Using machine learning to translate applicant work history into predictors of performance and turnover

Sajjadiani, Sima; Sojourner, Aaron J.; Kammeyer-Mueller, John D.; Mykerezi, Elton

doi:10.1037/apl0000405

Cited by 173AI and HR TechnologiesOnline Learning and AnalyticsEmployer Branding and e-HRM

Using machine learning to translate applicant work history into predictors of performance and turnover

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Sajjadiani, Sima, Sojourner, Aaron J., Kammeyer-Mueller, John D., Mykerezi, Elton·2019·Journal of Applied Psychology

DOI 10.1037/apl0000405

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Abstract

Work history information reflected in resumes and job application forms is commonly used to screen job applicants; however, there is little consensus as to how to systematically translate information about one's work-related past into predictors of future work outcomes. In this article, we apply machine learning techniques to job application form data (including previous job descriptions and stated reasons for changing jobs) to develop interpretable measures of work experience relevance, tenure history, and history of involuntary turnover, history of avoiding bad jobs, and history of approaching better jobs. We empirically examine our model on a longitudinal sample of 16,071 applicants for public school teaching positions, and predict subsequent work outcomes including student evaluations, expert observations of performance, value-added to student test scores, voluntary turnover, and involuntary turnover. We found that work experience relevance and a history of approaching better jobs were linked to positive work outcomes, whereas a history of avoiding bad jobs was associated with negative outcomes. We also quantify the extent to which our model can improve the quality of selection process above the conventional methods of assessing work history, while lowering the risk of adverse impact. (PsycINFO Database Record (c) 2019 APA, all rights reserved).

