The rise of hybrids: plastic knowledge in human–AI interaction

Antonio La Sala · Ryan Patrick Fuller · Laura T. Riolli · Valerio Temperini

21 citations0/0 files storedJournal of Knowledge Management

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AI and HR Technologies Corporate Management and Leadership Impact of AI and Big Data on Business and Society

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.

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2016The future of employment: How susceptible are jobs to computerisation?1986Perceived organizational support.2003The Skill Content of Recent Technological Change: An Empirical Exploration 2020Human Trust in Artificial Intelligence: Review of Empirical Research 2010Developing a capacity for organizational resilience through strategic human resource management 2021Artificial intelligence capability: Conceptualization, measurement calibration, and empirical study on its impact on organizational creativity and firm performance 2019Artificial Intelligence in Human Resources Management: Challenges and a Path Forward 2005What Is Competence?

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