Review of Literature on Corporate Governance and Firm Performance: A Text Mining Approach

This article synthesises existing literature on “corporate governance” (CG) and its impact on “firms’ performance.” The study followed the PRISMA framework, analysing 442 Scopus-indexed journal documents. The paper critically examines intersection of CG and firm performance through the lens of text mining. We have used Latent Dirichlet Allocation (LDA) algorithm for topic modelling. After topic modelling analysis, from a total of 442 unique documents’ metadata, we identified 10 latent topics in our paper. Out of which we discarded four irrelevant topics from the analysis since they were not associated to Corporate Governance, which is our field of study. So, our selected topics were effects of CG on FP, CG disclosure, Agency theory, Ownership Structure, Board Size and Board Diversity. These selected six topics represents a distinct theme with assigned probabilities. The probabilities demonstrate relative importance of each token to its respective topic. This analysis reveals the comprehensive intellectual foundation of the CG field. The study adds to the literature by identifying six relevant topics of academic discussion. This study provides academics and practitioners with a road map for targeted investigation and strategic implementation in enhancing firm performance by highlighting the most important themes in corporate governance research. To our knowledge, very few prior studies has used the LDA approach in reviewing corporate governance and firm performance literature.