Conflict Management and Outcomes in Franchise Relationships: The Role of Regulation
Bibliographic record
Abstract
Franchise relationships are prone to conflict. To safeguard the rights of individual franchisees, several states have legislated greater franchisor disclosure (registration law) ex ante and/or franchisor “termination for good cause” (relationship law) ex post. The impact of regulatory oversight on franchisor–franchisee conflict, however, remains unclear. Relying on agency theory arguments, the authors first assess the influence of the regulatory context, both by itself and in combination with the franchise ownership structure, on the incidence of litigated conflict. Conditional on litigation, they also predict the impact of franchise regulation on both the parties’ litigation initiation and resolution choices and the resulting outcomes. The authors test the hypotheses using a unique multisource archival database of 411 instances of litigation across 75 franchise systems observed over 17 years. The results indicate that the regulatory context, by itself as well as in combination with the franchise ownership structure, significantly shapes parties’ conflict management choices. The authors also find evidence of a trade-off between prevailing in the particular conflict and achieving franchise system growth objectives.
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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 | 0.066 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.007 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".