Evidence map›Paper›PMID 42780726›Full record

ArticleFrontiers in psychology2026

Human-AI co-creation in generative AI video: content characteristics, flow experience, and continued creation intention-a hybrid SEM-ANN study.

Min Hou, Jiayi Zhang, Mengyi Liu, Jing Pu

Abstract read
In one paragraph

Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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

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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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.

Min HouSchool of Arts and Media, Sichuan Agricultural University, Ya'an, China.
Jiayi ZhangSchool of Arts and Media, Sichuan Agricultural University, Ya'an, China.
Mengyi LiuSchool of Arts and Media, Sichuan Agricultural University, Ya'an, China.
Jing PuSchool of Arts and Media, Sichuan Agricultural University, Ya'an, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: Grounded in the stimulus-organism-response (S-O-R) theory and flow theory, this study investigates the pathways through which five dimensions of artificial intelligence (AI) video content characteristics-generation quality, emotional resonance, visual novelty, interaction, and narrative coherence-influence university students' AI video creation willingness (AW), with a particular focus on the mediating role of flow experience. Methods: A hybrid approach combining structural equation modeling (SEM) and artificial neural network (ANN) analysis was adopted. Using the Jimeng (Dreamina) AI video generation platform as the research context, questionnaire data were collected from 512 Chinese university students with more than 1 month of usage experience. Results: All five AI video content characteristics significantly and positively influenced flow experience, which in turn significantly affected AW. Among these characteristics, visual novelty and narrative coherence exerted the most prominent effects on AW. Bootstrap mediation tests confirmed that flow experience significantly mediated all examined pathways. ANN sensitivity analysis further verified that visual novelty was the primary predictor of flow experience, while flow experience was the core predictor of creation willingness. Discussion: This study extends the application boundary of S-O-R theory to generative AI creation contexts and elucidates the psychological mechanism through which external stimuli are transformed into behavioral responses. The findings also provide theoretical grounding and practical guidance for AI video platforms to optimize user experience and enhance users' willingness to adopt AI video creation.

Indexed as

AI video creation willingnesscontent characteristicsflow experienceflow theorygenerative AI videoSEM–ANN hybrid analysisS-O-R framework

Identifiers

PMID42780726
PMCPMC13597746

What OpenQuestion holds

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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.