Growth Through Franchises in Knowledge-Intensive Industries: Interplay of Routine Program and Expansion Mode
Why this work is in the frame
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Bibliographic record
Abstract
Thanks to technological developments produced by scientists and engineers, franchising has grown to become a business model of choice for firm expansion in knowledge-intensive industries. We propose a formal model to explore to what degree franchisors should adapt their business practices or routines to successfully expand their franchises in newly targeted markets. By simultaneously considering the franchise's need to adapt locally in a new market and the level of business routine tacitness at the time of expansion, we integrate previously separate agency cost logics into one model. We offer refinements to the belief that expanding through a franchisee is the best when the business routines need adaptation, but expanding through a company-owned unit is best when these routines can be replicated.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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