Mapping the conceptual structure of innovation in artificial intelligence research: A bibliometric analysis and systematic literature review

This study uses bibliometric analysis and a systematic literature review to map the conceptual structure of artificial intelligence innovations (AI-I) in the social sciences between 2000 and 2023. It explicitly focuses on non-economic aspects conducive to AI-I, namely social, technological, cultural, sustainable, personal, moral, and ethical. Our analysis reveals that 1225 articles and proceeding papers have been published, and terms such as “technology,” “big data,” “management,” “performance,” “future,” and “impact” are the most frequently used when discussing innovation and AI. According to our time-zone analysis, the last two years have shown a significant emphasis on concepts such as “transformation,” “corporate social responsibility,” and “resource-based view.” In terms of citations, the countries that receive the highest number of references in the AI-I field are the United Kingdom, the United States, Germany, Australia, and China. The most prolific authors in terms of publications are David Teece, Erik Brynjolfsson, and Anjan Chatterjee. Given that most studies highlight the economic side of AI-I, we selected the most prolific 163 articles from all social science research areas. These studies legitimize the main non-economic aspects that highlight both certainties and uncertainties conducive to such innovations. Although the technological component is the most popular in our analysis of the non-economic aspects of the AI-I subfield, we find an important emphasis on ethical/moral dimensions conducive to slow innovation principles. We also observe a growing interest in the cultural dimension, specifically exploring potential factors that can lead to better human acceptance of these innovations.