Date of Award


Degree Type


Degree Name

Doctor of Philosophy (PhD)




Anti-Black racism persists in the United States with harmful consequences for Black people. White people are able to disrupt the racial status quo and propel the conversation about racial justice forward by confronting racism. Confronting perpetrators of racism can reduce prejudice, yet people hesitate to confront because they fear social backlash, even from those with whom they share a social bond. Two online studies asked participants to complete a task eliciting stereotypical responding while being observed by a supposed interaction partner with whom they shared either a high or low desire to get along. Participants were confronted by their interaction partner for racism or in a rude way. I tested whether a perpetrator's trust in a confronter would influence the strength of the association between their motivation to form a social bond and the outcomes of a racial confrontation (i.e., negative emotionality, backlash, and prejudice reduction). As predicted, results showed that relative to participants with high affiliative motivation, trusting a confronter with whom one has low affiliative motivation decreased negative confronter-directed emotions (Study 1, n = 538) and backlash, and increased intentions to control future bias (Study 2, n = 869). Contrary to predictions, negative self-directed emotions following a confrontation for racism increased negative stereotype endorsement and attitudes toward Black people. These results indicate that harnessing the power of trust might mitigate backlash for White ally confronters who are strangers, while promoting a perpetrator's behavior change. The effectiveness of online manipulations of affiliative motivation as well as the impact of this moment in history are discussed in terms of their influence on the results of these studies.

Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 3.0 License.