경영정보학 분야의 인공지능 연구동향 탐색: 네트워크 분석과 전문가 인터뷰 기반 접근

초록

Recent advances in artificial intelligence have introduced a new turning point in Management Information Systems research, yet systematic analyses of the structural relationships and flows among research topics remain limited. To address this gap, this study integrates citation network analysis and expert interviews techniques to explore the evolutionary patterns of AI research in the Management Information Systems (MIS) field and to identify strategic future research directions. Based on a dataset of 339,373 papers from the AMiner DBLP database spanning 1958 to 2022, we segmented the data into seven time periods and conducted cluster analysis. For the most recent period, where citation links are relatively sparse, we applied a GraphSAGE model trained on previous periods to predict topic clusters and complemented the findings with expert surveys to derive future research priorities. The analysis revealed a structural shift in MIS-AI research from a cohesive, centralized pattern to a more decentralized and diverse configuration, with new clusters emerging around machine learning, security, and human-centered computing. This study holds academic significance by combining quantitative network analysis with qualitative expert interviews to provide a dynamic view of the research landscape, offering practical insights for data-driven strategy formulation and interdisciplinary research design.

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