Virtual characters, virtual influence? Assessing the efficacy of virtual and human influencers and the influence of need for interaction and consumer envy
Bibliographic record
Abstract
Virtual influencers are digital figures created to mimic human influencers' physical features and conduct. Brands are increasingly drawn to these figures for their possible advantages (e.g., control over their communication strategy and reduced communication costs) in attracting and maintaining large followings. However, research on this topic remains nascent: Few studies have investigated why consumers react differently to human vs. virtual influencers; consequently, the field may underestimate some individual traits that orient this mechanism. Given that gap, this paper presents three experimental studies that not only test how the type of influencer (i.e., human or virtual) affects consumers' perceptions and intentions, but also explore the moderating role of two consumer-related factors: the need for interaction and the inclination toward envy. The results reveal that human influencers generally elicit heightened perceptions of credibility and authenticity, which in turn affect consumers’ behavioral intentions (i.e., to spread positive word-of-mouth for or purchase the advertised product). Additionally, consumers characterized by higher levels of the need for interaction and/or lower levels of envy seem to prefer the human influencers. In short, our research offers useful insights for digital marketing scholars and practitioners aiming to incorporate influencers into their digital campaigns.
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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 | 0.003 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 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) | 0.003 | 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".