Contractor Selection

The current issue and full text archive of this journal is available at www.emeraldinsight.com/1471-4175.htm CI 10,3 Contractor selection innovation: examination of two decades’ published research Gary Holt Department of Civil and Building Engineering, Loughborough University, Loughborough, UK Abstract Purpose – The “problem” of selecting a contractor has attracted significant academic research endeavour over the last two decades. The principal aim here is to examine that research via published academic outputs for the period circa 1990-2009. Design/methodology/approach – A sample of published contractor selection (CSn) research is critically 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 methodological approach, followed by documentary synthesis then questionnaire surveys. Preferred research tools are found to be system interrogation, rank order analysis and Likert scale/importance indices, with hypothesis testing and “other” methods used less so. Almost two-thirds of research products are CSn models, with derived or proffered processes, and knowledge relating to CSn criteria, between them representing approximately the remaining third of output. Research limitations/implications – It is suggested that many of the CSn models exhibit as much complexity as the original “problem” they sought to resolve, while the reliability and longevity of suggested “cocktails” of CSn criteria (in practice), might be questioned. A call for future research products 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 is proposed. Practical implications – The paper signals a possible need for greater industrial engagement in the research domain. Originality/value – The findings are novel to this paper. Keywords Procurement, Contractor workers, Tendering, Subcontractoring, Clients Paper type Literature review 304 Received 16 April 2009 Accepted 15 October 2009 Construction Innovation Vol. 10 No. 3, 2010 pp. 304-328 q Emerald Group Publishing Limited 1471-4175 DOI 10.1108/14714171011060097 Introduction The construction industry is recognised for many distinct characteristics and in a procurement context, its separation of design from production and resulting transient fragmentation, 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 undertaking the production function[1] has for decades, been the subject of research, debate and published guidance. The combined constructive comments of the anonymous reviewers are acknowledged, for helping develop and improve the paper. Contractor selection (CSn) normally comprises a “theoretically infinite” group of contractors (Holt, 1996) whose attributes (El-Sawalhi et al., 2008) are – often on the basis of past performance (Kadefors, et al., 2007; Holt et al., 1994a) – assessed against client objectives 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 be considered against the potential for these capabilities to significantly change over short periods. In practice, CSn procedures vary significantly (Palaneeswaran and Kumaraswamy, 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 be expanded upon as this study unfolds. Given the importance of CSn to project performance (Holt, 1998), numerous construction industry reports have focused on it (Cooke and Williams, 2004, p. 1.2). For example, The Simon Committee (1944) strongly advocated selective rather than open tendering (Masterman, 1996, p. 8), while Emmerson (1962) highlighted the issue of construction design being “removed” from production as referred to above. Shortly after Emmerson, Banwell (1964) reiterated aspects of The Simon Committee (et al.), advocating what were then “non-conventional” procurement methods such as negotiation, and a follow-on report from the National Economic Development Office, also supported greater use of serial tendering and negotiated contracts (NEDO, 1967). In the title of its report, The Tavistock Institute (Tavistock, 1966), reflected that the building industry was characterised by “[. . .] interdependence and uncertainty” concluding somewhat ironically (given said calls for increased negotiation), that the industry “thrived” on the latter (Woodward, 1997). The oft-cited Latham (1993, 1994) reports were quite influential in procurement terms, raising issues of mistrust between stakeholders (Cooke and Williams, 2004, p. 4), whilst among other aspects, promoting greater use of selection based on quality in addition to price (i.e. value). Egan (1998) subsequently encouraged longer term procurement relationships in favour of selective methods, as a mechanism to achieving quality and efficiency improvement; while in a second report (Egan, 2003), arguably rooted the term “integrated supply chains” into procurement dialogue. Aside from government, the Construction Industry Institute offered explicit CSn guidance (CII, 1988) along with direction on choosing contractors when striving to improve 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 of information technology within construction, it was inevitable that problems (and their avoidance) 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 competition towards integrated supply chain mechanisms that encourage mutual benefit, organisational learning, partnerships, profitability and quality – while embracing information technology – is witnessed (Li et al., 2000; Love et al., 2002b; Walker and Johannes, 2003; Akintoye and Main, 2007). The emphasis within the literature has moved from “lowest cost” to “value” (CIRIA, 1998, 2009) (while the author’s anecdotal