Can(’t) touch this: The effect of form realism and product domain in virtual influencer endorsements
Bibliographic record
Abstract
• Form realism of virtual influencers positively affects brand outcomes. • Perceived proximal sensory capacities of virtual influencers mediate this effect. • Domain (physical vs. non-physical) of the endorsed products moderates this effect. The prevalence of virtual agents across various domains has led to the emergence of virtual influencers on social media platforms as computer-generated alternatives to human social media influencers. This research sheds light on the factors that influence virtual influencers’ effectiveness in brand endorsements by examining their form realism and its interaction with the product domain. Four experiments show that virtual influencers’ form realism and the domain (physical vs. non-physical) of the products they endorse affect virtual influencers’ effectiveness as brand endorsers. Virtual influencers with high (vs. low) form realism generate a more positive attitude toward the brand. The underlying process driving this effect is the perceived lack of proximal sensory capabilities of virtual influencers with low form realism compared to those with high form realism. Importantly, there are no differences in brand attitudes for high (vs. low) form realism when virtual influencers endorse products belonging to non-physical (vs. physical) domains, where the proximal sensory capabilities of virtual influencers are less prominent. This research contributes to the literature by examining an emerging influencer type within the brand endorsement context. This research also offers practical implications for retailers regarding selecting the right influencer and crafting effective endorsement campaigns.
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 | 0.003 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.025 | 0.002 |
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".