See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/264834873

Productivity determinants in Oman construction industry

Article  in  International Journal of Productivity and Quality Management · January 2013

DOI: 10.1504/IJPQM.2013.056736

CITATIONS

21READS

2,149

2 authors, including:

Some of the authors of this publication are also working on these related projects:

Project Mangement View project

Mohammad Miftaur Rahman Khan Khadem

University of Sharjah

29 PUBLICATIONS   274 CITATIONS   

SEE PROFILE

All content following this page was uploaded by Mohammad Miftaur Rahman Khan Khadem on 22 October 2014.

The user has requested enhancement of the downloaded file.

426 Int. J. Productivity and Quality Management, Vol. 12, No. 4, 2013

Copyright © 2013 Inderscience Enterprises Ltd.

Productivity determinants in Oman construction industry

Md. Anisul Islam* Department of Mechanical and Industrial Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoud 123, Muscat, Oman E-mail: m091852@squ.edu.om and Department of Industrial and Production Engineering, Shah Jalal University of Science and Technology, Sylhet-3114, Bangladesh E-mail: anis134ipe@yahoo.com E-mail: anis-ipe@sust.edu *Corresponding author

Mohammad Miftaur Rahman Khan Khadem Department of Mechanical and Industrial Engineering, Sultan Qaboos University, P.O. Box 33, Al-Khoud 123, Muscat, Oman E-mail: khadem@squ.edu.om

Abstract: Spectacular socio-economic development has taken place all around Oman during the last four decades. As a consequence, thriving construction industries are apparent everywhere in the country and not surprisingly, they are going through some productivity problems. Therefore, in light of improving productivity, this paper focuses on exploring the major determinants of productivity, their co-relationship, and the problem categories responsible for construction delay in the context of Oman construction industry. A semi-structured questionnaire approach is chosen as a method of survey from the parties involved in construction industry, such as, owners, consultants, contractors, and foremen/workers. Twenty five major factors of productivity, which are further grouped into ten critical variables by principle components analysis, are identified to be important in this study. Lack of professionalism, fairness in financial transactions, incompetent supervisors, lack of materials, and incomplete drawing are found as top five factors of productivity. In addition, management, people, collaboration, health and safety, logistics, commitment, operational activity, authority, quality, and financial matters are reported as critical variables of productivity. Owners and consultants are determined as top problem categories for construction delay. The comparisons of top five productivity factors found in this study to several other countries’ productivity factors are also shown. Overall, this study is expected to have substantial implication for policy makers and researchers in the area of construction productivity in Oman.

Productivity determinants in Oman construction industry 427

Keywords: productivity determinants; principle components analysis; construction industry; Oman.

Reference to this paper should be made as follows: Islam, M.A. and Khadem, M.M.R.K. (2013) ‘Productivity determinants in Oman construction industry’, Int. J. Productivity and Quality Management, Vol. 12, No. 4, pp.426–448.

Biographical notes: Md. Anisul Islam is an Assistant Professor in the Department of Industrial and Production Engineering at Shah Jalal University of Science and Technology, Sylhet, Bangladesh. Currently, he is working as a Research Assistant in the Department of Mechanical and Industrial Engineering; and Operations Management and Business Statistics at Sultan Qaboos University, Oman. His research interests are in productivity improvement techniques, operations management, and management science. He has published papers in many journals such as International Journal of Fashion Design, Technology and Education, International Journal of Industrial and System Engineering, Journal of Engineering and Technology, and many at international conferences.

Mohammad Miftaur Rahman Khan Khadem is an Assistant Professor in the Department of Mechanical and Industrial Engineering at Sultan Qaboos University, Muscat, Oman. He received his BS in Mechanical Engineering from Bangladesh Institute of Technology, Khulna, Bangladesh; MS in Mechanical Engineering from the University of South Alabama, USA; and PhD in Industrial Engineering from the University of Wisconsin – Milwaukee, USA. His research interests include lean manufacturing, human factors, healthcare system, manufacturing systems modelling, simulation and optimisation, production control, artificial intelligence and e-manufacturing. He is a member of IIE, INFORMS and SME. He has served as a guest editor in International Journal of Industrial and System Engineering; and International Journal of Industrial Engineering.

1 Introduction

Productivity means almost everything in any business (Krugman, 1997) and a long-term practice of productivity concepts constitutes less cost, less time, and higher quality of product/service that ultimately results in greater economic growth in business organisation (Odhigu et al., 2012). In addition, productivity indices are reckoned as indicators of business performance for stakeholders (Yu and Lee, 2002). As consequences, because of its largest size and its assistance to all other industries to some extent, construction productivity contributes significantly to the gross national product (GNP) both in industrialised and semi-industrialised countries (Chia, 2012; Navon, 2005). Moreover, construction productivity is considered as a vital issue in national and international competitiveness, increasing living status, and also in achievement of societal goal in countries (Ghoddousi and Hosseini, 2012; Chia et al., 2012).

428 M.A. Islam and M.M.R.K. Khadem

But the ever-changing multidisciplinary (Jaffar et al., 2011) and instinct desegregated behaviours (Hohns, 1979) of construction industries make productivity principles complex. As a result, the construction industry has been facing higher level of productivity concerns for a long time that necessitate continuous investigation of the productivity variables or factors. Moreover, lack of systematic investigation impedes the pursuit of benefits from established productivity concepts in this industry, while other industries, for example manufacturing industries (Mojahed and Aghazadeh, 2008), and service industries such as bank (Sufian, 2010) hold the major parts of gain. Thus, investigating the productivity factors in the purpose of finding the current and future problems in construction industry is ever essential, especially in unfocused countries (Shehata and El-Gohary, 2011; Goodrum et al., 2009).

As the common Asian saying, stated by Long et al. (2004), “a problem well defined is a problem half solved”, and it is always better to be proactive in problem analysis in which potential problems for the future can be well anticipated. In particular, construction productivity can be characterised by several levels (e.g., industry, firm, project, and activity level) and their collaborations from different upstream and downstream industries (Huang et al., 2009; Dzeng and Wu, 2012). Productivity in construction industry is studied in literature by three most common indices such as single factor-labour productivity index, multi factor production index, and capital productivity index (Li and Liu, 2012; Chia et al., 2012; Allan et al., 2010; Ruddock and Ruddock, 2011; O’Brien and Associates, Inc., 2008); or by productivity measurement methods such as Hicks-Moorsteen index (Arora and Arora, 2012), Malmquist productivity index (Sufian, 2012; Li and Song, 2012; Li and Liu, 2010), data envelopment analysis (Ray and Ray, 2012; Xue et al.,2008); or by improvement tools such as Six Sigma (Desai, 2012), cluster concepts (Phusavat et al., 2012). Despite of various areas of improvement, it is impossible to bring productivity development in construction industries except the well identified productivity factors, both present and future, affect it (Jaaskelainen, 2010; Mojahed and Aghazadeh, 2008). Some of these factors have positive effect and some have negative effect on overall productivity. The factors have negative effect on productivity, are considered as problem categories for construction industry.

In general, every kind of development needs some physical infrastructure, which is an ultimate work for construction industry that in turn significantly influence on socio-economic development of countries (Bielsa and Duarte, 2011). Similar scenario prevails in the Sultanate of Oman (hereafter referred as Oman) with dramatic socio-economic progress presently where construction industry contributed 3% to the GDP in 2004 and it is expected to raise 10% by 2020 (Oman Chamber of Commerce and Industry, 2005). Most of the construction farms are belong to small and medium industries and they have been growing by 34.7% in 2007 as a result of large scale public and private investment in various infrastructure developments, such as industries, tourism and commercial property projects, etc. (PwC, 2012). Moreover, under the long term economic plan called ‘vision 2020’, currently Oman Government is spending 23% more money in 2012 in compare to 2011 fiscal year in construction industry during the eighth five-year development plan (2011–2015) (The Consulate General of the Sultanate of Oman-Australia, 2012). But then, no systematic investigation has been done so far on productivity area in the construction industry in Oman, although several risk factors are identified in the construction industry in Oman (Ballal et al., 2007). Hence, it is urgently needed that factors of productivity are to be well explored that make practitioners and

Productivity determinants in Oman construction industry 429

researchers’ know-how on the present and future problems and prospects of construction industry in Oman.

This research focuses on identifying:

1 the major productivity determinants and their co-relationship

2 the problem categories those are responsible for construction delay in Oman construction industry.

