Travel Information Research Articles

Influencer Credibility as A Source of Travel Information for Generation Z to Bali

The development of technology is increasingly rapid, so tourism business managers must keep up with existing technological developments. Social media, especially Instagram, is one of the most widely used platforms in the world, especially for Generation Z. However, some respondents use Instagram for information and entertainment and there is a problem that the information obtained is sometimes not in accordance with reality, especially tourist information. This shows that there is still a lack of credible influencer filtering used in disseminating information about a product or place. This study aims to examine the differences in the interests of Generation Z in assessing the credibility of influencers as a source of tourism information to Bali. In the study, the researcher used a quantitative approach with data analysis used, namely the Multivariate of Variance Test (MANOVA). The number of samples used was 110 respondents per type of tourist. The sampling technique is non-probability sampling with a purposive sampling approach. The results of the study revealed that both domestic and foreign tourist groups judged influencers by their attractiveness to create a feeling of trust in the content disseminated. Meanwhile, for the gender group, they have the same perspective in assessing the credibility of an influencer.

Experience-Sharing to Support Inclusive Travel for Blind and Partially Sighted People

This research aimed to understand the challenges blind and partially sighted people experience in tourism and the travel information they need to plan their travel experiences effectively. To this end, we designed a mixed-methods study consisting of semi-structured interviews and co-design workshops, which were conducted to identify the needs of BPS people and the barriers experienced due to a lack of access to such information. The findings provide insights into the information-seeking process and highlight the role of experience sharing in cultivating a sense of agency, contribution, and interdependence. This study also contributes an accessible tourism ecosystem map based on our findings to highlight the different sources of travel information and their potential role in supporting inclusive travel for BPS people. The findings have implications for research and tourism service design and serve as a tool to motivate research on technologies to support inclusive leisure travel for BPS people and to inform the design of inclusive tourism services.

Variable Message Signs in Traffic Management: A Systematic Review of User Behavior and Future Innovations

Effective traffic management is crucial in addressing the growing complexities of urban mobility, and variable message signs (VMSs) play a vital role in delivering real-time information to road users. Despite their widespread application, there is limited comprehensive understanding of how VMS influence user behavior and optimize traffic flow. This systematic literature review aims to address this gap by examining the effectiveness of VMS in shaping user interactions and enhancing traffic management systems. Using the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology, a thorough analysis of relevant studies was conducted to identify key factors influencing VMS impact, including message content and characteristics, complementary sources of information, user demographics, VMS location, and users’ reliance on these signs. Additionally, the review explores the implications of displaying non-critical information on VMS and introduces virtual dynamic message signs (VDMSs) as an innovative approach for delivering public traveler information. The study identifies several research gaps, such as the integration of VMS with vehicle-to-everything (V2X) technologies, navigation systems, the need for validation in real-world scenarios, and understanding behavioral responses to non-critical information on VMS. This review highlights the importance of optimizing VMS for improved user engagement and traffic management, providing valuable insights and directions for future research in this evolving field.

What Do Travelers Know about Traveler's Diarrhea? Impact of a Pre-Travel Consultation in the Lisbon Area, Portugal.

Traveler's diarrhea (TD) is one of the most common travel-related health problems, largely interfering with planned activities and potentially contributing to antimicrobial resistance. This study aimed to characterize the knowledge about TD among pre-travel consultation users of one Portuguese travel clinic and determine the impact of the consultation on knowledge levels. Using a quasi-experimental, separate-sample pretest-posttest design, participants were randomly assigned to two groups: control/pre-consultation group (CG) or experimental/post-consultation group (EG). An anonymous self-administered questionnaire was used. A total of 470 participants were analyzed (227 CG; 243 EG). The EG/post-consultation group showed significant improvement in knowledge, with correct answers increasing from 63% to 75% (p < 0.001). However, knowledge gaps persisted: over 50% were unaware of TD's self-limited nature, 30% did not recognize loperamide as a symptom reliever, and 36% believed all travelers should take antibiotics to prevent TD. The educational level and previous travel outside Europe influenced baseline knowledge; previous travel medicine consultations and information on TD improved knowledge in both groups and made it easier to acquire knowledge on the subject. Thus, a pre-travel consultation effectively increased travelers' TD knowledge. However, post-consultation knowledge levels remained suboptimal, indicating the need for targeted interventions to increase travelers' literacy and optimize pre-travel consultations.

