An exploratory configurational analysis of collaborative innovation in megaprojects

Abstract

Abstract Despite an increasing emphasis on collaborative innovation in megaprojects, it is still empirically not known how the combinations of drivers could lead to collaborative innovation. Using a configurational approach, this study investigates the combinations of drivers contributing to collaborative innovation. First, questionnaire surveys were used to collect data from a sample of Chinese megaprojects. Data were then analysed by fuzzy-set qualitative comparative analysis (fsQCA). The findings demonstrate that four equifinal combinations can promote collaborative innovation: (1) learning, responding to project and clients’ requirements, improving efficiency, responding to competition, and responding to external changes; (2) learning, responding to project and clients’ requirements, and improving efficiency; (3) learning, gaining rewards, and responding to competition; (4) responding to project and clients’ requirements, improving efficiency, gaining rewards, and responding to external changes. Overall, this study contributes to collaborative innovation research and provides practical suggestions to better manage and promote collaborative innovation.