Artificial Intelligence Services and Customer Satisfaction in Tourism Information Systems

Abstract

Introduction: The use of AI in tourism is becoming more widespread due to chatbots, recommendation engines, virtual assistants, AI pricing, and prediction analysis. However, there is a need for an empirical analysis of the different ways in which AI services influence customer satisfaction. Objective: This research attempts to analyze the effect of the variables ease of use, personalization, reaction time, service quality and trust/reliability on customer satisfaction with AI-based tourism services. Methodology: Data was collected through questionnaires from 200 tourists using statistical tools like descriptive statistics, mean score test, multiple regression, and structural equation modeling in AMOS. Results: The average scores were 4.3 on the ease of use, 4.4 on personalization, 4.2 on response time, 4.1 on service quality, 3.9 on trust and reliability, and 4.2 on customer satisfaction. According to the regression analysis, there were positive correlations between the ease of use (β = 0.28), personalization (β = 0.35), response time (β = 0.22), service quality (β = 0.18), and trust and reliability (β = 0.15) and customer satisfaction, with R² = 0.75. As for the AMOS model, it had acceptable to excellent fit with χ²/df = 1.68, GFI = 0.931, AGFI = 0.907, CFI = 0.957, TLI = 0.949, NFI = 0.924, RMSEA = 0.046, and RMR = 0.039. All structural paths that were tested proved to be statistically significant, with p < 0.05. Conclusion: Personalization turned out to be the best predictor of customer satisfaction, followed by the ease of use and response time. The results suggest that AI-enabled tourism information services could improve customer satisfaction if they provided personalized, responsive, reliable, and user-friendly support.