Artificial Intelligence and Competitive Performance: The Quality Management Mechanism from a Cross-Boundary Ambidexterity Perspective

Abstract

While artificial intelligence (AI) is increasingly used in business, the quality management (QM) practices through which AI implementation is associated with competitive performance (CP) remain underexplored. Drawing on the cross-boundary ambidexterity perspective, this study distinguishes internal and external AI implementation and four QM practices defined by organizational boundary and learning orientation. Structural equation modeling (SEM) of survey data from 555 Chinese manufacturing enterprises indicates positive indirect associations between AI implementation and CP through QM practices in the corresponding organizational domain. External AI implementation is associated with CP through external QM practices, while internal AI implementation is associated with CP through internal QM practices. Market turbulence weakens the performance associations of exploitative QM practices and strengthens those of exploratory QM practices. The study extends research on AI implementation and QM by identifying distinct organizational pathways to current performance relative to competitors. The findings offer guidance on aligning AI investments with specific QM objectives and adjusting quality priorities to changing market conditions.