Franchisor–Franchisee Bankruptcy and the Efficacy of Franchisee Governance
Bibliographic record
Abstract
Franchisors’ long-term viability is tied to the ongoing operations of their franchisees. To ensure the ongoing performance of franchisees, franchisors deploy multiple governance mechanisms. This study assesses how governance mechanisms deployed to enhance franchisee ability (via selection and socialization) and motivation (via incentives and monitoring) impact franchisee bankruptcy. The authors examine the individual and joint effects of deploying governance mechanisms that share the same underlying objective, namely, to enhance franchisee ability and motivation. They also assess how motivation-inducing mechanisms may serve to counter the motivation-dampening effect of an increased royalty rate. Relying on data from multiple archival sources, the authors identify all bankruptcy filings by franchisees and their franchisors across 1,115 franchise systems over a 13-year observation window. Their findings document a positive and significant relationship between franchisee and franchisor bankruptcy. They also find main and interaction effects of the ability- and motivation-influencing governance mechanisms on the likelihood of franchisee bankruptcy, and the existence of significant bankruptcy spillovers among franchisees within the same franchise system. They discuss implications for franchise theory and management.
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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 | 0.004 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".