Segmenting Cruise Consumers by Motivation for an Emerging Market: A Case of China

Consumer Perceptions of Brand Localness and Globalness in Emerging Markets: A Cross-Cultural Context

The globalization of markets and consumer behavior has changed dramatically in recent years. Similarly, global and local brands are facing many challenges in emerging markets. Thus, in this backdrop, this research is intended to examine the impact of consumer perceptions of brand localness and globalness on brand attitude in order to predict consumer behavioral intentions (purchase intention, price premium, and word of mouth) in cross-cultural emerging markets (China and Pakistan). Additionally, this research considered the moderating effects of consumer ethnocentrism and brand familiarity as a control variable. This study used an online survey to examine 1,562 responses from Chinese (n = 768) and Pakistani (n = 794) consumers regarding local and global brands. The proposed hypotheses were analyzed by using the partial least square-structural equation modeling method. The findings indicated that the consumer perceptions of brand localness and brand globalness had a substantial impact on brand attitude, which in turn favorably influenced consumer behavioral intentions in China and Pakistan. The brand attitude was a crucial mediator in both markets but was more critical in China than Pakistan. The interaction moderating effects of consumer ethnocentrism and consumer perceptions of brand localness positively influenced brand attitude in China, whereas consumer ethnocentrism and consumer perceptions of brand globalness negatively influenced brand attitude in Pakistan. Interestingly, brand familiarity was discovered a substantial control variable in both markets, except for purchase intention in Pakistan. This research contributed to Fishbein’s attitude theory and social identity theory. This research offers important recommendations to local and global marketers and brand managers in formulating and employing several positioning, market segmentation, and targeting strategies that may assist them in competing effectively in emerging markets.

Exploration of Topic Classification in the Tourism Field with Text Mining Technology—A Case Study of the Academic Journal Papers

This study collects abstracts of SSCI tourism journal papers between 2010 and 2019 from the WoS (Web of Science) database and uses a novel method of topic classification to explore the vocabulary characteristics of the classified articles. The corpora of abstracts are given quantitative Term Frequency–Inverse Document Frequency (TF–IDF) weights. A hierarchical K-means cluster analysis is then performed to automatically classify the articles; co-word analysis techniques are used to show the characteristics of feature words for distinct clusters, titles, and the consistency of the classified articles. Based on the results for 5783 abstracts, cluster analysis classifies the number of K-means clusters into six categories: travel, culture, sustainability, model, behavior, and hotel. A cross-check method is applied to assess the consistency of the topic classifications, list titles and keywords of the documents with the three smallest distances in each category and apply a strategic diagram to present the features of the distinct categories.