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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, 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 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 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.

Mobility as a service (MaaS) is a new model of bundling personal transport options from multiple providers to provide frictionless service. The model integrates information and service to enable users to search, book and pay through a single digital platform. The aim of MaaS is to minimize individual car ownership and revolutionize personal mobility habits in favour of sustainable modes. Thus, it is important to understand consumers' readiness for MaaS. This study explains the factors that influence MaaS adoption.

A comprehensive review of the related literature has been conducted, leading to the development of a conceptual model for investigating the key factors that influence MaaS adoption. The model draws upon the digital platform, schema congruity and planned behaviour theories. The model is then tested using structural equation modelling and survey data collected from 331 respondents.

The findings indicate that MaaS information schema congruity, the digital platform's personalizability, customizability, functional integration, network integration and governance of data together with attitude towards MaaS and subjective norm concerning MaaS influence MaaS adoption. Furthermore, MaaS digital platform expectation and MaaS information schema congruity contribute to users' attitude towards MaaS use.

The paper highlights the design and informational attributes of MaaS platforms that influence their adoption and contributes an integrated research model that links digital platform characteristics, behavioural factors and information schema congruity. Future research can use the constructs and measures we developed in this study to build deeper understanding of what contributes to the success of digital platforms.

This study aims to investigate low-carbon footprint travel choices, considering both destination attributes and climate change perceptions, and examine the impacts of nudging (a communication tool to alter individuals’ choices in a predictable way) on tourists’ preferences for carbon mitigation in destinations.

A discrete choice experiment questionnaire was administered to a sample of 958 Hong Kong respondents. Hybrid choice modeling was used to examine the respondents’ preferences for destination attributes and to explain preference heterogeneity using tourists’ climate change perceptions. The respondents’ willingness to pay for the destination attributes was also calculated to measure the monetary value of the attributes.

Destination type, carbon emissions and travel cost had significant effects on tourists’ choices of destination. Nudging increased tourists’ preference for low-carbon footprint choices. Tourists with higher climate change perceptions were more likely than others to select low-carbon destinations with carbon offset projects.

The findings of this study provide an impetus for destination management organizations to support local carbon offset projects, implement policies that mitigate carbon emissions and develop sustainable tourism to fulfill tourists’ demand for low-carbon footprint travel choices. Based on the findings, policymakers could promote sustainable tourism by publishing relevant climate change information on social media.

This study addressed a gap in the literature on tourist travel choice by considering carbon emission-related attributes and climate change perceptions and by confirming the role of nudging in increasing the choice of low-carbon destinations.

This study aims to investigate the engagement gap between Metaverse and in-person travel, the influence of Metaverse tourism on tourists and the industry and the challenges and responses associated with Metaverse technology. The study presents practical cases and highlights the implications of this research for practice, society and future research.

This study uses a literature review to explore concerns about Metaverse technology in tourism. It analyzes the difference between in-person travel and Metaverse tourism, the impact on tourists and the industry and challenges and responses to Metaverse. The review shows a rising trend in Metaverse tourism research.

These findings suggest differences between Metaverse tourism and in-person travel. By providing personalized travel options, social interaction, immersive experiences and soliciting visitor feedback, it is possible to enhance the tourist experience. Additionally, the study highlights the opportunities and challenges that Metaverse tourism presents to the tourism industry. The study provides practical cases in the tourism industry and implications for practice, society and future research.

The study’s implications for Metaverse tourism are practical, societal and future research-related. Metaverse technology can enhance the tourist experience through personalized options, social interaction, immersive experiences and feedback. This inclusivity can promote social equity and cultural exchange. Further research is needed to explore the social effects of Metaverse tourism and its long-term impacts on local communities, economies and the environment.

This study contributes by exploring the impact of Metaverse tourism, supporting academic research and practice. It fills a knowledge gap by analyzing the application of Metaverse technology in tourism, providing insights for researchers and practitioners. It offers practical guidance by identifying opportunities and challenges in Metaverse tourism, fostering industry innovation. Additionally, it informs policymakers about the impact of Metaverse tourism on development.

