On the road to HR legitimacy in SMEs: the signalling power of HR metrics
Bibliographic record
Abstract
In this study, we examine why and how HR metrics can help increase the legitimacy of the HR function in organizations. Drawing on signalling theory, we conceptualize HR metrics as a communicational tool that signals the value of the HR function’s activities and contributions. We apprehend HR legitimacy through two key concepts: the strength of the HRM system and the underlying philosophies, or raison d’être, attributed to the HR function, namely maximizing business performance and promoting employee well-being. Testing our model among a sample of 218 HR professionals in Canadian small and medium-sized enterprises (SMEs), we find that the relationships between HR metrics and HR philosophies are partially mediated by HRM strength. These results suggest that HR professionals can tap into the signalling power of HR metrics to enhance the legitimacy of the HR function by fostering shared perceptions that HR goals, activities, and contributions are valuable and aligned with the interests of both management and employees. This suggests that the use of HR metrics and analytics can help HR functions pursue a pluralist philosophy that seeks to enhance mutuality in employment relationships. This research is among the first to use a quantitative design to capture HR legitimacy.
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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 | 0.035 | 0.169 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.004 | 0.018 |
| Scholarly communication | 0.010 | 0.012 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".