Search Results (312)

Emerging contaminants (ECs) represent a growing environmental problem due to their presence in various environmental matrices. In Mexico, research on ECs remains limited. This scoping review, conducted in accordance with the PRISMA-ScR guidelines, analyzes their environmental fate during the period 2004–2025, considering their distribution in surface and groundwater, sediments, and influents and effluents from wastewater treatment plants (WWTPs). The reviewed studies demonstrate the presence of pharmaceutically active compounds (PhACs), endocrine-disrupting compounds (EDCs), personal care products, phthalates, bisphenols, pesticides, illicit drugs, xanthines, perfluoroalkyl and polyfluoroalkyl substances (PFAS), metabolites, and industrial compounds. The Apatlaco, Cuautla, and Santa Catarina rivers exhibit the greatest diversity of ECs, while groundwater shows evidence of infiltration of PhACs, phthalates, bisphenols, and triclosan, associated with urban, industrial, and tourist discharges. In sediments, PhACs and EDCs accumulate at concentrations in the ng/g range, while phthalates reach concentrations of thousands of ng/g, indicating their affinity for the solid phase. At Mexican wastewater treatment plants, conventional treatment methods do not completely remove ECs, allowing residual amounts to be discharged into receiving bodies of water. Overall, national monitoring should be expanded to include metabolites, transformation products, and advanced treatment assessment. Full article

Community-based tourism (CBT) destinations create sustainability value that tourists may not fully observe during short visits. This study examines how perceived sustainability communication is associated with tourist support. Its originality lies in showing that sustainability communication in CBT is not directly associated with destination image or behavioral intention but appears to operate through destination personality as a symbolic interpretation mechanism before destination evaluation. Drawing on signaling theory and the stimulus–organism–response framework, perceived sustainability communication is conceptualized as a sustainability signal, destination personality as symbolic interpretation, destination image as cognitive–affective evaluation, and behavioral intention as tourist support. Data from 390 domestic tourists visiting six CBT destinations in Surat Thani Province, Thailand, were analyzed using PLS-SEM. The results show that perceived sustainability communication was associated with destination personality, which was subsequently associated with destination image and behavioral intention. Destination image was also associated with behavioral intention, while the direct associations with destination image and behavioral intention were not significant. Destination personality and destination image sequentially mediated the relationship. IPMA identified destination personality as the primary managerial priority, followed by destination image. The findings suggest that sustainability messages become behaviorally meaningful through symbolic interpretation and destination evaluation. Full article

The socio-cultural sustainability of heritage tourism depends in part on how historical memories and cultural meanings are communicated, interpreted, and passed on over time. However, the family’s role as a key socialization agent in shaping tourists’ interpretations of nationally significant heritage remains underexplored. This study examines the relationships among family historical communication (FHC), tourists’ subjective interpretation of heritage, and behavioral intentions. Using the case of sustainable heritage tourism in Dandong, China, the study conceptualizes tourists’ subjective heritage interpretation in terms of three dimensions: educational experience (cognitive), historical identity (affective), and inheritance value (evaluative). The study contributes by treating family historical communication as a pre-trip interpretive context that shapes heritage meaning-making and by proposing a multidimensional framework linking prior social experiences, subjective interpretation, and behavioral intentions. Survey data from 611 heritage tourists were analyzed using partial least squares structural equation modeling (PLS-SEM) to test the hypothesized relationships and age-cohort moderation effects. The results show that FHC is positively associated with all three meaning-making dimensions as well as with behavioral intentions. The mediation analysis further indicates that the indirect effects of FHC on behavioral intentions are stronger through historical identity and inheritance value than through educational experience. In addition, significant age-cohort moderation is found only in the relationship between FHC and educational experience. By highlighting the family as an important bridge between national narratives and personal meaning, this study offers implications for the tourism-supportive and socio-cultural dimensions of sustainable heritage management and inclusive destination governance. Full article

