The Digital Consumer

Authored by: Donna L. Hoffman , Thomas P. Novak , Randy Stein

The Routledge Companion to Digital Consumption

Print publication date:  December  2012
Online publication date:  May  2013

Print ISBN: 9780415679923
eBook ISBN: 9780203105306
Adobe ISBN: 9781136253379




The time for predicting whether social media applications will become indispensable to people in their everyday lives is over, because that time has arrived. In the United States, Facebook usage now surpasses Google, accounting for 25 percent of all pages views and 10 percent of all internet activity (Dougherty 2010). One quarter of total online time is spent on social media, with social media usage increasing 82 percent between December 2008 and December 2009 (Nielsen Wire 2010). Facebook is not the only social media application enjoying a phenomenal surge in usage. Thirty-five hours of video are uploaded to every minute (Schmidt 2010). As of May 2011, receives 50 million unique daily visitors (Kincaid 2011). And as early as 2006, one in three South Koreans was a member of the Korean social networking site (BBC News 2006).

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The Digital Consumer



The time for predicting whether social media applications will become indispensable to people in their everyday lives is over, because that time has arrived. In the United States, Facebook usage now surpasses Google, accounting for 25 percent of all pages views and 10 percent of all internet activity (Dougherty 2010). One quarter of total online time is spent on social media, with social media usage increasing 82 percent between December 2008 and December 2009 (Nielsen Wire 2010). Facebook is not the only social media application enjoying a phenomenal surge in usage. Thirty-five hours of video are uploaded to every minute (Schmidt 2010). As of May 2011, receives 50 million unique daily visitors (Kincaid 2011). And as early as 2006, one in three South Koreans was a member of the Korean social networking site (BBC News 2006).

As social media continues to evolve and become even more ubiquitous, and as user-generated content replaces marketer-generated content, researchers are beginning to examine how social media is likely to shape consumer behavior. For example, who is influencing whom in social media? How do online reviews, and information about friends’ buying behavior, influence consumers’ attitudes and purchase behavior? How do people decide what content to create (e.g., what goes in a Facebook profile, and how do users decide what to Tweet about?) What content is most likely to go viral? More generally, why do people use social media at all and become customers of social media applications?

Of course, the ubiquity and scope of social media usage make such investigations daunting. Research on specific topics in how social media impacts consumer behavior has begun to proliferate in the past several years, but organizing and drawing conclusions from this work present significant challenges. Since social media applications themselves are just coming out of a nascent stage, theoretical frameworks guiding broad research questions are still scarce. That is, while many specific topics have been covered, drawing generalizations can be difficult.

On this note, Novak (2008) synthesized research on social media to identify 22 distinct motivations why people use social media, noting that the lack of an organizing framework hampered conceptual progress. From this broad range of goals, Hoffman and Novak (forthcoming) developed a social media version of the 4Ps marketing mix model to account for the goals users have when using social media. Specifically, they identified four higher-order goals they argued account for the lion’s share of motivations people have in using social media applications: connect, create, consume, and control. People might use social media simply to connect with other users. They might also use an application to create content, for example, by posting updates on Facebook or tweeting brief reviews with Twitter. Alternatively, people might consume content that other users have created. Finally, users might also choose to exercise control over how they use social media, such as by adjusting privacy settings or by modifying the visual appearance of an application. These “4Cs” are not necessarily mutually exclusive as the same behavior could fulfill one or more of the goals; for example, playing Farmville could fulfill the need to connect for one user, and the need to control for another. Similarly, people may approach social media use with more than one goal at the same time, for example with the goals of creating content in order to connect with others. Together, the ability to fulfill these four goals online likely explains why people spend so much time on social media applications.

Since the 4Cs framework helps understand why people use social media, it also indicates the implications social media use should have for consumption behavior. A review of the literature on social media use according to the 4Cs therefore provides a useful organizing framework, as well as a mechanism to identify important areas for future research. Our review that follows is organized by the 4Cs higher-order social media goals. The research we discuss is organized by the primary goal that consumers in a given study were most directly trying to fulfill, or by the type of goal that was most directly examined. For each goal, we define how it relates to social media use, describe research relevant to the goal, and conclude with ideas for future research relating to how consumers might be seeking to fulfill these goals through their social media usage behaviors.


