Contractor selection innovation:examination of two decades’

published researchGary Holt

Department of Civil and Building Engineering, Loughborough University,Loughborough, UK

Abstract

Purpose – The “problem” of selecting a contractor has attracted significant academic researchendeavour over the last two decades. The principal aim here is to examine that research via publishedacademic outputs for the period circa 1990-2009.

Design/methodology/approach – A sample of published contractor selection (CSn) research iscritically appraised. Aspects highlighted include: stated aims and research justification;methodological approaches employed; research tools used; and products of CSn research.

Findings – Main research foci are observed as: modelling the CSn process; studying selection criteria;and “interrogation” of existing CSn systems. Foci justifiers are linked mainly to the “importance” and“difficulties” of CSn decision making. Deterministic modelling of CSn is the favoured methodologicalapproach, followed by documentary synthesis then questionnaire surveys. Preferred research tools arefound to be system interrogation, rank order analysis and Likert scale/importance indices, withhypothesis testing and “other” methods used less so. Almost two-thirds of research products are CSnmodels, with derived or proffered processes, and knowledge relating to CSn criteria, between themrepresenting approximately the remaining third of output.

Research limitations/implications – It is suggested that many of the CSn models exhibit as muchcomplexity as the original “problem” they sought to resolve, while the reliability and longevity ofsuggested “cocktails” of CSn criteria (in practice), might be questioned. A call for future researchproducts to more closely consider end-user impact and potential for “take-up” by industry is made.An empirical follow-on study to assess (inter alia) practitioner use and “value” of CSn research isproposed.

Practical implications – The paper signals a possible need for greater industrial engagement in theresearch domain.

Originality/value – The findings are novel to this paper.

Keywords Procurement, Contractor workers, Tendering, Subcontractoring, Clients

Paper type Literature review

IntroductionThe construction industry is recognised for many distinct characteristics and in aprocurement context, its separation of design from production and resulting transientfragmentation, arguably reigns supreme (Langford and Male, 2001, Chapter 2; Walker,2002, Chapter 1; Loosemore et al., 2003, p. 2; Cooke and Williams, 2004, p. 2). Resultantly,selecting the most appropriate contractor for the purpose of their undertakingthe production function[1] has for decades, been the subject of research, debate andpublished guidance.

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/1471-4175.htm

The combined constructive comments of the anonymous reviewers are acknowledged, forhelping develop and improve the paper.

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Received 16 April 2009Accepted 15 October 2009

Construction InnovationVol. 10 No. 3, 2010pp. 304-328q Emerald Group Publishing Limited1471-4175DOI 10.1108/14714171011060097

Contractor selection (CSn) normally comprises a “theoretically infinite” group ofcontractors (Holt, 1996) whose attributes (El-Sawalhi et al., 2008) are – often on the basisof past performance (Kadefors, et al., 2007; Holt et al., 1994a) – assessed against clientobjectives or “criteria” (Russell and Skibniewski, 1988; Wong and Holt, 2003).

Effectively, this is the “judging” of contractor(s’) capabilities (Ng and Skitmore, 1999;Marzouk, 2008) but that judgment – often performed as a “snapshot in time” – must beconsidered against the potential for these capabilities to significantly change over shortperiods. In practice, CSn procedures vary significantly (Palaneeswaran andKumaraswamy, 2001; Egemen and Mohamed, 2005) and are typified by: non-linearity(Lam et al., 2005); uncertainty (Watt et al., 2009); subjectivity (Holt et al., 1993; Watt et al.,2009); and volatility/competitiveness (Fong and Choi, 2000). These aspects will beexpanded upon as this study unfolds.

Given the importance of CSn to project performance (Holt, 1998), numerousconstruction industry reports have focused on it (Cooke and Williams, 2004, p. 1.2). Forexample, The Simon Committee (1944) strongly advocated selective rather than opentendering (Masterman, 1996, p. 8), while Emmerson (1962) highlighted the issue ofconstruction design being “removed” from production as referred to above. Shortly afterEmmerson, Banwell (1964) reiterated aspects of The Simon Committee (et al.),advocating what were then “non-conventional” procurement methods such asnegotiation, and a follow-on report from the National Economic Development Office,also supported greater use of serial tendering and negotiated contracts (NEDO, 1967). Inthe title of its report, The Tavistock Institute (Tavistock, 1966), reflected that thebuilding industry was characterised by “[. . .] interdependence and uncertainty”concluding somewhat ironically (given said calls for increased negotiation), that theindustry “thrived” on the latter (Woodward, 1997).

The oft-cited Latham (1993, 1994) reports were quite influential in procurementterms, raising issues of mistrust between stakeholders (Cooke and Williams, 2004, p. 4),whilst among other aspects, promoting greater use of selection based on quality inaddition to price (i.e. value). Egan (1998) subsequently encouraged longer termprocurement relationships in favour of selective methods, as a mechanism to achievingquality and efficiency improvement; while in a second report (Egan, 2003), arguablyrooted the term “integrated supply chains” into procurement dialogue.

