The Influence of Global Brand Distribution on Brand Popularity on Social Media

Abstract

The literature on consumer choice between global and local brands is focused on sales-based measures of brand globalness (BG). When managers need to establish an effective social media campaign to raise awareness of their brands’ activities on social media, the literature focus may not provide clear guidance on how such measures can be applied to social media. This research contributes to global brand research by combining the literature streams on consumer social media engagement and global branding marketing strategies. First, to provide a managerial tool for this task, the authors propose two novel measures, BG and country brand popularity (CBP), based on consumers’ brand activities on social media. Using these measures, the authors hypothesize that CBP is influenced by cultural, social, and economic factors, which is motivated by motivation–opportunity–ability theory. Using Facebook data covering the top 100 brands that operate across 50 countries in each of 51 industries, they show that CBP is influenced by BG and cultural, social, and economic characteristics.

Keywords

brand globalization localization multilevel model social media

In most companies, a global brand is something to be celebrated—it means that customers all over the world have adopted a company’s offerings. Yet global brand management has always been challenging—customers across the world may purchase the brand, but they may comprise segments that differ in terms of liking and loyalty, depending on such factors as culture and local brand options. Furthermore, global brand management has become increasingly complex with the advent of social media, through which different customers may comment on and compare their brand evaluations with others residing in the same or different countries. How might brand managers proceed? How do they assess the fandom of their customers across the globe? Is the brand popular, and if so, where?

The global brand manager often seems to be walking a tightrope. On the one hand, global brands can enhance brand attitudes and customers’ willingness to pay (Davvetas, Sichtmann, and Diamantopoulos 2015). On the other hand, such a situation may also carry disadvantages, such as when these brands are manufactured in unfavorable countries of origin or when consumers prefer local brands (Kim and Park 2017; Riefler 2012). Thus, there are still many rules to learn as to when marketers might expect consumer preferences for a local versus global brand. The challenge becomes more complicated in a social media context, where all kinds of brands are discussed, often within active brand communities connecting like-minded consumers (Adjei, Noble, and Noble 2010). Obviously, social media is extraordinarily important to marketers—customer feedback and social media communications are abundant on a level that businesses have never seen before, and online discussions can also emphasize local or global brand preferences.

Social media may help a brand manager better understand consumers’ acceptance of and attitudes toward a brand. Companies may also be better informed if they understand where the brand might be popular elsewhere in the world in the company’s expansion endeavors. In addition, to understand a market’s customer receptivity (or the extent to which a brand must be tailored to local tastes), it is imperative to understand the country’s comprehensive profile, encompassing its social, economic, and cultural aspects (Eisingerich and Rubera 2010; Moon et al. 2016; Özsomer 2012). Thus, information regarding consumers’ online support for local or global brands should be studied partly as a function of societal markers, the national economy, and culture to provide clearer guidance for strategic global brand management. These contemporary issues—global brands and social media—are among the most important marketing issues of our time. Marketers have a strong tradition in global brand research, and a similarly strong literature stream is developing on social media. Research is still needed to combine discussions in the context of global brand management (Hudson et al. 2016; Johnston et al. 2018).

Given this theorizing, our research contributes to the literature streams of global brand and social media in that (1) we develop two theoretical brand measures derived from social media—BG and country brand population (CBP)—to complement traditional measures predominantly based on consumer surveys and corporations’ financial statements, and (2) we identify and test a comprehensive range of factors that influence brand popularity in the environment of social media, unlike previous studies that focused on a few selected factors such as culture or product quality (see Table 1). Specifically, first, this research concentrates on the two theoretical constructs (BG and CBP) to improve our understanding of factors that influence brand popularity on social media as a target variable for brand managers. To accomplish this broad research objective, we first focus on measuring the two key constructs theoretically on social media. In contrast to most studies using consumer surveys to learn about consumers’ preferences between local versus global brands (Batra et al. 2017; Davvetas and Diamantopoulos 2016; Hsieh 2002; Özsomer 2012; Strizhakova and Coulter 2015), we use brand preference distributions across countries revealed on a social media platform. Our proposed measure generates a continuous scale of BG (cf. the dichotomous scale used by existing studies; Batra et al. 2017; Davvetas and Diamantopoulos 2016). This continuous scale reflects the varying degrees of BG more realistically. To be consistent with our measurement of BG, we also use brand popularity expressed by consumers on social media as our measure of brand popularity, whereas existing studies have commonly used perceptual brand attitudes or purchase intentions (Gao et al. 2018; Riefler 2012; Xie, Batra, and Peng 2015). In measuring these two constructs, we use publicly available information on social media to understand a given brand’s particular context to better anticipate consumer responses. More specifically, the enrichment of social media data allows us to produce granular-level data covering various industries in many countries. These data ultimately lead to an efficient analytical branding tool for marketing managers, so that they can take advantage of easily accessible and publicly available data (on both their own and competitor brands) directly from consumers.

Table 1.

Summary of Research on Measuring Global/Local Brands and Brand Popularity.

Article	Defining/Measuring BG	Defining/Measuring Brand Popularity	Factors Influencing Brand Popularity
Banerjee (2008)	Country-specific brand reception	Brand competitive advantage (conceptual, nonempirical paper)	Culture and match with brand heritage
Batra et al. (2017)	Brand consistency; global versus local (dichotomous)	Survey perceptions of best brand, value, performance, quality, brand personality	Culture: Power and hedonism, and benevolence, universalism, and self-direction
Davvetas and Diamantopoulos (2018)	Regret in buying global or local brands	Purchase selection of local or global brand	Global brand superiority, ability to justify choice
Gao et al. (2018)	Consumer emotional worth of brand connection	Interviewees gained emotional benefits from sharing brand ties	Online information seeking, need for social ties
Gürhan-Canli, Sarial-Abi, and Hayran (2018)	Preferences for global versus local brands	Brand attitude, preference (conceptual, nonempirical paper)	Cognitive (quality), motivational (signaling)
Hsieh (2002)	Cross-country consistency in branding strategy	Brand image cohesiveness	Product (auto) attributes
Hudson et al. (2016)	Interplay of consumer–brand relationships and social media	Engage with brand via social media	Brand relationship quality, uncertainty avoidance
Johnston et al. (2018)	Global effectiveness of social media advertising	Attitude toward brand’s social media advertising	Credibility, value, attitude toward social media
Özsomer (2012)	Perceived BG (survey)	Likelihood of purchasing the global brand	Local brand quality perception
Özsomer and Altaras (2008)	Same brand name in multiple countries	Brand attitude (conceptual, nonempirical paper)	Quality, social responsibility, prestige, price
Raj (1985)	Cross-country consistency in branding strategy	Dummy variables for categories of levels of repeat purchasing	Number of brands in product category
Riefler (2012)	Overall globalization attitudes affecting liking global brands	Attitude and purchase intention	Attitude about global consumption
Strizhakova and Coulter (2015)	Brands’ signaling of identity and quality	Purchase local versus global brand	Ethnocentrism and global connectedness
Torelli et al. (2012)	Global brand consistency and local preferences	Brand meanings and concepts to resonate with different consumer cultures	Culture: individual (openness, self-enhancement) versus collective (self-transcendence, conservation)
Xie, Batra, and Peng (2015)	Perceived BG and localness	Favorable behavioral intention	Brand’s quality, prestige, and potential for identity expressiveness
Zhang (2015)	Domestic versus foreign country-of-origin	Country-of-origin image	Quality of firms and brands
This research	Brand preference distribution across countries in many industries; data from social media; continuous scale (instead of dichotomous scale)	Number of likes in each country; data directly from consumers in many industries; suitable for social media campaigns	Comprehensive coverage of social, economic, cultural factors; interactions of BG with brand indices (brand home country and social-signaling industry)

Next, using these two novel measures based on social media, we identify how the three-dimensional national profile (composed of cultural, social, and economic characteristics) helps us understand different patterns of brand acceptance across countries. Our identification of the three categories of influencing factors is based on motivation–opportunity–ability (MOA) theory. This theory suggests that marketers’ communication effectiveness concerning brand management is influenced by consumers’ motivation (cultural), opportunity (social), and ability (economic) to process brand information (MacInnis, Moorman, and Jaworski 1991; Petty and Cacioppo 1986). Our examination of these three broad categories is more comprehensive than most existing studies that have vetted well-focused, influential factors pertaining to culture (Banerjee 2008; Batra et al. 2017; Hudson et al. 2016; Torelli et al. 2012), global connectedness (Riefler 2012; Strizhakova and Coulter 2015), consumer perceptions (Gürhan-Canli, Sarial-Abi, and Hayran 2018; Hudson et al. 2016; Özsomer 2012; Torelli et al. 2012; Xie, Batra, and Peng 2015; Zhang 2015), or consumer networking (Gao et al. 2008; Özsomer and Altaras 2008). Table 1 highlights more detailed contributions of our research, in which we compare this article with extant studies on global/local brands and brand popularity.

