The Role of Digital and Social Media Marketing in Consumer

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THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER
BEHAVIOR
Andrew T. Stephen
L’Oréal Professor of Marketing
University of Oxford, Saïd Business School
Park End Street, Oxford OX1 1HP, United Kingdom
Email. [e-mail]
Submitted to Current Opinion in Psychology special issue on consumer behavior
October 12, 2015
* Corresponding author: Andrew Stephen, L’Oréal Professor of Marketing, Saïd Business
School, University of Oxford, Park End Street, Oxford OX1 1HP, United Kingdom
([e-mail]). The author thanks Nancy Puccinelli for providing comments
on an earlier draft, Cait Lamberton for discussions about the digital marketing literature that
helped inspire some of the opinions expressed in this article, and the special issue editors for
their helpful feedback.

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THE ROLE OF DIGITAL AND SOCIAL MEDIA MARKETING IN CONSUMER
BEHAVIOR
ABSTRACT
This article reviews recently published research about consumers in digital and social
media marketing settings. Five themes are identified: (i) consumer digital culture, (ii) responses
to digital advertising, (iii) effects of digital environments on consumer behavior, (iv) mobile
environments, and (v) online word of mouth (WOM). Collectively these articles shed light from
many different angles on how consumers experience, influence, and are influenced by the digital
environments in which they are situated as part of their daily lives. Much is still to be
understood, and existing knowledge tends to be disproportionately focused on WOM, which is
only part of the digital consumer experience. Several directions for future research are advanced
to encourage researchers to consider a broader range of phenomena.

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INTRODUCTION
Using the internet, social media, mobile apps, and other digital communication
technologies has become part of billions of people’s daily lives. For instance, the current rate of
internet use among American adults is about 87% and is closer to 100% for demographic groups
such as college-educated and higher-income adults [1]. Younger people—the next generation of
mass consumers—have similarly high levels [2]. People also spend increasing time online. For
example, in the UK, over the last decade the number of hours spent online by adults has more
than doubled, and now averages 20.5 hours per week [3]. Social media has fueled part of this
growth: worldwide there are now more than 2 billion people using social media [4], and
Facebook alone now has approximately 1 billion active users per day [5].
Clearly, people are exposing themselves to more and more digital and social media. This
is for many purposes, including in their roles as consumers as they search for information about
products,1 purchase and consume them, and communicate with others about their experiences.
Marketers have responded to this fundamental shift by increasing their use of digital marketing
channels. In fact, by 2017 approximately one-third of global advertising spending is forecast to
be in digital channels [6]. Thus, future consumer marketing will largely be carried out in digital
settings, particularly social media and mobile. It is therefore necessary for consumer research to
examine and understand consumer behavior in digital environments. This has been happening
over the last decade, with increasing amounts of research focusing on digital consumer behavior
issues. The literature is still relatively nascent, however, and more research is of course needed—
particularly given the ever-changing nature of the digital/social media/mobile environments in
which consumers are situated and interact with brands and each other. This article attempts to
1 For convenience, I use the term “product” throughout this article to refer to any kind of marketed offer from a firm.
This can include specific products or services, as well as brands (multiple products or services) as a whole.

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take stock of very recent developments on these issues in the consumer behavior/psychology
literature, and in doing so hopes to spur new, relevant research.
This review is based on articles published in between January 2013 and September 2015
in the four leading consumer research journals: Journal of Consumer Research (JCR), Journal of
Consumer Psychology (JCP), Journal of Marketing (JM), and Journal of Marketing Research
(JMR). Articles related to digital marketing, social media, and online word of mouth are featured
in this review. In total, 29 articles were published on these topics in the consumer behavior
literature in the last few years, suggesting that this is an increasingly popular domain within
consumer research. In addition to these articles, there were three review articles worth
mentioning: (i) Berger’s review of word-of-mouth and interpersonal communication research
[7], (ii) You et al.’s meta-analysis of online word-of-mouth effects [8], and (iii) Yadav and
Pavlou’s review of marketing in computer-mediated environments [9].
RESEARCH THEMES AND FINDINGS
Five distinct research themes emerge in recent consumer research on digital marketing
and social media. The five themes are (i) consumer digital culture, (ii) advertising, (iii) impacts
of digital environments, (iv) mobile, and (v) online WOM and reviews. The most popular themes
are online WOM, which is covered by almost half of the articles, and advertising, represented by
slightly over one-quarter of the articles. I now discuss each theme.
Consumer Digital Culture
Consumer digital culture research considers, quite deeply, the digital environments in
which consumers are situated. A key aspect of this work has been understanding how

