Journal of Public Administration

Journal of Public Administration

Structural Modeling of Responsible Artificial Intelligence-Based Human Resource Management and Its Implications for Employee Flourishing

Document Type : Research Paper

Authors
Abbasali Rastgar 1
Minasadat Mousavi 2
1 Prof., Department of Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.
2 Ph.D., Department of Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran.
10.22059/jipa.2026.413478.3897
Abstract
Objective
This study aimed to identify, explain, and model the structural and hierarchical relationships between the components of responsible Artificial Intelligence (AI) in Human Resource Management (HRM) and their subsequent outcomes on employee flourishing within digital organizations. The core research problem addresses a critical gap in the extant literature: with the rapid integration of algorithms and intelligent decision-support systems into HRM, most organizations have disproportionately focused on technical efficiency, processing speed, and cost reduction, thereby neglecting the ethical dimensions and human consequences of these technologies. This study seeks to demonstrate how principles such as algorithmic transparency, accountability, fairness, privacy, and psychological safety can lead to positive work experiences and optimal employee outcomes within the context of human-machine interaction. Consequently, this research aims to explain how abstract AI ethical principles translate into psychological and behavioral outcomes for employees, thereby bridging the theoretical gap between normative AI principles and sustainable HRM practices.
Methods
Utilizing an exploratory-sequential mixed-methods design, this study was conducted within a pragmatic research paradigm. In the qualitative phase, semi-structured interviews were conducted with 17 dual-domain experts, comprising HRM specialists and AI or digital transformation experts, who were selected through purposive criterion and snowball sampling techniques. The qualitative data were analyzed using thematic analysis to extract relevant themes and dimensions. In the second (quantitative) phase, Interpretive Structural Modeling (ISM) was employed to map the hierarchical structure and interactions among the identified components. Prior to constructing the Structural Self-Interaction Matrix (SSIM), the dimensions and themes extracted from the qualitative phase were re-evaluated and validated by an expert panel in order to prevent the imposition of researcher bias and ensure the credibility of the findings. Furthermore, the analysis was grounded in Socio-Technical Systems (STS) theory to elucidate the alignment between the technical subsystem, comprising algorithms and infrastructure, and the social subsystem, encompassing humans and organizational interactions, in the implementation of responsible AI.
Results
The qualitative phase identified 15 sub-themes, which were subsequently categorized under the broader dimensions of responsible AI and employee flourishing. The ISM analysis revealed that implementing responsible AI in HR processes is not a one-dimensional technological intervention, but rather a multi-layered, systemic process structured across eight hierarchical levels. At the foundational levels, namely, the governance and technical layers, variables such as ethical policy-making, robust data infrastructure, and fair algorithmic design function as primary drivers. These foundational variables subsequently influence the middle layers, corresponding to the socio-psychological subsystem, by fostering organizational trust, enhancing perceived procedural justice, and mitigating ethical risks. Ultimately, at the highest level of the model, designated as the outcome subsystem, employee flourishing is realized through constructs such as meaning at work, active engagement, personal growth, and optimal performance. The findings collectively confirm that social and ethical components play a crucial mediating role in translating technical capabilities into meaningful human outcomes.
Conclusion
By proposing a three-layered structural-interpretive model, comprising governing-technical, socio-psychological, and outcome layers, this study addresses a significant gap in the literature at the intersection of responsible AI and HRM. The results demonstrate that the success and sustainability of intelligent technologies in organizations rely fundamentally on the joint optimization of both technical and social subsystems. In the absence of adequate attention to the psychological and ethical needs of employees, AI implementation may trigger work alienation and decrease organizational commitment, thereby undermining the very objectives it seeks to achieve. The proposed model serves as a strategic roadmap for organizational policymakers, HR managers, and system developers seeking to design human-centered technological solutions and promote human resource flourishing in the era of digital transformation. These insights offer both theoretical contributions and practical guidance for organizations striving to balance technological advancement with ethical responsibility and employee well-being.
