Cost overrun in the Malaysian construction industry projects: A deeper insight

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International Journal of Project Management xx (2014) xxx–xxx

JPMA-01643; No of Pages 10

Cost overrun in the Malaysian construction industry projects:A deeper insight

Zayyana Shehu a,⁎, Intan Rohani Endut c, Akintola Akintoye b, Gary D. Holt b,d

a 3 Lines Technologies UK LTD, 165-168 Regent Street, London W1B 5TD, United Kingdomb Professor of Construction Management and Economics, Grenfell-Baines School of Architecture, Construction and Environment, University of Central Lancashire,

Preston, PR1 2HE, United Kingdomc Faculty of Civil Engineering, University Teknologi MARA, 40450 Shah Alam, Selangor, Malaysia

d Professor of Innovation in Machinery Management, Birmingham City Business School,Birmingham, B42 2SU, United Kingdom

Received 20 April 2013; received in revised form 2 April 2014; accepted 8 April 2014

Abstract

The construction industry drives economic growth and development in Malaysia, but unfortunately, its projects often suffer from cost overruns(that is, negative cost variance such that final project cost exceeds contract sum). This can lead to conflict and litigation, or in the extreme, projectsmay even be abandoned. To better understand this phenomenon, a questionnaire survey of Malaysian quantity-surveying consultants wasundertaken to obtain project characteristics and cost performance data, in relation to a sample of 359 recently completed construction projects. Datawere analysed in terms of project financial outturn based on: contract values; project sector; type of project; procurement route; nature of projects;and tendering method used. The findings offer stakeholders descriptive statistical cost performance information in relation to these characteristics.These statistics will support first-order project management decision-making within Malaysia particularly; and internationally more generally, witha view to helping minimise project cost variance in the future.© 2014 Elsevier Ltd. APM and IPMA. All rights reserved.

Keywords: Malaysia; Construction industry; Project management; Cost variance; Project cost data

1. Introduction

Cost overruns frequent the construction industries of many(both developed and developing) countries (Enshassi et al., 2009;Sweis et al., 2013) and the significance of this, has attracted muchresearch over recent decades (Arditi et al., 1985; Creedy, 2004;Dawood, 1998; Dlakwa and Culpin, 1990; Doloi, 2013;Frimpong et al., 2003; Kaming et al., 1997; Koushki et al.,2005; Mansfield et al., 1994). This is because cost is arguably oneof the most fundamental criteria for measuring the success of anyproject (Becker et al., 2014; Hajarat and Smith, 1993; Memonet al., 2013; San Cristóbal, 2009). Although, cost still retainsintrinsic relationships with other performance criteria such as

⁎ Corresponding author. Tel.: +447729355179.E-mail address: [email protected] (Z. Shehu).

http://dx.doi.org/10.1016/j.ijproman.2014.04.0040263-7863/00/© 2014 Elsevier Ltd. APM and IPMA. All rights reserved.

Please cite this article as: Z. Shehu, et al., 2014. Cost overrun in the Malaysian10.1016/j.ijproman.2014.04.004

constr

time, quality and value-for-money (Holt, 2010). Nonetheless,despite its academic attention, negative construction project costvariance (the difference between contract sum and a greater finalproject cost) remains. This is especially a problem for Malaysia'sconstruction industry and, its broader developing economy(Ramanathan et al., 2012).

Project costs are commonly categorised as either direct orindirect for contracting, accounting, taxation and other purposes(Becker et al., 2014). However, according to Holland andHobson(1999) there is no universally-accepted categorization frameworkfor the construction industry, to partition construction costs intodirect and indirect groupings. Therefore, this research considersthe both of these cost classifications, to study Malaysianconstruction project cost overruns.

The difference between agreed contract sum and final projectcost can be expressed as a ratio (Kaka and Price, 1991) whereby a

uction industry projects: A deeper insight, Int. J. Proj. Manag. http://dx.doi.org/

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http://dx.doi.org/10.1016/j.ijproman.2014.04.004






Table 1Project type cost ratios.

