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As independent travel has become increasingly popular in recent years, factors influencing users’ attitudes and behaviors should be explored. Besides, the rise of video platforms like YouTube, Bilibili and TikTok has led to the flourishing of user generated content (UGC), among which travel vlogs have become convenient and essential references for tourists before and while they travel on their own.

This research used quantitative analysis through a survey designed for young people, as most vlog viewers are in those age groups, to explore the attitudes and perceptions of vlog preference, attitudes and cognizance of travel vlogs among different gender, guided by the decision-making theory and preference theory with 455 participants.

Survey results showed that both genders have common choices regarding travel vlog preference, attitudes, sharing and cognizance. Yet, gender differences appear in some cognition of travel vlogs and specific preferences, such as information searching habits, trusty travel and information sources.

Existing studies focus on the influence of platforms on users, but scant studies focus on user perceptions of vlogs as a new information carrier for young people in East Asia, especially regarding gender differences, which is crucial in media-based marketing. This research provides practical suggestions to marketers and vloggers to attract and engage different genders of young people and theoretical implications on decision and preference theories.

One of the most well-known, seminal models in the tourism field is the one proposed 50 years ago by Stanley C. Plog. His venturesomeness model has been widely cited in journal articles, textbooks, and has also been used as a reference for planning and designing tourism marketing projects. However, empirical research using Plog's psychographics has yielded varied results, some of which have corroborated his model, while others have found partial or no support for some postulates. Therefore, the purpose of this chapter is to offer an exhaustive review of 47 studies in the literature which have employed Plog's venturesomeness concept to examine travelers' personality traits, attitudes, and behavior, synthetizing empirical findings and drawing conclusions from the cumulative results. A discussion of the model's contribution to the current body of knowledge, managerial implications for tourism practitioners, and directions for future research are presented.

The purpose of this paper is to provide a guiding framework for studying the travel patterns of PT users. The combination of public transit (PT) users’ travel data and user profiling (UP) technology to draw a portrait of PT users can effectively understand users’ travel patterns, which is important to help optimize the scheduling of PT operations and planning of the network.

To achieve the purpose, the paper presents a three-level classification method to construct the labeling framework. A station area attribute mining method based on the term frequency-inverse document frequency weighting algorithm is proposed to determine the point of interest attributes of user travel stations, and the spatial correlation patterns of user travel stations are calculated by Moran’s Index. User travel feature labels are extracted from travel data containing Beijing PT data for one consecutive week.

In this paper, a universal PT user labeling system is obtained and some related methods are conducted including four categories of user-preferred travel area patterns mining and a station area attribute mining method. In the application of the Beijing case, a precise exploration of the spatiotemporal characteristics of PT users is conducted, resulting in the final Beijing PTUP system.

This paper combines UP technology with big data analysis techniques to study the travel patterns of PT users. A user profile label framework is constructed, and data visualization, statistical analysis and K-means clustering are applied to extract specific labels instructed by this system framework. Through these analytical processes, the user labeling system is improved, and its applicability is validated through the analysis of a Beijing PT case.

To succeed in a competitive tourist market and attract more foreign tourists, it is essential to have a clear understanding of what travellers are seeking and endeavour to meet those needs, as well as key influential factors in their travel decision-making process. The purpose of the study is to develop and examine tourists’ pre-trip motivational model using the push–pull theory.

A tourists’ pre-trip motivational model was developed and then tested based on a sample of 320 Chinese and non-Chinese visitors to Melbourne, Australia, to assess the suitability of the new model. Data were analysed by descriptive and inferential statistical techniques, such as principal component analysis and independent T-tests.

The analysis revealed statistically significant differences between studied samples in terms of the push and pull factors. In comparison with non-Chinese tourists, Chinese visitors to Melbourne assigned higher importance to resting and relaxing opportunities, family-oriented activities, as well as safety and a high level of service. The identified differences should be reflected in marketing and promotional activities provided to Chinese and non-Chinese travellers.

