likelihood (FIML) method is more efficient than list wise deletion, pairwise deletion

and similar response pattern imputation (Enders & Bandalos, 2001).

The reliability test discussed here refers to the widely used Cronbach’s α>0.7

coefficient (Cronbach, 1951). This is an acceptable indication of the internal

consistency of constructs. However, in SEM, the composite reliability statistics

indexed in Bagozzi and Yi (1988) are needed as an indicator of internal consistency

of indicators within a construct. Fornell and Larcker (1981a)’s average variance

extracted (AVE) method, however, can be used to retest the validity of constructs

instead. Composite reliability is preferred as informative statistics.

Of the articles reviewed, only 14.3% (12 of 84) provide multivariate normality

test results or qualitatively state that this requirement was met. In some cases, other

multivariate normality tests are applied instead. For example, a Chi-square Q-Q plot

of each variable was used to assess multi-normality (Ding & Ng, 2010). The

estimation methods used are rarely mentioned and often ignored. 65.5% (55 of 84)

present Cronbach’s α values, but only a few (e.g. Cegarra-Navarro and Sánchez-

Polo (2011); (Chou & Yang, 2012)) provide composite reliability statistics.

Validity of constructs

Construct validity is necessary for reliable model testing and theory

development. Related issues have been criticized for decades in many research fields

such as marketing (Jarvis, et al., 2003). It covers both “the degree of agreement of

indicators hypothesized to measure a construct and the distinction between those

indictors and indicators of a different construct(s)” (Bagozzi & Yi, 2012). The two

common tests are for convergent validity as mentioned above and discriminant

validity.

Convergent validity measures the degree of positive correlation of one MV and

other MVs within the same construct, since MVs within the same construct should

share a comparatively high proportion of commonality (Hair, 2014). This is done by

assessing factor loadings, in which standardized factor loadings of the MVs larger

than (≈0.7) are taken to indicate a sufficient latent variable contribution (Hair,

2014), while standardized factor loadings less than 0.5 are considered for deletion

5.0


(Xiong, Skitmore, Xia, et al., 2014). On the construct level, AVE is usually used to

measure convergent validity and should be larger than 0.5 to indicate a satisfactory

convergent validity (Fornell & Larcker, 1981a)

Discriminant validity aims to test whether a construct is truly distinct from

other constructs, which is critical to model development. The Fornell and Larcker

(1981a) criterion is widely used for assessing discriminant validity. This insists that

the AVE of one construct should be higher than its highest squared correlation with

other constructs (i.e. the square root of each construct’s AVE should be larger than

its highest correlation with other constructs).

Only 19.0% (16 of 84) of the articles reviewed conducted related convergent

tests without evaluating their suitability at this stage. With the MV factor loadings

provided in 53 articles, we calculated the AVE values of each construct and found

64.2% articles to be of questionable convergent validity (i.e. having at least one

construct’s AVE less than 0.5). For articles that considered convergent validity, 25%

(4 of 16) are questionable, 62.5% (10 of 16) are satisfactory with AVEs of all

constructs larger than 0.5, and 12.5% (2 of 16) of the articles did not disclose the MV

standardized factor loadings. 19.0% (16 of 84) conducted related discriminant tests

without evaluating their suitability at this stage, with only 12 articles conducting both

convergent and discriminant validity tests. 25 articles reported the correlation matrix

among latent variables, with 17 of these also reporting the standardized factor

loadings. After retesting the Fornell-Lacker criterion in these 17 applications, 29.4%

(5 of 17) have questionable discriminant validity (i.e. at least one construct’s AVE <

its highest squared correlation with other constructs). In addition, discriminant

problems are possibly more serious, since some suspicious models did not report the

authentic correlation matrix between constructs. For example, in the final model

presented in (Wong, Cheung, & Fan, 2009), the paths from double-loop learning to

project efficiency and project effectiveness are 0.91 and 0.95 respectively. The AVE

values of the latter two constructs are 0.65 and 0.50 respectively, likely suggesting a

flawed discriminant validity assessment. 16.7% (14 of 84) conducted exploratory

factor analysis (EFA) including principal component analysis or factor analysis

before doing the confirmatory factor analysis (CFA). Table 3.2 provides a summary

of the main results of this section.

Table 3.2 Issues related to model development


Categories Tested items Number Percentage

Procedure

details

EFA before CFA/SEM 14 16.67%

Internal consistency reliability reported 55 65.48%

Convergent reliability considered 16 (of 55) 19.05%

Discriminant validity considered 16 (of 55) 19.05%

Construct

validity

retested

Reported standardized factor loadings 53 63.10%

Reported correlations between latent variables 25 29.76%

Reported both 17 20.24%

Convergent validity questionable 34 (of 53) 64.15%

Discriminant validity questionable 5 (of 17) 29.41%

Issues relating to model evaluation and reporting of results

Assessing the goodness of fit (GOF) of developed models is important for

model improvement and the discussion of findings. Many criteria have been

developed for this purpose and can be grouped into three broad categories: absolute

indices, incremental fit indices and parsimonious fit indices. Since numerous

statistics have been developed to measure model fit, this review presents only those

that are most important and commonly used.

Absolute fit indices

The Chi-square (χ2) test is the traditional measure for assessing overall model

fit by analysing the discrepancy between the sample and the proposed model (Hu &

Bentler, 1999). A probability, p, larger than 0.05 (Hair, 2006) is conventionally taken

to indicate a sufficiently good fit. This is not to be confused with the p values in t-

tests, where p<0.05 is preferred. However, χ2 statistics have been criticized for

being sensitive to sample size and for only providing a dichotomous ‘accept or

reject’ result (Kline, 2005; McDonald & Ho, 2002). The comparative χ2 of the χ2

to degrees of freedom ratio can be used to minimise the impact of sample size

(Hooper, Coughlan, & Mullen, 2008). Values of this ratio less than 2 indicate a good

fit (Marsh & Hau, 1996; Reisinger & Turner, 1999). In practice, several criteria are

often used for measuring the same GOF index. Those mostly used are summarised in

Table 3.3. For example, Kline (2005) and Pesämaa, Eriksson, and Hair (2009)

suggest ratio values of 3 and 5 respectively for the comparative χ2 index. Other

statistics in this category are also well developed (Hooper, et al., 2008; Hu &

Bentler, 1999; Marsh & Hau, 1996).


The absolute indices measure the fit between the tested model and the sample

data (McDonald & Ho, 2002) and are the most fundamental indication of how well

the proposed theory fits the real world (Hooper, et al., 2008). In addition to the χ2

test, the absolute indices include the root mean square error of approximation

(RMSEA), goodness-of-fit index (GFI), adjusted goodness-of-fit index (AGFI), root

mean square residual (RMR) and standardized root mean square residual (SRMR).

RMSEA, as a very informative statistic, measures how well the parameter estimates

generated in the proposed model fit the population matrix (Byrne, 2001). An

RMSEA<0.05 indicates an excellent fit (Marsh & Hau, 1996); 0.08>RMSEA>0.05

indicates an acceptable error of approximation (Browne, Cudeck, Bollen, & Long,

1993); and RMSEA>0.10 indicates poor fit (Byrne, 2001). In addition, a 0.06

RMSEA cut-off proposed by Hu and Bentler (1999) has some support (Hooper, et

al., 2008). There is no best criterion and current results can be evaluated separately

by each since most have well-developed theoretical support.

For the articles reviewed, 36% (9 of 25) reported p values of χ2 tests that

were confused with those of the t-tests; 48% (12 of 25) correctly stated or applied the

probability criterion level of the χ2 tests; the remaining four had unclear results.

Only 48% (12 of 25) have the recommended χ2 with p>0.05 (Hair, 2006; Marsh &

Hau, 1996). However, 83.7% (41 of 49) have a comparative χ2 ratio of less than

two, indicating a good fit. 86.9% (73 of 84) reported values of RMSEA, with 97.3%,

75.3%, 41.1%, and 27.4% of these having values less than 0.1, 0.08, 0.06 and 0.05

respectively. The results are presented in Table 3.4.

Incremental fit indices

The incremental fit indices, also known as relative fit indices, are a group

statistic obtained by comparison with a baseline model (Jöreskog & Sörbom, 1996;

McDonald & Ho, 2002). These indices include the normed fit index (NFI),

comparative fit index (CFI), Tucker-Lewis Index (TLI/NNFI), incremental fit index

(IFI) and relative fit index (RFI). NFI measures a model by comparing the χ2 test

value of the model to the χ2 value of the null model in which all of the MVs are

assumed to be uncorrelated (Hooper, et al., 2008). A NFI>0.9 is generally taken to

indicate a good fit (Hair, 2006; Marsh & Hau, 1996), although Hu and Bentler

propose a stricter cut-off value of 0.95 (Hu & Bentler, 1999). However, NFI is


sensitive to sample size and is underestimated when the sample size is small

(Hooper, et al., 2008). Therefore, NFI is not recommended for sole use (Kline,

2005). CFI is an extension of NFI that takes into account sample size and performs

well in small sample situations. Definitions of other statistics are provided in Hooper

et al (Hooper, et al., 2008), Hu and Bentler (1999) and Marsh and Hau (1996).

Descriptions and criteria for incremental fit statistics are summarised in Table 3.3.

As shown in Table 3.4, CFI is the most widely reported statistic in this category, with

80.95% (68 of 84) of the reviewed articles reporting values of CFI and 72.1% and

38.2% of models having CFI>0.90 and CFI>0.95 respectively.

Parsimonious fit indices

The parsimonious fit indices aim to avoid models becoming overly complex in

the search for improved GOF without necessary theoretical considerations (Mulaik,

et al., 1989). These indices include the parsimony normed-fit index (PNFI),

parsimony comparative fit index (PCFI) and parsimony goodness-of-fit index

(PGFI). PNFI, for example, is a modified form of NFI obtained by adjusting the

degrees of freedom. Although PNFI >0.5 is usually accepted in practice (e.g. (Chen

& Fong, 2012)), Mulaik et al note that it is possible to obtain a good fit model with a

value less than 0.5 (Mulaik, et al., 1989).

Table 3.3 GOF evaluation criteria and practical results

Fit index Evaluation criteria No. Proportion

Chi-square test

Probability

Reported number 25

p>0.05 (Marsh & Hau, 1996) (Hair,

2006) 12 48.0%

p>0.01 (Pesämaa, et al., 2009) 12 48.0%

Chi-square/df

Reported or not 49

smaller than 2 (Marsh & Hau, 1996) (Reisinger & Turner, 1999)

41 83.7%

Smaller than 3 (Kline, 2005) 48 98.0%

Smaller than 5 (Pesämaa, et al., 2009) 49 100.0%


RMSEA

Reported number 73

Smaller than 0.05 (Marsh & Hau,

1996) 20 27.4%

Smaller than 0.06 (Hu & Bentler,

1999) 30 41.1%

Smaller than 0.08 (Browne, et al.,

1993) 55 75.3%

Smaller than 0.1 (Byrne, 2001) 71 97.3%

GFI Reported number 53

Greater than 0.95(Hooper, et al., 2008) 9 17.0%


Greater than 0.90 (Marsh & Hau,

1996) (Hair, 2006) 21 39.6%

AGFI

Reported number 25

Greater than 0.95 (Hooper, et al., 2008) 1 4.0%


1996) 5 20.0%

Greater than 0.80 (Gefen, 2000) 15 60.0%

RMR

Reported number 15

Smaller than 0.05 (Chen & Fong,

2012) 9 60.0%


1999) 12 80.0%

SRMR

Reported number 5

Smaller than 0.05 (Xiong, Skitmore,

Xia, et al., 2014) 2 40.0%


1999) 4 80.0%


CFI

Reported number 68

Greater than 0.95 (Hu & Bentler, 1999) 26 38.2%


1996) (Hair, 2006) 49 72.1%

NFI

Reported number 33

Greater than 0.95(Hu & Bentler, 1999) 9 27.3%


1996) (Hair, 2006) 21 63.6%

TLI/NNFI

Reported number 43


Greater (Hair, 2006) 24 55.8%

IFI

Reported number 25



1996) 19 76.0%

RFI Reported number 7


1996) (Hair, 2006) 1 14.3%

Parsimonious fit

PNFI Reported number 6

Greater than 0.50 (Chen & Fong,

2012) 5 83.3%

PCFI Reported number 2

Greater than 0.50 (Chen & Fong, 2012) 2 100.0%

PGFI Reported number 2

Greater than 0.50 (Xiong, Skitmore,

Xia, et al., 2014) 2 100.0%

Table 3.4 Description of reported GOF indices

Fit index No. Proportion Mean SD Median Range

Chi-square test

Chi-square 50 59.52% /

Probability level 25 29.76% /


Chi-square/d.f. 49 58.33% 1.76 0.49 1.68 (1.02, 3.5)


RMSEA 73 86.90% 0.068 0.039 0.066 (0.000,0.329)

GFI 53 63.10% 0.856 0.086 0.846 (0.620, 0.983)

AGFI 25 29.76% 0.808 0.111 0.829 (0.530, 0.950)

RMR 15 17.86% 0.065 0.061 0.049 (0.013, 0.230)

SRMR 5 5.95% 0.071 0.045 0.057 (0.038, 0.150)


CFI 68 80.95% 0.918 0.064 0.934 (0.744, 1.000)

NFI 33 39.29% 0.893 0.083 0.913 (0.690, 0.998)

TLI(NNFI) 42 50.00% 0.880 0.105 0.901 (0.428, 1.016)

IFI 25 29.76% 0.927 0.055 0.941 (0.941, 1.000)

RFI 7 8.33% 0.773 0.110 0.730 (0.670, 0.994)

Parsimonious fit indices

PNFI 6 7.14% 0.583 0.154 0.650 (0.277, 0.688)

PCFI 2 2.38% 0.748 0.027 0.748 (0.729, 0.767)

PGFI 2 2.38% 0.653 0.028 0.653 (0.633, 0.673)

3.2.4 Discussion and recommendations

SEM is a very useful and versatile technique for both theoretical research and

experimental studies, and applications in construction research continue to increase.

Every method of statistical analysis, however, has its strengths and limitations and it

is important to understand these properties and characteristics in order to make

suitable choices among available alternatives. This is especially the case with SEM,

where many pitfalls await the unwary researcher in terms of sample size, construct

validity assessment, goodness of fit measures, etc. Many of these are identified in

this review of all the 84 articles containing SEM in solving construction research

problems over the period 1998-2012, including questionable convergent and

discriminant validity, and misunderstood p values in Chi-square tests. These and

many other important issues such as longitudinal studies, mediation effects,

moderation effects and multi group analysis are discussed and recommendations for

selected issues are summarised in Table 3.5.

The three-step procedure can be helpful for researchers in organizing their

application of SEM. At the research design stage, researchers can evaluate if SEM is

suitable and how to design their models and hypotheses. In the model development

stage, researchers can evaluate whether it is possible to solve the proposed models

accordingly. Many problems in model development are related to carelessness over

some critical issues in the research design stage. Therefore, researchers are


encouraged to ensure a suitable MVs to LVs ratio, sample size and construct type as

early as possible. For example, it is inadvisable to apply single indicator constructs

without sufficient theoretical support, it being better to use manifest variables

directly if necessary (Ringle, et al., 2012). In the model development stage, a two-

step procedure is recommended: (1) the CFA phase: correlate all constructs together

firstly to test reliability and validity and refine or even change models accordingly;

and (2) the SEM phase: replace the correlations among constructs to the proposed

causal relations in the theoretical model and refine the models again.

It is also noticed that 16.7% (14 of 84) of the articles conducted an exploratory

factor analysis (EFA) before doing the confirmatory factor analysis (CFA). However,

its value and necessity are uncertain. Instead, the motivational differences between

EFA and CFA (see more in Thompson (2004)) should be considered, as should the

fact that CFA can handle MVs categorization and model refinements well. Since

model evaluations have been presented in detailed in Section 3.3, they are not

presented in Table 3.5. Additionally, it is recommended for researchers to present a

graphical form of the developed model for its clarity. It is a fact that few models are

perfectly correct and this can be a guide for researchers to assess and report their

models comprehensively (Shah & Goldstein, 2006). Since the principal of parsimony

is useful in selecting the best model from all candidate models especially when the

other two types of indices are comparable (Mulaik, et al., 1989), it is recommended

for further research to report more on parsimonious fit indices.

Table 3.5 Recommendations for selected issues in SEM

Issue Recommendation

Number of MVs per LV

Use three or more MVs per LV (Kline, 2005). The single indicator

construct is not recommended for its inadequate representation and

model deterioration; unless a single MV can present the LV perfectly

(Sarkar, et al., 1998; Shah & Goldstein, 2006).

Formative vs reflective

constructs

Check the causal directions between LVs and MVs as discussed in 3.1.2

section. Current SEM software (i.e. LISREL, AMOS and EQS) only

handles reflective constructs well. Solving formative constructs needs

additional constraints (Shah & Goldstein, 2006), however, and other

methods such as partial least square structural modelling are needed.

Model identification Calculate the d.f. values before data collection to make sure that it is

possible solve the original model and the alternatives.

Sample size issues Try to have a sample size larger than 100 (Bagozzi & Yi, 2012) or the

sample size to unknown parameters ratio should be larger than 5:1

(Bentler & Chou, 1987). Use bootstrapping to confirm the reliability of

results. Report GOF indices adjustments for small samples, such as

NNFI and Chi-square/d.f (Hooper, et al., 2008; Shah & Goldstein,

2006).

Multivariate normality Multivariate normality of data is an inherent assumption when applying

the ML and violations of this will cause problems such as inflated


goodness of fit (MacCallum, et al., 1992).. It is recommended to use

estimation methods such as “ML, Robust” in EQS (Shah & Goldstein,

2006) and normal ML available in AMOS and LISREL as they are

robust to moderate violations of normality (Shah & Goldstein, 2006).

Some other distribution-free methods such as ULS and ADF can be

used (Shah & Goldstein, 2006).

Convergent validity

Assessing construct validity is necessary for making reliable

conclusions. The AVE of constructs should be larger than 0.5. Factor

loadings less than 0.5 should be considered for deletion (Hair, Sarstedt,

et al., 2012).

Discriminant validity The AVE of one construct should be higher than its highest squared

correlation with other constructs (Fornell & Larcker, 1981a).

