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The normative literature suggests that there are more than seventy decision factors that contractors must consider if they intend to success in their bid and projects. In addition, such factors have been grouped in relation to project characteristics, client attributes, contractors' business administration and external factors. The extant literature suggests that the relationships between many of the bid decision factors are orthogonal and may not explain how bid-decisions shape project outcomes.

This knowledge gap has stagnated research in this area. Building on findings of recent studies, this study unbundles bid decision themes by analysing key factors amongst contractors' administrative characteristics. In addition, the study determines intrinsic predictive correlation between the sub-themes. A total of 17 variables were listed in a structured questionnaire survey and presented to participants recruited through purposive snowballing. In total, 50 responses were received and formed the basis of descriptive and inferential statistical analyses.

The study found that the 17 factors are significant in bid decisions and consequently in bid successes. However, the explanatory variables attached to the themes are most efficient if categorised into six sub-themes. These are contractor's administration depth, strategic direction, commercial intention and own market advantage, resources, experience as well as openness to technology imposed by new projects. Furthermore, four theoretical positions are significant. Contractors bid for projects strategically; they are less reliant on their resource capabilities in bid decisions; every bid decision aligns with a risk strategy, and financial stability enables them to establish appropriate risk mitigation arrangements that could ensure project success.

These findings provide some support for the conceptual premise that bid decisions need to be rational, considered and strategically contribute to project outcomes.

Contractors of different scales, operating in different construction industries of varying institutional and economic settings, have different considerations when making bid or no-bid and mark-up decisions. Focusing on the large and medium-sized contractors in the Jilin province, China, the purpose of this study is to examine important factors affecting their decision to bid (d2b) and mark-up decisions and investigate differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

This study used a survey design for timely data collection from a large population. Contractors’ bidding attitudes was collected using an online survey questionnaire with a list of 40 key factors. Statistical analytical methods were applied for comparing the two groups of contractors.

The results of this study indicate that factors related to client conditions are most critical for both large and medium-sized contractors in their d2b and mark-up decisions. The results also show statistically significant differences between the two groups of contractors on a subset of factors affecting their d2b and mark-up decisions. The large contractors have placed more emphasis on projects' potential financial and strategic benefits. Another notable finding is that both groups of contractors have placed great emphasise on “government legislations” in their d2b and mark-up decisions.

These findings should be interpreted in consideration of several limitations. Firstly, the sample size is relatively small, and the focus was on a single province in the China construction industry. Next, this study only explores differences between large and medium-sized contractors in evaluating the importance of the various factors affecting their d2b and mark-up decisions.

Contractors could refer list of critical factors in competing for jobs in Jilin province or other provinces of similar institutional and economic settings. Construction clients, on the other hand, should consider the list of critical factors in the formulation of their competitive tendering procedures, thus enhancing the efficiency in their procurement of construction services.

Research on contractors’ bidding decision-making in the context of Chinese construction industry remains scarce; the research findings have implications for the industry stakeholders.

This study aims to investigate clients' attributes, their key decision variables and causal relationships between the decision variables. In addition, the purpose of the study is to map-out from these analyses, the attributes of project clients that motivate contractors' bid decision.

A total of 50 responses were obtained from a questionnaire survey. 50% of participants are contractors. 44% are claims consultants, whilst 6% are manufacturers and clients. Beyond measures of central tendencies, analysis focussed on causal relationships by way of correlation, analysis of variance and reductionism.

All 20 factors considered have significant correlations with at least one other factor. Findings also show the factors can be clustered into six: reputation, financial strength, relationship with the bidder, organisational attributes, history with project attributes and project organisation.

Evidence suggests stakeholders have often struggled to consider the many decision factors reported in normative literature, numbering hundreds. By clustering the factors into sub-themes, bid decisioning has been made more efficient. The study also explains how client attributes could determine project success and contractor participation. Different stakeholders can use findings of this study for training and further studies.

