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Religious tourism is one of the oldest and fastest-growing segments. This study analyzes religious tourism through the pilgrimage of Muslims to the holy city of Mecca and has the following objectives: (1) establish the motivational dimensions of religious tourism; (2) identify the motivational dimensions that predict the satisfaction of religious tourism and (3) determine the motivational dimensions that predict return, recommend and say positive things about religious tourism applied to the pilgrimage to a sacred city.

The sample consisted of Muslim pilgrims who had visited Mecca. The sample was collected in Bahrain, a country located on the Persian Gulf, where most of its population is Muslim. A total of 380 valid questionnaires were obtained online. For the data analysis, factorial analysis and the multiple regression method enter were performed.

The results show that religious motivations are more important when visiting a sacred city than secular ones. Three motivational dimensions were found: religious, social and cultural and shopping. The three dimensions found have a significant relationship with satisfaction and loyalty. Likewise, it was found that the religious motivational dimension is the factor that most predicts satisfaction and loyalty in the behavior of religious visits to a sacred city.

The main limitation of the present study was the temporality in which the sample was taken because the demand may vary at another time of the year and therefore vary its results.

The authors of the study recommend that holy cities increase the religious motivations of these travelers by periodically researching their needs and organizing services to suit their desired spiritual experience. Also, to improve the social and cultural part, travel agencies and tourist companies to Mecca should promote social and cultural motivation among travelers in an appropriate way by providing service packages that involve visits to cultural and social sites such as museums and cultural centers.

This research will serve as a management guide for public institutions and private companies to develop more efficient planning in religious destinations and sacred cities.

This study is the first to analyze the construct of motivations in the pilgrimage to the city of Mecca, to then establish what the main motivations are that predict satisfaction and loyalty in a religious city. Thus, its results provide important information for tourist destination managers and tourism service providers.

This paper reports the results of research that examines the interrelationships between efficacy of sustainability values (SV) and pro-sustainable behaviors of potential tourists. A partially mediated model is postulated and tested to help explain additional error variance in predicting consumers’ destination choice decisions in tourism, hence voiding a critical research gap. Coined as the “environmentally intellectualist behavior,” a new mediator variable is tested to explain additional error variance in human-value models.

The study is based on data collected from two representative samples of potential tourists from the USA and Canada. Data analyses include exploratory and confirmatory factor analyses that were used to examine the underlying domain structures of SV, followed by a predictive model using structural equation modeling.

The study findings suggest that values are salient factors that underlie pro-sustainable tourism and travel behavior. Moreover, the results confirm the existence of a higher-order sustainability construct. The study contributes original insights to the field by demonstrating that there are direct and indirect positive relationships between SV, environmental behaviors and decisions of consumers who take a pro-sustainable stance when traveling.

By modeling values as antecedents to attitudes and testing interrelationships between SV and the mediator variables coined as the environmentally intellectual behavior, the authors developed and tested a predictive model to explain destination- and product choice decisions. The model tested herein advances the value theory in two fundamental ways: first, this study demonstrates that SV can be modeled as higher-order factors. Second, values are antecedents to attitude and other variables, therefore must be included in consumer behavior models. Finally, the culture or origin of tourists matters when examining the impact of values on tourists’ choice decisions. Political actions and environmental attitudes can be modeled as mediators to explain additional error variance.

Air transport accounts for nearly 40% worth of the global trade cargo volume, where more than 50% of the air cargo is carried on passenger flights. Therefore, this paper aims to focus on identifying the influencing factors for both passenger and cargo demand-driven networks to smoothen the global supply chain.

The data for the study was collected through literature reviews and interviews with industry experts. The analytical hierarchy process was used to analyze the expert's opinions on the critical factors affecting air cargo demand growth. Regression analysis was conducted using the selected variables to develop a model to calculate air cargo demand growth.

According to the expert opinion, it was identified that facilities under airport capacities and facilities are mainly affected by the air cargo carried by combi carriers. The model was developed considering the air connectivity index and air cargo demand at destination variables.

The factors identified here are mainly related to the current situation in Sri Lanka. Applying this methodology to other economic zones will add new factors related to their economic contexts and could be generalized as the influencing factors for the growth of air cargo demand by finding more results.

