How Formal and Informal Hierarchies Shape Conflict within Cooperatives: A Field Experiment in Ghana
Bibliographic record
Abstract
As an organizational form, cooperatives are increasingly being used throughout the world across different industries and sectors. While it has been suggested that various benefits can be derived from shared ownership, cooperatives are often characterized by conflict among members that, in turn, can lead to eventual failure of the cooperatives. Existing theory has suggested that the choice of formal control structure can play an important role in mitigating conflict, but a longstanding debate exists as to whether flat versus hierarchical control structures are more effective. To add further insight into this theoretical discussion, we conducted a field experiment involving 40 newly formed cooperatives in rural Ghana, which were randomly assigned to either a flat or hierarchical control structure. The quantitative results of our field experiment and subsequent qualitative data suggest that formal hierarchical control structures lead to lower levels of collective psychological ownership, which in turn result in higher levels of conflict compared to flat control structures within cooperatives. However, our results also suggest that the extent to which the choice of formal control structures influences conflict among cooperative members can be highly dependent on the absence or presence of an informal hierarchy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".