Joint intent detection and slot filling with syntactic and semantic features using multichannel CNN-BiLSTM

Understanding spoken language is crucial for conversational agents, with intent detection and slot filling being the primary tasks in natural language understanding (NLU). Enhancing the NLU tasks can lead to an accurate and efficient virtual assistant thereby reducing the need for human intervention and expanding their applicability in other domains. Traditionally, these tasks have been addressed individually, but recent studies have highlighted their interconnection, suggesting better results when solved together. Recent advances in natural language processing have shown that pretrained word embeddings can enhance text representation and improve the generalization capabilities of models. However, the challenge of poor generalization in joint learning models for intent detection and slot filling remains due to limited annotated datasets. Additionally, traditional models face difficulties in capturing both the semantic and syntactic nuances of language, which are vital for accurate intent detection and slot filling. This study proposes a hybridized text representation method using a multichannel convolutional neural network with three embedding channels: non-contextual embeddings for semantic information, part-of-speech (POS) tag embeddings for syntactic features, and contextual embeddings for deeper contextual understanding. Specifically, we utilized word2vec for non-contextual embeddings, one-hot vectors for POS tags, and bidirectional encoder representations from transformers (BERT) for contextual embeddings. These embeddings are processed through a convolutional layer and a shared bidirectional long short-term memory (BiLSTM) network, followed by two softmax functions for intent detection and slot filling. Experiments on the air travel information system (ATIS) and SNIPS datasets demonstrated that our model significantly outperformed the baseline models, achieving an intent accuracy of 97.90% and slot filling F1-score of 98.86% on the ATIS dataset, and an intent accuracy of 98.88% and slot filling F1-score of 97.07% on the SNIPS dataset. These results highlight the effectiveness of our proposed approach in advancing dialogue systems, and paving the way for more accurate and efficient natural language understanding in real-world applications.

The influence of residential location and public transit options on commuters’ intention to use travel apps for different travel purposes

While living in city centers is usually linked to higher accessibility levels, shorter travel times, and higher levels of public transit (PT) utilization, the opposite is true for residents of suburban areas. This assumption holds in metropolitan contexts, where central areas offer better accessibility and are associated with higher levels of PT use. In metropolitan peripheries, a large part of commuting is done on an interurban basis, so that the level of use of public transit can be linked to the supply and information available. This work aims to understand the conditions in terms of transit supply and land use, considering the most frequent trip, psychological variables, and the modal choice of commuters’ motorized modes, in the intention to use the new real-time multi-modal travel app, such as advanced traveler information systems (ATIS) for digital mobility-management assistance. A Structural Equation Model is developed to empirically test a sample of 768 respondents collected in two suburban corridors in the Lisbon Metropolitan Area (LMA). Finally, our findings indicate that residential location, mode choice, and trip complexity have a relevant influence on the intention to adopt travel apps. Male students belonging to Generation Y/Z are the most likely users of travel apps. Regardless of the reason, travel patterns associated with more complex (more transfers) and more frequent trips can reinforce the intention to use apps. Also, it is worth noting that students are frequent public transit users, and public transit is also related to trip complexity.

The social media usage and their effect on Tourism marketing behavior A Study among University International Students in Indonesia

This study investigates the impact of social media on tourism marketing behavior among international students in Indonesia. Employing a descriptive research method utilizing a qualitative approach, in some groups it was conducted with international students to explore their travel experiences and the influence of social media on their destination choices. The findings reveal that social media plays a significant role in shaping students' travel decisions, with a particular emphasis on the importance of visual platforms like Instagram and YouTube and their specific interests in how and where to visit with the help of these platforms. This research contributes to the existing literature on the role of social media in tourism marketing and provides insights into the preferences and behaviors of international students, a key demographic in the tourism industry. The discussion covers the range of interests that students have in artificial, cultural, and natural tourism as well as more niche types including MICE, health, and information tourism. Notably, participants stressed the need for thorough travel information, especially about lodging, meals, transportation, and guided tours information that is mostly found online. The study emphasizes how social media use and the diverse range of tourist experiences that foreign students in Indonesia are looking for are intricately related. Overall, this investigation clarifies the mutually beneficial relationship between social media dynamics and traveller behaviours, revealing information that is essential for academics and industry participants to comprehend in order to fully utilize digital platforms in modern tourism.

