Avatars as information: Perception of consumers based on their avatars in virtual worlds
Bibliographic record
Abstract
Abstract The presence of consumers and companies in the virtual worlds has increased in recent years. It is predicted that 80% of active Internet consumers and Fortune 500 companies will have an avatar or presence in a virtual community, including social networks, by the end of 2011 (eMarketer, 2007 ). The increase in the number of consumers with avatars emphasizes the need for a better understanding of who these consumers behind the avatars really are in order to convert these individuals to online and real‐world customers. The objective of this paper is to investigate how avatars reflect the personality of their creators (targets) in virtual worlds. Using the Brunswik Lens Model as the theoretical framework, an investigation of real consumers in the virtual worldSecond Lifereveals that perceivers who view targets' avatar use particular thin‐slices of observations such as avatar cues (e.g., attractiveness, gender, hairstyle) to form accurate personality impressions about targets. The findings support the premise that real‐life companies that intend to expand to virtual worlds can use member avatars as a proxy for member personality and lifestyles. As a future research direction, avatars and other consumer‐generated media could be used as the basis for targeting and segmentation of online consumers. © 2010 Wiley Periodicals, Inc.
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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 | 0.001 | 0.005 |
| 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 | 0.001 |
| 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".