AI Personalization Wins Indian Hotel Guests Through Value, Not Speed Alone

Artificial intelligence is quietly rewriting the rules of hospitality in India, and a new study suggests that the path to a satisfied guest runs not through faster robots or slicker chatbots, but through something far more old-fashioned: value for money. Research published in Discover Artificial Intelligence used structural equation modelling on survey data from 276 respondents to map exactly how AI-enabled personalization shapes customer satisfaction and customer experience in the Indian hospitality industry. The results challenge much of the Western literature on the so-called dark side of AI, showing that Indian consumers in this sample weighed tangible value and efficiency far more heavily than lingering privacy fears or abstract service quality perceptions.

The study arrives at a moment when the hospitality sector, which contributes roughly 10 percent of global GDP, is under intense pressure to reinvent itself. India’s travel and tourism market is expected to grow from an estimated US$75 billion in FY20 to US$125 billion by FY27, with international tourist arrivals projected to reach 30.5 million by 2028 and the sector supporting around 40 million jobs. At the same time, global AI spending is forecast to exceed $300 billion by 2026, and McKinsey estimates that generative AI could add between $2.6 trillion and $4.4 trillion annually to the world economy. Against that backdrop, the authors set out to answer a deceptively simple question: which factors actually convert AI-powered personalization into satisfied, loyal guests?

The research team, led by Prashant Chaudhary of Dr. Vishwanath Karad MIT World Peace University, together with Nilesh Kate and Sujoy Kumar Jana of the Pune Institute of Business Management and Pradip Padhye of Symbiosis International University, built a conceptual model containing seven constructs: service quality, expectancy, response time, perceived value, customer experience, perceived risk and customer satisfaction. Drawing on models such as the European Customer Satisfaction Index and expectancy-value theory, they formulated twelve hypotheses about how these variables interact. Data were collected through a structured questionnaire distributed via Google Forms using convenience sampling, with all respondents over 18 years old and most drawn from southern India. The instrument comprised twenty-five items rated on five-point Likert scales, covering everything from the punctuality of AI-driven services to perceptions of price fairness and data privacy.

Methodologically, the analysis was rigorous by the standards of survey-based hospitality research. Confirmatory factor analysis verified the measurement model, with fit indices including the Chi-square/degree of freedom ratio, the Comparative Fit Index, the Tucker-Lewis Index and the root mean square error of approximation all falling within acceptable ranges. Reliability was strong: the Cronbach’s alpha of 0.870 comfortably exceeded the conventional 0.70 threshold, and both convergent validity, with construct reliability above 0.7 and average variance explained above 0.5, and discriminant validity were established. The team then applied structural equation modelling using IBM AMOS 20.0 and SPSS 22.0 to test the causal structure implied by their hypotheses, examining standardized path coefficients across the full network of relationships.

The headline finding is striking in its clarity. Perceived value emerged as the strongest and most consistent predictor of customer satisfaction, with a standardized path coefficient of 0.65. In other words, guests who felt that AI-assisted hospitality services delivered cost-effectiveness, convenience and benefits worth the price paid were the ones most satisfied and most inclined toward loyalty. Service expectancy, in turn, was the strongest predictor of perceived value at 0.60, and also significantly shaped perceived risk at 0.25. This chain, from expectancy through value to satisfaction, formed the backbone of the supported model and suggests that how much customers believe in and engage with AI technologies fundamentally determines how much value they perceive, and therefore how satisfied they become.

Response time told a more complicated story. It significantly influenced perceived value with a negative coefficient of −0.10, a counter-intuitive result the authors interpret as reflecting a preference for empathetic, human-like interaction over sheer automated speed. Faster AI-mediated responses were associated with somewhat lower perceived value in this sample, hinting that pure automation without warmth may feel hollow to guests who still crave human connection. At the same time, response time positively shaped perceived risk at 0.39, indicating that slower AI responses heightened worries about privacy and reliability. These nuanced dynamics suggest that the tempo of AI service delivery matters, but not in the simplistic way that faster-is-always-better intuition would predict.

Perhaps the most provocative results are the ones that failed to materialize. Seven of the twelve hypotheses were rejected. Service quality showed no significant direct effect on perceived value, perceived risk or customer experience, a pattern the authors attribute partly to shared-variance suppression, since expectancy and response time, which correlated moderately with service quality, absorbed much of the explanatory power. Customer experience likewise had no significant direct effect on satisfaction, and perceived risk did not significantly predict satisfaction at all. The authors caution that the perceived risk items were worded in a reassuring, trust-oriented direction rather than as threat measures, so the null result should be read as evidence that trust was already accounted for through expectancy and value, not that privacy concerns are irrelevant to Indian consumers.

These departures from Western findings carry real theoretical weight. Prior studies, including work by Wirtz and colleagues on frontline service robots and research highlighting customer discomfort with automation-driven lack of transparency, have emphasized the risks and ethical complexities of AI in service settings. The Indian data instead suggest a region-specific pattern: when the value proposition of AI, such as efficient, personalized and convenient service, is tangible and evident, consumers demonstrate a higher tolerance for AI-related risk and data privacy concerns. The young, price-conscious, largely student-dominated sample, 66 percent of whom were students aged mostly between 18 and 30, may amplify this utilitarian orientation, and the authors are careful to flag the convenience sampling and southern-India concentration as limitations that warrant caution in generalizing to older or non-student segments.

The practical implications for hotel chains, budget accommodations and online travel platforms are nonetheless concrete. The findings recommend that hospitality operators integrate AI into systems, processes and communication channels to raise service expectancy, and blend AI-powered analytics with human intervention to achieve something approaching hyper-personalization. AI tools enhanced with multilingual capabilities can break language barriers in booking experiences, while automation frees human staff for tasks requiring empathy and creativity. According to a Dun & Bradstreet survey cited in the study, 100 percent of surveyed Indian organizations have AI projects underway, around 73 percent report measurable returns, and 69 percent plan to increase AI investments. An Adobe survey of more than 5,000 Asia-Pacific consumers found that nearly 95 percent of Indian consumers trust AI-powered technologies to improve their experience, a strikingly higher figure than in New Zealand at 54 percent or Australia at 57 percent.

Ultimately, the study’s novelty lies in identifying perceived value and service expectancy, rather than service quality alone, as the critical levers through which AI integration can lift consumer satisfaction and loyalty in emerging markets such as India. The authors propose a revised, more parsimonious model in which expectancy and response time act primarily through perceived value and risk to drive satisfaction, and they call for future research using stratified national samples, risk-worded measurement instruments, and longitudinal or experimental designs to establish causality. For an industry racing toward digital transformation in the post-pandemic landscape, the message is clear: algorithms and chatbots may deliver the service, but it is the perception of value, carefully calibrated and honestly communicated, that wins the guest. AI, deployed strategically and blended with human ingenuity, becomes a catalyst for loyalty, but only when guests can see, in hard terms, what the technology is worth to them.

Subject of Research: The effects of artificial intelligence-enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry

Article Title: The effects of artificial intelligence enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry

Article References: Chaudhary, P., Kate, N., Jana, S. K., & Padhye, P. (2026). The effects of artificial intelligence enabled personalization on customer satisfaction and customer experience in the Indian hospitality industry. Discover Artificial Intelligence, 6(1), Article 1183. https://doi.org/10.1007/s44163-026-02250-8

Image Credits: AI Generated

DOI: 10.1007/s44163-026-02250-8

Keywords: artificial intelligence, hospitality industry, customer satisfaction, customer experience, personalization, perceived value, India, structural equation modelling, service quality, perceived risk, loyalty, emerging markets