key determinants of performance of design-bid-build projects in singapore

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Key determinants of performance of design-bid-buildprojects in SingaporeFlorence Yean Yng Linga Department of Building , National University of Singapore , 4 Architecture Drive,Singapore , 117566 E-mail:Published online: 13 May 2010.

To cite this article: Florence Yean Yng Ling (2004) Key determinants of performance of design-bid-build projects inSingapore , Building Research & Information, 32:2, 128-139, DOI: 10.1080/096132103200048497

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Key determinants of performance ofdesign-bid-build projects in Singapore

FlorenceYeanYng Ling

Department of Building, National University of Singapore,4 Architecture Drive, Singapore117566

E-mail: [email protected]

What are the factors affecting the performance of design-bid-build projects? Ten performance metrics and 60 factors are

identified that project team members can use to measure and manage the success levels of construction projects in four

broad categories: cost, time, quality and administrative burden. A survey and correlation analysis of 65 design-bid-build

projects in Singapore is used to suggest the factors that affect each performance metric significantly. Contractor

characteristics are the key determinants affecting the highest number of performance metrics. The most significant

variable is the contractor’s past record for completing projects on budget and to an acceptable level of quality. Based on

this evidence, it is recommended that clients and consultants pay closer attention to a wider array of characteristics (not

just the initial price) involving contractor selection. Another key determinant is the project character with the most

important variable being the percentage of repetitive elements.

Keywords: design-bid-build, performance criteria, performance management, quality control, selection criteria, success

factors, Singapore

Quels sont les facteurs qui affectent les performances des projets conduits selon le scenario ‘‘conception-soumission-

construction’’? L’article recense dix metriques de performances et 60 facteurs que les membres d’equipes de projet

peuvent utiliser pour mesurer et gerer les niveaux de reussite de projets de construction dans quatre grandes categories:

cout, temps, qualite et charge administrative. Une etude et une analyse de correlation portant sur 65 projets de type

‘‘conception-soumission-construction’’ menes a Singapour permettent de suggerer les facteurs qui affectent de maniere

significative chaque metrique de performances. Les caracteristiques des contractants sont les principaux determinants qui

affectent le plus grand nombre de metriques de performances. Les variables les plus significatives sont les antecedents du

contractant en matiere d’achevement de projets dans le respect des budgets et avec un niveau acceptable de qualite.

S’appuyant sur ces preuves, il est recommande aux clients et aux consultants de consacrer davantage d’attention a un

grand nombre de caracteristiques (et non seulement le prix initial) lors du choix d’un contractant. Un autre determinant

majeur est le caractere du projet, la variable la plus importante etant le pourcentage d’elements repetitifs.

Mots cles: ‘‘conception-soumission-construction’’, criteres de performances, gestion de la performance, controle de la

qualite, criteres de selection, facteurs de reussite, Singapour

IntroductionConstruction project clients are interested in maximiz-ing project success. Success in a project is generallyoperationalized into time, cost and quality perfor-mance (Hatush and Skitmore, 1997). Several studieson critical success factors have been conducted (e.g.Jaselskis and Ashley, 1991; Chua et al., 1999). These

studies have focused on one or two aspects of perfor-mance. However, performance of a project is multifa-ceted and may include unit cost, construction anddelivery speeds, and the level of client satisfaction.

The aim is to investigate the factors that affect perfor-mance of design-bid-build (DBB) building projects.

BUILDING RESEARCH & INFORMATION (2004) 32(2), March–April, 128–139

Building Research & Information ISSN 0961-3218 print ⁄ISSN 1466-4321 online # 2004 Taylor & Francis Ltdhttp: ⁄ ⁄www.tandf.co.uk ⁄journals

DOI: 10.1080 ⁄096132103200048497

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Under this, the specific objectives are to identify keydeterminants that affect the following:

� cost performance (operationalized as unit cost andcost growth)

� time performance (operationalized as constructionspeed, delivery speed and schedule growth)

� quality performance (operationalized as workman-ship quality, system quality, technological capabil-ity and equipment quality)

� other performance (operationalized as a client’sadministrative burden and a client’s satisfaction)

This study provides evidence to project team members(Tables 5–8) on the key variables that they must paycloser attention to in order to improve the success oftheir projects. Those significant variables that are con-trollable could then be properly managed to increasethe chances of project success. This study focuses onDBB project performance because it is the main pro-curement method used in Singapore.

In the next section, project success is defined andoperationalized. The literature relating to project

performance and critical success factors is reviewed.Then the research method is presented, followed by theresults. A discussion follows, whereby the factors thataffect the ten performance metrics significantly areidentified and ways to manage them suggested.

BackgroundMuch research has been conducted on how project successcan be measured. Baccarini’s (1999) definition of projectsuccess includes meeting time, cost and quality objectivesand satisfying project stake holders. He also defined pro-duct success as meeting quality output standards, and pro-cess success as meeting time and budget objectives.Konchar and Sanvido (1998) and Molenaar and Songer(1998) also defined various metrics to measure project per-formance. Based on these studies, ten performance metricsare selected for the present study (Table 1). These perfor-mance measures have been chosen because they addressthe different facets of project success and, taken together,they might provide a holistic picture of how well the pro-ject had performed. More importantly, they cover thethree main project objectives of cost, time and quality, andinclude an element about client satisfaction.