Keywords
Responsible artificial intelligence
Employee thriving
Transparency and accountability
Organizational trust
Socio‑technical systems theory
Subjects
Alawamleh, M., Al-Twal, A., Lahlouh, L. & Jame, R. O. (2023). Interpretive structural modelling of organizational innovation factors: An emerging market perspective. Journal of Open Innovation: Technology, Market, and Complexity, 9(2), 100067.
Bankins, S., Formosa, P. & O’Kane, P. (2024). The ethical implications of artificial intelligence (AI) in human resource management: A review and framework. Human Resource Management Review, 34(1), 100940.
Blau, P. M. (1964). Exchange and power in social life. Wiley.
Braun, V. & Clarke, V. (2021). One size fits all? What counts as quality practice in (reflexive) thematic analysis? Qualitative research in psychology, 18(3), 328-352
Budhwar, P., Malik, A., De Silva, M. T. & Thevisuthan, P. (2023). Artificial intelligence – challenges and opportunities for international HRM: a review and research agenda. The International Journal of Human Resource Management, 34(10), 1968-2007.
Bunjak, A., Černe, M. & Popovič, A. (2023). Artificial intelligence and employees’ well-being: A systematic literature review and future research agenda. Journal of Business Research, 162, 113886.
Chang, Y. L. & Ke, J. (2024). Socially responsible artificial intelligence empowered people analytics: a novel framework towards sustainability. Human Resource Development Review, 23(1), 88-120.
Cherns, A. (1976). The principles of sociotechnical design. Human relations, 29(8), 783-792.
Chowdhury, S., Dey, P. K., Joel-Edgar, S., Bhattacharya, S., Rodriguez-Espindola, O., Abadie, A. & Truong, L. (2023). Unlocking the value of artificial intelligence in human resource management through AI capability framework. Human Resource Management Review, 33(1), 100899.
Creswell, J. W. & Plano Clark, V. L. (2023). Revisiting mixed methods research designs twenty years later. Handbook of mixed methods research designs, 1(1), 21-36.
Das, S. S., Pramod, V. R., & Kiron, K. (2024). Interpretive structural modeling (ISM): A literature review. International Journal of Creative Research Thoughts, 12(5), 863-868.
European Commission. (2024). Artificial Intelligence Act: Regulation of the European Parliament and of the Council laying down harmonised rules on artificial intelligence. Official Journal of the European Union.
Field, A. (2018). Discovering statistics using IBM SPSS statistics. Sage.
Haji Esmaeili, H., Tahmasebi, R. & Babashahi, J. (2026). Designing an Inclusive Talent Development Framework in the Organization (Case Study: Social Security Organization). Journal of Public Administration, 18(1), 203-233. https://doi.org/10.22059/jipa.2025.399679.3749 (in Persian)
Khair, M. A., Mahadasa, R., Tuli, F. A. & Ande, J. R. P. K. (2020). Beyond human judgment: exploring the impact of artificial intelligence on HR decision-making efficiency and fairness. Global Disclosure of Economics and Business, 9(2), 163-176.
Leicht-Deobald, U., Busch, T., Schank, C., Weibel, A., Scherer, S. & Schildhauer, T. (2022). The challenges of algorithm-based HR decision-making for personal integrity. Journal of Business Ethics, 179(3), 661-683.
Malik, A., Budhwar, P. & Kazmi, B. A. (2023). Artificial intelligence (AI)-assisted HR decision making: A multi-level framework. Human Resource Management Review, 33(1), 100910.
