When does social media experience improve franchisee performance? A knowledge value chain perspective
Bibliographic record
Abstract
This study examines how franchisee owners’ social media experience affects their store performance. Using a novel dataset of 8.53 million Facebook posts and 8 million Google reviews from McDonald’s stores, the authors address two questions: (1) Does an owner’s greater social media experience improve the satisfaction of customers? (2) When does the knowledge from the store’s physical environment alter the effect of social media experience on customer satisfaction? For the first question, the authors predicted and found that an owner’s greater social media experience enhanced customer satisfaction, which in turn drove the growth of store sales. For the second question, the authors identify key contextual moderators based on the knowledge value chain framework. The findings reveal that an owner’s greater local experience and the market’s customer diversity diminish , whereas an owner’s greater vicarious social media experience from sister stores amplifies , the positive effect of social media experience. These findings contribute to the literature on social media and franchising by uncovering how the digital and physical experiences of a franchisee owner jointly shape customer experiences. Practically, our results underscore the importance of integrating social media insights with knowledge from the physical environment to improve owners’ performance.
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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 | 0.016 |
| 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 | 0.008 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.009 | 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".