Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 17

The DOI matches its Crossref record; 6 of the 6 references it cites were found in a registry or an index.

Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement

Trinkenreich, Bianca, Santos, Fabio, Stol, Klaas-Jan·2024·ACM Transactions on Software Engineering and Methodology

Abstract

In this study of burnout and engagement, we address three major themes. First, we offer a review of prior studies of burnout among IT professionals and link these studies to the Job Demands-Resources (JD-R) model. Informed by the JD-R model, we identify three factors that are organizational job resources and posit that these (a) increase engagement and (b) decrease burnout. Second, we extend the JD-R by considering software professionals’ intention to stay as a consequence of these two affective states, burnout and engagement. Third, we focus on the importance of factors for intention to stay, and actual retention behavior. We use a unique dataset of over 13,000 respondents at one global IT organization, enriched with employment status 90 days after the initial survey. 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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1145/3691629

Cites

6 of 6 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 6 references

Research works printed but not linked: 116

  1. Tanay Agrawal. 2021. Hyperparameter optimization using scikit-learn. In Hyperparameter Optimization in Machine Learning: Make Your Machine Learning and Deep Learning Models More Efficient. Springer, 31–51. · DOI
  2. D. Alao and A. B. Adeyemo. 2013. Analyzing employee attrition using decision tree algorithms. Computing, Information Systems, Development Informatics and Allied Research Journal 4, 1 (2013), 17–28.
  3. Amitabh Anand, Jessica Doll, and Prantika Ray. 2023. Drowning in silence: A scale development and validation of quiet quitting and quiet firing. International Journal of Organizational Analysis 32, 4 (2023), 721–743. https://www.emerald.com/insight/content/doi/10.1108/ijoa-01-2023-3600/full/html · DOI
  4. Sigrún Andradóttir. 2014. A review of random search methods. In Handbook of Simulation Optimization. Michael C. Fu (Ed.), Springer, 277–292.
  5. 10.1145/3555228.3555263 · DOI
  6. 10.1287/mnsc.49.4.571.14424 · DOI · Paper page
  7. 10.1080/14783363.2018.1447367 · DOI
  8. Lingfeng Bao, Zhenchang Xing, Xin Xia, David Lo, and Shanping Li. 2017. Who will leave the company? A large-scale industry study of developer turnover by mining monthly work report. In 2017 IEEE/ACM 14th International Conference on Mining Software Repositories (MSR). IEEE, 170–181.
  9. 10.1016/0090-2616(90)90061-S · DOI · Paper page
  10. 10.1016/j.infsof.2007.09.004 · DOI · Paper page
  11. Max Bramer. 2007. Avoiding overfitting of Decision Trees. In Principles of Data Mining. Springer London, London, 119–134.
  12. Marie Carasco-Saul, Woocheol Kim, and Taesung Kim. 2015. Leadership and employee engagement: Proposing research agendas through a review of literature. Human Resource Development Review 14, 1 (2015), 38–63. · DOI
  13. 10.1016/j.knosys.2015.07.019 · DOI
  14. 10.1037/0021-9010.86.3.499 · DOI · Paper page
  15. 10.36941/jicd-2019-0010 · DOI
  16. Francesca Fallucchi, Marco Coladangelo, Romeo Giuliano, and Ernesto William De Luca. 2020. Predicting employee attrition using machine learning techniques. Computers 9, 4 (2020), 86. · DOI
  17. Alan Felstead, Duncan Gallie, Francis Green, and Hande Inanc. 2015. Fits, misfits and interactions: Learning at work, job satisfaction and job-related well-being. Human Resource Management Journal 25, 3 (2015), 294–310. · DOI
