Defect intention : the specific challenges faced by women in open source that may predict (or influence) their intention to leave an open source software project/community

Background Open source is largely accepted as an important innovation driver in the technology industry. Even though inclusion and diversity is beneficial for the success of technology projects (including open source software projects), many statistics are pointing out that diversity in open source is even worse than in the technology sector in general. The unequal representation of minorities (in this limited scope study represented by women) has negative effects on the innovation potential of many tech-related companies and is a major cause of corporate companies’ concerns. To attract more women and increase their retention in open source software projects and communities, the understanding of reasons behind the decisions on why they leave/defect an open source project can be is essential for the development of the effective retention strategies in OSS. Objective Based on the extensive literature review conducted by Trinkenreich, et al. (2021), only a few studies make a theoretical connection to why women leave (or avoid) open source software projects. This study aimed to explore the challenges faced by women in open source that may predict (or influence) their intention to leave/defect an open source software project/community. Thus, the following research question was formulated: What are the specific challenges faced by women in OS that may predict (or influence) their intention to leave an OSS project/community? Methodology The initial in-depth literature review discovered a list of socio-cultural challenges faced by women when contributing to open source projects. Trinkenreich, et al. (2021) have grouped these challenges conceptually as follows: (1) Lack of peer parity; (2) Non-inclusive communication; (3) Toxic culture; (4) Impostor syndrome; (5) Community reception issues; (6) Stereotyping; (7) Work-life balance issues, (8) Gender-identified contributions. Additionally, one of the authors of this study found an existing dataset on the state of diversity, equity, and inclusion in open source as of 2021. The survey ‘2021 Diversity, Equity, and Inclusion in Open Source’ was developed and distributed by the Linux Foundation. The data for this survey was gathered in 2021 from 2,350 individuals, particularly, from the Foundation’s subscribers and community members, on questions about their sense of inclusion and belongingness in OS communities. The authors of this study made the initial mapping of the questions from the Linux Foundation survey against challenge-clustering developed by Trinkenreich, et al. (2021). This helped to isolate the following groups of challenges for this study: (1) Non-Inclusive Communication & Community Reception Issues; (2) Toxic Culture; and (3) Gender-Identified Contributions & Stereotyping, that are likely to contribute to women leaving/defecting an OSS project/community. Altogether, this helped to formulate two hypotheses: null (H0) and alternative (HA) which highlight the relationships between different variables in the dataset. The hypotheses were tested using multiple regression analysis. To test the hypotheses and answer the research question, the authors of this study did not design the survey questions themselves but rather observed them directly through the questions of the Linux Foundation survey. In the context of this study (viz., a small-scale applied research project) capitalizing on the secondary data made sense as explained further in the study. A multiple regression was carried out to explore whether any of the challenges (e.g., lack of response to or rejection of contributions or questions; experience of conflict or interpersonal tension between you and another contributor; experience of written or spoken language that made a women feel unwelcome; experience of threats of violence, stalking; experience of unsolicited sexual advances or comments; experience of stereotyping based on perceived demographic characteristics; experience of impersonation or malicious publication of personal information; experience of background-based harassment) could significantly predict (or influence) women’s intention to leave/defect an open source software project/community. Results The results of multiple regression analysis reject the null hypothesis. The following predictors (i.e., independent variables): Q17_04_violence_stalking_experience, Q17_06_stereotyping_experience, and Q18_background_based_harassment are statistically significant and thus contribute to the regression models because their statistical significance (i.e., the p-value) is less than 0.05. Based on the findings of the study, the challenges that may predict (or influence) women’s intention to leave/defect an open source software project/community can be formulated as follows: o For the sample ‘North America (Unites States, Canada, Mexico)’ § [Model 1] experience of threats of violence, stalking directed at women in the context of an open source project § [Model 2] experience of threats of violence, stalking and of harassment connected to their background directed at women in the context of an open source project o For the sample ‘Europe’ § [Model 1] experience of stereotyping based on perceived demographic characteristics directed at women in the context of an open source project § [Model 2] experience of stereotyping based on perceived demographic characteristics and threats of violence, stalking directed at women in the context of an open source project Conclusions Women’s intention to leave/defect an OSS project/community can be explained by the following prediction models (i.e., regression equations): o For the sample ‘North America (Unites States, Canada, Mexico)’ § [Model 1] Y = 0.892 – (0.413 * Q17_04_violence_stalking_experience) § [Model 2] Y = 0.991 – (0.328 * Q17_04_violence_stalking_experience) – (0.228 * Q18_background_based_harassment) o For the sample ‘Europe’ § [Model 1] Y = 0.938 – (0.345 * Q17_06_stereotyping_experience) § [Model 2] Y = 0.953 – (0.285 * Q17_06_stereotyping_experience) – (0.242 * Q17_04_violence_stalking_experience) The results of the study also indicate that the models were a significant predictor of women’s intention to leave/defect an OSS project/community: o For the sample ‘North America (Unites States, Canada, Mexico)’ § [Model 1] F(1,134) = 31.671, p = <0.001 § [Model 2] F(2,133) = 20.342, p = <0.001 o For the sample ‘Europe’ § [Model 1] F(1,104) = 19.874, p = <0.001 § [Model 2] F(2,103) = 13.118, p = <0.001 Contribution to theory and practice Academic value: The findings of this study extend the knowledge about specific challenges faced by women in OS that may predict (or influence) their intention to leave an OSS project/community. Insights for adopting ‘Innovation by All’ workplace culture: The findings of this study provide OSS projects/communities with insights into the hindrances and determinants associated with women’s participation in OS. These insights, in their turn, can be valuable to understand and be aware of when an OSS team/community aims to adopt an ‘Innovation by All’ workplace culture and by doing so - attain greater team productivity, more innovative and more revolutionary ideas, greater agility, and higher rates of ideas’ implementation, decision-making, and innovation. Internal analysis: The results of this study can be used to inform OSS teams/communities about the most critical aspects they need to address in order to attract more and retain existing female talent. Thus, the findings of this study can serve as an internal analysis for an OSS team/ community to take further actions on including and diversifying their project teams and ensuring that all members stay and keep on contributing to OSS projects. Recommendations for future research The following research proposals are suggested: (1) An extensive quantitative study amongst female contributors of various OSS projects/communities and a comparative analysis of these communities based on different parameters. (2) A replication of this study that examines/explores the specific challenges faced by the representatives of other minority groups in OS that may predict (or influence) their intention to leave an OSS project/community. (3) A comparative study (e.g., women versus men; women versus binary/no-gender participants; and so on) about challenges faced by them in OS that may predict (or influence) the intention to leave an OSS project/community.

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