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The bid/no-bid decision is critical to the success of construction contractors. The factors affecting the bid/no-bid decision are either qualitative or quantitative. Previous studies on modeling the bidding decision have not extensively focused on distinguishing qualitative and quantitative factors. Thus, the purpose of this paper is to improve the bidding decision in construction projects by developing tools that consider both qualitative and quantitative factors affecting the bidding decision.

This study proposes a mixed qualitative-quantitative approach to deal with both qualitative and quantitative factors. The mixed qualitative-quantitative approach is developed by combining a rule-based expert system and fuzzy-based expert system. The rule-based expert system is used to evaluate the project based on qualitative factors and the fuzzy expert system is used to evaluate the project based on the quantitative factors in order to reach the comprehensive bid/no-bid decision.

Three real bidding projects are used to investigate the applicability and functionality of the proposed mixed approach and are tested with experts of a construction company in Alberta, Canada. The results demonstrate that the mixed approach provides a more reliable, accurate and practical tool that can assist decision-makers involved in the bid/no-bid decision.

This study contributes theoretically to the body of knowledge by (1) proposing a novel approach capable of modeling all types of factors (either qualitative or quantitative) affecting the bidding decision, and (2) providing means to acquire, store and reuse expert knowledge. Practical contribution of this paper is to provide decision-makers with a comprehensive model that mimics the decision-making process and stores experts' knowledge in the form of rules. Therefore, the model reduces the administrative burden on the decision-makers, saves time and effort and reduces bias and human errors during the bidding process.

The purpose of this paper is to explore the information that manufacturing companies have available when competitively bidding for service contracts.

A semi-structured interview study was undertaken with industrialists in various sectors, which are currently facing the issue of servitisation.

One of the main findings was that, despite the novelty of the process, the decision makers at the competitive bidding stage have an understanding of the involved uncertainties. In particular, the uncertainty arising from the customer as the user of the product and evaluator of the competitive bids in addition to the uncertainty connected to the competitors were identified as the main influences on the pricing decision.

The research implications show the influences and considerations during the decision-making process at the competitive bidding stage for service contracts. These include the customer and the competitors.

Shortcomings in the current industrial practice were identified such as the approaches used to communicate the cost estimate for the service contract. The approaches currently used contradict research findings in the area of communicating uncertainty information, which means that further research is to be done to identify optimal approaches to displaying the uncertainty connected to the communicated information.

This paper offers a basis for research to understand the challenges industry faces when competitively bidding for service contracts. This can be used to develop novel approaches in supporting the decision maker such as a model that presents the probability of winning in comparison to the probability of making a profit.

Discusses how a repetitive competitive bidding model, developed previously, can be adapted to the differing features of the situation. Focuses, first, on identifying the implications for decision analysis of the strategic nature of repetitive bidding and then on the impact of data to aid in winning a tender. Uses a real example from the construction industry which, for confidentiality reasons, is called Whernside Ltd. States the company is one operating a world‐wide spread of construction and development activities such as; civil engineering, private housing, property development, building, dredging, mining, mechanical engineering, foundation engineering and concrete products manufacture. Tabulates the company's turnover and profit in detail, and demonstrates that if bidding activity is to be continued, the formulation of a long‐term strategy must aim to set a perspective in the prescription of individual bids, so the long‐term accrued benefits are, in some sense, optimal. Documents the tactical bidding decision structure using figures by aid of explanation and goes into great detail by way of pinpointing the bid process. Concludes that from the discussion conclusions may be drawn and the three main ones paragraphed and spelt out with recommendations.

Bid/no-bid decision is a significant and strategic decision, which must be finalized at an early stage of the bidding process. Such decision-making may have significant impact on the performance of the contractors. Using Chi-square Automatic Interaction Detector (CHAID) and Classification and Regression (CRT) decision tree algorithms, this paper aims to develop bid/no-bid models for design-bid-build projects for contractors.

