Toward A Place‐Based Understanding of Business Sustainability: The Role of Green Competitors and Green Locales in Firms' Voluntary Environmental Engagement
Bibliographic record
Abstract
Abstract Management research has extensively considered who, what, when, why, which and how aspects pertaining to firms' voluntary environmental practices, yet the where aspect, which would consider the role of a firm's location on its environmental practices, has received remarkably less attention. We explore three research questions relating social and physical attributes of a firm's location with its engagement in a voluntary environmental program (VEP). Drawing on a sample of hotels participating in a Costa Rican VEP, we find that the number of VEP certified competitors (i.e. green competitors) and firm proximity to a sacrosanct environment (i.e. a green locale) are positively related to a firm's level of VEP engagement. We also find an interaction effect such that the relationship between the number of VEP certified competitors and the level of VEP engagement is positively moderated by firm proximity to a green locale. We argue that firms' voluntary environmental engagement can be enhanced by developing green clusters amid green corridors. Copyright © 2017 John Wiley & Sons, Ltd and ERP Environment
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.013 |
| Scholarly communication | 0.009 | 0.009 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 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".