Polarized public opinion responding to corporate social advocacy: Social network analysis of boycotters and advocators

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Polarized public opinion responding to corporate social advocacy: Social network analysis of boycotters and advocators Hyejoon Rima, YoungAh Leeb,*, Sanglim Yoob a b

Hubbard School of Journalism and Mass Communication, University of Minnesota, United States Department of Journalism, Ball State University, United States

A R T I C LE I N FO

A B S T R A C T

Keywords: Corporate social advocacy (CSA) Corporate social responsibility Boycott Twitter Social network analysis NodeXL

Despite growing attention to corporate social-political advocacy, little is known about how publics mobilize and establish relationships in social media when firms are involved in hot-button issues. Using the social network approach, this study examines a network structure which emerged around boycotting and advocating for Starbucks and Budweiser when these two brands responded to President Donald Trump’s immigration ban executive order in 2017. The study identified three unique characteristics in the boycotters’ networks. The boycotters appeared not only in the aggregated brand boycotting networks, but also in the advocators’ networks. In addition, boycotters in Budweiser and Starbucks networks were engaged in boycotting other brands or organizations which were opposed to Republicans or President Trump’s policy. Finally, the network of boycotters was very dense and highly connected among subgroups while that of advocators was sparse. Theoretical and practical implications are discussed.

1. Introduction There has been increasing attention to corporate social advocacy (CSA), which refers to a company’s public stance on salient sociopolitical issues as a way of engaging with publics and gaining legitimacy (Coombs & Holladay, 2018; Dodd & Supa, 2014, 2015). Over recent decades, corporate social responsibility (CSR) has been widely recognized as a desired business action that benefits both society and businesses (Du, Bhattacharya, & Sen, 2007; Carroll & Brown, 2018; Carroll, 1999; McWilliams & Siegel, 2001). Most recently, publics’ expectations of companies have moved beyond traditional CSR practices where a company supports non-controversial and universal topics such as education, and the eradication of poverty. Now, in light of the socially-charged climate, publics expect companies to explicitly express their stances on social and political issues (Nalick, Josefy, Zardkoohi, & Bierman, 2016; Wettstein & Baur, 2016). Recent surveys indicate that publics believe that CEOs can influence public policy and legislative decisions (Edelman, 2018; Weber Shandwick, 2018), and more than half of consumers would buy or boycott brands based on a brand’ issue position (Edelman, 2017). However, we have observed that a company’s championing of one side of a divisive issue can provoke polarized reactions. Publics may respond to it by boycotting or advocating for the company depending on preexisting beliefs (Chatterji & Toffel, 2018; Feng, 2016). ⁎

Organizations have used and benefited from social media platforms for monitoring external environments and issue management tools (Luo, Jiang, & Kulemeka, 2015). As CSA has become an integral part of corporates’ strategic issue management and legitimacy, it is crucial to understand the patterns and characteristics of relationships that the public establishes around the issues for which they advocate. Publics in the social media era not only express their opinions but take part in collective actions and social movements (Park, Lim, & Park, 2015). About two-thirds of American adults (65 %) use social networking sites (SNS) and this trend has influenced the way people share information and build relationships (b). Literature has documented the evidence of the strategic use of SNS for connecting and mobilizing publics to collective action (e.g., Choi & Park, 2014; Feng, 2016; Park et al., 2015). Social media, as catalyst and facilitator with much greater influence on various social issues, has substantially contributed to increased awareness of CSA. Simultaneously, it has stimulated publics issue engagement and mobilization (Feng, 2016). Despite growing attention to CSA, little is known about how publics are mobilized and how they establish relationships in social media. Using the social network approach, the study explores the characteristics of the network structure of brand boycotters and advocators emerging around CSA when Starbucks and Budweiser responded to President Donald Trump’s immigration ban executive order in 2017. Network analysis allowed for identification of social actors and the

Corresponding author. E-mail addresses: [email protected] (H. Rim), [email protected] (Y. Lee), [email protected] (S. Yoo).

https://doi.org/10.1016/j.pubrev.2019.101869 Received 27 February 2019; Received in revised form 24 November 2019; Accepted 25 November 2019 0363-8111/ © 2019 Elsevier Inc. All rights reserved.

Please cite this article as: Hyejoon Rim, YoungAh Lee and Sanglim Yoo, Public Relations Review, https://doi.org/10.1016/j.pubrev.2019.101869

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are desirable, proper, or appropriate within some socially constructed systems of norms, values, beliefs, and definitions” (Suchman, 1995, p. 574). Organizations strive to gain their legitimacy for continued existence in society by aligning their behavior with stakeholders’ expectations. Dowling and Pfeffer (1975) also stressed the importance of fitting corporate behavior within the social values and expectations of stakeholders. In a similar vein, Chatterji and Toffel (2018) point out that firms engage in CSA as a way of standing up for diverse stakeholders such as employees, customers, partners, the community, and their environment. Some researchers suggest that a CEO’s personal characteristics such as their political ideology (Chin, Hambrick, & Treviño, 2013), commitment to business ethics (Muller & Kolk, 2010), and even their personal need for media attention and image reinforcement (Petrenko, Aime, Ridge, & Hill, 2016) can also influence an organization’s CSA efforts. As noted earlier, CSA may provoke acrimonious debate and influences a company’s financial performance. For instance, when Starbucks or Chick-fil-A made a public stance on the topic of same-sex marriage, the organizations stepped into a controversial social debate. In their experiment study using these two brands, Dodd and Supa (2015) revealed that the congruence between the company’s stance on an issue and the consumer’s stance on an issue predicted publics’ reaction to CSA. The findings showed that publics sharing their views with the company showed a greater purchase intention, whereas publics who were against the company’s stance showed lesser intention to purchase the company’s product. The study also compared the extent to which the publics’ purchase intention was influenced by their stance aligned with or against a company’s stance, and suggested that their purchase intentions were less influenced by a company’s stance when they were supporters of same-sex marriage.