evidence suggests that, among smaller contractors at least, “cost” often remains the ultimate selection criterion). The “credit crunch” has certainly placed strain on the Contractor selection innovation 305 CI 10,3 306 “literature’s call” for value, with price competition presently fierce[2] (RICS, 2008, p. 3), resulting from unprecedented negative forces acting on international construction activity (The Times, 2008; CECA, 2008; CIOB, 2009; ASCE, 2009). The issue of CSn – and academic research in the field for that matter – has been an evolving one over the last two decades; moulded by reaction to changes in the procurement environment and in the case of academic CSn research, to advancements in the science of methodology. Regarding the latter, developing techniques such as neural networks, fuzzy decision making, and web-based technologies have witnessed application among a range of construction management problems (Graham et al., 2006; Vries and de and Steins, 2008; Han et al., 2008, respectively). Somewhat expectedly therefore, examples of their use in CSn research are equally easy to find, for example, see Albino and Garavelli (1998), Singh and Tiong (2005) and Palaneeswaran and Kumaraswamy (2005), respectively. Aim and objectives of this study This 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 of construction CSn, published over a time window of circa 20 years prior to 2009. In so doing, such review and concomitant synthesis sought accord with contention of Fellows and Liu (2003, p. 64) that a literature review must provide, “[. . .] a summary of the state-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 of literature, with particular clusters of synthesis, emanating from focus on five areas of the subject defined by objectives (1)-(5) above. Based on observation of outcomes resulting from satisfying these objectives, the contribution of published academic research is considered, and future research direction intimated. The literature review: rationale and method The literature review is discussed in terms of its: . rationale; and . method. Rationale Levin (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 this question 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, some regard a review as simply an “essay” (Davies, 2007, p. 39), for this study the “answer” to Levin connects with two particular definitions: (1) that a review can be, “an expert’s general review of current literature on a particular topic” (Walliman, 2006, p. 182); and (2) that it should overview, “[. . .] who the key writers are, [. . .] prevailing theories and hypotheses [. . .] what questions are being asked, [. . .] what methods and methodologies are appropriate [. . .] and show relationships [. . .] so that key themes emerge” (Emerald, 2009). Literature reviews have frequently been used – to good effect – in construction management research (CMR). For example, Sidwell and Budiawan (2001) undertook a review to highlight problems with competitive tendering in relation to contractor-led innovation; Wong et al. (2005) reviewed the literature to investigate intelligent building research and discover three research foci as a basis for summarising future research direction; Donohoe (2005) reviewed literature and case law to consider a (then) recent appeal case and its implications for building surveyors; and constraints on information communication technology were identified through the literature by Peansupap and Walker (2006). More recently, common measures for improving constructability have been studied via review (Wong et al., 2007). This is but an indicative sample. Method The academic literature, upon which this study focussed, was identified via online search among selected construction management journals and other research databases (see later). Additional kinds of literature such as trade journals, comments, magazine articles and theses[3] do exist and are not excluded herein; but academic journal papers were afforded emphasis, because they represent the most important wealth of literature available (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 to scientific progress (Smith, 1999, p. 9). Search keywords were limited to “CSn” intending only to identify research directly relating to CSn – or to consider this conversely – to avoid contractor research in a broader 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 be included in this kind of (targeted) literature search, those chosen (Table I) were considered to represent contemporary eminence in the field of construction management, and hence, able to yield a representative sample (note journal acronyms in Table I which are used hereafter). By way of supporting this contention, several ASCE publications, BRI, CME, CI, ECAM, and some in the “others” category of the table are all included 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 an analysis by Hughes (2009) relating to papers submitted for the 2007 UK Research Assessment Exercise (RAE, 2008) – ranked virtually all journals targeted in the present study highly. Notwithstanding this emphasis on journal papers, some conference papers in the field were also identified via the Royal Institution of Chartered Surveyors conference series (RICS, 2009); the Association of Researchers in Construction Management database (ARCOM, 2009); and the American Society of Civil Engineers database (ASCE, 2008). Contractor selection innovation 307 CI 10,3 Source/database No. papers returned from searcha 86 133 35 121 8 164 312 No. papers relevant