Category Cost ratio Mean Min Max Median SD

Overall 1.04 1.02 0.20 1.89 1.01 0.16Public project 1.04 1.01 0.20 1.89 1.00 0.16Private project 1.05 1.06 0.91 1.73 1.03 0.14New build 1.05 1.01 0.20 1.89 1.00 0.16Refurbishment 1.01 1.03 0.59 1.48 1.01 0.16

2 Z. Shehu et al. / International Journal of Project Management xx (2014) xxx–xxx

ratio N1.0 represents cost overrun. According to Cleveland(1995), an accurate project cost estimate can provide a good basisfor project control during construction; while inaccurate costestimation is detrimental to both contractors and clients. Thisbecause an overestimated cost will likely be unacceptable to theclient at project feasibility stage, whereas an underestimated costwill typically lead to an increased outturn cost (ratio N1.0). Thelatter situation typically translates to financial losses for thecontractor and/or client (depending on who assumes the burdenaccording to contract terms) (Akintoye, 2000; DeMarco, 2005).Given this, the aim of this study was to investigate certain projectcharacteristics influencing Malaysian construction project over-runs, through an industry-wide survey and subsequent analysis,of real project outturn data.

2. Cost overruns and their imperative in Malaysianconstruction projects

Construction cost overrun has attracted attention at bothnational and global levels. Using the factor analysis technique,Le-Hoai et al. (2009) compared causes of construction time andcost overruns in Asia and Africa, to categorize them into sevenprincipal factors: slowness and lack of constraint; incompetence;design; market and estimate; financial capability; government;and workers. Nawaz et al.'s (2013) work on cost performance inPakistan listed factors that are responsible for cost overruns, toinclude corruption and bribery, political interests, poor sitemanagement, delay in site mobilization, rigid attitude byconsultants, extra work without approvals, and frequent changesduring execution. Rosenfeld (2014) meanwhile undertook aroot-cause analysis of construction-cost overruns and identified15 universal root causes, among which, premature tenderdocuments; too many changes in owners’ requirements ordefinitions; and unrealistic tender-prices were featured.

Different strategies are continually being developed to addressconstruction cost overrun. For example, the UK GovernmentConstruction Strategy report by the Procurement/Lean ClientTask Group (UK Government, 2012) proposed an IntegratedProject Insurance; to cover excessive cost overrun as a means ofproviding cost effective financial security to any funder and coverall for all supply chain members. The rationale underpinning thisis to remove the potential for a ‘blame culture’ and the‘passing-on’ of liability within the construction team. The USConstruction Industry Institute (CII) have conducted extensiveresearch into indirect construction costs (IDCC) based on expertopinions, data collection interviews and analysis of 47 case studyproject surveys. CII published a comprehensive guide on processimprovement opportunities to reduce IDCC (CII, 2014a). Theyalso offer a performance assessment system, through whichonline users can submit project data to assess (inter-alia) costperformance, against best practice statistics (CII, 2014b).

The construction industry in Malaysia plays a vital role in thecountry's development (Azhar et al., 2008; Endut, 2008; Memonet al., 2013). It contributes significantly to national economicgrowth (Sambasivan and Soon, 2007); creates employment bothdirectly and indirectly (Ramanathan et al., 2012); and improvescitizens' quality of life through provision of essential socio-

Please cite this article as: Z. Shehu, et al., 2014. Cost overrun in the Malaysian constr10.1016/j.ijproman.2014.04.004

economic infrastructure and public facilities (Memon et al.,2013). According to Mansor (2010), cited by Memon et al.(2013), the 10th Malaysia Plan allows RM230 billion(≈72.4 billion USD at 2014 conversion) for ‘development’,and RM20 billion (≈6.3 billion USD) facilitation funds to createimpetus in driving demand for the sector.

Of the RM230 billion development expenditure, 60%, orRM138 billion (≈43.4USD), was expended on physical develop-ment to be undertaken directly by the construction sector. Withsuch high levels of capital investment and considering said roleconstruction plays in Malaysian economics, the industry faces tworecurrent (and inter-related) problems. These are: i) slippage ofproject-schedules (‘time overrun’); and ii) negative cost variance(‘project cost overrun’) (Endut et al., 2006; Ramanathan et al.,2012; Sambasivan and Soon, 2007). A result of these (and otherproblems linked to them) is that many clients are left with a feelingof dissatisfaction, relating to their construction project experience(Egan, 1998; Nzekwe-Excel, 2012).

This research adopts the axiom for evaluating the project costratio (CR) proffered by Endut (2008) viz: Cost Ratio (CR) =(Final Cost ∕ Contract Cost). As explained in the Introduction, theideal CR is 1.0; so any value above this can be considered as costoverrun. Table 1 shows the cost ratios of public, private, newbuild and refurbishment projects derived from the sample dataused in this study. It can be seen that the CR values for allcategories of Malaysian projects exceeded 1.0.

