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Using social role theory to predict how gender and ethnicity of aviation job candidates affects perceived job classifications

Author

Listed:
  • Woods, Stephen
  • Harris, Megan
  • Rice, Stephen
  • Boquet, Albert
  • Rice, Connor
  • Rosales, Danielle
  • Domingo, Cassandra
  • Bivens, Matthew
  • Lange, Ryan
  • Winter, Scott
  • Crouse, Sean

Abstract

Prior investigations reveal a challenging landscape for women and minorities in the aviation industry as they grapple with issues that prevent them from landing high-paying, prestigious roles. The existing research does not investigate public perceptions' biases toward the suitability of individuals based on their genders and ethnicities to particular aviation roles. To bridge this knowledge gap, we employed Social Role Theory in two separate studies, requesting that participants assign certain aviation roles to candidates based on images. Each image portrayed only slight variations, namely in gender (male or female) and ethnicity (Caucasian, African, Hispanic, or Asian descent). Surprisingly, our findings showed a bias towards picturing Caucasians in the pilot's seat, while other ethnicities were stereotypically relegated to less remunerative service roles. Candidates of African descent were most frequently assigned the least lucrative service roles, such as ground crew or TSA agents. Notably, males were favored for pilot roles over females, who were most commonly envisioned as flight attendants. Piloting roles were most often bestowed upon white and Asian males, while flight attendant roles saw a higher representation of Asian, Hispanic, and Caucasian females. We delve into these data's significant theoretical and pragmatic repercussions in the following discussion.

Suggested Citation

  • Woods, Stephen & Harris, Megan & Rice, Stephen & Boquet, Albert & Rice, Connor & Rosales, Danielle & Domingo, Cassandra & Bivens, Matthew & Lange, Ryan & Winter, Scott & Crouse, Sean, 2024. "Using social role theory to predict how gender and ethnicity of aviation job candidates affects perceived job classifications," Technology in Society, Elsevier, vol. 76(C).
  • Handle: RePEc:eee:teinso:v:76:y:2024:i:c:s0160791x24000290
    DOI: 10.1016/j.techsoc.2024.102481
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    References listed on IDEAS

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