#Sponsored: Understanding the Boundary Conditions of Resistance Coping Activation in Influencer Advertising
Bibliographic record
Abstract
Influencer advertising has sparked controversy among both consumers and regulators, in that influencer advertising’s very effectiveness is built on deceit, because consumers are often unaware of the persuasive intent. Empirical evidence on influencer advertising is built largely on the premise that disclosure will activate consumers’ reactance, as consumers will recognize the persuasive intent. Using a mixed-method approach (focus groups and survey), we contribute to the growing body of research on influencer advertising by demonstrating the role of three important boundary conditions in the relationship between knowledge of persuasive intent and activation of “resistant coping” mechanisms: trust, overconfidence, and transparency. Based on our focus-group results, we propose that two groups of outcome variables need further research attention: (1) consumers’ moral and affective advertising literacy and (2) other individual-level psychological outcomes, such as cognitive dissonance and reduced control over one’s time and productivity. In our further empirical test, we focus more specifically on perceptions of moral appropriateness of advertising, and we illustrate its importance for understanding how influencer advertising works.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.017 | 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".