The problem of construction projects exceeding contract sumexists among both developed and developing countries(Anastasopoulos et al., 2010; Sambasivan and Soon, 2007). Ascan be seen from the cost ratio analyses in Table 1, the overallCR = 1.04. But, it has been claimed that the extent of overrun,may be greater among developing economies (Memon et al.,2011). For instance, Kaming et al. (1997) indicated that morethan 92% of Indonesian building projects experienced costoverruns, whilst Kolltveit and Gronhaug (2004) revealedoverruns from between 6 and 160% among Norwegian projects.Meanwhile, Ganuza-Fernandez (1996) (cited by Perez-Castrilloand Riedinger, 2004), suggested that as much as 77% of Spanishconstruction projects suffered in this way and one-third of theseextended approximately 20% beyond contract sum. It is cleartherefore, that this is a global phenomenon (see additionally, forinstance, Ali and Kamaruzzaman, 2010; Doloi et al., 2012;Enshassi et al., 2009; and Memon et al., 2013). Approximatelyhalf of all Malaysian construction projects experience between0.03 and 72.88% cost overruns and so is little different to othercountries in this respect (Memon et al., 2013).

Scholars includingMorris and Hough (1987), Ellis (1985) andFlyvbjerg et al. (2004) suggest that larger projects experience




Table 2Project characteristics.

Characteristic Number a Percent b

TypeNew build 301 83.8Refurbishment 58 16.2

Nature of worksInfrastructure 139 38.7Educational 111 30.9Residential 52 14.5Office 29 8.1Commercial 13 3.6Health 11 3.1Recreational 3 0.8Industrial 1 0.3

LocationSelangor 118 32.9Perak 56 15.6Wilayah Persekutuan 30 8.4Kelantan 24 6.7Johor 23 6.4Kedah 22 6.1Pulau Pinang 22 6.1Pahang 13 3.6Terengganu 13 3.6Negeri Sembilan 12 3.3Melaka 10 2.8Perlis 9 2.5Sabah 6 1.7Sarawak 1 0.3Terengganu 13 3.6Negeri Sembilan 12 3.3Melaka 10 2.8Perlis 9 2.5Sabah 6 1.7Sarawak 1 0.3

SectorPublic 308 85.8Private 51 14.2

ProcurementTraditional 291 81.1Design and build 57 15.9Project management 9 2.5Man. contracting 1 0.3Turnkey 1 0.3

TenderOpen 176 49.0Selected 118 32.9Negotiated 65 18.1

Totals for all characteristics:a Ʃ = 359.b Ʃ = 100%.

3Z. Shehu et al. / International Journal of Project Management xx (2014) xxx–xxx

greater (value) overruns, but disparity is apparent among theliterature regarding this in relation to, for instance, geographicallocation or project typology. On the former for example, Odeck(2004) suggested a range of between 59 and 183% amongNorwegian public road works; Pickrell (1990) observed ±10%among US Department of Transportation projects; and Fourancreet al. (1990) suggested up to 500% negative cost variance amongUK Transport and Road Research Laboratory data. Flyvbjerget al. (2003), in studying 258 Danish transport infrastructureprojects, found a range of between −80 and 280% overrun —and the likelihood of actual costs being larger than contract sumat 0.86 and likelihood of them being lower at 0.14. Regardingproject types, Jahren and Ashe (1990) suggested a range of −10to 20% for naval facility construction projects; Kolltveit andGronhaug (2004) found large-scale projects overran between 6and 160%; and Skamris and Flyvbjerg (1997) suggested between50 to 100% among construction projects generally.

Linking project size to the geographic focus of this study, itis noteworthy that in Malaysia, projects costing less thanRM10 million (≈3.2 million USD) are normally constructedby small contractors (CIDB, 2005a). These, according to Takim(2005) perform worse than large contractors and especially interms of planning and coordination; which often leads to costoverrun in this cost range (Endut, 2008). However, theMalaysianexperience is not project size-constrained; and as suggested byIbrahim et al. (2010) remains therefore, a ‘common’ problemlacking investigation especially at the industry ‘cause and effect’level. It has been confirmed that some of the effects of Malaysiancost overrun include but are not confined to, arbitration,abandonment, delays (time overrun), disputes and litigation(Ibironke et al., 2013; Sambasivan and Soon, 2007).