to studyb 24 13 6 14 2 10 12 12 93 308 Table I. Results of searches ASCE Database http://pubs.asce.org/research/ Building and Environment www.sciencedirect.com/science/journal/03601323 ISSN: 0360-1323 (print) Building Research and Information www.tandf.co.uk/journals/rbri ISSN: 0961-3218 (print) Construction Management and Economics www.tandf.co.uk/journals/titles/01446193.html ISSN: 0144-6193 (print) Construction Innovation http://info.emeraldinsight.com/products/journals/ journals.htm?id¼CI ISSN: 1471-4175 (print) Engineering, Construction and Architectural Management http://info.emeraldinsight.com/products/journals/ journals.htm?id¼ecam ISSN: 0969-9988 (print) International Journal of Project Management www.sciencedirect.com/science/journal/02637863 ISSN: 0263-7863 (print) Other, non-specified journals Total Notes: aUsing search terms “Contractor þ Selection”; bsubjectively chosen – refer to narrative The decision regarding a paper’s relevance to this study was a subjective one based entirely on the author’s experiential judgement. This decisional characteristic of literature reviews has previously been recognised. For example, Birmingham (2000) suggested that the problem of what to include or otherwise is an, “[. . .] evaluative decision” 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 a paper’s coverage of a subject, along with its scientific quality. Similarly, the categorisation of papers (regarding, for example, subject focus and methodological standpoint), was a result of subjective evaluation and harmonized with Cooper’s (1998) suggestion that “[. . .] literature reviews can focus on research, research methods, theories, applications [. . .] to identify 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 works identified/included from each. The sample does not purport to be exhaustive or a definitive listing in the field, taking into account the above issue of “inclusion” and the fact that new research is always working through to publication. However, given the scope, depth, and targeted nature of the review, the sample was considered appropriate to meaningful analysis. Analysis of the literature The analysis is focussed under each of the headings: “CSn research focus and justification” (to observe favoured aspects of study and their motives for attention); “Methodological approaches to CSn research” (to consider methodological predilection); “Research tools used to solve CSn problems” (to identify solutions chosen); and “Products of research” (to highlight the results of this combined effort). Accordingly, a discussion ensues; salient observations are highlighted; and suggestions for future research direction in the field are proffered. CSn research focus and justification The majority of CSn research has focussed on the selection “problem” as a process, with justification centred on decisional aspects relating to (inter alia) its issues of difficulty, uncertainty, and subjectivity announced in the introduction. Coupled with this, the importance of the selection decision is often mentioned. Differing Csn models have been proffered as solutions thereto. On justification, Hatush and Skitmore (1998) highlighted the important link between CSn and project success in terms of achieving schedule, cost and quality; such link additionally highlighted by many others (Russell and Jaselskis, 1992). Kumaraswamy (1996) stated that, “The right choice of contractor is crucial to success”; further examples confirming that CSn, “[. . .] is important to ensure the success of projects” (Bubshait and ´ Al-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 most important tasks facing a client who wishes to achieve successful project outcomes. As Kumaraswamy 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 and hence, addressing these difficulties, has been the ambition of many researchers. Hatush and Skitmore (1997) confirmed, “One of the most difficult decisions taken by the client [. . .] is selecting the contractor”; while Albino and Garavelli (1998) referred to the complexity 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 due to lack of information” in harmony with Watt et al. (2009), who stated that the decision is, “[. . .] plagued with uncertainties”. Similar rhetoric includes, “[. . .] the task is challenging” (Fong and Choi, 2000); and its description as, “[. . .] a complicated two-group non-linear classification problem” by Lam et al. (2005). Elsewhere, Pongpeng and 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 that prequalification should be an, “[. . .] important issue for contractors who seek to obtain work” and that they might therefore direct [more] attention to their qualifying attributes. The need to aggregate both quantitative and linguistic selection data was the ¨ justification 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, “[. . .] few efforts [previously] focused on CSn in refurbishment projects”; while Holt and Edwards (2005) justified their treatise, by highlighting problems of selecting from a preponderance of “cowboy” firms in the domestic building sector. Some research has substantiated the targeting of a country-specific CSn setting or problem. Examples include Lai et al. (2004) who studied Chinese bid evaluation; Contractor selection innovation 309 CI 10,3 310 Ferguson et al. (1995) focussing on Dutch tendering practice; Kumaraswamy (1996) who presented a Hong Kong perspective; and observation of European contract award by Lambropoulos (2007) and Topcu (2004). Other, setting-specific