Marketing Science Institute Working Paper Series 2016 Report No. 16-115
How Earned and Owned Social Media Impact Shareholder Value Through Consumer Mindset Metrics
Anatoli Colicev, Ashwin Malshe, Koen Pauwels, and Peter O’Connor
“How Earned and Owned Social Media Impact Shareholder Value Through Consumer Mindset Metrics” © 2016 Anatoli Colicev, Ashwin Malshe, Koen Pauwels, and Peter O’Connor; Report Summary © 2016 Marketing Science Institute
MSI working papers are distributed for the benefit of MSI corporate and academic members and the general public. Reports are not to be reproduced or published in any form or by any means, electronic or mechanical, without written permission. Report Summary
Although recent studies have shown strong effects of social media metrics on sales and business outcomes, it is unclear why and how these effects occur. In this study, Anatoli Colicev, Ashwin Malshe, Koen Pauwels, and Peter O’Connor propose and study a chain of effects from social media to shareholder value through consumer mindset metrics.
They test their proposed paths in a unique data set that captures information on daily earned and owned media from social networking websites, consumer mindset metrics (salience, purchase intent, and advocacy), and stock market returns for 45 brands in 11 industries. They merge these data with data on advertising, firm size, and announcements of new products, dividends, earnings, and M&A to derive a final dataset.
Findings Owned social media (i.e., brand posts, tweets, replies to users, and retweets) drive brand salience and advocacy but not purchase intent. Owned social media also drive earned social media brand community size and engagement (i.e., likes and “people talking about that,” user retweets, and YouTube video views), which both in turn drive salience and purchase intent. Only purchase intent leads to higher stock returns. Positive and negative comments on social media affect brand advocacy, and negative comments directly drive abnormal returns. Finally, new product announcements, rather than advertising, have a substantial impact on positive social media comments and on brand advocacy.
These findings suggest that managers can use owned social media to positively shape conversations on earned social media. Quantifying the differential impacts of growing a brand’s community and improving social media engagement and valence should help managers to design more effective social media strategies.
The findings also highlight the role of owned and earned social media in predicting consumer mindset metrics. Managers can use changes in owned and earned social media, which are available in very short time intervals, as proxies for mindset metrics, which are often measured less frequently due to costly sampling requirements.
Finally, the study offers insights on the interdependence among social media, customer mindset, and firm performance metrics, and suggests how to elevate all three via the managerial levers of owned social media, new product announcements, and advertising.
Anatoli Colicev is a doctoral candidate, Information Systems, Decision Sciences, and Statistics, and Ashwin Malshe is Assistant Professor, Marketing, both at ESSEC Business School, Paris- Singapore. Koen Pauwels is Professor, Marketing, Özyeğin University, İstanbul. Peter O’Connor is Professor, Information Systems, Decision Sciences, and Statistics, ESSEC Business School, Paris-Singapore.
Marketing Science Institute Working Paper Series 1 Acknowledgments The authors thank seminar participants at 2015 Marketing Strategy Meets Wall Street Conference, Big Data Conference 2015, ESSEC Business School, Paul College (UNH), College of Business (UT San Antonio), and Özyeğin University for their comments and YouGov for providing BrandIndex data. Anatoli Colicev and Peter O’Connor acknowledge funding from ESSEC Research Center.
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Introduction US companies now spend on average 10% of their marketing budgets on various types of social media (The CMO Survey 2015). Companies routinely invest in building their own digital assets with social media components, which are commonly known as “owned social media”. For example, most Fortune 500 companies have Twitter accounts (73%), Facebook fan pages (66%), and YouTube channels (62%; Heggestuen and Danova 2013). Companies may also derive social media exposure through user-generated brand mentions, comments, recommendations, etc. Such “earned social media” refer to the activities within the social media sphere that a company does not directly generate (Stephen and Galak 2012). Studies report that at least 42% of Facebook users have mentioned a brand in their status updates (Mazin 2011) and an estimated 19% of all the tweets by Twitter users are brand-related (Jansen et al. 2009). However, almost 87% of marketers are still not able to quantitatively measure the impact of social media on business performance (The CMO Survey 2015). With growing investments in social media, understanding whether and how social media may impact consumers and consequent shareholder value is essential for academicians and practitioners alike (Kumar 2015). To demonstrate the accountability of social media efforts, managers must show that social media lead to the creation of shareholder wealth (Rao and Bharadwaj 2008). Recent studies document a predictive relationship between earned social media and stock market metrics, such as firm value and firm risk (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012), thereby making a case for monitoring earned social media. However, firms typically not only monitor but also engage in social media activities, such as creating, developing, and managing online content; facilitating consumer conversations; and influencing consumer mindset metrics (e.g. brand salience) (Peters et al. 2013). How owned and earned social media drive these mindset metrics, which in turn drive shareholder value, is not well understood (Srinivasan, Hsu, and Fournier 2012). Accordingly our research questions are: how do social media relate to key consumer mindset metrics, such as brand salience, purchase intent, and advocacy? Through which paths do specific social media components such as the volume and valence of earned media affect stock market performance? To address these questions, we propose and study the chain of effects from social media to shareholder value through consumer mindset metrics. Recent studies have shown strong effects
Marketing Science Institute Working Paper Series 3 of social media metrics on sales (e.g. Stephen and Galak 2012), retail business outcomes (e.g. Hewett et al. 2015), and financial metrics (e.g. Tirunillai and Tellis 2012). However, it is unclear why and how these effects occur. For instance, Srinivasan, Rutz, and Pauwels (2015) speculate that a few dozen Facebook ‘Unfriends’ do not significantly decrease US-wide sales by themselves, but instead represent a broader customer issue. Likewise, Luo, Zhang, and Duan (2013) argue that investors react to earned social media because it offers unfiltered insights into what brand consumers are currently thinking and feeling, which should affect future consumer behavior. In this paper, we empirically show that social media impact shareholder value partially by influencing consumer mindset metrics.
(Tables and figures follow References.)
Table 1 compares our research with related current literature on six different aspects. Following Stephen and Galak (2012), we define OSM as brand-initiated, brand-related social media activities that are fully under the brand’s control. Similarly, we define ESM as brand- related social media activities which are initiated by external entities, such as consumers or journalists. We are among the first few to study the impact of earned social media and owned social media (henceforth ESM and OSM respectively) through consumer mindset metrics on shareholder value. Past studies do not capture such mechanism and relate social media directly to performance indicators such as reach (Schulze, Schöler, and Skiera 2014), sales (Stephen and Galak 2012), or shareholder value (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012). Recently, Nam and Kannan (2014) proposed that the effect of social tags can affect financial markets via consumer perceptions (i.e., customer-based brand equity). However, they do not have a direct metric of consumer perceptions, and instead infer them from the social tags. In contrast, our causal chain captures both direct effects of social media on shareholder value and indirect effects through consumer mindset metrics. We include a wide array of metrics from social networking websites such as Facebook, Twitter, and YouTube. This contrasts to the most of the extant research, which uses a limited set of social media information such as Facebook posts (Goh, Heng, and Lin 2013), YouTube views (Yoganarasimhan 2012), Facebook likes and unlikes (Srinivasan, Rutz, and Pauwels 2015), blogs (Stephen and Galak 2012), apps (Schulze, Schöler, and Skiera 2014), online reviews
Marketing Science Institute Working Paper Series 4 (Tirunillai and Tellis 2012), and social tags (Nam and Kannan 2014). We also distinguish between three dimensions of ESM based on the efforts required by social media participants: the Size of their brand community, the extent of their Engagement, and the Valence of their comments. Whereas joining a community by liking a Facebook page is a low effort ESM activity, engaging with brands by watching videos or retweeting brand tweets involves a higher level of effort. In addition, providing positive or negative customer feedback (valence) involves even higher levels of efforts. Our data and model allow us to capture the differential impacts of OSM and ESM on consumer mindset metrics, and shareholder value over time, thereby presenting a more granular picture of the role of social media. Finally, we cover a larger number of brands (45) and markets (11) compared to most extant studies. We offer implications for theory and practice. By highlighting the role of consumer mindset metrics in the social media-shareholder value link, we add to our collective understanding of the ways in which social media benefits businesses. Overall, our Granger Causality tests provide empirical support for the conceptual cause-and-effect conclusions in Katsikeas et al. (2015). Accordingly, our analysis addresses their call for “studies linking different aspects of performance and identifying contingency factors that may affect the strength of such relationships.” (Katsikeas et al. 2015 p.32). Specifically, the size of the online brand community improves brand salience, but engagement has the largest effect on brand purchase intent, which directly increases shareholder value. Moreover, positive and negative comments on social media have symmetric effects on brand advocacy, but only negative comments directly drive shareholder value – in line with the argument that ESM helps overcome the information asymmetry faced by investors (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012). Thus, the nature of the role of social media activities in increasing shareholder value is twofold: it helps shape consumer mindset and it directly assists in informing investors. We provide managers with key insights on the interdependence among various performance metrics. Within the same organization managers may have multiple performance objectives (Katsikeas et al. 2015). Social media managers focus mainly on key social media metrics, brand managers are concerned with improving the mindset metrics, while senior managers such as chief marketing officers (CMOs) are pressured to deliver stock market returns. Accordingly, it is unclear which metrics are most important across various settings (Katsikeas et al. 2015). To tackle this issue, our study provides key insights in how to elevate all three set of metrics with
Marketing Science Institute Working Paper Series 5 the following managerial levers: owned social media, new product announcements and advertising. We quantify the differential impacts of growing a brand’s community, improving social media engagement, and social media valence, which should help managers to design more effective social media strategies. While earned social media are not under the control of marketers, our research suggests that managers can use owned social media to positively shape conversations on earned social media. Moreover, we show that new product announcements, rather than advertising, have a substantial impact on positive social media comments and on brand advocacy. Our study also highlights the role of owned and earned social media in predicting consumer mindset metrics. Managers can use changes in owned and earned social media, which are available in very short time intervals, as proxies for mindset metrics, which are often measured less frequently due to costly sampling requirementsi.
Conceptual Framework The impact of marketing mix elements on consumer mindset metrics and firm performance is well-documented (Joshi and Hanssens 2010; Srinivasan, Vanhuele, and Pauwels 2010). These metrics help understand the state of mind of consumers and are sensitive to marketing actions (Keller and Lehmann 2006). Nowadays, prospective customers learn about brands increasingly through social media instead of traditional marketing (e.g. Court et al. 2009; Pauwels and van Ewijk 2013). Just as marketing may impact performance both directly and indirectly through consumer mindset (Hanssens et al. 2014), we argue that social media effects on performance materialize both through a “mind-set route” and the direct “informational route” investigated in Srinivasan, Rutz, and Pauwels (2015) and Tirunillai and Tellis (2012). To investigate both routes of influence, we conceptualize the effects of Earned Social Media (ESM) and Owned Social Media (OSM) on three key mindset metrics—Salience, Purchase Intent, and Advocacy and through these on shareholder value. Following Stephen and Galak (2012), we define OSM as brand-initiated, brand-related social media activities that are fully under the brand’s control. Similarly, we define ESM as brand-related social media activities which are initiated by external entities, such as consumers or journalists. Figure 1 shows our conceptual framework specifying the different forms in which we observe social media.
Marketing Science Institute Working Paper Series 6 In the following sections, we discuss four conceptual building blocks: (1) Types of OSM and ESM differ in source credibility and customer effort and their impact on mindset metrics (H1-H2), (2) Mindset metrics affect stock market performance (H3), (3) OSM and ESM affect stock market performance above and beyond its effect through mindset metrics (H4), and (4) Several feedback loops are expected among these variables.
Owned and earned social media types differ in source credibility and customer effort As shown in Table 2, we propose that specific ESM and OSM activities differ on the dimensions of source credibility and required consumer effort, which in turn drive their impact on consumer mindset metrics.
