Designing the right fit: socially cued versus functional AI interfaces in hospitality self-service technologies

What is it about?

Purpose: This study examines how alignment between self-service technology (SST) interface characteristics and service task demands shapes user evaluations in hospitality contexts. Drawing on Task-Technology Fit (TTF) and Social-Technology Fit (STF), it investigates how Functional SSTs (F-SSTs) and socially cued SSTs (SC-SSTs) interact with cognitively oriented tasks (CT) and socially oriented tasks (ST) to influence user outcomes. Design/methodology/approach: A 2 × 2 between-subjects experiment was conducted with 410 U.S. participants recruited via MTurk. Structural equation modeling tested fit-based mechanisms linking task-interface alignment to cognitive load, social presence, perceived ease of use (PEOU), reduced service dehumanization (RSD), and intention to use, including the moderating role of sensory appeal. Findings: F-SSTs enhance TTF and reduces cognitive load in CT, improving PEOU and intention to use. SC-SSTs strengthen STF and increase social presence in ST. However, social presence does not reduce RSD, and RSD does not directly influence intention. Sensory appeal amplifies fit-based effects. Practical implications: SST effectiveness depends on task-interface alignment. Low-level social cueing enhances interaction experience but does not shift broader impersonality judgments, while adoption is driven primarily by usability. Managers should align interface design with task objectives and targeted outcomes. Originality/value: The study demonstrates that SST evaluations are driven by task-interface fit rather than interface features alone. By distinguishing interaction-level experience from higher-order dehumanization judgments, it refines TTF and STF and clarifies the limits of socially cued interfaces in service contexts.

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