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The understanding of how to mitigate opportunism in construction projects is still limited and conflicting. The complexity of causalities and interdependence among antecedents of opportunism (transaction characteristics and governance mechanisms) is the major obstacle to current research. This study takes a holistic perspective to explore the different combinations of conditions that lead to high opportunism and low opportunism in project management.

Through 2 phases of the interview and questionnaire survey, the 91 valid survey data were collected from the buyer–seller relationships in construction projects and analyzed by adopting fuzzy-set qualitative comparative analysis.

A single transaction characteristic is rarely sufficient to explain opportunism, and combinations of different transaction characteristics and governance mechanisms (performance ambiguity, asset specificity, buyer's requirement certainty, informal control, and formal control) have different effects on opportunism. In the case of extremely unsatisfactory transaction characteristics, even the combination of formal and informal control cannot prevent high opportunism. The combination including low-formal control and high-asset specificity easily leads to high opportunism. Besides, performance ambiguity is a vital factor in mitigating high opportunism or achieving low opportunism.

Previous studies have always addressed the role of one or some factors independently and separately. This study is one of the first to explore the different combinations of conditions that result in high opportunism and low opportunism in project management based on transaction costs economics and agency theory.

Community detection of dynamic networks provides more effective information than static network community detection in the real world. The mainstream method for community detection in dynamic networks is evolutionary clustering, which uses temporal smoothness of community structures to connect snapshots of networks in adjacent time intervals. However, the error accumulation issues limit the effectiveness of evolutionary clustering. While the multi-objective evolutionary approach can solve the issue of fixed settings of the two objective function weight parameters in the evolutionary clustering framework, the traditional multi-objective evolutionary approach lacks self-adaptability.

This paper proposes a community detection algorithm that integrates evolutionary clustering and decomposition-based multi-objective optimization methods. In this approach, a benchmark correction procedure is added to the evolutionary clustering framework to prevent the division results from drifting.

Experimental results demonstrate the superior accuracy of this method compared to similar algorithms in both real and synthetic dynamic datasets.

To enhance the clustering results, adaptive variances and crossover probabilities are designed based on the relative change amounts of the subproblems decomposed by MOEA/D (A Multiobjective Optimization Evolutionary Algorithm based on Decomposition) to dynamically adjust the focus of different evolutionary stages.