Green human resource management in a family business setting: a fuzzy set approach
Bibliographic record
Abstract
Sustainability in family businesses as an entrepreneurial initiative is an emerging research field. The literature assumes that owners, managers, and employees in family businesses adopt homogeneous sustainability practices as a global construct mixing social and environmental issues. However, green human resource management (GHRM), considered as an innovative process, is focused on understanding how regular managers and employees become green-oriented and aim for business sustainability by applying green policies and practices. Based on previous green HRM research, this study proposes to apply a fuzzy set qualitative comparative analysis to identify the combinations of key practices observed in 513 family businesses in Quebec. This perspective helps to explain some of the boundary conditions and combinations of green HRM practices in family businesses based on more realistic insights by avoiding a merely linear analysis. We highlight four different pathways connecting green HRM system to economic performance. This study is the first to explore green HRM practices in family businesses. The paper concludes by discussing how the findings advance knowledge on sustainability in family business research.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".