Abstract
As live streaming has become increasingly prevalent in recent years, concerns over addiction to it are rising. Drawing on self-presentation theory, this study examines how viewers’ desire for online self-presentation contributes to live streaming addiction. An online survey collected a sample of 400 responses from viewers of live streams. Partial Least Squares-Structural Equation Modeling (PLS-SEM) was utilized to test our proposed research model. Our findings indicate that social influence and personal control both enhance viewers’ inclination to engage in online self-presentation. This inclination indirectly enhances addiction via viewer engagement, which was measured through a two-dimensional construct: monetary and non-monetary engagement. Using moderated mediation analysis and structural equation modeling–artificial neural network (SEM-ANN) methods, we further validated the mediating effect of viewer engagement, with streamer expertise as the moderator. Streamer expertise positively moderates the relationship between online self-presentation desire and engagement, whereas it negatively moderates the impact of engagement on addiction. This study makes three key contributions to the literature: (1) it extends the focus of live streaming research to addiction, highlighting its dark side; (2) it applies self-presentation theory to the live streaming context, thereby enhancing our understanding of online self-presentation; and (3) it offers deeper insights into how the contextual factor-streamers’ expertise-influences addiction mechanisms in live streaming. Furthermore, the findings provide valuable practical implications for platforms and policymakers regarding viewer management.
| Original language | English |
|---|---|
| Journal | Journal of Retailing and Consumer Services |
| Volume | 91 |
| Issue number | 104763 |
| DOIs | |
| Publication status | Accepted/In press - 2026 |
Keywords
- Live streaming addiction
- Live streaming
- Self-presentation theory,
- Engagement
Projects
- 1 Active
-
国家自然科学基金青年科学基金项目(C类)Young Scientists Fund (C) of the National Natural Science Foundation of China (NSFC: 72502020)
Song, X. (PI)
1/01/26 → 31/12/28
Project: Governmental Research Project
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