As the use of social networking sites (SNSs) in hiring increases, human resources professionals have become concerned with the increased probability of discriminatory hiring decisions. At the same time, there is increasing evidence that discrimination towards overweight and obese applicants has risen in the past decade. The present study addressed these concerns by examining the impact of an applicant's weight in a SNS profile picture on the decision to hire the applicant for a sales position. The impact of the applicant's gender, body mass index, and stereotype-consistent behaviors on hiring intentions were examined. In addition, the type of sales position (face to face or over the phone) were also examined. Results indicated that only overweight, and not obese, candidates experienced discrimination. Moreover, whereas the types of behaviors disclosed on social media profiles impact hiring intentions, there were no interaction effects between applicant weight and the types of behaviors disclosed. Analyses suggested that stereotype-consistent behaviors associated with conscientiousness (i.e., laziness, discipline) were a stronger predictor of hiring intentions than behavior that was less work-related (i.e., unhealthy behaviors). In addition, results suggested that weight discrimination did not occur more for the in person position than the phone position. Overall, the present study suggests that disclosing behaviors on social media that reflect negative traits (i.e., lazy, undisciplined, unhealthy) may impact hiring intentions, regardless of the applicant's weight. Moreover, while applicants who are overweight are less likely to be hired than their average weight counterparts, this discrimination does not occur more often when the applicant engages in stereotype-consistent behavior.
Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-5974 |
Date | 01 January 2016 |
Creators | McHugh, Bridget |
Publisher | University of Central Florida |
Source Sets | University of Central Florida |
Language | English |
Detected Language | English |
Type | text |
Format | application/pdf |
Source | Electronic Theses and Dissertations |
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