The COVID-19 emergency reshaped work perceptions worldwide, exacerbating employee turnover, particularly in tourism and hospitality. While often examined from a traditional human resource-based perspective, this issue requires examining mechanisms through which employees can be attracted, satisfied, and retained. This study adopts an internal marketing perspective and offers a new application of the theory of consumption values (TCV), viewing employees as internal customers and examining the values driving employment choices within tourism and hospitality. Eighty-one in-depth interviews were conducted, providing insights into TCV values (i.e., social, conditional, functional, epistemic, emotional). Findings highlight workplace collaboration and relationships as social value, and work-life balance and flexibility as conditional value. A novel theme, transformative value, emerged when employers foster openmindedness and identity building. Besides its theoretical contribution, the study offers recommendations for improving employee attraction and retention in tourism and hospitality
This article explores the connections between job satisfaction, affective commitment, and turnover intention among hotel employees in Spain and Türkiye. In order to compare how these relationships differ between Turkish and Spanish employees, the study draws on Hofstede’s cultural theory and Social Exchange Theory (SET) to explain the exchange relationships in the workplace. We conducted a questionnaire survey to gather data from 270 hotel employees, including 132 from Spain (November–December 2023) and 138 from Türkiye (January 2024), working in three-, four-, and five-star hotels across Spain and Türkiye. We employed SmartPLS for multigroup analysis (PLSMGA) and partial least squares structural equation modeling (PLS-SEM), while SPSS was utilized for descriptive analysis. The findings indicate that affective commitment is significantly influenced by relationships with superiors and satisfaction with the reward system, but not by physical conditions or relationships with colleagues. Turnover intention is negatively impacted by affective commitment. The findings offer actionable, culture-specific HR insights for multinational hotels.
Film nostalgia has gained increasing research attention for its role in shaping tourists’ perceptions of destinations. Drawing on appraisal theory of emotion, this study adopts a quantitative approach and examines the impact of Hong Kong films on tourists’ responses based on 759 valid responses. Specifically, it examines how film nostalgia influences tourists’ forgiveness through the mediating role of place attachment, and how forgiveness, in turn, affects behavioral intentions following exposure to negative information about a destination, including intentions to recommend, defend, and visit it. The study further examines whether different types of destination negative information (moral vs. competence) moderate these relationships. The findings reveal a significant mediating role of place attachment, highlighting how the emotional bond formed through nostalgic media can promote forgiveness. The results also suggest that the type of destination negative information functions as an important contextual factor shaping tourist responses. These findings indicate that destinations can leverage nostalgic film elements to strengthen emotional connections, mitigate the impact of negative information, and enhance long-term destination resilience.
This study investigates the hedonic (pleasure-oriented) and eudaimonic (meaning-oriented) dimensions of adventure well-being by analyzing Tripadvisor reviews from seven Himalayan trekking destinations in India. Using Structural Topic Modeling (STM), sentiment, and emotion analysis, the study identifies 14 experiential themes that cluster into hedonic, eudaimonic, and instrumental domains. Hedonic experiences are associated with aesthetic immersion, relaxation, and sensory enjoyment, whereas eudaimonic experiences are linked to challenge, achievement, cultural engagement, and self-reflection. The findings further indicate that these dimensions frequently overlap, with adventure experiences often generating pleasure, personal growth, and meaning simultaneously. Instrumental themes provide the structural and logistical support enabling these outcomes. Drawing on Self-Determination Theory (SDT) as an interpretive lens, the findings suggest that tourists’ narratives are broadly consistent with experiences associated with autonomy, competence, and relatedness, while the Himalayan context appears to imbue adventure experiences with ecological and spiritual significance. The study provides contextual insights into hedonic and eudaimonic dimensions of adventure well-being. It offers practical insights for designing experiences that balance enjoyment with personal growth and sustainability
Air pollution, particularly fine particulate matter (PM2.5), defined as airborne particles with an aerodynamic diameter of 2.5 μm or smaller, has become a critical concern for tourism destinations in Northern Thailand, shaping visitor experiences and threatening destination sustainability. Drawing on the Theory of Planned Behavior (TPB), this study examines how perceived PM2.5 affects tourist revisit intention (RI) through three channels: a risk-avoidance/shortcut pathway (direct deterrence), a cognitive pathway (influencing TPB beliefs: attitude, subjective norm, perceived behavioral control), and an experiential pathway (through satisfaction). We surveyed 417 domestic and international tourists across eight Upper Northern Thai provinces (January–April 2025) using structured questionnaires measuring TPB constructs alongside destination image (DI), perceived value (PV), and environmental perceptions. Data were analyzed using Kendall’s tau, regression, and structural equation modeling (SEM) to test hypothesized direct and indirect relationships. Results show that perceived PM2.5 reduces tourist’s revisit intention and satisfaction, while DI and PV enhance SAT and RI. Satisfaction plays a central mediating role, transmitting the effects of PM2.5, DI, and PV onto revisit intention. These findings highlight that improving air quality management and strengthening perceived destination value are essential for maintaining satisfaction and encouraging repeat visitation in haze-affected destinations.
