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A taxonomy framework and process model to explore AI-enabled workplace inclusion

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  • Lazazzara, Alessandra
  • Za, Stefano
  • Georgiadou, Andri

Abstract

This study develops a taxonomy framework and a process model to explain how artificial intelligence (AI) reshapes workplace inclusion through human resource management (HRM) practices. We analyze 25 empirical studies using a hybrid inductive–deductive method informed by Nickerson et al.’s (2013) taxonomy development framework. The resulting taxonomy classifies AI-enabled HRM practices according to their strategic goals, types of human-AI interaction, inclusion typologies, evaluation methods, and mitigation strategies. We extend this taxonomy with a process model that illustrates how different forms of AI agency – ranging from assisting to automating − shape inclusion outcomes and require differentiated mitigation strategies. Our analysis reveals three interconnected dimensions of AI-enabled workplace inclusion emerge in such contexts: inclusion in work (individual experiences), inclusion at work (organizational climate), and inclusion of work (human-AI interaction). Each dimension demands distinct context-sensitive mitigation strategies depending on the level AI agency involved By linking AI agency to differentiated forms of inclusion and tailored mitigation strategies, this study advances theoretical understanding of AI-enabled inclusion. It also offers actionable guidance for organizations implementing AI in HRM practices while safeguarding workplace inclusion.

Suggested Citation

  • Lazazzara, Alessandra & Za, Stefano & Georgiadou, Andri, 2025. "A taxonomy framework and process model to explore AI-enabled workplace inclusion," Journal of Business Research, Elsevier, vol. 201(C).
    • Handle: RePEc:eee:jbrese:v:201:y:2025:i:c:s014829632500520x
    DOI: 10.1016/j.jbusres.2025.115697
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