relationships among these actors embedded in networks from a holistic perspective (Danowski, Gluesing, & Riopelle, 2011; Yang & Taylor, 2015). In examining the properties of semantic and hyperlink networks for this case, the study aims at identifying the characteristics of the network structure, the pattern of interactions between users, and the political stances of users who engage with advocating and boycotting networks. Furthermore, as highlighted in Himelboim, Golan, Moon and Suto's, (2014) study, this research pays particular attention to examining the characteristics of brokers to understand the role of mediated public relations. This comparative analysis of social networks provides insights into the distinct roles of social media in network formation and how organizations position themselves in such social movements. 2. Literature review 2.1. Corporate social advocacy (CSA) CSA is defined as a planned and/or ad hoc expression of an organization’s stance on controversial social-political issues that spans boundaries between strategic issue management and CSR (Dodd & Supa, 2014). CSA consists of planned initiatives by the organization when such a public stance is made based on a strategic business decision. In some cases, this type of social advocacy can be initiated by executives’ unscripted remarks, such as a CEO’s tweet or “making an off-the-cuff remark to a journalist” regarding the controversial socialpolitical issue (Dodd & Supa, 2014; Nalick et al., 2016). Some scholars focused on corporate engagement in the political arena and conceptualized corporate political advocacy, which refers to “voicing or showing explicit and public support for certain individuals, groups, or ideals and values with the aim of convincing and persuading others to do the same” (Wettstein & Baur, 2016, p. 200). The study adopts the term of CSA, but broadly embraces the notion of corporate political advocacy given that the nature of the case study (i.e., the immigration ban executive order) touches politically charged social issues. CSA can be considered as a form of corporate advocacy, which is defined as “the research, analysis, design, and mass dissemination of arguments on issues contested in the public dialogue in an attempt to create a favorable, reasonable and informed public opinion which in turn influences institutions’ operating environment” (Heath, 1980, p. 371). While corporate advocacy is intended to influence certain issue frames to generate a publics’ favorable perception toward a company, CSA is characterized as positioning the company on one side of the values it supports by explicitly expressing its stance on controversial issues. There would be no direct benefit from voicing support of contentious issues. Rather the response may provoke polarized public reactions (Chatterji & Toffel, 2018). Previous research has documented several characteristics of CSA. First, unlike CSR, the social-political issues that the organization advocates for or against are not directly related to the organization’s core business. Second, CSA is different from a corporate political activity, so-called lobbying efforts. While the purpose of engaging in political activity is to influence government policies only to benefit firms (Hillman, Keim, & Schuler, 2004), CSA seeks to promote a specific value that is beyond the company’s immediate economic interests (Dodd & Supa, 2014; Wettestein & Baur, 2016). Similarly, although CSA is characterized by not having profit or market motivations, CSA can have direct and indirect impacts on the bottomline outcomes of organizations (Dodd & Supa, 2014; Nalick et al., 2016). Dodd and Supa (2014) argue that sociopolitical issues are controversial and can potentially isolate organizational stakeholders. Simultaneously, it may attract activist groups who relate to the issue. Nalick et al. (2016) noted that some stakeholders will view firm’s sociopolitical involvement as beneficial, while others will perceive it as “discriminatory” (p. 388). CSA is closely related to organizational legitimacy, which is defined as “a generalized perception or assumption that the actions of an entity

2.2. Publics’ response to CSA: brand boycott and brand advocacy Due to the nature of CSA, championing one side of controversial social issues may result in dividing stakeholders into polarized groups, depending on the value the public embraces. For example, when Starbucks takes its stance in support of same-sex marriage, advocators and boycotters were created simultaneously, based on the individual’s value of and view on the issue. Publics arise around specific issues, and in the context of CSA, boycotters and advocators can be considered as active publics. According to the situational theory of publics, active publics are characterized as those who have a high level of involvement with the issue, recognize the problem, and do not feel constrained from attempting to fix the problem (Grunig, 1997). Subsequently, they are active in communicative action and are more likely to organize or be part of collective actions, such as activism (Grunig, 1997; Kim & Grunig, 2011). Publics are organized around goals used to exert their power, and the movement is portrayed as consumer activism, where like-minded publics are formed as communities to facilitate their social endeavor (Schneider & Kozinets, 2011). In responding to CSA, because not all publics welcome the values the company advocates, those active publics may take part in boycotting or advocating for the brand. Ciszek and Logan (2018) analyzed public discourses on Ben and Jerry’s Facebook posts regarding the company’s support for the Black Lives Matter movement, and they found evidence of competing views represented as antagonists’ call to boycott as well as the supporters’ rallying for a boycott. 2.2.1. Brand boycott and brand advocacy Emerging technology stimulates communication and social interactions among users who share a common interest. This further develops the online community through a computer-mediated mechanism (Chang, Hsieh, & Tseng, 2013). Swimberghe, Flurry, and Parker, (2011)) noted that organizations have experienced consumer backlash as a result of decisions to support controversial causes. Consumer boycott is defined as “an attempt by one or more parties to achieve 2

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structural hole explains that because information flows more within than between groups, brokers have competitive advantages for gaining nonredundant information while controlling information flow between groups, thus benefiting from diverse ideas and resources derived from holes in the networks (Burt, 1992, Burt et al., 2013; Granovetter, 1973). The role of brokers, therefore, is of particular importance in generating social capital as it is created by a network where resources, information, and knowledge are embedded in relationships (Burt, 1992, 1999; Sommerfeldt, 2013). In a similar vein, in public relations research, Himelboim et al. (2014) highlighted the critical role of brokers as being social mediators, which refers to “communicative relationships and interactions with key social mediators that influence the relationship between an organization and its publics” (p. 361). Social mediators help organizations to reach out to publics beyond their targeted public and to establish symbolic relationships by mediating the relationships between them through social media (Himelboim et al., 2014). The types of social mediators include formal (e.g., media organizations) and informal (e.g., grassroots). Formal mediators have a formally and societally assigned role as information providers, whereas informal mediators are not associated with specific social institutions. In addition to their structural position, the roles of mediators are determined by their network centrality, which refers to the prominence of connectivity in a network, and the directionality of relationships. Himelboim et al. (2014) further explain the characteristics of the bridging hub, which is a mediator with high in-degree centrality (e.g., a large number of users who follow the actor). In the context of grassroots networks, the mediator would exhibit a high “betweenness” centrality, and therefore, has the power to spread and alter the information (Newman, 2003).

