Harnessing Eco-innovation Strategies for Sustainable Development in Nigeria’s Agri-food Industry
Bibliographic record
Abstract
The exposure of Nigeria to climate change, population expansion, and rising food consumption has made the adoption of eco-innovative solutions to be more critical. Therefore, the study aims to examine the impact of eco-innovation strategies on the sustainability of agri-food industry in Nigeria. The study used a mixed-method research approach, beginning with qualitative data collection through literature reviews, followed by a survey (n = 384) targeting agripreneurs in the agri-food industry across Bayelsa, Edo, Delta, and Rivers State. Quantitative data were analysed using descriptive and inferential statistics, while thematic analysis provided qualitative insights. The study revealed that government-sponsored innovation policies emerged as the most impactful driver of sustainability. Integration of renewable energy and promotion of sustainable consumption practices also demonstrated positive effect on sustainability. Sustainable product design had the least positive effect on sustainability. This study holds several theoretical implications, particularly through the lens of Regulatory Focus Theory (RFT) — whether a promotion or prevention focus can influence the adoption of eco-innovation strategies in the agri-food industry. By linking RFT with sustainability initiatives, the study suggests that firms with a promotion-focused mindset, which emphasizes growth and positive outcomes, are more likely to engage in proactive eco-innovation, such as sustainable product design and the integration of renewable energy. In contrast, firms with a prevention focus, which is more concerned with avoiding losses and maintaining safety, are more inclined to adopt reactive sustainability practices, such as compliance with environmental regulations.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".