Dynamic capabilities for strategic green advantage: green electricity purchasing in North American firms, SMEs, NGOs and agencies
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
North American businesses, social economy organisations and government agencies are tackling the challenges of declining non-renewable energy resources and climate change by voluntarily purchasing green electricity (GE). This study uses a survey of 213 organisations that voluntarily purchase GE to test the influence of green institutional and green resource-based factors on the purchase decision. Components of green institutional theory and the green resource-based view of the firm were found to have only a secondary or indirect influence on the voluntary decision to purchase GE. In contrast, the overwhelming importance attributed by respondents to the role of champions suggests that internal agency should be incorporated into future studies examining voluntary environmental decisions from an organisational perspective. The dynamic capabilities process, defined as the interaction between champions and environmental structures, can generate strategic green advantage if champions use environmental structures to emphasise: 1) environmental benefits; 2) marketing and green image benefits; 3) the GE purchase as a hedge against fossil-fuel price uncertainty.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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