Assessing CAusAl RelAtionships Between ConstRuCtion ... - Core

Assessing Causal Relationships Between Construction Resources and Cost Overrun Using PLS Path Modelling Focusing in Southern and Central Region of Malaysia

Assessing Causal Relationships Between Construction Resources and Cost Overrun Using PLS Path Modelling Focusing in Southern and Central Region of Malaysia A. H. Memon1, I. A. Rahman2, A. A. Abdul Azis3 , Faculty of Civil and Environmental Engineering, University Tun Hussein Onn Malaysia, 86400 Parit Raja, Johor, Malaysia 1 2

, Civil Engineering Department, Universitas Bakrie, Jakarta, Indonesia

2 3

ABSTRACT Construction resources are fundamental requirement for any construction project to achieve project completion within estimated budget. The fundamental construction resources include material, manpower, machinery and money. These resources have significant effect on construction cost. Construction cost is now a days facing problem of coverrun. Hence, it is very critical to assess impact of various resources on cost overrun. Data collection was carried out using structured questionnaire survey amongst client, consultant and contractors in southern and central region of Malaysia. A total of 234 questionnaire were collected including 128 in southern part and 106 in central part. Data was analysed with PLS path modelling using SmartPLS v2.0 software. The result showed that resources have significant effect on cost overrun. Extracted variance showed that in southern region, around 40% of cost variation can be caused because of construction resource while in central region variance is achieved as 59%. Material resource was found as common and most significant resource in southern and central region. GoF value was achieved as 0.529 and 0.566 for southern and central regions respectively confirming the explaining power of the proposed path model. KEYWORDS: Construction resources, central Malaysia, southern Malaysia, PLS path modelling

1.0 INTRODUCTION Malaysia is one the fast growing economy of the world. In spite of global economic crises in last decades, it is undergoing rapid development Corresponding author email: [email protected]

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and being popularized for tourism facilities by increased infrastructure development including roads, bridges and hospitals and hence huge amounts have been allocated for construction development under 10th Malaysian Plan (Mansor, 2010). But unfortunately, construction industry in Malaysia is facing a serious problem of cost overrun. It is evident from investigation of 308 public and 51 private projects by (Endut, Akintoye, & Kelly, 2009) investigated. The authors found that only 46.8% of public projects and 37.2% of private projects were completed within the estimated cost. In a survey to study cost performance in southern region of Peninsular Malaysia (Memon, Rahman, Azis, Rasiah, & Hanas, 2011) found that only 15% respondents mentioned that their projects were finished with cost while 85% of respondents agreed that mostly they face cost overrun in their projects. The overrun in construction cost affect significantly on development plans of the country. Hence a serious attention is required to on cost studies of construction to achieve the projects completed within estimate cost. These overrun can be caused because various factors and it is very critical and essential to understand the sources that cause construction cost overrun. Amongst, construction resources related issues are major factors which affect construction cost performance. Resources are organizational assets. Resource planning should take into consideration not only what is best for an individual project, but also what is best for the organization as whole. For successful construction project, prior and adequate arrangement for provision of resource involved in construction such as type and quantity of material, manpower, machines and finance is very essential at each stage of construction. Various studies have indicated different resource-related issues which cause cost overrun. For any project, fundamental Construction resources include Material, Manpower (Labour), Machinery (Equipments) and Money (Finance) and hence this study is limited in addressing the factors related to these four categories.

1.1

Material Resource

Materials are the essence of any construction projects which represents a represent a substantial proportion of the total value of the project. A material management essentially focus on identifying, acquiring, storing, distributing and disposing of materials. Adequate planning of material is necessary to ensure regular and timely supply of the material in proper quantity required for execution of the construction activities because the late or irregular delivery or wrong type material delivery during construction are major factors that contribute to the delay of the project and ineffective utilization of manpower which lead to cost overrun. 68

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1.2

Manpower Resource

Manpower resources or worker are also significant resources for any construction project as none of work can be executed without manpower. Hence, efficient use of manpower is very critical for any construction projects. Good results certainly cannot be achieved without the adequate availability of skilled and unskilled manpower, most suitable allocation and management of human or manpower resource. Effective manpower management can reduce labour costs and thereby increase profits for company.

1.3

Machines or Equipment Resources

Another important resource required for any projects is machine or equipment. Machinery resource is very beneficial as it help in improving efficiency especially when the amount of work is in bulk quantity it makes work faster with fewer resources. Hence, the selection and utilization of equipment on a project must be an integral part of the total plan to choose the appropriate type and number of the equipment required for any project depend.