We define social media as the set of

web-based and mobile tools and applications that allow people to create (consume) content that can be consumed (created) by others and which enables and facilitates connections.

Thus, social media are not so much about specific technologies (i.e. Facebook today, Viewdle tomorrow), but rather what the technologies let consumers do. Arguably, more than any other reason, people use social media to connect with others. Indeed, the ability to connect with large numbers of other users across time and space is largely responsible for much of the success of social media. However, interactions via social media are quite different than offline interactions: conversations are replaced with tweets, wall posts, video uploads, status updates, and “likes.” Users are no longer limited to information gleaned from their friends in real-time, real-life interactions; instead, they can display their musical interests, photos of what they did last weekend and relationship status with just a few keystrokes. Social media therefore changes the way people connect with each other, both in the type of interactions that take place and in the quantity and quality of information that is available.

These changes naturally lead to several questions concerning the changing landscape of people’s social lives. When seeking a connection with others online, what information do consumers get from others, and how do they use it? Also, does connection with others online lead to positive effects on well-being?

As far as seeking connections with others online, social media is unique in that first impressions can be formed by looking at someone’s profile and content created, rather than from a face-to-face meeting. Despite this difference, liking stems from cues of sociability in both modes of meeting: liking of people in real-life meetings is predicted by non-verbal expressivity, while liking from examining a Facebook profile is predicted by the expressivity of the profile (e.g. number of photos posted and how much contact the person has with others; Weisbuch et al. 2009). However, impressions will be shaped by the method utilized to find information. People who actively choose which information to use on Facebook to form an impression of another user like the user less than those passively given a set amount of information on the same user (Waggoner et al. 2009). Notably, Waggoner et al. also found increased amounts of information increased confidence in judgments in passive perceivers (those who are given information to look at), but not active perceivers (those who decide what information to look at, and how much of it to look at). Ironically, this suggests that actively trying to find someone to form a bond with over a social network might be a more difficult task than it initially seems due to the overabundance of information available.

The amount of information available on social media sites is also changing the way we acquire information about our real-life friends. Before social networking websites, impressions of friends were limited by information obtained through face-to-face (i.e. direct) interactions. However, social media enables computer-mediated interactions, so what people post on their Facebook profiles need not be limited to what they choose to share during in-person interactions. Indeed, members of social networks are unaware of disagreements with friends, even on topics they say they discuss, instead applying stereotypes and projecting their own views on their friends (Goel et al. 2010). Presumably, as more and more information about friends becomes available online, differences between friends will become more salient. Research has yet to address how noticing this chasm cascades back into real-life interactions, but it is clear that users of social media now have access to information that directly contradicts their natural tendency to assume that their friends’ beliefs and attitudes are similar to their own.

Since online interaction has the potential to at least partially replace offline interaction, research examining the impact of social media use on well-being has begun to emerge in recent years. Research has uncovered several relevant moderators and caveat-laden relationships between social media use and well-being. For example, though direct communication between pairs on Facebook (e.g., trading wall posts) increases feelings of bonding and decreases felt loneliness, it is paradoxically those who consume the most content that feel least bonded and most lonely (Burke et al. 2010). Also, Facebook usage is correlated with feelings of both connection and disconnection, because feelings of disconnection motivate increased use of Facebook, which ultimately leads to feelings of connection (Sheldon et al. 2011). However, given the myriad of people using social media and reasons why they use it, there is unlikely to be a clear-cut, unidimensional and unidirectional effect of social media on well-being.

Recently, Hoffman and Novak (2011a) evaluated the relationship between social media goal pursuit and the experience of feeling connected. Results showed that social goals and connect goals lead to different levels of relatedness, but the relationship between goal pursuit and relatedness is moderated by online social identity and motivational orientations. Further research is necessary to connect these feelings of connection to life outcomes. Indeed, users themselves may overestimate the effect of social media usage on their well-being. Research done after the university shootings at Virginia Tech and Northern Illinois University found that students who turned to Internet-related activities for support after the shootings thought those activities were beneficial, but there was no actual effect on well-being, as measured by depressive and PTSD symptoms (Vicary and Fraley 2010). Of course, not all sources of stress that might lead people to seek support online are as extreme as those school shootings, but it is clear that care must be taken when judging the accuracy of users’ predictions about their own well-being.

