A Sustainable Business Model for Network Enhanced Green Innovativeness Capability Development
Bibliographic record
Abstract
This paper proposes a sustainable business model that determines the key criteria to facilitate green innovativeness capability development for green enterprise in the micro-firm context. Micro-firms, those firms with less than ten full-time employees, need to be continuously innovating in order to sustain their enterprise in the emerging green economy. This context is often characterised by continuous sustained transformation of ideas and knowledge into new products, processes or services in a resource constrained environment exasperated by the micro-firm’s size. Prior studies have found that facilitated business networks have a positive impact on micro-firm sustainability as these networks enhance the firm’s constrained resource base, enable innovativeness capability development and act as an additional resource in the micro-firm. This study uses a qualitative interpretative multiple case, cross-country approach to explore micro-firm green enterprise, encompassing green network activities in Ireland and Canada over a twelve month period. The findings assist in understanding the impact of facilitated networks on green innovativeness capability development in the micro-firm environment. They offer fresh insights and open new research paths to green innovativeness capability development in micro-firms and have implications for evolving green enterprises, facilitated networks and policy makers. The proposed innovativeness capability development framework can be used as a guideline for micro-firm support organisations including facilitated networks in assisting micro-firms in reaching their green innovativeness goals and objectives. It can also be used by green micro-enterprises in the attainment of the green innovativeness capabilities for the green economy.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".