This paper proposes a hybrid content-based recommender system aimed at enhancing personalized tourism experiences and supporting the promotion of tourism in Morocco, with particular emphasis on the Drâa-Tafilalet region. The proposed approach integrates three machine learning models—Decision Tree, k-nearest neighbors, and Support Vector Machine—to predict user ratings for historical tourism sites. Tourist attraction metadata and user-generated reviews are represented using TF-IDF vectorization, while the predictions produced by the individual models are combined through an inverse-error weighting strategy. The system is evaluated using two datasets: a subset of the Yelp dataset comprising reviews of historical buildings in the United States, and a regional Drâa-Tafilalet dataset. Experimental results, assessed using MAE and RMSE, indicate that the weighted hybrid model outperforms the individual recommendation models by achieving lower prediction errors. These findings demonstrate the potential of hybrid content-based recommendation approaches to improve the accuracy of personalized tourism recommendations, support tourist decision-making, and promote underrepresented regional destinations. Full article

Small rural businesses face significant challenges due to geographical constraints, transportation costs, small market size, and low population density. On top of that, the energy crisis that arose after the start of the 2022 Russia–Ukraine war and the sanctions imposed by the EU and the US have created a stifling energy environment. The latter has exposed the businesses to the risk of energy poverty. The current study examines energy poverty within three business sectors that are prominent in the Greek countryside. These are entities firstly involved in the processing, manufacturing, and standardization of agricultural products; secondly, involved in the trade of agricultural products; and lastly, certain businesses operating in the tourist area. More specifically, this research examines the energy needs and energy obligations of these businesses as well as the energy efficiency of their facilities by simultaneously exploring the impact of European and national energy policies on addressing energy poverty in rural micro-businesses. To detect the opinions, experiences, perceptions, estimations, and expectations of entrepreneurs who maintain these businesses in rural areas, a qualitative approach was adopted utilizing personal in-depth interviews. Overall, fifteen micro-entrepreneurs were interviewed. Findings revealed that energy costs for rural businesses are becoming a major issue for their survival. Moreover, they have a substantial effect on their operational costs, exceeding other expenses and leading to an increase in energy poverty. These findings have also been confirmed by statistical data. Energy costs for small businesses range from 15% to 35% depending on the business, and during peak periods or crises, they exceed 40%. In addition, fees and taxes account for over 40% of electricity bills. Full article

In this study, the researchers aim to analyse the motivations of active golf tourists in Spain using the AFE-CFA-Cluster methodology. To this end, a survey was conducted in Spain amongst 381 players, both in person and online, who stated that they had undertaken one golf trip at least once in their lives. The survey consisted of three sections of questions: the first concerned preferences regarding both the destination and the golf courses; the second comprised 20 questions relating to motivations, divided into five categories: business opportunities, financial benefits, escape and relaxation, learning and challenge, and social interaction and camaraderie; and, finally, the third section focused on socio-demographic aspects. The results obtained from the exploratory factor analysis, which were subsequently confirmed by confirmatory factor analysis, revealed the composition of four motivational constructs: business opportunity, financial savings, escape and relaxation, and learning and challenge, resulting in a total of five homogeneous groups of golf tourists: experiential golfers, wellness-oriented golfers, multifunctional golfers, low-involvement golfers and learning-oriented golfers. These results may be useful for companies in the sector and marketing managers in defining the various existing segments of golf tourists and applying specific marketing strategies for each. Full article

Despite growing interest in destination branding, empirical evidence is still limited in explaining how brand personality influences tourist behavior mediated by integrated aspects. This study addresses this research gap by proposing and testing a structural model that considers the mediating role of tourist experience, authenticity, and trust in the relationship between brand personality and tourist behavior. The methodology used was quantitative and causal–correlational, using structural equation modeling and a sample of 514 Peruvian tourists selected through non-probabilistic convenience sampling. The results show that brand personality significantly influences tourist experience and destination authenticity, while its direct effect on trust is weak. In addition, experience positively influences trust and authenticity. Significantly, authenticity and experience show direct and positive effects on tourist behavior, while trust has a negative effect. These findings contribute to the advancement of the literature by integrating a single explanatory model with experiential, cognitive, and relational variables, broadening the understanding of the indirect role of brand personality in the tourism context. From a practical standpoint, the results suggest that destination managers should focus on enhancing brand personality and authenticity. 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

