Literature Review

Running head: THE EVOLUTION OF VIRALITY AND SOCIAL SHARING IN DIGITAL MARKETING 1

THE EVOLUTION OF VIRALITY AND SOCIAL SHARING IN DIGITAL MARKETING   2

Journal of Marketing Research Vol. XLIX (April 2012), 192 –205

*Jonah Berger is Joseph G. Campbell Assistant Professor of Marketing (e-mail: [email protected]), and Katherine L. Milkman is Assistant Professor of Operations and Information Management (e-mail: kmilkman@ wharton.upenn.edu), the Wharton School, University of Pennsylvania. Michael Buckley, Jason Chen, Michael Durkheimer, Henning Krohnstad, Heidi Liu, Lauren McDevitt, Areeb Pirani, Jason Pollack, and Ronnie Wang all provided helpful research assistance. Hector Castro and Premal Vora created the web crawler that made this project possible, and Roger Booth and James W. Pennebaker provided access to LIWC. Devin Pope and Bill Simpson provided helpful suggestions on our analysis strategy. Thanks to Max Bazerman, John Beshears, Jonathan Haidt, Chip Heath, Yoshi Kashima, Dacher Keltner, Kim Peters, Mark Schaller, Deborah Small, and Andrew Stephen for helpful comments on prior versions of the article. The Dean’s Research Initiative and the Wharton Interactive Media Initiative helped fund this research. Ravi Dhar served as associate editor for this article.

JONAH BERGER and KATHERINE L. MILKMAN*

Why are certain pieces of online content (e.g., advertisements, videos, news articles) more viral than others? This article takes a psychological approach to understanding diffusion. Using a unique data set of all the New York Times articles published over a three-month period, the authors examine how emotion shapes virality. The results indicate that positive content is more viral than negative content, but the relationship between emotion and social transmission is more complex than valence alone. Virality is partially driven by physiological arousal. Content that evokes high-arousal positive (awe) or negative (anger or anxiety) emotions is more viral. Content that evokes low-arousal, or deactivating, emotions (e.g., sadness) is less viral. These results hold even when the authors control for how surprising, interesting, or practically useful content is (all of which are positively linked to virality), as well as external drivers of attention (e.g., how prominently content was featured). Experimental results further demonstrate the causal impact of specific emotion on transmission and illustrate that it is driven by the level of activation induced. Taken together, these findings shed light on why people share content and how to design more effective viral marketing campaigns.

Keywords: word of mouth, viral marketing, social transmission, online content

What Makes Online Content Viral?

© 2012, American Marketing Association ISSN: 0022-2437 (print), 1547-7193 (electronic) 192

Sharing online content is an integral part of modern life. People forward newspaper articles to their friends, pass YouTube videos to their relatives, and send restaurant reviews to their neighbors. Indeed, 59% of people report that they frequently share online content with others (Allsop, Bassett, and Hoskins 2007), and someone tweets a link to a New York Times story once every four seconds (Harris 2010). Such social transmission also has an important impact on

both consumers and brands. Decades of research suggest

that interpersonal communication affects attitudes and deci- sion making (Asch 1956; Katz and Lazarsfeld 1955), and recent work has demonstrated the causal impact of word of mouth on product adoption and sales (Chevalier and Mayz – lin 2006; Godes and Mayzlin 2009). Although it is clear that social transmission is both fre-

quent and important, less is known about why certain pieces of online content are more viral than others. Some customer service experiences spread throughout the blogosphere, while others are never shared. Some newspaper articles earn a position on their website’s “most e-mailed list,” while oth- ers languish. Companies often create online ad campaigns or encourage consumer-generated content in the hope that people will share this content with others, but some of these efforts take off while others fail. Is virality just random, as some argue (e.g., Cashmore 2009), or might certain charac- teristics predict whether content will be highly shared? This article examines how content characteristics affect

virality. In particular, we focus on how emotion shapes social transmission. We do so in two ways. First, we analyze a unique data set of nearly 7000 New York Times articles to examine which articles make the newspaper’s “most e- mailed list.” Controlling for external drivers of attention, such as where an article was featured online and for how long, we examine how content’s valence (i.e., whether an

What Makes Online Content Viral? 193

article is positive or negative) and the specific emotions it evokes (e.g., anger, sadness, awe) affect whether it is highly shared. Second, we experimentally manipulate the specific emotion evoked by content to directly test the causal impact of arousal on social transmission. This research makes several important contributions. First,

research on word of mouth and viral marketing has focused on its impact (i.e., on diffusion and sales; Godes and May- zlin 2004, 2009; Goldenberg et al. 2009). However, there has been less attention to its causes or what drives people to share content with others and what type of content is more likely to be shared. By combining a large-scale examination of real transmission in the field with tightly controlled experiments, we both demonstrate characteristics of viral online content and shed light on the underlying processes that drive people to share. Second, our findings provide insight into how to design successful viral marketing campaigns. Word of mouth and social media are viewed as cheaper and more effective than traditional media, but their utility hinges on people transmitting content that helps the brand. If no one shares a company’s content or if consumers share content that por- trays the company negatively, the benefit of social transmis- sion is lost. Consequently, understanding what drives peo- ple to share can help organizations and policy makers avoid consumer backlash and craft contagious content.

CONTENT CHARACTERISTICS AND SOCIAL TRANSMISSION

One reason people may share stories, news, and informa- tion is because they contain useful information. Coupons or articles about good restaurants help people save money and eat better. Consumers may share such practically useful content for altruistic reasons (e.g., to help others) or for self- enhancement purposes (e.g., to appear knowledgeable, see Wojnicki and Godes 2008). Practically useful content also has social exchange value (Homans 1958), and people may share it to generate reciprocity (Fehr, Kirchsteiger, and Riedl 1998). Emotional aspects of content may also affect whether it is

shared (Heath, Bell, and Sternberg 2001). People report dis- cussing many of their emotional experiences with others, and customers report greater word of mouth at the extremes of satisfaction (i.e., highly satisfied or highly dissatisfied; Anderson 1998). People may share emotionally charged con- tent to make sense of their experiences, reduce dissonance, or deepen social connections (Festinger, Riecken, and Schachter 1956; Peters and Kashima 2007; Rime et al. 1991). Emotional Valence and Social Transmission These observations imply that emotionally evocative

content may be particularly viral, but which is more likely to be shared—positive or negative content? While there is a lay belief that people are more likely to pass along negative news (Godes et al. 2005), this has never been tested. Fur- thermore, the study on which this notion is based actually focused on understanding what types of news people encounter, not what they transmit (see Goodman 1999). Consequently, researchers have noted that “more rigorous research into the relative probabilities of transmission of positive and negative information would be valuable to both academics and managers” (Godes et al. 2005, p. 419). We hypothesize that more positive content will be more

viral. Consumers often share content for self-presentation

purposes (Wojnicki and Godes 2008) or to communicate identity, and consequently, positive content may be shared more because it reflects positively on the sender. Most peo- ple would prefer to be known as someone who shares upbeat stories or makes others feel good rather than some- one who shares things that makes others sad or upset. Shar- ing positive content may also help boost others’ mood or provide information about potential rewards (e.g., this restaurant is worth trying). The Role of Activation in Social Transmission Importantly, however, the social transmission of emo-

tional content may be driven by more than just valence. In addition to being positive or negative, emotions also differ on the level of physiological arousal or activation they evoke (Smith and Ellsworth 1985). Anger, anxiety, and sad- ness are all negative emotions, for example, but while anger and anxiety are characterized by states of heightened arousal or activation, sadness is characterized by low arousal or deactivation (Barrett and Russell 1998). We suggest that these differences in arousal shape social

transmission (see also Berger 2011). Arousal is a state of mobilization. While low arousal or deactivation is charac- terized by relaxation, high arousal or activation is character- ized by activity (for a review, see Heilman 1997). Indeed, this excitatory state has been shown to increase action- related behaviors such as getting up to help others (Gaertner and Dovidio 1977) and responding faster to offers in nego- tiations (Brooks and Schweitzer 2011). Given that sharing information requires action, we suggest that activation should have similar effects on social transmission and boost the likelihood that content is highly shared. If this is the case, even two emotions of the same valence

may have different effects on sharing if they induce differ- ent levels of activation. Consider something that makes peo- ple sad versus something that makes people angry. Both emotions are negative, so a simple valence-based perspec- tive would suggest that content that induces either emotion should be less viral (e.g., people want to make their friends feel good rather than bad). An arousal- or activation-based analysis, however, provides a more nuanced perspective. Although both emotions are negative, anger might increase transmission (because it is characterized by high activation), while sadness might actually decrease transmission (because it is characterized by deactivation or inaction).

THE CURRENT RESEARCH We examine how content characteristics drive social

transmission and virality. In particular, we not only examine whether positive content is more viral than negative content but go beyond mere valence to examine how specific emo- tions evoked by content, and the activation they induce, drive social transmission. We study transmission in two ways. First, we investigate

the virality of almost 7000 articles from one of the world’s most popular newspapers: the New York Times (Study 1). Controlling for a host of factors (e.g., where articles are fea- tured, how much interest they evoke), we examine how the emotionality, valence, and specific emotions evoked by an article affect its likelihood of making the New York Times’ most e-mailed list. Second, we conduct a series of lab experiments (Studies 2a, 2b, and 3) to test the underlying

process we believe to be responsible for the observed effects. By directly manipulating specific emotions and measuring the activation they induce, we test our hypothe- sis that content that evokes high-arousal emotion is more likely to be shared.

STUDY 1: A FIELD STUDY OF EMOTIONS AND VIRALITY

Our first study investigates what types of New York Times articles are highly shared. The New York Times covers a wide range of topics (e.g., world news, sports, travel), and its articles are shared with a mix of friends (42%), relatives (40%), colleagues (10%), and others (7%),1 making it an ideal venue for examining the link between content charac- teristics and virality. The New York Times continually reports which articles from its website have been the most e-mailed in the past 24 hours, and we examine how (1) an article’s valence and (2) the extent to which it evokes vari- ous specific emotions (e.g., anger or sadness) affect whether it makes the New York Times’ most e-mailed list. Negative emotions have been much better distinguished

from one another than positive emotions (Keltner and Lerner 2010). Consequently, when considering specific emotions, our archival analysis focuses on negative emo- tions because they are straightforward to differentiate and classify. Anger, anxiety, and sadness are often described as basic, or universal, emotions (Ekman, Friesen, and Ellsworth 1982), and on the basis of our previous theorizing about activation, we predict that negative emotions characterized by activation (i.e., anger and anxiety) will be positively linked to virality, while negative emotions characterized by deacti- vation (i.e., sadness) will be negatively linked to virality. We also examine whether awe, a high-arousal positive

emotion, is linked to virality. Awe is characterized by a feeling of admiration and elevation in the face of something greater than oneself (e.g., a new scientific discovery, someone over- coming adversity; see Keltner and Haidt 2003). It is gener- ated by stimuli that open the mind to unconsidered possibil- ities, and the arousal it induces may promote transmission. Importantly, our empirical analyses control for several

potentially confounding variables. First, as we noted previ- ously, practically useful content may be more viral because it provides information. Self-presentation motives also shape transmission (Wojnicki and Godes 2008), and people may share interesting or surprising content because it is entertain- ing and reflects positively on them (i.e., suggests that they know interesting or entertaining things). Consequently, we control for these factors to examine the link between emotion and virality beyond them (though their relationships with virality may be of interest to some scholars and practitioners). Second, our analyses also control for things beyond the

content itself. Articles that appear on the front page of the newspaper or spend more time in prominent positions on the New York Times’ home page may receive more attention and thus mechanically have a better chance of making the most e-mailed list. Consequently, we control for these and other potential external drivers of attention.2 Including these

controls also enables us to compare the relative impact of placement versus content characteristics in shaping virality. While being heavily advertised, or in this case prominently featured, should likely increase the chance content makes the most e-mailed list, we examine whether content charac- teristics (e.g., whether an article is positive or awe-inspiring) are of similar importance. Data We collected information about all New York Times arti-

cles that appeared on the newspaper’s home page (www. nytimes. com) between August 30 and November 30, 2008 (6956 articles). We captured data using a web crawler that visited the New York Times’ home page every 15 minutes during the period in question. It recorded information about every article on the home page and each article on the most e-mailed list (updated every 15 minutes). We captured each article’s title, full text, author(s), topic area (e.g., opinion, sports), and two-sentence summary created by the New York Times. We also captured each article’s section, page, and publication date if it appeared in the print paper, as well as the dates, times, locations, and durations of all appearances it made on the New York Times’ home page. Of the articles in our data set, 20% earned a position on the most e-mailed list. Article Coding We coded the articles on several dimensions. First, we

used automated sentiment analysis to quantify the positivity (i.e., valence) and emotionality (i.e., affect ladenness) of each article. These methods are well established (Pang and Lee 2008) and increase coding ease and objectivity. Auto- mated ratings were also significantly positively correlated with manual coders’ ratings of a subset of articles. A com- puter program (LIWC) counted the number of positive and negative words in each article using a list of 7630 words clas- sified as positive or negative by human readers (Pennebaker, Booth, and Francis 2007). We quantified positivity as the difference between the percentage of positive and negative words in an article. We quantified emotionality as the per- centage of words that were classified as either positive or negative. Second, we relied on human coders to classify the extent

to which content exhibited other, more specific characteris- tics (e.g., evoked anger) because automated coding systems were not available for these variables. In addition to coding whether articles contained practically useful information or evoked interest or surprise (control variables), coders quan- tified the extent to which each article evoked anxiety, anger, awe, or sadness.3 Coders were blind to our hypotheses. They received the title and summary of each article, a web link to the article’s full text, and detailed coding instructions (see the Web Appendix at www.marketingpower.com/jmr_ webappendix). Given the overwhelming number of articles in our data set, we selected a random subsample for coding

194 JOURNAL OF MARKETING RESEARCH, APRIL 2012

1These figures are based on 343 New York Times readers who were asked with whom they had most recently shared an article. 2Discussion with newspaper staff indicated that editorial decisions about

how to feature articles on the home page are made independently of (and well before) their appearance on the most e-mailed list.

3Given that prior work has examined how the emotion of disgust might affect the transmission of urban legends (Heath, Bell, and Sternberg 2001), we also include disgust in our analysis. (The rest of the results remain unchanged regardless of whether it is in the model.) While we do not find any significant relationship between disgust and virality, this may be due in part to the notion that in general, New York Times articles elicit little of this emotion.

What Makes Online Content Viral? 195

(n = 2566). For each dimension (awe, anger, anxiety, sad- ness, surprise, practical utility, and interest), a separate group of three independent raters rated each article on a five-point Likert scale according to the extent to which it was characterized by the construct in question (1 = “not at all,” and 5 = “extremely”). We gave raters feedback on their coding of a test set of articles until it was clear that they understood the relevant construct. Interrater reliability was high on all dimensions (all ’s > .70), indicating that con- tent tends to evoke similar emotions across people. We averaged scores across coders and standardized them (for sample articles that scored highly on the different dimen- sions, see Table 1; for summary statistics, see Table 2; and for correlations between variables, see the Appendix). We assigned all uncoded articles a score of zero on each dimen- sion after standardization (i.e., we assigned uncoded articles the mean value), and we included a dummy in regression analyses to control for uncoded stories (for a discussion of this standard imputation methodology, see Cohen and Cohen 1983). This enabled us to use the full set of articles collected to analyze the relationship between other content characteristics (that did not require manual coding) and virality. Using only the coded subset of articles provides similar results.

Additional Controls As we discussed previously, external factors (separate

from content characteristics) may affect an article’s virality by functioning like advertising. Consequently, we rigor- ously control for such factors in our analyses (for a list of all independent variables including controls, see Table 3). Appearance in the physical newspaper. To characterize

where an article appeared in the physical newspaper, we created dummy variables to control for the article’s section (e.g., Section A). We also created indicator variables to quantify the page in a given section (e.g., A1) where an arti- cle appeared in print to control for the possibility that appearing earlier in some sections has a different effect than appearing earlier in others. Appearance on the home page. To characterize how much

time an article spent in prominent positions on the home page, we created variables that indicated where, when, and for how long every article was featured on the New York Times home page. The home page layout remained the same throughout the period of data collection. Articles could appear in several dozen positions on the home page, so we aggregated positions into seven general regions based on locations that likely receive similar amounts of attention (Figure 1). We included variables indicating the amount of time an article spent in each of these seven regions as controls after Winsorization of the top 1% of outliers (to prevent extreme outliers from exerting undue influence on our results; for summary statistics, see Tables WA1 and WA2 in the Web Appendix at www.marketingpower. com/ jmr_ webappendix). Release timing and author fame. To control for the possi-

bility that articles released at different times of day receive different amounts of attention, we created controls for the time of day (6 A.M.–6 P.M. or 6 P.M.–6 A.M. eastern standard time) when an article first appeared online. We control for author fame to ensure that our results are not driven by the tastes of particularly popular writers whose stories may be more likely to be shared. To quantify author fame, we cap- ture the number of Google hits returned by a search for each first author’s full name (as of February 15, 2009). Because

Table 1 ARTICLES THAT SCORED HIGHLY ON DIFFERENT DIMENSIONS

Primary Predictors Emotionality

•“Redefining Depression as Mere Sadness” •“When All Else Fails, Blaming the Patient Often Comes Next”

Positivity •“Wide-Eyed New Arrivals Falling in Love with the City” •“Tony Award for Philanthropy”

(Low Scoring) •“Web Rumors Tied to Korean Actress’s Suicide” •“Germany: Baby Polar Bear’s Feeder Dies”

Awe •“Rare Treatment Is Reported to Cure AIDS Patient” •“The Promise and Power of RNA”

Anger •“What Red Ink? Wall Street Paid Hefty Bonuses” •“Loan Titans Paid McCain Adviser Nearly $2 Million”

Anxiety •“For Stocks, Worst Single-Day Drop in Two Decades” •“Home Prices Seem Far from Bottom”

Sadness •“Maimed on 9/11, Trying to Be Whole Again” •“Obama Pays Tribute to His Grandmother After She Dies”

Control Variables Practical Utility

•“Voter Resources” •“It Comes in Beige or Black, but You Make It Green” (a story about being environmentally friendly when disposing of old computers)

Interest •“Love, Sex and the Changing Landscape of Infidelity” •“Teams Prepare for the Courtship of LeBron James”

Surprise •“Passion for Food Adjusts to Fit Passion for Running” (a story about a restaurateur who runs marathons) •“Pecking, but No Order, on Streets of East Harlem” (a story about chickens in Harlem)

Table 2 PREDICTOR VARIABLE SUMMARY STATISTICS

M SD Primary Predictor Variables Emotionalitya 7.43% 1.92% Positivitya .98% 1.84% Awea 1.81 .71 Angera 1.47 .51 Anxietya 1.55 .64 Sadnessa 1.31 .41

Other Control Variables Practical utilitya 1.66 1.01 Interesta 2.71 .85 Surprisea 2.25 .87 Word count 1021.35 668.94 Complexitya 11.08 1.54 Author fame 9.13 2.54 Author female .29 .45 Author male .66 .48 aThese summary statistics pertain to the variable in question before

standardization.

of its skew, we use the logarithm of this variable as a con- trol in our analyses. We also control for variables that might both influence transmission and the likelihood that an arti- cle possesses certain characteristics (e.g., evokes anger). Writing complexity. We control for how difficult a piece

of writing is to read using the SMOG Complexity Index (McLaughlin 1969). This widely used index variable essen- tially measures the grade-level appropriateness of the writ- ing. Alternate complexity measures yield similar results. Author gender. Because male and female authors have

different writing styles (Koppel, Argamon, and Shimoni 2002; Milkman, Carmona, and Gleason 2007), we control for the gender of an article’s first author (male, female, or unknown due to a missing byline). We classify gender using a first name mapping list from prior research (Morton, Zettelmeyer, and Silva-Risso 2003). For names that were classified as gender neutral or did not appear on this list, research assistants determined author gender by finding the authors online. Article length and day dummies. We also control for an

article’s length in words. Longer articles may be more likely to go into enough detail to inspire awe or evoke anger but may simply be more viral because they contain more infor-

mation. Finally, we use day dummies to control for both competition among articles to make the most e-mailed list (i.e., other content that came out the same day) as well as any other time-specific effects (e.g., world events that might affect article characteristics and reader interest). Analysis Strategy Almost all articles that make the most e-mailed list do so

only once (i.e., they do not leave the list and reappear), so we model list making as a single event (for further discus- sion, see the Web Appendix at www.marketingpower.com/ jmr_ webappendix). We rely on the following logistic regression specification:

where makes_itat is a variable that takes a value of 1 when an article a released online on day t earns a position on the most e-mailed list and 0 otherwise, and t is an unobserved day-specific effect. Our primary predictor variables quantify the extent to which article a published on day t was coded as positive, emotional, awe inspiring, anger inducing, anxiety inducing, or sadness inducing. The term Xat is a vector of the other control variables described previously (see Table 3). We estimate the equation with fixed effects for the day of an article’s release, clustering standard errors by day of release. (Results are similar if fixed effects are not included.) Results Is positive or negative content more viral? First, we

examine content valence. The results indicate that content is more likely to become viral the more positive it is (Table 4, Model 1). Model 2 shows that more affect-laden content, regardless of valence, is more likely to make the most e- mailed list, but the returns to increased positivity persist even controlling for emotionality more generally. From a different perspective, when we include both the percentage of positive and negative words in an article as separate pre- dictors (instead of emotionality and valence), both are posi- tively associated with making the most e-mailed list. How- ever, the coefficient on positive words is considerably larger than that on negative words. This indicates that while more positive or more negative content is more viral than content that does not evoke emotion, positive content is more viral than negative content. The nature of our data set is particularly useful here

because it enables us to disentangle preferential transmis- sion from mere base rates (see Godes et al. 2005). For example, if it were observed that there was more positive than negative word of mouth overall, it would be unclear whether this outcome was driven by (1) what people encounter (e.g., people may come across more positive events than negative ones) or (2) what people prefer to pass on (i.e., positive or negative content). Thus, without know- ing what people could have shared, it is difficult to infer much about what they prefer to share. Access to the full cor-

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Table 3 PREDICTOR VARIABLES

Variable Where It Came from Main Independent Variables Emotionality Coded through textual analysis

(LIWC) Positivity Coded through textual analysis

(LIWC) Awe Manually coded Anger Manually coded Anxiety Manually coded Sadness Manually coded

Content Controls Practical utility Manually coded Interest Manually coded Surprise Manually coded

Other Control Variables Word count Coded through textual analysis

(LIWC) Author fame Log of number of hits returned by

Google search of author’s name Writing complexity SMOG Complexity Index

(McLaughlin 1969) Author gender List mapping names to genders

(Morton et al. 2003) Author byline missing Captured by web crawler Article section Captured by web crawler Hours spent in different places on Captured by web crawler the home page

Section of the physical paper Captured by web crawler (e.g., A)

Page in section in the physical Captured by web crawler paper (e.g., A1)

Time of day the article appeared Captured by web crawler Day the article appeared Captured by web crawler Category of the article (e.g., sports) Captured by web crawler

196 JOURNAL OF MARKETING RESEARCH, APRIL 2012

What Makes Online Content Viral? 197

Figure 1 HOME PAGE LOCATION CLASSIFICATIONS

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Notes: Portions with “X” through them always featured Associated Press and Reuters news stories, videos, blogs, or advertisements rather than articles by New York Times reporters.

pus of articles published by the New York Times over the analysis period as well as all content that made the most e- mailed list enables us to separate these possibilities. Taking into account all published articles, our results show that an article is more likely to make the most e-mailed list the more positive it is. How do specific emotions affect virality? The relation-

ships between specific emotions and virality suggest that the role of emotion in transmission is more complex than mere valence alone (Table 4, Model 3). While more awe-inspiring (a positive emotion) content is more viral and sadness- inducing (a negative emotion) content is less viral, some negative emotions are positively associated with virality. More anxiety- and anger-inducing stories are both more likely to make the most e-mailed list. This suggests that transmission is about more than simply sharing positive things and avoiding sharing negative ones. Consistent with our theorizing, content that evokes high-arousal emotions (i.e., awe, anger, and anxiety), regardless of their valence, is more viral. Other factors. These results persist when we control for a

host of other factors (Table 4, Model 4). More notably, informative (practically useful), interesting, and surprising articles are more likely to make the New York Times’ most e-mailed list, but our focal results are significant even after we control for these content characteristics. Similarly, being

featured for longer in more prominent positions on the New York Times home page (e.g., the lead story vs. at the bottom of the page) is positively associated with making the most e-mailed list, but the relationships between emotional char- acteristics of content and virality persist even after we con- trol for this type of “advertising.” This suggests that the heightened virality of stories that evoke certain emotions is not simply driven by editors featuring those types of stories, which could mechanically increase their virality.4 Longer articles, articles by more famous authors, and articles writ- ten by women are also more likely to make the most e- mailed list, but our results are robust to including these fac- tors as well. Robustness checks. The results are also robust to control-

ling for an article’s general topic (20 areas classified by the New York Times, such as science and health; Table 4, Model 5). This indicates that our findings are not merely driven by certain areas tending to both evoke certain emotions and be particularly likely to make the most e-mailed list. Rather,

4Furthermore, regressing the various content characteristics on being featured suggests that topical section (e.g., national news vs. sports), rather than an integral affect, determines where articles are featured. The results show that general topical areas (e.g., opinion) are strongly related to whether and where articles are featured on the home page, while emotional characteristics are not.

