Knowledge governance and innovation ambidexterity in the platform context: exploring the role of knowledge transformation
Authors: Zhou Qian , Wang Shuxiang , Wang Liya , Xu Wei 
Published: February 2025

Purpose

Open innovation platform has become an effective field through which enterprises can acquire valuable knowledge for incremental and breakthrough innovation. However, as more entities join the innovation platform, the knowledge activities in the platform ecosystem are now facing higher complexity and vulnerability due to the differences in the knowledge demands as well as conflicting interest claims of participants. The lack of mature governance mechanisms has caused opportunistic behaviors like knowledge infringement, leakage and hiding, which seriously hinder the in-depth knowledge sharing and effective utilization. What’s more, the enthusiasm for collaborative innovation also reduced among multi-subjects. Therefore, the purpose of this study is to improve platform participants’ innovation ambidexterity under the guidance of scientific design of platform knowledge governance mechanisms through improved knowledge transformation processes.

Design/methodology/approach

Therefore, based on knowledge governance theory and knowledge transformation model (SECI, socialization-externalization-combination-internalization), the study explored the influence of relationship and contractual knowledge governance on the innovation ambidexterity of platform participants through the mediation effect of knowledge transformation. To better analyze complex causal relationships among variables and the chain multiple mediation effect, structural equation modeling is used, coupled with bootstrap analysis verification.

Findings

Platform contractual governance and relationship governance can positively influence the innovation ambidexterity of participants through knowledge trading and reuse, as well as through knowledge sharing and creation. The findings not only contribute to optimizing the effectiveness of knowledge activities on digital platforms but also provide empirical evidence and practical insights to support enterprises’ incremental and breakthrough innovation according to their own knowledge bases.

Practical implications

The findings offer valuable insights for providing decision-making guidance not only for platform-leading enterprises but also for individual and enterprise users on effectively using open innovation platforms to conduct knowledge seeking, trading or sharing and knowledge reuse or creation to enlarge the incremental innovation value and to trigger breakthrough innovation value in their product and technology developments.

Social implications

Through diverse knowledge governance mechanisms, platform-leading enterprises do not only act as “economic agents” with private attributes to reduce knowledge asymmetry in the public trading market, diffuse knowledge broadly and mitigate cooperation costs to increase economic value; they also serve as “social actors” for multilateral participants to increase the cohesion of knowledge sharing and creation to provide sustainable knowledge fuel for the higher level of breakthrough innovation. Overall, knowledge arrangement efficiency can be optimized, and breakthrough innovation value can be activated in a well-governed platform, gradually escaping the diminishing marginal benefits of exploitative innovation.

Originality/value

This study has extended the views of the knowledge transformation model under the platform context and proposed dualistic knowledge transformation pathways, named “tacit knowledge socialization” and “explicit knowledge combination,” respectively. Besides, it discovered that under the contractual and relationship knowledge governance mechanisms’ guiding, participants in open innovation platforms may choose different knowledge searching and exchange ways according to their knowledge needs and thus trigger the different knowledge transform process. Then, “tacit knowledge socialization” transformation can show larger positive impact on breakthrough innovation, while “explicit knowledge combination” transformation makes larger impact on incremental innovation.