3. Methodology

Based predominantly on review of extant literature in thefield, a questionnaire was designed to investigate Malaysianconstruction project overrun through an industry-wide survey.Information sought in the first part of the questionnaire isrelated to the respondent and their project, mainly for thepurposes of analysing data in relation to these aspects later.This included: general information about the respondentcompany; name of project; start and completion dates; location;number of storeys; gross floor area (in the case of buildingprojects); contract and actual duration periods; pre-contractbudget; contract sum and final account cost.

The second part collected data regarding specific features ofthe projects that were identified from the literature as havingpotential bearing on cost variance. For instance, see Pearl et al.(2003), Kaka and Price (1991), Chan et al. (2010), and Park andPapadopoulou (2012). These features included: the project sector(public or private); type of project (new build or refurbishment);nature of project (residential, infrastructure, commercial, office,educational or health); procurement method (traditional, designand build or project management); and tendering method (open,selective or negotiated). The first draft questionnaire was piloted(Altman et al., 2006) among a sample of 20 academics andconstruction consultants combined (Denscombe, 2010; Lancaster


et al., 2004) and following minor adjustments resulting from thisexercise, was used for the main survey.

The survey targeted the entire population of quantity-surveying consultants (150 companies) in Malaysia; who wereinvited to offer the information described above and resultantly,data were provided in relation to a total of 359 projects. Table 2summarises the main characteristics of these projects, which canbe summarised as: 301 new build, 58 refurbishment; 51 private





sector and 308 public sector; and a predominance of infrastruc-ture projects (39% of all sample projects) that were procured viatraditional means (81%). Within Malaysia, new build infrastruc-ture projects and particularly roads and utilities, predominate(Adnan and Morledge, 2003). As in any developing country,there is a tendency towards public sector funded projects ininfrastructure, along with other social amenities such as schools,drainage systems and hospitals (Ofori, 1994).

4. Data analysis and discussion

Before analysing the sample data in terms of cost variance(using descriptive analyses and regression analysis), it is useful tofirst consider the projects they represent in a little more detail.Following the clear predilection for traditional procurement; 16%of the projects employed design and build and all remainingprocurement variants accounted for only 3%. Whilst each projectmay lend itself to particular procurement requirements, theoptimal choice can reduce project costs by an average of 5%(Alhazmi and McCaffer, 2000). The sample comprised mainlyinfrastructure, educational and residential work (togetherrepresenting 93% of responses) and approximately half of thesework types, were secured via open tenders. Only one fifth ofprojects were negotiated. The projects were spread throughoutmany geographical locations in Malaysia, with the biggestconcentration (approximately one-third of the sample) being inSelangor. This reflects that Selangor is one of the most developedstates in Malaysia with more project developments than anyother, and, because its government department was very willingto provide project data for this study.

4.1. Project cost overrun

Several scholars (Doloi et al., 2012; Enshassi et al., 2009;Memon et al., 2013) have identified with the need for greaterinsight into cost estimation (contract sum) and overrun inconstruction projects. Logically one may assume, first becausethe cost estimate plays a major role in project decision-makingprocesses (Magnussen and Olsson, 2006) and second, becauseits negative variance during the construction phase, leads to

Table 3Contract sum vis-à-vis final cost.

Category Class range a Contract sum Final cost

Freq. Percent Suma Freq. Percent Suma

Small 0.0–5.0 209 58.2 432.0 201 56.0 422.2Medium 5.1–10.0 44 12.3 832.9 49 13.6 884.8

10.1–20.0 38 10.6 38 10.6Large 20.1–30.0 14 3.9 1214.5 12 3.3 1236.0

30.1–40.0 11 3.1 18 5.040.1–50.0 11 3.1 7 1.9

Very large N50.1 32 8.9 4147.6 34 9.5 4340.6Totals 359 100 6627.0 359 100 6883.6Mean a 18.46 19.17Minimuma 0.10 0.10Maximuma 563.30 567.30Std. Deviation a 46.70 49.04a RM million.


perceptions of poor project outcome (Latham, 1994; Memonet al., 2013). Table 3 presents an analysis by class range ofcontract sum vis-à-vis final cost for those data analysed.

Most contract sums were in the class ≤ RM5 (≤1.5USD)million (n = 209), and the lowest frequency (n = 22) was in therange RM30.1 (9.75USD) million to RM50.1 (15.7USD) million.This supports Langdon and Seah (1997) who established that theMalaysian construction industry executed many more smallprojects than larger ones; but contradicts that literature whichsuggests that larger projects have the higher cost overruns(Flyvbjerg et al., 2004; Morris and Hough, 1987). Indeed,comparison of the contract sum and final cost columns in Table 3,suggests very little difference in terms of trends. The differencesthat do exist may be due to an advance transition of activitieswithin the industry, in terms of the phase of national development.The relationship between contract costs and final costs was furtherevaluated using regression analysis— Fig. 1 shows the graphicalresult for all sample projects. The high R2 value (0.98) suggestsminimal discrepancy between these two sub-sets of data with theimplication being, that cost overrun when considered among allprojects combined was minimal. The figure also shows that thedistribution of cost variance (both negative and positive) is muchgreater among lower contract values.

