Boundary Conditions of the High‐Investment Human Resource Systems‐Small‐Firm Labor Productivity Relationship
Bibliographic record
Abstract
Although a few published, multiindustry, firm‐level, empirical studies have linked systems of high‐investment or high‐performance human resource management practices to enhanced small‐firm performance, this stream of strategic human resource management research is underdeveloped and equivocal. Accordingly, in this study, we use a sample of for‐profit, private‐sector, small Canadian firms with fewer than 100 employees from a variety of industry sectors to examine boundary conditions of the relationship between firm‐level high‐investment human resource systems and objective small‐firm labor productivity. Congruent with contingency theory, this study's results indicate that the extent and nature of the influence of high‐investment human resource systems on objective small‐firm labor productivity is contingent on internal (differentiation strategy and firm capital intensity) and external boundary conditions (industry dynamism and industry growth). Implications and limitations of this research study as well as avenues for future research are discussed.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".