Abstract
As the computing industry vocalizes the prioritization of diverse experiences, minimal improvements have been made to provide an equitable and representative work experience for diverse professionals. With women representing less than 28% of the STEM workforce and racially diverse professionals representing less than 37% of STEM professions, both gender and racially diverse candidates represent a minority within the industry (Fry et al., 2021). This contrast in representation is exacerbated by diverse professionals leaving the field at a disproportionate rate compared to their Caucasian male colleagues (Fry et al., 2021). This research was conducted to report findings surrounding the motivation behind diverse professionals’ decision to exit the computing industry alongside recommendations for improvements to workplace inclusivity. These conclusions were identified through the analysis of two research questions: 1) Why do underrepresented, diverse professionals exit the Computing field and 2) What improvements can organizational leadership implement to retain and grow diverse employees? 330 participants provided their insight to questions regarding work experience and opportunities for improvement. Utilizing NVivo 20, grounded theory qualitative analysis was conducted to find commonalities in contributor responses which formed a basis of participant voices through three selective codes: 1) Compensation Equity, 2) Representation, and 3) Inclusive Work Environment. These cohesions provide a foundation upon which organizational leadership can increase equity and representation while implementing survey feedback for retention improvements within their organization. Future opportunities for research should include a larger sample size with diverse geographic representation, considerations for ethnic diversity, and challenges faced by LGBTQ+ professionals within the industry.
Cite
CITATION STYLE
Williams, E. (2023). Actualizing gender and racial diversity inclusion in computing fields. Issues in Information Systems, 24(4), 255–272. https://doi.org/10.48009/4_iis_2023_120
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