A web-based travel package management system integrated with QR code

The tourism industry is associated with people travelling either domestically or abroad, for business, social, or recreational reasons. This industry integrates numerous services and activities such as tourism organization, marketing, hotel industry, transportation services, food and beverage activities, retail stores and various other activities under one umbrella. In addition to generating income and jobs, tourism is the best way to promote the nation's image. As the economy and living conditions have improved, so too has the tourist sector, and more people are turning to vacations as a way to alleviate stress and unwind. Currently, travel agencies handle a lot of data with low efficiency and a lot of mistakes. Therefore, this study developed a comprehensive integration of QR code for package travel management system that caters to users in Malaysia, offering functionalities such as itinerary customization, QR code generation and access to essential travel information. The development of this project adopts the Waterfall Methodology, encompassing phases such as planning, requirement analysis, design, implementation and testing. The application is crafted using the Visual Studio Code. The study conducted a User Acceptance Testing (UAT) using questionnaires to evaluate the system's effectiveness and user-friendliness to thirty respondents including family members of frequent travellers and randomly selected community members. Positive feedbacks with Perceived Ease of Use (PEU) scoring 3.6, Attitude of Using (ATT) at 3.7, Perceived Usefulness (PU) scoring 3.4, and Intention to Use (IU) obtaining 3.9, were gathered from the analysis and findings that emphasizing the QR code system's potential as a cost-effective and efficient solution for travellers to monitor and optimize their travel experiences. Future work involves adding maps for easy navigation and implementing augmented reality (AR) for virtual destination previews.

주요 수입원으로 부동산 의존 여부에 따른 서비스업의 재무비율과 재무성과 간의 영향관계에 관한 연구

[Purpose] This paper analyzes the impact of financial ratios on the financial performance of two different types of service industries, called Group 1 and Group 2. Group 1 consists of maritime transport, air transport, hotel and car rental industries, which depend on real estate as a major source of income, while Group 2 consists of travel agencies, trading companies and information service industries, which do not depend on real estate as a major source of income. [Methodology] This study primarily analyzed 12 representative financial ratios, divided into four categories：profitability ratios, stability ratios, growth ratios, and activity ratios. Secondary data based on the Value Search from 2015 to 2019, was analyzed, and a multiple regression model was employed using SPSS 26.0. [Findings] The analysis showed that for Group 1, focusing on increasing low asset turnover with a relatively low debt ratio is a good way to improve a company’s profitability；for Group 2, focusing on relatively high asset turnover with relatively high debt ratio can improve a company’s profitability. [Implications] The financial performance of service industry was impacted by different types of financial ratios according to real estate dependence as major source of income.

Estimating the value of self‐drive recreation in the remote Kimberley: Sensitivity to multi‐destination trips

AbstractThe remote and iconic Kimberley region, in northern Western Australia, is a contested landscape with growing pressure for different types of land use and development. The region has high biodiversity values and impressive scenery that provide attractive recreation experiences for visitors. We estimate the value of domestic recreation to the region using a travel cost model. Analysis includes two approaches for accounting for multi‐destination trips (MDTs) given travel to remote sites are more likely to include these than shorter trips, and four specifications for the value of travel time. We estimate a total consumer surplus of A$110.34 million annually and find that adjusting travel costs for MDTs using stated survey information produces a better fitting model than using revealed travel information. These findings highlight both the importance of estimating recreation values for use in land use planning in the Kimberley and for improving the travel cost method for remote sites where per‐trip travel costs are high.

How do affective and cognitive involvement affect tourism intentions: a perspective on blogger impact

ABSTRACT Social Network Services (SNS) platforms are crucial sources of travel information. Xiaohongshu, a highly esteemed platform in China and globally, provides travel guides and lifestyle information. Empirical studies on how influencers, as well as the affective and rational factors of travel user-generated content (UGC) affect travel intentions are limited. Based on involvement theory and dual-system theory, this study uses topic modelling, sentiment analysis, and semantic network visualization to analyze travel posts and comments on Xiaohongshu. It reveals the affective and functional factors of posts by bloggers of varying impact and their effects on audience affective and cognitive involvement, processing systems, and travel intentions. The results are as follows: (1) Posts by medium-impact bloggers are more successful in generating and enhancing travel intentions. (2) Affective factors trigger affective involvement and processing, driving heuristic travel intentions; functional factors drive cognitive processing through cognitive involvement, leading to cognitive travel intentions. (3) The proper emotional intensity of affective and functional factors effectively transmits information, builds audience confidence, and maximises the enhancement of travel intentions. This study enriches the research viewpoint and theoretical foundation of enhancing travel intentions, providing practical recommendations for tourism practitioners in creating appropriate and effective emotional promotional content.

