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Avatars Versus Agents: A Meta-Analysis Quantifying the Effect of Agency on Social Influence
Abstract
Existing research has investigated whether virtual representations perceived to be controlled by humans (i.e., avatars) or those perceived to be controlled by computer algorithms (i.e., agents) are more influential. A meta-analysis (N = 32) examined the model of social influence in virtual environments (Blascovich, 2002) and investigated whether agents and avatars in virtual environments elicit different levels of social influence. Results indicated that perceived avatars produced stronger responses than perceived agents. Level of immersion (desktop vs. fully immersive), dependent variable type (subjective vs. objective), task type (competitive vs. cooperative vs. neutral), and actual control of the representation (human vs. computer) were examined as moderators. An interaction effect revealed that studies conducted on a desktop that used objective measures showed a stronger effect for agency than those that were conducted on a desktop but used subjective measures. Competitive and cooperative tasks showed greater agency effects than neutral tasks. Studies in which both conditions were actually human controlled showed greater agency effects than studies in which both conditions were actually computer controlled. We discuss theoretical and design implications for human–computer interaction and computer-mediated communication.
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“Additionally, considerable evidence suggests that humans perceive media agents differently from how they perceive humans (e.g., Blascovich et al., 2002; Fox et al., 2015; Krämer et al., 2012).”
From Building a Stronger CASA: Extending the Computers Are Social Actors Paradigm (2020) · cites this paper as Fox et al., 2015
In the citing paper
“Previous work has found that the mere perceived identity of the partner as computer or human has profound effects, even when actual identity does not (Fox et al., 2015; Lucas, Gratch, King, & Morency, 2014).”
From Psychological, Relational, and Emotional Effects of Self-Disclosure After Conversations With a Chatbot (2018) · cites this paper as Fox et al., 2015
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Metadata
- Authors
- Fox, J., Ahn, S. J. (., Janssen, J. H., Yeykelis, L., Segovia, K. Y., & Bailenson, J. N.
- Affiliations
- The Ohio State University (US)
- University of Georgia (US)
- Eindhoven University of Technology (NL)
- Stanford University (US)
- Venue
- Human–Computer Interaction
- Publisher
- Informa UK Limited
- Published
- 2014-05-12
- Volume
- 30
- Issue
- 5
- Pages
- 401-432
- ISSN
- 0737-0024 (ISSN-L)
- 1532-7051
- OpenAlex
- W1964044925
- Cited by
- 320
- References
- 94
- Citations by year
- 2026: 24 · 2025: 40 · 2024: 44 · 2023: 51 · 2022: 34 · 2021: 31 · 2020: 27 · 2019: 18 · 2018: 15 · 2017: 18 · 2016: 9 · 2015: 8 · 2014: 1
- FWCI
- 1.71
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Human-Computer Interaction (publishedVersion)
- Funders
- NSF (0966838)
- Topics
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Physical Sciences › Computer Science › Human-Computer Interaction › Virtual Reality Applications and Impacts
- Social Sciences › Sociology and Political Science › Digital Games and Media
- Keywords
- social influence, avatars, computational agents, perceived agency, virtual worlds, immersion, computer-mediated communication
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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