The study focused on self-care, and specifically on mindfulness practices, a category of mental health behavior that has surged in popularity through apps, podcasts, and guided audio exercises. Unlike many physical health behaviors whose outcomes are distant and probabilistic, self-care is something people can do immediately and privately, which makes it an ideal testbed for framing effects. The researchers distinguished between two types of framed messages. Gain-framed messages emphasized the benefits of self-care, describing what people could obtain by practicing it, such as improved wellbeing and reduced distress. Nonloss-framed messages highlighted the distress that self-care could prevent, framing the behavior in terms of avoiding negative outcomes. A control condition received information unrelated to self-care, giving the team a baseline against which to compare the two framing strategies.

Participants were recruited through social media and university mass email and were randomly assigned to view one of the three message types. The design went beyond a simple comparison, however, because the authors were interested in whether framing effects depend on who is receiving the message. They measured two candidate moderators. The first was regulatory focus, a trait-level orientation from psychological theory that describes whether people chronically approach rewards, a promotion focus, or prioritize avoiding losses, a prevention focus. The second was perceived risk of worsening mental health, essentially how much a person believes their mental state could deteriorate. According to the matching hypothesis that has guided much of this literature, gain frames should work best for promotion-focused individuals, while nonloss frames should resonate with prevention-focused individuals or those who feel at risk.

The outcome measures captured three stages of behavioral engagement: intention to practice self-care, actual uptake, and depth of engagement, with assessments taken immediately after the message and again one week later. Uptake and engagement were operationalized around mindfulness audio materials, allowing the researchers to observe not just what participants said they would do but what they actually did, including how long they played the audio. The analysis relied on analyses of covariance and regression models, with attention to correcting for multiple testing, a methodological caution the authors credit in part to consultations during the review process.

The headline finding was a gain-framing advantage for self-care intention. Participants who read the gain-framed message reported higher intention to practice self-care than those in the control condition, and the data pointed suggestively in the same direction when compared with the nonloss-framed condition. In other words, telling people what they stand to gain from caring for their minds appears to be a more effective motivational strategy than telling them what they might avoid losing, at least when it comes to forming intentions. This aligns with a broader pattern in the framing literature, where gain frames tend to outperform loss frames for behaviors perceived as preventive and low-risk.

Interestingly, the behavioral data told a slightly different story. There was tentative evidence that participants in the nonloss-framing condition played the mindfulness audio for a longer duration than those in the control condition. This divergence between intention and behavior is a recurring puzzle in health psychology: the message that best motivates stated plans is not always the message that best drives actual engagement. The authors describe this behavioral effect as tentative, reflecting the statistical caution they applied, but it hints that nonloss framing may have a subtle role in sustaining engagement even when it does not win the intention contest.

The moderation results are where the study delivers its most consequential message. For people with lower perceived risks of worsening mental health, gain framing produced higher self-care intention than both the control condition and, tentatively, the nonloss condition. This suggests that when individuals do not feel their mental health is in jeopardy, emphasizing positive benefits is the more persuasive route. However, no evidence of moderation by perceived risk emerged for the other dependent variables, and, perhaps most strikingly, the results showed no support for moderation by regulatory focus at all. The long-standing theoretical expectation that promotion-focused people respond better to gain frames and prevention-focused people to nonloss frames did not hold in this mental health context.

This null result matters because it contradicts a substantial body of work on regulatory focus and framing interactions in physical health. The authors are explicit about the implication: their findings do not support applying tailored framing based on regulatory focus when encouraging mental health self-care. For intervention designers, that is both a warning and a relief. Tailoring messages to individual personality profiles is technically demanding, requiring assessment of traits and dynamic message generation. If that complexity does not buy measurable improvements in this domain, resources may be better spent elsewhere. At the same time, the finding that perceived risk moderates the framing effect offers a simpler, more actionable tailoring variable: people who feel vulnerable may not need benefit-focused persuasion, while those who feel secure may respond well to it.

The study was conducted with ethics approval from the Survey and Behavioural Research Ethics Committee of The Chinese University of Hong Kong, and it forms part of a larger research program funded by the Research Grants Council of Hong Kong on personalized nudging to promote uptake of and engagement in self-care through digital mental health interventions. The work began as part of the first author’s doctoral dissertation and evolved substantially through peer review, with the authors acknowledging input from colleagues on study design, moderation methodology, and statistical corrections. The team is transparent about the limitations inherent in their design, noting that the effects they were hunting for may be very small.