Previous studies have considered bid decisioning indexically – factors were long, analyses were largely inconclusive, and causal relationships are orthogonal. Findings in this study have shown depth: 20 originating client-specific factors were clustered into six sub-themes, and correlations were established. The methodology used for the study is confirmatory and conclusive.

With the aim to provide a global view of factors affecting mark-up size on construction projects, this study performs a meta-analytical review of the relevant studies over the past 20 years.

The analytical process involved the identification and evaluation of the importance of critical factors affecting mark-up size on construction projects, and the assessment of the generalisability of findings of the meta-analysis. A random-effects model was adopted in the statistical meta-analysis.

The results show that there are 23 critical factors, and the top five factors are: (1) competitiveness of other bidders; (2) number of bidders; (3) relationship and past experience with client; (4) experience on similar project; and (5) project size. A heterogeneity test further shows that there is no statistically significant heterogeneity across the studies, reinforcing the generalisability of the findings to a global context.

The list of critical factors from a global perspective should form a good basis for future efforts in bidding model development.

The research findings have practical implications to both construction clients and contractors in formulating their contracting practices and strategies.

This is the first meta-analysis of a sizeable collection of replicated studies on factors affecting mark-up size on construction projects in the literature.

Prefabricated components sustainable supplier (PCSS) selection is critical to the success of prefabricated projects. However, limited studies have addressed the uncertainty and complexities during the selection process, particularly in multi-criterion group decision-making (MCGDM) circumstances. Hence, the research aims to develop a group decision-making model using a modified fuzzy MCGDM approach for PCSS selection under uncertain situation.

The proposed study develops a framework for sorting decisions in PCSS selection by using the hesitant fuzzy technique for order preference by similarity to ideal solution (HF-TOPSIS) method. The maximum consistency (MC) model is used to calculate the weights of decision makers (DMs) based on the cardinality and sequence of decision data.

The proposed framework has been successfully applied and illustrated in the case example of CB01 contract section in Hong Kong-Zhuhai-Macao Bridge (HZMB) megaproject. The results show various complicated decision-making scenarios can be addressed through the proposed approach. The MC model is able to calculate the weights of DMs based on the cardinality and sequence of decision data.

The research contributes to improving accuracy and reliability decision-making processes for PCSS selection, especially under hesitant and fuzzy situations in prefabricated megaprojects.

In industrial buyer–supplier relationships, being an attractive customer has been found to result in superior supplier performance. However, there is a limited understanding of how these benefits transfer to the public domain. This study aims to explore the influence of customer attractiveness on supplier resource mobilization efforts toward the public sector.

A qualitative approach was used, focusing on in-depth interviews with 23 informants from 3 critical and complex supplier markets. The data were processed using inductive coding and thematic analysis.

The findings indicate that customer attractiveness in the public sector influences suppliers’ mobilization efforts on several dimensions. In addition to stimulating competition in the tender phase, customer attractiveness can yield important benefits to quality, supply stability and innovation during the business relationship. It appears imperative for the public sector to improve its standings with suppliers to both mitigate the apparent risk of sub-par treatment and to unlock the preferential supplier treatment associated with being an attractive customer.

Receiving increased mobilization from suppliers will result in better use of public money and help improve resilience and innovation in public procurement.

This study extends the research on customer attractiveness in the public sector by being the first to explore the range and nature of its influence on supplier mobilization efforts.

This study aims to build on social capital theory (SCT) and its dimensions by examining the role of social capital in the public procurement process and by identifying related contingencies that may influence procurement performance.

A systematic literature review and a thematic analysis regarding social capital in procurement are conducted. The antecedent–behaviour–consequence (ABC) model is used for illuminating linkages between social capital, contingencies and procurement performance.

The dimensions of social capital are investigated in the procurement process; however, the extent of social capital role can vary between the phases of the process. It is concluded that the contingencies of social dynamics are linked with social capital and may influence the outcomes and performance of the procurement process.

Social capital can ease interactions between public buyers and private suppliers by contributing to effective tendering, improving social interaction in negotiations and balancing rigidity in contract management, supporting the interests of both parties. The provided framework helps decision makers to comprehend the social dynamics in public procurement.

