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The ‘lowest‐price wins’ philosophy has been a consistent theme of contractor selection over the years. To comprehensively elucidate this selection preference and compare it with the use of a multi‐criteria selection (MCS) approach in the tenderer evaluation process, this paper investigates MCS tender price selection preferences. That is, project‐specific criteria (PSC) and lowest‐price wins selection practices of UK construction clients, in both building and civil engineering works at in detail via results of the empirical survey. The investigation provides further insight into the evaluation of contractors' attributes (i.e. PSC). Levels of importance assigned (LIA) for each criterion were analysed (i.e. quantitative analysis of the differences in opinions and, variance amongst the respondents) in a multivariate statistical method. Importance attached by construction clients to the ‘lowest‐price wins’ philosophy is also presented. Contrast was made between the MCS approach and the ‘lowest‐price wins’ option amongst the surveyed construction clients. It was found that increased awareness of the use of PSC prevailed amongst the survey construction clients. This indicated that cost has to be tempered with the evaluation of PSC and the attempt of construction clients searching for a new evaluation paradigm (i.e. adoption of MCS approach rather than basing on the lowest‐price wins alone).

This paper seeks to introduce an evolved hybrid genetic algorithm and neural network (GNN) model. The model is developed to predict contractor performance given the current attributes in a process to pre‐qualify the most appropriate contractor. The predicted performance is used to pre‐qualify the contractors.

Hypothetical and real‐life case studies from projects executed in the Gaza Strip and West Bank were collected through structured questionnaires. The evaluation of the contractor's attributes and the corresponding actual performance of the contractor in terms of time, cost, and quality overrun (OR) were collected. The weighted contractor's attributes were used as inputs to the GNN model. The corresponding time, cost, and quality ORs for the same cases were fed as outputs to the GNN model in a supervised learning back propagation neural network (NN). (The adopted training and testing process to develop a trained model is presented.) The training process, including choosing the topology of the required NN using genetic algorithms, is explained.

The results revealed that there is a satisfactory relationship between the contractor attributes and the corresponding performance in terms of contractor's deviation from the client objectives. The accuracy of the model in terms of mean absolute percentage error (MAPE), R², average absolute error and mean square error revealed that the model has sufficient accuracy for implementation. The average MAPE for time, cost and quality OR is 15 per cent. Consequently, the model accuracy is 85 per cent.

The GNN model is able to predict future contractor performance for given attributes.

This paper describes a multiple‐criteria decision support system (MCDSS) for the selection of the most appropriate contractor. The system can accommodate the unique characteristics of a project in addition to the qualifications and capabilities of those contractors assessed. The system first evaluates the list of contractors by matching their qualifications with specific project conditions. A short list of eligible contractors is thus selected and further the MCDSS compares the current capabilities of the short listed contractors and their plans for the project under consideration, to select the most appropriate contractor. The Delphi method was used to evoke expertise and obtain reliable assessment values for all criteria related to the contractor qualification, while the analytic hierarchy process (AHP) was used to assess the specific project conditions. The system can be easily modified to adopt specific conditions of the proposed project and also to facilitate the decision maker in explaining the reasons for the elimination of excluded contractor.

This paper aims to propose a new model for project selection using the combined approach of analytic hierarchy process (AHP) and linear programming (LP). The key selection criteria for construction projects are also identified and assessed based on the perception of construction professionals.

Fifteen project selection criteria are identified based on review of related literature. A questionnaire is developed and distributed among construction professionals in the United Arab Emirates to assess the importance of these criteria. The questionnaire was designed to enable pair-wise comparisons using the AHP methodology. Based on the responses of 42 professionals, the weight of each criterion was calculated using Expert Choice software. Selected criteria are then used to compare available projects. AHP is applied first to prioritize the competing projects, in line with owner strategic goals and objectives. Next, the priority ratios (weights) obtained from the AHP model are used as the coefficients of the decision variables in a LP model to allocate the available budget in an optimal way to maximize the owner’s benefit.

Results indicate that project-specific criteria are the most important with an overall weight of 0.48. The top three criteria are profit, financial standing and risk with corresponding weights of 0.161, 0.114 and 0.1, respectively. The proposed model provided more benefit to the owner than using the traditional project selection method.

There are several methods for project selection. However, the optimization techniques do not consider qualitative factors. Other techniques consider both qualitative and quantitative factors but do not consider budget limitations to optimize the selection decision. The model proposed here combines both approaches and takes into consideration unique criteria that are specific to the construction industry.

A quantitative contractor selection technique which embraces the pre‐qualification, evaluation and final selection processes is being developed. The emphasis is on evaluating contractors' performance potential in terms of their ability to achieve time, cost and quality standards. This approach is in contrast to the majority of current selection techniques which tend to prequalify, then discriminate predominantly on the cost component of tenders. The conceptual model is applied to a hypothetical but realistic scenario of a contractor competing for a small industrial contract. This illustrates the mechanics of the new technique, emphasizing that contractor selection should include identifying the contractor with the best performance potential and not merely the lowest bidder.