This research examines how sacred meaning is constructed through tourism in emotionally charged Indian settings: spiritual, dark (thanatouristic), and hybrid. Using a two-study mixed-method approach, it reveals two dimensions of authenticity, feeling and setting, that shape enduring visitor–site connections. Study 1 uses grounded theory to uncover meaning-making processes in sacred and dark settings. The findings reveal that sacralization is an emergent, co-constructed phenomenon, occurring at the intersection of a site’s material “setting authenticity” and a visitor’s internal “feeling authenticity.” This suggests that sacred meaning is neither inherent to the location nor purely subjective, but a dynamic product of their interaction. Study 2 demonstrates that site type influences sacralization via different authenticity pathways, moderated by traveler mindset and time perception. By linking sacralization to cultural and experiential sustainability, this study offers a novel framework for destination design and meaningful tourism experience management
This study examines the synergy between information and communication technology (ICT) and destination performance (DP) across 84 destination countries from 2000 to 2019 using canonical correlation analysis. Incorporating the ICT penetration and the economic contribution of ICT as the proxies for measuring the development of ICT, this study concludes that both dimensions of ICT are significantly associated with DP. This study also conducts a three-step sensitivity analysis tostrengthen the credibility of our findings. The conclusion is consistently supported given various heterogeneous factors, including different levels of tourism performance, the change of time, and different levels of economic development. This study contributes to the current tourism literature byillustrating a strong interaction between ICT and DP, indicating that the importance of ICT in tourism has been gradually growing over the past two decades while urgently calling for developing economies to capitalize on ICT-related resources to enhance tourism performance.
The Indian tourism sector, while economically significant, faces increasing pressure to address its environmental impact. Although carbon taxes have not yet been introduced in India, the growing emphasis on climate responsibility indicates that such regulations may soon become standard. This highlights the urgent need for a proactive, technology-led decarbonization strategy. This study introduces a framework integrating artificial intelligence and blockchain technology to support transparent and efficient carbon credit trading tailored to Indian tourism enterprises. Based on data from 10 leading firms on the National Stock Exchange, the research proposes the Emission Reduction Progress Factor, a composite index combining emissions performance, financial outcomes, and customer satisfaction. A blockchain-based matching system using Entropy Optimal Transport ensures secure, peer-to-peer carbon credit exchanges, minimizing transaction costs and enhancing transparency through decentralized smart contracts. The framework also includes a cost model to estimate energy use, trading fees, and penalties. Strategic decision-making is supported by a nonlinear optimization model based on particle swarm optimization, which aligns emissions reduction with operational profitability. Findings indicate that high-emission firms benefit financially from trading, while low-emission firms gain through surplus credit sales and enhanced market positioning. The framework offers a scalable model to institutionalize sustainable tourism practices in India.
The integration of artificial intelligence (AI) chatbots in tourism has transformed travelers’ information-seeking behaviors, offering efficient and personalized support for tasks such as itinerary planning. However, algorithm aversion (AA), the tendency to distrust AI systems due to perceived errors or a lack of human-like judgment, poses a significant barrier to adoption. This study investigates how psychological and behavioral factors influence AA in travelers’ use of AI chatbots, utilizing Technology Readiness Theory (TRT) to create a novel framework integrating Perceived AI Autonomy (PAA), Advisory Algorithm Preference (AAP), Human Agent Preference (HAP), and TRT dimensions (optimism, innovativeness, discomfort, insecurity). Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to analyze data from 298 travelers, the results reveal that PAA and DC significantly increase AA, while optimism (OPT) reduces it. However, HAP and innovativeness (INN) show no significant effects on AA. In addition, AA strongly predicts user dissatisfaction (UD). The study contributes mainly to extending the existing framework of TRT to describe and explain AI adoption in tourism.
This research note presents a rapid and comparative review of generative artificial intelligence (GenAI) applications within tourism, hospitality, and marketing research. This study reviewed 51 empirical, peer-reviewed articles published between 2020 and 2024. A narrative synthesis approach was used to identify the major theories, key variables, and methodological patterns, as well as to explore how Gen-AI has been utilized as a research subject, tool, and output generator across disciplines. Findings reveal a surge in Gen-AI-related publications in tourism and hospitality since 2022. While hospitality research primarily tests existing theories, tourism studies emphasize user acceptance and emotional responses. In contrast, marketing research demonstrates broader theoretical innovation and methodological diversity. The findings also reveal that most tourism and hospitality studies treated Gen-AI as a research subject while marketing studies are more likely to adopt it as a tool and incorporated into research design. Practically, the study provides researchers and practitioners with a clearer understanding of how Gen-AI is being applied, guiding future research design, industry adoption, and interdisciplinary collaboration.