198 JOURNAL OF MARKETING RESEARCH, APRIL 2012

Table 4 AN ARTICLE’S LIKELIHOOD OF MAKING THE NEW YORK TIMES’ MOST E-MAILED LIST AS A FUNCTION OF ITS CONTENT

CHARACTERISTICS

Specific Including Including Section Only Coded Positivity Emotionality Emotions Controls Dummies Articles (1) (2) (3) (4) (5) (6)

Emotion Predictors Positivity .13*** .11*** .17*** .16*** .14*** .23***

(.03) (.03) (.03) (.04) (.04) (.05) Emotionality — .27*** .26*** .22*** .09* .29***

— (.03) (.03) (.04) (.04) (.06) Specific Emotions Awe — — .46*** .34*** .30*** .36***

— — (.05) (.05) (.06) (.06) Anger — — .44*** .38*** .29** .37***

— — (.06) (.09) (.10) (.10) Anxiety — — .20*** .24*** .21*** .27***

— — (.05) (.07) (.07) (.07) Sadness — — –.19*** –.17* –.12† –.16*

— — (.05) (.07) (.07) (.07) Content Controls Practical utility — — — .34*** .18** .27***

— — — (.06) (.07) (.06) Interest — — — .29*** .31*** .27***

— — — (.06) (.07) (.07) Surprise — — — .16** .24*** .18**

— — — (.06) (.06) (.06) Home Page Location Control Variables Top feature — — — .13*** .11*** .11***

— — — (.02) (.02) (.03) Near top feature — — — .11*** .10*** .12***

— — — (.01) (.01) (.01) Right column — — — .14*** .10*** .15***

— — — (.01) (.02) (.02) Middle feature bar — — — .06*** .05*** .06***

— — — (.00) (.01) (.01) Bulleted subfeature — — — .04** .04** .05*

— — — (.01) (.01) (.02) More news — — — .01 .06*** –.01

— — — (.01) (.01) (.02) Bottom list ¥ 10 — — — .06** .11*** .08**

— — — (.02) (.03) (.03) Other Control Variables Word count ¥ 10–3 — — — .52*** .71*** .57***

— — — (.11) (.12) (.18) Complexity — — — .05 .05 .06

— — — (.04) (.04) (.07) First author fame — — — .17*** .15*** .15***

— — — (.02) (.02) (.03) Female first author — — — .36*** .33*** .27*

— — — (.08) (.09) (.13) Uncredited — — — .39 –.56* .50

— — — (.26) (.27) (.37) Newspaper location and web timing controls No No No Yes Yes Yes

Article section dummies (e.g., arts, books) No No No No Yes No

Observations 6956 6956 6956 6956 6956 2566 McFadden’s R2 .00 .04 .07 .28 .36 .32 Log-pseudo-likelihood –3245.85 –3118.45 –3034.17 –2331.37 –2084.85 –904.76 †Significant at the 10% level. *Significant at 5% level. **Significant at 1% level. ***Significant at the .1% level. Notes: The logistic regressions models that appear in this table predict whether an article makes the New York Times’ most emailed list. Successive models

include added control variables, with the exception of Model 6. Model 6 presents our primary regression specification (see Model 4), including only observa- tions of articles whose content was hand-coded by research assistants. All models include day fixed effects. Models 4–6 include disgust (hand-coded) as a control because disgust has been linked to transmission in previous research (Heath et al. 2001), and including this control allows for a more conservative test of our hypotheses. Its effect is never significant, and dropping this control variable does not change any of our results.

What Makes Online Content Viral? 199

this more conservative test of our hypothesis suggests that the observed relationships between emotion and virality hold not only across topics but also within them. Even among opinion or health articles, for example, awe-inspiring arti- cles are more viral. Finally, our results remain meaningfully unchanged in

terms of magnitude and significance if we perform a host of other robustness checks, including analyzing only the 2566 hand-coded articles (Table 4, Model 6), removing day fixed effects, and using alternate ways of quantifying emotion (for more robustness checks and analyses using article rank or time on the most e-mailed list as alternate dependent measures, see the Web Appendix at www.marketingpower. com/ jmr_webappendix). These results indicate that the observed results are not an artifact of the particular regres- sion specifications we rely on in our primary analyses. Discussion Analysis of more than three months of New York Times

articles sheds light on what types of online content become viral and why. Contributing to the debate on whether posi- tive or negative content is more likely to be shared, our results demonstrate that more positive content is more viral. Importantly, however, our findings also reveal that virality is driven by more than just valence. Sadness, anger, and anxiety are all negative emotions, but while sadder content is less viral, content that evokes more anxiety or anger is actually more viral. These findings are consistent with our hypothesis about how arousal shapes social transmission. Positive and negative emotions characterized by activation or arousal (i.e., awe, anxiety, and anger) are positively linked to virality, while emotions characterized by deactiva- tion (i.e., sadness) are negatively linked to virality. More broadly, our results suggest that while external

drivers of attention (e.g., being prominently featured) shape what becomes viral, content characteristics are of similar importance (see Figure 2). For example, a one-standard- deviation increase in the amount of anger an article evokes increases the odds that it will make the most e-mailed list by 34% (Table 4, Model 4). This increase is equivalent to spending an additional 2.9 hours as the lead story on the New York Times website, which is nearly four times the average number of hours articles spend in that position. Similarly, a one-standard-deviation increase in awe increases the odds of making the most e-mailed list by 30%. These field results are consistent with the notion that acti-

vation drives social transmission. To more directly test the process behind our specific emotions findings, we turn to the laboratory. STUDY 2: HOW HIGH-AROUSAL EMOTIONS AFFECT

TRANSMISSION Our experiments had three main goals. First, we wanted

to directly test the causal impact of specific emotions on sharing. The field study illustrates that content that evokes activating emotions is more likely to be viral, but by manip- ulating specific emotions in a more controlled setting, we can more cleanly examine how they affect transmission. Second, we wanted to test the hypothesized mechanism behind these effects—namely, whether the arousal induced by content drives transmission. Third, while the New York Times provided a broad domain to study transmission, we

wanted to test whether our findings would generalize to other marketing content. We asked participants how likely they would be to share

a story about a recent advertising campaign (Study 2a) or customer service experience (Study 2b) and manipulated whether the story in question evoked more or less of a spe- cific emotion (amusement in Study 2a and anger in Study 2b). To test the generalizability of the effects, we examined how both positive (amusement, Study 2a) and negative (anger, Study 2b) high-arousal emotions characterized influ- ence transmission. If arousal increases sharing, content that evokes more of an activating emotion (amusement or anger) should be more likely to be shared. Finally, we measured experienced activation to test whether it drives the effect of emotion on sharing. Study2a: Amusement Participants (N = 49) were randomly assigned to read

either a high- or low-amusement version of a story about a recent advertising campaign for Jimmy Dean sausages. The two versions were adapted from prior work (McGraw and Warren 2010) showing that they differed on how much humor they evoked (a pretest showed that they did not differ in how much interest they evoked). In the low-amusement condition, Jimmy Dean decides to hire a farmer as the new spokesperson for the company’s line of pork products. In the high-amusement condition, Jimmy Dean decides to hire a rabbi (which is funny given that the company makes pork products and that pork is not considered kosher). After read- ing about the campaign, participants were asked how likely

Figure 2 PERCENTAGE CHANGE IN FITTED PROBABILITY OF MAKING THE LIST FOR A ONE-STANDARD-DEVIATION INCREASE ABOVE THE MEAN IN AN ARTICLE CHARACTERISTIC

% Change in Fitted Probability of Making the List –20% 20% 40%0%

21%

34%

–16%

30%

13%

18%

25%

14%

30%

20%

Anxiety (+1SD)

Anger (+1SD)

Sadness (+1SD)

Awe (+1SD)

Positivity (+1SD)

Emotionality (+1SD)

Interest (+1SD)

Surprise (+1SD)

Practical Value (+1SD)

Time at top of home page (+1SD)

200 JOURNAL OF MARKETING RESEARCH, APRIL 2012

they would be to share it with others (1 = “not at all likely,” and 7 = “extremely likely”). Participants also rated their level of arousal using three

seven-point scales (“How do you feel right now?” Scales were anchored at “very passive/very active,” “very mellow/ very fired up,” and “very low energy/very high energy”:  = 82; we adapted this measure from Berger [2011] and aver- aged the responses to form an activation index). Results. As we predicted, participants reported they

would be more likely to share the advertising campaign when it induced more amusement, and this was driven by the arousal it evoked. First, participants reported that they would be more likely to share the advertisement if they were in the high-amusement (M = 3.97) as opposed to low- amusement condition (M = 2.92; F(1, 47) = 10.89, p < .005). Second, the results were similar for arousal; the high- amusement condition (M = 3.73) evoked more arousal than the low-amusement condition (M = 2.92; F(1, 47) = 5.24, p < .05). Third, as we predicted, this boost in arousal medi- ated the effect of the amusement condition on sharing. Con- dition was linked to arousal (high_amusement = .39, SE = .17; t(47) = 2.29, p < .05); arousal was linked to sharing (activa- tion = .58, SE = .11; t(47) = 5.06, p < .001); and when we included both the amusement condition and arousal in a regression predicting sharing, arousal mediated the effect of amusement on transmission (Sobel z = 2.02, p < .05). Study2b: Anger Participants (N = 45) were randomly assigned to read

either a high- or low-anger version of a story about a (real) negative customer service experience with United Airlines (Negroni 2009). We pretested the two versions to ensure that they evoked different amounts of anger but not other specific emotions, interest, or positivity in general. In both conditions, the story described a music group traveling on United Airlines to begin a week-long-tour of shows in Nebraska. As they were about to leave, however, they noticed that the United baggage handlers were mishandling their guitars. They asked for help from flight attendants, but by the time they landed, the guitars had been damaged. In the high-anger condition, the story was titled “United Smashes Guitars,” and it described how the baggage han- dlers seemed not to care about the guitars and how United was unwilling to pay for the damages. In the low-anger con- dition, the story was titled “United Dents Guitars,” and it described the baggage handlers as having dropped the gui- tars but United was willing to help pay for the damages. After reading the story, participants rated how likely they would be to share the customer service experience as well as their arousal using the scales from Study 2a. Results. As we predicted, participants reported that they

would be more likely to share the customer service experi- ence when it induced more anger, and this was driven by the arousal it evoked. First, participants reported being more likely to share the customer service experience if they were in the high-anger condition (M = 5.71) as opposed to low- anger condition (M = 3.37; F(1, 43) = 18.06, p < .001). Sec- ond, the results were similar for arousal; the high-anger con- dition (M = 4.48) evoked more arousal than the low-anger condition (M = 3.00; F(1, 43) = 10.44, p < .005). Third, as in Study 2a, this boost in arousal mediated the effect of con- dition on sharing. Regression analyses show that condition

was linked to arousal (high_anger = .74, SE = .23; t(44) = 3.23, p < .005); arousal was linked to sharing (activation = .65, SE = .17; t(44) = 3.85, p < .001); and when we included both anger condition and arousal in a regression, arousal mediated the effect of anger on transmission (Sobel z = 1.95, p = .05). Discussion The experimental results reinforce the findings from our

archival field study, support our hypothesized process, and generalize our findings to a broader range of content. First, consistent with our analysis of the New York Times’ most e- mailed list, the amount of emotion content evoked influ- enced transmission. People reported that they would be more likely to share an advertisement when it evoked more amusement (Study 2a) and a customer service experience when it evoked more anger (Study 2b). Second, the results underscore our hypothesized mechanism: Arousal mediated the impact of emotion on social transmission. Content that evokes more anger or amusement is more likely to be shared, and this is driven by the level of activation it induces. STUDY 3: HOW DEACTIVATING EMOTIONS AFFECT

TRANSMISSION Our final experiment further tests the role of arousal by

examining how deactivating emotions affect transmission. Studies 2a and 2b show that increasing the amount of high- arousal emotions boosts social transmission due to the acti- vation it induces, but if our theory is correct, these effects should reverse for low-arousal emotions. Content that evokes more sadness, for example, should be less likely to be shared because it deactivates rather than activates. Note that this is a particularly strong test of our theory

because the prediction goes against several alternative explanations for our findings in Study 2. It could be argued that evoking more of any specific emotion makes content better or more compelling, but such an explanation would suggest that evoking more sadness should increase (rather than decrease) transmission. Method Participants (N = 47) were randomly assigned to read

either a high- or low-sadness version of a news article. We pretested the two versions to ensure that they evoked differ- ent amounts of sadness but not other specific emotions, interest, or positivity in general. In both conditions, the arti- cle described someone who had to have titanium pins implanted in her hands and relearn her grip after sustaining injuries. The difference between conditions was the source of the injury. In the high-sadness condition, the story was taken directly from our New York Times data set. It was titled “Maimed on 9/11: Trying to Be Whole Again,” and it detailed how someone who worked in the World Trade Cen- ter sustained an injury during the September 11 attacks. In the low-sadness condition, the story was titled “Trying to Be Better Again,” and it detailed how the person sustained the injury falling down the stairs at her office. After reading one of these two versions of the story, participants answered the same sharing and arousal questions as in Study 2. As we predicted, when the context evoked a deactivating

(i.e., de-arousing) emotion, the effects on transmission were

What Makes Online Content Viral? 201

reversed. First, participants were less likely to share the story if they were in the high-sadness condition (M = 2.39) as opposed to the low-sadness condition (M = 3.80; F(1, 46) = 10.78, p < .005). Second, the results were similar for arousal; the high-sadness condition (M = 2.75) evoked less arousal than the low-sadness condition (M = 3.89; F(1, 46) = 10.29, p < .005). Third, as we hypothesized, this decrease in arousal mediated the effect of condition on sharing. Condition was linked to arousal (high_sadness = –.57, SE = .18; t(46) = –3.21, p < .005); arousal was linked to sharing (activation = .67, SE = .15, t(46) = 4.52, p < .001); and when we included both sadness condition and arousal in a regression predict- ing sharing, arousal mediated the effect of sadness on trans- mission (Sobel z = –2.32, p < .05). Discussion The results of Study 3 further underscore the role of

arousal in social transmission. Consistent with the findings of our field study, when content evoked more of a low- arousal emotion, it was less likely to be shared. Further- more, these effects were again driven by arousal. When a story evoked more sadness, it decreased arousal, which in turn decreased transmission. The finding that the effect of specific emotion intensity on transmission reversed when the emotion was deactivating provides even stronger evi- dence for our theoretical perspective. While it could be argued that content evoking more emotion is more interest- ing or engaging (and, indeed, pretest results suggest that this is the case in this experiment), these results show that such increased emotion may actually decrease transmission if the specific emotion evoked is characterized by deactivation.

GENERAL DISCUSSION The emergence of social media (e.g., Facebook, Twitter)

has boosted interest in word of mouth and viral marketing. It is clear that consumers often share online content and that social transmission influences product adoption and sales, but less is known about why consumers share content or why certain content becomes viral. Furthermore, although diffusion research has examined how certain people (e.g., social hubs, influentials) and social network structures might influence social transmission, but less attention has been given to how characteristics of content that spread across social ties might shape collective outcomes. The current research takes a multimethod approach to

studying virality. By combining a broad analysis of virality in the field with a series of controlled laboratory experi- ments, we document characteristics of viral content while also shedding light on what drives social transmission. Our findings make several contributions to the existing

literature. First, they inform the ongoing debate about whether people tend to share positive or negative content. While common wisdom suggests that people tend to pass along negative news more than positive news, our results indicate that positive news is actually more viral. Further- more, by examining the full corpus of New York Times con- tent (i.e., all articles available), we determine that positive content is more likely to be highly shared, even after we control for how frequently it occurs. Second, our results illustrate that the relationship between

emotion and virality is more complex than valence alone and that arousal drives social transmission. Consistent with

our theorizing, online content that evoked high-arousal emotions was more viral, regardless of whether those emo- tions were of a positive (i.e., awe) or negative (i.e., anger or anxiety) nature. Online content that evoked more of a deac- tivating emotion (i.e., sadness), however, was actually less likely to be viral. Experimentally manipulating specific emotions in a controlled environment confirms the hypothe- sized causal relationship between activation and social transmission. When marketing content evoked more of spe- cific emotions characterized by arousal (i.e., amusement in Study 2a or anger in Study 2b), it was more likely to be shared, but when it evoked specific emotion characterized by deactivation (i.e., sadness in Study 3), it was less likely to be shared. In addition, these effects were mediated by arousal, further underscoring its impact on social transmission. Demonstrating these relationships in both the laboratory

and the field, as well as across a large and diverse body of content, underscores their generality. Furthermore, although not a focus of our analysis, our field study also adds to the literature by demonstrating that more practically useful, interesting, and surprising content is more viral. Finally, the naturalistic setting allows us to measure the relative impor- tance of content characteristics and external drivers of atten- tion in shaping virality. While being featured prominently, for example, increases the likelihood that content will be highly shared, our results suggest that content characteris- tics are of similar importance. Theoretical Implications This research links psychological and sociological

approaches to studying diffusion. Prior research has mod- eled product adoption (Bass 1969) and examined how social networks shape diffusion and sales (Van den Bulte and Wuyts 2007). However, macrolevel collective outcomes (such as what becomes viral) also depend on microlevel individual decisions about what to share. Consequently, when trying to understand collective outcomes, it is impor- tant to consider the underlying individual-level psychologi- cal processes that drive social transmission (Berger 2011; Berger and Schwartz 2011). Along these lines, this work suggests that the emotion (and activation) that content evokes helps determine which cultural items succeed in the marketplace of ideas. Our findings also suggest that social transmission is

about more than just value exchange or self-presentation (see also Berger and Schwartz 2011). Consistent with the notion that people share to entertain others, surprising and interesting content is highly viral. Similarly, consistent with the notion that people share to inform others or boost their mood, practically useful and positive content is more viral. These effects are all consistent with the idea that people may share content to help others, generate reciprocity, or boost their reputation (e.g., show they know entertaining or useful things). Even after we control for these effects, how- ever, we find that highly arousing content (e.g., anxiety evoking, anger evoking) is more likely to make the most e- mailed list. Such content does not clearly produce immedi- ate economic value in the traditional sense or even neces- sarily reflect favorably on the self. This suggests that social transmission may be less about motivation and more about the transmitter’s internal states.

202 JOURNAL OF MARKETING RESEARCH, APRIL 2012

It is also worthwhile to consider these findings in relation to literature on characteristics of effective advertising. Just as certain characteristics of advertisements may make them more effective, certain characteristics of content may make it more likely to be shared. While there is likely some over- lap in these factors (e.g., creative advertisements are more effective [Goldenberg, Mazursky, and Solomon 1999] and are likely shared more), there may also be some important differences. For example, while negative emotions may hurt brand and product attitudes (Edell and Burke 1987), we have shown that some negative emotions can actually increase social transmission. Directions for Further Research Future work might examine how audience size moderates

what people share. People often e-mail online content to a particular friend or two, but in other cases they may broad- cast content to a much larger audience (e.g., tweeting, blog- ging, posting it on their Facebook wall). Although the for- mer (i.e., narrowcasting) can involve niche information (e.g., sending an article about rowing to a friend who likes crew), broadcasting likely requires posting content that has broader appeal. It also seems likely that whereas narrow- casting is recipient focused (i.e., what a recipient would enjoy), broadcasting is self focused (i.e., what someone wants to say about him- or herself or show others). Consequently, self-presentation motives, identity signaling (e.g., Berger and Heath 2007), or affiliation goals may play a stronger role in shaping what people share with larger audiences. Although our data do not allow us to speak to this issue

in great detail, we were able to investigate the link between article characteristics and blogging. Halfway into our data collection, we built a supplementary web crawler to capture the New York Times’ list of the 25 articles that had appeared in the most blogs over the previous 24 hours. Analysis sug- gests that similar factors drive both virality and blogging: More emotional, positive, interesting, and anger-inducing and fewer sadness-inducing stories are likely to make the most blogged list. Notably, the effect of practical utility reverses: Although a practically useful story is more likely to make the most e-mailed list, practically useful content is marginally less likely to be blogged about. This may be due in part to the nature of blogs as commentary. While movie reviews, technology perspectives, and recipes all contain useful information, they are already commentary, and thus there may not be much added value from a blogger con- tributing his or her spin on the issue. Further research might also examine how the effects

observed here are moderated by situational factors. Given that the weather can affect people’s moods (Keller et al. 2005), for example, it may affect the type of content that is shared. People might be more likely to share positive stories on overcast days, for example, to make others feel happier. Other cues in the environment might also shape social trans- mission by making certain topics more accessible (Berger and Fitzsimons 2008; Berger and Schwartz 2011; Nedun- gadi 1990). When the World Series is going on, for exam- ple, people may be more likely to share a sports story because that topic has been primed. These findings also raise broader questions, such as how

much of social transmission is driven by the sender versus the receiver and how much of it is motivated versus unmoti-

vated. While intuition might suggest that much of transmis- sion is motivated (i.e., wanting to look good to others) and based on the receiver and what he or she would find of value, the current results highlight the important role of the sender’s internal states in whether something is shared. That said, a deeper understanding of these issues requires further research. Marketing Implications These findings also have important marketing implica-

tions. Considering the specific emotions content evokes should help companies maximize revenue when placing advertisements and should help online content providers when pricing access to content (e.g., potentially charging more for content that is more likely to be shared). It might also be useful to feature or design content that evokes acti- vating emotions because such content is likely to be shared (thus increasing page views). Our findings also shed light on how to design successful

viral marketing campaigns and craft contagious content. While marketers often produce content that paints their product in a positive light, our results suggest that content will be more likely to be shared if it evokes high-arousal emotions. Advertisements that make consumers content or relaxed, for example, will not be as viral as those that amuse them. Furthermore, while some marketers might shy away from advertisements that evoke negative emotions, our results suggest that negative emotion can actually increase transmission if it is characterized by activation. BMW, for example, created a series of short online films called “The Hire” that they hoped would go viral and which included car chases and story lines that often evoked anxiety (with such titles as “Ambush” and “Hostage”). While one might be concerned that negative emotion would hurt the brand, our results suggest that it should increase transmission because anxiety induces arousal. (Incidentally, “The Hire” was highly successful, generating millions of views). Fol- lowing this line of reasoning, public health information should be more likely to be passed on if it is framed to evoke anger or anxiety rather than sadness. Similar points apply to managing online consumer senti-

ment. While some consumer-generated content (e.g., reviews, blog posts) is positive, much is negative and can build into consumer backlashes if it is not carefully man- aged. Mothers offended by a Motrin ad campaign, for exam- ple, banded together and began posting negative YouTube videos and tweets (Petrecca 2008). Although it is impossi- ble to address all negative sentiment, our results indicate that certain types of negativity may be more important to address because they are more likely to be shared. Customer experiences that evoke anxiety or anger, for example, should be more likely to be shared than those that evoke sadness (and textual analysis can be used to distinguish dif- ferent types of posts). Consequently, it may be more impor- tant to rectify experiences that make consumers anxious rather than disappointed. In conclusion, this research illuminates how content char-

acteristics shape whether it becomes viral. When attempting to generate word of mouth, marketers often try targeting “influentials,” or opinion leaders (i.e., some small set of special people who, whether through having more social ties or being more persuasive, theoretically have more influ- ence than others). Although this approach is pervasive,

What Makes Online Content Viral? 203

recent research has cast doubt on its value (Bakshy et al. 2011; Watts 2007) and suggests that it is far from cost effec- tive. Rather than targeting “special” people, the current research suggests that it may be more beneficial to focus on crafting contagious content. By considering how psycho- logical processes shape social transmission, it is possible to gain deeper insight into collective outcomes, such as what becomes viral.

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The future of social media in marketing.

Authors: Gil Appel, Lauren Grewal, Rhonda Hadi and Andrew T. Stephen Date: Jan. 2020

From: Journal of the Academy of Marketing Science(Vol. 48, Issue 1) Publisher: Springer

Document Type: Report Length: 14,610 words DOI: http://dx.doi.org/10.1007/s11747-019-00695-1

Abstract: Social media allows people to freely interact with others and offers multiple ways for marketers to reach and engage with consumers. Considering the numerous ways social media affects individuals and businesses alike, in this article, the authors focus on where they believe the future of social media lies when considering marketing-related topics and issues. Drawing on academic research, discussions with industry leaders, and popular discourse, the authors identify nine themes, organized by predicted imminence (i.e., the immediate, near, and far futures), that they believe will meaningfully shape the future of social media through three lenses: consumer, industry, and public policy. Within each theme, the authors describe the digital landscape, present and discuss their predictions, and identify relevant future research directions for academics and practitioners.

Author(s): Gil Appel 1 , Lauren Grewal 2 , Rhonda Hadi 3 , Andrew T. Stephen 3 4

Author Affiliations:

(1) grid.42505.36, 0000 0001 2156 6853, Marshall School of Business, University of Southern California, , 701 Exposition Blvd, 90089, Los Angeles, CA, USA

(2) grid.254880.3, 0000 0001 2179 2404, Tuck School of Business, Dartmouth College, , 100 Tuck Hall, 03755, Hanover, NH, USA

(3) grid.4991.5, 0000 0004 1936 8948, Saïd Business School, University of Oxford, , Park End Street, OX1 1HP, Oxford, UK

(4) grid.1002.3, 0000 0004 1936 7857, Monash Business School, Monash University, , Melbourne, Australia

Introduction

Social media is used by billions of people around the world and has fast become one of the defining technologies of our time. Facebook, for example, reported having 2.38 billion monthly active users and 1.56 billion daily active users as of March 31, 2019 (Facebook 2019). Globally, the total number of social media users is estimated to grow to 3.29 billion users in 2022, which will be 42.3% of the world's population (eMarketer 2018). Given the massive potential audience available who are spending many hours a day using social media across the various platforms, it is not surprising that marketers have embraced

social media as a marketing channel. Academically, social media has also been embraced, and an extensive body of research on social media marketing and related topics, such as online word of mouth (WOM) and online networks, has been developed. Despite what academics and practitioners have studied and learned over the last 15-20 years on this topic, due to the fast- paced and ever-changing nature of social media-and how consumers use it-the future of social media in marketing might not be merely a continuation of what we have already seen. Therefore, we ask a pertinent question, what is the future of social media in marketing?

