Organizational sensemaking and environmental performance: A longitudinal study of publicly traded firms' sustainability reports
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.
Bibliographic record
Abstract
Abstract Environmental strategy research has often used organizational interpretation as a key lens for understanding how firms engage in sensemaking around natural environmental issues and environmental performance. This work has rarely empirically tested the proposed relationships of organizational interpretation in firms' sensemaking around environmental issues nor the relationship between firms' environmental sensemaking and environmental performance. We empirically test this relationship, capturing environmental sensemaking through computer‐aided text analysis (CATA) of published sustainability reports, and environmental performance with the Trucost environmental dataset. Mixed‐effects general linear modeling on a bespoke longitudinal dataset of 117 publicly traded companies from 2005 to 2018 reveals the three stages of the organization interpretation model of sensemaking—scanning, interpreting, and responding—align as expected. We also find firms' environmental scanning relates with year‐over‐year improvement in environmental performance, yet environmental interpreting correlates with worsening environmental performance. Additionally, larger firms and firms in industries with high carbon emissions gather more environmental data and exhibit more extensive environmental interpreting. This research provides insight for scholars by testing environmental sensemaking and exploring the boundary conditions of sensemaking and performance, and for practitioners and policymakers by offering a new framework for analyzing and interpreting sustainability reports and corporate environmental performance.
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Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it