Search Results (198)

This study investigates the resilience dynamics of the Maldives’ tourism sector through a longitudinal analysis of tourist arrivals from six global regions (2008–2024), focusing on spatiotemporal behavioral shifts induced by external shocks such as the COVID-19 pandemic. Using ANOVA and time-series data, the findings reveal divergent recovery trajectories across regions, highlighting resilience as a differentiated and adaptive process. European markets exhibited a rapid, V-shaped rebound, surpassing pre-pandemic levels by 2022, reflecting the “One Island, One Resort” model’s alignment with post-crisis preferences for safety, isolation, and controlled environments. Conversely, Asian markets experienced a more gradual, L-shaped recovery due to extended mobility restrictions and slower border reopening. The analysis further demonstrates that tourism seasonality has been structurally reconfigured, with European arrivals still driven by climatic “push” factors (winter-sun demand). In contrast, Middle Eastern travel is anchored in cultural and religious “pull” factors, such as halal tourism and school vacations. These findings emphasize that tourism resilience is spatially, temporally, and behaviorally contingent, rather than uniform. Accordingly, policymakers should move beyond one-size-fits-all recovery models and implement spatially targeted, adaptive strategies, including customized marketing, diversified tourism offerings, and crisis-ready governance frameworks, to mitigate seasonality and reinforce the Maldives’ long-term capacity to withstand future shocks. Full article

Tourism today represents a strategic engine of economic growth, contributing substantially to GDP, employment, and export revenues. Accounting for approximately 10% of global GDP, the sector plays a significant role in tourism-intensive countries. Tourism has shown remarkable resilience and recovery capacity in the post-COVID era, reaffirming its status not only as an economic sector but also as a spatial phenomenon. Due to its inherent place-based nature, tourism cannot be outsourced: it relies on the unique cultural, environmental, and territorial assets of specific locations. While this makes tourism a powerful driver of local development, it also presents challenges related to environmental stress, cultural commodification, and social tensions, especially in over-visited destinations such as Venice, Barcelona, or Lisbon. This paper introduces the concept of “anti-gravity tourism”, a novel framework inspired by physics, to describe planning strategies aimed at counteracting the gravitational pull exerted by mass tourism hotspots. Using the STESY model, the study applies spatial analysis to four case study areas, identifying Destination Areas (DAs) through clustering techniques and developing spatial design proposals aligned with the principles of the New Urban Agenda (NUA). The results highlight how “anti-gravity” strategies can be operationalized through context-sensitive planning tools to mitigate overtourism, support territorial equity, and maximize positive externalities. Ultimately, the paper argues for a paradigm shift towards tourism policies that ensure long-term sustainability by balancing economic growth with social inclusion and environmental stewardship. Full article

This study presents the HISTOESE (History Education for Sustainable Environments) Model, an empirically grounded and practice-based framework for cultural heritage education and sustainability. Developed through a qualitative, design-based research approach, it analyzes a longitudinal corpus of 50 master’s dissertations and supervised teaching reports (2008–2025) from the Polytechnic Institute of Viana do Castelo, Portugal. Although the empirical basis derives from teacher education, the HISTOESE model fosters school–community partnerships that indirectly support cultural tourism and sustainable, place-based recovery. Using inductive thematic analysis, the study traced how project-based learning (PBL) activities mobilised local heritage, community collaboration, and sustainable pedagogical practices. Three key findings emerged: (1) local-context pedagogies strengthened children’s historical consciousness and heritage literacy; (2) inclusive, low-cost and upcycling strategies fostered community participation; and (3) partnerships with museums, tourism offices, and cultural associations generated visible cultural events that reinforced post-COVID resilience. The HISTOESE framework synthesises these insights into four interrelated pillars—Proximity and Contextualization, Inclusive and Sustainable Practices, Recognition and Valorisation and Active Citizenship and Collaboration—providing a transferable model for teacher education and community-based cultural sustainability. Practical implications concern curriculum design, heritage–tourism interfaces, and collaborative strategies for sustainable development. Full article

