ArticleHeliyon2022
Investigating switching intention of e-commerce live streaming users.
Article in Heliyon, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Factors influencing customers' switching intentions to new energy vehicles based on the push-pull mooring model using SEM-ANN approach in Shanxi, China.Scientific reports · 2025Article
- The interactive impact of social interaction and product involvement on customer stickiness in the context of live streaming e-commerce.Frontiers in psychology · 2025Article
- Understanding Users' App-Switching Behavior During the Mobile Search: An Empirical Study from the Perspective of Push-Pull-Mooring Framework.Behavioral sciences (Basel, Switzerland) · 2024Article
- Review
- How information and communication overload affect consumers' platform switching behavior in social commerce.Heliyon · 2024Article
- A reverse mechanism of advance selling driven by information asymmetry and competition.Heliyon · 2023Article
- Mind over Matter: Examining the Role of Cognitive Dissonance and Self-Efficacy in Discontinuous Usage Intentions on Pan-Entertainment Mobile Live Broadcast Platforms.Behavioral sciences (Basel, Switzerland) · 2023Article
Corrections and comments
- Erratum issued
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
As a new way of shopping, e-commerce live streaming (ELS) has gained unprecedented growth and popularity in the past years, especially in China. Because of the considerable rivalry in the ELS market, users frequently switch between ELS platforms. However, the switching intention of ELS users is yet to be explored for gaining new knowledge and practical insights. This study aims to improve the understanding of ELS users' switching intentions by developing an extended Push-Pull-Mooring (PPM) model. Using structural equation modeling, the study model was examined based on 443 valid responses from an online survey questionnaire. SmartPLS 3.3.2 was used to validate the causal model, and most of the study hypotheses were supported. According to the results, push effects (dissatisfaction, privacy concern, and negativity perceived value), pull effects (attractiveness of alternatives, perceived usefulness, perceived ease of use, and knowledge-based trust), and mooring effects (switching cost, social influence, and inertia) significantly influence ELS users' switching intentions. Furthermore, we found that mooring effects had a moderating role on the link between push effects and ELS user switching intention. However, the link between pull effects and ELS user switching intention was not found. The findings should aid ELS providers in deciphering ELS users' intentions in switching to other platforms and developing relevant theories, services, and regulations. The present study expands on previous research by introducing the PPM as a general model and demonstrating its effectiveness in explaining user switching intentions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.