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Human-made vs. AI-generated: how provenance labels drive strategic curation via perceived effort
Han Sol Lim1, Bong Gyou Lee1, Yoon Hi Sung2
1Graduate School of Information, Yonsei University, Seoul, Republic of Korea.
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.
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