Sectoral vulnerability to distinct crisis types in small, open, and geopolitically exposed markets—such as Jordan—remains insufficiently quantified, constraining targeted policy design and portfolio allocation. This study’s primary purpose is to establish a transparent, comparable metric of sector-level market resilience that reveals how crisis typology reorders vulnerabilities and shapes recovery speed. Applying this framework, we assess Jordan’s equity market across three archetypal episodes—the Global Financial Crisis, the Arab Spring, and COVID-19—to clarify how shock channels reconfigure sectoral risk. Using daily Amman Stock Exchange sector indices (2001–2025), we estimate $GARCH\left(\mathrm{1,1}\right)$ models for each sector–crisis window and summarize volatility dynamics by persistence $(\alpha +\beta )$, interpreted as an inverse proxy for resilience; complementary diagnostics include maximum drawdown and days-to-recovery, with nonparametric (Kruskal–Wallis) and rank-based (Spearman, Friedman) tests to evaluate within-crisis differences and cross-crisis reordering. Results show pronounced heterogeneity in every crisis and shifting sectoral rankings: financials—especially banking—display the highest persistence during the GFC; tourism and transportation dominate during COVID-19; and tourism/electric-related industries are most persistent around the Arab Spring. Meanwhile, food & beverages, pharmaceuticals/medical, and education recurrently exhibit lower persistence. Higher persistence aligns with slower post-shock normalization. We conclude that resilience is sector-specific and contingent on crisis characteristics, implying targeted policy and portfolio responses; regulators should prioritize liquidity backstops, timely disclosure, and contingency planning for fragile sectors, while investors can mitigate crisis risk via dynamic sector allocation and volatility-aware risk management in emerging markets. Full article

The Sohodol Gorges has become a location of interest for tourists seeking ecological experiences and outdoor activities. The main purpose of the present study is to evaluate the attitudes of Romanian tourists toward the development of geotourism in this region following the COVID-19 pandemic. In conjunction with the research questions, hypotheses, variables, and research methodology, the following research objectives were emphasized in this study of the Oltenia region: (1) investigate how certain socio-demographic variables, such as age, gender, level of education, and occupation, influence tourists’ perceptions of the various aspects of geotourism development in the Sohodol Gorges; (2) analyze the different dimensions of geotourism, including its economic, ecological, and socio-cultural impacts, thus contributing to a deeper understanding of how geotourism is perceived in the study area in the post-pandemic context. For a qualitative evaluation of the information presented in this study, the authors used a qualitative survey with open questions and closed questions as a data collection method. For data processing and analysis, the EViews version 12.0 software package was used, enabling complex statistical analyses such as multiple regressions and correlation coefficient determination. These techniques were essential for identifying and interpreting the relationships between demographic variables and tourist perceptions. The research results provide a detailed picture of the influence that demographic and behavioral factors have on tourists’ perceptions in the context of post-COVID-19 geotourism development in the Sohodol Gorges of Romania. Education level and age play a significant role in shaping economic and environmental perceptions, indicating that tourists with higher education levels are more aware of the economic and ecological impact of tourism. Full article

Despite a substantial volume of literature on the consequences of the recent pandemic, the relationship between psychological constructs that affect individual mindset and confidence, as well as travel intention, is missing. This study seeks to examine the travelers’ behavioral intentions and psychological constructs. The study was conducted among 398 Malaysian medical students. The study was conducted from the end of 2022 to the middle of 2023. The study followed PLS-SEM to estimate relationships between variables and predict dependent variables. The results revealed a negative correlation between travel risk, severity, travel barriers, and travel intention, while COVID-19 vaccination effectiveness and self-efficacy positively influenced travel intention. The present study reveals that individuals with higher levels of self-efficacy or confidence in overcoming obstacles and coping with the challenges of new circumstances exhibit a stronger intention to travel (ITT). This study contributes to understanding the cognitive process of individuals’ intentions to travel and the coping mechanisms during the post-pandemic. Utilizing the health belief model, this study validates how individual health behavior regarding perceived risk affects travel decisions or intentions. This study provides valuable insight into consumer behavior for decision-making in the aviation and tourism industries and for policymakers after the global health crisis. Full article

