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Purpose The purpose of this paper is to investigate the effects of experiences with local food in Ghana on satisfaction, favorability and behavioral intention. Design/methodology/approach Unlike previous studies that have used multiple regression analysis or structural equation modeling, this study adopts impact-range performance analysis (IRPA) and impact asymmetry analysis (IAA). A total of 336 questionnaires were used in the data analyses. Findings Factor analysis generates five domains of experience of consuming local food. Socialization and boasting and experience with various menus and quality of food contribute most to explaining the three dependent variables. Originality/value This study has significant value because it extends the study of local food consumption experience to the understudied area of African food tourism, particularly Ghanaian food and tourists to Ghana.

This study aimed to identify variations of three types of perceived image including affective, cognitive, and overall image over three points in time and to test the efficacy of image in explaining satisfaction, knowledge, and attachment with a destination. Although previous studies used results collected through a crosssectional survey, this study surveyed the same samples at three different times, that is, before, during, and after travel, to enrich our understanding of how image develops through the three key stages of a trip. The findings indicate that there is significant variation in perceived image domains, extracted as a result of factor analysis, and overall image across time. To predict satisfaction, attachment, and knowledge, "vividness" of the affective image domains and "diverse tourism attraction" of the cognitive image domains showed significance on regression models. Interestingly, "developed tourism industry" was not reported being significant predictor in any model. The results suggest that future studies need to measure destination image over time in line with traveler's movement.

Robots and artificial intelligence represent a newly emerging trend in tourism and hospitality.However, studies examining how cultural perceptions influence tourists' experiences interacting with service robots are lacking. In response to the industrial trend, the experiential components of robot-staffed hotels are assessed in this study. A qualitative approach is adopted to compare the semantic networks of Japanese and non-Japanese tourists' online reviews, using 1,498 reviews from nine robot-staffed hotels in Japan. The results indicate that hotel guests' interaction with robots is one of the main experiential components in robotstaffed hotels. The semantic network analysis results demonstrate noticeable differences, with Japanese reviews demonstrating more emotional responses to human-robot interaction and non-Japanese reviews valuing the functional and technical aspects of robot-provided services more.

This study attempts to investigate basic emotions incorporated in online reviews of fine dining Cantonese restaurants in Hong Kong and to investigate antecedents and consequences according to each emotion. This study adopts semantic network analysis and a machine learning algorithm to achieve its research objectives. A total of 2,118 reviews were used for the analysis. Five emotions -joy, sadness, disgust, surprise, and anger -accounted for 72% of prediction accuracy. Given that the five types of emotions in this study were closely associated with service, food, and reputation, the three components are considered the core elements of a fine dining restaurant experience. Results of this study imply that restaurants should understand customers' emotion based on big data analysis. The integration of emotion theory and practical implications can provide meaningful evidence on how to capitalize on big data.