Given the increasing importance of social media in brand management, this research examines how cross-country cultural distance plays a role in determining brand popularity on social media. Based on cultural discount theory, this research develops hypotheses on how cultural, socioeconomic, and brand-related variables influence such online brand popularity. Our empirical analysis shows that online brand popularity is influenced by a country’s cultural characteristics, cultural distance, and interactions involving the cultural distance and each of three factors (economic prosperity measured by per capita GDP, social media accessibility measured by Internet penetration, and the brand’s global status measured by brand globalness). Further, this research notes two widely used national cultural dimension systems—Hofstede’s and Schwartz’—and empirically finds that Hofstede’s system has better validity in this research context. Finally, in focusing on the potential managerial applications of this research, we cluster and profile three groups of countries in international brand management: (1) individualistic, low uncertainty avoidance cultures, (2) high masculinity, short-term orientation cultures, and (3) collectivistic, high uncertainty avoidance cultures. This research has implications for marketing managers who make various brand decisions, ranging from selecting countries to enter, to tweaking glocalization strategies for optimal product offerings.
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We thank Timo Mandler, a graduate student at the University of Hamburg, for providing crucial data for this research. We also thank Dawn Iacobucci, E. Bronson Ingram Professor of Management in Marketing at Vanderbilt University, for her insightful comments on this research. We appreciate the support of Hankuk University of Foreign Studies Research Fund of 2020.
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Kim, MY., Moon, S. The effects of cultural distance on online brand popularity. J Brand Manag (2021). https://doi.org/10.1057/s41262-020-00227-1
- Cross-cultural research
- Social media
- Brand communities
- Regression analysis
- Multidimensional scaling