In addition to these ten measures, some authors haveproposed other measures. These are not used here,

Table 1 Performance metrics

No. Performance metrics (Dependentvariables)

De¢nition

CostY1 Unit cost ($/m2) (Final project cost/area)/indexY2 Cost growth (%) [(Final project cost ^ contract project cost)/

contract project cost] * 100

TimeY3 Construction speed (m2/month) Area/(as-built construction end date^

as-built construction start date/30)Y4 Delivery speed (m2/month) Area/total time, where total time¼ (as-built

construction end date^date ¢rstconsultant was engaged/30)

Y5 Schedule growth (%) [(Total time 7 total as-planned time)/totalas-planned time] *100

QualityY6 Workmanship quality Ease of commissioning and extent of defect

recti¢cation (5¼exceed owner’sexpectation,1¼not satisfactory)

Y7 System quality Performance of building elements, interiorspace and environment (5¼exceedowner’s expectation,1¼not satisfactory)

Y8 Equipment quality in the building(HVAC, etc.)

Performance of equipment (5¼exceedowner’s expectation,1¼not satisfactory)

OthersY9 Client’s administrative burden 5¼no burden,1¼not satisfactory/very

heavy burdenY10 Client’s satisfaction 5¼exceed client’s expectation,1¼not

satisfactory

Source: Konchar and Sanvido (1988); Molenaar and Songer (1998)

Project performance determinants

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primarily because of difficulty in collecting objectivedata. For example, Yasamis et al. (2002) proposedmeasuring corporate quality culture, which includesleadership and employee empowerment, which are dif-ficult to measure objectively.

Research projects on key determinants of performanceof DBB projects have been conducted and are nowreviewed. For brevity, works relating to critical successfactors of design-build (DB) projects (e.g. Chan et al.,2001) are not discussed because the present paperfocuses on DBB project performance.

Pinto and Slevin (1988) used regression analysisto establish that ten critical success factors are relatedsignificantly to project success: project mission; topmanagement support; project schedule and plans;client consultation; personnel; technical expertise; clientacceptance; monitoring and feedback; communication;and troubleshooting. They also found four externalfactors that are significantly related to project success:characteristics of the team leader; power and politics;environmental events; and urgency of the project.

Jaselskis and Ashley (1991) investigated optimal allo-cation of project management resources for achievingsuccess. Determinants for construction budget perfor-mance were identified using logistic regression. Themost important variable was ‘implementation of con-structability programme’.

Sanvido et al. (1992) examined the contribution of dif-ferent factors to project success. These include projectteam experience; contracts; resources; and informationavailable. They recommended broad strategic princi-ples for achieving project success.

Chua et al. (1997) identified key management factorsthat affect budget performance. They used 20 recordsof projects, whereby 80% were located in the US andthe rest in other parts of the world. Sixty per cent of theprojects were cost reimbursable and 40% lump sumcontracts. The study identified eight key factors affect-ing construction budget performance: organizationallevels between project manager and craftsmen; projectmanager experience; level of design completion at thestart of the project; constructability programme; pro-ject team workmanship rate; frequency of controlmeetings; frequency of budget updates; and controlsystem budget.

Kog et al. (1999) used the same set of data as Chuaet al. (1997) to find out the key factors affecting construc-tion schedule performance. Using the same methodol-ogy, five factors were identified: frequency ofmeetings; amount of time project manager devotes tothe project; project manager’s experience; monetaryincentives to designers; and implementation of con-structability programme.

In the US, Konchar and Sanvido (1998) conducted anempirical study that examined explanatory and inter-acting variables to predict performance of DB, DBBand construction management at risk projects. Usingmultivariate regression analysis, they developed modelsto predict unit cost, construction and delivery speedsbased on 316 projects.

Chua et al. (1999) used the analytic hierarchy process(AHP) to ascertain critical success factors affectingbudget, schedule and quality performance. The mainlimitation of this work is that quantitative data werenot used. Instead, 20 practitioners were subjected tothe AHP technology to rate the importance of differentpairs of variables basing on their opinions.

From the review above, a few gaps in knowledgeemerge. First, none of the studies covered the variousperformance metrics comprehensively. Instead, theyconcentrated only on one or selected areas of projectperformance. Second, many of these studies concen-trated on selected aspects of the project when tryingto determine what factors affect performance. Forexample, Kog et al. (1999) concentrated on issuesrelating to the project manager. In reality, clients andconsultants also play an important part in project suc-cess. Therefore, a need exists to identify the key deter-minants that affect the different aspects of projectperformance. This would allow project team membersto concentrate on the more important variables andmanage them well, thereby improving the potential forproject success.

Research methodBased on the review of previous research, ten perfor-mance measures (Table 1) were identified. In addition,60 potential factors affecting project performance werealso identified (Table 2) from the literature review. Thefactors affecting project success are categorized intoattributes relating to the project, contractors, clientsand consultants.

