Facebook was created to be a marketing and advertising tool.

Advertising strategies have been changed drastically as a result of development of social media. Online advertisers have used social media (i.e. Facebook) to connect customers with companies, developing new opportunities for customers know about their brands and products (Comscore Media, 2009). To draw customers’ attention, online marketers have employed social networking sites to customize ads more appealing to customers. That is reasonable to understand why more companies are employing social media as marketing tools and why social networking sites like Facebook are preferred to non-virtual media in creating ads (Waters et al., 2011).

Statistical figures show that Facebook is an ideal platform for advertisement. Facebook has 1.39 billion active users visiting the website per month and 92% of social marketing companies have chosen Facebook as a marketing tool, that help Facebook reach the level of revenue of 12.7 billion in 2014 (Rudolph, 2015). Additionally, many companies or service providers, either small or large, choose to create online ad on Facebook because it is less expensive than other media.

Online advertising has moved to a new phase after IBM teamed up with Facebook on May 6, 2015 in attempt to create better advertisings on the world's largest social network by incorporating Facebook's targeting technology into IBM's services for marketers (Finley, 2015). For example, a retailer can develop an ad of a product that a customer previously views on its websites and place it to the customer's Facebook feed. Through this cooperation, both companies hope that the advertising campaigns more effective. “These new capabilities will allow Facebook to deliver more relevant, more personalized experiences,” said Jay Henderson, a director of strategy for IBM commerce (Finley, 2015). These “personalized experiences” are associated with a number of benefits, for instance, reducing customer's resistance against ad (Baek and Morimoto, 2012), enhancing ad credibility (Xu, 2006) and improving brand awareness (Johns and Perrott, 2008).

Although researchers have attempted to investigate the impact of personalized ads in traditional media (Baek and Morimoto, 2012, Yu and Cude, 2009), website (Awad and Krishnan, 2006, Bleier and Eisenbeiss, 2015, Ho and Bodoff, 2014), or mobile (Kim and Han, 2014, Xu, 2006), little has been made to examine the effects of personalized ads on Facebook (Keyzer et al., 2015, Tucker, 2014). Given the fact that Facebook has grown to be the most popular social media and that personalization has been increasingly utilized as an advertising strategy that makes an ad more relevant to the users, the impact of personalized advertising on Facebook is worth investigating (Taylor et al., 2011). The current research is developed to fill the research gap. The primary objectives of this research are threefold: (1) Develop a comprehensive model that captures the effects of perceived personalized ads on Facebook on customer attitudinal and behavioral reactions (ad credibility, ad avoidance, ad skepticism, ad attitude, and behavioral intention) to the ad; (2) Test hypothesized relationships using two data sets collected through an online survey; and (3) Develop appropriate customer segments based on personal views of personalized ads on Facebook.

The research is organized as follows. First, related literature on personalized advertising is elaborated which is then followed by hypotheses development. Methodology is then presented with formative and reflective measurement model and structural model, mediation tests, and cluster analysis. The paper ends with discussions and conclusion.

Perceived personalization

The concept of personalization emerged as early as late nineteenth century (Ross, 1992) and usually referred to segmentation and targeting and profiling while other researchers use this term in the context of mass customization, or one-on-one marketing (Petrison et al., 1997). Broader meaning of personalization has been employed in practice that includes, but is not limited to, tailoring the product, tailoring content of message, or location diagnosis (Wind and Rangaswamy, 2001).

Personalization

The effects of perceived personalization

Drawing on the foundation of perceived utility (i.e., benefits or rewards) of targeted advertising (Weilbacher and Walsh, 1952), personalized advertising is a strategic approach to optimizing advertising messages through matching with customer characteristics and interests. A personalized advertisement well-tailored to a customers’ need provides useful information, and therefore affects the way customers respond when they are exposed to the ad – the response that is measured by ad avoidance, ad

Mediation effects

As earlier discussed, a personalized ad is tailored according to a viewer's geographic location, preferences or prior interactive activities, the viewers believe that ad to be more trustworthy and more credible. Stated differently, personalized ads improve ad credibility (Kim and Han, 2014; MacKenzie and Lutz, 1989). Credibility plays a role in changing customer perception about the ad. A credible message is more persuasive (Perloff, 1993; Choi and Rifon, 2002). Therefore, any message that is

