A <scp>quarter‐century review of HRM in small and medium‐sized enterprises</scp>: <scp>Capturing what we know</scp>, <scp>exploring where we need to go</scp>
Bibliographic record
Abstract
Abstract Despite the proliferation of HRM research, only a small fraction explores the context of small and medium‐sized enterprises (SMEs). Where HRM in SMEs has received attention, the literature base remains fragmented and variable, comprising a plurality of definitions, explanations, and methods. To advance understanding, this paper uses a quarter‐century systematic review drawing on an evidence base of 137 peer‐reviewed articles. A cumulative framework is presented capturing key developments and synthesizing existing areas of research focus. Analysis of limitations and knowledge‐gaps finds a failure to differentiate across various types of SMEs, limited appreciation of SME characteristics and contextual conditions, and a dominance of managerial perspectives. An agenda for future research on HRM in SMEs is outlined with respect to definitional parameters, HR practices, HRM–performance, key determinants, and presenting issues. The paper concludes that SMEs offer a unique, fruitful, and timely context for investigations of HRM.
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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 | 0.011 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.016 | 0.021 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".