Towards a Model for Self-Disclosure on Social Network Sites: A Pilot Study

Introduction

Resilience is an important concept in the context of healthcare systems implementation. Resilience has been defined as the ability of health systems to adapt in the face of crises such as Covid-19 and to plan for future shocks (Heeks, 2018). Resilience in health system relies on the constituents of health information systems ecosystem such as Information technology, people (nurses, medical doctors, policymakers, patients and others) and processes with people being at the centre of building resilient health systems. However, people who are part of healthcare ecosystems are affected by factors related to privacy and security when engaging with healthcare systems that need further investigation.

Hoy & Milne (2010) and Yao (2011) argue that online platforms including health systems’ users (patients, doctors) are worried about their privacy being violated. Further, on Social Network Sites (SNS) like Twitter and Facebook, self-disclosure of personal information is a critical aspect for the users, in terms of security and privacy. This was also supported by Marwick & Hargittai (2019) who posited that some SNS users in addition to their concerns about security and privacy infringement perceive privacy violations as inevitable. Hence, people (users) disclosing their personal information (self-disclosure) has become an issue as it poses a risk to their privacy.

Recent privacy violation cases such as the Cambridge Analytica (CA) data harvest have shown that it is to the best of SNS users to protect themselves by controlling what information they disclose. The tech company, CA, reportedly harvested over 50 million Facebook profiles with the purpose to build software to influence and predict choices during the United States 2016 presidential election (Graham-Harrison & Cadwalladr, 2018). In fact, it has been reported by the CA whistleblower Wylie Christopher that the company used the data to predict and influence voters during President Donald Trump’s campaign in the 2016 presidential election.

Issues associated with self-disclosure have been reported in many studies. For instance, Krasnova et al., 2010 and Min & Kim, 2015) investigated online users’ information (personal) sharing enticement. Moreover, self-disclosure has been investigated considering traits such as culture (Krasnova et al., 2012b), and social context (Cui, 2015). While there is an agreement amongst scholars on online users behaviour and self-disclosure, debates continue regarding the factors that may affect that behaviour and the extent to which these factors affect online users. It was argued by Yao (2011) that self-disclosure of SNS user may be caused by his/her behaviour online. In the same vein, Krasnova et al. (2010) stated that behavior is determined by factors like platform enjoyment and the quest for maintaining existing relationship. Factors such as perceived similarity, keeping up with trends or social ties have also been mentioned as driving factors for online self-disclosure behaviors (Mahamadou Kante, 2022). Additionally, Zlatolas et al. (2015) reported privacy concerns as affecting the behaviour of SNS users to self-disclosure. Hence, the main objective of this study was to design and test a structural model for self-disclosure as a comprehensive pilot study phase. The specific objectives were:

• 1.

  To propose a conceptual model of SNSs’ users self-disclosure

• 2.

  To establish the construct validity of the proposed conceptual model

Factors Affecting Self-Disclosure

An indispensable feature of any academic project is the literature review (Müller-Bloch & Kranz, 2015). We conducted an online literature search (Google scholar) in an attempt to single out factors that may affect SNSs’ self-disclosure practices following the framework proposed by Levy & Ellis (Levy & Ellis, 2006). The proposed Framework (Figure 1) follows a systematic data processing approach which includes three main stages:

• •

  Inputs: literature gathering and screening;

• •

  Processing;

• •

  Outputs: writing the literature.

The literature search was done exclusively digitally and details about the search strategy used are presented in Table 1.

Figure 1.

The three stages of effective literature review process

Source (Levy & Ellis, 2006)