Search Results (296)

Background: Pre-exposure prophylaxis (PrEP) offers over 99% protection against HIV when used consistently, but stigma continues to undermine persistence in care. While much research has described the external manifestations of PrEP-related stigma, less is known about how individuals cope with these stigmas and how such coping processes influence persistence. Guided by Social Cognitive Theory, this study examined the psychosocial strategies men who have sex with men (MSM) in Tanzania use to cope with PrEP-related stigma and sustain persistence in care. Methods: Thirty-two in-depth interviews were conducted with purposefully selected MSM aged 18–38 years at Ngamiani Health Centre in Tanga region. The sampling included both persistent and non-persistent PrEP users with variation in age and sexual position preferences. Participants were sampled for variation in persistence status (persistent and non-persistent), age, and sexual position preference to capture heterogeneity in stigma experiences and coping processes. Interviews were conducted in Kiswahili, audio-recorded, transcribed, translated, and analyzed using reflexive thematic analysis. Results: Participants described PrEP-related stigma as socially constructed through narratives that equated PrEP with HIV treatment, labeled it a “gay pill,” associated it with promiscuity, or linked it to bodily harm or increased HIV risk. These stigmas impact persistence in care through discouraging clinic visits and daily pill taking. However, some participants remained persistent in care despite stigma by using protective mental strategies such as personal agency, mental time travel, and affirmation from supportive social connections, which buffered emotional impacts and sustained persistence. Conclusions: Persistence in PrEP care is shaped not only by stigma in the social environment but also by how individuals interpret and respond to it. Interventions should therefore combine structural stigma-reduction efforts with mental health-informed strategies that strengthen agency and supportive social relationships to sustain PrEP engagement among MSM. Full article

This study addresses a pivotal strategic issue in hospitality e-commerce: how hotels can optimize cooperation with heterogeneous online travel agencies (OTAs). Moving beyond the conventional question of whether to cooperate, we investigate the interrelated decisions of which OTA type to partner with (quality-focused vs. price-focused) and which business model to adopt (merchant vs. agency). We develop a game-theoretic model that incorporates key e-commerce factors, including hotel capacity constraints, cross-channel spillover effects, and differential consumer acceptance of OTA types. Our analysis yields a contingent decision framework. We demonstrate that OTA cooperation becomes beneficial only when a hotel’s room capacity exceeds its direct-channel demand. The optimal strategy evolves with capacity: hotels with moderate capacity should partner with a single OTA type—predominantly the quality-focused one—while larger hotels should engage both types to maximize market coverage. In terms of business models, smaller hotels benefit from the risk-shifting merchant model, whereas larger hotels capture higher margins through the agency model. A key finding is the general superiority of a differentiated approach: applying the agency model to quality-focused OTAs and the merchant model to price-focused OTAs. This research provides a structured analytical framework to guide hotel managers in crafting e-commerce platform strategies and offers scholars a foundation for further inquiry into platform competition and contract design in digital marketplaces. Full article

This study empirically examines how organizational learning influences problem-solving competency and organizational effectiveness in the context of online travel agencies (OTAs) and tests the moderating role of digital empowerment. Using agency lists registered under Korea’s Tourism Promotion Act, we employed stratified sampling by region and simple random sampling within strata. Data collection was commissioned by the Tourism/Leisure HRD Council. A survey was carried out from 2 to 19 June 2025; of the 210 questionnaires returned, 204 valid responses were analyzed. Measures were adapted from prior studies on a five-point Likert scale. Analyses conducted in SPSS 27.0 included descriptive statistics, exploratory factor analysis (EFA), reliability testing (Cronbach’s α), correlation analysis, and simple and hierarchical regressions. The results indicate that (1) organizational learning has a significant positive effect on problem-solving competency (β = 0.541, p < 0.001, R² = 0.293); (2) organizational learning positively affects organizational effectiveness (β = 0.436, p < 0.001, R² = 0.190); and (3) problem-solving competency positively influences organizational effectiveness (β = 0.624, p < 0.001, R² = 0.389). Regarding moderation, digital empowerment did not significantly moderate the organizational learning → problem-solving link but did significantly moderate the organizational learning → organizational effectiveness relationship (p < 0.05), suggesting that digital empowerment enhances the conversion efficiency of learning into performance. Theoretically, this study substantiates the learning–problem-solving–performance mechanism in a service/tourism setting and identifies digital empowerment as a catalytic moderator that strengthens the translation of learning into organizational outcomes. Practically, the findings imply that OTAs can amplify organizational effectiveness by building digital empowerment structures—data-driven decision systems, process automation, and real-time customer-response capabilities—which enable learned knowledge to materialize into performance. Future research should incorporate digital maturity, leadership, customer orientation, and related variables into extended models. Full article

