The DOI matches its Crossref record; 148 of the 151 references it cites were found in a registry or an index.
The Challenges of Algorithm-Based HR Decision-Making for Personal Integrity
Open accessAbstract
Organizations increasingly rely on algorithm-based HR decision-making to monitor their employees. This trend is reinforced by the technology industry claiming that its decision-making tools are efficient and objective, downplaying their potential biases. In our manuscript, we identify an important challenge arising from the efficiency-driven logic of algorithm-based HR decision-making, namely that it may shift the delicate balance between employees' personal integrity and compliance more in the direction of compliance. We suggest that critical data literacy, ethical awareness, the use of participatory design methods, and private regulatory regimes within civil society can help overcome these challenges. Our paper contributes to literature on workplace monitoring, critical data studies, personal integrity, and literature at the intersection between HR management and corporate responsibility.
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“AI-driven algorithmic monitoring thus allows to collect and record a vast array of new information and metrics, such as emotions (Chan, 2018;Pinheiro, Ramos, Donizete, Picanço, & De Oliveira, 2017), movements, sleep time, physical and health condition, social media activity, internet browser history (Angrave, Charlwood, Kirkpatrick, Lawrence, & Stuart, 2016;Leicht-Deobald et al., 2019)”
From Algorithms as work designers: How algorithmic management influences the design of jobs (2022) · cites this paper as Leicht-Deobald et al., 2019
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In the citing paper
“These so-called predictive algorithms operate regression-based forecasting techniques that, for example, help managers predict which employees are likely to leave the organization (and thus how to make decisions about retention), or to predict the future performance of a job candidate (and thus help hiring managers with selection decisions) (Cheng & Hackett, 2021;Leicht-Deobald et al., 2019).”
From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“More generally, IT developers and therefore end-user employees, managers or independent contractors are not aware of the basis upon which computer-programmed steps are taken and which data are drawn on when HRM algorithms generate a selected type of output (Gal et al., 2020;Leicht-Deobald et al., 2019).”
From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019
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In the citing paper
“For instance, algorithms can assist Human Resources (HR) to filter job applicants (Leicht-Deobald et al., 2019), fire inadequately fast warehouse workers, and improve work morale through fine-grained people analytics (Gal et al., 2020).”
From Algorithmic management in a work context (2021) · cites this paper as Leicht-Deobald et al., 2019
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“they argue that algorithmic HR systems can promote an efficiencydriven logic that changes the balance between employee personal integrity and organizational compliance.”
From Designing “Contestability” in Automated HR Decisions: Appeal Mechanisms, Evidence, and Outcomes (2025) · cites this paper as Leicht-Deobald et al. (2019)
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In the citing paper
“If algorithmic systems are treated as authoritative, employees may feel pressure to conform to model-driven expectations, even when those expectations conflict with their judgment, identity, or context.”
From Designing “Contestability” in Automated HR Decisions: Appeal Mechanisms, Evidence, and Outcomes (2025) · cites this paper as Leicht-Deobald et al. (2019)
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In the citing paper
“Software algorithms support HRM decision making in two ways, through providing information (or augmentation) and with automation (Leicht-Deobald et al., 2019;Raisch & Krakowski, in press).”
From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019
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“Notwithstanding their benefits such as adaptability, the use of software algorithms in HRM creates challenges (Gal et al., 2020;Leicht-Deobald et al., 2019;Tambe et al., 2019).”
From Algorithmic human resource management: Synthesizing developments and cross-disciplinary insights on digital HRM (2021) · cites this paper as Leicht-Deobald et al., 2019
In the citing paper
“Algorithmic management, therefore, makes a unique demand for the “blind trust” of workers (Leicht-Deobald et al. 2019).”
From Artificial intelligence and work: a critical review of recent research from the social sciences (2024) · cites this paper as Leicht-Deobald et al. 2019
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Citations
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Cited by
Sorted by citations
Algorithms at Work: The New Contested Terrain of Control
Kellogg, K. C., Valentine, M. A., & Christin, A. (2020) · Academy of Management Annals · doi:10.5465/annals.2018.0174 · Cited by 2,732
Köchling, A., & Wehner, M. C. (2020) · Business Research · doi:10.1007/s40685-020-00134-w · Cited by 511
Algorithmic management in a work context
Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021) · Big Data & Society · doi:10.1177/20539517211020332 · Cited by 532
“For instance, algorithms can assist Human Resources (HR) to filter job applicants (Leicht-Deobald et al., 2019), fire inadequately fast warehouse workers, and improve work morale through fine-grained people analytics (Gal et al., 2020).”
BackgroundAlgorithms as work designers: How algorithmic management influences the design of jobs
Parent-Rocheleau, X., & Parker, S. K. (2022) · Human Resource Management Review · doi:10.1016/j.hrmr.2021.100838 · Cited by 512
Ethical framework for Artificial Intelligence and Digital technologies
Ashok, M., Madan, R., Joha, A., & Sivarajah, U. (2022) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2021.102433 · Cited by 469
Metadata
- Authors
- Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Schafheitle, S., Wildhaber, I., & Kasper, G.
- Affiliations
- University of St.Gallen (CH)
- University of Vechta (DE)
- Venue
- Journal of Business Ethics
- Published
- 2019-06-07
- Volume
- 160
- Issue
- 2
- Pages
- 377-392
- ISSN
- 0167-4544 (ISSN-L)
- 1573-0697
- PMC
- PMC6868110
- OpenAlex
- W2948100573
- Cited by
- 347
- References
- 151
- Citations by year
- 2026: 82 · 2025: 73 · 2024: 50 · 2023: 43 · 2022: 53 · 2021: 34 · 2020: 11 · 2019: 1
- FWCI
- 43.30
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Business Ethics (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Alexandria (UniSG) (University of St.Gallen) (acceptedVersion)
- KOPS (University of Konstanz) (submittedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Alexandria (UniSG) (University of St.Gallen) (submittedVersion)
- Zürcher Hochschule für Angewandte Wissenschaften digital collection (Zurich University of Applied Sciences) (submittedVersion, cc-by-nc-nd)
- Funders
- Swiss National Science Foundation (407540_167208)
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Business, Management and Accounting › Industrial relations › Legal and Labor Studies
- Keywords
- algorithmic decision-making, human resource management, personal integrity, workplace monitoring, employee compliance, algorithmic bias, critical data literacy, moral consciousness, participatory design, corporate social responsibility, critical data studies
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