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How does artificial intelligence impact employees’ engagement in lean organisations?

Abstract

Driven by the digital transformation currently pursued by organisations, artificial intelligence (AI) applications have become more frequent. Nevertheless, its impact on employees’ behaviors and attitudes is still poorly known. As employees’ engagement (EE) is a key element for a successful Lean Production (LP) implementation, there is the need to understand such AI’s implications on EE in this scenario. This paper aims to investigate the impact of AI on EE in lean organisations. We performed a qualitative-empirical approach in which we first interviewed twelve academic experts to grasp the investigated problem. Then, we conducted a multi-case study in manufacturing organisations undergoing a LP implementation to refine such understanding based on the observation of real-world evidence. Identifying commonalities between these stages allowed the formulation of propositions for future theory testing and validation. Findings indicate that AI may positively impact EE dimensions (physical, cognitive, and emotional) in human-centred work environments, such as lean organisations, although not at the same extent. Results also suggest that employees’ psychological conditions (safety, meaningfulness, and availability) are positively affected by the relationship between AI and EE. The demystification of AI’s effect on EE helps practitioners anticipate potential issues that can impair the LP implementation in the Fourth Industrial Revolution era.
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Category

Academic article

Language

English

Author(s)

  • Guilherme Luz Tortorella
  • Daryl Powell
  • Peter Hines
  • Alejandro Mac Cawley Vergara
  • Diego Tlapa-Mendoza
  • Roberto Vassolo

Affiliation

  • SINTEF Manufacturing
  • Ireland
  • University of South-Eastern Norway
  • Autonomous University of Baja California
  • Austral University, Buenos Aires
  • Federal University of Santa Catarina
  • Pontifical Catholic University of Chile
  • University of Melbourne

Year

2024

Published in

International Journal of Production Research

ISSN

0020-7543

View this publication at Norwegian Research Information Repository