The models in this study have been developed using CHAID and CRT algorithms. Thirty-four bid/no-bid key factors were collected via extensive research. The bid/no-bid factors were listed based on their importance index as a result of a questionnaire distributed among the construction professionals. These factors were divided into five main risk categories – owner, project, bidding situation, contract and contractor – which were taken as inputs for the models. Split-sample validation was applied for testing and measuring the accuracy of the CHAID and CRT models. Moreover, Spearman's rank correlation and Analysis of Variance (ANOVA) tests were employed to identify the statistical features of the received 169 responses.

The key bid/no-bid factors in construction industry were categorized in five related groups and ranked based on the relative importance index. It was found that the top 6 ranked bid/no-bid factors were (1) current workload, (2) need for work, (3) previous experience with employer; (4) timely payment by the employer; (5) availability of other projects for bidding (6) reputation of employer in the industry. Matrix comparison between all bid/no-bid groups was performed using Spearman's correlation to measure the relationship between each of the two paired groups. It was concluded that all the relationships were positive.

Existing bidding models require many inputs and advanced understanding of mathematics and software to run the model. Contractors tend to use easy, fast and available support methods. Excluding a great number of the bid/no-bid factors may affect the final decision. This paper proposes a bid/no-bid decision tree models for contractors of different sizes. It is the first study in the literature, to the best of authors' knowledge, to study bid/no-bid decision with the proposed decision tree algorithm. The proposed models in this study overcome the shortfalls of most previous models such as avoiding the complexity and difficulties of applying the concept. The proposed model will provide the contractors with a bid/no-bid decision based on the input for the defined bid factor groups. The proposed models display the soft spots and hot spots between the independent and dependent variables, which leads to a better decision. The proposed models display the result effectively in visual terms, easy to understand and easy to apply. The proposed models are a form of multiple effect (or variable) analysis which allows the companies to explain, describe, predict or classify an outcome.

This study aims to examine the influences of shifting the bidding system of Thai public infrastructure projects from e-auction to e-bidding.

A questionnaire survey was conducted with owners or senior managers with direct responsibility in deciding the mark-up of 72 small and medium-sized contractor firms. Five senior professionals were interviewed to provide insights into and to strengthen the discussion of the findings. The Wilcoxon test was applied to analyze the difference in the importance of the factors between e-auction and e-bidding.

The results revealed a shift in the importance of the factors, from those related to the financial aspects in the e-auction to the situational aspects in the e-bidding. The comparison test results also suggested that the majority of factors become significantly less important in the e-bidding system, with “identity of competitors” and “general expense of the bidding process” having the most apparent mean differences. The interview results supported by data on winning prices and estimations strongly indicated that bid collusions likely exist in the e-auction. By shifting to e-bidding, the data also show that the Thai Government can save public money in its infrastructure project development.

The study provides an analysis from the perspectives of contractor firms on how e-auction and e-bidding options influence bid mark-up decisions. Many studies have focused on the issues and advantages provided by the e-procurement mainly from the owner (government)’s perspective but how the change influences the contractor’s attitude has been less explored.

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.

The purpose of this study is to investigate and rank the critical factors influencing the bid/no bid criteria and their importance in the Australian construction industry.

The research study has been undertaken by conducting an extensive literature review on bid/no bid decision-making criteria. As a result, the researchers identified 26 most common bid/no bid decision-making criteria that are accordingly grouped into five distinct categories, namely, “project”, “market”,“contractor”, “client” and “contract”. The literature review was followed by a national survey that was designed and utilised by the researchers to collect data for this purpose. The survey was sent to potential 450 Australian construction companies in various locations and responses were received from 81 Australian construction companies. Response data were subjected to descriptive and inferential statistics. Kruskal Wallis one-way analysis of variance (ANOVA) was applied to detect significant differences between the mean score grouped according to the organisation size (contract value).

The descriptive and empirical analysis demonstrated a disparity of ranking of the 26 bid/no bid criteria factors among the groups; however no statistically significant differences among the 26 bid/no criteria factors despite the absolute differences in the rankings and mean scores in the following four factors: (1) “bidding condition”, (2) “strength/weaknesses”, (3) “contract payment terms” and (4) “number of competitors/bidders”. Based on the overall sample, the highly ranked four factors were “client financial capability”, “project risk”, “project future benefits and profitability” and “number of competitors/bidders”. The following were the least ranked: “contractors’ financial situation”, “project duration” and “contractors’ material availability”. “Client financial capability”and “project risk” were jointly ranked as the most important by large, whereas “client financial capability” was also rated highly for smaller Australian construction contractors (ACCs). The medium ACCs had “project risk”as highly ranked.

