The DOI matches its Crossref record; 14 of the 14 references it cites were found in a registry or an index.
Impulse Buying Behaviors in Live Streaming Commerce Based on the Stimulus-Organism-Response Framework
Open accessAbstract
Live streaming commerce, which evolved from social commerce, has continued to flourish rapidly over the past few years in China. It is a new business model that allows vendors to directly face and interact with consumers. This study focuses on the impulsive buying behavior on consumers in live streaming commerce. We proposed a research model based on the stimulus organism response (S-O-R) framework to explore the reaction and behavior of consumers after certain stimuli factors. A total of 433 valid sample questionnaires with the shopping experience in the live streaming platform were taken. This research adopted PLS-SEM statistical analysis as an empirical research evaluation. After the empirical investigation, we found that perceived enjoyment positively affects the urge to buy impulsively. Perceived usefulness positively affects perceived enjoyment. However, perceived usefulness does not positively affect the urge to buy impulsively. Attractiveness and expertise positively affect perceived enjoyment. Product usefulness and purchase convenience positively affect perceived usefulness. We found that consumers in live streaming commerce are easier to have impulsive buying through the presentation and urging of the live streamer in a short period. In this paper, we build a model for impulsive buying in live streaming commerce. We verify this model under the Chinese context. The findings of this paper provide concrete suggestions to vendors.
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
Podsakoff, P. M., MacKenzie, S. B., Lee, J.-Y., & Podsakoff, N. P. (2003) · Journal of Applied Psychology · doi:10.1037/0021-9010.88.5.879 · Cited by 82,194
Ohanian, R. (1990) · Journal of Advertising · doi:10.1080/00913367.1990.10673191 · Cited by 4,043
The Role of Live Streaming in Building Consumer Trust and Engagement with Social Commerce Sellers
Wongkitrungrueng, A., & Assarut, N. (2020) · Journal of Business Research · doi:10.1016/j.jbusres.2018.08.032 · Cited by 1,477 · cited 4×
“Currently, most scholars believe that the main influencing factor of live streaming is the charm of live streamers [1,5,6,13,16].”
BackgroundInfluentialThe significance of impulse buying today.
Stern, H. (1962) · Journal of Marketing · doi:10.1177/002224296202600212 · Cited by 1,049
How live streaming influences purchase intentions in social commerce: an IT affordance perspective.
Sun, Y., Shao, X., Li, X., Guo, Y., & Nie, K. (2019) · Electronic Commerce Research and Applications · doi:10.1016/j.elerap.2019.100886 · Cited by 1,038
Social motivations of live-streaming viewer engagement on Twitch
Hilvert-Bruce, Z., Neill, J. T., Sjöblom, M., & Hamari, J. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.02.013 · Cited by 870 · cited 1×
“Live streaming commerce not only enables consumers to experience shopping benefits but also assists in developing virtual social relationships with live streamers during realtime interactions [10,17].”
BackgroundKamboj, S., Sarmah, B., Gupta, S., & Dwivedi, Y. (2018) · International Journal of Information Management · doi:10.1016/j.ijinfomgt.2017.12.001 · Cited by 591
Liu, Y., Li, H., & Hu, F. (2013) · Decision Support Systems · doi:10.1016/j.dss.2013.04.001 · Cited by 541
Enhancing consumer engagement in e-commerce live streaming via relational bonds.
Hu, M., & Chaudhry, S. S. (2020) · Internet Research · doi:10.1108/intr-03-2019-0082 · Cited by 539
The influence of perceived value on purchase intention in social commerce context
Gan, C., & Wang, W. (2017) · Internet Research · doi:10.1108/intr-06-2016-0164 · Cited by 442
Cited by
Xie, C., Yu, J., Huang, S. (., & Zhang, J. (2022) · Tourism Management · doi:10.1016/j.tourman.2022.104513 · Cited by 231
Li, M., Wang, Q., & Cao, Y. (2022) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph19074378 · Cited by 180
Characteristics and roles of streamers in e-commerce live streaming
Zhang, S., Huang, C., Li, X., & Ren, A. (2022) · The Service Industries Journal · doi:10.1080/02642069.2022.2068530 · Cited by 130
Zhong, Y., Zhang, Y., Luo, M., Wei, J., Liao, S., Tan, K.-L., & Yap, S. S.-N. (2022) · Young Consumers · doi:10.1108/yc-08-2021-1367 · Cited by 66
Li, G., Jiang, Y., & Chang, L. (2022) · Frontiers in Psychology · doi:10.3389/fpsyg.2022.918196 · Cited by 59
Internet Celebrities’ Impact on Luxury Fashion Impulse Buying
Chen, M., Xie, Z., Zhang, J., & Li, Y. (2021) · Journal of Theoretical and Applied Electronic Commerce Research · doi:10.3390/jtaer16060136 · Cited by 56
Wang, J., & Oh, J. I. (2023) · Sustainability · doi:10.3390/su151310028 · Cited by 32
Exploring Features and Products in E-Commerce on Consumers Behavior Using Cognitive Affective
Pahrudin, P., Caraka, R. E., Liu, L.-W., Ugiana Gio, P., Anita Yunikawati, N., & Pardamean, B. (2025) · IEEE Access · doi:10.1109/access.2025.3553192 · Cited by 11
From greenwashing to green trust: Understanding the consumer behavior in the fashion industry
Zhao, J., Zhou, H., Zhou, M., Zheng, C., Zhou, L., & Yang, F. (2026) · Acta Psychologica · doi:10.1016/j.actpsy.2026.106850
Metadata
- Authors
- Lee, C.-H., & Chen, C.-W.
- Venue
- Information
- Publisher
- MDPI AG
- Published
- 2021-06-08
- Volume
- 12
- Issue
- 6
- Pages
- 241
- ISSN
- 2078-2489 (ISSN-L)
- OpenAlex
- W3166629752
- Cited by
- 286
- References
- 14
- FWCI
- 39.74
- Type
- journal article
- Language
- en
- Open access
- gold
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
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.