Previous studies have been conducted using different factors and models to forecast air cargo demand, and those did not consider demand from combi and all-cargo carriers together. More than 98% of air cargo trades in Sri Lanka are happening through combi carriers. Hence, Sri Lanka will be a best case study to analyze the behavior of combi carriers.

This study aims to explore the connection between demographic traits and the choice of attraction patterns among international repeat tourists.

The study employed a questionnaire survey to collect data from 1550 international repeat tourists who visited Tanzania between November 2022 and July 2023. Convenient sampling was employed as tourists were selected from the three international airports of Tanzania, namely Kilimanjaro International Airport, Julius Nyerere International Airport, and Abeid Aman Karume International Airport. A multinomial logistic regression model was used to examine the impact of socio-demographic characteristics on the selection of attraction patterns among international repeat tourists.

The study revealed that demographic factors, including age, marital status, income level, occupation, and education level, exhibit statistically significant correlations with preferences for distinct attraction patterns. This significance was established through a p-value of less than 0.05 for all the aforementioned variables.

This study is primarily focused on international repeat tourists, thereby limiting insights into the preferences of domestic tourists. To better inform strategies aimed at attracting a larger domestic tourist base, future research may prioritize the investigation of choice of attractions patterns among domestic tourists in relation to their demographic characteristics.

This study contributes to the nuanced understanding of international tourist behavior by unraveling the extent to which demographic traits impact tourists’ choices of attraction patterns, thereby providing insights crucial for effective marketing strategies, improved visitor experiences, and sustainable tourism development strategies.

This paper aims to bridge the extended reality framework and the luxury hospitality literature by providing insights into how immersive technologies using artificial intelligence (AI) can shape luxury value and consumer differentiation.

The authors conducted three experimental studies comparing immersive AI versus traditional hospitality across luxury contexts (hotels, restaurants and spas). Study 1 investigates the effect of immersive AI (vs traditional hospitality) on customers’ behavioral intentions and the need for differentiation using virtual-assisted reality. Study 2 tests the underlying mechanism of the need for differentiation and luxury value in an augmented reality context. Study 3 provides additional support for the proposed underlying mechanism using virtual-assisted reality in luxury hospitality.

The findings reveal that immersive AI (vs traditional) luxury hospitality reduces customers’ behavioral intentions of using such services and perceived luxury value. Moreover, the findings indicate that the intention to use immersive AI (vs traditional) luxury hospitality services is contingent upon customers’ need for differentiation.

The findings have important theoretical and managerial implications for immersive technologies in luxury hospitality. They shed light on the dynamics between integrating immersive AI into luxury hospitality and its impact on customers’ differentiation motives and perceived luxury value. The findings reveal the detrimental effect of using immersive AI (vs traditional hospitality) within this context.

COVID-19, like many previous crises, proved once more that some hospitality and tourism organizations are more crises resilient than others. Despite increasing frequency and magnitude of crises, little is known about the features of crises resilient organizations and mitigation strategies they adopt. If the characteristics of such resiliency are identified, those strengths might be targeted. Hence, the purpose of this study is to identify characteristics of crises resilient organizations by analyzing the interface between different organizational characteristics, recovery strategies they adopted and impacts of COVID-19 on individual hospitality and tourism organizations.

A global sample of 202 respondents from 20 countries and four continents, representing different sectors of the hospitality and tourism industry, participated in the survey. Descriptive analysis and cluster analysis were used to rank the items and group hospitality and tourism organizations based on their crises resiliency.

Service quality, loyal customers, branding, high paid in capital, domestic market base, hygiene and safety image, information and communication technology adoption, product and market diversification and restructuring debts emerged as major characteristics and strategies of crises resilient organizations. Using cluster analysis, four different groups of organizations were identified. Based on the impacts of COVID-19 on these organizations, Cluster-1 emerged as significantly more crises resilient, whereas Cluster-4 organizations were significantly more vulnerable to crises. Their characteristics and mitigation strategies they adopted were discussed.

The paper not only identified features of crises resilient organizations and successful mitigation strategies but also measured their impact on various performance indicators. Future studies might use characteristics, mitigation strategies and performance indicators identified in this study.

Based on the findings, tourism organizations would focus on strengthening characteristics and implementing strategies that make crises resilient organizations. Public bodies and destination management would also set their decision criteria based on these findings to create a more resilient tourism industry.

