What the paper studied
The authors tested how two design qualities of AI travel chatbots — perceived usefulness (does the bot help me plan and book?) and service convenience (is it fast, accessible, low-effort?) — affect customer loyalty to an online travel agency (OTA), and whether customer experience mediates that link. They surveyed 200 adult OTA users in DKI Jakarta who had used an AI chatbot in the past six months and analyzed the data with PLS-SEM (SmartPLS 3) using a Stimulus–Organism–Response framework.
Key findings
- Both stimuli matter directly. Perceived usefulness (β = 0.202, p = 0.001) and service convenience (β = 0.192, p < 0.001) both have direct positive effects on customer loyalty.
- Customer experience is the dominant driver of loyalty. Customer experience has the largest direct effect on loyalty (β = 0.488, t = 8.145, p < 0.001) — bigger than either stimulus on its own.
- Experience mediates the path from chatbot quality to loyalty. Indirect effects: PU → CE → CL (β = 0.199, p < 0.001) and SC → CE → CL (β = 0.210, p < 0.001). Functional chatbot quality builds loyalty only after it produces a good experience.
- Service convenience is the stronger experience-driver. SC → CE (β = 0.430) outperforms PU → CE (β = 0.408).
- Model power. The model explains ~46% of variance in customer experience and ~55% in customer loyalty.
- Sample skews mobile and active. Users were 50/50 male/female, mostly age 26–41, mostly with bachelor's degrees, with ~88% having used the chatbot more than once in six months. Main use cases: booking tickets/hotels (31%), tour package info (22.5%), travel recommendations (18%).
Why it matters for hospitality
The Indonesian OTA market is huge and switch-heavy — 71.4% of Indonesians have used an OTA, but customers switch platforms easily. The lesson generalizes: in any commoditized travel marketplace, a chatbot that is technically capable but feels generic will not retain customers. What converts utility into retention is the experience layer — personalization, accessibility, frictionless interaction, and the feeling that the bot "gets" you. Hospitality brands building chatbots should not stop at "it answers the question" — they need to instrument and design for the experience that flows from the answer.
Practical takeaways
- Treat customer experience as the primary KPI for travel chatbots, not just task completion or response time.
- Invest in service convenience (speed, multilingual support, 24/7 access, simple UI, low effort) as the strongest lever for experience and loyalty.
- Make perceived usefulness concrete: relevant recommendations, accurate availability, integrated booking, and clear next steps — not just a wall of text.
- Build a human escalation path. The authors flag that chatbots must integrate with human service when they can't solve complex problems; this protects experience quality and trust.
- Continuously measure: customer feedback, complaint history, time-to-resolution, post-interaction satisfaction. Don't assume deployment = success.
- Personalize using known guest context (loyalty tier, prior bookings, preferences) — generic chatbot experiences don't drive loyalty.
- For Indonesia and similar high-switching markets, frictionless cross-channel handoffs (chat → booking → confirmation → in-stay) are a defensive moat against competitor OTAs.