  18. Peter A. Flach and Meelis Kull. 2015. Precision-recall-gain curves: PR analysis done right. In Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015. Corinna Cortes, Neil D. Lawrence, Daniel D. Lee, Masashi Sugiyama, and Roman Garnett (Eds.), 838–846.
  19. Sandro Formica and Fabiola Sfodera. 2022. The Great Resignation and Quiet Quitting paradigm shifts: An overview of current situation and future research directions. Journal of Hospitality Marketing & Management 31, 8 (2022), 899–907. · DOI · Paper page
  20. Nicole Forsgren and Jez Humble. 2016. The role of continuous delivery in IT and organizational performance. In Proceedings of the Western Decision Sciences Institute (WDSI), 1–15.
  21. Nicole Forsgren, Jez Humble, Gene Kim, A. Brown, and N. Kersten. 2018. Accelerate: State of DevOps Strategies for a New Economy. Report. DevOps Research & Assessment (DORA).
  22. 10.1177/239700221002400204 · DOI
  23. 10.2486/indhealth.32.231 · DOI
  24. Jason C. Gawke, Marjan J. Gorgievski, and Arnold B. Bakker. 2018. Personal costs and benefits of employee intrapreneurship: Disentangling the employee intrapreneurship, well-being, and job performance relationship. Journal of Occupational Health Psychology 23, 4 (2018), 508. · DOI
  25. Robin Genuer and Jean-Michel Poggi. 2020. Random Forests. Springer International Publishing, Cham, 33–55. · DOI
  26. 10.1016/0378-7206(92)90043-F · DOI
  27. Daniel Graziotin and Fabian Fagerholm. 2019. Happiness and the productivity of software engineers. In Rethinking Productivity in Software Engineering. Caitlin Sadowski and Thomas Zimmermann (Eds.), Springer, 109–124. · DOI
  28. 10.1109/semotion.2017.5 · DOI
  29. 10.1016/j.jss.2018.02.041 · DOI
  30. Hazel Grünewald, Petra Kneip, and Arjan Kozica. 2019. The use of gamification in workplace learning to encourage employee motivation and engagement. In The Wiley Handbook of Global Workplace Learning. Vanessa Hammer Kenon and Sunay Vasant Palsole (Eds.), Wiley, 557–575. · DOI
  31. 10.1108/IMDS-04-2016-0130 · DOI · Paper page
  32. Joseph F. Hair, Claudia Binz Astrachan, Ovidiu I. Moisescu, Lăcrămioara Radomir, Marko Sarstedt, Santha Vaithilingam, and Christian M. Ringle. 2021. Executing and interpreting applications of PLS-SEM: Updates for family business researchers. Journal of Family Business Strategy 12, 3 (2021), 100392. · DOI · Paper page
  33. 10.2753/MTP1069-6679190202 · DOI · Paper page
  34. 10.1108/EBR-11-2018-0203 · DOI · Paper page
  35. Joseph F. Hair Jr, G. Tomas M. Hult, Christian M. Ringle, and Marko Sarstedt. 2022. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (rd ed.). Sage Publications.
  36. 10.5555/3158980 · doi:10.5555/3158980
  37. Deneen M. Hatmaker and Shahidul Hassan. 2023. When do women receive managerial support? The effects of gender congruence and the manager-employee relationship. Public Management Review 25, 1 (2023), 22–41. · DOI
  38. 10.1145/3387904.3389263 · DOI
  39. Jörg Henseler. 2020. Composite-Based Structural Equation Modeling: Analyzing Latent and Emergent Variables. Guilford Publications.
  40. 10.1177/1094428114526928 · DOI · Paper page
  41. 10.1108/IMDS-09-2015-0382 · DOI · Paper page
  42. 10.1007/s11747-014-0403-8 · DOI · Paper page
  43. 10.1108/IMR-09-2014-0304 · DOI · Paper page
  44. Geert Hofstede. 2001. Culture’s Consequences: Comparing Values, Behaviors, Institutions and Organizations across Nations. Sage Publications.
  45. 10.1016/S1048-9843(96)90024-7 · DOI · Paper page
  46. 10.1080/09585190410001677331 · DOI
  47. Muhammad Jawad, Munazza Naz, and Sohail Rizwan. 2023. Leadership support, innovative work behavior, employee work engagement, and corporate reputation: Examining the effect of female in not government organizations. Corporate Social Responsibility and Environmental Management 30, 2 (2023), 708–719. · DOI