4.2. Actual cost overrun in Malaysian construction projects

Cost overruns are distributed across seven class ranges, fromb0% to N30.1%, as shown in Table 4. Projects that werecompleted below contract sum are reflected in the negativeminimum overall result (−80%). There are several reasons whythis could be, including a reduction of work at client request,design changes or increased efficiency from (for instance) rawmaterial selection. Overall, the sample experienced a meanpercentage overrun of 2% and among positive overrun projectsthis was 11.7%.

Approximately 45% of projects were completed at or belowcontract sum; the remainder overran — supporting extantliterature stating that negative variance is more common thanpositive variance (Flyvbjerg et al., 2003; Morris and Hough,1987; Skamris and Flyvbjerg, 1997). The mean value (2%)reflects the balance of negative and positive variance. Aibinuand Jagboro (2002) suggested an allowance of 17% of total cost

700

R2= 0.98

0

100

200

300

400

500

600

Final cost (RM million)

Con

trac

t sum

(R

M m

illio

n)

0 100 200 300 400 500 600

Fig. 1. Contract sum vis-à-vis final cost all sample.




Table 4Overrun frequency analysis.

Class range (%) Freq. %

b0 151 42.10 9 2.50.1–5 72 20.15.1–10 45 12.510.1–20 53 14.820.1–30 13 3.6N30.1 16 4.4Totals 359 100.0

OverallMean (%) 2.084Minimum (%) −80.38Maximum (%) 88.76Std. Deviation 16.44

Positive overrunMean (%) 11.69Std. Deviation 13.48

Table 6Analysis by project type.

Class range (%) New build Refurbishment

Freq. % Freq. %

b0 127 43.2 24 41.40 7 2.3 2 3.40.1–5 64 20.6 9 15.55.1–10 38 12.3 6 10.410.1–20 43 14.3 10 17.220.1–30 9 3.0 4 6.9N30.0 13 4.3 3 5.2Totals 301 100 58 100

OverallMean (%) 1.86 3.20Minimum (%) −80.38 −43.69Maximum (%) 88.76 48.37Std. Deviation 16.43 16.59

Positive overrunMean (%) 11.35 13.40Std. Deviation 13.76 11.99


estimate (based on Nigerian data) and the United StatesDepartment of Energy recommended a 15–20 per centallowance for budget estimates for new buildings. Comparedwith the literature, the cost overruns from −80.38% to 88.76%for Malaysian construction projects could be seen as typical(42% below contact sum, 3% on budget and 55% costing morethan contract sum).

4.3. Cost overrun based on project sector

The final cost of public works is often considerably higher thanthe price at which the contract is awarded in the tendering process(Bucciol et al., 2013). Usually, private projects perform betterthan public projects (Flyvbjerg et al., 2002; Sweis et al., 2013) butthe current research produced different results (Table 5). Based onmean overall frequency, public sector projects (1.37%) performedbetter than private sector ones (6.43%).

Table 5Analysis by project sector.

Class range (%) Public Private

Freq. % Freq. %

b0 137 44.5 14 27.50 4 1.3 5 9.80.1–5 62 20.1 10 19.65.1–10 31 10.1 14 27.510.1–20 50 16.2 3 5.920.1–30 11 3.6 2 3.9N30.0 13 4.2 3 5.9Totals 308 100 51 100

OverallMean (%) 1.37 6.427Minimum (%) −80.38 −9.230Maximum (%) 88.76 72.880Std. Deviation 16.66 14.494


4.4. Cost overrun based on type of project

Table 6 analyses cost variance by type of projects. The highestfrequency of cost overrun for new build projects was 20.6% (inthe 0.1 to 5 per cent class range); and for refurbishment projects17.2% (in the 10.1 to 20 per cent class range). For new build, 45%of projects were completed at or below contract sum with asimilar statistic among refurbishment projects being 45%.Overall, the trend between both project types was similarsuggesting that new build vis-à-vis refurbishment was not astrong discriminator of cost variance; although the overall meanvalue for positive overrun is consistent with Reyers andMansfield's (2001) contention, that refurbishment contingencieswere higher compared with new build projects of the same value.

