What the paper studied
The study examined how AI-driven personalization relates to loyalty toward smart tourism platforms. It separated two traveler responses: perceived recommendation discovery, meaning recommendations reveal appealing options users had not considered, and perceived AI recommendation quality, meaning users judge recommendations favorably.
Researchers analyzed an online survey of 1,232 eligible users of AI-enabled smart tourism platforms, recruited through travel-related social media groups. They used partial least squares structural equation modeling to examine relationships among personalization, recommendation responses, and loyalty. They also considered users’ motivation to think deeply, called need for cognition, and perceived privacy risk.
Because the survey captured one point in time, the results show associations rather than causal effects or changes in loyalty over time.
Key findings
- AI-driven personalization was positively associated with platform loyalty and with both recommendation discovery and recommendation quality.
- Discovery and quality were each positively associated with loyalty. Both represented significant indirect associations between personalization and loyalty, suggesting two distinct ways travelers may value recommendations.
- Users with higher need for cognition reported higher recommendation quality. The personalization–quality relationship, and its indirect association with loyalty, were stronger among these users.
- Higher perceived privacy risk was associated with lower recommendation discovery. It also weakened the personalization–discovery relationship and its indirect association with loyalty.
- The supplied industry implications describe the cognitive-motivation and privacy interactions as small. These are secondary design considerations, not grounds for making personality or privacy-risk segmentation the center of personalization strategy.
Why it matters for hospitality
For booking platforms and hotel recommendation tools, relevance alone may not capture the full value of personalization. Travelers may appreciate both dependable decision support and help discovering unfamiliar accommodations, destinations, or activities connected to their goals.
This distinction matters for distribution and guest experience. A recommendation can be a good match without expanding a traveler’s options; an interesting discovery can still fail if its information is outdated or unsuitable. Teams should therefore assess quality and discovery separately rather than relying only on clicks.
The findings concern loyalty toward the platform, not necessarily loyalty toward an individual hotel brand. They also do not establish increases in bookings, revenue, or actual repeat use. Those outcomes require additional measurement.
Practical takeaways
- Separate core recommendations from a clearly labelled discovery section. Keep core options aligned with stated constraints and explain why exploratory alternatives could fit.
- Check relevance, availability, information freshness, and comparison usefulness before introducing discovery-oriented content. These are proposed operational safeguards, not tested interventions from this survey.
- Offer concise recommendation cards with optional explanations and comparison detail. Let travelers choose information depth rather than inferring personality and imposing different interfaces.
- Make privacy controls visible where recommendations appear. Consider explanations of data use, options to exclude information, and controls to pause personalization or delete search history.
- Track quality ratings and discovery-related saves alongside willingness to recommend and reuse intentions. Use longitudinal platform data to test whether favorable intentions translate into return sessions and bookings.