Pretest

The objective of the pre-test was to see whether or not participants perceived an online ad to be personalized. Before the survey was administered, participants were given instructions of the survey to ensure that only those who were qualified (i.e., having a Facebook account) could proceed to the questionnaire. Next, the definition of personalized advertising was provided. Then, participants were asked to report whether they saw a personalized advertisement on their Facebook account (“Yes” or

PLS approach

Structural equation analysis (SEM) is a popular statistical method for multivariable data analysis. There are two types of SEM: covariance-based (CB-SEM) and variance-based (partial least squares SEM or PLS-SEM) (Ringle et al., 2015). PLS-SEM is used to estimate path relationships on the basis of available data with an objective of minimizing the error terms of endogenous variable. This method can work with a small sample size in a complex model. It is not bound by the normal data distribution

Formative measurement model

In this measurement model, indicators of each formative construct, serving as possible independent drivers of the latent construct, should not correlate with each other. This can be checked by multicollinearity test or by VIF (Diamantopoulos and Winklhofer, 2001). Maximum VIF values for formative constructs (ad skepticism, ad credibility, and ad avoidance) is 4.758 which is lower than the threshold of 5 (see Table 3). Therefore it is confirmed that the problem of multicollinearity does not

Ad credibility and avoidance: mediating the link between personalized ads and ad attitude

Mediation effect was tested using PROCESS by (Hayes, 2013) with application of the serial multiple mediator model. The model consists of four indirect effects as products of regression coefficients connecting between PER and ATTD. The procedure was implemented with 95% bias-corrected bootstraps confidence intervals based on 10,000 bootstrap samples. The results were reported in Table 6.

The first indirect effect, labeled "Ind1" in the output, is the specific indirect effect of personalized ads

Cluster analysis

After the model was tested using the estimation sample, two samples were combined to identify the market segments on the basis of four exogenous variables in the model that included ad avoidance, ad skepticism, personalized ad, and ad credibility. A two-step cluster analysis was employed to carry out the task and mean scores from all respondents of the combined sample served as the input for the analysis (Punj and Stewart, 1983).

In the first step, Ward's hierarchical clustering with square

Cluster profiling

The first validation test was performed with respect to two dependent variables of the model (ad attitude, and behavior intention). The results revealed that the three clusters differ significantly in these two variables. Particularly, Chi-square test showed that three clusters were significantly different in ad attitude (χ2(2) = 811.89, p <; 0.01) and in behavioral intention (χ2(2) = 674.90, p <; 0.01) with Ad Lover having the highest scores and Ad Haters having the lowest scores.

There was no

Discussion

The paper has developed a comprehensive model that captures the relationship between personalized ads on Facebook on customer's responses to the ads. The model was tested through two phases using different data sets (validation and estimation). The results collected through MTurk revealed that all hypothesized relationships are supported except the effects of personalized ads on ad avoidance, ad skepticism on ad avoidance, and ad skepticism on ad attitude. In addition, mediation tests

Theoretical implications

The findings of the research offer a number of theoretical implications. In consistence with the previous research focusing on traditional media (Baek and Morimoto, 2012, Pavlou and Stewart, 2000, Tam and Ho, 2005), the results of the current research lends evidence that perceived personalization plays a role in enhancing customer perception of and customer response to an ad on social networking sites. Through Facebook, an ad can be made personalized using different criteria, such as gender,

Managerial implications

This research is the first of the kind implemented in response to the application of new advertising technology as part of integrated efforts between IBM and Facebook to see whether personalization in advertising changes customer's perception about the ads on Facebook. Therefore, the findings of this research also provide implications for advertisers and marketers. An ability of online retailers to integrate their ads into a Facebook's user's account has added a new dimension of advertising

Limitations and future research

There are some limitations associated with the current study that open avenue for future research. First, the results may be biased based on the selection of two product categories. Further research could employ more variable of product categories and brands to improve external validity. Second, the data were collected from online customers using questionnaire, another research method (i.e., experimental design) could be used to see whether the results hold the same that would help improve

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