Turnover Events, Vicarious Information, and the Reduced Likelihood of Outlet-Level Exit Among Small Multiunit Organizations
Bibliographic record
Abstract
A key question for organizational learning research is to identify opportunities and constraints for firms to gain useful information from the activities and performance of other firms. We argue that market-level turnover events generate and release vicarious information that small multiunit organizations can use to enhance their likelihood of survival. We focus on two specific turnover events, ownership transfers and contemporaneous exit-entry pairs (cases in which both outlet entry and outlet exit occur within the same market within the same time period), because these events are likely to generate and release information without altering the total number of outlets in a market. We find that the likelihood of a multiunit owner's outlet exit declines when there are many ownership transfers and exit-entry pairs in other markets where the owner also operates outlets. We conclude that these turnover events, even in just one market where a small multiunit organization is present, generate vicarious information substantial enough to increase the survival likelihood of all outlets of that multiunit organization. Our theory and supporting results show how organizational learning-based arguments can be combined with our knowledge of multiunit organizations to build a theory of relationships between geographically separated turnover events.
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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 | 0.021 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".