From a managerial perspective, our research should prove useful to managers making global branding decisions, including (1) resource allocation within existing markets in establishing stores or forging retail alliances, (2) selections of the most promising new countries for entrance by forecasting which customer bases are most likely to embrace the brand (in part by locating similarities among current countries with satisfied customers and potential expansion territories whose citizenry may echo the profile of currently content customers in an established market country), and (3) decisions regarding whether the company takes its global brand into countries beyond the country of origin “as is” or whether the company tailors the brand for local consumer preferences. Moreover, because our data from multiple sources (including actual social media activities) comprise various industries and countries, our approach can provide managerial guidance at the industry and country levels.

Literature Review

Global Brands

In industry, popular indicators of a brand’s global status emphasize sales of the brand across multiple countries, such as indexing the number of countries where the brand is marketed. This idea has intuitive appeal—the more countries in which the brand sells, the greater its globalness (Johansson and Ronkainen 2005; Kim and Chung 1997). Nielsen (2001) considers global brands to be those with at least 5% of sales coming from outside the home region and total revenues of at least $1 billion. Similarly, the annual list of the world’s most valuable global brands from Interbrand and BusinessWeek are those with more than one-third of sales generated outside their home country, awareness beyond their home customer base, and publicly available marketing and financial data. These indicators of a brand’s status emphasize the global reach and breadth of market penetration.

By comparison, academic marketers tend to define BG with an emphasis on cross-country consistency in the branding strategy (Hsieh 2002; Raj 1985). For example, Özsomer and Altaras (2008, p. 1) define global brands as “those that have widespread regional/global awareness, availability, acceptance, and demand and are often found under the same name with consistent positioning, personality, look, and feel in major markets enabled by centrally coordinated marketing strategies and programs.” Thus, a brand is considered “global” if it maintains an active presence in multiple country markets with consistent brand positioning, typically using the same name (Özsomer 2012).

A frequent topic in the literature concerns the receptivity of consumers in a country regarding preferences for a global or local brand. Global brands can signal quality, but local brands may be better tailored for local tastes. Studies on global brand management have identified various factors influencing the success of global brands (Gupta, Pansari, and Kumar 2018; Talay, Townsend, and Yeniyurt 2015). Notably, Gürhan-Canli, Sarial-Abi, and Hayran (2018) review this literature and find research opportunities in the effects of culture on consumers and brand preferences (Banerjee 2008; Batra et al. 2017; Hudson et al. 2016; Torelli et al. 2012). Regarding the influence of culture, Banerjee (2008) finds that a close match between country culture and brand culture enhances the effectiveness of brand strategy execution significantly (Moon et al. 2016). In particular, ethnocentrism has received strong attention as part of the national culture spectrum. Specifically, consumers high on ethnocentrism can take pride in purchasing local brands (Strizhakova and Coulter 2015), whereas consumers’ global orientation positively influences their attitudes toward global brands (Guo 2013; Riefler 2012; Strizhakova and Coulter 2015).

Xie, Batra, and Peng (2015) show that both global and local brands can be successful, depending on consumer perceptions (e.g., a brand’s quality and prestige, identity expressiveness). Concerning the influence of consumer perceptions on the success of global/local brands, several studies focused on product quality (Gürhan-Canli, Sarial-Abi, and Hayran 2018; Hudson et al. 2016; Özsomer 2012; Torelli et al. 2012; Xie, Batra, and Peng 2015; Zhang 2015) or consumer networking (Gao et al. 2008; Özsomer and Altaras 2008). In contrast, Davvetas and Diamantopoulos (2018) study consumers’ feelings of satisfaction or regret when buying global versus local brands, and the role of the ease with which consumers could justify their purchases. Compared with this literature, Table 1 indicates the intended contributions of our research: our definition and measurement of both BG and brand popularity and our tests of the factors that are expected to influence brand popularity.

Recent studies have highlighted the relationship of social media and BG, as the influence of social media on consumer behavior has increased. For example, Gao et al. (2018) study types of social media use (e.g., functional or social connections) to understand differences in global brands and local effects. Johnston et al. (2018) investigate cross-cultural differences in social media advertising. Our research concentrating on BG on social media is intended to add to this new research stream.

Social Media

Research on social media abounds, given this exciting time of growth (Hansen, Kupfer, and Hennig-Thurau 2018). A large-scale meta-analysis demonstrates that social media is positively correlated with sales (Rosario et al. 2016). Social media activity is increasingly recognized as an important firm asset (Kumar 2018), proving to be beneficial in new product launches (Gelper, Peres, and Eliashberg 2018) and in choosing brand alliance partners (Kupfer et al. 2018).

Many marketing scholars observe a self-reinforcing feedback loop that strengthens consumers’ perceptions of and attitudes toward brands in terms of their online engagement with various social media brand platforms. For example, John et al. (2018) show that consumers who are fond of a brand are more likely to endorse it on social media. In addition, Peng et al. (2018) show that the mutual interconnectedness of users is self-reinforcing, boosting users’ likelihood of sharing content and thereby enhancing the efficacy of a marketer’s media campaign.

Marketing scholars are also beginning to delineate forms of social media, such as “owned” (purchased) or “earned” (spontaneous consumer posts). Colicev et al. (2013) show that earned social media volume boosts brand awareness and purchase intentions, whereas social media valence influences customer satisfaction. Owned social media posts are effective for brands with strong reputations, thus lending credibility to the postings and boosting purchase intentions for high-involvement, utilitarian brands.

Our study uses online social media data, taking advantage of its potential for tapping consumer perceptions. Culotta and Cutler (2016) model text data on 200 brands and three attributes (ecofriendliness, luxury, and nutrition) transmitted by consumers on Twitter. Their results show strong correlations with traditional survey data; thus, they argue for online social media data sources as timely and relevant for collecting consumer information. Klostermann et al. (2018) show how to extract and analyze data from online social networks in multiple media formats, including images, texts, and social tags.

Next, we define our central construct measures and show how they are derived in a straightforward manner from online social media sites such as Facebook. After describing the measures, we present our research hypotheses.

Conceptual Foundations and Hypothesis Development

Many factors may influence brand popularity on social media. Therefore, to be systematic and orderly, we categorize those factors, theorizing such relationships into four groups: (1) brand characteristics, (2) social access, (3) economic abilities, and (4) cultural dimensions. Our focus is on individual countries across various industries. Thus, we develop our hypotheses at the country level.

Brand Popularity

We consider brand popularity and combine the literature streams of global brands and social media to determine the extent of consumer receptivity and preferences for global brands through social media. Brand popularity has been defined as the extent to which a brand is widely sought after and purchased by the general population (Kim and Chung 1997). Brand popularity is considered the accumulation of market acceptance and brand goodwill over time. It is a relatively long-term concept that does not significantly change in the short run. Conversely, market sales is a relatively short-term concept that fluctuates periodically in response to marketing actions, such as price cuts and heavy promotions. Likewise, Raj (1985) finds a positive relationship between brand popularity and loyalty, drawing on over 900 product categories. Brand popularity has a strong presence on social media, which influences the commercial success of the brand. A long-term brand fan base on Facebook is formed over years and can be regarded as the accumulation of marketing acceptance and brand goodwill over time (De Vries, Gensler, and Leeflang 2012).