The overrun mean values for new build and refurbishmentprojects were 1.86% and 3.20% respectively, reiterating an earlierobservation that refurbishment projects are more prone tonegative cost variance. This sympathises with the findings ofother scholars such as Ashworth and Skitmore (1983), Quah(1992) and Reyes and Mansfield (2001). Such tendency has beenattributed to increased complexity of refurbishment projectswhen compared to new build (Baccarini, 1996; Murray, 2000).

4.5. Cost overrun based on procurement methods

The most common procurement method used within con-struction is traditional (e.g. Idoro, 2012) although other, more‘mutually beneficial’ routes such as partnering, design and build,management contracting, and project management have allwitnessed increased usage over recent years (Holt, 2010). Inthis analysis, three procurement methods were considered:traditional, design and build, and project management. This wasbecause management contracting and construction managementwere only used in only five of the sample projects, and such asmall number made their inclusion in comparative data analysisunjustifiable (and unreliable). Table 7 presents the frequencies of




Table 7Analysis by procurement method.

Class range (%) Traditional D & Build Project Man.

Freq. % Freq. % Freq. %

b0 124 42.6 26 44.8 1 11.10 6 2.1 3 5.2 0 0.00.1–5 51 17.5 18 31.0 4 44.45.1–10 37 12.7 3 5.2 3 33.310.1–20 51 17.5 2 3.4 0 0.020.1–30 10 3.4 2 3.4 1 11.1N30.1 12 4.1 4 6.9 0 0.0Totals 291 100 58 100 9 100

OverallMean (%) 1.770 2.90 6.49Minimum (%) −80.38 −43.69 −3.36Maximum (%) 72.88 88.76 29.36Std. Deviation 15.84 20.10 9.19

Positive overrunMean (%) 11.65 13.24 7.72Std. Deviation 11.52 22.35 8.99


cost variance in relation to the seven class ranges amongprocurement types.

Traditional procurement was most popular (291 projects);followed by design and build (58 projects), then projectmanagement (9 projects). Project management experienced thehighest frequency of overruns in the 0.1 to 5 per cent class range(44.4%), as did design and build (31.0%). Traditional procure-ment however, experienced the highest frequency of overruns inthe smaller (percentage overrun) class ranges and these tailed offmarkedly for overruns above 20.1%. These observations con-tradict Akintoye (1994), whose study suggested that design andbuild could account for up to one-fifth reduction in project costcompared with traditional. The mean percentage cost overrunvalues show that among this sample, traditional procurementyielded better cost performance (traditional 1.7, design and build2.9, and project management 6.5%).

Table 8Analysis by nature of project.

Class range(% overrun)

Residential Infrastructure Commercial

Freq. % Freq. % Freq.

b0 29 55.8 50 36.0 20 2 3.8 2 1.4 10.1–5 10 19.2 30 21.6 15.1–10 7 13.5 15 10.8 310.1–20 2 3.8 28 20.1 320.1–30 1 1.9 7 5.0 0N30.1 1 1.9 7 5.0 2Totals 52 100 139 100 13

OverallMean (%) −2.53 3.24 11.04Minimum (%) −27.76 −80.38 −6.78Maximum (%) 48.37 88.76 40.84Std. Deviation 12.76 19.03 15.14



4.6. Cost overrun based on nature of projects

The nature of the project can influence cost variance (Pearlet al., 2003). Table 8 presents cost variance analysis for thedifferent types of project among the sample data; whichcomprised mainly infrastructure (n = 139) and educational(n = 111) with health projects representing the smallest group(n = 11). For four of the six project types, the highest overrunfrequency was in the range of 0.1 to 5 per cent cost overrun class.

Infrastructure projects had a mean value of 3.2 per centoverrun with a range of between −80.38 and 88.76%. Thirty-sixper cent of these projects experienced positive cost variance;1.4% were to contract sum and 62.6% experienced negativevariance (overrun). These findings support those of Odeck (2004)who studied 620 Norwegian road projects (see also Flyvbjerget al., 2002; Skamris and Flyvbjerg, 1997). Residential projectsexperienced cost variance between −7.76 and 48.37% with amean value of −2.53%. Okuwoga's (1998) Nigerian housingprojects study found that 25% of the sample completed with lessthan a 15 per cent cost overrun — indicating that residentialprojects in Malaysia perform better than their Nigerian develop-ing country counterparts. Educational projects experienced costvariance from −44.20 to 53.14% with a mean of −0.83. Overall,best-cost performance was exhibited by the residential projectgroup (−2.5%); the worst being the office projects with a mean of11.8%.

