Search Results (1,400)

This research focuses on the effects of online destination brand experience on destination brand authenticity, destination brand engagement, and external search behavior and behavioral intention. It also investigates the mediating effect of destination brand authenticity on the relationship between online destination brand experience and destination brand engagement. The research population consisted of visitors who had experienced the Zeugma and Gaziantep cultural tourism destinations. The Smart PLS (Partial Least Squares) statistical program was used for data analysis. The analysis results showed that online destination brand experience positively affected destination brand authenticity and destination brand engagement. Destination brand engagement influenced external search behavior and behavioral intention positively. However, the findings revealed that the social engagement dimension of destination brand engagement did not have a significant effect on external search behavior. Furthermore, the effect of the cognitive engagement dimension on behavioral intention was also insignificant. Finally, it was found that destination brand authenticity partially mediated the relationship between online destination brand experience and destination brand engagement. Full article

Tourism supply chains have become increasingly vulnerable to demand volatility amid disruption and recovery, creating a need for forecasting inputs that are both timely and operationally meaningful. This study examines whether digital marketing demand indicators can serve as empirically grounded inputs for planning-oriented analysis of tourism supply chains under disruption and recovery conditions. Using annual global data for 2005–2025, the analysis combines digital marketing, tourism demand, booking-readiness, and planning-related indicators in a time-series modeling framework. Ordinary least squares models with HC3 (Heteroskedasticity-Consistent version 3) robust standard errors and lagged specifications are used to test the relationships among digital marketing intensity, tourism demand, digital transaction readiness, and capacity- and travel-related planning outcomes across pre-disruption, disruption, and recovery periods. The results show that digital marketing demand indicators are positively associated with tourism demand intensity, online booking readiness, and digital transaction capacity, while tourism demand intensity is positively linked to travel intensity and air-passenger throughput. Lagged digital marketing indicators also significantly predict subsequent planning-related outcomes, supporting their use as forward-looking inputs for simulation-based tourism planning. Moreover, the moderation analysis indicates that this predictive relationship is significantly moderated by the disruption and recovery regimes, with the link between lagged digital marketing and capacity pressure steepening during disruption and reversing sign during recovery. Overall, the findings indicate that digital marketing demand forecasts can provide useful and empirically grounded inputs for tourism simulation and strategic planning, while underscoring the need to recalibrate these inputs across disruption and recovery regimes. Full article

Green labels on online travel platforms have become an important mechanism for disclosing environmental information and guiding sustainable tourism consumption. However, when a platform simultaneously provides green certification and competes with suppliers through its self-operated business, green label adoption may reshape both market competition and environmental outcomes. This study develops a game-theoretic model of a co-opetitive tourism platform supply chain consisting of a tourism service supplier (TSS) and an online travel platform (OTP). Two label adoption strategies are compared: the TSS’s self-labeling strategy and its adoption of the OTP-certified green label. The results show that, under self-labeling, the OTP can gain a competitive advantage by setting a higher price and greenness level, although this advantage weakens as consumer recognition of the TSS’s self-label increases. Under platform-certified labeling, the OTP raises the common green standard, which intensifies price competition between the two parties. In most cases, adopting the OTP’s green label improves supply chain profits; however, under certain combinations of competition intensity and platform label credibility, it may reduce the profits of both members and increase environmental damage. These findings suggest that platform-led green certification does not necessarily improve environmental performance and should be designed as a governance mechanism rather than a purely marketing instrument. Full article

This study identifies the factors that influence the use of the Airbnb platform by Generation Z and Y in Greece, classifies them into groups according to their attitudes, and profiles the tourists of each generation according to their preferences regarding Airbnb bookings. The researchers conducted a primary survey using a sample of 576 citizens. Factor analysis was conducted initially to identify the main factors that affect each generation in using Airbnb after the COVID-19 pandemic. Cluster analysis was performed to classify each generation into groups. Quadratic discriminant analysis was conducted in the third phase to check cluster predictability. Non-parametric tests, including the chi-square test, were performed to profile tourists of each generation according to their preferences regarding Airbnb bookings. The results of this study indicate that people of Generations Z and Y preferred to use Airbnb accommodations even after the COVID-19 pandemic. The fact that Airbnb is safer than a conventional hotel due to COVID-19, the easy booking process and access to house amenities, as well as other marketing issues, affects most people of Generation Z and Y. This market segmentation study is quite essential in the tourism industry, especially in a country where tourism is of great importance to its economy. It highlights the impact of the pandemic on decisions and attitudes regarding the use of Airbnb. Full article