certain objectives by urging individual consumers to refrain from making selected purchase in the marketplace” (Friedman, 1985, p. 97). Consumers participate in a boycott to express dissatisfaction with a company or government policies and behaviors (Shaw, Newholm, & Dickinson, 2006) and to resolve the ethical problems they encounter (Klein, Smith, & John, 2004). Past research argued that boycotters threated corporations by exerting negative word-of-mouth to harm the corporate reputation (King, 2008; Whetten & Mackey, 2002) and to reject the organization’s product and brand (Swimberghe et al., 2011). The organizers for the boycott movement can be individuals as well as activist groups or organizations (Hestres, 2013; Nalick et al., 2016; Park et al., 2015). Scholars noted that publics’ increased attention to CSR further stimulated boycott movements (Klein, et al., 2004). By promoting boycotts, consumers express their disagreement with a company’s policy and attempt to influence change or modify the target company’s behaviors (Sen, Gurham-Canli, & Morwitz, 2001; Yuksel, 2013). Studies have also suggested internalized motivations for boycott participation, such as maintaining self-esteem, enhancing self-identity and sense of belonging, and moral self-realization (Farah & Newman, 2010). Swimberghe et al. (2011) particularly focused on investigating how religion motivates consumer activism. They found that Christian conservatism predicted consumer activist behavior. The study found that the stronger the consumers held conservative beliefs, the more negatively they evaluated the company that supported issues or causes in conflict with their values (Swimberghe et al., 2011). Consumers may organize collective actions in SNS, not only to boycott a brand, but also to express their support for a brand. Muniz and O’guinn (2001) introduced the concept of brand community, which refers to “a specialized, non-geographically bound community, based on a structured set of social relationships among admirers of a brand.” (p. 412) Brand community is characterized by shared consciousness, rituals and traditions, and a sense of moral responsibility (Muniz & O’guinn, 2001). These characteristics explain how advocators created social networks in response to CSA. In particular, Muniz and O’guinn (2001) described a sense of moral responsibility as an element that generates collective action and contributes to group cohesion. Scholars provide the evidence of brand community in computer-mediated environments (Chang et al., 2013; Muniz & O’guinn, 2001).

2.4. President Trump’s immigration ban of 2017, CSA and social networks In 2017, about a week after inauguration, President Trump signed an executive order restricting entry into the United States for citizens from seven Muslim-majority nations. The travel ban sparked fierce opposition and outrage. People rallied in U.S. cities and at airports to voice outrage over President Trump’s executive order, and companies challenged the immigration ban. While major American companies’ reaction to the ban ranged from silence to challenging, some technology and retail companies were most vocal opposing the travel ban (Abrams et al., 2017). Soon after the order was signed, Twitter co-founder and CEO Jack Dorsey tweeted, criticizing its humanitarian and economic impact. Linked-In CEO, Jeff Weiner, also committed to creating economic opportunity regardless of ethnicities and nationalities. Some companies, such as Lyft and Google, committed to supporting immigrant-rights organizations, while Airbnb offered free housing to people affected by the travel ban. Starbucks was one of the companies that jumped in, giving voice to this politically-charged issue. CEO Howard Schultz denounced Trump’s executive order and pledged to hire 10,000 refugees over the next five years in 75 countries where it operates. Responding to Trump’s order and the response by Schultz, consumers immediately chose sides. Some consumers called for boycotting Starbucks, using the hashtag #BoycottStarbucks, while others started to advocate for the company, using #DrinkStarbucks. Another brand that experienced backlash was Budweiser. The company released a 60-second advertisement during the Super Bowl, “Born the Hard Way," which chronicled the journey of Adolphus Busch, its immigrant founder. In the commercial, Busch heard upon his arrival, “You’re not wanted here! Go home!” Although the company denied a connection between the commercial and the immigration ban, consumers immediately responded to the advertisement by using the hashtag #BoycottBudweiser. Taking Trump’s immigration ban as a case that triggered companies’ engagement in CSA, the present study examines social networks that emerged around boycotting and advocating for Starbucks and Budweiser. First, to understand how the two opposite groups (i.e.,

2.3. Social network approach and social mediators The emergence of digital technology and the growing use of SNS has changed the landscape of relationship building among social entities, as well as publics’ engagement in social movement (Hestres, 2014; Karpf, 2012). Using online communication, publics gather and share information, network with others, facilitate conversations, and organize collective actions (Makarem & Jae, 2016; Park et al., 2015). The ability to communicate about the issue with low cost and beyond geographic barriers enables social actors to easily publicize their goals and establish supportive networks (Karpf, 2012). In social networks, publics can easily engage in political and social discourses, and mobilize to advocate for the issues they support (Choi & Park, 2014; Park et al., 2015). Network theories explain human behavior by focusing on relationships among members of societies and the patterns of relationships (Wasserman & Faust, 1994). In the social media sphere, a network structure is created among the social actors (i.e., nodes) and the relational ties between them (i.e., links). Then brokers (mediators) connect the users inside of their networks (i.e., cluster) to the outside subgroups. Among several network concepts that emerged in the 1970s on the advantage of bridges connecting groups, brokerage has gained academic attention in terms of “connecting across clusters to engage diverse information” (Burt, Kilduff, & Tasselli, 2013, p.530). Network brokers enact brokerage in the structural hole, which refers to a gap between disconnected members in a social network (Burt, 1992), by building relations with disconnected groups. The theory of the 3

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to commit and engage in change not only in the business world but also in a broader community regarding environmental, social and educational issues. Thus, these two brands with clear CSA positioning and with substantial Twitter presence (i.e. Starbucks with 11,913,943 followers, Budweiser with 160,000 followers) can offer empirical insights into the relationship between CSA and its business impacts.

