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BBN-Based Approach for Identifying the Governance Factors of Megaprojects

In: Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate

Author

Listed:
  • Lan Luo

    (Nanchang University)

  • Fenghao Gu

    (Nanchang University)

  • Yue Yang

    (Nanchang University)

  • Qiushi Bo

    (Nanchang University)

Abstract

The governance factors explored in previous studies are static, sticking to fixed scenes and factors. In the context of the increasingly complex environment of contemporary megaprojects, the results of static research show inadaptability, failing to accurately grasp the key factors of governance. This research proposes an approach to identify the factors affecting the governance of megaprojects in China based on the Bayesian belief network (BBN). Firstly, the governance factors of megaprojects are determined based on the literature review including a total of 33 factors. Secondly, the BBN-based model of megaprojects is constructed in line with 235 samples effectively collected. Finally, further analysis is conducted on the basis of the obtained model, including predictive, diagnostic, sensitivity, and influence chain analysis. The results indicate that cultural development and government decisions should be placed with more attention in the practices of megaprojects. The research contributes to (a) the state of knowledge by exploring the dynamic causal relationship of influencing factors and (b) the state of practice by proposing a megaprojects governance model by combining machine learning and expert advice.

Suggested Citation

  • Lan Luo & Fenghao Gu & Yue Yang & Qiushi Bo, 2022. "BBN-Based Approach for Identifying the Governance Factors of Megaprojects," Lecture Notes in Operations Research, in: Hongling Guo & Dongping Fang & Weisheng Lu & Yi Peng (ed.), Proceedings of the 26th International Symposium on Advancement of Construction Management and Real Estate, pages 760-777, Springer.
    • Handle: RePEc:spr:lnopch:978-981-19-5256-2_60
    DOI: 10.1007/978-981-19-5256-2_60
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