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“Second, if not taking or implementing the termination decision, algorithms may facilitate employees' dismissal through the prediction of employee behaviors such as departure (Punnose & Ajit, 2016;Rosenbaum, 2019;Sajjadiani et al., 2019;Sikaroudi, Mohammad, Ghousi, & Sikaroudi, 2015;Zhao, Hryniewicki, Cheng, Fu, & Zhu, 2018), performance (Edwards & Edwards, 2019;Greenfield, 2018;Sajjadiani et al.,”
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A review of text analysis in human resource management research: Methodological diversity, constructs identified, and validation best practices
Campion, E. D., & Campion, M. A. (2025) · Human Resource Management Review · doi:10.1016/j.hrmr.2025.101078 · Cited by 7 · see citing works
“For example, Campion et al. (2024) and Koenig et al. (2023) predicted job performance after hire and Sajjadiani et al. (2019) predicted turnover.”
Influential
“I Put In My Two Weeks Notice!” Employee Behavior and Wellness Prior to Exit
Moon, Y.-K., & O'Brien, K. E. (2023) · Journal of Career Development · doi:10.1177/08948453231163499 · Cited by 8
“In addition, companies may also utilize machine learning technique to build up their customized withdrawal pro ﬁ les based on their accumulated exit interviews and performance records (Sajjadiani et al., 2019).”
Method
An Integrated Strategic Architectural Framework for AI - Augmented HRM
Krishnan, P. (2024) · International Journal of Science and Research (IJSR) · doi:10.21275/sr241001224937 · Cited by 7
Can Legal and Professional Personnel Selection Principles be Met With Machine Learning (Artificial Intelligence)?
Campion, M. A. (2025) · Human Resource Management · doi:10.1002/hrm.70025 · Cited by 4 · see citing works
“The initial supportive research was also in this context (e.g., Campion et al. 2016; Campion, Campion, Johnson, et al. 2024; Sajjadiani et al. 2019).”
Unraveling the Most Important Predictors of Eudaimonic and Hedonic Well-Being in Korean Adults: A Machine Learning Approach
Jyung, M., Lee, S., & Choi, I. (2024) · Journal of Happiness Studies · doi:10.1007/s10902-024-00792-1 · Cited by 5 · see citing works
“…across various domains, such as emotion (Prout et al., 2020), personality (Gladstone et al., 2019), health (Kim et al., 2015 ; Lee et al., 2020a), and workplace dynamics (Sajjadiani et al., 2019), its use for systematically comparing potential predictors of happiness remains surprisingly sparse.”
Addressing diversity in hiring procedures: a generative adversarial network approach
Marra, T., & Kubiak, E. (2024) · AI and Ethics · doi:10.1007/s43681-024-00445-2 · Cited by 6 · see citing works
“Other research also indicates that algorithmic approaches yield more optimal decisions regarding employees’ performance outcomes [101, 136] and even streamline hiring fill rates [69].”
Can a Commercial Screening Tool Help Select Better Teachers?
Chi, O. L., & Lenard, M. (2022) · Educational Evaluation and Policy Analysis · doi:10.3102/01623737221131547 · Cited by 7 · see citing works
“Sajjadiani et al. (2019), applying machine learning techniques to data from the Minneapolis Public School District, find that the relevance of applicants’ work experience and their attributions for leaving past jobs predict teacher performance (student evaluations, observation scores, and…”
Method
A psychometric view of technology-based assessments
Liou, G., Bonner, C. V., & Tay, L. (2022) · International Journal of Testing · doi:10.1080/15305058.2022.2070757 · Cited by 5 · see citing works
“Sajjadiani et al. (2019) recently coined the term “inter-algorithm reliabil-ity” as the assessment of the level of convergence in scores when applying different algorithms to test the same construct with the same data.”
BackgroundMethod
Resumes vs. application forms: Why the stubborn reliance on resumes?
Risavy, S. D., Robie, C., Fisher, P. A., & Rasheed, S. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.884205 · Cited by 9 · see citing works
“As organizations rely more on machine learning (Sajjadiani et al., 2019), it will be important for future research to assess whether bias might be better mitigated when the input into algorithms comes from application forms as opposed to resumes.”
Background
Predictions of attrition among US Marine Corps: Comparison of four predictive methods
Vanegas, J. M. A., Wine, W., & Drasgow, F. (2021) · Military Psychology · doi:10.1080/08995605.2021.1978754 · Cited by 8 · see citing works
Evidence on the Dimensionality and Reliability of Professional References’ Ratings of Teacher Applicants
Goldhaber, D. D., Grout, C., Wolff, M., & Martinková, P. (2021) · Economics of Education Review · doi:10.1016/j.econedurev.2021.102130 · Cited by 6 · see citing works
Checks and balances: leveraging artificial intelligence for tri-balance personnel selection systems
Zhang, Q. (2025) · The International Journal of Human Resource Management · doi:10.1080/09585192.2025.2498563 · Cited by 6 · see citing works
“…(HRM) represents an inevitable and ongoing trend, with applications rapidly expanding across key HRM functions, including job design (Parent-Rocheleau & Parker, 2022), performance management (Sajjadiani et al., 2019), talent management (Wiblen & Marler, 2021), and training (Campion et al., 2024).”
How job crafters are selected during recruitment: An invisible filter based on job crafting experiences and applicant gender
Ji, S., & Chen, Z. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114348 · Cited by 8 · see citing works
Shedding Light on the Black Box: Integrating Prediction Models and Explainability Using Explainable Machine Learning
Zhang, Y. E., Zheng, Y., Wang, D., Gu, X., Zyphur, M. J., Xiao, L., Liao, S., & Deng, Y. (2025) · Organizational Research Methods · doi:10.1177/10944281251323248 · Cited by 6 · see citing works
“…ranging from natural sciences such as health (Esteva et al., 2017; Klute et al., 2019) to social sciences such as economics (Akyildirim et al., 2021) and organizational research (Naja ﬁ -Zangeneh et al., 2021; Sajjadiani et al., 2019), ful ﬁ lling critical prediction tasks such as classi ﬁ cation.”