Source credibility. Prior academic as well as practitioner literature has highlighted that consumers assign different levels of credibility to OSM versus ESM due to their perceived source. Originally proposed as an attribute of the communicator (Riley et al. 1954), credibility has been applied to media, with dozens of studies comparing the relative credibility of newspapers, radio, television, and the Internet (Rieh and Danielson 2007). According to this research stream, the two main components of credibility are (1) perceived trustworthiness and (2) perceived expertise, with trustworthiness having a higher impact on sales than expertise (Erdem and Swait 2004). Because ESM is largely uncontrolled by companies, it enjoys more credibility among consumers compared to other forms of media (Ho-Dac, Carson, and Moore 2013). As a result, ESM should be more powerful in affecting the consumer mindset. OSM is likely to be perceived as less credible because it comes from the company with a clear commercial objective and often appears pushy to customers (Stephen, Sciandra, and Inman 2015). Indeed, a recent survey by Nielsen (2013) reports highest consumer trust in earned media (84%) than in owned media (69%). Apart from the source credibility, OSM and ESM also differ on how much effort consumers need to put in their creation. By definition, consumers are not involved in OSM creation, but consumer efforts are likely to vary for different types of ESM.
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Consumer effort. Different forms of online expressions usually vary by the effort and motivation required from the consumer (e.g. Hoffman and Fodor 2010). First, joining a brand community (typically with a simple click on a branded page such as a brand’s Facebook page) is a one-time action that represents rather low effort. In contrast, interacting with the brand’s content represents a moderate-level of effort and is often labelled as ‘Engagement’ (e.g. Peters et al. 2013; Rooderkerk and Pauwels 2016). Examples of ESM Engagement include visiting a brand’s Facebook page and interacting, sharing brand tweets on Twitter, and watching videos posted by a brand on YouTube. Finally, a high level of effort is involved when consumers themselves create content such as positive or negative comments on a brand’s owned digital asset (e.g., Facebook brand page). Based on this rationale, we split ESM into three types—brand community size (BCS), engagement (ENG), and valence. Table 2 summarizes differences between these three types of ESM and also provides examples.
Consumer mindset metrics pyramid We conceptualize the consumer mindset metrics as a three-layer pyramid (see Figure 1) with brand Salience as the bottom layer, Purchase Intent as the middle layer, and Advocacy as the top layer. The consumer mindset pyramid represents decreasing sensitivity to ESM and OSM (from high sensitivity at the bottom to low at the top). Salience is relative straightforward to increase: both ESM and OSM are easily accessible to prospective customers, who use such online information to find out about brands (e.g. Court et al. 2009). On the other hand, Purchase Intent depends on many other factors, such as the actual value proposition and fit with the prospective customer (Keller 1993). Therefore, compared to Salience, Purchase Intent should be harder to increase with OSM and ESM. Finally, Advocacy depends on customer satisfaction with the actual brand experience as well as on a host of other factors (Berger and Milkman 2012; Reichheld and Aspinall 1993). Therefore, we expect Advocacy to have a relative low level of sensitivity to ESM and OSM. Our rationale is reflected in the higher offline advertising elasticity of awareness (0.06), then brand consideration (0.02) and finally brand liking (0.002) reported in Srinivasan, Vanhuele, and Pauwels (2010, table 6).
Marketing Science Institute Working Paper Series 8 H1: Earned social media have (a) the highest impact on Salience (bottom of the consumer mindset pyramid), (b) relatively lower impact on Purchase Intent, and (c) the lowest impact on Advocacy (top of the consumer mindset pyramid). H2: Owned social media have (a) the highest impact on Salience (bottom of the consumer mindset pyramid), (b) relatively lower impact on Purchase Intent, and (c) the lowest impact on Advocacy (top of the consumer mindset pyramid).
Are there differences between OSM and different types of ESM in their ability to increase brand Salience, Purchase Intent and Advocacy? While extant literature does not offer formal hypotheses, it does give insights into the specific strengths of each social media type to affect the consumer mindset.
OSM and consumer mindset metrics. The OSM-Salience link follows from the rich past research on how firm-controlled communication can affect brand salience. For example, brands that use OSM to disseminate positive news, information (e.g. new product launches) and content (e.g., YouTube videos) can impact their salience in consumer minds. Therefore, OSM shows consumers which brands are active on social media and educates people about brands’ products, thereby creating the top of the mind brand recall and making consumers more likely to remember the brand (McCann 2013). Second, such consumers are likely to form brand perceptions based on the quality of this content and the underlying messages broadcast to them (Stephen, Sciandra, and Inman 2015). Therefore, OSM aims at impacting the Purchase Intent of consumers. To what extent it is successful for many brands is an empirical question. Similarly, OSM may affect Advocacy by providing prompt handling of customer service requests (Stephen, Sciandra, and Inman 2015). A recent study by McCann (2013) reports that 65% of consumers who get a response to their complaints, or manage to help others, feel more valued as consumers and more likely to recommend the brand to others. Marketers that promptly respond to customer remarks create a positive image making the consumer more salient in consumer minds (Stephen, Sciandra, and Inman 2015) and establish better relationships with their customer base (Ma, Sun, and Kekre 2015). As such, a well-managed social media presence enables high level of transparency; fostering cooperation and trust, making consumers remember the brand and therefore aiming at increasing brand Advocacy.
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ESM and consumer mindset metrics: ESM acts as a credible source of brand-related information for users especially on negative aspects that firms do not communicate through OSM. We expect ESM to impact Salience because it delivers information about how many other people have experienced or used the product and how popular the product is in the market (Babić et al. 2015). To this end, ESM Brand Community Size may suffice as an indicator of interest towards the brand (Goh, Heng, and Lin 2013). However, Purchase Intent requires more content that reduces prospect’s uncertainty about the brand, which may come to the prospect as customers interact with the brand content (such as commenting on it, sharing it on Facebook or retweeting it). Indeed, Babić et al. (2015) and Goh, Heng, and Lin (2013) call it a bandwagon effect wherein the mere availability of consumers’ opinions has an influence on other consumers, regardless of whether these opinions are positive or negative (Godes and Mayzlin 2009). Once a brand is well known though, negative comments should hurt Purchase Intent and Advocacy (Berger and Milkman 2012). In addition, positive (negative) connotation of the content generates higher (lower) product sales by enhancing (lowering) customers’ quality expectations and attitudes toward a product (Tang, Fang, and Wang 2014). Previous studies have noted the performance implications of the positive and negative valence of content in different forms, such as text (Godes and Mayzlin 2004) and brand-related comments on social media (Schweidel and Moe 2014). In summary, we expect OSM and ESM Brand Community Size to be key drivers of Salience, ESM engagement to be a key drive of Purchase Intent and ESM valence (Positive Comments) to be a key driver of Advocacy.
Consumer mindset metrics increase stock market performance In general, favorable brand attributes (e.g. salience, purchase intent, and advocacy) provide incremental information content to accounting performance measures in explaining stock returns (Mizik and Jacobson 2008). Such consumer mindset metrics are offered for a fee to stock market investors, who are always looking for even the smallest informational advantage over competing investors. Hence, such changes are reflected in stock prices quickly (Luo, Raithel, and Wiles 2013). Prior research in marketing has documented that consumer mindset metrics are strongly correlated with future product demand and cash flows, and, thus serve as a credible signal to
Marketing Science Institute Working Paper Series 10 stock market investors (Mizik and Jacobson 2008), who typically incorporate such information into their valuation of the firm. Strong consumer mindset metrics allow for rapid product quality identification, thereby reducing investor search costs (Mizik 2014). Investors prefer well-known brands with strong reputations and a large consumer base (Rego, Billett, and Morgan 2009) with positive brand attitude (Aaker and Jacobson 2001). High consumer mindset metric values translate into higher stock performance (Johansson, Dimofte, and Mazvancheryl 2012; Mizik and Jacobson 2008). H3: Changes in consumer mindset metrics affect shareholder value
Direct investor informational route of social media on shareholder value. The prior literature suggests that product reviews, blog posts and online consumer ratings have value- relevant informational content for stock market investors and thus may have a direct impact on financial market metrics (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012). Luo, Zhang, and Duan (2013) argue that social media possess valuable information that is relevant for future consumer decisions. As this information gets updated and spreads rapidly through online social networks, it provides real-time information to investors. Marketing literature has shown that the financial market reacts to information about brands (e.g., ESM), largely because investors view such information as an indicator of changes in firm value (Mizik and Jacobson 2008). Such changes may not always reflect the brand’s standing on its product market. For instance, negative ESM Engagement can be about a non-product related issue (e.g. management scandal), which does not immediately and directly affect consumer mindset metrics, but can affect investors as they might believe that this event would hurt the firm and lead to customer concerns in future. On the positive side, an unexpected increase in ESM through more “likes” on a brand’s Facebook page can signal gains in brand popularity and potentially a larger customer base in the future, thereby leading to (1) higher expected cash flows and (2) lower cash flow risk (Nam and Kannan 2014). Finally, more ESM engagement may indicate a more enthusiastic and, thus, faster adoption of new products, which may signal accelerated future cash flows. Similarly, unexpected changes in OSM may provide investors with clues for firms’ yet undisclosed marketing strategies. For example, an increase in the OSM intensity may signal an increase in overall marketing intensity. Thus, overall we expect a direct impact of ESM and OSM on financial market metrics.
Marketing Science Institute Working Paper Series 11 H4: Changes in Earned and Owned Social Media metrics affect shareholder value
Feedback loops among OSM, ESM and consumer mindset metrics. Feedback loops between OSM and ESM have been demonstrated in the “echoverse” analysis (Hewett et al. 2015) and in the studies that explore which type of OSM firms should use to increase ESM (Homburg, Ehm, and Artz 2015; Stephen, Sciandra, and Inman 2015). Moreover, investors may have both an initial and a delayed reaction to OSM and ESM, as they wait for further information to reduce uncertainty about the future value impact of brand-related events (Pauwels and van Ewijk 2013). For instance, investors may wait and see whether a few negative/positive online posts actually translate into changes in broader brand perceptions, which have been shown to affect brand sales and customer life time value (Keller and Lehmann 2006; Srinivasan, Vanhuele, and Pauwels 2010; Stahl et al. 2012). Such interdependencies may play out over several days or even weeks, just as investor reaction to e.g. new products (Pauwels et al. 2004).
Data Sample To address our research questions, we require a dataset combining stock market performance with OSM, ESM and consumer mindset metrics at the same (daily) time interval. We select brands based on several criteria regarding feasibility, validity, and reliability of the study (Tirunillai and Tellis 2012). First, the brands must have rich data available on owned and earned media across the time period of investigation. We could obtain such data on 184 brands. Second, firms need to have data on consumer mindset metrics (122 brands). Third, firms need to follow corporate branding strategy so that changes in shareholder value are more clearly attributable to changes in consumer perceptions of only one brand (84 brands). Fourth, firms have to be listed on one of the three U.S. stock exchanges (NASDAQ/NYSE/AMEX) as we use shareholder value as the dependent variable (45 brands). The use of these five criteria leads to our selection of the following nine industries and 45 brands: airlines (Delta, Southwest), automobiles (Ford, General Motors, Honda and Toyota), software and hardware (HP, IBM, Microsoft, Dell and Sony), energy (GE, BP, Shell and Chevron), clothing (Gap, Nike, Nordstrom), banking (American Express, Citibank, MetLife, Progressive, Wells Fargo), restaurant (McDonald’s, Starbucks and Burger King), retail (Amazon, Best Buy, Dillard’s,
Marketing Science Institute Working Paper Series 12 Expedia, Home Depot, Lowe’s, Macy’s, Nordstrom, Target, Safeway, Sears, Walmart and Walgreen’s), and cable and telecommunications (AT&T, Dish Network, Disney Channel, Netflix, Time Warner, Verizon). We merge social media dataset (ENG, BCS, OSM and Positive and Negative valence), consumer mindset metrics, and abnormal returns with advertising, firm size, new products, dividends, earnings, and M&A announcements to derive a final dataset composed of a balanced panel of 45 brands covering 273 trading days (October 31, 2012 through November 29, 2013) resulting in 12, 285 brand-day observations. In the social media we include Facebook, Twitter, and YouTube. The raw data on various Facebook and Twitter metrics are procured from a third- party vendor who archives historical social media data from these platforms. We get YouTube metrics from Social Blade (Social Blade 2014), a statistics website that tracks social media metrics. Consumer mindset metrics are derived from the daily online consumer survey conducted by YouGov. Stock prices, dividend announcements, and earnings announcements are from Chicago Research for Stock Prices (CRSP) and other firm financial metrics are from S&P Compustat. We obtain new product and M&A announcements from Factiva. Finally, advertising data are from Kantar Media.
