AI-Assisted Decision-Making in Sports Tourism: How Perceived Sycophancy Shapes Destination Fit and Risk Judgment
DOI:
https://doi.org/10.33152/jmphss-10.2.10Keywords:
perceived AI sycophancy, sports tourism, destination fit, risk judgment, generative artificial intelligence, qualitative researchAbstract
Generative artificial intelligence is increasingly used in tourism planning, yet little is known about how tourists interpret highly affirmative, preference-consistent AI responses in consequential sports-tourism decisions. This study explores tourists’ reported experiences of perceived AI sycophancy and how such interactions are associated with judgments of destination fit, personal capability, risk evidence, information verification, and companion negotiation. Using an exploratory qualitative design, 40 adults who had used generative AI when planning mountain or outdoor sports tourism in Chongqing, China, were interviewed. A grounded-theory-informed coding procedure was used to develop 22 open codes and four interconnected categories: preference precedence and sycophantic confirmation, algorithm-reinforced destination fit, risk evidence reordering, and algorithmic self-echo. Participants described how preference-consistent responses could make prior wishes appear externally validated, strengthen perceived person–destination fit, reduce the salience of restrictive risk evidence, and provide apparently neutral support during companion disagreement. These patterns were less evident in accounts involving substantial sports experience, active multi-source verification, professional guidance, or multi-party cross-questioning. As this study is based on the participants' retrospective reports and not independent audits of AI conversation logs, the results are limited to perceived sycophantic behaviour and reported decision processes rather than verified model behaviour or causal effects. This paper provides a relational model of AI-assisted tourism decision-making and suggests that for high-stakes travel planning, epistemic independence, explicit uncertainty communication, and external verification need to be ensured.
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