Language and gender : Computerized text analyses predict gender ratios from organizational descriptions
Stille, Lotta ; Sikström, Sverker LU
; Lindqvist, Anna
LU
; Renström, Emma A.
LU
and Gustafsson Sendén, Marie
(2023)
In Frontiers in Psychology
13.
- Abstract
Previous research has shown that language in job adverts implicitly communicates gender stereotypes, which, in turn, influence employees’ perceived fit with the job. In this way, language both reflects and maintains a gender segregated job market. The aim of this study was to test whether, and how, language in organizational descriptions reflects gender segregation in the organizations by the use of computational text analyses. We analyzed large Swedish companies’ organizational descriptions from LinkedIn (N = 409), testing whether the language in the organizational descriptions is associated with the organizations’ employee gender ratio, and how organizational descriptions for organizations with a majority of women and men employees... (More)
Previous research has shown that language in job adverts implicitly communicates gender stereotypes, which, in turn, influence employees’ perceived fit with the job. In this way, language both reflects and maintains a gender segregated job market. The aim of this study was to test whether, and how, language in organizational descriptions reflects gender segregation in the organizations by the use of computational text analyses. We analyzed large Swedish companies’ organizational descriptions from LinkedIn (N = 409), testing whether the language in the organizational descriptions is associated with the organizations’ employee gender ratio, and how organizational descriptions for organizations with a majority of women and men employees differ. The statistical analyses showed that language in the organizational descriptions predicted the employee gender ratio in organizations well. Word clouds depicting words that differentiate between organizations with a majority of women and men employees showed that the language of organizations with a higher percentage of women employees was characterized by a local focus and emphasis on within-organizations relations, whereas the language of organizations with a higher percentage of men employees was characterized by an international focus and emphasis on sales and customer relations. These results imply that the language in organizational descriptions reflects gender segregation and stereotypes that women are associated with local and men with global workplaces. As language communicates subtle signals in regards to what potential candidate is most sought after in recruitment situations, differences in organizational descriptions can hinder underrepresented gender groups to apply to these jobs. As a consequence, such practices may contribute to gender segregation on the job market.
(Less)
- author
- Stille, Lotta
; Sikström, Sverker
LU
; Lindqvist, Anna
LU
; Renström, Emma A.
LU
and Gustafsson Sendén, Marie
- organization
- publishing date
- 2023-01
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- gender segregation, job market, natural language processing, organizational descriptions, perceived fit
- in
- Frontiers in Psychology
- volume
- 13
- article number
- 1020614
- publisher
- Frontiers Media S. A.
- external identifiers
-
- pmid:36698572
- scopus:85146862294
- ISSN
- 1664-1078
- DOI
- 10.3389/fpsyg.2022.1020614
- language
- English
- LU publication?
- yes
- id
- 16fba054-6a58-4a1f-8144-f652c5b70dd3
- date added to LUP
- 2023-02-13 09:28:31
- date last changed
- 2024-04-18 18:14:26
@article{16fba054-6a58-4a1f-8144-f652c5b70dd3,
abstract = {{<p>Previous research has shown that language in job adverts implicitly communicates gender stereotypes, which, in turn, influence employees’ perceived fit with the job. In this way, language both reflects and maintains a gender segregated job market. The aim of this study was to test whether, and how, language in organizational descriptions reflects gender segregation in the organizations by the use of computational text analyses. We analyzed large Swedish companies’ organizational descriptions from LinkedIn (N = 409), testing whether the language in the organizational descriptions is associated with the organizations’ employee gender ratio, and how organizational descriptions for organizations with a majority of women and men employees differ. The statistical analyses showed that language in the organizational descriptions predicted the employee gender ratio in organizations well. Word clouds depicting words that differentiate between organizations with a majority of women and men employees showed that the language of organizations with a higher percentage of women employees was characterized by a local focus and emphasis on within-organizations relations, whereas the language of organizations with a higher percentage of men employees was characterized by an international focus and emphasis on sales and customer relations. These results imply that the language in organizational descriptions reflects gender segregation and stereotypes that women are associated with local and men with global workplaces. As language communicates subtle signals in regards to what potential candidate is most sought after in recruitment situations, differences in organizational descriptions can hinder underrepresented gender groups to apply to these jobs. As a consequence, such practices may contribute to gender segregation on the job market.</p>}},
author = {{Stille, Lotta and Sikström, Sverker and Lindqvist, Anna and Renström, Emma A. and Gustafsson Sendén, Marie}},
issn = {{1664-1078}},
keywords = {{gender segregation; job market; natural language processing; organizational descriptions; perceived fit}},
language = {{eng}},
publisher = {{Frontiers Media S. A.}},
series = {{Frontiers in Psychology}},
title = {{Language and gender : Computerized text analyses predict gender ratios from organizational descriptions}},
url = {{http://dx.doi.org/10.3389/fpsyg.2022.1020614}},
doi = {{10.3389/fpsyg.2022.1020614}},
volume = {{13}},
year = {{2023}},
}