Organizational sensemaking and environmental performance: A longitudinal study of publicly traded firms' sustainability reports
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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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".