While most research on connection online focuses on the use of social media to become more connected, it is of course possible to prefer an online version of an activity to avoid social interactions with others. For example, some users prefer online gambling to casino gambling, viewing the online version as a way to experience the joy of gambling with the added bonus of anonymity, preferring not to have to interact with the other gamblers (Cotte and LaTour 2010). While that study did not involve social media, per se, the implication is clear: users may turn to social media to participate in stigmatized behavior, or simply to have a more low-intensity form of social interaction. The effects on well-being of using social media in this way still need to be addressed.

While less researched, it is worth noting that social media is not only a way for users to connect with each other, but also offers businesses these opportunities as well. Online sellers can increase their economic value by connecting with other sellers on social networks, because it makes the seller’s marketplace more accessible, essentially creating an “online shopping mall” (Stephen and Toubia 2010). Presumably, seamless integration into customers’ social networks would also provide greater feelings of trust between customers and sellers, though further research is necessary to address this question.

Is the connection that people experience online a “real” connection? Considerable research has established that behavior in online virtual environments parallels real-world behavior in many important ways, indicating that the connections people establish online are as real as the ones they establish in the physical world. Slater et al. (2006) replicated Milgram’s (1963) obedience study and found that participants administering virtual electric shocks to virtual subjects behaved in similar ways as did participants in Milgram’s original study. Yee et al. (2007) found that single and mixed gender dyad norms for interpersonal distance in avatar-based online social environments paralleled norms in the real world. Similarly, social cues involving the size of successive requests for cooperation made in online environments (i.e. large followed by moderate, vs. small followed by moderate) produced identical outcomes in both virtual and physical world settings (Eastwick and Gardner 2008).

It is clear from research on how people are connecting online that the wealth of information and connection opportunities available is a double-edged sword. The relationship between social media usage and actual feelings of connection is complex (Burke et al. 2010; Sheldon et al. 2011). Perhaps this is because, with increased information and connection opportunities, expectations may inflate and therefore be more difficult to meet (cf. Goel et al. 2010; Vicary and Fraley, 2010; Waggoner et al. 2009). The amount of information available on social networks could also de-motivate effective connection-seeking behavior (see Iyengar and Lepper 2000). Future research should address what strategies users could or should use to cut down on this information overload.

Thus, it is clear that social media presents users with a wide array of opportunities to connect with others. However, important questions for the future revolve around how people deal with the massive amount of information available, how feelings of connection are actually derived from interactions online, and online connections improve psychological well-being, if they do so at all.


Undoubtedly one of the competitive advantages of social media is the amount of user-generated content. People now have access to a huge amount of information created by others, with moment-to-moment access to others users’ current moods, thoughts, reviews, and often their purchasing behaviors. However, how does consuming user-generated content online influence users’ behaviors, both online and offline?

Notably, some social media websites, like are set up for the explicit purpose of shaping users’ behaviors with their user-generated content. Users can look online for reviews of businesses and products when they need help with a purchasing decision. However, consuming content online, even when users are not explicitly looking for information to help with a decision, is likely to impact behavior, both online and offline. For example, updates posted through social media applications, such as Facebook, Cyworld, and Twitter, often contain references to what products they are using. Even if users are consuming online content just to pass the time, consuming content from friends online may influence subsequent attitudes and behavior.

Guided by the distinction that users can be influenced by consuming content online intentionally or not, two broad research areas emerge. First, when users explicitly consume content online to shape their decisions, what determines whether online word of mouth will shape user opinions, and in what direction? Second, more indirectly how does the everyday consumption of content online (e.g., aside from an explicit, directed search for reviews) shape users’ opinions and behaviors? However, a more fundamental question needs to be addressed first. If we are to discuss how consumers influence each other through the content they create online, how do we know who is most influential? Users will obviously tend to consume and be influenced by content created by those who are most influential, yet what is meant by “influential” is not clear.

