Join In . . . and Drop Out? Firm Adoption of and Disengagement From Voluntary Environmental Programs
Bibliographic record
Abstract
Voluntary environmental programs (VEPs) offer opportunities for companies and stakeholders to improve environmental outcomes valued by society in the absence of regulatory mandates. Research has addressed numerous antecedents for firm adoption of VEPs, enhancing knowledge of how stakeholders and firms engage on substantive issues of public importance. However, program adoption is dynamic, and stagnant participation rates may threaten program longevity when firms do not realize expected benefits. Prior literature has not sufficiently addressed the factors that compel firms to drop out. In this study I articulate three consequential drivers of firm commitment to VEPs—transparency, effort, and achievement—and empirically estimate their effects on firm disengagement from one such prominent program: CDP (formerly known as Carbon Disclosure Project). Findings indicate that firm transparency and effort represent powerful commitment mechanisms driving continued program participation. This study contributes to theory over multiple literatures related to VEP participation and offers practical guidance for both VEPs and firms.
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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 | 0.005 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".