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Research on the Causal Chain of Vertical Collusion in Bidding for Government Investment Projects

Year: 2025
Type: article
Abstract: Effective control of collusive behavior in the government bidding process is a prerequisite for promoting the high-quality development of social economy, consolidating the results of the anti-corruption struggle and the smooth operation of the project. Based on risk theory and Bayesian networks, this paper uses GeNIE 4.0 software to construct a Bayesian network structure model to analyze the causal chain of vertical collusion in government project tendering processes. The study found three key causal chain paths: ① external supervision subject to collusion gains, external environment, competitive pressure, and lack of compliance awareness of the formation of “external supervision subject → response subject → vertical collusion” regulatory failure of vertical collusion path; ② rights subject to collusion gains, the external environment, and the lack of publicity of policies and regulations. External supervision is influenced by collusion gains, the external environment, competitive pressure, and compliance awareness of the formation of the “rights subject → external supervision subject → response subject → vertical collusion” internal and external collusion path; the rights of the subject, affected by the collusion gains, the external environment, and the lack of publicity of policies and regulations, form a collusion path of uncontrolled rights. It is also found that, at the subject level, the subject of rights and the subject of external supervision are in a key position in the control process; at the factor level, the control of collusion proceeds, the publicity of policies and regulations, and the external environment can significantly enhance the control effect of vertical collusion and provide a precise focus for the collusion chain to break the chain.
Source: Systems
Authors: Dai Yao, Yun Chen, Chongsen Ma
Language: English
Open Access status: gold
Estimated APC paid: $2,896
OpenAlex ID: W4414326303
FWCI:
Cites: 23
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