Evaluation of the Impact of Artificial Intelligence-Based Decision-Making Systems on Organizational Agility and Operational Performance in the Automotive Industry

Abstract

This study aimed to evaluate the effects of artificial intelligence-based decision-making systems on organizational agility and operational performance in the automotive and auto-parts industries in Tehran. This quantitative study employed a cross-sectional descriptive-correlational design. The target population consisted of managers and experts working in automotive and auto-parts companies in Tehran, from whom 384 participants were selected through convenience sampling based on Cochran’s sample-size formula. Data were collected using a standardized questionnaire with a five-point Likert scale. Face and content validity were confirmed by experts, while convergent and discriminant validity and construct reliability were assessed using average variance extracted, composite reliability, and the Fornell–Larcker criterion. Descriptive analyses were conducted in SPSS, and the research hypotheses were tested using partial least squares structural equation modeling in SmartPLS 3. Inferential results demonstrated that AI-based decision-making systems had a strong, positive, and significant effect on organizational agility (β=0.821, p<0.001) and a positive significant effect on operational performance (β=0.383, p=0.024). The model explained 67.4% of the variance in organizational agility (R²=0.674) and 14.7% of the variance in operational performance (R²=0.147). Effect sizes were 0.892 for organizational agility and 0.172 for operational performance. Predictive relevance values were Q²=0.418 and Q²=0.083, respectively, while the overall GoF value of 0.531 indicated strong structural model fit. AI-based decision-making systems represent an important strategic capability for automotive firms because they substantially strengthen organizational agility and also contribute to improved operational performance. Their integration into managerial and operational processes can enhance responsiveness, flexibility, decision quality, efficiency, and organizational adaptability. Accordingly, automotive companies should prioritize the systematic adoption of AI-supported decision-making while simultaneously developing the data, technological, and human capabilities required for effective implementation.