On 1 November 2024, the Federal Highway Administration issued a final rule updating the 23 CFR Part 630 Subpart J on Work Zone Safety and Mobility, detailing performance measures and reporting requirements. The rule suggests that state agencies should define formal performance measures that can be tracked consistently for the continuity of work zone program management across states. The objective is to help identify work zones needing mobility or safety improvements, as well as provide quantitative feedback on the best practices. The emergence of connected vehicle data over the past few years provides a scalable approach for agencies to calculate and monitor the performance measures defined in the CFR, covering, but not limited to, speed, travel time, queue length and duration, hard braking events and speed differentials. This paper describes techniques that use connected vehicle data to estimate different measures that map into the performance measures defined in this rule. A 2024 work zone in Illinois along I-24 was chosen to demonstrate the utility of the measures. The paper concludes with a discussion of ongoing work applying these derived measures to 101 work zones across 9 states in 2025 to demonstrate scalability. Full article

The rise in social media has transformed Online Travel Agencies (OTAs) into platforms where users actively share their experiences and opinions. However, conventional opinion mining approaches often fail to capture nuanced emotional expressions or connect them to user influence. To address this gap, this study introduces an ontology-driven opinion mining framework that integrates multi-class emotion classification, aspect-based analysis, and influence modeling using Indonesian-language discussions from the social media platform X. The framework combines an OTA-specific ontology that formally represents service aspects such as booking support, financial, platform experience, and event with fine-tuned IndoBERT for emotion recognition and sentiment polarity detection, and Social Network Analysis (SNA) enhanced by entropy weighting and TOPSIS to quantify and rank user influence. The results show that the fine-tuned IndoBERT performs strongly with respect to identification and sentiment polarity detection, with moderate results for multi-class emotion classification. Emotion labels enrich the ontology by linking user opinions to their affective context, enabling the deeper interpretation of customer experiences and service-related issues. The influence analysis further reveals that structural network properties, particularly betweenness, closeness, and eigenvector centrality, serve as the primary determinants of user influence, while engagement indicators act as discriminative amplifiers that highlight users whose content attains high visibility. Overall, the proposed framework offers a comprehensive and interpretable approach to understanding public perception in Indonesian-language OTA discussions. It advances opinion mining for low-resource languages by bridging semantic ontology modeling, emotional understanding, and influence analysis, while providing practical insights for OTAs to enhance service responsiveness, manage emotional engagement, and strengthen digital communication strategies. Full article

Highway interchanges are vulnerable components of transport networks, often prone to congestion and crashes. Traditional monitoring methods like loop detectors or travel time queries often fail to capture the granular spatiotemporal distribution of bottlenecks in detail. To address this gap, this study introduces a new approach to quantify congestion and analyze bottleneck dynamics at Atlanta’s Tom Moreland Interchange, one of the nation’s most congested sites. A percent Traffic Congestion (pTC) metric was developed from the Google Maps Traffic Layer for twelve directional routes and validated against observed travel times obtained independently through the Google Maps Routes API. Traffic imagery collected every ten minutes for four months and 746 crash records were analyzed. Findings reveal distinct spatial patterns and temporal dynamics of congestion, with northbound I-85 and eastbound I-285 most affected during afternoon peaks. A quadratic model provided the best fit between pTC and travel times (R² = 0.85), confirming pTC as a reliable congestion indicator. An LSTM model using pTC time series also accurately predicted mobility trends at the I-285 west to I-85 north bottleneck. Additionally, Seasonal-Trend decomposition using LOESS (STL) identified congestion anomalies, and their association was analyzed with crashes. The proposed methodology offers transportation agencies a cost-effective framework for monitoring, measuring, and understanding congestion in complex interchanges. Full article