Recent research has addressed – with some degree of disagreement – which users are responsible for the most effective change via word of mouth. Using computer simulations, Watts and Dodds (2007) found that, contrary to the long-standing idea that a minority of exceptional individuals are responsible for the diffusion of information in social networks, diffusion is actually due to a larger mass of easily influenced individuals. However, Goldenberg et al. (2009) note that there are two types of highly influential people – innovators and followers – who impact information diffusion, albeit in different ways. Innovators adopt new products and trends earlier in the diffusion process and affect speed of adoption, while followers have a greater impact on market size. Similarly, arguing for the need to segment influential users from non-influential users, research has shown that only one-fifth of a user’s friends on a social network site impact the user’s general behavior on the site (Trusov et al. 2010). This suggests that users will exercise a fair amount of selectivity in choosing whose reviews to read.

What then determines whether online word of mouth will actually shape user opinions? While most word of mouth is positive, negative word of mouth is actually most effective (Chen et al. 2011). However, the relationship between word of mouth and consumer behavior also varies by product type and who is consuming the online content. Online reviews are more influential for products consumers are unfamiliar with, and for relatively experienced online users (Zhu and Zhang 2010). Consumers also have the ability to look at multiple reviews from the same user. When doing this, consumers might find some that reviewers like the same things as them, while other reviewers dislike the same things as them. Consumers are more likely to follow the opinions of the former, due to the ambiguity of reasons for negative reviews (Gershoffet al. 2007).The particular goal that users have when examining reviews also impacts how they process reviews. Users explicitly trying to make a decision are impressed with breadth of information on a topic, while consumers simply trying to learn are more impressed with deep knowledge of the focal topic (Weiss et al. 2008).

Of course, not all consumers will read reviews so closely that the details of the review become relevant for their choices. Thus, it is important to look at how content containing thin slices of opinions on products in turn shape viewers’ opinions, albeit in ways that are less direct than full reviews do. Indeed, social media sites such as, and the “like” feature on Facebook, provide a simpler bit of information than a full review, just noting whether a product or business is popular, and who in particular is using it.

Intriguingly, online popularity is self-propagating. Studies of online music markets (Salganik et al. 2006; Salganik and Watts 2008) have shown that quality takes a back seat to perceived popularity, at least initially. Although the highest quality songs tended to do well over time, if other songs were perceived as popular, they became and remained popular, regardless of quality. Even when users do not have an explicit goal to find a product to consume, though, they still might be influenced – positively or negatively – by simply seeing what other users are purchasing. This effect also appears to occur when consumers simply observe online content trends, such as brand search trends or YouTube viewing statistics. In a series of studies examining the impact of brand volume trends on brand attitudes, Hoffman and Novak (2011b) found that when consumers simply viewed positive (negative) volume trends about a brand, they were more likely to have positive (negative) attitudes toward the brand, even after controlling for valence effects. They also found that the effects were more pronounced when consumers believed the trends were generated from others more similar to them. Relatedly, those who are moderately connected to others tend to copy the purchases of those in their social networks; those who are on the low end of connection do not copy others’ purchases, and those on the high end actively avoid making purchases that mimic people in their networks (Iyengar et al. 2009).

Simple popularity information delivered this way is unique to social media, begging the question of whether online interactions impact consumption in ways that other ways of learning about products cannot. Indeed, word of mouth from social networking sites leads to longer carryover effects on consumer behavior than traditional marketing efforts, such as promotional events and media appearances (Trusov et al. 2009). Compared to another source of information about others’ consumption behavior–simple observational learning–while negative word of mouth is more influential than positive, the reverse is true for observational learning (Chen et al. 2011).

Additional research is needed to address whether consumers construct word-of-mouth communications, such as reviews, differently online than they would offline. For example, an examination of offline word of mouth has shown that people tend to use abstract language when an experience matches expectations, and readers likewise infer a positive experience from abstract language (Shellekens et al. 2010). Given the emergence of word-of-mouth websites like, care needs to be taken before assuming that these results will apply to online reviews as well.

Thus, several lines of research suggest that the online consumption marketplace has several unique features not shared by the offline marketplace. Online content consumption inundates users with information on products and how other users are using them. The research conducted thus far suggests that additional work should continue to examine what moderates user-to-user influence, as well as how content created and delivered on social media differs from its offline counterparts.


