Navigating Sustainability Transitions in Emerging Economies: The Temporal Impacts of Environmental Innovations and the Role of Quality Management Systems
Bibliographic record
Abstract
Environmental innovation plays a critical role in advancing sustainability transitions in agriculture. However, the implementation of such innovations often introduces short-term operational inefficiencies before delivering long-term environmental and economic benefits. Existing research primarily focuses on cross-sectional analyses of environmental innovation, overlooking the short-term complexities of its adoption, particularly in the agricultural sector. This study employs a multiple-case study approach to examine how agricultural firms navigate sustainability transitions and mitigate initial setbacks. Drawing on corporate responsibility reports from six agricultural firms in emerging economies, this research investigates the temporal effects of environmental innovation and the moderating role of Quality Management Systems in mitigating the transition challenges. This study contributes to the literature by bridging the gap between environmental innovation theory and its practical, short-term implementation challenges in agriculture. The insights provide policy recommendations for designing effective support mechanisms that encourage agricultural firms in emerging economies to sustain their commitment to environmental innovation, despite early adaptation costs.
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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 | 0.005 |
| 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 | 0.004 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".