Online paid advertising is a dynamic form of online marketing that requires precision and a quick response to market changes. Automated tools are used to greatly simplify the process of creating and managing Pay-Per-Click (PPC) campaigns. The purpose of this research is to evaluate the impact of implementing an automated PPC management tool (Dotidot) on campaign performance in a travel agency compared with standard Google Ads campaigns. A structured multi-criteria procedure is first applied to select the most suitable tool for the Czech and Slovak markets. The core contribution of the paper is the observational case study of Dotidot and its performance comparison. The subject of the research is a comparative analysis of three PPC automated tools: Conviu, Dotidot, and BlueWinston. The significance of this topic lies in highlighting the relatively new possibilities of digital marketing and choosing the right tool for using automation. This research can also stimulate other researchers in this field and expand knowledge. The analysis is carried out using the method of systematic comparison based on established criteria, the result of which is a recommendation of the preferred tool and a brief discussion of its implementation options. The analyzed tools are used mainly on the Czech and Slovak markets and are oriented towards e-commerce (electronic commerce). In addition to e-commerce, a case study of the use of digital promotion in sports organizations is also presented. The research results in a systematic comparison of Conviu, Dotidot, and BlueWinston tools according to predefined criteria. The research also includes a brief discussion on the possibilities of implementing the recommended tool in practice, as well as an assessment of its benefits and limitations. Full article

Autonomous vehicles (AVs), including privately owned self-driving cars and shared autonomous vehicles (SAVs), hold great potential to transform urban mobility by enhancing safety, accessibility, efficiency, and sustainability. However, their widespread deployment also carries the risk of significantly increasing vehicle miles traveled (VMT), a phenomenon known as the rebound effect. This paper examines the VMT rebound effects resulting from AV and SAV deployment, drawing on recent studies and global case insights. We conducted a systematic narrative review of 48 studies published between 2019 and 2025, drawing on academic sources and credible agency reports. We do not conduct a meta analysis. We quantify how different automation levels (SAE Levels 3, 4, 5) impact VMT and identify the primary factors driving VMT growth, namely: reduced perceived travel time cost, induced demand from new user groups, modal shifts away from transit, and empty VMT. Global case studies from North America, Europe, Asia, and the Middle East are reviewed alongside regional policy responses. Quantitative analyses indicate moderate to significant VMT increases under most scenarios—for example, approximately 10 to 20% increases with conditional automation and potentially over 50% with high/full automation, under the circumstances of no effective policy interventions. Meanwhile, aggressive ride-sharing and policy interventions, including road pricing and transit integration, can mitigate or even reverse these increases. The discussion provides a critical assessment of policy strategies such as mileage pricing, SAV incentives, and integrated land-use/transport planning to manage VMT growth. We conclude that without proactive policies, widespread AV adoption is likely to induce a rise in VMT, but that a suite of well-designed measures can steer automated mobility towards sustainable outcomes. These findings help policymakers and planners balance AV benefits with congestion, energy use, and climate goals. Full article

Short-term traffic flow prediction is a critical component of efficient management in Intelligent Transportation Systems (ITS), providing real-time travel guidance for commuters and supporting informed decision-making by transportation authorities. To address the current challenges of insufficient prediction accuracy and excessive reliance on time-series features, we propose a spatio-temporal feature-integrated short-term traffic flow prediction model named KAN-CNN-BiLSTM. In this model, traffic flow data from the target road segment and its two adjacent segments are jointly fed into the model to fully leverage spatio-temporal features for prediction. Subsequently, a Convolutional Neural Network (CNN) extracts spatial features from the combined traffic flow data. To overcome the limitation of traditional LSTMs, which can only process unidirectional time series, we introduce a bidirectional long short-term memory network (BiLSTM) with symmetric time series extraction capability. This enables simultaneous capture of historical and future traffic flow dependencies. Finally, we replace the conventional fully connected network with a Kolmogorov–Arnold network (KAN) to enhance the representation of complex nonlinear features. Experimental results using traffic flow data from the UK Highways Agency website demonstrate that the KAN-CNN-BiLSTM model outperforms existing mainstream methods, achieving superior prediction accuracy and minimal error. The model’s MAE, RMSE, MAPE, and R² values are 27.4696, 40.3923, 8.65%, and 0.9615, respectively. Full article

