Generative AI and Knowledge Creation: Comparing Proposals for Revising the SECI Model

Abstract

The use of Generative Artificial Intelligence (GenAI) is deeply impacting all Knowledge Management (KM) processes. In particular, due to its ability to generate new content in different forms, the new technology is deemed capable of deeply transforming the knowledge creation process, which is considered the highest and most impactful stage of KM processes. Despite this, a comprehensive understanding of how companies can leverage GenAI to create organizational knowledge is lacking, both empirically and theoretically. Regarding the latter, scholars have recently underlined that prior research has yet to focus on the transformation of the SECI model and Ba theory, the most widely used conceptual frameworks for interpreting the organizational knowledge creation process, in the era of human-intelligence interaction. However, in the last two years, some studies have examined whether the SECI model needs to be revised in light of GenAI. Based on a review of the 19 systematically identified articles on Scopus, the present paper identifies, discusses, and compares the three different conceptual approaches adopted by scholars in dealing with the topic in question: a) applying the SECI model in its original version; b) adapting the original SECI model with small adjustments; c) developing a new SECI-based model. The paper compares the three approaches, highlighting how they assume different notions of the role of GenAI in the knowledge creation process and the types of knowledge involved. The academic and practical implications that arise from the study are discussed in the conclusions.