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consumers’ identities and self-concepts extend into digital worlds, such as work by Belk [10,
11]. Belk [10] extended his prior work on the “extended self” to incorporate the digital
environments in which consumers now situate themselves, which is an important piece of theory
development because it considers concepts such as the ability for consumers to have multiple
selves due to possessing multiple online “personas.” Belk also suggests many areas for future
research. Other research under this theme looked at more specific phenomena. McQuarrie et al.
[12] focused on fashion blogging as a means of documenting the “megaphone effect,” which is
the ability for regular consumers to access large audiences through digital/social media. This is
an important effect and they discussed how bloggers go about building audiences and
accumulating social (or cultural) capital through demonstrations of “good taste.” In a social
media setting this essentially means that a blogger (or “influencer”) makes recommendations that
signal her expertise to others. This is in a specific setting, but has implications for understanding
consumers’ content-generation behaviors on social media more generally, since signaling
positive personal attributes is likely a common motivation for posting certain things on sites like
Facebook. Together, these articles make an important conceptual contribution around how we
see consumers in a digital world, particularly by implying an expanded conception of what it is
to be a consumer in today’s digital world.
Advertising
Digital advertising is a major topic in the marketing literature and, with respect to
consumer behavior, considers how consumers respond to various aspects of digital ads. A
number of recent articles considered behavioral aspects of digital advertising from various
perspectives. One interesting perspective taken in a few articles [13-15] was based around how

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to overcome (assumed) psychological reactance due to personalization of digital ad targeting.
Schumann et al. [13] considered how negative reactions to personalization could be overcome
with normative reciprocity appeals (instead of utility appeals). Lambrecht and Tucker [14]
studied ad retargeting, which is when personalized recommendations based on prior web-
browsing history are made when a consumer returns to a website. Negative responses to
retargeting are found, but this is mitigated when consumers’ preferences have become more
precise. Tucker [15] found that personalized website ads are more favorably received when
consumers have a higher perception of being in control of the personal/private information used
for personalization, which directly corresponds to literature on psychological reactance and
suggests a theoretical way forward for research into consumer digital privacy, which is lacking.
Other articles have considered a variety of digital ad response aspects [16-20]. Luo et al.
[16] looked at drivers of popularity for group-buying ads (i.e., Groupon-like “daily deals”),
finding social influence (e.g., social proof due to others buying a deal) to be a major driver of
deal popularity. Jerath et al. [17] studied responses to search engine advertising, finding that
when consumers search for less-popular keywords their searches are more effortful. Puccinelli et
al. [18] examined digital video ads (e.g., that run on sites like Hulu and YouTube), focusing on
how TV show emotion interacted with ads’ energy levels to affect consumers’ responses. They
find that affective matching between show and ad matters such that when consumers experience
“deactivating” emotions (e.g., sadness) it is harder to view energetic ads. Dinner et al. [19]
considered how digital display and search ads drive online and offline purchasing for a retailer,
finding that digital ads are more effective than offline ads in driving online behavior. Finally,
Goldstein et al. [20] studied “annoying” (e.g., obtrusive, low quality) website ads and showed
how they create economic costs for advertisers (i.e., waste) and cognitive costs for consumers.

