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This study aims to investigate the effectiveness of two types of social media videos used for destination image development: induced/commercial-oriented content and organic content (where content is made without commercial interest, such as vlogs classified as user-generated content).

Experimental research using “Emotive EEG” (electroencephalogram) in a controlled environment was conducted with 30 participants (20 males, 10 females), age range 18 to 26. Emotive EEG recording was performed while the participants watched both types of video clips. Test results for both groups indicate that induced content is preferred over organic content.

This study opens up future research avenues where neuromarketing’s “Marketer Friendly” EEG equipment can be applied to the customer selection process.

Marketing analysts can gauge the interest and response of customers on different types of social media video content for destination marketing based on the findings of this study.

The main purpose of this research paper is to generate a holistic bibliometric study of the tourism industry and COVID-19 fields, to further investigate the current interests and trends emerging from scientific collaboration and thematic analysis and to identify research gaps that indicate future research directions.

This study conducts several analyses, which include the co-authorship and social network analysis, co-citation and keyword co-occurrence knowledge structures. The authors generate a knowledge map of the leading articles and link them with previous literature to elucidate the debates and consensus in research on COVID-19 and tourism.

Research interests concentrate in the USA, China, Europe and the Oceania areas, so more cross-continental collaborations are expected among them and with other regions. Popular topics are tourism sustainable transformation, crisis management and multidisciplinary fields like tourism, hospitality, information technology and environmental sciences. This paper also identifies underexplored topics for future investigation on the social, environmental, cultural and governance dimensions of sustainable tourism.

This paper contributes to guiding tourism researchers in identifying and finding publication references and future collaborations. Moreover, the investigation of knowledge structures could be beneficial for scholars hoping to broaden the current understanding of this field and discover potential for future tourism research, especially in the global pandemic and other severe health crises.

This study enriches the existing literature in the fields of tourism and the pandemic and highlights current interests and research trends exploring scientific collaboration, thematic analysis and knowledge mapping.

Road traffic emissions are generally believed to contribute immensely to air pollution, but the effect of road traffic data sets on air quality (AQ) predictions has not been fully investigated. This paper aims to investigate the effects traffic data set have on the performance of machine learning (ML) predictive models in AQ prediction.

To achieve this, the authors have set up an experiment with the control data set having only the AQ data set and meteorological (Met) data set, while the experimental data set is made up of the AQ data set, Met data set and traffic data set. Several ML models (such as extra trees regressor, eXtreme gradient boosting regressor, random forest regressor, K-neighbors regressor and two others) were trained, tested and compared on these individual combinations of data sets to predict the volume of PM_2.5, PM₁₀, NO₂ and O₃ in the atmosphere at various times of the day.

The result obtained showed that various ML algorithms react differently to the traffic data set despite generally contributing to the performance improvement of all the ML algorithms considered in this study by at least 20% and an error reduction of at least 18.97%.

This research is limited in terms of the study area, and the result cannot be generalized outside of the UK as some of the inherent conditions may not be similar elsewhere. Additionally, only the ML algorithms commonly used in literature are considered in this research, therefore, leaving out a few other ML algorithms.

This study reinforces the belief that the traffic data set has a significant effect on improving the performance of air pollution ML prediction models. Hence, there is an indication that ML algorithms behave differently when trained with a form of traffic data set in the development of an AQ prediction model. This implies that developers and researchers in AQ prediction need to identify the ML algorithms that behave in their best interest before implementation.

The result of this study will enable researchers to focus more on algorithms of benefit when using traffic data sets in AQ prediction.

Drawing on the stimulus–organism–response framework, this study examined the environmental stimuli driving tourists' internal, or organismic, states. In addition, the authors investigated the association of the identified organismic variables with the response variables during the COVID-19 pandemic. Specifically, the study examined how the associations between tourists' anticipation of recovery and the national government's smart governance, on one hand, and tourists' desire to travel domestically, their attitude toward domestic travel and their willingness to exhibit prosocial behaviors, on the other, further drive the satisfaction they derive from domestic travel.

The authors used an online questionnaire to collect self-report, single-wave data from individuals residing in India, an emerging market (N = 421).

The findings demonstrate (1) the association of anticipated recovery on the desire to travel and prosocial behavior; (2) the association of smart governance on attitude (although negative); (3) the association of desire, attitude and prosocial behavior on satisfaction; and (4) the lack of any moderation effect for perceived severity.

This study is the first empirical study to investigate the impact of tourists' perceptions and dispositions and the efficacy of the national government on tourists' desire to travel domestically and on their satisfaction with domestic travel. The findings can help emerging market multinationals and global brands engage better with domestic consumers in emerging markets within the context of the current pandemic. In addition, the findings can help to prepare these players to handle future disruptions caused by global health contingencies.

The purpose of this research is to examine, from the self-efficacy standpoint, the factors that influence medical tourists' use of social media for travel planning. To that end, this study presents a conceptual framework for evaluating medical tourists' online behaviour and empirically tests the model's validity by examining various dimensions of self-efficacy.

A sample of 224 people who searched for medical tourism information online was used in this study. Partial least squares structural equation modelling (PLS-SEM) was employed to test the proposed model with data from an online questionnaire survey.

The findings suggest that sources of self-efficacy reinforce one's belief in the ability to use social media for medical travel planning purposes. According to the structural model analysis results, with the exception of social persuasion (SP), all of the proposed factors were significant sources of self-efficacy.