One of the major innovations of online social media is the shift of the job of content creation from marketers to consumers. Consumers post personal status updates, send tweets, post reviews for local businesses and products they are using, and“check-in”online when they reach a notable destination offline. This ability to create content leads to two questions: First, how can we predict who is most likely to create content?, and, second, how do consumers decide what content to create?

Given the high volume of people using social media, and the effort involved in creating content, it follows that not every user has an active hand in creation. On Twitter, for example, half of all tweets are generated by a mere 20,000 users, and most content is actually generated by media outlets (Wu et al. 2011). Personality attributes predict who is creating what content: for example, trait narcissism predicts creating content that is self-promoting (Buffaradi and Campbell 2008). Additionally, those high in need of uniqueness are less likely to generate positive reviews for, or recommend, products that signal a lack of uniqueness (Cheema and Kaikati 2010). Future research will undoubtedly uncover additional relationships between chronic dispositions and social media usage behaviors.

On message boards, the distinction between those who create content and those who only consume it separates users into two groups: posters and lurkers. In addition to differing with respect to whether they create content, posters and lurkers react differently to created content. Posters are affected primarily by negative opinions and adjust their opinions downward, while lurkers (which, as the Wu et al. (2011) research suggests, account for the majority of users) are less impacted by negative opinions (Schlosser 2005).

As far as content creation is concerned, one robust finding seems to be that social media users tend to create content based on information that creates positive affect. That is, content that leads to positive affect tends to go “viral.” For example, eliciting positive affect makes tweets more likely to spread (Bakshy et al.2011). However, Bakshy et al. note that, despite this relationship, predicting a priori which tweets will go viral is difficult to predict. Perhaps suggestive on this point, Berger and Milkman (2011) showed that awe-inspiring content is especially likely to go viral, with users tending to send articles that inspire the awe. Thus, discriminating among types of positive affect might be one way to gain power in a priori predictions of which content will spread.

Of course, everyone on a social network is a creator of content to the extent that users must decide what content to put in their profiles. How do users decide what goes in their profiles? Do they represent themselves accurately? Though it seems intuitive that users might exaggerate ideal personality attributes on their profiles, Back et al. (2010) found that Facebook profiles are, in fact, accurate. Observers’ ratings of users’ personalities based on their Facebook profiles, correlated with those users’ actual, rather than ideal, personalities.

Research is just beginning to reveal which people are more likely to create content, and what they create. One fruitful area for future exploration might be an examination of what goals people tend to have when creating content. For example, what might prompt a user to post a status update on Facebook about a coffee shop he just visited? Important here might be tying the reason in to the other goals one has for using social media. For example, a consumer might want to vent about a negative experience (influencing other users’ consume goals), but also might be posting the status update to indirectly invite friends to join him (exercising a connect goal). The goal users have in mind when creating content will surely impact the extent to which the content is likely to spread, so examining content creation in this way should provide a fuller picture of how people consume content as well.


Usage of social media provides people with not only the chance to create content, but also the opportunity to choose which content they consume. In the process of doing so, users can personalize and customize their privacy and usage settings, essentially creating a user-maintained checks and balances system giving them control over the extent to which their own content is private and others’ content is delivered to them. Since these settings are the gatekeeper from the application to the user, it is naturally important to examine what factors shape how users determine what these settings should be. However, research addressing this has been scant relative to the other social media goal types, perhaps since adjusting control settings, while important, is not necessarily seen as part of a user’s everyday social media use. This would be an incorrect perspective, since the extent to which a consumer seeks out and implements control over a social media application directly impacts the manner in which one creates and consumes content, and connects with others. Control thus underlies the other “Cs.”

Consumers will likely exercise weaker control over content on websites to which they feel a more intimate connection. For example, research on online social capital has shown that social capital is generated when people exhibit volunteerism, reciprocity, and social trust (Mathwick et al. 2008). Presumably, this means that consumers will be more likely to share their content when other consumers – and social media application owners, like Facebook – exhibit those three characteristics. There are other steps that website owners can take to encourage content sharing, as well. Eliciting advice from users increases intimacy, while, conversely, eliciting expectations decreases intimacy (Liu and Gal 2011). However, additional research is necessary to understand how social media usage builds online social capital, particularly as that usage becomes more routinized and part of consumers’ daily routines (Hoffman 2012). However, research directly addressing willingness to disclose information paints a cautionary picture. Users may actually be more likely to disclose information when cues that disclosure is dangerous are present (John et al. 2011). For example, an unprofessional-looking website might actually suppress privacy concerns, increasing disclosure, while also (paradoxically) decreasing felt security. In concert with the research on trust cited above, this research suggests there may be counter-intuitive, and perhaps potentially dangerous ways of“tricking” users into disclosure they ordinarily would not do.