The tourism industry is currently experiencing a substantial digital transformation, with Online Travel Agencies (OTAs) committed to integrating innovations such as artificial intelligence (AI) in order to enhance service delivery and personalize user experiences. This study investigates the relationship between the utilization of AI and the technical quality scores of tourism websites, aiming to identify significant associations and variances in the critical conversion phase. An exploratory research design is employed to evaluate the technical quality of three prominent international tourism websites, i.e., Booking.com, Airbnb.com, and Tripadvisor.com. The investigation uses Google Lighthouse, with Performance, Accessibility, Best Practices, and Search Engine Optimization (SEO) as variables analyzed across both desktop and mobile versions, as well as on pages with and without AI functionality. Data analysis is performed using JASP (version 0.19.3), including linear regression analysis to quantify the predictive relationship. The analysis confirms that the Performance variable is the most sensitive to the influence of AI. AI integration demonstrates a significant positive influence on the Performance score of travel websites. The regression model indicates that AI usage explains 78.9% of the variation in the Performance score (${\mathrm{R}}^2$ = 0.789, p < 0.001), indicating a substantial correlation with technical sustainability. Nevertheless, there remains an ongoing necessity for optimization, particularly with regard to the enhancement of overall performance and improvement of scores for mobile devices. The study acknowledges certain limitations related to the sample size of AI applications and the accessibility of specific AI versions in particular geographic regions. Full article

Tourism enterprises are increasingly pressured to align competitiveness with sustainability, yet limited evidence exists from Central and Eastern Europe. This study investigates the role of eco-friendly orientation as a determinant of customer satisfaction and perceived competitiveness in the Visegrad Four (Slovakia, Czech Republic, Poland, and Hungary). Empirical research was conducted on a sample of 478 enterprises, including hotels, guesthouses, spas, agritourist facilities, and travel agencies. Data were collected between January and June 2025 using a standardized questionnaire and analyzed through descriptive statistics, factor analysis, Pearson correlation, multiple regression, and ANOVA. The findings demonstrate that enterprises adopting more extensive environmental practices report significantly higher customer satisfaction (r = 0.43, p < 0.01) and perceived competitiveness (r = 0.38, p < 0.01). Factor analysis identified three key dimensions of environmental orientation: ecological operations, ecological innovations, and ecological marketing. Regression analysis highlighted ecological marketing, particularly the adoption of certifications and eco-labels, as the strongest predictor of competitiveness. ANOVA revealed significant cross-country differences, with Slovak and Czech enterprises outperforming Polish and Hungarian counterparts. The results suggest that ecological initiatives are essential for long-term competitiveness, providing both strategic guidance for managers and policy implications for fostering supportive regulatory and financial frameworks across the region. This study makes a novel contribution by offering one of the first large-scale empirical analyses of the link between sustainability and competitiveness in Central and Eastern Europe, a region where such research is still scarce. Theoretically, it extends the application of Ecological Modernization Theory to the tourism sector, while practically it provides actionable recommendations for managers and policymakers on integrating eco-certification and ecological marketing into their strategies. These insights underline the dual role of environmental orientation as a driver of both customer satisfaction and competitive advantage. Full article

