Deviant Cyber-Sexual Activities in Young Adults: Exploring Prevalence and Predictions Using In-Person Sexual Activities and Social Learning Theory.

Abstract

Technology has shifted some human interactions to the virtual world. For many young adults, sexual encounters now occur through virtual means, as social media, picture exchanges, sexually explicit Web sites, and video chatting have become popular alternative outlets for these activities to occur. This study used the self-report responses of 812 undergraduate students (282 men and 530 women), collected from an online survey. In addition to using 10 personal demographic control variables, this study used five sexual activity/relationship characteristics (number of sexual partners, relationship status, age to first use pornography, frequency of sexual activity/intercourse, and frequency of masturbation), and the four constructs of Akers' social learning theory (identified as differential association, differential reinforcement, imitation/modeling, and definitions favorable) to predict a seven-item count of deviant cyber-sexual activities, and two measures of "sexting" behaviors. Gender, self-esteem, sexual orientation, race, and religion were strongly significant predictors in the models, but Akers' four elements of social learning performed the strongest in predicting the two measures of sexting and the overall deviant cyber-sexual activities scale. This finding indicates that peer associations and peer reinforcements have a strong influence on individuals' willingness to engage in deviant cyber-sexual activities. This study explored different avenues for young adults' engagement in sexual deviancy and the results suggest that sexual behaviors performed in-person may not be the strongest predictors of online sexual behavior.