The authors argue that public project managers do not consider the iron triangle (cost, quality and schedule) primary important in measuring the success of their projects. To investigate which success criteria are important to public project managers, the authors interviewed 26 Dutch project managers who are employed by the government and who are responsible for managing infrastructural projects. The paper aims to discuss these issues.

In this research the Q-methodology is applied. Q-methodology helps to find for correlations between subjects across a sample of variables. Q-factor analysis reduces the individual viewpoints down to a few factors. A factor can be seen as the mathematical representation of an “average” perspective shared by a group of people.

Findings are based on the individual rankings of 19 success criteria; the authors distinguished three common perspectives: the holistic and cooperative leader, the socially engaged, ambiguous manager and the executor of a top-down assignment. In none of the perspectives the iron triangle criteria formed the top three to measure project success.

The research results may have a national character. The way project success is perceived by public project managers may be culture dependent. For this the authors expand the research to other countries in the near future.

This paper contributes to the understanding of the public project manager by their private collaboration partners, like consultants, engineers and contractors. This will help them to understand their client and contribute to better collaboration in projects.

This paper shows that the difference in work attitude and value frame in the public sector leads to a specific view on project success.

A Hong Kong‐based study of factors affecting construction industry performance levels confirms that selection methodologies and decisions are critical, both at the upstream formulation of procurement and operational systems themselves as well as the downstream selection of various project participants. Needs are identified for a holistic approach to broader procurement choices that incorporate all procurement sub‐systems, an integration with synergistic operational sub‐systems and more informed and intelligent participant choices. The many interacting variables and potential information overload point to the consequential need for harnessing IT/IS (information technology/information systems) to enable smarter selections at all stages of the construction supply chain. Frameworks and sample modules of the proposed knowledge‐based decision aids are presented in the case of overall procurement system selection and contractor selection.

This paper sets out to empirically investigate whether a “project champion” can have an undue influence at the project selection stage and, if so, how this can be both identified and controlled.

The research methodology is based on a single case study and is part of a much wider research investigation.

The case clearly shows that a “project champion” can have a biased influence on project selection. It was shown that in this particular case the “project champion” deemed the project‐specific risks to be lower than that suggested by other appraisal team members and that the strategic benefits to be derived from the project to be higher. Both these influences make the project look more attractive and can result in a project being accepted, which may not be in the best interests of the organisation. By using the financial appraisal profile (FAP) model the organisation was able to identify this bias and reduce its influence.

Generalisability is an issue with case studies. It is believed that this paper not only provides empirical evidence confirming what may have intuitively been suspected, but also provides a solution to the problem.

By empirically identifying the adverse influence a project champion may have at the project selection stage will allow practitioners to take account of this bias.

This paper presents a model and case study that shows how project champions play a role and how this role may be used in evaluation of projects. Project champion roles in this environment have been only rarely studied.

Today’s growing numbers of contractor selection methodologies reflect the increasing awareness of the construction industry for improving its procurement process and performance. This paper investigates contractor classification methods that link clients’ selection aspirations and contractor performance. Multivariate techniques were used to study the intrinsic link between clients’ selection preferences, i.e. project‐specific criteria (PSC) and their respective levels of importance assigned (LIA), during tender evaluation for modelling contractor classification models in a data set of 68 case studies of UK construction projects. The logistic regression (LR) and multivariate discriminant analysis (MDA) were used. Results revealed that both techniques produced a good prediction on contractor performance and indicated that suitability of the equipment, past performance in cost and time on similar projects, contractor relationship with local authority, and contractor reputation/image are the most predominant PSC in the LR and MDA models among the 34 PSC. Suggests contractor classification models using multivariate techniques could be developed further.

This paper presents results of an empirical investigation involving private construction clients in Malaysia, which seeks to establish appropriate tender evaluation criteria and weightings for a risk-oriented tender evaluation system.

At the initial stage of this study, a list of significant risks is identified and gathered through literature review. These risks are then mapped onto tender evaluation criteria. Following this, the identified risks and their mappings are validated through a questionnaire survey to determine appropriate criteria for tender evaluation. Weightings for the selected evaluation criteria are established through an analytic hierarchy process (AHP) group decision-making (GDM) method.

In practice, different lists of criteria, covering tender's technical capability and financial performance, are often used by different client organisations. However, there is a paucity of research behind the selection of these criteria and the weighting being attributed to different criteria.

This study provides an important and a valuable insight into the actual criteria used during tender evaluation practice based on an analysis of documentary evidence. Both current practice and existing tender evaluation studies failed to address the risk element adequately. There is a lack of an explicit link between evaluation criteria and project risks. This study fills this knowledge gap by identifying tender evaluation criteria through reviewing criteria used in practice and examining their links to risk factors.