Aside from government, the Construction Industry Institute offered explicit CSnguidance (CII, 1988) along with direction on choosing contractors when striving toimprove construction safety (CII, 2003), while the issue of long-term (contractor/client)relationships surfaced yet again, when the European Construction Institute highlighted“value enhancement” via strategic alliances (ECI, 1999). Given the adoption ofinformation technology within construction, it was inevitable that problems (and theiravoidance) of CSn and e-tendering have also been commented upon (CRC, 2006).

Since the middle of the last century, a shift from promoting broadest CSn competitiontowards integrated supply chain mechanisms that encourage mutual benefit,organisational learning, partnerships, profitability and quality – while embracinginformation technology – is witnessed (Li et al., 2000; Love et al., 2002b; Walker andJohannes, 2003; Akintoye and Main, 2007). The emphasis within the literature hasmoved from “lowest cost” to “value” (CIRIA, 1998, 2009) (while the author’s anecdotalevidence suggests that, among smaller contractors at least, “cost” often remains theultimate selection criterion). The “credit crunch” has certainly placed strain on the

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“literature’s call” for value, with price competition presently fierce[2] (RICS, 2008, p. 3),resulting from unprecedented negative forces acting on international constructionactivity (The Times, 2008; CECA, 2008; CIOB, 2009; ASCE, 2009).

The issue of CSn – and academic research in the field for that matter – has been anevolving one over the last two decades; moulded by reaction to changes in theprocurement environment and in the case of academic CSn research, to advancements inthe science of methodology. Regarding the latter, developing techniques such as neuralnetworks, fuzzy decision making, and web-based technologies have witnessedapplication among a range of construction management problems (Graham et al.,2006; Vries and de and Steins, 2008; Han et al., 2008, respectively). Somewhat expectedlytherefore, examples of their use in CSn research are equally easy to find, for example, seeAlbino and Garavelli (1998), Singh and Tiong (2005) and Palaneeswaran andKumaraswamy (2005), respectively.

Aim and objectives of this studyThis backcloth of “evolvement” brings matters conveniently to the focus of this study,the aim of which was to critically appraise academic research within the field ofconstruction CSn, published over a time window of circa 20 years prior to 2009. In sodoing, such review and concomitant synthesis sought accord with contention of Fellowsand Liu (2003, p. 64) that a literature review must provide, “[. . .] a summary of thestate-of-the-art”.

Objectives related to this aim included particular consideration of:

(1) the foci of that research;

(2) stated research drivers;

(3) favoured methodological approaches;

(4) research tools employed; and

(5) the products of research effort.

That is, the study aspired to capture a temporal transit through the observed window ofliterature, with particular clusters of synthesis, emanating from focus on five areas of thesubject defined by objectives (1)-(5) above. Based on observation of outcomes resultingfrom satisfying these objectives, the contribution of published academic research isconsidered, and future research direction intimated.

The literature review: rationale and methodThe literature review is discussed in terms of its:

. rationale; and

. method.

RationaleLevin (2007, p. 75) asserted that to write a good literature review requires one to ask:“What am I reviewing the literature for?” In the present instance, some answers to thisquestion mirror “uses” of a review included in O’Leary’s (2004, p. 78) list, these being to“inform”, “argue rationale”, and “develop questions”. Notwithstanding that, someregard a review as simply an “essay” (Davies, 2007, p. 39), for this study the “answer” toLevin connects with two particular definitions:

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(1) that a review can be, “an expert’s general review of current literature on aparticular topic” (Walliman, 2006, p. 182); and

(2) that it should overview, “[. . .] who the key writers are, [. . .] prevailing theoriesand hypotheses [. . .] what questions are being asked, [. . .] what methods andmethodologies are appropriate [. . .] and show relationships [. . .] so that keythemes emerge” (Emerald, 2009).

Literature reviews have frequently been used – to good effect – in constructionmanagement research (CMR). For example, Sidwell and Budiawan (2001) undertook areview to highlight problems with competitive tendering in relation to contractor-ledinnovation; Wong et al. (2005) reviewed the literature to investigate intelligent buildingresearch and discover three research foci as a basis for summarising future researchdirection; Donohoe (2005) reviewed literature and case law to consider a (then) recentappeal case and its implications for building surveyors; and constraints on informationcommunication technology were identified through the literature by Peansupap andWalker (2006). More recently, common measures for improving constructability havebeen studied via review (Wong et al., 2007). This is but an indicative sample.

MethodThe academic literature, upon which this study focussed, was identified via onlinesearch among selected construction management journals and other research databases(see later). Additional kinds of literature such as trade journals, comments, magazinearticles and theses[3] do exist and are not excluded herein; but academic journal paperswere afforded emphasis, because they represent the most important wealth of literatureavailable (Fellows and Liu, 2003, p. 62). Further, journal papers are (should be)academically “robust” in that most are subject to blind review; a process stated as key toscientific progress (Smith, 1999, p. 9).

