The DOI matches its Crossref record; 89 of the 89 references it cites were found in a registry or an index.
How do virtual streamers affect purchase intention in the live streaming context? A presence perspective
Open accessAbstract
The rise of live streaming commerce has attracted attention from scholars and practitioners as a new opportunity to reach consumers. However, few studies have focused on the role of virtual streamers in this context. Based on the stimulus organism response framework, we explore the impact of three characteristics of virtual streamers—likeability, animacy, and responsiveness—on social presence, telepresence, and purchase intention. We use partial least squares structural equation modeling analysis with online survey data from 378 consumers concerning their experiences of viewing virtual streamers. The findings indicate that likeability, animacy, and responsiveness enhance social presence and telepresence, which then promote purchase intention. Likeability and responsiveness directly increase purchase intention, but animacy does not. Multi-group analysis reveals differences between the effects of humanoid and zoonotic virtual streamers on social presence and telepresence. These results shed light on the role of virtual streamer characteristics and contribute to the literature on live streaming commerce, presence, and virtual humans.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Evaluating structural equation models with unobservable variables and measurement error
Fornell, C., & Larcker, D. F. (1981) · Journal of Marketing Research · doi:10.1177/002224378101800104 · Cited by 109,190
A new criterion for assessing discriminant validity in variance-based structural equation modeling
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-014-0403-8 · Cited by 38,459
A primer on partial least squares structural equation modeling (PLS-SEM)
Leguina, A. (2015) · International Journal of Research & Method in Education · doi:10.1080/1743727x.2015.1005806 · Cited by 18,921
Accounting for common method variance in cross-sectional research designs
Lindell, M. K., & Whitney, D. J. (2001) · Journal of Applied Psychology · doi:10.1037/0021-9010.86.1.114 · Cited by 8,490
(1974) · Medical Entomology and Zoology · Cited by 7,295
Defining virtual reality: Dimensions determining telepresence
Steuer, J. (1992) · Journal of Communication · doi:10.1111/j.1460-2466.1992.tb00812.x · Cited by 5,459
Liang, H., Saraf, N., Hu, Q., & Xue, Y. (2007) · MIS Quarterly · doi:10.2307/25148781 · Cited by 3,921
The social psychology of telecommunications
Gray, G. R. H. (1977) · Telecommunications Policy · doi:10.1016/0308-5961(77)90016-7 · Cited by 3,461
Bartneck, C., Kulić, D., Croft, E., & Zoghbi, S. (2009) · International Journal of Social Robotics · doi:10.1007/s12369-008-0001-3 · Cited by 3,193
Testing measurement invariance of composites using partial least squares
Henseler, J., Ringle, C. M., & Sarstedt, M. (2016) · International Marketing Review · doi:10.1108/imr-09-2014-0304 · Cited by 2,964
Cited by
Tedjakusuma, A. P., Silalahi, A. D. K., Eunike, I. J., Phuong, D. T. T., & Riantama, D. (2025) · Digital Business · doi:10.1016/j.digbus.2025.100115 · Cited by 14
Metadata
- Authors
- Gao, W., Jiang, N., & Guo, Q.
- Venue
- Journal of Retailing and Consumer Services
- Publisher
- Elsevier BV
- Published
- 2023-07
- Volume
- 73
- Pages
- 103356
- ISSN
- 0969-6989 (ISSN-L)
- OpenAlex
- W4362723624
- Cited by
- 267
- References
- 89
- FWCI
- 23.46
- Type
- journal article
- Language
- en
- Open access
- hybrid
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 ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: How do virtual streamers affect purchase intention in the live streaming context? A presence perspective. https://app.cochat.ai/papers/10.1016/j.jretconser.2023.103356
- The paper
- Gao, W., Jiang, N., & Guo, Q. (2023). How do virtual streamers affect purchase intention in the live streaming context? A presence perspective. Journal of Retailing and Consumer Services. https://doi.org/10.1016/j.jretconser.2023.103356
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