Momeni, A., Yaghoubi, N. M., Roshan, S. A. & Pourezzat, A. A. (2026). The Design of an Artificial Intelligence Adoption Model in Smart Governance Using a Meta-Synthesis Approach. Journal of Public Administration, 18(1), 263-295. doi: 10.22059/jipa.2025.398124.3737 (in Persian)
Mousavi, M., Rastgar, A.A. & Shafiei Nikabadi, M. (2025). Artificial Intelligence in Human Resource Management with a Positive Approach: A Framework for Employee Flourishing. Business Intelligence Management Studies, 14(54), 175-216. doi: 10.22054/ims.2025.86244.2626 (in Persian)
National Institute of Standards and Technology (NIST). (2023). Artificial Intelligence Risk Management Framework (AI RMF 1.0). U.S. Department of Commerce. https://doi.org/10.6028/NIST.AI.100-1
Prikshat, V., Malik, A. & Budhwar, P. (2023). AI-augmented HRM: Antecedents, assimilation and multilevel consequences. Human Resource Management Review, 33(1), 100886.
Rastgar, A. & Mousavi, M. (2023). Investigating the Effect of Ethnic Diversity of Iranian Startup Team Members on the Investment Capital Raised (Study Case: Startups at Dubai Gitex Exhibition). Journal of Public Administration, 15(4), 751-765. doi: 10.22059/jipa.2023.355429.3295 (in Persian)
Sadeghi, S. (2024). Employee Well-being in the Age of AI: Perceptions, Concerns, Behaviors, and Outcomes. arXiv preprint arXiv:2412.04796.
Schulte, P. A. & Streit, J. M. (2025). Psychosocial risks and ethical implications of technology: considerations for decent work. Annals of Work Exposures and Health, 69(4), 360-376.
Soomro, S., Fan, M., Sohu, J. M., Soomro, S. & Shaikh, S. N. (2024). AI adoption: a bridge or a barrier? The moderating role of organizational support in the path toward employee well-being. Kybernetes.
Spreitzer, G., Sutcliffe, K., Dutton, J., Sonenshein, S. & Grant, A. M. (2005). A socially embedded model of thriving at work. Organization Science, 16(5), 537–549. https://doi.org/10.1287/orsc.1050.0153
Stahl, B. C., Antoniou, J., Bhalla, N., Brooks, L., Jansen, P., … & Way, C. (2023). A systematic review of artificial intelligence impact assessments. Artificial Intelligence Review, 56(3), 2097-2127.
Sushil. (2012). Interpreting the interpretive structural model. Global Journal of Flexible Systems Management, 13(2), 87-106.
Tambe, P., Cappelli, P. & Yakubovich, V. (2019). Artificial intelligence in human resources management: Challenges and a path forward. California Management Review, 61(4), 15–42. https://doi.org/10.1177/0008125619867910
Tripathi, A. & Kumar, V. (2025). Ethical practices of artificial intelligence: A management framework for responsible AI deployment in businesses. AI and Ethics, 1-12.
Trist, E. L. (1981). The evolution of socio-technical systems: A conceptual framework and an action research program. Occasional paper, 2(1981), 1981.
Vaezi, S. K. & Pashaei, F. (2025). Application of artificial intelligence for Generating Smart Behavioral Nudges. Journal of Public Administration, 17(4), 964-988. doi: 10.22059/jipa.2025.395752.3700 (in Persian)
Valtonen, A., Kimpimäki, J. P. & Savela, N. (2026). Employee wellbeing: a computational review on the consequences of workplace automation. Technovation, 152, 103424.
von Krogh, G. (2012). How does social software change knowledge management? Toward a strategic research agenda. The Journal of Strategic Information Systems, 21(2), 154–164. https://doi.org/10.1016/j.jsis.2012.04.003
Vrontis, D., Christofi, M., Pereira, V., Tarba, S., Makrides, A. & Trichina, E. (2023). Artificial intelligence, robotics, advanced technologies and human resource management: a systematic review. Artificial intelligence and international HRM, 172-201. 55.
Weber, R. (2021). Constructs and Indicators: An Ontological Analysis. MIS Quarterly, 45(4)
Journal of Public Administration
Volume 18, Issue 3
2026
Pages 775-804