boycotters and advocators) have converged and diverged in their message toward the brands. the study proposes to examine hashtags emerging in each networks’ Twitter discussion A hashtag is a technological feature for indexing keywords or topics on Twitter, which allows users to search, link, and interact with others. When describing the information they tweet, users tend to use several hashtags to facilitate searches and engagement. In their research, Pervin, Phan, Datta, Takeda, and Toriumi, (2015)) found that about 50 % of the hashtags appearing in tweets on the great Eastern Japan Earthquake included multiple hashtags. Similarly, Yang (2016) examined the use of hashtags for #BlackLivesMatter social media activism, and found that many other hashtags were used in combination with one another (e.g., #blackish, #ChicagoPD, #Trump, #FeeltheBern, #abff, #blacktwitter, #OscarSoWhite), such that #BlackLivesMatter activism became a “unifying theme of multiple stories about racial justice” (p. 15). Therefore, to get an insight on how users described the contents of their tweets, and how the representative hashtag for boycotting and advocating the brands were intertwined with other hashtags, we posed research questions as follows:

3.1. Data Using NodeXL, a Microsoft Excel application add-in for network analysis, we performed 16 data draws between February 5, 2017 and February 19, 2017 with the hashtags #DrinkStarbucks, #BoycottStarbucks, #DrinkBudweiser, and #BoycottBudweiser. This produced a total of 17,821 vertices and 29,543 edges, which have been used for the network analysis (RQ4). For content analysis (RQ2, RQ3), we further drew the 5169 original tweets, including the top 10 hashtags in the tweet for the entire graph, which helped us to identify the characteristics of brokers. Vertices are also called nodes, agents, and entities. In this dataset, they represent people who tweet and engage within this social network. Edges describes the connections between two Twitter users that is established by a tweet, mention, reply or hyperlink. While retweet, mention, reply and hyperlinks all play different types of communication roles within tweets (Lovejoy, Waters, & Saxton, 2012), the current study used the total number of edges in order to describe the size of connections in aggregate that reveal the emergent structure within two distinct groups. Twitter usernames, user information, user images, and follow-ups and replies to relationships were also collected. This information was used to compare advocating and boycotting Twitter terms and to collect user-specific information, including users’ political information and keywords in individual Twitter mentions. This study follows the social network analysis approach toward capturing the best possible picture of social network patterns rather than analyzing all social conversations surrounding an event. To do so, we collected the maximum 1000 Twitter users and their relevant tweets per day as regulated by the Twitter Application Programming Interface. This sampling method has been widely accepted in social network analysis literature and a recent study by Driscoll and Walker (2014) shows that Twitter data from the search API and the ‘firehose’ is more similar than different.

RQ1-1. What are the most frequently used hashtags of the two groups (i.e., brand boycotters and brand advocators) on Twitter, including the hashtag used for data draws? RQ1-2. What are the hashtags that are shared or unique on Twitter for the two groups (i.e., brand boycotters and brand advocators)? In addition, based on previous literature on mediated public relations and the theory of structural holes, the study proposes to examine the characteristics of brokers and the structure of network (1999, Burt, 1992; Himelboim et al., 2014). The social actors of public mobilization can be individuals, as well as Internet-mediated organizations, and they can influence an information flow (Burt, 1992; Hestres, 2014). Burt (1999) emphasized the importance of identifying brokers as they play a role as opinion leaders who can diffuse information between groups therefore having much potential in contributing to create social capital. To better understand the characteristics of actors who participate in brand advocacy and boycotting, this study identifies and compares the types of brokers and their political stances in the two opposing groups. Finally, to capture holistic relational structures created around boycotters and advocators, the research compares network structures between two groups.

3.2. Network analysis

RQ2. What are the primary types of brokers (i.e., individuals, brands, media outlets, other organizations) within each issue topic?

Using NodeXL, the network was created, and each dataset was mapped by analyzing Twitter data of users who follow, mention, and reply to one another. The clusters in the topic networks were identified using the Clauset-Newman-Moore algorithm (Clauset, Newman, & Moore, 2004), which is included in the NodeXL program. This algorithm enables us to analyze large network datasets and to efficiently find subgroups (Himelboim et al., 2014). Most frequently used hashtags on Twitter were auto-calculated by NodeXL for each dataset and screened by two researchers to confirm.

RQ3. Are there different political stances in terms of the composition of brokers in the two opposing groups? RQ4. Are there different network structures in terms of their distinctive connectivity in the two opposing groups? 3. Method This study employed social network analysis of Twitter user-interactions. In addition, to identify characteristics of brokers, the study conducted a quantitative content analysis of user descriptions and hashtags. Network analysis is used to understand patterns of connections and interactions, in aggregate, between and among individuals and organizations (Hansen, Shneiderman, & Smith, 2010). The interactions among any Twitter user who tweeted using the hashtags #DrinkStarbucks, #BoycottStarbucks, #DrinkBudweiser, and #BoycottBudweiser were analyzed. The social actors in this data include individuals, two companies (Starbucks and Budweiser) and media. Networks and relationships between these social actors were established when users followed, replied or mentioned one another. Starbucks and Budweiser were two major beverage brands with significant presence related to the social media debate surrounding the immigration ban. These two brands have publicly expressed their vision

3.2.1. Identifying brokers In Twitter networks, brokers can be identified in several ways. We operationalize Twitter brokers as the top 10 in terms of betweenness centrality per each dataset. Betweenness centrality measures “the extent to which an actor lies between other actors on their geodesics” (Valente, Coronges, Lakon, & Costenbader, 2008, p.3). Thus, high betweenness centrality actors have the ability to influence others around them in a network (Friedkin, 1991) via both direct and indirect pathways. An actor with high betweenness centrality has the potential to impact the spread of information through the network, by facilitation, hindering, or even altering the communication between others (Newman, 2003). Specifically, Rowley (2018) proposes that an actor or a firm’s position in networks measured by betweenness centrality and density could predict how an actor reacts to stakeholders’ influences. 4

