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dc.contributor.author
Tortorella, Guilherme Luz
dc.contributor.author
Powell, Daryl
dc.contributor.author
Hines, Peter
dc.contributor.author
Mac Cawley Vergara, Alejandro
dc.contributor.author
Tlapa Mendoza, Diego
dc.contributor.author
Vassolo, Roberto Santiago
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dc.date.available
2024-07-11T10:09:30Z
dc.date.issued
2024-06
dc.identifier.citation
Tortorella, Guilherme Luz; Powell, Daryl; Hines, Peter; Mac Cawley Vergara, Alejandro; Tlapa Mendoza, Diego; et al.; How does artificial intelligence impact employees’ engagement in lean organisations?; Taylor & Francis Ltd; International Journal Of Production Research; 6-2024; 1-17
dc.identifier.issn
0020-7543
dc.identifier.uri
http://hdl.handle.net/11336/239578
dc.description.abstract
Driven by the digital transformation currently pursued by organizations, 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 organizations. 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 organizations 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-centered work environments, such as lean organizations, 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. As digital transformation evolves, organizations undergoing a LP implementation must learn how to cope with the integration of AI into their processes and benefit from it without undermining the principles and behaviors that commonly drive a lean organization.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Taylor & Francis Ltd
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dc.rights
info:eu-repo/semantics/openAccess
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
Artificial intelligence
dc.subject
Industry 4.0
dc.subject
Lean production
dc.subject
Employees’ engagement
dc.subject.classification
Otras Economía y Negocios
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dc.subject.classification
Economía y Negocios
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dc.subject.classification
CIENCIAS SOCIALES
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dc.title
How does artificial intelligence impact employees’ engagement in lean organisations?
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-07-10T12:46:46Z
dc.journal.pagination
1-17
dc.journal.pais
Reino Unido
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dc.journal.ciudad
Londres
dc.description.fil
Fil: Tortorella, Guilherme Luz. University of Melbourne; Australia
dc.description.fil
Fil: Powell, Daryl. Norwegian University of Science and Technology; Noruega
dc.description.fil
Fil: Hines, Peter. South East Technological
University; Irlanda
dc.description.fil
Fil: Mac Cawley Vergara, Alejandro. Pontificia Universidad Católica de Chile; Chile
dc.description.fil
Fil: Tlapa Mendoza, Diego. Universidad Autonoma de Baja California (universidad Baja California);
dc.description.fil
Fil: Vassolo, Roberto Santiago. Universidad Austral. Instituto de Altos Estudios; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina
dc.journal.title
International Journal Of Production Research
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dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.tandfonline.com/doi/full/10.1080/00207543.2024.2368698
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/http://dx.doi.org/10.1080/00207543.2024.2368698
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