The research method had the aim of identifying theimportant factors (Table 2) that affect project perfor-mance (defined in Table 1). To achieve this, a retro-spective case study questionnaire (data collectioninstrument) was designed based on the factors and per-formance metrics uncovered from the literature review.The questionnaire asked respondent to provide specificinformation about the project’s final and initial costs,actual and planned durations, gross floor area, andtender closing date. Respondents also had to indicatethe project quality and client’s satisfaction level using thescales provided in Table 1. The other project datawere elicited using the questions listed in Table 2.A pilot study was first carried out to identify possibleinadequacies in the data collection instrument. The

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Table 2 Factors that may a¡ect project performance

Var. ref. Explanatory variables De¢nition

Project characteristicsX1 Gross £oor area of the project m2

X2 Contractual arrangement 1¼With quantities; 2¼drawings andspeci¢cations

X3* Payment mode to the contractor 1¼Lump sum; 2¼ cost plusX4 Form of contract 1¼Singapore Institute of Architects

Standard Conditions of Contract (whichhas its origins in JCT traditional forms)

2¼Singapore Public Sector StandardConditions of Contract

X5 Type of building 1¼Residential; 2¼warehouse;3¼ shops; 4¼ educational institution;5¼ others; 6¼ hotel;7¼manufacturing; 8¼ o⁄ce;9¼ healthcare

X6 Ownership of building 1¼Public sector; 2¼private sectorX7* Level of design complexity Scale1^5; 1¼not complex; 5¼highly

complexX8* Level of construction complexity Scale1^5; 1¼not complex; 5¼highly

complexX9 Level of technological advancement Scale1^5; 1¼not complex; 5¼highly

complexX10 Level of specialisation required of

contractorsScale1^5; 1¼not complex; 5¼highly

complexX11 Percent of repetitive elements 1¼0^10%; 2¼11^20%; 3¼21^30%;

4¼ 31^40%; 5¼ 41^50%; 6� 50%X12* Presence of special issues 1¼Yes; 2¼ noX13* Type of speci¢cation Scale1^5; 1¼performance;

3¼ combination; 5¼prescriptiveX14 Extent to which bid documents allow

additions to scopeScale1^5; 1¼prevented addition;

5¼ encouraged additionsX15 Flexibility of scope of works when

contractor is hiredScale1^5; 1¼ £exible; 5¼ not £exible

X16 Project scope de¢nition completion whenbids are invited

Scale1^5; 1¼high; 5¼ low

X17* Design completion when bids are invited 1¼0%; 2¼ up to10%; 3¼11^25%;4¼26^49%; 5¼ � 50%

X18 Design decisions made when bids areinvited

1¼up to10%; 2¼11^20%; 3¼21^30%;4¼ 31^49%; 5¼ � 50%

X19 Design completion when budget is ¢xed 1¼0%; 2¼1^5%; 3¼ 6^10%;4¼11^25%; 5¼26^50%; 6� 50%

X20* Bidder’s knowledge of the budget 1¼No; 2¼ yesX21 Importance for project to be completed

within budgetScale1^5; 1¼not crucial; 5¼ very critical

X22* Importance for project to be delivered Scale1^5; 1¼not crucial; 5¼ very criticalX23 Time given to contractors to bid Scale1^5; 1¼ inadequate; 5¼adequateX24* Time given to owners/consultants to

evaluate bidsScale1^5; 1¼ inadequate; 5¼adequate

X25* Extent to which the contract period isallowed to vary during bid evaluationstage

Scale1^5; 1¼ ¢rmly ¢xed; 5¼ variable

X26* Importance for the project to becompleted on time

Scale1^5; 1¼not crucial; 5¼ very critical

X27 Bidding procedure 1¼Competitive bid; 2¼ negotiationX28 Number of bidders 1¼1; 2¼ 2^3; 3¼ 4^5; 4¼ 6^7; 5¼ 8^12;

6�12X29 Prequali¢cation or short-listing 1¼No; 2¼ yesX30 Bid evaluation and selection criteria 1¼Price only; 2¼ ability only;

3¼ combination of price and abilityX31* Bidding environment Scale1^5; 1¼ low/scarcity of work;

5¼ high/plentiful

Owner and consultant characteristicsX32 Consultant’s level of construction

sophisticationScale1^5; 1¼ low; 5¼high

X33 Client’s level of constructionsophistication

Scale1^5; 1¼ low; 5¼high


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finalized form provided the basis for the implementa-tion of a questionnaire survey.

