New organizational forms in emerging economies: bridging the gap between agribusiness management and international development
Bibliographic record
Abstract
Purpose This editorial article introduces and analyzes a variety of new organizational forms that rapidly emerged in Africa, Asia, Latin America and Eastern Europe in the latest two decades. Among the others, these include: business model partnerships, business platforms, incubators and hubs, public–private partnerships, agribusiness companies' foundations and spin-offs, short supply chains, community-supported agriculture and other community self-organizing experiences. Building upon the recent literature and the five selected papers in this special issue, the authors discuss what is novel in these organizations and why, when and how they emerge and evolve over time. Design/methodology/approach The authors identify three elements that, when considered together, explain and predict the emergence and evolution of these new organizational forms: institutions, strategies and learning processes. Findings The authors demonstrate that societal actors seeking to (re)design these new organizational forms need to consider these three elements to combine the pursuit of their interests of their own constituencies with the sustainable development goals (SDGs). Originality/value Taking stock from the literature, the authors invite future research on new organizational forms to take explicitly the pursuit of the SDGs into consideration; to build upon a process ontology; and to deeply reflect on our positionality of scientists studying and sometimes engaging in these organizations.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.008 |
| Scholarly communication | 0.011 | 0.007 |
| Open science | 0.001 | 0.003 |
| 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".