S. Mostafa Rasoolimanesh, Rob Law, Dimitrios Buhalis and Cihan Cobanoglu
Dimitrios Buhalis, Xi Yu Leung, Daisy Fan, Simon Darcy, Ganghua Chen, Feifei Xu, Garry Wei-Han Tan, Robin Nunkoo and Anna Farmaki
Ümit Şengel, Gökhan Genç, Merve Işkın, Mustafa Çevrimkaya, Ioannis Assiouras, Burhanettin Zengin, Mehmet Sarıışık and Dimitrios Buhalis
The COVID-19 pandemic, which appeared in China in late 2019, has affected the world psychologically, socially and economically in 2020. Tourism is one of the areas where the…
Abstract
Purpose
The COVID-19 pandemic, which appeared in China in late 2019, has affected the world psychologically, socially and economically in 2020. Tourism is one of the areas where the effects of COVID-19 have been felt most clearly. The study aims to determine the effect of negative problem orientation (NPO) and perceived risk related to the COVID-19 pandemic on travel and destination visit intention.
Design/methodology/approach
This study employed a convenience and probabilistic sampling method for collecting data from 531 respondents using an online questionnaire. Partial least square structural equation modeling (PLS-SEM) was used for testing research model.
Findings
According to the findings, NPO and perceived risk related to the pandemic were found to have direct and indirect effects on the travel behavior of tourists. The results of this research provide theoretical and practical implications for hospitality and travel businesses on topics such as the psychological effects of the pandemic and the travel behaviors of tourists.
Originality/value
It is estimated that the pandemic will also affect tourist behavior due to its effects on human psychology. For this reason, a study conducted in the context of tourist behavior theories is expected to contribute to the literature, managers and future of the tourism.
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Keywords
Bin Yao, Richard T.R. Qiu, Daisy X.F. Fan, Anyu Liu and Dimitrios Buhalis
Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the…
Abstract
Purpose
Due to product diversity, traditional quality signals in the hotel industry such as star ratings and brand affiliation do not work well in the accommodation booking process on the sharing economy platform. From a suppliers’ perspective, this study aims to apply the signaling theory to the booking of Airbnb listings and explore the influence of quality signals on the odds of an Airbnb listing being booked.
Design/methodology/approach
A binomial logistic model is used to describe the influences of different attributes on the market demand. Because of the large sample size, sequential Bayesian updating method is utilized in hospitality and tourism field for the first attempt.
Findings
Results show that, in addition to host-specific information such as “Superhost” and identity verification, attributes including price, extra charges, region competitiveness and house rules are all effective signals in Airbnb. The signaling impact is more effective for the listings without any review comments.
Originality/value
This study contributes to the literature by incorporating the signaling theory in the analysis of booking probability of Airbnb accommodation. The research findings are valuable to hosts in improving their booking rates and revenue. In addition, government and industrial management organizations can have more efficient strategy and policy planning.
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Keywords
João J. Ferreira, Sérgio J. Teixeira, Fangfang Shi, Peter Wanke and Dimitrios Buhalis