Who or What to Believe: Trust and the Differential Persuasiveness of Human and Anthropomorphized Messengers
Bibliographic record
Abstract
Participants in three studies read advertisements in which messages were delivered either by people or by anthropomorphized agents—specifically, “talking” products. The results indicate that people low in interpersonal trust are more persuaded by anthropomorphized messengers than by human spokespeople because low trusters are more attentive to the nature of the messenger and believe that humans, more than partial humans (i.e., anthropomorphized agents), lack goodwill. People high in interpersonal trust are less attentive about who is trying to persuade them and so respond similarly to human and anthropomorphized messengers. However, when prompted to be attentive, they are more persuaded by human spokespeople than by anthropomorphized messengers due to their belief that humans, more than partial humans, act with goodwill. Under conditions in which attentiveness is low for all consumers, high and low trusters alike are unaffected by the nature of persuasion agents. The authors discuss the implications of the findings for advertisers considering the use of anthropomorphized “spokespeople.”
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.031 |
| 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 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".