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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.

Competitively bid construction projects are often completed for amounts greater than the original low‐bid amount. Improved methods of predicting a projects potential to experience large cost overruns would be useful to government agencies for planning capital budgets. The purpose of this paper is to explore the application of treesmaps to the analysis of competitively bid project cost overruns.

In this research a data visualization technique called treemaps was explored as a method of describing the nature of the bids for highway construction projects, and as a method of relating the characteristics of a projects bids to the observed increase in completed project cost. Ratios were calculated that measured bid characteristics for projects from Texas and California. Treemaps were constructed that related the level of the bidding ratios to the level of the difference between the completed project and the original low‐bid amount

It was found that the treemaps indicated that projects with high‐ratio values typically experienced a larger weighted average percentage difference between the low bid and completed project cost than projects with low ratio values.

Future research should include consideration of other project factors in the treemaps, and testing of the treemaps to determine their usefulness to practitioners.

Treemaps present a way of understanding complex relationships between construction bidding data and the cost increases that occur during construction without the need to construct complex mathematical models.

The paper shows that bidding ratios can be used as indicators of a projects potential for cost overruns. The paper presents a new way of visualizing the construction bidding ratio data.

This paper is an empirical analysis of first-price sealed-bid procurement auctions in Sweden, with and without combinatorial bidding. The data comprises procurement auctions of identical contracts (road resurfacing) with identical bidders conducted under the same time period (2009-2011) in two different regions in Sweden. Given the comparison of the suppliersʼ offered price per tons of asphalt, we cannot reject the hypothesis of identical distribution of standalone bids generated in both types of auction. The distribution of package bids within the combinatorial format is significantly lower than the distribution of standalone bids within the non-combinatorial format, suggesting substantial cost reduction of allowing package bidding. Also, within the combinatorial format, our analysis of data indicates higher costs when packages are predetermined by the purchaser rather than chosen freely by the suppliers.

The purpose of the paper is to perform bid mark‐up optimisation through the use of artificial neural networks (ANN) and a metric of the selected bid mark‐up's derived entropy. The scope is to provide an alternative, entropy‐based method for bid mark‐up optimisation that improves on the analytical models of Friedman and Gates.

The proposed method enables the incorporation of bid parameters through the use of ANN's pattern recognition capabilities and the integration of these parameters with a mark‐up selection process that relies on the entropy produced by possible mark‐up values. The entropy metric used is the product of the probability of winning over the bidder's competitors multiplied by the natural logarithm of the inverse of this probability.

The case study results show that the proposed entropy‐based bidding model compares favourably with the prevailing competitive bidding models of Friedman and Gates, resulting in higher optimisation with regards to the number of jobs won, the monetary value of contracts awarded and the value of “money left on the table”. Furthermore, the method allows for the incorporation of several objective and subjective bid parameters, in contrast to Friedman's and Gates's models, which are based solely on the bid mark‐up history of a bidder's competitors.

While the proposed method is a useful tool for the selection of optimal bid mark‐up values, it requires historical data on the bidding behaviour of key competitors, much like the classic bidding models of Friedman and Gates.

The method is suitable for quantifying objective and subjective competitive bidding parameters and for optimising bid mark‐up values.

Construction contract auctions are characterized by (1) a heavy emphasis on the lowest bid as it is that which usually determines the winner of the auction, (2) anticipated high outliers because of the presence of non‐competitive bids, (3) very small samples, and (4) uncertainty of the appropriate underlying density function model of the bids. This paper describes a method for simultaneously identifying outliers and density function by systematically identifying and removing candidate (high) outliers and examining the composite goodness‐of‐fit of the resulting reduced samples with censored normal and lognormal density function. The special importance of the lowest bid value in this context is utilized in the goodness‐of‐fit test by the probability of the lowest bid recorded for each auction as a lowest order statistic. Six different identification strategies are tested empirically by application, both independently and in pooled form, to eight sets of auction data gathered from around the world. The results indicate the most conservative identification strategy to be a multiple of the auction standard deviation assuming a lognormal composite density. Surprisingly, the normal density alternative was the second most conservative solution. The method is also used to evaluate some methods used in practice and to identify potential improvements.

The purpose of this paper is to try to understand the relationship between price and quality in public procurement by using data on the quality of civil engineering works and prices in tenders. By doing so, it tries to conduct a comprehensive examination of the quality of construction works, which has been difficult to recognize and evaluate.

The methodology of this analysis is to collect data on the quality of construction works from the rankings of construction performance evaluations revealed by public procurement authorities. In addition, the prices of bids of the providers in public procurement are also collected from the available data and analyzed. The relationship between the two was statistically analyzed in an objective and verifiable way.

