How Does the Enforcement of Labor Law Affect Other Firms? Exploring the Spillover Effects on Competitors’ Responsible HRM Practices
Bibliographic record
Abstract
How does the enforcement of labor regulations impact across a sector? This study questions whether penalizing one firm for labor violations induces competitors to improve or degrade their human resource (HR) standards. Prior work on interorganizational spillover effects focuses on other business areas (e.g., knowledge and technology, each of which has its own specific features), is heterogeneous, and does not directly engage the above question. Labor is an active agent and an internal stakeholder; moreover, corporations make decisions that are distinct to individuals. Hence, unit theories designed to understand other aspects of the spillover phenomena are not readily transferable to the domains encompassed in this study. Adopting an abductive exploratory design, we report that fines lead competitors to “relax” their adoption of responsible HR practices. Exploring further the boundary conditions of this discovery, we find that industry competition, the magnitude of fines, productivity, and the presence of labor unions moderate the above relationship. After that, we propose directions for future unit theorizing and the potential place of the latter within broader programmatic theorizing. At an applied level, the study helps HR managers better understand the likely consequences of a competitor being fined for breaching labor standards.
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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 | 0.004 | 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 | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".