1.4

Money

Money or finance is the most important resource as no work can be carried without enough and timely availability of money. Unavailability of money will affect all other resource and can cause stoppage of works. Hence effective management of money or finance is the most important, it will help in planning and decision making of the works and without management of money or finance; the management of other resource becomes useless. The design and specifications of a project depend upon it and without sufficient money or finance any project cannot be completed. Hence, this study focused on assessing effect of construction resources on construction cost overrun. However, this study was limited to compare effect of resource on cost in southern region (Johor, Negeri Sembilan and Melaka) and central region (Kuala Lumpur and Selangor state) of peninsular Malaysia only. Further, PLS Path modelling also known as PLS structural equation modelling, a graphical equivalent of a mathematical representation (Byrne, 2010) was adopted to assess relationship between resource and cost overrun. This approach very effective and popular especially in analyzing the cause–effect relations between latent constructs because of better functionality is better than other multivariate techniques including multiple regression, path analysis, and factor analysis (Ng, Wong, & Wong, 2010). ISSN: 2180-3811


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Lumpur and Selangor state) of peninsular Malaysia only. Further, PLS Path modelling also known as PLS structural equation modelling, a graphical equivalent of a mathematical representation (Byrne, 2010) was adopted to assess relationship between Journal of Engineering and Technology resource and cost overrun. This approach very effective and popular especially in analyzing the cause–effect relations between latent constructs because of better functionality is better than other multivariate techniques including multiple regression, and factor analysis (Ng, & Wong, 2010). MODEL 2.0 path analysis, DEVELOPMENT OFWong, HYPOTHETIC

Figure 1 shows the causal relationships between various resources and DEVELOPMENT OF HYPOTHETIC MODEL cost2.0overrun, where MAN represents material resource, MAN denotes manpower (labour) resource, MAC stands for resources machinery (equipments) Figure 1 shows the causal relationships between various and cost overrun, where MAN resource, MAN denotes manpowerresource. (labour) resource, resource andrepresents MON material stands for money (finance) Each of MAC stands for machinery (equipments) resource and MON stands for money (finance) construct is measured by various indicators. resource. Each of construct is measured by various indicators. MAN

MAT

Cost Overrun

MON

MAC

Figure 1. Causal relationship between construction resources andresources cost overrun and cost Figure 1. Causal relationship between construction overrun

In order to identify the indicator representing each construct or category of resource, a comprehensive literature review was carried out. Table 1 shows the items representing their relative construct.the indicator representing each construct or category In order to identify

of resource, a comprehensive literature review was carried out. Table 1 shows the items representing their relative construct. Table 1. Indicators/measurement items of constructs

Table 1. Indicators/measurement items of constructs Factor Description Machinery related Factors (MAC)

Source

MAC01

Late delivery of equipments

MAC02 MAC03

Insufficient Numbers of equipment Equipment availability and failure

MAC04

High cost of machinery and maintenance

Money or Finance Related Factors (MON) MON01 Delay payment to supplier /subcontractor MON02 Delay in progress payment by owner MON03 Poor financial control on site Cash flow and financial difficulties faced MON04 by contractors MON05

Mode of financing, bonds and payments

MON06

Financial difficulties of owner

Manpower Related Factors (MAN) MAN01 shortage of technical personnel MAN02 labour productivity

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MAN03

High cost of labour

MAN04 MAN05

Shortage of site workers Labour Absenteeism

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(Creed, 2005; Latin, Abiding, & Trigunarsyah, 2008) (Moura, Teixeira, & Pires, 2007) (Creedy, 2005; Moura et al., 2007) (Ameh, Soyingbe, & Odusami, 2010; Azhar, Farooqui, & Ahmed, 2008) (Moura et al., 2007) (Creedy, 2005) (Ameh et al., 2010; Azhar et al., 2008) (Le-Hoai, Lee, & Lee, 2008; Long, Ogunlana, Quang, & Lam, 2004) (Ameh et al., 2010; Omoregie & Radford, 2006) (Le-Hoai et al., 2008; Oladapo, 2007)

(Creedy, 2005) (Moura et al., 2007) (Ameh et al., 2010; Koushki, Al-Rashid, & 4Kartam, No. 12005) June 2013 (Ameh et al., 2010; Azhar et al., 2008) (Moura et al., 2007)