In this article, the authors reflect on the power of participatory feminist research in supporting gendered knowledges and cultures of equality through a community-based education project working with women construction workers in Sylhet, northern Bangladesh. We consider the impacts of their involvement in feminist qualitative research conducted as part of the Global Gender & Cultures of Equality (GlobalGRACE) project, an international research, education and capacity building project funded through the UK government’s Global Challenges Research Fund (2017–2022). We then explore what happened next, between 2022 and 2025, after the official project ended. GlobalGRACE aimed to enhance women’s wellbeing, support self-esteem and confidence building, and promote gender equality with women construction workers from socio-economically marginalised communities. Here we reflect on the ways participant women were able to bring their agency and situated gendered knowledges through their participation to create exhibitions of films and photography, build a Manifesto of Workers Rights, and emerge beyond the project as entrepreneurs, developing a lasting social enterprise supporting over 2800 women. Travelling through the GlobalGRACE project, we thus consider participant women’s experiences as they made this journey from day-labourers on building sites to creating Protity, a women’s social enterprise supporting one another to become independent entrepreneurs running their own small businesses in Sylhet, Bangladesh. Full article

Tourism is a strategic sector for the Peruvian economy, yet it also generates negative environmental impacts, highlighting the need to foster pro-environmental behaviors. In this context, travel and tourism agencies, as intermediaries between supply and demand, play a key role in promoting sustainable practices, particularly through social media platforms. This study aimed to analyze the extent to which travel and tourism agencies in Peru promote pro-environmental behaviors in their digital publications. A quantitative approach was used, employing an observation guide to analyze social media posts on Facebook, Instagram, and TikTok from a random sample of 369 agencies registered with the Ministry of Foreign Trade and Tourism between March and May 2025. The results show that although most agencies maintain an active presence on social media, primarily on Facebook and Instagram, the frequency of pro-environmental publications is low. Inferential analysis revealed no statistically significant differences among agency types, although retail agencies showed a lower probability of reaching higher levels of commitment compared to tour operators. These findings underscore the need to integrate sustainability as a cross-cutting axis in the digital strategies of the tourism sector. Full article

Boyacá, a department in the Colombian Andes, is recognized for its rich natural, cultural, and historical heritage, positioning it as an emerging tourism destination with strong potential for sustainable development. Its portfolio encompasses ecological, cultural, adventure, and rural modalities that contribute to local economies and community well-being. This study employs a mixed-methods design combining documentary analysis with fifty semi-structured interviews conducted across five provinces, engaging stakeholders such as hotel managers, travel agencies, restaurateurs, agroecological farmers, museum directors, café entrepreneurs, and ecotourism operators. The findings reveal that innovation, particularly through infrastructure, modernization, digital adoption, and service professionalization—is critical to strengthening competitiveness. However, persistent challenges include stakeholder fragmentation and the absence of a coordinated promotional strategy, both of which limit visibility and shared value generation. To address these challenges, the study proposes integrated innovation pathways, including thematic circuits (heritage, independence, and coffee routes), personalized tourism experiences, and the use of digital and experiential marketing tools. These approaches highlight the need for collaborative governance involving public, private, and community actors. The article contributes empirical evidence to the debate on tourism innovation and sustainability in emerging regions, underscoring Boyacá’s paradox: global recognition coexists with fragmented governance. It argues that innovation-driven strategies, aligned with sustainability principles, are essential for consolidating Boyacá as a competitive and regenerative tourism destination. Full article

This study examines the descriptive trajectories through which service innovation is associated with customer exit dynamics after service failures, drawing on a three-wave panel of 532 online travel agency users and employing partial least squares structural equation modeling with predictive assessment. We analyze how innovation is associated with switching intentions via brand hate and brand distrust over time. Results reveal distinct temporal patterns: service innovation is linked to consistent reductions in both hate and distrust, yet only hate emerges as a salient mediator whose marginal association with switching intensifies over time. In contrast, distrust, although mitigated by innovation, remains relatively stable and behaviorally inert. Rather than asserting a causal explanation, we document temporal associations—labelled here as a “dilution effect”—to indicate that innovation coincides with weakening negative emotions but only partial attenuation of their behavioral correlates. By distinguishing between the fading but influential role of hate and the persistent yet inert nature of distrust, this study clarifies differentiated pathways through which negative states coincide with customer exit. For managers, the results highlight the need for staged innovation strategies to dissipate hate, complemented by long-term trust-repair initiatives to address enduring distrust and reduce customer churn. Full article