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2020-02-10
Schneeberger, Tanja, Scholtes, Mirella, Hilpert, Bernhard, Langer, Markus, Gebhard, Patrick.  2019.  Can Social Agents elicit Shame as Humans do? 2019 8th International Conference on Affective Computing and Intelligent Interaction (ACII). :164–170.
This paper presents a study that examines whether social agents can elicit the social emotion shame as humans do. For that, we use job interviews, which are highly evaluative situations per se. We vary the interview style (shame-eliciting vs. neutral) and the job interviewer (human vs. social agent). Our dependent variables include observational data regarding the social signals of shame and shame regulation as well as self-assessment questionnaires regarding the felt uneasiness and discomfort in the situation. Our results indicate that social agents can elicit shame to the same amount as humans. This gives insights about the impact of social agents on users and the emotional connection between them.
2017-05-19
Zhang, Sixuan, Yu, Liang, Wakefield, Robin L., Leidner, Dorothy E..  2016.  Friend or Foe: Cyberbullying in Social Network Sites. SIGMIS Database. 47:51–71.

As the use of social media technologies proliferates in organizations, it is important to understand the nefarious behaviors, such as cyberbullying, that may accompany such technology use and how to discourage these behaviors. We draw from neutralization theory and the criminological theory of general deterrence to develop and empirically test a research model to explain why cyberbullying may occur and how the behavior may be discouraged. We created a research model of three second-order formative constructs to examine their predictive influence on intentions to cyberbully. We used PLS- SEM to analyze the responses of 174 Facebook users in two different cyberbullying scenarios. Our model suggests that neutralization techniques enable cyberbullying behavior and while sanction certainty is an important deterrent, sanction severity appears ineffective. We discuss the theoretical and practical implications of our model and results.