(Le-Hoai, Lee, & Lee, 2008; Long, Ogunlana, Quang, & Lam, 2004) (Ameh et al., 2010; Omoregie & Radford, Causal Relationships Between Construction Resources and Cost Overrun Using PLS Path MON05 Mode of financing, bonds and payments 2006) Modelling Focusing in Southern and Central Region of Malaysia MON06 Financial difficulties of owner (Le-Hoai et al., 2008; Oladapo, 2007) MON04

Assessing

Cash flow and financial difficulties faced by contractors

Manpower Related Factors (MAN) MAN01 shortage of technical personnel MAN02 labour productivity MAN03

High cost of labour

MAN04 MAN05 MAN06

Shortage of site workers Labour Absenteeism Severe overtime

Material Related Factors (MAT) MAT01

Fluctuation of prices of materials

MAT02

Shortages of materials

MAT03 MAT04

Late delivery of materials Changes in material Specification

3.0

(Creedy, 2005) (Moura et al., 2007) (Ameh et al., 2010; Koushki, Al-Rashid, & Kartam, 2005) (Ameh et al., 2010; Azhar et al., 2008) (Moura et al., 2007) (Long et al., 2004)

(Ameh et al., 2010; Enshassi, Al-Najjar, & Kumaraswamy, 2009) (Latif et al., 2008; Moura et al., 2007; Omoregie & Radford, 2006) (Latif et al., 2008; Moura et al., 2007) (Moura et al., 2007)

RESEARCH METHODS AND DATA COLLECTION

3.0 DataRESEARCH METHODS AND DATA COLLECTION collection was carried out using structured questionnaire survey amongst the client, consultant and contractors. A 5-points likert scale was adopted to understand the

Dataperception collection was carriedasout using structured of the respondents 1 for not significant, 2 forquestionnaire slightly significant,survey 3 moderately 4 for very significant and 5 for extremely significant.likert Partialscale amongst the significant, client, consultant and contractors. A 5-points Least Square path modelling also termed asofPartial Least Square Structural was adopted to(PLS) understand the perception the respondents as 1 for not Equation Modelling (PLS-SEM) was used to assess the hypothetic model. The PLS path significant, 2 for slightly significant, 3 moderately significant, 4 for modelling approach is a general method for estimating causal relationships in path very models that involve latent constructs which are indirectly measured by various significant and 5 for extremely significant. Partial Least Square (PLS) (Ringle, Sarstedt, & Mooi, 2010). PLS uses a component-based approach, pathindicators modelling also termed as Partial Least Square Structural Equation similar to principal components factor analysis (Compeau, Higgins, & Huff, 1999). The Modelling (PLS-SEM) was to assessfound theinhypothetic model. The use of PLS path modelling can used be predominantly the fields of marketing, PLS path modelling approach is a general method for estimating causal relationships in path models that involve latent constructs which are indirectly measured by various indicators (Ringle, Sarstedt, & Mooi, 2010). PLS uses a component-based approach, similar to principal components factor analysis (Compeau, Higgins, & Huff, 1999). The use of PLS path modelling can be predominantly found in the fields of marketing, strategic management, and management information systems (Henseler, Ringle, & Sinkovics, 2009). However, it is still new in the context of construction engineering and management. In modelling the willingness of construction organizations to participate in e-bidding (Aibinu & Al-Lawati, 2010) used PLS-SEM while (Lim, Ling, Ibbs, Raphael, & Ofori, 2011) adopted PLS-SEM for Empirical Analysis of the Determinants of Organizational Flexibility in the Construction Business and (Aibinu, Ling, & Ofori, 2011) used PLS-SEM for modelling organizational justice and cooperative behaviour in the construction project claims process.

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strategic management, and management information systems (Henseler, Ringle, &

2009). However, it is still new in the context of construction engineering and JournalSinkovics, of Engineering and Technology

management. In modelling the willingness of construction organizations to participate in e-bidding (Aibinu & Al-Lawati, 2010) used PLS-SEM while (Lim, Ling, Ibbs, Raphael, & Ofori, 2011) adopted PLS-SEM for Empirical Analysis of the Determinants Flexibility the Construction Business and (Aibinu, Ling, & Ofori, 4.0 of Organizational RESULTS ANDinDISCUSSIONS 2011) used PLS-SEM for modelling organizational justice and cooperative behaviour in project claims process. 4.1 the construction Respondent Demographics

