Making the Next Move: How Experiential and Vicarious Learning Shape the Locations of Chains' Acquisitions
Bibliographic record
Abstract
We examine acquisitions by multiunit chain organizations to determine why they acquire a particular target rather than others that are available to them and thus better understand chain growth. We advance experiential and vicarious learning processes as an explanation for chains' next spatial move. Our analysis of Ontario nursing home chains' acquisition location choices from 1971 to 1996 provides broad support for a learning perspective, demonstrating how experiential and vicarious processes shape and constrain the locations of chains' acquisitions. Experiential processes lead chains to replicate themselves by acquiring components geographically and organizationally similar to their own most recent and most similar prior acquisitions and their own current components. Vicarious processes lead chains to imitate location choices of other visible and comparable chains' most recent acquisitions, prior acquisitions nearest to potential targets, and their current components. Our study thus establishes organizational learning as a conceptual foundation for predicting the location of a chain's next acquisition and, more generally, the spatial expansion of chains over time.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.003 | 0.004 |
| 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".