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Using machine learning to translate applicant work history into predictors of performance and turnover
Open accessAbstract
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.,”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Sajjadiani et al., 2019
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Dima, J., Gilbert, M., Dextras-Gauthier, J., & Giraud, L. (2024) · Frontiers in Psychology · doi:10.3389/fpsyg.2024.1360401 · Cited by 164
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BackgroundMethodTian, X., Pavur, R., Han, H., & Zhang, L. (2022) · Business Process Management Journal · doi:10.1108/bpmj-08-2022-0389 · Cited by 109
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“…7: Examine New Technology That May Improve ME Analyses Finally, research is starting to show that new technology like artificial intelligence and machine learning algorithms can make a number of contributions to organizational research (Putka et al. 2018) and practice (Sajjadiani et al. 2019).”
BackgroundLee, P., Fyffe, S., Son, M., Jia, Z., & Yao, Z. (2022) · Journal of Business and Psychology · doi:10.1007/s10869-022-09864-6 · Cited by 61
“…sources in organizational settings, and the extracted information is used to improve organizational decision-making (e.g., Bhatia et al., 2021; Campion et al., 2016; Kobayashi et al., 2018; Langer et al., 2021, 2022; Putka et al., 2022; Sajjadiani et al., 2019; Tonidandel et al., 2021).”
Koenig, N., Tonidandel, S., Thompson, I. B., Albritton, B. H., Koohifar, F., Yankov, G. P., Speer, A. B., Hardy, J. H., Gibson, C., Frost, C., et al. (2023) · Personnel Psychology · doi:10.1111/peps.12608 · Cited by 78
Model-free selective inference under covariate shift via weighted conformal p -values
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Bevilacqua, M., Oketch, K., Qin, R., Stamey, W., Zhang, X., Gan, Y., Yang, K., & Abbasi, A. (2024) · ACM Transactions on Information Systems · doi:10.1145/3702639 · Cited by 51
“Most notably in educational settings, but also potentially in talent analytics, personnel selection, and job-to-candidate recommender systems [71, 103].”
People versus machines: introducing the HIRE framework
Will, P., Krpan, D., & Lordan, G. (2022) · Artificial Intelligence Review · doi:10.1007/s10462-022-10193-6 · Cited by 64
“Sajjadiani et al. (2019) finds that algorithms can maximise selecting hires on the basis of performance outcomes.”
BackgroundInfluentialKassir, S., Baker, L. J., Dolphin, J., & Polli, F. E. (2022) · AI and Ethics · doi:10.1007/s43681-022-00208-x · Cited by 50
“…through the lenses described above: use of machine learning out-of-sample testing as a local validation procedure [126], data-driven identification of job-relevant predictor–criterion relationships [127, 128], and simultaneous optimization of selection models for fairness and validity [129, 130].”
MethodValidity Concerns in Research Using Organic Data
Xu, H., Zhang, N., & Zhou, L. (2019) · Journal of Management · doi:10.1177/0149206319862027 · Cited by 53
Algorithmic Writing Assistance on Jobseekers' Resumes Increases HiresPreprint
Wiles, E., Munyikwa, Z., & Horton, J. (2023) · Management Sciences · doi:10.2139/ssrn.4356195 · Cited by 39
“In one related example, (Sajjadiani et al., 2019) analyze resumes of applicants to public school teaching jobs and find that spelling accuracy is associated with a higher probability of being hired.”
Campion, M. A., & Campion, E. D. (2023) · Personnel Psychology · doi:10.1111/peps.12621 · Cited by 58
“Examples include scoring candidate essays (Campion et al., 2016), deriving selection content from work history (Sajjadiani et al., 2019),scoringbiodataitems(Putkaetal.,2018),measuringorganizationalrecruitmentsignals(Banksetal.,2019),and evaluating post-hire job performance (Speer, 2018).”
