Start Date
11-8-2016
Description
Malware has become problematic in social networking sites (SNS), where users can click on links that come from legitimate users but that may be infected with non-authorized downloads. This may generate security risks for companies (e.g. unauthorized access to sensitive information), if employees use those social networks from their work devices. Considering the relevance of this phenomenon for organizations, this ERF paper explores the potential of malware infection from the point of view of the weakest link in security efforts: the user. This study proposes a theoretical model based on Theory of Reasoned Action that will aid in understanding the factors that determine a user’s intention to share potentially infected content in a SNS. This model will be empirically validated using a survey-based study involving Facebook users, and structural equation modelling techniques.
Recommended Citation
Camacho, Sonia, "Social Media Malware: Determinants of Users’ Intention to Share Potentially Infected Posts" (2016). AMCIS 2016 Proceedings. 18.
https://aisel.aisnet.org/amcis2016/Adoption/Presentations/18
Social Media Malware: Determinants of Users’ Intention to Share Potentially Infected Posts
Malware has become problematic in social networking sites (SNS), where users can click on links that come from legitimate users but that may be infected with non-authorized downloads. This may generate security risks for companies (e.g. unauthorized access to sensitive information), if employees use those social networks from their work devices. Considering the relevance of this phenomenon for organizations, this ERF paper explores the potential of malware infection from the point of view of the weakest link in security efforts: the user. This study proposes a theoretical model based on Theory of Reasoned Action that will aid in understanding the factors that determine a user’s intention to share potentially infected content in a SNS. This model will be empirically validated using a survey-based study involving Facebook users, and structural equation modelling techniques.