Improving social dynamics and solutions in public procurement.

This study extends social capital research in the field of public procurement and creates a framework connecting social capital and prevailing contingency factors to procurement process performance.

Insufficient time allocation for the bidding period occurs, causing drawbacks to both parties, the client and the bidder. Hence, this study aims to evaluate the time allocated for preparing a bid proposal as per the National Competitive Bidding (NCB) in the Sri Lankan context.

The study has adopted a mixed method approach and expert interviews and document review to detect, analyse and validate the issues, and solutions based on NCB along with the adequacy of the allocated bidding period used as main data collection tools. Both qualitative and quantitative data were analysed through manual content analysis and inferential analysis respectively.

Overall, 24 local issues with the existing competitive bidding process and solutions for each were identified. Among the 24 local issues, it was unanimously agreed by all interviewees that three specific issues require attention and improvement. These issues are related to the standard and incompleteness of bidding documents, inaccurate BOQ quantities measured by the consultant or the main contractor, and the excessive number of bidding document amendments by the consultant. It was revealed that a maximum of 42 calendar days (6 weeks) is sufficient for the bidding process while a minimum of 21 calendar days (3 weeks) is insufficient.

The findings of this study would be recommended that Information and Communication Technology Agency (ICTA) understand the necessity of revising the NCB reference to the time allocated for the preparation of bids. By recognising the importance of sufficient time allocation for bid preparation, this research serves as a practical guide for authorities involved in policy formulation, aiding them in implementing revisions that align with the dynamic requirements of bidding procedures.

This study aims to examine the reaction of anchor investors (AIs) to pre-IPO earnings management (EM). The authors use the unique detailed bid data from the Indian anchor experiment. The authors also study the reputed AIs’ EM detection ability and pricing behavior in response to pre-IPO EM.

The authors use unique AI bid data for 169 Indian IPO firms. Utilizing the logistic regression and Tobit regression models with industry and year-fixed effects, the authors examine the relationship between various measures of AI participation and proxies of short-term and long-term discretionary accruals.

The authors document that pre-IPO EM is positively associated with the likelihood of anchor backing but negatively related to the likelihood of reputed anchor backing. The findings indicate that AIs are misled by pre-IPO EM, but reputed AIs are not. The authors also observe that reputed AIs, compared to the non-reputed, pay less than the upper band with increasing EM. The findings are robust to using various AI measures and EM proxies.

The findings have significant implications for regulators in the implementation of AI concept in non-anchor markets and better implementation of policies in existing anchor settings. Findings can also be relevant for non-institutional investors in the IPO domain.

This is one of the few studies on institutional investors' IPO bidding behavior in response to pre-IPO EM. However, this is the first study to analyze AIs' IPO bidding behavior in response to pre-IPO EM.

Various approaches have emerged to assist practitioners in making more informed decisions in highway construction projects. However, industry practitioners are still using subjective ways to make decisions. Also, researchers have developed tools and techniques with similar objectives. Lack of information on what has been developed might lead to those issues. Therefore, this paper aims to review trends of evolution, pinpoint strengths and gaps in the literature and identifies potential future directions for decision-making research in highway construction projects.

A systematic review was conducted on published articles on decision-making in highway construction projects using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique.

The analysis of 101 articles revealed that existing decision-making research in highway construction projects targets improvements in four areas: feasibility, conceptual, detailed scope and detailed design. The four areas consist of sixteen subthemes that are detailed in this study. In addition, most research involved developing decision support tools and systems as well as decision-making models, techniques and frameworks. Lastly, several research areas have emerged, such as adding more decision criteria including those with uncertainties, expanding existing decision-making models into decision support systems, benchmarking decision criteria between different sample populations and exploring inter-and intra-relationships between decision criteria.

This paper provides an overview of existing research on decision-making in highway construction projects. Also, it reveals research gaps in the body of knowledge to point out directions for future research. Finally, industry practitioners can use the findings to develop strategies for effective decision-making processes.