This embodied conversational agent looks very human and as old as I feel! The effect of perceived agent anthropomorphism and consumer-agent age difference on brand attitude
Bibliographic record
Abstract
In a study with 320 Canadian participants, we explore the effect of perceived physical and non-physical anthropomorphism of an Embodied Conversational Agent (ECA) and the perceived actual and subjective age difference between an individual and this ECA on the ECA’s likeability and credibility. We also explore the effect of likeability and credibility on the attitude towards the website on which the ECA appears and the brand on the website. Perceived physical anthropomorphism has a positive effect on ECA likeability and credibility. The perceived differences between a consumer’s subjective and actual age and perceived ECA age have a negative effect on ECA likeability. This effect is attenuated by an interaction effect between the subjective age difference and perceived non-physical anthropomorphism: for a given level of the subjective age difference, the more the ECA is perceived as having human emotions and motivations, the more positive the effect on ECA likeability is. ECA likeability and credibility lead to more positive attitudes towards the website and the brand. For the conceptualization of our study, we draw upon the Attraction to Similarity Theory, complemented by insights from anthropomorphism studies, Self-Congruity Theory, and relational characteristics research. Theoretical and managerial contributions are discussed.
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".