Can anthropomorphism bring better persuasiveness? An empirical study on online health risk information
Bibliographic record
Abstract
Purpose Anthropomorphism presents a promising strategy for improving information presentation in online health communication. Although showing significant potential, its underlying mechanisms and boundary conditions remain underexplored and warrant further research. This study, therefore, aims to investigate how anthropomorphic cues in online health risk information influence information persuasiveness, specifically examining the underlying mechanisms and boundary conditions. Design/methodology/approach Three experiments (Experiment 1: N = 198; Experiment 2: N = 118; Experiment 3: N = 146) were conducted to examine the impact of anthropomorphism on information persuasiveness, explore pertinent psychological mechanisms and investigate the moderating role of narrative perspective. Findings Anthropomorphic cues were found to enhance the persuasiveness of online health risk information by increasing perceived severity and vulnerability, and by reducing psychological reactance. Furthermore, narrative perspective was shown to moderate the relationships between anthropomorphic cues and both perceived severity and vulnerability. Practical implications Guidance is provided to health information providers on the effective application of anthropomorphic strategies in disseminating online health risk information. In addition, the study highlights the importance of selecting appropriate narrative perspectives that align with the specific characteristics of different diseases in health persuasion. Originality/value This study advances the understanding of anthropomorphic health risk information in online settings by demonstrating its efficacy in mitigating psychological reactance. The findings show that narrative perspectives of injurants versus victims moderate the influence of anthropomorphic health risk information on individuals’ perceived severity and vulnerability.
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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 | 0.008 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.000 |
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".