The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.
Governing algorithms, empowering people: how ethical AI oversight shapes trust, technostress, and employee autonomy in AI-enabled HRM
Open accessAbstract
Introduction: Artificial intelligence is increasingly embedded in human resource management, yet its implications for employee autonomy depend not only on technological capability but also on how algorithmic systems are governed. This study examines how algorithmic transparency, perceived algorithmic fairness, and human-in-the-loop oversight shape employee autonomy through trust in AI, psychological safety, and technostress. Methods: Drawing on Social Exchange Theory and Conservation of Resources Theory, the study tested an integrated model using survey data from 290 employees working in organisations that employ AI-enabled HRM systems in India. The hypothesised direct and mediated relationships were examined using partial least squares structural equation modelling. Results: Algorithmic transparency, perceived algorithmic fairness, and human-in-the-loop oversight significantly strengthened employee trust in AI. Algorithmic transparency also enhanced psychological safety, while human-in-the-loop oversight reduced technostress. Trust in AI and psychological safety positively influenced employee autonomy, whereas technostress negatively influenced autonomy. Trust mediated the relationships between transparency, fairness, human oversight, and employee autonomy. Psychological safety mediated the transparency–autonomy relationship, while technostress mediated the relationship between human oversight and autonomy. Discussion: The findings demonstrate that employee autonomy in AI-enabled workplaces is shaped through relational, psychological, and resource-preservation mechanisms. Responsible AI-enabled HRM therefore requires more than technical efficiency; transparent decision processes, fairness safeguards, meaningful human oversight, and psychologically safe opportunities to question algorithmic decisions are important for preserving employee agency.
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.
Integrity signals
Registry
Cites
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.
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
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
The "what" and "why" of goal pursuits: Human needs and the self-determination of behavior
Deci, E. L., & Ryan, R. M. (2000) · Psychological Inquiry · doi:10.1207/s15327965pli1104_01 · Cited by 34,210
Psychological Safety and Learning Behavior in Work Teams
Edmondson, A. (1999) · Administrative Science Quarterly · doi:10.2307/2666999 · Cited by 12,026
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
Lee, M. K. (2018) · Big Data & Society · doi:10.1177/2053951718756684 · Cited by 1,338
Research works printed but not linked: 31
- Amershi, 2019, Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1
- Barocas, 2019, Fairness and Machine Learning
- Binns, 2018, Proceedings of the 2018 Chi Conference on Human Factors in Computing Systems, 1
- Blau, 1964, Exchange and power in social life
- Brod, 1984, Technostress: The Person Cost of the Computer Revolution
- Bujold, 2024, Responsible artificial intelligence in human resources management: a review of the empirical literature, AI Ethics, 4, 1185, 10.1007/s43681-023-00325-1 · DOI
- Cai, 2024, Effect of transparency of algorithmic performance management on proactive work behavior: role of motivation to improve performance and psychological ownership, Int. J. Asian Bus. Inf. Manag., 15, 1, 10.4018/IJABIM.348062 · DOI
- Cropanzano, 2005, Social exchange theory: an interdisciplinary review, J. Manag., 31, 874, 10.1177/0149206305279602 · DOI · Paper page
- Cummings, 2004, Designing Decision Support Systems for Revolutionary Command and Control Domains
- Dietvorst, 2016, Overcoming algorithm aversion: people will use imperfect algorithms if they can (even slightly) modify them, Manag. Sci., 64, 1155, 10.1287/mnsc.2016.2643 · DOI · Paper page
- Duggan, 2020, Algorithmic management and app-work in the gig economy: a research agenda for employment relations and HRM, Hum. Resour. Manag. J., 30, 114, 10.1111/1748-8583.12258 · DOI · Paper page
