The Moderating Effect of Perceived Organisational Support on the relationship between Perceived Gender Bias and Self-Esteem among Women in Science, Technology, Engineering and Mathematics (STEM) in Lagos, Nigeria

  • Wakil Ajibola Asekun Department of Psychology, University of Lagos
  • Oluwadamilola Aanuoluwapo Odunlade Department of Psychology, University of Lagos.
Keywords: Career aspirations, gender bias, organisational support, self esteem, STEM

Abstract

Women remain markedly underrepresented in Science, Technology, Engineering, and Mathematics (STEM), and the gender bias they encounter at work may carry psychological costs that extend beyond career outcomes. This study examined how perceived gender bias impacts the self-esteem and career aspirations of women in STEM in Lagos State. It also examined whether perceived organisational support moderates the relationship between perceived gender bias and self-esteem. Using a survey design, 150 women with at least 1 year of STEM work experience were recruited through convenience sampling across the technology (43.3%), academic (34.0%), and manufacturing (22.7%) sectors. Participants completed the Schedule of Sexist Events Questionnaire (SSEQ), the Career Aspiration Scale–Revised (CAS-R), the Index of Self-Esteem (ISE), and the Perceived Organisational Support Scale (POSS); the ISE was reverse-scored, so higher scores denote lower self-esteem. Data were analysed using Pearson correlations, simple linear regression and a one-way MANOVA. Findings indicate that perceived gender bias was significantly associated with lower self-esteem, but was unrelated to career aspirations. Age did not predict either outcome, while the perceived organisational support did not moderate the bias–self-esteem relationship. These findings suggest that gender bias operates as a psychological stressor that erodes self-esteem. We discuss how perceived gender bias could limit women's aspirations and undermine their potential to attain top positions in STEM, particularly their growth in the adoption of Artificial intelligence in corporate organizations. We recommend policies that sanction gender-based prejudices and discrimination.

Published
2026-07-21