BackgroundTeacher Preferences, Working Conditions, and Compensation StructurePreprint
Johnston, A. C. (2020) · SSRN Electronic Journal · doi:10.2139/ssrn.3532779 · Cited by 11
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“Prior research has shown that issues of underrepresentation embedded in employee data are often subtly inherited and further reinforced by algorithms during model training, thereby embedding bias into organizational decision systems in more covert and systemic ways (Sajjadiani et al. 2019).”
Renkema, M., & Tursunbayeva, A. (2024) · Futures · doi:10.1016/j.futures.2024.103453 · Cited by 53
Bruno, P., & Strunk, K. O. (2019) · Educational Evaluation and Policy Analysis · doi:10.3102/0162373719865561 · Cited by 34
“More recently, Sajjadiani et al. (2019) use machine learning techniques to discern aspects of prospective teachers’ work history from resumes (e.g., self-reported previous job titles or job descriptions) submitted during the application process.”
BackgroundAhmed, A., Younus, S., Ahmed, S. R., Algburi, S., & Fadhel, M. (2023) · International Service Availability Symposium · doi:10.1109/isas60782.2023.10391817 · Cited by 31
“Sajjadiani et al. (2019) [12] utilized machine learning to translate applicant work history into predictors of performance and turnover, showcasing the versatility of predictive models in HR processes.”
MethodThompson, I. B., Koenig, N., Mracek, D. L., & Tonidandel, S. (2023) · Journal of Business and Psychology · doi:10.1007/s10869-023-09874-y · Cited by 47
“For example, Sajjadiani et al. (2019) used machine learning to classify prior work experience and subsequently predict turnover.”
MethodKim, J., Dibrell, C., Kraft, E. M., & Marshall, D. R. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2020.08.057 · Cited by 31
Advice from artificial intelligence: a review and practical implications
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Greiner, M., & Kim, J. (2020) · Corporate Social Responsibility and Environmental Management · doi:10.1002/csr.2026 · Cited by 27
Text as Data Methods for Education Research
Fesler, L., Dee, T. S., Baker, R. B., & Evans, B. J. (2019) · Journal of Research on Educational Effectiveness · doi:10.1080/19345747.2019.1634168 · Cited by 31
“For example, suppose Sajjadiani et al. (2019) were interested in the proportion of teachers who leave voluntarily from their previous job.”
Benabou, A., & Touhami, F. (2025) · International journal of production management and engineering · doi:10.4995/ijpme.2025.21900 · Cited by 39
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Pitfalls of Machine Learning-Based Personnel Selection
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Campion, E. D., Campion, M. A., Johnson, J., Carretta, T., Romay, S., Dirr, B., Deregla, A., & Mouton, A. (2023) · Journal of Applied Psychology · doi:10.1037/apl0001144 · Cited by 24
“…Hannigan et al., 2019), there is increasing interest in the use of NLP by HR researchers, such as for selection (e.g., M. C. Campion et al., 2016; Sajjadiani et al., 2019; see E. D. Campion & Campion, 2020, for a review of text analysis in employment research), performance management (e.g.,…”
BackgroundValizade, D., Schulz, F., & Nicoara, C. (2022) · British Journal of Management · doi:10.1111/1467-8551.12678 · Cited by 30
Nguyen, T.-D. H., Bui, N. H., Nguyen, D., Yue, M.-C., & Nguyen, V. (2022) · Conference on Uncertainty in Artificial Intelligence · doi:10.48550/arxiv.2206.10833 · Cited by 23
“To aid the committee to make better decisions, machine learning models are increasingly used in both university admission [Waters and Miikkulainen, 2014] and job hiring [Sajjadiani et al., 2019].”
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“…these different levels is quintessential and valuable as the world of selection evolves to embrace new developments in gamification, mobile technology, statistical methods, machine learning, artificial intelligence, and Big Data (Landers et al., 2018; Putka et al., 2018; Sajjadiani et al., 2019).”
