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Record W4412419490 · doi:10.1016/j.bushor.2025.07.004

Measure what matters: A blueprint for a sustainability culture diagnostic

2025· article· en· W4412419490 on OpenAlex
Mark Klassen, C. Brooke Dobni, Norman T. Sheehan

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueBusiness Horizons · 2025
Typearticle
Languageen
FieldDecision Sciences
TopicComplex Systems and Decision Making
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsBlueprintMeasure (data warehouse)SustainabilityBusinessComputer scienceEngineeringData miningBiologyEcology

Abstract

fetched live from OpenAlex

While many recognize sustainability as a valid risk management tactic, some CEOs are facing opposition to their sustainability initiatives. Given this challenging environment, we argue that it is critical that CEOs successfully execute their sustainability agendas to avoid criticism. Unfortunately, the complexities surrounding the execution of sustainability initiatives make achieving good sustainability performance difficult. As such, this article makes two small but critical contributions to improving organizational sustainability performance. First, we argue that if CEOs want to improve their organization’s ability to improve sustainability outcomes, they need to start by measuring their organization’s sustainability culture. Second, we leverage an empirically validated innovation culture measurement model to operationalize a sustainability culture diagnostic tool that CEOs can use to measure their organization’s sustainability culture. Finally, we provide preliminary guidance on how CEOs can use the sustainability culture diagnostic to improve their organization’s sustainability culture and performance.

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.

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)0.003
metaresearch head score (Gemma)0.037
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.957
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.037
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.000
Scholarly communication0.0020.001
Open science0.0010.000
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 head0.071
GPT teacher head0.385
Teacher spread0.314 · 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