All new building projects exceeding US5 million andcompleted between 1993 and 2002 in Singapore wereidentified from the Singapore Building andConstruction Authority’s (BCA) website (2002). Both

private- and public-sector projects were investigated.Project data were collected from clients, consultantsand contractors using the questionnaire designed forthis study. To provide the data, respondents completedthe questionnaire and returned it to the researcher.A separate questionnaire was used for each project. Intotal, 400 randomly selected projects were identified,

Table 2 Continued

Var. ref. Explanatory variables De¢nition

X34* Consultant’s experience with similarprojects

Scale 1^5; 1¼no similar projects; 5¼nearly allthose types

X35* Client’s experience with similar projects Scale 1^5; 1¼no similar projects; 5¼nearly allthose types

X36 Consultant’s sta⁄ng level to attend tocontractor

Scale 1^5; 1¼ low; 5¼high

X37 Client’s sta⁄ng level to attend tocontractor


X38 Number of DBB projects handled byconsultant in the past

1¼0; 2¼1; 3¼2^3; 4¼ 4^6; 5¼7^10; 6�10

X39 Number of DBB projects handled byowner in the past

1¼0; 2¼1; 3¼2^3; 4¼ 4^6; 5¼7^10; 6�10

Contractor characteristicsX40* Contractor’s experience with similar types

of projectsScale 1^5; 1¼no similar projects; 5¼nearly all

those typesX41* Contractor’s experience with similar size

of projectsScale 1^5; 1¼no similar projects; 5¼nearly all

those typesX42 Contractor’s experience with projects in

SingaporeScale 1^5; 1¼no similar projects; 5¼nearly all

those typesX43* Subcontractors’experience and capability Scale 1^5; 1¼poor; 5¼excellentX44 Communication among project team

membersScale 1^5; 1¼poor; 5¼excellent

X45* Contractor’s prior working relationshipwith the owner

Scale 1^5; 1¼poor; 5¼excellent

X46 Contractor’s prior working relationshipwith consultants


X47 Contractor’s track record for completionon time


X48 Contractor’s track record for completionon budget


X49 Contractor’s track record for completionto acceptable quality


X50 Contractor’s sta⁄ng level Scale 1^5; 1¼ low; 5¼highX51 Adequacy of contractor’s plant and

equipmentScale 1^5; 1¼ low; 5¼high

X52 Magnitude of variations in contractor’spast projects


X53 Magnitude of claims and disputes incontractor’s past projects


X54 Contractor’s key personnel’smanagement ability


X55 Contractor’s ability in ¢nancialmanagement


X56 Contractor’s quality control andmanagement capability


X57 Contractor’s health and safetymanagement capability


X58 Contractor’s technical expertise Scale 1^5; 1¼poor; 5¼excellentX59 Contractor’s design capability 1¼No in-house capability; 5¼ full in-house de-

signersX60 Paid-up capital of contractor 1¼14^27K; 2¼ 28^82K; 3¼ 83^138K; 4¼139^

277K; 5¼278^833K; 6¼ 834K^1.39M;7¼1.4^2.8M; 8�2.8M

Note:* variable does not a¡ect project performance at the 0.05 signi¢cance level.

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and questionnaires were sent to 40, 57 and 60 clients,consultants and contractors, respectively. The list ofcompleted projects was first generated from theBCA’s website. Renovation, addition and alterationprojects, and projects that do not meet the above-mentioned criteria were deleted from the list, leavingonly new (green-field) building projects on the list. Theseremaining projects were numbered, and a table of randomnumbers was used to pick the projects randomly for study.

After the completed questionnaires were received, theywere checked for accuracy. The consistency of the rat-ings was checked visually and no major inconsistencieswere found. Some missing responses to specific ques-tions were found. A decision was made to insert themissing data with the rating that was most commonlyused by that respondent.

The statistical analyses undertaken and reported hereincluded the independent samples test andcorrelationana-lysis. Multiple linear regression modelling was also con-ducted, but the results were reported in (Ling et al., 2004).

The independent-samples t-test procedure comparesmeans for two groups of cases. This test requires obser-vations to be from independent random samples fromnormal distribution. To check whether or not the testshould be based on an assumption of equal variances,Levene’s test for equality of variance was alsoconducted. The results in Table 3 show that 65% ofthe responses were for residential projects. Therefore,

the independent-samples t-test was used to test whetherthe performance of residential and other types of build-ings were significantly different (Table 4).

Correlation analysis was undertaken to identify vari-ables/factors (Table 2) that were significantly corre-lated to each performance metric (Table 1). UsingPearson’s correlation analysis, predictor variables witha high degree of association with each of the ten perfor-mance measures were identified. Correlation analysiswas chosen because the correlation coefficient canmeasure the strength of any association between a pairof random variables (Newbold, 1991). It measureshow closely a change in one variable is tied to thechange in another variable, and vice versa. Unlike lin-ear regression, random variables are treated symmetri-cally, where the correlation between X1 and Y1 is thesame as the correlation between Y1 and X1. Note thatstatistical correlation does not necessarily mean causa-tion. This means that while some items show signifi-cant correlation, there may not be a scientificexplanation because there is no causation. The correla-tion coefficient is calculated using equation (1):

r ¼

Xn1

xiyi � nxy

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiXn1

x2i � n�2

x

! Xn1

y2i � n�2y

!vuutð1Þ

where r is the correlation coefficient, which is measuredon a scale of �1 to þ1, where 0 is ‘no correlation’ or‘no relationship’ between the scores, �1 is for ‘perfectnegative correlation’, and þ1 is for ‘perfect positivecorrelation’; and (x1, y1), (x2, y2), . . . , (xn, yn) denotea random sample of n pairs of observations on the ran-dom variables X and Y.

