Big Data-Driven Firm-User Interactive Innovation:Theoretical Framework and Cutting-Edge Topics
Big data-driven firm-user interactive innovation has become an important mode for firms to obtain market insight and user dynamics,and improve innovation agility and adaptability.Meanwhile,the key factors and mechanisms of such innovation mode have increasingly become hot topics in fields such as innovation,marketing,and information system.It combined systematic literature review design and keyword co-occurrence analysis to summarize research insights on firm-user interactive innovation in the context of big data.Aiming at the two research gaps of how context and tools affect the relationship between lead users and ordinary users,as well as the interaction mechanism between the two,a theoretical framework of"condition-path-performance"for big data-driven firm-user interactive innovation was proposed.Based on the framework,combined with literature and case evidence,it focused on the context and tool conditions in which firms interact with lead users and ordinary users to influence innovative products and big data cooperation assets,thus forming theoretical innovation.The analysis results indicate that the essence of interactive innovation path selection is the dynamic balance between innovation novelty-universality,as well as data analysis cost-benefit,under heterogeneous contexts and tool conditions.The dynamic changes in the two types of innovation paths are triggered by changes in the maturity of interactive innovation tools and innovation leadership.Finally,based on the research findings,three cutting-edge topics in the theoretical research of big data-driven firm-user interactive innovation were further refined,and their theoretical and practical significance was discussed.
product innovationbig data drivenfirm-user interactive innovationbig data-based cooperative asset
NETL
NSTL
万方数据
维普
