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Article
Publication date: 25 July 2024

Antonio La Sala, Ryan Fuller, Laura Riolli and Valerio Temperini

The aim of this research is twofold: first, to get more insights on digital maturity to face the emerging 4.0 augmented scenario by identifying artificial intelligence (AI…

Abstract

Purpose

The aim of this research is twofold: first, to get more insights on digital maturity to face the emerging 4.0 augmented scenario by identifying artificial intelligence (AI) competencies for becoming hybrid employees and leaders; and second, to investigate digital maturity, training and development support and HR satisfaction with the organization as valuable predictors of AI competency enhancement.

Design/methodology/approach

A survey was conducted on 123 participants coming from different industries and involved in functions dealing with the ramifications of Industry 4.0 technologies. The sample has included predominately small-to-medium organizations. A quantitative analysis based on both exploratory factor analysis and multiple linear regression was used to test the research hypotheses.

Findings

Three main competency clusters emerge as facilitators of AI–human interaction, i.e. leadership, technical and cognitive. The interplay among these clusters gives rise to plastic knowledge, a kind of moldable knowledge possessed by a particular human agent, here called hybrid. Moreover, organizational digital maturity, training and development support and satisfaction with the organization were significant predictors of AI competency enhancement.

Research limitations/implications

The size of the sample, the convenience sampling method and the geographical context of analysis (i.e. California) required prudence in generalizing results.

Originality/value

Hybrids’ plastic knowledge conceptualized and operationalized in the overall quantitative analysis allows them to fill in the knowledge gaps that an AI agent-human interplay may imply, generating alternative solutions and foreseeing possible outcomes.

Details

Journal of Knowledge Management, vol. 28 no. 10
Type: Research Article
ISSN: 1367-3270

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