Indeed, the authors themselves frame the path forward clearly. They call for field studies, longitudinal designs, and well-powered studies capable of detecting very small effects to determine whether gain framing holds an advantage for actual self-care behavior over longer periods. A single laboratory-style exposure followed by one week of follow-up may simply be too brief and too controlled a setting to capture how framing shapes habits that unfold over months. Still, for a field increasingly dominated by digital interventions that must decide, algorithmically and at scale, what message to show whom, this experiment offers a valuable calibration. The safest evidence-based default for encouraging mental health self-care appears to be gain framing, with perceived risk, not personality, as the most promising axis for personalization, and with a lingering, tantalizing hint that loss-avoidance language may quietly extend the time people spend actually practicing mindfulness once they begin.

Subject of Research: Message framing effects on mental health self-care intention and mindfulness engagement

Article Title: Impact of gain and nonloss framing in mental health self-care: investigating regulatory focus and perceived risk of worsening mental health as moderators

Article References: Yeung, S. K., Mak, W. W. S., Zhao, H., & Lee, J. C. M. (2026). Impact of gain and nonloss framing in mental health self-care: investigating regulatory focus and perceived risk of worsening mental health as moderators. BMC Psychology. https://doi.org/10.1186/s40359-026-05715-8

Image Credits: AI Generated

DOI: 10.1186/s40359-026-05715-8

Keywords: message framing, mental health self-care, mindfulness, regulatory focus, perceived risk, gain framing, nonloss framing, health communication, digital mental health interventions, health psychology, behavior change, randomized experiment

The researchers started from a well-established idea in consumer psychology: perceived risk theory. Since the 1960s, scholars have recognized that buyers do not simply weigh benefits; they also mentally simulate everything that could go wrong. A purchase can fail financially, functionally, socially, or psychologically, and the anticipation of those failures shapes decisions just as powerfully as any advertised advantage. What Sobhanifard and Molavi suspected was that green products carry an unusually heavy load of such perceived risks — risks that traditional models of sustainable consumption have tended to underestimate or ignore. If consumers hesitate at the shelf, the hesitation may have less to do with weak environmental values and more to do with a private, often unspoken calculus of what they stand to lose.

To test that idea, the team built a mixed-methods design that unfolded in three stages. First, they conducted a systematic review of the existing literature on green consumption and perceived risk, combing through prior studies to assemble a candidate list of risk factors. Second, they interviewed nine experts in the field and applied thematic analysis to those conversations, refining and expanding the list until it captured the full landscape of consumer anxieties. The result was a set of 21 distinct perceived risk factors — a far more granular taxonomy than the handful of risk categories found in classic textbooks. The factors ranged from the familiar, such as higher purchase price and doubts about product quality, to the subtle and rarely studied, such as the fear of being seen as a show-off for buying green, or the anxiety of not being able to explain a product’s environmental features to friends and family.

The third stage moved from theory to measurement. The researchers administered a structured questionnaire to 642 consumers in Iran, asking each respondent to rate, on a scale from 1 to 10, how strongly every one of the 21 factors discouraged them from purchasing green products. The questionnaire was deliberately concrete: rather than asking abstract questions about environmental concern, it asked about specific worries — whether a brand’s claims seemed believable, whether after-sales service and repairs would be adequate, whether the product might trigger health problems, whether buying it would demand extra time and effort spent comparing options and researching credentials. Respondents also reported demographic details and the share of household purchasing decisions they personally controlled, allowing the researchers to anchor their risk ratings in real shopping roles.

When the survey data came back, the team applied Exploratory Factor Analysis, a statistical technique that searches for hidden structure in a cloud of correlated variables. The analysis revealed that the 21 risk factors were not an undifferentiated list; they clustered into four underlying dimensions. The first, labeled Personal Factors, gathered the intimate, self-directed worries — including a construct the authors introduce as internal failure feeling, the anxiety, disappointment, or loss of self-esteem a buyer anticipates after a purchase goes wrong. The second dimension, Information and Trust, captured doubts about the credibility of product claims, the reliability of brands, and the honesty of green marketing, including the ever-present specter of greenwashing. The third, Environmental Assurance, reflected uncertainty about whether green products genuinely deliver on their ecological promises, from recyclable packaging to verifiable environmental performance. The fourth, Quality Expectations and Purchase Value, bundled concerns about durability, performance, limited variety, unfashionable design, and the nagging sense that a green product might simply be worse than its conventional rival.

Ranking the risks was where the study made its most distinctive methodological move. Instead of relying on regression coefficients or expert judgment alone, the researchers turned to Random Forest, a machine-learning algorithm that builds hundreds of decision trees on random subsets of the data and aggregates their predictions. Random Forest is prized for its robustness and for its ability to quantify the relative importance of each input variable, even when variables interact in nonlinear ways — exactly the situation one expects when psychological, social, and financial worries combine to influence a single purchase decision. Running the algorithm on the survey responses produced a clear hierarchy of barriers, and the six most influential risk factors emerged as internal failure feeling, unreliable information, brand distrust, additional effort, low variety and not being fashionable, and lack of reliable organizations and certifications.