Variable operationalization Table 3 shows the variable operationalization, which we detail below.
(Tables and figures follow References.)
Social media measures. In contrast to consumer mindset metrics, social media metrics are not designed to be representative of the entire population of current or prospective customers (Ruths and Pfeffer 2014). It is exactly because of their platform-specific dynamics and sample bias (e.g. Schweidel and Moe 2014) that we don’t expect a full overlap with the survey-based consumer mindset metrics (see also Pauwels and van Ewijk 2013). Moreover, as the social media space is vast and constantly changing (Smith, Fischer, and Yongjian 2012), it is infeasible to cover the entire spectrum of social media platforms. Still, to guard against platform-specific threats to generalizability, we obtain data from three diverse and popular social media platforms: Facebook, Twitter and YouTube. We sourced data from a third-
Marketing Science Institute Working Paper Series 13 party data provider that collects and archives social media data using a set of automated web based tools. We ascertained the validity of the data by using the following two-step procedure. In the first step, over a period of ten days, we accessed a brand’s Facebook page, Twitter account, and YouTube channel and manually collected the metrics that are displayed on the social media accounts (e.g., number of Facebook “likes” and “People Talking about That (PTAT) metric, Twitter “followers,” and numbers of YouTube “subscribers” and “video views”). We also counted brand’s daily Facebook posts and Twitter tweets over the same period. We repeated this procedure for all the brands in our sample. In the second step, we compared our data on all the above metrics with the data vendor’s records on the same metrics. We found no discrepancies between the two sets of metrics, thereby suggesting that the data provider reliably collects and archives data from Facebook, Twitter, and YouTube. As to the specific social media metrics, we follow our conceptual framework in selecting measures with varying perceived credibility and the efforts required from the consumer to participate (e.g. Hoffman and Fodor 2010).
Owned social media (OSM). Companies establish their Facebook and Twitter presence to serve as platforms for interacting with consumers. Facebook and Twitter are the two main social media platforms the companies use to spread company news (e.g. new product announcements) and engage with consumers. Accordingly, we collect the daily cumulative number of “brand posts” on Facebook as well as “brand tweets”, “brand replies to users”, and “brand retweets of user tweets” on Twitter. All these metrics correspond to the activities brands perform on their OSM. Collecting data about OSM on YouTube was not possible at the time of the studyii.
Earned social media Brand community size (BCS). Increases in brand community size (“Facebook likes”) are immediately disseminated through news feeds on Facebook and are good indicators of future sales while decreases in “likes’ can indicate a drop in consumer interest (Srinivasan, Rutz, and Pauwels 2015). Furthermore, having Twitter “followers” allows brands to establish a direct connection to their users, facilitating the flow of information and influence, driving and changing attitudes among prospective customers, and contributing to the acquisition of new customers (Malthouse et al. 2013). Finally, the number of YouTube subscribers increases brand awareness and allows for fast information spread (Yoganarasimhan 2012). Accordingly,
Marketing Science Institute Working Paper Series 14 we collect daily cumulative numbers of Facebook “likes”, Twitter “followers” and YouTube “subscribers” for each of the brands in our study as measures of BCS. These metrics collectively indicate the size of brand communities on three most important social media channels.
Engagement (ENG). Beyond simply joining a brand community, users can “engage” by visiting a brand’s Facebook page and interacting with the content, sharing brand tweets on Twitter, and watching videos posted by a brand on YouTube. Accordingly we collect the daily cumulative number of “People Talking about this” (PTAT) on Facebook, Twitter “user retweets,” and YouTube “video views”. First, PTAT is defined as by Facebook Insights “the number of people who have created a story from a brand page post”. This metric combines all the user actions that are directed towards a brand (e.g. user comments/shares/likes on brand posts, hashtags, and user posts on brand wall). PTAT is an important metric for measuring activity as content creators voluntarily engage in telling stories about a brand (Gensler et al., 2013). Second, the volume of retweets has been shown to impact brand fortunes (Kumar et al. 2013). Finally, YouTube video views capture engagement on a more visual level often with brand-related content such as product reviews, demonstrations, unboxing of products and events (Smith, Fischer, and Yongjian 2012).
Social Media Valence. While a measure like PTAT simply counts the numbers of voluntary engagement, this engagement could be positive or negative. Therefore, we measure valence as the number of positive and of negative sentiment comments of user posts on Facebook brand pagesiii. Consistent with the recommendation of the recent meta-analysis by Babić et al. (2015), our valence metric is a composite volume-valence metric, which captures the number as well as the polarity of the user posts. For deriving the valence of the textual data, the Naïve Bayes algorithm is a popular linear classifier and is known for its simplicity and high levels of efficiency. The probabilistic model of Naïve Bayes classifiers is based on the Bayes’ theorem and it classifies posts into positive or negative valence categories based on the input training set of lexical words. Recently, Tirunillai and Tellis (2012) used a similar approach within the marketing literature.
Marketing Science Institute Working Paper Series 15 Principal component analysis on the social media metrics. Given their nature, we can expect a high correlation amongst the 12 social media metrics. Indeed, pairwise correlation tests show typical correlations between 0.5 and 0.8 for each brand. Therefore, we follow Luo, Raithel, and Wiles (2013) and apply Principal Component Analysis (PCA) to reduce dimensionality of social media metricsiv. We use the factor scores to obtain the constructs of ENG, BCS and OSM. PCA reveals that each of the social media constructs is unidimensional (i.e., having only one factor with an eigenvalue larger than one). Furthermore, the extracted variance from the original variables is high, ranging from 53% to 96% on average across brands (detailed results are available in the Table A1 Web Appendix 1). This suggests that we can reliably estimate the model with the first principal components without a major information loss.
Consumer mindset metrics. We obtain data on consumer mindset metrics from YouGov Group, which uses online consumer panels to monitor brand perceptions. For the U.S. market, YouGov monitors multiple brands in various industries by surveying 5,000 randomly selected consumers (from a panel of 5 million consumers) each dayv. To assure representativeness, YouGov weights the sample by age, race, gender, education, income, and region. In any one survey, an individual respondent is asked about only one measure for an industry ensuring that none of the survey metrics influence each other and, therefore, reducing common method bias and measurement error. YouGov database has been previously used in the marketing literature (Luo, Raithel, and Wiles 2013) and presents at least four advantages. First, YouGov administers the same set of questions for each brand assuring consistency across brands for each of their metrics. Second, YouGov database is likely to be more reliable because it uses a large panel of consumers, capturing the general opinion of the crowd. Third, the large panel size and random selection of respondents imply that YouGov data coherently captures between-subject variance (Luo, Raithel, and Wiles 2013). Fourth, YouGov data are collected daily, thereby incorporating quick changes in consumer perceptions at a high frequency. We formulate consumer mindset metrics based on YouGov metrics that map well with Salience, Purchase Intent, and Advocacy. Our eight YouGov consumer mind-set metrics are: advertising awareness, received word-of-mouth, consideration, purchase intent, current customer, perceived value, satisfaction, and recommendationvi. We perform a factor analysis with Varimax rotation on these metrics and obtain a three factor solution (each of the three
Marketing Science Institute Working Paper Series 16 eigenvalues is higher than 1, with each factor representing one of the three key consumer mindset metrics: Salience (advertising Awareness and received word-of-mouth), Purchase Intent (consideration, purchase intent, current customer) and Advocacy (recommendation, perceived value, satisfaction). Each consumer mind-set metric loads higher on one single factor than on any other factors, indicating good discriminant validity of the factors (detailed results are available in the Table A2 and A3 Web Appendix 1).
Shareholder value. We use abnormal returns as a measure for shareholder value. We estimate abnormal returns from raw stock returns by controlling for the common risk factors documented in the finance literature (Carhart 1997; Fama and French 1993). This method for measuring abnormal returns is widely used in marketing (Tirunillai and Tellis 2012; Tuli and Bharadwaj 2009). We follow the procedure outlined in Kurt & Hulland (2013) to obtain abnormal stock returns from the University of Chicago’s CRSP database. The common factors are available from Wharton Research Data Service (WRDS). We specify brand’s returns as:
(1) − RR += ββ − RR )( 2,,10,, SMB ++ ββ 3 HML + β4 MOM + ε ,tititititftmiitfti where
,ti N σε ,ti ),0(~ where Ri,t is the returns for firm i in time t, Rm,t average market returns, Rf,t the risk-free rate,
SMBt size factor, HMLt value factor, MOMt momentum factor, β0i the intercept, βs are the factor coefficients, and εit is the model residual. The abnormal returns on time period t + 1 are calculated using the following formula. In period t+1: ˆˆ ˆ ˆ ˆ (2) ARt+ = ( + RR tfti + ) −− + ββ ( + − RR tftmii + ) + β21,1,101,1,1 SMB + + β31 HML + + β41 MOM tititi +1 We repeat this procedure for every brand for a rolling window of 250 trading days prior to the target day to get estimated daily abnormal returns. For our main model, we use the natural logarithm of 1+ARt+1. The range of correlations among social media metrics, consumer mindset metrics and abnormal returns is within the range reported in the meta-analysis by Katsikeas et al. (2015 p.44); [-.01, .41]. Similar to Katsikeas et al. (2015) , the mean correlation between performance metrics is low.
Marketing Science Institute Working Paper Series 17 Control variables. Following the firm valuation models widely used in marketing (e.g. Tirunillai and Tellis 2012) we include a comprehensive set of covariates in our model. The controls include advertising expenditure, firm size as market value of equity (MVE), new product introductions, mergers and acquisitions (M&A), earnings announcements, and dividend distributions. We provide a detailed description of these control variables in the Web Appendix 2.
Methodology For estimation of the conceptual model as depicted in Figure 1 we adopt the persistence- modeling framework using Vector Autoregression (VAR; Dekimpe and Hanssens 1995). For this study, VAR has several advantages over alternative model specifications. First, OSM and ESM can drive consumer mindset metrics and shareholder value in the short-run due to fast information dissemination as well as in the long-run due to increased brand awareness, which further highlights enduring effects due to consumer mindset metrics. VAR estimates both short- and long-term effects of historical owned and earned media on consumer mindset metrics and consequently on shareholder value. Second, as we measure how the unexpected changes in online social media metrics can lead to changes in the assessment of firm value by investors, VAR captures these unexpected changes and the dynamics of carryover effects over time through the generalized impulse response functions (GIRF), which are robust to the assumptions of causal ordering of the variables (Pesaran and Shin 1998). Third, the model allows for dynamic feedback loops for different endogenous variables (e.g., Salience can affect ENG; Lovett, Peres, and Shachar 2013). By capturing these feedback loops, VAR estimation yields a comprehensive picture of the full dynamic system of endogenous variables (Stock and Watson 2001). Finally, VAR allows controlling for non-stationarity, serial correlation, and reverse causality (Granger and Newbold 1986; Luo, Raithel, and Wiles 2013). Our analysis consists of several methodological steps (see Table A4 in the Web Appendix 2).