Background. Length of stay, operationalized here as nights per booking (NPB), is a first-order driver of yield, labor planning, and environmental pressure. The COVID-19 pandemic and the rise of long-stay remote workers (often labeled “slomads”, a slow-travel subset of digital nomads) plausibly altered stay-length distributions, yet national, booking-weighted evidence for the United States remains scarce. Purpose. This study quantifies COVID-19 pandemic-era and post-pandemic shifts in U.S. Airbnb stay lengths, and identifies whether higher averages reflect (i) more long stays or (ii) longer long stays. Methods. Using every U.S. Airbnb reservation created between 1 January 2019 and 31 December 2024 (collapsed to booking-count weights), the analysis combines: weighted descriptive statistics; parametric density fitting (Gamma, log-normal, Poisson–lognormal); weighted negative-binomial regression with month effects; a two-part (logit + NB) model for ≥28-night stays; and a monthly SARIMA$(0,1,1){(0,1,1)}_{12}$ with COVID-19 pandemic-phase indicators. Results. Mean NPB rose from 3.68 pre-COVID-19 to 4.36 during restrictions and then stabilized near 4.07 post-2021 (≈10% above 2019); the booking-weighted median shifted permanently from 2 to 3 nights. A two-parameter log-normal fits best by wide AIC/BIC margins, consistent with a heavy-tailed distribution. Negative-binomial estimates imply post-vaccine bookings are 6.5% shorter than restriction-era bookings, while pre-pandemic bookings are 16% shorter. In a two-part (threshold) model at 28 nights, the booking share of month-plus stays rose from 1.43% (pre) to 2.72% (restriction) and settled at 2.04% (post), whereas the conditional mean among long stays was in the mid-to-high 50 s (≈55–60 nights) and varied modestly across phases. Hence, a higher average NPB is driven primarily by a greater prevalence of month-plus bookings. A seasonal ARIMA model with pandemic-phase dummies improves fit over a dummy-free specification (likelihood-ratio = 8.39, df = 2, p = 0.015), indicating a structural level shift rather than higher-order dynamics. Contributions. The paper provides national-scale, booking-weighted evidence that U.S. short-term-rental stays became durably longer and more heavy-tailed after 2020, filling a gap in the tourism and revenue-management literature. Implications. Heavy-tailed pricing and inventory policies, and explicit regime indicators in forecasting, are recommended for practitioners; destination policy should reflect the larger month-plus segment. Full article

This study addresses the critical need for accurate tourism demand (TD) forecasting in Bulgaria using economic indicators, developing robust predictive models to navigate post-pandemic market volatility. The COVID-19 pandemic exposed tourism’s vulnerability to systemic shocks, highlighting deficiencies in traditional forecasting approaches. Bulgaria’s tourism industry, characterized by strong seasonal variations and economic sensitivity, requires enhanced methodologies for strategic planning in uncertain environments. The research employs comprehensive comparative analysis of machine learning (ML) and deep machine learning (DML) methodologies. Monthly overnight stay data from Bulgaria’s National Statistical Institute (2005–2024) were integrated with COVID-19 case data, Consumer Price Index (CPI) and Bulgarian Gross Domestic Product (GDP) variables for the same period. Multiple approaches were implemented including Prophet with external regressors, Ridge regression, LightGBM, and gradient boosting models using inverse MAE weighting optimization, alongside deep learning architectures such as Bidirectional LSTM with attention mechanisms and XGBoost configurations, as each model statistical significance was estimated. Contrary to prevailing assumptions about deep learning superiority, traditional machine learning ensemble approaches demonstrated superior performance. The ensemble model combining Prophet, LightGBM, and Ridge regression achieved optimal results with MAE of 156,847 and MAPE of 14.23%, outperforming individual models by 10.2%. Deep learning alternatives, particularly Bi-LSTM architectures, exhibited significant deficiencies with negative R² scores, indicating fundamental limitations in capturing seasonal tourism patterns, probable data dependence and overfitting. The findings, provide tourism stakeholders and policymakers with empirically validated forecasting tools for enhanced decision-making. The ensemble approach combined with statistical significance testing offers improved accuracy for investment planning, marketing budget allocation, and operational capacity management during economic volatility. Economic indicator integration enables proactive responses to market disruptions, supporting resilient tourism planning strategies and crisis management protocols. Full article