In this study, a factor is considered significantly corre-lated to the performance measure when the significancelevel, calculated using the Statistical Package for SocialSciences (SPSS), is less than 0.05 (i.e. p<0.05). Thecorrelation analysis was tested within each project forall the 65 sets of returned questionnaires.

ResultsData sets of 65 projects were received from 27 respon-dents. The respondents comprised 17, six and fourcontractors, consultants and clients, respectively. Onaverage, each respondent provided data of 2.4 projects.The details of the projects are shown in Table 3. Thekey determinants identified in this study would bemore applicable to medium-sized residential projectsup to 100 000 m2. The findings would apply equallyto public- and private-sector projects.

Table 3 Pro¢le of projects

Number %

Type of buildingResidential 42 65Industrial 12 18Commercial 3 5Institutional 8 12Total 65 100

Gross £oor area (m2)3000^10 000 7 1110 001^50 000 30 4650 001^100 0000 15 23>100 000 12 18Unknown 1 2Total 65 100

OwnershipPublic 29 45Private 36 55Total 65 100

Contract sum (US$)5^14.9 million 22 3415^29.9 million 17 2630^49.9 million 11 17�50 million 15 23Total 65 100


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The data comprised mostly residential projects. Tocheck that the findings would also be applicable to othertypes of projects such as industrial, commercial andinstitutional, (herein after referred to as ‘others’), furtherstatistical tests were carried out. Levene’s test for

equality of variances showed that except for Y8, it islegitimate to assume that there are equal variancesbetween two groups of projects: residential and others(Table 4). The independent samples test results showthat except for Y2, there is no significant differencebetween the means of residential and other types of pro-jects. The implication is that the results may be applic-able to the different types of building projectsinvestigated here, notwithstanding that 65% of the pro-jects are residential projects. For Y2, Table 4 shows thatcost growth of residential projects is significantly lowerthan other types of projects. One possible reason is thatresidential projects tend to be more repetitive and lesscomplex in nature and hence face less likelihood of highcost overruns.

Tables 5–8 show the correlation results. Their vari-ables are ranked according to how significant each cor-relates with the dependent variable. The smaller thesignificance level (i.e. nearer to zero), the stronger thecorrelation with the dependent variable. For example,X52 is ranked above X60 because the former is corre-lated with Y2 at the 0.012 significance level, while thelater is correlated at the 0.015 level (Table 5). If the sig-nificance levels of two variables are the same, the onewith a larger correlation coefficient (irrespective ofpositive or negative sign) is ranked higher. This isbecause a correlation coefficient is measured on a scaleof �1 to þ1, where 0 is ‘no correlation’, �1 is ‘perfectnegative correlation’, and þ1 is ‘perfect positive

Table 4 Comparison between performance of residential and other types of projects

Performancemetrics(seeTable 1)

Means Levene’s test for equality ofvariance

t-test for equality of means

F p t p (Two-tailed)

Y1 R 1456.06p

2.003 0.162 �0.825 0.412O 1703.43 X �0.711 0.483

Y2 R �0.88p

3.416 0.069 �2.059 0.044*O 4.15 X �1.593 0.124

Y3 R 2302.83p

1.961 0.166 0.635 0.528O 2000.70 X 0.713 0.479

Y4 R 1576.07p

1.056 0.308 0.919 0.362O 1251.57 X 1.028 0.308

Y5 R 6.39p

0.295 0.589 �0.596 0.553O 8.74 X �0.588 0.560

Y6 R 3.34p

0.494 0.485 �1.425 0.159O 3.64 X �1.373 0.177

Y7 R 3.46p

1.455 0.232 �0.245 0.807O 3.50 X �0.257 0.798

Y8 R 3.50p

8.878 0.004* 1.528 0.132O 3.26 X 1.712 0.092

Y9 R 3.76p

2.747 0.102 0.356 0.723O 3.70 X 0.333 0.741

Y10 R 3.19p

1.720 0.194 �0.329 0.743O 3.26 X �0.305 0.762

Levene’s test:p

¼ equal variances assumed; X¼equal variances not assumed.* Signi¢cant at the 0.05 level.R¼ residential; O¼ other types.

Table 5 Correlation results of cost performance metrics

Rank Var. ref. Correlationcoe⁄cient

p

Y1Unit cost1 X11 �0.460 0.0002 X4 �0.431 0.0003 X6 0.416 0.0014 X19 0.368 0.0045 X52 0.328 0.0086 X1 �0.32 0.0107 X29 0.311 0.0128 X58 �0.296 0.0189 X15 �0.289 0.02010 X28 �0.285 0.02211 X30 0.264 0.03512 X10 0.262 0.03713 X48 �0.251 0.045

Y2 Cost growth1 X52 0.309 0.0122 X60 �0.302 0.0153 X11 �0.287 0.0214 X48 �0.276 0.0265 X5 0271 0.0296 X49 �0.245 0.049

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correlation’. For example, X1 and X6 are both corre-lated to Y3 at the 0.000 significance level (Table 6).However, X1 is ranked higher than X6 because theircorrelation coefficients are 0.915 and �0.530, respec-tively. If there are three variables (Xi, Xj and Xk) thathave correlation coefficients of þ0.900, �0.900 and0.000 with respect to a dependent variable (Y), theinterpretation is that Xk is not correlated to Y, whileXi and Xj are both strongly correlated to Y. The differ-ence is that when Y increases, Xi increases while Xj

decreases, to the same extent.