What the study found was that the price of the work measured by the bid rate had a positive and significant relationship with the quality of the work measured by the construction performance rating. Furthermore, the panel data were analyzed using panel data for 51 firms that all appeared in the construction performance evaluation for 12 years and participated in at least three bids in each year, and the first-order term of the bid rate was negative and the second-order term was positive and was 10% significant, which is a U-shaped relationship.

This study is one of the investigations focused on public procurement construction work. One of the limitations of this study is that it is a study of one industry in the Asian region. However, this study focuses on the incentives of business operators. As such, the analysis is applicable to all situations faced by those running businesses.

The managerial implications of this study include the following: although, improving the quality of construction results is costly, competitive firms are also steadily improving their quality as it is a factor of competition. However, there will come a point where their efforts come to a halt at a certain level. It is necessary to operate the business with this situation in mind. In addition to the company's efforts, it is necessary to lobby the government.

As a policy implication for this analysis, it was possible to evaluate that steady improvement in construction results was confirmed, but the correlation between the relaxation of the competition and the increase in the bid rate was correlated with the decrease in the total amount of public works. This highlights the problem of having to think about measures for the efficient use of the. In addition to this, the fact that the competition situation has been eased can be considered as a problem that it will adversely affect the innovation stimulation of operators, and that it will become necessary to pay more attention to competition limiting acts.

The uniqueness of this paper is firstly that unique data representing the quality of public procurement, which had been difficult to analyze, were collected, organized and used in the study. In addition, not only did we find a positive relationship between quality and price but we also found a U-shaped relationship between them using panel data.

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 endowment effect is arguably one of the most robust phenomena documented in economics, behavioral decision theory and consumer research. However, the endowment effect has traditionally been studied as a fairly static phenomenon at the transactional level of analysis.

This paper documents this “contagious” endowment effect using lab experiments and such field data as eBay transactions and discuss the managerial implication of these findings.

This study suggests that the endowment effect is not limited to the level of the specific object, but can manifest itself with the more abstract class of objects to which a specific object happens to belong.

A logical next step would be to examine the boundary conditions – how similar does the subsequent object have to be for the endowment effect to transfer over to it? A related aspect would be whether there are boundary conditions arising from the quality of the endowment.

The effects reported here probably underlie the success of the many types of “bait and switch” schemes that have been used by the more unsavory type of marketer. As such, these findings might have implications for policy in the area of consumer protection.

This paper argues for and presents evidence consistent with the notion that the endowment effect is dynamic and can be transferred from one transaction to another and refer to this generalization of the endowment effect to other, similar products as “contagious endowment.”

Humanitarian logistics plays a critical role in the aid response to hunger and disasters worldwide. The US Department of Agriculture (USDA) uses a competitive bidding process to procure P.L. 480 Title II food aid, a $2 billion business annually. This paper describes a mixed‐integer program (MIP) decision tool that mimics the USDA bid approach in order to improve ocean carrier and food supplier bid pricing strategy.

First, the USDA bid process is detailed and the MIP decision tool is described. Then how the tool is run against historical data to approximate future USDA bid awards is explained, allowing the authors to subsequently advise food supplier and ocean carrier clients of expected price competition and pricing flexibility before they submit bids.

The MIP decision tool has demonstrated its effectiveness in supporting $8 million in food aid bids. Bidding implications for food aid carriers and suppliers are provided as well as suggestions for additional opportunities for humanitarian logistics research are offered.

Extant literature in procurement offers little practical support for bidder pricing preparation. Additionally, humanitarian logistics is an emerging, under‐researched field, and this paper is the first to address sourcing and distribution of Title II food aid.

This study aims to make some contribution to the process of corporate compliance governance in China.

This paper adopts qualitative method, literature research, case analysis and comparative methods to explore the Chinese compliance governance model in the field of collusive bidding crimes.

In the process of criminal prosecution of enterprises suspected of committing crimes, the judicial authorities should promote the restoration of normal production and operation of corporate enterprises by promoting the construction of corporate compliance, which is conducive to solving the difficult problem of attribution of collusive bidding crimes. In addition, corporate compliance under prosecutorial supervision is also conducive to optimizing the regulatory path of collusive bidding and achieving more effective prevention and control of unit crimes in the mode of co-regulation between the state and corporate.

Compliance governance corporate crime is at a nascent stage in China, and this study seeks to provide some reference for future compliance review governance in China through the analysis of specific business crime cases.