AI challenges conventional knowledge management: light the way for reframing SECI model and Ba theory

Abstract

Purpose: Nonaka’s SECI (Socialization-Externalization-Combination-Internalization) model and Ba theory have been dominant frameworks in knowledge management (KM) for decades, but less attention is given to their revolutionary changes in the era of human-intelligence interaction. Thus, this study aims to explore the profound impact of artificial intelligence (AI) on conventional SECI model and Ba theory. Design/methodology/approach: This study integrates systematic literature review (LDA) and abductive reasoning as research design to analyze the existing literature (12,075 results from Web of Science Core Collection) to find research gap and potential clues for proceeding our study and future research direction. Findings: This study reconstructs and reinterprets the AI-based SECI model and AI-enabled Ba. Specifically, it reimagines knowledge forms and functions, establishing a new paradigm for the AI-based SECI model through the dimensions of socialization, externalization, combination and internalization. Additionally, it examines knowledge-driven pathways via perceptual, cognitive and behavioral intelligence. It further develops AI-enabled Ba to conduct an in-depth analysis of knowledge sharing and creation, aligning these processes with an updated Ba framework. Notably, it replaces the traditional Dialoguing Ba with Interpretation Ba and the Systemizing Ba with Decision-making Ba. It introduces the concept of “AI-based knowledge force” and proposes a method for measuring its influence in the rising knowledge spiral. It also conceptualizes the basis and nature of human-intelligence symbiosis, emphasizing the shift from a human-centric to a human-intelligence relationship. The theory of affordances is employed to explore the relational dynamics in terms of the existence, perception, actualization and effects of affordances. Meanwhile, the doctrine of the mean is used to illuminate the nature of the relationship across technological and content dimensions. Practical implications: The findings inspire managers and decision-makers to adopt various AI-based strategies to accelerate knowledge transformation, thereby enhancing the overall AI-based knowledge force in human decision-making. These strategies can help rationally manage and innovate knowledge to boost knowledge reserves, as well as promote the development of AI technologies related to knowledge creation. Originality/value: This study leverages AI tool to reconstruct the conventional SECI model and Ba theory by establishing the AI-based SECI model and AI-enabled Ba, revealing the complete knowledge conversion process and its underlying mechanisms. It broadens the application of the theory of affordances and the doctrine of the mean in the knowledge creation literature, highlighting the relational basis and nature of human-intelligence symbiosis among humans, AI tools and the knowledge environment. As a result, our findings emphasize the need for synergistic collaboration between artificial agents and humans in KM.