Addressing this question is the goal of this article. It is important to consider the future of social media in the context of consumer behavior and marketing, since social media has become a vital marketing and communications channel for businesses, organizations and institutions alike, including those in the political sphere. Moreover, social media is culturally significant since it has become, for many, the primary domain in which they receive vast amounts of information, share content and aspects of their lives with others, and receive information about the world around them (even though that information might be of questionable accuracy). Vitally, social media is always changing. Social media as we know it today is different than even a year ago (let alone a decade ago), and social media a year from now will likely be different than now. This is due to constant innovation taking place on both the technology side (e.g., by the major platforms constantly adding new features and services) and the user/consumer side (e.g., people finding new uses for social media) of social media.

What is social media?

Definitionally, social media can be thought of in a few different ways. In a practical sense, it is a collection of software-based digital technologies-usually presented as apps and websites-that provide users with digital environments in which they can send and receive digital content or information over some type of online social network. In this sense, we can think of social media as the major platforms and their features, such as Facebook, Instagram, and Twitter. We can also in practical terms of social media as another type of digital marketing channel that marketers can use to communicate with consumers through advertising. But we can also think of social media more broadly, seeing it less as digital media and specific technology services, and more as digital places where people conduct significant parts of their lives. From this perspective, it means that social media becomes less about the specific technologies or platforms, and more about what people do in these environments. To date, this has tended to be largely about information sharing, and, in marketing, often thought of as a form of (online) word of mouth (WOM).

Building on these definitional perspectives, and thinking about the future, we consider social media to be a technology-centric- but not entirely technological-ecosystem in which a diverse and complex set of behaviors, interactions, and exchanges involving various kinds of interconnected actors (individuals and firms, organizations, and institutions) can occur. Social media is pervasive, widely used, and culturally relevant. This definitional perspective is deliberately broad because we believe that social media has essentially become almost anything-content, information, behaviors, people, organizations, institutions-that can exist in an interconnected, networked digital environment where interactivity is possible. It has evolved from being simply an online instantiation of WOM behaviors and content/information creation and sharing. It is pervasive across societies (and geographic borders) and culturally prominent at both local and global levels.

Throughout the paper we consider many of the definitional and phenomenological aspects described above and explore their implications for consumers and marketing in order to address our question about the future of marketing-related social media. By drawing on academic research, discussions with industry leaders, popular discourse, and our own expertise, we present and discuss a framework featuring nine themes that we believe will meaningfully shape the future of social media in marketing. These themes by no means represent a comprehensive list of all emerging trends in the social media domain and include aspects that are both familiar in extant social media marketing literature (e.g., online WOM, engagement, and user-generated content) and emergent (e.g., sensory considerations in human-computer interaction and new types of unstructured data, including text, audio, images, and video). The themes we present were chosen because they capture important changes in the social media space through the lenses of important stakeholders, including consumers, industry/practice, and public policy.

In addition to describing the nature and consequences of each theme, we identify research directions that academics and practitioners may wish to explore. While it is infeasible to forecast precisely what the future has in store or to project these on a specific timeline, we have organized the emergent themes into three time-progressive waves, according to imminence of impact (i.e., the immediate, near, and far future). Before presenting our framework for the future of social media in marketing and its implications for research (and practice and policy), we provide a brief overview of where social media currently stands as a major media and marketing channel.

Social media at present

The current social media landscape has two key aspects to it. First are the platforms-major and minor, established and emerging-that provide the underlying technologies and business models making up the industry and ecosystem. Second are the use cases; i.e., how various kinds of people and organizations are using these technologies and for what purposes.

The rise of social media, and the manner in which it has impacted both consumer behavior and marketing practice, has largely been driven by the platforms themselves. Some readers might recall the "early days" of social media where social networking

Focal stakeholders discussed

Predicted imminence

Individuals Firms Public policy

sites such as MySpace and Friendster were popular. These sites were precursors to Facebook and everything else that has developed over the last decade. Alongside these platforms, we continue to have other forms of social media such as messaging (which started with basic Internet Relay Chat services in the 1990s and the SMS text messaging built into early digital mobile telephone standards in the 2000s), and asynchronous online conversations arranged around specific topics of interest (e.g., threaded discussion forums, subreddits on Reddit). More recently, we have seen the rise of social media platforms where images and videos replace text, such as Instagram and Snapchat.

Across platforms, historically and to the present day, the dominant business model has involved monetization of users (audiences) by offering advertising services to anyone wishing to reach those audiences with digital content and marketing communications. Prior research has examined the usefulness of social media (in its various forms) for marketing purposes. For example, work by Trusov et al. (2009) and Stephen and Galak (2012) demonstrated that certain kinds of social interactions that now happen on social media (e.g., "refer a friend" features and discussions in online communities) can positively affect important marketing outcomes such as new customer acquisition and sales. More recently, the value of advertising on social media continues to be explored (e.g., Gordon et al. 2019), as well as how it interacts with other forms of media such as television (e.g., Fossen and Schweidel 2016, 2019) and affects new product adoption through diffusion of information mechanisms (e.g., Hennig-Thurau et al. 2015).

Although the rise (and fall) of various kinds of social media platforms has been important for understanding the social media landscape, our contention is that understanding the current situation of social media, at least from a marketing perspective, lies more in what the users do on these platforms than the technologies or services offered by these platforms. Presently, people around the world use social media in its various forms (e.g., news feeds on Facebook and Twitter, private messaging on WhatsApp and WeChat, and discussion forums on Reddit) for a number of purposes. These can generally be categorized as (1) digitally communicating and socializing with known others, such as family and friends, (2) doing the same but with unknown others but who share common interests, and (3) accessing and contributing to digital content such as news, gossip, and user- generated product reviews.

All of these use cases are essentially WOM in one form or another. This, at least, is how marketing scholars have mainly characterized social media, as discussed by Lamberton and Stephen (2016). Indeed, online WOM has been-and, we contend, will continue to be-important in marketing (e.g., in the meta-analysis by Babic Rosario et al. 2016 the authors found, on average, a positive correlation between online WOM and sales). The present perspective on social media is that people use it for creating, accessing, and spreading information via WOM to various types of others, be it known "strong ties" or "weak ties" in their networks or unknown "strangers." Some extant research has looked at social media from the WOM perspective of the consequences of the transmission of WOM (e.g., creating a Facebook post or tweeting) on others (e.g., Herhausen et al. 2019; Stephen and Lehmann 2016), the impact of the type of WOM content shared on others' behavior (e.g., Villarroel Ordenes et al. 2017; Villarroel Ordenes et al. 2018), and on the motivations that drive consumer posting on social media, including considerations of status and self-presentation (e.g., Grewal et al. 2019; Hennig-Thurau et al. 2004; Hollenbeck and Kaikati 2012; Toubia and Stephen 2013; Wallace et al. 2014).

While this current characterization of WOM appears reasonable, it considers social media only from a communications perspective (and as a type of media channel). However, as social media matures, broader social implications emerge. To appropriately consider the future, we must expand our perspective beyond the narrow communicative aspects of social media and consider instead how consumers might use it. Hence, in our vision for the future of social media in marketing in the following sections, we attempt to present a more expansive perspective of what social media is (and will become) and explain why this perspective is relevant to marketing research and practice.

Overview of framework for the future of social media in marketing

In the following sections we present a framework for the immediate, near, and far future of social media in marketing when considering various relevant stakeholders. Themes in the immediate future represent those which already exist in the current marketplace, and that we believe will continue shaping the social media landscape. The near future section examines trends that have shown early signs of manifesting, and that we believe will meaningfully alter the social media landscape in the imminent future. Finally, themes designated as being in the far future represent more speculative projections that we deem capable of long-term influence on the future of social media. The next sections delve into each of the themes in Table 1, organized around the predicted imminence of these theme's importance to marketing (i.e., the immediate, near, and far futures).

Framework for the future of social media as it relates to marketing issues

Immediate future Omni-social presence The rise of influencers

Privacy concerns on social media

Near future Combating loneliness and isolation

Integrated customer care Social media as a political tool

Far future Increased sensory richness

Online/offline integration and complete convergence

Social media by non- humans

The immediate future

To begin our discussion on the direction of social media, in this section, we highlight three themes that have surfaced in the current environment that we believe will continue to shape the social media landscape in the immediate future. These themes- omni-social presence, the rise of influencers, and trust and privacy concerns-reflect the ever-changing digital and social media landscape that we presently face. We believe that these different areas will influence a number of stakeholders such as individual social media users, firms and brands that utilize social media, and public policymakers (e.g., governments, regulators).

Omni-social presence

In its early days, social media activity was mostly confined to designated social media platforms such as Facebook and Twitter (or their now-defunct precursors). However, a proliferation of websites and applications that primarily serve separate purposes have capitalized on the opportunity to embed social media functionality into their interfaces. Similarly, all major mobile and desktop operating systems have in-built social media integration (e.g., sharing functions built into Apple's iOS). This has made social media pervasive and ubiquitous-and perhaps even omnipotent-and has extended the ecosystem beyond dedicated platforms.

Accordingly, consumers live in a world in which social media intersects with most aspects of their lives through digitally enabled social interactivity in such domains as travel (e.g., TripAdvisor), work (e.g., LinkedIn), food (e.g., Yelp), music (e.g., Spotify), and more. At the same time, traditional social media companies have augmented their platforms to provide a broader array of functionalities and services (e.g., Facebook's marketplace, Chowdry 2018; WeChat's payment system, Cheng 2017). These bidirectional trends suggest that the modern-day consumer is living in an increasingly "omni-social" world.

From a marketing perspective, the "omni-social" nature of the present environment suggests that virtually every part of a consumer's decision-making process is prone to social media influence. Need recognition might be activated when a consumer watches their favorite beauty influencer trying a new product on YouTube. A consumer shopping for a car might search for information by asking their Facebook friends what models they recommend. A hungry employee might sift through Yelp reviews to evaluate different lunch options. A traveler might use Airbnb to book future accommodation. Finally, a highly dissatisfied (or delighted) airline passenger might rant (rave) about their experience on Twitter. While the decision-making funnel is arguably growing flatter than the aforementioned examples would imply (Cortizo-Burgess 2014), these independent scenarios illustrate that social media has the propensity to influence the entire consumer-decision making process, from beginning to end.

Finally, perhaps the greatest indication of an "omni-social" phenomenon is the manner in which social media appears to be shaping culture itself. YouTube influencers are now cultural icons, with their own TV shows (Comm 2016) and product lines (McClure 2015). Creative content in television and movies is often deliberately designed to be "gifable" and meme-friendly (Bereznak 2018). "Made-for-Instagram museums" are encouraging artistic content and experiences that are optimized for selfie-taking and posting (Pardes 2017). These examples suggest that social media's influence is hardly restricted to the "online" world (we discuss the potential obsolescence of this term later in this paper), but is rather consistently shaping cultural artifacts (television, film, the arts) that transcend its traditional boundaries. We believe this trend will continue to manifest, perhaps making the term "social media" itself out-of-date, as it's omni-presence will be the default assumption for consumers, businesses, and artists in various domains.

This omni-social trend generates many questions to probe in future research. For example, how will social interactivity influence consumer behavior in areas that had traditionally been non-social? From a practitioner lens, it might also be interesting to explore how marketers can strategically address the flatter decision-making funnel that social media has enabled, and to examine how service providers can best alter experiential consumption when anticipating social media sharing behavior.

The rise of new forms of social influence (and influencers)

The idea of using celebrities (in consumer markets) or well-known opinion leaders (in business markets), who have a high social value, to influence others is a well-known marketing strategy (Knoll and Matthes 2017). However, the omnipresence of social media has tremendously increased the accessibility and appeal of this approach. For example, Selena Gomez has over 144 million followers on Instagram that she engages with each of her posts. In 2018, the exposure of a single photo shared by her was valued at $3.4 million (Maxim 2018). However, she comes at a high price: one post that Selena sponsors for a brand can cost upwards of $800,000 (Mejia 2018). However, putting high valuations on mere online exposures or collecting "likes" for specific posts can be somewhat speculative, as academic research shows that acquiring "likes" on social media might have no effect on consumers' attitudes or behaviors (John et al. 2017; Mochon et al. 2017). Moreover, Hennig-Thurau et al. (2015), show that while garnering positive WOM has little to no effect on consumer preferences, negative WOM can have a negative effect on consumer preferences.

While celebrities like Selena Gomez are possible influencers for major brands, these traditional celebrities are so expensive that smaller brands have begun, and will continue to, capitalize on the popularity and success of what are referred to as "micro- influencers," representing a new form of influencers. Micro-influencers are influencers who are not as well-known as celebrities, but who have strong and enthusiastic followings that are usually more targeted, amounting anywhere between a few thousand to hundreds of thousands of followers (Main 2017). In general, these types of influencers are considered to be more trustworthy and authentic than traditional celebrities, which is a major reason influencer marketing has grown increasingly appealing to brands (Enberg 2018). These individuals are often seen as credible "experts" in what they post about, encouraging others to want to view the content they create and engage with them. Furthermore, using these influencers allows the brand via first person narration (compared to ads), which is considered warmer and more personal, and was shown to be more effective in engaging consumers (Chang et al. 2019).

Considering the possible reach and engagement influencers command on social media, companies have either begun embracing influencers on social media, or plan to expand their efforts in this domain even more. For example, in recent conversations we had with social media executives, several of them stated the growing importance of influencers and mentioned how brands generally are looking to incorporate influencer marketing into their marketing strategies. Further, recent conversations with executives at some globally leading brands suggest that influencer marketing spending by big brands continues to rise.

While influencer marketing on social media is not new, we believe it has a lot of potential to develop further as an industry. In a recent working paper, Duani et al. (2018) show that consumers enjoy watching a live experience much more and for longer time periods than watching a prerecorded one. Hence, we think live streaming by influencers will continue to grow, in broad domains as well as niche ones. For example, streaming of video game playing on Twitch, a platform owned by Amazon, may still be niche but shows no signs of slowing down. However, live platforms are limited by the fact that the influencers, being human, need to sleep and do other activities offline. Virtual influencers (i.e., "CGI" influencers that look human but are not), on the other hand, have no such limitations. They never get tired or sick, they do not even eat (unless it is needed for a campaign). Some brands have started exploring the use of virtual influencers (Nolan 2018), and we believe that in coming years, along with stronger computing power and artificial intelligence algorithms, virtual influencers will become much more prominent on social media, being able to invariably represent and act on brand values and engage with followers anytime.

There are many interesting future research avenues to consider when thinking about the role of influencers on social media. First, determining what traits and qualities (e.g., authenticity, trust, credibility, and likability) make sponsored posts by a traditional celebrity influencer, versus a micro-influencer, or even compared to a CGI influencer, more or less successful is important to determine for marketers. Understanding whether success has to do with the actual influencer's characteristics, the type of content being posted, whether content is sponsored or not, and so on, are all relevant concerns for companies and social media platforms when determining partnerships and where to invest effort in influencers. In addition, research can focus on understanding the appeal of live influencer content, and how to successfully blend influencer content with more traditional marketing mix approaches.

Privacy concerns on social media

Consumer concerns regarding data privacy, and their ability to trust brands and platforms are not new (for a review on data privacy see Martin and Murphy 2017). Research in marketing and related disciplines has examined privacy and trust concerns from multiple angles and using different definitions of privacy. For example, research has focused on the connections between personalization and privacy (e.g., Aguirre et al. 2015; White et al. 2008), the relationship of privacy as it relates to consumer trust and firm performance (e.g., Martin 2018; Martin et al. 2017), and the legal and ethical aspects of data and digital privacy (e.g., Culnan and Williams 2009; Nill and Aalberts 2014). Despite this topic not seeming novel, the way consumers, brands, policy makers, and social media platforms are all adjusting and adapting to these concerns are still in flux and without clear resolution.

Making our understanding of privacy concerns even less straightforward is the fact that, across extant literature, a clear definition of privacy is hard to come by. In one commentary on privacy, Stewart (2017), defined privacy as "being left alone," as this allows an individual to determine invasions of privacy. We build from this definition of privacy to speculate on a major issue in privacy and trust moving forward. Specifically, how consumers are adapting and responding to the digital world, where "being left alone" isn't possible. For example, while research has shown benefits to personalization tactics (e.g., Chung et al.

2016), with eroding trust in social platforms and brands that advertise through them, many consumers would rather not share data and privacy for a more personalized experiences, are uncomfortable with their purchases being tracked and think it should be illegal for brands to be able to buy their data (Edelman 2018). These recent findings seem to be in conflict with previously established work on consumer privacy expectations. Therefore, understanding if previously studied factors that mitigated the negative effects of personalization (e.g., perceived utility; White et al. 2008) are still valued by consumers in an ever-changing digital landscape is essential for future work.

In line with rising privacy concerns, the way consumers view brands and social media is becoming increasingly negative. Consumers are deleting their social media presence, where research has shown that nearly 40% of digitally connected individuals admitted to deleting at least one social media account due to fears of their personal data being mishandled (Edelman 2018). This is a negative trend not only for social media platforms, but for the brands and advertisers who have grown dependent on these avenues for reaching consumers. Edelman found that nearly half of the surveyed consumers believed brands to be complicit in negative aspects of content on social media such as hate speech, inappropriate content, or fake news (Edelman 2018). Considering that social media has become one of the best places for brands to engage with consumers, build relationships, and provide customer service, it's not only in the best interest of social media platforms to "do better" in terms of policing content, but the onus of responsibility has been placed on brands to advocate for privacy, trust, and the removal of fake or hateful content.

Therefore, to combat these negative consumer beliefs, changes will need to be made by everyone who benefits from consumer engagement on social media. Social media platforms and brands need to consider three major concerns that are eroding consumer trust: personal information, intellectual property and information security (Information Technology Faculty 2018). Considering each of these concerns, specific actions and initiatives need to be taken for greater transparency and subsequent trust. We believe that brands and agencies need to hold social media accountable for their actions regarding consumer data (e.g., GDPR in the European Union) for consumers to feel "safe" and "in control," two factors shown necessary in cases of privacy concerns (e.g., Tucker 2014; Xu et al. 2012). As well, brands need to establish transparent policies regarding consumer data in a way that recognizes the laws, advertising restrictions, and a consumer's right to privacy (a view shared by others; e.g., Martin et al. 2017). All of this is managerially essential for brands to engender feelings of trust in the increasingly murky domain of social media.

Future research can be conducted to determine consumer reactions to different types of changes and policies regarding data and privacy. As well, another related and important direction for future research, will be to ascertain the spillover effects of distrust on social media. Specifically, is all content shared on social media seen as less trustworthy if the platform itself is distrusted? Does this extend to brand messages displayed online? Is there a negative spillover effect to other user-generated content shared through these platforms?

The near future

In the previous section, we discussed three areas where we believe social media is immediately in flux. In this section, we identify three trends that have shown early signs of manifesting, and which we believe will meaningfully alter the social media landscape in the near, or not-too-distant, future. Each of these topics impact the stakeholders we mentioned when discussing the immediate social media landscape.

Combatting loneliness and isolation

Social media has made it easier to reach people. When Facebook was founded in 2004, their mission was "to give people the power to build community and bring the world closer together.. use Facebook to stay connected with friends and family, to discover what's going on in the world, and to share and express what matters to them" (Facebook 2019). Despite this mission, and the reality that users are more "connected" to other people than ever before, loneliness and isolation are on the rise. Over the last fifty years in the U.S., loneliness and isolation rates have doubled, with Generation Z considered to be the loneliest generation (Cigna 2018). Considering these findings with the rise of social media, is the fear that Facebook is interfering with real friendships and ironically spreading the isolation it was designed to conquer something to be considered about (Marche 2012)?

The role of social media in this "loneliness epidemic" is being hotly debated. Some research has shown that social media negatively impacts consumer well-being. Specifically, heavy social media use has been associated with higher perceived social isolation, loneliness, and depression (Kross et al. 2013; Primack et al. 2017; Steers et al. 2014). Additionally, Facebook use has been shown to be negatively correlated with consumer well-being (Shakya and Christakis 2017) and correlational research has shown that limiting social media use to 10 min can decrease feelings of loneliness and depression due to less FOMO (e.g., "fear of missing out;" Hunt et al. 2018).

On the other hand, research has shown that social media use alone is not a predictor of loneliness as other factors have to be considered (Cigna 2018; Kim et al. 2009). In fact, while some research has shown no effect of social media on well-being (Orben et al. 2019), other research has shown that social media can benefit individuals through a number of different avenues such as teaching and developing socialization skills, allowing greater communication and access to a greater wealth of

resources, and helping with connection and belonging (American Psychological Association 2011; Baker and Algorta 2016; Marker et al. 2018). As well, a working paper by Crolic et al. (2019) argues that much of the evidence of social media use on consumer well-being is of questionable quality (e.g., small and non-representative samples, reliance on self-reported social media use), and show that some types of social media use are positively associated with psychological well-being over time.

Managerially speaking, companies are beginning to respond as a repercussion of studies highlighting a negative relationship between social media and negative wellbeing. For example, Facebook has created "time limit" tools (mobile operating systems, such as iOS, now also have these time-limiting features). Specifically, users can now check their daily times, set up reminder alerts that pop up when a self-imposed amount of time on the apps is hit, and there is the option to mute notifications for a set period of time (Priday 2018). These different features seem well-intentioned and are designed to try and give people a more positive social media experience. Whether these features will be used is unknown.

Future research can address whether or not consumers will use available "timing" tools on one of many devices in which their social media exists (i.e., fake self-policing) or on all of their devices to actually curb behavior. It could also be the case that users will actually spend less time on Facebook and Instagram, but possibly spend that extra time on other competing social media platforms, or attached to devices, which theoretically will not help combat loneliness. Understanding how (and which) consumers use these self-control tools and how impactful they are is a potentially valuable avenue for future research.

One aspect of social media that has yet to be considered in the loneliness discussion through empirical measures, is the quality of use (versus quantity). Facebook ads have begun saying, "The best part of Facebook isn't on Facebook. It's when it helps us get together" (Facebook 2019). There have been discussions around the authenticity of this type of message, but at its core, in addition to promoting quantity differences, it's speaking to how consumers use the platform. Possibly, to facilitate this message, social media platforms will find new ways to create friend suggestions between individuals who not only share similar interests and mutual friends to facilitate in-person friendships (e.g., locational data from the mobile app service). Currently there are apps that allow people to search for friends that are physically close (e.g., Bumble Friends), and perhaps social media will go in this same direction to address the loneliness epidemic and stay current.

Future research can examine whether the quantity of use, types of social media platforms, or the way social media is used causally impacts perceived loneliness. Specifically, understanding if the negative correlations found between social media use and well-being are due to the demographics of individuals who use a lot of social media, the way social media works, or the way users choose to engage with the platform will be important for understanding social media's role (or lack of role) in the loneliness epidemic.

Integrated customer care

Customer care via digital channels as we know it is going to change substantially in the near future. To date, many brands have used social media platforms as a place for providing customer care, addressing customers' specific questions, and fixing problems. In the future, social media-based customer care is expected to become even more customized, personalized, and ubiquitous. Customers will be able to engage with firms anywhere and anytime, and solutions to customers' problems will be more accessible and immediate, perhaps even pre-emptive using predictive approaches (i.e., before a customer even notices an issue or has a question pop into their mind).

Even today, we observe the benefits that companies gain from connecting with customers on social media for service- or care- related purposes. Customer care is implemented in dedicated smartphone apps and via direct messaging on social media platforms. However, it appears that firms want to make it even easier for customers to connect with them whenever and wherever they might need. Requiring a customer to download a brand specific app or to search through various social media platforms to connect with firms through the right branded account on a platform can be a cumbersome process. In those cases, customers might instead churn or engage in negative WOM, instead of connecting with the firm to bring up any troubles they might have.

The near future of customer care on social media appears to be more efficient and far-reaching. In a recent review on the future of customer relationship management, Haenlein (2017) describes "invisible CRM" as future systems that will make customer engagement simple and accessible for customers. New platforms have emerged to make the connection between customer and firm effortless. Much of this is via instant messaging applications for businesses, which several leading technology companies have recently launched as business-related features in existing platforms (e.g., contact business features in Facebook Messenger and WhatsApp or Apple's Business Chat).

These technologies allow businesses to directly communicate via social media messaging services with their customers. Amazon, Apple, Facebook, and Google are in the process, or have already released early versions of such platforms (Dequier 2018). Customers can message a company, ask them questions, or even order products and services through the messaging system, which is often built around chatbots and virtual assistants. This practice is expected to become more widespread, especially because it puts brands and companies into the social media messaging platforms their customers already use to communicate with others, it provides quicker-even instantaneous-responses, is economically scalable through the use of AI-

driven chatbots, and, despite the use of chatbots, can provide a more personalized level of customer service.

Another area that companies will greatly improve upon is data collection and analysis. While it is true that data collection on social media is already pervasive today, it is also heavily scrutinized. However, we believe that companies will adapt to the latest regulation changes (e.g., GDPR in Europe, CCPA in California) and improve on collecting and analyzing anonymized data (Kakatkar and Spann 2018). Furthermore, even under these new regulations, personalized data collection is still allowed, but severely limits firm's abilities to exploit consumers' data, and requires their consent for data collection.

