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Voluntary Standards and Ceremonial Adoption: Strategic Registration, Competition, and Audience in LEED Certification

2023· book-chapter· en· W4387992246 on OpenAlexaff
Anne Bowers, Hyeun J. Lee

Bibliographic record

Venuenot available
Typebook-chapter
Languageen
FieldEngineering
TopicSustainable Building Design and Assessment
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsCertificationCompetition (biology)IncentiveMarketingPublic relationsBusinessEnvironmental designTurnoverPolitical scienceManagementEngineeringEconomics

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Other · Consensus signal: none
Teacher disagreement score0.786
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.028
GPT teacher head0.243
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreOther

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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