Evaluating and identifying paths for cultural tourism development (CTD) in the Yellow River Basin (YRB) is crucial for establishing the Yellow River Cultural Tourism Belt in China. This study utilised Fuzzy-set Qualitative Comparative Analysis (fsQCA) to address the complex factors affecting CTD, unlike econometric approaches. An evaluation framework based on sustainable development and inclusive growth, consisting of 24 factors across three rule layers, was created to assess 78 cities in the YRB, China, using the entropy weight–TOPSIS method. Analysis with ArcGIS 10.5 revealed that from 2004 to 2019, CTD increased overall, with notable regional disparities: downstream and central regions thrived, while northern and southern regions lagged. High development followed three paths: policy-assisted consumer market-driven, economic investment-driven, and government-guided economic investment and innovation-driven development. Conversely, low development followed three paths: insufficient economic policies and innovation, deficient economy and consumer market, and insufficient economic and social investment. These findings could help develop sustainable cultural tourism in the birthplaces of global civilisations, specifically within the Chinese context. Full article

Under the dual pressures of heritage conservation and tourism growth, improving inclusive access to cultural tourism opportunities in historic urban areas has become an urgent planning issue under Sustainable Development Goal 11 and the Historic Urban Landscape approach. Taking the central urban area of Suzhou, China, as a case study, this study evaluates time-budgeted walkable accessibility, spatial equity, local mismatch, and accessibility-generating conditions from a 15 min city perspective. An integrated analytical framework was developed by combining kernel density analysis, GIS-based network accessibility modelling, Lorenz–Gini equity assessment, bivariate Local Indicators of Spatial Association (LISA), and XGBoost–SHAP interpretation. The results show that cultural tourism opportunities exhibit a clear core polarisation–peripheral attenuation pattern. Within the 15 min threshold, Gusu District records SACR and AAR values of 80.18% and 95.23%, respectively, indicating a pronounced historic-core accessibility advantage. Accommodation-tier differences do not form a simple monotonic relationship with accessibility, but are shaped by the spatial embedding of different accommodation-market segments within the cultural tourism opportunity field. HL units, namely high-tier accommodation near low accessibility, emerge as priority diagnostic areas of local mismatch, while delayed accessibility beyond 30 min becomes particularly evident among elderly visitors. The SHAP interpretation further indicates that leisure-strolling attractions show a more balanced supply–accommodation structure, whereas commercial–cultural mixed and heritage-core attractions are more strongly supply-led. By linking accessibility measurement, equity assessment, local mismatch diagnosis, and mechanism-based explanation, this study provides an operational basis for zonal and typology-oriented optimisation of cultural tourism accessibility in historic urban areas. Full article

Transportation plays a fundamental role in tourism development, serving as the critical link between tourism demand and supply. China’s domestic demand-oriented strategy has positioned tourism as an important driver of economic recovery during the post-COVID-19 transition period, highlighting the urgent need to strengthen tourism system resilience. Tourism economic resilience is measured via the entropy-weighted Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method, transportation accessibility is quantified using a composite index, and a Geographically Weighted Random Forest (GWRF) model is applied across 73 prefecture-level cities in the Yellow River Basin to map spatial patterns and examine the association between transportation accessibility and tourism economic resilience. The results reveal: (1) pronounced spatial disparities in both tourism resilience and accessibility, displaying a clear “core–periphery” pattern; (2) strong spatial coupling between high resilience and high accessibility in the east, and low–low clusters in the west (e.g., Qinghai, Gansu, Sichuan); and (3) a relatively strong association between transportation accessibility and tourism resilience, particularly in supporting recovery, adaptability, and renewal capacity. Other influential factors include tourist density, openness to external markets, and industrial structure. This study provides a quantitative foundation for understanding the spatially heterogeneous associations of transport infrastructure with tourism system resilience and offers both theoretical insights and practical guidance for formulating regionally differentiated, transport-led policy strategies to foster sustainable tourism development in river-basin economies. Full article

While virtual tourism (VT) has emerged as a disruptive force in destination marketing, the mechanism by which virtual immersion translates into physical visitation remains debated. Addressing the “virtual-to-real” conversion gap, this study proposes an integrated theoretical framework combining the Stimulus–Organism–Response (SOR) model with the Technology Acceptance Model (TAM). Unlike traditional studies, we position Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) as boundary conditions rather than direct antecedents. Empirical data were collected from 476 tourists with virtual experiences of Zhangjiajie National Forest Park and analyzed using Structural Equation Modeling (SEM). The results indicate that virtual experiences not only directly trigger visit intention but also indirectly foster it by enhancing destination attitude. Crucially, a novel “asymmetric moderation” effect was revealed: while technical attributes (PU and PEOU) do not influence the affective formation of attitude, they significantly moderate the translation of attitude and experience into behavioral intention. These findings suggest that while immersion drives “liking,” technical utility drives “going.” This study offers strategic insights for Destination Marketing Organizations (DMOs) to optimize VT platforms by balancing hedonic experience with functional utility to maximize actual visitor conversion. Full article

