Fighting for collusive bidding in the construction industry: A text mining-enabled approach

Abstract

Policy measures are crucial for regulating collusive bidding and are integral to effective governance. However, current research lacks a comparative exploration of strategies to combat collusive bidding through policy. Therefore, this study aims to identify more effective countermeasures by examining policy variations between regions with low and high incidences of collusive bidding. Using Latent Dirichlet Allocation (LDA) topic modeling, the study extracts key themes from these policies, while qualitative analysis highlights differences in approaches. It underscores that integrating electronic and information technology into bidding systems significantly reduces collusive practices. While increasing penalties can deter collusive bidding, achieving desired impacts requires thorough investigation and vigilant oversight. Additionally, strengthening external supervision enhances control over such activities. This study identifies critical governance strategies for addressing collusive bidding and advocates further research into more effective methods within the construction sector.