This study aims to reveal how ecotourists’ general perceptions, concerns, and intentions to act regarding climate change have been shaped in the context of their personality domains following the COVID-19 pandemic. Data were collected from 409 participants who took part in nature walking activities in Turkey in 2024 using a survey method. The data were analyzed using quantitative methods such as structural equation modeling (SEM) and multiple regression analyses. The findings reveal statistically significant relationships between Big-Five personality domains of ecotourists’ and their perceptions of climate change, concerns, intentions to act, and ecotourism intentions. The results reveal that attitudes toward climate change have become more pronounced, especially in the post-pandemic period, and that personality domains are a strong determinant in shaping these attitudes. This study is important for the development of sustainable tourism policies and for providing strategic recommendations to managers in the field of ecotourism. Full article

Amid ongoing societal development, tourists’ travel behavior patterns have been undergoing substantial transformations, and understanding their evolution has emerged as a key area of scholarly interest. Taking Beijing as a case study, this research aims to uncover the spatiotemporal evolution patterns of tourist flows and their underlying driving mechanisms. Based on digital footprint relational data, a dual-perspective analytical framework—“tourist perception–tourist flow network”—is constructed. By integrating the center-of-gravity model, social network analysis, and regression models, the study systematically examines the dynamic spatial structure of tourist flows in Beijing from 2012 to 2024. The findings reveal that in the post-pandemic period, Beijing tourists place greater emphasis on the cultural connotation and experiential aspects of destinations. The gravitational center of tourist flows remains relatively stable, with core historical and cultural blocks retaining strong appeal, though a slight shift has occurred due to policy influences and emerging attractions. The evolution of the spatial network structure reveals that tourism flows have become more dispersed, while the influence of core scenic spots continues to intensify. Government policy orientation, tourism information retrieval, and the agglomeration of tourism resources significantly promote the structure of tourist flows, whereas the general level of tourism resources exerts no notable influence. These findings offer theoretical insights and practical guidance for the sustainable development and regional coordination of tourism in Beijing, and provide a valuable reference for the spatial restructuring of urban tourism in the post-COVID-19 era. Full article

This paper investigates the impact of the transition from manufacturing to tourism on sectoral productivity, output prices, and labor costs. Using panel data econometric models for 15 selected EU countries from 2011 to 2023, the study confirms key dynamics predicted by Baumol’s cost disease (BCD) hypothesis. The findings reveal that higher productivity is positively associated with both implied prices and hourly labor costs across sectors, supporting the wage equalization mechanism central to BCD. However, the relationship between productivity and wages or prices is weaker in labor-intensive sectors like tourism, underscoring their structural vulnerability to wage-driven cost pressures. Additionally, the analysis captures the impact of major external shocks, including the COVID-19 pandemic and the Russo–Ukrainian war, treated as jointly sourced super-shocks. The regression results indicate significant price disruptions following these shocks, whereas no statistically significant trend in labor costs was detected in the post-treatment period. These results highlight the differential effects of external shocks on wages versus prices, emphasizing the challenges faced by low-productivity, labor-intensive sectors in managing cost dynamics. The findings provide valuable insights for policymakers addressing sectoral imbalances in the context of BCD and navigating the economic consequences of global disruptions. Full article

