Exploring the drivers of green SMEs: A multidimensional qualitative study
Bibliographic record
Abstract
Despite growing global attention to sustainable entrepreneurship, limited understanding persists regarding how financial, institutional, and leadership dynamics jointly shape the green transition of small and medium-sized enterprises (SMEs). Responding to this gap, the present study offers a novel, ecosystem-based perspective that examines how multiple drivers and barriers co-evolve to influence SME sustainability transitions. Drawing from institutional theory and the resource-based view, and using the United Arab Emirates as a representative context, the paper presents qualitative evidence from 96 stakeholders—including government officials, SME leaders, academics, consultants, and finance professionals. Findings include evidence of the complex interplay between internal capabilities and external institutional structures that determine the pace and depth of green transformation. The findings reveal that financial challenges—particularly high borrowing costs, investor risk aversion, and the absence of specialized green finance instruments—constitute the most significant barriers to sustainability adoption. Conversely, government support mechanisms, technological advancement, and leadership commitment act as powerful enablers, promoting innovation and resilience. The study further demonstrates that SMEs’ ability to signal environmental and social value to investors depends on coherent policy frameworks and integrated public–private financing mechanisms. By integrating insights informed by theory and empirical data, this research proposes a multidimensional framework linking green finance accessibility, institutional readiness, and organizational capability development, advancing theoretical understanding of SME sustainability transitions. The findings provide actionable guidance for policymakers, financial institutions, and ecosystem stakeholders seeking to strengthen national green SME ecosystems and offer comparative insights for other economies pursuing sustainable and inclusive growth.
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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 | 0.010 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".