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Social advertising (SA) on social networking sites has been considered to be further empowered by the internet technology and companies contemplate it as a potential tool in reaching and building more individual connections amongst brands and consumers (Obeidat et al., 2017; Gironda & Korgaonkar, 2018). In social advertising, the advertisers gather information about consumers’ peer networks and their affiliations with a brand, product, organization, etc., on social networking sites to highly personalize and target ads to contextualize their display. Consumers are increasingly relying on social networking sites (SNS) while discovering and purchasing products and services, which abet advertisers to design more targeted advertisements.
The content of the social ads entails user interactions’ and personal details (picture /name), that are properly agreed upon by the user to share on their pages (IAB, 2009). The SNS provides further assistance for the marketers with its massive consumer information source. For instance, geographic locations, browsing and search history, click through rates, demographics, psychographics, passions, and preferences (Tucker, 2014; Bleier & Eisenbeiss, 2015). Furthermore, this information helps marketers to predict future purchases and to design tailor-made the advertisements.
Companies associated with social media sites, advertising agencies and publishers to reach out to their prospective consumers’ in an effective way (Liu & Mattila, 2017). Moreover, these agencies use data mining techniques with complex targeting algorithms, to design and display a personalized commercial message or advertisement on SNS (Aguirre et al., 2015). Li and Shiu (2012) have explained SA as a recommendation system that enables users to share commercial information among their networks or friends in SNS.
According to CMO survey, the company’s investments on social media have been increased by 200 percent in past eight years with increased marketing budgets of 10.5 percent (CMO Survey, 2017). Tucker (2012) has stated that SA is more effective and have earned greater click-through rates than traditional advertising formats.
Much research in the context online marketing has focused on online-personalized advertisements. Research studies have stated that consumers perceive online-personalized ads are intrusive, coercive and annoying when the personalized content of the advert has intruded their privacy (Saegert, 1987; Stone, 2010; Aaker & Bruzzone, 1985; Edwards et al., 2002). The studies have stated that these intrusive personal advertisements have both positive and negative outcomes. The relative negative outcomes of intrusive ads are frustration (Meyers-Levy & Malaviya, 1999), negative attitude (Tsang et al., 2004), negative cognitions (Rains, 2013), anger (Dillard & Meijnders, 2002), ignoring or contradicting recommendations (Fitzsimons & Lehmann, 2004), feeling of outrage (Wang & Zuo, 2017), customer disengagement (Khuhro et al., 2017) and positive outcomes are trust and commitment (Darpy & Prim-Allaz, 2009), attitude (Akestam et al., 2017), openness to information (Jalali, 2011) and click through intentions (CTI) (Chen et al., 2017).
Many studies have focused on the negative outcomes of personalized advertisements but very little research has been done on positive outcomes of personalized advertisements (Tsang et al., 2004; Xu, 2006; Bang & Wojdynski, 2016; Pappas et al., 2017; Bleier & Eisenbeiss, 2015a, 2015b; Chen, 2017; Gironda & Korgaonkar, 2018; Krafft et al., 2017). In addition, to our knowledge the studies on social advertisements are sparse (Li & Lien, 2009). Addressing the research gaps proposed by Baek and Morimoto (2012), and Lambrecht and Tucker (2013) we examine how the consumer perceptions’ such as privacy concern, perceived personalization and e-WOM influence consumers’ click through intentions towards social advertisements.