3.2.5 Conclusions

Since it is hard to discuss everything important in SEM, the discussion and

recommendations section is organized to cover the common drawbacks of current

applications in our field. In doing this review of current SEM applications in solving

construction related problems, therefore, the goal has not been to cast doubts on the

SEM results to date. Rather, it has been to provide suggestions, recommendations

and guidelines for future SEM from research design to model development and

evaluation. It is hoped, therefore, that this review will be helpful for researchers to

enrich the body of knowledge. Other advanced techniques such as measurement

invariance and multitrait-multimethod studies are well developed in psychology, but

have seen little use in our field to date. Readers interested in applying these are

advised to consult the appropriate literature. Meanwhile, the intention of this paper

has been to contribute to the acceleration of research development in the construction

field by helping to create more technically informed researchers in the basic

application of structural equation modelling.

SEM can not only be a powerful tool for handling complex research problems

in traditional research topics, it can also be a helpful tool for construction academics

and technicians to assess the acceptance, usage and success of newly developed

technologies (e.g. Lee and Yu (2012); (Park, et al., 2012; Son, Park, et al., 2012;

Yang, et al., 2012)). This review will help them to design and apply SEM

applications in a more logical and efficient way.

Chapter 4: Work stress 87

Chapter 4: Work stress

STATEMENT OF CONTRIBUTION




field of expertise;











Work stress


dimensions of occupational stress: Evidence from construction cost estimators in

China, Construction Management and Economics, 2015. 33(5-6), 495-507.


Bo Xiong

Conducted a literature review, designed the questionnaire, collected

data, wrote the manuscript and acted as the corresponding author.

07/03/2016


Bo Xia Directed and guided this study, and assisted with questionnaire

design.

88 Chapter 4: Work stress




Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


4.1 INTRODUCTION

Occupational stress has been an important concept in organizational

management since the increased awareness of the prevalence of mental disorders

such as depression in the 1980s Tennant (2001). In the construction industry, because

of the complexity and dynamic uncertainty of its projects and often, workers and

professionals are frequently expected to confront and cope with stressful situations

(Leung, Ng, et al., 2005; Love, Edwards, & Irani, 2010). This, together with the

heavy workloads involved, can lead to serious occupational stress (Bowen, Edwards,

& Lingard, 2012). In addition to concerns of the wellbeing of those affected, such as

occupational illness and injuries (Lundstrom, Pugliese, Bartley, Cox, & Guither,

2002), the study of occupational stress is especially important in organisational terms

for the effects on organizational commitment, production performance and even

intentions to leave (Boyas, Wind, & Kang, 2012; Jamal, 1990; Leiter & Maslach,

1988).

Although identification and categorization studies of the stressors involved (i.e.

working conditions causing stress) are not uncommon in construction management

research (e.g. (Leung, Zhang, et al., 2008; Leung, Ng, et al., 2005; Richmond &

Skitmore, 2006)), the sub-dimensions of occupational stress (i.e. divisibility of

emotional reactions caused by work conditions) have received little treatment to date.

Additionally, occupational stress is widely regarded as a holistic concept with little

regard for the dimensions involved. Hurrell and McLaney (1988), for example, use

the general term "strain" to describe the emotional reaction to stressful conditions.

However, such reactions should not only include negative ones but also the joy of

stress (Hanson, 1987).

Realizing a similar situation in psychosomatic research, Levenstein et al.

(1993) developed a 30-question perceived-stress questionnaire (PSQ), validated with

responses from 230 medical subjects comprising in-patients, out-patients, students

and health workers in Italy. Fliege et al. (2005) later used an adapted version with

650 German subjects also in the medical context to conduct a principal component

analysis (PCA); identifying four underlying dimensions, comprising one stressor –

demand - and three emotional reactions in terms of worry, tension and joy (Fliege, et

al., 2005). However, this remarkable finding of exactly four dimensions has yet to be


confirmed empirically in other contexts, the construct consisting of three emotional

reactions validated and their effects determined.

This paper therefore firstly tests the applicability of an adapted Chinese PSQ

by applying exploratory factor analysis, and then validates the three dimensional

emotional reaction framework by conducting confirmatory factor analysis and

structural equation modelling. Two hypotheses are firstly tested:

Hypothesis 1 Fliege et al.’s (2005) four categories, comprising demand, worry,

tension and lack of joy are identifiable in the Chinese PSQ version.

Hypothesis 2 The three dimensional occupation stress framework of worry,

tension and lack of joy is reliable and valid in this context.

These two hypotheses are derived from the adapted and translated

measurement scale. Hypothesis 3 is developed to assess nomological validity in

exploring relationships between these internal dimensions and a related construct of

organizational commitment. The term organizational commitment is introduced for

this purpose, since some researchers (e.g. Leiter and Maslach, 1988; Jamal, 1990)

found a negative effect of occupational stress on organizational commitment.

Additionally, organizational commitment is an important concept highly correlated

with task performance and much other organizational behaviour, including

organizational citizen behaviour and turnover of employees (Chun, Shin, Choi, &

Kim, 2013; Porter, et al., 1976; Porter, et al., 1974). This leads to

Hypothesis 3 The three internal dimensions of occupational stress negatively

affect organizational commitment.

4.2 LITERATURE REVIEW

4.2.1 Occupational stress and its effects

Occupational stress can be regarded as adverse subjective emotions

experienced by employees when facing an imbalance between requirements and

ability and other working conditions (Bowen, Edwards, Lingard, & Cattell, 2014;

Leung, Zhang, et al., 2008). Worry, tension and lack of joy are found to be important

components of such emotions (Fliege et al., 2005). Occupational stress has become

an important topic in construction during recent decades since the industry has a high


exposure to uncertainties and many professionals experience high levels of stress.

According to a survey on stress conducted by the Chartered Institute of Building

(CIOB) in 2006, 68.2% of the 847 respondents admitted suffering stress and 26.6%

of these sought medical help. It is also revealed that only 15% of respondents thought

the industry had become less stressful and mental health was coped with well in

workplace (Campbell, 2006). A more recent survey by Bowen (2014a) indicates 55%

respondents of construction professionals in South Africa face high stress.

Occupational stress can also result in poor mental health according to Love et al.’s

(2010) survey among construction professionals in Australia. Occupational stress,

which is described as emotional stress in Leung, et al. (2010)’s survey of stress

among construction workers in Hong Kong, rather than demand-ability imbalance

causing accidents and injuries.

The influence of occupational stress on people is less easy to understand.

Evidence from a survey of 306 mainly American nurses indicates that perceived

social support from co-workers improves reported job performance and reduces

reported job stress (AbuAlRub, 2004), while Hon (2013), with evidence from 305

employees in 48 hotels and service organizations in China, finds co-worker support

is a significant moderator between working-creativity-caused stress and service

performance. Interestingly, AbuAlrub (2004) found job stress and job performance

had a U shape relationship, with mainly American nurses reporting moderate job

stress and believing their performance is worse than those reporting low/high job

stresses - which is consistent with Hanson’s (1987) statement that a medium stress

level is needed for more efficient work output (Gunning & Cooke, 1996). Jamal

(1984), on the other hand, in analysing sample data from 440 nurses working in

Canada, proposes employee professional and organizational commitment as

moderators in the stress-performance link, although this is only partially supported

by the data. In construction research, Bowen, Edwards, et al. (2014) examine four

categories of effects of occupational stress in terms of psychological effects,

physiological effects, sociological effects and substance usage (including alcohol,

cigarettes and even illegal drugs) in a survey of construction professionals in South

Africa.

In this study, the effect of occupational stress on organizational commitment is

targeted to validate the divisibility of occupational stress. The term “commitment” is


widely used in analysing both organizational and individual behaviours but no

commonly acknowledged definition has been developed (Becker, 1960). For

example, the activities of a committed person may be a result of considering

“generalized cultural expectations” (e.g. a trustworthy person do not change jobs

frequently) and “impersonal bureaucratic arrangements” (e.g. the economic loss of

quitting the current job) (Becker, 1960). However, the term “organizational

commitment” discussed here is not concerned with various definitions of general

commitment but the commitment related behaviours of employees characterized by:

(1) acceptance and appreciation of goals and values of the organization; (2)

willingness to make extra efforts for the success of the organization; and (3) a strong

desire to stay in the organization (Mowday, et al., 1979). Some academics (e.g.

Porter et al., 1974) point out that organizational commitment takes longer to build

but is more stable. Chun et al. (2013) analyse data from 3821 employees of 130

Korean companies and find organizational commitment positively affects the

financial performance of these companies via a mediation effect of organizational

citizen behaviour. Porter et al. (1974) conducted a longitudinal study to examine the

relationships between organizational commitment, job satisfaction and turnover with

evidence from psychiatric technician trainees, and found that general attitudes

concerning organizational commitment are important in deciding whether to stay or

leave (Porter et al., 1974) and that the level of organizational commitment declines

before leaving the current job (see Porter et al., 1976).

For the relationship between occupational stress and organizational

commitment, Leiter and Maslach (1988) found a significant negative effect of

occupational stress on organization commitment with empirical cases from 52 nurses

and support staff in a small hospital. Similarly, Jamal (1990) found low

organizational commitment and high turnover intention when employees face high

occupational stress and stressors, with empirical observations of a large hospital in

Canada with around 350 nurses. A recent survey in Iran conducted by Aghdasi,

Kiamanesh, and Ebrahim (2011) also found a significantly negative effect of

occupation stress on organizational commitment when exploring the relationship

between emotional intelligence and organizational commitment and the mediating

effect of occupation stress.


4.2.2 Stressors and coping strategies

Hurrell and McLaney (1988) point out that job stressors lead to psychological,

physiological and behavioural reactions of employees. In stress related research, the

importance of identifying internal and external stressors has been widely

acknowledged (Levenstein et al., 1993). For example, the 15-stressor inventory

developed in Jamal’s (1984) study measuring the relationship between occupational

stress and job performance of Canadian nurses categorize the 15 stressors into four

types in terms of role ambiguity, role conflict, overload and resource inadequacy.

Another psychometric instrument developed by Hurrell and Mclaney (1988)

categorizes 13 occupational stressors into workload, responsibility, role demands and

conflict. In construction, Leung et al. (2005a) find work overload, role conflict, job

ambiguity and working environment to be the ones most affecting stress levels of

consultant cost engineers (quantity surveyors) in Hong Kong. Organizational support

factors are also regarded as antecedents of stressors with this group, with stressors

such as lack of autonomy acting as mediators between organizational support and

employee stress (Leung et al., 2008b). Gunning and Cooke (1996) surveyed 39

construction professionals and 22 lecturers active in the Northern Ireland

construction industry and found "working to impossible deadlines", "client demands"

and "hiring/firing staff" to be three main causes of stress. Love et al. (2010) find

work-support to be an important predictor of occupational stress of consultants in the

Australian construction industry too, with lack of support resulting in the generally

poor mental health status of those affected (Love et al., 2010). Bowen et al. (2012)

evaluate the status of some stressors including job demands, job control, job support,

job certainty and opportunities, and the general work environment among South

African construction professionals. Ng, Skitmore, and Leung (2005) research on

measuring the manageability of stress in relation to Hong Kong construction projects

categorized 33 stressors into seven groups of work-nature related stressors, work-

time related stressors, organisation policy related stressors, organisation position

related stressors, situational/environmental stressors, relationship related stressors

and personal stressors.

Negative effects of occupational stress occur when insufficient resources are

available to cope with stressors (Cohen, Kamarck, & Mermelstein, 1983). The act of

coping, describes the situation when people defend themselves from threats to


current psychological conditions, such as integrity, and has gained in popularity since

1960s (Lazarus, 1993). In the Ways of Coping Questionnaire developed by Folkman

and Lazarus (1988), eight coping categories are presented: confrontational coping,

distancing, self-controlling, seeking social support, accepting responsibility, escape-

avoidance, planned problem solving and positive reappraisal. As mentioned earlier,

only 26.6% of the stressed people in the 2006 CIOB survey sought medical advice

and the mostly dependent coping mechanism is the support from other colleagues

(Campbell, 2006). Yip and Rowlinson (2006) exploratory factor analysis of the stress

coping behaviours of construction professionals in Hong Kong identified four main

categories of rational problem solving, resigned distancing, support seeking and

passive wishful thoughts. Adverse coping behaviours such as the consumption of

alcohol, cigarettes and illegal drugs have already been mentioned Africa (Bowen et

al., 2014a). The study of appropriate coping strategies is therefore an associated

common topic. Aiming to help project participants better cope with stresses, Ng et al.

(2005), for example, conducted a questionnaire survey to measure the manageability

of the stressors most confronted by Hong Kong construction project participants.

Richmond and Skitmore (2006) also provide 14 stress coping strategies such seeking

social support, improving communication and taking exercise for 50 identified

potential stressors by conducting interviews with project managers in the Australian

IT industry. In coping strategy selection, Haynes and Love (2004) found that active

coping is more useful than other strategies such as social coping and self-control in

their survey of male project managers in Australia.

4.2.3 Measures of occupation stress and divisibility

Hurrell et al. (1998) divide occupational stress research into two main types:

research studies on occupational stressors faced by employees in their working

environment, and studies of employees' emotional reactions (e.g. stain) to working

conditions. Although it is debatable whether the measurement of stress should

concentrate on stressors or stress reactions, it is acknowledged that both approaches

provide useful different perspectives (Hurrell et al., 1998; Fliege et al., 2005).

Therefore, some basic questions such as what is "occupational stress" and "what

dimensions should be included?" are also worth exploring.


The divisibility of occupational stressors has been widely acknowledged and

applied (Kahn, Wolfe, Quinn, Snoek, & Rosenthal, 1964; Leung, Ng, et al., 2005;

Porter, et al., 1974) as well as the difference between stressors and stress, but

occupational stress is still seen as a holistic concept in most studies. For example, in

Jamal’s (1990) study examining the effects of stress and stressors on employee job

satisfaction, organizational commitment and turnover intention among nurses in

Canada, total scores of eight stress-related items are used in further analyses. Since

occupational stress can be regarded as a result of the imbalance between job demands

and actual ability of employees (Bowen et al. 2014a), the total score of ten items

measuring such imbalance is used to describe occupational stress in (Leung, Zhang,

et al., 2008) and (Leung, Chan, Chong, & Sham, 2008) study of construction cost

engineers. However, such imbalance is more similar to a stressor rather than an

emotional reaction. In Bowen et al.’s (2014a) study of the stress effects of

construction professionals in South Africa, only a single 10-point scale was applied

to measure stress levels. Therefore, it is critical to contribute to the body of

knowledge to provide more attention to measurement-related issues of occupational

stress (Hurrell et al., 1998).

Levenstein et al. (1993) developed a perceived stress questionnaire (PSQ) to

explore the divisibility of occupational stress, but which has been criticised by Fliege

et al. (2005), however, for overlapping occupational stress and stressors.

Nevertheless, the developed PSQ reveals the existence of internal dimensions of

occupational stress. Additionally, Fliege et al. (2005) admit that their PSQ version

includes one occupational stressor, demands, and three other emotional stress

reactions. This study develops a Chinese PSQ version based on their work and aims

to demonstrate the divisibility of occupation stress.

4.3 RESEARCH METHOD

An adapted questionnaire was developed based on the PSQ constructed by

Levenstein et al. (1993) and Fliege et al. (2005). To assure content validity in the

Chinese context, a translation and back translation technique was applied. Principal

component analysis, confirmatory factor analysis and structural equation modelling

were used for testing construct validity.


4.3.1 Perceived stress questionnaire

The PSQ developed by Levenstein et al. (1993) and Fliege et al. (2005) was

used as the main instrument in the study. After analysing the results of Fliege et al’s

factor analysis and considering the likely drawbacks and suitability of these items in

the Chinese context, a modified 4x4 (four dimensions of stress, with each containing

four items) version was conjectured for the Chinese cost engineer context. Also,

while Fliege et al.’s PSQ refer to the respondent as “you”, the respondent were

address as “I” in this study make it easier for Chinese respondents to report more

personal emotional reactions. Additionally, the four-scale questionnaire response

format used in Levenstein et al. (1993) was changed to a seven-point Likert scale

format to elicit more finely grained information. Furthermore, a “don’t know”

option, omitted from Levenstein et al.’s and Fliege et al’s versions, was offered in the

questionnaire as a standard procedure for those unable to answer corresponding

questions.

The main part of the questionnaire is presented in Table 4.1. According to

Fliege et al.‘s (2005) categorization, Q1-Q4 belongs to demands, Q5-Q8 to worry,

Q9-Q12 to tension and Q13-Q16 belongs to joy. Q13-Q16 was reversed in the

analysis and named as AQ13-AQ16 indicating the lack of joy dimension to be

consistent with Levenstein et al.’s categorization.

Table 4.1 Perceived stress questionnaire No. Occupational stress 1-not at all to 7

very intensive Don't know

Q1 I have too many things to do 1 2 3 4 5 6 7 □

Q2 I do not have enough time for myself 1 2 3 4 5 6 7 □

Q3 I feel under pressure from deadlines 1 2 3 4 5 6 7 □

Q4 I feel I am in a hurry 1 2 3 4 5 6 7 □

Q5 I have many worries 1 2 3 4 5 6 7 □

Q6 My problems seem to be piling up 1 2 3 4 5 6 7 □

Q7 I fear I may not manage to attain my goals 1 2 3 4 5 6 7 □

Q8 I feel frustrated 1 2 3 4 5 6 7 □

Q9 I feel tense 1 2 3 4 5 6 7 □

Q10 I feel mentally exhausted 1 2 3 4 5 6 7 □

Q11 I have trouble relaxing 1 2 3 4 5 6 7 □


Q12 I find it hard to feel calm 1 2 3 4 5 6 7 □

Q13 I feel I am doing things I really like (R) 1 2 3 4 5 6 7 □

Q14 I am light hearted (R) 1 2 3 4 5 6 7 □

Q15 I feel safe and protected (R) 1 2 3 4 5 6 7 □

Q16 I am full of energy (R) 1 2 3 4 5 6 7 □

Note: Adapted from Fliege et al. (2005).

