Strategic Relational Human Resource Management in the Age of AI: Balancing Human and Technology Connections
Bibliographic record
Abstract
The increasing integration of artificial intelligence (AI) into human resource management (HRM) is reshaping workplace relationships and the strategic design of HRM systems. This book chapter examines the reciprocal influence between AI adoption and Strategic Relational Human Resource Management (SRHRM) systems, a framework that prioritizes HRM practices fostering positive workplace relationships and collective human and social capital emergence. This conceptual study employs a theory-building methodology to critically analyze how AI-driven HRM tools, such as algorithmic decision-making in recruitment, performance management, and promotion, contribute to sustainable competitive advantage through the effect on relational mechanisms like trust, fairness, and social connections. By integrating insights from strategic HRM, HRM digitalization, and human and social capital literature, this chapter develops a conceptual model depicting that organizations with strong SRHRM systems can strategically implement AI tools to align with relational values, ensuring technological advancements reinforce rather than impede human-centered HRM principles. The chapter advances theoretical understanding by reconciling fragmented perspectives on AI and relational HRM while offering practical guidance for designing AI-enabled SRHRM systems that balance operational efficiency with sustainable workplace relationships.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".