Automating the Systematic Literature Review Process in Management Science Using Artificial Intelligence

Abstract

ABSTRACT Systematic literature reviews (SLR) are essential for synthesizing research across disciplines, yet their manual execution is time-consuming and increasingly challenging due to the rapid proliferation of academic publications. This study examines the role of artificial intelligence (AI) in the SLR process within management sciences. Drawing on established SLR methodologies, particularly from health sciences where automation is widely applied, this research identifies AI’s role in contributing to key review stages, including literature identification, selection, data extraction, synthesis, and reporting. This study itself applied a systematic literature review methodology, querying Scopus and Web of Science, complemented by AI-based tools (Elicit and SciSpace) to extend coverage. Backward and forward citation searches were also conducted, resulting in a final sample of 93 publications. The findings from this sample suggest that AI enables researchers to shift roles from literature examiners to managers of the review process, overseeing AI tools executing repetitive and time-consuming tasks. However, despite the benefits of using AI in generating SLRs, its application in management research presents challenges, particularly in handling context-dependent and interpretative analyses. The study highlights both the transformative potential and the critical need for human oversight in AI-assisted reviews. Limitations include the reliance on existing automation techniques developed for health sciences and the exclusion of certain literature sources. Future research should explore AI’s effectiveness in managing SLRs, ethical considerations, and hybrid human–AI collaboration models. AI’s growing role in academic research entails the need to balance automation with scholarly rigor and methodological integrity.

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