Overall, the ten performance metrics are affected bybetween three and 14 variables to a significant degree.System quality (Y7) is significantly affected by the mostnumber of variables (14 variables) while ownersatisfaction (Y10) is affected by only three variables.The next section discusses these results.

The results also show that some 18 variables do not affectperformance metrics. These are marked ‘*’ on Table 2.The implication of this finding is that there is no need

Table 6 Correlation results of time performance metrics


p

Y3 Construction speed1 X1 0.915 0.0002 X6 �0.530 0.0003 X4 0.431 0.0004 X11 0.414 0.0015 X28 0.366 0.0036 X60 0.358 0.0047 X19 �0.359 0.0058 X32 0.346 0.0059 X9 0.314 0.01110 X29 �0.292 0.01911 X21 0.286 0.02212 X33 0.277 0.02713 X44 �0.266 0.034

Y4 Delivery speed1 X1 0.925 0.0002 X6 �0.519 0.0003 X4 0.422 0.0014 X11 0.401 0.0015 X19 �0.363 0.0046 X60 0.359 0.0047 X32 0.347 0.0058 X28 0.345 0.0059 X9 0.321 0.01010 X29 �0.287 0.02211 X33 0.283 0.02312 X21 0.281 0.025

Y5 Schedule growth1 X50 �0.449 0.0002 X48 �0.385 0.0023 X51 �0.366 0.0034 X49 �0.360 0.0045 X55 �0.337 0.0066 X58 �0.329 0.0087 X47 �0.321 0.0108 X53 �0.296 0.0189 X46 �0.293 0.01910 X21 �0.279 0.02611 X54 �0.279 0.02612 X14 �0.271 0.031

Table 7 Correlation results of quality performance metrics


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Y6Workmanship quality1 X27 �0.330 0.0072 X23 0.321 0.0093 X52 �0.304 0.0144 X53 �0.264 0.0345 X48 0.252 0.043

Y7 System quality1 X49 0.398 0.0012 X57 0.362 0.0033 X15 �0.358 0.0034 X50 0.357 0.0035 X16 �0.342 0.0056 X4 �0.335 0.0067 X47 0.326 0.0088 X48 0.320 0.0099 X58 0.316 0.01010 X2 �0.313 0.01111 X56 0.284 0.02212 X51 0.278 0.02513 X59 0.264 0.03414 X29 0.245 0.049

Y8 Equipment quality1 X49 0.405 0.0012 X48 0.366 0.0033 X37 0.355 0.0044 X27 0.353 0.0045 X38 0.303 0.0156 X15 �0.297 0.0167 X39 0.293 0.0188 X16 �0.290 0.0199 X42 �0.280 0.02410 X2 �0.254 0.04111 X55 0.248 0.046

Table 8 Correlation results of other performance metrics


p

Y9 Owner’sadministrative burden1 X49 0.379 0.0022 X50 0.368 0.0033 X16 �0.364 0.0034 X47 0.320 0.0095 X48 0.312 0.0116 X58 0.311 0.0127 X57 0.258 0.038

Y10 Owner’ssatisfaction1 X44 0.302 0.0142 X18 0.257 0.0393 X36 0.254 0.041


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to focus on these issues when managing projects as theydo not have significant effect on the project outcome.

On the other hand, several variables have a significantimpact on many performance metrics. For example,‘X48 Contractor’s track record for completion on bud-get’ and ‘X49 Contractor’s track record for completionto acceptable quality’ affect seven and five performanceoutcomes, respectively. The implication is that whenselecting contractors, choosing one with a good trackrecord for completion on budget and to an acceptablequality would contribute to good project performance.

DiscussionThis section discusses some of the variables that affectthe performance metrics defined in Table 1. The vari-ables are identified by a variable reference given in par-entheses. The detailed variable description, correlationcoefficients and significance levels are given in Table 2.

Determinants of cost performanceTable 5 shows that unit construction cost (Y1) isaffected by 13 variables. Of these, ten relate to projectcharacteristics while three relate to contractorcharacteristics. Clients interested in having lower unitcosts should instruct their consultants to producedesigns that have repetitive elements (X11) and thescope of works must not be flexible when the contrac-tor is hired (X15). Table 5 shows the correlation coef-ficients between Y1 and X28 and X29 are �0.285 and0.311, respectively. This means that to get a low unitcost (Y1), X28 must be high (negative correlation)while X29 must be low (positive correlation). Table 2shows that high X28 is associated with large numberof bidders while low X29 happens when no prequalifi-cation is undertaken. The implication is that whenthere is no short-listing or prequalification of bidders(X29) and a large number of bidders are invited to bid(X28), the unit cost may be lower. This may be becausewhen more bidders are involved, there is an increasedlikelihood of bidders making mistakes by missing outsignificant items in their bids.

