Background:
Facebook is a successful advertising platform as it offers profound advertising customization, due to extensive processing of user information (eMarketer, 2015; Facebook, 2015). Although key to Facebook’s business model, advertising is not the main motivation for its users to access the platform (Wilson, Gosling, & Graham, 2012). Users have to “accept” the presence of advertising alongside the content for which they visit Facebook. Hence, user acceptance is a crucial factor for both the advertiser and the social networking site.
Objective:
We investigate the impact of different choices and options in the creation of Facebook ads, related to the use of personal data (e.g. sensitivity of personal data) and advertising place (e.g. ad location), on user acceptance. Six factors were identified, based on theory and practice, and implemented in fictitious advertisements on a mock Facebook page. Each factor had two or three possible manifestations:
Factors related to the use of personal data
1) Social context: the ad either included a message “[A friend] likes [brand]” or not.
2) Data collector’s perceived risk: An energy company (pre-tested high perceived risk), and a movie company (pre-tested low perceived risk).
3) Data use transparency: a message about data use was either included in the ad or not.
4) Sensitivity of personal data in the ad: “no personal data”, “low sensitive”, or “high sensitive” personal data.
Factors related to advertising place
5) Ad location: “newsfeed”, a person’s “timeline”, or “fan page of an unrelated brand”.
6) Ad placement on the page: “left sidebar”, “right sidebar”, or “message stream”.
Product involvement, an influential processing variable, was included as a moderator (Dens & De Pelsmacker, 2010; Lee, Kim, & Sundar, 2015).
Methods:
An online full factorial survey was completed by 409 Facebook users (53% response rate), aged 25 to 55 years (M = 40.18, SD = 8.9, 54.5% female). By randomizing the manifestations of each factor, 217 (3*3*3*2*2*2) vignette-combinations were created. A sample of 100 vignettes was drawn and was divided over 20 decks. Each respondent was randomly assigned to one 5-vignette deck. For each vignette they indicated their user acceptance (7-point, Cronbach's alpha = .939).
Results:
Multilevel analysis was performed with the six factors as independent variables and user acceptance as the dependent (Auspurg & Hinz, 2014). Only a significant effect of placement on user acceptance was found. The right sidebar placement (M = 3.70) was better accepted than the message stream placement (b = -.13, t(1660.48) = 2.19, p = .029) and the left sidebar placement (b = -.18, t(1659.61) = -3.15, p = .002), which did not differ. Interaction analyses indicated product involvement as a moderator. A multilevel analysis was performed on both the low and high involvement group. Respondents scoring low on product involvement, accepted ads in the right sidebar placement best (M = 3.60), followed by the left sidebar placement (b = -.22, t(756.12) = -2.44, p = .015) and the message stream placement (b = -.47, t(766.29) = -5.05, p < .001). The high product involvement group accepted the message stream placement best (M = 3.98), followed by the right sidebar placement (b = -.15, t(900.77) = -1.94, p = .052), and the left sidebar placement (b = -.31, t(901.22) = -4.04, p < .001). In conclusion, user acceptance is primarily driven by ad placement. Yet, its influence depends on the degree of product involvement. High product involvement is related to higher acceptance of ads with a prominent placement. Ads for low involved products are better accepted when shown in the sidebar. These findings can be related to differences in processing of (native) in-stream ads compared to (banner) sidebar ads.
Future Work:
The results are the basis for more in-depth experimental analysis on the role of advertising placement on the acceptance of Facebook advertising, and the influence of intrusiveness as a mediator. A follow-up experiment is carried out and a full paper is expected in summer 2016.
References:
Auspurg, K., & Hinz, T. (2014). Factorial Survey Experiments. SAGE Publications.
Dens, N., & De Pelsmacker, P. (2010). Consumer response to different advertising appeals for new products: The moderating influence of branding strategy and product category involvement. Journal of Brand Management, 18(1), 50–65. http://doi.org/10.1057/bm.2010.22
eMarketer. (2015, March). Facebook and Twitter Will Take 33% Share of US Digital Display Market by 2017 - eMarketer. Retrieved December 18, 2015, from http://www.emarketer.com/Article/Facebook-Twitter-Will-Take-33-Share-of-US-Digital-Display-Market-by-2017/1012274
Facebook. (2015). About Advertising on Facebook. Retrieved December 18, 2015, from https://www.facebook.com/about/ads
Jung, J., Shim, S. W., Jin, H. S., & Khang, H. (2015). Factors affecting attitudes and behavioural intention towards social networking advertising: a case of Facebook users in South Korea. International Journal of Advertising, 35(2), 248–265. http://doi.org/10.1080/02650487.2015.1014777
Lee, S., Kim, K. J., & Sundar, S. S. (2015). Customization in location-based advertising: Effects of tailoring source, locational congruity, and product involvement on ad attitudes. Computers in Human Behavior, 51, Part A, 336–343. http://doi.org/10.1016/j.chb.2015.04.049
Wilson, R. E., Gosling, S. D., & Graham, L. T. (2012). A Review of Facebook Research in the Social Sciences. Perspectives on Psychological Science, 7(3), 203–220. http://doi.org/10.1177/1745691612442904