The study focuses on artificial intelligence (AI)-driven sustainable tourism offerings (STO), which attempted to explore the drivers, factors promoting the drivers, and the obstacles aligned to this topic in a specific climate-sensitive context: Small Island Developing States (SIDS). Drawing from an interpretative approach and semistructured interviews with tourism stakeholders from the supply and demand side followed by thematic analysis, key drivers identified were: speed, efficiency, customization, ecological crisis, the zero-waste culture, and resource constraints among others. Ethical rules and regulations, costs, human readiness, trust, complexity, and contextual adaptation were the key barriers of AI-powered STO. Some mediating factors were also extended. The study’s findings align and advance the unified theory of acceptance and use of technology (UTAUT) and diffusion of innovation (DOI) models by providing the first evidence of a set of 13 mix of internal and external constructs that affect the AI-driven STO in SIDS. Such insights can be used by policymakers, AI designers for the tourism arena, tourism businesses, and stakeholders of the industry to understand, test, and validate the use of AI-driven sustainable tourism marketing initiatives aiming at ecological conservations, economic resilience and stronger adaptation, and mitigation to climate change impacts, all while being contextually appropriate.
The tourism and hospitality (T&H) sector is experiencing reflective revolution, influenced by the rapid blend of AI and generative AI (GenAI) technologies. The objective of this study is to explore how these technologies influence ethical problems, reshape social interactions, and raise equity concerns while being evaluated critically through the sustainability’s perspective. This research is conducted using an established scientific method, known as a systematic literature review (SLR) using PRISMA guidelines. This study further utilized the integrated framework ADO-TCM (Antecedents–Decisions–Outcomes and Theories–Contexts–Methods) to get a more extensive and meaningful understanding of the topic. The proposed integrative framework illustrates that the outcome of AI/GenAI adoption in T&H is shaped by technological capabilities, governance, contextual values, and sustainability imperatives. This review’s limitations include the elimination of extraordinary keywords from databases, a focus on only English-language studies, and rigorous selection guidelines. The literature on AI’s impact on sustainability and ethics is expanding; however, there is still a lack in T&H literature. Most studies concentrated on areas like sustainable tourism, entrepreneurship, and economic growth. However, none addressed the ethical, social, or equity aspects of AI/GenAI in T&H.
The increasing adoption of artificial intelligence in tourism has intensified concerns about data privacy, transparency, and ethical governance. This study examines the current state of corporate digital responsibility among tourism operators in New Zealand and explores how AI-enabled tools may conceptually address identified gaps. A two-stage qualitative design was used. The first stage involved a website analysis of 55 Qualmark-licensed tour operators to assess privacy practices, legal transparency, and organizational governance. The second stage drew on interviews with three academic AI specialists and three tourism practitioners representing small, medium, and large organizations to examine perceived feasibility and organizational constraints. The findings reveal wide variation in the accessibility of privacy information, limited reference to New Zealand legislation, inconsistent disclosure of data management processes, and a general absence of visible governance roles such as Privacy Officers. Expert and practitioner insights indicate that AI-enabled tools may offer conditional support for improving transparency, compliance, and governance, with feasibility varying according to organizational capacity and digital maturity. The study advances conceptual understanding of corporate digital responsibility in tourism by positioning AI as a complementary mechanism to human oversight and institutional governance, rather than proposing operational or prescriptive solutions.
This study investigates the underexplored role of artificial intelligence (AI) in tourism crisis and disaster management. While AI has been extensively adopted in tourism to enhance personalization and efficiency, its integration into crisis contexts remains limited. Drawing on Mitroff’s five-phase model of crisis management, the research employs qualitative interviews with 20 global experts to explore AI applications across signal detection, preparation, containment, recovery, and learning. Findings highlight AI’s potential in early warning systems, risk modeling, automated communication, and postcrisis recovery strategies. However, ethical concerns, data privacy issues, and uneven access to technological resources present significant barriers. Larger tourism organizations are better positioned to adopt AI solutions, whereas small and medium-sized enterprises (SMEs) face challenges such as high implementation costs and limited digital capacity. The study highlights the importance of implementing inclusive policies and developing capacity-building initiatives to ensure equitable technological integration. It offers novel insights into AI’s potential to strengthen resilience in the tourism sector.