4.3.2 Translation and back translation

Translation and back translation is a widely accepted technique in cross-

cultural research since translation quality and equivalence between source and target

versions are critical (Brislin, 1970). Despites of its importance, this technique was

not yet widely acknowledged and used in construction research. Siu, Phillips, and

Leung (2003), for example, apply the technique in a safety attitudes questionnaire

used in some European studies when measuring the role of age on safety attitudes

and performance among Hong Kong construction workers. Ding and Ng (2007) also

apply the technique in validating their translated Chinese version of McAllister’s

trust scale. Because of differences in cultural backgrounds and languages, the

translation of questionnaires from English to Chinese needs be carried out with care

in this research. A two-stage translation and back translation technique was therefore

adopted. For the first stage, a Chinese version of the questionnaire was translated

from the English version by a bilingual PhD candidate with knowledge of PSQ, with

the preliminary Chinese draft emerging after several rounds of discussions with a

bilingual member of university academic staff. For the second stage, another pair of

bilingual assistants (i.e. PhD student and academic staff) without prior knowledge of

the PSQ English version of the questionnaire, translated the Chinese questions back

to English. The two English versions were then compared for significant inaccuracies

(Table 4.2). The discrepancies found were then corrected to produce the final

version.

Table 4.2 Translation and back translations No. Final Chinese version Back translation-1 Back translation-2

Q1 我有太多事情要做 I have a lot of things to do. I have too many works to do

Q2

我感到留给自己的时间

不够 I feel that I have limited time

to myself.

I feel not enough time for

myself


Q3

我感到来自截止日期的

压力 I feel the pressure from

deadlines. I feel deadline pressure

Q4 我感觉自己很着急 I feel that I am in a hurry. I feel I am in a hurry

Q5 我有很多担心 I have many worries. I have a lot of concerns

Q6 我的问题似乎越堆越多 It seems that my problems are

increasing.

My problems seem to be

accumulating

Q7

我担心我不能实现我的

目标（们） I am afraid that I cannot

achieve my goals.

I am concerned about not

realising my objective(s)

Q8 我感到受挫与沮丧 I feel frustrated and depressed. I feel frustrated and depressed

Q9 我感到紧张 I feel nervous. I feel nervous

Q10 我感觉到精神上的疲惫 I feel mentally exhausted. I feel mentally exhausted

Q11

我在放松身心上存在问

题 I have some problems on

relaxing my body and mind.

I have problem in physical and

psychological relaxation

Q12 我很难冷静 It is hard for me to keep calm.

I have difficulty in calming

down

Q13

我感觉我在做自己真正

喜欢的事情 I feel I am doing the things

that I like.

I think I am doing the work

that I truly like

Q14 我很轻松 I feel relaxed. I am very relaxed

Q15 我有安全感 I feel a sense of security. I feel secure

Q16 我感觉充满能量 I feel that I am full of energy. I feel energetic

4.3.3 Data collection and demographics

To validate the developed PSQ with empirical evidence from China, a

questionnaire applying the snowball sampling technique was used as recommended

in Shi, Ye, Lu, and Hu (2014) to approach potential participants rather than direct

delivery to companies, due to the sensitive questions asked in the PSQ. Young cost

engineers are targeted to validate the PSQ as it is impossible to cover all construction

populations in China. As indicated in some surveys including Love et al. (2010) and

Bowen et al. (2014a), different construction professionals differ largely in stress

levels and related effects. Construction cost engineers, with huge responsibilities and

high stress levels in construction projects, have been targeted as subjects in several

previous studies (e.g. (Bowen, et al., 2012; Leung, Chan, et al., 2008; Leung, Zhang,

et al., 2008; Leung, Ng, et al., 2005; Leung, Olomolaiye, et al., 2005)). Haynes and

Love (2004) found that less working experience is a significant predictor of high

occupational stress, while such a negative effect is not significant in (Bowen,

Govender, & Edwards, 2014). It is reasonable, therefore, to assume that young (i.e.

inexperienced) cost engineers have higher risks of becoming stressful at work due to


the imbalance between lack of experience and high demands. In Winefield and

Anstey (1991) survey of the occupational stress of general practitioners, emotional

exhaustion and depression of respondents younger than 40 are much higher than

those elder. Boyas et al. (2012) also found among child protection workers that

occupational stress levels and coping mechanisms differed greatly by age groups,

attributing this to differing social capital. Similarly, young doctors familiar with a

doctor's daily work find their job to be less stressful, emphasizing the effect of

experience (Bolanowski, 2005). Since five years’ experience is generally

acknowledged as the necessary time for practitioners to master construction cost

estimation skills (Skitmore, et al., 1990), potential respondents were restricted to

having no more than five years’ working experience. 144 valid responses were used

for further analyses. Of these, 74 (51.4%) are male and 69 (47.9%) are female (1

missing data); 42 (29.2%) are younger than 25, 100 (69.4%) range from 25 to 34 and

1 (0.7%) from 35 to 44 (1 missing data); and for their highest educational level, 13

(9%) possessed diplomas, 109 (75.7%) a bachelor’s degree and 22 (15.3%) a

master’s degree.

4.3.4 Data reliability

Cronbach's alpha is used to evaluate the internal consistency of the

questionnaire items. The overall value is 0.885, with 0.845, 0.834, 0.790 and 0.753

for the demands (Q1-Q4), worry (Q5-Q8), tension (Q9-Q12) and lack of joy (AQ13-

AQ16) dimensions respectively. Since all these values are larger than the 0.7 cut-off

value (Cronbach, 1951), the whole and the parts of the questionnaire are considered

to be acceptably consistent. Since the Cronbach alpha value is affected by length of

scale, the matrix of correlations of individual items is also examined for confirming

scale reliability (Ding and Ng, 2007). With a mean of the absolute values of item-

item correlations of 0.329 (SD=0.182), the results indicate an acceptable level of

reliability.

Although PCA deals well with non-normal distribution situations (Wang & Du,

2000)tests on sample distributions are still useful to reflect information concerning

the population distribution. Additionally, multivariate normality is an inherent

assumption when using the default maximum likelihood estimation method in

structural equation modelling (SEM) (MacCallum, et al., 1992; Xiong, et al., 2015a).


The sample skewness and kurtosis statistics can be used to test the normality of

distribution of variables and both should lie within the [-1, +1] interval (Hair, 2006).

Here, the skewness and kurtosis values of all 16 variables are within the range of -

0.86 to 0.45 and -0.54 to 0.46 respectively, which indicates the normal distribution

assumption to be satisfied.

4.4 DATA ANALYSIS AND DISCUSSION

Tests of these hypotheses are carried out with a sample of 144 predominately

young cost engineers working in the Chinese construction industry. As will be seen,

the first two hypotheses are supported and hypothesis 3 is supported partially as a

structural equation model indicates that only the lack of joy has a significantly

negative effect on organizational commitment.

4.4.1 Principal component analysis

Consistent with Levenstein et al. (1993) and Fliege et al.’s (2005) exploratory

study using PCA, the PCA confirms the hypothesized four-dimensional structure of

the PSQ, with a 0.840 Kaiser-Mayer-Olkin measure of sampling adequacy higher

than the cut-off value of 0.5 (Hair, 2006) and a highly significant p<0.0001 for

Bartlett’s test for sphericity, indicating that the items are suitable for factor analyses.

The forced 4-factor solution applying the varimax rotation, a widely applied

orthogonal rotation method maximizing the sum of the variances of the squared

loadings (Abdi, 2003) and used in Leventein et al. (1993) and Fliege et al. (2005),

explains 70.1% of the overall variance, with components 1, 2, 3 and 4 accounting for

38.1%, 16.6%, 10.0% and 5.4% respectively. The allocated components, means (M),

standard deviations (SD) and communalities (h2) of the items are summarised in

Table 4.3. For clarity, the largest factor loadings of each item are shown in bold.

Table 4.3 PCA with varimax rotation

Items Components Item parameters

1 2 3 4 M SD h2

Q1 0.009 0.856 0.019 -0.109 5.72 1.19 0.75

Q2 0.092 0.879 0.063 0.108 5.59 1.44 0.80

Q3 0.042 0.74 -0.106 0.366 5.49 1.45 0.70


Q4 0.256 0.643 0.108 0.524 5.22 1.43 0.77

Q5 0.327 0.47 0.077 0.645 5.26 1.41 0.75

Q6 0.379 0.297 0.077 0.699 4.60 1.50 0.73

Q7 0.402 -0.044 0.244 0.706 4.76 1.71 0.72

Q8 0.789 -0.045 0.2 0.344 3.78 1.67 0.78

Q9 0.789 0.149 0.187 0.258 4.08 1.63 0.75

Q10 0.663 0.304 0.384 0.184 4.44 1.58 0.71

Q11 0.695 0.13 0.345 0.022 3.86 1.64 0.62

Q12 0.683 -0.023 -0.072 0.245 3.32 1.71 0.53

AQ13 0.182 -0.102 0.625 -0.007 3.42 1.38 0.43

AQ14 0.147 0.371 0.776 -0.038 3.99 1.40 0.76

AQ15 0.108 0.068 0.822 0.171 3.55 1.40 0.72

AQ16 0.155 -0.279 0.667 0.395 3.38 1.27 0.70

4.4.2 Discussion-PCA results

With the exception of Q8 – “I feel frustrated and depressed” – the PCA

supports the hypothesised 4x4 structure. This anomaly is discussed below in terms of

the four dimensions involved, together with the relationship of the results with the

findings of previous studies of stress emotional reactions.

The tension dimension, comprising Q8-Q12, explains the largest proportion of

variance (38.1%) in the data, which is consistent with Jamal’s (1984) view of job-

related tension being regarded as occupational stress. According to Fliege et al.’s

(2005) original categorization, Q8 (“I feel frustrated”) is not included in this

dimension but in the worry dimension. This may be due to Fliege et al’s selection of

5 items from Levenstein et al.’s (1993) original 13 items for this dimension. If we

carry out a semantic analysis between Q5-Q7 and Q8, however, it is easy to see that

there are no words of “worry”, “afraid” or “fear” in Q8. Additionally, two Chinese

words are used to represent “frustrated” exactly and they are back translated as

“frustrated and depressed”. Therefore, it is reasonable to include Q8 in the tension

dimension. Also worth mentioning is the slightly low communality value (0.53) of

Q12 and a slight increase (0.009) of Cronbach's alpha value if deleted. This indicates

an inconsistent understanding of “calm” by the respondents, possibly related to the

fact that “calm” refers not only to “not excited or nervous” but also to “reasonable

and wise” in the Chinese culture, which is significantly influenced by Confucius’

wisdom . Therefore, some minor changes may be necessary for future applications of

Q12.


The demands dimension, comprising Q1-Q4, explains 16.6% of the variance in

the data. Cronbach's alpha value is rather high (0.845) but would not increase if any

item is deleted. According to Fliege et al.’s (2005) explanation, this dimension is

actually an extra stressor dimension that is similar to the term “overload” mentioned

in many stressor studies (e.g. Jamal, 1984; Leung et al., 2005a) and different in

nature to the other three dimensions.

The lack of joy dimension, comprising AQ13-AQ16, explains 10.0% of the

variance, and has an acceptable Cronbach alpha value of 0.753, but would be

increased a little (by 0.007) if AQ13 is deleted. Additionally, the communality of

AQ13 is comparatively low (0.43), indicating some confusion among respondents

when answering Q13 (“I feel I am doing the things that I like”), which is similarly

reflected in Levenstein et al.’s (1993) factor analysis results where the factor loading

on this item in the lack of joy factor is also comparatively low.

The worry dimension, comprising Q5-Q7, explains 5.4% of the variance, and

has a high Cronbach alpha value (0.803) that would not increase if any item was

deleted. The issue of Q8 is discussed above. To remain in the worry dimension, the

wording of Q8 needs to be changed to such as “I am afraid of/fear frustration” with a

greater emphasis on “worry”.

Investigating the differences among variables is a very informative way to

understand the multi-attributes of the sample. As shown in Table 4.3, items under the

demands sub-dimension among participants have comparatively high mean values,

indicating the young cost engineers experience a general “overload” feeling. The

average value of this sub-dimension (5.52) is higher than that (4.13) of the “work

overload” feeling among their counterparts in Hong Kong according to a 7 point

Likert scale survey by Leung et al. (2005a). Additionally, Leung et al. (2005a) found

that the “work overload” factor is the most predictive stressor of stress of

construction cost engineers in Hong Kong. This difference may be related to the

extensive construction work needed to cope with Mainland China’s rapid

urbanization, where the sub-sector of construction cost consultancy reached CNY

80.685 billion and 237,100 employees in 2011 after a 10% annual increase rate for

several years (Shi et al., 2014). With such a fast increasing market and following

needs to recruit new employees, therefore, it is not surprising to find that current

employees experience high “demands”. According to the results shown in Table 4.3,


young construction cost engineers also experience intense worry but with a little less

tension and less still lack of joy.

4.4.3 Validation with SEM

Although the PSQ developed by Levenstein et al. (1993) and Fliege et al.

(2005) helps in a obtaining a deeper understanding of occupational stress, the internal

dimensions of occupational stress need further construct validation. Since the PCA

results confirm the applicability of the PSQ, confirmatory factor analysis (CFA) is

applied for testing hypothesis 2 and hypothesis 3. In order to test nomological

validity and understand the potential different effects of the three different emotional

reactions, organizational commitment was introduced as the dependent variable in

SEM to test hypothesis 3. Five items were used, such as Mowday et al’s (1979)

organization commitment measure of "I really care about the fate of this

organization".

A CFA model is firstly developed to test the reliability and validity of a

construct consisting of three sub-dimensions of occupational stress in terms of worry,

tension and lack of joy. Since the CFA model, as presented in Table 4.4, is a good fit,

a further SEM model is developed to test hypothesis 3. Because the weightings of

manifest variables on latent variables in the CFA are quite similar to those in the

SEM, only the weightings in the SEM are presented in Table 4.4 for the sake of

clarity and simplicity.

Confirmatory factor analysis

Confirmatory factor analysis is a specific application of structural equation

modelling to validate established measurement constructs or model validation (Xiong

et al., 2015). For example, Molenaar et al. (2009) validated their five-dimensional

framework to measure corporate safety culture by applying CFA. (Wong, Cheung,

Yiu, & Pang, 2008) also applied CFA to validate a three-dimensional framework to

measure trust in construction contracting, while (Ding, Ng, Wang, & Zou, 2012)

validated a two-dimensional trust framework by CFA with empirical evidences in

construction. In this study, a three-dimensional framework for measuring

occupational stress is developed and tested with CFA. In such studies, model fit,

reliability and the validity of the constructs are critical for validating the developed


models (Xiong et al., 2015). The overall model fit as presented in Table 4.5 is

generally satisfactory. To assure the reliability of constructs, Cronbach's alpha is still

useful in determining the internal consistency of constructs. An alternative is

composite reliability, CR, where

𝐶𝑅 =(∑ 𝜆𝑖)2

(∑ 𝜆𝑖)2+∑ 𝑉𝑎𝑟(𝑒𝑖) (4.1)

as indexed in Bagozzi and Yi (1988) and recommended as a more informative

statistic in the SEM context for its ability to assess internal consistency of indicators

within a construct. A value larger than 0.7 indicates good quality (Bagozzi and Yi

1988). As Table 4.4 indicates, all CR values are acceptable.

Construct validity tests normally include convergent validity and discriminant

validity. Convergent validity measures the extent of positive correlations of one

manifest variable (MV) with other MVs within same constructs, since MVs should

share a comparatively high proportion within the same constructs (Hair, 2014). To

assess convergent validity, the standardized regression weights and squared multiple

correlations (SMCs) for each item are calculated. As presented in Table 4.4, all the

standardized regression weights are highly significant and above 0.5, ranging from

0.538 to 0.853, indicating acceptable validity (Xiong, Skitmore, Xia, et al., 2014). It

is worth mentioning that the standardized regression weights of Q12 and AQ13 are

close to the threshold, which is consistent with previous PCA results. They are still

kept to ensure the completeness of measurements as their deletion does not leading to

any improvement in the CFA and SEM results. Discriminant validity (that constructs

in the model are significantly different) can be confirmed by comparing the

unconstrained model and constrained alternatives. Since the unconstrained model is

significantly better than the model equally constrained correlations between

constructs (Chi-square (df=2)= 40.967, p=0.000), it is reasonable to regard these

constructs as different ones. Since this study aims to validate the divisibility of

occupational stress, nomological validity, another useful although little mentioned

construct validation, is recommended by applying structural equation modelling

(Ding and Ng, 2007).

Table 4.4 Standardized regression weights

Item Standardized regression weights SMC CR


Worry Tension Lack of joy OC

Q5 0.714

0.510

0.808 Q6 0.811

0.658

Q7 0.764

0.583

Q8

0.852

0.727

0.856

Q9

0.853

0.728

Q10

0.777

0.603

Q11

0.633

0.401

Q12

0.538

0.289

AQ13

0.538

0.289

0.766 AQ14

0.654

0.428

AQ15

0.786

0.618

AQ16

0.693

0.480

Q17

0.708 0.501

0.887

Q18

0.840 0.705

Q19

0.779 0.607

Q20

0.819 0.671

Q21 0.758 0.575


In some studies, the CFA phase is usually undertaken as a first step before

placing directional relationships between constructs in the model (Xiong, Skitmore,

Xia, et al., 2014). Since a good model fit is achieved in the CFA phase, a further

SEM model is developed to test Hypothesis 3. As indicted in Table 4.5, the model fit

is acceptable. The final results are presented in Figure 4.1 and Table 4.4. The

correlations between emotional reactions are also tested and presented as broken

lines in Figure 4.1, where the observed variables such as Q5 are shown in rectangles;

latent variables such as worry are shown in ellipses; with directional arrows

reflecting effects of sub-dimensions of occupational stress on organizational

commitment. It is found that only lack of joy has a significantly negative effect on

organization commitment, with the other two emotional reactions having no

significant effects.

Table 4.5 Goodness of fit

Goodness of fit measure Criteria CFA SEM

χ2/df <5.0 2.255 2.078

Absolute fit

RMSEA <0.1 0.093 0.087

AGFI >0.8 0.822 0.785

SRMR <0.08 0.065 0.062

Incremental fit

CFI >0.9 0.918 0.902


IFI >0.9 0.919 0.904

Parsimonious fit

PNFI >0.5 0.667 0.690

PGFI >0.5 0.578 0.621

Figure 4.1 Effects of dimensions of stress on organizational commitment

4.4.4 Validation with SEM Discussion of the CFA and SEM results

The CFA results validate the three sub-dimension construct of occupational

stress, which supports Hypothesis 2. It is also noticed that worry and tension are

highly correlated but lack of joy is less correlated. The high correlation agrees with

many previous studies, since occupational stress is simply regarded as a mix of

tension and worry (Hurrell Jr, Nelson, & Simmons, 1998). As richer meanings have

been identified in occupational stress (Levenstein et al., 1993, Fliege et al., 2005), it

will be necessary to take into account its multi-dimensional nature in subsequent

research.

