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Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions

Author

Listed:
  • Purva Grover

    (Indian Institute of Management Amritsar)

  • Arpan Kumar Kar

    (Indian Institute of Technology Delhi)

  • Yogesh K. Dwivedi

    (Swansea University)

Abstract

In this digital era, data is new oil and artificial intelligence (AI) is new electricity, which is needed in different elements of operations management (OM) such as manufacturing, product development, services and supply chain. This study explores the feasibility of AI utilization within an organization on six factors such as job-fit, complexity, long-term consequences, affect towards use, social factors and facilitating conditions for different elements of OM by mining the collective intelligence of experts on Twitter and through academic literature. The study provides guidelines for managers for AI applications in different components of OM and concludes by presenting the limitations of the study along with future research directions.

Suggested Citation

  • Purva Grover & Arpan Kumar Kar & Yogesh K. Dwivedi, 2022. "Understanding artificial intelligence adoption in operations management: insights from the review of academic literature and social media discussions," Annals of Operations Research, Springer, vol. 308(1), pages 177-213, January.
    • Handle: RePEc:spr:annopr:v:308:y:2022:i:1:d:10.1007_s10479-020-03683-9
    DOI: 10.1007/s10479-020-03683-9
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    2. Bag, Surajit & Sabbir Rahman, Muhammad & Rogers, Helen & Srivastava, Gautam & Harm Christiaan Pretorius, Jan, 2023. "Climate change adaptation and disaster risk reduction in the garment industry supply chain network," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 171(C).
    3. Ahmad M. Alghamdi & Salvatore Flavio Pileggi & Osama Sohaib, 2023. "Social Media Analysis to Enhance Sustainable Knowledge Management: A Concise Literature Review," Sustainability, MDPI, vol. 15(13), pages 1-30, June.
    4. Ming Tang & Huchang Liao, 2023. "Group Structure and Information Distribution on the Emergence of Collective Intelligence," Decision Analysis, INFORMS, vol. 20(2), pages 133-150, June.
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    6. Taiga Saito & Shivam Gupta, 2022. "Big Data Applications with Theoretical Models and Social Media in Financial Management," CIRJE F-Series CIRJE-F-1205, CIRJE, Faculty of Economics, University of Tokyo.
    7. Kinkel, Steffen & Capestro, Mauro & Di Maria, Eleonora & Bettiol, Marco, 2023. "Artificial intelligence and relocation of production activities: An empirical cross-national study," International Journal of Production Economics, Elsevier, vol. 261(C).
    8. Taiga Saito & Shivam Gupta, 2022. "Big data applications with theoretical models and social media in financial management," CARF F-Series CARF-F-550, Center for Advanced Research in Finance, Faculty of Economics, The University of Tokyo.
    9. Tsan-Ming Choi & Alexandre Dolgui & Dmitry Ivanov & Erwin Pesch, 2022. "OR and analytics for digital, resilient, and sustainable manufacturing 4.0," Annals of Operations Research, Springer, vol. 310(1), pages 1-6, March.
    10. D’Amico, Elettra & Belitski, Maksim & Colombelli, Alessandra, 2023. "Evaluating Internal and External Knowledge Sources in Adopting Artificial Intelligence," Department of Economics and Statistics Cognetti de Martiis LEI & BRICK - Laboratory of Economics of Innovation "Franco Momigliano", Bureau of Research in Innovation, Complexity and Knowledge, Collegio 202303, University of Turin.
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    12. D’Amico, Elettra & Belitski, Maksim & Colombelli, Alessandra, 2023. "Evaluating Internal and External Knowledge Sources in Adopting Artificial Intelligence," Department of Economics and Statistics Cognetti de Martiis. Working Papers 202315, University of Turin.
    13. Hu, Qian & Pan, Zhao, 2023. "Can AI benefit individual resilience? The mediation roles of AI routinization and infusion," Journal of Retailing and Consumer Services, Elsevier, vol. 73(C).

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