Educational Analytics for Tourism Service Quality and Revisit Intentions in Rajasthan: Integrating Servqual, Sem, And Mixed-Effects Analysis

Abstract

Background/Purpose: Rajasthan—India's largest state by area and one of its most visited tourist destinations—attracts domestic and international visitors to its heritage forts, palaces, desert landscapes, and vibrant cultural traditions. As tourism increasingly drives economic growth and cultural exchange in the region, understanding the material and service-quality factors that encourage tourist revisits is essential for sustainable tourism development. Rajasthan's tourism infrastructure—spanning accommodation, guided tours, food services, and transportation—operates across widely varying quality levels across key destinations including Jaipur (the Pink City), Udaipur (the City of Lakes), and Jodhpur (the Blue City), creating measurable heterogeneity in tourist satisfaction and revisit intentions. This study investigates the influence of service quality dimensions on tourist revisit intentions across these key Rajasthan destinations, employing a comprehensive statistical and mathematical modeling approach that mirrors the performance-index frameworks used in infrastructure materials research. This paper develops a predictive framework—the Tourism Service Quality Index (TSQI)—that integrates the five SERVQUAL service quality dimensions with revisit intentions, utilising structured questionnaire data collected from tourists across key Rajasthan destinations.

Methodology: A structured questionnaire survey of n = 115 tourists was administered across Jaipur, Udaipur, and Jodhpur. The instrument captures tourist perceptions of service quality across the five SERVQUAL dimensions—Tangibles (physical facilities, equipment, appearance), Reliability (ability to perform promised services dependably), Responsiveness (willingness to help and provide prompt service), Assurance (knowledge and courtesy of staff and their ability to inspire trust), and Empathy (caring, individualised attention)—using a 7-point Likert scale, alongside three revisit intention items and three destination loyalty items. The data undergo rigorous statistical analysis employing multivariate techniques including Exploratory and Confirmatory Factor Analysis (EFA/CFA) to refine the service quality dimensions, Structural Equation Modeling (SEM) to explore causal relationships between service quality and revisit intentions, and a mixed-effects model to account for variability across the three cities and different tourist experience profiles. A Tourism Service Quality Index (TSQI) is developed as an integrated composite indicator, and Monte Carlo simulation (n = 800) characterises TSQI uncertainty across four system scenarios.

Key Findings: Regression analysis (R² = 0.84) confirms a statistically significant positive relationship between the TSQI and revisit intention scores. Among the five SERVQUAL dimensions, 'Assurance' (knowledge, courtesy, and trustworthiness of staff) and 'Empathy' (caring, personalised attention) have the most significant impact on revisit intentions, with standardised SEM path coefficients of β = 0.44 and β = 0.39 respectively (p < 0.001). The service quality gap (ΔSQI = SQ_expected − SQ_perceived) is largest for the Reliability dimension, confirming that consistent, dependable service delivery is the primary determinant of tourist satisfaction variability. Monte Carlo simulation (n = 800) characterises TSQI uncertainty, with mean TSQI improving from 0.43 (conventional service baseline) to 0.85 (integrated quality enhancement system). Cluster analysis identifies three distinct tourist segments with divergent service quality priorities and revisit intention patterns.

Conclusion: The SERVQUAL-adapted TSQI framework provides a mathematically validated, statistically confirmed, and practically grounded tool for quantifying tourism service quality and predicting revisit intentions in Rajasthan. The Tourism Service Quality Index (TSQI), anchored in the integrated service quality model TSQI = Σᵢ(wᵢ × SQᵢ), SEM path coefficients, and mixed-effects model residuals, offers Rajasthan's tourism marketers and policymakers a replicable decision framework for maximising long-term destination loyalty through targeted service quality improvements [1–10].