This study aims to predict the effect of cultural distance, perceived risk and electronic word of mouth (eWOM) on higher education institutes' students' destination image. In addition, it examines the mediating role of destination image in relation to students' travel intentions.
An online survey was employed to collect data from 200 graduate and postgraduate students. The partial least squares was employed to analyse the hypothesised relationships.
The results of this study found support for the positive effect of cultural distance and eWOM on destination image. Additionally, the mediating effect of destination image was also supported.
This research confirms the vital role of destination image as an antecedent of students' future intention to visit the destination. Moreover, this study contributes to marketing theory by predicting the critical drivers of higher education students' destination image and discussing their applications in the education sector.
This study explores the pioneering approach of utilising machine learning (ML) models and integrating social media data for predicting tourist arrivals in Sri Lanka.
Two sets of experiments are performed in this research. First, the predictive accuracy of three ML models, support vector regression (SVR), random forest (RF) and artificial neural network (ANN), is compared against the seasonal autoregressive integrated moving average (SARIMA) model using historical tourist arrivals as features. Subsequently, the impact of incorporating social media data from TripAdvisor and Google Trends as additional features is investigated.
The findings reveal that the ML models generally outperform the SARIMA model, particularly from 2019 to 2021, when several unexpected events occurred in Sri Lanka. When integrating social media data, the RF model performs significantly better during most years, whereas the SVR model does not exhibit significant improvement. Although adding social media data to the ANN model does not yield superior forecasts, it exhibits proficiency in capturing data trends.
The findings offer substantial implications for the industry's growth and resilience, allowing stakeholders to make accurate data-driven decisions to navigate the unpredictable dynamics of Sri Lanka's tourism sector.
This study presents the first exploration of ML models and the integration of social media data for forecasting Sri Lankan tourist arrivals, contributing to the advancement of research in this domain.
By merging good countryside and rural capitals frameworks, a model for reimagining the island's development is formulated, which is then applied to the female perspective to provide valuable insights from a group that is often marginalized in rural areas. As Croatian islands are highly tourism-oriented, this study finds it important to explore possibilities for future island development that can provide balanced and vibrant settlements on the islands.
The present paper synthesizes Shucksmith's (2018) model of a good countryside, which serves as a goal, with Gkartzios et al.'s (2022) capitals framework, which is viewed as a means of attaining a good countryside, specifically a good island. The research is delimited to the island of Brac, Croatia. By conducting interviews with female respondents, this study aims to capture the female perspective on envisioning potential futures of “good” island living, a perspective that is frequently underestimated despite its significant contributions to the creation of an ideal locale.
The results demonstrate that there is a substantial amount of socio-cultural rural capital that is leveraged to strengthen relatedness and rights as development objectives. However, low levels of economic, built and land-based rural capital pose challenges to achieving repair and re-enchantment, which are crucial for settlements that rely on tourism.
These findings bear immense implications for policymakers and planners, underscoring the imperative to account for the perspectives and needs of diverse social groups, including women, in the design and implementation of development strategies for islands. By doing so, a sustainable and equitable future, rich in tourism potential, can be cultivated on the island.
Meat overconsumption by tourists is one of the key issues in the sustainability of tourist destinations. The objective of this paper is to assess the impact that a promotion of meatless gastronomy and its actual increased availability would have on the attractiveness and visitation of a popular European urban destination.
The research uses an innovative foresight approach that combines environmental scanning and scenarios. The authors formulated the scenarios using the Delphi technique and working with 27 scholars whose insights into the topic were enhanced through theses that resulted from the environmental scanning. They provided their insights into how a promotion of meatless gastronomy would affect a destination. Subsequently, the authors synthetized their insights and formulated the scenarios.
Based on two defined scenarios, the authors found that a meatless image represents an opportunity for the future development of an urban European destination. A long-term growth in visitor numbers can be achieved while ensuring environmental, economic and socio-cultural sustainability providing that relevant stakeholders are involved in the promotional activities.
Management of tourists' unsustainable eating habits is currently an unexplored issue. The present research addresses this topic and provides valuable insights that will help to address sustainability issues while making tourism destinations more attractive.
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