Rational, emotional or both: Decoding service robot adoption in tourism from elaboration likelihood model perspective

Abstract

Purpose This study aims to decode the adoption of tourism service robots by examining both rational and emotional factors. Using the elaboration likelihood model (ELM) as a theoretical framework, it explores how intelligent service quality and external cues shape users’ adoption intentions. Additionally, it investigates how individual regulatory focus and individual innovativeness influence the processes. Design/methodology/approach A conceptual model grounded in ELM was developed, incorporating variables of intelligent service quality and external cues. Data were collected via a survey questionnaire, yielding 398 valid responses. The study used hierarchical linear regression analysis to examine how both rational evaluation and emotional persuasion routes contribute to the adoption of service robots. Findings The results reveal that both intelligent service quality (information content quality and service task quality) and external cues (source credibility and social influence) significantly affect the adoption of service robots through the central and peripheral routes, respectively. Additionally, the effect of these routes is moderated by individual regulatory focus and innovativeness. Originality/value This study innovatively applies ELM to service robot adoption in tourism, bridging the gap between rational and emotional factors in decision-making. It offers insights into the dual-process theoretical framework, highlighting that effective adoption strategies must align with user-specific characteristics. These insights provide valuable practical guidance for stakeholders in the tourism industry.