Exploring the role of generative artificial intelligence in quality management: A comprehensive bibliometric and thematic analysis and conceptual framework

This study examines the emerging role of generative artificial intelligence (GenAI) in quality management (QM) by mapping its intellectual foundations, thematic evolution, and future research directions. Responding to the fragmented nature of current scholarship, the study adopts a hybrid bibliometric–thematic approach based on 84 Scopus-indexed Q1/Q2 journal articles published between 2014 and 2026. The bibliometric analysis identifies publication trends, influential papers, dissemination routes, author networks, keyword structures, and journal-level knowledge linkages, while the thematic analysis interprets how GenAI is being applied and conceptualized across quality-related contexts. The findings show that GenAI–QM research is rapidly accelerating, particularly from 2023 onward, but remains interdisciplinary and only partially consolidated within traditional QM theory. Four dominant themes are identified: genAI-enabled inspection, data augmentation, and anomaly detection; industrial applications for defect reduction, predictive maintenance, and process optimization; integration with total quality management (TQM), Six Sigma, Quality 4.0, and digital transformation; and governance, ethics, trust, and organizational readiness. Building on these findings, the study proposes an integrated conceptual framework explaining how GenAI capabilities are translated into quality-management processes, aligned with established QM paradigms, conditioned by governance and organizational enablers, and converted into operational, managerial, and strategic quality outcomes. This study contributes to QM scholarship by theorizing GenAI as a generative quality capability that connects data creation, anomaly interpretation, decision support, governance, and organizational learning. Rather than presenting GenAI as a set of isolated applications, the study explains how its capabilities can be embedded within TQM, Six Sigma, and Quality 4.0 logics to support the transition from reactive quality control toward predictive, adaptive, and learning-oriented quality management systems.