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

The research, conducted by Elizabeth Kafui Senya of Accra Technical University and Anthony Bordoh of the University of Education, Winneba, focuses on the Accra Metropolitan Area, the commercial heart of Ghana and the hub of its tourism economy. Published in SN Social Sciences, the study surveyed 90 senior managers drawn from hotels, restaurants, shopping malls, car rental services and allied venues across the Greater Accra Region. Using a multi-stage sampling procedure, the researchers first selected nine Municipal and Metropolitan District Assemblies and then reached the managers who had lived through the pandemic’s worst disruptions at first hand.

The findings document a cascade of operational failures that unfolded in a predictable but devastating sequence. Mobility restrictions imposed by the government, including the Executive Instrument E.I. 63 that limited movement and gatherings, triggered an acute contraction in demand. With travellers staying home and international visitors absent, revenues collapsed. Firms responded by retrenching workers, deepening the economic pain in a sector that is one of Ghana’s most important employers. Supply chains fractured at the same time, as suppliers of food, beverages and hospitality consumables faced their own lockdown constraints. The cancellation of international conferences removed another critical revenue stream, one that fills hotel rooms and restaurant tables in Accra throughout the year, particularly during the peak season.

These results align with a broader international literature on the pandemic’s impact on hospitality. Studies from India, Spain and global assessments of tourism during COVID-19 all recorded demand shocks, workforce losses and supply disruption. But the Ghanaian study adds an important dimension: the perspective of firms operating in an emerging economy where government support was limited and where many businesses had no financial reserves to fall back on. The authors argue that structural vulnerabilities in developing economies made the pandemic disproportionately damaging, and that understanding how firms in such contexts coped is essential for building crisis-ready industries worldwide.

Methodologically, the study is notable for the rigour of its measurement. The researchers employed a quantitative cross-sectional survey design consistent with the positivist research paradigm, distributing structured questionnaires to the participating managers. To test whether their survey instruments reliably captured the concepts they intended to measure, they turned to Confirmatory Factor Analysis embedded within Partial Least Squares Structural Equation Modelling, a statistical framework widely used in business research for handling complex relationships between latent constructs. The psychometric results were exceptionally strong. Cronbach’s alpha, a measure of internal consistency, ranged from 0.979 to 0.987 across the study’s three constructs, far above the conventional threshold of 0.70. Composite reliability values ranged from 0.981 to 0.990, and the average variance extracted, which indicates how much of the variance in the indicators is captured by the underlying construct, fell between 0.795 and 0.898, well above the 0.50 benchmark typically required for convergent validity. All factor loadings exceeded 0.70, meaning every survey item contributed meaningfully to its construct.

For readers unfamiliar with these statistics, the numbers matter because they tell us the findings rest on solid measurement rather than noisy data. When Cronbach’s alpha approaches 0.99, respondents answered related questions in a highly consistent way, suggesting the survey tapped a coherent underlying reality. High average variance extracted means the constructs are distinct and well defined. In practical terms, the study’s conclusions about disruption and resilience are not artefacts of a poorly designed questionnaire; they reflect carefully validated evidence from the field.

So what actually helped firms survive? The managers surveyed identified a clear hierarchy of resilience levers. At the top of the list were prophylactic health programmes, preventive measures designed to protect staff and customers from infection and to keep operations running safely. Closely linked to this was a call for equitable health infrastructure, reflecting the recognition that business resilience in a pandemic is inseparable from public health capacity. Firms that could trust the surrounding health system, and that invested in their own protective protocols, were better positioned to reassure customers and maintain operations under restrictions.

Insurance uptake emerged as another critical strategy. In an economy where many small and medium-sized hospitality businesses operate without formal risk coverage, the pandemic exposed the cost of that gap. Managers who had insurance, or who recognised its value during the crisis, identified it as a key buffer against revenue collapse. The study suggests that expanding insurance penetration in the sector should be a policy priority, not only for individual firms but for the industry’s collective resilience.

Diversification of delivery services and digitalisation rounded out the resilience agenda. With dine-in traffic restricted, restaurants and hotels that could pivot to delivery, takeaway and online ordering maintained at least a trickle of revenue. Digital tools, from online booking platforms to mobile payment systems and social media marketing, allowed firms to reach customers without physical contact and to adapt their offerings as conditions changed. The pandemic, in effect, accelerated a digital transition that many Ghanaian hospitality firms had postponed, and the managers surveyed now regard digital capability as a core survival asset rather than an optional extra.

The study’s contribution goes beyond documenting damage. By providing empirical, firm-level evidence from an understudied Sub-Saharan emerging economy, it fills a gap in the crisis management and organisational resilience literature, which has historically been dominated by research from Europe, North America and Asia. The authors argue that resilience in hospitality is not a one-size-fits-all formula: strategies that work in well-resourced markets may be unavailable or insufficient in contexts with weaker institutions and thinner financial safety nets. Their findings point toward a resilience agenda tailored to developing economies, one that combines firm-level actions such as digitalisation, diversification and insurance with system-level investments in health infrastructure and public health preparedness. As the world braces for future pandemics and climate-related disruptions, the experience of Accra’s hotels, restaurants and malls offers a sobering lesson: the businesses most vulnerable to global shocks are often those with the fewest buffers, and strengthening their resilience requires action at every level, from the individual firm to the national health system.

Subject of Research: Firm-level operational disruptions and resilience strategies in Ghana's hospitality industry during the COVID-19 pandemic

Article Title: Operational disruptions and resilience strategies in Ghana’s hospitality industry during the COVID-19 pandemic: firm-level evidence from the Accra Metropolitan Area

Article References: Senya, E. K., & Bordoh, A. (2026). Operational disruptions and resilience strategies in Ghana’s hospitality industry during the COVID-19 pandemic: firm-level evidence from the Accra Metropolitan Area. SN Social Sciences, 6(10), Article 452. https://doi.org/10.1007/s43545-026-01744-7

Image Credits: AI Generated

DOI: 10.1007/s43545-026-01744-7

Keywords: COVID-19, hospitality industry, organisational resilience, Ghana, Accra, PLS-SEM, confirmatory factor analysis, pandemic recovery, Sub-Saharan Africa, supply chain disruption, digitalisation, insurance uptake