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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.

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 study aims to deepen the understanding of consumer engagement and satisfaction within the health and wellness tourism sector, a rapidly growing niche in the global tourism industry. It focuses on identifying key elements that influence consumer perceptions and experiences in this domain.

Employing a quantitative approach, this research utilizes Dynamic Correlated Topic Models (DCTM) and sentiment analysis techniques to analyze user-generated content from TripAdvisor. The methodology involves parsing through extensive online reviews to extract thematic patterns and emotional sentiments related to various wellness tourism experiences.

The findings reveal that wellness and relaxation, spa and therapy services, and cultural immersion are significant factors influencing consumer satisfaction in health and wellness tourism. These elements contribute to a more profound and emotionally satisfying tourist experience, highlighting the shift from traditional tourism to more holistic, wellness-focused travel.

The study is limited by its focus on user-generated content from a single platform, which may not fully represent the diverse range of consumer experiences in health and wellness tourism. Future research could expand to include other platforms and cross-reference with qualitative data.

The study offers valuable implications for destination managers and marketers in the health and wellness tourism industry, suggesting that enhancing and promoting wellness-centric experiences can significantly improve consumer satisfaction and engagement.

The research underscores the growing importance of health and wellness in societal values, reflecting a shift in consumer preferences towards travel experiences that offer mental, physical, and spiritual benefits. This has broader implications for how destinations can cater to the evolving demands of socially conscious travelers.

This research contributes original insights into the evolving field of health and wellness tourism by integrating advanced text mining techniques to analyze consumer feedback, offering a novel perspective on what drives engagement and satisfaction in this sector.

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.

This study aims to investigate how user-generated content (UGC) and firm-generated content (FGC) in social media affect attitudes toward halal tourism destinations, destination image and visit intentions among female Muslim (Muslimah) travelers. It also examines the role of travel habits as the moderating variable.

This study adopts the Theory of Interpersonal Behavior (TIB) and the Theory of Planned Behavior (TPB) to explore the motivation of Muslimah travelers to visit halal destinations. The survey was completed by 284 Indonesian Muslimah respondents who had visited halal tourism destinations in Indonesia. The SEM-PLS statistical technique was used to verify the associations between variables.

This study’s findings offer valuable insights for both theoretical understanding and practical applications. They suggest that both UGC and FGC significantly predict attitudes toward halal tourism and destination image, thereby influencing the visiting intentions of Muslimah travelers. Although UGC does not directly influence visiting intentions, it does play a significant role in shaping visiting intentions through its mediating effect on attitudes toward halal tourism destinations. In addition, the results indicate that habit positively moderates the relationship between attitudes toward halal tourism and visiting intentions.

This research contributes uniquely and significantly in two aspects. First, it merges the TIB with the TPB to elucidate the decision-making process of Muslimah travelers when selecting halal destinations. This integration illuminates new insights into the intentions of Muslimah tourists. Second, this study examines the respective influences of UGC and FGC on the visiting intentions of Muslimah travelers to halal tourism destinations. By comparing UGC and FGC, the research provides valuable insights into the decision-making processes of Muslimah tourists. Given Indonesia’s status as home to the largest Muslim population globally, this study offers a novel perspective on the traveling behavior of Muslimahs and practical recommendations for the Indonesian tourism sector in the establishment and promotion of Muslim-friendly destinations within the country.

Solo travel is one of the fastest-growing tourism segments, especially among women. This study aims to identify which research topics of interest are more evident in solo female travelers (SFTs) and provide future researchers directions to explore some other research areas in SFTs.

Adopting the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, the searched keywords included “female travel*” OR “women travel*” AND solo, extracted from Web of Science, Scopus, Emerald and ProQuest databases conducted in English.

Out of the 163 initial records, 25 papers were included. The reviews revealed four research topics that are more evident in SFT studies, namely, push and pull factors, perceived risks and travel constraints, risk management and negotiation strategy and travel experience. More work is required on SFTs beyond the existing research streams.

The papers selected for this study were chosen based on the review’s selection criteria and might omit other SFT studies.

This paper contributes to uncovering the literature on SFTs to date.

In the realm of postpandemic perspectives, tourism was awakened to see that atop economic viability is resiliency. Shift in the demand to visit ecotourism destinations became a valuable opportunity to the local community to capitalize on natural resources as primary precursor for livelihood, substantiating their communal role as bailiffs in the conservation of protected landscapes and mitigators of the negative tourism implications. As tourism initiates in rethinking sustainability and inclusivity, the postpandemic paradigm in destination planning became anew. Aside from being an agritourism destination, Nueva Ecija has been promoted as an emerging ecotourism destination. Various literature has evaluated local ecotourism destinations in the Philippines, yet few literature examined the potential of Nueva Ecija. The emerging popularity of the province's ecotourism postpandemic beckons the inflow of tourists compelling for an in-place policy toward sustainability. The study aims to underscore the destination branding of Nueva Ecija as a tourist destination using a strategic framework derived from ecotourism concepts and best practices. Qualitative data from various tourism stakeholders were collected, and internal and external factors were analyzed. Results of the study may serve as a foundation toward a unified framework to assess and strengthen ecotourism destinations toward sustainability and tourism destination resiliency.

The technology acceptance model (TAM) is a widely used framework explaining why users accept new technologies. Still, its relevance is questioned because of evolving consumer behavior, demographics and technology. Contrary to a research paper or systematic literature review, the purpose of this critical reflection paper is to discuss TAM's relevance and limitations in hospitality and tourism research.

This paper uses a critical reflective approach, enabling a comprehensive review and synthesis of recent academic literature on TAM. The critical evaluation encompasses its historical trajectory, evolutionary growth, identified limitations and, more specifically, its relevance in the context of hospitality and tourism research.

TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures.

To optimize TAM's efficacy, the authors propose several strategic recommendations. These include embedding TAM within the specific context of the industry, delving into TAM-driven artificial intelligence adoption, integrating industry-specific factors, acknowledging cultural nuances and using comprehensive research methods, such as mixed methods approach. It is imperative for researchers to critically assess TAM's suitability for their studies and be open to exploring alternative models or methods that can adeptly navigate the distinctive dynamics of the industry.

This critical reflection paper prompts a profound exploration of technology adoption within the dynamic hospitality and tourism sector, makes insightful inquiries into TAM's future potential and presents recommendations.