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often followed by #boycottbudweiser (n = 193; 28.97 %), #resist (theresistance) (n = 96; 14.41 %), and #maga (makeamericagreatagain) (n = 95; 14.26 %). For the aggregated Boycott Budweiser networks (top hashtags, n = 3,812), #boycottbudweiser (n = 2,036; 53.41%) were used most often followed by #superbowlsunday (superbowl) (n = 769; 20.17 %), #drinkbudweiser (drinkbudweiser) (n = 238; 6.24 %), #boycottstarbucks (n = 200; 5.24 %), and #maga (n = 174; 4.56 %). For the aggregated Drink Starbucks networks (top hashtags, n = 604), #drinkstarbucks (drinkstarbuckstofightbigotry) (n = 337; 55.79 %) was the most dominant hashtag followed by #boycottstarbucks (n = 135; 22.35 %), #trumpleaks (n = 48; 7.94 %), #theresistance (n = 48; 7.94 %), and #maga (n = 12; 1.98 %). In comparison, within the aggregated Boycott Starbucks networks (top hashtags, n = 24,791), #boycottstarbucks (n = 17,244; 69.55 %) was the most frequently appearing hashtag followed by #maga (n = 2,080; 8.39 %), #starbucks (n = 960; 3.87 %), #americafirst (n = 844; 3.40 %), and #blackriflecoffee (n = 725; 2.92 %). Regarding the hashtags that are shared or unique on Twitter, for the Budweiser issue topic, #drinkbudweiser (32.43 % of drink networks vs. 6.24 % of boycott networks), #boycottbudweiser (28.97 % vs. 53.41 %), #maga (14.26 % vs. 4.56 %) and #resist (14.41 % vs. 2.49 %) appeared for both the advocating networks and the boycotting networks. Within the Drink Budweiser networks, unique hashtags appeared which were related to alcohol (i.e., beer, alcohol, wine, vodka) (9.9 %) while unique hashtags for the Boycott Budweiser networks included various boycotting hashtags against brands that took a political stance (total 10 %): #boycottstarbucks (5.24 %); #boycottpepsi (1.46 %); #boycottnordstrom (1.31 %); #boycotthollywood (0.78 %); #boycottburlingtoncoatfactory (0.52 %); #boycottoscar (0.52 %); #boycottnfl (0.20 %). In the case of the Starbucks issue topic, #drinkstarbucks (55.79 % of Drink Starbucks networks vs. 0.17 % of Boycott Starbucks networks), #boycottstarbucks (22.35 % vs. 69.55 %), #maga (1.98 % vs. 8.39 %) emerged for both the advocating networks and the boycotting networks. Especially for the Drink Starbucks networks, unique hashtags appeared, including #trumpleaks (7.94 %), while unique hashtags for the Boycott Starbucks networks included various boycotting hashtags (total 5.72 %), such as #boycottbudweiser (2.04 %), #boycottnfl (0.88 %), and #boycottnordstrom (0.97 %), which were seen as taking a different stance from President Trump (Garcia, 2017; Wolf, 2017)(see Table 2). RQ2 asked about the primary types of brokers (individuals, brands, media outlets, other organizations) within each issue topic. For the Budweiser issue networks (total brokers, n = 77), individuals were the most prominent brokers (n = 66, 85.7 %) followed by Budweiser’s official Twitter account (n = 3, 3.9 %), media outlets (n = 5, 6.5 %) and others (n = 3, 3.9 %). In the case of the Starbucks issue networks (total brokers, n = 80), individuals were the most influential brokers (n = 65, 81.3 %) followed by the Starbucks official Twitter account (n = 8, 10 %), media outlets (n = 4, 5.0 %) and others (n = 3, 3.8 %). RQ3 examined political stances in terms of the composition of brokers in the two opposing groups. First, a chi-square test of independence was performed to examine the relationship between network types and political stances of brokers for the Budweiser issue networks. Because there were two cells (33.3 %) that had less than five of the expected value, we performed an additional test using the Freeman-Halton extension of the Fisher exact probability test for a tworows by three-columns contingency table, which showed a significant difference between political stances (p = .038). Most prominent brokers for the Boycott Budweiser networks were conservatives (n=26, 65 %) followed by the not apparent category (n=13, 32.5 %) and liberals (n=1, 2.5 %). For the Drink Budweiser networks, there were some liberals (n=7, 18.9 %) but the most prominent brokers were also conservatives (n= 17, 45.9 %) followed by the not apparent category (n=13, 35.1 %). These conservative actors in the Drink Budweiser

Hence, the current study identified the top 10 most influential gatekeepers among each dataset measured by their betweenness centrality score in order to identify a reasonable slice of the most influential information controllers. 3.2.2. Comparing aggregated patterns As NodeXL can only analyze the Twitter network by date, it is hard to observe aggregated patterns of network by terms. To overcome this limit, the R package igraph was used to visualize aggregated network patterns by key terms and also to calculate connectivity between aggregated network nodes. Igraph is a C language-based R package for creating and manipulating graphs and analyzing networks (Csárdi and Nepusz, n.d.). It is a powerful network analysis tool that is capable of handling large amounts of network data efficiently. Tweets on the Tweeter network addressing four terms were collected during four separate dates in February 2017. Using the relationship between edges, aggregated networks for four key terms were visualized consecutively. To understand the structure of the complex network graphs, the modularity of each network was calculated. The modularity of a graph measures how good the division is, or how separated the different vertex types are from each other. Modularity is often used in optimization methods to detect community structure in networks. Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules. The value of the modularity ranges from negative one to positive one. It is positive if the number of edges within groups exceeds the number expected on the basis of chance. In this study, community structure of a graph was measured using the walktrap clustering algorithm, following a method suggested by Pons and Latapy (2006). 3.3. Content analysis A content analysis was conducted to examine the characteristics of social mediators emerged in the Budweiser issue networks (total brokers, n = 77) and the Starbucks issue networks (total brokers, n = 80). Based on preliminary review of all four datasets, a coding protocol was developed in terms of the primary types of brokers (i.e., individuals, brands, media outlets and other organizations; Himelboim et al., 2014) and the political stance (conservative, liberal, independent, and not apparent; Hargittai, Gallo, & Kane, 2008). Two coders visited each Twitter account to identify the types of brokers and political stances using the cues from the author’s profile and tweets, such as political associations, the presence of banners supporting politicians or political parties, political affiliation clues, or supporting or opposing certain values (Hargittai et al., 2008). The examples of conservative values include elimination of abortion and wealth transfers, whereas liberal values include equal opportunity regardless of race, sex, gender or any other background, and environmental protection. Each coder independently coded approximately 20 % of the brokers (n = 30) to check intercoder reliability. The intercoder reliability check estimates for Krippendorff’s alpha was 1.00 for primary type variable and 0.83 for political stance variable (Hayes & Krippendorff, 2007). 4. Results At 16 points throughout the data collection term during February 2017, Tweets with #DrinkStarbucks, #BoycottStarbucks, #DrinkBudweiser, and #BoycottBudweiser were collected and recorded for research. In each dataset, top hashtags in the tweet on the entire graph, calculated by NodeXL, were identified. First, RQ1-1 and RQ1-2 examined to what degree the two opposite groups had converged and diverged in their message toward the brands so we could understand descriptive semantic trends between boycotting groups and advocating groups within each issue topic. Within the aggregated Drink Budweiser networks (top hashtags, n = 666), #drinkbudweiser (dontboycottbudweiser) (n = 216; 32.43 %) appeared most 5

