Franchising research in marketing: suggestions for future research
Bibliographic record
Abstract
In their thoughtful review of franchising research conducted over the prior decade, Dant, Grünhagen, and Windsperger in 2011 sounded the alarm regarding the marketing discipline’s ceding of the franchising phenomenon to scholars in management and economics. The authors use their seminal work as a point of departure, reviewing the trends emerging from an examination of empirical research on franchising published in marketing journals over the ten-year period spanning 2005 to 2014. They note with some satisfaction an increasing interest in issues unique to franchising, including ownership structure, franchisee/franchisor selection, governance, and the performance of both franchisors and their franchisees. The review also describes recent methodological advances, and assesses the extent to which franchising research in marketing has been influential (as reflected in citations) to other areas of study. For scholars with an interest in franchising, the chapter provides information on emerging data sources and suggests possible directions for future research.
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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 | 0.013 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.018 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.019 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.019 | 0.003 |
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".