As applications like Foursquare and features like Facebook’s check-in become more popular, understanding how users adjust privacy settings will be important for illuminating the extent to which consumers are willing to remove boundaries between their online and offline worlds. Consumers also should be made aware of potential hazards that may make them likely to disclose inappropriately.

Control in social media settings extends not just to control over content, but also to control over interactions with other people, or even control or influence over oneself. Fox and Bailenson (2009) found that individuals who viewed an avatar of themselves exercising in a virtual online environment were more likely to voluntarily exercise in the real world the next day. Similarly, Yee and Bailenson (2007) found that participants who negotiated in online settings with taller, rather than shorter, avatars were more aggressive in subsequent face-to-face negotiations with actual people. Thus, to the degree that choices people make in online environments influence their subsequent offline behavior, people may choose to engage in specific types of online behavior in order to gain this control over themselves or others. For example, participation in virtual worlds may positively influence quality of life for those with physical disabilities (Novak 2012). Marketers can also exert this control by manipulating how people interact with online social environments. Yang and Chattopadhyay (2009) have shown that consumers who are given the opportunity to customize a pre-specified type of online persona (i.e. a conservative, business-line persona), will be more receptive to advertising targeted to that persona.

From the handful of research done on control behaviors thus far, it is clear that control behaviors online represent an important way that users express traits that are important to them. Research has started to uncover how features of the application shape how users balance this self-expression with privacy, but other questions such as what dispositions impact control behavior, and how social influence online impacts control behavior are still largely open. Since control behaviors are the gateway for all other behaviors online, predicting the extent to which users put barriers between their offline and online worlds will become increasingly important as social media applications continue to become pervasive in everyday life.


In this chapter, we used the 4Cs framework to organize current research on the impact of social media usage on consumer behavior and we have identified several promising directions for future research. We hope that consumer behavior researchers can further refine and extend this framework as additional research emerges. From the wide range of research we summarized here, it is clear that many fascinating phenomena of social media usage have been identified. However, the diversity of these findings also underscores the point that these investigations were guided by quite myriad questions, making theoretical generalizations difficult. We suspect and hope that, in the future, theoretical debates (such as the one we cited over which users are most influential online) will become more common. With that in mind, we will briefly discuss the potential of three interrelated research areas of social media usage that could benefit from a theoretical framework.

First is the impact of social media usage on well-being. Research has certainly uncovered that online interactions can be meaningful (Burke et al. 2010; Sheldon et al., 2011). However, we have also reviewed how charting the impact of meaningful interactions on well-being can be difficult (e.g., Vicary and Fraley 2010). Moreover, social media presents unprecedented access to information about one’s friends in at least two ways that real-life interactions cannot match – information on friends’ beliefs (Goel et al. 2010) and information on friends’ consumption behavior (Iyengar et al. 2009; Salganik et al. 2006; Salganik and Watts 2008). Ideas about how people deal with this mass of information will be necessary to gauge the impact of social media usage on well-being.

Second is the extent to which online interactions differ from offline interactions, specifically the long-term consequences of decisions made online. That is, as just mentioned, we have good reason to think that social media will change how people make friends and purchasing decisions. However, we do not know anything about the long-term satisfaction of these decisions. Since the inputs to these decisions have changed, new theories may be necessary to address the life cycles of these decisions.

Third is the distinction between the explicit and implicit ways users are influenced by social media. That is, we have identified how users process information when they explicitly look at reviews online (e.g., Shellekens et al. 2010). However, we have also reviewed more subtle ways in which social media shapes consumer behavior (Iyengar et al. 2009) and interactions between friends (Berger and Milkman 2011). This suggests that future theories of how social media influences users will need to take into account how implicit attitudes and specific types of emotions shape social media usage in ways that might not be accessible to users themselves.


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