The Transfer of Experience in Groups of Organizations: Implications for Performance and Competition
Bibliographic record
Abstract
Groups of organizations are pervasive, although there is little systematic knowledge about how they affect their members. We examine one dimension of the operation of organization groups, the transfer of experience. Our core argument is that organization groups may create benefits for their members, but problems for those outside the group. Within the group they can facilitate the transfer of experience among their members by creating mechanisms for communication, incentives for helping, and by promoting understanding. The predicted pattern of experience transfer should improve performance of those within the group, but also has implications for those outside it. Experience accumulated in one organization group strengthens the competitiveness of its organizations, and thereby harms competitors outside the group. Thus, organization groups are fundamental both for the functioning of their members and the competitive dynamics of their industries. Our longitudinal analysis of the profitability of kibbutz agriculture supports both these claims. Between 1954 and 1965 (the years of this study), almost all kibbutzim were part of organization groups. Kibbutzim became more profitable as a function of the experience of others in their group. Their profitability was reduced, however, as a function of experience of others outside their group.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.013 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".