From fame and followers to fortune: How person-brands capture value in the creator economy
Bibliographic record
Abstract
Person-brands are key market actors in the creator economy, yet we lack a comprehensive understanding about how they capture the value they create. This research answers how by analyzing over 100 person-brands across diverse markets. We develop a novel framework for theorizing value capture by introducing the concept of value capture mode. We then detail three modes—Advertiser, Entrepreneur, and Professional—each involving unique strategies, resource bundling practices, activities, and risks. Our research reveals how person-brands bundle resources from value networks in the creator economy comprising audiences, brands, and platforms. It further explains how person-brands grow value capture through concentration, mode-spanning, and field-bridging approaches, providing insights into value network expansion and extraction by person-brands. We offer recommendations and research avenues for three key actors in the creator economy: person-brands, organizations, and platforms. For person-brands, we recommend targeting high-return opportunities through scalable, recurring, aligned, and recursive activities, and optimizing network positions by building communities, balancing centralization and diversification, establishing direct communication channels, and developing independent platforms. For organizations, we suggest investing in person-brands as partners rather than marketing tools, with specific strategies for each mode. For platforms, we recommend providing tailored tools and features to support person-brands’ value capture efforts in each mode.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.010 | 0.017 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 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".