Artificial Intelligence, Machine Learning and the Reconstruction of Employee Psychology
Sharma, R. K. (2020) · NHRD Network Journal · doi:10.1177/2631454120971912 · Cited by 3 · see citing works
“As Sajjadiani et al. (2019) indicate, the initial keywords and heuristics in an ML process are keyed in by human beings.”
BackgroundInfluential
Classification Performance of Supervised Machine Learning to Predict Human Resource Management Outcomes: A Meta‐Analysis Using Cross‐Classified Multilevel Modeling
Vanhove, A. J., Graham, B. Z., Titareva, T., & Udomvisawakul, A. (2025) · Human Resource Management · doi:10.1002/hrm.70012 · Cited by 5 · see citing works
Integration of Artificial Intelligence in Human Resource Management: Analyzing Opportunities, Challenges, and Human-AI Collaboration
Benabou, A., & Touhami, F. (2025) · Management and Accounting Review · doi:10.24191/mar.v24i01-15 · Cited by 3 · see citing works
“Additionally, AI algorithms can autonomously determine job terminations, especially in the gig economy, and predict behaviors like attrition or fraud, enabling proactive issue resolution (Sima et al., 2019).”
Using Practice Employment Tests in Recruitment and Selection to Equalize Preparation Opportunities
Campion, E. D., & Campion, M. A. (2025) · Human Resource Management · doi:10.1002/hrm.22287 · Cited by 7 · see citing works
Predicting and Explaining Assessment Center Judgments: A Cross‐Validated Behavioral Approach to Performance Judgments in Interpersonal Assessment Center Exercises
Grunenberg, E., Stachl, C., Breil, S. M., Schäpers, P., & Back, M. D. (2024) · Human Resource Management · doi:10.1002/hrm.22252 · Cited by 6 · see citing works
The role of sociopolitical workplace networks in involuntary employee turnover
Floyd, T. M., Gerbasi, A., & Labianca, G. (2023) · Social Networks · doi:10.1016/j.socnet.2023.10.005 · Cited by 4 · see citing works
Analysis of Human Resource Predictive Models Utilizing Machine Learning
Rahate, V., Mohanty, V., Arora, A., Bagul, D., Disawal, V., & Kulkarni, A. (2023) · 2023 International Conference on Communication, Security and Artificial Intelligence (ICCSAI) · doi:10.1109/iccsai59793.2023.10421177 · Cited by 3
“Using the historical records kept by HR departments, researchers may develop and train an AI model that can predict employee turnover [15].”
Background
Gendered competencies and gender composition: A human versus algorithm evaluator comparison
Merritt, S. M., Ryan, A. M., Gardner, C. L., Liff, J., & Mondragon, N. J. (2023) · International Journal of Selection and Assessment · doi:10.1111/ijsa.12459 · Cited by 6 · see citing works
Reconsider what your MBA negotiation course taught you: The possible adverse effects of high salary requests
Maaravi, Y., & Segal, S. (2022) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2022.103803 · Cited by 2 · see citing works
Impact of Machine Learning on the Productivity of Employees in Workplace
Wardhani, R., Kant, K., Sreeram, A., Gupta, M., Erwandy, E., & Bora, P. (2022) · 2022 4th International Conference on Inventive Research in Computing Applications (ICIRCA) · doi:10.1109/icirca54612.2022.9985471 · Cited by 3
“Research has found that the implementation of machine learning can increase the productivity level, specialisation within job roles, and enhanced the significance of “human skills” including quantitative s kills, creativity and problem solving [12].”
Background
Machine Learning Approach to Personality Assessment and Its Application to Personnel Selection
Ock, J., & An, H. (2021) · Korean Journal of Industrial and Organizational Psychology · doi:10.24230/kjiop.v34i2.213-236 · Cited by 5 · see citing works
“Also, with regards to the reliability of machine-learning based personality assessments, the effect of rater (or in the case of machine learning, algorithm) is an important source of error that would be useful to identify and model (Sajjadiani et al., 2019).”
Background
A Feature Interaction Based Neural Network Approach: Predicting Job Turnover in Early Career Graduates in South Korea
Byeon, H. (2024) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2024.0150833 · Cited by 3
“By identifying the factors that contribute to early job departure, graduates can better prepare themselves to meet the challenges of their initial employment experiences [6,7].”
Background
A new type of mental health assessment using artificial intelligence technique
Jiang, L., Tian, X., Ren, P., & Fang, L. U. O. (2021) · Advances in Psychological Science · doi:10.3724/sp.j.1042.2022.00157 · Cited by 9 · see citing works
The Machines Aren’t Taking Over (Yet): An Empirical Comparison of Traditional, Profiling, and Machine Learning Approaches to Criterion-Related Validation
Allen, K., Affourtit, M., & Reddock, C. M. (2020) · Personnel Assessment and Decisions · doi:10.25035/pad.2020.03.002 · Cited by 4 · see citing works
Multi-Algorithm Optimization of Explanation Stability in Ensemble Learning for Human Capital Attrition Risk Prediction
Qi, Z.-H., Chen, Z.-W., & Si, F. (2026) · Mathematics · doi:10.3390/math14183283 · Cited by 2 · see citing works
Structured approach to job mapping for exemplary performers: leveraging the enhanced DACUM method
Sadique, F., & Rothwell, W. J. (2026) · Journal of Workplace Learning · doi:10.1108/jwl-06-2025-0177 · Cited by 2 · see citing works
Utilizing Big Data Technologies in Employee Selection: A Systematic Literature Review
Xie, F., Aman, A., Fernandez, D., & Ghazali, M. F. (2026) · International Journal of Selection and Assessment · doi:10.1111/ijsa.70057 · Cited by 2 · see citing works
Integrating Textual Features with Survival Analysis for Predicting Employee Turnover
Ke, Q., & Xu, Y.-Z. (2026) · Behavioral Science · doi:10.3390/bs16020174 · Cited by 2
“Sajjadiani et al. (2019) found that gender significantly predicts turnover, with female turnover rates higher than male turnover rates.”
Screening Teachers Effectively Across Roles and Classrooms
Bruno, P. (2024) · Journal of Education Human Resources · doi:10.3138/jehr-2023-0103 · Cited by 3 · see citing works