2 Literature review

Productivity is explained as a ratio of a measure of output to a measure of certain or all of the inputs used to produce this output (Grimes, 2007). In construction industry, inputs include the resources associated with manpower, machinery, equipment, materials and transportation; outputs include the physical element that associated with the improvement of construction project (Gouett et al., 2011; Dzeng and Wu, 2012). These outputs and the compositions of input mix in construction industries change over time, and it is difficult to establish a benchmark of these relations (Chia, 2012). In addition, inputs and outputs can be characterised by tangible and intangible factors and in some construction industries, intangible factors are important twice than tangible (Haskel et al., 2011). As a result, it is greatly suggested to study the common factors affecting productivity in the purpose of improving construction productivity (Ghoddousi and Hosseini, 2012). These factors of construction productivity include both positive factors and negative factors. Negative factors affect productivity adversely by increasing non-productive time and cost. Thus, emphasising on positive factors that have progressive affect, and checking and amending negative factors that have adverse effect on productivity, will eventually improve construction productivity. All known factors affecting productivity positively or negatively, are useful for productivity forecasting in construction industries (Lema, 1995).

Earlier studies showed that huge scope of study remains open in construction productivity both in developed and developing countries (Ofori, 2006; Makulsawatudom et al., 2004). It is appeared in literature that productivity in construction industries have been studied in various research works through common factors affecting productivity (Lam and Wong, 2011; Alinaitwe et al., 2007; Mojahed and Aghazadeh, 2008; Enshassi et al., 2009; Long et al., 2004; Makulsawatudom et al., 2004); or by a particular factor of productivity such as, craftsmen (Jarkas and Radosavljevic, 2012; Kaming et al., 1997), sub-contractors (Ng and Tang, 2010), workers (Li and Liu, 2012; James et al., 2012; Jarkas and Bitar, 2012; Moselhi and Khan, 2012; Kazaz and Ulubeyli, 2007; Zakeri et al., 1997; Abdel-Razek et al., 2007); or by entire network of the industry, i.e., supply chain (Setijono, 2010; Dey et al., 2008), even in some cases through the causes of construction delay (Doloi et al., 2011; Faridi and El-Sayegh, 2006).

Numerous productivity factors have been found out by the previous research works. For examples, Jarkas et al. (2012) stated that skill of labour, shortage of materials, labour supervision, shortage of experienced labour, communication between site management and labour force, lack of construction managers’ leadership, high temperature weather, delays in ‘responding to request for information’, lack of providing labour with transportation, and proportion of work subcontracted, were the top ten productivity factors in Qatar construction industry. Al-Ghamdi et al. (2011) found that various

430 M.A. Islam and M.M.R.K. Khadem

corporate management factors, such as managers in dissimilar cultural contexts, at different times, and operating in different types of business environment, as poor productivity factors in private sector firms across the Saudi Arabia. Ghoddousi and Hosseini (2012) explained top seven factors affecting Iran construction projects such as materials/tools, construction technology and method, planning, supervision system, reworks, weather, and jobsite condition. Dai et al. (2009) studied US construction industry, stated that construction equipment, materials, tools and consumables, engineering drawing management, direction and coordination, project management, training, craft worker qualification, superintendent competency, and foreman competency, in a descending order of their negative impact on construction productivity. In Italy, Pellegrino et al. (2012) summarised ten factors influencing construction productivity such as, experience and skill of construction crew, size of the construction crew, site management, design deficiencies or mistakes, delivery delay – equipment deficiencies, storage area, planning/time scheduling, quality acceptance/control, weather conditions, work repetition. Rivas et al. (2011) in Chilean construction industry found that materials, tools, rework, equipment, truck availability, and the workers’ motivational dynamics were the factors affecting construction productivity, Salary expectations of workers and midlevel employees were also found to be the most important reason for turnover. Long et al. (2004) identified that inaccurate time estimation, slow site clearance, and excessive change order were the top three productivity factors in Vietnam. He had identified a total of 20 factors; those were grouped further into five factors by principle components analysis. In case of Gaza strip, major productivity factors were escalation of material prices, differentiation of currency prices, and cash flow of project according to contractors opinion (Enshassi et al., 2009), whereas the lack of materials, incomplete drawing, and incompetent supervisors were the significant factors in construction industries in Thailand (Makulsawatudom et al., 2004). In addition, conflict, poor workmanship, and incompetence of contractors were the causes of poor productivity in South African construction industries (Hanson, 2006). Moreover, education of owners, skill of workers and past productivity records were determined as crucial factors of firm-level productivity in Nigeria (Adebowale and Oyelaran-Oyeyinka, 2012).

Experiencing delay is a common concern of any construction industry, as it increases costs that ultimately aggregate poor productivity in construction industry (El-Maghraby et al., 2011). Thus, in the sense of an elaborate investigation of the industry, finding the parties responsible for productivity delay are important. Delay in material delivery by vendors, non-availability of drawing/design on time, and financial constraints of contractors were the causes of delay in Indian construction industries (Doloi et al., 2011). In Kuwait construction industry, it was found that change orders, financial constraints, owner’s lack of experience, materials, weather, labour, contractor, and combination, were the most delay factor (Koushki et al., 2005). In Saudi Arabian construction industry, it was observed that shortage of manpower – labour related, contractor experience-contractor related, shortage of material – material related, lack of finance to complete the work – owner related, short original contract duration – contract relationship related, and late in reviewing and approving design – consultant related, factors were the causes of construction delay (Al-Kharashi and Skitmore, 2009). A research work in Thailand construction industry studied by Toor and Ogunlana (2008), presented that factors related to designers, contractors and consultants were rated among the top problems. Problems such as lack of resources, poor contractor management, shortage of labour, design delays, planning and scheduling deficiencies, changed orders

Productivity determinants in Oman construction industry 431

and contractors’ financial difficulties were also stressed in this study. In the USA, design change, inaccurate evaluation of projects time, complexity of works, risk and uncertainty associated with projects, non-performance of subcontractors and nominated suppliers, lack of proper training and experiencing of PM, were found as top delay factors in construction industry (Olawale and Sun, 2010). In UAE, preparation and approval of drawings, inadequate early planning of the project and slowness of the owner’s decision-making process were the causes of construction delay (Faridi and El-Sayegh, 2006). Preparation and approval of drawings, unsuitable leadership style of construction/project manager and preparation and approval of drawings delayed construction works in Lebanon (Mezher and Tawil, 1998). In Jordan, the parties such as client, contractor and labour and equipment were the most responsible for construction delay (Odeh and Battaineh, 2002); however, design changes by the owner, poor labour productivity and inadequate planning of contractor were identified as causes of construction delay in Indonesia (Kaming et al., 1997). A similar study by Long et al. (2004) found that consultant, contractor, coordinator were the most occurred problem categories in Vietnamese construction projects. In Malaysia, contractor, labour, and equipment and materials were prime problem categories for construction delay (Sambasivan and Soon, 2007).

Thus, productivity concepts in construction industry can be carried out in different aspects and each aspect has its own significance on productivity. Moreover, factors have co-relationship in each aspect and it is important to understand their co-relationship (Doloi et al., 2011).

3 Methodology

3.1 Survey questionnaire

In this research work, an effective semi-structured questionnaire-based survey (Alinaitwe et al., 2007) has been adopted. A primary questionnaire has been organised first from the standard literature in this area. Since Omani construction industries have not been investigated before, related literature published based on neighbour countries have been focused more in primary questionnaires. The questionnaire, is written in English, is categorised into two parts. The first part is aimed to prime determinants of productivity and second part is intended to construction delay factors. Initially, 30 factors of productivity and six categories of delay have been used for a pilot test. An expert panel, comprising two professionals and one senior academician, is maintained in this pilot study to make questionnaires suitable in present context. Based on their comments, a final draft of questionnaire having total of 25 factors of productivity, seven factors of delay has been adopted for final survey in this study.

Each respondent is requested to rate the factors on a five-point Likert scales (such as, 1 = very low, 2 = low, 3 = average, 4 = high, and 5 = very high). Respondents in this study have been reached by various methods such as, e-mail, telephone, by colleagues, and directly, to deliver and receive the feedback. However, direct (by hand) method is chosen preferably to motivate the respondents (Long et al., 2004). Some respondents are requested to be interviewed at their suitable time. Heterogeneity in this survey is assured by selecting a group of mixed respondents, which is very essential in finding various factors in construction industries (Sambasivan and Soon, 2007).

