Virtual influencers in marketing: addressing authenticity challenges through anthropomorphism
Bibliographic record
Abstract
Purpose Virtual influencers are emerging as prominent digital brand endorsers on social media; however, their perceived authenticity remains a critical challenge to their marketing effectiveness. This research addresses this gap and investigates how the anthropomorphism of virtual influencers – in appearance and behavior – differentially affects perceived authenticity through the mechanism of social presence, ultimately shaping audiences’ purchase intentions. Design/methodology/approach Drawing from social presence theory and anthropomorphism literature, we developed a theoretical framework to examine how two dimensions of anthropomorphism – appearance and behavior – translate into perceived authenticity through social presence. Additionally, we explore how these relationships vary across virtual influencer types, such as human-like and animal-like personas. An online survey was conducted via Credamo. A dataset of responses from 415 followers of virtual influencers was analyzed using SmartPLS 4.0. Findings The empirical findings reveal that both anthropomorphic appearance and behavior positively impact perceived authenticity via social presence, driving purchase intentions. Notably, anthropomorphic behavior plays a more significant role than appearance, influencing perceived authenticity both directly and indirectly via social presence. In contrast, anthropomorphic appearance affects perceived authenticity only indirectly, with a weaker effect than anthropomorphic behavior. Additionally, the strengths of these relationships vary across influencer types, such as human-like and animal-like virtual influencers. Originality/value This research pioneers scholarly efforts to address the authenticity challenges associated with virtual influencers, emphasizing that the authenticity gap is not a fixed limitation but a dynamic issue that can be addressed through deliberate design and operational strategies for virtual influencers. It advances virtual influencer research by investigating two key dimensions of anthropomorphism – appearance and behavior – and elucidating the relationship between anthropomorphism and authenticity through the lens of social presence. It uncovers variations in the effects of anthropomorphism across different types of virtual influencers, offering a sound framework to understand the dynamic interactions among factors studied.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".