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Impacts of Digital Environments
A still-emerging theme in recent years is how digital/social media environments impact
consumer behavior [21-23]. The consequences can be thought of as environment-integral (i.e.,
digital environments influence behavior in those environments) or environment-incidental (i.e.,
digital environments influence behavior in other, unrelated environments). It is interesting to see
how the various informational and social characteristics of digital/social environments, such as
being exposed to other consumers’ opinions (e.g., reviews) or choices (e.g., bids in online
auctions), or even just to friends’ lives through social media, can impact subsequent behaviors.
For instance, with respect to environment-integral consequences, Lamberton et al. [21] and
Norton et al. [22] considered learning from strangers in digital environments. They find that
consumers in competitive online settings infer interpersonal dissimilarity and act aggressively
against ambiguous others (strangers) [21], and find that seeing online that others made the same
choices as oneself can reduce, not increase, confidence in one’s choices if others’ justifications
(e.g., in online reviews) are dissimilar [22]. Adopting a different perspective, Wilcox and
Stephen [23] examined an environment-incidental response with respect to how using Facebook
affected self-control. They found that when exposed to closer friends on Facebook, consumers
subsequently exhibited lower self-control in choices related to, for example, healthy behaviors
(e.g., choosing a cookie instead of a healthier granola bar).
Mobile
Consumer behavior in mobile settings is also increasingly important, as consumers use
mobile devices more frequently. This is particularly interesting in shopping contexts. In an in-
store shopping setting, Hui et al. [24] studied how consumers respond to mobile coupons in

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physical stores, finding in a field experiment that mobile offers requiring consumers to deviate
from their planned shopping paths can increase unplanned spending. In an online shopping
setting, Brasel and Gips [25] focused on shopping on mobile devices (e.g., tablets) and
specifically on how touching products (instead of clicking with a mouse) can increase feelings of
psychological ownership and endowment. This is an interesting contribution because work on
how consumers physically interface with mobile devices and how that influences decision
making is scant but, as this article showed, important. Unrelated to shopping is work by Bart et
al. [26] that considered how mobile display ads—which are very small and carry very little (if
any) information—influence consumers’ brand attitudes and purchase intentions. They found
that in many product categories mobile display ads have no effect, but that they do lift attitudes
and intentions for high-involvement, utilitarian products (e.g., financial services).
Online WOM and Reviews
WOM is the most-represented topic in digital and social marketing research, which is
unsurprising given the reliance consumers seem to have on socially sourced online information.
A number of sub-themes were covered recently. First, an interesting set of articles considered
linguistic properties of online WOM and/or reviews [27-33], generally showing how perceptions
of reviews and how influential they are can depend on subtle language-based properties. For
instance, Kronrod and Danziger [27] showed that figurative (vs. literal) language in online
reviews positively affected consumer attitudes and choice for hedonic goods. Moore [28]
considered explanatory language in online reviews, finding that whether consumers explained
actions or reactions affected perceived review helpfulness. Hamilton et al. [29] considered
negative WOM, finding that using softening language when conveying negative opinions (e.g.,

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“I don’t want to be negative, but…”) increases perceived reviewer credibility and likability.
Tang et al. [30] considered two kinds of neutral language, mixed (positive and negative) versus
indifferent. They show that mixed neutral (vs. indifferent) WOM amplifies effects of WOM on
purchasing. Ludwig et al. [31] studied affective language in reviews and examined how a review
with linguistic style that is consistent with the typical linguistic style used for that product group
influenced sales, finding that positive affect increases conversions (but at a diminishing rate),
negative affect decreases conversions, and congruent linguistic styles are beneficial. Chen and
Lurie [32] examined temporal contiguity language in online reviews (i.e., reviewers indicating
they recently had the experience), finding that consumers discount positive reviewer opinions
less if the experience was seemingly recent (i.e., presence of temporal contiguity cues).
Another important topic recently examined is differences between online and offline
WOM. Lovett et al. [33] found that online WOM is driven by social and functional brand
characteristics whereas offline WOM is driven by emotional brand characteristics. Eisingerich et
al. [34] studied differences between transmitting WOM in social media (e.g., on Facebook)
versus offline (in person), showing that consumers are less inclined to transmit WOM in social
media because of a higher perceived social risk.
Finally, other recent articles considered additional online WOM-related issues. For
instance, He and Bond [35] considered when online reviews provide good versus bad forecasts
of consumer brand enjoyment, finding that the forecast error/discpreancy depends on the degree
to which a reviewer’s and consumer’s preferences are similar. Cascio et al. [36] identified neural
correlates of susceptibility to others’ opinions in online WOM settings, with susceptibility to
social influence being related to brain regions involved with shifting personal preferences and
considering others. He and Bond [37] focused on sets of online reviews (cf. single reviews) and

