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Generative AI-Assisted Literature Analysis for Case Papers in Business and Management

Author(s)

Ziqi Ding1, Shuting He1, Xiaoyan Liu1, Ao Zhang2,*

Affiliation(s)

1School of International Business, Jilin International Studies University, Changchun, Jilin, China 2School of Accounting, Jilin University of Finance and Economics, Changchun, Jilin, China *Corresponding Author

Abstract

Against the background of the rapid development of generative artificial intelligence technology, the field of academic research is undergoing profound changes. As a core link in academic research, literature analysis in case papers of business and management has long been plagued by pain points such as low efficiency, strong subjectivity, and insufficient systematicness. Taking case papers in the field of financial digitalization as the research object, this paper systematically explores the implementation path, application effect and future prospect of generative AI-assisted literature analysis. Research shows that generative AI can significantly improve the efficiency of literature analysis and realize large-scale literature processing, but it still has limitations in the in-depth mining of logic and the guarantee of academic rigor. The literature analysis framework of “AI preliminary analysis-manual revision-AI batch analysis” constructed in this paper provides a new paradigm of literature research with both efficiency and quality for scholars in the field of business and management.

Keywords

Generative AI; Business and Management; Literature Analysis; Financial Digitalization

References

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