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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.

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.

Making the right bid/no-bid decision is critical to the success and development of construction contracting enterprises. Decision makers’ personal characteristics, such as risk perception and propensity, have great impact on bid/no-bid decisions, which is the major concern of this research. The purpose of this paper is to explore the relationship among decision makers’ risk perception, risk propensity, and their bid/no-bid decision making of construction projects, as well as the factors influencing the risk perception and propensity.

In total, four hypotheses were proposed based on an extensive literature review. Experimental questionnaires were distributed to employees working in Chinese construction contracting enterprises with knowledge of construction bidding, and 134 valid questionnaires were obtained. Multivariate statistical analysis through SPSS 19.0 was used to analyze the acquired data.

Data analysis shows that in the context of international construction contracting: risk perception has a negative influence on bid/no-bid decision making; while risk propensity produces a positive influence and the probability and magnitude of potential gain or loss both have significant impacts on risk perception, and the probability plays a more important role.

This research studied the bid/no-bid decision making of construction projects from the new perspectives of risk perception and risk propensity of the decision makers.

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.

Factors affecting bid/no-bid decisions of international projects are more complex than those of domestic projects. The purpose of this paper is to identify and rank decision factors considered by variously sized Chinese international contractors (CICs) and categorize those groups of factors important to experienced practitioners.

The analysis of factors identified by a literature review is conducted based on data derived from questionnaire results received from 119 CIC project and bidding managers. The relative importance of factors is measured by mean value and standard deviation. The discrepancy in rank and importance value perceived by variously sized CICs are explored by ranking disparity analysis, non-parametric test and Spearman’s rank correlation coefficient test. Finally, factor analysis is used to categorize the underlying groups of factors considered by CICs.

In sum, 41 factors are identified through the literature review as having an impact on the bid decision. Significant disparities in ranking and importance are found in several factors, which partially affect the consistency of the ranking of factors perceived between large and small-medium CICs. Ultimately, nine major factors are identified as impacting the bidding decision, with “contractor’s capability” and “country risk of the host country,” being the most important.

The weight of a factor considered in a decision varies across contractors as a function of contractor size. Whether using models or subjective judgment in making decisions, it is beneficial to fully understand the main groups of factors influencing the decision. Vulnerability to country risk emerges as the first criterion accessed in the bid decision.

A comprehensive set of factors is established for CICs, including both general factors common to domestic projects, and international factors unique to international projects. All factors are grouped by inferential analysis from the perspective of contractors, which reveals the underlying mechanism of the bid decision-making process. While the data were collected from CICs, the methodology in exploring factors, along with implications, is determined to be applicable internationally.

Decisions by construction contractors to bid (or not to bid) require the thorough assessment and evaluation of factors relevant to the decision, as well as the quantification of their combined impact, to produce successful bid/no-bid decisions. The purpose of this study is to present a fuzzy fault tree model to assist construction contractors to more efficiently bid for future projects.

The proposed model consist of two stages: first, identification of the factors that affect bidding decision using a questionnaire survey after an extensive literature review, and second, usage of the identified factors to build a fuzzy fault tree model to simulate the bidding decision.

A list of 15 factors that affect bid/no-bid decisions was identified. Analysis of factors revealed that the highest-ranking factors were related to financial aspects of the project. A case study is presented to demonstrate the capabilities of the model, and a fuzzy important analysis is performed on the basic events to demonstrate the differences between three contractors’ bid/no-bid decisions. The results reveal that there is variation between the decisions of each contractor based on their willingness to participate. Besides, the influence of evaluation factors on the final decision for each contractor is different.

The study contributes to the body of knowledge on tendering and bidding practices. The proposed model incorporated the fuzzy set theory, which suits human subjectivity. The proposed methodology overcomes the limitations of previous models as it can, using the linear pool opinion principle, combine and weigh the evaluations of multiple experts. In addition, the model is convenient for situations where historical data are not available.

The purpose of this paper is to identify and rank the factors that affect the bid/no bid decision according to their relative importance from the perspective of the contracting parties operating in the Gaza Strip, Palestine.

The objectives of this research were investigated through a postal questionnaire, which covered a randomly selected sample of 63 contractors, 29 clients and 13 consultants operating in the construction industry in the Gaza strip. The questionnaire was structured based on related literature, the pilot study and actual factors affecting bidders' decisions to bid or not that arise from special conditions in the Gaza Strip. A total of 78 factors that affect the bid/no bid decision were identified. These factors were then ranked according to their relative importance to contracting parties operating in the Gaza Strip, Palestine.

The results illustrate that the financial capability of the contractors, the financial capability of the clients, the financial values of the project, the due date of the payments, the availability of construction raw materials in local markets, and the stability of the construction industry were the most critical factors affecting the bid/no bid decision, as agreed by all respondents.

