Changes in productivity and labour relations

António Moniz, Marta Candeias and Nuno Boavida
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Nuno Boavida: CICS.NOVA - Interdisciplinary Centre of Social Sciences, New University of Lisbon, NOVA - Universidade Nova de Lisboa = NOVA University Lisbon

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Abstract: New technologies, sustainability policies, protectionism and consumers preferences are pushing for the reorganisation of the automotive cluster. The emergence of artificial intelligence (AI) has the potential to create disruptive effects in the employment systems across the world. The future deployment of broad-spectrum algorithms capable of being used in wide areas of application (e.g., industrial robotics, software and data communication) can lead to considerable changes in current work patterns, swiftly render many unemployed across the globe and profoundly destabilise labour relations. In this paper, we identify the probable penetration of AI in the automotive sector and to study its effects on work organisation, employment, and industrial relations systems, in Portugal. These changes are put in place to enhance the product quality, control costs, and improve productivity. We study these implications on productivity and industrial relations collecting new data and obtain results based on secondary statistical analyses and case studies in the automotive industry. Finally, changes in the productivity and labour market will be discussed considering the employment and skills changes in the automotive sector when investment on automation becomes a clear trend in the automotive sector.

Keywords: artificial intelligence; automotive cluster; cyber-physical systems; automation; labour relations; Portugal; productivity (search for similar items in EconPapers)
Date: 2022-05-03
Note: View the original document on HAL open archive server: https://hal.science/hal-04670873v1
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Published in International Journal of Automotive Technology and Management, 2022, 22 (2), pp.222-244. ⟨10.1504/IJATM.2022.124366⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04670873

DOI: 10.1504/IJATM.2022.124366

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