Barriers to Green Entrepreneurship: An ISM-Based Investigation
Bibliographic record
Abstract
Green entrepreneurship is a novel sustainability term. A strategy has recently been put forward to make a business greener by minimizing the harmful impact on the environment and committing to sustainability while maintaining financial imperishability. However, some barriers prevent its implementation to its full potential. This study aims at investigating such barriers following the interpretive structural modeling (ISM) approach for analyzing relationships among them and for their prioritization, for the effective construction of green entrepreneurship. The study revealed that collaboration among stakeholders of business activities is vital to green entrepreneurship. Results also show that R&D and technology are foundational to overcoming other barriers, such as the costs associated with green initiatives, lack of knowledge and subjective awareness in the market, shortage of investors and involvement of private sectors, government regulations, cultural differences, dominating industries, lack of incentive and support mechanism, and bureaucracy. Subsequently, the results indicated that overcoming these barriers will enable us to change the short-term mindset of investors towards green entrepreneurship. Implications of this study include using the revealed set of barriers and their modeled relationships for policymaking as well as the development of better targeted and more effective strategies to overcome these barriers, enabling its implementation to its full potential.
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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 | 0.008 | 0.022 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 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".