Document Type



Doctor of Philosophy


Psychology, Industrial and Organizational

Date of Defense


Graduate Advisor

Stephanie Merritt, Ph.D.


Bettina Casad, Ph.D.

Jim Breaugh, Ph.D.

Susan Kashubeck-West, Ph.D.


The present study examined the effects of information included in candidates’ online networking profiles on recruiters’ perceptions and ratings of their likelihood of inviting the candidate for a job interview. Specifically, this study used a status generalization theory perspective to examine the weighting of information related to candidate physical attractiveness, gender, and qualification to predict perceived expectations for intellectual competence, likability, and social skills. These expectations then predicted whether the candidate should be recommended for a job interview. While participants relied almost exclusively on qualification information when making judgments of intellectual competence, candidates placed increased weight on attractiveness when rating likability and social skills. Using a unique policy-capturing HLM framework, these relationships were examined within high- and low-customer visibility positions and within both masculine- and feminine-typed jobs. The degree of in-person versus face-to-face customer contact required for the position did not affect participants’ reliance on attractiveness, and participants did not exhibit gender bias even when the position was described as stereotypically masculine or stereotypically feminine. Finally, this study examined the moderating effects of implicit and explicit attractiveness attitudes on expectations and found that more biased explicit, but not implicit, attitudes strengthened the degree to which participants relied on attractiveness information in making recruitment decisions. Because physical attractiveness discrimination is not directly covered under current employment law, it is important to examine attractiveness biases in organizational contexts to determine if recruitment and selection methods are functioning at the highest degree of validity possible. This has particular implications for training interventions that can be implemented to both reduce attractiveness biases and to increase the validity and fairness of selection systems.