We believe that in the future, companies will be able recognize early indications of problems within customer chatter, behavior, or even physiological data (e.g., monitoring the sensors in our smart watches) before customers themselves even realize they are experiencing a problem. For example, WeWork, the shared workspace company, collects data on how workers move and act in a workspace, building highly personalized workspaces based on trends in the data. Taking this type of approach to customer care will enable "seamless service," where companies would be able to identify and address consumer problems when they are still small and scattered, and while only a small number of customers are experiencing problems. Customer healthcare is a pioneer in this area, where using twitter and review sites were shown to predict poor healthcare quality (Greaves et al. 2013), listen to patients to analyze trending terms (Baktha et al. 2017; Padrez et al. 2016), or even predict disease outbreaks (Schmidt 2012).

Companies, wanting to better understand and mimic human interactions, will invest a lot of R&D efforts into developing better Natural Language Processing, voice and image recognition, emotional analysis, and speech synthesis tools (Sheth 2017). For example, Duplex, Google's latest AI assistant, can already call services on its own and seamlessly book reservations for their users (Welch 2018). In the future, AI systems will act as human ability augmenters, allowing us to accomplish more, in less time, and better results (Guszcza 2018).

For marketers, this will reduce the need for call centers and agents, reducing points of friction in service and increasing the convenience for customers (Kaplan and Haenlein 2019). However, some raise the question that the increased dependence on automation may result in a loss of compassion and empathy. In a recent study, Force (2018) shows that interacting with brands on social media lowered people's empathy. In response to such concerns, and to educate and incentivize people to interact with machines in a similar way they do with people, Google programmed their AI assistant to respond in a nicer way if you use a polite, rather than a commanding approach (Kumparak 2018). While this might help, more research is needed to understand the effect of an AI rich world on human behavior. As well, future research can examine how consumer generated data can help companies preemptively predict consumer distress. Another interesting path for research would be to better understand the difference in consumer engagement between the various platforms, and the long-term effects of service communications with non-human AI and IoT.

Social media as a political tool

Social media is a platform to share thoughts and opinions. This is especially true in the case of disseminating political sentiments. Famously, President Barack Obama's victory in the 2008 election was partially attributed to his ability to drive and engage voters on social media (Carr 2008). Indeed, Bond et al. (2012) have shown that with simple interventions, social media platforms can increase targeted audiences' likelihood of voting. Social media is considered one of the major drivers of the 2010 wave of revolutions in Arab countries, also known as the Arab Spring (Brown et al. 2012).

While social media is not new to politics, we believe that social media is transitioning to take a much larger role as a political tool in the intermediate future. First evidence for this could be seen in the 2016 U.S. presidential election, as social media took on a different shape, with many purported attempts to influence voter's opinions, thoughts, and actions. This is especially true for then-candidate and now-President Donald Trump. His use of Twitter attracted a lot of attention during the campaign and has continued to do so during his term in office. Yet, he is not alone, and many politicians changed the way they work and interact with constituents, with a recent example of Congresswoman Alexandria Ocasio-Cortez that even ran a workshop for fellow congress members on social media (Dwyer 2019).

While such platforms allow for a rapid dissemination of ideas and concepts (Bonilla and Rosa 2015; Bode 2016), there are some, both in academia and industry that have raised ethical concerns about using social media for political purposes. Given that people choose who to follow, this selective behavior is said to potentially create echo chambers, wherein, users are exposed only to ideas by like-minded people, exhibiting increased political homophily (Bakshy et al. 2015). People's preference to group with like-minded people is not new. Social in-groups have been shown to promote social identification and promote in- group members to conform to similar ideas (Castano et al. 2002; Harton and Bourgeois 2004). Furthermore, it was also shown that group members strongly disassociate and distance themselves from outgroup members (Berger and Heath 2008; White and Dahl 2007). Thus, it is not surprising to find that customized newsfeeds within social media exacerbate this problem by generating news coverage that is unique to specific users, locking them in their purported echo chambers (Oremus 2016).

While social media platforms admit that echo chambers could pose a problem, a solution is not clear (Fiegerman 2018). One reason that echo chambers present such a problem, is their proneness to fake news. Fake news are fabricated stories that try to disguise themselves as authentic content, in order to affect other social media users. Fake news was widely used in the

2016 U.S. elections, with accusations that foreign governments, such as Iran and Russia, were using bots (i.e., online automatic algorithms), to spread falsified content attacking Hillary Clinton and supporting President Trump (Kelly et al. 2018). Recent research has furthermore shown how the Chinese government strategically uses millions of online comments to distract the Chinese public from discussing sensitive issues and promote nationalism (King et al. 2017). In their latest incarnation, fake news uses an advanced AI technique called "Deep Fake" to generate ultra-realistic forged images and videos of political leaders while manipulating what those leaders say (Schwartz 2018). Such methods can easily fool even the sharpest viewer. In response, research has begun to explore ways that social media platforms can combat fake news through algorithms that determine the quality of shared content (e.g., Pennycook and Rand 2019).

One factor that has helped the rise of fake news is echo chambers. This occurs as the repeated sharing of fake news by group members enhance familiarity and support (Schwarz and Newman 2017). Repetition of such articles by bots can only increase that effect. Recent research has shown that in a perceived social setting, such as social media, participants were less likely to fact-check information (Jun et al. 2017), and avoided information that didn't fit well with their intuition (Woolley and Risen 2018). Schwarz and Newman (2017) state that misinformation might be difficult to correct, especially if the correction is not issued immediately and the fake news has already settled into the minds of users. It was also shown that even a single exposure to fake news can create long term effect on users, making their effect larger than previously thought (Pennycook et al. 2019).

Notably, some research has found that exposure to opposing views (i.e., removing online echo chambers) may in fact increase (versus decrease) polarization (Bail et al. 2018). Accordingly, more work from policy makers, businesses, and academics is needed to understand and potentially combat political extremism. For example, policy makers and social media platforms will continually be challenged to fight "fake news" without censoring free speech. Accordingly, research that weighs the risk of limited freedom of expression versus the harms of spreading fake news would yield both theoretical and practically meaningful insights.

The far future

In this section, we highlight three emerging trends we believe will have a have long-term influence on the future of social media. Note that although we label these trends as being in the "far" future, many of the issues described here are already present or emerging. However, they represent more complex issues that we believe will take longer to address and be of mainstream importance for marketing than the six issues discussed previously under the immediate and near futures.

Increased sensory richness

In its early days, the majority of social media posts (e.g., on Facebook, Twitter) were text. Soon, these platforms allowed for the posting of pictures and then videos, and separate platforms dedicated themselves to focus on these specific forms of media (e.g., Instagram and Pinterest for pictures, Instagram and SnapChat for short videos). These shifts have had demonstrable consequences on social media usage and its consequences as some scholars suggest that image-based posts convey greater social presence than text alone (e.g., Pittman and Reich 2016). Importantly however, a plethora of new technologies in the market suggest that the future of social media will be more sensory-rich.

One notable technology that has already started infiltrating social media is augmented reality (AR). Perhaps the most recognizable examples of this are Snapchat's filters, which use a device's camera to superimpose real-time visual and/or video overlays on people's faces (including features such as makeup, dog ears, etc.). The company has even launched filters to specifically be used on users' cats (Ritschel 2018). Other social media players quickly joined the AR bandwagon, including Instagram's recent adoption of AR filters (Rao 2017) and Apple's Memoji messaging (Tillman 2018). This likely represents only the tip of the iceberg, particularly given that Facebook, one of the industry's largest investors in AR technology, has confirmed it is working on AR glasses (Constine 2018). Notably, the company plans to launch a developer platform, so that people can build augmented-reality features that live inside Facebook, Instagram, Messenger and Whatsapp (Wagner 2017). These developments are supported by academic research suggesting that AR often provides more authentic (and hence positive) situated experiences (Hilken et al. 2017). Accordingly, whether viewed through glasses or through traditional mobile and tablet devices, the future of social media is likely to look much more visually augmented.

While AR allows users to interact within their current environments, virtual reality (VR) immerses the user in other places, and this technology is also likely to increasingly permeate social media interactions. While the Facebook-owned company Oculus VR has mostly been focusing on the areas of immersive gaming and film, the company recently announced the launch of Oculus Rooms where users can spend time with other users in a virtual world (playing games together, watching media together, or just chatting; Wagner 2018). Concurrently, Facebook Spaces allows friends to meet online in virtual reality and similarly engage with one another, with the added ability to share content (e.g., photos) from their Facebook profiles (Whigham 2018). In both cases, avatars are customized to represent users within the VR-created space. As VR technology is becoming more affordable and mainstream (Colville 2018) we believe social media will inevitably play a role in the technology's increasing usage.

While AR and VR technologies bring visual richness, other developments suggest that the future of social media might also be

more audible. A new player to the social media space, HearMeOut, recently introduced a platform that enables users to share and listen to 42-s audio posts (Perry 2018). Allowing users to use social media in a hands-free and eyes-free manner not only allows them to safely interact with social media when multitasking (particularly when driving), but voice is also said to add a certain richness and authenticity that is often missing from mere text-based posts (Katai 2018). Given that podcasts are more popular than ever before (Bhaskar 2018) and voice-based search queries are the fastest-growing mobile search type (Robbio 2018), it seems likely that this communication modality will accordingly show up more on social media use going forward.

Finally, there are early indications that social media might literally feel different in the future. As mobile phones are held in one's hands and wearable technology is strapped onto one's skin, companies and brands are exploring opportunities to communicate to users through touch. Indeed, haptic feedback (technology that recreates the sense of touch by applying forces, vibrations, or motions to the user; Brave et al. 2001) is increasingly being integrated into interfaces and applications, with purposes that go beyond mere call or message notifications. For example, some companies are experimenting with integrating haptics into media content (e.g., in mobile ads for Stoli vodka, users feel their phone shake as a woman shakes a cocktail; Johnson 2015), mobile games, and interpersonal chat (e.g., an app called Mumble! translates text messages into haptic outputs; Ozcivelek 2015). Given the high levels of investment into haptic technology (it is predicted to be a $20 billion industry by 2022; Magnarelli 2018) and the communicative benefits that stem from haptic engagement (Haans and IJsselsteijn 2006), we believe it is only a matter of time before this modality is integrated into social media platforms.

Future research might explore how any of the new sensory formats mentioned above might alter the nature of content creation and consumption. Substantively-focused researchers might also investigate how practitioners can use these tools to enhance their offerings and augment their interactions with customers. It is also interesting to consider how such sensory-rich formats can be used to bridge the gap between the online and offline spaces, which is the next theme we explore.

Online/offline integration and complete convergence

A discussion occurring across industry and academia is on how marketers can appropriately integrate online and offline efforts (i.e., an omnichannel approach). Reports from industry sources have shown that consumers respond better to integrated marketing campaigns (e.g., a 73% boost over standard email campaigns; Safko 2010). In academia meanwhile, the majority of research considering online promotions and advertisements has typically focused on how consumers respond to these strategies through online only measures (e.g., Manchanda et al. 2006), though this has begun to change in recent years with more research examining offline consequences to omnichannel strategies (Lobschat et al. 2017; Kumar et al. 2017).

Considering the interest in integrated marketing strategies over the last few years, numerous strategies have been utilized to follow online and offline promotions and their impacts on behavior such as the usage of hashtags to bring conversations online, call-to-actions, utilizing matching strategies on "traditional" avenues like television with social media. While there is currently online/offline integration strategies in marketing, we believe the future will go even further in blurring the lines between what is offline and online to not just increase the effectiveness of marketing promotions, but to completely change the way customers and companies interact with one another, and the way social media influences consumer behavior not only online, but offline.

For brands, there are a number of possible trends in omnichannel marketing that are pertinent. As mentioned earlier, a notable technology that has begun infiltrating social media is augmented reality (AR). In addition to what already exists (e.g., Snapchat's filters, Pokémon Go), the future holds even more possibilities. For example, Ikea has been working to create an AR app that allows users to take photos of a space at home to exactly , down to the millimeter size and lighting in the room, showcase what a piece of furniture would look like in a consumer's home (Lovejoy 2017). Another set of examples of AR comes from beauty company L'Oréal. In 2014 for the flagship L'Oréal Paris brand they released a mobile app called Makeup Genius that allowed consumers to virtually try on makeup on their phones (Stephen and Brooks 2018). Since then, they have developed AR apps for hair color and nail polish, as well as integrating AR into mobile ecommerce webpages for their luxury beauty brand Lancôme. AR-based digital services such as these are likely to be at the heart of the next stage of offline/online integration.

AR, and similar technology, will likely move above and beyond being a tool to help consumers make better decisions about their purchases. Conceivably, similar to promotions that currently exist to excitse consumers and create communities, AR will be incorporated into promotions that integrate offline and online actions. For example, contests on social media will advance to the stage where users get to vote on the best use of AR technology in conjunction with a brand's products (e.g., instead of users submitting pictures of their apartments to show why they should win free furniture, they could use AR to show how they would lay out the furniture if they were to win it from IKEA).

Another way that the future of online/offline integration on social media needs to be discussed is in the sense of a digital self. Drawing on the extended self in the digital age (Belk 2013), the way consumers consider online actions as relevant to their offline selves may be changing. For example, Belk (2013) spoke of how consumers may be re-embodied through avatars they create to represent themselves online, influencing their offline selves and creating a multiplicity of selves (i.e., consumers have more choice when it comes to their self-representation). As research has shown how digital and social media can be used for self-presentation, affiliation, and expression (Back et al. 2010; Gosling et al. 2007; Toubia and Stephen 2013; Wilcox and Stephen 2012), what does it mean for the future if consumers can create who they want to be?

In addition, when considering digital selves, what does this mean for how consumers engage with brands and products? Currently, social media practice is one where brands encourage consumer engagement online (Chae et al. 2017; Godes and Mayzlin 2009), yet the implications for how these types of actions on the part of the brand to integrate online social media actions and real-life behavior play out are unclear. Research has begun to delve into the individual-level consequences of a consumer's social media actions on marketing relevant outcomes (Grewal et al. 2019; John et al. 2017; Mochon et al. 2017; Zhang et al. 2017), however much is still unknown. As well, while there is recent work examining how the device used to create and view content online impacts consumer perceptions and behaviors (e.g., Grewal and Stephen 2019), to date research has not examined these questions in the context of social media. Therefore, future research could address how digital selves (both those held offline and those that only exist online), social media actions, and if the way consumers reach and use various platforms (i.e., device type, app vs. webpage, etc.) impact consumer behavior, interpersonal relationships, and brand-related measures (e.g., well-being, loyalty, purchase behaviors).

Social media by non-humans

The buzz surrounding AI has not escaped social media. Indeed, social bots (computer algorithms that automatically produce content and interact with social media users; Ferrara et al. 2016) have inhabited social media platforms for the last decade (Lee et al. 2011), and have become increasingly pervasive. For example, experts estimate that up to 15% of active Twitter accounts are bots (Varol et al. 2017), and that percentage appears to be on the rise (Romano 2018). While academics and practitioners are highly concerned with bot detection (Knight 2018), in the vast majority of current cases, users do not appear to recognize when they are interacting with bots (as opposed to other human users) on social media (Stocking and Sumida 2018). While some of these bots are said to be benign, and even useful (e.g., acting as information aggregators), they have also been shown to disrupt political discourse (as mentioned earlier), steal personal information, and spread misinformation (Ferrara et al. 2016).

Of course, social bots are not only a problem for social media users but are also a nagging concern plaguing marketers. Given that companies often assess marketing success on social media through metrics like Likes, Shares, and Clicks, the existence of bots poses a growing threat to accurate marketing metrics and methods for ROI estimation, such as attribution modelling (Bilton 2014). Similarly, when these bots act as "fake followers," it can inflate the worth of influencers' audiences (Bogost 2018). This can also be used nefariously by individuals and firms, as shown in a New York Times Magazine expose that documented the market used by some influencers to purchase such "fake" followers to inflate their social media reach (Confessore et al. 2018). As discussed above in relation to influencer marketing, where it has been commonplace for influencers to be paid for posts at rates proportionate to their follower counts, there have been perverse incentives to game the system by having non- human "fake" bot followers. This, however, erodes consumer trust in the social media ecosystem, which is a growing issue and a near-term problem for many firms using social media channels for marketing purposes.

However, there are instances when consumers do know they are interacting with bots, and do not seem to mind. For example, a number of virtual influencers (created with CGI, as mentioned earlier) seem to be garnering sizeable audiences, despite the fact they are clearly non-human (Walker 2018). One of the most popular of these virtual influencers, Lil Miquela, has over 1.5 million followers on Instagram despite openly confessing, "I am not a human being.. I'm a robot" (Yurieff 2018). Future research might try to understand the underlying appeal of these virtual influencers, and the potential boundary conditions of their success.

Another category of social bots gaining increasing attention are therapy bots. These applications (e.g., "Woebot;" Molteni 2017) aim to support the mental health of users by proactively checking in on them, "listening" and chatting to users at any time and recommending activities to improve users' wellbeing (de Jesus 2018). Similar bots are being used to "coach" users, and help them quit maladaptive behaviors, like smoking (e.g., QuitGenius; Crook 2018). Interestingly, by being explicitly non-human, these agents are perceived to be less judgmental, and might accordingly be easier for users to confide in.

Finally, the Internet of Things revolution has ushered in with it the opportunity for a number of tangible products and interfaces to "communicate" via social media. For example, in what started as a design experiment, "Brad," a connected toaster, was given the ability to "communicate" with other connected toasters, and to tweet his "feelings" when neglected or under-used (Vanhemert 2014). While this experiment was deliberately designed to raise questions about the future of consumer-product relationships (and product-product "relationships"), the proliferation of autonomous tangible devices does suggest a future in which they have a "voice," even in the absence of humans (Hoffman and Novak 2018).

Going forward, we believe the presence of bots on social media will be more normalized, but also more regulated (e.g., a recent law passed in California prevents bots from masquerading as humans; Smith 2018). Further, consumers and companies alike will be become increasingly interested in how bots communicate and interact with each other outside of human involvement. This brings up interesting potential research questions for academics and practitioners alike. How will the presence of non-humans change the nature of content creation and conversation in social media? And how should companies best account for the presence of non-humans in their attribution models?

Future research directions and conclusion

Time Theme Brief description of theme Suggested research directions and example research questions

Immediate future

Omni-social presence

Consumers now live in a world in which most aspects of their lives can potentially intersect with social media and this digitally enabled social interactivity is shaping culture itself.

* How will social interactivity influence consumer behavior in areas that had traditionally been non-social?

* How might marketers strategically address the flatter decision-making funnel that social media enables?

* How might service providers best alter experiential consumption when anticipating social media sharing?

The rise of influencers

Prominent social media actors are leveraging their influence to collaborate with brands. Companies incorporate influencers into their marketing mix and are creating "virtual influencers" of their own.

* What drives the appeal of live influencer content?

* How can marketers strategically identify and employ influencers as part of the marketing mix?

* How virtual influencers affect consumers' perception of brands?

* Is there a difference between virtual and real influencers in their effect on consumers?

Privacy concerns on social media

Consumer trust in social media is on the decline. Consumers worry about the privacy of their data, and this worry and distrust is transferring from just the platforms to brands and companies.

* Who and what is trusted on social media? What makes this trust higher or lower?

* What can be done to win back consumer trust on the part of the platforms and brands?

This article has presented nine themes pertinent to the future of social media as it relates to (and is perhaps influenced by) marketing. The themes have implications for individuals/consumers, businesses and organizations, and also public policymakers and governments. These themes, which represent our own thinking and a synthesis of views from extant research, industry experts, and popular public discourse, are of course not the full story of what the future of social media will entail. They are, however, a set of important issues that we believe will be worth considering in both academic research and marketing practice.

To stimulate future research on these themes and related topics, we present a summary of suggested research directions in Table 2. These are organized around our nine themes and capture many of the suggested research directions mentioned earlier. As a sub-field within the field of marketing, social media is already substantial and the potential for future research- based on identified needs for new knowledge and answers to perplexing questions-suggests that this sub-field will become even more important over time. We encourage researchers to consider the kinds of research directions in Table 2 as examples of issues they could explore further. We also encourage researchers in marketing to treat social media as a place where interesting (and often very new) consumer behaviors exist and can be studied. As we discussed earlier in the paper, social media as a set of platform businesses and technologies is interesting, but it is how people use social media and the associated technologies that is ultimately of interest to marketing academics and practitioners. Thus, we urge scholars to not be overly enticed by the technological "shiny new toys" at the expense of considering the behaviors associated with those technologies and platforms.

Suggested directions for future research

* Is there any way for consumers to feel as though losing some data privacy is worth it due to benefits?

Near Future

Combating loneliness and isolation

There is conflicting research that exists regarding social media's role in causing consumer loneliness and isolation, leading to calls to revolutionize how social media is used.

* What about social media impacts loneliness perceptions (e.g., quantity of use, use type, platform)?

* Are there individual characteristics correlated with social media use and loneliness?

* Are there ways for social media platforms to encourage more meaningful connections vs. social comparison?

Integrated customer care

Social media, using improved analytics tools, and unprecedented knowledge on consumers will allow for an almost "invisible" customer care. Customers will be able to interact with firms seamlessly from almost any device.

* How can marketers preemptively predict and respond to consumer distress?

* Do customers engage and perceive customer service differently on different platforms (e.g., AI assistant, chatbots, mobile messaging)?

* How will the increased interaction with AI and IoT affect consumer behavior?

Social Media as a Political Tool

Social media is used by politicians to directly engage with voters, evoking series of new challenges for policymakers, such as increased polarization, echo chambers, and fake news.

* What can be done to reduce polarization in social media?

* What is the effect of eco chambers on long term behaviors?

* How can we successfully identify and negate the effects of fake news?

Far Future Increased Sensory Richness

A plethora of new technologies, including augmented reality, virtual reality, voice activation, and haptic integration market suggest that the future of social media will become increasingly sensory- rich.

* How might these new sensory formats alter the nature of content creation and consumption?

* How might practitioners use these tools to enhance their offerings and augment their interactions with customers?

* How might such sensory-rich formats be used to bridge the gap between the online and offline spaces?

Online/Offline Integration and Complete Convergence

The lines between what is offline and online are blurring, changing how consumers interact with other consumers, companies, and products and experiences.

* How is tech like AR going to change the way consumers interact with brands, social media platforms, other consumers, and offline experiences?

* What are some repercussions of digital selves considering consumer behavior and brand-related measures?

* How do digital selves that differ from offline personas, impact consumer attitudes and behaviors?

Social Media by Non-Humans

Artificial intelligence in the form of bots, virtual influencers, and IoT devices will increasingly permeate the social media sphere.

* How will the presence of non- humans change the nature of content creation and conversation in social media?

* What is the underlying appeal of virtual influencers?

* How should companies account for the presence of non-humans in their attribution models?

Finally, while we relied heavily (though not exclusively) on North American examples to illustrate the emergent themes, there are likely interesting insights to be drawn by explicitly exploring cross-cultural differences in social media usage. For example, variations in regulatory policies (e.g., GDPR in the European Union) may lead to meaningful differences in how trust and privacy concerns manifest. Further, social media as a political tool might be more influential in regions where the mainstream media is notoriously government controlled and censored (e.g., as was the case in many of the Arab Spring countries). While such cross-cultural variation is outside the scope of this particular paper, we believe it represents an area of future research with great theoretical and practical value.

In reviewing the social media ecosystem and considering where it is heading in the context of consumers and marketing practice, we have concluded that this is an area that is very much still in a state of flux. The future of social media in marketing is exciting, but also uncertain. If nothing else, it is vitally important that we better understand social media since it has become highly culturally relevant, a dominant form of communication and expression, a major media type used by companies for advertising and other forms of communication, and even has geopolitical ramifications. We hope that the ideas discussed here stimulate many new ideas and research, which we ultimately hope to see being mentioned and shared across every type of social media platform.

Acknowledgements

The authors thank the special issue editors and reviewers for their comments, and the Oxford Future of Marketing Initiative for supporting this research. The authors contributed equally and are listed in alphabetical order or, if preferred, order of Marvel superhero fandom from highest to lowest and order of Bon Jovi fandom from lowest to highest.

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pp. 79+. Gale OneFile: Communications and Mass Media, link.gale.com/apps/doc/A610971747/PPCM?u=miam11506&sid=bookmark-PPCM&xid=0196002d. Accessed 4 Feb. 2022.

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Forster 17

Advertising on Social Network Sites: A Structural Equation Modelling Approach

Anant Saxena Uday Khanna

Abstract Social networking sites (SNSs) emerged as one of the most powerful media for advertising across the globe. Globally, companies are shifting a larger pie of their advertising budgets towards social networking sites for better reach and interactive platform. The companies are also looking at it as a low-cost model, which could reap results in minimum time possible for the targeted ‘Facebook generation’. These very facts motivate researchers to study the value of advertisements on social networking sites like Facebook, LinkedIn, Twitter and others. The article is an empirical study to understand the implications of different variables in advertisements on the delivery of advertising value to the respondents. Confirmatory factor analysis (CFA) has been conducted to test the reliability of instrument being used for data collection. Further, a model has been proposed for measuring advertising value through structural equation modelling. The predicted results confirm the roles of different variables, namely, information, entertainment and irritation, in accessing value of advertisements displayed on social networking sites.