Data4.0collection was carried out in two regions of Malaysia i.e. southern RESULTS AND DISCUSSIONS and central part of Malaysia amongst the client, consultant and 4.1 Respondent Demographics contractors. A total of 400 questionnaire sets were distributed as 200 in southern part intwo central regions. Assouthern resultand 128 completed Data collection wasand carried200 out in regions of Malaysia i.e. central part questionnaire set with return rateandofcontractors. 64% percentage southern of Malaysia amongst the client, consultant A total of 400 from questionnaire sets were distributed as 200 in southern part and 200 in central As results 128 were regions and 106 set with 53% return rate fromregions. central region completed questionnaire set with return rate of 64% percentage from southern regions received The53% respondents involved in survey wereback. engaged in and 106back. set with return rate from central region were received The respondents involvedtypes in survey engaged in handling variousfor types of construction handling various ofwere construction project many years. The project for many years. The demographic summarized in table 2. demographic are summarized in are table 2. Table 1. Demographics of respondents

Table 2. Demographics of respondents Southern Frequency %

Central Frequency %

Type of Organization Client Consultant Contractor

21 27 80

16.4 21.1 62.5

18 52 36

17.0 49.0 34.0

Size of Projects Handling Small Projects Large Project

31 97

24.3 73.7

11 95

10.4 89.6

Working Experience Less than 5 years 6 – 10 years More than 10 Years

24 52 52

18.8 40.6 40.6

7 20 79

6.6 18.9 74.5

Table 2 shows that majority of respondent had experience of handling large projects and

had2experience more than 10 yearsof which showed that the respondents were competent Table shows that majority respondent had experience of handling enough and capable to participate in the survey large projects and had experience more than 10 years which showed that the respondents were competent enough and capable to participate 4.2 PLS Path Modelling in the survey.

4.2

The statistical software application Smart PLS 2.0 (Ringle, Wende, & Will, 2005) was used PLS to compute the PLS path model. Analysis was carried out in two stages to calculate Path Modelling PLS model criteria including (i) assessment of construct validity and (ii) assessment of path Model.

The statistical software application Smart PLS 2.0 (Ringle, Wende, & Will, 2005) was used to compute the PLS path model. Analysis was carried out in two stages to calculate PLS model criteria including (i) assessment of construct validity and (ii) assessment of path Model.

2.1.1 Assessment of Construct Validity Construct validity was assess to measure of the internal consistency. Composite reliability scores (CR), Cronbach’s alpha and average variance extracted (AVE) tests were used to determine the construct validity of measured constructs. The results of construct validity are shown in table 3. 72

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Construct validity was assess to measure of the internal consistency. Composite reliability scores (CR), Cronbach’s alpha and average variance extracted (AVE) tests were usedRelationships to determineBetween the construct validityResources of measured constructs. TheUsing results ofPath Assessing Causal Construction and Cost Overrun PLS construct validity are shownModelling in table 3.Focusing in Southern and Central Region of Malaysia Table 3 .Construct validity