Given our interest in social media and a brand’s global fans (and to be consistent with the literature), we define BG as a brand’s social media fans distributed across countries. If a brand is liked by customers in more countries and in a more proportional manner, it is considered global. For example, we show that the Facebook popularity of McDonald’s comes from over 20 countries, without a great deal of dominance from a few countries, whereas Walmart is a brand that is liked mostly in two countries (the United States and Mexico). Our measure captures that difference, representing Walmart as more local than McDonald’s, in spite of its large sales. As a brand operates in more countries, the brand manager should adopt better-coordinated and more-refined branding strategies to be successful in the brand’s operation countries.

One reason that marketing managers struggle with the globalization, glocalization, and localization questions is that their decisions have strong consequences for their brands. In particular, a global brand frequently faces fierce challenges from local brands, which are more culturally customized for local consumers (Moon et al. 2016; Zhang 2015). Therefore, for many consumers in any country, a decision between purchasing a global versus a domestic brand heightens the desire to support their domestic brands. Given that the global brand is less customized to the country, BG seems to lower its CBP. For example, Japanese consumers choosing between two comparable TVs frequently choose the domestic Sony brand over the global best-selling imported Samsung brand.

Banerjee (2008) argues that brand managers who desire to establish a lasting bond with consumers must do so by identifying the unique aspects of the country’s cultural heritage and by skillfully fitting the brand into that culture to be accepted by the local consumers. According to cultural discount theory, a product rooted in one culture will be less attractive elsewhere because foreign consumers will find it more difficult to appreciate the unique styles and values embedded in the product (Lee 2006).

Generally speaking, to make the global brand successful in individual countries, brand managers must comprehensively consider social, economic, and cultural variables. As the global product expands into more countries and becomes more global, it needs to enter less successful countries with different profiles after exhausting easy-target countries with similar characteristics to those of the brand’s home country (Elliott and Cameron 1994). For example, drinking coffee at Starbucks in South Korea carries an element of enjoying something associated with American culture for the Korean consumer. However, Starbucks nevertheless faces strong competition from local coffee shops with domestically invested channel power. Generally, when a brand is in a large number of countries, it is more difficult to adapt it to numerous different local environments.

There are many reasons for consumers to prefer a local brand, such as ease of access and distribution, lower costs (which are translated into lower prices), products manufactured to suit peculiar local tastes and preferences, and nationalism and pride in one’s own country and its products (Davvetas and Diamantopoulos 2016; Hsieh 2002). Yet some consumers do not care as much about a product’s country of origin (Riefler 2012). Global/local tensions exist, even when global brands are considered of higher quality because the products are not tailored to the local market. Local brands appear to be more meaningful and authentic in unique ways, given that consumers use those brands to signal aspects of their self-identity (Strizhakova, Coulter, and Price 2011).

Furthermore, the variety of numerous local brands dominating global brands in distribution opportunities within a given national market often allows local brands to have an advantage with local consumers. From a brand manager’s perspective, in terms of expanding a brand into more country markets, the manager will first select the best countries where the brand can be most successful. Next, the brand manager will consider more challenging markets. Therefore, as a base reference point for further hypotheses in the next section, we investigate the following:

H₁: As a brand expands into more countries (i.e., increases the brand’s global distribution), the average country-specific brand popularity in the brand’s operation countries diminishes.

This hypothesis reflects a relationship stating that some brands (countries, industries) begin with high BG acceptance, whereas others are more valued in their local, tailored form. To accommodate real-world complexities, in our model we show how this basic relationship is modified by the country’s social, economic, and cultural characteristics.

Brand Characteristics: Brand Home Country and Social-Signaling Brands

The country-of-origin effects on consumer responses are well documented in international marketing research (Peterson and Jolibert 1995). The country-of-origin construct is known to be composed of multiple dimensions, such as the country of manufacture, parts, design, brand, and corporation ownership (Pharr 2005).

Following this research focus, we compare how favorably brands are received between their home countries and other countries. Most global brands have strong customer bases in their home countries, given that they are initially created to meet the preferences of their own cultures. Those brands are then often dispersed into other countries, based on their own economic and cultural strengths (Tellis, Stremersch, and Yin 2003). Thus, brands are expected to be received more positively in their home countries than in foreign countries (Lee 2006). Moreover, domestic brands often have strong distribution power in their own countries. The local origin of global brands also provides a price premium for those brands (Winit et al. 2014). Therefore, we expect the brand home country to accept its own domestic brand more favorably than foreign countries.

The positive effect of the home-country brand may be weakened for global brands because they are designed to appeal to multiple countries with various cultures and tastes, compared with local brands, which are predominantly focused on domestic markets. Thus, as global brands expand into more countries, they may lose some of their strong connections with their home consumers (Gürhan-Canli, Sarial-Abi, and Hayran 2018). Brand management is usually simpler and more efficient for local brands that serve only a few countries sharing similar cultural characteristics. In particular, the interaction effect of BG and the brand’s home country on CBP needs to be examined with more general data covering various countries and products. Thus, we propose the following hypotheses:

H_2a: Brands tend to be more popular in their home countries than in other countries.

H_2b: High BG tends to weaken the positive effect of the brand home country on CBP.

Davvetas and Diamantopoulos (2018) maintain that little is known about how product category influences consumer choices between global and local brands. Thus, we hypothesize the difference in brand popularity between social signal product categories (e.g., automobiles, clothing, travel) and other categories (e.g., banks, chemicals, health care). In this context, consumers may consider the brands they consume as a social signal (Strizhakova and Coulter 2015). Because consumers are conscious of how they are perceived by others, they are more likely to express their consumption experiences with social-signaling products that are more visible to other people (Talay, Townsend, and Yeniyurt 2015). Such social signaling can often be considered the symbolization of consumers’ social statuses; as a result, social-signaling products are expected to have heightened popularity on social media.

From a schema theory perspective, Davvetas and Diamantopoulos (2018) show that in the social-signaling product categories consumers use for identity construction, global brands have an advantage over local brands. Other scholars have similarly found effects for social signaling, such as Strizhakova and Coulter (2015). They show that local purchases decreased with the consumer’s perception of global connectedness, as consumers regard global brands as passages to global consumer culture. Similarly, consumers perceive BG as an identity currency that promotes their self-image as modern and cosmopolitan (Strizhakova, Coulter, and Price 2011). Thus, we posit the following hypotheses:

H_3a: Brands in social-signaling product categories tend to be more popular than brands in other product categories.

H_3b: High BG tends to strengthen the positive effect of social-signaling brands on CBP.

Social Access and Economic Abilities

Consumer research shows that marketers’ communication effectiveness is driven by consumers’ motivation, opportunity, and ability to process brand information (Petty and Cacioppo 1986). Enhanced levels of brand information processing are desired to evoke enduring brand attitudes and memories, thereby leading to more effective communication results. First, motivation is defined as consumers’ desire or readiness to process brand information they receive or encounter. We posit that motivation can be partly captured by cultural factors, as different cultural orientations motivate consumers differently to process various parts of brand-related information. We introduce the national cultural dimensions in the next section.

Next, opportunity is defined in our research context as the extent to which consumers can access brand information. In particular, consumers need to access social media to become familiar with available brand information. Because much of social media is in English, more nationals speaking English suggests that more people can access that brand information. In addition, with respect to online social media participation, presumably higher internet and cell phone penetration rates indicate greater opportunities to have such brand information.

