The integration of Generative AI into business and management stands out as a pivotal transformation. This paper employs bibliometric analysis to scrutinize academic perspectives on the applications of Generative AI across diverse business research fields. Concurrently, text mining based on tweets and websites is deployed to probe cutting-edge industry applications of Generative AI in business and management. The latent Dirrichlet allocation (LDA) topic modeling method unveils the profound potential of Generative AI, i.e. 1) creating new interfaces for service providers and personalized experience; 2) creating new content to augment human creativity; 3) improving efficiency and productivity, and 4) enabling more new applications, business models and use in the practical business applications. Delving into the contemporary research topics in information systems, marketing, management, and other business research, this study undertakes a comprehensive bibliometric and thematic analysis to integrate findings from both contemporary academic research fields and business application spheres to identify the research gap and explore future research context in GenAI. Moreover, this study underscores the ethical and practical challenges that emerge, advocating for interdisciplinary study on GenAI. The paper concludes by providing suggestions for future research, underscoring the importance of combining technological expertise with a human-centered approach.
Wang, May Ying and Wang, Pengqi, "Decoding business applications of generative AI: A bibliometric analysis and text mining approach" (2023). ICEB 2023 Proceedings (Chiayi, Taiwan). 15.