Table 5 shows six key determinants of cost growth(Y2), of which four relate to contractor characteristicsand two to project characteristics. Table 5 shows thecorrelation coefficients between Y2 and X48 andX49 are �0.276 and �0.245, respectively. This meansthat to get a low cost growth (Y2), X48 and X49 mustbe high (negative correlation). Table 2 shows that highX48 and X49 are achieved through selecting contrac-tors who have good track record for completion onbudget and to acceptable quality. Dissanayaka andKumaraswamy (1999) found that good track recordsgive clients more confidence in the construction team.The results also show that when contractors’ firms arelarge (X60), i.e. have high paid-up capital, cost growth

is likely to be small, implying that larger contractorshave better cost control capability. This finding agreeswith Hatush and Skitmore (1997) who found thatfinancial status, financial stability and credit ratingsaffect project cost performance. The implication of thefindings is that clients who are on a tight budget withno room for cost over-run should choose contractorscarefully.

Determinants of time performanceProject speeds comprised construction speed (speedduring construction) and delivery speed (speed of thewhole project from inception to completion). Table 6shows that 13 and 12 variables affect construction anddelivery speeds, respectively. The 12 determinants ofdelivery speed are a subset of construction speed.This indicates that for DBB projects, managing con-struction speed will automatically take care of deliveryspeed. In both cases, nine and two variables relate toproject and consultant/client characteristics, respec-tively. This indicates that contractors play a small rolein project speeds. It may thus be argued that theupstream activities (such as planning and design)have a profound effect on the speed of downstreamactivities (physical construction). This is confirmed bythe findings that show that when consultants (X32)and clients (X33) have high level of constructionsophistication, project speed is likely to be higher.Having the necessary construction sophistication, thesepeople will know how to design or demand for designsthat are constructible (e.g. high percentage of repetitiveelements (X11)), and are likely to know how to man-age the project better than someone who has no con-struction knowledge.

A project with low schedule growth is completed ontime or early. Table 6 shows 12 key determinants ofschedule growth (Y5), of which two relate to projectcharacteristics and the rest to contractor characteris-tics. Schedule growth can be controlled by choosing theright contractor for the job. The contractors shouldhave the necessary technical capabilities (X58). Thisagrees with Walker and Shen’s (2002) case study thatfound that good construction time performance isobtained when firms have ability supported by compe-tence. Low schedule growth is obtained when contrac-tors have adequate plant and equipment (X51), and thenecessary ability in financial management (X55) and gen-eral management (X54). As Chan and Kumaraswamy(1997) discovered, poor site management and supervi-sion (i.e. weak general management, X54) lead to timeoverruns. Contractors with good management abilitycan undertake proper planning, and this further contri-butes to good time performance (Walker, 1996).Contractors should also have adequate staffing level(X50). This is consistent with Proverb and Holt’s(2000) finding that construction time is affected by thenumber of supervisors, who presumably will help

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ensure that the project progresses smoothly with no sche-dule growth.

Contractors should also have a good track record forcompleting projects on budget (X48), on time (X47)and to acceptable quality (X49). The good track recordindicates contractors’ ability to carry out the job dili-gently, with minimal rework and hence minimum sche-dule growth. An excellent prior working relationshipbetween contractors and consultants (X46) is alsoimportant, as this gives rise to a high degree of interac-tion, which had been found to improve performance(Pocock et al., 1996). This also brings about good teambuilding and communications, which further enhancetime performance (Walker, 1996).

The findings on time performance show that clientsand consultants play an important role in constructionand delivery speeds even though they do not undertakephysical construction. This confirms that upstreamactivities have an important bearing on downstreamactivities. On the other hand, contractors play a moreimportant role in ensuring that the project is completedon time. They need to have proper knowledge, skillsand abilities to execute and manage the project toensure timely completion.

Determinants of quality performanceProjects with high workmanship quality are easy tocommission and have minimum defect rectification.Table 7 shows five variables that control workmanshipquality. Workmanship quality will be high if contrac-tors have low number of variations (X52) and fewclaims and disputes (X53) in their past projects.Their good workmanship minimized the number ofcall-backs to rectify defects and consequently there isno need for disputes.

System quality refers to performance of building ele-ments, interior space and environment. Table 7 shows14 key determinants of system quality (Y7), of whichfive relate to project characteristics and the rest to con-tractor characteristics.

Contractors play an important role in system quality.Those that are prequalified or short-listed (X29) basedon good track record for completion on time (X47), onbudget (X48) and to acceptable quality (X49) will pro-duce higher system quality. At the same time, contrac-tors with technical expertise (X58) and who havecapability in health and safety management (X57),quality control and management (X56), and in design(X59) are more likely to produce high system quality.When system quality is very important, contractorsmust have adequate level of staffing (X50) and plantand equipment (X51).

To obtain high equipment quality, Table 7 showsthat more emphasis must be placed on project

characteristics. These include: project scope must bewell defined when bids are invited (X16); the contractshould be based on bills of quantities (X2); and opencompetitive bidding (X27) is avoided. This is becauseconsultants will usually hand-pick and negotiate withcontractors who have a good track record for comple-tion to acceptable quality (X49), on budget (X48), andhave the necessary financial management ability (X55).