Finally, ability refers to consumers’ skills or proficiencies in interpreting brand information. The availability and accessibility of brand-relevant knowledge structures provide the foundation for processing ability (MacInnis, Moorman, and Jaworski 1991). In our research context, ability can be seen as the economic capabilities that allow more nationals to have sufficient leisure time for social media engagement and to have sufficient discretionary income to purchase certain products (Moon et al. 2016; Tellis, Stremersch, and Yin 2003). In terms of economic indicators, we consider per capita gross domestic product (GDP) and trade indices. We also consider the economic equality level of the country because it should indicate that more nationals can engage in social media as a basic economic necessity. Thus, a lower Gini value (representing higher equality) should result in the potentiality that more people could enjoy the economic benefits of social media (economic accessibility).

Both social and economic factors should contribute to consumers’ perceptions of brand popularity. Gao et al. (2018) delineate the global and local effects of international social media brand–user ties as a company relational resource—some ties seemed social in nature (e.g., following a celebrity’s brand endorsements), whereas other ties were more functional (e.g., meeting consumers’ informational needs). Gupta, Pansari, and Kumar (2018) use cross-cultural dimensions and country economic indicators to understand how customer engagement is related to customer expectations and satisfaction. We hypothesize the following:

H₄: Countries with better social access tend to show higher CBP.

H₅: Countries with stronger economic abilities tend to show higher CBP.

National Cultural Dimensions

Next, we discuss how BG combines with culture to affect CBP on social media. Cross-cultural differences have been studied in numerous domains of consumer research, including product ownership and usage, brand loyalty, retail formats, and media usage (Park and Rabolt 2009). This research provides evidence that even a brand with constant positioning may be perceived differently in various cultures. Importantly, an international rollout is more likely to be successful if it is pursued in a country with a similar context as one in which the brand is already performing well (Roth 1995). Thus, we propose hypotheses regarding the effects of national culture on social media brand popularity in individual countries. The overarching theoretical framework consists of answering the question, “How popular will my brand X be in country Y, given its cultural profile Z?”

We recognize that there are multiple cross-cultural frameworks measuring national cultures (Chhokar, Brodbeck, and House 2008). We decided to proceed with Hofstede’s six national cultural dimensions (power distance, individualism, masculinity, uncertainty avoidance, long-term orientation, and indulgence) because of their vast research coverage to enhance comparisons and research contributions (Song et al. 2018). In addition, the country profiles are readily accessible (www.geert-hofstede.com), which enhances the ease with which others may adopt our approach.

We begin with power distance, which involves the extent to which society members accept and expect that power is distributed unequally (Hofstede, Hofstede, and Minkov 2010). High-power-distance societies (e.g., India, Indonesia, Malaysia, Mexico, Singapore) are more autocratic and hierarchical, and their members accept differences in power and wealth more readily than in low-power-distance, egalitarian societies (e.g., Denmark, Germany, Sweden, the United Kingdom, the United States). Because of their acceptance of existing authority, high-power-distance-society members are more reluctant to try something new with an unestablished authority (Yaveroglu and Donthu 2002). This characteristic may lead high-power-distance members to stay with familiar local brands and resist new imported foreign brands. Thus, we hypothesize:

H_6a: Cultures with greater power distance tend to show lesser CBP.

In individualistic cultures (e.g., Australia, Canada, Hungary, Italy, the United Kingdom, the United States), individuals think and behave independently of one another, whereas for collectivistic cultures (e.g., Bangladesh, China, Singapore, South Korea, Taiwan, Thailand), group membership mutually obligates individuals (Oyserman, Coon, and Kemmelmeier 2002). In collectivistic societies, an individual’s identity may be determined more by one’s role in interpersonal relationships than by personal achievement, as in individualistic cultures. Thus, collectivism is likely to enhance brand popularity in two ways: (1) the stronger online connectivity of collectivistic societies may increase online activities, leading to greater brand popularity, and (2) individuals may like a reigning popular brand because they are connected with others who already like the brand and thus experience social pressures to conform to group norms (McGrath and O’Toole 2014). Therefore, we hypothesize the following:

H_6b: Individualistic cultures tend to show less CBP than collectivistic cultures.

In masculine societies, gender roles are distinct: men are expected to be assertive, tough, and focused on material success, while women are expected to be modest, tender, and concerned with quality of life (Hofstede, Hofstede, and Minkov 2010). High-masculinity cultures tend to be more competitive (e.g., Germany, Iraq, Italy, Japan, Venezuela), whereas low-masculinity cultures value consensus seeking (e.g., Brazil, Denmark, the Netherlands, Sweden, Thailand) to a greater extent. In feminine cultures, supporting popular brands may contribute to appeasing societal attitudes instead of trying to promote lesser-known brands.

Furthermore, brand attitudes may converge toward supporting and strengthening already-popular products, in part because there would simply be greater access to and experience with the existing popular brands. In contrast, some consumers in masculine cultures may wish to embrace a variety of brands, with different opinions “competing” for the support of these various brands. Thus, as more brands compete, the CBP for an individual brand in masculine countries tends to weaken. We hypothesize:

H_6c: Masculine cultures tend to show less CBP than feminine cultures.

Uncertainty avoidance involves the degree to which the society’s members attempt to cope with anxiety by reducing uncertainty (Hofstede, Hofstede, and Minkov 2010). Cultures high in uncertainty avoidance (e.g., Belgium, Chile, Japan, South Korea, Spain) can be more tentative in decision making, requiring greater information before making choices, compared with cultures lower in uncertainty avoidance (e.g., Denmark, Hong Kong, India, Malaysia, Singapore).

High uncertainty avoidance in evaluating brands can increase brand-based social media activities when consumers attempt to reduce uncertainty by exchanging information with others. Johnston et al. (2018) study cross-cultural differences in social media advertising and, in high-uncertainty-avoidance cultures, find a heightened impact of infotainment and credibility on social media advertising value. McGrath and O’Toole (2014) find that high uncertainty avoidance tends to motivate joint problem solving and cooperation, which can induce more interactions among like-minded people. Friends, family, and coworkers would have more experience with popular brands. Thus, the apparent risk in purchasing brands would be reduced, compared with the uncertainty and associated discomfort that would be experienced with less popular brands. These tendencies with high uncertainty avoidance are likely to enhance online brand popularity. Therefore, we hypothesize:

H_6d: High-uncertainty-avoidance cultures tend to show greater CBP.

Optimal marketing choices are partly a function of consumers’ time horizon orientation. A long-term orientation in buyer–seller relationships would heighten mutual dependence and trust, which in turn should strengthen long-term business relationships (e.g., Germany, Indonesia, Japan, Russia, South Korea, Switzerland, Taiwan) (Nevins and Money 2008). By comparison, short-term perspectives concentrate on the present and involve exchanges more typical of transaction-based relationships (e.g., Australia, Finland, Mexico, South Africa, the United States) (McGrath and O’Toole 2014).

Given this distinction, we expect that the immediacy of time (in a short-term-oriented culture) may be analogously experienced as immediacy, based on proximity, thereby leading to more favorable attitudes toward and support for incumbent popular brands. This mechanism makes agreeing on popular brands easier, thus contributing to a heightened CBP. We test whether engagement with social media may be more active for short-term-oriented societies because such activities generate immediate results and responses (without clear practical long-term commitment or associated benefits). Accordingly, we hypothesize:

H_6e: Long-term-orientation cultures are likely to show less CBP.

Finally, as Hofstede’s research continues to develop and expand, cross-cultural dimensions have been added to the framework. One new and still understudied dimension is indulgence. High-indulgence cultures (e.g., Venezuela, Australia, Canada) allow the relatively free pursuit of basic and natural human drives pertaining to enjoying life, whereas high-restraint cultures suppress such pursuits of human needs and control them by strict social norms (e.g., Egypt, India, South Korea) (Thampi, Jyotishi, and Bishu 2015). Thus, we anticipate the following:

H_6f: High-indulgence cultures are likely to show less CBP.

Data and New Measures

To test the hypotheses, we collected social media–based brand information, as well as countries’ social, economic, and cultural variables from multiple sources, as summarized in Table 2 and Web Appendix Table A1. Using the data descriptions, we specify how two key variables, BG and CBP, are measured in a novel way in this research.

Table 2.

Summary Statistics of Variables.

