International Franchising: Evidence from US and Canadian Franchisors in Mexico
Bibliographic record
Abstract
The contracting practices of franchisors outside of theirdomestic markets have received limited attention in the empirical literature on franchising, mostly due to data limitations. We exploit a newly assembled data set that allows us not only to describe the contracting practices of US and Canadian franchisors in Mexico but, most importantly, to compare them to their domestic counterparts. We briefly but systematically review the two theoretical frameworks that have been used most to study franchisors' domestic and international operations, namely agency and internationalization theory, and use implications derived from these to guide our analyses. We focus in turn on franchisors' decision to operate in the Mexican market, their propensity to enter via company-owned versus franchised units as compared to the same decision domestically, and finally the financial contract terms they adopt (royalty rate, franchise fee and advertising fee) for their franchise agreements in Mexico compared to their home market. Our empirical results confirm hypotheses derived from the theories, particularly with respect to the decision to operate in Mexico. But we also find some surprises - for example, the vast majority of US and Canadian franchisors employ exactly the same financial contract terms in Mexico as in their home market. We argue that this tendency is probably best explained by the same arguments used in the franchising literature to explain contract uniformity within domestic markets. Further implications for future research and practice are also discussed.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".