Version 1
: Received: 28 March 2018 / Approved: 29 March 2018 / Online: 29 March 2018 (14:35:35 CEST)
How to cite:
Mensah, A.; Ampong, G.O.A.; Adu, A.S.Y.; Addae, J.A.; Omoregie, O.K.; Ofori, K.S. Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective. Preprints2018, 2018030251. https://doi.org/10.20944/preprints201803.0251.v1
Mensah, A.; Ampong, G.O.A.; Adu, A.S.Y.; Addae, J.A.; Omoregie, O.K.; Ofori, K.S. Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective. Preprints 2018, 2018030251. https://doi.org/10.20944/preprints201803.0251.v1
Mensah, A.; Ampong, G.O.A.; Adu, A.S.Y.; Addae, J.A.; Omoregie, O.K.; Ofori, K.S. Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective. Preprints2018, 2018030251. https://doi.org/10.20944/preprints201803.0251.v1
APA Style
Mensah, A., Ampong, G.O.A., Adu, A.S.Y., Addae, J.A., Omoregie, O.K., & Ofori, K.S. (2018). Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective. Preprints. https://doi.org/10.20944/preprints201803.0251.v1
Chicago/Turabian Style
Mensah, A., Osaretin Kayode Omoregie and Kwame Simpe Ofori. 2018 "Examining Self-Disclosure on Social Networking Sites: A Flow Theory and Privacy Perspective" Preprints. https://doi.org/10.20944/preprints201803.0251.v1
Abstract
Social media and other web 2.0 tools have provided users the platform to interact and also disclose personal information not only with their friends and acquaintances, but also with relative strangers with unprecedented ease. This has enhanced the ability of people to share more about themselves, their families, and their friends through a variety of media including text, photo, and video, thus developing and sustaining social and business relationships. The purpose of the paper is to identify the factors that predict self-disclosure on social networking sites within the Ghanaian context. Data was collected from 452 students in three leading universities in Ghana and analyzed with Partial Least Square-Structural Equation Modeling. Results from the study revealed that all variables in the proposed model with the exception of interaction and perceived control were significant predictors of self-disclosure with privacy risk being the most significant predictor. In all, the model accounted for 54.6 percent of the variance in self disclosure. The implications and limitations of the current study are discussed and directions for future research proposed.
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
