PurposeThis study aimed to bibliometrically and visually analyze and review hospitality and tourism marketing studies published from 2000–2020.Design/methodology/approachA total of 3,942 articles collected from the databases of Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCI-E) in the Web of Science (WoS) along with their references were used for analyses. The bibliometric software HistCiteTM and literature measurement visualization tools, VOSviewer and CiteSpace, were employed to analyze the selected articles.FindingsThe results of the study demonstrated top influential scholars and institutions, intellectual structure and emerging trends of the study topics, and future research opportunities in the field of hospitality and tourism marketing.Research limitations/implicationsFirst, academic influence of a scholar was evaluated by citations of his/her publications, which did not take the order of authorship into consideration. Second, this study was restricted to the English language journals. Third, other types of published documents related to the studied field such as review papers were not considered by this research.Originality/valueIn comparison to traditional qualitative analysis such as content analysis, bibliometric analysis is a more objective approach to vividly demonstrate trends and performance of a research field, offers unique insights for its advancement with wider inclusiveness of a larger amount of data.
Global interests in organic foods are of importance to researchers and the food industry. Traditional questionnaire-based methods do not provide a broad picture. To meet this need, worldwide interests in organic foods were studied by integrating query data from the Google search engine and deep learning methods. The results show that organic oil, organic milk, organic chicken, and organic apples are the most interested organic foods; people from Singapore, US, New Zealand, Australia, United Kingdom and Canada care about organic foods the most; consumers' interest in organic foods has no correlation with GDP and life expectancy but has significant correlations with other dimensions of culture such as individualism, uncertainty avoidance, and long-term orientation. A recurrent neural network (RNN) model structure is useful in predicting people's interests in major organic foods over time.INDEX TERMS Organic food, search engine, search interest, neural network, data modeling, deep learning, consumer behavior.
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