Model specification. Based on the unit root and cointegration tests, we specify the VAR model in Equation (3):
Marketing Science Institute Working Paper Series 18 Abret Abret ⎡eAbret,t ⎤ ⎡ t ⎤ ⎡ −nt ⎤ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ eSalience,t Saliencet Salience −nt ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ⎥ ⎢Purchase ⎥ ⎢Purchase ⎥ ePurchase,t t n n −nt ⎢ ⎥ ⎢ ⎥ ⎛ 11 """ γγ 19 ⎞⎢ ⎥ ⎛ 11 "" φφ 1p ⎞⎡x1 ⎤ Advocacy ⎜ ⎟ Advocacy ⎜ ⎟ ⎢eAdvocacy,t ⎥ ⎢ t ⎥ p ⎢ −nt ⎥ ⎢ ⎥ ⎜! ⎟ ⎜! ⎟ x2 ⎢ ⎥ ⎢Positive ⎥ = ⎢Positive ⎥ + ⎢ ⎥ + e (3) ⎢ t ⎥ ∑⎜ ⎟⎢ −nt ⎥ ⎜ ⎟⎢ ⎥ ⎢ Positive,t ⎥ n=1 ⎜! ⎟ ⎜! ⎟ ! ⎢ ⎥ ⎢Negativet ⎥ ⎢Negative nt ⎥ ⎢ ⎥ e ⎜ ⎟ − ⎜ φφ ⎟ x ⎢ Negative,t ⎥ ⎢ ⎥ ⎝ 91 """ γγ 99 ⎠⎢ ⎥ ⎝ 91 "" 9 p ⎠⎣⎢ p ⎦⎥ ENGt ENG −nt ⎢e ⎥ ⎢ ⎥ ⎢ ⎥ ⎢ ENG,t ⎥ ⎢BCS t ⎥ ⎢BCS −nt ⎥ e ⎢ ⎥ ⎢ ⎥ ⎢ BCS ,t ⎥ OSM OSM ⎢ ⎥ ⎣ t ⎦ ⎣ −nt ⎦ e ⎣ OSM ,t ⎦ Where Abret = abnormal returns, Salience= Salience of the brand, Purchase = Purchase intention, Advocacy = Brand advocacy, Positive = valence metric of positive posts, Negative= valence metric of negative posts, ENG= earned social media engagement, BCS = earned social media brand community size, OSM= owned social media. All variables are included in logs, with the exception of positive and negative comments, which on some days take 0 values. The off-
n diagonal terms of the matrix −Γ γ kl estimate the indirect effects among the endogenous variables and the diagonal terms estimate the direct effects. The exogenous vector Χ contains p control variables – advertising expenditure, new product announcements, mergers and acquisitions, dividend distributions announcements, earnings announcements, market capitalization, and a deterministic trend t to capture the impact of omitted, gradually changing variables. We perform standard diagnostic tests for auto-correlation, normality and heteroscedasticity of VAR residuals and find no violations of these assumptions at 5% level of significance (details of model specification are available in Table A5 in Web Appendix 3). We estimate this nine-equation VAR model for each brand separately for three reasons. First, we want to isolate time series support or refutation for our hypotheses and resulting recommendations. For instance, brand A may enjoy a large ESM Brand Community Size, a higher Purchase Intent and better stock market performance than brand B, but that does not mean that brand B can increase its performance by increasing its ESM BCS. Estimating the model for each brand allows us to both show for how many brands a relationship holds, and give brand-specific advice (based on a second-stage analysis). Second, our specification may differ by brand, depending on the unit root and cointegration tests. Likewise, the data misses positive and negative comments for six brands, and uses brand-specific PCA factors – both of which do not
Marketing Science Institute Working Paper Series 19 present an issue with brand-specific estimation. Finally, brand-specific estimation allows easier comparison with previous papers that demonstrated the effect of consumer mindset metrics on stock market performance (Luo, Raithel, and Wiles 2013), of consumer mindset metrics on market performance (Srinivasan, Vanhuele, and Pauwels 2010), of social media on market performance (e.g. Demirci et al. 2014) and of earned social media on stock market performance (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012). The optimal lag order (“n”) is chosen using Akaike Information Criterion (AIC) and taking into account the serial auto-correlation LM test. Our goal is to balance lag-selection criteria with auto-correlation bias (Slotegraaf and Pauwels 2008). We first select the appropriate lag based on AIC, estimate the model and check whether we should add lags in accordance with diagnostic tests on residual autocorrelation (Franses 2005). We refer to Table A5 in Web Appendix 3 for details on the observation-to-parameter ratio, which exceeds the threshold value of 5 (Leeflang et al. 2015) in all cases. After the 9-equation VAR model estimation, we also estimate the model without the three consumer mindset metrics. Comparing the explanatory power of this 6-equation model with the full model gives us a first indication of the importance of including consumer mindset metrics. For a more formal test, we follow Srinivasan, Vanhuele, and Pauwels (2010) in calculating the Forecast Error Variance Decomposition (a.k.a. the dynamic R2) of abnormal returns.
Forecast error variance decomposition (FEVD). From the VAR parameters, we derive FEVDs to investigate whether, and to what extent, ENG, BCS, OSM, and valence metrics explain consumer mindset metrics and firm value. FEVD provides a measure of the relative impact of shocks initiated by each of the individual endogenous variables in a VARX model (Nijs, Srinivasan, and Pauwels 2007) over time. This relative value of endogenous variables is established based on FEVD in 30 days reducing sensitivity to short-term fluctuations (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012). We use the Cholesky ordering based on the results of Granger causality tests to impose a causal ordering on the variables. To assess the statistical significance of FEVD estimates, we obtain standard errors using Monte Carlo simulations with 1,000 runs (Srinivasan, Vanhuele, and Pauwels 2010).
Marketing Science Institute Working Paper Series 20 Generalized Impulse Response Function (GIRF). Having established that both social media metrics and consumer mindset metrics drive a substantial portion of abnormal returns, we now estimate their elasticities. The GIRF tracks the over-time impact of an unexpected shock to a variable (the impulse variable) on the response variable, without imposing a causal ordering (Pesaran and Shin 1998). We derive standard errors using Monte Carlo simulation with 1,000 runs in each case (Benkwitz, Lütkepohl, and Wolters 2001). Finally, we follow established practice in marketing and assess the statistical significance of each impulse response value by applying a one-standard-error band (Sims and Zha 1999; Slotegraaf and Pauwels 2008; Trusov, Bucklin, and Pauwels 2009). We define long-term impact as the accumulated impact of the impulse response function until it reaches its asymptote designated by four consecutive non- significant impulse responses (Nijs, Srinivasan, and Pauwels 2007).
Aggregation over brands. We estimate our brand-by-brand VAR models and aggregate our results across brands by means of the added Z method (Rosenthal 1991). The added Z method allows for the combination of p-values across different brands (Gijsenberg 2014) for each effect in the model. We take each brand-specific estimate and its standard error to obtain the Z score (standard-normal statistic). Next, we sum the Zs and divide the sum by the square root of the number of included brands (45). Moreover, the overall effect size is the weighted average (by the inverse of the standard error) response parameter across the included brands.
Results Granger causality. The results of the Granger Causality Tests (see Table A6 in Web Appendix 4) show the support for the dynamic relationships in our conceptual framework (Figure 1). Consistent with Figure 1, Engagement, Brand Community Size, and OSM Granger cause consumer mindset metrics Salience, Purchase Intent and Advocacy (p < .05) while Purchase Intent and all social media metrics Granger cause abnormal returns (p < .05). Moreover, we find that the feedback loops do exist between our variables, highlighting the need for a multiple equation system.
Stationarity, unit roots, and cointegration. We check the nature of the time-series data by performing unit root tests for each variable. We provide a detailed explanation in Web Appendix
Marketing Science Institute Working Paper Series 21 4 in Table A7 and A8. OSM, Engagement, consumer mindset metrics, and abnormal returns are stationary for all brands so they enter the system in levels. For some brands, Brand Community Size and valence metrics enter the VAR system differenced. Finally, we do not find any cointegrating equation among our variables (see Table A9 in Web Appendix 4) eliminating the need for Vector Error Correction models. Relative Importance of Metrics. Table 4 shows the average FEVD (dynamic R2) for each variable in the model.
First, consistent with recent research on the direct informational route, social media variables explain a 7.2% variance in abnormal returns. However, our three consumer mindset metrics explain an additional 4.6% of variance in abnormal returns, demonstrating the importance of accounting for the consumer mindset route. Consistent with our conceptual framework, social media metrics do a better job explaining the variance in Salience (8.9%) than in Purchase Intent (7.9%), (statistically significant differences for 34 out of 45 brands) and finally in Advocacy (6.8%) (statistically significant differences with Salience for 31 brands and with Purchase Intent for 30 brands). Overall these results indicate the important role of social media in explaining consumer mindset metrics and abnormal returns. The results of FEVD for the restricted model without consumer mindset metrics are reported in Web Appendix 5 in Table A10. In the full model the R2 for abnormal returns is 12.7% (see Table 4) while in the restricted model the R2 drops to 8.9% implying that consumer mindset metrics do play an important role in explaining abnormal returns. At the same time, the explained variance of abnormal returns by social media metrics is substantial after accounting for consumer mindset metrics, indicating that the mediation of their effects through consumer mindset metrics is partialvii.
Cumulative Effects of social media on consumer mindset metrics (H1-H2). To assess H1 and H2, Table 5 presents the impacts of social media metrics on consumer mindset metrics.
Marketing Science Institute Working Paper Series 22 Overall, the cumulative elasticities indeed support H1 and H2 that ESM and OSM affect consumer mindset metrics, and that they affect the consumer mindset metrics differently. First, Salience is positively affected by OSM (0.617, p < .01), Brand Community Size (1.002, p < .01), Engagement (0.559, p < .01), and Positive valence (0.688., p < .01), and negatively by Negative valence (-0.325, p < .1). Second, Purchase Intent is positively affected by Brand Community Size (0.489. p < .01) and ENG (0.526, p < .01), and negatively by OSM (-0.344, p < .05) and Negative valence (-0.336, p < .1). Finally, Advocacy is only affected by OSM (0.329, p <.1) and Positive valence (0.343, p <.1) at the 10% significance level. Moreover, we observe these significant effects for 73%-80% of brands in the case of Salience and Purchase Intent, but only 65%-73% of brands in the case of Advocacy. T-tests confirm that the influence of ESM on Salience (1.002 by Brand Community Size) is significantly larger than that on Purchase Intent (0.526 by Engagement) followed by Advocacy (0.343 by Positive valence), in support of H1a-c. For OSM, the influence on Salience (0.617) is higher than that on Advocacy (0.329), but the negative effect on Purchase Intent is unexpected and results in only partial support for H2. Further analysis shows a diverse mix of positive versus negative effects of OSM on Purchase Intent for different brands, which we explore in the second-stage analysis for managerial recommendations.
Cumulative effects of consumer mindset metrics and social media on shareholder value (H3- H4). Table 6 shows the average cumulative effects of each metric on abnormal returns.
As to consumer mindset metrics, we find that Purchase Intent increases abnormal returns (0.621, p < .01), while the small effects for Salience and Advocacy do not reach statistical significance. Thus, we find partial support for H3. As to the size of consumer mindset metrics effects, we can compare the 0.621 abnormal returns elasticity to the 0.3-0.6 sales elasticities reported in Srinivasan, Vanhuele, and Pauwels (2010). Given the average market capitalization of $ 3 billion in our sample, a unit shock to Purchase Intent increases firm value by about $18 million.