432 M.A. Islam and M.M.R.K. Khadem

3.2 Respondents characteristic

To ensure the heterogeneity in this study, a total of 296 respondents are randomly selected from different parties, such as owners, consultants/designers, contractors, foremen, and experienced workers (at least five years), involved in construction industries in Oman. Of them 138 respondents are found valid for analysis that represents overall response rate is 46.6%. Some respondents are afraid to give the rank as most of the parties are expatriates, which is also the general scenario in construction industries in Oman. Seven respondents from owner, foreman, and worker level are not considered in the analysis due to ill ranking, perhaps because of their linguistic deficiency. It shows the lacking of research culture presently in Omani construction industries. Table 1 indicates a brief demography of respondents. Table 1 A brief demography of respondents

Parties Distributed Responses received Response rate Proportion

Owner 32 11 34.4% 7.9% Consultant/designer 103 59 57.3% 42.7% Contractor/subcontractor 76 40 52.6% 28.9% Foreman 61 18 29.5% 29.5% Worker 24 10 41.6% 7.3% Total 296 138 46.6% 100%

The highest proportion (57.3%) of respondents in this study is found from consultant/designer professionals, which is followed by contractor/subcontractor (52.6%). Next highest proportion (41.66%) from workers, although number of worker respondents are only ten. The other parties are occupied 34.37% of owner and 29.50% from foremen. Judgement from foremen and workers could be considered as a single group of respondents in this study, as because it is very frequent that experienced workers become foremen in small and medium construction industry in Oman.

3.3 How is the analysis performed?

After collecting the information (retrieved data from the success respondents), some descriptive statistics such as mean, standard deviation, relative importance index (RII) and factor analysis, principle components analysis have been utilised for this analysis. RII is preferred mostly in ranking the attributes, as mean and standard deviation is not an appropriate choice in ranking due to inability to show any relationship between the attributes (Faridi and El-Sayegh, 2006; Iyer and Jha, 2005). Thus, the factors are ranked in descending order of RII. RII is calculated by following equation:

WRIIA N∑

=∗

where

W the weight given to each factor by the respondents

A highest weight

N total number of respondents.

Productivity determinants in Oman construction industry 433

Factors of productivity are grouped into different variables and it is essential to understand the structure of inter-relationship among the factors within each variable. Principal components analysis is used to obtain the optimal ways of combining factors into a small number of variables and factor analysis is employed to get co-relationship among the factors (Doloi, 2009; Long et al., 2004). The validity and reliability of questionnaires (in terms of internal consistency) is tested by the Cronbach’s alpha (Cα) of each variable (Teerajetgul et al., 2009). Although there is no acceptable limit of Cα, as it can be extended by large number of variables (Zhang, 2005). Numerous statistical tests such as Kaiser-Meyer-Olkin measure of sampling adequacy (KMO MSA) and Bartlett’s test of sphericity are applied to test the adequacy of the collected data for factor analysis (Field, 2005). The value of KMO MSA can be varied from 0 to 1. It is advisable to have minimum value of KMO MSA is 0.5; value near to 1 is more reliable result by factor analysis (Kaiser, 1974). Moreover, for the accurate factor analysis, the strength of the data is measured by the communality of each factor used in factor analysis. The value 0.4 to 0.7 of communality has been recommended for any further analysis in principle components analysis (Costello and Osborne, 2005).

4 Result calculation

4.1 Result 01: Ranking of productivity determinants by RII and mean

Ranking of productivity determinants represents the importance of the particular determinants that influence production efficiency. A total of 25 determinants/factors are ranked in descending order of RII and mean value of the determinants in this study (detail shown in Table 2). Table 2 Ranking of the determinants/factors

Factors RII Mean(SD) Rank

Lack of professionalism 0.891 4.45 (0.59) 1 Fairness in financial transactions 0.864 4.32 (0.54) 2 Incompetent supervisors 0.792 3.96 (0.75) 3 Shortage of materials 0.785 3.92 (1.06) 4 Incomplete drawing 0.781 3.90 (0.66) 5 Safety (accident) 0.729 3.64 (0.68) 6 Scheduled working overtime 0.716 3.58 (0.92) 7 Rework 0.711 3.56 (0.59) 8 Bureaucracy 0.710 3.55 (0.92) 9 Contractor’s financial difficulties 0.697 3.48 (0.97) 10 Changing of foremen 0.694 3.47 (1.01) 11 Poor communication 0.659 3.29 (0.91) 12 Inspection delay 0.658 3.29 (0.74) 13 Instruction time 0.648 3.24 (0.95) 14 Change orders 0.646 3.23 (0.85) 15 Shift/work timing 0.625 3.13 (0.84) 16 Poor site condition 0.623 3.11 (1.03) 17

434 M.A. Islam and M.M.R.K. Khadem

Table 2 Ranking of the determinants/factors (continued)

Factors RII Mean(SD) Rank

Low technology 0.613 3.06 (0.97) 18 Staff team morality 0.598 2.99 (0.83) 19 Interferences from other trade 0.591 2.95 (0.70) 20 Lack of responsibility 0.585 2.92 (0.92) 21 Work load 0.563 2.82 (0.93) 22 Lack of skilled manpower 0.561 2.80 (0.86) 23 Obtaining permission from local authority 0.548 2.74 (0.84) 24 Tool/equipment breakdown 0.502 2.51 (0.71) 25

Table 2 suggests that productivity in small and medium construction industries in Oman has been affected mostly by ‘lack of professionalism’ of all parties involves in these industries and less affected by ‘tool/equipment breakdown’, that indicates construction industries in Oman are still labour oriented. The co-relationship among the factors is not known from RIIs and means of the data. Table 3 Communalities of the each factor

Factors Initial Extractions

Lack of professionalism 1.000 0.622 Fairness in financial transactions 1.000 0.689 Incompetent supervisors 1.000 0.662 Shortage of materials 1.000 0.737 Incomplete drawing 1.000 0.550 Safety (accident) 1.000 0.618 Scheduled working overtime 1.000 0.665 Rework 1.000 0.700 Bureaucracy 1.000 0.698 Contractor’s financial difficulties 1.000 0.643 Changing of foremen 1.000 0.735 Poor communication 1.000 0.703 Inspection delay 1.000 0.634 Instruction time 1.000 0.693 Change orders 1.000 0.777 Shift/work timing 1.000 0.722 Poor site condition 1.000 0.611 Low technology 1.000 0.619 Staff team morality 1.000 0.770 Interferences from other trade 1.000 0.672 Lack of responsibility 1.000 0.799 Work load 1.000 0.676 Lack of skilled manpower 1.000 0.764 Obtaining permission from local authority 1.000 0.607 Tool/equipment breakdown 1.000 0.732

Note: Extraction method: principal components analysis.

Productivity determinants in Oman construction industry 435

4.2 Result 02: Reduction of these factors into the critical variables by principle components analysis

Prior to principle components analysis, strength of each factor is examined by communality to decide the accuracy of factor analysis (Ng and Tang, 2010). The communities of all factors are shown in Table 3. It reveals that each factor has communality greater than 0.5, which suggests their accuracy valid for factor analysis.

Principle components analysis with varimax orthogonal rotation is used to reduce 25 factors into highly predictive variables of productivity. Table 4 shows a total of ten variables are developed, named as management, people, collaboration, commitment, health and safety, logistics, operational activity, authority, quality, and financial matters with Eigen values greater than 1.00. Table 4 Ten variables are found by principle components analysis

Variable no. Variable labels Eigenvalue Percentage

of variance Factors Factor loading

Shortage of materials 0.807 Poor communication 0.797

Instruction time 0.710

1 Management (4 factors)

3.937 15.748

Scheduled working overtime 0.626 Lack of responsibility 0.789 2 People

(2 factors) 2.441 9.766

Staff team morality 0.776 Change orders 0.828

Rework 0.724 Inspection delay 0.719

3 Collaboration (4 factors)

1.985 7.939

Interferences from other trade 0.525 Lack of professionalism 0.772 4 Commitment

(2 factors) 1.604 6.415

Changing of foremen 0.644 Safety (accident) 0.862 5 Health and safety

(2 factors) 1.517 6.068

Work load 0.706 Tool/equipment breakdown 0.740 6 Logistics

(2 factors) 1.251 5.004

Low technology 0.590 Shift/work timing 0.856 7 Operational activity

(2 factors) 1.199 4.797

Poor site condition 0.560 Obtaining permission from

local authority 0.728 8 Authority

(2 factors) 1.135 4.541

Bureaucracy 0.593 Incompetent supervisors 0.595

Lack of skilled manpower 0.588 9 Quality

(3 factors) 1.024 4.096

Incomplete drawing 0.464 Contractor’s financial

difficulties 0.722 10 Financial matters

(2 factors) 1.005 4.022

Fairness in financial transactions

0.638

436 M.A. Islam and M.M.R.K. Khadem

4.3 Result 03: Reliability test of respondent’s answer and factor analysis

KMO MSA and Bartlett’s test of sphericity are measured to test the adequacy of the collected data from respondents used in factor analysis. The result of Bartlett’s test of sphericity are approx. Chi-square = 847.883, df = 300 and significance level, p = 0.000 and KMO MSA value is 0.694, which is suggested to be acceptable for factor analysis (Hair et al., 1998). A statistic Cronbach’s alpha (Cα) for investigating the internal consistency of questionnaire is examined for each variable (shown in Table 5). Table 5 Cronbach’s alpha for each variable