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Grunenberg, E., Peters, H., Francis, M. J., Back, M. D., & Matz, S. (2024) · Frontiers in Social Psychology · doi:10.3389/frsps.2023.1290295 · Cited by 29
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Jangbahadur, U., Ahlawat, S., Rozera, P., & Gupta, N. (2024) · Evidence-based HRM a Global Forum for Empirical Scholarship · doi:10.1108/ebhrm-02-2023-0038 · Cited by 32
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Local supply, temporal dynamics, and unrealized potential in teacher hiring
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“candidates’ future effectiveness in the classroom (Biasi forthcoming; Goldhaber, Grout, and Huntington-Klein 2017; Jacob et al. 2018; Rockoff et al. 2011; Sajjadiani et al. 2019), few schools use such measures and those that do tend not to select applicants based on the information with”
BackgroundEthical AI in HR: A Case Study of Tech Hiring
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Speer, A. B., Oswald, F. L., & Putka, D. J. (2025) · Organizational Research Methods · doi:10.1177/10944281251346404 · Cited by 15
“…as the criterion (Fan et al., 2023); human resource information systems (HRIS) data as predictors and employee turnover as the criterion (e.g., Sajjadiani et al., 2019; Speer et al., 2019); and prehire assessment data as predictors (e.g., GMA tests, interview responses) with employee job…”
InfluentialOptimize your leadership pipeline: Leveraging HR analytics for c-suite executive development
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Turnover reasons are more complex than “people quit bosses”: An approach-avoidance perspective
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Campion, E. D., & Campion, M. A. (2025) · Human Resource Management Review · doi:10.1016/j.hrmr.2025.101078 · Cited by 9
“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
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MethodAn 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
Campion, M. A. (2025) · Human Resource Management · doi:10.1002/hrm.70025 · Cited by 4
“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).”
Jyung, M., Lee, S., & Choi, I. (2024) · Journal of Happiness Studies · doi:10.1007/s10902-024-00792-1 · Cited by 5
“…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
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“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
“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…”
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Liou, G., Bonner, C. V., & Tay, L. (2022) · International Journal of Testing · doi:10.1080/15305058.2022.2070757 · Cited by 7
“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.”
BackgroundMethodResumes 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
“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.”
BackgroundPredictions 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 10
Goldhaber, D. D., Grout, C., Wolff, M., & Martinková, P. (2021) · Economics of Education Review · doi:10.1016/j.econedurev.2021.102130 · Cited by 6
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 11
“…(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).”
Ji, S., & Chen, Z. (2023) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114348 · Cited by 8
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 7
“…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
“As Sajjadiani et al. (2019) indicate, the initial keywords and heuristics in an ML process are keyed in by human beings.”
BackgroundInfluentialVanhove, A. J., Graham, B. Z., Titareva, T., & Udomvisawakul, A. (2025) · Human Resource Management · doi:10.1002/hrm.70012 · Cited by 6
Benabou, A., & Touhami, F. (2025) · Management and Accounting Review · doi:10.24191/mar.v24i01-15 · Cited by 4
“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
Grunenberg, E., Stachl, C., Breil, S. M., Schäpers, P., & Back, M. D. (2024) · Human Resource Management · doi:10.1002/hrm.22252 · Cited by 6
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
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].”
BackgroundGendered 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
Maaravi, Y., & Segal, S. (2022) · Journal of Vocational Behavior · doi:10.1016/j.jvb.2022.103803 · Cited by 2
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].”
BackgroundMachine 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 6
“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).”
BackgroundByeon, 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].”
BackgroundA 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
Allen, K., Affourtit, M., & Reddock, C. M. (2020) · Personnel Assessment and Decisions · doi:10.25035/pad.2020.03.002 · Cited by 5
Qi, Z.-H., Chen, Z.-W., & Si, F. (2026) · Mathematics · doi:10.3390/math14183283 · Cited by 2
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
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
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
Metadata
- Authors
- Sajjadiani, S., Sojourner, A. J., Kammeyer-Mueller, J. D., & Mykerezi, E.
- Venue
- Journal of Applied Psychology
- Published
- 2019-10
- Volume
- 104
- Issue
- 10
- Pages
- 1207-1225
- ISSN
- 0021-9010 (ISSN-L)
- 1939-1854
- 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
- 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
About these checks
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