Tourism drives economic growth but also intensifies environmental pressure at travel destinations, particularly by exacerbating local challenges in waste management. Rather than merely testing the theoretical validity of the norm activation model (NAM), this study utilizes its key constructs—specifically moral and accountability variables—as a strategic framework to examine the psychological drivers of waste reduction in the urban context of Vientiane, Laos. Data from 382 domestic tourists were analyzed using ordinary least squares regression. Ascription of responsibility (AR) (β = 0.219, p < 0.001) was the strongest predictor of intention, followed by personal norm (PN) (β = 0.173, p < 0.01) and actual waste management behavior (β = 0.160, p < 0.01). Notably, environmental knowledge and awareness of consequences—factors often emphasized in traditional environmental campaigns—had no significant influence. The findings demonstrate that, in addressing urban waste challenges in developing regions, fostering internalized moral sentiments (AR and PN) is far more effective than mere pro-environmental education. This study concludes that sustainable waste management may benefit from operationalized interventions that activate personal accountability rather than relying solely on general environmental awareness. Full article

Urban tourism destinations increasingly rely on specialized segments such as wine tourism. However, limited research examines how electronic word-of-mouth (e-WOM) and personal recommendations jointly influence destination loyalty through experiential and relational pathways in urban settings. This study develops and tests an integrative model linking e-WOM to destination loyalty through experience, trust, and satisfaction as mediating mechanisms, grounded in social exchange theory and expectancy-disconfirmation theory. Data were collected from 326 tourists visiting Jerez de la Frontera (Spain) via intercept surveys at transportation terminals and analyzed using structural equation modelling with maximum likelihood estimation and bootstrapping. All nine hypothesized relationships were positive and significant, collectively accounting for 89.3% of the variance in destination loyalty. Satisfaction emerged as the strongest predictor of loyalty, far exceeding the direct effects of experience and trust, while e-WOM exerted the strongest influence on experience. Indirect effects confirmed that e-WOM influences loyalty through experience and trust pathways. This research provides the first model integrating pre-visit communication influences with experiential and relational constructs to explain destination loyalty in an urban context. Findings indicate that urban destinations should prioritize visitor satisfaction as the primary loyalty driver, while strategically managing digital reputation and ensuring alignment between online recommendations and actual destination experiences. Full article

The concentration of tourist flows along short urban links caused by cruise stops in medium-sized Mediterranean ports exacerbates traffic and localized environmental externalities. This study evaluates the willingness to pay (WTP) of cruise passengers for an electric tram that would connect the Gaženica Port with Zadar’s historic center, an intervention designed to cut travel time and reduce on-street congestion and emissions. Over the course of two seasons, a two-wave, two-site, in-person survey was conducted at the port and in the city center. The instrument adopts a double-bounded dichotomous choice (DBDC) contingent valuation design with randomized starting bids that were calibrated using a pre-test that benchmarked prevailing transport pricing. Primary WTP estimates are obtained from a binary choice model with socio-demographic and environmental covariates; whereby inference relies on cluster-robust errors. Robustness is assessed through three complementary checks that do not require additional data: (i) a bivariate specification to account for within-respondent correlation between first and follow-up bids; (ii) Turnbull nonparametric bounds for the interval-censored WTP distribution; and (iii) starting-point tests using split-sample estimation and bid-set indicators. A spike adjustment based on “no–no at the lowest bid” responses is explored where appropriate. Beyond its methodological contribution, this research advances the sustainable tourism development discourse by quantifying visitors’ financial support for low-emission urban mobility infrastructure that mitigates environmental stresses while preserving residential life quality. The results integrate cruise tourist management with the more general goals of resilient and sustainable urban destinations by offering a decision-ready value input for port-city mobility planning in historic Mediterranean centers. Full article

