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This study aims to construct a “contractual–relational–governmental” 3D governance framework for new infrastructure projects (NIPs) within China’s distinct institutional context. The primary objective is to explore the impact of multiple governance mechanisms on the NIP performance, thus identifying the key governance mechanisms and proposing targeted performance improvement strategies.

The research design follows a sequential mixed methodology of integrating qualitative and quantitative data collection and analysis. Firstly, project governance and performance indicators were collected from relevant literature and expert interviews. Secondly, a questionnaire was developed, and data were collected through on-site and online means. Finally, the partial least square structural equation model (PLS-SEM) was utilized to examine and analyze the relationships between governance mechanisms and NIP performance.

Contractual, relational and governmental governance all have a certain role in promoting the NIP performance. Specifically, contract stringency, trust and governmental decision are the core elements of contractual, relational and governmental governance, respectively, while commitment does not significantly affect NIP performance. Generally, relational and governmental governance exert a more substantial influence compared to contractual governance, with governmental decision and trust being the most effective.

This paper contributes to the field by introducing PLS-SEM as a measurement tool for exploring the impact of multiple governance mechanisms on governance performance in NIPs. The results offer valuable insights for project managers, enabling them to concentrate on core factors while refining and optimizing governance mechanisms and strategies.

Abnormal behaviors such as rework, backlog, changes and claims generated by project organizations are unavoidable in complex projects. When abnormal behaviors emerge, the previously normal state of interactions between organizations will be altered to some extent. However, previous studies have ignored the associations and interactions between organizations in the context of abnormal organizational behaviors (AOBs), making this challenging to cope with AOBs. As a result, the objective of this paper is to explore how to reduce AOBs in complex projects at the organizational level from a network perspective.

To overcome the inherent limitations of a single case study, this research integrated two data collection methods: questionnaire survey and expert scoring method. The questionnaire survey captured the universal data on the influence possibility of AOBs between complex project organizations and the expert scoring method got the influence probability scores of AOBs between organizations in the case. Using these data, four organizational influence network models of AOBs based on a case were developed to demonstrate how to destroy AOBs networks in complex projects using network attack theory (NAT).

First, the findings show that controlling AOBs generated by key organizations preferentially and improving the ability of key organizations can weaken AOBs network, enabling more effective coping strategies. Second, the owners, government, material suppliers and designers are identified as key organizations across all four influence networks of AOBs. Third, change and claim behaviors are more manageable from the organizational level.

Project managers can target specific organizations for intervention, weaken the AOBs network by applying NAT and achieve better project outcomes through coping strategies. Additionally, by taking a network perspective, this research provides a novel approach to comprehending the associations and interactions between organizations in the context of complex projects.

This paper proposes a new approach to investigating AOBs in complex projects by simultaneously examining rework, backlog, change and claim. Leveraging NAT as a novel tool for managing the harmful effects of influence networks, this study extends the knowledge body in the field of organizational behavior (OB) management and complex project management.