PRIVATE SOCIAL MEDIA GROUP AND WOMEN CAREER DEVELOPMENT IN TECHNOLOGY: A PLS-SEM AND BAYESIAN NETWORK APPROACH

This dissertation provides a new insight on how private social media group to support women in technology careers based on a combined model of Partial Least Squares Structural Equation Modeling and Bayesian network, which can be more clearly applied to social media platforms. Career satisfaction is heavily influenced by meaningful connections. Women who Tech is a private Facebook group of nearly 10,000 women who practice within the technology field. This private group adds value to education and improves its members’ career satisfaction. Within this private group, a questionnaire was conducted to ascertain the group’s impact on members, mainly focusing on their intention to use this group for exploring career information and the support of that to career in technology. Participants were surveyed regarding demographics, exclusive Women who Tech use’s personal and emotional experiences, to investigate the women-only private social media group’s value and impact. The survey was completed by 324 members. After running PLS-SEM and Bayesian network analysis, we can see that Women who Tech provides an opportunity within private social media group as an effective venue to network among women in tech.