Marketing Science Institute Working Paper Series 23 As to social media metrics, we find that Brand Community Size (-0.394, p < .05) and Negative valence of Engagement (-0.369, p < .1) negatively impact abnormal returns while the other metrics of online social media are not significantly related to abnormal returns. Thus, we find partial support for H4. The negative abnormal returns effect of brand community size is surprising, but may be due to the large firms in our sample ‘buying’ more social media subscribers at a price that investors deem too high (Bennet 2012). Future research should investigate this matter. In sum, we find that ESM and OSM have different effects on consumer mindset metrics, with a general tendency of lower-effort social media to affect the lower levels of the pyramid (OSM and ESM Brand Community Size on Salience), and higher-effort social media to affect the higher levels of the pyramid (ESM Engagement on Purchase Intent and Positive valence on Advocacy). Also, abnormal returns are significantly affected by a consumer mindset metrics (Purchase Intent) and a social media metric (Negative valence). A key unexpected result is the negative effect of OSM on Purchase Intent, which we explore in a second stage analysis.
Second-stage analysis: which brands obtain most benefits from owned social media?. Web Appendix 7 provides the specifics and results of our second stage analysis, on which we report the relevant findings for OSM below. We consistently find that firms with better standing obtain a higher impact. First, the OSM-Salience effect is higher for firms with better perceived quality, higher Transparency and higher Philanthropy. Second, OSM-Purchase Intent effect is positive for firms with better Governance, a larger size and higher advertising growth. We infer that such firms mostly reinforce good news with their OSM, while firms with worse Governance may use OSM mostly in a crisis situation (e.g. product recall, corporate fraud), providing a possible explanation for the average negative effect of OSM on Purchase Intent. Third, the OSM- Advocacy effect is higher for firms with better Resource management and Human rights as well as for the firms that have large increases in advertising. Firms with better Resource management have more efficient operations leading to decreased resource consumption. It’s likely that this efficiency translates into more efficient customer service and conflict resolution using OSM, which in turn translates into better value and customer satisfaction. We believe that the effect of increased advertising is linked to the objectives for increasing advertising spending. For
Marketing Science Institute Working Paper Series 24 example, if firms increase advertising at the time of new product announcements, increased OSM may lead to higher Advocacy.
The impact of new product announcements and advertising. To further actionable insights, we also quantified the effects of the exogenous variables New Product Announcements and Advertising in Table A11 in the Web Appendix 6. We find that product announcements have the highest positive impact on Advocacy, followed by Positive user comments and Abnormal returns. Moreover, product announcement reduce the negative user comments. In contrast, advertising only increases OSM, ESM Brand Community Size and Engagement – not ESM valence nor Abnormal Returns.
Feedback loops among consumer mindset and social media metrics. Beyond the assessment of the hypotheses, our methodology also reveals dynamic effects from consumer mindset metrics to social media metrics and among each type of metric. We report these in Table 7.
First, we find that Advocacy drives ESM Brand Community Size (0.299, p < .1) implying that consumers who recommend a brand bring new members to the brand’s social media community. Second, we find that Salience drives OSM (0.550, p < .01) and Positive valence (0.444, p < .05). This implies that as more consumers become aware of a brand, they generate more positive comments while the brand also increases OSM. Third, OSM increases both ESM Brand Community Size (1.049, p < .01) and Engagement (1.409, p < .01), while Engagement, ESM Brand Community Size, Positive valence, and Negative valence all drive OSM. In other words, firms both stimulate ESM through their Owned Social Media and are stimulated by ESM to produce more OSM.
Discussion The aim of this study was to scrutinize and quantify the chain of effects from social media to the financial markets through consumer mindset metrics. Recent studies have shown the strong effect of social media metrics on sales (e.g. Stephen and Galak 2012), retail business outcomes
Marketing Science Institute Working Paper Series 25 (e.g. Hewett et al. 2015), and financial metrics (e.g. Tirunillai and Tellis 2012). However, it is unclear why and how these effects occur. Our analysis addresses the call for “studies linking different aspects of performance and identifying contingency factors that may affect the strength of such relationships” (Katsikeas et al. 2015 p.32). Using VAR models, we link three measures of Earned Social Media (ESM), namely engagement, brand community size and valence, as well as Owned Social Media (OSM) to three key consumer mindset metrics and to shareholder value measured by abnormal returns. Based on these findings, we present the theoretical and managerial implications of our research.
Theoretical contributions Our research makes three key contributions to marketing theory. First, we show that social media do not have a mere informational role in affecting shareholder wealth. Thus, our study advances current knowledge beyond the direct impact of social media on shareholder wealth. Instead, earned and owned social media impact consumer mindset metrics, which in turn affect shareholder value. In addition to providing immediate signals to investors, social media affect the mindset of consumers represented by various consumer mindset metrics. Social media have the power to shift consumer attitudes that can translate in future boost in demand for products, thereby affecting the firm long-term valuation that investors also take into account when making their investing decisions. Accordingly, our research emphasizes Keller and Lehmann's (2006) contention that consumer perceptions play a crucial role in shareholder value creation. However, even after we account for consumer mindset metrics, the direct impact of social media on firm value documented in prior literature (Luo, Zhang, and Duan 2013; Tirunillai and Tellis 2012) doesn’t disappear completely. Thus, the nature of the role of social media activities is twofold: they help form consumer perceptions (mind-set route) and they assist in informing investors (informational route). Accordingly, we offer a key theoretical insight for providing a more complete picture of the effects of social media on shareholder wealth. Second, by considering different types of social media our research underscores that not all social media are created equal and, therefore, may not affect shareholder value in a similar fashion. Firms typically not only monitor their earned media but also engage actively on social media channels for creating, developing, and managing online content, facilitating consumer conversations, and influencing consumer perceptions (Peters et al. 2013). We show that, while
Marketing Science Institute Working Paper Series 26 OSM has a substantial impact on Salience and Advocacy, it does not typically increase Purchase Intent, which is the main driver of abnormal returns. In contrast, the Size and Engagement of ESM do substantially affect Purchase Intent. Finally, positive ESM valence increases Advocacy, while negative ESM valence decreases abnormal returns. Thus, we demonstrate the value relevance of ESM and OSM by using a mechanism that is more grounded in marketing theory. Our research contributes to the marketing-finance interface by answering the recent call for research on the return on investment (ROI) on social media marketing (Kumar 2015). Much of the extant social media marketing literature focuses on strategies that will improve performance of OSM. By identifying the important role that ESM plays in shaping consumer mindset and creating shareholder value, our research highlights the need for more research on strategies that will lead to better ESM performance.
Managerial implications From a practical standpoint, our findings emphasize the importance of both types of social media (ESM and OSM) for shaping consumer mindset metrics and increasing shareholder value. First, our study highlights the role of OSM and ESM in predicting consumer mindset metrics. Specifically, the Size of the online brand community improves brand Salience, but Engagement has the larger effect on brand Purchase Intent, which directly increases stock market performance. Moreover, positive and negative comments on social media impact brand Advocacy, but only negative comments directly drive abnormal returns. These effects have a substantial long-term impact on consumer mindset metrics. Thus, managers can use changes in OSM and ESM, which are available in very short time intervals, as proxies for consumer mindset metrics, which can be measured less frequently due to costly sampling requirements. Moreover, managers can quantify by how much their new products increase all social media metrics and abnormal returns. Second, by highlighting the role of consumer mindset metrics in social media-shareholder value link, we add to our collective understanding of the ways in which social media benefits businesses. Improving purchase intent is a key goal in marketing, and can now be systematically related to its drivers in social media metrics, and its consequences for shareholder value. Finally, we provide managers with key insights into the interdependence among various performance metrics related to social media, consumer mindset, and shareholder value. Our study suggests
Marketing Science Institute Working Paper Series 27 how to increase these metrics with key managerial levers: owned social media, new product announcements, and advertising. We find that although OSM has a relatively smaller impact on consumer mindset metrics, it positively influences both measures of ESM. In most cases ESM activities are not controllable by marketers, yet they can use OSM to improve ESM engagement and increase brand community size. In the unreported second stage analysisviii we also find that OSM has substantially higher effects of consumer mindset metrics for firms with higher reputation (superior product quality, governance, transparency, philanthropy, resource management, and human rights). In other words, getting one’s house in order yields more credibility to one’s controlled social media. Likewise, firms that have increased advertising may enjoy synergy or halo effects from OSM. In contrast, managers of the firms with lower credibility must carefully evaluate the way they are using social media. For example, the companies with negative public perception about their product or service quality (e.g., airlines) can use OSM to tackle customer complaints, which hopefully increases perceived quality and positive word of mouth. While our second stage analysis was exploratory, these preliminary findings call for future research to formally investigate our tentative explanations.
Limitations and future research Like any scientific research, our research also has a few limitations, which further research could overcome given more time and wider data availability. First, data limitations allow us to study only large U.S. brands. In other contexts, our results might not hold due to the specific nature of social networks in different regions. For example in China, users typically participate in alternative forms of social media (e.g., WeChat), which have their own unique characteristics. Future studies can investigate how the culture-specific characteristics of social media platforms in different countries drive consumer mindset metrics and the firm value. Second, we have used only limited text data on Facebook user posts for our valence metric. It is possible that the sentiment expressed in different forms (e.g. user comment on brand’s Facebook page) or on different platforms (e.g. Twitter or YouTube) could alter our findings. For example, Twitter tweets are limited to 140 characters, yielding a unique vocabulary specific to Twitter users. Moreover, user anonymity is far more pervasive on Twitter than Facebook, potentially changing the sentiment of comments (e.g. Schweidel and Moe 2014). Future studies can incorporate more extensive data on sentiment on these platforms. Finally, we include paid media merely as a
Marketing Science Institute Working Paper Series 28 control variable in the empirical model as we do not have access to daily paid social media data. Future studies might consider the endogenous role of paid media in driving consumer mindset metrics and thus firm value. This paper provides the missing link of consumer mindset metrics in the value chain from social media metrics to shareholder value. Understanding this indirect route should help us generate specific conditions under which owned and earned social media move the needle for our organizations.
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Marketing Science Institute Working Paper Series 35 Figure 1: Conceptual Framework
Marketing Science Institute Working Paper Series 36 Table 1 Review of studies estimating social media effects on performance Owned Brand Consumer Stock Earned Coverage of Study Social Community Mindset market Social Media multiple markets Media size Metrics effects Stephen and Galak Yes (owned Yes (user Yes(the No No No, 1 firm (2012) blog) posts on number of specialized in blogs and forum microloans community member forums) registration) Schulze, Schöler, and Yes Yes No No No Yes, 759 Facebook Skiera (2014) apps in 22 categories Srinivasan, Rutz, and No Yes Yes No No No, 1 brand from Pauwels (2015) (Facebook (Facebook fast moving Likes and Likes) consumer goods Unlikes) Luo, Zhang, and Duan No Yes (internet No No Yes No, 9 brands of (2013) search) computer and software Tirunillai and Tellis No Yes (rating, No No Yes Yes, 15 brands (2012) volume and from 6 markets valence of reviews) Goh, Heng, and Lin Yes(firm- Yes (user No No No No, 1 firm in 1 (2013) generated comments on market posts on brand’s Facebook) Facebook posts) Pauwels, Stacey, and No Yes No No No No, 1 retailer in 1 Lackman (2013) (conversation market topic) Kumar et al. (2016) Yes (firm- No No No (survey No No, 1 retailer in 1 generated on consumer (customer market (wine and posts) profiles) spending spirits) and cross- buying)
Marketing Science Institute Working Paper Series 37 Demirci et al. (2014) Yes Yes (website No No No Yes, 4 firms in 4 visits) markets Nam and Kannan No Yes No No Yes Yes, 44 firms in 14 (2014) (bookmarks, markets social tags) This study Yes Yes Yes Yes Yes Yes, 45 brands (Facebook, (Facebook, from 11 markets Twitter, Twitter, YouTube) YouTube)
Marketing Science Institute Working Paper Series 38 Table 2 Social Media Metrics Characteristics Metric Definition Consumer Credibility Examples Effort Owned Social Brand-related social None Low Brand’s Facebook post on Media media activity that is brand’s official Facebook page generated by the brand Brand’s tweet on brand’s and therefore under the official Twitter account brand’s full control Earned Social Brand-related social Low to High Medium to Media media activity that is High generated by external entities such as customers or journalists. 1.Earned Social Joining an online brand Low Medium “Like” on brand’s Facebook Media Brand community. page) Community “Follow” on brand’s Twitter Size account.