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Table 1 Most Frequently Used Hashtags on Twitter of the Two Opposing Networks. Company Budweiser

Top hashtags Advocating network

Boycotting network

Starbucks

Advocating network

Boycotting network

Count

Percentage (%)

#drinkbudweiser* #boycottbudweiser #resist #maga hashtags related to alcohol (e.g., beer, wine, vodka) Subtotal #boycottbudweiser* #superbowlsunday #drinkbudweiser #boycottstarbucks #maga Others Subtotal

216 193 96 95 66

32.43 28.97 14.41 14.26 9.91

666 2,036 769 238 200 174 395 3,812

100 53.41 20.17 6.24 5.24 4.56 10.36 100

#drinkstarbucks* #boycottstarbucks #trumpleaks #theresistance #maga Others Subtotal #boycottstarbucks* #maga #starbucks #americafirst #blackriflecoffee Others Subtotal

337 135 48 48 12 24 604 17,244 2,080 960 844 725 2,938 24,791

55.79 22.35 7.94 7.94 1.98 3.97 100 69.55 8.39 3.87 3.40 2.92 11.85 100

Fig. 1. Aggregated Budweiser boycott network.

prominent brokers were also conservatives (n = 22, 55 %). These conservative actors in the Drink Starbucks networks tweeted “#drinkstarbucks” and “#boycottstarbucks” simultaneously. Also, they encouraged each other by tweeting the phrase “team boycottstarbucks drinkstarbucks seems trending thing right now thanks potus.” Our findings indicate that the brokers in the advocators and boycotters’ groups are different in terms of their political stance. For example, the advocator brokers included both conservatives and liberals, while conservatives dominated the boycotter brokers. RQ4 compared network structures in terms of their distinctive connectivity in the two opposing groups. Figs. 1 and 2 clearly show that in the case of boycotting a certain brand, communities, which are densely connected subgraphs, tend to overlap each other, sharing vertices between communities. The modularity of the Budweiser boycott Tweeter network is 0.75 and that of Starbucks boycott is 0.79. Both denote relatively complex network community structure. In contrast, the community structures showing support of a certain brand are relatively evident (see Fig. 3 and 4). The modularity of Budweiser support Tweeter network is 0.56 and that of Starbucks support network is 0.58. Connectivity between communities was remarkably low and simple. Figs. 5 and 6 show the decomposed graph for the Budweiser boycott network and Starbucks Boycott network respectively. The decomposition algorithm of igraph decomposes a graph into connected components, visualizing the most distinctive connectivity of each network. Interactions between vertices during February 2017 regarding

Note. * denotes the hashtag used for each data draw.

networks were tweeting “#drinkbudweiser” and “#boycottbudweiser” simultaneously (Tables 1 and 2). A similar pattern was found for the brokers in the Starbucks network. A chi-square test showed a significant difference, χ2 (2, N = 80) = 11.852, p < .005. Most prominent brokers for the Boycott Starbucks networks were conservatives (n = 32, 80 %) followed by the not apparent category (n = 8, 20 %). There were no liberals found in this network. For the Drink Starbucks networks, there were some liberals (n = 10, 25 %) and some not apparent (n = 8, 20 %), but the most Table 2 Shared and Unique Hashtags on Twitter of the Two Opposing Networks. Company

Budweiser

Shared

Unique

Starbucks

Shared

Unique

#drinkbudweiser #boycottbudweiser #maga #resist hashtags related to alcohol (i.e. beer, wine, vodka) #boycottstarbucks #boycottpepsi #boycottnordstrom #boycotthollywood #boycottburlingtoncoatfactory #boycottoscar #boycottnfl #drinkstarbucks #boycottstarbucks #maga #trumpleaks #boycottbudweiser #boycottnordstrom #boycottsuperbowl #boycottnfl #boycotthollywood #boycottliberals

6

Advocating network (%)

Boycotting network (%)

32.43 28.97 14.26 14.41 9.9

6.24 53.41 4.56 2.49

55.79 22.35 1.98 7.94

5.24 1.46 1.31 0.78 0.52 0.52 0.20 .17 69.55 8.39 2.04 0.97 0.88 0.88 0.42 0.21

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Fig. 2. Aggregated Starbucks boycott network. Fig. 6. Decomposed aggregated Starbucks boycott network only showing the connected components.

boycotting a certain brand were commonly very intense and their connectivity was high.

