Refining Literature Review Strategies: Analyzing Big Data Trends Across Journal Tiers
Narges Mashhadi Nejad, Marcelo J. Alvarado-Vargas, Mehrdad Jalali Sepehr · Academy of Management Proceedings · 2024
This study investigates the influence of academic journal tiers on literature reviews, particularly in the context of big data research in business operations. This study analyzes 1,000 academic articles and conference proceedings across various journal tiers using topic modeling to guide researchers in selecting appropriate journal tiers while conducting a literature review. It examines whether researchers should prioritize different tiers of academic journals based on their goals of depth or breadth. LDA is used for topic modeling, analyzing themes across A*, A, B, and C level journals. The analysis reveals that higher-tier journals (A* and A) offer depth and specialized research. Lower-tier journals (B and C), however, provide a wider range of themes, contributing to the breadth of research topics. Quantitatively, the study employs the Shannon Diversity Index to assess thematic diversity across journal tiers. The results indicate a progressive increase in thematic diversity from A* to B tiers, but this expansion plateaus with the inclusion of C tier journals. Statistical tests confirm that highly cited journals exceed a threshold for thematic depth (Hypothesis 1) and that including a broader range of journal tiers enhances thematic breadth up to a point (Hypothesis 2). The study concludes that the selection of journal tiers for literature reviews must balance depth and breadth.