Research has demonstrated how age stereotypes influence judgment and decision making at work, but older workers are more than just older. All individuals are members of multiple demographic categories, yet we know surprisingly little about how multiple category membership affects judgments and decision making at work. Competing models have been suggested, such as the category activation and inhibition model (Kulik et al., 2007) and the intersectional salience of ageism at work model (Marcus & Fritzsche, 2015). However, empirical tests of these models are scarce. In the present study, the age and gender of job applicants were manipulated in a mock job interview. Job context was also manipulated through a recruitment ad that described the ideal applicant using age and gender stereotypic language. One hundred and seventy-three human resource professionals rated the mock interview. It was expected that when the demographic characteristics of the job applicant matched the stereotypes identified by the job ad, hiring professionals would rate the applicant as more suitable in hireability, qualifications, and recommended starting salary. Results showed a bias against older job applicants, as they were rated as less qualified and as requiring higher starting salaries than younger job applicants, even though their interview transcripts were identical. Moreover, a 3-way interaction showed that the highest salaries were suggested for older job applicants whose gender matched the gender stereotypes presented in the job ad. These results illustrate a hurdle faced by older workers; they will be perceived as less capable yet more expensive. Ageism emerged as the most salient category in this study of individuals seeking re-employment beyond traditional working age, but the results suggest intersectional effects as well. Future research should further examine how ageism is experienced by different multi-group members in other job contexts.
| Identifer | oai:union.ndltd.org:ucf.edu/oai:stars.library.ucf.edu:etd-7433 |
| Date | 01 May 2019 |
| Creators | Perez, Alyssa |
| Publisher | STARS |
| 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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