The findings of the present study agree with Hatushand Skitmore (1997) who found that contractors’experience, ability and management personnel affectproject quality. It also accords with Proverbs andHolt (2000) who found that contractors’ practicesalone account for 47% of construction time perfor-mance. Two contractor characteristics – contractor’strack record for completion on budget (X48) and con-tractor’s track record for completion to acceptablequality (X49) – are found to affect seven and five per-formance metrics, respectively. This means that onemajor selection criterion is the contractor’s trackrecord.

Clients may use the attributes identified in the presentstudy to prequalify or select contractors to help themachieve a high standard of quality. These clients mayneed to be prepared to trade-off low cost for highquality.

Determinants of other performance measuresTable 8 shows seven key determinants of client’sadministrative burden (Y9), of which only one relatesto project characteristics and the rest to contractorcharacteristics. To have minimum administrative bur-den, the project scope definition must be high whenbids are invited (X16). This is because when the projectscope is already fixed, contractors can proceed with thework without continually checking with the client.This will also minimize change orders, whether arisingfrom client-initiated variations or necessary variationsfor the works (Chan and Kumaraswamy, 1997).

Clients who want minimum administrative burdenshould select contractors carefully. In particular, thecontractor must have good track record for completingpast projects on time (X47), on budget (X48) and toacceptable quality (X49). Contractors should also havetechnical expertise (X58) and health and safety man-agement capability (X57). In addition, they need tohave high level of staffing (X50) to execute the project.

Three variables affect client satisfaction (Y10) asshown in Table 8. First, many design decisions shouldhave been made when bids are invited (X18). Whenconsultants confirm a client’s requirements, the chancefor clients being satisfied with the project is higher.Consultants should also have adequate staffing levelto attend to contractors (X36). In particular, if theysupervise the contractors closely, the project may have


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better performance, and this leads to higher client’ssatisfaction. Good communication among consultantsand contractors (X44) will also lead to higher clientsatisfaction. The constant contact will help to ensurethat there are minimum miscommunications.

LimitationsOne possible limitation of this study is that the 65 pro-ject data sets were used to ascertain the determinants often performance metrics may appear to be small.However, with Singapore’s annual construction volumebeing approximately US$8 billion (BCA, 2002), the 65data sets collected, which have a total contract sum ofUS$2.6 billion, are not small when compared with theoverall size of the Singapore construction industry.However, it is recognized that Singapore is a uniquemarket because it is small, concentrated and the govern-ment’s actions have a profound effect on the construc-tion industry. Generalizing the findings to constructionindustries that have different features must thereforebe undertaken with care.

One of the limitations of this study is that the responseswere obtained from just 17 contractors, six consultantsand four clients in Singapore. There is therefore thepossibility that these projects are not truly representa-tive of the population.

Another limitation is that some subjective data wereelicited from respondents, such as asking contractorsto rate the level of client’s satisfaction and administra-tive burden. The respondents may not have rated someof the questions unbiasedly or they may not knowwhat the ‘real’ answer is.

ConclusionVariables that affect specific aspects of project successwere identified through correlation studies. The resultsshow that some variables affect more performancemetrics than others. It is concluded that project success,based on the performance metrics defined here, can beachieved by controlling key determinants, which arelisted in Tables 5–8.

Contractor characteristics affect five performancemetrics to a greater extent than project or client/consul-tant characteristics. The performance metrics affectedare: cost and schedule growths; workmanship and sys-tem quality; and the client’s administrative burden.The implication of this finding is that contractors playa big role in bringing about project success. In particular,clients who want their projects to have more certainty (interms of minimum cost and schedule growths) and highquality must select their contractors carefully.

Project characteristics play a more important role inunit cost, and construction and delivery speeds. Thepractical application of this finding to clients who wantto use their facilities in a short time and/or have a

limited budget for their new buildings is that they needto control project characteristics. These include choos-ing the form of contract (X4) carefully and increasingthe percentage of repetitive elements (X11).

The results also show that client and consultant char-acteristics play a lesser direct role in project success.Notwithstanding this, consultants still play an impor-tant role in project success by controlling some of theproject characteristics. For example, they would needto take the right actions such as choosing the appropri-ate form of contract (X4), producing designs whichhave a high percentage of repetitive elements (X11),and determining the bidding procedure (X29) andnumber of bidders (X28). Furthermore, one of consul-tants’ most important roles is to select the ‘right’ con-tractor for the project based on the contractor-relatedkey determinants identified above.

The present study has identified the key determinantsthat affect ten areas of project performance. Project suc-cess may be attained if the key determinants are prop-erly controlled and managed. Chief among these keydeterminants is proper contractor selection by clientsand consultants. Contractors should be selected basedon their track records and experience and not merelytheir bid price. On contractors’ part, they may achieveexcellent project performance if they strengthen theirmanagerial and technical capabilities.

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key determinants of performance of design-bid-build projects in singapore

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