5. Discussion The study examined the network structures of boycotters and advocators emerged around CSA, when Starbucks and Budweiser responded to President Trump’s immigration ban executive order. By comparing the tweeter terms and the relationships among the social actors embedded in networks of boycotters and advocators, the study identified three unique characteristics in the boycotters’ networks which are worth discussing. First, the boycotters appeared not only in the aggregated brand boycotting networks but also in the advocators’ networks. When examining top hashtags, except for some unique hashtags appearing in advocating networks (e.g., #beer, #TrumpLeaks), most hashtags, including #BoycottBudweiser and #DrinkBudweiser were shared in both networks. Interestingly, people using the hashtag #MAGA (Make America Great Again) were found across all four networks that this study examined. For example, a Starbucks boycotter (Subu44burke, 2017) tweeted “#BoycottNFL #BoycottStarbucks #MAGA believe it or not, the media is putting a spell on all of us” in the Starbucks boycotting networks while another boycotter (Kcnationdefense, 2017) appeared in the advocating networks with “@Starbucks can't find qualified #Americans or looking 4 cheaper labor? #DrinkStarbucks #MAGA #ReasonsToProtest #MAGA.” Consequently, the hashtag #MAGA appeared in both networks. A closer examination of top brokers indicated that boycotters often use the hashtags that are shared by supporters (e.g., #drinkstarbucks) along with the boycotting hashtags. A hashtag plays as a channel for the users to find out information of their interest and to reach out beyond their network community (Bruns & Burgess, 2011). In so doing, terms can be searched and will appear in the advocators’ networks, and thus may influence or alter information flows. This pattern may be related to the boycotters’ signaling behavior, identified as one of the tactics boycotters utilize. Scholars have suggested that when boycotters are instrumentally motivated, they actively exhibit tactics to influence changes such as signaling and informing general consumers about appropriate behaviors (Klein et al., 2004; Makarem & Jae, 2016). Moreover, boycotters are characterized by having high perceived efficacy; they believe that their actions can make a difference on the actions of other consumers (John & Klein, 2003). Although explicitly measuring the participant motivation was out of the scope for this study, by examining their tweets, we found that they were actively retweeting boycotters’ tweets, celebrating and empowering each other. Second, our data showed that boycotters in Budweiser and Starbucks networks were also engaged in boycotting other brands or

Fig. 3. Aggregated Budweiser advocator network.

Fig. 4. Aggregated Starbucks advocator network.

Fig. 5. Decomposed aggregated Budweiser boycott network only showing the connected components.

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bonding given the opportunities that brokers have. This implies a critical role of public relations in positioning structural position and/or in identifying and facilitating relationships with opinion brokers. As Sommerfeldt (2013) suggested, the quality relationship among the actors facilitates information exchange and corporation, which ultimately contributes to the creation of social capital. With an exploratory yet holistic approach, this network analysis study advances our knowledge on how publics’ respond and build relationships when a company delves into contentious social-political issues. The characteristics of boycotter networks that we discovered concur with recent social network studies on political polarization on Twitter. For instance, scholars argue that network actors often utilize hashtags which target various politically-opposed audiences (Conover, Ratkiewicz, Francisco, Flammini, & Menczer, 2010; Hong, 2016). In a highly segregated partisan network ecology, Twitter users not only use retweets and mentions but also content injection by annotating their tweets with opposing hashtags in order to expose opposing audiences to their own information. It is noteworthy that Twitter users react to and repeat their communication patterns, such as content injection using hashtags, with corporate brands. In a broader sense, boycotters’ use of content injection shows evidence of the “eco chamber” (Key, 1966) view that social media exacerbates the public divide through a highly fragmented and customized network structure. In particular, boycotters’ engagement with opposing networks is not encouraging civic conversations on a divided issue but rather reinforcing pre-existing partisanship. In addition, our findings shed light on the potential impact of CSA at the societal level where it shapes public opinion (Carroll & Brown, 2018; Nalick et al., 2016). A recent survey showed increased CEO credibility and public expectation for businesses to be agents of change (Edelman, 2018). CEO endorsements can be particularly influential in the social media environment because they possess a higher level of social connectivity, credibility, and social status, which are important attributes of online opinion leaders (Feng, 2016). Scholars noted that when business leaders tap into sociopolitical issues, it creates media attention and increased issue awareness among stakeholders, which is critical for empowering publics to be a part of collective actions (Coombs & Holladay, 2018; Dodd & Supa, 2014). The study also provides several practical implications. When organizations tap into such divisive issues, they should be aware of potentially adverse public responses and anticipate communicative challenges. The intention of engaging in CSA might not be driven by political ideology. For example, Brian Moynihan, CEO of Bank of America mentioned that “it’s not exactly political activism, but it is action on issues beyond business” (as cited in Chatterji & Toffel, 2018, p. 81). Yet, social issues are polarized by nature and are often tied to political ideology (Coombs & Holladay, 2018). Public relations practitioners should be mindful of how their engagement is being perceived by publics and the consequences of being at the center of public discourses. Coombs and Holladay (2018) noted that “the firm must define the social issue in a positive manner and legitimate its involvement with the issue,” (p. 85), emphasizing the importance of communication in social issues management. The company’s message should be compelling and authentic to legitimate the company’s engagement (Chatterji & Toffel, 2018; Coombs & Holladay, 2018). Some important questions remain. A dilemma for organizations in this politically and socially-charged environment would include whether they should take public stances or remain silent to avoid a fierce public movement in social media. CSA triggers consumer movement in an active manner, and as the study showed, boycotters tend to be more vocal than supporters. Then, to what extent could such a backlash hurt a company’s financial and reputational objectives? This exploratory study did not examine its impact on organizational bottom-lines, but past research and public poll results showed contradictory findings which imply both gains and losses (e.g., Dodd & Supa, 2014, 2015; King, 2008; Nalick et al., 2016; Wettstein & Baur, 2016). One market

