Reconceptualizing Franchisee Performance: A Configurational Approach in a Base‐of‐the‐Pyramid Context
Bibliographic record
Abstract
Abstract This paper proposes and tests a new conceptual framing for franchisee performance that draws on institutional complexity to explore the interaction of corporate, market, and relational logics of performance. Extant research draws on corporate and market logics to explain performance; however, this does not explain individual franchisee performance in complex institutional environments such as Base‐of‐the‐Pyramid (BoP) markets where relational logics may be more important, thereby limiting explanations of how franchisee outlets perform. Drawing on data from a network of 58 franchise outlets in the context of Kenya, we conduct a configurational analysis related to sales outcomes. We leverage fuzzy‐set qualitative comparative analysis (fsQCA) to map out the conditions under which franchisees exhibit higher sales performance. Results show that three distinct configurations can lead to increased sales performance. Our results paint a nuanced picture of combinations of factors that result in franchisee success with relevance to the BoP context and beyond.
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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 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.005 | 0.023 |
| Scholarly communication | 0.011 | 0.011 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".