Table 3 .Construct validity Construct

Southern Region

Central Region

AVE

CR

Alpha

AVE

CR

Alpha

Machinery

0.686

0.897

0.848

0.602

0.851

0.760

Manpower

0.533

0.870

0.823

0.514

0.862

0.814

Material

0.568

0.838

0.740

0.537

0.772

0.772

Money

0.647

0.916

0.892

0.507

0.860

0.806

According toto(Fornell & Larcker, 1981) as cited as by cited (Aibinuby & (Aibinu Al-Lawati,& 2010; Akter, According (Fornell & Larcker, 1981) Al-Lawati, Ambra, & Ray, 2011) AVE for each construct should be higher than 0.5. This means 2010; Akter, Ambra, & Ray, 2011) for each should that at least 50% of measurement varianceAVE is captured by theconstruct latent variables. The be higher than Thisthemeans at least 50% of The measurement variance reliability test0.5. depicts degree that of internal consistency. most commonly used reliability coefficient is CR and Cronbach’sThe alpha. Reliability test can bedepicts used to check how is captured by the latent variables. reliability the degree well a construct is measured by its assigned indicators. CR value can vary between 0 of and internal consistency. The most commonly used reliability coefficient 1. Researchers argue that composite reliability value for a good model should be is more CR and Cronbach’s Reliability can of bealpha used check how0 well than 0.7 (Akter et al.,alpha. 2011). Similarly, the value cantoalso vary from to 1. A commonisthreshold for sufficient of Cronbach’s alpha isCR 0.6value and if the value a construct measured by its values assigned indicators. can vary is more than 0.7,1. data is considered argue as highlythat acceptable (Akter reliability et al., 2011; Wong between 0 and Researchers composite value&for Cheung, 2005; Yang & Ou, 2008). Hence, it can be summarized that for satisfactory a good model more 0.7and (Akter et al., 2011). construct validityshould the cut-offbe values for than AVE, CR Cronbach Alpha are 0.5,Similarly, 0.7 and the0.7value of alpha alsothat vary fromVariance 0 to 1. Extracted A common threshold respectively. Tablecan 2 shows Average (AVEs), Compositefor Reliabilityvalues (CRs), and alphas exceeded the cut-off of the 0.5, value 0.7, andis0.7 in both sufficient of Cronbach’s alpha is 0.6values and if more than cases of southern region and central region analysis. Thus, the constructs are considered 0.7, data is considered as highly acceptable (Akter et al., 2011; Wong satisfactory with the evidence of adequate reliability, convergent validity. & Cheung, 2005; Yang & Ou, 2008). Hence, it can be summarized that Assessmentconstruct of Path Model for2.1.1 satisfactory validity the cut-off values for AVE, CR and Cronbach Alpha are 0.5, 0.7 and 0.7 respectively. Table 2 shows that Path model can be assessed with path co-efficient and explained variance on the Average Variance Extracted (AVEs), Composite Reliability (CRs), and endogenous latent variables. Figure 2 shows the results of path model. Figure 2 (a) alphas exceeded cut-off values ofwhile 0.5, figure 0.7, 2(b) andshows 0.7 the in path both cases shows the path modelthe results for southern region model for central region.and Figure 2(a) shows that material resource have highest path coof results southern region central region analysis. Thus, the constructs with value of 0.452 showing are highly significantreliability, resource areefficient considered satisfactory with the thematerial evidence of adequate affecting construction cost in southern part. Second most significant factor was money convergent validity. (finance) resource. From figure 2(b) it is perceived that in central regions are material resource are most significant resource affecting construction cost with path co-efficient

2.1.1 Assessment of Path Model with value of 0.419 and second ranked significant resource was Money. The results are similar in southern and central part which shows that material resource is most critical

Path model can be assessed with path co-efficient and explained resource in Malaysia. variance on the endogenous latent variables. Figure 2 shows the results of path model. Figure 2 (a) shows the path model results for southern region while figure 2(b) shows the path model results for central region. Figure 2(a) shows that material resource have highest path co-efficient with value of 0.452 showing the material are highly significant resource affecting construction cost in southern part. Second most significant factor was money (finance) resource. From figure 2(b) it is perceived that in central regions are material resource is most significant resource affecting construction cost with path co-efficient with value of 0.419 and second ranked significant resource was money. The results are similar in southern and central part which show that material resource is most critical resource in Malaysia. ISSN: 2180-3811


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MAN

MAT

0.452

MAN

MAT

0.419

0.127

0.143

Cost Overrun

MAT 0.206

Cost Overrun

R2 =0.408

0.298

MAN

MAT 0.022

MON

MAC

0.452

MAC

0.127

R2 =0.594

MAN

0.285

MON

0.419

0.143

Cost

Cost

Figure 1.1. Path modelmodel results results Overrun Figure Path Overrun Figure 2. (a)RPath model results for Figure 2. (b) Path model results for =0.408 R =0.594 Figure 2. (a) Path model results Figure 2.Regions (b) Path model Southern Regions 0.298 Central 0.285 0.206 0.022 for Central Regions for Southern Regions results 2