Spatiotemporal characteristics and influencing factors of network attention to resort hotels in China

An increasing number of people are gathering travel information online prior to their trips as a result of the Internet's rapid expansion. The amount of network attention receives can indicate how many people are looking for something. The orderly development of Chinese resort hotels can be guided by research on the spatiotemporal characteristics of their network attention. Using Chinese resort hotels as the study subject, everyday information about resort hotels in 31 Chinese provinces was gathered via the Baidu Index platform between 2018 and 2022, and mathematical statistics and other methods were used to study the spatiotemporal distribution characteristics and influence of Chinese resort hotels network attention. Findings reveal that, from 2018 to 2022, network attention to resort hotels across the country fluctuated significantly across seasons, and there was a “precursor effect” reaction before the week of network attention. Moreover, the spatial distribution of network attention to Chinese resort hotels was uneven, showing an overall trend of “east–central–west” decline. Level of economic development, degree of network development, leisure time, and population size are the main factors affecting the spatiotemporal distribution of Chinese resort hotels network attention.

A Personalized Recommendation Algorithm for Tourist Attractions Using User Behavior Data

With the widespread application of 5G and cloud computing technologies, smart tourism has become a hotspot and development trend in the tourism industry. This paper develops a cloud-based intelligent tourism application for the Android platform, innovatively integrating edge computing technology to achieve faster data processing speeds and lower latency, providing users with instant travel information and services. The application adopts a personalized recommendation algorithm to intelligently recommend tourist attractions and routes based on user behavior and preferences, and introduces a geo-tagged social function, allowing users to share travel experiences and reviews, enhancing user interaction and community sense. Furthermore, the application incorporates augmented reality (AR) technology, providing virtual tour guide services through the mobile camera, bringing users an immersive travel guide experience. This paper details the design architecture, key technology implementation, and expected effects of this intelligent tourism application, aiming to enhance the tourism experience while promoting the digital transformation and development of the tourism industry through technological innovation.

Evaluation and determinants of metro users' regularity: Insights from transit one-card data

Regularity is typically defined based on the repetitive travel behavior of individuals, referring to how often travelers would utilize a specific service within a given spatio-temporal context. However, previous research on metro users' regularity primarily utilized basic metric, for example metro trip frequency, to measure regularity. What's more, metro smart card data typically encompasses time, spatial features, and card type information, lacking individual attributes such as age, gender, and type of residence, which limits in-depth analysis correlating individual attributes with travel behavior. The study obtained transit one-card data from Nanjing, China, which enabled us to extract metro user's travel and individual information. Thus, the entropy rate methodology was employed to measure metro users' regularity, while machine learning techniques were used to analyze non-linear effects of built environment, travel-related, and individual attributes on regularity. Results indicate that the built environment, travel-related, and individual attributes account for 66.66%, 33.31%, and 0.03% of the total relative importance, respectively. Two most influential variables impacting regularity, namely entertainment POIs at the origin level (17.77%) and weekdays (17.51%), belong to the built environment and travel-related attributes, respectively. In terms of individual attributes, age exhibits a greater impact on regularity compared to gender and type of residence, manifested in the variation of regularity among different age groups. This finding can assist metro policymakers in understanding metro users' travel behavior, aiming to enhance operational efficiency and optimize the user experience.

ETIAS system and new proposals to advance the use of AI in public services

Eu-LISA is launching the European Travel Information and Authorization System (ETIAS), which seems an example of a different, human rights-oriented approach to AI within the law enforcement. However, the reality is quite different: the usual problems of the use of AI—lack of transparency, bias, opacity, just to name a few—are still on board. This paper critically assesses these promises of ETIAS and argues that it has serious issues that have not been properly dealt with. So as to argue the need to address these issues, the paper addresses ETIAS within the wider context of human rights and solidarity-based data governance. In this respect, ETIAS is seen as a tool which uses data for high value purposes, such as EU safety and security, yet it also calls for serious risk mitigation measures. Indeed, the risks related to law enforcement on the borders and in migration management are extremely serious due to the vulnerability of people who escape from poverty, wars, regimes, and other disasters. In the third part of this article, we articulate three proposals of such risk mitigation measures. We argue in favour of strengthening critical general safeguards in ETIAS, then elaborate a principle that should guide AI-based public service development (P4P principle) and end with a few IPR-related requirements for private sector involvement in such services. Adopting these measures could contribute to reduce the risk of building EU AI expertise upon data coming from the most vulnerable social groups of our planet.