Key Words Advertising Value, Social Networking Sites, Structural Equation Modelling

Introduction Social networking websites (SNSs) have emerged as the ‘need of an hour’. Their journey started with the launch of sixdegrees.com in the year 1997, which attracted millions of users at that time. The site allowed the users to create profiles listing their friends with the ability to surf the friends list (Boyd and Ellison, 2007). This has been followed by an array of SNSs like Facebook, Orkut, Linkedin and MySpace in the year 2003–2004. Within a short span of time, these websites become an addiction for youngsters as these give them opportunity and platform to express their feelings and emotions in the society. Websites like Facebook, Orkut, Twitter and MySpace have become household names and an integral part of people’s life so much that it has become tough for regular users to imagine a life without them. Globally, Internet users spend more than four and a half hours per week on SNSs, more time than they spend on e-mail (Anderson et al., 2011). As more and more of what people think and do ends up getting expressed on SNSs, it is expected that SNSs affect the buying decisions greatly. In addition, the huge viewer’s base of these websites makes them a favourable media for advertisements by different companies. According to a study done by comScore, Inc., a market research firm, SNSs accounted for more than 20 per cent, that is, one in

five, display ads of all display ads viewed online, with Facebook and MySpace combining to deliver more than 80 per cent of ads among sites in the social networking category (comScore, 2009). According to Rizavi et al. (2011) social networking websites act as a good platform for advertising that attract millions of users from different countries, speaking multiple languages belonging to different demographics. According to Trusov et al. (2009) referrals and recommendations on SNSs have a significant impact on new customer acquisition and retention. This fact led marketers to turn to Internet platforms like SNSs, blogs and other social media as an avenue for cost-effective marketing, employing e-mail campaigns, website adver- tisements and viral marketing. Also from a marketing perspective, these websites give potential customers the opportunity to virtually explore a business, encourage them to visit and at last share their views and experiences with their friends (Phillips et al., 2010). Understanding the effectiveness of SNSs in promoting product and services through advertisements, companies across the globe have increased their advertising budget for SNSs which has led to increase in revenue generation for social networking website companies. According to a report released by Interactive Advertising Bureau (IAB), Internet advertising revenues totaled $14.9 billion in 2011, up 23 per cent from the $12.1 billion reported in 2010

Vision 17(1) 17–25 © 2013 MDI

SAGE Publications Los Angeles, London,

New Delhi, Singapore, Washington DC

DOI: 10.1177/0972262912469560 http://vision.sagepub.com

Article

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(PricewaterhouseCoopers LLP., 2011). India shares the same story in terms of Internet advertising revenues.

According to a report, the size of Internet advertising industry was INR 7.7 billion in 2010 registering a growth of 28.3 per cent over INR 6 billion in 2009 (PricewaterhouseCoopers Private Limited, 2011). The same report highlights that in India SNSs have shown a remarkable growth of 43 per cent in 2010 over 2009, with a 54 per cent growth in advertising on SNSs in 2010– 2011 (PricewaterhouseCoopers Private Limited, 2011). Considering the fact that advertising on SNSs is on a new high, this research focus on studying the value of advertisements being displayed on SNSs.

Literature Review and Hypothesis Web advertising continues to be a major area of advertising research from a long time. A number of studies have been done discussing advertisements on the Web and their effects. Berthon et al. (1996) have discussed the role of World Wide Web as an advertising medium in the mar- keting communication mix and proved that World Wide Web is a new medium for advertising characterized by ease-of-entry, relatively low set-up costs, globalness, time independence and interactivity. In spite of the acceptance of World Wide Web as an effective media for advertising, few studies have focused on the value of advertisements displayed on this medium. R.H. Ducoffe introduced the concept of advertisement value in 1995. According to Ducoffe (1995) advertising value is defined as the utility or worth of the advertisement. Ducoffe (1996), in his another study on World Wide Web, proved the significant impact (either +ve or –ve) of entertainment, information and irritation on advertisement value. Brackett and Carr (2001) in their study on cyberspace advertising reports that information, entertainment, irritation and credibility significantly affect advertisement value which in turn affects attitude towards advertisements. Discussion on different predictors of advertisement value with reference to SNSs advertisements is hereby illustrated:

1. Information: Information content is an important determinant of advertisement effectiveness. Comp- anies advertise for one main reason—providing information about their product, services and brand to consumers. Consumers reported that supplying information is the primary reason why they approve advertising (Bauer et al., 1968). According to Norris (1984) information in advertisements enables the customers to evaluate the products more rationally leading to improved markets with low prices and high quality of the product. Information content on Internet can be delivered better in comparison to

television medium, reason being short time span of television advertisements. Yoon and Kim (2001) mentioned that Internet advertising differs from tra- ditional advertising as it delivers unlimited informa- tion beyond time and space and it gives unlimited amount and sources of information. Web advertise- ments provide information and generate awareness without interactive involvement (Berthon et al., 1996). On the contrary, information delivered through SNSs advertisements is different from tra- ditional Web advertisements because SNSs provide a medium that is interactive in nature. A person could scan and share information with online friends and followers, thus making the advertisement infor- mation viral in nature. Large media companies have realized the potential of SNSs to reach and deepen relationships with the ‘subscribed’ audience (Jhih- Syuan and Pena, 2011). This specialty of SNSs advertisement makes it the most competitive plat- form for sharing information about products and services. As the delivery and importance of infor- mation for SNSs advertisements is different from other forms of advertisements, it is important to note its effect on advertisement value. Based on this rationale, the hypothesis tested is:

H1: There is a significant positive impact of infor- mation content of advertisements on the value of advertisements displayed on social networking websites.

2. Entertainment: An advertisement that is full of information but nil in entertainment content is not worthy. According to McQuail (1994) an advertise- ment entertains when it fulfils the audience needs for escapism, diversion, aesthetic enjoyment or emotional release. The ability of advertising to entertain can enhance the experience of advertising. In addition, an advertisement could be information for one and entertainment for other person at the same time (Alwitt and Prabhaker, 1992). Consumers who found advertising to be entertaining also evalu- ate it as informative (Ducoffe, 1995). This shows that entertainment and information are interrelated concepts when talking about advertisements. SNSs platform is interactive in nature and display banner advertisements of different brands at the same platform and same time; they have the power to entertain the audience. Kim and Lee (2010) noted that college students use SNSs for six main reasons: entertainment, passing time, social interaction, information seeking, information provi- ding, and professional advancement. According to

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Taylor et al. (2011) SNSs advertisements provide entertainment value to the audience. The same study reported that entertainment exhibits almost four times more strength of influence on favourable con- sumers’ attitude towards advertisements than infor- mation. With reference to the existing literature, it is important to find the impact of entertainment on advertisement value of SNSs advertisements. In the same vein, the hypothesis tested is:

H2: There is a significant positive impact of enter- tainment content of advertisements on the value of advertisements displayed on social networking websites.

3. Irritation: Irritation from advertisements arises when we feel discomfort in watching advertisement due to any reason. The reason can be personal or social. A personal reason could be distraction while focusing on a particular task on World Wide Web. According to Wells et al. (1971) irritation is one amongst six dimensions of personal reactions towards advertising. It is the degree to which the viewer disliked the contents that he had seen. The words that came into the mind of the viewer at time of getting irritated from an advertisement are ‘terrible’, ‘stupid’, ‘ridiculous’, ‘irritating’ and ‘phony’. An advertisement can be rewarding for some viewers and yet be an irritant and unrewarding for others (Alwitt and Prabhaker, 1992). According to Aaker and Bruzzone (1985), increase in irritation can lead to general reduction in the effectiveness of advertisement. In case of Internet advertising, it also generates considerable irritation (Schlosser et al., 1999). As online behaviour including use of SNSs is highly goal oriented, advertisements on SNSs might irritate the user (Taylor et al., 2011). The lit- erature suggested that irritation has a negative effect on the effectiveness of advertisement irrespective of the media. Based on this rationale the hypothesis tested is:

H3: There is a significant negative impact of irrita- tion content of advertisements on the value of adver- tisements displayed on social networking websites.

A considerable amount of research on determinants of Web advertising effectiveness and value has been done (Berthon et al., 1996; Brown et al., 2007; Ducoffe, 1995; Lei, 2000; Schlosser et al., 1999; Yoon and Kim, 2001); however, these studies were more focused on traditional websites rather than SNSs. Advertising through SNSs is different from traditional websites due to several reasons.

First, advertisements on SNSs are different not only in form and substance but also in delivery method. Some of the messages are ‘pushed’ upon consumers while others rely on consumers to ‘pull’ content; some generate revenue whereas some are non-paid content delivered through media content (Taylor et al., 2011). Second, SNSs have their own unique user-to-user interface (Safko and Brake, 2009). Third, SNSs users are increasing day by day all over the world, which makes this medium suitable for advertising. As SNSs advertising is different from traditional Web advertising and a little is known about value of SNSs advertisements, this study tries to fill this research gap by providing a model, which tests the interrelationships between different determinants of advertisement value.

Model Testing The importance of advertisements displayed on SNSs is increasing day by day. According to Stelzner (2011) 88 per cent of the marketers have reported that their social media advertisements have generated more exposure for their businesses. This leads the authors to test a model for accessing the value of advertisements displayed on SNSs by employing structural equation modelling (SEM) approach. Use of SEM technique gives us the opportunity to examine multiple dependence techniques simultaneously. SEM approach is a statistical methodology that combines the strength of factor analysis and path analysis. According to Singh (2009) SEM is considered as a more advanced technique than other multivariate techniques because it can estimate a series of interrelated dependence relationship simultaneously. According to Byrne (1998) SEM technique is better because:

1. It accounts for measurement errors in course of model testing.

2. It can incorporate observed (indicator) variables as well as latent (unobserved) variables at same time during model testing,

3. It tests a priori relationships rather than allowing the technique or data to define the nature of relationship between the variables.

In the present study, SEM analysis is conducted in two major steps; first, to test the measurement model and second, a structural model. Measurement model provides the series of relationships that suggests how observed variables represent latent variables (Figure 1), tested by means of confirmatory factor analysis (CFA). Structural model tests the conceptual representation of the relationships between the latent variables. It tells whether the proposed model is eligible to represent a proposed concept and conceptual relationships between the variables or not (Figure 2).

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Figure 1. Measurement Model

ENTERTAINMENT

INFORMATION

IRRITATION

ADD.VALUE

ENTERTAIN 3

ENTERTAIN 2

ENTERTAIN 1

INFO 1

INFO 2

INFO 3

IRRITATION 1

IRRITATION 2

IRRITATION 3

ADDVALUE 1

ADDVALUE 2

ADDVALUE 3

0.57

0.17

0.19

0.47

0.40

0.51

0.45

0.80

0.39

0.80

0.86

0.52

0.23

0.75

0.65

0.76

0.77

0.73

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Figure 2. Structural Model

Method Sample Design

The research focuses on social networking websites with college students as sample respondents. The college students were selected as sample for two basic reasons. First, student sample is more homogeneous (less variable) in terms of socio-economic background, demographics and education (Peterson, 2001). Second, a number of studies have reported that students are the main users of social networking websites (Dwyer et al., 2007; Pempek et al., 2009; Subrahmanyam et al., 2008). With this rationale, present study sample includes postgraduate management students of a reputed college based in India. 276 students have responded to an online questionnaire mailed to 300 students. The questionnaires were mailed with Google documents facility to form and mail online forms/ questionnaires. After removing incomplete questionnaires, only 189 questionnaires were found to be useable for analysis and further study. Resulting sample consists of 71 per cent males and 29 per cent females. Subjects were asked to report their reactions to instrument statements by considering their

perceptions of advertisements on SNSs in general, not a single advertisement or advertisement for any particular product. The objective of this generalization is to assess the value of advertisement on social networking websites across different advertisements of product and service categories.

Sample Size and SEM Analysis

Sample size is a key issue when performing SEM analysis. According to Bentler and Bonett (1980) and Hair et al. (2007) chi-square value is sensitive to increase in sample size, while it lacks power to discriminate between good fit and poor fit models with small sample size (Kenny and McCoach, 2003). Hair et al. (2007) mentioned that 15 res- ponses per parameter is an appropriate ratio for sample size. Going on with this approach a sample size of 189 res- pondents for measuring 12 parameters was appropriate.

Research Instrument

For measuring the advertisement value of advertisements displayed on social media, a 12 item scale developed by

ADD. VALUE

ADDVALUE 3

ADDVALUE 2

ADDVALUE 1

IN1 IN2 IN3

e3 e2 e1

IRR1 IRR2 IRR3

e9 e8 e7

EN1 EN2 EN3

e6 e5 e4

INFORMATION

IRRITATION

ENTERTAINMENT

e10

e11

e13

e12

0.55 0.84 0.38

0.27

0.15

0.87 0.58

0.77 0.90 0.51

0.40

0.36 0.38

0.75

0.76

0.72

0.25

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Ducoffe (1995) was used. The instrument was modified as per the need of the study. A five-item Likert scale was used as a response scale, from strongly disagrees to strongly agree.

Measurement Model Measurement model is a specification of the measurement theory that shows how constructs are operationalized by sets of measured items. Confirmatory factor analysis is used to test the reliability of a measurement model. Unlike exploratory factor analysis, CFA allows the researcher to tell the SEM programme which variable belongs to which factor before the analysis (Hair et al., 2007). According to Salisbury et al. (2001) CFA allows the researcher to specify the actual relationship between the items and factors as well as linkages between them.

Construct Validity

According to Hair et al. (2007) construct validity is the extent to which a set of measured items actually represents theoretical latent construct; those items are designed to measure. The reliability of advertisement value scale was examined by specifying a model in CFA using AMOS 19. Reliability of an instrument can also be calculated by Cronbach’s alpha, but use of SEM technique makes such a practice unnecessary and redundant (Bagozzi and Yi, 2012). The results (see Table 1) confirm the overall fit of a measurement model when employed to CFA.

According to Hair et al. (2007) one incremental fit index (CFI), one goodness of fit index (GFI), one absolute fit index (GFI, SRMR) and one badness of fit index (SRMR), with chi-square statistic should be used to assess a model’s goodness of fit. Our study results show all the different types of indices in the acceptable range.

Convergent and Discriminant Validity

Convergent validity exists when the items that are indicators of a specific construct converge or share a high proportion of variance in common. In general, ‘factor loading’ and ‘variance extracted’ measures are used to measure convergent validity. We have used factor loading measure in our study to measure convergent validity (Hair et al., 2007; Salisbury et al., 2001). All the factor loadings are statistically significant, a minimum requirement for convergence (Hair et al., 2007). Furthermore, except items ‘Info 3’ and ‘Irritation 1’ all factor loadings are in the range of 0.50 to 0.80, which is more than acceptable value of 0.50 (Hair et al., 2007) (see Figure 1). According to Chin et al. (1997) discriminant validity exists if the correlation between the constructs is not equal to 1. Following the rule, our study shows the discriminant validity between the constructs (see Figure 1).

Structural Model After assessing the eligibility of scale for measuring different variables in the study, the next step is to test the hypothesized relationships in a structural model. Ducoffe (1996) has proved the respective role of information, entertainment and irritation on advertisement value for the advertisements on the Web. In our study, we try to explore the impact of these respective variables on advertisement value vis-à-vis SNSs.

Performance of the Model

Hypothesized relationships are supported by the overall model fit indices obtained. All of the fit indices are above the recommended values. The c2/df value 2.31 met the recommended value of less than 3 (Carmines and McIver, 1981). Hair et al. (2007) argues that chi-square value is sensitive to sample size and number of variables; therefore, c2/df value is not taken as a sole indicator of model fit. Other model fit indicators taken are also within the recommended range (see Table 2). In sum, various model fit indices indicates that the proposed model fitted well with the present data set.

Table 1. Model Fit Indices for Measurement Model

Statistic Recommended

Value Obtained Value

Chi-square c2 92.616 Df 48 c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 1.93

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.92

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.88

SRMR (Hu and Bentler, 1999)

< 0.08 0.06

CFI (Watchravesringkan et al., 2008)

> 0.80 0.92

Note: AGFI: Adjusted goodness of fit index; SRMR: Standardized root mean square residual; CFI: Comparative fit index

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Table 2. Model Fit Indices for Structural Model

Statistic Recommended

Value Obtained Value

Chi-square c2 115.539 Df 50 c2/df (Hinkin, 1995),

(Carmines and McIver, 1981)

< 3.00 2.31

GFI (Hooper et al., 2008), (Hair et al., 2007)

> 0.90 0.91

AGFI (Muenjohn and Armstrong, 2008)

> 0.80 0.86

RMSEA (MacCallum et al., 1996)

< 0.10 0.08

CFI (Watchravesringkan et al., 2008)

> 0.80 0.88

Note: SMSEA: Root Mean Square Error of Approximation

Estimated Standardized Path Coefficients

Figure 2 shows the standardized path coefficients of the four constructs under investigation. All the path coefficients were significant at the level of 0.01 with the direction of influence as hypothesized (+ or −). Information and entertainment were positively associated with advertisement value whereas irritation is negatively asso- ciated with advertisement value; thus all the hypotheses framed are statistically supported. A significant correlation between information and entertainment also indicates that the consumers who find advertisement to be entertaining are more likely to evaluate it as informative. These results are consistent with another study (Ducoffe, 1995). Finally, the squared multiple correlations (R2) indicates that the present model explains 38 per cent of the variance in advertisement value.

Discussion and Implication The study yielded important new insights about a topic that is important for both industry practitioners and aca- demicians. The concept of advertisement value and factors affecting it had been widely tested for various types of advertisements in a number of studies but lack of work for advertisements displayed on social networking websites was the motivating factor to do research in the particular domain. The study tests the model to assess advertisement value by employing SEM approach. SEM combines the strength of factor analysis and path analysis. It enables us to test whether observed variables completely describes latent variables or not. In addition, SEM is a more successful technique than other multivariate techniques as it can estimate a series of interrelated dependence relationship simultaneously. It tells whether the proposed

model is eligible to represent a proposed concept and conceptual relationships between the variables or not. The results of CFA suggest that the observed variables are suitable enough to represent different latent variables, that is, information, entertainment, irritation and advertisement value in the particular domain of social networking advertising.

The findings of structural model analysis suggest that the proposed model for accessing the value of advertisements displayed on SNSs fits well. In addition, the proposed hypotheses assessing the relationships between the variables are statistically supported. The findings suggest that when advertisements displayed on SNSs provide entertainment and information content or impressions, it increases the worth of the advertisement. On the one hand, as has been proved true for other types of media advertising, consumers derive utility from advertisements that provide some useful or functional information and increase hedonic value by entertaining them. On the other hand, irritation decreases the net worth of the advertisements displayed on SNSs. This suggests that the companies using SNSs media for advertising their products and services should reduce the contents, which irritate the viewers’ base.

It is worth noting that ‘information’ exhibited around 1.6 times more strength of influence on advertisement value than entertainment. This suggests that companies should firstly focus on providing information content in their advertisements to make their advertisements worth for consumers. In addition, it is interesting to note that findings of this study show a significant correlation between information and entertainment, which indicates that consumers who find advertisement to be entertaining are more likely to evaluate it as informative.

Limitations Although the study has been done taking into account the methodological rigour, some limitations remain. First, the sampling used is convenience sampling. Second, exploration of other variables that affects the value of advertisement is needed.

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Anant Saxena and Uday Khanna 25

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Anant Saxena ([email protected]) is working as a Research Associate at IMT Ghaziabad, UP, India. He is currently researching the role of common service center (CSC) project in Indian governance and also working on the impact of green marketing on consumer purchase decision in India. He has published research papers in national and international journals of repute. His research interests are marketing through social media, information technology & government policies and e-marketing.

Uday Khanna ([email protected]) is an Assistant Professor at the Faculty of Management Studies at Graphic Era University, Dehradun, India. His areas of interest are Marketing, Marketing Research and Sales and Distribution. He is currently researching the quality of corporate governance of Indian companies. He has published some good papers in national and international journals of repute. He has rich industry experience in FMCG companies of repute like Gillette India Ltd and Hindustan Pencils Ltd.

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ScienceDirect Journal of Interactive Marketing 28 (2014) 43–54

Consumer Decision-making Processes in Mobile Viral Marketing Campaigns

Christian Pescher & Philipp Reichhart & Martin Spann ⁎

Institute of Electronic Commerce and Digital Markets, Ludwig-Maximilians-Universität München, Geschwister-Scholl-Platz 1, D-80539 München, Germany

Available online 22 November 2013

Abstract

The high penetration of cell phones in today's global environment offers a wide range of promising mobile marketing activities, including mobile viral marketing campaigns. However, the success of these campaigns, which remains unexplored, depends on the consumers' willingness to actively forward the advertisements that they receive to acquaintances, e.g., to make mobile referrals. Therefore, it is important to identify and understand the factors that influence consumer referral behavior via mobile devices. The authors analyze a three-stage model of consumer referral behavior via mobile devices in a field study of a firm-created mobile viral marketing campaign. The findings suggest that consumers who place high importance on the purposive value and entertainment value of a message are likely to enter the interest and referral stages. Accounting for consumers' egocentric social networks, we find that tie strength has a negative influence on the reading and decision to refer stages and that degree centrality has no influence on the decision-making process. © 2013 Direct Marketing Educational Foundation, Inc. Published by Elsevier Inc. All rights reserved.

Keywords: Mobile commerce; Referral behavior; Sociometric indicators; Mobile viral marketing

Introduction

The effectiveness of traditional marketing tools appears to be diminishing as consumers often perceive advertising to be irrelevant or simply overwhelming in quantity (Porter and Golan 2006). Therefore, viral marketing campaigns may provide an efficient alternative for transmitting advertising messages to consumers, a claim supported by the increasing number of successful viral marketing campaigns in recent years. One famous example of a viral marketing campaign is Hotmail, which acquired more than 12 million customers in less than 18 months via a small message attached at the end of each outgoing mail from a Hotmail account informing consumers about the free Hotmail service (Krishnamurthy 2001). In addition to Hotmail, several other companies, such as the National Broadcasting Company (NBC) and Proctor & Gamble, have successfully launched viral marketing campaigns (Godes and Mayzlin 2009).

In general, a viral marketing campaign is initiated by a firm that actively sends a stimulus to selected or unselected consumers. However, after this initial seeding, the viral marketing campaign

⁎ Corresponding author. E-mail addresses: [email protected] (C. Pescher),

[email protected] (P. Reichhart), [email protected] (M. Spann).

1094-9968/$ -see front matter © 2013 Direct Marketing Educational Foundat http://dx.doi.org/10.1016/j.intmar.2013.08.001

ion, In

relies on peer-to-peer communications for its successful diffusion among potential customers. Therefore, viral market- ing campaigns build on the idea that consumers attribute higher credibility to information received from other consumers via referrals than to information received via traditional advertising (Godes and Mayzlin 2005). Thus, the success of viral marketing campaigns requires that consumers value the message that they receive and actively forward it to other consumers within their social networks.

Mobile devices such as cell phones enhance consumers' ability to quickly, easily and electronically exchange informa- tion about products and to receive mobile advertisements immediately at any time and in any location (e.g., using mobile text message ads) (Drossos et al. 2007). As cell phones have the potential to reach most consumers due to their high penetration rate (cf., EITO 2010), they appear to be well suited for viral marketing campaigns. As a result, an increasing number of companies are using mobile devices for marketing activities.

Research on mobile marketing has thus far devoted limited attention to viral marketing campaigns, particularly with respect to the decision-making process of consumer referral behavior for mobile viral marketing campaigns, e.g., via mobile text messages. Thus, the factors that influence this process remain largely unexplored. The literature on consumer decision-making suggests that consumers undergo a multi-stage process after receiving a

c. Published by Elsevier Inc. All rights reserved.

44 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

stimulus (e.g., a mobile text message) and before taking action (e.g., forwarding the text message to friends) (Bettman 1979; De Bruyn and Lilien 2008). At different stages of the process, various factors that influence consumer decision-making can be measured using psychographic, sociometric, and demographic variables as well as by consumer usage characteristics. Whereas previous studies have mainly focused on selected dimensions, our study considers variables from all categories.

De Bruyn and Lilien (2008) analyzed viral marketing in an online environment and discussed relational indicators of business students who had received unsolicited e-mails from friends. This study provided an important contribution and amplified our understanding about how viral campaigns work. The present paper differs from the work of De Bruyn and Lilien (2008) and goes beyond their findings in four important ways: actor, medium, setting, and consumer characteristics. The first difference is the actor involved. In viral campaigns, the initiator, usually a company, sends the message to the seeding points (first level). Next, the seeding points forward the message to their contacts (second level), and so on. Whereas De Bruyn and Lilien (2008) focused on the second-level actors, the present study focuses on the first-level actors, e.g., the direct contacts of the company. We believe that for the success of a campaign, additional insights into the behavior of first-level actors are very important because if they do not forward the message, it will never reach the second-level actors. The second difference is the medium used in the campaign. Although we cannot explicitly rule out that participants of De Bruyn and Lilien's (2008) campaign used mobile devices, they conducted their campaign at a time when the use of the Internet via mobile devices was still very uncommon. Therefore, it is reasonable to assume that at least the majority of their participants used a desktop or a laptop computer when they participated in De Bruyn and Lilien's (2008) campaign. In contrast, the present study explicitly uses only text messages to mobile devices. In addition, mobile phones are a very personal media which is used in a more active way compared to desktop or laptop computers (Bacile, Ye, and Swilley 2014). The third difference is the setting in which the viral campaign takes place. Whereas the participants in the study by De Bruyn and Lilien (2008) were business students from a northeastern US university, we conduct a mobile marketing campaign in a field setting using randomly selected customers. The fourth and most important difference is that De Bruyn and Lilien (2008) focused exclusively on relational characteristics. In addition to relational characteristics, this paper also considers variables that describe demographic factors, psychographic factors, and usage characteristics. As these variables yield significant results, the study and its findings go beyond the findings of De Bruyn and Lilien (2008).

