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This paper aims to identify and assess the key criteria for selecting green suppliers in the United Arab Emirates (UAE) construction industry.

A total of 20 criteria were identified and shortlisted through an extensive literature review. These criteria were grouped into four categories: technical and commercial bid, company characteristics, environmental and socioeconomic. A questionnaire was then developed and distributed to construction professionals in the UAE. A total of 39 professionals responded to the survey including contractors, consultants, owners and suppliers. The respondents performed pairwise comparisons among the selection criteria. Data was then analyzed using the Expert Choice Software.

The research findings highlighted that the technical and commercial bid category was ranked as the most important with a weight of 0.338, followed by socioeconomic, company characteristics and environmental categories weighing 0.239, 0.225 and 0.199, respectively. The UAE construction professionals also ranked health and safety, material’s quality and tender price as the top three most important criteria when selecting a sustainable supplier.

This research addresses the lack of literature toward green supplier selection in the UAE. In addition, it assists contractors in selecting the appropriate supplier and promotes sustainable practices in the construction industry.

Material suppliers play an important role in the successful delivery of construction projects. Selecting the appropriate supplier is of paramount importance to project success. Several methods can be used to evaluate and select the best-fit suppliers. However, the selection criteria in such methods are primarily based on traditional construction projects rather than sustainable construction projects. Recently, there is an increase in the number of sustainable construction projects in the UAE. Therefore, identifying and assessing the key criteria for selecting green suppliers is needed. This paper fills the gap in literature as to selecting green suppliers in construction projects.

Due to the increasing size and complexity of construction projects, construction engineering and management involves the coordination of many complex and dynamic processes and relies on the analysis of uncertain, imprecise and incomplete information, including subjective and linguistically expressed information. Various modelling and computing techniques have been used by construction researchers and applied to practical construction problems in order to overcome these challenges, including fuzzy hybrid techniques. Fuzzy hybrid techniques combine the human-like reasoning capabilities of fuzzy logic with the capabilities of other techniques, such as optimization, machine learning, multi-criteria decision-making (MCDM) and simulation, to capitalise on their strengths and overcome their limitations. Based on a review of construction literature, this chapter identifies the most common types of fuzzy hybrid techniques applied to construction problems and reviews selected papers in each category of fuzzy hybrid technique to illustrate their capabilities for addressing construction challenges. Finally, this chapter discusses areas for future development of fuzzy hybrid techniques that will increase their capabilities for solving construction-related problems. The contributions of this chapter are threefold: (1) the limitations of some standard techniques for solving construction problems are discussed, as are the ways that fuzzy methods have been hybridized with these techniques in order to address their limitations; (2) a review of existing applications of fuzzy hybrid techniques in construction is provided in order to illustrate the capabilities of these techniques for solving a variety of construction problems and (3) potential improvements in each category of fuzzy hybrid technique in construction are provided, as areas for future research.

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.

The purpose of this study is to investigate project selection in the context of business management with the specific aim of understanding the role of business models in project selection. The logic followed for making decisions and selecting projects significantly influences construction companies’ success.

The research objective is pursued by conducting a multiple-case study. Managers acting in key decision-making roles from eight construction companies are interviewed. A conceptual framework is developed for analysing the interview data and the prevailing project selection practices in construction.

The findings suggest that project selection is not guided by any specific business model, but that the decision-making process is dominated more by short-term factors such as need of work and profitability. Thus, estimation know-how largely determines the kind of projects companies are willing to consider, regardless of their competence to deliver them.

The study produces a hypothesis that ignorance of business models in project selection and their general underutilisation in management have negative effects on performance of the construction industry. More consistent management practice would enable the development of business models and processes, contributing to performance and help companies to distinguish themselves from each other.

As opposed to previous studies that have produced bidding models that emulate the current industry practices, this research analyses the prevailing logic of project selection from a more critical perspective. In addition, the project selection practices of Finnish construction companies have not been investigated previously.

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.

Failures of public–private partnership (PPP) projects are often ascribed to the agency problem, which arise under conditions of inadequate and asymmetric information when a principal (the client) hires an agent (the contractor). This paper aims to identify the causes and effects of moral hazard and adverse selection on PPP construction projects using a synthesis of extant literature (to determine key variables) and analysis of survey questionnaire data collected.

Mean score ranking was used to rank the causes and effects of moral hazard and adverse selection problems in PPP construction projects. One sample t-test was conducted to establish the relative significance of these variables.

Effort dimensions (which are not verifiable), low transfer of risk, lack of accurate information about project conditions, wrong party chosen to execute project and renegotiation of contracts were the most significant causes of moral hazard and adverse selection problems in PPP construction projects. In addition, reduction of competition, high transaction costs, consequences on profitability of project, siphoning of funds and negative implications on enforceability of contract were the most significant effects of moral hazard and adverse selection problems in PPP construction projects.

Application of these findings will help mitigating moral hazard and adverse selection problems occurring when undertaking PPP construction projects.

Research findings provide guidance to construction stakeholders in the PPP sector on the different causes and effects of adverse selection and moral hazard. This pioneering study is the first to conduct an empirical assessment of the causes and effects of moral hazard and adverse selection of PPP construction projects in a developing country.

Contractor selection is critical in green buildings (GBs) since the preferred contractor has the responsibility to achieve construction sustainability as well as relationship sustainability. The developer satisfaction reflecting requirements can boost the cooperative relationship among stakeholders and act as an evaluation scale for the success of GB projects, which needs to be emphasized in the selection process but little involved in the existing research. This study explores improving GB contractor (GBC) selection by integrating developer satisfaction into selection procedures.