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considered how consumers interpret opinion dispersion and whether it is attributed to the product
or to reviewers’ tastes being heterogeneous. Anderson and Simester [38] documented the
prevalence of deceptive reviews posted by people who have not purchased a product, suggesting
that the practice is not limited to competitors but includes existing customers with no financial
incentive to bias online ratings. Finally, Barasch and Berger [39] examined social transmission
behavior when consumers broadcast (to many, e.g., through mass-audience posts on Facebook or
Twitter) versus narrowcast (to few, e.g., through messages to a few friends), finding that people
share information that makes themselves not look bad when broadcasting (i.e., self focus) but
share information that will be helpful to receivers when narrowcasting (i.e., other focus).
RECOMMENDATIONS FOR FUTURE RESEARCH
The digital/social media consumer behavior literature is fast-growing and largely focuses
on phenomena that are practically relevant and theoretically interesting. Researchers have mostly
considered how consumers use information (e.g., online WOM, reviews) available to them in
digital/social media environments. Future research should continue this approach, although in a
more expanded fashion. Consumers’ behaviors other than those related to online WOM/reviews
should be considered, and other types of information found (and inferences made) in online
environments should be considered. For example, it would be interesting to consider the complex
interplay between transmitter, receiver, linguistic/content, and context factors when it comes to
antecedents and consequences of online WOM.
Another high-potential direction for future research is to consider how various kinds of
digital environments (including social media and mobile) impact a wide variety of consumer
outcomes, including psychological and economic constructs. Few articles have done this, though

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it is likely that a multitude of consumer outcomes are influenced by the digital environments in
which they are increasingly situated. It is also possible that some adverse consequences may be
detected, similar to Wilcox and Stephen’s [23] finding linking Facebook use to lower self
control. In addition to this, the ways that consumers physically interact (i.e., interface) with
digital environments needs deeper exploration, given what Brasel and Gips [25] found in terms
of feelings of endowment when using touch-based interfaces to shop. In studying the impacts of
digital environments on consumers, it will also be necessary to consider longer-term responses
because these effects may be subtle but cumulatively important. Thus, one-shot experimental
studies should be complemented by longitudinal experiments and archival data capturing
consumers’ digital exposures, online social interactions, and behaviors over time.
Finally, researchers should consider emerging important topics, particularly consumer
privacy issues in the context of digital marketing and social media. Tucker [15] considered this
to an extent, though a comprehensive understanding of how consumers think about their privacy,
what they want to do to protect it, and how they value (or devalue) digital media services that
protect (or not) privacy is still needed.
In conclusion, there has been much recent activity in the consumer behavior/psychology
literature related to digital and social media marketing, and many important contributions to
knowledge have been made. To move this literature forward, particularly given the fast-moving
nature of digital settings, research that attempts to broaden our understandings of key
phenomena, examines brand-new phenomena, and develops theories in an area that lacks an
established theoretical base will be most valuable.

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Services,” Journal of Marketing, 78 (1), 59-75.