The paper provides supportive practical solutions for contractors, clients and consultants to enhance and improve bidding decisions. It is recommended that clients and consultants consider the financial capabilities, technical capabilities and staff competencies of the contractors during the awarding stage, and not simply focus on the lowest bid.

This study aims to investigate and ranks the critical factors influencing the bid or no-bid decision and their importance for the indigenous small building contractors within the Tanzanian construction industry.

An interpretivist epistemological design was adopted to extensively manually review and search extant literature on bid or no-bid decision-making criteria. A total of 30 most common bid or no-bid decision-making criteria were identified. These were included in a questionnaire survey data collection instrument. The survey was distributed to 40 small indigenous (local) building contractors in Dar es Salaam, Tanzania. In total, 33 responses were received. Response data was subjected to both descriptive and inferential statistics.

The results show a disparity of ranking of the 30 bid/no-bid criteria factors among the two grades of small contractors, with 11 factors having statistically significant differences (p = < 0.05). Based on the overall sample, the most highly ranked seven factors in ascending order were: availability of capital; financial capacity of the client; project size; profitability; project type; need of work; and current workload. The following were the least ranked: tax liability; the degree of safety; availability of other projects; availability of labor; bidding document price; and uncertainty because of weather conditions. Availability of capital and financial capacity of the client were jointly ranked as the most important by Class VI contractors. In comparison, availability of capital and need of study were rated highly for Class VII contractors.

The sample consisted of indigenous small building contractors in one industry operating in Tanzania only and did not include the perceptions of the foreign contractors based in Dar es Salaam. Future studies are required to expand the current research and investigate this specific aspect further.

The identified “bid/no-bid criteria” information will allow indigenous small building contractors to enhance the efficiency and effectiveness of their bidding decision-making process. Emergent findings will enable said contractors to: better fit into the competitive construction business environment; increase their awareness of existing decision-making practices; and develop appropriate strategies for evaluation of opportunities encountered. Cumulatively, these findings benefit small indigenous building contractors by increasing their understanding of the factors influencing bid decision.

The study represents the first empirical study in Tanzania on the critical factors influencing the bid or no-bid decision among the indigenous small building contractors, which face fierce competition from foreign contractors.

The purpose of this paper is to introduce a quantitative method for assisting contractors to select appropriate projects for bidding by considering multiple attributes and integrating decision group member opinions.

The fuzzy technique for order preference by similarity to ideal solution (TOPSIS) method is used to help contractors make decision on project selection and the linguistic terms are defined for representing the triangular fuzzy numbers for ratings of alternatives and weights of criteria.

The selection of appropriate projects for bidding is a multiple attribute group decision‐making exercise. In a real decision process, there are many uncertainties and ambiguities, and time limitations mean that decision makers cannot always make precise judgments. The numerical example demonstrates that the fuzzy TOPSIS approach can be used to simulate the decision process in project selection, and the results provide contractors with valuable insight into the project selection problem.

Selecting appropriate projects for bidding is to use a contractor's limited resources more efficiently and increase the probability of winning contracts. Therefore, there is a need for a quantitative method to help contractors make better decision on project selection. That leads to the formulation of this paper. The fuzzy TOPSIS method can assist contractors to make better decisions in bidding.

The decision to bid or not to bid for new projects determines contractors’ propensity for business success or failure. The purpose of this paper is to investigate the factors that affect the decision of indigenous construction contractors to bid or not to bid in Nigeria.

Analysis was conducted on data from questionnaires received from 64 engineering management employees of leading construction companies which are members of Nigeria’s Federation of Construction Industry. The study identified 41 significant decision factors often considered by Nigerian indigenous contractors before the bid. Mean item scores were obtained for each of the factors. Principal component analysis was used to point out the most significant decision factors.

Results revealed significant orthogonal relationships between the factors. Only 11 of the 41 factors are statistically significant to influence contractors’ decision to bid or not to bid. Most of the significant items were amongst the least rated items by the participants. The post hoc decision factors include consultant’s interpretation of project specifications, previous relationship between the intending bidder and client, availability of other projects at the time of bidding, technological complexity of the project under consideration and prequalification requirements. Others include the propensity for resource price fluctuation, business capacity of partners, amount of own work vs subcontracted work, required rate of return on investment and difficulty in obtaining finance.

The practical implication of these findings are as follows: the orthogonal relationship between the decision factors implies non-linear relationship between the factors and actual decision to bid or not to bid, and that bid success is often not predictable by bid behaviour; many of the bid decision factors rated highest by indigenous contractors seldom impact the contractors’ actual bid decisions; local and international players can adopt the significant decision factors elicited in this study for managing their structures for inter-organizational partnerships.