Unlocking employer insights: Using large language models to explore human-centric aspects in the context of industry 5.0

This paper aims to enhance the understanding of Industry 5.0 by introducing an innovative AI-based methodology that proficiently maps employer expressions related to well-being using job postings. This process involves creating a comprehensive dictionary of well-being expressions, which is then comp...

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Bibliographic Details
Published in:Technological forecasting & social change Vol. 208; p. 123719
Main Authors: Grybauskas, Andrius, Cárdenas-Rubio, Jeisson
Format: Journal Article
Language:English
Published: Elsevier Inc 01.11.2024
Subjects:
Human-centricity
Industry 5.0
Job vacancies
LLM
Well-being
J28
Well-being
J63
L86
Industry 5.0
L23
Job vacancies
LLM
O33
Human-centricity
ISSN:0040-1625
Online Access:Get full text
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Summary:This paper aims to enhance the understanding of Industry 5.0 by introducing an innovative AI-based methodology that proficiently maps employer expressions related to well-being using job postings. This process involves creating a comprehensive dictionary of well-being expressions, which is then compared with existing academic literature. This approach facilitates empirical well-being analysis from employers’ perspectives. Bridging theoretical and practical realms, we offer valuable insights to academia and industry about well-being (human-centricity) interpretation by employers. The findings highlight UK employers’ prioritisation of self-realisation and a positive work atmosphere to attract job seekers. Nonetheless, many vacancies do not explicitly emphasise well-being to attract potential workers. •Industry 5.0 values human well-being, emphasizing its role in organizational practices.•Fine-tuned LLMs can extract well-being variables from job postings, giving insights. valuable insights across various domains.•UK employers prioritize self-realization, yet well-being isn’t often highlighted.•It’s unclear if employers’ focus on well-being is a lasting change or a short-term shift.
ISSN:0040-1625
DOI:10.1016/j.techfore.2024.123719