Fig. 2 shows a graphical analysis of cost variance among thesample based on contract sum. This identified that the smallerthe project value, the greater was the propensity of costvariance and this interestingly, demonstrated an approximatelysimilar negative/positive distribution. A very similar set ofcircumstances was observed when conducting the same graph-ical analysis among final project costs. This confirmed thattendency for cost variance decreased with project value; as didthe degree of variance between contract sum and final cost.However, in considering these observations it is noted that the

Office Educational Health

% Freq. % Freq. % Freq. %

23.1 6 20.7 57 51.4 6 54.57.7 1 3.4 1 0.9 1 9.17.7 5 17.2 24 21.6 2 18.223.1 7 24.1 11 9.9 1 9.123.1 5 17.2 14 12.6 0 0.00.0 1 3.4 2 1.8 1 9.115.4 4 13.8 2 1.8 0 0.0100 29 100 111 100 11 100

11.86 −0.83 −1.87−9.57 −44.20 −16.6772.88 53.14 26.6917.16 13.07 12.22

16.38 8.87 9.3117.35 9.84 11.99




-60

-40

-20

0

20

40

60

80

Perc

enta

ge c

ost v

aria

nce

Contract cost (RM million)

0 50 100 150 200 250

Fig. 2. Cost variance among the sample.


number of large projects in the sample was small. Here, themain objective was to explore the distribution of the costoverrun regardless of project size; but future research mightconsider a more equally stratified sample (in this context) tocompare results.

Table 10Analysis by contract sum.

Class range Small a Mediumb Large c Very large d

4.7. Cost overrun based on tendering method

As highlighted in the Methodology section, within Malaysiathere are three main tendering methods: open, selective, andnegotiated. Open tender is the most common method among bothpublic and private sector construction projects, albeit selectiveand negotiated methods are becoming increasingly popular forpublic sector projects. Table 9 presents analysis in respect of costoverrun for these three tendering types. The highest frequency ofcost overrun was in the 0.1 to 5 per cent class range for all threetender methods (21, 17 and 23% of projects for open, selectiveand negotiated respectively) — open and selective achievingslightly better performance based on this specific metric. Basedon mean cost overrun values, selective (0.67%) performed betterthan negotiated (2.5%) and open tender methods (2.8%) implying

Table 9Analysis by tendering method.

Class range(% overrun)

Open tender Select. tender Neg. tender

Freq. % Freq. % Freq. %

b0 67 38.1 58 49.2 26 40.00 1 0.6 4 3.4 4 6.20.1–5 37 21.0 20 16.9 15 23.15.1–10 20 11.4 16 13.6 9 13.810.1–20 32 18.2 14 11.9 7 10.820.1–30 8 4.5 3 2.5 2 3.1N30.1 11 6.3 3 2.5 2 3.1Totals 176 100 118 100 65 100

OverallMean (%) 2.87 0.67 2.50Minimum (%) −80.38 −44.20 −43.69Maximum (%) 72.88 53.14 88.76Std. Deviation 17.62 13.34 18.21



that selective tendering is the method offering best-costperformance. This is borne out in that 52% of projects werecompleted at or below contract sum using selective tendering,with corresponding statistics being 46 and 39% for negotiatedand open tender methods respectively.

4.8. Cost overrun based on contract sum

CIDB of Malaysia introduced different categories of projectsize for the construction industry excellence award 2005 (CIDB,2005b), with division of projects into small scale — which arecontract values less than RM3 million (≈0.93 million USD);medium scale — which are between RM3 million andRM50 million (≈15.6 million USD), and major scale— havinga contract value more than RM50 million. For the purpose of dataanalysis, these categories were used to help design four companysize classifications: small (RM0–5 m), medium (RM5.1–20 m),

(% overrun)Freq. % Freq. % Freq. % Freq. %

b0 97 48.3 20 24.7 18 48.6 10 29.40 7 3.5 1 1.2 0 0.0 1 2.90.1–5 36 17.9 20 24.7 4 10.8 12 35.35.1–10 24 11.9 8 9.9 4 10.8 9 26.510.1–20 29 14.4 16 19.8 7 18.9 1 2.920.1–30 3 1.5 10 12.3 1 2.7 0 0.0N30.1 5 2.5 6 7.4 3 8.1 1 2.9Totals 201 100 87 100 37 100 34 100

OverallMean (%) −1.16 8.04 4.70 3.19Minimum (%) −80.38 −21.23 −28.45 −22.87Maximum (%) 72.88 49.77 73.33 88.76Std. Deviation 16.21 13.75 19.14 16.73

Positive overrunMean (%) 10.16 13.88 17.09 8.01Std. Deviation 11.43 12.29 18.43 17.90a RM0–5 m.b RM5.1–20 m.c RM20.1–50 m.d NRM50 m.





large (RM20.1–50 m) and very large (NRM50 m) — as shownin Table 10.

