Advancing understanding of HRM in small and medium-sized enterprises (SMEs): critical questions and future prospects
Bibliographic record
Abstract
A notable paradox of HRM research is that while small and medium-sized enterprises (SMEs) form the dominant private sector employer across the globe, they remain dramatically underrepresented in scholarship. This is significant as there are a number of SME specific characteristics that shape HRM in this context, raising questions around the relevance and applicability of dominant understanding of HRM. In this paper we outline six such SME characteristics captured by the acronym RECIPE and outline their implications for HRM. We then introduce seven special issue papers which serve to advance understanding of HRM in SMEs. Drawing together key insights, we conclude by proposing a number of routes for future research and deeper contextualisation of HRM in SMEs. These include broadening the theoretical palette, challenging conventional assumptions, moving beyond an exclusive HPWS focus, incorporating employee perspectives, coupled with the need to cast a wider methodological net.
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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 | 0.037 | 0.034 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.004 | 0.025 |
| Scholarly communication | 0.016 | 0.033 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.009 | 0.014 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".