This research not only identifies how hospitality and tourism organizations are affected by COVID-19 but also how these impacts change based on different organizational characteristics and strategies. Understanding which organizational characteristics affect the crises vulnerability of hospitality and tourism organizations might inform risk and crises management literature and structural design elements in tourism businesses, hence offer both theoretical and practical implications.

It has been established that values, beliefs and norms are good predictors of pro-environmental behaviour; however, it is less explored how these constructs can be influenced. The purpose of this paper is to assess whether the COVID-19 pandemic, as a shock event, was a “game changer” or it had limited impacts on deep-rooted travel patterns.

To understand whether the pandemic has influenced people’s value orientations with regards to sustainable travel, the authors have made use of the value–belief–norm (VBN)theory of environmentalism and conducted a large-scale survey in Germany, Italy, France, The Netherlands and UK (N = 1545).

The findings showed that the pandemic caused only temporary change in deep-rooted travel patterns as (self-reported) pre-pandemic behaviour of respondents align with their value orientations and proved to be a good predictor of projected travel behaviour. Based on projected travel behaviour and value orientations, four market segments were identified – Frontrunners, Laggards, Comfortable Crowd and Entitled Stewards. While all segments showed willingness to adapt, the authors found variations in the extent of adaption and in the phases of the customer journey where travellers are likely to make changes.

According to our knowledge, using the VBN theory of environmentalism to understand the impact of shock events on deep-rooted travel patterns represents a new perspective. Moreover, to the best of authors’ knowledge, this study is one of the first, written in the midst of the COVID-19 pandemic, which looks at the impact of the health crisis on deep-rooted travel patterns, instead of the commonly studied risk perception.

The explosion of data due to the sophistication of information and communication technology makes it simple for prospective tourists to learn about previous hotel guests' experiences. They prioritize the rating score when selecting a hotel. However, rating scores are less reliable for suggesting a personalized preference for each aspect, especially when they are in a limited number. This study aims to recommend ratings and personalized preference hotels using cross-domain and aspect-based features.

We propose an aspect-based cross-domain personalized recommendation (AsCDPR), a novel framework for rating prediction and personalized customer preference recommendations. We incorporate a cross-domain personalized approach and aspect-based features of items from the review text. We extracted aspect-based feature vectors from two domains using bidirectional long short-term memory and then mapped them by a multilayer perceptron (MLP). The cross-domain recommendation module trains MLP to analyze sentiment and predict item ratings and the polarities of the aspect based on user preferences.

Expanded by its synonyms, aspect-based features significantly improve the performance of sentiment analysis on accuracy and the F1-score matrix. With relatively low mean absolute error and root mean square error values, AsCDPR outperforms matrix factorization, collaborative matrix factorization, EMCDPR and Personalized transfer of user preferences for cross-domain recommendation. These values are 1.3657 and 1.6682, respectively.

This study assists users in recommending hotels based on their priority preferences. Users do not need to read other people's reviews to capture the key aspects of items. This model could enhance system reliability in the hospitality industry by providing personalized recommendations.

This study introduces a new approach that embeds aspect-based features of items in a cross-domain personalized recommendation. AsCDPR predicts ratings and provides recommendations based on priority aspects of each user's preferences.

The purpose of this study is to look at the factors that drive people to participate in the sharing economy (SE). Based on the Technology Acceptance Model (TAM) and the Theory of Planned Behavior (TPB), the study proposes a mathematical model. The study’s ultimate objective is to help businesses attract more involved customers and promote collaborative consumption as a sustainable alternative to typical consumption patterns. The study offers a conceptual framework established via a thorough literature review to examine Indian customers’ use behavior toward SE platforms. A one-sample two-tailed t-test is used to assess the framework’s efficacy. The research fills gap in the literature on the SE by investigating the factors that determine subjective norms (SN), attitudes (A), and perceived behavioral control (PBC). A framework is provided that takes behavioral intention (BI) contemplated as a mediating variable. The research improves TAM and TPB by including new factors such as technical characteristics. This research adds to the body of knowledge on the digital SE by underlining the relevance of usage behavior in comprehending Indian customers, where A, SN, and PBC are important aspects. The research presents a paradigm for better understanding customers’ attitudes and behaviors toward various SE platforms, which might help academics, practitioners, and policy makers situate their initiatives within the larger field of sharing. The study’s categorizations of Indian consumers’ A, SN, PBC, and BI toward the SE might potentially advise on future research and government policies.