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Leveraging partial-least squares structural quation modeling and machine learning, we find that the data mostly support our theoretical model, with some variation across different subgroups of respondents. An importance-performance map analysis suggests that managers may wish to focus on interventions regarding burnout as a predictor of intention to leave. The Machine Learning model suggests that engagement and opportunities to learn are the top two most important factors that explain whether software professionals leave an organization.<\/jats:p>","DOI":"10.1145\/3691629","type":"journal-article","created":{"date-parts":[[2024,9,2]],"date-time":"2024-09-02T15:09:04Z","timestamp":1725289744000},"page":"1-45","update-policy":"https:\/\/doi.org\/10.1145\/crossmark-policy","source":"Crossref","is-referenced-by-count":16,"title":["Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement"],"prefix":"10.1145","volume":"33","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7302-6082","authenticated-orcid":false,"given":"Bianca","family":"Trinkenreich","sequence":"first","affiliation":[{"name":"Colorado State University, Fort Collins, CO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8069-3158","authenticated-orcid":false,"given":"Fabio","family":"Santos","sequence":"additional","affiliation":[{"name":"Colorado State University, Fort Collins, CO, USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1038-5050","authenticated-orcid":false,"given":"Klaas-Jan","family":"Stol","sequence":"additional","affiliation":[{"name":"Lero, Cork, Ireland and University College Cork, Cork, Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"320","published-online":{"date-parts":[[2024,12,3]]},"reference":[{"key":"e_1_3_2_2_2","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1007\/978-1-4842-6579-6_2","volume-title":"Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient","author":"Agrawal Tanay","year":"2021","unstructured":"Tanay Agrawal. 2021. 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