Anthropomorphism of virtual influencers: A congruence perspective
Bibliographic record
Abstract
Anthropomorphism is a defining element in virtual influencer design and a key driver of how audiences perceive and respond to them. Building on prior work that examined individual anthropomorphic features, this research aims to investigate how congruence between two core dimensions of anthropomorphism—appearance and behavior—shapes audience responses, with a particular focus on purchase intention and the mediating role of psychological distance. Data were collected through an online survey of Instagram users who followed virtual influencers (N = 362) and analyzed using polynomial regression with response surface analysis (PRRSA). Results indicate that anthropomorphic congruence enhances purchase intention following a commensurate compatibility pattern along the misfit line and a linear effect along the fit line. When incongruence occurs, behavior-dominant anthropomorphism elicits more favorable responses than appearance-dominant anthropomorphism. Psychological distance partially mediates the relationship between anthropomorphic congruence and purchase intention. Overall, this research demonstrates the importance of anthropomorphic congruence for enhancing virtual influencer effectiveness and reveals asymmetric effects between appearance and behavior. It advances theoretical understanding of anthropomorphic design in human–AI interactions and offers actionable implications for marketers to develop effective virtual influencer strategies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".