justification includes the work of Yiu et al. (2002) that addressed the “dearth of research” aimed specifically at building and maintenance CSn; Aziz (2008) whose study related to highway pavement project contractors; a study aimed at apartment block maintenance contractors by ˙ Zavadskas and Vilutiene (2006); and Juan et al. (2009) whose focus was housing refurbishment contractors. Finally, regarding research focus, it was perhaps inevitable that the broader aspect of research into main CSn would eventually be applied to subcontractors; and recent outputs confirm this. For example, see Kumaraswamy and Mathews (2000), Ng and Tang (2008), Mbachu (2008), Ng et al. (2008) and Arslan et al. (2008). By categorising each paper making up the sample (n ¼ 93) into one principal research focus, the results shown in Table II were achieved. Two caveats are acknowledged 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 is elucidated within the “Discussion” later. Methodological approaches to CSn research Analysis 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 by Skitmore and Mills (1999), which did not fall into a methodological category. Statistical/deterministic modelling generally sets out to mathematically model a process, such that the derived model can replicate a previous event as a means of predicting a future one (Fellows and Liu, 2003, p. 76); and being different from stochastic modelling, which draws on probability (Fellows and Liu, 2003). Although, as elsewhere highlighted, “[. . .] models whose solutions include chaotic dynamics are a link between deterministic 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 methodological approach among the sample (almost 40 per cent of papers); being represented by a range of selection models. For instance, Tam and Harris (1996) used a discriminant function to classify contractors into performance groups; Wong et al. (2003) used a similar function and logistic regression to achieve contractor classification; Lam et al. (2005) employed principal component analysis during prequalification decision making to, “alleviate multicollinearity and largely reduce the dimensionality of the prequalification data set”; Research focus Description Example papers Number 39 16 13 8 7 4 3 3 93 %a 42 17 14 9 8 4 3 3 100 Selection model Assessment criteria Pongpeng and Liston (2003) and Abdelrahman et al. (2008) ¨ Russell and Skibniewski (1988), Waara and Brochner (2006) and Holt et al. (1994b) Survey of practice Decision support Ferguson et al. (1995) and Palaneeswaran and Kumaraswamy (2001) Lam et al. (2000) and Shen et al. (2003) Ng et al. (2005) and Mbachu (2008) Jennings and Holt (1998) and Singh and Tiong (2006) Holt (1997) and Lai et al. (2004) Skitmore and Mills (1999) Contractor performance Opinion survey Case study Other Totals Model embracing the contractor evaluation and selection process to yield optimum result Investigation of assessment criteria (e.g. for prequalification, project specific evaluation, or tender evaluation) Survey of for example, present selection or evaluation methods Model to embrace selection or a part thereof, explicitly stated as decision support or ‘aid’ Contractor evaluation directly related to past or predicted contractor performance Survey of opinion (for example, of clients, contractors or consultants) In depth analysis of a past case or an existing/ proposed system For any papers not fitting into the above foci Note: aPercentage of sample, rounded Contractor selection innovation 311 Table II. Analysis of “research focus” CI 10,3 40 No. 35 36 Percent 312 30 27 25 20 20 15 10 5 6 3 1 Other (note) Figure 1. Summary of methodological approaches employed 0 Stat./ detmnst. model Lit./doc. synthesis Postal survey Statistical Interviews inquiry ˜ while Bendana et al. (2008) presented a fuzzy-logic-based selection method. Deterministic techniques that embrace CSn variables represented on different measurement scales are appropriate to the multivariate character of CSn input data. Literature/documentary synthesis, is taken here to include examination of all kinds of existing (written) knowledge or data, as a means of conceptualising, developing theory, or presenting propositions (Davies, 2007, p. 3). Almost one-third of the sample employed this methodology, such as within the review of research on transaction costs related to CSn method (Lingard et al., 1998); the comparative overview of documented knowledge of practices in design/build (sic) CSn (Palaneeswaran and Kumaraswamy, 2000b); the benchmarking of public sector selection practices (Palaneeswaran and Kumaraswamy, 2000a); and an examination of the management and tender evaluation literature by Watt et al. (2009). Many texts expand the virtues of this “interrogative” research methodology, see for example, Silverman (2001) and May (2001, Chapter 8). Within the broader field of CMR, questionnaires are favoured, having even been used to study the journals within which the results of these kinds of surveys are often published (Chau, 1997). But, such persistent usage has been questioned of late. For instance, McCaffer (2008) asked, “How do you move from analyses of questionnaire returns to making a real change in industry’s practices?” Surveys were used in 22 per cent of the papers making up the sample, such as to rate the importance of a list of 35 prequalification criteria (Ng et al., 1999); to study the perceived importance of factors affecting choice of contractors in the UK (Holt et al., 1994c); and to compare use of ` project-specific vis-a-vis “value-specific” tender selection criteria (Wong et al., 2000). 