Evidence map›Paper›PMID 41100548›Full record

ArticlePloS one2025

Exploring the impact of differences between AI streamers and human streamers on consumer purchase intention in live e-commerce: A grounded theory approach.

Jie Li, Jinni Chen, Yi Shi, Yi Cui

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Jie LiHangzhou Dianzi University Information Engineering College, Hangzhou, Zhejiang, China.
Jinni ChenCommunication University of China, Beijing, China.
Yi ShiCommunication University of China, Beijing, China.
Yi CuiCommunication University of China, Beijing, China.ORCID https://orcid.org/0000-0001-6109-0968

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

With the rapid development of live e-commerce, AI streamers have gradually emerged as a new industry trend. However, significant differences exist between AI streamers and human streamers in terms of interaction styles, emotional expression, and user experience. How these differences influence consumer purchase intention has not yet been systematically investigated. Based on this, the present study employs grounded theory and the stimulus-organism-response model to explore the mechanism through which the differences between AI streamers and human streamers in live e-commerce affect consumer purchase intention. Data were collected through in-depth interviews and analyzed using open coding, axial coding, and selective coding. A research model was constructed, incorporating sensory differences, affective differences, and functional differences (stimulus factors), perceived entertainment and social presence (organism factor), and purchase intention (response factor), with consumer purpose as a moderating factor. Our results reveal that AI and human streamers differ in sensory, affective, and functional dimensions. Under purchase motivation, AI streamers, with their standardized and concise sensory presentation, better meet the need for quick decision-making, thus outperforming human streamers in functional differences. In contrast, under viewing motivation, human streamers excel in all three dimensions. These differences affect purchase intention through the mediating roles of perceived entertainment and social presence. Consumer purpose (purchase vs. viewing) moderates these effects. This study bridges the gap in understanding how AI and human streamers' differences shape consumer behavior, offering practical insights for e-commerce platforms to optimize streamer strategies and guiding the anthropomorphic development of AI streamers.

Indexed as

Artificial IntelligenceCommerceConsumer BehaviorIntentionAdultFemaleGrounded TheoryHumansMaleMiddle AgedMotivationYoung Adult

Identifiers

PMID41100548
PMCPMC12530592

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LicenceCC BY
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Registered trials

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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.