Search Results (45)

This study examines how information source and fire manager attributes affect the use of 33 different information sources used for fire management. The approach is like that of recreation demand models that predict an individual’s travel to recreation sites based on individual and site characteristics. Here, we predict “visits” to websites rather than campsites. The study develops and estimates a random utility model, using survey data from a representative sample of US Southwest fire managers. Results were consistent with predictions of economic value of information models. Significant predictors included the agency that a manager worked for, a manager‘s self-reported role within the agency, the interagency dispatch centers where they worked, the total number of fire management decisions, and the geographic and administrative scope of the dispatch center management area. Manager personal attributes (education, age, experience) only minutely improved model fit. Information use varied significantly by type of information source. The probability of use was greater for general weather or climate websites/portals than for specialized sources (such as drought, ENSO, or fire decision support tools (DSTs)). Fire management-specific sources (excluding fire DSTs) had a greater probability of use than general sources. Manager reliance on different sources of information shifted when moving from before to during the fire season. Future research could explore the internal dynamics of agencies and dispatch centers affecting information use, why fire managers do not use decision support systems more to support decisions, and the role of different types (and not just years) of experience. Full article

Real-time navigation systems are increasingly used to provide optimal driving routes together with accurate travel time predictions that reflect dynamic urban traffic conditions. Recent advances have focused on integrating structured traffic data from traditional APIs with unstructured, context-rich information extracted via semantic crawling of news websites and social media platforms. This survey reviews state-of-the-art approaches that combine these heterogeneous data sources to improve route planning and travel time estimation, with special attention to the challenges posed by incident detection, event extraction, and multimodal data fusion. We discuss core methodologies including natural language processing techniques for event recognition, machine learning models for traffic prediction, and graph-based routing algorithms, highlighting their advantages and limitations. Finally, we outline open research directions for building context-aware navigation systems able to adapt to real urban mobility conditions. Full article

Croatia’s tourism market is highly exposed to digital platforms and peer-to-peer information flows, yet evidence on how Croatian users differentiate between promotional formats (digital channels, agency websites, traditional media and word-of-mouth) remains fragmented and rarely translated into actionable priorities. This study aims to identify the underlying dimensions of perceived promotional influence and to prioritize promotional formats for destination choice in Croatia by integrating PCA and the Analytic Hierarchy Process (AHP). An online survey (N = 299) was used to extract promotional dimensions via PCA and to test group differences by gender, age and primary information source, while AHP translated expert judgments into a comparative priority structure. Results consistently indicate that word-of-mouth is the most persuasive driver of destination choice, but its perceived importance varies significantly across demographic segments and information-source profiles. Younger respondents place greater emphasis on digital channels (especially social media and travel agency websites), whereas older respondents show higher reliance on traditional formats. The combined PCA–AHP approach provides a structured bridge between user perceptions and managerial prioritization, offering segment-specific guidance for more efficient allocation of promotional resources in Croatian destination marketing. 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

Background/Objectives: The “TROVAILMIOVACCINO” platform was developed to help adults in Italy identify vaccines recommended for them based on individual characteristics, in line with the Italian National Immunization Plan (NIP). The website directs users to an anonymous online questionnaire addressing key factors such as age, sex, pregnancy status, travel history, medical conditions, and risky behaviors. It is intended for adults aged 18 and over and can be filled out either by individuals or by others on their behalf, such as healthcare professionals. The purpose of the study was to assess the platform’s reach, the health status of users, and its ability to inform users. Methods: Data were organized into tables and analyzed using frequencies, percentages, and statistical tests to assess user demographics and health conditions. Significant differences among sociodemographic groups were evaluated using the Chi-square and Fisher’s exact tests. Results: Over 30 months, the website was accessed 1897 times, with 1622 users (85.5%) completing the questionnaire for personal interest. The majority of users were aged 18–49 years (61.5%), with a nearly equal male–female distribution. Healthcare workers represented the most common professional group (29.2%) among users. Older individuals were more likely to have the questionnaire completed by someone else. Among respondents, 25.8% reported having a single medical condition, with cardiovascular diseases (11.9%), diabetes (6.7%), and respiratory diseases (4.8%) being the most frequent. The most common risk condition reported was potential contact with newborns. Conclusions: The findings highlight the value of the platform in reaching diverse user groups and offering tailored vaccine recommendations. Full article

