Artificial Intelligence, Decision Quality, and Supply Chain Performance: A Dynamic Capabilities Perspective
DOI:
https://doi.org/10.66578/btis.v2i2.31Keywords:
Artificial intelligence, Supply chain analytics, Decision quality, Supply chain adaptability, Operational performance, AI-enabled decision-makingAbstract
This study examines how artificial intelligence (AI)-enabled capability influences supply chain adaptability and operational performance through analytics capability and decision quality. Drawing on the dynamic capabilities view and decision-support systems theory, the study develops a decision-centric framework in which analytics capability supports sensing, decision quality represents the seizing mechanism, and supply chain adaptability reflects reconfiguring processes. Using survey data from 300 supply chain professionals across manufacturing, logistics, and retail sectors, the model is tested using partial least squares structural equation modeling (PLS-SEM). The results show that AI-enabled capability significantly enhances analytics capability, which in turn improves decision quality. Decision quality has a strong positive effect on supply chain adaptability, which subsequently improves operational performance. In addition, environmental uncertainty strengthens the relationship between decision quality and supply chain adaptability. The findings highlight decision quality as a critical micro-foundational capability that translates analytical insights into adaptive operational actions. By shifting the focus from data availability to decision effectiveness, this study provides a process-oriented explanation of how AI-enabled analytics create operational value in complex supply chain environments. From a practical perspective, the results suggest that organizations should integrate AI-driven analytics with structured decision-making processes to enhance adaptability and improve operational performance.
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