The main goal and contribution of this work is, first, to analyze consumers' decision-making processes regarding their forwarding behavior in response to mobile advertising via their cell phone (i.e., text messages) in a mobile environment using a real-world field study. To analyze consumers' decision-making processes, we use a three-stage sequential response model of the consumer decision-making process. Additionally, we inte- grate consumers' egocentric social networks into a theoretical

framework to consider social relationships (e.g., tie strength, degree centrality) when analyzing mobile viral marketing campaigns. Thus, to understand referral behavior, we integrate psychographic (e.g., usage intensity) and sociometric (e.g., tie strength) indicators of consumer characteristics. We are then able to determine the factors that influence a consumer's decision to refer a mobile stimulus and are able to identify the factors that lead to reading the advertising message and to the decision to learn more about the product.

Related Literature

Viral Marketing and Factors that Influence Consumer Referral Behavior

Viral marketing campaigns focus on the information spread of customers, that is, their referral behavior regarding information or an advertisement. Companies are interested in cost-effective marketing campaigns that perform well. Viral marketing cam- paigns aim to meet these two goals and can, accordingly, have a positive influence on company performance (Godes and Mayzlin 2009). Companies can spread a marketing message with the objective of encouraging customers to forward the message to their contacts (e.g., friends or acquaintances) (Van der Lans et al. 2010). In this way, the company then benefits from referrals among consumers (Porter and Golan 2006). Referrals that result from a viral marketing campaign attract new customers who are likely to be more loyal and, therefore, more profitable than customers acquired through regular marketing investments (Trusov, Bucklin, and Pauwels 2009).

Two streams of research can be identified. The first is the influence of viral marketing on consumers, and the second is research that has analyzed the factors that lead to participating in viral marketing campaigns. First, previous research identified that viral marketing influences consumer preferences and pur- chase decisions (East, Hammond, and Lomax 2008). Further, an influence on the pre-purchase attitudes was identified by Herr, Kardes, and Kim (1991). In addition, viral marketing also influences the post-usage perceptions of products (Bone 1995).

Second, previous research has identified satisfaction, customer commitment and product-related aspects as the most important reasons for participating in viral marketing campaigns (cf., Bowman and Narayandas 2001; De Matos and Rossi 2008; Maxham and Netemeyer 2002; Moldovan, Goldenberg, and Chattopadhyay 2011). With respect to psychological motives, self-enhancement was identified as a motive for consumers to generate referrals (De Angelis et al. 2012; Wojnicki and Godes 2008). The importance of self-enhancement in addition to social benefits, economic incentives and concern for others was identified as a motive behind making online referrals (Hennig-Thurau et al. 2004). Referrals can be differentiated into positive and negative referrals. Anxiety reduction, advice seeking and vengeance are factors that contribute to negative referrals (Sundaram, Mitra, and Webster 1998).

Within the referral process, the relationships and social network position of the consumer are also influential. For example, Bampo et al. (2008) found that network structure is

45C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

important in viral marketing campaigns. Furthermore, it has been determined that consumers are more likely to activate strong ties than weak ties when actively searching for information (Brown and Reingen 1987) because strong ties tend to be high-quality relationships (Bian 1997; Portes 1998). In addition, targeting consumers who have a high degree centrality (e.g., quantity of relationships) leads to a higher number of visible actions, such as page visits, than do random seeding strategies (Hinz et al. 2011). Kleijnen et al. (2009) analyzed the intention to use mobile services using sociometric variables and evaluated how consumers' network positions influence their intentions to use mobile services. However, the previous study contributes to the literature by analyzing a different research question than is examined in our paper. Specifically, Kleijnen et al. (2009) focused on the intention to use services, while our study focuses on consumers' decision-making processes until they make a referral. In summary, previous research focused on the consumers' psychographic constructs or relationships and social networks to explore why consumers participate in viral marketing campaigns and why they make referrals, two constructs that are rarely analyzed together. Iyengar, Van den Bulte, and Valente (2011) used both constructs jointly and found that correlations between the two are low. However, this study did not take place in an online or mobile context but rather in the context of referrals for new prescription drugs between specialists. In contrast, our study analyzes both aspects together within a mobile viral marketing campaign.

In addition to offline- or online-based viral marketing activities, an increasing number of companies are conducting marketing campaigns using mobile phones, and promising approaches include mobile viral marketing campaigns. Research on mobile viral marketing is relatively unexplored because most research in the field of mobile marketing analyzes marketing activities such as mobile couponing (Dickinger and Kleijnen 2008; Reichhart, Pescher, and Spann 2013), the acceptance of advertising text messages (Tsang, Ho, and Liang 2004) or the attitudes toward (Tsang, Ho, and Liang 2004) and the acceptance of mobile marketing (Sultan, Rohm, and Gao 2009). In the context of mobile viral marketing research, Hinz et al. (2011) studied mobile viral marketing for a mobile phone service provider and determined that the most effective seeding strategy for customer acquisition is to focus on well-connected individuals. In contrast to our study, their referrals were conducted via the Internet (i.e., the companies' online referral system) rather than via a mobile device (i.e., forwarding the text message immediately). Nevertheless, generat- ing referrals using a mobile device can affect referral behavior. Palka, Pousttchi, and Wiedemann (2009) postulated a grounded theory of mobile viral marketing campaigns and found that trust and perceived risk are important factors in the viral marketing process. In comparison to our study, they used qualitative methods and did not conduct a real-world field study. Okazaki (2008) identified, for Japanese adolescents, consumer characteristics such as purposive value and entertainment value are the main factors in mobile viral marketing campaigns and that these factors significantly influence the adolescents' attitudes toward viral marketing campaigns. Furthermore, both purposive value and entertainment value are influenced by the antecedents'

group-person connectivity, commitment to the brand, and relationship with the mobile device. In contrast to our study, Okazaki (2008) did not analyze whether referrals were made, nor did he analyze the referrals that were directly made via a mobile device by forwarding the mobile text message. Instead, he analyzed the general viral effect in the form of telling or recommending the mobile advertising campaign. Further, our field study analyzes the entire consumer decision-making process for a mobile viral marketing campaign via text messages across the three stages: from stage one, reading, to stage two, interest, to stage three, decision to refer.

To summarize, in contrast to the existing studies in the field of mobile viral marketing, we analyze consumers' egocentric networks via measures such as tie strength and degree centrality. These sociometric factors are analyzed jointly with psychographic constructs across the three stages in the decision-making process. Thus, our study uses a real-world mobile viral marketing campaign and enables us to test the relative importance of social embeddedness and consumer characteristics with respect to consumers' decision to forward mobile messages.

Decision-making Process and Specifics of the Mobile Environment

Consumer decision-making is a multiple-stage process (Bettman 1979; De Bruyn and Lilien 2008; Lavidge and Steiner 1961). In a viral marketing campaign, the final goal is to generate a high number of referrals. Therefore, our model of consumer forwarding behavior is designed for the specific situation of mobile viral marketing campaigns.

The process and first stage begin with the consumer reading a mobile advertising text message on his or her mobile phone. If this text message sparks the consumer's interest and the consumer wants to learn more about the offered product, he/she enters the interest stage, which is the second stage of the model. If the consumer finds the product interesting after learning about it, he or she makes a referral, which is the third stage of our model (decision to refer).

In this study, we analyze the stages of the consumer decision-making processes within a mobile environment, i.e., within a mobile viral marketing campaign. There are several differences between mobile viral marketing and online or offline viral marketing. A mobile text message is more intrusive than an e-mail because it appears immediately on the full screen. Consumers usually carry their mobile phone with them and a mobile message may also reach them in a private moment. Contrary, consumers may need to purposely look into their e-mail accounts to receive e-mails. Therefore a mobile message can be more personal compared to an e-mail. In comparison to offline face-to-face referrals, mobile referrals do not possess this personal aspect and can be transmitted digitally within a few minutes to several friends in different places simultaneously. This is not possible in the offline world. Additionally, a mobile referral can reach the recipient faster than an e-mail or an offline referral. Thus, the mobile device may influence the referral behavior due to its faster digital transmission of information.

46 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Development of Hypotheses

While the factors that influence the stages of the decision- making process can be divided into two groups, we analyze them jointly in this study. The first group consists of the psychographic indicators of consumer characteristics, thus focusing on each consumer's motivation to participate in the campaign and his or her usage behavior. The second group of factors includes sociometric indicators of consumer character- istics, thus providing information about the type of relationship that the consumer has with his or her contacts and his or her resulting social network.

Psychographic Indicators of Consumer Characteristics

As mentioned in the related literature section, according to Okazaki (2008), in viral marketing campaigns, purposive value and entertainment value are the primary value dimensions for consumers. This insight is based on the finding that consumers gain two types of benefits from participating in sales promotions: hedonic and utilitarian benefits (Chandon, Wansink, and Laurent 2000). Hedonic benefits are primarily intrinsic and can be associated with entertainment value. Consumers participate voluntarily and derive value from the fun of interacting with peers by forwarding a referral (e.g., an ad might be of interest to peer recipients) (Dholakia, Bagozzi, and Pearo 2004). A previous study found that the entertainment factor influences intended use in mobile campaigns (Palka, Pousttchi, and Wiedemann 2009). Okazaki (2008) found that in a mobile viral marketing campaign, the entertainment value directly influences the recipient's attitude toward the campaign, which, in turn, influences the recipient's intention to participate in a mobile viral campaign. Phelps et al. (2004) showed that the entertainment value is a factor that increases consumers' forwarding behavior in viral marketing campaigns conducted via e-mail. Thus, we may presume that consumers who place high importance on the entertainment value of exchanging messages are more likely to enter the reading and interest stages than consumers who do not value entertainment to the same degree. Additionally, the entertainment value can also influence the decision to refer (i.e., forwarding) behavior because a text message that addresses consumers who place high importance on entertainment value causes the recipient to think about forwarding the text message and motivates them to forward the mobile advertisement to friends (i.e., decision to refer stage).

H1. Consumers who place high importance on the entertainment value of a message are more likely to a) enter the reading stage, b) enter the interest stage and c) enter the decision to refer stage.

As utilitarian benefits are instrumental and functional, they can be associated with purposive value (Okazaki 2008). Dholakia, Bagozzi, and Pearo (2004) analyzed the influence of purposive value in network-based virtual communities and found that purposive value is a predictor of social identity and a key motive for an individual to participate in virtual communities. With respect to the mobile context, previous research found that purposive value has a direct, significant

influence on a consumer's attitude toward a mobile viral marketing campaign and that this attitude significantly influences the intention to participate in mobile marketing campaigns (Okazaki 2008). For some consumers, forwarding a (mobile) advertisement in a viral marketing campaign can have a personal and a social meaning (e.g., doing something good for friends by forwarding the ad). Thus, we hypothesize that consumers who place high importance on the purposive value of exchanging messages will display a greater likelihood to enter the reading and interest stages. We also hypothesize that consumers who place high importance on the purposive value of a message are more likely to make the decision to forward the message.

H2. Consumers who place high importance on the purposive value of a message are more likely to a) enter the reading stage, b) enter the interest stage and c) enter the decision to refer stage.

The intensity of usage (e.g., a high quantity of written text messages) positively influences the probability of trial and adoption (Steenkamp and Gielens 2003). Thus, consumers with high usage intensities are more likely to actively participate in a mobile viral marketing campaign. As mobile viral marketing campaigns are a fairly new form of advertising, consumers with high usage intensities are more likely to participate in mobile viral marketing campaigns and are more likely to forward messages than consumers with low usage intensities. Therefore, we propose that usage intensity has an effect on the decision to forward a mobile advertising text message. The likelihood of deciding to forward the mobile advertisement increases with the usage intensity of mobile text messages. This proposition is consistent with Neslin, Henderson, and Quelch (1985), who found that the promotional acceleration effect is stronger for heavy users than it is for other consumers. Godes and Mayzlin (2009) analyzed the effectiveness of referral activities and argued that the sales impact from less loyal customers is greater, but they also highlighted that this greater sales impact does not mean that the overall referrals by less loyal customers have a greater impact than those by highly loyal customers. They concluded that companies who want to implement an exogenous referral program to drive sales should focus on both less loyal and highly loyal customers because focusing only on highly loyal or less loyal customers is not necessarily the cornerstone of a successful viral marketing campaign. In the online context, a previous study found that experience with the Internet influences channel usage behavior (Frambach, Roest, and Krishnan 2007). Thus, as consumers with high usage intensity are used to communicating with mobile phones, they know how to write, read and forward mobile text messages. Accordingly, it is likely that the threshold to forward a text message is lower for consumers with high usage intensity than it is for other consumers and that such consumers are thus more inclined to refer. Further, the minimal effort required to directly forward a mobile text message via a cell phone increases the decision to refer. Thus, we hypothesize that heavy mobile users will be more likely to refer than will light users.

H3. The usage intensity of the referral medium has a positive influence on the likelihood of making the decision to refer.

47C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Sociometric Indicators of Consumer Characteristics

Sociometric indicators describe the interaction structure of an individual consumer with his or her surroundings. When consumers receive an interesting mobile advertising message, it is likely that they want to find out more about it. Once the consumer has visited the product homepage, he or she then considers not only whether the message is worth forwarding but also to whom it should be forwarded.

Sociometric indicators provide information about the social network of each individual consumer. This individual network influences the likelihood of knowing someone who may be interested in the offered product. Thus, social networks have a significant impact on the decision-making process in a viral marketing campaign. The decision to forward the mobile advertising message depends on two factors: the quality and the quantity of relations, i.e., the tie strength and the degree centrality.

Tie strength is an important factor in viral marketing and increases with the amount of time spent with the potential recipient and with the degree of emotional intensity between the sender and the potential recipient (Marsden and Campbell 1984). Consumers perceive strong ties to be more influential than weak ties (Brown and Reingen 1987) because the strong ties seem more trustworthy (Rogers 1995). Therefore, because consumers are more motivated to provide high-value informa- tion to strong ties (Frenzen and Nakamoto 1993), tie strength is an indicator of the quality of the relationship.

Reagans and McEvily (2003) studied how social network factors influence knowledge transfer at an R&D firm. To measure the tie strength, they used two items that are analogous to those that we used (Burt 1984). Their results indicated that tie strength positively influences the ease of knowledge transfer. Thus, network ties increase a person's capability to send complex ideas to heterogeneous persons. Overall, they highlighted the importance of tie strength with respect to the knowledge transfer process, and they postulate that tie strength holds a privileged position. Other studies found that weak ties make non-redundant information available (Levin and Cross 2004). In an online setting, participants were more likely to share information with strong ties than with weak ties (Norman and Russell 2006). With respect to viral marketing conducted via e-mail, previous research has found that tie strength has a significant influence on whether the recipient examines an e-mail message sent from a friend (i.e., opens and reads the message) (De Bruyn and Lilien 2008). Tie strength was also determined to be less relevant in an online setting compared to an offline setting (Brown, Broderick, and Lee 2007). In a non-mobile or non-online context, stronger ties are more likely to activate the referral flow (Reingen and Kernan 1986). Furthermore, tie strength is positively related to the amount of time spent receiving positive referrals (van Hoye and Lievens 1994).

As previously mentioned, research on word-of-mouth behav- ior has shown that people engage in word-of-mouth for reasons such as altruism (Sundaram, Mitra, and Webster 1998). However, Sundaram, Mitra, and Webster (1998) did not control for the quality of a relationship between sender and recipient. Research concerning referral reward programs has identified that offering a

reward increases the referral intensity and has a particular impact on weak ties (Ryu and Feick 2007). Brown and Reingen 1987 found that while strongly tied individuals exchange more information and communicate more frequently, weak ties play an important bridging role. Additionally, Granovetter (1973) stated that one is significantly more likely to be a bridge in the case of weak ties than in strong ties. In job search, when using personal networks, it was found that weak ties have a higher rate of effectiveness when addressing specialists for jobs compared to strong ties (Bian 1997) and that the income of people using weak ties was greater than those who used strong ties (Lin, Ensel, and Vaughn 1981). At the information level, consumers who are connected via strong ties tend to share the same information that is rarely new to them, while consumers obtain important information from weak ties who tend to possess information that is “new” to them (Granovetter 1973). Consistent with this finding, Levin and Cross (2004) found that novel insights and new information are more likely to pass along weak ties than between strong ties. As in our study, viral marketing information can be perceived as a novel insight or new information. Given that consumers are more likely to send a message to someone if the content is new to the receiver, it is more likely that consumers will forward the text message to a weak tie than to a strong tie. Thus, we presume that consumers prefer to forward mobile advertising text messages, such as the one used in our study (for a new music CD), to other customers with whom they are connected through weak ties.

Similarly, Frenzen and Nakamoto (1993) postulate that the motivation to share information or refer a product is driven by the value of the information and the cost of sharing. They identified (though only for weak ties) an influence of word-of-mouth that is spread by value and opportunity costs. In our case, when customers forward a mobile advertising message, the opportunity cost is low because forwarding can be done easily and without any effort (e.g., compared to meeting the friend personally in the city). In the case of strong ties, the preferences of the recipient (e.g., for products) are well known to the sender, whereas these preferences are unclear for weak ties. Thus, people who want to do something beneficial for their contacts know the likelihood that it will benefit the recipient in the case of strong ties, but they do not know the benefit it may bring to weak ties. In our study, this benefit involved sharing information to acquire a free new music CD. Because the costs of sharing are low using cell phones, people are more inclined to forward such information. With respect to strong ties, people know whether the information is relevant. Furthermore, relationships to strong ties are more important than relationships to weak ties. Importantly, people do not want to displease strong ties by sending irrelevant information or cause information overload with unsuitable information. In the case of receiving annoying information, the recipient could ask the sender not to forward text messages anymore. This sanction is more painful when received from strong ties (e.g., good friends) than from weak ties (e.g., acquaintances) because the weak ties are less important to the sender. This is similar to the finding that people who are dissatisfied (e.g., with a product or service) are more likely to advise against the purchasing of the product to strong ties rather than to weak ties (Wirtz and Chew 2002), which may also be due to the sanction issue. In

48 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

mobile viral marketing campaigns, because the sharing of information is easy and not costly, factors such as knowledge about the preferences of the recipient or the fear of annoying strong ties become more important when deciding whether and to whom to refer the message.

Thus, we hypothesize the following:

H4. Tie strength has a negative influence on the likelihood of making the decision to refer.

Diffusion occurs via replication or transfer, e.g., of used goods (Borgatti 2005). Within the group of replication processes, replications can occur one at a time (serial duplication), e.g., gossip or viral infections, or simultaneously (parallel duplication), e.g., e-mail broadcasts or text messages on mobile phones. Degree centrality is a measure that is suitable for analyzing processes in which a message is duplicated simulta- neously because it can be interpreted as a measure of immediate influence — the ability to “infect” others directly (Borgatti 2005, p 62). Hubs are identified using degree centrality because they are actors who possess a high number of direct contacts (Goldenberg et al. 2009) and because they know a high number of people to whom they can forward a message and can thus influence more people (Hinz et al. 2011). Hubs also adopt earlier in the diffusion process. In detail, innovative hubs increase the speed of the adoption process, while follower hubs influence the size of the total market (Goldenberg et al. 2009). Further, hubs tend to be opinion leaders (Kratzer and Lettl 2009; Rogers and Cartano 1962) because they have a high status and serve as reference points in the information diffusion process. Small groups of opinion leaders often initiate the diffusion process of innovations (Van den Bulte and Joshi 2007). Czepiel (1974) analyzed whether centrality in opinion networks influenced the adoption and found no significance for this, a finding that is contrary to other literature in the field (e.g., Goldenberg et al. 2009). Furthermore, targeting central customers leads to a significant increase in the spread of marketing messages (Kiss and Bichler 2008). In a viral marketing campaign for a mobile service where referrals are conducted via the Internet (i.e., online referral system) and not via forwarding a mobile text message, the results showed that high centrality increases the likelihood of participa- tion (Hinz et al. 2011). Therefore, we hypothesize that degree centrality has a positive influence on the decision to refer.

H5. Degree centrality has a positive influence on the likelihood of making the decision to refer.

Empirical Study

Goal and Research Design

The goal of this empirical study is to test the hypotheses derived above using a three-stage model that represents the stages of a consumer's decision-making process in a mobile referral context.

In our field study, we conducted a mobile marketing campaign. The randomly chosen participants had previously agreed to receive mobile advertising text messages on their cell

phones (opt-in program). We sent a text message to 26,137 randomly chosen customers that included a link to a website and the notice that they could download a recently released music CD for free. The only purpose of this website was to give the campaign's participants the option to download the music CD for free. In the text message, they were also asked to forward the message to their contacts. The exact text of the message stated, “Amazing! You & your friends will receive a new CD as an MP3 for free! No subscription! Available online: URL.com -N Forward this text message to your friends now!”

One week later, we sent another text message to all of the participants who received the initial mobile advertising text message with a link to an online survey. This second text message contained the request to participate in an online survey. We provided one 100 Euro and two 50 Euro prizes as incentives to participate in the survey. The winners were drawn in a lottery. In the questionnaire, the participants provided information about their behavior during different stages of the referral process and about their psychological constructs and egocentric networks (Burt 1984). Egocentric networks are networks that analyze the focal actor and the actor's direct friends as well as the relations that exist between them (Burt 1984).

Measures

The psychographic constructs “purposive value” and “enter- tainment value” were adapted from Dholakia, Bagozzi, and Pearo (2004) and Okazaki (2008). Concerning the operationalization of both psychographic constructs, i.e., purposive value and entertain- ment value, we addressed the consumers' characteristics to identify the consumers who place high importance on the purposive value of messages in general. We analyzed the consumer's characteristics concerning both constructs, i.e., the importance of the purposive value and the entertainment value for respondents. Items were measured on a seven-point Likert scale, using scale points from “do not agree at all” (1) to “totally agree” (7). We operationalized the “usage intensity” by surveying the number of text messages each participant wrote per day (see Appendix A for details).

To measure consumers' social networks, we surveyed their egocentric networks (Burt 1984; Fischer 1982; McCallister and Fischer 1978; Straits 2000). Egocentric networks are defined as the direct relationships between an individual consumer (or ego) and other consumers (or alters) and the relationships that exist between the alters. These are small networks of one focal actor called the “ego”, the participant in the survey, and his or her contacts, called “alters”. The difference between a regular network and an egocentric network is that in the latter, all of the necessary information is obtained from one actor, which makes it a feasible method for obtaining samples that are representa- tive of a large-scale population. To access the respondents' core networks, we used the term “generator,” which was taken from Burt (1984), and adapted it to the specific characteristics of this study (see Appendix A for details). The participants first generated their list of alters by identifying their most frequent contacts and were then asked a series of questions, which helped us to gain additional insights in their social network, including the strength of the relationship with each contact. Because each egocentric

49C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

network is calculated based on information from a single respondent, such networks are usually treated as undirected. Based on this information, we calculated the degree centrality, which is the number of ties for a node, and average tie strength for each network. Marsden (2002) showed that the egocentric centrality measures are generally good proxies for sociometric centrality measures.

Model

The model consists of a funnel of three successive decisions: (i) reading the message, (ii) visiting the homepage (interest) and (iii) forwarding the message (decision to refer). In each stage, the number of observations diminishes because only consumers who took action in the last stage can take another action in the next stage, i.e., only consumers who read the message can access the homepage. Fig. 1 shows that the model is hierarchical, nested, and sequential, which leads to desirable statistical properties. Therefore, we use Maddala's (1983, p 49) sequential response model, which is also known as a model for nested dichotomies (Fox 1997). We follow the argumentation of De Bruyn and Lilien (2008), who adapted a model of Maddala (1983, pp 49) to the context of consumer decision-making (see Appendix B for our model). We fit the model by simultaneously maximizing the likelihood functions of the three dichotomous models in Stata 12. Each likelihood function incorporates the estimated probabilities of the preceding stages.

De Bruyn and Lilien (2008) argued that consumers do not drop out at random in this process because it is a process of self-selection, which may raise statistical concerns. However, the parameter estimates for the structure of the model used in this paper have been shown to be unaffected by changes in the marginal distributions of the variables (Bishop, Fienberg, and Holland 1975; Mare 1980).

Consumer does not read

message n=309

Consumer does not visit

homepage n=194

Consumer does not for-

ward messag n=296

Consumer receives message

n=943

Consumer reads

message n=634

Fig. 1. Consumers' decision-making

Results

Descriptive Statistics and Bias Tests

In all, 943 subjects responded to the survey. Of these, 634 read the initial message (reading), 440 visited the homepage (interest) and 144 consumers forwarded the message (decision to refer). Table 1 shows the correlations and descriptive statistics among the variables in this study.

First, we compare the demographic characteristics of the survey respondents with the demographic characteristics of the entire customer sample. Of the 943 survey respondents, 28% are female, which is in line with the entire sample. In addition we observed the age distribution of the survey respondents as well as the entire customer sample and found that the groups are essentially consistent (see Table 2).

Second, we compared consumers who read the text message (Nread = 634) with those who did not read the initial text message (Nnoread = 309) with respect to their surveyed demographics and cell phone usage behavior. We found no significant difference between the groups for demographics (female: Mread = 27.0%, Mnonread = 30.1%, p N .31; age: Mread = 29.2 yrs, Mnonread = 30.6 yrs, p N .06), monthly cell phone usage (Mread = 29.28 €, Mnonread = 29.62 €, p N .95) or age and gender.

Three-stage Decision-making Model

We include all explanatory variables in the estimation of every stage to avoid an omitted variable bias. Table 3 shows the results of our sample selection model.

Entertainment Value and Purposive Value (H1 and H2) We find significant influence concerning consumers who place

high importance on the entertainment value of exchanging mobile

e

Consumer forwards message

n=144

U N

A W

A R

E R

E A

D IN

G

S TA

G E

IN T

E R

E S

T

S TA

G E

D E

C IS

IO N

T O

R E

F E

R

S TA

G E

Consumer visits

homepage n=440

process in the viral campaign.

Table 1 Descriptive statistics.

