The DOI matches its Crossref record; 53 of the 53 references it cites were found in a registry or an index.
Happiness Begets Money: Emotion and Engagement in Live Streaming
Abstract
Live streaming offers an unprecedented opportunity for content creators (broadcasters) to deliver their content to consumers (viewers) in real time. In a live stream, viewers may send virtual gifts (tips) to the broadcaster and engage with likes and chats free of charge. These activities reflect viewers’ underlying emotion and are likely to be affected by the broadcaster’s emotion. This article examines the role of emotion in interactive and dynamic business settings such as live streaming. To account for the possibility that broadcaster emotion, viewer emotion, and viewer activities influence each other, the authors estimate a panel vector autoregression model on data at the minute level from 1,450 live streams. The results suggest that a happier broadcaster makes the audience happier and begets intensified viewer activities, in particular tips. In addition, broadcasters reciprocate viewer engagement with more smiles. Further analyses suggest that these effects are pronounced only after a live stream has been active for a while, and they manifest only in streams by broadcasters who have more experience, receive more tips, or are more popular in past live streams. These results help platforms and broadcasters optimize marketing interventions such as broadcaster emotion enhancement in live streaming and quantify the financial returns.
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
Investigating causal relations by econometric models and cross-spectral methods
Granger, C. W. J. (1969) · Econometrica · doi:10.2307/1912791 · Cited by 22,989
Large Sample Properties of Generalized Method of Moments Estimators
Hansen, L. P. (1982) · Econometrica · doi:10.2307/1912775 · Cited by 13,467
Sims, C. A. (1980) · Econometrica · doi:10.2307/1912017 · Cited by 12,727
Core affect and the psychological construction of emotion.
Russell, J. A. (2003) · Psychological Review · doi:10.1037/0033-295x.110.1.145 · Cited by 6,113
Generalized impulse response analysis in linear multivariate models
Pesaran, H. H., & Shin, Y. (1998) · Economics Letters · doi:10.1016/s0165-1765(97)00214-0 · Cited by 6,097
New Introduction to Multiple Time Series Analysis
Lütkepohl, H. (2005) · Springer eBooks · doi:10.1007/978-3-540-27752-1 · Cited by 5,918
Estimating Vector Autoregressions with Panel Data
Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988) · Econometrica · doi:10.2307/1913103 · Cited by 4,239
Annual report readability, current earnings, and earnings persistence
Li, F. (2008) · Journal of Accounting and Economics · doi:10.1016/j.jacceco.2008.02.003 · Cited by 2,838
Trusov, M., Bucklin, R. E., & Pauwels, K. (2009) · Journal of Marketing · doi:10.1509/jmkg.73.5.90 · Cited by 2,451
Financial development and dynamic investment behavior: Evidence from panel VAR
Love, I., & Zicchino, L. (2006) · The Quarterly Review of Economics and Finance · doi:10.1016/j.qref.2005.11.007 · Cited by 1,785
Cited by
Li, M., Wang, Q., & Cao, Y. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19074378 · Cited by 180
Li, G., Jiang, Y., & Chang, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.918196 · Cited by 59
Ye, Y., Zhou, Z., & Duan, H. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.1041476 · Cited by 19
Metadata
- Authors
- Lin, Y., Yao, D., & Chen, X.
- Venue
- Journal of Marketing Research
- Publisher
- SAGE Publications
- Published
- 2021-04-20
- Volume
- 58
- Issue
- 3
- Pages
- 417-438
- ISSN
- 0022-2437 (ISSN-L)
- 1547-7193
- OpenAlex
- W3135549087
- Cited by
- 394
- References
- 53
- FWCI
- 72.22
- Type
- journal article
- Language
- en
- Open access
- closed
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: Happiness Begets Money: Emotion and Engagement in Live Streaming. https://app.cochat.ai/papers/10.1177/00222437211002477
- The paper
- Lin, Y., Yao, D., & Chen, X. (2021). Happiness Begets Money: Emotion and Engagement in Live Streaming. Journal of Marketing Research. https://doi.org/10.1177/00222437211002477
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.