Variables Cronbach’s alpha (Cα)

Management 0.650 People 0.773 Collaboration 0.623 Commitment 0.611 Health and safety 0.729 Logistics 0.767 Operational activity 0.697 Authority 0.617 Quality 0.682 Financial matters 0.710

4.4 Result 04: Ranking of problem categories responsible for construction delay by means of RII

In order to elaborate the exploration of the construction industries, it is essential to identify the responsible factors of construction delay. Table 6 shows that the owner, consultant and coordination are the top three problem categories, which are followed by contractor and foreman/worker in Omani construction industries. Rules and regulations, and technology are ranked 6th and 7th respectively at overall seven problem categories used in this study. Owner, consultant and coordination are essential at early design and planning stage of a construction project. So, the ranking found in present study indicates that problems are more severe at early stage than execution stage where contractor and foreman/worker are comparatively more involved. Rules and regulations and technology are required at all levels of a project. Table 6 Ranking of problem categories responsible for construction delay

Rank Problem categories RII Mean (SD)

1 Owner 0.745 3.72 (0.88) 2 Consultant 0.689 3.44 (0.90) 3 Coordination 0.640 3.20 (0.94) 4 Contractor 0.613 3.06 (1.13) 5 Foreman/worker 0.592 2.96 (0.80) 6 Rules and regulations 0.518 2.59 (0.82) 7 Technology 0.458 2.29 (0.76)

Productivity determinants in Oman construction industry 437

The respondent’s agreement on ranking the problem categories is examined by the Spearman’s correlation coefficient at 1% significance level. It is found that the Spearman’s correlation coefficients between owner and consultant, contractor and consultant, contractor and owner, foreman/worker and consultant, foreman/worker and contractor are found 0.878, 0.579, 0.582, 0.592, and 0.542 respectively that connotes a better degree of agreements among the respondents.

5 Discussion of reduced variables

5.1 Management

The first variable is named as ‘management’ that illustrates 15.74% of total variances of the linear component and eigenvalue is 3.937. Management at every level of construction industries plays prime role in construction productivity. According to Deming (1982), management can resolve almost 85% of business problem. In this study, management variable includes four productivity factors such as, shortage of material, poor communication, instruction time, and scheduled working overtime. Shortage of material is ranked fourth in overall ranking of productivity factors. Shortage of material is common in any construction industry (Makulsawatudom et al., 2004). In Oman, shortage of materials is occurred not only for scarcity of material, rather it is due to improper material planning by contractor, inaccurate specification by designers or consultants, wrong calculation of lead time, poor communication among contractors, site managers, foremen and vendors. Usage of low quality of material is common in case of change order and reworks. It is observed that some materials come from abroad; in this case material shortage or material quality mostly depends on fluctuation of prices. Scheduled working overtime is ranked seventh in overall ranking. Surprisingly, working at overtime is obligatory by management not only for site workers but also for employees at consultancy and contractor firms. Most of the time, it is forced and unpaid overtime. Site workers do not have the potentiality to ignore the overtime. Scheduled overtime is happened both at afternoon (before lunch) and evening, which ultimately results to their poor productivity. Instruction time for worker, is ranked 14th, is another evidence of mismanagement in a construction industries. Workers do not know the work schedule even in the morning of the working day. Foremen or contractors are used to act as a policeman (to impose the order) in site, which is an effective way of control for unskilled and temporary foreign worker according to foremen and contractors thought. Poor communication is ranked 12th. It results to poor coordination among the parties (clearing authority, owners, contractors, and consultants) (Chan and Yeong, 1995). It creates delays in getting permission from authority, changing order and design, and reworks, which are responsible for delaying the whole project work (Doloi et al., 2011; Hanna et al., 1999).

5.2 People

The second variable is people, has two factors, such as lack of responsibility and staff team morality, illustrating 9.76% of total variances of the linear component and eigenvalue is 2.441. People are directly responsible and most important factor in construction industries (Ng et al., 2004). Enshassi et al. (2009) report that people is one

438 M.A. Islam and M.M.R.K. Khadem

of the top three variables in Gaza trip’s construction performance. The factor lack of responsibility is ranked 21st and staff team morality is ranked 19th in productivity factor. Construction industries are full of unskilled and temporary workforce in this region. Moreover, no financial awards, high rate of accidents, no health insurance especially for workers, and no recreation facility in jobsite, results in lack of responsibility and less morality among people in construction industries.

5.3 Collaboration

The third variable is collaboration, illustrating 7.93% of total variances of the linear component and eigenvalue is 1.985. It has four factors such as: change orders, rework, inspection delay, and interferences from other trade. Collaboration among diversified parties provides strong teamwork in construction industry (Sveiby and Simons, 2002). Lack of understanding, and coordination among the parties results in less knowledge exchange, fear in relationship, and non-impressed behaviour towards others. A strong team with trusty collaboration among the parties can mitigate the adverse impact of these factors on productivity. Change orders, reworks and inspection delays are ranked as 15th, 7th, 13th respectively and interferences from other trade is ranked 20th in overall ranking of productivity factors.

5.4 Commitment

All party’s commitment is required for successful completion of any construction work (Iyer and Jha, 2005). Commitment is the fourth variable in this study, illustrating 6.42% of total variances of the linear component and eigenvalue is 1.604. It has two factors: lack of professionalism and changing of foremen. Lack of professionalism has highest impact on productivity as it is the first ranked factor in overall ranking of productivity, according to respondents in this study. It is found that the lack of long-term vision and reluctant attitude at industry and company level of construction industries imparts lack of professionalism at all levels of industries. Moreover, most of the contractors are expatriates, consultants are fresh and expatriate mostly, foremen and workers are unskilled and 100% expatriates in Oman construction industries. So, cultural dissimilarity, lack of trust makes them unprofessional in workplace. Changing of foremen, is ranked 11th in overall ranking, is very frequent in Oman due to lack of commitment from owners and contractors. Foremen are changed by owner or by contractor in purpose of paying less money. Sometime contractor is replaced by less paid foremen by owner.

5.5 Health and safety

The fifth variable is health and safety, illustrating 6.06% of total variances of the linear component and eigenvalue is 1.517. It has two factors such as, safety (accident), and work load. Lack of safety in organisation produces more accident, injuries and fatalities, which increase absence of employees, diminish productivity and result in large financial loss (Kazaz and Ulubeyli, 2007). On the other hand, causes of unsafe workplace are not only due to limited PPEs but also the unwillingness of employees to comply with safety

Productivity determinants in Oman construction industry 439

rules and regulations. Improper work planning, (for instance, habit to wait maximum to start the work) and tendency to take more work order raise more safety concern in workplace. Safety (accident) is ranked 6th and workload is ranked 22nd in overall ranking.

5.6 Logistics

The sixth variable is logistics, illustrating 5.00% of total variances of the linear component and eigenvalue is 1.251. It has two factors such as, tool/equipment breakdown and low technology. Proper and full geared construction work cannot be acquired without suitable tool/equipment support (Long et al., 2004). Improper maintenance and inaccurate equipment capacity calculation by contractor or foremen are the main causes of weak logistics support (Makulsawatudom et al., 2004). Technology is essential in everywhere, like in management, procurement, designing, generation and sharing the information, in any construction project. The usage of technology in work is essential for successful survival in today’s competitive construction business (Long et al., 2004). Here, it is found that limited access to modern technology, lack of timely and irrelevant information are the causes of large production cycles and slower decision making in construction industries. Tool/equipment breakdown is ranked 25th and low technology is ranked 18th in overall ranking of productivity.