- Fornell, 1981, Evaluating structural equation models with unobservable variables and measurement error, J. Mark. Res., 18, 39, 10.1177/002224378101800104 · DOI · Paper page
- Greenberg, 1987, A taxonomy of organizational justice theories, Acad. Manag. Rev., 12, 9, 10.2307/257990 · DOI
- Grgic-Hlaca, 2018, Proceedings of the 2018 World Wide Web Conference, 903
- Hackman, 1976, Motivation through the design of work: test of a theory, Organ. Behav. Hum. Perform., 16, 250, 10.1016/0030-5073(76)90016-7 · DOI · Paper page
- Hair, 2019, When to use and how to report the results of PLS-SEM, Eur. Bus. Rev., 31, 2, 10.1108/ebr-11-2018-0203 · DOI · Paper page
- Heerink, 2010, Assessing acceptance of assistive social agent technology by older adults: the almere model, Int. J. Soc. Robot., 2, 361, 10.1007/s12369-010-0068-5 · DOI · Paper page
- Henseler, 2015, A new criterion for assessing discriminant validity in variance-based structural equation modeling, J. Acad. Mark. Sci., 43, 115, 10.1007/s11747-014-0403-8 · DOI · Paper page
- Hobfoll, 1989, Conservation of resources: a new attempt at conceptualizing stress, Am. Psychol., 44, 513, 10.1037/0003-066X.44.3.513 · DOI · Paper page
- Hobfoll, 2018, Conservation of resources in the organizational context: the reality of resources and their consequences, Annu. Rev. Organ. Psychol. Organ. Behav., 5, 103, 10.1146/annurev-orgpsych-032117-104640 · DOI · Paper page
- Köchling, 2025, This (AI)n’t fair? Employee reactions to artificial intelligence (AI) in career development systems, Rev. Manag. Sci., 19, 1195, 10.1007/s11846-024-00789-3 · DOI
- Lepri, 2018, Fair, transparent, and accountable algorithmic decision-making processes: The premise, the proposed solutions, and the open challenges, Philosophy & Technology, 31, 611, 10.1007/s13347-017-0279-x · DOI · Paper page
- Liu, 2023, Proceedings of the 27th Pacific Asia Conference on Information Systems (PACIS 2023), Paper 147
- Malik, 2023, Artificial intelligence (AI)-assisted HRM: towards an extended strategic framework, Hum. Resour. Manag. Rev., 33, 100940, 10.1016/j.hrmr.2022.100940 · DOI
- Meijerink, 2023, The duality of algorithmic management: toward a research agenda on HRM algorithms, autonomy and value creation, Hum. Resour. Manag. Rev., 33, Article 100876, 10.1016/j.hrmr.2021.100876 · DOI
- Noponen, 2024, Taylorism on steroids or enabling autonomy? A systematic review of algorithmic management, Manag. Rev. Q., 74, 1695, 10.1007/s11301-023-00345-5 · DOI
- Nunnally, 1978, An Overview of Psychological Measurement, 97, 10.1007/978-1-4684-2490-4_4 · DOI
- Ragu-Nathan, 2008, The consequences of technostress for end users in organizations: conceptual development and empirical validation, Inf. Syst. Res., 19, 417, 10.1287/isre.1070.0165 · DOI · Paper page
- Richter, 2016, A critical look at the use of SEM in international business research, International Marketing Review, 33, 376, 10.1108/IMR-04-2014-0148 · DOI
- Tarafdar, 2007, The impact of technostress on role stress and productivity, J. Manag. Inf. Syst., 24, 301, 10.2753/mis0742-1222240109 · DOI · Paper page
- Wieringa, 2020, Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, 1
Metadata
- Authors
- Patnaik, T., Gnankob, R. I., & Nanda, N. S.
- Affiliations
- Woxsen School of Business (IN)
- Vignana Jyothi Institute of Management (IN)
- Corresponding author
- Patnaik, Tamrisha
- Venue
- Frontiers in Artificial Intelligence
- Publisher
- Frontiers Media SA
- Published
- 2026-08-31
- Volume
- 9
- Pages
- 1911545-1911545
- ISSN
- 2624-8212 (ISSN-L)
- PMC
- PMC13572705
- OpenAlex
- W7204826046
- Cited by
- 3
- References
- 5
- Citations by year
- 2026: 3
- FWCI
- 17.02
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Frontiers in Artificial Intelligence (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Physical Sciences › Computer Science › Artificial Intelligence › Ethics and Social Impacts of AI
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › Job Satisfaction and Organizational Behavior
- Keywords
- employee autonomy, algorithmic transparency, human-in-the-loop oversight, trust in AI, psychological safety, technostress, responsible AI governance, social exchange theory, conservation of resources theory
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
