Exploring the Relationship between Open Innovation and Innovation Performance in Big Data Environment: The Moderating Effects of Innovation Expropriability

Abstract

In the era of big data, the industry environment is complex and volatile, and enterprises need to innovate their business models by creating innovative value networks and reconstructing transaction systems to avoid being eroded in the process of global economic transformation. The traditional closed innovation processes are no longer deemed suitable for advancing firm innovation. Instead, open innovation has become a pivotal strategic choice in the quest to foster innovative developments. This study builds upon the paradox of open innovation (OI) in the age of big data, and introduces the innovation expropriability theory. Utilizing survey data from high-tech industrial park firms in the Yangtze River Delta region of China, it empirically examines the influence of open innovation on firm innovation performance (FIP). Furthermore, it explores the independent and joint moderating effects of rival absorptive capacity (RAC) and appropriability regimes (ARs). The findings reveal that OI significantly improves FIP. The effect of OI on FIP is positively moderated by ARs, while the independent moderating effect of RAC is not significant. However, a joint moderating effect is observed between RAC and ARs. Further investigation reveals that the moderating role of RAC depends on the presence of ARs, indicating a matching relationship between RAC and ARs. This research holds significant implications for firms in implementing open innovation strategies and ensuring effective intellectual property protection to enhance innovation performance in the era of big data.