Social Media Virtual Special Issue Journal of Advertising

Journal of Advertising

Social Media Advertising and eWOM from 2011 to present

Guest Editor: Louisa Ha, Bowling Green State University

Social media are the fastest growing advertising media worldwide. Just Facebook’s 2016 global advertising revenue is already 27 billion U.S. dollars, a 58% growth from 2015 (Statista, 2017). Despite social media’s heavy reliance of advertising as revenue source, the study of advertising on social media is still scarce compared to other media. Partly it is due to the difficulty of measuring the ads because they are customized and programmed based on individual attributes. Another problem is that social media ads are frequently in native format, similar to the social media content, and vary in forms from sponsored posts (Facebook) to promoted tweets (Twitter) to sponsored filters and stories (Snapchat).   

Journal of Advertising has published articles addressing social media both as an advertising medium and as a non-paid electronic word-of-mouth (eWOM) medium. This virtual theme collection selected 10 articles that are useful reference and for future research in social media advertising and e-WOM published 2011-2017. 

Most articles published in JA on social media focused on the user-generated content characteristic of social media and saw them as a mix of interpersonal and media communication. Cho, Huh and Faber (2014) conducted a field experiment to examine the influence of sender trust (interpersonal relationship trust) and advertiser trust (mediated and calculated trust) on four stages of e-mail advertising effects of a tax preparer service in the United States.  Fernado, Suganthi and Sivakumaran (2014) used greenwashed ads with questionable environment claims as a specific case to examine if blogs by online news organizations and environmental protection NGOs set the agenda for specialized greenwashing blog users in the U.S. 

Consumer-generated advertising (CGA) is advertising created by consumers on a brand either as a remix, a parody and an open competition entry. It is one specific type of social media advertising that two articles of this selection examined. Campbell et al. (2011) found comments around those CGA ads were mostly not about the brand, but the consumer as the ad creator and viewpoints shown in the ads. Lawrence, Fournier and Brunel’s (2013) multimethod study confirmed the main benefit of CGA is the perceived trustworthiness of the creator as independent from the advertiser and the consumers’ engagement with the creator and the ad, not because of consumers’ identification with the creator.     

The viral potential of social media in sharing of advertising and consumer comments is another aspect scholars show great interest (Cho, Huh and Faber, 2014; Roy et al., 2017). Jin and Phua (2014) conducted two between-subject lab experiments to demonstrate the effects of different attributes of celebrities’ tweets on brands as influential eWOM on their followers. 

Cross-national comparative study of social media use can reveal similarities and differences in social media effects. Minton et al.’s (2012) three-country survey of consumers shows how cultures influence social media use and motivations for sustainable behaviors such as recycling, organic food purchase and donation to green charities.

The blurring of editorial content and advertising by native advertising in social media also aroused concern from advertising scholars. Wojdynski & Evans’ (2016) experiment found very few participants can distinguish native ads from editorial content. Vanwesenbeeck, Walrave and Ponnet’s (2016) experiment specifically examined how parental mediation style may affect young adolescent’s response to social network game advertising because such embedded advertising format makes young adolescent hard to differentiate advertising from the game itself.

Scholars also took advantage of the advancement in big data computational method to study social media.  Roy et al. (2017) proposed an algorithm called Trust in Social Media Score (TSM) which combines survey data and computational data to measure trust in social media by identifying the most trustworthy and trusting individuals in three types of social media. Liu, Burns and Hou (2017) used big data LDA Modeling technique and Sentiment Analysis to identify the issues and concerns of specific brands as well as merits of specific brands that help in generating insights and directions for brand management.  

Statista (2017). Facebook's advertising revenue worldwide from 2009 to 2016 (in million U.S. dollars)

Read the full introduction to the collection here.

Access articles chosen by guest editor Louisa Ha for FREE until August 31, 2017.