Managing a Mission-driven Franchise Organization: An Empirical Investigation of Organizational Practice and Individual Outcomes
Bibliographic record
Abstract
The purpose of this research is to investigate the interplay among organizational practice, the adoption of a management philosophy and individual outcomes in a mission-driven multinational education franchise system. A sample of 1,700 potential survey participants throughout the focal organization in both Canada and the United States was used, including 1,282 franchisees and 418 employees. A total of 152 responses were received, including 83 responses from franchisees and 69 responses from corporate employees. The findings of the study provide evidence that the adoption of a management philosophy will mediate the relationships between a philosophy-oriented organizational practice and the individual outcomes in the context of a mission-driven education industry franchise–franchisor setup in North America. One obvious limitation is that the data collection was conducted from within a single organization. The findings of the study are specific to the North American region. Hence, the generalizability is limited. The article builds upon Wang’s ( Journal of Business, 2011, Ethics, 101(1), 111–126) study by adding new measures. The empirical evidence of the study confirms that it is possible for a franchise organization to utilize corporate mission and management philosophy to govern a high level of unity between all stakeholders in a franchise system.
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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 | 0.010 | 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 | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".