Dynamic Eco Innovation Practices: A Systematic Review of State of the Art and Future Direction for Eco Innovation Study
Bibliographic record
Abstract
Purpose: The purpose of this paper is to discuss the role of dynamic eco innovation practice in order to achievesustainability in manufacturing industries. The outcomes of this paper attempts to describe core categories of ecoinnovation practices in manufacturing industry, drivers of eco innovation-practices and framework of dynamiceco-innovation practices. At the end of the paper provides insight of new research direction for eco innovationresearch in new millennium particularly in developing countries.Design/methodology/Approach: The selected papers that have been reviewed were retrieved from Googlescholar database with high citation index. A list of items/variables was developed for eco innovation measuresbased on dynamic capabilities theory pillar in eco innovation /green manufacturing through systematic literaturereview.Findings: Manufacturing acknowledges technical target in form of both product and process eco innovation as apivotal role to attain sustainability development in ecology, economy and society. There are four eco innovationdrivers captured in literature namely regulatory push, technology push, market pull, and firm strategies. However,underlined dynamic capabilities theory, four measurements uncovered consists of technology collaboration,green human resource, eco innovation culture and environmental management system strategy. Exploring thefactors of eco innovation effort considered as the heart of new research direction in new millennium.Originality/value: Eco innovation is a new research area and limited knowledge established under micro level.Thus, this paper attempts to provide a clear direction of dynamic eco innovation practices towards ecoinnovation efforts in manufacturing industry en route for building the sustainable development echoes toeconomy, ecology and society.
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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 | 0.019 | 0.067 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.019 | 0.018 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".