Abstract
Megaproject management has continuously faced new challenges that require new theories and methods. The network perspective has been an increasing trend in this context. However, there is a lack of consensus concerning the applicability of network thinking to megaprojects and the status of network analytic methods with respect to the facilitation of megaproject management. This paper aims to present the full status of the network perspective in the context of megaproject management, integrate network thinking, clarify the application of network analytic methods, and accelerate the network paradigm in the context of megaproject management. This research included a systematic review using the Preferred Reporting Items for Systematic Review and Meta-Analyses framework. A total of 152 relevant studies were retrieved from three major databases and reviewed. The findings of this study were based on a novel integrated framework that included the constitutive and the analytic perspectives of the network paradigm. The constitutive perspective sheds light on the megaproject network, its definition, characteristics pertaining to network thinking, and megaproject governance through network logic. The analytic perspective illuminates the applicability of network methods to megaproject issues in detailed subknowledge areas, including stakeholder management, risk management, communication, coordination and collaboration, knowledge management, and interface management. Furthermore, these two perspectives were generalized and converged into pure categories of network research, including network mechanisms and antecedents, network patterns and dynamics, network functions and outcomes, and network controls and optimization. Based on meticulous discussions concerning the integrated framework, limitations and future directions for progress in this area were proposed to facilitate a deeper exchange of ideas in the context of any future research efforts.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
Acknowledgments
This material is based in part upon work supported by the National Natural Science Foundation of China under Grant Nos. 71771178, 72072131, and 72001160, the Shanghai Pujiang Program under Grant No. 2020PJC109, and the Fundamental Research Funds for the Central Universities under Grant No. 22120200364. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of any funding agencies. Also, the anonymous reviewers are acknowledged—their combined observations helped improve the paper markedly.
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Volume 148 • Issue 7 • July 2022
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