Global franchising in emerging and transitioning economies
Bibliographic record
Abstract
Franchising has experienced phenomenal growth both in the US and abroad in recent years. Figures vary, but it is estimated that U.S. franchising generates $800 billion worth of business in gross sales and represents 40 percent of the retail trade (Swartz, 2001). While in the US, Canada and parts of Western Europe franchising has reached domestic market saturation, emerging markets remain relatively untapped. Emerging markets, accounting for 80% of the world’s population and 60% of the world’s natural resources, present the most dynamic potential for long-term growth to businesses, in general, and to franchisors, in specific. The U.S. Department of Commerce estimated that over 75% of the expected growth in world trade over the next two decades will come from emerging countries, particularly Big Emerging Countries, which account for over half the world’s population but only 25% of its GDP. Emerging markets are among the fastest growing markets for international franchisors. Several surveys conducted by Arthur Andersen showed that more and more franchisors are seeking opportunities in emerging markets. A recent article in Franchising World (Amies, 1999) stated: “Franchises are springing up in the most unlikely, and for many of us unheard-of, places...Those franchisors who can establish a beach-head on these wilder shores could do very well, but the risks are great.” This article is a step in the direction of educating its target markets about international franchising opportunities and threats in emerging economies.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 0.001 |
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".