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A phenomenological study to understand gender diversity and inclusion in the tech industry

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Abstract

Various researches have established that diversity with inclusion yields a competitive advantage and promotes innovation in workplaces. Gender diversity and retention of top women talent, however, remains a problem. This study explored the reasons leading to the lower representation of women in the tech industry and the impact of the diversity and inclusion initiatives on the lives of working women. Purposive sampling has been used to select four women and two men working in tech, and in-depth interviews over the telephone were conducted for a phenomenological study. Analysis of data was carried out by Interpretative Phenomenological Analysis (IPA). The results highlight common issues women face in the tech industry and compare it to their male counterparts' experiences. Women highlight unconscious biases, internal and external obstacles, lack of women mentors at senior positions, and family restrictions. This paper gives a deeper understanding of the lived experience of entry-level men and women currently working in the tech industry, which will help identify gaps in our current approach towards inclusivity and the way forward for these practices for employees in STEM fields. The study is significant for organizations struggling to maintain gender balance in the tech industry despite implementing several diversity and inclusion practices. It will help the organizations understand a relatively new term, including inclusion and adopting specific measures to create an inclusive work environment.

Imprint

Faiza Khan, Sanjay Bhattacharya. A phenomenological study to understand gender diversity and inclusion in the tech industry. Cardiometry; Issue 22; May 2022; p.386-394; DOI: 10.18137/cardiometry.2022.22.386394; Available from: https://www.cardiometry.net/issues/no22-may-2022/phenomenological_study_understand

Ключевые слова

Gender diversity,  Inclusion,  Leadership,  Phenomenology,  Interpretative Phenomenological Analysis (IPA)
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