The Impact of B Lab Certification on Firm Growth
Bibliographic record
Abstract
We investigate the impact of B Lab certification—a rapidly growing type of third-party certification for organizations with social and/or environmental missions—on the short-term growth rates of certifying firms. To date, this kind of certification has generally been regarded as an unalloyed good for the organizations that adopt it; but prior research has overlooked the possibility that it may also entail attentional deficits and internal organizational disruption, leading to a short-term growth slowdown. Our study reports results based on a novel, hand-collected dataset of 249 mainly privately held North American certified B Corporations over 2011–2014. Our results, derived from a difference-in-difference framework, and augmented with insights from a set of in-depth interviews, identifies a short-term growth slowdown arising from certification, which is more pronounced for the smallest and youngest firms. These findings highlight the need for management theorists to pay greater attention to internal re-organization costs and external benefits flowing from B Lab certification; they also carry important practical implications for organizations contemplating certification.
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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 | 0.002 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".