The SEM results reveal that the three emotional reactions have different effects

on organizational commitment. Lack of joy has a largely negative effect on

organizational commitment (as lack of joy increases, organizational commitment

decreases) but worry and tension do not. This is generally consistent with previous


studies of Leiter and Maslach (1988), Jamal (1990) and Aghdasi et al. (2011) in that

occupational stress negatively affects organizational commitment, while this study

finds that the main contributor is lack of joy other than worry and tension.

Acknowledging the differences between lack of joy discussed here and the term "job

satisfaction", these findings are consistent with (Tett & Meyer, 1993), where job

satisfaction was found to be highly correlated with organizational commitment and

turnover. Similarly, Currivan (1999) found greater intensity of job stressors

comprising role ambiguity, role conflict and workload leads to lower job satisfaction,

which also leads to weaker organizational commitment. It is surprising to find that

worry and tension, although highly correlated with workloads, do not have a

significant effect on organizational commitment, as greater workloads have been

found to lead to weaker organizational commitment in some previous studies (e.g.

(Currivan, 1999; De Cuyper & De Witte, 2006)).

This puzzling paradox demonstrates the necessity to understand the sub-

dimensions of occupational stress in terms of worry, tension and lack of joy, since

these emotional reactions may have different causes and effects. An additional

stepwise principal component regression analysis as used in Gan, Zuo, Ye, Skitmore,

and Xiong (2015) was conducted to predict organizational commitment not only with

worry, tension and lack of joy but also gender, experience and even demands in a

second test. The result is same in that only lack of joy is significant predictor. The

insignificant effects of worry and tension on organizational commitment may

possibly be attributed to a U-shape relationship between stress and job performance

and stress and organizational relationship (AbuAlrub, 2004; Leung et al., 2005b). As

Leung et al. (2005b) point out, a moderate stress level would result in better

performance among cost engineers, while stress measured as the imbalance between

actual ability and job expectations is more a proxy for job fit rather than stress level

(Lauver & Kristof-Brown, 2001).

4.5 CONCLUSION

The applicability of a revised PSQ based on Levenstein et al. (1993) and Fliege

et al.’s (2005) studies in China is demonstrated, which means Hypothesis 1 is

supported. A translation and back translation technique and principal component


analysis are used to firstly validate the questionnaire data. This confirms it can be

used in future studies, in contrast with many previous studies that suffer from

measurement deficiencies concerning occupational stress. In order to record distinct

stressor and emotional reactions, a further three-dimensional framework for

measuring occupational stress is developed.

The second contribution is that the divisibility of occupational stress is

demonstrated in this study, which means Hypothesis 2 is supported. The three sub-

dimensions in terms of worry, tension and lack of joy are developed and validated by

structural equation modelling. Since the model comprising three emotional reactions

is supported with CFA, further research would benefit from treating occupational

stress as a multi-dimensional concept. The measurement issue is always a most

critical in this kind of research and some previous studies use one or several stressors

such as workload and ability imbalance to indicate occupational stress. Such

practices could be reasonable in some situations, but would be problematic when

exploring the relationship between stressors and stress. Some research directly asks

for respondents’ general perception of stress, which may be defined differently from

person to person and hard to measure without the help of more observable measured

items. This new framework identifies the core characters and manifest variables of

occupational stress, which helps the standardization of occupational stress

measurement and provides a standard way to develop a measurement framework.

Another theoretical implication of this study is that sub-dimensions may act

differently in organizational contexts. As proposed in Hypothesis 3, three dimensions

are presumed to negatively affect organizational commitment, while only lack of joy

has significantly negative effect. Therefore, more exact descriptions should be used

when examining and describing the relationship between occupational stress and

occupational stressors or other organizational influences.

It should also be noted that the empirical work in this study is limited to the

specific context of construction professionals in China, although findings from this

research may also interest researchers outside the construction field. Further research

will benefit from applying the findings of this research in other settings and

exploring relationships between the sub-dimensions of occupational stress with other

managerial factors. In addition, much work remains to be done in identifying

uncovered dimensions of occupational stress and improve measurement accuracy.

Chapter 5: Job satisfaction 109

Chapter 5: Job satisfaction

5.1 THE NEXUS BETWEEN JOB SATISFACTION AND JOB

PERFORMANCE OF CONSTRUCTION COST ENGINEERS

5.1.1 Introduction

The relationship between job satisfaction and performance (S-P) has been an

important topic of study for academics and organisation managers for many decades

since the Hawthorne studies and the human relations movement in the 1930s (Judge

et al., 2001). It is initially proposed that “happier workers produce more” which

gains popularity as an argument because of its consistency with intuition. In the

1960s, some researchers (Lawler and Porter, 1967) argued that job satisfaction was

induced by performance for rewards, and that good performers gain more rewards

and are happier. Both opinions are supported by theory. The former opinion is

supported by the theory of reciprocity — that an employee has a natural intention to

respond reciprocally to perceived kindness and unkindness (Falk & Fischbacher,

2006). The latter opinion is based on motivation theory, which reasons that rewards,

led by the job performance of employees, result in satisfaction and even higher

subsequent performance in response to the effects of organisational commitment and

goal setting (Latham & Pinder, 2005). However, convincing empirical evidence for

both assumptions are still lacking. Some researchers (Fisher, (2003) describe the S-P

nexus as simple "folk wisdom".

Reviewing previous studies, weak and inconsistent empirical evidence for the

S-P nexus can be attributed to changing definitions of concepts and divisibility of

abstract terms. For example, satisfaction may have several facets, especially

economic satisfaction (ES) and production-related/noneconomic satisfaction (PS)

(Xiong et al., 2014). Similarly, dimensions of job performance include task

performance, organisational citizen behaviour and even anti-productive behaviours

(Viswesvaran & Ones, 2000). This study divides job satisfaction into economic

satisfaction and noneconomic satisfaction, and uses task performance (TP) as the

measure of job performance. It is proposed that PS increases TP and then TP

increases ES. A literature review is firstly conducted and then a conceptual

110 Chapter 5: Job satisfaction

framework is proposed. Statistical analyses are further applied to validate the

hypothesised model.

5.1.2 Literature review

Linkage between individual satisfaction and performance

Studies on the relationships between job satisfaction and job performance

comprise an appreciable portion of behaviour research in management (Organ,

1988b). Additionally, the discrepancy between the strong intuition among

practitioners that satisfaction has an obvious influence on productivity and low

correlations for these elements of performance obtained in empirical studies has

made this an appealing topic for researchers for decades (Judge et al., 2001). There

are three mainstream hypotheses on the S-P nexus: (1) job satisfaction causes job

performance; (2) job performance causes job satisfaction; (3) there are other complex

relationships between the two including moderators, mediators or antecedent

variables.

The first of these hypotheses again goes back to the Hawthorne studies and

human relations movement, when the idea that improvement in employee morale

leads to production improvement became widely accepted (Schwab & Cummings,

1970). Despite little supporting empirical evidence, the hypothesis that job attitudes

affect employee behaviour became accepted as logically reasonable (Judge et al.,

2001) and used as a common assumption in many studies. The second hypothesis

reverses the cause and the effect, with Lawler and Porter (1967), for example,

pointing out that rewards were not adequately considered in previous research, and it

was therefore reasonable to assume that satisfaction follows the rewards produced by

performance. Although there is some empirical evidence in favour of the second

hypothesis (Judge et al., 2001), it is still insufficient to be convincing and has been

criticised as containing a hidden and questionable presumption that performance and

rewards are closely linked for individual workers (Fisher, 2003).

Because of the weak empirical evidence relating to the first two hypotheses,

some researchers have turned to exploring common antecedent variables for

satisfaction and performance in terms of mediators and moderators in the job S-P

linkage (Judge et al., 2001; Schwab & Cummings, 1970). Some researchers such as


Schwab and Cummings, 1970) argue that the unsatisfactory outcomes of S-P linkage

research have been mainly caused by the ambiguity of definitions of job satisfaction.

Although some measures of job satisfaction such as the Job Descriptive Index

(Smith, 1969) and Minnesota Satisfaction Questionnaire (Weiss, Dawis, & England,

1967) have been developed, job satisfaction is still seen as a holistic concept in

applications connecting satisfaction and job performance. It has been suggested that

researchers should explore the relationship between specific attitude measures and

specific job behaviours, rather than the link between general satisfaction and a

specific behaviour (Fisher, 2003). Lai (2007), for example, divided the job

satisfaction of dealers in the motor industry into social satisfaction and economic

satisfaction, and found that noneconomic satisfaction was much more important than

economic satisfaction in influencing performance. This dichotomy is also consistent

with Brown’s (2001) finding that economic satisfaction should be treated separately

for analysis, since it is highly related to pay factors like pay equity.

However, some previous studies (Janssen and Van Yperen, 2004) fail to

connect satisfaction with performance, while other studies (Lai, 2007; Nerkar et al.,

1996) assume that all disaggregated satisfaction facets share common unidirectional

relationships with performance; for instance, all satisfaction sub-dimensions lead to

performance. Therefore, the vital unanswered question is whether it is possible that

the low correlation observed in previous studies between overall satisfaction and

performance was caused by different or even conflicting causal relationships

between satisfaction sub-dimensions and performance. For example, economic

satisfaction (satisfaction with pay) generated by receiving rewards is caused by

performance rather than being a cause of performance, while some other satisfaction

dimensions (such as satisfaction with co-workers and supervisors) may enhance

performance.

Another explanation for unsatisfactory previous research results can also be

attributed to changes in the conceptualisation of job performance. In early

organisational studies such as the Hawthorne studies, job performance is considered

to be virtually the same as task performance, defined as

… the proficiency with which incumbents perform activities that are formally

recognized as part of their jobs; activities that contribute to the organization’s

technical core either directly by implementing a part of its technological


process, or indirectly by providing it with needed materials or services.

(Borman & Motowidlo, 1993a, p73)

In recent decades, another category of employee behaviour, known as

organisational citizen behaviour (OCB), has been identified and accepted by both

academics and practitioners. This assumes that job responsibilities, expressed active

involvement in the organisation, and innovation for the benefit of the organisation

take place even without reward expectations (Eisenberger et al., 1990). Job

performance nowadays includes task performance, OCB and even counterproductive

behaviours in some situations (Viswesvaran & Ones, 2000). As an early stage

exploration, this study focuses on task performance (TP).

Conceptual model development

Many conceptual models describing job satisfaction and performance have

been proposed, as presented in Figure 5.1 adapted from Judge, et al. (2001). The first

three models assume there are causal relationships between job satisfaction and job

performance. Model 4 and Model 5 assume there are antecedents or moderators

affecting the S-P nexus.

Figure 5.1 Main conceptual models of the S-P nexus

Following the majority of previous studies (Judge et al., 2001; Organ, 1988b),

this paper assumes there is a positive correlation between overall job satisfaction and


job performance. In addition to the overall S-P nexus, a fine-grained hypothesised

model is developed by using two broad dimensions of job satisfaction in terms of ES

and PS. The theory of reciprocity and motivation theory are used to develop the

conceptual model, as presented in Figure 5.2.

Figure 5.2 Proposed conceptual model for this study


Questionnaire survey

To explore the S-P nexus, related items of the questionnaire survey concerning

interactions between person and environment are used, as presented in Table 5.1.

Respondents are construction cost engineers, also known as quantity surveyors in

China. To measure job satisfaction, eight items as presented in Table 5.1 are used,

based on previous works (Smith, 1969; Cotton and Tuttle, 1986; Xiong, et al., 2014).

Following the previous works of Skitmore and Marston (1999b) and Leunget al.

(2005), five items such as “I estimate the budget of the project without overrunning”

are used to measure task performance of the professionals.

Table 5.1 Measures of Job satisfaction No. Job satisfaction measures 1-not at all to 7 very intensive Don't know

Q1 Satisfaction with pay 1 2 3 4 5 6 7 □

Q2 Satisfaction with promotional opportunities 1 2 3 4 5 6 7 □

Q3 Satisfaction with organisational welfare 1 2 3 4 5 6 7 □

Q4 Satisfaction with work itself 1 2 3 4 5 6 7 □

Q5 Satisfaction with supervision 1 2 3 4 5 6 7 □

Q6 Satisfaction with co-workers 1 2 3 4 5 6 7 □

Q7 Satisfaction with workload 1 2 3 4 5 6 7 □

Q8 Satisfaction with current tasks 1 2 3 4 5 6 7 □

Because of cultural and linguistic differences, the translation of questionnaires

from English to Chinese needed be carried out with care. To ensure content validity,

the translation and back translation technique (see detailed steps in Xiong, Skitmore,

and Xia, 2015b) was applied with the assistance of four bilingual researchers.


Data collection and demographics

The snowball sampling technique is useful to gather sensitive information,

especially in a situation where random sampling is not available. Snowball sampling

allows researchers to access informants through contact information provided by

other informants, and has been the most widely employed sampling method in many

disciplines across the social sciences (Noy, 2008). Considering the study context, this

technique is appropriate to this study. 285 complete responses among 310 returned

ones were considered valid for further analysis in this study. The majority of

respondents have a bachelor degree or higher education level. Respondents are

almost evenly distributed across some characteristics, including gender

(male/female), working city/state, company type (property developer/construction

company/consulting company) and employment sector (public/private). To evaluate

the internal consistency of the questionnaire items, Cronbach's alpha is calculated in

SPSS 21.0, with the overall value equal to 0.868, indicating good consistency.

5.1.4 Results

Principal component analysis

The PCA confirms a two-dimensional structure of job satisfaction, with a 0.836

Kaiser-Mayer-Olkin measure of sampling adequacy higher than the cut-off value of

0.5, and a highly significant p<0.0001 for Bartlett’s test for sphericity indicating that

the items are suitable for factor analyses. The solution gained by applying varimax

rotation explains 65.7% of overall variance, with component 1 and component 2

accounting for 50.5% and 15.2% respectively. Loadings with components, means,

standard deviations and communities (h2) of items are summarised in Table 5.2.

Table 5.2 Principal component analysis with varimax rotation

Items Components Item parameters

1 2 Mean SD h2

Q1 0.130 0.857 3.860 1.325 0.751

Q2 0.263 0.813 3.912 1.328 0.730

Q3 0.242 0.817 3.891 1.391 0.726

Q4 0.652 0.387 4.488 1.165 0.576

Q5 0.628 0.383 4.656 1.439 0.540

Q6 0.792 -0.087 5.193 1.163 0.635


Q7 0.680 0.347 4.284 1.327 0.583

Q8 0.797 0.287 4.442 1.254 0.717

Correlation and regression analysis

To investigate the necessity for distinguishing ES and PS, effects of sub-

dimensional satisfaction on task performance are explored by applying regression

analysis. Average values of ES, PS, and task performance are calculated. Overall

satisfaction is attained by calculating the average of ES and PS assuming equal

weight. Correlations of these factors are presented in Table 5.3. Regression analysis

is applied as Model A, presented in Figure 5.3. If these two dimensions share

consistency (Nerkar et al., 1996), their effects on task performance should be

consistent. However, it is found that only PS has a significant positive effect on task

performance. Model B is then developed as a conceptual model, presented in Figure

5.3. The attained results confirm that PS positively affects task performance, and TP

positively affects ES.

Table 5.3 Correlations between factors

Factors A B C

A. task Performance 1 B. ES_A 0.193** 1

C. PS_A 0.344** 0.558** 1 D. overall satisfaction 0.296** 0.905** 0.858**

Note: **. Correlation is significant at the 0.01 level (2-tailed).

Figure 5.3 Model evaluations by regression analysis


The form of interaction

The above linear regression results reveal overall positive effects of PS on TP

and TP on ES. It is found in studies of stress that although work stress has an overall

negative effect on job performance, the relationship would be better seen as an n-

shaped or inverted U-shaped one, as there is a quadratic effect of stress on

performance (Leung et al., 2005; Xiong et al., 2015b). Considering the similarity

between stress and satisfaction, this study explores the relationship form of two

relationships in terms of PS-TP and TP-ES. Results are presented in Table 5.4.

Table 5.4 forms of effects Relationships Relationship form

PS-TP

TP-ES

5.1.5 Discussion and conclusions

In previous studies on the nexus between job satisfaction and job performance,

job satisfaction has been widely taken as a holistic term without investigating the

internal dimensions. In the literature review, a two-dimensional structure of job

satisfaction is proposed. As presented in Table 5.2, economic satisfaction (ES) and

production-related satisfaction (PS) are different components. Similarly, job

performance is a multi-attribute concept. This study focuses on task performance

only.


To evaluate the validity of the proposed model in Figure 5.2, correlation and

regression analyses are applied. Comparing the modelling results of Model A and

Model B, it is necessary to distinguish ES and PS. Additionally, this study proposes a

new model to describe relationships between the sub-dimensions of job satisfaction

and performance. In addition to support from theories including reciprocity theory

and motivation theory, this model is demonstrated as valid by empirical evidence

gained from construction professionals in China.

In addition to the overall positive linear effects of PS-TP and TP-ES as

presented in Figure 5.3, it is found that an n-shaped relationship would be better to

describe the effect of TP on ES. Findings in this study will benefit further studies on

the nexus between job satisfaction and performance.

There are a few limitations worth mentioning. Following the stimulus-

organism-response paradigm in studying employee behaviour, the antecedents of job

satisfaction and performance include working environment factors like

organisational support and individual characteristics (Xiong, 2015). Without

considering these factors thoroughly, situations like Model 4 and Model 5 as

described by Judge et al. (2001) need further investigation in future research.


5.2 EXAMINING THE INFLUENCE OF PARTICIPANT PERFORMANCE

FACTORS ON CONTRACTOR SATISFACTION: A STRUCTURAL

EQUATION MODEL





field of expertise;











Examining the influence of participant performance factors on contractor

satisfaction: A structural equation model

Bo Xiong,* Martin Skitmore, Bo Xia, Md Asrul Masrom, Kunhui Ye, Adrian

Bridge. International Journal of Project Management, 2014, 32(3), 482-491.


Bo Xiong



07/03/2016

Martin Skitmore Directed and guided this study, and proofread the manuscript..

Bo Xia Assisted with the interpretation of results and manuscript

revisions.

Md Asrul Masrom provided data for validation.

Kunhui Ye Assisted with the interpretation of results and manuscript

revisions.

Adrian Bridge Assisted with questionnaire design.





Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


5.2.1 Introduction

The construction industry plays an important role in providing employment

opportunities and enhancing economic development, especially in developing

countries such as China, India, and Malaysia (Doloi et al. 2012; Ye and Xiong 2011;

Yong and Mustaffa 2012). However, the industry has a poor record for project

success in terms of cost, time, quality, etc. Participant satisfaction is a crucial aspect

of this, as noted by Al-Tmeemy et al. (2011) and Leung et al. (2004), in addition to

qualified project completion.

Participant satisfaction describes the level of “happiness” of project

participants and slow decisions made by clients, poor labour productivity, and

architects' reluctance to change, for example, contribute to both reduced satisfaction

and unsuccessful projects (Doloi et al. 2012). Enhanced satisfaction, therefore, not

only helps to improve motivation and cooperation among participants but also

increases the likelihood of successful project completion, making its evaluation

important in judging the success or otherwise of a project.

Construction contractors are responsible for the actual production work

involved (cost management, schedule management, quality management etc.) in

projects and so their performance is critical to the success of projects. Furthermore,

replacing a contractor with another during project execution is very costly. It is

therefore important to understand the factors influencing contractor performance, and

measuring the degree of contractor satisfaction offers a means of achieving this as

well as providing an opportunity to enhance the effectiveness of cooperation between

contractors and other participants. That is to say, contractor satisfaction is central to

maintaining the cohesiveness and level of teamwork needed for a project (Chan et al

2002).

Previous satisfaction research in construction, however, is concerned much

more with the satisfaction of clients and customers than that of contractors. In

addition, current limited studies on measuring contractor satisfaction consider only

the effects of client behaviour and regard satisfaction holistically (Soetanto and

Proverbs 2002). A more detailed, multi-dimensional account of contractor

satisfaction will take into account the behaviour of the different participants

involved.


Structural equation modelling (SEM) enables this to be done. Developed

from data collected by a postal survey of Malaysian construction contractors, a

structural equation model demonstrates that project participants appear to

fundamentally influence contractor satisfaction on two dimensions: economic-related

satisfaction and production-related satisfaction. Corresponding hypotheses are also

developed and tested by applying SEM, describing the causal relationships involved

in terms of satisfaction dimensions and associated participant performance factors.

5.2.2 Introduction

The concept of customer satisfaction emerged in the early 1980s in the USA

and subsequently widely used in the fields of psychology, business, marketing and

economics (Liu and Leung 2002). Defined as the response to the difference between

‘How much is there?’ and ‘How much should there be?' (Wanous and Lawler 1972),

satisfaction is particularly useful in the measurement of performance outcomes

(Nerkar et al. 1996).

In the construction industry, the term ‘satisfaction’ has become progressively

used over the past decade, its increased attention being taken to indicate a positive

change from a pure focus on business performance to a greater emphasis on

stakeholder performance (Love and Holt 2000). Therefore, in addition to the

traditional objective outcome measures of time, cost and quality, measuring

satisfaction has become another effective way of helping to improve project

performance, especially for large and complex projects (Cheng et al. 2006; Ling et

al. 2008; Toor and Ogunlana 2010). Furthermore, satisfaction can boost repeat

business and increase long-term profitability (Wirtz 2001).

There exist a variety of applications of satisfaction measurement in the

construction context. These comprise studies of client satisfaction levels associated

with contractor and consultant performance (Cheng et al. 2006; Mbachu and Nkado

2006); customer satisfaction with the products and services of the industry (Maloney

2002; Yang and Peng 2008); and home buyer and occupant satisfaction in terms of

comfort (Paul and Taylor 2008; Torbica and Stroh 2001). Leung et al. (2004) also

measures the degree of correlation between project participant satisfaction and

potential contributing factors.


However, although there are studies measuring contractor performance,

contractor satisfaction has received much less attention. The sole example to date is

that of Soetanto and Proverbs (2002), who establish an overall contractor satisfaction

regression equation based on responses from 80 top UK contractors. However, this is

restricted to the measurement of contractor satisfaction exclusively in response to

client behaviour. Extending this to accommodate the influence of other participants

has yet to be undertaken.

Satisfaction in the construction industry is also viewed as a holistic entity in

current research on client satisfaction, homebuyer satisfaction and contractor

satisfaction (Cheng et al., 2006; Kärnä et al., 2009; Paul and Taylor 2008; Soetanto

and Proverbs 2002). However, research conducted in the manufacturing industry

demonstrates the importance of distinguishing economic satisfaction from non-

economic satisfaction in manufacturer-distributor relationships (del Bosque

Rodríguez et al., 2006). Although construction is uniquely different to manufacturing

in many ways, the role of manufacturers in the production and transfer of products to

the market via distributers has some similarity with the role of construction

contractors, who construct and transfer products to clients directly or via client to end

users. It is likely, therefore, that construction contractor satisfaction will benefit from

receiving a similar decomposition.


To examine the influence of participant performance factors on contractor

satisfaction, two main research methods are adopted: questionnaire survey and

structural equation modelling (SEM). Eighteen hypotheses are first proposed

according to the literature review. A conceptual model is then developed based on

these hypotheses by SEM. In the questionnaire design, Keline's (2005) principle,

which uses three measurement variables to reflect one latent variable, is applied in

order to obtain a stable equation structural model. 125 complete and reliable

responses collected from contractors in Malaysia comprise the basis for the data

analysis.

Hypotheses

One conceptualisation of satisfaction is in the form of an input-process-output

system where, although the internal process is still unknown, performance outcomes


provide an input leading to satisfaction/dissatisfaction as the output (Soetanto and

Proverbs 2002). Performance outcomes are determined by different construction

project participants, with contractors, as performance assessors, having their own

psychological interpretation of the performance levels of others (Soetanto and

Proverbs 2002). Thus, the satisfaction of contractors is treated as being caused by

participant performance.

The Construction Industry Development Board (CIDB), which was

established by the Malaysian Federal Government in 1994 and is in charge of

planning direction of the industry, reported in its 2006-2015 construction industry

plan that project failures are not solely caused by contractors, but also by other

participants, such as the architect, engineer, subcontractors and suppliers (CIDB

2006). It is clear, therefore, that project success depends on the efforts of all

participants, as unsatisfactory work by any one participant can lead to the failure of a

whole project. In addition, delayed government projects in Malaysia are known to be

due not only to the poor performance of contractors, but also to a lack of

communication between participants, inadequate client finance and late provision of

construction drawings by consultants (Sambasivan and Soon 2007).

Adapting del Bosque Rodríguez et al. (2006), contractor satisfaction is

divided into two dimensions: economic-related satisfaction (ES) and production-

related satisfaction (PS). The former dimension refers to contractor satisfaction with

economic issues such as project cost, project profitability and potential business

opportunities arising from current projects. In contrast, production-related

satisfaction refers to contractor satisfaction with production quality, including project

quality, safety and timely completion.

The measurement of contractor satisfaction should therefore take into account

the effects of several participants. Perhaps the most important of these is the client,

who plays an important role in both project completion and contractor satisfaction.

Several infrastructure projects in Jordan, for example, have suffered in terms of

delays due to client-related factors, including finance, payments for completed work,

and slow decision making (Odeh and Battaineh 2002). Similarly, massive client-led

changes in project scope have caused up to 70% poor time performance in Saudi

Arabian projects (Assaf and Al-Hejji 2006). Also, Park's (2009) survey of 27

contractors found effective preplanning and client clarity of intention to be the most


important factors affecting scope dimension and project success in South Korea. This

suggests corresponding hypotheses of:

• H1: The client's clarity of objectives (OC) has a positive influence on ES.

• H2: OC has a positive influence on PS.

• H3: OC has a positive influence on DC

• H4: OC has a positive influence on construction risk management (RM).

• H5: The client's promptness of payment (PP) has a positive influence on ES.

• H6: PP has a positive influence on PS.

Suitable design is another crucial factor to project success, with contractors

regarding defective design as a major risk in South Korea (Park 2009), for example,

while accounting for 50% of quality failures in Malaysia (CIDB 2006), leading to the

corresponding hypotheses:

• H7: DC has a positive influence on ES.

• H8: DC has a positive influence on PS.

An increasing number of project uncertainties have a fundamental effect on

project performance in the UK (Atkinson et al. 2006). These uncertainties lead to

negative relationships between parties, conflicts, mismatched objectives and

adversarial relationships (Harmon 2003). Construction risk management provides a

means of overcoming this to some extent and is therefore necessary to project

success, with the corresponding hypotheses being:

• H9: RM has a significant influence on ES.

• H10: RM has a significant influence on PS.

• H11: RM has a positive influence on PP.

• H12: RM has a positive influence on the effectiveness of other project

participants' work (EW)


• H13: RM has a positive influence on respect and trust among project

participants (RT)

The ineffective contribution of other project participants is recognised as a

major cause of project failure, being attributed to poor schedule performance in

Saudi Arabia for example, particularly in public projects (Al-Kharashi and Skitmore

2009). Similarly, the performance of subcontractors and suppliers is also an

important factor contributing to the success and quality of construction projects in

Finland (Kärnä et al. 2009), giving rise to the corresponding hypotheses of:

• H14: EW has a positive influence on ES.

• H15: EW has a positive influence on PS.

Participant attitudes during the project are also very important in influencing

collaborative work and service quality (Ling and Chong 2005; Soetanto and Proverbs

2002). Similarly, enhancing understanding and trust among project participants is

beneficial in increasing the satisfaction levels of all participants (Lehtiranta et al.

2012). The corresponding hypotheses are:

• H16: RT has a positive influence on ES.

• H17: RT has a positive influence on PS.

• H18: RT has a positive influence on EW

All these hypotheses together comprise a conceptual model, which is also

regarded as the structural component in the perspective of SEM, as illustrated in

Figure 5.4.


Figure 5.4 Structural component


The structural equation modelling (SEM) technique is widely used to explore

and test causal relationships in the social sciences, such as in psychology, education

and health. SEM is a combination of factor analysis, multiple correlation, regression

and path analysis. Compared with other multivariate analysis methods, such as

multiple regression and neural networks, SEM has the ability to (1) estimate multiple

and interrelated dependence relationships; (2) represent unobserved concepts in these

relationships; (3) consider measurement errors in estimation; and (4) define a model

explaining an entire set of relationships (Keline 2005; Cho et al. 2009).

Because of these advantages, SEM is being increasingly used in construction-

related studies. For example, Islam and Faniran (2005) construct an SEM model to

investigate three factors influencing project-planning effectiveness; Cho et al. (2009)

use SEM to explore the effects of project characteristics on project performance;

while Anvuur and Kumaraswamy (2012) investigate the effects of four job cognition

variables on four cooperative behaviours. SEM is also recommended for increased

use in the construction industry due to its suitability in solving construction-related

problems (Oke et al. 2012). Likewise, SEM applied here aims to provide a way to

investigate the effects of participant performance factors on two contractor

satisfaction dimensions.


SEM describes the relationships between two kinds of variables: latent and

observed. Latent variables cannot be observed directly due to their abstract character.

In contrast, observed variables contain objective facts or use an item rating scale in a

questionnaire. Several observed variables can reflect one latent variable (Byrne 2010;

Islam and Faniran 2005). One structural equation model divides into two

components: the measurement and the structural component. The measurement

component consists of the measurement errors of the measurement variables and the

relationships between observed variables and the represented latent variable. The

structural component expresses the relationships among latent variables. Thus, a

structural equation model consists of one structural component and several

measurement components (Washington et al. 2011). A two-step modelling method is

usually used to develop a structural equation model in preference to establishing the

model directly (Anvuur and Kumaraswamy 2012; Byrne 2010). This comprises, first,

a confirmatory factor analysis (CFA) followed by SEM. The aim of the CFA is to

test the validity of the measurement components and provide the foundation for the

next step. If the goodness of fit is satisfactory in the CFA phase, the next step is to

replace the correlations between the latent variable with hypothesized causal

relationships and then test the model.

Of course, as with all analyses of this kind, the existence of statistical

correlation or association does not prove causation or influence but simply lends

support to the logical or intuitive belief in their presence. Bearing this in mind, the

word 'influence' denotes "appears to influence" rather than to indicate any irrefutable

proof of such influence.

To apply SEM, many computer software systems, such as AMOS, EQS and

LISREL, have been developed (Jyh-Bin and Shen-Fen 2008). Of these, the SPSS

AMOS version 19 is used to construct and analyse the contractor satisfaction model.

Based on SEM, Figure 5.4 shows the structural component composed of all the

hypotheses that describe the direct relationships between two variables.

Structural equation modelling Questionnaire Survey

The hypotheses shown in Figure 5.4 are tested according to Keline’s three-

variable principle, where three observed variables are used to reflect a latent variable

(Keline 2005). To do this, the observed variables are extracted from Masrom's (2011)

larger questionnaire of Malaysian contractors, formerly used to construct a multiple


regression contractor satisfaction model from 95 contributing factors. Bearing in

mind the requirement of high reliability and clear classification, both subjective

methods (e.g. brainstorming) and statistic methods (e.g. reliability testing) were used

to obtain the measurement framework as shown in Table 5.5.

Table 5.5 Constructs and measurement of SEM

Latent variables Abbr. No. Items

Performance variables: Which performance level would you rate? (1=very bad, 5=very good)

Client's clarity of

objectives OC Q1 Quality of project brief (e.g. needs and requirements)

Q2 Completeness of project brief

Q3 Certainty of project brief

Client's promptness of

payments PP Q4 Ease of final account settlement

Q5 Speed of final account settlement

Q6 Promptness of progress payment made by the client

Designer carefulness DC Q7 Design constructability

Q8 Comprehensiveness of design

Q9 Flexibility of design to accommodate changes

Construction risk

management RM Q10 Efficiency of risk control (e.g. identification, evaluation)

Q11 Effectiveness of conflict management

Q12 Appropriateness of sharing risks with other participants

Effictiveness of other

project participants EW Q13 Productivity of project manpower

Q14 Efficiency of subcontractor to undertake their work

Q15 Supplier effectiveness in material supply

Respect and trust among

project participants RT Q16 Participants’ respect and friendliness during the project

Q17 Trust between participants and project team

Q18 Understanding between participants and project team

Satisfaction variables: Which satisfaction level would you rate? (1=very dissatisfied, 5=very satisfied)

Economic-related

satisfaction ES Q19 Project cost management performance (actual vs budget)

Q20 Project profitability

Q21 Potential business development in future

Production-related

satisfaction PS Q22 Schedule performance (actual vs budget)

Q23 Construction product quality performance

Q24 Safety of worksite


Data

The data comprise 125 responses from senior experienced personnel, with a

41.7% valid response rate. This is comparable with the previous SEM studies, e.g.

Islam and Faniran (2005) with 52 cases (61% response rate), Cho et al's (2009) 151

cases and Anvuur and Kumaraswamy's (2012) 153 cases (18% response rate), while

exceeding the minimum of 100 cases for SEM suggested by Gorsuch (1983) and

Bagozzi and Yi (2012). Of the respondents, 17.6% companies have been in business

for 1-5 years; 28% for 6-10 years; 20.8% for 11-15 years; 12% for 16-20 years; and

21.6 % companies for more than 20 years. Concerning company size, 53.6% are

large companies (G7), and 46.4% are small to medium companies (G1-G6)

according to the company size criteria and corresponding tendering capability in

Malaysia (CIBD 2006). Table 5.6 describes the basic characteristics of the

respondents, and further details are contained in Masrom (2011).

Table 5.6 Details of respondents

Respondent's information Groups Frequency Percent Cumulative Percent

Education level Certificate 11 8.8 8.8

Diploma 39 31.2 40

Bachelor degree 69 55.2 95.2

Master degree 6 4.8 100

PHD 0 0 100

Education background Architecture 9 7.2 7.2

Project management 32 25.6 32.8

Quantity surveying 31 24.8 57.6

Civil engineering 40 32 89.6

other 13 10.4 100

Management position Top level 61 48.8 48.8

Middle level 57 45.6 94.4

Low level 7 5.6 100

Experience 1-5 years 22 17.6 17.6

6-10 years 52 41.6 59.2

11-15 years 26 20.8 80

16+ years 25 20 100

Reliability test

Cronbach’s alpha value is used to test the reliability of the hypothesized

construct based on the data. If a Cronbach’s alpha value is above 0.7, the received

data is deemed to be acceptable for significant consistency (Cho et al. 2009; Doloi et


al. 2012). As shown in Table 5.7, the items measured in eight variables and the

overall construct are sufficiently reliable.

Table 5.7 Reliability test of the questionnaire responses Variables All

24

Q1-3 Q4-6 Q7-9 Q10-

12

Q13-

15

Q16-

18

Q19-

21

Q22-

24

Chronbach’s

Alpha value

0.922 0.873 0.863 0.839 0.870 0.793 0.861 0.814 0.758

5.2.4 Results

A two-step method is used to develop the structural equation model.

Confirmatory factor analysis (CFA) provides the first step, and demonstrates a

satisfactory goodness of fit. Since the goodness of fit is satisfactory in the CFA

phase, the next step replaces the correlations between the latent variables with

hypothesized causal relationships as shown in Figure 5.4. Maximum likelihood

estimation is used to conduct both steps.


The measurement components are similar in structure. For example, as shown

in Figure 5.5, the client's clarity of objectives (OC) is reflected in three observed

items: Q1-Q3; and their measurement errors. The observed variables are shown in

rectangles, the latent variable in ellipses, measurement errors in circles and with

arrows indicating the direction of effects. To identify a measurement component, one

coefficient between the latent item and measurement items is given the value of unity

firstly before calculating the next step of standardization (Keline 2005). Likewise, a

starting value of unity is given between Q1 and OC. A dummy variable is used to

denote company size, with 0=small/medium and 1=large contractors.


Figure 5.5 Measurement component

Table 5.8 presents the standardized regression weights and squared multiple

correlations (SMCs) for each observed item. Statistically significant standardized

regression weights of 0.5 or higher indicate good convergent validity (Anvuur and

Kumaraswamy 2012). In this case, all the regression weights (factor loadings) are

highly significant and range from 0.65 to 0.93 with the SMCs ranging from 0.42 to

0.86. For example, the SMC for ‘quality of project brief’ (Q1) is 0.67, indicating that

67% of the variance in ‘quality of project brief’ is explained by ‘client clarity of

objectives' (OC).

