Dynamic capabilities for strategic green advantage: green electricity purchasing in North American firms, SMEs, NGOs and agencies
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.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".