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[14]** Lambrecht, Anja and Tucker, Catherine (2013), “When Does Retargeting Work?
Information Specificity in Online Advertising,” Journal of Marketing Research, 50 (5), 561-576.
Retargeting means that consumers are targeted with personalized ads designed based on prior
browsing history when they return to a website. The authors report a field experiment on a travel
website and find that, generally, retargeted ads do worse than control ads that are generic/not
personalized. This negative response is not found if, based on browsing histories, consumers’
preferences have evolved in the sense that they have gotten more precise.
[15]** Tucker, Catherine E. (2014), “Social Networks, Personalized Advertising, and Privacy
Controls,” Journal of Marketing Research, 51 (5), 546-562.
The author studies how consumers’ perceptions of control over personal information used for
social media ad targeting (i.e., personalized ads) influence likelihood to click on ads, using data
from a field experiment. Personalized ads did not perform well. However, when the website gave
users more control over their personal information, personalized ads performed better. This
finding is consistent with the idea that reactance-type responses can be mitigated by giving
consumers a sense of (perceived) control or freedom of choice.
[16] Luo, Xueming, Andrews, Michelle, Song, Yiping, and Aspara, Jaakko (2014), “Group-
Buying Deal Popularity,” Journal of Marketing, 78 (2), 20-33.
[17] Jerath, Kinshuk, Ma, Liye, and Park, Young-Hoon (2014), “Consumer Click Behavior at a
Search Engine: The Role of Keyword Popularity,” Journal of Marketing Research, 51 (4), 480-
486.
[18]** Puccinelli, Nancy M. Wilcox, Keith, and Grewal, Dhruv (2015), “Consumers' Response
to Commercials: When the Energy Level in the Commercial Conflicts with the Media Context,”
Journal of Marketing, 79 (2), 1-18.
The authors report six studies looking at the interplay between focal media content (e.g., a TV
show or a movie) and digital video ads that come after viewing that content. They show that after
watching content that evokes a deactivating emotion, consumers view energetic ads less and
have lower recall compared to when they do not experience a deactivating emotion.
[19] Dinner, Isaac M., Van Heerde, Harald J., and Neslin, Scott A. (2014), “Driving Online and
Offline Sales: The Cross-Channel Effects of Traditional, Online Display, and Paid Search
Advertising,” Journal of Marketing Research, 51 (5), 527-545.
[20] Goldstein, Daniel G. Suri, Siddharth, McAfee, R. Preston, Ekstrand-Abueg, Matthew, and
Diaz, Fernando (2014), “The Economic and Cognitive Costs of Annoying Display
Advertisements,” Journal of Marketing Research, 51 (6), 742-752.
[21] Lamberton, Cait Poynor, Naylor, Rebecca Walker, and Haws, Kelly L. (2013), “Same
destination, different paths: When and how does observing others' choices and reasoning alter
confidence in our own choices?” Journal of Consumer Psychology, 23 (1), 74-89.

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[22] Norton, David A., Lamberton, Cait Poynor, and Naylor, Rebecca Walker (2013), “The
Devil You (Don't) Know: Interpersonal Ambiguity and Inference Making in Competitive
Contexts,” Journal of Consumer Research, 40 (2), 239-254.
[23] Wilcox, Keith, and Stephen, Andrew T. (2013), “Are Close Friends the Enemy? Online
Social Networks, Self-Esteem, and Self-Control,” Journal of Consumer Research, 40 (1), 90-
103.
The authors show that using Facebook, even for just five minutes, can potentially lower
subsequent self-control in unrelated tasks (e.g., persistence with a mentally challenging task,
healthy food choices). This effect occurs when consumers’ Facebook “friends” are mostly strong
ties (more close friends than acquaintances), and, perversely, occurs because time on Facebook
being exposed to the lives of one’s reasonably close friends boosts self-esteem (which in turn
lowers self-control).
[24] Hui, Sam K., Inman, J. Jeffrey, Huang, Yanliu, and Suher, Jacob (2013), “The Effect of In-
Store Travel Distance on Unplanned Spending: Applications to Mobile Promotion Strategies,”
Journal of Marketing, 77 (2), 1-16.
[25]** Brasel, S. Adam, and Gips, James (2014), “Tablets, touchscreens, and touchpads: How
varying touch interfaces trigger psychological ownership and endowment,” Journal of Consumer
Psychology, 24 (2), 226-233.
The authors show that touchscreen interfaces—e.g., iPhones, iPads—impact online shopping
behavior by enhancing the endowment effect. This is because touching (vs. clicking with a
mouse) increases perceptions of psychological ownership for products when browsing online.
This effect is stronger for products with high haptic importance (i.e., products where
touching/feeling them is important in the evaluation).
[26]* Bart, Yakov, Stephen, Andrew T., and Sarvary, Miklos (2014), “Which Products Are Best
Suited to Mobile Advertising? A Field Study of Mobile Display Advertising Effects on
Consumer Attitudes and Intentions,” Journal of Marketing Research, 51 (3), 270-285.
A study of 54 mobile display ad campaigns, each with test (saw ad) and control (did not see ad)
groups, finds that mobile display ads only positively affect brand favorability and purchase
intention for products that are both utilitarian and high involvement (e.g., financial services). The
authors explain that this could be because these types of products trigger more deliberate “central
route” processing (following the elaboration likelihood model) that leads to greater persuasive
effectiveness.
[27] Kronrod, Ann, and Danziger, Shai (2013), “Wii Will Rock You! The Use and Effect of
Figurative Language in Consumer Reviews of Hedonic and Utilitarian Consumption,” Journal of
Consumer Research, 40 (4), 726-739.
[28]** Moore, Sarah G. (2015), “Attitude Predictability and Helpfulness in Online Reviews: The
Role of Explained Actions and Reactions,” Journal of Consumer Research, 42 (1), 30-44.