A systematic framework of GBC selection including twenty-five criteria from literature review and experts survey is firstly constructed. Both tactical and strategic criteria are further classified into Kano categories (must-be, one-dimensional, and attractive categories) using the fuzzy Kano model (FKM), and weighted by the developer satisfaction index. The model proposed by this study combining FKM and TOPSIS divides the selection process into the filtration phase and selection phase by Kano categories. The proposed model is finally verified through performance comparison among multiple methods in a case.

Selection criteria are measured linearly and nonlinearly, showing criteria having nonlinear satisfaction change accounts for two-thirds of all. Criteria at tactical level tend to be must-be or one-dimensional categories for the developer, and most strategic criteria are classed as the attractive category, indicating that adding strategic criteria is necessary for long-term cooperation. The proposed model, using developer satisfaction to improve the selection process, ensures the selected GBC to be the most satisfactory with requirements of the developer and makes the performance of GBCs easily distinguishable.

This study contributes to the existing body of knowledge for promoting relationship sustainability by supplementing an integrated model with emphasis on developer satisfaction in GBC selection, so as to establish a good initial foundation due to the match between performances of GBCs and needs of developers. It not only helps maximize developer satisfaction in GBC selection by applying satisfaction to pre-construction management, but also instructs GBCs to prioritize performance improvements. The framework is also conducive for developers to classify selection criteria and select other participants (like green suppliers) from the satisfaction perspective in GBs.

The purpose of this paper is to propose a subcontractor selection model to fully consider the impact of construction enterprise demands on subcontractor selection. The objectives are to understand the translating process of specific enterprise demands to the evaluating criteria and the weight calculation process.

A three-stage model of subcontractor selection was designed based on quality function deployment (QFD), analytic hierarchy process (AHP) and improved grey correlation analysis (IGCA). In the proposed model, specific enterprise demands are translated by the QFD method, and the weights of the criteria are determined by the IGCA. The AHP is used to quantify the exporters' experience and construct the judgment matrix, which is used as inputting of the grey correlation analysis.

The proposed model provides a feasible process for subcontractor selection by fully considering the actual requirements of the project. By combining the company requirements and project requirement to put forward the requirements of the target subcontractor, the selection process ensures that the selected subcontractor and the project have a higher “fit”

Few researches on construction subcontractor selection have taken into account the “voice” of the company's stakeholders. Therefore, this paper designs a three-stage construction subcontractor selection model by introducing QFD to achieve the transmission of “voice” in the subcontractor selection process, so as to take full consideration of the project objectives and the needs of the company's stakeholders. Meanwhile, in order to decrease the subjective of weight calculation, this paper designs an AHP-IGCA allocation method to determine the weight of relevant indicators. By integrating the proposed weight calculation method and QFD method, the subcontractor selection results become more reasonable and objective.

The construction industry has long been criticized for unethical conduct. The owner usually manages the contractor's opportunistic behaviors by employing a professional supervisor, but there is a risk of covert collusion between the supervisor and contractor. Based on the principal–agent theory and collusion theory, this paper aims to investigate optimal collusion-proof incentive contracts.

This paper presents a game-theoretic framework comprising an owner, supervisor and contractor, who interact and pursue maximized self-profits. Built upon the fixed-price incentive contract, cost-reimbursement contract, and revenue-sharing contract, different collusion-proof incentive contracts are investigated. A real project case is used to validate the developed model and derived results.

This paper shows that the presence of unethical collusion undermines the owner's interests. Especially, the possibility of agent collusion may induce the owner to abandon extracting quality information from the supervisor. Furthermore, information asymmetry significantly affects the construction contract selection, and the application conditions for different incentive contracts are provided.

This study still has some limitations that deserve further exploration. First, this study explores contractor–supervisor collusion but ignores the possibility of the supervisor abusing authority to extort the contractor. Second, to focus on collusion, this paper ignores the supervision costs. What's the optimal supervision effort that the owner should induce the supervisor to exert? Finally, this paper assumes that the colluders involved always keep their promises. However, what if the colluders may break their promises?

Several collusion-proof incentive contracts are explored in a project management setting. The proposed incentive contracts can provide the project owner with effective and practical tools to inhibit covert collusion in construction management and thus safeguard construction project quality.

This study expands the organization collusion theory to the field of construction management and investigates the optimal collusion-proof incentive contracts. In addition, this study is the first to investigate the effects of information asymmetry on contract selection.

The construction industry has a significant function in improving the quality of life in the urban environment; meanwhile, greening the supply chain is becoming a seriously pressing issue in the construction industry. This paper seeks to select green suppliers in construction projects implemented at Iranian state universities via multicriteria decision-making (MCDM) models through rough set theory.

A mixed methodology design was conducted through a literature review of studies concerned with green suppliers to identify the related criteria and the rough MCDM techniques. As such, 15 criteria were finalized through content validity ratio (introduced by Lawshe, 1975). The weights of the criteria were calculated through the rough AHP and the suppliers were prioritized using the rough TOPSIS to contribute to the Construction Department.

The study proposed a hierarchical structure of the decision process for green supplier selection in construction projects. According to the weighting results, environmental awareness, green social responsibility and the environmental management system were the most important criteria.

Because the scope of the study was limited to state universities and the methods worked according to the experts' views, the results should be generalized with more caution. The validity of the results should be examined by applying the model to similar contexts.

The study conceptualizes green supplier selection in construction projects at state universities. Furthermore, the method used makes it possible to deal with the uncertainty arising from experts' limited awareness of only part of the problem rather than the whole system under investigation.