Digital nomads blur the boundaries between tourism, work, and temporary residence, yet little is known about how local businesses interpret this ambiguous population. This study examines how Greek SMEs classify digital nomads and how these classifications shape perceived business benefits and harms, support for protective policy guardrails, and firm-level adaptation intentions. Using survey data from 747 SME owner-managers and managers in tourism-linked and adjacent sectors, the study tests an integrated framework with PLS-SEM and multi-group analysis. The findings show that SME responses are interpretive rather than automatic. Residency-Based Visitor Beliefs positively predicted support for protective policy guardrails (β = 0.334, p < 0.001), but did not directly predict adaptation intentions. Perceived Touristness positively predicted both guardrail support (β = 0.110, p < 0.001) and adaptation intentions (β = 0.181, p < 0.001). Perceived Business Benefits was the strongest predictor of adaptation intentions (β = 0.390, p < 0.001), while Perceived Business Harms also increased both guardrail support (β = 0.175, p < 0.001) and adaptation intentions (β = 0.310, p < 0.001). Mediation results showed that the effects of Residency-Based Visitor Beliefs on adaptation were fully transmitted through benefits and harms, whereas Perceived Touristness operated indirectly only through harms. Multi-group analysis further revealed significant heterogeneity across firm size, years in operation, and tourism dependence. The study contributes to digital nomad and tourism research by introducing a business-side classification perspective and by linking classification, evaluation, and response in a single model. Overall, the findings show that whether digital nomads are classified as tourists by businesses has measurable implications for regulatory preferences and market adaptation. Full article

This paper examines the trustworthiness of generative AI as a tourism recommender system by analyzing how ChatGPT5.2 responds to an open-ended, zero-shot prompt: “Recommend me a list of 10 German Christmas Markets.” Using German Christmas markets as a case study, outputs, texts in reasoning panels, and cited sources of fifteen replicates (carried out over five consecutive days) were systematically documented and analyzed. The results show a consistent and patterned selection which is dominated by a small canon of markets (Nürnberg, Dresden, Köln, München, and Stuttgart). The generative AI model does not neutrally sample from the entire pool of approximately 2000 German markets but instead reproduces a narrow canon of “iconic” destinations. Analysis of reasoning traces and follow-up conversations demonstrates that ChatGPT5.2 applies hidden selection criteria, including canonical status, landmark setting, branding strength, and perceived trip-planning usefulness, while also introducing undisclosed filters such as geographic spread across Germany and stylistic diversity. Although the model claims to use source triangulation and quality checks, the evidence shows substantial reliance on tourism marketing pages, travel media, blogs, and social media, especially for descriptive commentary. The study concludes that generative AI tourism recommendations are useful but non-neutral and should be interpreted as “curated,” bias-bearing constructs rather than transparent information retrieval. The implications of this on tourism management and the marketing of Christmas markets are discussed. Full article

This study investigates how user-generated content (UGC) and perceived experience of destination-generated social media content (DGC) shape satisfaction, destination image, and electronic word-of-mouth (eWOM) intention in lesser-explored tourism destinations. A dual-content model grounded in the stimulus-organism-response (SOR) framework is tested using partial least squares structural equation modeling (PLS-SEM) with data from 300 tourists who interact with destinations’ social media. Results reveal that UGC exerts limited influence on satisfaction, destination image, and eWOM intention, which diverges from much prior literature but is consistent with the scarcity and lower trustworthiness of UGC in small destinations. In contrast, perceived experience of DGC strongly enhances destination image and eWOM intention, highlighting the relevance of pre-visit digital experiences. In addition, moderation analysis shows that openness to experience significantly influences selected relationships, with stronger effects observed among tourists who are lower in openness. The findings underscore the importance of integrating pre-visit digital interactions and individual differences into destination marketing models and provide practical insights for destination management organizations (DMOs) in lesser-known destinations, emphasizing the strategic value of high-quality official content to compensate for limited UGC. This research advances destination marketing literature by jointly examining UGC and DGC and by introducing perceived experience of DGC and personality as key explanatory elements. Full article

Digital technologies are reshaping how tourists and hospitality consumers search for, personalize, interpret, and share experiences. This study examines customer value co-creation (VCC) as a mechanism linking digital-age participation with consumer outcomes in tourism and hospitality. A PRISMA 2020-guided meta-analysis was conducted using Scopus, Web of Science Core Collection, and Hospitality & Tourism Complete. Forty peer-reviewed studies met the eligibility criteria. Random-effects models synthesized unadjusted correlations between VCC and its main antecedents and outcomes. VCC was positively associated with customer engagement, perceived innovation, and sustainability/CSR-related perceptions. On the outcome side, the strongest and most mature associations were observed for satisfaction (r = 0.64), loyalty (r = 0.61), and perceived value (r = 0.52). Extended outcomes, including experience evaluations, well-being, image, and equity-related indicators, were also positive on average but less empirically mature. High heterogeneity and wide prediction intervals show that VCC is better understood as a context-dependent mechanism rather than a universally strong predictor. Exploratory evidence suggests that digitally intensive service environments may strengthen the VCC–loyalty association. Although the evidence base is not cultural-tourism-specific, the findings are relevant to cultural and heritage settings where digital touchpoints can support interpretation, perceived authenticity, symbolic meaning, and post-visit advocacy. Full article