Search keywords were limited to “CSn” intending only to identify research directlyrelating to CSn – or to consider this conversely – to avoid contractor research in abroader sense such as that relating to: performance measurement per se (Luu, et al.,2008); specific contractor assessment such as safety performance (Hinze and Gambatese,2003); or contractor metrics such as financial stability (Chan, et al., 2005).

While it is difficult to definitively justify which journals should, or should not beincluded in this kind of (targeted) literature search, those chosen (Table I) wereconsidered to represent contemporary eminence in the field of constructionmanagement, and hence, able to yield a representative sample (note journal acronymsin Table I which are used hereafter). By way of supporting this contention, several ASCEpublications, BRI, CME, CI, ECAM, and some in the “others” category of the table are allincluded in the Australian Business Deans Council list of “top” journals in the field(ABDC, 2009); while a list of journals “most respected” by UK academics – based on ananalysis by Hughes (2009) relating to papers submitted for the 2007 UK ResearchAssessment Exercise (RAE, 2008) – ranked virtually all journals targeted in the presentstudy highly. Notwithstanding this emphasis on journal papers, some conference papersin the field were also identified via the Royal Institution of Chartered Surveyorsconference series (RICS, 2009); the Association of Researchers in ConstructionManagement database (ARCOM, 2009); and the American Society of Civil Engineersdatabase (ASCE, 2008).

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The decision regarding a paper’s relevance to this study was a subjective onebased entirely on the author’s experiential judgement. This decisional characteristic ofliterature reviews has previously been recognised. For example, Birmingham (2000)suggested that the problem of what to include or otherwise is an, “[. . .] evaluativedecision” based on the researcher’s own judgement of “adequacy”; while Fink (2005, p. 5)confirmed its being a subjective task that might use screening criteria for evaluating apaper’s coverage of a subject, along with its scientific quality. Similarly, the categorisationof papers (regarding, for example, subject focus and methodological standpoint), was aresult of subjective evaluation and harmonized with Cooper’s (1998) suggestion that “[. . .]literature reviews can focus on research, research methods, theories, applications [. . .] toidentify the central issues in a field” while also noting that, “identifying related work[assists in helping] to rationalise the work and identify a focus” (Hart, 1998).

Table I summarises the search sources and respective numbers of worksidentified/included from each. The sample does not purport to be exhaustive or adefinitive listing in the field, taking into account the above issue of “inclusion” and thefact that new research is always working through to publication. However, given thescope, depth, and targeted nature of the review, the sample was considered appropriateto meaningful analysis.

Analysis of the literatureThe analysis is focussed under each of the headings: “CSn research focus andjustification” (to observe favoured aspects of study and their motives for attention);“Methodological approaches to CSn research” (to consider methodological predilection);

Source/databaseNo. papers returned

from searchaNo. papers relevant

to studyb

ASCE Databasehttp://pubs.asce.org/research/ 86 24Building and Environmentwww.sciencedirect.com/science/journal/03601323ISSN: 0360-1323 (print) 133 13Building Research and Informationwww.tandf.co.uk/journals/rbri ISSN: 0961-3218 (print) 35 6Construction Management and Economicswww.tandf.co.uk/journals/titles/01446193.htmlISSN: 0144-6193 (print) 121 14Construction Innovationhttp://info.emeraldinsight.com/products/journals/journals.htm?id¼CI ISSN: 1471-4175 (print) 8 2Engineering, Construction and Architectural Managementhttp://info.emeraldinsight.com/products/journals/journals.htm?id¼ecam ISSN: 0969-9988 (print) 164 10International Journal of Project Managementwww.sciencedirect.com/science/journal/02637863ISSN: 0263-7863 (print) 312 12Other, non-specified journals 12Total 93

Notes: aUsing search terms “Contractor þ Selection”; bsubjectively chosen – refer to narrativeTable I.Results of searches

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“Research tools used to solve CSn problems” (to identify solutions chosen); and“Products of research” (to highlight the results of this combined effort). Accordingly, adiscussion ensues; salient observations are highlighted; and suggestions for futureresearch direction in the field are proffered.

CSn research focus and justificationThe majority of CSn research has focussed on the selection “problem” as a process, withjustification centred on decisional aspects relating to (inter alia) its issues of difficulty,uncertainty, and subjectivity announced in the introduction. Coupled with this, theimportance of the selection decision is often mentioned. Differing Csn models have beenproffered as solutions thereto.

On justification, Hatush and Skitmore (1998) highlighted the important link betweenCSn and project success in terms of achieving schedule, cost and quality; such linkadditionally highlighted by many others (Russell and Jaselskis, 1992). Kumaraswamy(1996) stated that, “The right choice of contractor is crucial to success”; further examplesconfirming that CSn, “[. . .] is important to ensure the success of projects” (Bubshait andAl-Gobali, 1996); “[. . .] is a decisive event for project success” (Alarcon and Mourgues,2002); “[. . .] the project’s success level is largely dependent on it” (Li, et al., 2005); and(selecting a capable bidder) is crucial to, “ensure the success of construction projects”(Li, et al., 2007). Fong and Choi (2000) meanwhile, identified that CSn is one of the mostimportant tasks facing a client who wishes to achieve successful project outcomes. AsKumaraswamy et al. (2000) summed it up, “[. . .] smarter selection does in fact matter”.