Large language-model chatbots such as ChatGPT and DeepSeek are quickly gaining traction as an easy, first-stop tool for trip planning because they offer instant, conversational advice that once required sifting through multiple websites or guidebooks. Yet little is known about the biases that shape the destination suggestions these systems provide. This study conducts a controlled, persona-based audit of the two models, generating 6480 recommendations for 216 traveller profiles that vary by origin country, age, gender identity and trip theme. Six observable bias families (popularity, geographic, cultural, stereotype, demographic and reinforcement) are quantified using tourism rankings, Hofstede scores, a 150-term cliché lexicon and information-theoretic distance measures. Findings reveal measurable bias in every bias category. DeepSeek is more likely than ChatGPT to suggest off-list cities and recommends domestic travel more often, while both models still favour mainstream destinations. DeepSeek also points users toward culturally more distant destinations on all six Hofstede dimensions and employs a denser, superlative-heavy cliché register; ChatGPT shows wider lexical variety but remains strongly promotional. Demographic analysis uncovers moderate gender gaps and extreme divergence for non-binary personas, tempered by a “protective” tendency to guide non-binary travellers toward countries with higher LGBTQI acceptance. Reinforcement bias is minimal, with over 90 percent of follow-up suggestions being novel in both systems. These results confirm that unconstrained LLMs are not neutral filters but active amplifiers of structural imbalances. The paper proposes a public-interest re-ranking layer, hosted by a body such as UN Tourism, that balances exposure fairness, seasonality smoothing, low-carbon routing, cultural congruence, safety safeguards and stereotype penalties, transforming conversational AI from an opaque gatekeeper into a sustainability-oriented travel recommendation tool. Full article

This study explores how AI chatbot experiences on travel websites influence consumer engagement, with satisfaction from using AI chatbots as a mediating factor. Grounded in the Stimulus-Organism-Response (S-O-R) framework, the research shifts the focus from utilitarian models to examine how chatbot attributes—e.g., ease of use, information quality, security, anthropomorphism, and omnipresence—affect satisfaction of using AI chatbots and subsequent consumer engagement behaviours. Survey data from 519 Portuguese travellers were analysed using partial least squares structural equation modelling (PLS-SEM). The study contributes to theory by (1) demonstrating S-O-R’s advantages over utilitarian models in capturing relational and emotional dimensions of AI interactions, (2) identifying satisfaction with using AI chatbots as a pivotal mediator between AI chatbot experience and consumer engagement, and (3) revealing generational disparities in drivers of engagement. Notably, satisfaction strongly influences engagement for Generation X, while direct experience matters more for Generation Z. Millennials exhibit a distinct preference for hybrid human–AI service handoffs. The practical implications include prioritizing natural language processing for ease of use, implementing generational customization (e.g., gamification for Gen Z, reliability assurances for Gen X), and ensuring seamless human escalation for Millennials. These insights equip travel businesses to design AI chatbots that foster long-term loyalty and competitive differentiation. Full article

Online travel agencies (OTAs) function as e-commerce platforms that facilitate transactions between accommodation providers and consumers, enabling users to efficiently search for, compare, and book travel and lodging services. As the number of OTAs continues to grow, delivering superior service quality has become essential for increasing customer repurchase intentions. Despite its significance, existing research has primarily focused on factors such as website quality, pricing strategies, brand image, and perceived value as determinants of repurchase intention. However, relatively little attention has been paid to the alignment between online information disclosure and customers’ actual offline experiences. To address this gap, the present study introduces the concept of the information disclosure gap and examines its effects on search cost, customer satisfaction, and trust, as well as the subsequent influence of these variables on repurchase intention. A questionnaire-based survey method was conducted with individuals in Taiwan who had prior experience using OTAs, yielding 365 valid responses. This study offers practical insights and recommendations for both OTAs and accommodation providers aimed at reducing the information disclosure gap and strengthening customer repurchase intention. Full article