Artificial intelligence (AI) is reshaping tourism businesses by improving decision making, service personalization, operational efficiency, and data-driven management. Beyond these organizational benefits, AI may also strengthen firms’ capacity to cope with market volatility, demand shocks, cost pressures, and other sources of financial fragility. This study provides a systematic literature review and bibliometric analysis of 146 Web of Science articles on AI in tourism published between 2019 and 2023. Following a structured screening process, it identifies the intellectual structure, thematic evolution, and main performance-related drivers associated with AI adoption. The findings show a rapidly expanding field centered on business performance, information technology, big data, robotics, and AI-enabled service innovation. The literature suggests that AI contributes to resilience by enhancing forecasting, resource allocation, customer management, and organizational adaptability under uncertainty. However, explicitly financial perspectives—such as financial vulnerability, resilience, liquidity, solvency, and risk management—remain underdeveloped. This study contributes by reframing AI in tourism as a potential resilience-building capability rather than only a tool for service innovation. Its main limitations are the reliance on Web of Science and a fixed 2019–2023 bibliometric corpus, which future research should extend. Full article

As the “fifth major factor of production,” data plays a crucial role in fostering China’s tourism industry, advancing high-quality economic development, and gaining competitive market advantages. Serving as institutional infrastructure for data factor rights confirmation, pricing, trading, and value conversion, data trading platforms are central to the market-based allocation of data factors. The efficient flow and value realization of data elements have paved the way for the rapid development of digital tourism; new forms of digital tourism represent a profound transformation of the industry resulting from integration and innovation with other sectors. Based on the platform ecosystem theory, we select the panel data of 297 Chinese cities from 2012 to 2024 and innovatively use the Double/Debiased Machine Learning (DDML) model to empirically test the impact of data trading platforms on the new forms of digital tourism and its mechanisms. It is found that the construction of data trading platforms effectively empowers the development of new forms of digital tourism, and this conclusion still holds after a series of robustness tests, such as changing the sample split ratio, replacing the machine learning algorithm, and the instrumental variables method. Mechanism analysis indicates that data trading platforms significantly promote new forms of digital tourism through dual pathways of talent agglomeration and technological innovation, an effect further strengthened by increased government support. Heterogeneity analysis found that the empowerment effect is more significant in cities with lower resource endowment and common administrative level and historical cities, which can be effectively transformed into an employment support effect. Spatial effect analysis reveals that the establishment of data trading platforms exerts a positive pull effect on new forms of tourism in surrounding cities within a 30 km core zone. However, this effect gradually weakens with increasing distance, turning into a significant negative siphon effect beyond 60 km. The findings provide theoretical basis and empirical support for regionally differentiated digital tourism development policies. Full article

This study analyzes domestic tourism mobility in Spain using aggregated and anonymized mobile phone data, with a particular focus on the outbound market of the municipality of Madrid and its territorial redistribution between September 2019 and September 2025. Using experimental statistics from the National Institute of Statistics (INE), a monthly series of origin–destination flows to all Spanish municipalities was constructed, harmonizing the municipal database and incorporating intensive indicators to improve inter-territorial comparability. The spatiotemporal dynamics were integrated into a Space–Time Cube (monthly resolution), and Emerging Hot Spot Analysis (EHSA) was applied to classify the persistence, intensification, or attenuation of high- and low-intensity clusters. Additionally, the grouping of time series allowed for the identification of seasonal patterns associated with coastal, urban, and nearby inland destinations. The results show: (i) a synchronous disruption in the spring of 2020 linked to COVID-19; (ii) a staggered recovery beginning in 2021, consolidating in 2023–2025; and (iii) a dual structural pattern, with a strong concentration of volumes in large urban and coastal hubs, along with high relative intensities in small municipalities in the ring surrounding Madrid. EHSA identifies intensifying hotspots in established coastal systems (Costa del Sol and Costa Blanca) and cooling or attenuated dynamics in parts of the inland region, consistent with the reconfiguration of the “tourism radius” following the pandemic. Limitations arising from statistical confidentiality and the representativeness of the source are discussed, and future research directions are proposed based on the integration of the information with expenditure and transportation data and on spatiotemporal modeling to support destination planning and management. Full article