Human Resource Systems and Ethical Climates: A Resource‐Based Perspective
Bibliographic record
Abstract
We know very little about how ethical climates are built and the potential role of a firm's HR system in facilitating the development of this resource. The resource‐based view (RBV) of the firm suggests that human resource systems directly influence a firm's performance through the development of resources that are deeply woven in a firm's history and culture. How this occurs though has not been thoroughly considered in the research literature. Drawing on the theoretical insights from the resource‐based view of the firm, this article explores how HR systems can foster the development and maintenance of five types of ethical climates. In so doing, this article improves our conceptual understanding of why ethical climates may be seen as having strategic value for firms and how HR systems may influence that value. In addition, it contributes to theory by extending the domain of the resource‐based view of the firm by exploring its integration with the varied types of ethical climates. © 2014 Wiley Periodicals, Inc.
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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 | 0.009 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.038 |
| Scholarly communication | 0.013 | 0.008 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".