Online Social Networks: Why We Disclose

Abstract

On online social networks such as Facebook, massive self-disclosure by users has attracted the attention of Industry players and policymakers worldwide. Despite the Impressive scope of this phenomenon, very little Is understood about what motivates users to disclose personal Information. Integrating focus group results Into a theoretical privacy calculus framework, we develop and empirically test a Structural Equation Model of self-disclosure with 259 subjects. We find that users are primarily motivated to disclose Information because of the convenience of maintaining and developing relationships and platform enjoyment. Countervailing these benefits, privacy risks represent a critical barrier to information disclosure. However, users’ perception of risk can be mitigated by their trust in the network provider and availability of control options. Based on these findings, we offer recommendations for network providers.

Risk Hurts Fun: The Influence of Perceived Privacy Risk on Social Network Site Usage

“…Krasnova et al 2010a Drawing from the works presented above, we describe Perceived Privacy Risk as the degree to which a person believes that using an SNS has negative consequences with regards to his/her privacy (cf. Chen 2013; Dinev and Hart 2006;Featherman and Pavlou 2003;Kim et al 2008;Krasnova et al 2010b;Peter and Ryan 1976;Wu et al 2009). …”

“…Indeed, multiple studies have confirmed a negative influence of Perceived Privacy Risk on SNS members' information disclosure behavior. For example, Lo (2010) found that Perceived Privacy Risk negatively influences the general Willingness to provide personal information to SNSs; Krasnova et al (2010b) found that it has a negative influence on the actual amount of self-disclosed information.…”

“…Parameters were estimated using maximum likelihood and, since our data was not distributed joint multivariate normal, a bias-corrected bootstrapping approach with 2000 replications was used to test for significance (Byrne 2001;Krasnova et al 2010b). …”

“…Parameters were estimated using maximum likelihood and significance was assessed by using a biascorrected bootstrapping approach with 2000 replications (Byrne 2001;Krasnova et al 2010b). Fit measures indicated a good model fit (Bollen-Stine corrected p-value = .279, CMIN/DF = 1.466, GFI = .985, AGFI = .968, CFI = .996, RMSEA = .034, SRMR = .018).…”

Direct Marketing to Online Social Network (OSN) Users in Nigeria

“…Krasnova et al (2009) in their study reported that commercial agents or marketers target message to specific interest groups the same way through search engine or traditional advertising channels. The other way of reaching consumers is for brands, setting up their profiles on Social Networking Sites, accepting "friends" they can keep up to date with the latest brands.…”