Evidence map›Paper›PMID 36299521›Full record

ArticleHeliyon2022

Investigating switching intention of e-commerce live streaming users.

Dingyu Ye, Fufan Liu, Dongmin Cho, Zhengzhi Jia

Erratum issuedAbstract read
In one paragraph

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.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

7 citing papers in PubMed.

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  4. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Dingyu YeDepartment of Design and Manufacturing Engineering, Jeonbuk National University, Jeollabuk-do, Republic of Korea.
Fufan LiuFaculty of Information Technology, University of Jyväskylä, Finland.
Dongmin ChoDepartment of Industrial Design, Jeonbuk National University, Jeollabuk-do, Republic of Korea.
Zhengzhi JiaDepartment of International Communication, Birmingham City University, Birmingham, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

E-commerce live streamingPush-pull-mooring modelStructural equation modelingSwitching intention

Identifiers

PMID36299521
PMCPMC9589182

What OpenQuestion holds

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LicenceCC BY-NC-ND
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Registered trials

None linked

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.