Voluntary Standards and Ceremonial Adoption: Strategic Registration, Competition, and Audience in LEED Certification
Bibliographic record
Abstract
Abstract We study ceremonial adoption of voluntary standards, where participants adopt the standard in principle but do not change their practices. Ceremonial adoption can benefit individual participants, who may be able to reap the benefits of association with the standard at lower cost, but it can be problematic for overall levels of adoption. We conceive of ceremonial adoption as an interaction between strategic incentives of participants and social ties to their audiences, such that not all participants are likely to ceremonially adopt. Our setting is the Leadership in Energy and Environmental Design (LEED) certification for sustainable construction. We study the conditions under which projects register for LEED certification, allowing them to claim affiliation with LEED, but then do not actually finish certification. While our data are correlational in nature, our results suggest that studying the competition for audience members (in our case, occupants) can provide greater understanding of certification behavior as well as overall levels of adoption. Our findings have implications for organizations that design and maintain voluntary standards and for organization theorists who wish to understand field-level change. Thus, we provide more evidence that strategy and organizational theory interact in important and often unexamined ways.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.009 | 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".