Clustering, Knowledge Sharing, and Intrabrand Competition: A Multiyear Analysis of an Evolving Franchise System
Bibliographic record
Abstract
As franchise systems expand, the clustering and resulting proximity of same-brand outlets often become contentious issues. The increased interactions among outlets may facilitate knowledge sharing, even while inducing intrabrand competition. Prior research has considered each possibility—knowledge sharing or intrabrand competition—in isolation, resulting in conflicting recommendations to the central question of whether multiple same-brand outlets should be close to or distant from one another. In this study, the authors take the perspective of the focal outlet and show that the opportunity to share knowledge afforded by clustering-based proximity may or may not be realized, depending on the motivation and ability of the proximal outlets to share knowledge, the focal outlet's ability to absorb knowledge, and the governance context. An analysis of more than 8,000 observations on the 988 outlets of a U.S.-based automotive service franchise system from 1977 to 2012, and corresponding outlet-level sales information from 2004 to 2012, provides support for the authors’ hypotheses.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| 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".