5.7 Operational activity

The seventh variable is operational activity, illustrating 4.79% of total variances of the linear component and eigenvalue is 1.199. It has two factors such as, shift/work timing and poor site condition. Extreme hot weather is natural in this region due to very high temperature and humidity. Even though, employees and workers are become habituate to, shift/work timing in this weather incur lower work productivity, thus proper estimation of workers and employees’ work productivity are essential. For example, hot weather is advisably considered before estimating labour productivity in Indian construction (Doloi et al., 2011). Limited and costly public transportation makes site condition poor in this region. Moreover, incompetent foremen and contractor in work measurement and work design make workplace miserable in some cases. Shift/work timing is ranked 16th and poor site condition is ranked 17th in overall ranking of productivity.

5.8 Authority

The eighth variable is authority, illustrating 4.54% of total variances of the linear component and eigenvalue is 1.135. It has two factors such as, obtaining permission from local authority and bureaucracy. Obtaining permission from local authority is ranked 24th in overall ranking. Slow response manner, lack of communication are the common barriers of obtaining permission from local authority. Lack of intimacy among the parties involved in industry causes late permission from local authority. The 9th overall ranked factor is bureaucracy, which is apparent in both obtaining authority permission and owners dealing that brings productivity troublesome in this region. Bureaucracy is found to make management work slow in Vietnam (Long et al., 2004).

440 M.A. Islam and M.M.R.K. Khadem

5.9 Quality

The ninth variable is quality, illustrating 4.09% of total variances of the linear component and eigenvalue is 1.024. It has three factors such as, incompetent supervisors, incomplete drawing and lack of skilled manpower. Incompetent supervisors is ranked 3rd, are due to wrong selection of supervisors, unskilled or non-professional workforce. Improper planning, schedule problems, lacks of coordination among the parties are often visible due to incompetent supervisors. Incomplete drawing, ranked 5th, is apparent due to inexperience designers, quick requirements by owners, lack of budget, and lack of coordination. Lack of skilled manpower, ranked 23rd, is a major concern in Omani construction industries. Consultants (local or expatriate) are often new in their job without enough previous training. It is surprising that not a single consultant is found in this study having any training experience, even before or after joining the job. Workers suddenly come from different job into construction and become construction workers, who ultimately become foremen/supervisors without any further training.

5.10 Financial matters

The tenth variable is financial matters, illustrating 4.02% of total variances of the linear component and eigenvalue is 1.005. It has two factors such as, contractor’s financial difficulties and fairness in financial transactions. According to the respondents, coming into business without long-term planning, not setting strategic vision, and tendency to participate in maximum bid are the reasons of constructor’s financial difficulties in Oman. These causes are finally responsible for incompetent project team and poor site management (Long et al., 2004). Fairness in financial transactions makes workers, and employees motivated and dedicated to their works. In this study, it is found that unfairness in financial transactions are occurred frequently in these industries due to unprofessional manner of both owners of construction works and owners of consultancy firms, and contractors because of lack of trust, lack of communication among the parties involved in construction industry. Workers often face unfairness in their wage payment. It is found in this study that workers have been paying same payment since last five years in spite of continuous inflation in worldwide. Not been paying agreed payment is frequent in these industries not only for expatriate workers, also for expatriate consultants and contractors, especially whose from some particular countries. Contractor’s financial difficulties and fairness in financial transactions is ranked 10th and 2nd respectively in overall ranking of productivity, indicates that these factors have good impact on construction productivity in Oman.

6 Comparison of explored factors of construction industry among countries

The purpose of studying the comparison of construction factors among countries is to know some similar and dissimilar factors of construction industry around the world, which is act as a basis of investigating any unexplored sectors in any country. Table 7 shows the detail comparisons.

Productivity determinants in Oman construction industry 441

Table 7 Comparison of explored factors of construction industries among countries

Maj

or p

rodu

ctiv

ity fa

ctor

s of c

onst

ruct

ion

indu

stry

C

ount

ry

1 2

3 4

5 Ex

plor

ed a

rea

Iran

(201

2)

Mat

eria

ls/to

ols

Con

stru

ctio

n te

chno

logy

an

d m

etho

d Pl

anni

ng

Supe

rvis

ion

syst

em

Rew

ork

Prod

uctiv

ity fa

ctor

s

Indo

nesi

a (1

997)

La

ck o

f mat

eria

ls

Rew

ork

Abs

ente

eism

In

terf

eren

ce

Lack

of t

ools

Pr

oduc

tivity

fact

ors

Thai

land

(200

4)

Lack

of m

ater

ials

In

com

plet

e dr

awin

g In

com

pete

nt su

perv

isio

n La

ck o

f too

ls a

nd

equi

pmen

t A

bsen

teei

sm

Prod

uctiv

ity fa

ctor

s

Thai

land

(200

8)

Vis

iona

ry le

ader

ship

In

cent

ive

or re

war

d Co

llabo

ratio

n Tr

ust

Info

rmat

ion

tech

nolo

gy

Key

kno

wle

dge

fact

ors

Vie

tnam

(200

4)

Inac

cura

te ti

me

estim

atio

n Sl

ow si

te c

lear

ance

Ex

cess

ive

chan

ge o

rder

s Sl

ow g

over

nmen

t per

mits

Seve

re o

verti

me

Prod

uctiv

ity fa

ctor

s

USA

(200

9)

Con

stru

ctio

n eq

uipm

ent

Mat

eria

ls

Tool

s an

d co

nsum

able

go

ods

Engi

neer

ing

draw

ing

man

agem

ent

Dire

ctio

n an

d co

ordi

natio

n Pr

oduc

tivity

fact

ors

Gaz

a st

rip (2

009)

Pr

ojec

t com

plex

ity

Num

ber o

f new

pr

ojec

t/yea

r M

anag

emen

t-lab

our

rela

tions

hip

Abs

ente

eism

rate

Se

quen

cing

of w

ork

Prod

uctiv

ity fa

ctor

s

Leba

non

(199

8)

Slow

ness

of t

he o

wne

r’s

deci

sion

-mak

ing

proc

ess

NA

Pr

epar

atio

n an

d ap

prov

al

of d

raw

ings

O

btai

ning

per

mit

from

th

e m

unic

ipal

ity

NA

D

elay

s fac

tors

(c

ontra

ctor

s’ p

ersp

ectiv

e)

Indi

a (2

011)

D

elay

is m

ater

ial

deliv

ery

by v

endo

rs

Non

-ava

ilabi

lity

of

draw

ing

on ti

me

Fina

ncia

l con

stra

ints

of

cont

ract

or

Incr

ease

in sc

ope

of w

ork

Obt

aini

ng p

erm

issio

n fr

om lo

cal a

utho

rity

Del

ay fa

ctor

s

UA

E (2

006)

Pr

epar

atio

n an

d ap

prov

al

of d

raw

ings

In

adeq

uate

ear

ly

plan

ning

of t

he p

roje

ct

Slow

ness

of t

he o

wne

r’s

deci

sion

-mak

ing

proc

ess

Shor

tage

of m

anpo

wer

Po

or s

uper

visi

on a

nd

poor

site

man

agem

ent

Del

ays f

acto

rs

Chi

le (2

011)

M

ater

ials

To

ols

Rew

ork

Equi

pmen

t Tr

uck

avai

labi

lity

Prod

uctiv

ity fa

ctor

Ita

ly (2

012)

Ex

perie

nce

and

skill

of

cons

truct

ion

crew

Si

ze o

f the

con

stru

ctio

n cr

ew

Site

man

agem

ent

Des

ign

defic

ienc

ies o

r m

ista

kes

Del

iver

y de

lay

– eq

uipm

ent d

efic

ienc

ies

Prod

uctiv

ity fa

ctor

Saud

i Ara

bian

(2

009)

Sh

orta

ge o

f man

pow

er

Con

tract

or e

xper

ienc

e Sh

orta

ge o

f mat

eria

ls

Lack

of f

inan

ce

Shor

t con

tract

dur

atio

n D

elay

fact

or

Qat

ar (2

012)

Sk

ill o

f lab

our

Shor

tage

of m

ater

ials

La

bour

supe

rvis

ion

Shor

tage

of e

xper

ienc

ed

labo

ur

Com

mun

icat

ion

betw

een

site

man

agem

ent a

nd

labo

ur fo

rce

Prod

uctiv

ity fa

ctor

Kuw

ait (

2012

) C

hang

e or

ders

Fi

nanc

ial c

onst

rain

ts O

wne

r’s l

ack

of

expe

rienc

e M

ater

ials

W

eath

er

Del

ay/c

ost f

acto

r

Om

an (2

007)

