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.