Gigification, job engagement and satisfaction: the moderating role of AI enabled system automation in operations management

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Braganza A., Chen W., Canhoto A., Sap S.

Production Planning and Control, vol.33, no.16, pp.1534-1547, 2022 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 33 Issue: 16
  • Publication Date: 2022
  • Doi Number: 10.1080/09537287.2021.1882692
  • Journal Name: Production Planning and Control
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, ABI/INFORM, Aerospace Database, Business Source Elite, Business Source Premier, Communication Abstracts, Compendex, INSPEC, Metadex, Civil Engineering Abstracts
  • Page Numbers: pp.1534-1547
  • Keywords: Artificial intelligence, job satisfaction, job engagement, gigification, operations management, system automation, supply chain management
  • Kayseri University Affiliated: No


© 2021 Informa UK Limited, trading as Taylor & Francis Group.Innovative and highly efficient Artificial Intelligence System Automation (AI-SA) is reshaping jobs and the nature of work throughout supply chain and operations management. It can have one of three effects on existing jobs: no effect, eliminate whole jobs, or eliminate those parts of a job that are automated. This paper focuses on the jobs that remain after the effects of AI-SA, albeit with alterations. We use the term Gigification to describe these jobs, as we posit that the jobs that remain share characteristics of gig work. Our study examines the relationship between Gigification, job engagement and job satisfaction. We develop a theoretical framework to examine the impact of system automation on job satisfaction and job engagement, which we test via 232 survey responses. Our findings show that, while Gigification increases job satisfaction and engagement, AI-SA weakens the positive impact of Gigification on these important worker outcomes. We posit that, over time, the effects of AI-SA on workers is that full-time, permanent jobs will give way to gigified jobs. For future research, we suggest further theory development and testing of the Gigification of operations and supply chain work.