The issue of importance is closely linked to the decisional difficulties of CSn andhence, addressing these difficulties, has been the ambition of many researchers. Hatushand Skitmore (1997) confirmed, “One of the most difficult decisions taken by the client[. . .] is selecting the contractor”; while Albino and Garavelli (1998) referred to thecomplexity of the decision in terms of, “analysis and judgement”. El-Sawalhi et al. (2007)identified with the need to, “provide uncertain, incomplete, or imprecise assessments dueto lack of information” in harmony with Watt et al. (2009), who stated that the decision is,“[. . .] plagued with uncertainties”. Similar rhetoric includes, “[. . .] the task ischallenging” (Fong and Choi, 2000); and its description as, “[. . .] a complicatedtwo-group non-linear classification problem” by Lam et al. (2005). Elsewhere, Pongpengand Liston (2003) recognised that that whether in public or private sector arenas,multiple decision makers are involved.

In contrast, others have justified (and focused) their work differently. Khosrowshahi(1999) for instance, in developing a CSn model for the public sector, suggested thatprequalification should be an, “[. . .] important issue for contractors who seek to obtainwork” and that they might therefore direct [more] attention to their qualifying attributes.The need to aggregate both quantitative and linguistic selection data was thejustification of a model suggested by Sonmez et al. (2001); while Phillips et al. (2007)described the need to achieve best value in delivering savings identified in the Gershon(2004) review, as the basis for their study. Juan et al. (2009) addressed the, “[. . .] fewefforts [previously] focused on CSn in refurbishment projects”; while Holt and Edwards(2005) justified their treatise, by highlighting problems of selecting from apreponderance of “cowboy” firms in the domestic building sector.

Some research has substantiated the targeting of a country-specific CSn setting orproblem. Examples include Lai et al. (2004) who studied Chinese bid evaluation;

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Ferguson et al. (1995) focussing on Dutch tendering practice; Kumaraswamy (1996) whopresented a Hong Kong perspective; and observation of European contract award byLambropoulos (2007) and Topcu (2004). Other, setting-specific justification includes thework of Yiu et al. (2002) that addressed the “dearth of research” aimed specifically atbuilding and maintenance CSn; Aziz (2008) whose study related to highway pavementproject contractors; a study aimed at apartment block maintenance contractors byZavadskas and Vilutiene (2006); and Juan et al. (2009) whose focus was housingrefurbishment contractors. Finally, regarding research focus, it was perhaps inevitablethat the broader aspect of research into main CSn would eventually be applied tosubcontractors; and recent outputs confirm this. For example, see Kumaraswamyand Mathews (2000), Ng and Tang (2008), Mbachu (2008), Ng et al. (2008) andArslan et al. (2008).

By categorising each paper making up the sample (n ¼ 93) into one principalresearch focus, the results shown in Table II were achieved. Two caveats areacknowledged regarding this categorisation:

(1) it was a subjective, “evaluative” process (as described in the introduction); and

(2) many papers spanned more than one research focus.

Nonetheless, the exercise does provide thematic insight (Emerald, 2009) that iselucidated within the “Discussion” later.

Methodological approaches to CSn researchAnalysis of methodological approaches was based on the chosen categories of:

. statistical/deterministic modelling;

. literature/documentary analysis;

. surveys;

. other (non-deterministic) forms of statistical enquiry; and

. interviewing.

The sixth classification designated “Other” (Figure 1), represented the “note” offered bySkitmore and Mills (1999), which did not fall into a methodological category.

Statistical/deterministic modelling generally sets out to mathematically model aprocess, such that the derived model can replicate a previous event as a means ofpredicting a future one (Fellows and Liu, 2003, p. 76); and being different from stochasticmodelling, which draws on probability (Fellows and Liu, 2003). Although, as elsewherehighlighted, “[. . .] models whose solutions include chaotic dynamics are a link betweendeterministic and stochastic [types]” (Metcalfe, 1996, p. 208). In the present context,typical deterministic examples tend to comprise an algorithm that given specific inputs,will yield contractor classification output and/or optimum choice.

Statistical/deterministic modelling of CSn was the most favoured methodologicalapproach among the sample (almost 40 per cent of papers); being represented by a rangeof selection models. For instance, Tam and Harris (1996) used a discriminant function toclassify contractors into performance groups; Wong et al. (2003) used a similar functionand logistic regression to achieve contractor classification; Lam et al. (2005) employedprincipal component analysis during prequalification decision making to, “alleviatemulticollinearity and largely reduce the dimensionality of the prequalification data set”;

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while Bendana et al. (2008) presented a fuzzy-logic-based selection method.Deterministic techniques that embrace CSn variables represented on differentmeasurement scales are appropriate to the multivariate character of CSn input data.

