Algorithmic Dispatch as a Service Experience Stimulus: A Conceptual Model Integrating SOR, Expectation–Confirmation, and Algorithmic Fairness in Food-Delivery Platforms

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摘要

Food-delivery platforms increasingly rely on algorithmic dispatch systems to match riders, optimize routes, and predict delivery times. While existing research has primarily examined algorithmic management from the perspective of workers, the customer-side psychological mechanisms triggered by algorithmic operations remain insufficiently explored. This article develops a comprehensive conceptual framework integrating the Stimulus–Organism–Response (SOR) model with Expectation–Confirmation Theory (ECT) and emerging research on algorithmic fairness to explain how algorithmic stimuli shape customer experience and behavioral intentions. The framework proposes that key algo-rithmic features—dispatch efficiency, transparency, fairness cues, and information quality—serve as external stimuli that influence internal cognitive and affective responses, including expectation confirmation, perceived service quality, perceived algorithmic fairness, and satisfaction. These internal states ultimately drive trust, repurchase intention, and platform loyalty. By synthesizing theories from consumer behavior, human–algorithm interaction, and digital service management, this paper advances a custom-er-centered understanding of algorithm-driven service delivery. The model provides actionable insights for improving user experience, enhancing algorithmic transparency, and designing fairer platform mechanisms that foster sustainable customer loyalty.

文献引用

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