Framing SECI model through an artificial knowledge generation perspective: a case study analysis in the GenAI era

Abstract

Purpose This study aims to examine the influence of generative artificial intelligence (GenAI) on the phases of knowledge creation as delineated by Nonaka’s SECI model. Furthermore, it delineates the fundamental interactions among the components of a theoretical framework for the artificial knowledge generation through a continuous interaction between human and artificial dimensions. Design/methodology/approach The authors used an exploratory single-case study to reach their aim. Data were primarily collected through semi-structured interviews and triangulated via direct observation and document analysis within a company operating in the cybersecurity sector. An inductive coding tree was derived from qualitative data analysis performed by using content analysis methodologies. Findings Data indicates that GenAI significantly influences the SECI phases of knowledge creation. In addition, the influence of this disruptive technology on knowledge generation cannot be adequately described by relying exclusively on the original SECI model, highlighting the need for its extension in the context of artificial knowledge generation. Research limitations/implications The study identifies developing AI-driven mechanisms that appear to introduce novel knowledge conversion dynamics absent from the original model. These findings resulted in the definition of a novel framework that offers a more thorough comprehension of human−machine collaboration in knowledge management. Originality/value The originality of this study lies in the systematic analysis of GenAI’s influence on the SECI model and the subsequent development of a novel preliminary and empirically informed theoretical framework that extends the SECI model by incorporating a machine dimension into knowledge generation processes.