{"title":"An exploratory study of factors influencing career decisions of Generation Z women in Data Science","authors":"M. Bhore, Poornima Tapas","doi":"10.4102/sajhrm.v21i0.2168","DOIUrl":null,"url":null,"abstract":"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.","PeriodicalId":21526,"journal":{"name":"Sa Journal of Human Resource Management","volume":"27 1","pages":""},"PeriodicalIF":1.2000,"publicationDate":"2023-03-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Sa Journal of Human Resource Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4102/sajhrm.v21i0.2168","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MANAGEMENT","Score":null,"Total":0}
引用次数: 0
Abstract
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.