Literature/documentary synthesis, is taken here to include examination of all kinds ofexisting (written) knowledge or data, as a means of conceptualising, developing theory,or presenting propositions (Davies, 2007, p. 3). Almost one-third of the sample employedthis methodology, such as within the review of research on transaction costs related toCSn method (Lingard et al., 1998); the comparative overview of documented knowledgeof practices in design/build (sic) CSn (Palaneeswaran and Kumaraswamy, 2000b); thebenchmarking of public sector selection practices (Palaneeswaran and Kumaraswamy,2000a); and an examination of the management and tender evaluation literature byWatt et al. (2009). Many texts expand the virtues of this “interrogative” researchmethodology, see for example, Silverman (2001) and May (2001, Chapter 8).

Within the broader field of CMR, questionnaires are favoured, having even been usedto study the journals within which the results of these kinds of surveys are oftenpublished (Chau, 1997). But, such persistent usage has been questioned of late.For instance, McCaffer (2008) asked, “How do you move from analyses of questionnairereturns to making a real change in industry’s practices?” Surveys were used in 22 per centof the papers making up the sample, such as to rate the importance of a list of35 prequalification criteria (Ng et al., 1999); to study the perceived importance of factorsaffecting choice of contractors in the UK (Holt et al., 1994c); and to compare use ofproject-specific vis-a-vis “value-specific” tender selection criteria (Wong et al., 2000).

Figure 1.Summary ofmethodologicalapproaches employed

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Other, non-deterministic forms of statistical enquiry such as correlation analysis(Colman and Pulford, 2006); hypothesis testing (Colman and Pulford, 2006, sections 7-8);and ordinal or rank analysis (Meddis, 1984) were used in six studies (Chinyio et al., 1998;Holt, 1998). Interviewing and/or case studies were employed among three studies,including Hatush and Skitmore (1997) who developed a questionnaire for structuredinterviews to investigate project success factors; and Palaneeswaran andKumaraswamy (2001) who combined “correspondence” with interviews to seekinternational expert opinion on CSn.

Research tools used to solve CSn problemsSpecific research tools used among the sample were examined (in respect of16 classifications), to look in more detail at the mechanisms by which research problemswere resolved and the “popularity” of these. These classifications and respective usagewere ordered by rank based on the number of times each was observed among thesample (see column 1, Table III).

The number of “usage observations” (column 3, total ¼ 113) is greater than thesample (n ¼ 93) because some researchers used more than one classification of tool.To offer some examples, Assaf et al. (1996) used rank order analysis with regressionanalysis; while Chau et al. (2003) combined Likert scale/importance ranking withmulti-attribute utility theory. Often, the combining of several methods in this wayunderpins a triangulated CMR methodology (Love, et al., 2002a; Edwards and Holt, 2010).

Here, system interrogation was defined as any method of inquiry that observedqualitative data and/or observation of existing systems, and was the favoured researchtool (40 observations). Examples include Holt et al.’s (1995b) review of UK selectionpractice; a cross-sectional review of CSn methodologies embedded in the literature(Holt, 1998); and the study of, “[. . .] current literature and actual experience refinedthrough a review of public-sector owners’ selection procedures” by Al-Reshaid andKartam (2005).

Forms of rank order analysis were second-favourite tool. Zavadskas and Vilutiene(2006) presented values of qualitative performance criteria obtained from a survey, andin part, rank analysed these using a quadrant analysis matrix. Marzouk (2008) applied asuperiority and inferiority ranking model to CSn, while several other researchesdemonstrated rank analysis of one form or another (Hatush and Skitmore, 1997;Wong et al., 2000; Chau et al., 2003). The latter two studies (as did several others)employed Likert/importance ranks as a means of abstracting subjective personalopinion (Likert, 1932; Oppenheim, 2000). This was the third favoured research tool and(given that such analysis is often used to analyse survey returns), mirrors the earlieridentified popularity of questionnaires.

Other tools used include: muilti-attribute analysis (Assaf and Jannadi, 1994) andmulti-attribute utility theory (Holt et al., 1994d, 1995a) (especially among “earlier”studies in the field), along with several interpretations of fuzzy theory (Singh andTiong, 2005). Artificial neural networks (Lam et al., 2000), mutivariate discriminantanalysis (Wong and Holt, 2003), analytical hierarchy process (Abdelrahman et al., 2008)and cluster analysis (Chinyio et al., 1998) were all observed four times each. Slightlylesser use (three observations) was noted for regression analysis (Tam and Harris, 1996),variance analysis (Hatush and Skitmore, 1997), correlation analysis (Zavadskas andVilutiene, 2006) and evidential or case-based reasoning (Juan, 2009).

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ssaf

and

Jan

nad

i(1

994)

and

Hol

tet

al.