Several of these findings deserve close attention. The top-ranked factor, internal failure feeling, is a novel construct in the green consumption literature, and its prominence suggests that the emotional stakes of buying green are higher than most models assume. A consumer who pays a premium for an eco-friendly product and then feels let down does not merely lose money; the authors suggest the buyer may experience a personal sense of failure, a wound to self-image that makes future green purchases even less likely. Equally striking is the weight placed on trust-related risks. Unreliable information and brand distrust ranked second and third, and the absence of credible certifications and verification organizations ranked sixth. In an environment saturated with vague environmental claims, consumers appear to be making a rational response: when they cannot verify a product’s green credentials, they default to skepticism, and skepticism kills the sale.

The remaining top barriers point to friction in the shopping experience itself. Additional effort — the extra time and energy needed to find, compare, and research green alternatives — acts as a silent tax on sustainable choices, one that conventional products do not impose. And the perception that green products offer low variety and lag behind current fashion suggests that sustainability still carries an aesthetic penalty in consumers’ minds, a stereotype that better design and marketing could directly attack. Together, the six barriers sketch a portrait of the hesitant green consumer: not indifferent to the environment, but wary of being deceived, disappointed, embarrassed, or exhausted by the act of buying responsibly.

The study’s implications extend well beyond Iran, even though its empirical base is a single national sample of 642 consumers — a limitation the authors’ framework does not erase, and one reason readers should treat the specific rankings as a hypothesis to be tested in other markets rather than a universal law. Still, the practical recipe that emerges is concrete. Policymakers could narrow the trust gap by establishing and enforcing credible certification schemes, so that a green label on a package means something a shopper can verify. Manufacturers could reduce internal failure feelings by guaranteeing performance and offering money-back protections, and could shrink the effort barrier by making green options easier to find and compare. Marketers, meanwhile, face the most delicate task: rebuilding brand credibility in a marketplace where exaggerated claims have taught consumers to doubt. The green gap, this research suggests, will not close through more appeals to conscience. It will close when buying green stops feeling like a gamble.

Subject of Research: Perceived risk factors in sustainable consumption and the green gap between consumer intentions and purchasing behavior

Article Title: Bridging the green gap through understanding the role of perceived risks in sustainable consumption decisions

Article References: Sobhanifard, Y., & Molavi, R. (2026). Bridging the green gap through understanding the role of perceived risks in sustainable consumption decisions. Discover Sustainability. https://doi.org/10.1007/s43621-026-04767-5

Image Credits: AI Generated

DOI: 10.1007/s43621-026-04767-5

Keywords: green gap, perceived risk, sustainable consumption, green products, consumer behavior, greenwashing, Random Forest, exploratory factor analysis, brand trust, environmental psychology, green certifications, purchase decisions

The research, published in Discover Psychology, was led by Riketa Parmar and Vipul Patel of Ganpat University’s V. M. Patel College of Management Studies, together with Mruga H. Mehta and Jigneshkumar P. Desai of Parul University. The team set out to build and test what they call the Green Banking Consumer Adoption Model for India, or GB-CAM-India. Rather than relying on a single behavioural theory, the model deliberately stitches together three of the most widely used frameworks in consumer and environmental psychology: the Technology Acceptance Model, the Theory of Planned Behaviour, and the Value-Belief-Norm theory. To these it adds constructs tailored to the Indian market, including perceived trust, perceived risk and customer satisfaction.

The logic behind the integration is worth unpacking. The Technology Acceptance Model, developed originally to explain how people come to use new technologies, focuses on two perceptions: that a system is useful and that it is easy to use. The Theory of Planned Behaviour adds the role of attitudes, subjective norms, meaning the social pressure we feel from people around us, and perceived behavioural control, our sense that we are capable of performing the behaviour. The Value-Belief-Norm theory, rooted in environmental sociology, traces pro-environmental action back through a chain of personal values, ecological beliefs and the activation of moral norms. Green banking sits at the intersection of all three: it is a technology, a planned consumer behaviour and an environmentally significant act at once, which is precisely why the authors argued that no single theory could capture the whole picture.

To test the model empirically, the researchers collected primary survey data from 760 respondents spread across five regions of Gujarat, a western Indian state with a large and diverse banking population. For each theoretical construct, they computed composite, or summed, scale scores and then subjected the data to a battery of statistical checks. Exploratory factor analysis was used to confirm that the survey items clustered into the intended dimensions, and reliability analysis confirmed internal consistency. The psychometric credentials of the measures were strong: the Kaiser-Meyer-Olkin measure of sampling adequacy reached 0.906, a value well above the conventional 0.6 threshold, every composite reliability value was at least 0.829, and all average variance extracted values were at least 0.545, calculated from the standardised loadings produced by the factor analysis. In plain terms, the survey instruments measured what they claimed to measure, and they did so consistently.