Fi

nanc

ial

capa

bilit

y/fa

ilure

M

anag

emen

t tea

m

Var

iatio

n or

ders

and

ad

ditio

nal w

orks

A

ccur

acy

of p

roje

ct

prog

ram

me

Mat

eria

l pr

ices

/ava

ilabi

lity/

su

pply

/qua

lity

Ris

k fa

ctor

s on

tim

e an

d co

st o

verr

uns

Om

an (2

012)

in

pres

ent s

tudy

La

ck o

f pro

fess

iona

lism

Fa

irnes

s in

finan

cial

tra

nsac

tions

In

com

pete

nt su

perv

isor

s Sh

orta

ge o

f mat

eria

ls

Inco

mpl

ete

draw

ing

Prod

uctiv

ity fa

ctor

s

442 M.A. Islam and M.M.R.K. Khadem

Table 7 shows top five factors of productivity in Thailand were lack of material, incomplete drawing, incompetent supervisors, lack of tools and equipment, and absenteeism (Makulsawatudom et al., 2004), whereas another study called key knowledge factors of Thai construction practice by Teerajetgul et al. (2009) had indicated the top six critical factors were visionary leadership, incentive or rewards, collaboration, trust, information technology, and individual competency or skills.

In Iran, it was found that material/tool shortage, construction technology and method, planning, supervisor system, and rework were the top factors of construction productivity (Ghoddousi and Hosseini, 2012). In Chile construction industry, top five factors affecting productivity were materials, tools, rework, equipment, and truck availability (Rivas et al., 2011). In Italy, major five productivity factor were experience and skill of construction crew, size of the construction crew, site management, design deficiencies or mistakes and delivery delay – equipment deficiencies (Pellegrino et al., 2012). In Qatar construction industry, top five productivity factors were skill of labour, shortage of materials, labour supervision, shortage of experienced labour, and communication between site management and labour force (Jarkas et al., 2012). In Indonesia, lack of materials, rework, absenteeism, interferences and lack of tools were reported as the top productivity factors of construction industries (Kaming et al., 1997). However, inaccurate time estimation, slow site clearance, excessive change orders, slow government permits and severe overtime were the major factors of construction productivity in Vietnam (Long et al., 2004), whereas construction equipment, materials, tools and consumable goods, engineering drawing management, and direction and coordination were the top ranked productivity factors in case of construction industry in the USA (Dai et al., 2009). In some countries, construction industries were investigated through the construction delay factors, for example, Saudi Arabia (Al-Kharashi and Skitmore, 2009); Kuwait (Koushki et al., 2005); Lebanon (Mezher and Tawil, 1998); India (Doloi et al., 2011); and UAE (Faridi and El-Sayegh, 2006).

In addition, Omani construction projects have been studied once before with a sample size of 43 professional respondents that identified a total of 32 significant risk factors. Of them, financial capability/failure, management team, variation orders and additional works, accuracy of project programme, material prices/availability/supply/quality were ranked as the top five risk factors (Ballal et al., 2007). The present study involves in identification of productivity factors and problem categories responsible for construction delay in Oman reflects the earlier result found by Ballal et al. (2007). Contractor’s financial difficulty was identified as top significant risk factor on time and cost overruns in Omani construction industries, whereas in present study it is identified as 10th significant factor of productivity. It seems that Omani construction industries overcome this problem presently, but practically, it is found that along with some other new issues (mentioned in Section 5.10) contractor’s financial difficulties results in some other new factors such as, unfairness in financial transactions, and unprofessional manner in Omani construction industries. As a result, present study shows that lack of professionalism is the top significant factor of productivity, which is followed by fairness in financial transactions, incompetent supervisors, shortage of material, and incomplete drawing as productivity factors in case of Omani small and medium construction industries. Materials shortage/availability was also found as fourth significant factor in previous study. It seems that the material shortage still has greater influences on Omani construction works. Although lack of materials is one of the main concerns in construction industries in today’s very fast urbanisation in around the world (Ghoddousi

Productivity determinants in Oman construction industry 443

and Hosseini, 2012; Rivas et al., 2011; Zakeri et al., 1997; Kaming et al., 1997; Makulsawatudom et al., 2004).

7 Conclusions

Worldwide, construction industries play a major role in socio-economic development, as because for any infrastructure development construction industry is essential. The same picture is true for Oman, in which various new sectors of development are continuing for the purpose of reducing large economic dependencies on oil and petroleum. Each sector of them involves huge construction works and commonly, each of them is experiencing construction productivity problems widely. In order to improve the productivity problem, it is advisable to identity the factors/determinants involve in construction industries. In this response, this study is aimed to investigate the determinants of productivity and their co-relationship; and to identify the most problem categories responsible for construction delay in the context of small and medium construction industries in Oman. A list of total 25 factors of productivity collected from published literature are given to rank each factor on a five-point Likert scales to several parties involved in Omani construction industries that has total numbers of employees less than 50. After calculating the RII of each factor, it is found that lack of professionalism, fairness in financial transactions, incompetent supervisors, shortage of materials, and incomplete drawings are the top five significant factors of productivity. In addition, these 25 factors are further grouped into ten critical variables of productivity by means of principle components analysis. The critical variables are: management, people, collaboration, health and safety, logistics, commitment, operational, authority, quality, and financial matters. Moreover, beside the productivity factor, the respondents are also requested to rank a total of 7 problem categories responsible for construction delay in this context. According to highest RII found from the respondent’s result, the problem categories are as follows: owner, consultant, coordination, contractor, foreman/worker, rules and regulations, and technology. The order of problem categories are the similar reflection of the productivity factors identified in this study. Because the top factors of productivity such as lack of professionalism and fairness in financial transactions are due to the owners of construction works, and owners of consultancy firms and contractors. In a comparison of the results of present study to different countries construction productivity factors depicts that Omani construction industries is confronting some unique challenges.

7.1 Implications of the study

In general, identifying the productivity factors and delay factors of construction industries are quite common in research communities. But the tradition of implementing any research outcomes into practice is yet too common in some construction industries like Oman, because construction works are dealt in this region still by an ad-hoc management. Thus, factors found in this study should be integrated in the main thought of construction processes for improving productivity obstructions. As a subsequent, this research work would be considered as a great implication to the researchers and the decision makers in the area construction productivity in the country, by the way that focus on the ten critical factors and 25 significant factors found will not only improve productivity and profit, but also will act as competitive weapons in the continued fierce construction market. These

444 M.A. Islam and M.M.R.K. Khadem

factors can be used as decision making variables for the managers that will facilitate productivity of the business; in addition, these factors can be used as a pivot for any other productivity study in the businesses and industries.

7.2 Contributions of the study

The novel contributions of this research are recognised from its maiden study of critical productivity factors identification in this context. The outcomes would provide construction industry turn signal and road maps for a successful survival in Oman and in other industries as well.

7.3 Limitations and future study

The presence of scepticism within the industry about the benefit of research works to their own businesses is one of the basic limitations this study encountered. For this reason, respondents were not willing to provide useful information timely. In addition, though best endeavours were given in this study and findings do make a substantial contribution, this study has some other limitations. Such as respondents were from different professional levels and their numbers were not equal, so each level of respondent may have different ranking of productivity, in spite of their degree of agreement was found significant in this study. However, a regression model could have been generated from the factors of productivity and a detail reason of poor productivity and delay, and their impact on construction productivity in Oman has to be studied thoroughly which are the recommended for future work.

Acknowledgements

The authors would like to thank The Research Centre (TRC) of Sultanate of Oman for financial support for this research study under the project no: RC/ENG/MIED/10/01. The authors also would like to thank the anonymous reviewers of the paper for their constructive comments and suggestions.

References Abdel-Razek, R.H., Elshakour, M.H.A. and Abdel-Hamid, M. (2007) ‘Labor productivity:

benchmarking and variability in Egyptian projects’, International Journal of Project Management, Vol. 25, No. 2, pp.189–197.

Adebowale, B.O.A. and Oyelaran-Oyeyinka, B. (2012) ‘Determinants of productivity and inter-firm collaboration in Nigerian clusters’, International Journal of Technology and Globalisation, Vol. 6, No. 3, pp.188–205.

