Clustering, Knowledge Sharing, and Intrabrand Competition: A Multiyear Analysis of an Evolving Franchise System
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it