Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions

Yogesh K. Dwivedi, A. Sharma, Nripendra P. Rana, M. Giannakis, P. Goel and Vincent Dutot ()
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Yogesh K. Dwivedi: Swansea University, School of CS&IT, Jain Deemed-to-be University, Bangalore, India
A. Sharma: O.P. Jindal Global University
Nripendra P. Rana: Qatar University
Vincent Dutot: Métis Lab EM Normandie - EM Normandie - École de Management de Normandie = EM Normandie Business School

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Abstract: Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems. \textcopyright 2023 The Author(s)

Keywords: AI; artificial intelligence; Artificial intelligence; Artificial intelligence research; Behavioral research; Big data; Big data analytic; Big data analytics; Data analytics; Data Analytics; decision making; Decision making; Digital devices; health care; machine learning; Machine learning; Machine-learning; research; Research agenda; Research topics; social change; Social changes; Structural topic modeling; Supply chains; technological development; Topic modeling; Topic Modeling; trend analysis (search for similar items in EconPapers)
Date: 2023
Note: View the original document on HAL open archive server: https://hal.science/hal-04292607v1
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Published in Technological Forecasting and Social Change, 2023, 192, ⟨10.1016/j.techfore.2023.122579⟩

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Persistent link: https://EconPapers.repec.org/RePEc:hal:journl:hal-04292607

DOI: 10.1016/j.techfore.2023.122579

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