Mostrar el registro sencillo del ítem

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  
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  
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  
dc.subject.classification
Economía y Negocios  
dc.subject.classification
CIENCIAS SOCIALES  
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  
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  
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