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 structured interviews to investigate project success factors; and Palaneeswaran and Kumaraswamy (2001) who combined “correspondence” with interviews to seek international expert opinion on CSn. Research tools used to solve CSn problems Specific research tools used among the sample were examined (in respect of 16 classifications), to look in more detail at the mechanisms by which research problems were resolved and the “popularity” of these. These classifications and respective usage were ordered by rank based on the number of times each was observed among the sample (see column 1, Table III). The number of “usage observations” (column 3, total ¼ 113) is greater than the sample (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 regression analysis; while Chau et al. (2003) combined Likert scale/importance ranking with multi-attribute utility theory. Often, the combining of several methods in this way underpins a triangulated CMR methodology (Love, et al., 2002a; Edwards and Holt, 2010). Here, system interrogation was defined as any method of inquiry that observed qualitative data and/or observation of existing systems, and was the favoured research tool (40 observations). Examples include Holt et al.’s (1995b) review of UK selection practice; a cross-sectional review of CSn methodologies embedded in the literature (Holt, 1998); and the study of, “[. . .] current literature and actual experience refined through a review of public-sector owners’ selection procedures” by Al-Reshaid and Kartam (2005). ˙ Forms of rank order analysis were second-favourite tool. Zavadskas and Vilutiene (2006) presented values of qualitative performance criteria obtained from a survey, and in part, rank analysed these using a quadrant analysis matrix. Marzouk (2008) applied a superiority and inferiority ranking model to CSn, while several other researches demonstrated 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 personal opinion (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 earlier identified popularity of questionnaires. Other tools used include: muilti-attribute analysis (Assaf and Jannadi, 1994) and multi-attribute utility theory (Holt et al., 1994d, 1995a) (especially among “earlier” studies in the field), along with several interpretations of fuzzy theory (Singh and Tiong, 2005). Artificial neural networks (Lam et al., 2000), mutivariate discriminant analysis (Wong and Holt, 2003), analytical hierarchy process (Abdelrahman et al., 2008) and cluster analysis (Chinyio et al., 1998) were all observed four times each. Slightly lesser use (three observations) was noted for regression analysis (Tam and Harris, 1996), variance analysis (Hatush and Skitmore, 1997), correlation analysis (Zavadskas and ˙ Vilutiene, 2006) and evidential or case-based reasoning (Juan, 2009). Contractor selection innovation 313 CI 10,3 314 Rank by use 40 11 10 6 6 5 4 4 4 4 3 3 2 2 2 1 ( £ 7 instances)c 113 35.4 9.7 8.8 5.3 5.3 4.4 3.5 3.5 3.5 3.5 2.7 2.7 1.8 1.8 1.8 6.2 100 1 2 3 ¼4 ¼4 6 ¼7 ¼7 ¼7 ¼7 ¼ 11 ¼ 11 ¼ 13 ¼ 13 ¼ 13 16 Table III. Analysis of research tools No. of observationsa Percentage of observationsb Example publications Holt (1998) and Al-Reshaid and Kartam (2005) Assaf et al. (1996) and Wong et al. (2001) Wong et al. (2000) and Chau et al. (2003) Assaf and Jannadi (1994) and Holt et al. (1994d) Holt et al. (1995a) and Holt et al. (1994e) ˜ Bendana et al. (2008) and Juan et al. (2009) Khosrowshahi (1999) and Lam et al. (2000) Tam and Harris (1996) and Ng et al. (1999) Mustafa and Ryan (1990) and Mahdi et al. (2002) Holt (1997) and Chinyio et al. (1998) Wong (2004) and Aziz (2008) Hatush and Skitmore (1997) and Wong et al. (2003) ˙ Zavadskas and Vilutiene (2006) Juan (2009) Ng and Tang (2008) Darvish et al. (2008) and Ip et al. (2004) Research tool System interrogation Other rank analysis Likert/importance ranks MAA MAUT Fuzzy theory ANN MDA AHP Cluster analysis Regression analysis Variance analysis Corelation analysis Evidential/CB reasoning Hypothesis testing Other Totals c Notes: aNumber of observations differs from sample size due to some papers utilising more than one research tool; bminor error in total due to rounding; other, one observed instance each of analytical network process, principal component analysis, factor analysis, combined matrix determination, genetic neural network, graph theory and matrix methods, branch and bound algorithm; key: MAA, multi-attribute analysis; MAUT, multi-attribute utility theory; ANN, artificial neural network; MDA, multivariate discriminant analysis; AHP, analytical hierarchy process; CB, case-based (reasoning) Seven papers used “novel” tools in this setting, viz.: analytic network process (Cheng and Li, 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), and branch and bound algorithm (Ip et al., 2004). Products of research Figure 2 shows that the research products of the sample may be broadly grouped into four categories, the largest of which (63 per cent) comprised CSn models of one form or another. Of products presented, 17 per cent derived, or proffered, processes (derived representing or conceptualising an existing process; proffered suggesting an improvement or variation of existing process). Suggested groupings of selection criteria for given CSn scenarios and/or developed importance metrics attached to these, evolved as a result of 15 per cent of the studies undertaken; 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 the sample (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 and Byfield, 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 and Kumaraswamy, 2005; El-Sawalhi et al., 2007); Contractor selection innovation 315 60 59 50 40 30 20 16 10 0 Model Process Criteria 14 4 Misc. No. Percent Figure 2. Broad classifications of research products CI 10,3 . . 