The impact of brand equity on vertical integration in franchise systems
Bibliographic record
Abstract
• In franchise systems, higher brand equity leads to less forward vertical integration. • The effect is stronger in franchise systems with more resources and weaker in systems with international presence. • Brand equity is a safeguarding asset that bridles partner opportunism through contractual self-enforcement. • Findings advise against forward vertical integration in franchise systems with strong brand equity. • Brand investments are dual investments, directly in the brand and indirectly in the channel. Brand equity and vertical integration are focal, strategic elements of a franchise system that can profoundly influence franchise performance. Despite the recognized importance of these two strategic levers and the longstanding research interest in the topic, our understanding of the interplay between brand equity and vertical integration (company ownership of outlets) in a franchise system remains incomplete. In this study, we revisit the five-decade-old question of how brand equity affects vertical integration in a franchise system and present some novel, nuanced insights into the topic. Evidence from a Bayesian Panel Vector Autoregressive model on a large panel data set shows that brand equity has a powerful, lagging inverse effect on vertical integration, such that higher brand equity leads to less downstream vertical integration in a franchise system. Reverse causality analyses identify a less pronounced but present reciprocal effect. Boundary conditions analyses reveal that the negative effect of brand equity on vertical integration is weaker in franchise systems with international presence and in retail-focused (vs. service-focused) franchises, and stronger in franchise systems with more financial resources. These findings (a) challenge traditional views (e.g., transaction cost theory, resource-based view, ownership redirection hypothesis) on the topic by demonstrating a negative effect for brand equity on vertical integration in franchise systems and showing that greater financial resources amplify this effect, and (b) shed new light on the intricate dynamics (temporal causation, reverse causation) and contingencies of this debated effect. Managerially, this research draws attention to the underrecognized strategic benefit of brand equity in mitigating channel governance issues and advise against unnecessary vertical integration, especially when brand equity is robust.
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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 | 0.002 | 0.007 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".