Artificial Intelligence (AI) has revolutionised the tourism industry, offering personalised experiences and streamlining operations. AI provides customised recommendations for travellers through data analysis and machine learning, making their journeys more meaningful. It has also improved efficiency through automated processes, chatbots and enhanced security measures. AI's ability to analyse large volumes of data enables tourism organisations to make data-driven decisions and target their marketing strategies effectively. One of the most notable contributions of AI in tourism is its ability to offer personalised recommendations. By analysing vast travel history, preferences and online behaviour, AI systems can provide tailored suggestions for destinations, accommodations, activities and dining options. This level of customisation enhances the overall travel experience, making it more relevant and satisfying for individual travellers. AI has also greatly improved operational efficiency within the tourism sector. Chatbots, powered by natural language processing, are increasingly being deployed by hotels, airlines and travel agencies to provide instant customer support and assistance. These chatbots can answer queries, offer recommendations and handle booking processes, reducing waiting times and enhancing customer satisfaction. In addition, facial recognition technology allows for quick and accurate identity verification at airports, hotels and other travel-related facilities. This improves security and provides travellers with a seamless and efficient experience. As technology advances, we expect AI to play a more prominent role in augmented reality, voice recognition and virtual assistants, further enhancing the travel experience and facilitating seamless interactions. In conclusion, AI has transformed the tourism industry by providing personalised recommendations, improving operational efficiency, enhancing security measures and enabling data-driven destination management.

After each of the COVID-19-induced lockdowns, an unprecedented surge in leisure travel was observed, resulting in tourists flocking to places of tourist interest. This phenomenon was termed revenge travel in popular literature. The purpose of this study is to explore the phenomenon of revenge travel in detail through an academic lens. It examines the psychological and emotional motivations for revenge travel while studying the differences in travel behaviour pre- and post-pandemic.

The study applies an interpretive phenomenological approach to explore post-pandemic travel behaviour. Data were collected via personal in-depth semi-structured interviews. Thematic analysis of the transcripts was conducted to arrive at discussion themes.

Drawing on the established theories, the findings of the study indicate that lockdown fatigue coupled with mortality salience induced individuals towards leisure travel. This in turn led to mood alleviation and compensation for the deprivation undergone.

The study reveals important insights into post-pandemic preferences of travel destinations (off-beat locations near urban clusters), accommodation options (more travellers choosing home stays and stand-alone properties) and vacation itineraries (a gradual shift towards slow tourism with more focus on immersive experiences). Further, there are indications that hotels may develop “workcations” and “staycations” as a new line of offering.

The study adds to the small body of knowledge on revenge travel. It adopts a phenomenological approach, thereby capturing the “lived experiences” of the participants and providing an in-depth look into the psychological and emotional motivations of revenge travel that have not been explored previously. The study provides insights into the travellers' psychology post a period of withdrawal and restraint.

Tourism is considered one of the globe's most prominent sectors, generating considerable forex revenues and employment generation, contributing to world peace and solidarity among many nations. However, it is negatively influenced by different factors like the spread of diseases, terrorist attacks, outbreaks of war, etc. The COVID-19 pandemic triggered unforeseen upheavals, resulting in demand and supply uncertainties in nearly every area of the economy (El-Erian, 2020). Thus, it is relevant to study the impact of the pandemic on the tourism industry. This chapter explains the journey of tourism during the COVID-19 pandemic by portraying the status of global tourism, how it impacted the Indian economy and its revival strategies, with special mention to Kerala tourism. The pandemic also resulted in a considerable change in the travel intentions of tourists, their travel preferences and their attitude towards travel. Hence, this chapter also presents the changed travel intentions of tourists that will help the industry players modify their products per the tourist's expectations. Finally, this chapter presents how the tourism industry recovered from the pandemic from both the supplier and demand perspectives, which will be helpful for all tourism stakeholders.