Mean StD 1 2 3 4 5

1. Entertainment value 4.19 1.53 2. Purposive value 3.81 2.01 .46⁎⁎

3. Usage intensity 7.53 19.73 .14⁎⁎ .08 ⁎⁎

4. Tie strength 3.10 .80 .11⁎⁎ .05 .01 5. Degree centrality 3.04 1.46 .08⁎⁎ .03 .05 .02 6. Age 29.69 11.08 −.10⁎⁎ .05 −.14⁎⁎ .02 −.17⁎⁎

Notes: means, standard deviations and correlations, N = 943. ⁎⁎ p b .05.

Table 3 Results of the three-stage model.

Stage: Reading Stage: Interest Stage: Decision to refer

Coefficient SE Coefficient SE Coefficient SE

Entertainment value .116 ⁎⁎ .054 .181 ⁎⁎ .070 .204 ⁎⁎ .101 Purposive value .062 .045 .167 ⁎⁎ .063 .600 ⁎⁎ .095 Usage Intensity −.003 .003 .008 .008 .010 ⁎ .006 Tie strength −.219 ⁎⁎ .094 −.156 .114 −.461 ⁎⁎ .153 Degree centrality −.021 .050 −.010 .064 .114 .085 Age −.012 ⁎ .006 −.014 .009 .036 ⁎⁎ .012 Gender a .098 .160 .580 ⁎⁎ .200 −.229 .283 Intercept 1.069 ⁎⁎ .461 −.100 .570 −4.264 ⁎⁎ .814 n 943 Log likelihood −1174.81 Wald chi2 180.58 Prob N chi2 b.01

a Dummy coding (0 = female, 1 = male). ⁎ p b .10. ⁎⁎ p b .05.

50 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

text messages with regard to the reading stage (coefficient: .116/H1a), the interest stage (coefficient: .181/H1b) and the decision to refer stage (coefficient: .204/H1c). Furthermore, we find a significant increase in the likelihood of entering the interest stage (coefficient: .167/H2b) as well as the decision to refer stage (coefficient: .600/H2c) for consumers who place high importance on purposive value. However, no support can be found for H2a.

Overall, consumers who place higher importance on the entertainment value of exchanging text messages and who place higher importance on the purposive value demonstrate an increased likelihood of entering the interest and decision to refer stages.

Usage Intensity (H3) Our results show that the influence of usage intensity on the

decision to refer a mobile text message is only significant at the 10% level (coefficient .010/H3). Thus, consumers who are used to writing mobile text messages are more likely to forward mobile text messages as well as the advertising text message. This result is consistent with previous findings that usage intensity is positively related to user behavior (Gatignon and Robertson 1991).

Tie Strength (H4) Our results support H4: Tie strength significantly decreases

the likelihood that consumers decide to send and forward a mobile text message (coefficient: −.461/H4). Thus, lower levels of tie strength increase the likelihood of the decision to send advertising text messages. A previous study found that the

Table 2 Demographics of survey respondents and the entire customer sample.

Percentage

Survey respondents Entire customer sample

Age b20 20% 11% 20–29 37% 36% 30–39 22% 28% 40–49 15% 17% 50–59 4% 6% ≥60 1% 2%

receivers of unsolicited e-mails tend to pay more attention to messages from close contacts (i.e., high-quality contacts) (De Bruyn and Lilien 2008). However, their study focused on consumers who are actively searching for relevant information and are thus receivers of information. In contrast, the present study addresses mobile advertising text messages that are sent from a consumer to the consumer's contacts without being solicited. In other words, we focus on the sender of the message. The difference between the sender and the receiver of a message provides a solid explanation for the differences in the results between the two studies. Further, we find a negative influence of tie strength on the likelihood to read the message, which may be explained by the low tendency of consumers with strong tie relationships to read firm-initiated text messages.

To gain deeper insight into the negative impact of tie strength on the decision to forward a message, we conduct an additional analysis and find that the average tie strength between senders and receivers of forwarded messages is 2.99. This tie strength is significantly less than the average tie strength of 3.09 for connections where no text messages were forwarded (p b .05).

Degree Centrality (H5) In testing H5, we focus on the influence of degree centrality

on the decision to refer and find that the number of contacts (i.e., degree centrality) has no influence on the decision to refer. Thus, H5 is not supported.

In addition to testing the hypotheses, we examine the influence of gender on each stage of the decision-making process. The results show that gender has a significant influence only on the interest stage (coefficient: .580). No significant influence of gender is identified for the reading or decision to refer stages. Further, we test the effect of age in the decision-making process and find that age has a significant positive influence on the decision to refer stage (coefficient: .036).

51C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Discussion

This paper develops a three-stage model to analyze the decision-making process of consumers in mobile viral marketing campaigns. This is an important area to study because companies actively approach only a small number of consumers in viral marketing campaigns. Therefore, additional information about these consumers, their decision-making processes and the factors that influence the consumers may help determine the success or failure of viral marketing campaigns. Only a few extant studies focus on mobile viral marketing campaigns. Due to the increased use and penetration rate of cell phones and smartphones, most consumers can be addressed via this new medium and channel. Furthermore, cell phones combine unique characteristics such as ubiquitous computing, always on and immediate reactions, thus making mobile phones an attractive marketing channel for viral marketing campaigns.

The three-stage model approach allows us to gain a deeper understanding of consumers' decision-making processes in mobile viral marketing campaigns. This study has produced several key findings.

The first key finding is the important role of consumers who place high importance on the purposive value and entertainment value of a message during the decision-making process. We find that the entertainment value significantly influences all three stages. Purposive value significantly influences the interest stage and the decision to refer stage, but it does not influence the reading stage. This finding is relevant when conducting a mobile viral marketing campaign. To attract customer attention, and thus to successfully pass the reading stage, companies should develop campaigns that entertain customers. To have a significant influence on the decision to refer, it is important to address consumers who place importance on both purposive and entertainment value. However, Dholakia, Bagozzi, and Pearo (2004) found that in online communities, purposive value has no direct effect on participation behavior. The differences between their results and our results can potentially be attributed to the differences between online technology and cell phones. We suggest that purposive value is mobile-specific because the mobile setting differs from the Internet. People tend to spend a considerable amount of time in online communities and therefore have potentially large amounts of content to read. This makes it difficult to identify the content that may be meaningful to the contacts. On the contrary, at least to date, customers receive a limited amount of mobile text messages. Thus, the purposive value can be judged, and if customers believe that the text message is useful and has purpose, they will be interested in it and will eventually decide to forward it.

We find that tie strength plays an important role in the last stage of the decision-making process and has a negative influence on both the reading stage and the decision to refer stage. In other words, consumers are more likely to pass messages on to weak ties (i.e., low-quality contacts). Further, usage intensity has a positive influence at the 10-percent level, which indicates that heavy users may be influential in mobile viral marketing campaigns because they have a lower threshold to pass information to their contacts. Thus, heavy users are

more influential because of their communication habits. An interesting finding is that degree centrality has no influence on the decision-making process.

Our results indicate that one reason for the failure of mobile viral marketing campaigns is that consumers tend to forward messages to consumers on whom they have limited impact. Consumers with low-quality contacts (i.e., weak ties) are more likely to forward the message because the perceived risk, which can include social sanctions, of forwarding a message is low. Accordingly, the results of this study indicate a behavior that might limit the impact of viral campaigns. Specifically, customers who predominantly possess weak ties are more likely to read the message, and they are more likely to pass it on to other customers with predominantly weak ties. In contrast, the receivers of the message tend to make their purchase decisions based on the content that they receive from high-quality (i.e., strong tie) contacts because such ties are perceived to be more trustworthy when making a purchase decision (Rogers 1995). Thus, the viral process may lead to a high number of referrals that are ignored by their receivers, thus potentially limiting the success of the campaign. Future research should therefore examine the circumstances under which consumers make their purchase decisions and the products that they choose based on recommendations from strong and weak ties.

This study has several limitations that open additional avenues for future research. First, because of our setting and because we conducted a viral marketing campaign in a mobile environment via text messages, we were not able to directly observe the referrals made. In our study, this variable is self-reported via a survey, which is a limitation of the study. Further research should conduct an experimental setting where it is possible to observe the forwarding behavior using behavioral data rather than survey data. Second, the partici- pants of this study were members of an opt-in program and had already agreed to receive mobile advertising text messages for advertising reasons. Therefore, it remains unclear how consumers who have not consented to receiving messages would react to unsolicited mobile advertising messages. Third, mobile marketing is still an emerging field in comparison to e-mail marketing. Therefore, it is likely that consumers will pay attention to these (mobile marketing) campaigns because they are rather novel. It remains to be seen how these results may change with the increasing prevalence of mobile marketing campaigns in the future. Fourth, we studied only one product category, a new music CD. Future research should analyze what products or services are (more or less) suitable for mobile viral marketing campaigns. Fifth, in this study, we only focused on data from the responses of consumers who were seeded by the company. To further generalize the results, future research could also analyze consumers who receive a message from a friend. Finally, we only analyzed mobile viral marketing via text messages. Future studies could analyze and compare the findings with mobile viral marketing campaigns using media-rich formats such as multimedia messages because these formats can offer more entertaining content to recipients.

52 C. Pescher et al. / Journal of Interactive Marketing 28 (2014) 43–54

Appendix A. Measurement Scales

Entertainment value (Cronbach's α = .804)

I want to entertain my friends by sending enjoyable information. Information exchange is fun by itself. It is a good way to spend time in an enjoyable way.

Purposive value (Cronbach's α = .738)

I feel like spreading information I have discovered to my friends. I want to send information to my friends who may be interested in it.

Usage intensity Number of mobile text messages each survey participant wrote per day.

Network items (Burt 1984)

Degree centrality (=number of contacts the respondent names): Looking back over the last six months, who are the people with whom you discussed an important personal matter? Please write their first names or initials in the boxes below. Tie strength: How close do you feel to these people?

Note: Items for entertainment value & purposive value on seven-point Likert

Appendix B. Sequential Logit Model Specification

Consider the following model (cf. De Bruyn and Lilien 2008):

Y = 1 if the recipient has not read the text message. Y = 2 if the recipient has read the text message but has not

visited the website. Y = 3 if the recipient has visited the website but has not

forwarded the message. Y = 4 if the recipient has forwarded the message.

The probabilities can be written as follows (Amemiya 1975):

P1(Y = 1) = F(β1′x) P2(Y = 2) = [1 − F(β1′x)]F(β2′x) P3(Y = 3) = [1 − F(β1′x)][1 − F(β2′x)]F(β3′x) P4(Y = 4) = [1 − F(β1′x)][1 − F(β2′x)][1 − F(β3′x)].

The parameters β1 are estimated for the entire sample by dividing the sample into those who read the text message and those who did not. The parameters β2 are estimated from the subsample of recipients who read the text message by dividing it into two groups: those who visited the website and those who did not. The parameters β3 are estimated from the subsample of recipients who visited the website by dividing the subsample into two groups: those who forwarded the message and those who did not.

The likelihood functions for the above sequential logit model can be maximized by sequentially maximizing the likelihood functions of the three dichotomous models (De Bruyn and Lilien 2008; Maddala 1983).

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Christian Pescher: Research interests include B2B and B2C e-commerce, innovation, and social networks in marketing and forecasting.

Philipp Reichhart: Research interests include e- and m-commerce, mobile marketing, consumer behavior, word of mouth, social network and location- based services.

Martin Spann is a professor of electronic commerce and digital markets at the Munich School of Management, Ludwig-Maximilians-University Munich, Germany. His research interests include e-commerce, mobile marketing, prediction markets, interactive pricing mechanisms, and social networks. He has published in Management Science, Marketing Science, Journal of Marketing, Information Systems Research, MIS Quarterly, Journal of Product Innovation Management, Journal of Interactive Marketing, Decision Support Systems, and other journals.

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Original Article

Introduction: The Disruption of Traditional Advertising

For decades, the advertising industry was based on an asym- metrical communication model, where marketers would engage audiences via paid media channels. The advent of social media platforms completely transformed the general media landscape, along with the advertising model, as audi- ences shifted from the role of content receivers to content creators, distributors, and commentators (Keller, 2009; Scott, 2015). Simply put, the empowerment of audiences from mere viewers to active content distributors effectively flipped the advertising model on its head. Where paid media (in this case, advertising) was once supported by earned and owned media, the modern advertising model uses owned, shared, and earned media as the key media planning strategy, sup- ported by paid media (Pearson, 2016). Recognizing the increased potential for free content distribution, marketers realized that creating highly engaging advertising content could expand potential reach, a cheaper and more credible tactic than traditional paid advertising (Cho, Huh, & Faber, 2014; Golan & Zaidner, 2008). This fundamental disruption of the advertising and marketing world led to growing interest in content creation, co-creation, and distribution.

Generally defined, advertising refers to the “paid non- personal communication from an identified sponsor using mass media to persuade or influence an audience” (Wells, Moriarty, & Burnett, 2000, p. 6). Consistent with most, but not all, of these requirements, Porter and Golan (2006) defined viral advertising as “unpaid peer-to-peer communi- cation of provocative content originating from an identified sponsor using the Internet to persuade or influence an audi- ence to pass along the content to others” (p. 33).

The expanding literature on viral advertising recognizes the ways in which peer-to-peer distribution of advertising content are redefining the industry. When examined holisti- cally, the literature has several limitations. First, existing viral advertising research is limited primarily to advertising spread within one step of the original source (e.g., predicting the number of message shares), while information on social

847516 SMSXXX10.1177/2056305119847516Social Media <span class="symbol" cstyle="Mathematical">+</span> SocietyHimelboim and Golan research-article20192019

1University of Georgia, USA 2University of South Florida, USA

Corresponding Author: Itai Himelboim, Department of Advertising and Public Relations, Grady College of Journalism and Mass Communication, University of Georgia, Athens, GA 30602-3018, USA. Email: [email protected]

A Social Networks Approach to Viral Advertising: The Role of Primary, Contextual, and Low Influencers

Itai Himelboim1 and Guy J. Golan2

Abstract The diffusion of social networking platforms ushered in a new age of peer-to-peer distributed online advertising content, widely referred to as viral advertising. The current study proposes a social networks approach to the study of viral advertising and identifying influencers. Expanding beyond the conventional retweets metrics to include Twitter mentions as connection in the network, this study identifies three groups of influencers, based on their connectivity in their networks: Hubs, or highly retweeted users, are Primary Influencers; Bridges, or highly mentioned users who associate connect users who would otherwise be disconnected, are Contextual Influencers, and Isolates are the Low Influence users. Each of these users’ roles in viral advertising is discussed and illustrated through the Heineken’s Worlds Apart campaign as a case study. Providing a unique examination of viral advertising from a network paradigm, our study advances scholarship on social media influencers and their contribution to content virality on digital platforms.

Keywords viral advertising, social networks, Twitter, viral marketing, social media influencers

2 Social Media + Society

media often spreads beyond a single step from the original source. Second, in focusing on the characteristics of shared content or sharing users, researchers make the assumption that all shares are equal in terms of their impact. However, sharing-impact varies among users, based on their connectiv- ity. Third, the metaphor of virality, the idea that content is spread gradually among individuals and their immediate contacts, may not fully capture what is often a complex multi-actor process of content distribution. Cascades of con- tent distribution were found to be centered on a small num- ber of distributors, creating a hierarchical, rather than egalitarian, pattern of content distribution (Baños, Borge- Holthoefer, & Moreno, 2013).

This study proposes a social networks approach to address these limitations, using Heineken’s Worlds Apart campaign as a case study. Data are collected for all Twitter users post- ing links to the original advertisement on YouTube, and the subsequent retweets and mention relationships. While a growing body of scholarship examines the potential impact of social media influencers in online marketing campaigns, they often treat all influencers as one and the same (Evans, Phua, Lim, & Jun, 2017; Phua & Kim, 2018).

We argue that different types of influencers impact social networks in different degrees and ways. Informed by a body of scholarship in social networks, we propose that there are three types of influencers: primary, contextual, and low influencers. Primary influencers are hubs, users who attract large and disproportionate retweets from other users in the network. Contextual influencers play a role of bridges in the network by providing context regarding the overall discus- sion and thus help to understand the distribution of content beyond the quantity of retweets. Low influencers are users who shared a link to online content; however, these users were neither retweeted nor mentioned by anyone else in the network. While low influencers have limited individual con- tributions to content distribution, their aggregate influence is substantial.

Social Media Influencers

An emergent body of scholarship in the field of marketing, advertising, and public relations examines the intermediary function of influencers between brands and consumers, orga- nizations, and stakeholders in social media engagement (De Veirman, Cauberghe, & Hudders, 2017; Freberg, Graham, McGaughey, & Freberg, 2011; Phua, Jin, & Kim, 2016). At the most basic level, influencer is identified by their number of followers and their ability to impact social media conver- sation regarding brands or topics (Watts & Dodds, 2007). While the term social media influencer is ubiquitously applied, there are few formal definitions of what an influ- encer actually is. Brown and Hayes (2008) defined influenc- ers broadly as individuals who hold influence over potential buyers of a brand or product to aid in the marketing activities of the brand. Others narrow the definition of an influencer to

reflect on the latest marketing trend in which social media celebrities are paid by advertisers to promote products (Abidin, 2016; Evans et al., 2017; Senft, 2008).

Moving beyond definitions, scholars attempt to theorize why it is that some social media users grow more influential than others via relationship building. To explain the influ- ence of influencers, media scholars often depend on the parasocial relationship explanation (Daniel, Crawford, & Westerman, 2018; Lou & Yuan, 2018; Rasmussen, 2018). Moving beyond a temporary parasocial interaction (as origi- nally conceptualized by Horton & Wohl, 1956), parasocial relationships between audience members and mediated characters are formed over a period of time and provide audience members with a sense of engagement with on- screen characters (Klimmt, Hartmann, & Schramm, 2006; Tukachinsky, 2010). In the context of social media, such parasocial relationships provide influencers with unique social capital that leads to audience trust (Tsai & Men, 2017; Tsiotsou, 2015).

Indeed, the central role of trust in parasocial relation- ships may provide a plausible explanation for the influencer phenomenon and the rise of influencer marketing (Audrezet, De Kerviler, & Moulard, 2018). Trust has been identified as a key predictor of several advertising consequences includ- ing recall, attitude, and likelihood to share (Cho et al., 2014; Lou & Yuan, 2018; Okazaki, Katsukura, & Nishiyama, 2007). Abidin (2016), building on the concept of parasocial relations, identified four ways that influencers appropriated and mobilized intimacies: commercial, interactive, recipro- cal, and disclosive. Influencers are identified not only based on their sheer number of such parasocial relationships, such as subscribers or followers on social media, but primarily based on their ability to impact social media conversation and subsequent behavior regarding brands or topics (Watts & Dodds, 2007).

We propose to complement existing conceptualization of influencers by shifting the focus from influencers’ engage- ment or the nature of individual connections with them, to their ability to reach large, unique, and relevant audiences and to shape the conversation about brands and topics. It is the distribution of content that allows influencers to influ- ence, and therefore provides a key theoretical framework for identifying social media influencers. We next discuss viral advertising as a theoretical framework for content reach, fol- lowed by its limitations. We then take a social networks approach to theorize social media influencers, bridging both bodies of literature.

Viral Advertising

As explained by Golan and Zaidner (2008), there are several key differences between viral and traditional advertising. First, viral advertising earns audience eyeballs, as opposed to paying for them. This is a major departure from the tradi- tional advertising exchange, where brands purchase media

Himelboim and Golan 3

space and interrupt an audience’s media consumption with advertisements. Second, viral advertisements provide such increased value to audiences that they transform audiences from passive content receivers to active social distributors who play a key role in advertisement distribution. Third, although there are limited studies speaking to this point, it is worth noting that information sharing has been shown to increase a user’s followers on Twitter, which is a long-term benefit for marketers (Hemsley, 2016).

What Makes Advertising Go Viral?

Why do some advertisements receive wide-scale viewership via audience distribution, while others do not? Scholars offer different approaches to this question, one focusing on con- tent characteristics (Brown, Bhadury, & Pope, 2010; Golan & Zaidner, 2008; Petrescu, 2014) and another examining virality attribute factors such as brand relationships (Hayes & King, 2014; Ketelaar et al., 2016; Shan & King, 2015).

Porter and Golan (2006) specifically identify provocative content as contributing to advertising virality. Other studies identify appeals to sexuality, as well as shock, violence, and other inflammatory content as key elements of message viral- ity (Brown et al., 2010; Golan & Zaidner, 2008; Petrescu, 2014). Eckler and Bolls (2011) argue that the emotional tone of advertisement is directly related to audience intention to forward ads to others. Yet advertising content, tone, and emo- tion cannot fully account for ad virality. Scholars point to a variety of other variables significantly related to advertising virality including brand relationship (Hayes & King, 2014; Ketelaar et al., 2016; Shan & King, 2015), attitude toward the ad (Hsieh, Hsieh, & Tang, 2012; Huang, Su, Zhou, & Liu, 2013), and credibility of the sender/referrer (Cho et al., 2014; Phelps, Lewis, Mobilio, Perry, & Raman, 2004).

Hayes, King, and Ramirez (2016) advanced research on viral advertising by illustrating the importance of interper- sonal relationship strength in referral acceptance. Their study suggested that individuals are motivated to share advertising content based on reputational enhancement and reciprocal altruism. Alhabash and McAlister (2015) conceptualized virality based on three key components: viral reach, affective evaluation, and message deliberation. The authors linked virality and online audience behaviors in what they refer to as viral behavioral intentions (VBI). This linkage is sup- ported by later research indicating that the virality of digital advertising is often related to several VBIs motivated by a variety of audience-based characteristics (Alhabash, Baek, Cunningham, & Hagerstrom, 2015; Alhabash et al., 2013).

Limitations of Viral Advertising Research

In essence, viral advertising represents a “peer-to-peer com- munication” strategy that depends on distribution of content (Petrescu & Korgaonkar, 2011; Porter & Golan, 2006). Despite the fact that most peer-to-peer social media shares

include multiple distribution phases (e.g., from user A to user B to user C), existing viral advertising research is mostly limited to one-step advertisement spread (e.g., predicting number of message shares). Studies suggest that while con- tent may be shared by many users, most viral content is spread beyond this single step (Bakshy, Hofman, Mason, & Watts, 2011). The body of literature concerning viral adver- tising does not examine advertising spread beyond a user’s immediate set of connections.

Second, the literature conceptualizes virality based on such sharing metrics as shares or retweets. In doing so, schol- ars fail to account for the possibility that the overall impact of such user actions may not result in equal content distribu- tion outcomes. In fact, studies on virality of content and cas- cades of information flow highlight that “popularity is largely driven by the size of the largest broadcast” (Goel, Anderson, Hofman, & Watts, 2015, p. 180). In other words, it is not only the number of consumer-to-consumer interactions but the connectivity of these consumers with others that determines the impact of viral advertising. One user’s retweet may count more than another user.

A third limitation is the more subtle assumption of virality as metaphor. The idea that content is spread gradually from one source to that source’s immediate small group of connec- tions, to their neighbors, and so on is a powerful metaphor that resonates well with many scholars (Miles, 2014; Porter & Golan, 2006). However, research shows no foundation for such an egalitarian assumption. Connections are distributed in a skewed manner across individuals, a phenomenon referred to in ways that vary by discipline:

in economics it goes by the name “fat tails,” in physics it is referred to as “critical fluctuations,” in computer science and biology it is “the edge of chaos,” and in demographics and linguistics it is called “Zipf’s law.” (Newman, 2000)

At the end of the day, most pieces of shared content are not re-shared by others, and thus are spread by very few. Similarly, from an advertisement and social media perspec- tive, Nielsen (2006) presented the “1-9-90 rule,” suggesting that content is created by 1% of users and distributed by 9% to the remaining 90% of content receivers. Baños et al. (2013) showed that only a small minority of content dis- tributors will account for content virality. In addition, Pei, Muchnik, Andrade, Zheng, and Makse (2014) suggested that “due to the lack of data and severe privacy restrictions that limit access to behavioral data required to directly infer performance of each user, it is important to develop and validate social network topological measures capable to identify superspreaders” (p. 8).

To address these key gaps in the literature of viral adver- tising and subsequently our ability to theorize influential users in terms of their content diffusion, we take a social net- works approach, which focuses on patterns of connectivity among users. We propose that social media influencers are

4 Social Media + Society

ultimately determined by their position in an issue or brand- specific conversation network, allowing their posted content to be distributed in a strategic manner. As such, these influ- encers play key roles in the virality of any advertising cam- paign on social media. A social networks approach, as illustrated by Himelboim, Golan, Moon, and Suto (2014) provides for a macro-understanding of social media relation- ships, content flow, and the role of social media influencers within the network.

The Social Networks Approach

The social networks conceptual framework shifts the focus from individual traits to patterns of social relationships (Wasserman & Faust, 1994). Applying a social networks approach to social media activity allows researchers to cap- ture content virality and identify key social media influenc- ers that affect the conversation about a brand and reach key groups of consumers. A social network is formed when con- nections (“links”) are created among social actors (“nodes”), such as individuals and organizations. The collections of these connections aggregate into emergent patterns or net- work structures. On Twitter, social networks are composed of users and the connections they form with other users when they retweet, mention, and reply to (Hansen, Shneiderman, & Smith, 2011).

The network approach can bridge the viral advertising and social media influencer’s bodies of literature. As dis- cussed earlier, social media platforms allow individuals to maintain parasocial relationships with influencers (Abidin, 2016). In the case of Twitter, such engagement is manifested in the form of mentions, likes, and retweets. In social net- works research, these relationships are conceptualized as links in a network.

