A cross-country exploration of the perceived impact of facilitated networks on green innovation capability development in the micro-firm
Bibliographic record
Abstract
The motivation for this research originates in the current global debate on the need for a sustainable green economy. As micro-firms (those firms with less than ten employees), represent 90.8% of all businesses in Ireland (CSO, 2011) and 75% in Canada (Industry Canada, 2013), the development of their green innovation capability development is vital in pursuit of green economy goals. This thesis studies explores the perceived impact facilitated network engagement has on green innovation capability development in the micro-firm. This study uses a interpretive multiple case, cross-country approach studying micro-firms in Ireland and Canada over a twelve month period. The proactive implementation of green innovation is influenced by the owner/manager (O/M)’s natural environment orientation (NEO) and the potential for economic gain. The findings show that facilitated networks play a role in the development of innovation capabilties and provide an additional resource that the O/M can draw from. In particular, the network allows the O/M to test new ideas, comprehend legislation and identify potential supports in pursuit of green innovation capability development within the micro-firm. This study has academic, practitioner and policy implications as it assists in understanding the impact of inter-firm collaboration on green innovation capability development. This study offers a framework that can be used as a guideline for micro-firm support organisations including facilitated networks to assist micro-firms in reaching their green innovation goals and objectives. At a national level, government run systematic and collective marketing initiatives, which engage with enterprise and networks could help to promote the financial savings and opportunities of green innovation. In the absence of regulations, the onus is on the individual to take accountability for their own green innovation. This exploratory study provides a basis on which further research can be undertaken in the area of green innovation, facilitated networks and the micro-firm. The framework could potentially be applied in other countries and in further micro-firms to test its applicability for the development of green innovation capabilities.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".