The integration of artificial intelligence (AI) in higher education is reshaping pedagogical practices across all disciplines; however, its role in tourism education remains underexplored. To bridge the gap, therefore, this study examines the factors influencing tourism students’ acceptance of AI-enhanced learning tools by drawing on an extended unified theory of acceptance and use of technology (UTAUT) framework. This research incorporates perceived trust, perceived risk, and attitude as additional constructs to conduct a quantitative survey of graduate, postgraduate students, research scholars, and tourism educators from multiple institutions and regions across India. The study’s findings established that performance expectancy, effort expectancy, and social influence positively predicted students’ attitudes. Facilitating conditions, perceived trust, and attitude positively predicted behavioral intentions; perceived risk did not. Gender significantly moderated the links from performance expectancy → attitude, effort expectancy → attitude, and perceived risk → intention, but not the other paths. The study’s findings extend UTAUT by incorporating context-specific constructs. Furthermore, it offers practical insights for implementing AI-driven pedagogy in tourism education.
Generative artificial intelligence (GenAI) has demonstrated a significant impact across tourism domains, such as planning, marketing, and product development; however, tourism education remains conservative in GenAI adoption. Drawing on Kolb’s Experiential Learning Theory (ELT), the study explores the role of text-to-image GenAI in enhancing experiential learning for new urban tourism education. Using a constructivist, participatory action research paradigm, this mixed-methods research employed an abductive approach to analyze visual and textual data derived from AI-generated images and essays written by 70 undergraduate tourism students. The results demonstrate how students articulated the new urban tourism concepts—the extraordinary mundane, encounters and contact zones, and urban coproduction. The study further analyzed the distinct roles GenAI assumes within the ELT stages: concrete experience, reflective observation, abstract conceptualization, and active experimentation. It proposes a pioneering GenAI-integrated ELT model introducing four roles for AI: AI-assisted inquiry, AI-augmented elicitation, AI-enhanced innovation, and AI-informed scaffolding. Overall, this study broadens theoretical and methodological perspectives in the tourism and education domains and suggests GenAI as a collaborative learning agent for instructional scaffolding.
This paper analyzes the nature of seasonality in tourism by developing a new index-based approach and examines the effect of seasonality on the general price level. The new concentration index measures the amplitude and stability of the seasonality pattern. The performance of the measure is validated by existing widely used measures. The validation is based on data from India, one of the world's major tourist destinations. The new concentration index captures the nature and various dimensions of seasonality very well; offers an alternative measure of seasonality in the tourism literature; provides a framework for seasonality analysis at annual and sub-annual levels, and may be used in further analysis. Based on monthly data from January 2005 to June 2023, findings indicate that the amplitude of seasonality is not high and the pattern is quite stable over the years, except during COVID-19. The estimated autoregressive moving average with exogenous variable (ARMAX) model from the time series econometrics literature indicates that seasonality has a positive and significant impact on prices, i.e., during peak seasons of tourism, prices, especially transport and recreation prices move up significantly. This is an important finding for policymakers and market players alike.
Achieving sustained growth in tourism’s economic impact requires a detailed understanding of the factors that determine tourist expenditure. The Pareto principle suggests that 20% of customers account for 80% of total sales; however, whether this distributional rule applies to tourist expenditure remains uncertain. This study revisited the Pareto principle in the context of inbound tourism in Japan by analyzing large-scale micro-data from official surveys. The results revealed that the top 20% of high-spending tourists contribute roughly half of the total expenditure, which is significant, but not at the hypothesized 80% level. These findings underscore the partial concentration of spending within a relatively small subgroup, indicating that a strategic focus on high spenders remains vital. To further explore the determinants of this high-spending segment, this study employed unconditional quantile regression to identify key drivers such as tourists’ income, types of travel arrangements, and types of activities. This study contributes to the literature by refining our understanding of expenditure distribution in inbound tourism and offering actionable insights for policymakers and destination management organizations seeking to enhance tourist expenditure through targeted policies and marketing strategies.
The Gulf Cooperation Council (GCC) countries are becoming a pivotal center for global tourism, shifting away from their traditional dependence on oil and gas revenues. Despite the growing body of research on tourism in the GCC countries, existing studies remain fragmented and lack a cohesive, comprehensive perspective. The study aims to offer a comprehensive review of the current state of research on tourism in the GCC countries and suggest directions for future research. Using bibliographic coupling and content analysis, we identify six key dominant areas of research on Gulf tourism: employee motivation and strategy, Islamic influences, tourism policy, ecotourism and corporate governance, mega-sporting events, and destination image. We explore trends and identify research gaps, providing a nuanced overview of tourism in the GCC countries. We also present a unified framework and future research directions, paving the way for new insights in this rapidly evolving field.
