Evidence map›Paper›PMID 40050942›Full record

ArticleBMC psychology2025

AI anxiety and knowledge payment: the roles of perceived value and self-efficacy.

Jinsong Chen, Miao He, Jinhua Sun

Abstract read
In one paragraph

Article in BMC psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing 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

6 citing papers in PubMed.

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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

3 authors.

Jinsong ChenGuizhou University of Finance and Economics, Guiyang, China.
Miao HeGuizhou University of Finance and Economics, Guiyang, China.
Jinhua SunSchool of Management, Chongqing University of Technology, Chongqing, China. sjh1009@163.com.

Funding

The Special Project for Rural Cadres Residency under the "Great Earth Thesis" program at Guizhou University of Finance and Economics in 2022, Project Number: 2023ZCGBA04 2023ZCGBA04
6 · The paper itself

Abstract

backgroundThe integration of Artificial Intelligence (AI) into daily life raises significant challenges and uncertainties, notably concerning job security and skill relevance. This has led to the emergence of 'AI anxiety'-a stress response to potential impacts of AI on individuals' futures. This study examines AI anxiety's effects on individuals' willingness to pay for knowledge, focusing on the roles of perceived value and self-efficacy.

methodsThis study consisted of two experiments. Study 1 utilized a one-factor between-subjects design (AI anxiety vs. neutral emotion) with 297 participants to examine the mediating role of perceived value in the relationship between AI anxiety and willingness to pay for knowledge. Study 2 employed a 2 × 2 between-subjects design (AI anxiety vs. neutral emotion )×self-efficacy (high vs. low) with 506 participants to investigate the moderating effect of self-efficacy on this relationship.

resultsStudy 1 showed that AI anxiety significantly increases the willingness to pay for knowledge, with perceived value partially mediating this effect. Study 2 demonstrated that self-efficacy moderates the influence of AI anxiety: higher self-efficacy levels weaken, whereas lower levels strengthen, the willingness to pay for knowledge. Furthermore, self-efficacy also negatively moderates the effect of AI anxiety on perceived value.

conclusionsAI anxiety positively influences payment for knowledge, with critical roles for perceived value and self-efficacy. These findings offer a new framework for understanding AI anxiety's impact on consumer behavior and provide actionable insights for platforms and policymakers.

Indexed as

AnxietyArtificial IntelligenceKnowledgeSelf EfficacyAdultFemaleHumansMaleMiddle AgedYoung AdultAI anxietyArtificial intelligenceKnowledge paymentPerceived valueSelf-efficacy

Identifiers

PMID40050942
PMCPMC11887247

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LicenceCC BY-NC-ND
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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.