The COVID-19 pandemic revealed long-standing vulnerabilities in island tourism economies such as Crete and Chios, including seasonality, overdependence on mass tourism, and limited innovation adoption. This study investigates how sustainable tourism, regional development, and innovation interconnect in these two cases. Drawing on structured questionnaires addressed to tourism stakeholders in Hersonissos (Crete) and Chios, we apply a comparative analysis through descriptive and inferential statistics. The findings reveal a common interest in diversifying tourism offerings through digital transformation and alternative tourism models. Hersonissos demonstrates higher engagement with innovation-led strategies, while Chios reflects a cautious but growing inclination toward eco-tourism and cultural tourism initiatives. The study employs a comparative cross-sectional design based on structured questionnaires addressed to 71 tourism stakeholders in Hersonissos and Chios. Data were collected through purposive sampling that was completed in July 2023. As a practical outcome, we propose the Regional Innovation Index in Tourism—a composite tool designed to assess innovation readiness at the local level. This index enables policymakers to evaluate the strategic capacity of tourism regions and supports the formulation of long-term, place-based development strategies aligned with EU policy goals and the transition to a green economy. Full article

Within China’s “Central China Rising” strategy, urban tourismification operates as a production mode that reconfigures spatial, economic, and ecological systems—mirroring global overtourism challenges seen in Barcelona and Venice, where rapid infrastructure development often prioritizes economic gains over ecological resilience (cf. Lines 43–46). This study examines 80 central Chinese cities (2010–2021), proposing the Urban Tourismification–Transportation Quality–Ecological Resilience System (UTTES) framework. Using entropy weighting, improved coupling coordination degree (CCD), GM (1,1) forecasting, and spatial Durbin models, we analyze coordination relationships, driving factors, and mechanisms. Key findings reveal the following: (1) UTTES coordination peaked in 2019 (pre-COVID), showing a spatial “center-periphery” gradient with provincial capitals leading. (2) Projections indicate transportation efficiency as a critical bottleneck—most cities will achieve good coordination post-2026. (3) Economic activity, social restructuring, and policy support drive the system, with spatial spillovers creating dual-path mechanisms (economic growth vs. manufacturing/environmental barriers). The UTTES framework advances a replicable methodology for diagnosing Tourism–Transportation–Ecology synergies in rapidly developing regions, integrating multidimensional indicators to balance environmental governance and tourism dynamics. Full article

This study aims to identify the factors that influence the behavior of Costa Rican tourists visiting rural destinations after the COVID-19 pandemic, thereby contributing to sustainable rural tourism development. The study applies data analysis using the partial least squares (PLS) regression technique to evaluate a reflective measurement model, based on data collected via a questionnaire. The evidence indicates that for the analyzed destination, the most significant factors influencing the behavior of Costa Rican tourists visiting rural destinations after the COVID-19 pandemic are learning, ICTs, and, in particular, relaxation. Although biosecurity and social influence do not affect the intention to return, these findings highlight the crucial role of learning and ICTs in the tourist experience and loyalty to the destination. This study contributes novel empirical insights to the still limited post-pandemic research on rural tourism, by providing current information on changes in tourist behavior in a specific post-pandemic context. Additionally, it focuses on a popular rural tourist destination in Costa Rica, offering a deeper understanding of a less explored tourism segment, as most previous studies have examined urban or international tourism. Specifically, this research addresses the gap regarding domestic tourist behavior in rural areas using a quantitative approach (PLS), revealing key drivers of return intention. The findings may also be relevant for rural destinations facing similar post-pandemic challenges in other countries. Full article

This study explores how rural tourism destinations in the Eastern Carpathians of Romania have recovered in the aftermath of the COVID-19 pandemic. Using data from 2016–2019 and 2021–2023, five core indicators—tourist arrivals, overnight stays, accommodation capacity, occupancy rates, and active units—were analyzed at the local level. Based on these indicators, a cluster analysis was conducted for us to identify groups of communes with similar tourism performance profiles. After clustering, composite indicators were calculated to track how each group evolved over time. The findings show that recovery has not been uniform: while some destinations bounced back or even improved, others continue to face structural challenges. These results suggest that local infrastructure, destination type, and governance capacity all play a role in shaping recovery paths. The paper offers a spatial overview of rural tourism dynamics and highlights the value of using data-driven tools for understanding uneven development in post-crisis contexts. Full article