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SUPPLY CHAIN MANAGERS' SATISFACTION WITH ARTIFICIAL INTELLIGENCE INTEGRATION: AN EXPECTATION CONFIRMATION THEORY APPROACH
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DOI：10.17270/J.LOG.001412.png)
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Abstract
En 中文
Background: Artificial intelligence (AI) is increasingly integrated into supply chain management (SCM) to support forecasting, coordination, and disruption management. However, outcomes remain uneven because the value derived from AI depends not only on technical capabilities but also on how managers form expectations, evaluate performance during use, and update beliefs over time. Drawing on Expectation Confirmation Theory (ECT), this study examines managers' satisfaction with AI-enabled decision support in SCM. Methods: A latent-variable model linking initial expectations, perceived performance, belief confirmation, and satisfaction was operationalized using 7-point Likert items adapted from prior information systems and AI research. Data were collected from 279 firms across primary, secondary, tertiary, and quaternary sectors using Computer-Assisted Telephone Interviewing (CATI) and Computer-Assisted Web Interviewing (CAWI). Partial Least Squares Structural Equation Modeling (PLS-SEM) was used to assess measurement quality and test the hypothesized relationships. Results: the measurement model demonstrated high reliability and validity (rho A, composite reliability, and Cronbach's alpha > .94; AVE > .78; HTMT < .85). Initial expectations positively influenced perceived performance (beta = .922, p < .001) and belief confirmation (beta = .208, p < .001). Perceived performance strongly predicted belief confirmation (beta = .786, p < .001). Satisfaction was explained by both belief confirmation (beta = .361, p < .001) and perceived performance (beta = .643, p < .001), with strong explanatory power (R & sup2; = .850 for perceived performance, .961 for belief confirmation, .998 for satisfaction). Conclusions: The findings support ECT as an explanation of managers' satisfaction with AI-enabled decision support in SCM. Perceived performance emerges as the primary driver, shaping both belief confirmation and satisfaction. Practically, organizations should manage expectations, enhance the transparency and reliability of AI tools, and support managers in interpreting AI-generated insights to facilitate sustained integration of AI into supply chain decision-making.
Keywords:
Artificial intelligence
supply chain management
expectation confirmation theory
perceived performance
belief confirmation
managers' satisfaction
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