The social networks approach allows us to capture the distribution of a specific piece of content (i.e., an advertise- ment) and identify users in key positions in the network that are responsible for the distribution of ads, as social media influencers. It should be noted that even in studies on infor- mation diffusion in related disciplines, it is quite rare to track the virality of a single piece of content, rather than the over- all diffusion of messages in a broader conversation.

Viral advertising research often focuses on the most visi- ble type of content that is spread, shared, or retweeted on Twitter. Social media influencers are often examined by their number of connections in a social media platform (De Veirman et al., 2017). However, a link to a video advertise- ment, or any other source of paid advertising content, may be posted by more than a single user who contributes to its dif- fusion. In other words, while the advertisement itself may have a single point of origin (e.g., a YouTube video page), this advertisement may have multiple users who may account for multiple points of origin for distribution on Twitter. While a particular video may have gained many views and shares on its platform of origin (“gone viral”), not all shares on

Twitter contributed equally to its virality. We therefore ini- tialize our understanding of content distribution patterns by asking,

RQ1: What is the distribution structure of a viral adver- tisement on Twitter?

A single network can have different types of links, or ties, that connect its users. On Twitter, users can be connected, among others, by relationships of retweets and mentions. A network of advertising virality captures users who posted content with a hyperlink to a given ad. Such Twitter users share a link to a given advertisement via a tweet, expanding its reach one step away from the source (YouTube). Some studies have examined the overall network structure to explain virality. Pei et al. (2014) used social network analysis on LiveJournal, Twitter, Facebook, and APS journals and found that users who spread the most content were located in the K-Core (a metrics of subgroup cohesiveness in the net- work). At the node-level, a few users are expected to contrib- ute further to the virality by having their tweets shared, or retweeted, by many additional users. Such users capture virality beyond a single step away from the source. Users with many connections in the network are known as social hubs (Goldenberg, Libai, & Muller, 2001) or simply Hubs. Using computer simulations, Hinz, Skiera, Barrot, and Becker (2011) found that seeding messages to hubs outper- formed a random seeding strategy and seeding to low-degree users, in terms of number of referrals. Kaplan and Haenlein (2011) also illustrated the role that hubs play in integrative social media and viral marketing campaigns.

Recognizing that the emergent literature on social media influencers is somewhat undermined by the various uses of the term influence to reflect different functions of influence, we recommend the categorization of influencers into three different types, based on the type of relationships, links in the network, that makes them central in a network.

Social networks literature repeatedly shows that given the opportunity to interact freely, connections among users will be distributed unequally, as a few will enjoy large and dis- proportionate number of relationships initiated with them, while most will have very few ties. On Twitter, content posted by a few users will enjoy major distribution via retweeting, while the rest will gain little shares, if any. Indeed, Araujo, Neijens, and Vliegenthart (2017), define influentials as “users with above average ability to stimulate retweets to their own messages” (p. 503), consistent with conceptualization of influencers based on impact on content distribution (Cha, Haddadi, Benevenuto, & Gummadi, 2010; Kwak, Lee, Park, & Moon, 2010). Hubs as conceptualized in social networks literature, therefore, are one type of social media influencers as conceptualized in social media scholar- ship, as each one makes a major contribution to content dis- tribution. One type of influencer, from a social networks conceptualization, is therefore the Primary Influencer, as it

Himelboim and Golan 5

is one of few members responsible for the distribution of content in the network. We therefore present the following research question:

RQ2: Which users serve as Primary Influencers in a viral advertising network?

On Twitter, retweets are attributed to the original tweet; therefore, operationalizing links in this network only as retweets fails to capture information flow beyond one step away from a user who shared a link to an ad. In other words, since users are unlikely to share the same link more than once, the network of retweets will create distinct subsets of users, each retweeting a single tweet. These subsets are com- pletely, or almost completely, disconnected from one another. As discussed earlier, a key limitation of viral advertising lit- erature is that studies are limited to the extent they measure diffusion from a single source. In order to maximize insights from the social networks approach to viral advertising, other types of ties should be considered.

The practice of mentioning users on Twitter, using the @ symbol, serves two main purposes. First, it associates a post with another user (e.g., an individual, an organization, a brand), serving as metadata for that tweet. Second, it serves as a secondary route of content distribution. When a tweet mentions a given user, that tweet will appear on the recipi- ent’s Notifications tabs and Home timeline view if the author of the tweet follows the sender. Conceptualizing mentions on Twitter as links in a social network captures the context of the virality of advertisements by connecting users beyond immediate retweeting of a single source. In other words, this practice bridges the otherwise disconnected subsets of retweeting users. In social network literature, bridging is a concept that can advance the understanding of advertisement virality and the key users who play a key role in it.

Bridges and Structural Holes

Burt’s (1992, 2001) theory of structural holes examines social actors (e.g., individuals and organizations) in unique positions in a social network, where they connect other actors that otherwise would be less connected, if connected at all. In Burt’s (2005) words, “A bridge is a (strong or weak) relation- ship for which there is no effective indirect connection through third parties. In other words, a bridge is a relation- ship that spans a structural hole” (p. 24). A lack of relation- ships among social actors, or groups of actors, in a network gives those positioned in structural holes strategic benefits, such as control, access to novel information, and resource brokerage (Burt, 1992, 2001). Actors that fill structural holes are viewed as attractive relationship partners precisely because of their structural position and related advantages (Burt, 1992, 2001).

The nature of Twitter retweets, however, rarely allows bridges to form as retweets that are associated with an

original tweet (unless modified retweets are used). In other words, the spread of retweets remains within a single step away from the author who posted that message. Therefore, this additional type of structural characteristic is not enough to characterize a new type of influential user in viral adver- tising. Conceptualizing a second type of parasocial relation- ship on the network—mentions (the inclusion of a reference to another Twitter user in a post)—as links in a network allows bridges to form as they provide an additional connec- tion among users. While mentions do not represent primary stages in content distribution, they do provide meaningful points of context that allow researchers to better understand the overall virality of an advertisement.

Since content distribution or virality on Twitter does not take place in a vacuum but rather is often responsive to the broader online conversion, the distribution of any specific tweet may be impacted by contextual factors. For example, the distribution of a tweet about a pharmaceutical company may be impacted by related actors linked to the industry in news coverage. On Twitter, users often provide context to their posted content, among others, by mentioning related users via their handles (@). While such users do not take an active role in the conversation, they are nominated, so to speak, as influencers in the network, as they provide addi- tional explanation for content virality. In other words, they allow researchers and practitioners to understand that the vast distribution of an ad on Twitter is driven by a larger context.

We therefore define a second type of social networks- driven influencer type as Contextual Influencer—highly mentioned users who bridge otherwise separated groups of retweeting users.

RQ3: Which users serve as Contextual Influencers in viral advertising networks?

Beyond a few users in key positions—Hubs or Bridges— many users’ content sharing is more limited. Each user con- tributes little to advertisement virality, as they reach only their immediate Twitter followers. However, as such users are often the majority of distribution agents, they ultimately make a major contribution to ad virality. We call users who are isolated in the network (defined as incurring no retweets for their shared video tweets) Low Influence users.

RQ4: What percentage do Low Influencers make of all users in the network?

Proof of Concept: Heineken’s “Worlds Apart” Viral Advertisement

To illustrate the conceptual framework proposed in the cur- rent study, we selected a popular Heineken advertisement on YouTube, titled “Heineken | Worlds Apart | #OpenYourWorld.” Heineken described the ad as, “Heineken presents ‘Worlds Apart’ An Experiment. Can two strangers with opposing

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views prove that there’s more that unites than divides us?” In this ad, Heineken harnesses a social issue, political and social polarization, and the importance of a constructive conversa- tion across opinions and ideologies. This campaign received accolades from the advertising industry and popular press, as it was compared to a Pepsi campaign that drew on similar social themes but failed to resonate with social media audi- ences (Al-Sa’afin, 2017). The video was posted on April 24, 2017, and attracted almost 15 million views by September 30, 2017. This advertisement became viral via a range of platforms, including Twitter. The advertisement was selected for this study for its high degree of virality (AdAge.com, 2017).

Method

Data

We used the social media analytics and library platform Crimson Hexagon to capture all Tweets that included the URL to the YouTube video ad (https://www.youtube.com/ watch?v=_yyDUOw-BlM). Crimson Hexagon is a Twitter Certified social media data analysis archive, and collects all publicly available tweets directly from the Twitter “fire- house.” The data collected for this study capture all public Twitter posts that used the hyperlink to the ad in question (including shortened hyperlinks). We captured all 18,942 tweets posted by 13,009 users between April 20, 2017, when the video was posted, and September 20, 2017. We elected for a longer period of data collection time, due to the fact that viral advertising content often results in mainstream and trade media (Wallsten, 2010). Furthermore, the explor- atory nature of this study required a more inclusive data collection period to account for unexpected waves of engagement (see Figure 1).

Note that the users @Youtube and @Heineken were removed from the network data analysis as these handles were automatically added to any tweet shared from YouTube, and therefore created artificial connections between all tweets, potentially misinforming the analysis.

The Network

A customized application was used to extract the retweets, mentions, and replies relationships from the list of tweets; the 5,765 retweets relationships; and the 7,212 mentions ties (including 392 replies, which serve the same function of appearing on a target user wall). The treatment of mentions and replies as a single link-type is a common practice in Twitter network analysis (Isa & Himelboim, 2018; Lee, Yoon, Smith, Park, & Park, 2017; Yep, Brown, Fagliarone, & Shulman, 2017). The MS Excel add-on network analysis application, NodeXL, was used to calculate user- and net- work-level analysis, as well as for visualization.

For each user in the network, two types of centrality measurements were calculated, using NodeXL. In-degree centrality was measured as the number of connections initi- ated with a given actor (Wasserman & Faust, 1994). On Twitter, in-degree centrality is based on ties or relationships that others have initiated with a user (e.g., the number of users who have retweeted or mentioned that specific user). Users with the highest values in this metric can be consid- ered Hubs, highlighting users who have successfully gained attention to their messages. We determined the cutoff point for identifying hubs by plotting the distribution of in-degree by number of users and the drop point in the scree plot-like graph. Betweenness centrality measures the extent that the actor falls on the shortest path between other pairs of actors in the network (Wasserman & Faust, 1994). The more people depend on a user to make connections with other

Figure 1. Twitter activity of posts including a hyperlink to the Heineken viral advertisement.

Himelboim and Golan 7

people, the higher that user’s betweenness centrality value becomes. This value is therefore associated with Bridges in a network.

Findings

The study identified a total of 13,009 users who posted a link to the original Heineken advertisement on YouTube. With almost 15 million views of this video on YouTube, this num- ber of tweets may appear low. However, each tweet posted by a user reaches all its Twitter followers. A message’s poten- tial reach or impressions are therefore calculated as the total number of Twitter walls, or user accounts, on which these tweets appeared. This metric is calculated by adding up all followers of all users who posted an original tweet with the advertisement URL and the followers of all users who retweeted such posts (Sterne, 2010). For the 13,009 users, the total reach was 48,962,936 users (the sum followers of all users in the network), meaning that for almost 50 million users, this advertisement appeared on their own Twitter pages. While it does not necessarily mean that the users all saw the ad, or even the link to it, and it does not take dupli- cates into account, the high reach value illustrates the poten- tial advertising distribution of the 13,009 tweets.

Characteristics and Viral Advertising—Time

The vast majority of advertising spread on Twitter (16,152 tweets, 85.27%) were posted within the first 2½ weeks fol- lowing the date the video was posted (April 20, 2017—May 6, 2017). The remaining posts were spread over the remain- ing 4.5 months of data collection. Notably, within the first 5 days of the video’s publication, only 248 tweets linked to it. This relatively low-engagement period was followed by highly retweeted activity of individual users such as @Cait_ Kahle, a consumer public relations professional, who tweeted on April 26, “Incredible perspective from @ Heineken via an advertisement #OpenYourWorld: https://t .co/ApmYwteLwn.” This tweet gained 463 retweets. @ CaseyNeistat, a photographer, posted on April 27, “y’all see that heineken commercial yet? it should win ALL ad awards—https://t.co/gFDXwy7F31,” gaining 1,172 retweets (see Figure 1).

Characteristics of Viral Advertising—Distribution of Spread and Reach

RQ1: What is the distribution structure of a viral adver- tisement on Twitter?

The distribution histogram of tweets was found to be highly skewed. Of the 7,422 users who posted an original tweet with a link to the advertising video on YouTube, 5,875 received no engagement from others (79.19% of original

tweets and 45.16% of total tweets). These are isolated users in the network in terms of advertising spread. For 933 users, only one retweet occurred (i.e., two users spread); 214 users were retweeted by two users, 214 by three users, 75 by four, and 29 users had five retweets each. At the other end of this skewed distribution, a few single tweets were shared many times (1,154; 435; 336; 310; and 102 retweets for each of the top five most shared users). Figure 2 illustrates the distribution.

RQ2: Which users serve as Hubs in a viral advertising network?

The top users, those who posted the most retweeted tweets linking to the Heineken advertisement video, were primarily individuals with no affiliation to the brand: @CaseyNeistat (Casey Neistat), a videographer (1,214 retweets); Cait_Kahle (Caitlin Kahle), a consumer public relations professional (465 retweets); @ChrisRGun (The Cuntacular Chris), a user who posts political jokes and videos (99 retweets); @jrisco (Javier Risco), a Mexican journalist (96 retweets); @ MaverickGamersX (Maverick Gamers), an aggregator for video game/film/entertainment industry news and reviews (88 retweets); @willwillynash (Will Nash), a director of short films and music videos (70 retweets); @anmintei, a professor at the University of Tokyo (53 retweets); @ TheSeanODonnell (Sean O’Donnell), an actor and producer (49 retweets); @COPicard2017, a fan account for Jean-Luc Picard (42 retweets); and @rands, a vice president at Slack (42 retweets).

Characteristics of Viral Advertising—The network

RQ3: Which users serve as bridges in viral advertising networks?

Social network analysis maps and examines patterns of spread of tweets across Twitter users. However, the network of retweets will create a set of disconnected silos, because

Figure 2. The histogram of URL spread by Twitter users. Both axes are in logarithmic scales.

8 Social Media + Society

any retweet of a post will be attributed to the original tweet. Understanding the network of social ties beyond retweets reveals the cross-silo interactions. We therefore expanded the dataset to include not only retweets but also mentions and replies within the set of 13,009 users who posted content with the ad’s URL.

Figure 3 illustrates the social networks created when using only retweets as links. For illustration purposes, only clusters created by the most retweeted users were included. Each highlighted user is one of the top retweeted users, surrounded by users who retweeted their post’s link to Heineken’s adver- tisement on YouTube. Clearly, such a network does not pro- vide more information than was previously gained from identifying the top retweeted posts. The hubs were discussed in an earlier analysis. This figure highlights the limitation of examining retweets as the sole relationships in Twitter activity surrounding this viral advertisement.

Adding mention relationships into the network adds another layer of connectivity. The retweeting clusters are no longer siloed and new clusters are formed. The top between- ness user added in this network is @Pepsi (in-degree = 352; betweenness centrality = 1,851,055.41).

Examining the content that included @Pepsi reveals an important theme of the tweets helping to spread the Heineken ad: the controversial Pepsi advertisement criticized for cultural and racial insensitivities. In fact, 315 of the tweets mentioning @Pepsi and linking to the Heineken ad were not retweets and were not retweeted, and would have otherwise been potentially ignored, as they “failed” to attract retweets. The other cluster contained two additional bridges in the network: @Heineken_ UK (in-degree = 181; betweenness centrality = 730,191.83) and @publicislondon, a London advertising agency (in-degree = 63; betweenness = 740,877.09). These two users gained no

meaningful retweets, and therefore did not reach the surface of the initial retweets analysis. However, they were highly men- tioned by users who posted links to the advertisement, contrib- uting to its virality. This finding highlights the potential significance of advertising agencies in the distribution of a viral ad on Twitter beyond their ability to inspire retweets. This anal- ysis points to the strategic application of the agencies’ Twitter networks as distribution mechanisms.

Figure 4 illustrates the connectivity power of mentions, allowing for more in-depth understanding of the viral distri- bution process. Specifically, we can see the siloed clusters created by highly retweeted users, and the connections cre- ated by highly mentioned users.

RQ4: What percentage do Low Influencers make of all users in the network?

Of the total 13,009 users who shared a link to the Heineken advertisement, 5,875 (45.16%) were not retweeted even one time, making them isolated in the network. In other words, while each of these users did not make a major contribution to the virality of the ad, as a whole, through comprising almost half of the users, they did make a major contribution, thus supporting the idea of Low Influence users as important for viral advertising. The total potential reach of these Low Influence users, calculated as the sum of the number of their followers, is 9,091,133, 18.56% of the total potential reach (48,962,936) of all users in the network.

Discussion

The current study aims to advance social media virality and social media influencers in advertising scholarship by

Figure 3. The retweets-based social network of Heineken’s viral advertisement.

Himelboim and Golan 9

incorporating a social networks approach. We propose and empirically identify three distinct types of social media influencers and thus highlight the multifaceted nature of dis- tribution in viral advertising. Using the Heineken’s Worlds Apart ad for proof of concept, we identified three types of key users, based on their network connectivity: Primary Influencers (retweeted hubs), Contextual Influencers (bridges), and Low Influencers (network isolates). As men- tioned, viral advertising largely depends on audience partici- pation in content distribution. Our analysis highlights the distinct role of each of the three influencers in ad distribu- tion. The current study aims to advance the understanding of the viral advertising process by offering a more macro-view of advertisements on Twitter. This view broadens the analy- sis of ad distribution beyond the single peer-to-peer flow, allowing for a multi-step structure available only through network analysis. Moving away from the question of what makes an advertising viral and toward the question of who makes an advertising viral, our study points to a highly skewed nature of distribution. The results of our analysis indicate that a small number of users disproportionately con- tributed to the distribution of the ad on Twitter, while the vast majority of users made a more modest contribution individ- ually, but a major contribution as a whole.

The Skewed Distribution of Primary Influence

One unique characteristic of viral advertising on Twitter is the multiple points of origin for an advertisement video.

While the ad has a single source, often YouTube or the brand website, it starts a potential cascade of sharing on Twitter via multiple tweets. This study demonstrates the differential contribution of individual Twitter accounts, either affiliated or not affiliated with a brand. Consistent with previous scho- larship on viral advertising (Petrescu, 2014; Phelps et al., 2004) and information sharing on Twitter (Araujo et al., 2017), we found that a small number of users had more influ- ence on content distribution than most. Scholarship often points to celebrities, elites, and media organizations as key influencers due to their large Twitter following (Himelboim et al., 2014; Jin & Phua, 2014). The current study points to a more nuanced explanation.

Taking a social networks approach, the structure of inter- actions created by acts of sharing only (i.e., retweets) fails to explain the overall structure of information flow in the net- work in two main ways. First, the major clusters of retweets remain isolated. Such a siloed structure cannot explain the virality of advertising beyond a set of a few highly shared tweets. Second, the approach ignores the majority of users who made much more moderate contributions to the virality of the ad, as they received little or no retweets.

Bridges: The Contextual Influencers

Viral advertising does not take place in vacuum and content sharing takes place in a broader conversational context. A second type of social media influencers—Contextual Influencers—are those who conceptualize and operationalize

Figure 4. The retweets and mentioned-based social network of Heineken’s viral advertisement.

10 Social Media + Society

as users who do not necessarily take an active part in the con- versation but are brought into the chatter by users who men- tioned them, as they contribute to advertising virality by sharing a link to an ad. Social media influencers are often characterized by the amount of social connections they have, in terms of followers or subscribers (Abidin, 2016). Contextual influencers are defined by Twitter mentions, a unique type of parasocial relationship that captures best the idea of a sense of engagement of audience members with on-screen characters (Tukachinsky, 2010).

Building upon literature about Bridges and structural holes, highly mentioned users play a role by symbolically connecting many users who were not retweeted, based on parasocial relationships that they form in the network. In other words, introducing both a new type of ties, mentions of users as links, and an additional node-level structural net- work position, a bridge provides context for understanding certain mechanisms in the distribution of viral advertising that have been ignored by recent scholarship, which has focused solely on highly retweeted users.

Contextual influencers, in the mention-based clusters of low engagement users, provide the context and a possible explanation for the advertisement’s virality. In the case study, a common reason for posting the video was in comparison with a failed competing campaign (Pepsi) and in the context of the advertising agency behind that campaign, Publicis London (Publicis, 2017). As one may recall, both Pepsi and Heineken launched campaigns aiming to gain advertising distribution through the production of socially provocative advertisements. While Pepsi was often criticized for its cam- paign, Heineken was praised. Even though Pepsi did not take an active part in distributing the Heineken ad, as it never posted a link to it, Pepsi is an influencer in the network because it influenced the virality of the Heineken ad. It is the comparison between the two campaigns that people posted about, when they distributed the Heineken ad link.

Further analysis of patterns of mentions among users in examples of viral advertising spread on Twitter can provide researchers and practitioners with an understanding of the triggers of distributing advertising content on Twitter. In other words, this proposed methodology shifts the focus from aiming to explain the reason for high levels of retweets, to explaining the reasons for high level of posts, even if these posts received no engagement. It should be noted that while @HeinekenUK appeared together in a cluster with @PublicisLondon, it had limited influence net- work connectivity.

Isolates: The Influence of the Low Influence Users

Expanding the breadth of the network to include mentions as network links also revealed clusters of users who contrib- uted to the virality of the advertisement but in a more hidden manner, as they did not attract many retweets. However, these users vastly contributed to the reach of the ad on

Twitter as whole. In the Heineken case, almost four of five original tweets posted with the Heineken video were not retweeted, representing about 45% of all tweets in our data- set. At face value, the contribution from each of these users seems minimal. However, when aggregated, we find that these seemingly non-successful content distributors played an important role in the overall distribution of the advertise- ment. Furthermore, the video appeared on the walls of all of their followers, even if they were not retweeted, expanding their contribution to the virality even further. In the Heineken case, they accounted for almost a fifth of the total potential reach. Considering Nielsen’s (2006) idea of the 1-9-90 rule, findings suggest that within the majority of seemly non- influential users, many have limited influence on advertis- ing distribution. But when aggregated, these users make a major impact on advertising virality.

There is no doubt that influencer analysis is an important factor in understanding viral distribution of Internet content. As made evident by previous studies, not all Twitter users are equal in their ability to promote content, with elites, corpora- tions, and celebrities often leading the discussion. The results of our analysis point to an often-overlooked phenomenon, the influence of non-influencers, a phenomenon that occurs when individuals fail to meaningfully contribute to the structure of a network, but play an important role in shaping the network when grouped with other non-influencers, ultimately making a meaningful contribution to viral advertising.

Conclusions and Limitations

Recognizing the potential of the network approach in informing and advancing research on viral advertising, our study demonstrated the multi-level ad distribution process. Whereas most previous studies focused on what may lead to advertisement virality (Chu, 2011; Golan & Zaidner, 2008; Hayes & King, 2014), our study addresses another impor- tant question: who makes advertising viral? We identified three key types of influencers. The first are the highly retweeted users in the network, each individually makes a major contribution to the distribution of ads. The second are highly mentioned users who make a crucial yet passive con- tribution to content virality. These serve as Bridges, filling structural holes left in the retweets-only networks. Third are Low Influencers, who each introduced the ad to Twitter by posting an original tweet with a link. Individually their con- tribution to ad virality does not go beyond their group of followers; however, their aggregated influence on virality is vast, making them influential in the network.

Finally, the current study advances the methodological approach to the study of viral advertising. Network analysis is the only method that allows for a meaningful representa- tion of the viral advertising distribution process. The defini- tion of an advertisement as a single paid form of media requires a third approach where data are collected based on a single piece of content, namely, a hyperlink to an

Himelboim and Golan 11

advertisement. Collecting Twitter data based on a single URL results in a dataset that captures the spread of a single ad across a range of distributors, as opposed to the traditional data collection strategy based on a set of keywords capturing a conversation about a brand. We argue this strategy is not only unique to viral advertisement, but is the most appropri- ate strategy overall. These conceptual and methodological contributions are applicable beyond the study of viral adver- tising and the field of marketing, as social media influencers play a key function of content distribution on online plat- forms with implications for scholars and practitioners alike across discipline.

The proposed conceptual framework had the key limita- tion of testing only a single dataset. Future studies should apply this network approach across viral advertising cam- paigns and across a range of brands. Similarities and differ- ences in the two types of influential users proposed here could lead to better understanding of the nature of viral advertising on social media. In the same vein, our analysis of the Pepsi account as a key bridge evidenced the potential contribution of non-affiliated accounts to the overall viral network. Our study did not examine other key users, and thus did not account for their potential influence. Furthermore, any study about engagement is susceptible to a bias made by fake engagement, an ongoing issue for researchers and prac- titioners (Pathak, 2017).

The current study is also limited by its use of a single case study on a single social media platform. Social media con- tent is almost never distributed via a single platform alone, but rather becomes viral through the integration and distribu- tion of content across platforms as individuals share links to content in multiple ways. We recognize this issue as a limita- tion of our study and call upon future studies to consider this consideration in their design.

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) received no financial support for the research, author- ship, and/or publication of this article.

ORCID iD

Itai Himelboim https://orcid.org/0000-0001-7981-5613

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Author Biographies

Itai Himelboim (PhD, University of Minnesota) is an associate pro- fessor of Advertising and Public Relations and the director of the SEE Suite, the social media engagement and evaluation lab, at Grady College of Journalism and Mass Communication, of the University of Georgia. His research interests include social media analytics and network analysis of large social media activity related to advertising, health, and politics.

Guy J. Golan (PhD, University of Florida) is the director of the Center for Media & Public Opinion. He has published more than 40 peer-reviewed journal articles focused on media effect, political communication, and strategic communication.