Understanding innovation diffusion and adoption strategies in megaproject networks through a fuzzy system dynamic model

• 作者: Yan ZHANG His-Hsien WEI Dong ZHAO Yilong HAN Jiayu CHEN
• 来源期刊: 工程管理前沿（英文版）
• 年，卷（期）: 2021,（1）
• 所属期刊栏目: Special issue:City and infrastructure engineering and management
• 页码范围: 32-47
• 页数: 16页
• 语种: 英文

摘要

Innovation and knowledge diffusion in megaprojects is one of the most complicated issues in project management.Compared with conventional projects,megaprojects typically entail large-scale investments,long construction periods,and conflicting stakeholder interests,which result in a distinctive pattern of innovation diffusion.However,traditional investigation of innovation diffusion relies on subjective feedback from experts and frequently neglects inter-organizational knowledge creation,which frequently emerges in megaprojects.Therefore,this study adopted project network theory and modeled innovation diffusion in megaprojects as intra-and inter-organizational learning processes.In addition,system dynamics and fuzzy systems were combined to interpret experts' subject options as quantitative coefficients of the project network model.This integrated model will assist in developing an insightful understanding of the mechanisms of innovation diffusion in megaprojects.Three typical network structures,namely,a traditional megaproject procurement organization (TMO),the environ megaproject organization (EMO),and an integrated megaproject organization (IMO),were examined under six management scenarios to verify the proposed analytic paradigm.Assessment of project network productivity suggested that the projectivity of the TMO was insensitive to technical and administrative innovations,the EMO could achieve substantial improvement from technical innovations,and the IMO trended incompatibly with administrative innovations.Thus,industry practitioners and project managers can design and reform agile project coordination by using the proposed quantitative model to encourage innovation adoption and reduce productivity loss at the start of newly established collaborations.