2. Earned It involves consumer Medium Medium Visiting a brand’s Facebook Social Media interactions with the page and interacting, Engagement brand’s content Sharing brand tweets on Twitter (“retweets”) 3. Earned Consists of positive or High High Positive or negative comments Social Media negative consumer on brand’s Facebook page Valence feedback
Marketing Science Institute Working Paper Series 39 Table 3 Measures and Data Sources Variable Type Description Source ABRET Dependent Abnormal returns CRSP Salience Dependent Consumer mindset metric representing brand YouGov Salience. We apply factor analysis with Varimax rotation on the YouGov metrics and obtain a three factor solution with Salience emerging as the first factor. The two variables that loaded on this factor are word-of-mouth exposure and awareness Purchase Intent Dependent Consumer mindset metric representing brand YouGov Purchase Intent. We apply factor analysis with Varimax rotation on the YouGov metrics and obtain a three factor solution with Purchase Intent emerging as the second factor. The three variables that loaded on this factor are consideration set inclusion, purchase intent and whether the respondent is a current customer Advocacy Consumer mindset metric representing brand YouGov Advocacy. We apply factor analysis with Varimax rotation on the YouGov metrics and obtain a three factor solution with Advocacy emerging as the third factor. The three variables that loaded on this factor are perceived value, satisfaction and recommendation ESM BCS Dependent Earned Social Media Brand Community Size. A Facebook, one-dimensional factor extracted from PCA on 3 Twitter metrics (# of likes on Facebook, # of followers on and Twitter, and # of subscribers on YouTube YouTube1 ESM ENG Dependent Earned Social Media Engagement. A one- Facebook, dimensional factor extracted from PCA on 3 Twitter
Marketing Science Institute Working Paper Series 40 metrics (daily number of PTAT* on Facebook, and retweets by users on Twitter and video views on YouTube1 YouTube) OSM Dependent Owned Social Media. A one-dimensional factor Facebook, extracted from PCA on 4 metrics (# of own posts Twitter1 on Facebook, # of own tweets, # of replies to users, and # of brand own retweets on Twitter) Negative Dependent The number of negative user posts on brands’ Facebook1 Facebook page. Positive Dependent The number of positive user posts on brands’ Facebook1 Facebook page. Paid Media Control The dollar amount spent on advertising (TV, radio, Kantar newspapers) Media Dividend Control Corporate action on distributing dividends to their Factiva distributions shareholders Earning Control Corporate action on announcing the quarterly Factiva announcements earnings to their shareholders Merger and Control Whether a firm has undergone identity changes Factiva Acquisitions New Product Control Announcements of new products Factiva Announcements Mktcap Control The market capitalization of the firm given by the CRSP number of shares outstanding times the price of the stock * People Talking About This” (“PTAT”) metric implies that users voluntarily engage in telling a story about a brand (Gensler et al., 2013). The “PTAT” metric is defined by Facebook Insights as “the number of people who have created a story from a brand page post”. ** To be able to perform the analysis at the daily level we attributed the constant previous months’ advertising expenditure to each day of the current month. 1 Facebook and Twitter data were purchased from a third-party service while YouTube data were collected from www.SocialBlade.com
Marketing Science Institute Working Paper Series 41
Table 4 Forecast Error Variance Decomposition of endogenous variables
Variance ABRET Advocacy Purchase Salience Negative Positive ENG BCS OSM explained R2=12.7% R2=14.7% R2=15.3% R2=18.3% R2=22.7% R2=25.7% R2=18.7% R2=26.9% R2=30.1% by ABRET 88.664 1.041 1.339 1.252 1.154 1.021 1.053 1.079 1.372 Advocacy 1.611 89.580 1.111 1.236 1.106 1.035 1.062 1.237 1.547 Purchase 1.486 1.446 86.630 1.352 1.213 1.197 1.182 1.246 1.353 Salience 1.501 1.505 3.479 87.736 1.561 1.622 1.178 1.417 1.587 Negative 1.255 1.537 1.553 1.315 67.586 2.640 1.769 1.636 0.886 Positive 1.537 1.154 1.609 1.929 24.441 86.876 1.996 2.676 1.059 ENG 1.402 1.041 1.500 1.562 1.071 1.417 85.639 0.725 1.004 BCS 1.708 1.537 1.705 2.007 1.725 2.786 3.639 87.578 1.563 OSM 1.274 1.569 1.568 2.131 1.396 1.405 3.072 3.099 89.934 Total Social 7.176 6.838 7.935 8.944 ------Media Total Mindset 4.598 ------3.880 3.854 3.422 3.900 4.487 Metrics Note: The values in the table represent the VAR model results at brand level for FEVD of 45 brands. Each value is an average computed from each brand’s FEVD at day 30 in order to capture all short and long term fluctuations. Similarly, we compute the average R2 for the focal variable across the brands
Marketing Science Institute Working Paper Series 42
Table 5 Impulse Responses of consumer mindset metrics to ENG, BCS and OSM
Cumulative impact % Significant
Panel A: Salience OSM 0.617*** 78 ESM BCS 1.002*** 73 ESM ENG 0.559*** 73 Positive 0.688*** 73 Negative -0.325* 79 Panel B: Purchase Intent OSM -0.344** 80 ESM BCS 0.489*** 76 ESM ENG 0.526*** 76 Positive -0.054 ns 68 Negative -0.336* 74 Panel C: Advocacy OSM 0.329* 73 ESM BCS 0.047 ns 78 ESM ENG 0.020 ns 47 Positive 0.343* 65 Negative -0.2835ns 77 ***p<0.01 **p<0.05 *p<0.1 ns=non-significant Notes: Estimates from the VAR model and the impulse response functions. Effects both across all brands can be evaluated by means of the added Z method (Rosenthal 1991). The details for individual firms can be found in WEB Appendix 4.
Marketing Science Institute Working Paper Series 43 Table 6 Impulse Responses of ABRET to consumer mindset metrics and SM variables
Dependent variable: Abnormal Returns
Cumulative impact % Significant
Salience 0.0491 ns 76 Purchase 0.6214*** 64 Advocacy -0.0499 ns 82 OSM -0.0385 ns 56 ESM BCS -0.3947** 80 ESM ENG -0.1111 ns 71 Positive -0.0498 ns 68 Negative -0.3690* 67 ***p<0.01 **p<0.05 *p<0.1 ns=non-significant Notes: Estimates from the VAR model and the impulse response functions. Effects both across all brands can be evaluated by means of the added Z method (Rosenthal 1991). The details for individual firms can be found in WEB Appendix 4.
Marketing Science Institute Working Paper Series 44 Table 7 ENG, BCS, OSM, positive and negative effects on each other Cumulative % Significant Cumulative impact % Significant impact Panel A: ESM BCS Panel B: ESM ENG OSM 1.0497*** 71 1.4092*** 67 ESM ENG 1.1299*** 69 -- -- ESM BCS -- -- 1.1798*** 87 Positive 1.2316*** 84 0.5360*** 73 Negative 0.8945*** 77 0.7329*** 59 Salience 0.1402 ns 87 0.0309 ns 62 Purchase Intent 0.0007 ns 80 0.2823 ns 67 ns Advocacy 0.2997* 71 -0.0791 67 Panel C: OSM Panel D: Positive OSM -- -- 0.2664 ns 65 ns ESM ENG 1.4710*** 64 -0.0899 62 ESM BCS 0.6214*** 82 1.2592*** 84 Positive 0.2910ns 57 -- -- Negative 0.5679*** 62 4.1522*** 100 Salience 0.5502*** 64 0.4448** 76 Purchase Intent -0.2108 ns 67 0.1842 ns 70 Advocacy 0.1770 ns 73 0.0517 ns 62 Panel E: Negative OSM 0.2383 ns 56 ESM ENG 0.6159*** 51 ESM BCS 0.7112*** 74 Positive 3.6828*** 92 Negative -- -- Salience -0.1782 ns 85 Purchase Intent 0.1549 ns 79 Advocacy 0.0183 ns 59
Marketing Science Institute Working Paper Series 45 ***p<0.01 **p<0.05 *p<0.1 ns=non-significant Notes: Estimates from the VAR model and the impulse response functions. Effects both across all brands can be evaluated by means of the added Z method (Rosenthal 1991). The details for individual firms can be found in WEB Appendix 4.
Marketing Science Institute Working Paper Series 46
i For example, American Customer Satisfaction Index (ACSI) publishes data for any given brand only annually. ii This omission was caused by an inability to collect the data, either in an automated fashion as was done with the other social media activity variables, or even manually. For example, the website Klout.com, which claims to measure the impact of individual’s social media activity and is commonly used as a performance or notoriety metric by many online marketing professionals, has only managed to incorporate data from YouTube in late 2015. iii Because six brands (BP, Disney, IBM, McDonalds’s, Starbucks, and Nike) prohibited user posts over our sample period, we collect the user posts for the remaining 39 out of 45 brands. Thus, 6 of our brand-specific models do not have the variables of ‘positive’ and ‘negative’ comments, and our reported average elasticities for these variables are based on the 39 remaining brands. The text corpus for sentiment analysis consists of 465,034 user posts for the 39 brands. Next we run Naïve Bayes classifier and extract sentiment from each post for a given brand on a given day. There could be more than one user post per day so we take the daily cumulative number of positive and negative posts as our two social media valence metrics. iv As a robustness check we consider an alternative approach to reducing our set of variables. We sum up together the measured variables under each construct that according to our PCA load together. We find no significant difference in the results v While other alternative data providers of consumer mindset metrics are available (e.g. Young and Rubicam 5 Pillars) they are collected less frequently, at quarterly or yearly level. vi The exact questions used in the YouGov survey are available upon request vii We don’t perform formal mediation tests as specified in Zhao, Lynch, and Chen (2010) because these tests are mostly applicable to the coefficients of single equation models and their properties for impulse response functions used in VAR are not studied.
viii We omitted the analysis for brevity. Results are available upon request from the authors.
Marketing Science Institute Working Paper Series 47 Web Appendix
This Web Appendix contains all the tests and details supporting our analysis and the robustness
of our results. We also provide some background about these analyses to facilitate navigation
through the various tables and figures.
Web Appendix 1- Principal Component and Factor analysis
We apply Principal Component Analysis (PCA) on our three constructs (OSM, ENG, and BCS).
First, for the PCA analysis of social media variables, there is a large variance shared among each
of the indicators with its respective constructs. These findings are stable across brands and within
brands over time. We rely on our PCA structure with 1 factor for each construct (see Table A1).
Marketing Science Institute Working Paper Series 48 Table A1 Variance explained by the first principal component in each construct for each brand OSM ENG BCS American Express 0.774 0.592 0.968 Verizon FiOS 0.833 0.633 0.985 Best Buy 0.791 0.575 0.916 BP 0.869 0.614 0.997 Chevron 0.734 0.652 0.985 Coca Cola 0.760 0.658 0.960 Citibank 0.982 0.596 0.994 Target 0.836 0.641 0.920 Delta 0.805 0.536 0.988 Dillard's 0.969 0.457 0.975 Disney 0.858 0.773 0.979 Macy's 0.827 0.574 0.904 Ford 0.821 0.524 0.925 Gap 0.826 0.457 0.905 GE 0.788 0.588 0.978 General Motors 0.893 0.794 0.984 HP 0.774 0.563 0.988 Home Depot 0.831 0.498 0.977 Honda 0.840 0.654 0.979 IBM 0.825 0.698 0.984 Sears 0.814 0.488 0.975 Lowe's 0.834 0.547 0.985 McDonald's 0.808 0.594 0.971 Nike 0.804 0.449 0.979 Nordstrom 0.783 0.600 0.983 Wells Fargo 0.794 0.645 0.979 Safeway 0.744 0.618 0.963 Sony 0.839 0.530 0.990 Southwest 0.832 0.448 0.987 AT & T 0.827 0.489 0.986 Walmart 0.826 0.617 0.970 Walgreen's 0.819 0.648 0.991 Microsoft 0.847 0.662 0.945 Shell 0.823 0.641 0.941 Progressive 0.824 0.686 0.978 Dell 0.850 0.498 0.968 Toyota 0.844 0.506 0.975 Time Warner 0.808 0.602 0.907 Starbucks 0.814 0.397 0.976 DISH Network 0.821 0.666 0.960 Amazon.com 0.972 0.556 0.969
Marketing Science Institute Working Paper Series 49 Expedia 0.824 0.751 0.948 MetLife 0.811 0.566 0.726 Netflix 0.834 0.710 0.982 Burger King 0.822 0.628 0.897 Average 0.827 0.591 0.960
We apply factor analysis with Varimax rotation on the YouGov metrics and obtain a three factor
solution with each of the factors representing a key mindset metric: salience, purchase intent and
advocacy. We report the Cronbach’s alpha and the correlation between the metrics for each
factor in Table A2 and A3.
