Bibliographic record
Abstract
A growing body of research has examined the efficacy of influencer marketing and how social media influencers (SMIs) produce influence through strategically manufacturing authenticity and relatability. Less clear, however, is what benefits consumers derive from influencers and how they incorporate influencer content into their own identity projects. In other words, advertisers and influencers do not know how consumers actually “consume” influence. The current research addresses this gap through developing a novel perspective on influencer marketing that highlights how consumers actively incorporate influencer content into their own practice performances. Based on a market ethnography of millennial and gen Z beauty consumers, this research uncovers six distinct actions through which consumers consume influence. Findings also challenge and update another core assumption of influencer marketing: that consumers generally perceive influencers to be similar to them. Altogether, this research introduces the Influencer Marketing Dartboard as a conceptual and managerial tool to better leverage influencers for marketing. Three contributions are offered that advance the influencer marketing and practice theory literatures: a deeper understanding of how companies can effectively utilize SMIs, a clearer differentiation between SMIs and celebrity endorsers, and insights into how mediated practices facilitate consumers’ identity projects.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.004 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".