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The author examines one type of linguistic property of online reviews, the use of explanations by
review authors. Explanations can be about actions such as why a consumer decided to buy the
brand, or reactions such as why they feel the way they do about the brand. It is found that
reviews for utilitarian products have more action explanations, whereas for hedonic products it is
the opposite (i.e., more reaction explanations). When utilitarian (hedonic) products’ reviews have
action (reaction) explanations, they lead to higher review helpfulness perceptions, predictability
of product attitudes, and, ultimately, product choice.
[29] Hamilton, Ryan, Vohs, Kathleen D., and McGill, Ann L. (2014), “We'll Be Honest, This
Won't Be the Best Article You'll Ever Read: The Use of Dispreferred Markers in Word-of-
Mouth Communication,” Journal of Consumer Research, 41 (1), 197-212.
[30] Tang, Tanya, Fang, Eric, and Wang, Feng (2014), “Is Neutral Really Neutral? The Effects
of Neutral User-Generated Content on Product Sales,” Journal of Marketing, 78 (4), 41-58.
[31] Ludwig, Stephan, de Ruyter, Ko, Friedman, Mike, Brueggen, Elisabeth C.,
Wetzels, Martin, and Pfann, Gerard (2013), “More Than Words: The Influence of Affective
Content and Linguistic Style Matches in Online Reviews on Conversion Rates,” Journal of
Marketing, 77 (1), 87-103.
[32]* Chen, Zoey, and Lurie, Nicholas H. (2013), “Temporal Contiguity and Negativity Bias in
the Impact of Online Word of Mouth,” Journal of Marketing Research, 50 (4), 463-476.
[33]** Lovett, Mitchell J., Peres, Renana, and Shachar, Ron (2013), “On brands and Word of
Mouth,” Journal of Marketing Research, 50 (4), 427-444.
The authors used a dataset featuring both online and offline WOM for approximately 600 brands,
which were characterized on 13 different dimensions. These were grouped into social, emotional,
and functional drivers of WOM. They found that the most important driver of offline WOM is
emotional brand characteristics. Social and functional characteristics, however, were found to be
the most important drivers of online WOM.
[34] Eisingerich, Andreas B., Chun, HaeEun, Liu, Yeyi, Jia, He, and Bell, Simon J. (2015),
“Why recommend a brand face-to-face but not on Facebook? How word-of-mouth on online
social sites differs from traditional word-of-mouth,” Journal of Consumer Psychology, 25 (1),
120-128.
[35] He, Stephen X. and Bond, Samuel D. (2013), “Word-of-mouth and the forecasting of
consumption enjoyment,” Journal of Consumer Psychology, 23 (4), 464-482.
[36] Cascio, Christopher N., O'Donnell, Matthew Brook, Bayer, Joseph, Tinney, Francis J., Jr.,
and Falk, Emily B. (2015), “Neural Correlates of Susceptibility to Group Opinions in Online
Word-of-Mouth Recommendations,” Journal of Marketing Research, 52 (4), 559-575.

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[37] He, Stephen X. and Bond, Samuel D. (2015), “Why Is the Crowd Divided? Attribution for
Dispersion in Online Word of Mouth,” Journal of Consumer Research, 41 (6), 1509-1527.
[38] Anderson, Eric T. and Simester, Duncan I. (2014), “Reviews Without a Purchase: Low
Ratings, Loyal Customers, and Deception,” Journal of Marketing Research, 51 (3), 249-269.
[39] Barasch, Alixandra and Berger, Jonah (2014), “Broadcasting and Narrowcasting: How
Audience Size Affects What People Share,” Journal of Marketing Research, 51 (3), 286-299.