Sexual harassment is a deviant social behavior as well as an extreme social issue in different societies and regimes. Scholars have provided a general feature description of cybersexual harassment, including unwanted sexual attention, image-based abuse, simulated rape, rape threats, hate speech, trolling, aiming, cyberbullying, and cyberstalking (Barak, 2005; Citron, 2014; Morahan-Martin, 2000). In the digital age, the Internet has primarily fostered sexual harassment by equipping individuals with technologies as tools to facilitate sexually-based harms (Sheridan & Grant, 2007; Anastasia & Nicola, 2017). Since cybersexual harassment imposes damages on personal emotional and psychological health and impinges on social security, it is essential to explore the potential antecedents of cybersexual harassment.
The social learning theory posits that deviant behaviors can be simulated and learned by individuals’ interaction with social associations and environments (Akers, 1989, 1990; Bandura, 1977). Four principal concepts compose of Social Learning Theory: Differential association, differential reinforcement, the definition/attitude of behavior, and imitation (Bandura, 1977; Akers et al., 1990; Lakers, 2011). Informed by SLT, this study attempts to shed light on the potential predictors of cybersexual behavior as well as investigating the role of cyber anonymity in engaging adults in cybersexual harassment behavior. Moreover, we will also gauge the association between cyber anonymity and components of social learning theory to establish a model to enhance the understanding of cybersexual harassment. Therefore, this study posits:
H1: An increase in perceived cyber anonymity will predict increased individuals’ intention to engage in cybersexual harassment behavior.
H2: Social negative influence of different social associations will positively predict individuals’ intention to engage in cybersexual harassment behavior.
H3: Perceived punishments will negatively predict individuals’ cybersexual harassment behavior.
H4: Perceived rewards will positively predict individuals’ cybersexual harassment behavior.
H5: The neutralization attitude toward cybersexual harassment will positively predict individuals’ cybersexual harassment behavior.
H6: Cyber anonymity is positively associated with the (a) social negative influence, (b) perceived rewards, and (c) neutralization attitude about conducting cybersexual harassment behavior.
H7: Cyber anonymity is negatively associated with the perceived punishments of conducting cybersexual harassment behavior.
We conducted a self-reported cross-sectional online survey amongst 500 Chinese citizens who are over 18 years old. To ensure anonymity between the respondents and researchers, we selected to do an online survey using a reliable research platform- wjx.cn, with 2.6 million sample base members on its platform. All the measures of variables were based on the adaption from the already established scales.
We intend to run a hierarchical regression model and Structure Equation Model (SEM) using SPSS and Mplus for a preliminary model building. The dependent variable is the individual’s intention to engage in cybersexual harassment behavior. The independent variables include social negative influence, perceived online anonymity, perceived benefits and perceived costs of cybersexual harassment, neutralization attitudes toward cybersexual harassment.