  48. Damien Joseph and Soon Ang. 2003. Turnover of IT professionals: A quantitative analysis of the literature. In Proceedings of the 2003 SIGMIS Conference on Computer Personnel Research: Freedom in Philadelphia–Leveraging Differences and Diversity in the IT Workforce, 130–132.
  49. E. Kevin Kelloway and Arla L. Day. 2005. Building healthy workplaces: What we know so far. Canadian Journal of Behavioural Science/Revue canadienne des sciences du comportement 37, 4 (2005), 223. · DOI · Paper page
  50. Renata Korsakienė, Asta Stankevičienė, Agnė Šimelytė, and Milda Talačkienė. 2015. Factors driving turnover and retention of information technology professionals. Journal of Business Economics and Management 16, 1 (2015), 1–17. · DOI
  51. Hengky Latan, Joseph F. Hair, and Richard Noonan (Eds.). 2023. Partial Least Squares Path Modeling: Basic Concepts, Methodological Issues and Applications (nd ed.). Springer. · DOI
  52. 10.1109/ICGSE.2017.11 · DOI
  53. 10.1108/02621710410529785 · DOI · Paper page
  54. Mingxiao Lu, Abdullah Al Mamun, Xuelin Chen, Qing Yang, and Mohammad Masukujjaman. 2023. Quiet quitting during COVID-19: The role of psychological empowerment. Humanities and Social Sciences Communications 10, 1 (2023), 1–16.
  55. 10.1016/j.eswa.2018.05.024 · DOI
  56. Ayman Mahmoud Maaitah. 2018. The role of leadership style on turnover intention. International Review of Management and Marketing 8, 5 (2018), 24.
  57. Thalmus Mahand and Cam Caldwell. 2023. Quiet quitting—Causes and opportunities. Business and Management Research 12, 1 (2023), 9–18. · DOI
  58. Rafael Gomes Mantovani, Tomáš Horváth, Ricardo Cerri, Sylvio Barbon Junior, Joaquin Vanschoren, and André Carlos Ponce de Leon Ferreira de Carvalho. 2024. Better trees: An empirical study on hyperparameter tuning of classification decision tree induction algorithms. Data Mining and Knowledge Discovery 38 (2024), 1364–1416. Retrieved from https://link.springer.com/article/10.1007/s10618-024-01002-5 · DOI
  59. Laurențiu P. Maricuțoiu, Coralia Sulea, and Alina Iancu. 2017. Work engagement or burnout: Which comes first? A meta-analysis of longitudinal evidence. Burnout Research 5 (2017), 35–43. · DOI
  60. 10.1177/002224297704100112 · DOI · Paper page
  61. 10.1016/j.patrec.2006.06.018 · DOI
  62. Christina Maslach, Susan E. Jackson, and Michael P. Leiter. 1997. Maslach Burnout Inventory. Scarecrow Education.
  63. Tiago Massoni, Nilton Ginani, Wallison Silva, Zeus Barros, and Georgia Moura. 2019. Relating voluntary turnover with job characteristics, satisfaction and work exhaustion-An initial study with Brazilian developers. In 2019 IEEE/ACM 12th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE). IEEE, 85–88. · DOI
  64. Katherine S. McGilton. 2010. Development and psychometric testing of the supportive supervisory scale. Journal of Nursing Scholarship 42, 2 (2010), 223–232. · DOI
  65. 10.1016/j.aap.2009.08.009 · DOI
  66. 10.2307/3250982 · DOI · Paper page
  67. Stanley A Mulaik. 2010. Foundations of factor analysis (2nd ed.). CRC press.
  68. Anthony M. Musolf, Emily R. Holzinger, James D. Malley, and Joan E. Bailey-Wilson. 2022. What makes a good prediction? Feature importance and beginning to open the black box of machine learning in genetics. Human Genetics 141, 9 (2022), 1515–1528. · DOI
  69. Vishnuprasad Nagadevara and Vasanthi Srinivasan. 2008. Early prediction of employee attrition in software companies-application of data mining techniques. Research and Practice in Human Resource Management 16 (2008), 2020–2032.
  70. Ramyashilpa D. Nayak. 2014. Anxiety and mental health of software professionals and mechanical professionals. International Journal of Humanities and Social Science Invention 3, 2 (2014), 52–56.
