Building Micro‐Foundations for Positive Workplace Relationships: Validation of a Strategic Relational Human Resource Management Measure
Bibliographic record
Abstract
ABSTRACT A growing number of studies have recognized the pivotal role of relational Human Resource Management (HRM) systems in fostering positive interpersonal relationships in the workplace. These systems are tailored to fulfill specific relational objectives through collective‐level mechanisms. However, there has been a notable neglect of strategies for establishing the general foundations of positive workplace relationships and the contributions of individual actors in relationship‐building activities. Drawing upon the multilevel micro‐foundational structure framework and strategic human capital theory, this study introduces and validates a new measure of strategic relational HRM (SRHRM) systems. This measure incorporates a set of interrelated HRM practices aimed at reinforcing individual employees' relational knowledge, skills, and abilities, which serve as micro‐foundations for the development and maintenance of workplace relationships. Our methodology encompasses a meticulous validation process for the SRHRM measure. This involves employing four diverse samples from North America and Asia to assess its content validity, internal consistency, convergent and discriminant validity, as well as criterion‐related validity. Our findings provide substantial support for the application of the SRHRM measure in future empirical investigations.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.010 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".