Literature DB >> 26457739

Women drive better if not stereotyped.

Angelica Moè1, Mara Cadinu2, Anne Maass3.   

Abstract

A commonly held stereotype is that women are poor drivers. This stereotype is recognized and endorsed by women and girls very early on, long before taking their driving licence, nevertheless they are less involved in accidents and drive safer and less fast than men. In line with the stereotype threat theory, the present study tests the hypothesis that making the driving stereotype salient will lead women to underperform in a driving simulation task. In Experiment 1women in the stereotype threat condition were told that the aim of the study was to detect gender differences in driving whereas in a control condition no study aim was provided. In Experiment 2, two conditions were compared: stereotype threat (same instructions as in Experiment 1), and stereotype boost (the alleged goal was to compare driving ability of young vs. old people). As predicted, the results of both experiments showed that women under stereotype threat, as compared to either control or stereotype boost participants, doubled the number of mistakes. Nevertheless, they overall expected/self-reported to drive/have driven poorly. Importantly, their level of expectation was a significant predictor of their actual driving performance only in the stereotype threat condition. Implications of these effects of stereotype threat on women's driving performance and self-assessment are discussed.
Copyright © 2015 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Driving; Stereotype threat; Women

Mesh:

Year:  2015        PMID: 26457739     DOI: 10.1016/j.aap.2015.09.021

Source DB:  PubMed          Journal:  Accid Anal Prev        ISSN: 0001-4575


  2 in total

1.  Mobile Phone Use "on the Road": A Self-Report Study on Young Drivers.

Authors:  Angelo Fraschetti; Pierluigi Cordellieri; Giulia Lausi; Emanuela Mari; Elena Paoli; Jessica Burrai; Alessandro Quaglieri; Michela Baldi; Alessandra Pizzo; Anna Maria Giannini
Journal:  Front Psychol       Date:  2021-08-16

2.  Analysing the effect of gender on the human-machine interaction in level 3 automated vehicles.

Authors:  Shuo Li; Phil Blythe; Yanghanzi Zhang; Simon Edwards; Weihong Guo; Yanjie Ji; Paul Goodman; Graeme Hill; Anil Namdeo
Journal:  Sci Rep       Date:  2022-07-08       Impact factor: 4.996

  2 in total

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