Table A2 Variance explained and factor loadings by each factor in factor analysis averaged across brands
Salience Purchase Intent Advocacy (Factor1) (Factor2) (Factor3) (2.70) (1.70) (2.46) Ad-Awareness 91* 31 -2
WOM-exposure 73* 53 13
Consideration-Set 25 80* 44
Purchase Intent 7 78* 41
Current Customer 25 88* 13
Perceived Value 22 18 89*
Satisfaction -4 15 89*
Recommendation -4 10 95*
Note: In the parenthesis below the factor we report eigenvalues that are more than 1 for
each factor
*the highest loading
Marketing Science Institute Working Paper Series 50 Table A3 Reliability and correlations between the individual variables for each construct averaged across brands
Salience (Cronbach Alpha 0.843) WOM-exposure
Ad-awareness 0.72967 (<.0001)
Purchase Intent (Cronbach Alpha 0.880)
Current Consideration customer
0.71901 Consideration <.0001
Purchase 0.66325 0.76839 <.0001 <.0001
Advocacy (Cronbach Alpha 0.905)
Satisfaction Recommend
0.78025 Recommend <.0001 Perceived Value 0.70322 0.80730 <.0001 <.0001
Marketing Science Institute Working Paper Series 51 Control Variables
We control for advertising as previous research has a wide evidence that advertising has an impact on
customer acquisition (Trusov, Bucklin, & Pauwels, 2009) and financial markets (Joshi & Hanssens,
2010). We express advertising expenditures as total monthly dollars spent on different media
platforms (television, radio, newspapers) as reported by Kantar Media. As these data are
available to us at monthly frequency while the rest of the variables are at daily frequency, we
substitute the immediately previous month’s advertising expenditure for daily observations in the
current month. We emphasize that as firms do not change their advertising budget on a daily
level, it is unlikely to introduce any major bias in our model.
We control for firm size using MVE, which is the product of the number of shares
outstanding and closing price of the stock at the end of the day. New product introductions and
M&A announcements are obtained by following the procedure outlined in Sood & Tellis, (2009).
We search across major newspapers, dailies, and news wire services on Factiva. For each day,
we identify announcements of new products introductions and M&As for each firm in each
category. These two variables are count variables representing the total number of
announcements related to the two events for each brand per period. We also control for earnings
and dividend announcements by using two more count variables. We obtain them from the CRSP
event file.
Marketing Science Institute Working Paper Series 52 Web Appendix 2 –VARX steps
Our analysis consists of several methodological steps (see Table A4) which we apply to each
brand separately (e.g. Pauwels & Hanssens, 2007).
Table A4 Analysis steps in the Vector Autoregression Modeling Approach
Methodological Step Relevant Literature Research Question 1. Unit Root Tests
What is the temporal causality among Granger causality test (Granger, 1969) variables?
Augmented Dickey-Fuller Test (Enders, 2014) Are variables stationary or evolving?
(Maddala & Kim, KPSS test* Are the results robust to null hypothesis? 1998)
(Johansen, Mosconi, Are evolving variables in long-term Cointegration test & Nielsen, 2000) equilibrium?
2. Model of Dynamic Interactions
How do owned and earned media, brand community size, brand equity and firm (Dekimpe & Vector autoregressive (VAR) model value variables interact in the long run Hanssens, 1999) and short run, accounting for the unit roots and cointegration?
What is the immediate effect of an 3. Generalized impulse response (Pesaran & Shin, impulse without imposing a causal functions (GIRF) 1998) ordering?
4. Forecast error variance (Nijs, Srinivasan, & What fraction of performance variance decomposition (FEVD) Pauwels, 2007) comes from each marketing action?
Note: *Null hypothesis: Series are stationary
Marketing Science Institute Working Paper Series 53 Web Appendix 3-Model Specification and Parameter-to-observations ratio
We specify the relationship among the metrics of OSM and ESM, Positive and Negative
Valence, consumer mindset metrics and abnormal returns through the following VAR model
with exogenous variables in the following matrix notation:
p eX Υt = ∑ ΥΓ − + Φ + ttntn n=1
Where, ∈{},, ,210 ...,TTTTt is the time period index, Υ is the vector of the endogenous variables in
the system, Γn are the coefficients matrices of the lags of endogenous variables, Χ is the vector of
control variables and Φ is its coefficients, and ε is the error term.
The number of parameters per equation for the model is 17 for number of lags (p) =1. This
includes lagged endogenous variables (9), intercept (1), and deterministic trend (1), and control
variables (6). The number of parameters per equation for (p) =2, for example, is 26, including 9
additional parameters for the lags. The VARX models are estimated equation by equation and
OLS is as efficient as SUR since the independent variables are identical across each equation
(Zellner, 1962). Therefore, a VARX model of order 1 estimates 17 parameters from 260
observations (a 15.3 observation-to-parameter ratio), while a model of order 2 estimates 26
parameters from 260 observations (a 10.0 ratio) etc. We provide the detailed number of lags and
accordingly parameters estimated for each brand. The average observation-to-parameter ratio is
8.5 which is above the minimum suggested threshold in (p.69 Leeflang, Wieringa, Bijmolt, &
Pauwels, 2015) and high compared to other similar studies (see for e.g. Srinivasan, Vanhuele, &
Pauwels, 2010)).
The optimal lag order (“n”) is chosen by Akaike Information Criterion (AIC) taking into account
the Breusch–Godfrey serial correlation Lagrange multiplier test (Breusch, 1978). Our goal is to
balance lag-selection criteria with auto-correlation bias (Slotegraaf & Pauwels, 2008). We first
select the appropriate lag based on AIC, estimate the model and check whether we should add
Marketing Science Institute Working Paper Series 54 lags to pass diagnostic tests on residual autocorrelation (Franses, 2005). We add lags until we
have no serial auto-correlation in the model.
Table A5 Lag length specification and parameters-to-observations ratio for each brand
LAG Parameters Parameter to Observations Ratio American Express 3 35 7.43 Verizon FiOS 3 35 7.43 Best Buy 3 35 7.43 BP 2 26 10.00 Chevron 3 35 7.43 Coca Cola 3 35 7.43 Citibank 4 44 5.91 Target 3 35 7.43 Delta 4 44 5.91 Dillard's 1 17 15.29 Disney Channel 2 26 10.00 Macy's 1 17 15.29 Ford 1 17 15.29 Gap 4 44 5.91 GE 2 26 10.00 General Motors 3 35 7.43 HP 3 35 7.43 Home Depot 1 17 15.29 Honda 5 53 4.91 IBM 4 44 5.91 Sears 5 53 4.91 Lowe's 3 35 7.43 McDonald's 1 17 15.29 Nike 4 44 5.91 Nordstrom 4 44 5.91 Wells Fargo 4 44 5.91 Safeway 4 44 5.91 Sony 1 17 15.29 Southwest 3 35 7.43 AT & T 3 35 7.43 Walmart 4 44 5.91 Walgreen's 4 44 5.91 Microsoft 4 44 5.91 Shell 4 44 5.91 Progressive 4 44 5.91 Dell 2 26 10.00 Toyota 4 44 5.91 Time Warner 3 35 7.43 Starbucks 1 17 15.29 DISH Network 1 17 15.29
Marketing Science Institute Working Paper Series 55 Amazon.com 3 35 7.43 Expedia 3 35 7.43 MetLife 2 26 10.00 Netflix 3 35 7.43 Burger King 4 44 5.91 Average 2.9 34.6 8.5
Marketing Science Institute Working Paper Series 56 Web Appendix 4- Granger Causality, Unit roots, Cointegration and Granger Causality
Granger Causality
The dynamic relationships in Figure 1 are established through Granger Causality tests
(Granger, 1969). Granger causality of variable Y by a variable X means that we can predict Y
better by knowing the past values of X than by only knowing the past values of Y. This
procedure, also known as temporal causality, provides the closest causality test possible with
non-experimental data (Srinivasan, Rutz, & Pauwels, 2015). We apply Dumitrescu and Hurlin
(2012) panel causality test which is a test of Granger non-causality (Granger, 1969) accounting
for heterogeneity across brands. As the causal relationships that exist for a brand can also exist
for other brands, the use of cross-sectional information involves taking into account the
heterogeneity across brands in the definition of the causal relationship. Accordingly, Dumitrescu
and Hurlin (2012) statistic takes correctly into account brand heterogeneity when estimating the
causal relationships between key endogenous variables and provides an overall Granger causality
statistic for the whole sample averaged across brands. In order to avoid erroneous conclusions,
we check whether a variable Granger causes another variable at any lag up to 30th lag and report
the results with the lag that has the highest statistical significance (Trusov et al., 2009).
We first provide the results of Granger Causality tests in table A6.
Marketing Science Institute Working Paper Series 57 Table A6 Granger Causality
Response ABRET Salience Purchase Advocacy Negative Positive OSM BCS ENG to ABRET -- 0.007 0.003 0.144 0.055 0.186 0.091 0.013 0.013
Salience 0.115 -- 0.006 0.003 0.073 0.150 0.001 0.003 0.020
Purchase 0.035 0.053 -- 0.005 0.116 0.099 0.021 0.065 0.103
Advocacy 0.102 0.095 0.141 -- 0.195 0.000 0.001 0.052 0.196
Negative 0.035 0.005 0.422 0.004 -- 0.000 0.254 0.000 0.296
Positive 0.000 0.013 0.192 0.043 0.000 -- 0.122 0.000 0.257
OSM 0.000 0.000 0.001 0.021 0.080 0.075 -- 0.000 0.000
BCS 0.000 0.000 0.012 0.011 0.007 0.001 0.000 -- 0.000
ENG 0.012 0.026 0.094 0.011 0.375 0.240 0.000 0.003 --
Minimum p-value across 30 lags. The null hypotheses assume that the variables shown in the left-most column do not Granger cause the variables shown in top-most row.
Next we discuss the detailed brand by brand unit root tests. A mean-reverting trends
exhibits stationarity and a trend that changes permanently exhibits evolution. Following Enders
(2014) we use the augmented Dickey–Fuller test (ADF) with evolution as the null hypothesis.
We complement the ADF test with the KPSS test (stationarity as the null hypothesis) proposed
by Kwiatkowski et al. (1992). Each test is estimated in two forms: with and without a
deterministic trend. Ideally they should converge in their results (Maddala & Kim, 1998). In the
case that one of the two tests rejects stationarity, we further check for the existence of a long-
term equilibrium (cointegration). In addition we also conduct panel unit root tests. In Table A7
we present the ADF test (KPSS shows no discrepancies with ADF) results for our endogenous
variables.
