Abstract
Bidder collusion seriously undermines the fair competition of the construction project market, and effective identification of collusion behaviors is of vital importance to the implementation of proactive regulation and supervision. In this paper, the data of construction project bidders from 2011 to 2018 are selected in Shaanxi Province, China, and a bidder network of construction projects is constructed. The collusion suspicion of bidders is analyzed from the macro-, meso-, and microlevels. The results show that the bidder network has features as small world at macrolevels, and it is easy for bidders to involve in collusion. The network community formed by construction, supervision, and survey and design bidding enterprises is analyzed at the mesolevel, and the collusion of supervision enterprises is found to have the highest suspicion At the microlevel, the characteristic value judgment and community division are adopted to analyze the collusion suspicion, which is divided into high, medium, and low according to the possibility. Through a comparison with the actual data, it is found that the method proposed in this paper can effectively identify the collusion behavior of construction project bidders. This paper proposes red, yellow, and green warning mechanism and formulates hierarchical accurate management preparedness, which can provide some suggestions to help prevent bidders from colluding.
Highlights
Bidding has increasingly become an important way to promote free competition in the construction industry market
We used Python to extract the original data and generated the bidders’ adjacency matrix. e nodes of the network represent the bidders, and the edges of the network indicate that two bidders participate in the bidding together. e number of bidding by the two bidders determines the weight of the edge and constructs an undirected weighted network. 2Python was used to calculate the basic properties of the network, and we constructed the bidder network. e number of network nodes is 5293, the number of edges is 19875, and the network density is 0.001. erefore, the bidder network was relatively sparse
A complex network of bidders in construction projects was established with the application of the theory and method of complex network. e possibility of bidding collusion was judged from the macro, meso, and microlevels by using the characteristic values and community division of the networks
Summary
Bidding has increasingly become an important way to promote free competition in the construction industry market. Governments of all countries attach great importance to the problem of collusion and adopt a series of policies to prevent collusion (e.g., National Research Council 2011, European Commission 2013, and Australian Government Competition Policy Review 2015) [6]. E Construction Industry Development Board (CIDB), the Organization of Economic Co-operation and Development (OECD) developed several guideline, the Competition Commission of South Africa (CCSA), and the World Bank, which provide the best practice standards and codes of conduct for national and international bidding [7]. To improve the transparency of supervision can be another effective measure to reduce corruption. These measures can prevent collusion to a certain extent, collusion is still happening. Long-term collusive bidding has created cartels of construction contractors, and if ignored or undetected for a long time, it will help to establish increasingly organized communities among the winning bidders [8]. erefore, it is Advances in Civil Engineering of great significance to identify colluding groups in tens of thousands of companies based on bidding behaviors to achieve prior supervision
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