2

Further, R² of the endogenous latent variable is used assess the explained variance. According to (Cohen, 1988), R² of endogenous can be assessed as substantial when the MON MON MAC MAC Further, of the endogenous latent the explained value isR² 0.26, moderate at value of 0.13 and variable weak when is theused value assess is 0.02. From figure variance. 1988),latent R² of endogenous canin be assessed 2(a), it isAccording perceived that to R² (Cohen, of the endogenous variable (cost overrun) southern region is 0.408 which shows that 40% of variance can be explained by endogenous i.e.weak as substantial when the value moderate Figureis 1. 0.26, Path model results at value of 0.13 and cost overrun. One the model other hand, figure 2(b) shows thatFigure R² of2.the endogenous latentfor Figure 2. (a) Path results for (b) Path model results when the(cost value is 0.02. From figure 2(a),shows it isthat perceived that R² of the variable overrun) central part is 0.594 which 59% of variance Southern in Regions Central Regions can be explained by endogenous i.e. cost overrun. R² of both models is higher than the cut-off endogenous latent variable (cost overrun) in southern region is 0.408 value and hence the model lies at a very satisfactory level. Also, from the figure 2 (a) which shows thatendogenous 40% of variance can is beused explained byexplained endogenous Further, R² of the latent variable assess the variance. and 2(b) it can be seen that resources have high impact on construction cost in central According to (Cohen, 1988), R² of endogenous can be assessed as substantial when i.e. cost overrun. One the other hand, figure 2(b) shows that R² of region as compared to southern region. We conducted a global fit measure (GoF) for thethe value 0.26,modelling, moderate at value of 0.13 and weak the value From figure PLS ispath which is (cost defined as the geometric mean of is the0.02. average endogenous latent variable overrun) inwhen central part is 0.594 which 2(a), it that is perceived R² the endogenous latent variable (cost in southern communality and average R2of (for endogenous constructs). GoF was overrun) estimatedi.e. for shows 59% ofthat variance can be explained byvalue endogenous cost globalisvalidation of PLS modelthat with40% following equationcan (1) as by (Akter et al., region 0.408 which shows of variance beadopted explained by endogenous i.e. overrun. R² of both models is higher than the cut-off value and hence 2011). cost overrun. One the other hand, figure 2(b) shows that R² of the endogenous latent

the model lies at a very satisfactory level. theoffigure 2 can (a) be variable (cost overrun) in central part is 0.594 whichAlso, showsfrom that 59% variance 2 and 2(b) it can be seen that resources have high impact on construction GoF AVE (1) the cut-off explained by endogenous i.e. cost R overrun. R² of both models is higher than valueinand henceGoF the (South model very 0satisfactory level. Also, from the figure 2 (a) cost central region as0lies to region. We conducted ) .compared 687at 0a.408 .529southern 2(b) fit it can be seen that 0resources high impact on construction cost in central aand global measure (GoF) PLShave path modelling, which is defined as GoF (central ) .54for0.594 0.566 region as compared to southern region. We conducted a global fit measure (GoF) the geometric mean of the average communality and average R2 (for for PLS pathstudy, modelling, is defined the geometric mean model of the In this GoF value which obtained 0.529 was forastheestimated complete (main for average endogenous constructs). GoFwas value foreffects) global validation communality and and average endogenous constructs). estimated for southern region 0.566R2 for(for central regions, which exceedsGoF the value cut-offwas value in ofglobal PLS model with following (1) as adopted (Akter et al., comparison of baseline obtained guidelines suggested by (1) [29]asasadopted 0.1by as GoFsmall, validation of PLS modelusing withequation following equation by (Akter et al., 0.25 as GoFmedium and 0.36 as GoFlarge. This shows that the model has substantial 2011). 2011). explaining power.

GoF

AVE

R

2

(1)

GoF (South)

0.687 0.408

GoF (central)

0.54 0.594

0.529 0.566

In this study, GoF value obtained was 0.529 for the complete (main effects) model for

Insouthern this study, valuefor obtained was 0.529 the complete region GoF and 0.566 central regions, whichfor exceeds the cut-off(main value in comparison of baseline obtained region using guidelines suggested by [29]regions, as 0.1 as which GoFsmall, effects) model for southern and 0.566 for central 0.25 as GoFmedium 0.36 as This shows that the obtained model has substantial exceeds the cut-offand value in GoFlarge. comparison of baseline using explaining power. guidelines suggested by [29] as 0.1 as GoFsmall, 0.25 as GoFmedium and 0.36 as GoFlarge. This shows that the model has substantial explaining power. 74

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5.0 SUMMARY AND CONCLUSION This study assessed the causal relationships to assess the effect of construction resource on cost overrun in central and southern regions of peninsular Malaysia. The findings of study can be summarized as:  Data collection was carried out questionnaire survey amongst southern and central regions of peninsular Malaysia  A total of 128 and 106 completed questionnaire were received from southern and central region respectively  Data was analyzed with SmartPLS v.20  Path model analysis showed that material resource are most significant resources causing cost overrun and is common problem in both southern and central regions  Approximately 40% variation in cost overrun is resulted from resources in southern part while 59% of variation is caused because of resources in central region of Malaysia.  GoF values showed that the model has enough explaining power to generalize the model for explaining cost overrun problems.

6.0 ACKNOWLEDGEMENT The authors would like to thank Universiti Tun Hussein Onn Malaysia for supporting this study. Also, we are thankful to construction practitioners for providing comprehensive and important information, and a lot of cooperation which made data collection easier.

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