The strategy/governance structure fit relationship: theory and evidence in franchising arrangements
Bibliographic record
Abstract
Abstract Despite widespread recognition of the importance of strategy–structure fit, e.g., diversification and divisionalization, research has yet to address the possibility of similar fit issues for other structural forms of organization, such as the choice of franchised vs. company‐owned governance structures. In this study, we depart from the usual debates regarding the superiority of one governance structure over another and argue that performance differences between these two alternative governance structures may be attributable more to the matching of one structure with a correspondingly appropriate strategy. Specifically, we posit that stores will act in fit‐enhancing ways by pursuing strategies that are more congruent with their governance structure; i.e., that franchised stores, with their more flexible and decentralized structures, will be more likely to pursue strategies that emphasize flexibility and local adaptation, whereas company‐owned stores will tend to pursue strategies that emphasize predictability and control. We also argue that those stores acting in such a manner will enjoy subsequent performance benefits. We develop these ideas around strategy/governance structure fit and test our hypotheses using longitudinal data from over 6000 stores within one of the biggest U.S. restaurant chains from 1991 to 1997. Copyright © 2004 John Wiley & Sons, Ltd.
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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 | 0.011 | 0.056 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".