The study provides useful information for Destination Marketing Organisations in tourism cities wanting to develop specifically customised tourist products, services and promotion programs tailored to each market.

The proposed extended push–pull model represents a holistic and complex model of the travel decision-making process with the multiple linkages between motivations for travelling, preferences of destination attributes, information source usage, trip expectations, possible constraints for travelling and evaluation of destination choice criteria. Understanding all these factors, their relationship and their influence on the final destination choice is a prerequisite for effective and successful actions on attraction and retention of visitors for all tourist destinations. The developed tourists’ pre-trip motivational model may be used as a conceptual framework to guide subsequent motivational studies in tourism.

Postpandemic efforts to rebuild have steered the global economy toward a more sustainable trajectory. It is imperative to acknowledge the pressing need for further enhancements in the sustainable development of the tourism industry. This study aims to examine the influence of personal factors, including environmental concern, cultural interest, travel lifestyle and involvement, on tourist satisfaction and revisit intention, using the theory of planned behavior (TPB) as its framework.

The data was gathered through surveys conducted in three of Türkiye’s most famous slow travel destinations: Seferihisar, Gökçeada and Akyaka. The analysis was carried out using SPSS and SmartPLS software, with subsequent structural model testing.

This study presents an extended model that incorporates four individual factors, tourist satisfaction, TPB and revisit intention. All hypotheses have been rigorously tested, and the model accounts for 60.4% of the variance in revisit intention. The findings are comprehensively discussed in this article, supported by relevant theoretical frameworks.

Future research avenues could delve into the evolution of slow tourism in both developed and developing countries, assess disparities in revisit intentions between slow tourism and mass tourism destinations and investigate the prospects of sustainable tourism development in the postpandemic era.

The authors use the TPB to examine individual factors, tourist satisfaction and revisit intentions, aiming to build an extended model to gain a deeper understanding of the slow tourist decision-making process.

This study aims to understand how traveling Instagrammers should be characterized by bridging socio-psychological concepts with Instagram usage and travel typologies.

Using analysis of variance, differences between non-Instagrammers and passive and active users were identified with regard to their personality traits and human values; traveling Instagrammers and their travel types were characterized in more detail using k-means clustering, and, finally, posting behavior was examined using multiple regression.

The results demonstrate significant differences between the user groups in terms of their personality traits, their human values and their travel behavior. The multiple regressions provide deeper insights into tourists’ Instagram posting.

This study was conducted on 1,100 German travelers, meaning that the results are only valid for this cultural context. All data were collected using an online panel and participants received incentives, and thus extrinsic rewards, instead of using merely intrinsic motivation to take part in the survey.

This study contributes to a better understanding of Instagram usage behavior when traveling and adds valuable insights to this field of research. At the same time, the results are relevant for tourism businesses wishing to optimize their communication and marketing strategies.

This study aims to reveal the travel insurance behavior of digital nomads systematically. For this purpose, the habits and preferences of digital nomads for travel and health insurance were investigated based on the problems they experience during their travels.

Within the scope of multimethod qualitative research, the insurance behaviors of digital nomads were investigated in depth by using both interview and content analysis techniques for social media comments. In the first stage of the study, the interview technique was used to describe the main factors underlying the insurance behaviors of digital nomads. In the second stage, the comments of digital nomads on social media platforms were evaluated by content analysis and themes regarding the insurance behaviors of digital nomads were reached.

The inadequacies of the current global system and national order determine the travel insurance behavior of digital nomads, who are generally opposed to the existing order and tend to escape from authority and routine. It was found that digital nomads care about the speed at which insurance companies meet their demands, the coverage of insurance policies (medical and travel-related components of insurance) and the cost of insurance behavior.