The findings can assist medical tourism providers in optimising online searches for medical travel information, as well as medical tourism destination marketers in directing the tourism providers' marketing efforts towards the use of social media to target potential medical tourists more efficiently and on a larger scale.

This study is being conducted in response to a significant knowledge gap in identifying the factors that influence medical tourists' online behaviour. This information can help medical tourism destination marketers gain a competitive advantage by using social media to target potential medical tourists more effectively and on a larger scale.

Underpinned by sensation-seeking theory (SST) and regulatory focus theory (RFT), this paper highlights the crucial role of adventurousness in self-protective behavior and future travel avoidance. Furthermore, this paper investigates safety-seeking tendency as a moderator and travel anxiety post-COVID-19 as a mediator.

Data were gathered from 574 potential visitors to St. Catherine post-COVID-19 and analyzed using Smart-PLS approach.

Adventurousness negatively and significantly affected travel anxiety, while the latter negatively influenced self-protective behavior and positively influenced future travel avoidance. Besides, the findings proved that travel anxiety partially mediated the adventurousness linkage with self-protective behavior and future travel avoidance. Moreover, safety-seeking tendencies dampened travel anxiety's connection with self-protective behavior and future travel avoidance.

This paper provides valuable insights into travel research in theory and practice to revive tourist attractions post-COVID-19 in developing countries via an adventure tourism pattern. The study helps figure out how to deal with the pandemic and restore the monument of heavenly religions, St. Catherine—sacred mountain peaks, mosques, churches and many monasteries—in addition to its charming and picturesque nature.

The current paper examines a traveler's adventurous nature and post-COVID-19 behavior when visiting St. Catherine and their behaviors related to future avoidance and self-protection. This paper adds the first investigation of travel anxiety and safety-seeking through the lens of SST and RFT theories in the Egyptian tourism context.

The purpose of this study has to conduct an integrated effective evaluation system to discover consumers' travel attitudes of the culinary experiences or cuisines and establishing an optimal mutual relationship of a brand equity evaluation model.

Based on a sample of 624 participants, the results support that social media engagement has indirect effects on culinary brand equity development through travel attitudes and information sharing.

The study finds that values are expressed as foundational attributes that have indirect effects on brand equity through hedonic function, perceived quality, brand awareness and brand image. In contrast, the social interaction mechanism strengthens subdimensional relationships. These findings extend the customer brand equity literature and the nature of tourists' perspectives in the context of Taiwan's culinary destination brand equity.

It is a suggestion for tourism and hospitality managers to identify the different characteristics of attitudes towards visits and the customers' desert of participation in food-related activities when it derives to the classifications of food and culinary tourism.

The current study extended the findings and asserted that social interaction leads to and strengthens the relationships between memorable culinary hedonic experiences and perceived quality and improves tourists' positive awareness and image compared to other tourism experiences.

The aim of this study is to identify the travel constraints of people with special needs (PwSN) and the strategies used to overcome them. The article also intends to analyze the differences in travel constraints and negotiation strategies according to the type of disability and/or special need.

A qualitative study was conducted in which a sample of PwSN (people with disabilities – mobility, sensory and cognitive – seniors and people with food allergies) were interviewed in depth.

PwSN face a wide range of constraints when engaging in tourism activities. These constraints are very diverse and influence people with different intensity and have to be overcome using different negotiation strategies. However, differences in the constraints were observed according to the type of special needs.

One of the most significant practical implications of this research is the need for raising awareness regarding human diversity among tourism stakeholders. It is critical to link sectoral policies that are reflected in the social and business reality, creating public–private partnerships to raise the sharing of knowledge, equipment and services. By addressing the constraints that prevent PwSN from traveling, the tourism sector can create more opportunities for them to participate in social activities, improving their quality of life and contributing to a more sustainable and inclusive industry.

Traveling can present significant challenges for people with special needs, which affect the quality of their tourism experience. Despite growing academic attention to this issue in recent years, research in this field has primarily focused on specific aspects of disability. This study seeks not only to identify the barriers to travel faced by people with different special needs but also to explore the negotiation strategies used to overcome these barriers and the differences according to the type of special need.

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.

As Chinese Generation Z consumers have distrust over traditional advertising and marketing, this study focuses on user-generated content (UGC) travel apps to demonstrate the structural relationship among the expertise of amateur information publisher, interactive atmosphere, information quality, expectation confirmation, perceived trust, experiential satisfaction and switching intention.

This study carried out a questionnaire-based survey among Chinese Generation Z consumers and obtained 356 valid questionnaire copies that were analyzed with the structural equation.

The expertise of amateur information publisher and the interactive atmosphere of UGC travel apps have positive effects on perceived trust; expectation confirmation and perceived trust have positive effects on experiential satisfaction, and experiential satisfaction has negative effects on switching intention.

The results should encourage UGC travel app developers to use visual effects so as to achieve a higher quality of information, foster a cordial interactive atmosphere and enhance the expertise of information publishers by selecting the best candidate, thus spurring the design of marketing activities popular among the Chinese Generation Z consumers.

This study argues that new-generation consumers have their specific appeals and in the tourist market. It explores the UGC travel apps, which are popular among Generation Z consumers in order to deepen marketing personnel's understanding of the relationship among the expertise of amateur information publisher, interactive atmosphere, information quality, expectation confirmation, perceived trust, experiential satisfaction and switching intention.