The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.
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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“Sajjadiani et al. (2019), for example, highlight how machine learning can be used as an alternative way to screen work history.”
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“Sajjadiani et al. (2019) suggèrent d’ailleurs que l'apprentissage automatique peut considérablement améliorer le processus de sélection en éliminant les biais des recruteurs et même certaines influences exercées par les candidats pour dévier le processus de sélection.”
AI-Driven Talent Management: Transforming Recruitment, Retention, and Workforce Analytics
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“Additionally, machine learning helps companies in the recruitment process by systematically eliminating recruiter bias (Sajjadiani et al. 2019).”
Perceived value of external job mobility: development and validation of job mobility mindset
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How Predictive of Teacher Retention Are Ratings of Applicants from Professional References?
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“Finally, Sajjadiani et al. (2019) examine the potential of using machine learning to interpret applicants’ work history in the Minneapolis Public School District (an approach that could reduce assessments of applicants after the initial set up).”
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Strategic framework for ai-driven human resource management architecture
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“Existing research includes predicting workforce attrition (Alduayj and Rajpoot 2018, Alsheref et al. 2022, Raza et al. 2022), productivity (Florez and Cortissoz 2017, Alaloul et al. 2022), and performance (Chalfin et al. 2016, Sajjadiani et al. 2019).”
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“The notion of a competency gap is rooted in the competency-based HRM literature [31], [32], [33].”
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“Predictive analytics further support retention management by identifying employees at risk of early attrition, helping firms to introduce targeted interventions and strategic workforce planning [7].”
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Work Design and Compensation Strategies in a Post‐ COVID World: Evidence From Online Job Postings
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“Similarly, the change to focus on employees’ needs denotes the importance to keep learning and developing capabilities to remain relevant in the dynamic of the jobs market (Dhanpat et al., 2020; Sajjadiani et al., 2019).”
Inampudi, P., Rao, P. B., Soumya, Arpitha, S., Ujwala, B., & Chandrashekaran, M. S. (2025) · Lex localis - Journal of Local Self-Government · doi:10.52152/xxpkx217 · Cited by 1
“Some authors have shown that AI can do tasks that human HR professionals might find difficult without technology, such as predicting the severity of workplace incidents (Kakhki et al., 2019), intentions to leave an organisation (Albert, 2019; Sajjadiani et al., 2019), and individual performance.”
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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
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