Priming skepticism: Unintended consequences of one‐sided persuasion knowledge access
Bibliographic record
Abstract
Abstract Scholars have historically assumed that consumers' persuasion knowledge is invariably linked to skepticism about advertising and marketing. As a result, studies have often used skepticism‐focused stimuli to prompt persuasion knowledge access. However, as originally conceptualized, persuasion knowledge also includes an understanding of persuasion tactics that are trusted and believed, which suggests that accessing persuasion knowledge does not necessarily make consumers more skeptical. In this paper, we propose that, for at least some persuasion knowledge research questions, skepticism‐focused interventions may be too “one‐sided” because they bias participants to consider only the skeptical side of persuasion knowledge. The purpose of the present research is to test whether the “one‐sided” persuasion knowledge interventions that are used in persuasion knowledge research encourage skepticism more than balanced interventions that focus consumers on the negative and positive motives that may underlie persuasive communication. Across three experiments with three distinct subject pools and over 2,500 participants, we demonstrate that one‐sided versus balanced manipulations of persuasion knowledge can have differential effects on consumer skepticism. This is an important finding because skepticism‐focused operationalizations are frequently employed in persuasion knowledge research.
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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 | 0.011 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".