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How Do Social Media Change Advertising and Its Theories?

Beyond being cheaper to advertise on social media and able to reach consumers directly (and indirectly) relatively instantaneously, social media have changed advertising in two broad senses.

Traditional advertising has been defined as an intentional process, “a paid nonpersonal communication from an identified sponsor m using mass media to persuade or influence an audience” (Richards and Curran 2002). Digital and social media, on the other hand, de-emphasized and de-clarified the boundaries of traditional advertising. Advertising via social media is the sum of intentional, paid for, and carefully designed and placed messages as well as the thorny world of electronic word-of-mouth (eWOM), where persuasion takes place without direct payment and possibly without advertiser intent. An intermediary anchor in the continuum between direct/paid and indirect/free is the emerging field of paid influencer marketing.

The second broad aspect of how social media revolutionized advertising lies in the essence and nature of social media themselves. While traditional media present persuasive messages to consumers who are – in most part – passively interacting with content, social media offers much more that traditional channels. The ability to engage with content is entirely different. Persuasive messages are not solely placed by brands, but our friends, those who are close or not that close to us (Shan and King, 2015) share persuasive messages with variable persuasive effects. We also, as consumers, interact with content by liking, tagging, sharing, retweeting, and commenting on these messages, thus creating opportunities for deliberation and instating perceptual markers that could influence how we behave offline. Finally, as consumers, we are also content generators (i.e., eWOM).

These qualitive differences fueled by social media affordances also reshape our thinking about the persuasive process in general. From our basic thinking of information processing; how we encode, store, retrieve and act upon information – to the ways in which we observe persuasive effects on attitudes and behaviors. In a recent study focused on the psychophysiological responses predicting viral behaviors (e.g., liking, sharing, and commenting; Alhabash, Almutairi, Lou, and Kim 2018), we re-envisioned Lang’s (2000; 2006) limited capacity model of motivated mediated message processing (LC4MP). The nature of information processing has been evolving due to the amount of information available via social media (i.e., information overload), therefore emphasizing automatic processing, shrinking the psychological process spanning exposure, motivational processing (approach vs. avoidance), and behaviors (e.g., clicks). These influences are heightened in the social media environment that’s complex, multi-modal, and combines different sources of influence at a single slice of time. For example, one could be receiving a message about a brand within her social media feed, where her friends are discussing issues of variable relevance and nature (e.g., emotionality), full with built-in structures of simultaneous processing (e.g. notifications, display ads, and other competing online/device activities), while another person could be receiving the same exact message in a totally different environment.

In one of our recent chapters (Alhabash, Hussain, and Mundel 2017), we revised prominent persuasion models. The elaboration likelihood model, situated in the sender-message-receiver (SMR) paradigm, was re-envisioned to reflect the blurring lines between senders and receivers, as well as messages and channels, which has been argued to reshape the way we process information. Additionally, Ajzen and Fishbein’s (Ajzen 1991) theory of planned behavior was re-imagined to include online or viral behaviors and viral behavioral intentions (i.e., online message engagement) as important, significant mediators of the relationship between attitudes, subjective norms, and perceived behavioral control, one on side, and offline behavioral intentions and behaviors, respectively, on the other side.

This virtual issue has been dedicated to reflecting the changing nature of advertising through the theoretical and practical lenses of social media. The selected articles fit – though not exclusively – into three wide lenses of investigating social media advertising: (1) focus on source and/or receiver attributes and factors; (2) examination of message and/or channel features; and (3) investigating both online and offline effects of social media advertising. It is worth mentioning that the studies are loosely categorized into these wide lenses, which implies that their focus extends, in most cases, across the three organizing areas.


Understanding why consumers engage with social media advertising is critical to examining its effectiveness. Joa, Kim, and Ha (2018) and Lee, Ham, and Kim (2013) examine the different factors contributing to our understanding of why consumers watch and share social media advertising, respectively. Joa and colleagues (2018) explored the factors contributing to ad-avoidance for “mandatory full-length advertising [and] skippable advertising” (p. 1). Situated in defining consumers as prosumers, who are not only consumers and producers of content online, the authors showed that the entertainment value of an ad is one of the strongest predictors of ad viewing behaviors for both mandatory and skippable ads on YouTube. While conducted a few years earlier, Lee et al.’s (2013) study arrived at similar findings. Applying the Theory of Reasoned Actions, the authors showed that entertainment and escapism motives for sharing online video ads were significant predictors of attitudes toward sharing online videos, which in turn was associated with greater online video ad sharing intentions. Chu (2011) showed that being a member of a brand’s Facebook page significant influenced attitudes toward social media and advertising, yet did not influence pass-along (sharing) behavioral intentions. Finally, Shan and King (2015) illustrates the blurring distinction between sources and receivers. In their study, Shan and King (2015) manipulated both the strength of the consumer-brand relationship as well as the strength of tie between the participant and the viral ad referrer. Interestingly, Shan and King (2015) found that the effects of interpersonal ties (with the ad referrer) were stronger than the consumer-brand relationship effect, in that the consumer-brand relationship’s effect was only evident when the viral ad was shared by an acquaintance (weak tie).

