Patterns in management research on artificial intelligence: A longitudinal analysis using structural topic modeling

Abstract The field of management research on applied artificial intelligence (AI) is growing rapidly as the technology is maturing in industrial applications. Due to the nascent state of the scientific discourse, it is hard to discern thematic focal points or how well managerial and technical topics are integrated. Based on 10,036 publications over 25 years, we map the topic landscape of AI-related management research, longitudinal patterns of topics, and structural changes of topic networks and research communities. Our model identifies 71 unique topics, indicating a strong but myopic focus on technological capabilities and applications. The lagged response to technological paradigm shifts indicates a double pacing problem. Network structures of thematic research communities reveal increased centralization and interconnections, suggesting the field’s role in transferring basic AI research to industrial implementation. However, topics in technology management of AI seem to be separated from recent advances in AI. We propose mechanisms to foster an integrative discourse on applied AI that allows management research to act as a sense-giving institution. This includes focusing on fundamental technological characteristics instead of applications and strengthening the role of journals as discourse intermediaries.