Valuing Influence with Social Learning
Bibliographic record
Abstract
Problem definition: Influencer marketing has become a prevalent strategy to promote products through social media. This paper examines the value of influencer marketing when followers not only learn from the influencer’s signal but can also engage in social learning by observing peers’ purchase behaviors and reviews. Methodology/results: We adopt an information design framework to analyze how a firm should value an influencer based on two key dimensions: the accuracy of the influencer’s past recommendations (informativeness) and the extent to which followers rely exclusively on the influencer versus learning from peers (charisma). Managerial implications: Our model uncovers insights about the interaction between information design and social learning. First, the naive intuition that the influencer is less valuable with social learning does not always hold. The influencer holds greater value under the social learning context when customers have a moderate intention to buy as her endorsement reinforces customer convictions, making them resilient against later negative feedback from other followers. Second, when the firm can strategically select an influencer, the optimal information structure is biased toward the positive signals: always endorse good products (true-positive rate of one) but sometimes endorse bad products (nonzero false-positive rate). Third, the optimal influencer when social learning exists has a lower false-positive rate than the one without social learning, meaning that when there exists subsequent social learning, it becomes even more important to have an influencer whose positive endorsement is trustworthy. In other words, the optimal influencer should be able to reveal more information with social learning than without social learning. Funding: This work was supported by the National Science Foundation [Grant 2208189]. Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2025.0348 .
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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 | 0.065 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.006 | 0.010 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 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".