Variable	Scale	Data Source	Mean	SD
Dependent Variable Components
Brand-specific local fans	# of likes	socialbakers.com	281,041	845,227
Country Facebook penetration	%	allin1social.com	42.0	15.7
Country population	thousand	World Bank	79,881	181,453
Brand Variables
BG	0∼1	socialbakers.com	.36	.35
Number of countries for brand	1, 2, 3,…	socialbakers.com	11.7	7.5
Brand home country	Binary	Facebook, Wikipedia	.14	.36
Social-signaling industry	Binary	Researchers’ judgments	.62	.44
Social Media Accessibility Variables
English speakers	%	Wikipedia	36.6	31.8
Internet penetration	%	World Bank	60.8	22.3
Cell phone penetration	%	World Bank	120.14	29.9
Economic Variables
Per capita GDP	USD	World Bank	23,612	19,646
Trade	% of GDP	World Bank	91.9	80.3
Gini	0∼100%	World Bank, CIA	37.6	9.2
Cultural Variables
Power distance	0∼100	geert-hofstede.com	62.4	20.4
Individualism	0∼100	geert-hofstede.com	43.6	24.2
Masculinity	0∼100	geert-hofstede.com	52.2	17.2
Uncertainty avoidance	0∼100	geert-hofstede.com	66.5	22.8
Long-term orientation	0∼100	geert-hofstede.com	44.6	22.7
Indulgence	0∼100	geert-hofstede.com	52.4	21.8

Notes: Brand home country = 1 if the brand is in its home-country market, and 0 otherwise. Gini: 0% = perfect equality, 100% = maximal inequality.

The measures of BG and CBP are managerially useful because they provide information that is complementary to extant measures. Financial asset information may not be available, depending on the firm’s status as public or private. Even when such information is available, it is frequently attributable to the whole firm across all country markets, but is not attributable to specific brands in individual countries. As a result, marketing managers may have access to their own brand data, but not their competitors’ data, thereby making brand comparisons difficult.

There are brand-ranking service providers, such as Interbrand and Brand Finance; however, most brands are not large enough to garner the attention of such brand-ranking systems. Even if marketing managers may approximate their likely Interbrand value, they may not have sufficient resources to estimate the value of competitor brands to understand those brands’ relative strengths. In addition, brand valuators tend not to provide brand assessments per country.

Furthermore, while valuation approaches are obviously useful, they do not incorporate social media–based information directly from consumers. Marketers believe that online endorsements are valuable, and they spend significant amounts of resources in managing their social media webpages. Indeed, Coca-Cola and Disney have surpassed 100 million fans, and Apple achieved 11 million “likes” for merely posting a photo of a new iPhone. These numbers contain information that is different from financial and brand valuations, and we use that information in our new BG and CBP measures.

Given their viral power, social network sites are recognized as powerful marketing communication tools for managers to maximize brand exposure and create dialogues with consumers (Lobel, Sadler, and Varshney 2016). As a result, companies invest heavily in social media to foster relationships between their brands and customers, and consumers appear to be responding—some brands show strong popularity through millions of brand fans (Facebook) or positive brand reviews (Amazon). De Vries, Gensler, and Leeflang (2012) find that more than half of social media users follow some brands on social media. Thus, social media outlets and brand community websites constitute excellent vehicles for brand managers to strengthen their connections with consumers. Therefore, our measures of BG and CBP are based on social media indicators.

Measuring BG

We measure BG for brand b, BG_b using the normalized Herfindahl–Hirschman Index (HHI) (Hirschman 1964). This index is a statistical measure of concentration. It has high visibility as a statistical index due to its use by the Department of Justice and the Federal Reserve for identifying near-monopolies and other antitrust issues in analyses involving the competitive effects of company mergers. Moreover, it is used to measure the concentration of income across households or the market concentration in various industries. We extend this popular measure to the degree of concentration of country-specific local fans across countries for a brand. This extension is consistent with our earlier conceptual definition of BG as “the extent to which a brand is popular in many countries.” More specifically, the raw HHI for brand b is computed using the following equation:

H H I_{b} = \sum_{i = 1}^{N_{b}} {(s h a r e_{b_{i}})}^{2},

where $s h a r e_{b_{i}}$ is the share of country i for brand b, and N_b is the number of countries included for the brand. To understand HHI, it is important to note that the share term is squared (share²); thus, a greater weight is placed on a larger share.

Consider an example of an entirely local brand, for which one country has the entire share, 100%; then $s h a r e^{2} = {1.00}^{2}$ , or $H H I = 1.00$ (indicative of high concentration as a perfect local brand), compared with a brand with even shares across, say, four countries; then each $s h a r e^{2} = {.25}^{2}$ , for a sum of $H H I = 4 (.0625) = .25$ (indicative of a broader share distribution as a more global brand). Thus, greater values of HHI reflect greater concentrations, as more local brands and lesser values of HHI reflect a spread of brand fans across more countries as more global brands. Next, given that HHI_b depends on the number of countries in each brand (which varies across brands), we normalize HHI:

\begin{array}{l} N H H I_{b} = \frac{(H H I_{b} - 1 / N_{b})}{(1 - 1 / N_{b})} for N_{b} > 1, \\ = 1 for N_{b} = 1. \end{array}

The normalized index NHHI is now comparable across all brands, irrespective of the varying numbers of countries for individual brands. Finally, note that the higher the index, the less global the brand is (i.e., the more concentrated it is). Thus, for convenience, we transform NHHI to $B G_{b}$ , such that $B G_{b}$ ranges from 0 (perfect brand localness) to 1 (perfect brand globalness), as follows:

B G_{b} = 1 - N H H I_{b} .

Web Appendix Figure A1 displays a histogram of BG represented in our data, which demonstrates that this measure produces a well-balanced distribution of BG. There are a relatively high proportion of extremely local brands and a relatively high proportion of extremely global brands at both ends of BG. These modes reflect the typical reality that some brands are popular in one or two countries only (for local brands), whereas other brands are truly popular in many countries proportionally (for global brands).

Brands that are high or low on BG are listed under the plot; for example, France’s Peugeot, India’s Tata Nano, and Japan’s Yamaha are relatively domestic, whereas BMWs, Corvettes, and Hondas are relatively global brands on social media. Similarly, in retail contexts, fans for store brands such as Macy’s and Sam’s Club are relatively concentrated in their home countries, whereas brands such as IKEA, Marks and Spencer, and Tesco have online fans in many countries (through distributional access, brand reputation, and tourism).

CBP as the Outcome Variable

In our research, the main outcome variable is CBP. The goal is to ascertain a brand’s online popularity in a given country in response to our knowledge of the brand’s globalness as well as other predictors. We define the popularity (p) for brand b in industry i in country c, $p_{i b c}$ as follows:

p_{i b c} = \frac{o f l o c a l f a n s i n c o u n t r y c f o r b r a n d b i n i n d u s t r y i}{(c o u n t r y c^{'} s p o p u l a t i o n) \times (F a c e b o o k p e n e t r a t i o n i n c o u n t r y c)}

The denominator has two country-specific components (population and Facebook penetration) to indicate the potential maximum number of fans who may like any brand in the given country c. This adjusted proportion measure ( $p_{i b c}$ ) allows for a direct and fair comparison of the brand popularity across countries.

Notably, although CBP and BG are somewhat related, given that both draw on social media data, the two measures are conceptually and operationally distinctive. In Equation 3, BG is built on an index of brand shares across countries; therefore, the globalness of a brand is constant across countries. That is, Nike has one BG score but a different CBP score for each country. In Equation 4, CBP is constructed on the basis of a brand’s fans within a country and is adjusted by the Facebook indexing for each country separately and, therefore, varies across countries. In our data, BG and CBP are modestly negatively correlated ( $r = - .23$ ). This level of correlation implies that the two measures are different from each other.

