Do AI-related papers in top management science journals contribute greater academic influence and online attention?

Abstract

Purpose This study aimed to investigate whether research papers incorporating AI-related themes or methodologies exhibited statistically significant associations within the management science disciplines amidst the rapid advancement of artificial intelligence (AI). Design/methodology/approach A mixed-method approach was used. Based on the UTD 24 journals in the field of management studies, this article systematically collected over 50,000 top-tier papers from six sub-disciplines of management and identified research literature containing elements related to AI through natural language processing methods. Subsequently, two quantitative analyses were carried out. Firstly, the academic influence was quantified through citation analysis, which was a widely recognized approach for evaluating the impact of research works. Secondly, online attention was examined by leveraging the comprehensive altmetric attention score, a metric that captures the broader engagement and dissemination of academic papers across various online platforms. Findings This study revealed contrasting associations of AI integration in management research. In terms of academic impact, AI-related papers in the management (general/strategy) and accounting sub-disciplines exhibited negative academic associations compared to non-AI counterparts. But this negative effect has been gradually weakening in recent years. Even the study found that after 2019, management (general/strategy) exhibited positive associations with the academic influence. While AI-related papers of the marketing sub-discipline outperformed non-AI papers in citation counts since 2013. Regarding online attention, the AI-related research in management (general/strategy) domain attained stronger online attention. Additionally, it was noteworthy that the female first authors might have achieved better academic or online engagement across some sub-disciplines. Practical implications This research might prove to be an invaluable resource for researchers and journal editors in sub-fields. For researchers, this work would illuminate new pathways for exploring AI-related themes and methodologies within their specific domains. For journal editors, it could offer some inspiration for adjusting their research directions and review standards. For example, editors could encourage more submissions that bridge the gap between theory and practice in AI-related research, or that explore emerging AI applications in under-researched areas. Originality/value The proliferation of AI technologies has spurred increased scholarly attention across various domains of management research, prompting questions about its value-added contributions in social science. Firstly, this study pioneered the cross-disciplinary analysis of AI research in the management research domain, revealing divergent associations. Secondly, NLP-driven vocabulary detection was utilized, ensuring comprehensive topic recognition and optimizing the methodology for future interdisciplinary research assessments. Thirdly, female first authors were demonstrated to have superior academic and online visibility compared to their male counterparts across several management domains, which was somewhat different from the conclusions drawn by previous research.

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