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Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective

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  • Sudatta Kar
  • Arpan Kumar Kar
  • Manmohan Prasad Gupta

Abstract

Artificial intelligence (AI) in business processes and academic research in AI has significantly increased. However, the adoption of AI in organizational strategy is yet to be explored in extant literature. This study proposes two conceptual frameworks showing hierarchical relationships among the various drivers and barriers to AI adoption in organizational strategy. In a two‐step approach, the literature study is first done to identify eight drivers of and nine barriers to AI adoption and validated by academic and industry experts. In the second step, MICMAC (matrice d'impacts croises‐multiplication appliqúe a un classment or cross‐impact matrix multiplication applied to classification) analysis categorizes the drivers and barriers to AI adoption in organizational strategy. Total interpretive structural modeling (TISM) is developed to understand the complex and hierarchical associations among the drivers and barriers. This is the first attempt to model the drivers and barriers using a methodology like TISM, which provides a comprehensive conceptual framework with hierarchical relationships and relative importance of the drivers and barriers to AI adoption. AI solutions' decision‐making ability and accuracy are the most influential drivers that influence other driving factors. Lack of an AI adoption strategy, lack of AI talent, and lack of leadership commitment are the most significant barriers that affect other barriers. Recommendations for senior leadership are discussed to focus on the leading drivers and barriers. Also, the limitations and future research scope are addressed.

Suggested Citation

  • Sudatta Kar & Arpan Kumar Kar & Manmohan Prasad Gupta, 2021. "Modeling Drivers and Barriers of Artificial Intelligence Adoption: Insights from a Strategic Management Perspective," Intelligent Systems in Accounting, Finance and Management, John Wiley & Sons, Ltd., vol. 28(4), pages 217-238, October.
    • Handle: RePEc:wly:isacfm:v:28:y:2021:i:4:p:217-238
    DOI: 10.1002/isaf.1503
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    as
    1. Nam, Taewoo, 2019. "Technology usage, expected job sustainability, and perceived job insecurity," Technological Forecasting and Social Change, Elsevier, vol. 138(C), pages 155-165.
    2. Brougham, David & Haar, Jarrod, 2018. "Smart Technology, Artificial Intelligence, Robotics, and Algorithms (STARA): Employees’ perceptions of our future workplace," Journal of Management & Organization, Cambridge University Press, vol. 24(2), pages 239-257, March.
    3. Rajesh, R., 2017. "Technological capabilities and supply chain resilience of firms: A relational analysis using Total Interpretive Structural Modeling (TISM)," Technological Forecasting and Social Change, Elsevier, vol. 118(C), pages 161-169.
    4. Miglė Černikovaitė & Žaneta Karazijienė & Lina Bivainienė & Valdas Dambrava, 2021. "Assessing Customer Preferences for Shopping Centers: Effects of Functional and Communication Factors," Sustainability, MDPI, vol. 13(6), pages 1-17, March.
    5. Nisreen Ameen & Ali Tarhini & Alexander Reppel & Amitabh Anand, 2021. "Customer experiences in the age of artificial intelligence," Post-Print halshs-03045430, HAL.
    6. J. Jena & Sumati Sidharth & Lakshman S. Thakur & Devendra Kumar Pathak & V.C. Pandey, 2017. "Total Interpretive Structural Modeling (TISM): approach and application," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 14(2), pages 162-181, May.
    7. Nida Shahid & Tim Rappon & Whitney Berta, 2019. "Applications of artificial neural networks in health care organizational decision-making: A scoping review," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-22, February.
    8. Viswanath Venkatesh, 2022. "Adoption and use of AI tools: a research agenda grounded in UTAUT," Annals of Operations Research, Springer, vol. 308(1), pages 641-652, January.
    9. De Bruyn, Arnaud & Viswanathan, Vijay & Beh, Yean Shan & Brock, Jürgen Kai-Uwe & von Wangenheim, Florian, 2020. "Artificial Intelligence and Marketing: Pitfalls and Opportunities," Journal of Interactive Marketing, Elsevier, vol. 51(C), pages 91-105.
    10. Puneeta Ajmera & Vineet Jain, 2019. "Modelling the barriers of Health 4.0–the fourth healthcare industrial revolution in India by TISM," Operations Management Research, Springer, vol. 12(3), pages 129-145, December.
    11. Giacomo Damioli & Vincent Van Roy & Daniel Vertesy, 2021. "The impact of artificial intelligence on labor productivity," Eurasian Business Review, Springer;Eurasia Business and Economics Society, vol. 11(1), pages 1-25, March.
    12. Sanjay Prasad & Ravi Shankar & Rachita Gupta & Sreejit Roy, 2018. "A TISM modeling of critical success factors of blockchain based cloud services," Journal of Advances in Management Research, Emerald Group Publishing Limited, vol. 15(4), pages 434-456, September.
    13. Paschen, Jeannette & Wilson, Matthew & Ferreira, João J., 2020. "Collaborative intelligence: How human and artificial intelligence create value along the B2B sales funnel," Business Horizons, Elsevier, vol. 63(3), pages 403-414.
    14. Tom Coupe, 2019. "Automation, job characteristics and job insecurity," International Journal of Manpower, Emerald Group Publishing Limited, vol. 40(7), pages 1288-1304, September.
    15. Ajay Agrawal & Joshua Gans & Avi Goldfarb, 2019. "The Economics of Artificial Intelligence: An Agenda," NBER Books, National Bureau of Economic Research, Inc, number agra-1.
    16. Shankar, Ravi & Gupta, Rachita & Pathak, Devendra Kumar, 2018. "Modeling critical success factors of traceability for food logistics system," Transportation Research Part E: Logistics and Transportation Review, Elsevier, vol. 119(C), pages 205-222.
    17. George Baryannis & Sahar Validi & Samir Dani & Grigoris Antoniou, 2019. "Supply chain risk management and artificial intelligence: state of the art and future research directions," International Journal of Production Research, Taylor & Francis Journals, vol. 57(7), pages 2179-2202, April.
    18. Aditya Vasan Srinivasan & Mona de Boer, 2020. "Improving trust in data and algorithms in the medium of AI," Maandblad Voor Accountancy en Bedrijfseconomie Articles, Maandblad Voor Accountancy en Bedrijfseconomie, vol. 94(3-4), pages 147-160, April.
    19. Lene Pettersen, 2019. "Why Artificial Intelligence Will Not Outsmart Complex Knowledge Work," Work, Employment & Society, British Sociological Association, vol. 33(6), pages 1058-1067, December.
    20. Quentin André & Ziv Carmon & Klaus Wertenbroch & Alia Crum & Douglas Frank & William Goldstein & Joel Huber & Leaf Boven & Bernd Weber & Haiyang Yang, 2018. "Consumer Choice and Autonomy in the Age of Artificial Intelligence and Big Data," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 5(1), pages 28-37, March.
    21. Agrawal, Ajay & Gans, Joshua & Goldfarb, Avi (ed.), 2019. "The Economics of Artificial Intelligence," National Bureau of Economic Research Books, University of Chicago Press, number 9780226613338.
    22. Cubric, Marija, 2020. "Drivers, barriers and social considerations for AI adoption in business and management: A tertiary study," Technology in Society, Elsevier, vol. 62(C).
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    3. 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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