Creating Shared Value Through Meta-Organizational Common Good Human Resource Management
Bibliographic record
Abstract
Research on the role of Human Resource Management (HRM) in fostering sustainability has pointed to the difficulty of changing organizations that have been built and optimized for economic profit. An emerging stream of research on sustainable businesses therefore focuses on organizations that are designed for sustainability from inception. Based on qualitative data, this empirical study investigates the design and functionality of HRM systems in companies that have received awards for their high level of environmental sustainability. Moreover, it examines how these systems contribute to the creation of shared value for the common good. Integrating existing insights from common good HRM and meta-organizational HRM, we elaborate theory of shared HR value creation by firms with exceptional environmental performance. We identify five principles through which common good HR practices are implemented and related outcomes of the HRM system. Our elaborated framework highlights relevant inputs, practices and outputs required for common good HRM and meta-organizational characteristics enabling the shared value creation of the HRM system. Our research has implications for designing strategically targeted HRM systems for environmental sustainability.
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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 | 0.015 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.003 | 0.014 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.002 | 0.011 |
| Research integrity | 0.001 | 0.002 |
| 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".