An Evolutionary Game Analysis of Organizational Relational Behavior in Megaprojects Considering the Reciprocal Preference

Chunxi Luo (), Xian Zheng and Chunlin Wu
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Chunxi Luo: Zhongnan University of Economics and Law
Xian Zheng: Zhongnan University of Economics and Law
Chunlin Wu: Beihang University

A chapter in Proceedings of the 27th International Symposium on Advancement of Construction Management and Real Estate, 2023, pp 898-910 from Springer

Abstract: Abstract The relational behavior of participating organizations is vital to improving megaproject performance. Although extant research has focused on promoting relational behavior by identifying various factors, it neglects the mutual influence of participating organizations to make relational behavior adoption decisions, especially considering the heterogeneous reciprocal preferences of distinct organizations. To fill this gap, this study constructs an evolutionary game model between two parties with different reciprocal preferences to analyze the evolutionary stability of each organization’s choice. We discuss and simulate the impact of initial relational behavior adoption willingness and reciprocal preferences of different participating organizations, resulting in both factors having a positive effect on the adoption of relational behavior. We further tested their relational behavior choices under different work scenarios (i.e., complex subproject, innovative subproject, interdependent subproject, and simple subproject) in megaprojects. The results are that the first three scenarios all end up with relational behavior adopted by both sides, while the last scenarios render non-adoption of relational behavior by both sides. This research promotes a better understanding for the dynamic mutual influence of participating organizations in megaprojects and unveils the driving paths to enhance relational behavior adoption from a perspective of organizations’ reciprocal preferences.

Keywords: Megaprojects; Relational behavior; Evolutionary game; Reciprocal preference (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/978-981-99-3626-7_69

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