organizations opposed to Republicans or President Trump’s policy. For example, the hashtags that appeared in boycotters’ networks also included #BoycottNordstrom and #BoycottNFL. This might be because boycotters in this case study were more likely driven by their partisanship rather than motivated by their experiences or relationships with a particular brand. A comparable pattern was observed in the political rumor diffusion on Twitter during the 2012 U.S. presidential election (Shin, Jian, Driscoll, & Bar, 2017). Shin et al. (2017) found that when people spread rumors, they did so selectively, not based on the rumor per se, but based on the rumors’ target. They explained the pattern as “the circulation of rumors occurs within ‘echo chambers’ defined by political homophily” (Shin et al., 2017, p. 1226). Similarly, past research has provided evidences that consumers engage in boycotts as a mean of political participation (Sen, Gürhan-Canli, & Morwitz, 2001; Stolle, Hooghe, & Micheletti, 2005). Sen et al. (2001) noted that consumers were motivated to be a part of consumer activism in order to establish “political or social/ethical control” (p. 400). The 2017 Edelman Earned Brand report noted that “ideology dominates the cultural conversation.” Third, the network of boycotters was very dense and highly connected among subgroups, while that of advocators was sparse and illconnected among subgroups. The higher level of connectivity between boycotters may reflect the role of emotion that triggers their behaviors. As discussed earlier, boycott is a form of expressive motivations. Negative emotions, such as anger, dissatisfaction, or outrage play a key role in increasing boycott participation (Lindenmeier, Schleer, & Pricl, 2012). Research in political communication also suggests that negative emotion drives political participation and public mobilization (e.g., Valentino, Gregorowicz, & Groenendyk, 2009). In addition to the antagonistic feelings that motivate boycotters taking in actions, a high level of perceived efficacy may explain the network structure of boycotters. As discussed earlier, boycotters tend to believe that their actions are effective and will make a difference in outcomes (John & Klein, 2003). Given that boycotters are internalized with self-efficacy and driven by anger, they are more likely to be vocal and mobilized, compared to the supporters group who are positively motivated. In regard to the characteristics of brokers, individuals or informal mediators were the most prominent, followed by each brand’s official Twitter and media outlets. Himelboim et al. (2014) noted that social media is a relational sphere where interactions occur among organizations, stakeholders, activists, competitors, bloggers, and journalists. This highlights the importance of identifying social mediators. While activist organizations were known to be engaged in boycotts as a facilitator (Den Hond & De Bakker, 2007; Feng, 2016; Schneider & Kozinets, 2011), in this particular case, individual activists with a conservative stance were more likely to be engaged in deliberate boycotting activities. This might imply a grassroots nature of boycotting behaviors responding to the CSA. It should be noted that the brand’s official Twitter account also played an important role in facilitating information flow as an information bridge. This finding supports the notion of mediated public relations (Himelboim et al., 2014). By securing a central position in their social networks, organizations can monitor emerging issue trends, but also form publics around shared values and for collective action. 5.1. Theoretical and practical implications This study answered a call from Yang and Taylor (2015) to further examine public discourses and relationships in organizational network ecologies. In addition, the study applied social network perspectives in the context of CSA and examined the characteristics of network structure and brokers. The density of network structure lends two potential promises to public relations scholarship. As cohesive social ties facilitate trust and cooperation, the highly aggregated boycotters’ network may perform better in creating social capital (Coleman, 1988). On the other hand, Burt (1992), 1999) credits the function of bridging than 8

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survey indicated decreased purchase intention for Starbucks when participants were asked immediately after the company’s announcement (YouGov, 2017). Interestingly, the Starbucks quarterly report issued in April 2017, covering the period of the boycotting movement in social media, showed a 4 % sales growth in the U.S. market (Starbucks, 2017). Given that previous research suggests both positive and negative effects of engaging in CSA, public relations practitioners should be aware of the potential risks and benefits and the complex communicative challenges.

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5.2. Limitations and future studies There are several limitations that need to be addressed in future research. First, as discussed above, the current study is exploratory in nature and limited to empirically demonstrating the extent of gains versus losses from CSA. Future research should look at how publics’ opinions are swayed by boycotting messages around social media. Furthermore, in terms of understanding different types of brokers, we have not analyzed the category of individuals if they have distinctive traits such as sports celebrities, activist or politicians. Future study needs to tap into potential links between the boycotting movement and its reputational threats or business outcomes coming from different types of individual brokers. Second, the current study findings are limited to Twitter networks. Only 24 % of American adults use Twitter, and notably young adults, urban residents, and college-educated adults use Twitter at higher rates (Pew Research Center, 2018a, 2018b). Therefore, the authors recognize that Twitter data-based analysis does not represent all social media. In addition, while the use of hashtags helps to identify the relevancy of a message for a particular topic, our data is limited to the four hashtags used for data collection. We acknowledge that the sample size is limited to capture the full corpus of tweets for two campaigns and suggest larger representative data sets in future research. Third, the case we examined in this study was limited to a politically charged social issue, and thus, the social networks data were not representative of entire issue topics. For the issues that are politically neutral or socially charged, the characteristics of boycotters and advocators might be different. In a similar vein, the size of advocators and boycotters in this study was not balanced. For future study, exploring the network structures representing more polarized groups for the socially-charged issue would be a necessary step to further understand publics’ responses to CSA. Finally, CSA may have significant impacts on shaping corporate culture and engaging with employees. For companies such as Nike and Starbucks, taking a public stance on President Trump’s immigration ban was mostly announced in a CEO letter’s form directed to employees. To better gauge potential gains versus loss as a result of CSA, we suggest a further examination about internal CSA influences on employees’ attitudes, moral, and commitment to the organization. Declaration of Competing Interest None. References Abrams, R., Barnes, B., Boudette, N. E., Cohen, P., Couturier, K., Drew, C., ... Bill, V. (2017). Starbukcs, exxon, apple: Companies challenging (or silent on) trump’s immigration ban. The New York Times. Retrieved from https://www.nytimes.com/ interactive/2017/business/trump-immigration-ban-company-reaction.html. Bruns, A., & Burgess, J. E. (2011). The use of Twitter hashtags in the formation of ad hoc publics. Proceedings of the 6th European Consortium for Political Research (ECPR) General Conference 2011. Burt, R. S. (1992). Structural holes: The social structure of competition. Cambridge, MA: Harvard University Press. Burt, R. S. (1999). The social capital of opinion leaders. The Annals of the American Academy of Political and Social Science, 566(1), 37–54. Burt, R. S., Kilduff, M., & Tasselli, S. (2013). Social network analysis: Foundations and

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