Integrating Green Practices and Environmental Performance; Evidence from Nigeria's SME Sector
Bibliographic record
Abstract
This study analysed the elements contributing to integrating green practices and environmental performance within Nigeria's wholesale/retail and warehousing/storage sectors. Meanwhile, the target respondents consist of selected SMEs owners and managers operating in the southwest of Nigeria who know how customers behave in relation to external green supply chains, green purchasing, customer integration, and environmental performance. To measure all the variables, validated items were adapted from prior studies. Thus, 164 copies of questionnaires were retrieved from the selected managers/owners of SMEs after testing for the validity and reliability of instruments through a pilot study. The relationship between the external green supply chain and Environmental Performance was the only direct hypothesis not supported by this investigation. Specifically, A significant relationship exists between customer integration, green purchasing, and environmental performance. This study adds to the body of knowledge by demonstrating how various aspects of green practices trigger customer integration and green purchasing factors in developing nations like Nigeria. As such, it advances the understanding of the topic by illuminating how policy frameworks are developed to encourage SMEs to adopt green practices. This study will enable SMEs in developing countries to embrace green practices by transferring eco-friendly technology that can speed their integration of green practices that lack ICT skills and infrastructure. Partnerships with multinational corporations, research institutions, and international organisations can facilitate this knowledge exchange.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".