Table 3 illustrates how BG and CBP function. The table depicts three iconic U.S. brands and demonstrates how our social media measures offer new information that is not redundant with simple brand valuation figures. The BG row contains the measure of BG for each brand. The long lists of countries with substantial fans for McDonald’s and Starbucks show how our BG index conveys the extent to which these brands are popular in many countries—the BG values are high for both brands. By comparison, the short list for Walmart translates into a low BG value, despite its gigantic sales as the biggest company in the world.

Table 3.

Illustration of BG and CBP.

	Variable:
Facebook	Global fans	55,380,902	38,063,705	34,963,808
	BG^a	.98	.81	.30
	CBP^b
	U.S.	.058	.095	.165
	Mexico	.036	.042	.011
	Brazil	.082	.005	–
	France	.028	.018	–
	Malaysia	.138	.045	–
	Philippines	.080	.057
Data from Facebook and socialbakers (2015)	Local fans: country distribution (%)	United States: 20%Brazil: 11%Germany: 5%Argentina: 4%Malaysia: 4%Philippines: 4%India: 3%Italy: 3%Mexico: 3%Poland: 3%United Kingdom: 3%Australia: 2%France: 2%Thailand: 2%Turkey: 2%Taiwan: 2%Venezuela: 2%Colombia: 1%Portugal: 1%South Korea: 1%Spain: 1%	United States: 47%Mexico: 7%Philippines: 6%Germany: 3%United Kingdom: 3%Canada: 2%France: 2%Indonesia: 2%Malaysia: 2%Taiwan: 2%Argentina: 1%Brazil: 1%India: 1%Italy: 1%Peru: 1%Spain: 1%Turkey: 1%	United States: 92%Mexico: 2%
Data from Facebook and socialbakers (2015)	Raw HHI	.06	.24	.85
Forbes data: World’s Most Valuable Brands (2014–2015)⁴	Brand value	$39.5 billion (6)	$10.5 billion (52)	$24.7 billion (20)
	Sales	$27.4 billion (345)	$17.0 billion (572)	$485.7 billion (1)
	Profit	$4.8 billion (124)	$2.5 billion (275)	$16.4 billion (18)
	Market cap	$99.8 billion (87)	$55.8 billion (126)	$261.3 billion (12)

^a BG = 1 – normalized HHI, a measure of country distribution, ranges from 0 (perfect brand localness) to 1 (perfect BG), defined in the text.

^b CBP = Number of country brand fans, adjusted for population and Facebook penetration.

^c Each number in parentheses indicates the brand’s rank on that variable.

The CBP row of Table 3 contains information on CBP and the number of a brand’s fans on Facebook in a given country, adjusted for the country’s population and its Facebook penetration. Several countries are listed for illustrative purposes. For example, the CBP indices for McDonald’s indicate that Malaysians enthusiastically embrace the brand, perhaps in part because McDonald’s was among the first fast-food chains to be halal certified; such an approval rating is important to Malaysia’s majority-Islamic population. In contrast, consumers in France may prefer their own renowned cuisine, which is not sufficiently represented on the McDonald’s menu to generate a great deal of online enthusiasm.

The final rows of Table 3 refer to the financial indicators for the brands. These figures (extracted from Forbes) indicate that Walmart is 18 times larger than McDonald’s in sales, while its brand value (i.e., net present value of estimated future cash flows attributable to the brand, per brandirectory.com/glossary) is approximately two-thirds that of McDonald’s. Even Starbucks, whose sales are only 4% of Walmart’s, has 42.5% of the brand value of Walmart. These company profiles indicate that our measure of BG adds information beyond a company’s financial performance indicators. Financial measures are obviously long established and are not the focus of the current research. However, what is important is that the variability in the brand popularity indices across brands and countries holds, independent of such financial indicators. This aspect clearly indicates that our measures of BG and CBP reflect something different from, and information that is complementary to, existing measures for brands.

Thus, based on our evaluation of the concepts and measures of BG in the relevant literature, we proceed with the social media index of BG computed as a function of a brand’s fan distribution across countries. Conceptually, if the brand is liked by many countries in a proportional manner, it is considered global. For example, the Facebook popularity of McDonald’s comes from fans in many countries (i.e., its overall Facebook popularity is not dominated by a few countries). Therefore, this brand is considered global. By contrast, if the brand is liked mostly by people from a few countries, the brand is considered local, such as Walmart, whose Facebook popularity is mainly from fans in only two countries.

Social, Economic, and Cultural Variables

Using socialbakers.com, a social media analytics website, we extracted data on the top 100 brands on Facebook in terms of the number of fans in each of the 51 industries available on the website, ranging from airlines to furniture and wellness services. For each brand included, we also obtained the brand’s number of fans in individual countries. Each brand’s fan distribution across countries allowed us to compute BG and CBP. Our coverage of the top 100 brands in the 51 industries resulted in 50 countries (see Web Appendix Table A1). This wide and deep selection of our data helps us produce generalizable results, applicable to many industries and countries.

We selected Facebook as our primary source of brand data because it has the most complete and widely reaching brand information as the most popular social media website. It is not unusual to see a focus on Facebook in marketing journals (Beukeboom, Kerkhof, and Vries 2015), in part due to its size and associated influence: as of the third quarter of 2018, Facebook had 2.3 billion monthly active users (Statista 2018). Furthermore, in our data, the number of a brand’s global fans on Facebook is positively correlated with the brand value ( $r = .35, p = .004$ ) and corporate value ( $r = .31, p = .016$ ) numbers provided by Brand Finance. These mildly positive correlations suggest that our BG and CBP measures matter compared with typical brand and financial valuation by providing complementary information to these primary financial indicators.

To understand how BG plays out in explaining CBP, we used the top 100 Facebook brands in each industry, which naturally comprised a mixture of global and local brands varying in BG, as demonstrated in Web Appendix Figure A1. The average number of local (i.e., country-specific) brand fans was approximately 281,000. The unit of analysis in the model presented in the following “CBP Regression Model” subsection is the level of a brand–country combination (N = 23,122). That is, as in Table 3, we obtain the popularity indices for brand X in country Y. There are multiple observations per brand X (across countries) and multiple observations per country Y (across brands). The brand X and country Y structure applies to each of the 51 industries (Z) in our data. Thus, our modeling takes into account this multilevel data structure.

In addition, we obtained brand variables, social media accessibility variables, economic variables, and cultural variables. Brand variables include BG, number of countries per brand, brand home country, and social-signaling industry. Social media accessibility variables are opportunity variables, according to the MOA theory framework. The proportion of English speakers was included, given that English is the dominant language used on Facebook and many other social media sites. Internet and cellular phone penetration variables were included, as these are two primary routes through which people access Facebook and other social media sites.

Our economic variables are ability variables, according to MOA theory. We included three commonly used national economic indicators: per capita GDP, trade, and the Gini coefficient. Improved national economic conditions allow more nationals to enjoy increased leisure time with more discretionary income, which suggests expanded active social media activities in the country. Finally, each country’s cultural profile from www.geert-hofstede.com refers to various motivation variables (according to MOA theory), given that different cultural orientations may provide different motivations for engaging in social media activities. For example, the strengthened group dynamics of collectivism may motivate increased social media activities, compared with individualism (McGrath and O’Toole 2014). Our data include all six national cultural dimensions from the website: (1) power distance, (2) individualism, (3) masculinity, (4) uncertainty avoidance, (5) long-term orientation, and (6) indulgence.

