Meta-Organization Formation and Sustainability in Sub-Saharan Africa
Bibliographic record
Abstract
In response to recent calls for theory to predict and explain the phenomenon of “meta-organizations,” we set out to identify the causes of their formation. Using a cross-case comparison of multiple case studies in sub-Saharan Africa, where nine focal firms varied in their response to the complexities of sustainability, we examined how and why some firms approached sustainability through a meta-organization while others did not. Our findings show that meta-organizations may be an effective means of managing the complexity of sustainability when participants exhibit an openness to innovative forms of collaboration—which, in turn, rests on complex systems framing and experiential embeddedness—and when they collectively undergo a four-stage process of meta-organization formation that transforms dormant resources into critical sources for achieving systemic goals. Our results also suggest that meta-organizations may be particularly well suited to addressing institutional and market voids, which typically constitute highly complex economic and social contexts. In addition to making contributions to the extant literature on interorganizational relationships and networks, this paper, to our knowledge, is the first to examine the appropriateness of the meta-organizational form in less developed economies, extending the potential generalizability of its application to multiple economic contexts.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.004 |
| 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".