Table 5.8 Standardized regression weights and SMCs

Item Standardized regression weights

SMC OC PP DC RM EW RT ES PS

Q1 0.82a

0.67

Q2 0.91

0.82

Q3 0.81

0.65

Q4

0.93a

0.86

Q5

0.91

0.83

Q6

0.65

0.42

Q7

0.79

0.63

Q8

0.88

0.78

Q9

0.74a

0.55

Q10

0.81a

0.65

Q11

0.87

0.76

Q12

0.81

0.66

Q13

0.69a

0.47

Q14

0.87

0.75

Q15

0.70

0.48

Q16

0.74

0.54

Q17

0.91

0.82

Q18

0.84a

0.71

Q19

0.78a

0.61

Q20

0.77

0.60

Q21

0.76

0.58

Q22

0.73 0.53

Q23

0.71 0.51

Q24 0.72a 0.52

Note: All results are from analyses that included company size as a control variable. All

factors without superscript ‘a’ are significant at p<0.001; Factors with superscript ‘a’ are

fixed to 1.00 before estimation.


Measuring the goodness of fit is an important part in developing structural

equation models and a large number of goodness of fit criteria has been developed

for this purpose (Washington et al., 2011). Generally, three types of model fit

measures are used to judge the fitness of the measurement components: absolute fit,

incremental fit and parsimonious fit (Ong and Musa 2012). Of these, Ong and

Musa’s criterion is used in this phase giving χ2 =311.391 (df =235, χ2/df = 1.325)

(Table 5.9). As the χ2/df value is between 1 and 2, this indicates an excellent fit

(Doloi et al. 2011).

Table 5.9 Results of goodness of fit (Adapted from Ong and Musa (2012))

Goodness of fit measure Index Criteria

χ2/df 1.325 <5.0

Absolute fit

RMSEA 0.051 <0.08

SRMR 0.045 <0.05

Incremental fit

CFI 0.957 >0.9

TLI 0.945 >0.9

Parsimonious fit

PNFI 0.665 >0.5

PGFI 0.610 >0.5


As a 'good model' goodness of fit is obtained in the CFA phase, the correlations

between the latent variables are replaced by hypothesized causal relationship as

shown in Figure 5.4. The final model is shown in Figure 5.6, where the observed

variables Q1 to Q24 are shown in rectangles; latent variables such as OC are shown

in ellipses; with the arrows reflecting the hypothesized direction of effect. Figure 5.6

includes eight measurement components and the structural component which refers

to all latent variables and their interrelationships shown in Figure 5.4.The

measurement errors and factor loadings between the latent variables and

measurement variables are not shown as they are very similar to those in Table 5.8.

The company size variable continues to be dummy coded and is not shown. The

standardized coefficients of the hypothesized causal relationships are shown, with the

coefficients not significant at p<0.05 being shown in parentheses. The influence of

company size variable on ES and PS is quite weak.


Figure 5.6 Final SEM model results

The SMC of ES in the model is 0.620, which indicates that 62% of the variance

in ES is explained by the six performance predictors and the dummy coded company

size variable. The SMC of PS is 0.713, indicating that 71.3% of the variance in ES is

explained by the six performance predictors and the dummy coded company size

variable. Both SMCs indicate usefulness in choosing contributing factors.

As can be seen in Figure 5.6, contractor satisfaction is significantly

influenced by the client's clarity of objectives (OC) and promptness of payment (PP),

designer carefulness (DC), construction risk management (RM) and effectiveness of

the other project participants (EW). Respect and trust among project participants

(RT) have no significant influence on economic-related satisfaction (ES) or on

production-related satisfaction (PS), but appears to affect ES and PS via EW. RT has

a positive effect on EW (r=0.414), which positively affects PS (r=0.466). However, a

significant test of indirect effects is needed to assess this fully.

The concept of indirect effects or mediation is invoked to investigate this

latter issue. In terms of the SEM model, if some variables act as mediators between

X and Y, then X has both a direct effect on Y and an indirect effect on Y via the


mediating factors. Figure 5.6 already shows the direct effects between variables in

terms of the calculated coefficients. The Sobel test based on the work of Sobel

(1982) determines the significance of mediation effects. The probability column of

Table 5.10 summarises the results for the seven paths, together with the values of

indirect effects in the corresponding column. Clearly, since RT->EW->PS and PR-

>EW->PS are not significant, OC->RT->EW->PS and OC->PR->EW->PS are also

not significant.

Table 5.10 P values and indirect effects (Sobel test)

Paths Probability Indirect effect

RT ->EW->PS 0.068 0.193

RM->EW->PS 0.083 0.188

RM->PP ->ES 0.058 0.100

OC->RM->ES 0.022 -0.311*

OC->RM->PS 0.012 -0.360*

OC->DC->ES 0.002 0.367*

OC->DC->PS 0.004 0.353*

* indirect effects when p < 0.05

5.2.5 Findings and discussion

The main research finding is that DC, OC and PP positively influence (have a

positive influence on) ES while DC, EW and OC positively influence PS, with RM

negatively influencing ES and PS. Also, other than H6, H14, H16 and H17, 14

hypotheses shown in Figure 5.4 are supported.

The model development results shown in Figure 5.6 support the hidden

assumption that contractor satisfaction is caused by participant performance and that

satisfaction is divisible into economic-related satisfaction (ES) and production-

related satisfaction (PS). In addition, the performance variables have different effects

on the two satisfaction dimensions, with client prompt payments (PP) having a

positive effect on ES but no significant effect on PS. While the effectiveness of other

project participants (EW) has a positive effect on PS but no significant effect on ES.

Client clarity of objectives (OC)

That OC positively influences ES (β=0.314) and PS (β=0.342) supports the

importance of clear objectives and demonstrates the positive relationship between

clear objectives and contractor satisfaction. It confirms Park's (2009) finding that

effective preplanning, clarity of contract and understanding of project requirements


rank highest in measuring critical success and Leung et al's (2004) assertion that goal

specificity is positively associated with goal commitment, which in turn is positively

associated with construction participant satisfaction. OC also has a positive effect on

DC (r=0.645) and RM (r=0.525). For indirect effects, OC influences ES (-0.311 and

0.367) by mediation of RM and DC respectively. Similarly, OC influences PS (-

0.360 and 0.353) by mediation of RM and DC respectively.

Client's promptness of payment (PP)

PP is characterized by ease and speed of final account settlement, and

promptness of progress payments made by the client. It has a positive effect on ES

(β=0.211), but its influence on PS is too weak to be significant. This confirms Yong

and Mustaffa's (2012) result in which the financial capability of client ranks the 1st

of 37 factors critical to project success in Malaysia, and Al-Kharashi and Skitmore's

(2009) finding that lack of finance and delay in progress payments are critical factors

for both clients and contractors in Saudi Arabian public projects. PP can ensure that

the contractor obtains sufficient cash flow during and after construction, and this is

probably why ES increases with PP. However, that the model does not show PP

having a significant influence on PS may be attributed to production issues such as

safety being influenced by government regulations etc.

Designer carefulness (DC)

DC refers to the quality of the designer's work, characterized by design

constructability, comprehensiveness of design, and the flexibility of the design to

accommodate changes in the measurement component. It is a key factor in the

model, with the strongest positive effect on both ES (β=0.569) and PS (β=0.548) and

supports H7 and H8 in which DC positively influences contractor satisfaction. This

confirms the widely acknowledged importance of design (Al-Kharashi and Skitmore

2009; Park 2009; Yong and Mustaffa 2012). OC's positive influence on DC supports

H3 and suggests that improving the clarity of project goals may also be beneficial in

improving the quality of design.

Construction risk management (RM)

RM is usually a duty of contractors, and it is characterized by the efficiency of

risk control, effectiveness of conflict management and appropriateness of sharing

risks with other participants in the measurement component. As can be seen in


Figure 5.6, RM is positively influenced by OC (r=0.525), confirming that the level of

risk management, associated with level of project uncertainty as Masrom (2011)

notes, is largely related to project characteristics and client clarity of objectives. This

is consistent with Siang and Ali’s (2012) findings that systematic risk management is

not implemented actively by most of contractors in Malaysia and all three case

companies, which are publicly listed in Malaysia, rate “avoid unsatisfactory projects

and to enhance margins” as the least important of ten benefits of risk management

(two selected 10th and one 9th). In view of this, it is not surprising to find that the

model also indicates RM to have a strong negative effect on both ES (β=-0.592) and

PS (β=-0.686), suggesting that contractors are unhappy with the effectiveness of their

risk management despite it being critical to project success. This makes Soetanto and

Proverbs' (2002) finding (that contractor satisfaction is negatively influenced by the

perception that clients know exactly what they want) more understandable in that

higher OC is associated with the lower ES and PS when mediated by RM. However,

the model is more complex, with consideration of the direct effects of OC and

indirect effects via DC and RM. Also of note is that RM is positively related to RT

(r=0.721) and EW (r=0.404), both of which are critical to project success. That is to

say, although risk management does not appear to bring satisfaction to contractors in

Malaysia directly, it is already regarded as an important way of enhancing the

productivity and harmony of participants.

Effectiveness of other project participants (EW)

EW is characterized by the efficiency of subcontractors in undertaking their

work, supplier effectiveness in material supply and the productivity of project

manpower, and has a significant effect on PS (β=0.466). This confirms Yong and

Mustaffa's (2012) finding that the allocation of manpower ranks as the most

important project-related factor critical to project success. On the other hand, EW

does not have a strong effect on ES, which may be due to the price of work being

based on workload rather than efficiency considerations.

Respect and trust among project participants (RT)

Atkinson et al. (2006) state that trust can be used as a way of reducing

uncertainty, while enhancing trust is regarded as a better way to solve hidden

problems in the construction process, with shared authorities among participants

being a critical factor contributing to project success (Yong and Mustaffa 2012). This


model confirms these findings in indicating that RT can enhance EW greatly

(r=0.414). Here, RT is characterized by the level of respect, understanding and trust

among participants. However, as Figure 5.6 shows, although RT can contribute to

project success, it does not have any significant effect on ES and PS. A similar

phenomenon occurs with Leung et al.'s (2004) finding that correlations do not exist

between the degree of participant satisfaction and their level of communication, or

the amount of authority clients and project managers have in setting project goals.

5.2.6 Conclusions

A framework is presented to measure construction contractor satisfaction,

which comprises two satisfaction dimensions: economic-related satisfaction (ES) and

production-related satisfaction (PS). This is used to develop a structural equation

model to investigate how project participants' performance affects contractor

satisfaction in terms of six factors: the client's clarity of objectives (OC) and

promptness of payments (PP), carefulness of the designer (DC), construction risk

management (RM), effectiveness of other project participants' work (EW) and

respect and trust among project participants (RT). The findings confirm 14

hypotheses and deny 4 hypotheses. In particular, the results support the view that

contractor satisfaction is a result of many participant effects and the six factors act

differently on ES and PS.

Three important implications can be concluded from these results. Firstly, it

is demonstrated that ES and PS provide a meaningful classification of contractor

satisfaction and that each is affected differently by the six predictors. Of special note

is that PP solely affects ES while EW solely affects PS. It is therefore necessary to

examine the internal dimensions of contractor satisfaction before their measurement,

as different types of satisfaction correlate differently with the different activities

involved.

Secondly, the developed model offers a potential means of improving

contractor satisfaction. For example, ES is influenced positively by OC, PP and DC,

and negatively by RM. Thus a possible way to improve ES and enhance project

success at the same time is for the client and designer to improve OC, PP and DC.

Reducing RM, on the other hand, is counter-productive as RM positively related to


EW and RT, both important to project’s successful delivery. On the contrary, if

construction risk management level needs to improve for assuring project success, it

may be possible for the participants to combine to increase contractor satisfaction in

other ways, such as by improving OC, PP and DC or OC, DC and EW. This reflects

the delicate difference between ES and PS.

Thirdly, a theoretical foundation is provided for participants, especially the

client, to estimate the potential contractor satisfaction to be gained from the project

prior to selecting the project contractor. In previous studies and practices, the client

chooses the contractor by comparing bid prices without considering contractor

satisfaction. It could be that an unsatisfactory contractor with the lowest tender price

is much worse than a satisfactory contractor with a higher priced tender. For this

concern, it is necessary to figure out a way to compare contractor satisfaction at

tendering stage. Besides graphical way, SEM can also be expressed in regression

equation way and many such cases can be found in Keline (2005). Based on direct

significant effects showed in Figure 5.6 and indirect significant effects showed in

Table 5.10, two equations are proposed as follows to calculate changes of ES and PS

with participant performance factors’ change:

ΔES=0.569ΔDC+0.314ΔOC+ 0.211ΔPP-0.592ΔRM+(0.367-0.311)ΔOC and

ΔPS=0.548ΔDC+0.466ΔEW+ 0.342ΔOC-0.686ΔRM+(0.353-0.360)ΔOC.

For each equation, the first four components refer to direct effects from

participant performance factors and the last component refers to the indirect effects.

With these two equations, the client can effectively identify a more satisfied

contractor by evaluating and measuring the variation of these performance factors

among different bidding contractors with focusing on significant factors. Similarly,

the model provides the opportunity for contractors to estimate potential satisfaction

and choose a project with a higher likely level of satisfaction, especially in

circumstances where many bidding opportunities arise with similar profit

expectations. Alternatively, a contractor may decide to bid for projects only where

the expected satisfaction exceeds a specific threshold value. Further, as it is

reasonable to speculate that better contractors will have a higher satisfaction

threshold value, it would then benefit clients to attract good contractors to bid by

improving corresponding aspects such as OC, DC, EW and PP.


It should be mentioned, however, that some potential limitations exist for

further development. The data are all from a sample of contractors in Malaysia and

therefore, although the conclusions are certainly valid for the sample, and probably

so for most Malaysian contractors, their applicability outside Malaysia is uncertain,

even in other developing countries. Differences in awareness and practices of risk

management should be considered particularly when applying similar research in

other countries. In addition, although the sample size of 125 used in the study meets

the requirements for conducting SEM generally, more data is needed for the

development of a complex model and improved model fit. For future research,

benefits are envisaged in further exploring the internal dimensions of contractor

satisfaction, a more detailed study of the relationship between contractor satisfaction

and project success, and the evaluation of satisfaction (for the client) to choose

contractors or (for contractors') decision to bid. The results also suggest that future

research in the Malaysia context may benefit from a more simplified data collection

instrument based on reduced number of hypotheses.


5.3 THE NEXUS BETWEEN CONTRACTOR SATISFACTION AND

PROJECT MANAGEMENT PERFORMANCE





field of expertise;











The nexus between contractor satisfaction and project management

performance

Bo Xiong*, Martin Skitmore, Md Asrul Masrom, Bo Xia. A fine-grained analysis of

contractor satisfaction in promoting project management performance, Submitted to

Project Management Journal, under revision.


Bo Xiong



07/03/2016


Md Asrul Masrom Provided data for validation and assisted in manuscript revision.

Bo Xia Assisted with the interpretation of results.





Martin Skitmore

___________________ _____________________ _________________

Name Signature Date


5.3.1 Introduction

Since the emergence of stakeholder theory (Freeman 1984), the notion of

stakeholder satisfaction (i.e. the satisfaction of organizations or groups of people) has

been widely accepted. With the increasing popularity of stakeholder management in

project-based industries such as construction, offshore engineering and software

development - accounting for almost 30% of the global economy (Turner 2008) -

studies of project participant satisfaction have become increasingly prominent over

the past decade, indicating a positive change in focus from individual performance to

a greater emphasis on stakeholder interests (Love and Holt, 2000; Toor and

Ogunlana, 2010). In addition to the traditional project management “iron triangle”

requirements in terms of measured cost, schedule and quality, satisfaction

measurement has been regarded as another effective means of improving

organisational performance (Cheng et al., 2006; Li et al., 2013; Toor and Ogunlana,

2010). Additionally, Project participant satisfaction becomes an early warning sign

of project outputs in complex projects, (Williams et al., 2012; Xiong et al., 2014).

Although the individual S-P nexus has been widely acknowledged and

explored, its counterpart nexus between organizational satisfaction and performance

has been little considered (Judge, et al., 2001). Pearsall and Ellis (2006), for example,

examine the effects of critical team member assertiveness on team performance and

team satisfaction, although without consideration of the S-P nexus. For the S-P nexus

of project participating companies, it could follow various studies at individual level

and assume that there is an overall S-P nexus or complex relationships among

performance and disaggregated satisfaction facets. A few extent studies on

contractor satisfaction like Xiong et al (2014) indicates that the need of satisfaction

disaggregation in revealing different effects of external factors on contractor

economic satisfaction and noneconomic satisfaction respectively. However, the

nexus between contractor satisfaction and contractor performance is still unknown.

This study aims to investigate the nature of the satisfaction-performance (S-P)

nexus of project participating companies with special cases from construction

contractors. A special concern is on dimensions of contractor satisfaction. In doing

this, two series of structural equation models are developed and compared. The first

group of models treats contractor satisfaction as a holistic concept in examining the


S-P nexus. In conducting a fine-grained analysis of contractor satisfaction, the

second group of S-P models divides contractor satisfaction into two facets of

economic-related satisfaction (ES) and production-related satisfaction (PS) following

Xiong et al. (2014). Analyses are done through a survey of 117 construction

companies' satisfaction and performance levels relating to their recent project work.

5.3.2 Theoretical background and hypotheses development

Satisfaction is defined as a response function of the discrepancy between ‘How

much is there?’ and ‘How much should there be?' (Nerkar, et al., 1996; Wanous &

Lawler, 1972b). In early research in industrial settings, satisfaction is mainly referred

to as job satisfaction. This became a popular term with the Hawthorne studies in

emphasizing linkages between employees' job satisfaction and their performance - a

relationship described as the “Holy Grail” by industrial-organizational researchers

(Judge et al., 2001). For the weak empirical evidence relating to the individual S-P

nexus as prompted some researchers (e.g. Judge et al., 2001) argue that the

unsatisfactory outcomes in S-P linkage research have been mainly caused by the

ambiguity in defining satisfaction, although some measures of job satisfaction such

as the Job Descriptive Index (JDI) (Smith, 1969) and Minnesota Satisfaction

Questionnaire (MSQ) (Weiss, et al., 1967) have been developed. Economic

satisfaction should be treated separately for analysis since it is highly related to pay

factors such as pay equity (Brown, 2001). Lai (2007) divides dealer’s satisfaction

into social satisfaction and economic satisfaction when investigating the mediating

effects of satisfaction in the Taiwan motor industry, finding noneconomic

satisfaction to be much more important than economic satisfaction in influencing

performance.