The majority of the participants were small construction contractors in Australia. The reason is that the researchers were not aware of the contractors’ size prior to inviting them for participation in the research study. Second, the findings may not generalise to other industries or to organisations operating in other countries.

The identified “bid/no bid criteria” increase the awareness of existing decision-making practices and play a critical role in the future decisions of the construction companies, where decision makers need to evaluate the next opportunities encountered. Furthermore, knowledge and possession of these identified “bid/no bid” criteria would enable contractors to select a project with a higher probability of success in the future, which will accordingly result in long-term financial benefits and higher performance. Finally, the awareness of these factors could contribute to changing the contractor’s behaviours when bidding in a competitive environment or market conditions.

The study contributes to the body of knowledge on tendering and bidding practices among contractors in Australia, an area previously under explored. Second, this study provides some insights on the factors influencing the bid/no bid decisions among the ACCs.

This article examines how bidding decisions are being tackled in practice. It appears very likely that in the future, these decisions will have to be made on a sounder, more structured, quantitative basis. The article suggests a formal, yet mathematically unsophisticated way of handling the bidding problem and considers how such an approach can help the decision maker. Such an approach is a valuable addition to the experience, judgment, intuition and common sense of the bidding strategist.

The “multi‐criteria selection” has become popular practice in selecting contractors, especially for public works, which involve multiple stakeholders. In line with this development, contractors need to consider more factors apart from offering a lower bidding price in formulating a bidding strategy. The purpose of this paper is to introduce a quantitative method, namely the fuzzy competence requirement (FCR) model, for assisting a contractor to formulate a better bidding strategy by better utilising its competence for meeting the best multiple criteria imposed by clients.

The fuzzy linear programming with multiple objectives technique is applied in this paper and an illustrative example is introduced to demonstrate the application of the proposed model.

The FCR model can assist in generating a range of bidding strategies by assuming different confidence levels that contractors may perceive. These strategies could be valuable references for contractors to develop their competitive bidding strategy, which enables contractors to efficiently utilise their competence to meet clients' multiple selection criteria.

The FCR model can provide contractors with useful information for formulating their bidding strategy. However, other factors, such as the sense of the market and estimation of rivals' bidding strategy, should also be considered in the bidding decision.

The FCR model can help contractors make better decisions in bidding by considering their competence and meeting client's multiple criteria.

This paper introduces a new model – FCR model – for helping contractors improve the efficiency of their bidding decision.

Unbalanced bidding can seriously imposed the government from obtaining the best value for the taxpayers' money in public procurement since it increases the owner's cost and decreases the fairness of the competitive bidding process. How to detect an unbalanced bid is a challenging task faced by theoretical researchers and practical actors. This study aims to develop an identification method of unbalanced bidding in the construction industry.

The identification of unbalanced bidding is considered as a multi-criteria decision-making (MCDM) problem. A data-driven unit price database from the historical bidding document is built to present the reference unit prices as benchmarks. According to the proposed extended TOPSIS method, the data-driven unit price is chosen as the positive ideal solution, and the unit price that has the furthest absolute distance measure as the negative ideal solution. The concept of relative distance is introduced to measure the distances between positive and negative ideal solutions and each bidding unit price. The unbalanced bidding degree is ranked by means of relative distance.

The proposed model can be used for the quantitative evaluation of unbalanced bidding from a decision-making perspective. The identification process is developed according to the decision-making process. The finding shows that the model will support owners to efficiently and effectively identify unbalanced bidding in the bid evaluation stage.

The data-driven reference unit prices improve the accuracy of the benchmark to evaluate the unbalanced bidding. The extended TOPSIS model is applied to identify unbalanced bidding; the owners can undertake objective decision-making to identify and prevent unbalanced bidding at the stage of procurement.