When does ownership concentration improve franchise store performance?
Bibliographic record
Abstract
Purpose This study aims to examine the performance outcomes of a store’s ownership concentration within a multi-unit franchise (MUF) network, emphasizing the nuanced effects under varying competitive conditions. Design/methodology/approach This study conducted a comprehensive analysis of all stores within the McDonald’s chain over an eight-year span. The research methodology incorporated a review of over 11 million customer evaluations to discern patterns in customer satisfaction and sales growth in relation to the store’s ownership concentration. Findings Stores with a pronounced ownership concentration showcased enhanced outcomes in both customer satisfaction and sales growth. However, the magnitude of these effects was moderated by the nature of competitive conditions, specifically focal market competition, non-focal market competition and legal safeguards. Research limitations/implications The study’s concentration on McDonald’s stores introduces a specificity that might limit the universal applicability of the findings to all franchise models or sectors. Additionally, the emphasis on the store level of analysis potentially overlooks broader systemic factors. Practical implications For managers and franchise owners, understanding the nuanced roles of ownership concentration can provide strategic insights. Recognizing how different competitive conditions can moderate the effects of ownership concentration can help in making informed decisions about power dynamics and competitive positioning. Social implications A store’s ownership concentration can have broader societal ramifications, potentially shaping consumer perceptions, community engagement and overall market health. As an owner’s stores concentrate spatially, they can contribute to a healthier market ecosystem, benefitting consumers and communities alike. Originality/value While the vertical power between the franchisor and franchisee owners have been studied, this study extends the discourse to power between MUF owners. This study provides novel insights by showing customer centric and firm centric performance outcomes of ownership concentration.
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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 | 0.003 | 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 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| 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".