  71. David E. Nelson. 1996. Validity of self reported data on injury prevention behavior: Lessons from observational and self reported surveys of safety belt use in the US. Injury Prevention 2, 1 (1996), 67–69. · DOI
  72. 10.1109/MS.2019.2924013 · DOI
  73. Ruan Oliveira, Tiago Massoni, Narallynne Araújo, Camila Sarmento, and Francielle Santos. 2021. Ants doing legwork: Investigating motivators for software development career abandonment. In Proceedings of the XXXV Brazilian Symposium on Software Engineering, 353–362. · DOI
  74. Pamela L. Perrewé, Wayne A. Hochwarter, Ana Maria Rossi, Alan Wallace, Isabelle Maignan, Stephanie L. Castro, David A. Ralston, Mina Westman, Guenther Vollmer, Moureen Tang, Paulina Wan, and Cheryl A. Van Deusen 2002. Are work stress relationships universal? A nine-region examination of role stressors, general self-efficacy, and burnout. Journal of International Management 8, 2 (2002), 163–187. · DOI
  75. 10.1002/9781118899281 · DOI
  76. A. M. Pines and Elliot Aronson. 1981. Burnout: From Tedium to Personal Growth. The Free Press.
  77. P. M. Usha and N. V. Balaji. 2019. Analysing employee attrition using machine learning. Karpagam Journal of Computer Science 13 (2019), 277–282.
  78. Philipp Probst and Anne-Laure Boulesteix. 2018. To tune or not to tune the number of trees in random forest. Journal of Machine Learning Research 18, 181 (2018), 1–18.
  79. 10.1002/widm.1301 · DOI · Paper page
  80. 10.1016/j.emj.2016.08.001 · DOI · Paper page
  81. 10.1016/j.lrp.2012.09.010 · DOI · Paper page
  82. 10.1108/IMDS-10-2015-0449 · DOI · Paper page
  83. Jeanine Romano, Jeffrey D. Kromrey, Jesse Coraggio, and Jeff Skowronek. 2006. Appropriate statistics for ordinal level data: Should we really be using t-test and Cohen’s d for evaluating group differences on the NSSE and other surveys. In Annual Meeting of the Florida Association of Institutional Research, Vol. 177, 34.
  84. Daniel Russo, Andres Ramon Masegosa Arredondo, and Klaas-Jan Stol. 2022. From anecdote to evidence: The relationship between personality and need for cognition of developer. Empirical Software Engineering 27, 71 (2022), 1–29.
  85. 10.1145/3447580 · DOI · Paper page
  86. 10.1371/journal.pone.0118432 · DOI · Paper page
  87. Fabio Santos, Jacob Penney, João Felipe Pimentel, Igor Wiese, Bianca Trinkenreich, Igor Steinmacher, and Marco A. Gerosa. 2023. Tell me who are you talking to and I will tell you what issues need your skills. In 2023 IEEE/ACM 20th International Conference on Mining Software Repositories (MSR), 611–623. · DOI
  88. Marko Sarstedt, Christian M. Ringle, and Joseph F. Hair. 2017. Treating unobserved heterogeneity in PLS-SEM: A multi-method approach. In Partial Least Squares Path Modeling. Hengky Latan and Richard Noonan (Eds.), Springer, 197–217. · DOI · Paper page
  89. Wilmar B. Schaufeli. 2004. The future of occupational health psychology. Applied Psychology 53, 4 (2004), 502–517. · DOI
  90. 10.1023/A:1015630930326 · DOI · Paper page
  91. Wilmar B. Schaufeli, Akihito Shimazu, Jari Hakanen, Marisa Salanova, and Hans De Witte. 2019. An ultra-short measure for work engagement: The UWES-3 validation across five countries. European Journal of Psychological Assessment 35, 4 (2019), 577. · DOI · Paper page
  92. 10.1177/154193120404801404 · DOI
  93. Peter H. Schönemann. 1971. The minimum average correlation between equivalent sets of uncorrelated factors. Psychometrika 36, 1 (1971), 21–30. · DOI
  94. 10.1016/j.jss.2019.110442 · DOI
  95. Haijian Shi. 2007. Best-First Decision Tree Learning. Ph.D. Dissertation. Citeseer.
  96. 10.1016/j.im.2013.08.003 · DOI
  97. Jordan Shropshire and Christopher Kadlec. 2012. I’m leaving the IT field: The impact of stress, job insecurity, and burnout on IT professionals. International Journal of Information and Communication Technology Research 2, 1 (2012), 6–16.