Marketing Science Institute Working Paper Series 58 Table A7
Unit roots test results and variable specification (the threshold is 5% p-value)
Brand Abret Purchase Salience Advocacy ENG OSM BCS Spec Positive Spec Negative Spec (BCS) Positive Negative
American Express 0.000 0.000 0.000 0.000 0.000 0.000 0.308 FD No obs No obs
Verizon FiOS 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Best Buy 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.001 Levels 0.000 Levels
BP 0.000 0.000 0.000 0.000 0.000 0.000 0.033 Levels No obs No obs
Chevron 0.000 0.000 0.000 0.000 0.000 0.000 0.104 FD 0.000 Levels 0.000 Levels
Coca Cola 0.000 0.000 0.000 0.000 0.000 0.000 0.018 Levels 0.000 Levels 0.000 Levels
Citibank 0.000 0.000 0.000 0.000 0.000 0.000 0.174 FD No obs No obs
Target 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Delta 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Dillard's 0.000 0.000 0.000 0.000 0.000 0.000 0.302 FD 0.000 Levels 0.000 Levels
Disney 0.000 0.000 0.000 0.000 0.000 0.003 0.040 Levels No obs 0.000 Levels
Macy's 0.000 0.000 0.000 0.000 0.000 0.000 0.065 FD 0.000 Levels 0.000 Levels
Ford 0.000 0.000 0.000 0.000 0.000 0.000 0.008 Levels 0.000 Levels 0.000 Levels
Gap 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.003 Levels 0.000 Levels
GE 0.000 0.000 0.000 0.000 0.000 0.000 0.030 Levels 0.000 Levels 0.000 Levels
General Motors 0.000 0.000 0.000 0.000 0.000 0.000 0.069 FD 0.000 Levels 0.000 Levels
HP 0.000 0.000 0.000 0.000 0.000 0.000 0.049 Levels 0.089 FD 0.000 Levels
Home Depot 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Honda 0.000 0.000 0.000 0.000 0.000 0.000 0.008 Levels 0.000 Levels 0.000 Levels
IBM 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels No obs 0.000 Levels
Sears 0.000 0.000 0.000 0.000 0.000 0.000 0.024 Levels 0.000 Levels 0.000 Levels
Lowe's 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
McDonald's 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels No obs No obs
Nike 0.000 0.000 0.000 0.000 0.000 0.002 0.000 Levels No obs No obs
Nordstrom 0.000 0.000 0.000 0.000 0.000 0.000 0.002 Levels 0.000 Levels 0.000 Levels
Wells Fargo 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Safeway 0.000 0.000 0.000 0.000 0.000 0.000 0.185 FD 0.000 Levels 0.000 Levels
Sony 0.000 0.000 0.000 0.000 0.000 0.000 0.088 FD 0.000 Levels 0.000 Levels
Southwest 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
AT & T 0.000 0.000 0.000 0.000 0.000 0.000 0.023 Levels 0.000 Levels 0.000 Levels
Walmart 0.000 0.000 0.000 0.000 0.000 0.000 0.032 Levels 0.000 Levels 0.000 Levels
Walgreen's 0.000 0.000 0.000 0.000 0.000 0.000 0.050 FD 0.000 Levels 0.000 Levels
Microsoft 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Levels 0.000 Levels 0.000 Levels
Shell 0.000 0.000 0.000 0.000 0.000 0.000 0.307 FD 0.004 Levels 0.050 Levels
Progressive 0.000 0.000 0.000 0.000 0.000 0.000 0.017 Levels 0.000 Levels 0.000 Levels
Dell 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Toyota 0.000 0.000 0.000 0.000 0.000 0.000 0.001 Levels 0.000 Levels 0.000 Levels
Time Warner 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels No obs No obs
Starbucks 0.000 0.000 0.000 0.000 0.000 0.000 0.003 Levels 0.000 Levels 0.000 Levels
DISH Network 0.000 0.000 0.000 0.000 0.000 0.000 0.006 Levels 0.000 Levels 0.000 Levels
Amazon.com 0.000 0.000 0.000 0.000 0.000 0.002 0.006 Levels 0.164 FD 0.013 Levels
Expedia 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
MetLife 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Marketing Science Institute Working Paper Series 59 Netflix 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.172 FD 0.140 FD
Burger King 0.000 0.000 0.000 0.000 0.000 0.000 0.000 Levels 0.000 Levels 0.000 Levels
Notes: FD=First Difference
Marketing Science Institute Working Paper Series 60 In addition, In table A8 we provide results for the Panel Unit roots tests.
Table A8 Panel Unit Roots
Panel and Levin, Lin Breitung Hadri* ADF-Fisher ADF (basis for
individual unit and Chu (individual (only individual (no intercept, no ADF-Fisher)
root tests (no intercept, intercept and intercept) trend) % of brand series with p-value< .05 no trend) trend)
Null Hypothesis Common Common No Unit root Individual Individual
Abret .000 .000 .780 .000 100.00
Salience .000 .000 .000 .000 100.00
Purchase .000 .000 .000 .000 100.00
Advocacy .000 .000 .000 .000 100.00
Positive .000 .000 .000 .000 91.82
Negative .000 .000 .000 .000 97.42
ENG .000 .000 .151 .000 100.00
BCS .000 .000 .000 .000 77.78
OSM .000 .000 .000 .000 100.00
Note: *Simulation evidence suggests that in various settings, Hadri's panel unit root test experiences significant size
distortion in the presence of autocorrelation when there is no unit root. In particular, the Hadri test appears to overreject
the null of stationarity, and may yield results that directly contradict those obtained using alternative test statistics (see
Hlouskova & Wagner, 2006 for details).
Next we test the system of the non-stationary variables (ESM, positive and negative) to
determine the number of cointegrating equations using Johansen Fisher Panel Cointegration Test
(Johansen et al., 2000). The null hypothesis of the trace statistic assumes that there are no more
than “n” cointegrating relations between the endogenous variables in the system. This method
tests the presence of cointegrating relation between the variables sequentially starting with rank
zero and accepts the first estimated value which fails to reject the null hypothesis as the
cointegrating rank. The trace statistic estimate and the critical value (at 5%) are given in the table
Marketing Science Institute Working Paper Series 61 A9. Based on the results we conclude that we don’t have any cointegrating equation among the
variables in the system and we do not need Vector Error Correction models.
Table A9 Johansen cointegration test results
Maximum Rank Trace Statistic Critical Value (5%) p-value
0 1001.06 35.01 0.000
1 507.28 18.40 0.000
2 127.74 3.84 0.000
Marketing Science Institute Working Paper Series 62 Web Appendix 5- FEVD of the model without consumer mindset metrics
The results of FEVD for the restricted model without consumer mindset metrics are reported in
Table A10. In the full model the R2 for abnormal returns is 12.7% (see Table 4 in the main
manuscript) while in the restricted model the R2 drops to 8.9% implying that consumer mindset
metrics do play an important role in explaining abnormal returns
Table A10 Variance Decomposition of endogenous variables in the model without the 3 consumer mindset metrics
Variance ABRET Negative Positive ENG BCS OSM explained by R2=8.9% R2=19.2% R2=22.5% R2=14.9% R2=23.5% R2=33.3%
ABRET 93.529 1.145 1.111 1.008 1.076 1.293
Negative 1.285 70.629 2.654 1.791 1.655 0.818
Positive 1.502 24.976 90.715 1.937 2.493 0.789
ENG 1.385 1.114 1.503 89.380 0.739 1.118
BCS 1.584 1.632 2.637 3.501 91.766 1.359
OSM 1.154 1.161 1.381 2.966 2.936 94.874
Note: The values in the table represent the VAR model results at brand level for FEVD of 45 brands. Each value is an average computed from each brand’s FEVD at day 30 in order to capture all short and long term fluctuations. Similarly, we compute the average R2 for the focal variable across the brands
Marketing Science Institute Working Paper Series 63 Web Appendix 6- Dynamic Multipliers
Although we treated the new product announcements and advertising exogenously (due
to non-centrality to our study and to avoid over-parametrization of the model), they constitute a
key strategic decisions for marketers. Therefore, it would be interesting to estimate their impact
on the endogenous variables in the model. To obtain the impact of exogenous variables in VAR
framework we manually wrote the reduced form of the model (from the structural form) that can
be compactly represented as:
t )()( += uxLByLA tt
1 p = 1()( 1 −− αα p LLLA )
LB = − β )1()(
Where yt are the endogenous variables at time t, xt are the exogenous variables at time t, L is the
lag operator, p is the order of the model, α is vector of parameters of the endogenous variables, β
is the vector of parameter of the exogenous variables (we do not include lags of exogenous
variables), and µ is the error term.
From the above reduced form of VAR model we obtain the final form:
LB )( y = x t LA )( t
Marketing Science Institute Working Paper Series 64 Table A11 Dynamic multipliers of endogenous variables to product announcements and advertising
The Effect of a The Effect of Endogenous Variables Product Advertising Announcement Abret 0.02082 -0.00082
Advocacy 0.08425 -0.00057
Purchase Intent 0.01389 0.00487
Salience 0.03641 0.00376
Negative -0.01182 -0.00091
Positive 0.04010 -0.00011
ESM ENG 0.00548 0.00019
ESM BCS 0.00003 0.00004
OSM 0.01266 0.00262
Note: The coefficient are dynamic multipliers (see Web Appendix 5 for details) of endogenous variables in the mode to for new product announcements and advertising
Marketing Science Institute Working Paper Series 65 Web Appendix 7- Second-Stage Analysis
As we have only 45 firm-level observations and 21 predictors, we lack enough degrees of
freedom for reliable estimation, which will likely result in a poor model fit. Therefore, an
ordinary logistic regression model may not be appropriate for this task. We instead use an L1
regularized logistic regression model, which forces the coefficients of unimportant variables to
equate to zero. To estimate the model, the algorithm maximizes the following objective function
using nonlinear programming or quadratic approximations (Hastie, Tibshirani, and Friedman
2011):
� � � � �� � �� ��� �� �� + � �� − ��� � + � − � �� ��,� � � � �
We estimate the three models using glmnet package in R. In all the three models, we can
correctly categorize between 82% and 89% of the sample cases. For brevity, we discuss the
results of the second-stage analysis for only the top three variables with positive coefficients for
each model separately.
We begin this analysis by modeling firm-level IRFs of each of the three consumer mindset
metrics to OSM as a function of a set of explanatory variables. The set of these variables, to a
certain extent, is arbitrary as there is little theory to guide us in the variable selection.
Nonetheless, we include three sets of variables that have high face validity. In order to capture
the preexisting notions and impressions about brands, we include the following set of YouGov
variables: Employer branding, perceived quality, and impression about the brand. In addition, we
add a variable indicating percentage of respondents who are former customers. Next, we include
the following set of financial metrics obtained from Compustat and Kantar: Firm size proxied by
log of total assets, profit margin, and change in advertising expenditure. Finally, in order to
capture public perception about the brand more granularly, we add a set of following corporate
social performance (CSP) measures: Community, Employees, Environment, and Corporate
Marketing Science Institute Working Paper Series 66 Governance. We also include components of CSP that constitute these four higher level
measures: Philanthropy, human rights, and sustainable products constituting Community CSP,
Compensation, diversity, and training for Employees CSP, Climate change and resource
management for Environment CSP, and Leadership ethics, and transparency for Corporate
Governance CSP. We obtained these metrics from CSR Hub database, which aggregates CSP of
US and foreign companies from 425 different sources. Thus, we use 21 different predictors (4
from YouGov, 2 from Compustat, 1 from Kantar, and 14 from CSR Hub) for the second-stage
model.
We run three separate models using regularized logistic regression with dummy variables
indicating the polarity of IRFs for Salience, Purchase intent, and Advocacy as dependent
variables. Details of model specification are provided in Web Appendix 7. The results from the
second stage analysis are descriptive and not causal. Nonetheless, they inform managers on the
steps to take to make their OSM effective. In particular, when marketers are finding it difficult to
calculate ROI of social media marketing spending (Headley, 2015), any insights that help
improve efficiency of social media budget allocation are valuable.
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