Reconfiguring Auditor Judgment Beyond the Human: Audit Data Analytics, Artificial Intelligence, and Audit Quality in Emerging Economies

Abstract

This study examines how audit data analytics (ADA) and artificial intelligence (AI) reconfigure auditor judgment and audit quality in emerging economies from a posthumanist perspective. Moving beyond anthropocentric assumptions that position professional judgment as an exclusively human cognitive act, the study conceptualises auditor judgment as a distributed and hybrid process emerging from human–AI assemblages embedded within regulatory, institutional, and technological infrastructures. Using survey data from 326 auditors across seven emerging economies and applying partial least squares structural equation modelling (PLS SEM), complemented by logistic regression analysis of audit report lag and financial restatements, the study investigates the relational effects of ADA intensity, AI integration, and contextual support on judgment quality and audit outcomes. The findings demonstrate that ADA and AI significantly enhance audit quality through both direct mechanisms and indirect pathways mediated by auditor judgment quality, while regulatory support and technological infrastructure strengthen these relationships. The study contributes to posthuman accounting scholarship by empirically demonstrating how audit quality emerges from socio technical entanglements rather than from isolated human expertise, with important implications for accountability, professional responsibility, and audit governance in algorithmically mediated environments.