An exploratory study of factors influencing career decisions of Generation Z women in Data Science

SA Journal of Human Resource Management

Field Value
Title An exploratory study of factors influencing career decisions of Generation Z women in Data Science
Creator Bhore, Milind Tapas, Poornima
Subject human resource management; career development; vocational guidance; Gen Z women; career; Data Science; technology; Science, Technology, Engineering, and Mathematics (STEM)
Description Orientation: Since April 2022, there has been a 30% increase in Data Science job openings globally. The majority of these positions are filled by Generation Z talent (Gen Z). According to research, businesses that promote gender diversity have higher earnings and revenues.Research purpose: The purpose of this study is to identify factors that will help organizations in designing policies and work environment to attract and foster Gen Z women employees in Data Science.Motivation for the study: There is limited research focusing on Gen Z women professionals and factors influencing their career choices in the field of Data Science in the Indian context.Research approach/design and method: Structured questionnaire was distributed online. Purposive sampling technique was adopted and 216 responses from Gen Z women studying in technology institutes pan India and working in Data Science were collected. Multiple linear regression statistical technique was leveraged for data analysis.Main findings: Technical education, job opportunities, compensation and conducive environment significantly and positively influence career decisions of Gen Z women in Data Science.Practical implications: Organizations will be able to define policies to encourage hiring of Gen Z women, break stereo types that prevent women from pursuing career in Data Science and create a conducive work environment that acknowledges and rewards the performance of Gen Z women.Contribution/value-add: The findings of this study will encourage more women from Gen Z to pursue careers in Data Science, boosting gender diversity and inclusivity in Data Science.
Publisher AOSIS
Date 2023-03-23
Type info:eu-repo/semantics/article info:eu-repo/semantics/publishedVersion — Survey
Format text/html application/epub+zip text/xml application/pdf
Identifier 10.4102/sajhrm.v21i0.2168
Source SA Journal of Human Resource Management; Vol 21 (2023); 9 pages 2071-078X 1683-7584
Language eng
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Coverage Asian region with focus in India — Gen Z; Women; Engineers
Rights Copyright (c) 2023 Milind Bhore, Poornima Tapas