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This study expands the empirical evidence amassed to date concerning tourists' decision-making processes in a situation of conflict between a country of origin's market and country of destination. It uses the constructs of animosity, national attachment, consumer ethnocentrism, country image, and perceptions of the state of bilateral relations in order to examine three intent to visit scenarios. To obtain the robust results, the study uses two methods of analysis from distinctly different repertoires of techniques, such as traditional statistical and data-mining approaches, and applies them to each scenario. The results consistently indicate a high level of influence of general animosity and ethnocentric tendencies in addition to country image and bilateral relation variables in tourists' decision making. The situational context of the study is the conflict between China, the country of origin, and South Korea, the country of destination, over the Terminal High Altitude Area Defense (THAAD) system. [ABSTRACT FROM AUTHOR]

Online public discussions of service failure incidents might result in significant reputational damage for service providers. This study examines the geographical and personal proximity factors that contribute to public involvement in such discussions, using a high-profile incident that happened in a hotel of a popular Chinese budget chain as an example. A geographically weighted regression model is developed to illustrate spatial variations of public response at a prefecture division level. Economic development, area status, and presence of the service provider on the market are identified as significant predictors. This analytical approach can assist hospitality organizations in developing regionally-tailored image recovery strategies. [ABSTRACT FROM AUTHOR]

Automated content analysis of online travel reviews allows identification of topics of travelers' satisfaction, yet its domain is not well researched. We suggest that the Anna Karenina principle positing a greater variability of the factors leading to business failure as opposed to those leading to success can be applied to the domain of visitors' reviews of historic and cultural attractions. The larger variability of issues in reviews of dissatisfied visitors is likely to result in limitations for automated topic modeling. We confirm our proposition using TripAdvisor reviews of the Terracotta Army museum in China, and validate the outcome with two additional sites. The study strongly suggests that application of unsupervised topic mining algorithms to negative reviews may be problematic and the results should be treated with caution. The main themes of dissatisfaction of visitors to all three sites are reported and practical implications for management of the attractions are discussed. [ABSTRACT FROM AUTHOR]