A project is a natural and an ideal organizational form to develop products

facing increasing product complexity, changing markets, cross-functional expertise

cooperation, customer-oriented innovation and technological uncertainty (Hobday,

2000). For many cross-functional projects, the final product is not produced by

routine practices of workers in assembly lines but by the collaboration of

participating project organizations (Turner, 2008). In projects, the performance of

participating companies is not only affected by environmental factors, but also

affected by perceived satisfaction. As indicated in the stakeholder theory developed

by Freeman (1984), stakeholder satisfaction management is useful for solving


corporate social-related issues effectively and generate good business performance

(Porter & Kramer, 2006). In many cases, perception of success by complex projects

has little to do whether they are completed on time, at cost and with the desired

quality, but achievement of the desired objectives of different stakeholders (Turner

and Zolin, 2012). Participant satisfaction has been accepted by many as a new

dimension of project success (Liu and Walker, 1998) and a new approach for early

warning sign (Xiong et al., 2014).

With the increasing popularity of project economics and stakeholder theory,

project participant satisfaction has become an emergent area (Orlitzky & Swanson,

2012). The organizational satisfaction levels of project participating companies in

the construction industry, such as clients and contractors, have also been studied by a

few researchers (e.g. Tang et al., 2003; Masrom et al. 2013). For example, client

satisfaction has especially been regarded by some researchers as a criterion for

defining success (De Wit, 1988; Munns and Bjeirmi, 1996). However, previous

studies focus on measuring and exploring the driving factors of participant

satisfaction (Leung, et al., 2004; Li, et al., 2013), contractor satisfaction (Masrom, et

al., 2013; Soetanto & Proverbs, 2002; Xiong, Skitmore, Xia, et al., 2014) and client

satisfaction (Cheng, et al., 2006; Mbachu & Nkado, 2006; Yang & Peng, 2008), all

of which have been studied in recent times. This study aims to explore the nexus

between satisfaction and performance of construction contractors by taking sub-

dimensions of contractor satisfaction into comparisons. Previous research indicates

that contractor satisfaction should include two dimensions: noneconomic satisfaction

and economic satisfaction. Geyskens, Steenkamp, and Kumar (1999)’s meta-analysis

of 71 studies of satisfaction in marketing relationships resonates with Lopez (1982)

results in demonstrating the necessity to distinguish between economic and

noneconomic satisfaction as distinct constructs with different causes and

consequences. Likewise, del Bosque Rodríguez, Agudo, and Gutiérrez (2006)

demonstrate the necessity of such a categorization by examining the determinants of

economic and noneconomic satisfaction in manufacturer-distributor relationships. In

addition to the supply-chain, such a separation of organizational satisfaction is also

applicable in construction projects. For example, a similar outcome was obtained by

Xiong et al (2014) in examining effects of other key project participants’

performance on the economic satisfaction (ES) and production satisfaction (PS) of


construction companies. However, few studies combine this categorization with

studies on the S-P nexus.

5.3.3 Methodology

Conceptual models and hypotheses

Project outcomes are generated by the combined efforts of different project

participants, and contractors, as key performance assessors, have their own

psychological interpretation of other participant's performance (Soetanto & Proverbs,

2002). For the influences of other project participants, Wang and Huang's (2006)

survey of construction supervising engineers in China found client performance to be

significantly correlated with project success. A major contributing factor seems to be

client-led changes in project scope, which cause up to 70% of poor schedule

performance in Saudi Arabian projects for example (Assaf & Al-Hejji, 2006). Poor

performance of designers also contributes significantly to delayed government

projects in Malaysia, where defective designs account for most quality problems

(Sambasivan & Soon, 2007). In addition, Xiong et al (2014) identified clients' scope

clarity and designer performance as the most significant variables of contractor

satisfaction. Therefore, the influences of clients and designer are considered as

antecedent variables when establishing models to test the S-P nexus. nexus of

construction contractors.

This study disaggregated satisfaction into two parts and proposed different

directional relationships based on previous studies. Puzzled by the weak empirical

evidence concerning both directional S-P links at individual level, Schwab and

Cummings (1970) point out that the unsatisfactory outcome may be caused by the

ambiguity in defining job satisfaction. Although some researchers follow this

statement and explore the relationships between disaggregated satisfaction

components and performance (e.g. Nerkar et al., 1996; Lai, 2007), these studies

assume that all disaggregated satisfaction facets share the same unidirectional

relationship with performance. If such an inherent assumption is correct and these

studies obtain significant results, the link between holistic satisfaction and

performance should also be significant, which is not consistent with the conclusions

achieved to date. To test the S-P nexus of the Malaysian construction contractors, it

is proposed that the directions between satisfaction components and performance are

different.


Two conceptual models describing the relationship between contractor

satisfaction (COS) and contractor project managenet performance (CPMP) are

developed.

Conceptual model 1

Conceptual model 1 is developed based on the assumption that COS is a

holistic concept comprising economic related satisfaction (ES) and production

related satisfaction (PS). ES refers to organisation satisfaction with economic issues

such as cost, profitability and potential business opportunities arising from current

business activities. In contrast, PS refers to organisation satisfaction with production

or service quality (Xiong et al. 2014). The contractor S-P nexus is explored by

including influences on the performance of clients and designers in terms of the

clarity of customer needs (OC) and carefulness in the design of products or services

to meet those needs (DP).

The following hypotheses are proposed: H1 concerns the relationships between

the performance of customers and designers separate from the performance and

satisfaction of the organisation. H2 concerns the linkage between organisation

satisfaction and performance, with H2A and H2B indicting that COS affects CPMP

and CPMP affects COS respectively (H2A and H2B are contained in two separate,

but otherwise identical models named Model 1A and Model 1B).

Hypothesis1A: OC has a positive direct effect on COS

Hypothesis1B: OC has a positive direct effect on CPMP

Hypothesis1C: OC has positive effects on DP.

Hypothesis1D: DP has a positive direct effect on COS

Hypothesis1E: DP has a positive direct effect on CPMP

Hypothesis2A: COS has a positive direct effect on CPMP

Hypothesis2B: CPMP has a positive direct effect on COS


Figure 5.7 Conceptual model 1

Conceptual model 2

Similar to conceptual model 1, the influences of OC and DP are considered in

exploring the contractor S-P nexus. A difference here is that satisfaction is divided

into ES and PS, prompted by Geyskens, et al. (1999), Rodríguez, et al.(2006), Lai

(2007) and Xiong et al (2014) as leading examples. Unlike these previous studies, the

different directional relationships that ES and PS may have with performance are

included here. As suggested by the previous literature, this research proposes that PS

has a positive effect on CPMP and CPMP has a positive effect on ES. The

hypotheses are as follows:

H3 concerns relationships between performance of customers and designers

separately with performance and satisfaction of the organisation. H4 concerns the

linkage between the two facets of organisation satisfaction and its performance.

Hypothesis3A: OC has a positive direct effect on ES

Hypothesis3B: OC has a positive direct effect on CPMP

Hypothesis3C: OC has a positive direct effect on PS


Hypothesis3D: OC has a positive direct effect on DP

Hypothesis3E: DP have positive direct effects on CPMP

Hypothesis3F: DP has positive effects on PS.

Hypothesis4A: PS has a positive direct effect on CPMP

Hypothesis4B: CPMP has a positive direct effect on ES

Figure 5.8 Conceptual model 2

Questionnaire survey

The data comprise questionnaire survey 117 usable responses from 136

received responses of Masrom (2012)’s survey of 300 Malaysian contractors

registered with Construction Industry Development Board (CIDB).Respondents are

mostly senior personnel and provided feedback based on the company’s most recent

construction project. The companies are evenly distributed in terms of size, with

53.0% being large companies (G7) and 47.0% small to medium companies (G1-G6)

categorized by company size and permitted tendering capability (see Masrom, 2012).

Company size is used as a control variable in model development, with 0 =

small/medium and 1 = large contractors. Of the company representatives, 93.2%

have a diploma or higher degree and 82.9% have more than 5 years’ working

experience. Table 5.11 presents basic information concerning the construction

projects involved.

Table 5.11 Description of projects


Project characters Groups Frequency Percent Cumulative

percent

Client type Federal government 40 34.19% 34.19%

State or local authority 30 25.64% 59.83%

Private sector 38 32.48% 92.31%

other 9 7.69% 100.00%

Procurement method Traditional (DBB) 75 64.10% 64.10%

Management contract 12 10.26% 74.36%

Design and build 25 21.37% 95.73%

other 5 4.27% 100.00%

Project duration < 1 year 59 50.43% 50.43%

1-2 year 35 29.91% 80.34%

2-3 year 14 11.97% 92.31%

> 3 year 9 7.69% 100.00%

The sample size here exceeds the recommended number of 100 cases

suggested by Bagozzi and Yi (2012) for SEM, and is comparable with previous SEM

studies in the construction industry (Xiong, et al., 2015a). For example, Cheung and

Chow (2011) used 103 responses to explore the underlying factors contributing to

withdrawal in construction project dispute negotiation; and Wong and Lam (2011)

used 107 responses to investigate the effect of organization learning and unlearning

on the performance of construction organizations. In addition to the comparatively

simple model structure, each construct contains at least three variables, which assures

the model identification of each measurement construct and requires a smaller

sample size for fitting the model (MacCallum, et al., 1996; Xiong, et al., 2015a).

To test the conceptual models and corresponding hypotheses, the measurement

framework in Table 5.12 was built based on Keline (2005)’s three-variable principle,

where three observed variables are used to reflect a latent variable . To do this, the

observed variables are extracted from Masrom's (2012) larger questionnaire of

Malaysian general contractors, involving 95 relevant indicators.

Table 5.12 Measurement constructs and items

Constructs No. Items Main sources

What performance level would you rate your project? (1=very bad, 5=very good)

Client's clarity of

objectives (OC)

Q1

Quality of project brief (e.g. needs and

requirements)

Soetanto and Proverbs

(2002);Soetanto and

Proverbs (2004); Assaf

and Al-Hejji (2006);

Park (2009); Masrom

(2012)

Q2 Completeness of project brief

Q3 Certainty of project brief


Designer

performance (DP)

Q4 Design constructability Tang, et al. (2003);

CIDB (2006); Yang and

Peng (2008);Park

(2009); Masrom (2012)

Q5 Comprehensiveness of design

Q6

Flexibility of design to accommodate

changes

Contractor project

management

performance (CPMP)

Q7 Productivity of project manpower Munns and Bjeirmi

(1996); Maloney (2002);

Soetanto and Proverbs

(2004); Tang, et al.

(2003); Cheng, et al.

(2006); Wang and Huang

(2006); Yang and Peng

(2008);Park (2009)

Q8

Two- way communication between

participants and your project team

Q9

Collaborative work between participants

and your project team

Q10

Quality of relationship between

subcontractors and your project team

Which satisfaction level would you rate? (1=very dissatisfied, 5=very satisfied)

Production-related

satisfaction (PS)

C11 Schedule performance (actual vs budget) Schwab and Cummings

(1970); Nerkar, et al.

(1996); Geyskens, et al.

(1999); del Bosque

Rodríguez, et al. (2006);

Lai (2007); Masrom

(2012); Masrom, et al.

(2013); Xiong, Skitmore,

Xia, et al. (2014)

C12 Construction product quality performance

C13 Safety of worksite

Economic-related

satisfaction (ES)

C14

Project cost management performance

(actual vs budget)

C15 Project profitability

C16 Potential business development in future

Chronbach’s alpha value is used to test the reliability of the hypothesized

construct based on the data, where a value exceeding 0.7 is taken as indicating the

received data is acceptable for meeting the consistency requirement (Cho, Hong, &

Hyun, 2009; Lai, 2007). As shown in Table 5.13, the items are measured in five

variables and the overall constructs are sufficiently satisfied.

Table 5.13 Reliability test

Variables All16 Q1-3 Q4-6 Q7-110 Q11-13 Q14-Q16

Cronbach’s Alpha value 0.914 0.874 0.85 0.805 0.753 0.806

Structural equation modelling: CB-SEM and PLS-SEM

Structural equation modelling (SEM) is widely accepted and used in exploring

and testing relationships among different constructs in the social science disciplines

and its evolution is regarded as the most important statistical progress in social

sciences in recent decades (Hair, Ringle, & Sarstedt, 2012). A structural equation

model includes observed variables and latent variables that are hard to observe


directly due to their abstract character and are represented by using several observed

variables (Byrne, 2010). According to model structures, one structural equation

model generally comprises a structural component consisting of the relationships

among latent variables and several measurement components, which consist of the

measurement errors of the measurement variables and the relationships between

observed variables and the represented latent variable (Washington, Karlaftis, &

Mannering, 2010). Compared with first generation models such as principle

component analysis and linear regression, SEM is a second generation multivariate

analysis method (Fornell & Larcker, 1987). It has many strengths, such as enabling

the use of one model to explore an entire set of complex relationships, or the use of

several observable items to represent ambiguous constructs (Cho, et al., 2009;

Fornell & Larcker, 1987; Keline, 2005). Additionally, the popularity of SEM is

enhanced by the availability of many SEM software packages offering graphical

interfaces for model development (Xiong, et al., 2015a).

There are two types of SME approaches: CB-SEM and PLS-SEM. CB-SEM is

appropriate for confirming theoretical hypotheses as it focuses on minimizing the

difference between the model-implied covariance matrix and the sample covariance

matrix, and obtaining accurate parameter estimates. In contrast, PLS-SEM is

preferred in prediction as it focuses on maximizing the explained variance of targeted

constructs (Hair, Ringle, et al., 2012). CB-SEM is more popular in theoretical studies

for its stricter rules concerning data and sample size and accurate estimates of

parameters, while PLS-SEM has recently increased in popularity for its ability to

provide accurate predictions of target variables with a comparatively small sample

size. Additionally, PLS-SEM can handle both reflective measurement constructs and

formative measurement constructs, while CB-SEM can only handle the reflective

measurement construct (Ringle, et al., 2012). In reflective constructs, changes in

latent variables lead to changes in observed variables, while changes in observed

variables do not lead to changes in latent variables, which is important for explaining

the selected latent variable when deleting an observed variable. In formative

constructs, changes in latent variables do not lead to changes in observed variables,

while changes in observed variables lead to changes in latent variables, which

changes the theoretical meaning significantly when deleting an observed variable

(Jarvis, et al., 2003).


In the current context, organisational satisfaction is represented by company

satisfaction (COS), which is composed of economic related satisfaction (ES) and

production related satisfaction (PS) with both ES and PS being reflected by several

observed variables, which means that COS in conceptual model 1 is a second order

reflective-formative model and therefore more suitable for PLS-SEM (Becker et al.,

2012). However, as Hair et al (2012) argue, CB-SEM and PLS-SEM have different

strengths and should be complementary rather than conflicting. In this case, both

methods are used - PLS-SEM for developing conceptual model 1 and CB-SEM for

developing conceptual model 2. The software SmartPLS 2.0 (Ringle, Wende, &

Will, 2005) and SPSS AMOS 21.0 are used for developing the models accordingly.

5.3.4 Results

Conceptual model 1

PLS-SEM has become a popular SEM method in recent years for its ability to

handle both reflective and formative measurement constructs (Becker et al., 2012;

Jarvis et al., 2003; Ringle et al., 2012). For this study, satisfaction is divided into

economic related satisfaction and non-economic satisfaction that is specific to

production related satisfaction. That is to say contractor satisfaction is a formative-

reflective construct. To test H2A and H2B separately, two models are constructed.

Each contains three latent variables (OC, DP, CPMP) with corresponding reflective

indicators, one second-order hierarchical latent variable (COS) and two first-order

constructs (reflective) that form the second-order construct (formative). The repeated

indicator approach is used for the conceptual model for its comparatively high

reliability and wide applications in reflective-formative construct problems (Becker,

Klein, & Wetzels, 2012; Chin, 2010; Ringle, et al., 2012). For this approach, a

higher-order latent variable comprising lower-order latent variables can be

constructed by representing all the observed variables belonging to the lower-order

latent variables. This approach can estimate the scores of latent variables

simultaneously instead of estimating different order constructs separately and then

uses the needed construct scores to test the proposed relationships as a separate step

(Hair Jr, Hult, Ringle, & Sarstedt, 2013; Ringle, et al., 2012).

Model fit evaluation


To validate the measurement components, three types of validity are assessed:

internal consistency reliability, convergent validity and discriminant validity.

Compared with Cronbach’s alpha values presented in Table 5.8, values of composite

reliability (CR) are preferred in the use of internal consistency reliability as such

measures do not assume that the observed variables share the same out-loadings

(Hair Jr, et al., 2013). The average variance extracted (AVE) is used to test

convergent validity. As Table 5.8 indicates, the values of composite reliability and

AVE are greater than the required 0.7 and 0.5 respectively (Fornell & Larcker,

1981b; Hair Jr, et al., 2013); all factor loadings of the observed variables on latent

variables are significant at the level of 0.01; the discriminant validity is satisfactory

as the square root of the average variance extracted for each construct is more than

the maximum correlations with other constructs (Fornell & Larcker, 1981b); and the

loadings are greater than the cross loadings by 0.1 as required (Hair Jr, et al., 2013).

Table 5.14 Validity test results

Latent

variables CR AVE

Correlations between constructs

OC DP CPMP ES PS

OC 0.923 0.800 0.894

DP 0.909 0.769 0.531 0.877

CPMP 0.872 0.631 0.499 0.635 0.795

ES 0.887 0.724 0.500 0.530 0.411 0.851

PS 0.861 0.674 0.480 0.484 0.469 0.640 0.821

COS 0.889 0.572 N/A N/A N/A N/A N/A

Note: The bold numbers in the diagonal row are the square roots of the average variance extracted.

Although the results presented are generated in Model 1A, the values of CR and AVE in Model 1B are

the same to within 0.001 and the differences in correlations between the two models to within 0.005.

Model development

Having confirmed their validity, the results of developing conceptual model

1are presented in Figure 5.9 and Figure 5.10 respectively to test H2A and H2B. COS

is a second order latent variable consisting of ES and PS and the relationships among

the three variables is calculated by the repeated indicator approach. Since satisfactory

results are achieved, only the links between three latent variables of OC, DP and

CPMP, and the second order COS are presented. As the company size control

variable has weak and insignificant effect in both Model 1A and Model 1B, its

results are not presented.


Figure 5.9 Model 1A testing H2A: COS causes CPMP

Figure 5.10 Model 1B testing H2B: CPMP causes COS Note for Fig. 5.9 and Fig.5.10: The values shown above the arrows are the of the path coefficients

validated by bootstrapping. t-statistics are shown in parentheses and their significance at the 1% (***)/

5% (**) level with values greater than 2.58 / 1.96. n.s below the arrows meaning the relationship is

not significant at the 5% level.

