Why Do Adults Engage in Cyberbullying on Social Media? An Integration of Online Disinhibition and Deindividuation Effects with the Social Structure and Social Learning Model

The dramatic increase in social media use has challenged traditional social structures and shifted a great deal of interpersonal communication from the physical world to cyberspace. Much of this social media communication has been positive: Anyone around the world who has access to the Internet has the potential to communicate with and attract a massive global audience. Unfortunately, such ubiquitous communication can be also used for negative purposes such as cyberbullying, which is the focus of this paper. Previous research on cyberbullying, consisting of 135 articles, has improved the understanding of why individuals—mostly adolescents—engage in cyberbullying. However, our study addresses two key gaps in this literature: (1) how the information technology (IT) artifact fosters/inhibits cyberbullying and (2) why people are socialized to engage in cyberbullying. To address these gaps, we propose the social media cyberbullying model (SMCBM), which modifies Akers’ [Akers RL (2011) Social Learning and Social Structure: A General Theory of Crime and Deviance, 2nd ed. (Transaction Publishers, New Brunswick, NJ)] social structure and social learning model. Because Akers developed his model for crimes in the physical world, we add a rich conceptualization of anonymity composed of five subconstructs as a key social media structural variable in the SMCBM to account for the IT artifact. We tested the SMCBM with 1,003 adults who have engaged in cyberbullying. The empirical findings support the SMCBM. Heavy social media use combined with anonymity facilitates the social learning process of cyberbullying in social media in a way that fosters cyberbullying. Our results indicate new directions for cyberbullying research and implications for anticyberbullying practices.

• 저널Information Systems Research · 27(4) · 962–986
• 토픽Bullying, Victimization, and Aggression · Social Psychology
• DOI10.1287/isre.2016.0671
• 저자Paul Benjamin Lowry, Jun Zhang, Chuang Wang, Mikko Siponen