Evidence map›Paper›PMID 42317700›Full record

ArticleFrontiers in psychology2026

Human-made vs. AI-generated: how provenance labels drive strategic curation via perceived effort.

Han Sol Lim, Bong Gyou Lee, Yoon Hi Sung, Chang Won Jung

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.

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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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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

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5 · Who and what money

Authors and funding

4 authors.

Han Sol LimGraduate School of Information, Yonsei University, Seoul, Republic of Korea.
Bong Gyou LeeGraduate School of Information, Yonsei University, Seoul, Republic of Korea.
Yoon Hi SungDepartment of Communication, Yonsei University, Seoul, Republic of Korea.
Chang Won JungDepartment of Media Communication, Dankook University, Gyeonggi-do, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: This study examined how content provenance labeling on short-form video platforms shapes users' perceived creator effort, algorithmic curation efficacy, and strategic curation intention. Whereas prior research has largely framed such labels as instruments for risk disclosure or warning, this study reconceptualizes provenance labels as value signals (i.e., attribution cues that signal and assign creative effort) and investigates the psychological mechanisms through which these signals create beliefs about algorithmic intervention and affect subsequent behavioral intentions. Methods: A between-subjects experiment was conducted with 618 short-form video users using a 3 (label type: human-made vs. AI-generated vs. unlabeled) × 2 (content type: eudaimonic vs. hedonic) factorial design to test labeling effects and the dual-pathway mechanism linking perceived effort with strategic curation. Results: The AI-generated label significantly reduced perceived effort, whereas the human-made label did not differ from the unlabeled condition, providing empirical evidence for an implicit human-made default assumption. Perceived effort increased strategic curation intentions via both rational and normative pathways. However, the AI-generated label weakened both pathways, producing an asymmetric dual-path effect that systematically undermined users' willingness to intervene in algorithmic curation. Discussion: The effort devaluation induced by AI labeling operated as a context-independent heuristic, unaffected by content type or perceived platform degradation. Moreover, an exploratory finding showed that greater algorithmic knowledge was associated with lower intervention intention, suggesting that user agency may be grounded less in technical knowledge than in subjective efficacy beliefs. This implies that future labeling policies should move beyond passive risk disclosure toward a human-centered value-certification framework that foregrounds human creativity and effort.

Indexed as

AI disclosurecontent provenancegenerative AIhuman-made labelingperceived effortshort-form videostrategic curationuser agency

Identifiers

PMID42317700
PMCPMC13272402

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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.