(199

4d)

¼4

MA

UT

65.

3H

oltet

al.

(199

5a)

and

Hol

tet

al.

(199

4e)

6F

uzz

yth

eory

54.

4B

end

anaet

al.

(200

8)an

dJu

anet

al.

(200

9)¼

7A

NN

43.

5K

hos

row

shah

i(1

999)

and

Lam

etal.

(200

0)¼

7M

DA

43.

5T

aman

dH

arri

s(1

996)

and

Nget

al.

(199

9)¼

7A

HP

43.

5M

ust

afa

and

Ry

an(1

990)

and

Mah

diet

al.

(200

2)¼

7C

lust

eran

aly

sis

43.

5H

olt

(199

7)an

dC

hin

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(199

8)¼

11R

egre

ssio

nan

aly

sis

32.

7W

ong

(200

4)an

dA

ziz

(200

8)¼

11V

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nce

anal

ysi

s3

2.7

Hat

ush

and

Sk

itm

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(199

7)an

dW

onget

al.

(200

3)¼

13C

orel

atio

nan

aly

sis

21.

8Z

avad

skas

and

Vil

uti

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(200

6)¼

13E

vid

enti

al/C

Bre

ason

ing

21.

8Ju

an(2

009)

¼13

Hy

pot

hes

iste

stin

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Ng

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g(2

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16O

ther

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7in

stan

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c6.

2D

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al.

(200

8)an

dIp

etal.

(200

4)T

otal

s11

310

0

Notes:

aN

um

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ofob

serv

atio

ns

dif

fers

from

sam

ple

size

du

eto

som

ep

aper

su

tili

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than

one

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arch

tool

;bm

inor

erro

rin

tota

ld

ue

toro

un

din

g;

c oth

er,o

ne

obse

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inst

ance

each

ofan

aly

tica

ln

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com

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ent

anal

ysi

s,fa

ctor

anal

ysi

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mu

lti-

attr

ibu

tean

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MA

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,m

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trib

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uti

lity

theo

ry;

AN

N,

arti

fici

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ln

etw

ork

;M

DA

,m

ult

ivar

iate

dis

crim

inan

tan

aly

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AH

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anal

yti

cal

hie

rarc

hy

pro

cess

;C

B,

case

-bas

ed(r

easo

nin

g)

Table III.Analysis of research tools

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314

Seven papers used “novel” tools in this setting, viz.: analytic network process (Cheng andLi, 2004), principal component analysis (Lam et al., 2005), factor analysis (Ng and Tang,2008), combined radix determination (Wang et al., 2007), genetic-neural network(El-Sawalhi et al., 2007), graph theory and matrix methods (Darvish et al., 2008), andbranch and bound algorithm (Ip et al., 2004).

Products of researchFigure 2 shows that the research products of the sample may be broadly grouped intofour categories, the largest of which (63 per cent) comprised CSn models of one form oranother. Of products presented, 17 per cent derived, or proffered, processes (derivedrepresenting or conceptualising an existing process; proffered suggesting animprovement or variation of existing process).

Suggested groupings of selection criteria for given CSn scenarios and/or developedimportance metrics attached to these, evolved as a result of 15 per cent of the studiesundertaken; with the remaining four per cent of studies comprising the “miscellaneous”grouping whose products did not fit with these three former classifications of output.

By considering authors’ own descriptions of the 59 CSn models contained within thesample (Figure 2), emphases of intended use were determined:

. 26 models were described as intended for contractor (or sub-contractor, or tender)“selection” ( Jaselskis and Russell, 1992; Fong and Choi, 2000; Kashiwagi andByfield, 2002; Cheng and Li, 2004; Arslan et al., 2008);

. eight were related to assessing or predicting contractor (or sub-contractor)“performance” (Holt et al., 1994a; Tam and Harris, 1996; Yasamis et al., 2002;El–Sawalhi et al., 2008);

. seven were related to “prequalification” (Russell and Skibniewski, 1990;Russell et al., 1990; Assaf and Jannadi, 1994; Lam et al., 2005; Palaneeswaran andKumaraswamy, 2005; El-Sawalhi et al., 2007);

Figure 2.Broad classifications of

research products

59

1614

40

10

20

30

40

50

60

Model Process Criteria Misc.

No. Percent

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. six addressed tender or bid “evaluation” (Mustafa and Ryan, 1990; Wong et al.,2001; Pongpeng and Liston, 2003; Watt et al., 2009); and

. four were designed for contractor/client “classification” (Holt, 1996; Chinyio et al.,1998; Wong and Holt, 2003; Wong et al., 2003).

The remainder had specific intended application such as for contractor “rating” (Albinoand Garavelli, 1998), contractor “ranking” (Darvish et al., 2008), or “assessingcompetitiveness” (Shen et al., 2003, 2006). See also Lo et al. (1998) in this respect.