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). 316 The remainder had specific intended application such as for contractor “rating” (Albino and Garavelli, 1998), contractor “ranking” (Darvish et al., 2008), or “assessing competitiveness” (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 better understanding of: practices in the public sector (Palaneeswaran and Kumaraswamy, 2000a); geographically specific CSn tradition (Kumaraswamy, 1996); procurement specific synthesis (Gransberg and Molenaar, 2003); and contrast between CSn stakeholders (Egemen and Mohamed, 2005). Equal diversity is evident in criteria products, including guidance on prequalification criteria (Hatush and Skitmore, 1997); with respect to cost-benefit (Ng and Skitmore, 2001); and reporting practitioner preference (Singh and Tiong, 2006). Discussion Figure 3 shows graphically the above findings of the review and may be cross-referenced with the following discussion. CSn importance The significance of selecting the most appropriate contractor for a given selection setting is of exceptional importance, largely, because of the positive “association” between employing a good contractor and achieving project aspirations. (Or its antipode the “association” between employing a poor contractor and likelihood of Interrelationships Observed research foci: 1. Selection models 2. Assessment criteria 3. Survey of practice 4. Decision support 5. Contractor performance 6. Opinion survey 7. Case study 8. Other Observed research justification: 1. Importance of selection decision 2. Difficulties of selection decision 3. Other, selection setting specific challenges Observed methodological design: 1. Deterministic models 2. Literature and documentary synthesis 3. Postal surveys 4. Statistical enquiry 5. Interviews Observed research tools: 1. System interrogation 2. Rank analysis 3. Likert / importance data 4. MAA 5. MAUT 6. Fuzzy theory 7. ANN 8. MDA 9. AHP 10. Cluster analysis 11. others… Observed research products Future research products? Models C.1990 Increasing complexity C.2010 Interrelationships Existing: mapping, formalisation, understanding Process Proffered: improvements, variations on existing Relevance to selection setting or scenario Importance:ranks, indices to selection setting Closer dialogue with industry and practitioners? More use of face-to-face research? Greater end-user impact and takeup? Web based technologies – transitional interface between academic products and industry? Criteria Figure 3. Model of CSn research Misc. Knowledge advancement in specific CSn contexts unsatisfactory project outturn). Such interrelationship, between an event that (normally) occurs at the outset of a project and one representing its conclusion, demonstrates CSn’s relevance in the overall context of construction “product” procurement – and helps explain why “importance” is a significant research justifier in this field. Uniqueness of the CSn environment The uniqueness of each construction product is matched by the (resulting) individuality of its CSn decisional parameters (such as CSn criteria to be applied). Accordingly, appropriate CSn criteria and their degrees of importance will fluctuate; both as a function of “objective” factors related to the project (such as time, quality, and value targets), 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 contractors operate and within which selection decisions are made – impacted for example – by inconstant macroeconomic, political, and commercial ambience. These characteristics, intuitively, explain the focus of much research observed in this study being the CSn process itself, and equally, why “importance” of that process is an oft-cited principal research justifier. Research foci and justifiers The uniqueness of construction “product procurement” however, demonstrates why some research has focussed on (and been justified by), distinctive CSn settings – set apart for example, by type of project, form of contractual arrangement, geographical characteristics, or some other peculiarity. This also helps illuminate the decision by several researchers, to study selection criteria for given selection scenarios. To seek for example, their applicability and concomitant importance levels which equally – even for the same criteria – will differ between these varying selection environments. A caveat here however, relates to the general applicability of any CSn research outputs, across differing selection settings and the “dynamic state” referred to above. Methodological considerations The methodological approaches observed lean toward deterministic modelling, reflecting the leading desire to model the selection process per se. That is, to determine optimal output (contractor identification) given numerous multivariate decisional input parameters. The predominance of literature and other documentary analysis 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” appear under-utilised in this branch of the field also. This is somewhat surprising in that given a desire to produce optimum selection models – that by definition are intended for practitioner 