This study investigated the factors influencing search efficiency on travel websites, focusing on the effects of gender, website design, and the distribution of effective versus ineffective areas in page layout on visual search efficiency and task performance. Using eye-tracking technology, three experiments were conducted with 48 participants (19 males, 29 females; Mage = 26.73). Among the tested websites, TC exhibited the highest efficiency in task completion time, followed by QN and TN (40.10 s < 83.88 s < 95.27 s). Analysis of fixation distributions indicated that participants focused on effective areas at rates of 20.53% (TC), 55.31% (QN), and 62.42% (TN), underscoring the significant impact of effective and interference area distribution on search efficiency. These findings provide empirical evidence for optimizing travel website design through visual layout improvements to enhance information retrieval and user experience, with TC serving as a prime example of a site with lower cognitive load that better aligns with sustainable tourism principles. Full article

Tourism refers to the activity of traveling for pleasure, recreation, or leisure purposes. It encompasses a wide range of activities and experiences, from sightseeing to cultural exploration. In today’s digital age, tourists often organize their excursions independently by utilizing information available on websites. However, due to constraints in designing customized tour routes such as travel time and budget, many still require assistance with vacation planning to optimize their experiences. Therefore, this paper proposes an algorithm for personalized tourism planning that considers tourists’ preferences. For instance, the algorithm can recommend places to visit and suggest activities based on tourist requirements. The proposed algorithm utilizes an extended model of the team orienteering problem with time windows (TOPTW) to account for mandatory locations and activities at each site. It offers trip planning that includes a set of locations and activities designed to maximize the overall score accumulated from visiting these locations. To solve the proposed model, the Adaptive Neighborhood Simulated Annealing (ANSA) algorithm is applied. ANSA is an enhanced version of the well-known Simulated Annealing algorithm (SA), providing an adaptive mechanism to manage the probability of selecting neighborhood moves during the SA search process. The computational results demonstrate that ANSA performs well in solving benchmark problems. Furthermore, a real-world attractive location in Tak Province, Thailand, is used as the case study in this paper to illustrate the effectiveness of the proposed model. Full article

Adopting advanced e-commerce practices is essential for enhancing user engagement and business performance, particularly in tourism. This study evaluates the e-commerce adoption of Kazakhstan’s tourism websites using an innovative Integrated Multi-Criteria Decision Analysis (IMCDA) methodology. Traditional evaluation methods overlook the interplay between website functionality, user experience, and strategic objectives. To address this gap, the IMCDA framework integrates qualitative and quantitative approaches by combining advanced Multi-Criteria Decision-Making (MCDM) techniques, including SPOTIS, ESP-COMET, RANCOM, and SITW, with content analysis and logistic regression. The study assessed 77 tourism websites, categorized into Online Travel Agencies (OTAs), Official Tourism Websites (OTWs), and Attraction Websites (AWs), based on 34 e-commerce features grouped into dimensions such as product information, functionality, reservations, payment systems, and customer relationship management (CRM). The findings reveal that OTAs significantly outperform OTWs and AWs in most dimensions, especially in online booking and CRM functionalities. At the same time, AWs lag in key e-commerce features like reservations and payment systems. This research highlights critical gaps in Kazakhstan’s tourism e-commerce ecosystem. It provides actionable recommendations, including enhancing CRM tools, integrating advanced booking systems, and leveraging collaborations with local financial technology providers like Kaspi Pay. The IMCDA framework offers a robust, adaptable evaluation model with practical implications for digital transformation and competitiveness in the tourism industry. This study contributes to advancing digital maturity in Kazakhstan’s tourism sector by addressing these gaps. It sets the foundation for future research to explore innovative strategies in e-commerce adoption across various regions and industries. Full article