Process products have contributed to a range of issues such as for betterunderstanding of: practices in the public sector (Palaneeswaran and Kumaraswamy,2000a); geographically specific CSn tradition (Kumaraswamy, 1996); procurementspecific synthesis (Gransberg and Molenaar, 2003); and contrast between CSnstakeholders (Egemen and Mohamed, 2005). Equal diversity is evident in criteriaproducts, including guidance on prequalification criteria (Hatush and Skitmore, 1997);with respect to cost-benefit (Ng and Skitmore, 2001); and reporting practitionerpreference (Singh and Tiong, 2006).

DiscussionFigure 3 shows graphically the above findings of the review and may becross-referenced with the following discussion.

CSn importanceThe significance of selecting the most appropriate contractor for a given selectionsetting is of exceptional importance, largely, because of the positive “association”between employing a good contractor and achieving project aspirations. (Or itsantipode the “association” between employing a poor contractor and likelihood of

Figure 3.Model of CSn research

Observed research foci:

1. Selection models2. Assessment criteria3. Survey of practice4. Decision support5. Contractor performance6. Opinion survey7. Case study8. Other

Observed research justification:1. Importance of selection decision2. Difficulties of selection decision3. Other, selection setting specific challenges

Observed methodologicaldesign:

1. Deterministic models2. Literature and documentary synthesis3. Postal surveys4. Statistical enquiry5. Interviews

Observed research tools:1. System interrogation2. Rank analysis3. Likert / importance data4. MAA5. MAUT6. Fuzzy theory7. ANN8. MDA9. AHP

Observed researchproducts

Misc.

Criteria

Process

Models C.2010

Future research products?

Closer dialogue with industry andpractitioners?

More use of face-to-face research?

Greater end-user impact and take-up?

Web based technologies – transitional interface betweenacademic products and industry?

Interrelationships

C.1990 Increasing complexity

Existing: mapping, formalisation, understanding

Proffered: improvements, variations on existing

Relevance to selection setting or scenario

Importance:ranks, indices to selection setting

Knowledge advancement in specific CSn contexts

Inte

rrel

atio

nshi

ps

10. Cluster analysis11. others…

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unsatisfactory project outturn). Such interrelationship, between an event that (normally)occurs at the outset of a project and one representing its conclusion, demonstrates CSn’srelevance in the overall context of construction “product” procurement – and helpsexplain why “importance” is a significant research justifier in this field.

Uniqueness of the CSn environmentThe uniqueness of each construction product is matched by the (resulting) individualityof its CSn decisional parameters (such as CSn criteria to be applied). Accordingly,appropriate CSn criteria and their degrees of importance will fluctuate; both as afunction of “objective” factors related to the project (such as time, quality, and valuetargets), along with more “subjective” counterparts, such as decision makers’predilection, disposition and perceived utility of (those) criteria.

To this, uniqueness may be added the dynamic state within which contractorsoperate and within which selection decisions are made – impacted for example – byinconstant macroeconomic, political, and commercial ambience. These characteristics,intuitively, explain the focus of much research observed in this study being the CSnprocess itself, and equally, why “importance” of that process is an oft-cited principalresearch justifier.

Research foci and justifiersThe uniqueness of construction “product procurement” however, demonstrates whysome research has focussed on (and been justified by), distinctive CSn settings – setapart for example, by type of project, form of contractual arrangement, geographicalcharacteristics, or some other peculiarity. This also helps illuminate the decision byseveral researchers, to study selection criteria for given selection scenarios. To seek forexample, their applicability and concomitant importance levels which equally – even forthe same criteria – will differ between these varying selection environments. A caveathere however, relates to the general applicability of any CSn research outputs, acrossdiffering selection settings and the “dynamic state” referred to above.

Methodological considerationsThe methodological approaches observed lean toward deterministic modelling,reflecting the leading desire to model the selection process per se. That is, todetermine optimal output (contractor identification) given numerous multivariatedecisional input parameters. The predominance of literature and other documentaryanalysis underlining methodological design (the basis of almost one-third of the sample),corresponds with the third most popular research focus, of surveying existing practice.The former “desire” can often, in part be informed by results of the latter approaches.

Postal surveys were the research tool of choice among one-quarter of the sample,whereas interviews only represented 4 per cent. It seems that in line with earlier calls for,“[. . .] closer dialogue with practitioners and more demonstrations of application [. . .]”within CMR generally (McCaffer, 2008); face-to-face research “tools” appearunder-utilised in this branch of the field also. This is somewhat surprising in thatgiven a desire to produce optimum selection models – that by definition are intended forpractitioner use – one might have expected to witness more discourse with “end-users”if these particular products of research are to be (of maximum utility to and ultimately)adopted by them.

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Favoured research tool was system interrogation of different kinds (35 per cent ofresearch tool observation on the sample), relating to the above recurring research focusof surveying existing practice. The use of rank analysis (10 per cent of observations)relates mainly to those studies that investigated and subsequently positioned in variousways, selection criteria. Predominant remaining research tools were statistical oralgorithmic in character, being applied principally to said modelling aspirations andother forms of interrogating data, accrued by way of the 25 per cent of studies employingquestionnaire and interview surveys.