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. Contractor selection innovation 317 CI 10,3 318 Favoured research tool was system interrogation of different kinds (35 per cent of research tool observation on the sample), relating to the above recurring research focus of surveying existing practice. The use of rank analysis (10 per cent of observations) relates mainly to those studies that investigated and subsequently positioned in various ways, selection criteria. Predominant remaining research tools were statistical or algorithmic in character, being applied principally to said modelling aspirations and other forms of interrogating data, accrued by way of the 25 per cent of studies employing questionnaire and interview surveys. General observations One general observation is that earlier studies tended toward more “simpler” modelling techniques; predominantly of an additive multi-attribute nature, and sometimes embracing utility theory. Later models have employed “newer” research tools such as artificial neural networks and fuzzy theory. A few recent studies meanwhile, have demonstrated use of tools such as “Combined Matrix Determination” and “Graph Theory 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” – for instance, “ready-to-use” computer package or web-based form (Ng et al., 2003). That is, algorithmic solutions alone require some form of transitional interface by which 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, an environment exists that, “[. . .] mandates procedures which make the implementation of superior 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 “impact value” 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 the present study. Other research products of the sample include formulation of existing, and proffering of proposed CSn systems (18 per cent of observations), along with knowledge of selection criteria (15 per cent). If the dialogue with practitioners mentioned above is performed, it would be valuable to also assess end-user adoption of these suggested “cocktails” of criteria, to identify degree of linkage between what academia suggests should impact contractor assessment decisions, and to what extent practitioners are actually aware of, or employ such knowledge in practice. Conclusion Much academic research has targeted CSn over the last two decades. The focus of this has, predominantly, been the decisional processes involved in CSn and decisional criteria applied thereto, with principal research “drivers” often cited as being to optimally model that complex decisional process, linked to the importance of the decisional task. Nonetheless, where equally complex statistical or algorithmic CSn “solutions” evolve from that research (as often observed among the sample), then paradoxically, these solutions 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 are applied. That is, while a set of criteria might be optimal in one setting; will this be the case in a different setting, or a similar setting at a different point in time? With the benefit of hindsight (i.e. since CSn research began in earnest) there is justification in asking whether such “recommended” criteria groupings can make the transition; from their academic evolvement, to sustained and reliable application in practice. Only dialogue with end-users could determine the answer. Statistical and/or deterministic modelling was the most prominent methodological approach 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 above comments regarding model complexity and relevance, similarly, apply here too. Specific research tools employed within CSn methodological research frameworks are equally varied. System interrogation – primarily as a function of qualitative data analysis – has frequently been used. This, predominantly subjective, interpretive tool may be welcomed by those within the broader field of CMR that espouse a shift from (more mechanistic) survey-based interrogative approaches; while simultaneously encouraging discourse between researchers and practitioners. Accrual of Likert scale data via questionnaire surveys and analysis of (often resulting) interval data using rank analysis methods, were also popular, but might be viewed as more convenient to the researcher than relevant to the research problem, in light of the above comments. Given said complexity of some evolving CSn models and an arguably under-utilisation of dialogue-based research methods in this subject domain, a call is made for future CSn research 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 research output can actually demonstrate real end-user impact? Accordingly, the answers here would better inform CSn research design if industry “impact” is the intention of research. (2) Identifying exemplars and/or barriers associated with (1). Such practical knowledge could then be fed back to the CSn research community, informing methodological designs with a view to maximising research value. (3) Helping to steer future CSn research direction – in the context of maximising its end-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 a natural 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 contractor design for example, are not overlooked. 2. Asserted partly on anecdotal evidence from the author’s own network of construction professionals. 3. Many research theses have addressed the CSn issue but are not considered here, because much of that research ultimately filters down into published papers. 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