Al-Ghamdi, S.M., Kazmi, A. and Banaemah, M.S. (2011) ‘Corporate management of productivity in private sector firms across Saudi Arabia’, Journal for Global Business Advancement, Vol. 4, No. 2, pp.118–142.

Alinaitwe, H.M., Mwakali, J.A. and Hansson, B. (2007) ‘Factors affecting the productivity of building craftsmen-studies of Uganda’, Journal of Civil Engineering and Management, Vol. 13, No. 3, pp.169–176.

Productivity determinants in Oman construction industry 445

Al-Kharashi, A. and Skitmore, M. (2009) ‘Causes of delays in Saudi Arabian public sector construction projects’, Construction Management and Economics, Vol. 27, No. 1, pp.3–23.

Allan, C., Dungan, A. and Peetz, D. (2010) ‘Anomalies’, damned ‘Anomalies’ and statistics: construction industry productivity in Australia’, Journal of Industrial Relations, Vol. 52, No. 1, pp.61–79.

Arora, H. and Arora, P. (2012) ‘Bank productivity measurement using Hicks-Moorsteen indices: evidence from Indian public sector banks’, International Journal of Business Performance Management, Vol. 13, Nos. 3/4, pp.386–407.

Ballal, T., Elhag, T. and Ambusaidy, S. (2007) ‘Project risk management in Oman: a survey of risk practices in the construction industry’, CIB World Building Congress, pp.549–557.

Bielsa, J. and Duarte, R. (2011) ‘Size and linkages of the Spanish construction industry: key sector or deformation of the economy?’, Cambridge Journal of Economics, Vol. 35, No. 2, pp.317–334.

Chan, A.P.C. and Yeong, C.M. (1995) ‘A comparison of strategies for reducing variations’, Construct Manage Economy, Vol. 13, No. 6, pp.467–473.

Chia, F.C. (2012) ‘Construction and economic development: the case of Malaysia’, International Journal of Construction Management, Vol. 12, No. 1, pp.23–35.

Chia, F.C., Skitmore, M., Runeson, G. and Bridge, A. (2012) ‘An analysis of construction productivity in Malaysia’, Construction Management and Economics, Vol. 30, No. 12, pp.1–15.

Costello, A.B. and Osborne, J.W. (2005) ‘Best practices in exploratory factor analysis: four recommendations for getting the most from your analysis’, Practical Assessment, Research & Evaluation, Vol. 10, No. 7, pp.1–9.

Dai, J., Goodrum, P.M., Maloney, W.F. and Srinivasan, C. (2009) ‘Latent structures of the factors affecting construction labor productivity’, Journal of Construction Engineering and Management, Vol. 135, No. 5, pp.397–406.

Deming, W.E. (1982) Quality, Productivity and Competitive Position, Center for Advance Engineering Study, Massachusetts Institute of Technology, Cambridge, Mass., USA.

Desai, D.A. (2012) ‘Quality and productivity improvement through Six Sigma in foundry industry’, International Journal of Productivity and Quality Management, Vol. 9, No. 2, pp.258–280.

Dey, P.K., Ho, W., Charoenngam, C. and Deewong, W. (2008) ‘Relationship characteristics within the supply chain of small and medium-sized construction enterprises in Thailand’, International Journal of Manufacturing Technology and Management,Vol. 15, No. 1, pp.102–118.

Doloi, H. (2009) ‘Analysis of pre-qualification criteria in contractor selection and their impacts on project success’, Construction Management and Economics, Vol. 27, No. 12, pp.1245–1263.

Doloi, H., Sawhney, A., Iyer, K.C. and Rentala, S. (2011) ‘Analyzing factors affecting delays in Indian construction projects’, International Journal of Project Management, Vol. 30, No. 4, pp.479–489.

Dzeng, R. and Wu, J. (2012) ‘The cost efficiency of construction industry in Taiwan’, The Open Construction and Building Technology Journal, Vol. 6, No. 2012, pp.8–16.

El-Maghraby, E., Frick, J. and Irgens, C. (2011) ‘Factors affecting pouring ready mix concrete production rate using tower cranes in Egypt’, International Journal of Data Analysis Techniques and Strategies, Vol. 3, No. 4, pp.353–374.

Enshassi, A., Mohamed, S. and Abushaban, S. (2009) ‘Factors affecting the performance of construction projects in the Gaza strip’, Journal of Civil Engineering and Management, Vol. 1, No. 3, pp.269–280.

Faridi, A. and El-Sayegh, S. (2006) ‘Significant factors causing delay in the UAE construction industry’, Construction Management and Economics, Vol. 24, No. 11, pp.1167–1176.

Field, A. (2005) Discovering Statistics Using SPSS, Sage Publications, London.

446 M.A. Islam and M.M.R.K. Khadem

Ghoddousi, P. and Hosseini, M.R. (2012) ‘A survey of the factors affecting the productivity of construction projects in Iran’, Technological and Economic Development of Economy, Vol. 18, No. 1, pp.99–116.

Goodrum, P.M., Zhai, D. and Yasin, M.F. (2009) ‘Relationship between changes in material technology and construction productivity’, Journal of Construction Engineering and Management, Vol. 135, No. 4, pp.278–287.

Gouett, M.C., Haas, C.T., Goodrum, P.M. and Caldas, C.H. (2011) ‘Activity analysis for direct-work rate improvement in construction’, American Society of Civil Engineers, Vol. 137, No. 12, pp.1117–1124.

Grimes, C.F. (2007) Employee Motivation, the Organizational Environment and Productivity, Accel-Team.Com, Accel-Team, Cumbria.

Hair, J.E. et al. (1998) Multivariate Data Analysis, 5th ed., Prentice Hall, Upper Saddle River, New Jersey.

Hanna, A.S., Russel, J.S., Nordheim, E.V. and Bruggink, M.J. (1999) ‘Impact of change orders on labor efficiency for electrical construction’, Journal of Construct Engineering Management, ASCE, Vol. 125, No. 4, pp.224–232.

Hanson, D.N. (2006) Causes of Client Dissatisfaction in the South African Building Industry and Ways of Improvement: The Contractors’ Perspectives, MSc thesis in Building, University of the Witwatersrand, Johannesburg, South Africa.

Haskel, J., Goodridge, P., Pesole, A., Awano, G., Franklin, M. and Kastrinaki, Z. (2011) Driving Economic Growth: Innovation, Knowledge Spending and Productivity Growth in the UK, NESTA, London.

Hohns, H.M. (1979) Preventing and Solving Construction Contract Disputes, Van Nostrand Reinhold, New York.

Huang, A.L., Chapman, R.E. and Butry, D.T. (2009) Metrics and Tools for Measuring Construction Productivity: Technical and Empirical Considerations, NIST Spec. Publ. 1101, National Institute of Standards and Technology, Gaithersburg, Maryland.

Iyer, K.C. and Jha, K.N. (2005) ‘Factors affecting cost performance: evidence from Indian construction projects’, International Journal of Project Management, Vol. 23, No. 4, pp.283–295.

Jaaskelainen, A. (2010) ‘Identifying factors affecting public service productivity’, International Journal of Services Technology and Management, Vol. 14, No. 4, pp.360–375.

Jaffar, N., Abdul-Tharim, A.H. and Mohd-Kamar, I.F. (2011) ‘A literature review of ergonomics risk factors in construction industry’, Procedia Engineering, 2nd International Building Control Conference, Vol. 20, pp.89–97.

James, P.M., Rust, A.A.B. and Kingma, L. (2012) ‘The well-being of workers in the South African construction industry: a model for employment assistance’, African Journal of Business Management, Vol. 6, No. 4, pp.1553–1558.

Jarkas, A.M. and Bitar, C.G. (2012) ‘Factors affecting construction labor productivity in Kuwait’, American Society of Civil Engineers, Vol. 138, No. 7, pp.811–820.

Jarkas, A.M. and Radosavljevic, M. (2012) ‘Motivational factors impacting the productivity of construction master craftsmen in Kuwait’, Journal of Management in Engineering, 17 October.

Jarkas, A.M., Kadri, C.Y. and. Younes, J.H. (2012) ‘A survey of factors influencing the productivity of construction operatives in the state of Qatar’, The International Journal of Construction Management, Vol. 12, No. 3, pp.1–23.

Kaiser, H.F. (1974) ‘An index of factorial simplicity’, Psychometrika, Vol. 39, No. 1, pp.31–36. Kaming, P.F., Olomolaiye, P.O., Holt, G.D. and Harris, F.C. (1997) ‘Factor influencing

construction time and cost overruns on high-rise projects in Indonesia’, Construction Management and Economics, Vol. 15, No. 1, pp.83–94.

