Enablers of green innovation in the micro-firm—perspectives from Ireland and Canada
Bibliographic record
Abstract
This research explores the enablers of green innovation in the micro-firm, applying a resource based view. The research poses the question, how are resources used to enable green innovation in a micro-firm? The authors apply a cross-country multi-case method, studying micro-firms in Ireland and Canada over a twelve month period. Results show that proactive use of green innovation enablers is influenced by; the owner/manager (O/M)’s natural environmental orientation and openness to engage with green innovation, their ability to identify, pool and bundle internal and external resources, their capacity to understand and implement green regulations and their ability to lever green potential for socio-economic gain. The findings show that engagement with both internal and external resource pools allow the O/M greater capacity to test new ideas, comprehend regulations and identify potential supports in pursuit of green innovation within the micro-firm. This study is important for a number of reasons. It explores green innovation resources within and outside the organisation and identifies the enablers of green innovation in micro-firms that could contribute to sustainable business goals.
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How this classification was reachedexpand
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 | 0.001 |
| 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 | 0.000 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".