Division of Criminal Justice, Department of Social Sciences, University of Texas at Tyler, 3900 University Blvd., Tyler, TX, 75799, USA.
Henry C. Lee College of Criminal Justice and Forensic Sciences, University of New Haven, West Haven, CT, USA.
Arch Sex Behav. 2019 Feb;48(2):619-630. doi: 10.1007/s10508-018-1251-2. Epub 2018 Jul 9.
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.
技术已经将一些人类互动转移到了虚拟世界。对于许多年轻人来说，性接触现在通过虚拟手段发生，因为社交媒体、图片交换、色情网站和视频聊天已经成为这些活动发生的流行替代渠道。本研究使用了 812 名本科生（282 名男性和 530 名女性）的在线调查自我报告回复。除了使用 10 个人口统计学控制变量外，本研究还使用了五个性活动/关系特征（性伴侣数量、关系状态、首次使用色情制品的年龄、性活动/性交频率和自慰频率）以及 Akers 社会学习理论的四个结构（识别为差异关联、差异强化、模仿/建模和有利定义）来预测七种异常网络性行为的计数，以及“发色情短信”行为的两个衡量标准。性别、自尊、性取向、种族和宗教在模型中是强烈的显著预测因素，但 Akers 社会学习的四个要素在预测“发色情短信”行为和总体异常网络性行为量表方面表现最强。这一发现表明，同伴之间的联系和同伴之间的强化对个人参与异常网络性行为的意愿有很大影响。本研究探讨了年轻人参与性行为偏差的不同途径，结果表明，面对面进行的性行为可能不是在线性行为的最强预测因素。