With the constructs validated, the team turned to multiple regression to test eight hypotheses linking the predictors to green banking adoption behaviour. The model performed respectably, explaining 46.8 percent of the variance in adoption behaviour, with an F-statistic of 82.51 on 8 and 751 degrees of freedom and a p-value below 0.001. Customer satisfaction dominated the results, with a standardised beta of 0.464, far ahead of any other predictor. Perceived ease of use came second at 0.193, followed by social influence at 0.083, both also significant at the 0.001 level. Perceived risk worked in the opposite direction, exerting a significant negative effect with a beta of minus 0.085 and a p-value of 0.002. The message for banks is that making green services feel safe, simple and satisfying matters more than preaching their environmental virtues.

One of the most technically interesting parts of the study concerns how satisfaction connects upstream perceptions to downstream behaviour. Using bootstrapped indirect-effect tests with 5,000 resamples and bias-corrected 95 percent confidence intervals, the researchers found that customer satisfaction carries significant indirect effects from perceived usefulness, perceived trust and environmental attitude through to adoption behaviour. Crucially, the corresponding direct paths from those three constructs to adoption were statistically nonsignificant. That pattern means satisfaction does not merely supplement these influences; it is the channel through which they operate. The authors are careful with terminology here, characterising these as indirect-only effects rather than partial mediation, a distinction that matters for how future researchers model the pathway.

The study also probed how the satisfaction-adoption relationship varies across the population. Income level emerged as a significant moderator of that path: higher-income consumers showed reduced sensitivity to satisfaction signals when deciding whether to adopt green banking. In other words, for wealthier customers, a pleasant service experience moves the adoption needle less than it does for lower-income consumers, a finding with clear implications for how banks segment their green marketing. The researchers additionally observed that mean adoption scores differed significantly across Gujarat’s five regions, with an F-statistic of 9.88 on 4 and 755 degrees of freedom and a p-value below 0.001. They are careful to note, however, that this is a group-difference finding rather than a formally tested regional moderation effect, an honest caveat that reflects the exploratory, composite-score design of the analysis.

The practical implications ripple outward from these statistics. For banks, the results suggest that investments in service quality, complaint resolution and overall customer experience may do more to accelerate green banking uptake than awareness campaigns alone, because satisfaction is the conduit through which usefulness, trust and environmental attitudes are converted into action. Reducing perceived risk, whether fear of fraud in digital channels or uncertainty about green products, should also pay dividends, given its significant negative coefficient. For policymakers working under the Reserve Bank of India’s Green Finance Framework, the regional differences in adoption scores across Gujarat hint that a one-size-fits-all national rollout may underperform, and that income-sensitive design could matter, since wealthier consumers respond differently to satisfaction cues than lower-income ones.

The authors are equally candid about the limits of their approach. Because the analysis relied on composite scores, exploratory factor analysis and multiple regression rather than a full structural equation model, the causal architecture of GB-CAM-India remains a proposal awaiting confirmatory testing. They explicitly flag directions for future SEM-based research that could estimate the full path model with latent variables, test the regional moderation hypothesis formally, and extend the sample beyond Gujarat to other Indian states. The survey design is also cross-sectional, so the direction of influence, while theoretically motivated, is inferred rather than observed over time.

Even with those caveats, the study lands at a propitious moment. As India’s financial sector aligns itself with sustainable development goals, understanding the psychology of the consumer becomes as important as the engineering of the green products themselves. What this research demonstrates is that the road to sustainable finance in India runs through the everyday experience of the bank customer: a useful app that is trusted, a service that satisfies, and a social environment that quietly nudges people toward greener choices. If satisfaction is the engine of green banking adoption, then the banks that win India’s sustainable finance transition may be those that treat environmental ambition and customer experience as a single, inseparable project.

Subject of Research: Consumer adoption of green banking services in India, modelled through an integrated TAM-TPB-VBN framework

Article Title: An integrated TAM–TPB–VBN framework with empirical validation from Gujarat for green banking consumer adoption in the Indian market

Article References: Parmar, R., Patel, V., Mehta, M. H., & Desai, J. P. (2026). An integrated TAM–TPB–VBN framework with empirical validation from Gujarat for green banking consumer adoption in the Indian market. Discover Psychology. https://doi.org/10.1007/s44202-026-00883-5

Image Credits: AI Generated

DOI: 10.1007/s44202-026-00883-5

Keywords: green banking, consumer adoption, Technology Acceptance Model, Theory of Planned Behaviour, Value-Belief-Norm theory, India, Gujarat, customer satisfaction, sustainable finance, perceived risk, social influence, survey research

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