This study investigates how AI-driven personalization shapes traveler satisfaction in a post-adoption tourism context, with particular attention to the mechanisms and boundary conditions through which personalization is associated with experiential outcomes. Using an integrated post-adoption framework, the study conceptualizes AI-driven personalization as an experiential input influencing satisfaction through trust formation, perceived value, and individual readiness to engage with technology. Survey data were collected from 347 tourists with direct experience of AI-enabled tourism services in Kosovo. The relationships were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results show that AI-driven personalization is positively associated with traveler satisfaction. It enhances trust in AI-powered systems, and trust is positively associated with perceived value. Perceived value mediates the relationship between trust in AI-powered systems and traveler satisfaction, highlighting value appraisal as a central post-adoption mechanism. AI-driven personalization is also indirectly associated with traveler satisfaction through a sequential mechanism, in which trust precedes perceived value in the experiential evaluation process. Technology readiness moderates the relationship between perceived value and traveler satisfaction, indicating heterogeneous experiential responses to AI-enabled tourism services. The study contributes to tourism and hospitality research by demonstrating a sequential relational–evaluative mechanism through which AI-driven personalization is associated with traveler satisfaction, shifting the focus from adoption-based explanations toward post-adoption experiential pathways. It further clarifies the role of trust as a relational mechanism preceding value formation and identifies technology readiness as a boundary condition shaping satisfaction outcomes in an emerging destination context. The findings also offer practical guidance for designing AI-enabled services that strengthen trust, enhance value perceptions, and align personalization strategies with varying levels of traveler technology readiness. Full article

The increasing availability of tourism information on digital platforms has improved tourists’ access to destination-related data. However, existing tourism information systems often lack effective integration between user preference information and geospatial data, limiting their ability to provide personalized and context-aware recommendations. This study proposes a personalized tourism recommendation system by integrating Geographic Information System (GIS) technology with association rule mining to analyze relationships between user preferences and spatial characteristics of tourist destinations. The proposed system provides map-based visualization, calculates distances between users and destinations, and generates personalized recommendations based on both user interests and spatial proximity. The implementation results demonstrate that the system can generate location-aware and personalized tourism recommendations, supporting users in identifying suitable destinations within their surrounding geographic context. The integration of geospatial processing with association rule mining improves recommendation relevance by incorporating both preference patterns and spatial proximity. Furthermore, the proposed framework has the potential to support more balanced spatial distribution of tourism activities by recommending geographically appropriate destinations rather than concentrating suggestions on highly popular locations. These findings highlight the value of combining geospatial technologies with data mining techniques to support tourism recommendation systems and spatially informed tourism planning. Full article

The rapid growth of digital tourism platforms has intensified information overload and decision complexity for both locals and travelers, while operators struggle to differentiate their offerings and sustain profitable, data-driven e-commerce models. This paper presents Doroob, a big data and artificial intelligence (AI)-driven, context-aware recommendation system that integrates traditional recommender techniques with real-time facial emotion recognition (FER) to enable intelligent tourism commerce. Doroob combines three AI-based recommendation strategies: smart adaptive recommendation (SAR) collaborative filtering, a Vowpal Wabbit-based context-aware model, and a LightFM hybrid model. It trained on datasets built from the Google Places API and enriched with ratings adapted from MovieLens. FER, implemented with DeepFace and OpenCV, analyzes short video segments as users browse destination details, converts emotion scores into 1–5 satisfaction ratings, and stores this implicit feedback alongside explicit ratings to support adaptive, emotion-aware personalization. Experimental results show that the context-aware model achieves the strongest top-K ranking performance, the hybrid LightFM model yields the highest AUC of 0.95, and the SAR model provides the most accurate rating predictions, demonstrating that combining contextual modeling and FER-based implicit feedback can enhance personalization, mitigate cold-start, and support data-driven promotion of local tourist services in intelligent e-commerce ecosystems. Full article