Empirical Analysis and Results

CBP Regression Model

To test the impact of BG and the national culture dimensions on CBP, we specify a model composed of the three-layer hierarchy: industry (highest level), brand, and country (lowest level) in the form of the three-level multilevel model (Bell et al. 2013):

\begin{array}{l} y_{i b c} = (α + α_{i} + α_{i b}) + β (x_{i b}, x_{c}, x_{i b} x_{c}) + ∊_{i b c} \\ E [α_{i} | x_{i b}, x_{c}] = σ_{i}^{2} \\ E [α_{i b} | x_{i b}, x_{c}] = σ_{i b}^{2} \\ E [∊_{i b c} | x_{i b}, x_{c}] = σ_{i b c}^{2} . \end{array}

The dependent variable $y_{i b c}$ is the logit function of the CBP proportion, $p_{i b c}$ , namely: $y_{i b c} = ℓ n [p_{i b c} / (1 - p_{i b c})]$ . The proportion p ranges from 0 to 1, and accordingly, the logit transformation extends the range of the dependent variable from −∞ to ∞. Regarding the parameters, α denotes the general intercept, α_i denotes the industry-level intercept for industry i, and α_ib denotes the brand-level intercept for brand b nested in industry i to which brand b belongs. Our data present two types of independent variables, x _ib and x _c. The vector x _ib contains multiple brand characteristics as independent variables (e.g., BG) nested in industry i, whereas x _c is a vector of multiple country characteristics as independent variables (e.g., national cultural dimensions). x _ib x _c indicates some interaction effects of BG and the national cultural measures. β is a vector of parameters to be estimated for both types of independent variables. The error term ∊_ibc follows the normal distribution.

Our model minimizes both observed and unobserved heterogeneity in the three levels of industry, brand, and country. The expressed brand and country variables from each industry in Table 4 reduce observed heterogeneity in the three levels. Furthermore, to account for unobserved industry- and brand-level heterogeneity, we model intercepts α_i and α_ib to effectively translate the model in Equation 5 into a random-effects model (Greene 2003). The error term ∊_ibc accounts for unobserved variations across industries, brands, and countries, whereas α_i and α_ib account for unobserved variations across industries and brands in individual industries, respectively. By contrast, the slope β is fixed across brands and countries because we intend to measure the average reactions across both brands and countries. In conclusion, our three-level multilevel model specification minimizes both observed and unobserved heterogeneity in the three levels of industry, brand, and country.

Table 4.

Estimation Results of the CBP Model.

Variable	BG Model		Benchmark Model		Hypothesis
Variable	Estimate	p-Value	Estimate	p-Value	Hypothesis
Intercept	−6.5843	<.0001	−6.9604	<.0001
BG	−1.0070	<.0001	N.A.		H₁
NCB	N.A.		−.0087	.0008
Brand home country	2.0539	<.0001	2.1858	<.0001	H₂
BG × Brand home country	−.6163	<.0001	N/A		H₂
NCB × Brand home country	N/A		−.7872	<.0001
Social-signaling industry (SSI)	.1487	.6423	.4839	.1385	H₃
BG × SSI	.2203	.0580	N.A.		H₃
NCB × SSI	N.A.		−.6604	<.0001
English speakers	.0085	<.0001	.0085	<.0001	H₄
Internet penetration	.0061	<.0001	.0061	<.0001
Cell phone penetration	.0003	.4854	.0003	.4920
Per capita GDP	.0766	.0040	.0791	.0030	H₅
Trade	.0033	<.0001	.0033	<.0001
Gini	−.0058	.0025	−.0057	.0033
Power distance	−.0027	.0003	−.0027	.0002	H₆
Individualism	−.0153	<.0001	−.0153	<.0001
Masculinity	−.0005	.5595	−.0006	.4718
Uncertainty avoidance	.0098	<.0001	.0098	<.0001
Long-term orientation	−.0100	<.0001	−.0099	<.0001
Indulgence	−.0089	<.0001	−.0088	<.0001
Brand home country (U.S.)	−.1017	.0207	−.1238	.0057
Country Market (U.S.)	−1.5068	<.0001	−1.4931	<.0001
σ_i ²	1.0694	<.0001	1.1029	<.0001
σ_ib ²	.8976	<.0001	.9491	<.0001
σ_ibc ²	1.0903	<.0001	1.0876	<.0001
AIC	67,819		67,929
BIC	67,865		67,975

Notes: N.A. = not applicable.

Hypothesis Testing Results

Before we test the proposed hypotheses, we compare our proposed model in Equation 5, which we refer to as the BG model, with a benchmark model that uses the number of countries for each brand (NCB) as the measure of BG (Hsieh 2002; Özsomer 2012), whereas our BG measure considers the proportions of brand popularity across countries. Table 4 compares the estimation results of both models and shows that our BG model fits the data better in terms of both Akaike information criterion (AIC) and Bayesian information criterion (BIC).

Although the improvement of the BG model over the benchmark model seems small, the actual improvement should be regarded as much larger, given that the AIC/BIC criteria are based on all of the variables used in both models. Thus, the pure improvement of the BG measure against the NCB measure is much larger than what the simple differences of the two models in the AIC and BIC values might suggest. Furthermore, whereas the NCB measure considers only the number of countries for the focal brand, the BG measure considers both the brand’s number of countries and the popularity distribution across the countries; that is, the NCB measure cannot capture various possible distributions across countries when multiple brands have the same number of countries.

Table 4 also provides the details of the empirical results regarding our BG model. First, the impact of BG is negative and significant, in support of H₁. This result supports the notion of a baseline effect in which a greater BG is associated with decreases in CBP. The brand home-country effect on CBP is positive, in support of H_2a, and its effect is negatively moderated by BG, in support of H_2b. Social signaling boosts CBP; as a result, H_3a is supported, and its moderation is accentuated by BG, in support of H_3b.

In testing H₄, two social access variables, English speakers and internet penetration, contribute significantly to CBP, whereas cell phone penetration does not. This finding suggests that in some countries, people may access Facebook primarily via the Internet, and not on their phones. Regarding H₅, all three economic variables (per capita GDP, trade, and the Gini index) produced the expected significant results. Specifically, the positive estimate of the trade variable indicates that national economies with open trade tend to increase CBP. The negative estimate of the Gini index variable indicates that inequalities in national economies tend to lower CBP, given that severe inequalities limit the economic activities of low-income consumers and their access to social media. These results make sense and enhance the validity of our CBP measure.

Next, turning to the effects of the six cross-cultural dimensions, five are empirically supported, with one insignificant effect from masculinity. First, countries with greater power distance are those that tend to have lesser CBP (H_6a supported). The impact of individualism is negative and significant, indicating that greater CBP tends to appear in more collectivistic cultures (H_6b supported). Masculinity has the predicted negative sign, but its effect is not significant; thus, H_6c is not supported. Uncertainty avoidance has a positively significant estimate (H_6d supported). Long-term orientation has a negatively significant estimate (H_6e supported). Finally, indulgent countries show lesser CBP (H_6f supported).

We added two variables to test the impact of the United States on CBP. As a superpower country, it has a dominant social, economic, and cultural status in brand competition. The country’s dominance in brand power was pronounced in the Brand Finance report on 500 global brands at the 2019 World Economic Forum. The United States had 20 brands on the top 50 brand list. To show how the country’s impact plays out in our model and data, we applied two variables: brand home country (U.S.) and country market (U.S.). U.S. brands turned out to be actually less popular in the home country than in other countries. This can happen when most other countries have a strong liking for U.S. brands; that is, paradoxically, the dominance of U.S. brands around the globe defies the common home-brand effect. Similarly, the negatively significant estimate of country market (U.S.) indicates that U.S. consumers show less support for brands on social media than other countries’ consumers, which can happen as more U.S. and foreign brands compete in the market. Many brand managers target the U.S. market as a key to their brands’ success in the global market. In brief, intensified competition created by more brands weakens U.S. consumers’ average preferences for individual brands in their own country.

Discussion

Theoretical and Methodological Implications

This research contributes to the global branding literature in both theoretical and methodological ways. First, from a theoretical perspective, we proposed hypotheses focused on how various factors (including BG) influence online brand popularity. Although most research has considered consumers’ brand choices or brand sales (Davvetas and Diamantopoulos 2018; Strizhakova and Coulter 2015), we focus on brand popularity on social media as a useful measure, based on the growing impact of social media on global branding strategies (Hudson et al. 2016; Johnston et al. 2018). We also focus on brand popularity in individual countries to help marketers establish effective branding strategies associated with identifying the next successful country markets in individual industries (see Table 1).