Kazaz, A. and Ulubeyli, S. (2007) ‘Drivers of productivity among construction workers: a study in a developing country’, Building and Environment, Vol. 42, No. 5, pp.2132–2140.

Productivity determinants in Oman construction industry 447

Koushki, P.A., Al-Rashid, K. and Kartam, N. (2005) ‘Delays and cost increases in the construction of private residential projects in Kuwait’, Construction Management and Economics, Vol. 23, No. 3, pp.285–294.

Krugman, P. (1997) The Age of Diminished Expectations, US Economic Policy in the 1990s, 3rd ed., New York Times book review, USA.

Lam, P.T.I. and Wong, F.W.H. (2011) ‘A comparative study of buildability perspectives between clients, consultants and contractors’, Construction Innovation: Information, Process, Management, Vol. 11, No. 3 pp.305–320.

Lema, N.M. (1995) Construction of Labour Productivity Modeling, University of Dar El-salaam, Tanzania.

Li, Q. and Song, Y. (2012) ‘Productivity growth in Chinese construction industry considering solid wastes generation’, Advanced Materials Research, Vol. 472–475, No. 2012, pp.3316–3319.

Li, Y. and Liu, C. (2012) ‘Labor productivity measurement with variable returns to scale in Australia’s construction industry’, Architectural Science Review, Vol. 55, No. 2, pp.110–118.

Long, N.D., Ogunlana, S., Quang, T. and Lam, K.C. (2004) ‘Large construction projects in developing countries: a case study from Vietnam’, International Journal of Project Management, Vol. 22, No. 7, pp.553–561.

Makulsawatudom, A., Emsley, M. and Sinthawanarong, K. (2004) ‘Critical factors influencing construction productivity in Thailand’, The Journal of KMITNB, Vol. 14, No. 3, pp.1–6.

Mezher, T.M. and Tawil, W. (1998) ‘Causes of delays in the construction industry in Lebanon’, Engineering, Construction and Architectural Management, Vol. 5, No. 3, pp.252–260.

Mojahed, S. and Aghazadeh, F. (2008) ‘Major factors influencing productivity of water and wastewater treatment plant construction: evidence from the deep south USA’, International Journal of Project Management, Vol. 26, No. 2, pp.195–202.

Moselhi, O. and Khan, Z. (2012) ‘Significance ranking of parameters impacting construction labour productivity’, Construction Innovation: Information, Process, Management, Vol. 12, No. 3, pp.272–296.

Navon, R. (2005) ‘Automated project performance control of construction projects’, Automation in Construction, Vol. 14, No. 4, pp.467–476.

Ng, S.T. and Tang, Z. (2010) ‘Labour-intensive construction sub-contractors: their critical success factors’, International Journal of Project Management, Vol. 28, No. 7, pp.732–740.

Ng, S.T., Skitmore, R.M., Lam, K.C. and Poon, A.W.C. (2004) ‘Demotivating factors influencing the productivity of civil engineering projects’, International Journal of Project Management, Vol. 22, No. 2, pp.139–146.

O’Brien, K.E. and Associates, Inc. (2008) Improvement of Onsite Productivity, K.E. O’Brien and Associates, Inc., Toronto, Ontario.

Odeh, A.M. and Battaineh, H.T. (2002) ‘Causes of construction delay: traditional contracts’, International Journal of Project Management, Vol. 20, No. 1, pp.67–73.

Odhigu, F.O., Yahya, A., Rani, N.S.A. and Shaikh, J.M. (2012) ‘Investigation into the impacts of procurement systems on the performance of construction projects in East Malaysia’, International Journal of Productivity and Quality Management, Vol. 9, No. 1, pp.103–135.

Ofori, G. (2006) ‘Revaluing construction in developing countries: a research agenda’, Journal of Construction in Developing Countries, Vol. 11, No. 1, pp.1–16.

Olawale, Y.A. and Sun, M. (2010) ‘Cost and time control of construction projects: inhibiting factors and mitigating measures in practice’, Construction Management and Economics, Vol. 28, No. 5, pp.509–526.

Oman Chamber of Commerce and Industry (2005) Annual Development Report [online] http://www.chamberoman.com/pdf/ChamberAnnualReport2005.pdf (accessed 16 October 2011).

448 M.A. Islam and M.M.R.K. Khadem

Pellegrino, R., Costantino, N., Pietroforte, R. and Sancilio, S. (2012) ‘Construction of multi-storey concrete structures in Italy: patterns of productivity and learning curves’, Construction Management and Economics, Vol. 30, No. 2, pp.103–115.

Phusavat, K., Nilmaneenava, S., Kanchana, R., Wernz, C. and Helo, P. (2012) ‘Identifying productivity indicators from business strategies’ surveys’, International Journal of Productivity and Quality Management, Vol. 9, No. 2, pp.158–176.

PwC (2012) Middle East Region, Oman [online] http://www.pwc.com/m1/en/countries/oman.jhtml (accessed 16 October 2011).

Ray, S. and Ray, I.A. (2012) ‘Malmquist indices of productivity change in India’s chemical industry: a subsector-level analysis’, International Journal of Economic Policy in Emerging Economies, Vol. 5, No. 1, pp.16–36.

Rivas, R.A., Borcherding, J.D., Gonzalez, V. and Alarcon, L.F. (2011) ‘Analysis of factors influencing productivity using craftsmen questionnaires: case study in a Chilean construction company’, Journal of Construction Engineering and Management, Vol. 137, No. 4, pp.312–320.

Ruddock, L. and Ruddock, S. (2011) ‘Evaluation of trends in the UK construction industry using growth and productivity accounts’, Construction Management and Economics, Vol. 29, No. 12, pp.1229–1239.

Sambasivan, M. and Soon, Y.W. (2007) ‘Causes and effects of delays in Malaysian construction industry’, International Journal of Project Management, Vol. 25, No. 2007, pp.517–526.

Setijono, D. (2010) ‘A conceptual framework for managing the performance of construction supply chain’, International Journal of Productivity and Quality Management, Vol. 5, No. 1, pp.1–20.

Shehata, M.E. and El-Gohary, K.M. (2011) ‘Towards improving construction labor productivity and projects’ performance’, Alexandria Engineering Journal, Vol. 50, No. 4, pp.321–330.

Sufian, F. (2010) ‘Sources of banks’ productivity growth in a developing economy: efficiency change or technological progress?’, International Journal of Management and Enterprise Development, Vol. 9, No. 2, pp.179–200.

Sufian, F. (2012) ‘Determinants of banks’ total factor productivity: the post-Asian financial crisis experience of the Philippines’, International Journal of Business Excellence, Vol. 5, Nos. 1/2, pp.77–100.

Sveiby, K. and Simons, R. (2002) ‘Collaborative climate and effectiveness of knowledge work – an empirical study’, Journal of Knowledge Management, Vol. 6, No. 5, pp.420–433.

Teerajetgul, W., Chareonngam, C. and Wethyavivorn, P. (2009) ‘Key knowledge factors in Thai construction practice’, International Journal of Project Management, Vol. 27, No. 8, pp.833–839.

The Consulate General of the Sultanate of Oman-Australia (2012) Govt. Spending in Construction Industry to Jump 23% at $10b [online] http://oman.org.au/general-news/387 (accessed 1 February).

Toor, S. and Ogunlana, S.O. (2008) ‘Problems causing delays in major construction projects in Thailand’, Construction Management and Economics, Vol. 26, No. 4, pp.395–408.

Xue, X., Shen, Q., Wang, Y. and Lu, J. (2008) ‘Measuring the productivity of the construction industry in China by using DEA-based Malmquist productivity indices’, Journal of Construction Engineering and Management, Vol. 134, No. 1, pp.64–71.

Yu, J. and Lee, H. (2002) ‘Productivity management system for construction projects’, Journal of the Architectural Institute of Korea, Vol. 18 No. 7, pp.103–113.

Zakeri, M., Olomolaiye, P., Holt, G.D. and Harris, F.C. (1997) ‘Factors Affecting the motivation of Iranian construction operatives’, Building and Environment, Vol. 32, No. 2, pp.161–166.

Zhang, X. (2005) ‘Concessionaire’s financial capability in developing build-operate-transfer type infrastructure projects’, Journal of Construction Engineering and Management, Vol. 131, No. 10, pp.1054–1064.

View publication statsView publication stats