  98. Brad Shuck, Thomas G. Reio Jr, and Tonette S. Rocco. 2011. Employee engagement: An examination of antecedent and outcome variables. Human Resource Development International 14, 4 (2011), 427–445. · DOI · Paper page
  99. 10.1111/j.2044-8325.1994.tb00571.x · DOI
  100. Maie Stein, Sylvie Vincent-Hoeper, and Sabine Gregersen. 2020. Why busy leaders may have exhausted followers: A multilevel perspective on supportive leadership. Leadership & Organization Development Journal 41, 6 (2020), 829–845. · DOI
  101. 10.1007/s10664-021-10062-w · DOI
  102. 10.1111/j.2517-6161.1974.tb00994.x · DOI · Paper page
  103. M. Subhashini and R. Gopinath. 2020. Employee attrition prediction in industry using machine learning techniques. International Journal of Advanced Research in Engineering and Technology 11, 12 (2020), 3329–3341.
  104. Carolyn Timms, Paula Brough, and Deborah Graham. 2012. Burnt-out but engaged: The co-existence of psychological burnout and engagement. Journal of Educational Administration 50, 3 (2012), 327–345. · DOI
  105. 10.1145/3510458.3513018 · DOI
  106. Bianca Trinkenreich, Klaas-Jan Stol, Igor Steinmacher, Marco Gerosa, Anita Sarma, Marcelo Lara, Michael Feathers, Nicholas Ross, and Kevin Bishop. 2023. A model for understanding and reducing developer burnout. In Proceedings of the International Conference on Software Engineering (Companion).
  107. Tien Rahayu Tulili, Andrea Capiluppi, and Ayushi Rastogi. 2022. Burnout in software engineering: A systematic mapping study. Information and Software Technology 155 (2022), 107116. · DOI
  108. 10.1177/2165079920953052 · DOI
  109. 10.1177/001872679905201005 · DOI
  110. Jacob Weisberg. 1994. Measuring workers’ burnout and intention to leave. International Journal of Manpower 15, 1 (1994), 4–14. · DOI
  111. Ron Westrum. 2004. A typology of organisational cultures. BMJ Quality & Safety 13, suppl 2 (2004), ii22–ii27.
  112. 10.1287/mnsc.1070.0733 · DOI
  113. Anja Wölbling, Kira Krämer, Clemens N. Buss, Katrin Dribbisch, Peter LoBue, and Abraham Taherivand. 2012. Design thinking: An innovative concept for developing user-centered software. In Management for Professionals. Alexander Maedche, Achim Botzenhardt, and Ludwig Neer (Eds.), Springer, Berlin, 121–136.
  114. Kin Fai Ellick Wong and Cecilia Cheng. 2020. The turnover intention–behaviour link: A culture-moderated meta-analysis. Journal of Management Studies 57, 6 (2020), 1174–1216. · DOI
  115. Guanping Xiao, Xiaoting Du, Yulei Sui, and Tao Yue. 2020. HINDBR: Heterogeneous information network based duplicate bug report prediction. In 2020 IEEE 31st International Symposium on Software Reliability Engineering (ISSRE). IEEE, 195–206. · DOI
  116. 10.1016/j.neucom.2020.07.061 · DOI · Paper page

Cited by

See all 17

Metadata

Authors
Trinkenreich, B., Santos, F., & Stol, K.-J.
Affiliations
  • Colorado State University, Fort Collins, CO, USA
  • Lero, Cork, Ireland and University College Cork, Cork, Ireland
Venue
ACM Transactions on Software Engineering and Methodology
Published
2024-11-30
Volume
33
Issue
8
Pages
1-45
ISSN
  • 1049-331X
  • 1557-7392
Cited by
17
References
6
Language
en
Funders
  • Sponsor Science Foundation Ireland (#15/SIRG/3293 and #13/RC/2094-P2)
  • European Regional Development Fund through the Southern and Eastern Regional Operational Programme to Lero—the Irish Software Research Centre

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement. https://app.cochat.ai/papers/10.1145/3691629
The paper
Trinkenreich, B., Santos, F., & Stol, K.-J. (2024). Predicting Attrition among Software Professionals: Antecedents and Consequences of Burnout and Engagement. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3691629

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.