Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions
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2023
- ISIDORE Id: 10670/1.010807...
- hal: hal-04292607
- doi: 10.1016/j.techfore.2023.122579
HALSHS : archive ouverte en Sciences de l’Homme et de la Société - notices sans texte intégral
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info:eu-repo/semantics/altIdentifier/doi/10.1016/j.techfore.2023.122579
http://creativecommons.org/licenses/by-nc-nd/ , info:eu-repo/semantics/OpenAccess
Mots-clés
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 analysisSujets proches
Scientific research Science research Science--ResearchCiter ce document
Yogesh K. Dwivedi et al., « Evolution of Artificial Intelligence Research in Technological Forecasting and Social Change: Research Topics, Trends, and Future Directions », HALSHS : archive ouverte en Sciences de l’Homme et de la Société - notices sans texte intégral, ID : 10.1016/j.techfore.2023.122579
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Résumé
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)