This study presents and examines a more rigorous theoretical model for the relationships among e-service quality (e-SQ), trust, reputation, and e-loyalty in the online travel context, with the latter variables considered simultaneously and with reputation assumed to moderate the effect of e-SQ on both trust and loyalty. The model was tested using the two-step approach of the product method in partial least squares structural equation modelling (PLS-SEM) using data from 257 U.S. respondents who had used travel websites for information search and booking in the 12 months prior to the study. Results revealed that e-SQ positively influenced e-loyalty both directly and indirectly (through trust); however, the effect of e-SQ on e-loyalty is negatively moderated when website reputation is high/positive. These results provide a better understanding of the way e-SQ, trust, and reputation influence e-loyalty in online travel when modelled accurately. Full article

Recently, urban spatial equity has become a research hotspot, but research focuses on the equity of work commuting from different dimensions. This paper aims to determine the fairness difference of work commuting in Chengdu from three different dimensions by analyzing job accessibility in Chengdu. Firstly, population residence and employment data are obtained by using mobile phone signaling data, real-time travel data are obtained by using Amap API, and regional housing information is obtained from a real estate website. Secondly, the differences in time and cost of job accessibility in different regions are calculated under different time thresholds. Finally, the equity of job accessibility is evaluated by using the Theil index and the Gini coefficient from three new perspectives: transport mode, house price economy, and spatial region. The experimental results show that (1) when time threshold increases, public transport in Chengdu is more equitable, while car traffic is opposite; (2) regions with higher prices are generally fairer; and (3) Chengdu’s equality disparities are more between areas than within areas. In addition to proposing a new accessibility formula based on travel impedance, this study suggests a new method for analyzing equity differences in Chinese cities that can serve as a reference for future researchers. At the same time, the results provide a scientific basis for optimizing the social spatial distribution of public transport services in Chengdu. Full article

During the COVID-19 epidemic, countries enacted autonomous measures to suspend long-distance travel. As a result, people used online platforms to share perspectives and disseminate their knowledge and skills. Internet learning content thus emerged as a primary solution. This study was conducted to assess the reactions of users to virtual tours. Participants were introduced to the 360-degree panoramic photography system of cultural monuments of the Taipei City Government and participated in an online cultural tour. A closed-ended questionnaire was distributed for their response. After compiling data from 31 participants, we analyzed the link between users’ demographic characteristics and their satisfaction levels with the online panoramic tour system. We discovered higher satisfaction rates of people with incomes exceeding that of the average participant. 83% of participants stated a willingness to explore scenic attractions virtually instead of physically traveling when unable to do so. The results of this study contribute to understanding the context of users’ post-visit satisfaction. The information gathered can be used to improve cultural heritage websites in terms of design, navigation, and cultural education, enabling virtual access to cultural sites and enriching users’ knowledge from home. Full article

Our study introduces an enhanced version of the Importance–Performance Analysis (IPA) method, a powerful tool that can be applied across various domains. This method plays a crucial role in our research, aiding in making well-informed decisions about smart tourism destination attributes. We achieved this by evaluating how 911 consumers from four different generations (Baby Boomers, Generation X, Millennials, and Generation Z) rated these attributes based on their most recent tourist destination visit. Unlike traditional methods that often rely on subjective opinions or complex statistical models, the Improved IPA (IIPA) method offers a clear approach to decision-making. It enables decision-makers to focus on the most crucial attributes that drive consumer interest, thereby optimizing resource allocation and marketing efforts. Specifically, to remain competitive, decision-makers for smart tourist destinations should focus on queuing-time forecast and applications, websites, and content accessible for travelers with disabilities for Baby Boomers; e-complaint handling for Generation X; smart emergency response system for Millennials; and tourist-flow forecast, real-time traffic broadcast, electronic-entrance guard systems, and accessible data about physical design features of accommodation, restaurants, and tourist attractions for Generation Z. Theoretically, this study advances the research on managerial decision-making by demonstrating the effectiveness of the IIPA as a clear and straightforward method for making optimal decisions about product or service attributes. In practice, the study provides decision-makers with valuable insights into the importance of different categories of smart attributes in shaping the overall holiday experience at a tourist destination for Baby Boomers, Generation X, Millennials, and Generation Z tourism consumers. Full article