Turning aligned interests into higher chain performance in franchising
Bibliographic record
Abstract
Purpose This study aims to study research on franchise chain performance that has focused on franchisors’ efforts to align their interests with those of franchisees to address partner uncertainty. In contrast, the question of what a franchisor should do to address another type of uncertainty and task uncertainty remains understudied. The authors suggest a franchisor’s coordination as a key means of alleviating task uncertainty and ongoing support and plural form as two mechanisms of coordination. The authors also posit that aligned interests between the franchisor and the franchisee improve, whereas one-sided interest impedes, chain performance. Furthermore, providing greater ongoing support or deploying plural form amplifies the positive effect of aligned interests on chain performance. Design/methodology/approach The authors relied on secondary data to test the hypotheses. The authors collected data for analysis from Bond’s franchisee guide and Nation’s Restaurant News restaurant database. They also tested the framework by analyzing 17-year, panel data of 71 restaurant chains operating in the USA and Canada using system generalized method of moments. Findings Results show that aligning interests does improve chain performance, but that the positive effect is amplified when aligned interests are matched with a chain’s provision of ongoing support or use of plural form. Originality/value The authors explicate why it is not enough to address the misaligned interests or lack of coordination alone; a chain manager needs to address both of these problems together. In addition, the authors explicate how two franchisee coordination mechanisms – ongoing support and plural form – help a chain augment the beneficial effect of aligning interests on chain performance. Without solving the twin problems of misaligned interests and coordination simultaneously, a chain is unlikely to achieve its full performance potential.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".