The distribution of sample projects was skewed towards thelower classification size, with decreasing numbers in eachcategory from 201 small projects to 34 very large projects. Allexcept the large project category experienced the greatestpercentage of overruns in the 0.1 to 5 per cent overrun class; theexception being large projects that experienced most (19%) ofprojects in the 10.1 to 20 per cent overrun class. Based on thepercentage of projects completing at or below the contract sum,small projects performed best (52%). The remaining order waslarge (49%); very large (32%); and medium (26%).

The mean cost variance percentage for small projects was −1.16% (best project size performance among the sample) and thelargest was 8.04% for medium projects (worst project sizeperformance). Hence, the small projects showed better mean costoverrun value compared with the other size classifications— butthe large variance between negative and positive cost variancevalues, suggests that small projects can still be prone to extremecost performance fluctuation, resulting from individual projectsituations. According to some scholars (Flyvbjerg et al., 2004;Jahren and Ashe, 1990), project size is an influence on costoverruns of infrastructure projects because other things beingequal, implementation phases can be longer for larger projects,which may induce overruns. The mean values of actual overrunsobserved in this study suggest that very large projects(NRM50 m) are prone to the smallest negative cost variance(8%); and large projects (RM20.1 to 55 m) are prone to thegreatest negative cost variance (17%). The record of accomplish-ment has also shown that larger projects are poorer than smallerones, and those cost overruns are particularly common in largeprojects (Ellis, 1985; Merewitz, 1973; Morris and Hough, 1987).In this study, the means show that very large projects have bettercost overrun compared with small, medium and large projects,somewhat contradicting Flyvbjerg et al. (2004).

5. Summary and conclusion

This research explored the construction cost performance ofprojects in terms of public and private sectors, new build vis-a-visrefurbishment, based on procurement methods, tenderingmethods, nature of project and project size. It has been establishedthat more than half of Malaysian construction projects (55%)experienced cost overruns and that public sector projectsperformed better than private sector projects. Whereas, analysisbased on procurement and tendering methods suggests thatdesign and build was associated with reduced cost overrun,followed by traditional then project management; whereas,selective tendering experienced 48% cost overrun above 0%,followed by negotiated method (52%) then open method (60%).

Health projects and commercial projects performed best interms of being completed to contract sum, while educationalprojects were worst in this respect (2%). In terms of negativecost variance, health projects also performed best along withresidential and educational classifications — office projectsperformed least well. In terms of project size, small and largeprojects performed better than medium and very large projects


although almost all very large projects were completed at below10% cost overrun.

The research findings offer stakeholders significant insightinto cost performance information in relation to certain charac-teristics of projects— project sector (public or private), nature ofproject (new build or refurbishment), procurement methods(traditional, design and build, and project management), andnature of project (residential, infrastructure, commercial, office,educational, and health). In addition, tendering method (open,selected and negotiated), and project size (small, medium, largeand very large) have also been explored in this context. Inso doing, these statistics will help project managers withfirst-order decision-making approximation – in Malaysia espe-cially, and internationally more generally – with a view tohelping minimise negative project cost variance. The termfirst-order is used here, because as highlighted by Chan andKumaraswamy (2002), detailed construction programmes usingadvance computer packages are essential to more accuratecosting of future construction projects and increasingly so,given the evermore increasing complexity of constructionprocesses and materials.

The methodology used in this research can be adoptedto explore time overrun in other international constructionindustries, in order to draw comparative results. It is therefore,unlikely that the findings presented here are applicable to everyconstruction industry, because regional factors may influencetime performance and/or alter the behaviour and characteristicsof research outcomes, relating to alternative geographicallocations. The findings and conclusions of this study therefore,should be viewed and interpreted in this context.

Conflict of interest

No conflict of interest.

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Cost overrun in the Malaysian construction industry projects: A deeper insight