Authors: Sajjadiani, S., Sojourner, A. J., Kammeyer-Mueller, J. D., & Mykerezi, E.
ORCID: Sajjadiani, Sima
Sojourner, Aaron J.
Mykerezi, Elton
Venue: Journal of Applied Psychology
Publisher: American Psychological Association (APA)
Published: 2019-10
Volume: 104
Issue: 10
Pages: 1207-1225
ISSN: 0021-9010 (ISSN-L)
1939-1854
DOI: 10.1037/apl0000405
OpenAlex: W2970702233
Cited by: 173
References: 88
Citations by year: 2026: 28 · 2025: 30 · 2024: 28 · 2023: 32 · 2022: 24 · 2021: 15 · 2020: 12 · 2019: 4
FWCI: 19.29
Type: journal article
Language: en
Open access: hybrid
doi.org
Licence: other-oa
www.apa.org/pubs/journals/resources/open-access.aspx (am, 175 d)
Locations: Journal of Applied Psychology (publishedVersion, other-oa)
PubMed (publishedVersion)
Funders: University of Minnesota
Institute of Education Sciences (R305H160036)
Topics: Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Employer Branding and e-HRM
Keywords: job application forms, involuntary turnover, voluntary turnover, job performance prediction, student assessment, value added, public school teaching, adverse impact, model interpretability
MeSH terms: Work Performance, Machine Learning, School Teachers, Adult, Female, Humans, Job Application, Longitudinal Studies, Male, Personnel Turnover, Data Mining

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