Because a country’s local-market context on social media is embedded in the country’s multifaceted profile, covering cultural, social media accessibility, and economic variables, we summarized those variables using MOA theory. This theory posits that the effectiveness of the marketer’s communication is determined by consumers’ motivation (cultural variables), opportunity (social media accessibility variables), and ability (economic variables) in processing brand-related information (MacInnis, Moorman, and Jaworski 1991; Petty and Cacioppo 1986). This rich array of predictors helps explain why a global brand may be popular in some countries (Batra et al. 2017; Torelli et al. 2012).

We found that increased BG is associated with reduced brand popularity in individual countries (not across countries). As global brands expand into more countries, they must enter more challenging countries after choosing easy target countries (Davvetas and Diamantopoulos 2016; Hsieh 2002). More challenging countries would be those with less social media access, developing nation status, and larger cultural distance from the brand’s home country. Furthermore, social-signaling products (e.g., sporting goods, jewelry, wine) tend to increase online brand popularity as consumers become conscious of showing their social-signaling products to others (Talay, Townsend, and Yeniyurt 2015).

From a methodological perspective, we conceptualized two new constructs, BG and CBP, and provided a novel and theoretically supported way to measure them. We incorporated social media indices derived from Facebook to understand which countries appreciate global or local brands. Our approach emphasizes how the popularity of the same brand is distributed across countries. If the popularity of a brand is distributed over more countries and in a more balanced way among those countries, the brand is characterized as being more global. These measures are continuous by measuring the degree of BG (0–1), whereas industry global brand rankings and most academic studies use binary classifications (global vs. local brands) (Batra et al. 2017).

Furthermore, our measurement is based on online popularity data, unlike most relevant studies, which use surveys to measure the consumer perceptions of BG. This access allowed us to collect information on the top 100 brands in each of the 51 industries across 50 countries. Such access offers a practically important advantage to any academic or brand manager who wishes to obtain comparable data. It would be practically almost impossible to collect this scope of data using existing approaches for brand evaluations by combining consumer surveys and corporate finance/accounting data. Nonpublic companies do not disclose their financial data. Even public companies do not release brand-level data in individual countries. For example, Coca-Cola, a public company, does not release sales numbers for individual brands (e.g., Dasani, Coke Zero, Sprite). While brand managers may gather necessary information on their own brands, they would not be able to collect comparable information on their competitors. Our measures of BG and CBP on social media can add these granular-level data by web scraping to supplement existing brand evaluation services.

Managerial Implications

For brands present in multiple countries, the degree of brand globalization or localization is an important strategic decision. Our research on brand popularity in individual countries can help guide brand managers to find countries where they can execute their branding strategies effectively, given the fit between the national profile and the brand. For example, to adjust their global standard products to local tastes, KFC serves Peking duck in Shanghai, and Coca-Cola produces carbonated fruit drinks in Shenzhen (Vorhauser-Smith 2012). All global brand managers face this “glocalization” tension, and the decision is frequently revisited, such as when crafting and updating any marketing communications used to support the brand in individual national markets (Hsieh 2002).

Our social media–based measure can be appropriate for this constantly flowing situation because social media instantly reflects what is happening to brands. Furthermore, managers in most industries can easily access the rich information produced by group dynamics in social media communities. To enhance the likelihood that our research could be useful to brand managers, we gathered data that are easily accessible on a representative social media platform; thus, any brand manager could similarly extract relevant data to strategize branding decisions.

A major advantage of our research is that our approach allows managers to evaluate their social media marketing strategy at the individual industry or country level, given that our data are from 51 industries and 50 countries (see Web Appendix Table A1). Our industry coverage is extensive, ranging from airlines to fashion accessories to retail, and our country coverage is similarly widely varied. Because such granular-level data are publicly available on social media (e.g., Facebook, Twitter, YouTube), our approach allows this level of analysis and is accessible to practitioners. To illustrate how the BG and CBP indices operate at the industry and country levels, we offer examples in Web Appendix Figure A2. Figure 2A, Panel A, indicates that, on average (i.e., aggregating over countries), brands such as Victoria’s Secret, Benetton, Hugo Boss, Rolex, Prada, Bulgari, Christian Louboutin, and Zara are truly global brands (at the right side of the plot), whereas the online fans of Levi’s, Tiffany, Coach, and Longchamp are more geographically concentrated (to the left) and, thus, may be characterized as more domestic. This figure shows that there is a high degree of CBP variation among global brands and among local brands, respectively.

Web Appendix Figure 2A, Panel B, shows country-level results for Victoria’s Secret and Christian Louboutin. Both are highly global brands (Panel A), yet they vary on the y-axis of CBP over countries. Panel B shows why Victoria’s Secret is a brand with greater overall CBP—it is most popular in the United States, but it is also popular in Mexico, Australia, Canada, Egypt, Germany, the Philippines, and Peru. In contrast, consumer social media support for Christian Louboutin comes from fewer countries. The bars of the CBP indices are greater overall for Victoria’s Secret; it is more popular in a greater number of countries and thus has a greater overall CBP index.

Our research is aimed at strengthening brand strategy decisions. By examining the social, economic, and cultural factors influencing brand popularity, brand managers can know which countries would require more significant brand glocalization. In countries that show greater appreciation for local versus global brands (such as in developing countries with economic inequality and low trade), the global brand manager may find it arduous and costly to achieve success. As a result, the choice may be to steer marketing efforts in a different direction.

Furthermore, our empirical analysis suggests that social-signaling products (e.g., accessories, coffee, automobiles) tend to be more successful in global markets. In promoting global brands, social media that reaches out practically everywhere can be an effective and efficient tool in international markets. Therefore, brand managers invest heavily in efforts to raise the online popularity level of their brands by encouraging their consumers to give favorable ratings or to post positive reviews about their brands on social media (De Vries, Gensler, and Leeflang 2012). There appears to be a virtuous cycle between online activities, such as consumers sharing their opinions on their favorite brands, and a brand’s level of awareness, popularity, and brand value.

Limitations and Future Research Directions

Our research has several limitations that may constrain our generalizations, and that may lead to interesting research opportunities. First, we drew data from Facebook because it is the world’s largest online social network, which heightens the relevance of our findings. However, a common criticism against Facebook is that the users are not representative, and in particular, that active users skew young and females (McAndrew and Jeong 2012). Furthermore, while Facebook has over 2 billion users, China hosts WeChat and Qzone, and other countries host their own popular networks as well (www.statista.com/statistics/272014/global-social-networks-ranked-by-number-of-users). China would be an interesting case study because it would likely allow the exploration and comparison of global and local brands in an economically and culturally unique environment. In addition, research comparing multiple social media platforms in establishing the predictors of online brand popularity would be enlightening, both across countries using cultural dimensions (as in the current research) or in a comparison of two global platforms (Facebook and YouTube, for example).

Regarding our conceptualization, a limitation might be that we had classified brands as global or local—two opposite realizations on a single continuum. Yet, some brands are successful both locally and globally. Thus, the research questions and findings might be recast in a way to allow a more continuous or multifaceted index of a brand’s globalness or localness.

Finally, as is the case with any real-world data set, we tested relationships captured via a stream of cross-sectional observations. We tried to represent and test the global branding aspects fairly in part by controlling for elements within the individual countries that are relatively stable, including their social, economic, and cultural profiles. Next steps might include conducting field experiments to establish elements of the relationships as plausibly causal. Researchers could manipulate branding profiles on social media and observe at least attitudinal responses characterizing the brands as global or local, with subsequent purchase intentions.

Supplemental Material

Supplemental Material, Web_Appendices - The Influence of Global Brand Distribution on Brand Popularity on Social Media

Supplemental Material, Web_Appendices for The Influence of Global Brand Distribution on Brand Popularity on Social Media by Moon-Yong Kim, Sangkil Moon and Dawn Iacobucci in Journal of International Marketing

Footnotes

Associate Editor

Kelly Hewett

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: They appreciate the support of Hankuk University of Foreign Studies Research Fund of 2019.

Online supplement:

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