These show that both COSCPMP and CPMPCOS are highly significant at

the 0.1 level (t-statistic=1.64), but still far from significant at the 0.05 level (t-


statistic=1.96). These results are generally consistent with many previous studies of

the S-P relationship in that they are positive but weak. The results of the hypothesis

tests are summarised in Table 5.16.

Table 5.15 Results of hypothesis tests

Hypotheses Model 1A Model 1B

H1A: OC has a positive direct effect on COS Supported Supported

H1B: OC has a positive direct effect on CPMP Not supported Not supported

H1C: OC has positive effects on DP. Supported Supported

H1D: DP have positive direct effects on COS Supported Supported

H1E: DP has a positive direct effect on CPMP Supported Supported

H2A: COS causes CPMP Not supported N/A

H2B: CPMP causes COS N/A Not supported

Conceptual model 2

The results gained in conceptual model 1 indicate the potential benefits of

satisfaction disaggregation. In developing model 2, although PLS-SEM is used to

obtain the significance of the links and coefficients, CB-SEM is preferred for its

ability to provide more accurate parameter estimates for the first-order reflective

constructs (Hair, Ringle, et al., 2012). When applying CB-SEM, a two-step

modelling method is often used (Anvuur & Kumaraswamy, 2011; Byrne, 2010;

Xiong, et al., 2015a). This involves firstly carrying out a confirmatory factor analysis

(CFA) from the correlations of all the latent variables and then, if the model fit

results of the CFA are acceptable, changing these to proposed directional

relationships for further analyses.


Table 5.16 Standardized regression weights and SMCs

Item

Standardized regression weights SMC

OC DP CPMP PS ES

Q1 0.894a 0.799

Q2 0.836 0.699

Q3 0.882 0.778

Q4 0.836 0.698

Q5 0.852 0.727

Q6 0.740a 0.548


Q7 0.627a 0.393

Q8 0.721 0.519

Q9 0.748 0.560

Q10 0.762 0.581

Q11 0.718a 0.516

Q12 0.707 0.500

Q13 0.717 0.514

Q14 0.768 0.590

Q15 0.797 0.635

Q16 0.732a 0.536

Table 5.16 provides the standardized regression weights and squared multiple

correlations (SMCs) for each observed item. All the regression weights (factor

loadings) range from 0.627 to 0.894 and, being above 0.5, are therefore highly

significant (Anvuur & Kumaraswamy, 2011). The SMCs range from 0.393 to 0.799

(mean=0.600, sd=0.113). The average SMCs of the items in the measurement models

are the average variance extracted (AVE) of latent variables and, ranging from 0.510

to 0.759, are all greater than the 0.5 threshold. In terms of goodness of fit as

presented in Table 5.11, χ2 = 187.214 (df = 170, χ2/df = 1.101, p=0.174) for the CFA

phase, the χ2/df value of less than 2 indicating a good fit (Xiong et al., 2015).


As a good model fit is obtained in the CFA phase, the correlations between the

latent variables are replaced by the hypothesized directional relationships of

conceptual model 2. The final model is shown in Figure 5.11. Company size is

excluded for being highly insignificant and weak. The observed variables Q1 to

Q16, measurement errors and connections of items are omitted due to space

limitations.


Figure 5.11 Model 2

Table 5.17 Goodness of fit

Goodness of fit measure Criteria CFA SEM

χ2/df <5.0 1.101 1.294

Absolute fit

RMSEA <0.08 0.021 0.036

SRMR <0.08 0.0466 0.0619

RMR <0.05 0.031 0.044

Incremental fit

CFI >0.9 0.991 0.973

TLI >0.9 0.987 0.962

Parsimonious fit

PNFI >0.5 0.646 0.648

PGFI >0.5 0.573 0.574

Table 5.18 Hypothesis direct effects

Hypotheses Model 2

H3A: OC has a positive direct effect on ES Supported

H3B: OC has a positive direct effect on CPMP Not supported

H3C: OC has a positive direct effect on PS Supported


H3D: OC has a positive direct effect on DP Supported

H3E: DP have positive direct effects on CPMP Supported

H3F: DP has positive effects on PS. Supported

H4A: PS has a positive direct effect on CPMP Supported

H4B: CPMP has a positive direct effect on ES Supported

Table 5.18 presents the results of testing the hypothesis direct effects. The

SMC of ES is 0.485, which indicates that 48.5% of the variance in ES is explained

by both direct effects from OC and CPMP and indirect effects from OC, DP and PS.

This is similar for CPMP and PS with SMCs of 0.685 and 0.462 respectively.

Following Anvuur and Kumaraswamy (2012), the bias-corrected bootstrap approach

is used with 500 resamples and maximum likelihood estimation to test the

significance of the indirect effects. This indicates the significance to range from

0.002 to 0.050. For example, the indirect effect of DP on CPMP via the mediation of

PS is 0.337×0.388=0.131, with a 95% confidence interval of (0.000, 0.322), p=0.045.

The total effects are the sum of the direct effects and indirect effects. The

standardized direct effects are shown in Figure 5.11, and Table 5.19 provides the

standardized indirect effects and total effects. To maintain consistency, and as the

effects of the insignificant link OCCPMP are small, Table 5.19 shows the

influencing effects on the dependent variables without deleting this link.

Table 5.19 Standardized direct/indirect/total effects

Effects Variables OC DP PS CPMP

Direct

effects

DP 0.626

PS 0.416 0.337

CPMP -0.091 0.601 0.388

ES 0.455 0 0 0.34

Indirect

effects

DP 0

PS 0.211 0

CPMP 0.62 0.131 0

ES 0.179 0.249 0.132 0

Total

effects

DP 0.626

PS 0.627 0.337

CPMP 0.528 0.732 0.388

ES 0.634 0.249 0.132 0.34


5.3.5 Discussion

This paper explored the project participant satisfaction-performance nexus with

empirical evidence from Malaysian construction contractors. This is partially

inspired by the question proposed by Judge et al. (2001) for future research of

“whether the S-P relationship will be stronger at group or organization level?” With

this concern in mind, two satisfaction dimensions including ES and PS are

introduced in the context of construction projects. Two series models were developed

to test the S-P link of construction contractors. When satisfaction is seen as the usual

holistic concept, the relationship between satisfaction and performance is weak -

similar to previous studies at the individual level. The good fit of Model 2 model

demonstrates the benefit of satisfaction disaggregation and the new S-P relationships

proposed in this study.

The development of the first conceptual model gives a similar result to

previous research at the individual level in that the linkage of satisfaction and

performance is positive but weak. Although Organ (1988) found a high probability of

the existence of a functional S-P relationship, and Judge et al (2001) obtained a 0.30

mean true correlation by conducting a meta-analysis, significant evidence of both

directional models was still lacking. Our findings for this model to test S-P links of

construction contractors are similarly insignificant. However, these results are

sufficiently counterintuitive that research continues in the measurement and

estimation of project participant satisfaction in order to improve satisfaction (e.g.,

Soetanto and Proverbs, 2002; Leung, 2004; Li et al, 2013; Masrom, 2013; Xiong et

al., 2014). This suggests the solution to the S-P problem to be more complex than

realized. Therefore, unlike previous work that assumes all satisfaction facets share

common unidirectional relationships with performance (e.g. Nerkar et al 1996; Lai

2007), the model 2 presented here hypothesises that PS has a positive effect on

CPMP and CPMP positively affects ES. In addition to providing a satisfactory

explanatory ability in the form of R2 of model 2, the hypothesized paths are

significant. The conflicting relationships of the two satisfaction dimensions on

performance validated here may also explain some of the previous findings at the

individual level. For example, Lai (2007) found that noneconomic satisfaction is

much stronger than economic satisfaction in determining the influence of decision


strategies on performance. This suggests that a key problem of the debate on the S-P

nexus is the conflicting dimensions of satisfaction, which has important implications

for future research at both individual and organizational levels.

5.3.6 Conclusions

The nature of the nexus between satisfaction and performance has been

debated for decades without satisfactory resolution. This paper provides results of the

empirical validation of hypothesised conceptual models among construction project

participants. When applied to case study data of 117 Malaysian construction

companies, model 1 indicates that, as do previous individual level studies, only a

weak relationship exists between satisfaction and performance. Model 2, on the other

hand, indicates a substantial effect of non-economic satisfaction on performance

which, in turn, has a substantial effect on economic satisfaction. This result is of

fundamental theoretical importance, with significant implications for future research

and practice.

A further contribution is the comprehensive use of CB-SEM and PLS-SEM in

solving the research problem. Although CB-SEM and PLS-SEM are increasingly

used in social science studies, they are rarely used together in same research (Hair et

al., 2012). This study combines the strengths of both approaches to provide a suitable

model development procedure for testing the formative-reflective construct at the

first step and then disaggregating the formative component at the second step. This

can be seen as a good demonstration of using both methods simultaneously and

provides a reference for similar future research.

Several limitations of the research should be noted. First, as a pioneer study of

the organizational satisfaction-performance nexus, the measures for satisfaction and

performance are less well established compared with widely used scales such as JDI

and MSQ in measuring individual satisfaction. Building a comprehensive analysis

framework will be beneficial for further studies. Otherwise, the inconsistency of

measures may lead to a significant bias and difficulty in conducting comprehensive

analyses (e.g. meta-analysis). In addition, the framework needs to be sufficiently

flexible since the items to measure noneconomic satisfaction may be different for

different industries and working roles. This study contributes to this aspect by


demonstrating the necessity to distinguish economic and non-economic dimensions.

A second limitation of the study is that, although two antecedent variables and one

control variable are considered here, some potential moderators may exist between

satisfaction and performance. For example, for trust among project participants, one

possible situation is that when trust is high, the positive link between noneconomic

satisfaction and performance will be larger, and vice versa. Additionally, industry

differences, culture differences, and country differences are also likely. Future

research will benefit by identifying the influence of potential antecedents, mediators

and moderators. Thirdly, although our hypotheses are verified by evidence from the

construction industry in Malaysia, the applicability of our results to other industries

and other countries is uncertain. More empirical studies of different industries and

contexts are necessary, therefore, to understand the bigger picture. Additionally, it

would be beneficial to test the hypothesis that noneconomic satisfaction positively

affects performance and performance positively affects economic satisfaction at the

individual level.

Finally, this study only focuses on the relationships between satisfaction and

performance of project participating companies. Further study may explore cross-

level influences, such as the impact of perceived organizational

economic/noneconomic satisfaction on individual performance and organizational

performance on individual satisfaction, and vice versa. Additional explorations on

how these findings could be beneficial to create harmonious relationships among

participants are necessary. Despite this study is derived from the context of

construction industry in Malaysia, models developed and findings achieved in this

study probably are useful for other industries and countries to improve project

planning, implementation and outcome assessments.

Chapter 5: Conclusions 163

Chapter 6: Conclusions

Job performance is one of the most important topics in the research area of

organisational and professional management. This research have a specific concern

for construction cost engineers, since they face uncertain works and their job

performance is critical to the success of construction projects. Therefore, this thesis

by publication contributes to developing efficient prediction techniques, providing

new understandings of person-environment interactions, and revealing relationships

between psychological reactions and performance.

The first major concern of this research was to developing an efficient

prediction technique by dealing with problems frequently occurred in previous

studies and practices. This research question was addressed by figuring out

overfitting and multicollinearity problems, developing the hybrid Akaike information

criterion-principal component regression (AIC-PCR) approach, and evaluating its

efficiency with an application of construction cost estimation. Another concern was

the role of psychological reactions in promoting job performance of cost engineers.

This question was addressed by developing a conceptual framework based on the P-

E fit theory, examining dimensions of work stress and job satisfaction, and

investigating relationships between these psychological reactions and job

performance.

In the next section, the main findings of this thesis by publication will be

summarised in detail. The following section describes limitations and offers

recommendations for future research.

6.1 SUMMARY AND DISCUSSION

In Chapter 2, the hybrid AIC-PCR approach is developed to deal with

overfitting and collinearity problems. AIC-PCR procedure and steps are also

introduced. and their usefulness for showing better predictive performance than

alternative methods, including MLR, ANN and SVR, is demonstrated in an

application of construction cost estimation. In section 6.2, path analysis is applied to

164 Chapter 6: Conclusions

examine effects of early cost drivers on the determination of construction

contingencies.

In Chapter 3, we firstly developed a conceptual model from a comprehensive

literature review, which can be used to understand the job performance of

construction professionals. Based on P-E fit theory and the S-O-R paradigm, a new

conceptual framework is achieved to be used as a reference for understanding the

role of psychological reactions. Another section on data analysis method, structural

equation modelling (SEM), is followed. SEM has been increasingly used in

construction research since the late 1990s, but previous SEM applications are not yet

satisfactory. Critical issues and suggestions for research design, model development

and model evaluation are introduced and discussed together with a review of

previous studies.

In Chapter 4, dimensions of work stress are explored and validated with

empirical evidence from construction cost engineers. The applicability of the adapted

perceived-stress questionnaire (PSQ) developed by Levenstein et al. (1993) and

Fliege et al. (2005) is tested by conducting a principal component analysis. SEM is

used to further validate the sub-dimensional model and examine differential effects

of three sub-dimensions on organisational commitment. These findings highlight the

necessity to bear in mind that work stress is a multi-dimensional concept which has

relationships with antecedents and outcomes.

In Chapter 5, a new model is proposed to describe the nexus between job

satisfaction and performance. Puzzled by the weak empirical evidence concerning S-

P links in previous studies assuming job satisfaction as the usual holistic concept,

this research followed Schwab and Cummings (1970)’s argument that the

unsatisfactory outcome may be caused by the ambiguity in definitions of satisfaction.

Additionally, this research proposes that previous unsatisfactory findings may be

because of the inconsistent causal directions between satisfaction components and

performance. Relationships between disaggregated satisfaction components and

performance are explored with empirical evidences from construction cost engineers.

The results obtained by dividing satisfaction into ES and PS demonstrate the benefit

of disaggregation, and reveal the true causal relationships involved. Therefore, our

findings on the relationship between satisfaction and performance can to some extent

help to mediate the decades of debate on the S-P link.


A specific contribution of section 5.1 is the investigation into the form of

effects for PS-TP and TP-ES. Although generally positively linear relationships are

found for both links, an inverted U-shaped relationship is found to be the better one

to explain the TP-ES. A specific contribution of section 5.2 is the comprehensive use

of Covariance based SEM (CB-SEM) and PLS-SEM in solving the S-P link problem.

Although CB-SEM and PLS-SEM are increasingly used in social science studies,

they are rarely used together in the same research (Hair et al., 2012). Combining the

strengths of both approaches provides a suitable procedure for development of a

model to test the formative-reflective construct at the first step and then disaggregate

the formative component at the second step. This approach provides a good

demonstration of using both methods simultaneously and a reference for similar

future research.

6.2 LIMITATIONS AND RECOMMENDATIONS

Besides of specific limitations discussed in several chapters, a few general

limitations of the thesis should be noted. As a, there are some drawbacks related to

the nature of the thesis by publication: some literature review parts might be

overlapping; several datasets are used; and relationships among chapters are not quite

consistent. Additionally, empirical evidences are generated from the construction

industry. Findings may be applicable in other industries with further validation.

The research presented in this thesis has several implications for future studies.

With regard to the first major objective of developing prediction technique in

construction cost estimation, the following studies could be undertaken in the future:

Estimation technique innovation through adaptation of AIC-PCR.

The AIC-PCR approach proposed in this thesis could benefit future research

and practice. In particular, predictors used in construction estimation easily

encounter collinearity and overfitting problems. As well as multiple linear

regression, case based reasoning (CBR) is a widely used approach in cost

estimation. The AIC-PCR approach could assist in determining similarly

important weights and improve estimation performance for CBR.

The impact of sustainability requirements on construction cost.


Sustainable development (SD) has been emphasised in twentieth-century

works such as Limits to Growth (1972) and Silent Spring (1962), and in some

late twentieth-century governmental reports such as Our Common Future

(1987), in which it is defined as “development that meets the needs of the

present without compromising the ability of future generations to meet their

own needs.” In recent years, sustainable development has been accepted as a

must. For example, more than 95% of major companies in Europe and the

USA accept its importance, and many are members of the World Business

Council for Sustainable Development (Giddings, Hopwood, & O'Brien,

2002). Because the construction industry has such large environmental

impacts (Hill & Bowen, 1997; Ofori, 2000), increased awareness of

sustainable building construction is considered to be key to reducing

environmental impacts and finding best practice (Pitt, Tucker, Riley, &

Longden, 2009). In this process, clients act as a key force and are paying

more attention to owning sustainable buildings (Gan et al., 2015). Buildings

labelled by related certifications such as Building Research Establishment

Environmental Assessment Methodology (BREEAM), Leadership in Energy

and Environmental Design (LEED) and Green Star are increasing. Although a

few academic articles argue that increased capital cost is the biggest barrier to

achieving sustainability in construction (Häkkinen & Belloni, 2011), potential

benefits gained by energy-saving are overlooked. Although some industry

reports (such as BRE and Cyril, 2005) argue that sustainability is a significant

factor in the life cycle costs of a building, these reports can be criticised for

using small sample sizes and neglecting interactions with other factors like

project size. Future research will benefit from examining the impact of

sustainability requirements on the construction costs in terms of both capital

costs and life cycle costs of a building.

With regard to the second major objective of exploring P-E interactions, the

following studies could be undertaken in the future:

Examining effects of organisational support on job performance via the

mediation of psychological reactions.

Organizational support and job performance have been important issues in

organizational management. Based on the conceptual model developed in this


thesis, psychological reactions in terms of job satisfaction and work stress

could mediate the impact of organisational support. Future research can

benefit from testing these direct and indirect effects.

Impact of personal characteristics on the relationships between organisational

support factors and job performance mediated by psychological reactions.

As indicated in many previous studies, moderating or direct effects of

personal characteristics on job performance of construction professionals are

worth exploring in the future. These characteristics may include age,

education level, personal traits, learning style, and so on.

Extensions and refinements of the stimulus-reactions-performance conceptual

model.

The conceptual model proposed in Section 2.1 can be used to understand

previous studies and to underpin future studies. Future research can benefit

from extending the model with new concepts and making necessary

adaptions. Longitudinal studies are also relevant to simulate P-E interactions

as a dynamic process.

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