General observationsOne general observation is that earlier studies tended toward more “simpler” modellingtechniques; predominantly of an additive multi-attribute nature, and sometimesembracing utility theory. Later models have employed “newer” research tools such asartificial neural networks and fuzzy theory. A few recent studies meanwhile, havedemonstrated use of tools such as “Combined Matrix Determination” and “GraphTheory and Matrix Models”. Referring to the earlier statement concerning “end-user”take-up, then given complexity of some models, one might speculate as to how“acceptable” these are to practitioners unless presented in a “user-friendly” – forinstance, “ready-to-use” computer package or web-based form (Ng et al., 2003).

That is, algorithmic solutions alone require some form of transitional interface bywhich non-academic users can employ them in practice. Of note on this issue, White(2005) suggested that with respect to tendering procedures in the public sector, anenvironment exists that, “[. . .] mandates procedures which make the implementation ofsuperior methods unlikely” – so one might reasonably ask, “How many of these “new”models are used in public sector practice?” The issue of industry take-up, or “impactvalue” of CSn research products, is perhaps best answered definitively by survey of(dialogue with) practitioners, and points to future research direction as a follow-on to thepresent study.

Other research products of the sample include formulation of existing, and profferingof proposed CSn systems (18 per cent of observations), along with knowledge of selectioncriteria (15 per cent). If the dialogue with practitioners mentioned above is performed, itwould be valuable to also assess end-user adoption of these suggested “cocktails” ofcriteria, to identify degree of linkage between what academia suggests should impactcontractor assessment decisions, and to what extent practitioners are actually aware of,or employ such knowledge in practice.

ConclusionMuch academic research has targeted CSn over the last two decades. The focus of thishas, predominantly, been the decisional processes involved in CSn and decisional criteriaapplied thereto, with principal research “drivers” often cited as being to optimally modelthat complex decisional process, linked to the importance of the decisional task.Nonetheless, where equally complex statistical or algorithmic CSn “solutions” evolvefrom that research (as often observed among the sample), then paradoxically, thesesolutions may simply be replacing one “demanding” state with another.

Similarly, one may question the reliability or relevance of cocktails of “optimal”selection criteria, given the transient and dynamic environments in which they areapplied. That is, while a set of criteria might be optimal in one setting; will this be the

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case in a different setting, or a similar setting at a different point in time? With the benefitof hindsight (i.e. since CSn research began in earnest) there is justification in askingwhether such “recommended” criteria groupings can make the transition; from theiracademic evolvement, to sustained and reliable application in practice. Only dialoguewith end-users could determine the answer.

Statistical and/or deterministic modelling was the most prominent methodologicalapproach observed; with earlier models tending towards (arguably simpler)multi-attribute analysis; while later models have employed (arguably more complex)deterministic techniques, such as artificial neural networks, and fuzzy logic. The abovecomments regarding model complexity and relevance, similarly, apply here too.

Specific research tools employed within CSn methodological research frameworksare equally varied. System interrogation – primarily as a function of qualitative dataanalysis – has frequently been used. This, predominantly subjective, interpretive toolmay be welcomed by those within the broader field of CMR that espouse a shift from(more mechanistic) survey-based interrogative approaches; while simultaneouslyencouraging discourse between researchers and practitioners. Accrual of Likert scaledata via questionnaire surveys and analysis of (often resulting) interval data using rankanalysis methods, were also popular, but might be viewed as more convenient to theresearcher than relevant to the research problem, in light of the above comments.

Given said complexity of some evolving CSn models and an arguably under-utilisationof dialogue-based research methods in this subject domain, a call is made for future CSnresearch to more enthusiastically employ the latter techniques, as a means of:

(1) Assessing the take-up of CSn research products by practitioners. For instance,what products actually filter through to practice? And which aspects of researchoutput can actually demonstrate real end-user impact? Accordingly, the answershere would better inform CSn research design if industry “impact” is the intentionof research.

(2) Identifying exemplars and/or barriers associated with (1). Such practicalknowledge could then be fed back to the CSn research community, informingmethodological designs with a view to maximising research value.

(3) Helping to steer future CSn research direction – in the context of maximising itsend-user impact and potential for industrial take-up.

In sum, CSn research should henceforth be targeted at satisfying, and perhaps its“success” be measured more by, the latter criterion (3). These propositions represent anatural extension of this review of the subject, and will form the basis of a follow-on,empirical study in the field.

Notes

1. Here, the term “production” is used loosely; procurement variants embracing contractordesign for example, are not overlooked.

2. Asserted partly on anecdotal evidence from the author’s own network of constructionprofessionals.

3. Many research theses have addressed the CSn issue but are not considered here, becausemuch of that research ultimately filters down into published papers. A searchable electronicindex of theses is however, available (ITT, 2009).

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Corresponding authorGary Holt can be contacted at: G.D.Holt@lboro.ac.uk

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