Message/Channel Factors

Eckler and Bolls (2011) study examined the effects of viral ad valence on forwarding intentions. Namely, the study’s novelty – at the time – lied in applying the LC4MP model to examining effects of emotional valence as an appeal on message effectiveness factors, where participants rated positive ads and brands within them more favorably, as well as expressed higher forwarding intentions for the ads, in comparison to coactive and negative ads, respectively. While the Eckler and Bolls’ (2011) study initiated the examination of message features’ effects on online and offline evaluations and behavioral intentions, Vargo’s (2016) tweet typology envisioned the factors pertinent to an online branded message that drive retweeting and likeing behaviors on the site. Using a large data set of branded tweets, Vargo’s (2016) study showcases the distinctiveness of online engagement behaviors (retweeting and liking). Finally, Kononova and Yuan’s (2015) sheds the light on the complexity of deciphering social media advertising effects as a function of ad placement. Their study manipulated the congruence and ad format (in-stream vs. display) in YouTube ads and examined their effects on memory recall and recognition, as well as attitudes toward the ads.

Online/Offline Effects

Alhabash et al.’s (2015; 2016) showcase the cross-over effects of social media advertising in relation to the online and offline environments. Alhabash et al. (2016) exposed participants to beer or water ads (for low and high familiar products), and showed that participants not only reported greater intentions to consume alcohol upon exposure to the beer than the water ads, they also indicated higher preference for selecting a bar (compared to a coffee shop) gift card. Those who were exposed to water ads expressed equal likelihood of choosing either type of gift card. In Alhabash et al.’s (2015), the authors presented evidence from four different studies, covering different advertising contexts (e.g., commercial advertising vs. social issue advertising), supporting the argument that viral behavioral intentions (or online engagement intentions) mediate the relationship between attitudes toward the ad (or message/post) and message-induced offline behavioral intentions.


This virtual issue aims to shed the light on the developments in thinking about the effects and implications of social media advertising. Different studies, presented here, highlight the complexity of studying advertising within the social media environment, thus making the call for further developments in our theoretical and methodological thinking when it comes to detecting these effects.


Ajzen, Icek (1991), “The theory of planned behavior,” Organizational behavior and Human Decision Processes, 50(2): 179-211.

Alhabash, Saleem, Anna R. McAlister, Chen Lou, and Amy Hagerstrom (2015), “From clicks to behaviors: The mediating effect of intentions to like, share, and comment on the relationship between message evaluations and offline behavioral intentions,” Journal of Interactive Advertising, 15(2), 82-96.

Alhabash, Saleem, Anna R. McAlister, Wonkyung Kim, Chen Lou, Carie Cunningham, Elizabeth Taylor Quilliam, and Jef I. Richards (2016), “Saw it on Facebook, drank it at the bar! Effects of exposure to Facebook alcohol ads on alcohol-related behaviors,” Journal of Interactive Advertising, 16(1): 44-58.

Alhabash, Saleem, Nasser Almutairi, Chen Lou, and Wonkyung Kim (2018), “Pathways to virality: Psychophysiological responses preceding likes, shares, comments, and status updates on Facebook,” Media Psychology, DOI: 10.1080/15213269.2017.1416296

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Chu, Shu-Chuan (2011), “Viral advertising in social media,” Journal of Interactive Advertising, 12(1): 30-43.

Eckler, Petya and Paul Bolls (2011), “Spreading the virus,” Journal of Interactive Advertising, 11(2): 1-11.

Gordon, Brett, Florian Zettelmeyer, Neha Bhargava, and Dan Chapsky (2016), “A Comparison of Approaches to Advertising Measurement: Evidence from Big Field Experiments at Facebook,” White paper, Kellogg School of Management, Northwestern University. Retrieved from: https://www.kellogg.northwestern.edu/faculty/gordon_b/files/kellogg_fb_whitepaper.pdf

Joa, Clair Youngnyo, Kisun Kim, and Louisa Ha (2018), “What makes people watch online in-stream video advertisements?,” Journal of Interactive Advertising, 18(1): 1-14.

Kononova, Anastasia and Shupei Yuan (2015), “Double-dipping effect? How combining YouTube environmental PSAs with thematically congruent advertisements in different formats affects memory and attitudes,” Journal of Interactive Advertising, 15(1): 2-15.

Lang, Annie (2006), “Using the limited capacity model of motivated mediated message processing to design effective cancer communication messages,” Journal of Communication, 56(Supple_1), S57-S80.

Lee, Joonghwa, Chang-Dae Ham, and Mikyoung Kim (2013), “Why people pass along online video advertising: From the perspectives of the interpersonal communication motives scale and the theory of reasoned action,” Journal of Interactive Advertising, 13(1): 1-13.

MDG Advertising (2018). How social media has changed the ad game [infographic]. Retrieved from: https://www.mdgadvertising.com/marketing-insights/infographics/how-social-media-changed-the-ad-game-infographic/

Richards, Jef I. & Catherin M. Curran (2002), “Oracles on “advertising”: Searching for a definition, Journal of Advertising, 31(2): 63-77.

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Vargo, Chris J. (2016), “Toward a tweet typology: Contributory consumer engagement with brand messages by content type,” Journal of Interactive Advertising, 16(2): 157-168.

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