IMPACT OF DESIGN, INFORMATION AND USEFULNESS OF TRAVEL WEBSITE ON TRAVEL PURCHASE INTENTION

This study explores the impact of design, information, and usefulness of travel websites on travel purchase intention. Utilizing a quantitative research design, data from 177 respondents were collected via a structured questionnaire administered through Google Forms. Regression analysis revealed that design, information, and usefulness significantly and positively influenced travel purchase intention with each predictor variable exhibiting a substantial effect on travel purchase decisions. These findings corroborate existing research emphasizing the importance of website design, information quality, and usefulness in shaping consumer purchase intention in the online travel domain. For instance, Kim &amp; Lennon, (2008) found that visually appealing design positively influences purchase intention, while Ruiz Mafe Sanz Blas, (2006) demonstrated the significant impact of informative content on purchase intention, aligning with the positive coefficients observed in this study. KEYWORDS- Travel website design, Information and usefulness of travel website and travel purchase intention

Identifying Traveler Groups Lacking in Seatbelt Usage and Suggesting Policy Measures

This study was aimed to identify the traveler groups who are exposed to potential of severe injury due to lack of seatbelt use. Moreover, policy measures have been suggested to enhance seatbelt use among such groups. A hybrid data collection approach was adopted to gather traveler information. Seatbelt usage questionnaire was used as the survey instrument. The survey was carried out in the parking lots of educational and commercial facilities around Bahrain. Statistical tests, namely, t-tests and Analysis of Variance (ANOVA), were used to compare the attributes of travelers. Literature review was used to verify the findings of this study and identify common traveler groups which are lacking in seatbelt use. The recommendations are also provided on policy measures which could be adopted globally to promote seatbelt use. It was found that occupation, education, gender, nationality and accident occurrence have significant impact on seatbelt wearing. It was also found that accident experience and seatbelt wearing, both are dependent on travelers’ social and cultural background rather than each other. The findings of this study matched previous literature. Groups lacking in seatbelt use included male passengers belonging to lower- and middle-income groups which fits the description of expatriate workers in Bahrain. Some policy measures, appropriate to these groups, have also been suggested.

An Adaptive Route Guidance Model Considering the Effect of Traffic Signals Based on Deep Reinforcement Learning

Navigation or route guidance systems are designed to provide drivers with real-time travel information and the associated recommended routes for their trips. Classical route choice models typically rely on utility theory to represent drivers’ route choice behavior. Such choices, however, may not be optimal from both the individual and the system perspectives. This is simply due to the fact that drivers usually have imperfect knowledge about the time-varying traffic conditions. In this article, we explore and propose a new model-free deep reinforcement learning (DRL) approach to solving the adaptive route guidance problem based on microsimulation. The proposed approach consists of three interconnected algorithms, including a network edge labeling algorithm, a routing plan identification algorithm, and an adaptive route guidance algorithm. Simulation experiments on both a toy network and a real-world network of Suzhou, China, are performed to demonstrate the effectiveness of the proposed approach in terms of guiding a single vehicle as well as multiple vehicles through complex traffic environments. Comparative results confirm that the DRL approach outperforms the traditional shortest path method by further reducing the average travel time in the network.

Tour Sentiments Personalized Recommendations System Using AI and Deep Learning

In a world saturated with travel information overload and generic recommendations, "Tour Sentiments: A Personalized Travel Recommendation System Using AI " emerges as a groundbreaking initiative. This project seeks to transform travel planning by harnessing the capabilities of Artificial Intelligence and real-time google Maps with street view and duration of travel. The primary goal of 'Tour Sentiments' is to create a personalized travel recommendation system that adapts to the changing emotions and preferences of travelers. By employing real-time sentiment analysis using google gemini, the system aims to offer tailored suggestions that align closely with individuals' current feelings and desires, departing from conventional one-size-fits-all approaches. Beyond mere efficiency, the ultimate vision of 'Tour Sentiments' is to elevate the travel experience, infusing it with joy, discovery, and lasting memories. Through the amalgamation of AI and sentiment analysis, the project aspires to make global exploration more accessible and captivating, setting a new standard for personalized travel guidance and enriching journeys worldwide.

Investigating Tourists’ Mobile Technology Resistance Behavior: An Application of Innovation Resistance Theory

ABSTRACT While numerous travel applications (apps) have been introduced to the public, technology barriers have prevented people from using new technology to seek travel information. This study employed the innovation resistance theory to predict users’ resistance behaviors by investigating passive innovation resistance, product-specific barriers, and active resistance at a dimensional level. Structural equation modeling with a sample of 362 users revealed that passive resistance dimensions – resist to change and status quo satisfaction- had significant effects on all product-specific barriers. However, only three barriers, in turn, influenced active resistance in a different pattern; usage barriers led to active resistance – opposition, particularly while value and observability barriers resulted in rejection. Applying the innovation resistance theory, this research provides a new insight into travelers’ innovation resistance behaviors, extending the literature on technology in the tourism context. The dimensional approach further enhanced our understanding of the complicated resistance decision-making processes in a specific but comprehensive way. Findings offer practical implications for mobile app development, thereby reducing product-specific barriers while improving innovative travel products that are essential and convenient during their trips.