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Exploring the generative AI adoption in service industry: A mixed-method analysis

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  • Gupta, Rohit
  • Rathore, Bhawana

Abstract

In the last few years, many service organisations have been exploring the use of Generative Artificial Intelligence (GAI) tools for their businesses and upgrading their existing processes. These tools have the potential and capability to transform the business world in various aspects. However, serval service organisations are facing many challenges while adopting the GAI tools in their organisations. In a similar context, this study explores the adoption of GAI barriers through two studies by a mixed-method approach. The first study is based on YouTube datasets of selected videos where GAI adoption challenges, problems, and barriers were discussed. Further, these YouTube datasets were analysed through text mining and empirical modelling techniques. In the second study, an extensive literature review was done and critical barriers to GAI adoption were identified based on the extensive literature review. Further, these barriers were analysed through three theoretical lenses and a hybrid fuzzy multicriteria decision-making approach. In addition, the results from the first study were further matched and verified with our second study. This establishes the relevance of adopting a mixed-method approach. Our major findings are: (i) trust, anticipation, and surprise emerged as the strongest emotions of the viewers who posted their comments on the YouTube videos; (ii) Five major barriers are revealed through topic analysis of YouTube transcripts and these are ethical, technological, regulations & policies, cost, and human resources; (iii) Six major barriers are identified through second study are privacy & security, return on investment, running cost, misuse, over-reliance, and Lack of digital infrastructure.

Suggested Citation

  • Gupta, Rohit & Rathore, Bhawana, 2024. "Exploring the generative AI adoption in service industry: A mixed-method analysis," Journal of Retailing and Consumer Services, Elsevier, vol. 81(C).
    • Handle: RePEc:eee:joreco:v:81:y:2024:i:c:s0969698924002935
    DOI: 10.1016/j.jretconser.2024.103997
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