Human resource professionals’ human and social capital in SMEs: small firm, big impact
Bibliographic record
Abstract
HR professionals have been widely ignored in the Human Resource (HR) management literature pertaining to small and medium-sized enterprises (SME). Given that entrepreneurs may be reluctant to hire HR professionals, it is important to investigate the value that HR people may bring to an SME. Addressing this literature gap, our research draws on resource orchestration theory (ROT) to link the interaction between HR professionals’ human and social capital to firm performance via the use of high-performance work practices (HPWPs). Further, this relationship depends on the SME’s size: in small firms, the link is strengthened because HR professionals’ human and social capital is more likely to create a competitive advantage in a context of resource poverty. We test the research model among a sample of 174 Canadian SMEs with fewer than 250 employees. Our results show that HR professionals’ human and social capital is more closely related to firm performance through the adoption of HPWPs in small SMEs than in large ones. Overall, our study highlights the value that HR professionals bring to small SMEs and advocates for a greater presence of HR professionals in SMEs.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".