This study aims to provide a new perspective to the literature by conducting multimethod qualitative research within the scope of the risks experienced by digital nomads and their travel insurance behavior. The study shows that digital nomads cannot escape from national regulations. They have to take out insurance in countries with low cost of living due to problems with the health system and security, and in some countries, it is due to country regulations. In practice, the study provides conclusions and recommendations for current and potential digital nomads, insurance companies and governments regarding the insurance behavior of digital nomads. In particular, the study concludes with recommendations for insurance companies in terms of insurance product development and the use of social policy tools for governments.

This study offers a perspective on the key factors underlying the travel insurance behavior of digital nomads.

This study aims to investigate the factors that impact the solo travel intentions of millennial women in rural and urban areas. By exploring these factors, this research also sheds light on the similarities and differences in travel behaviors and motivations of women in different geographical contexts within India.

This study, rooted in affordance-actualization theory and communication theory, aims to critically examine how ChatGPT influences users’ transition from new adopters to loyal advocates within the context of travel decision-making. It incorporates constructs including communication quality, personalization, anthropomorphism, cognitive and emotional trust (ET), loyalty and intention to adopt into a comprehensive model.

This study used quantitative methods to analyze data from 477 respondents, collected online through a self-administered questionnaire by Embrain, a leading market research company in South Korea. Lavaan package within R studio was used for evaluating the measurement model through confirmatory factor analysis and using structural equation modeling to examine the proposed hypotheses.

The findings reveal a pivotal need for enhancing ChatGPT’s communication quality, particularly in terms of accuracy, currency and understandability. Personalization emerges as a key driver for cognitive trust, while anthropomorphism significantly impacts ET. Interestingly, the study unveils that in the context of travel recommendations, users’ trust in ChatGPT predominantly operates at the cognitive level, significantly impacting loyalty and subsequent adoption intentions.

The findings of this research provide valuable insights for improving Generative AI (GenAI) technology and management practices in travel recommendations.

As one of the few empirical research papers in the burgeoning field of GenAI, this study proposes a highly explanatory model for the process from affordance to actualization in the context of using ChatGPT for travel recommendations.

This paper aims to deepen the current knowledge of seasonality by investigating visitors’ intentional and behavioural patterns during peak and off-peak seasons. It compares the variation in several key behavioural factors, namely, duration of stay, party size, revisit intention, spending and breakdown of spending in different sectors in hospitality and tourism including entertainment, restaurant, accommodation and transportation. Moreover, this research expands the understanding by examining the effectiveness of two innovative strategies of offering a digital app and organising a unique event to tackle seasonal imbalances through stimulating visitors’ intention to change their timing of visit from peak to off-peak periods.

The author initially used a Delphi approach to gather experts’ opinion on the two scenario settings: event organisation and a trip planner app. The scenarios aimed to potentially encourage visitors to change their visit time to off-peak seasons. Then, using a quantitative survey, the travel habits and spending behaviours of 310 participants were captured. Furthermore, the survey assessed their intention to travel during off-peak seasons in response to the implementation of the two innovative strategies.

The results revealed that although the number of visitors who travel in off-peak seasons may be lower, their daily spending is higher than peak season visitors. In addition to total spending per day, the duration of stay, part size, quality of accommodation and re-visit intention of visitors indicated significant variation between peak and off-peak seasons. According to the statistical analysis’ results, organising events (including festivals) proves more effective in encouraging visitors to travel during off-peak seasons compared to digital innovation (i.e. a trip planner app). This finding is in line with the tenets of the Jobs-to-be-Done Theory of innovation.

This study contributes by conceptualising the mechanism of seasonality and its impacts on subsectors of tourism and hospitality. To the best of the author’s knowledge, this is one of the few empirical research that compares the behavioural patterns of visitors including their average spending per day between peak and off-peak seasons. Previous studies focused on specific regions or sectors, whereas this research investigates visitors’ behaviour on a broader scale to provide more comprehensive view. Furthermore, this study is novel due to practising an outside-in approach through investigating the effectiveness of the two innovative strategies aimed at addressing seasonality in the hospitality and tourism industry from visitors’ point of view.