A governance lens on bias in internal auditing: A bibliometric analysis of insights, current trends and future directions

Abstract

This study aims to map the intellectual landscape and evolving knowledge structure of research on bias in internal auditing, a field increasingly connected to governance, professional judgment, ethical accountability, and technology-enabled audit environments (Bari et al., 2024; Hagendorff & Fabi, 2024). A bibliometric content analysis was conducted using 35 Scopus-indexed articles published between 1986 and 2025. Bibliometric techniques such as co-word analysis, three-field plots, and thematic mapping were applied using VOSviewer and RStudio (Biblioshiny). The results reveal a growing yet fragmented field, with key contributions emerging from high-impact journals in auditing, ethics, governance, and management. The conceptual analysis suggests a transition from traditional themes such as audit judgment, internal control, and professional objectivity toward emerging concerns involving algorithmic bias, digital ethics, explainability, and AI governance. Six thematic clusters were identified, including audit governance, algorithmic systems, professional bias, and cognitive behavior, highlighting a dynamic transition in how bias is conceptualized within internal auditing. This is among the first known bibliometric studies focusing specifically on bias in internal auditing. This study contributes by offering an initial bibliometric map of bias in internal auditing and by identifying governance-related key knowledge gaps and future research directions for scholars, practitioners, and regulators.