Quality Management as an Enabler of Enterprise AI Adoption: Boundary Conditions and Performance Implications
TL;DR
Positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China.
Abstract
While enterprise artificial intelligence (AI) adoption is crucial for high-quality economic development, many companies have yet to adopt AI in practice. Enterprise quality management (QM), serving as an internalized foundation of standardized processes and data governance, may critically enable AI adoption, yet this relationship remains underexplored. Utilizing panel data from Chinese A-share listed companies (2007–2023), we employ a fixed-effects regression model, supplemented by a series of methods to address endogeneity, including the instrumental variables approach, difference-in-differences, and event studies. Results indicate that QM significantly promotes enterprise AI adoption, which further enhances enterprise performance. This positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China. Theoretically, this study extends the literature on the antecedents of AI adoption by identifying enterprise QM as a crucial, yet overlooked, internal driver. Practically, aligning AI integration with established quality frameworks, cultivating leadership with IT expertise, and fostering a supportive environment provide a viable pathway to overcome AI adoption barriers.