Evidence map›Paper›PMID 42245597›Full record

ArticleFrontiers in psychology2026

Regional cognitive pathways to intention to use AI: a structural equation modeling approach.

Yewon Jang, Tae Gyoon Jeong, Tongjoo Suh

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
–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

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

3 authors.

Yewon JangCollege of Natural Resources and Life Science, Pusan National University, Miryang, Republic of Korea.
Tae Gyoon JeongCollege of Natural Resources and Life Science, Pusan National University, Miryang, Republic of Korea.
Tongjoo SuhCollege of Natural Resources and Life Science, Pusan National University, Miryang, Republic of Korea.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The aim of this study was to examine how the cognitive pathways linking artificial intelligence (AI) knowledge and AI application capability to the intention to use AI differ across regions. Perceived efficiency and AI concerns are modeled as perceptual factors associated with AI adoption decisions. Using nationwide survey data from Korea, a multi-group structural equation model distinguishing between megacity and non-megacity regions was estimated. The results showed that perceived efficiency is closely associated with the intention to use AI, whereas AI concerns show a weaker association. In the megacity region, AI application capability was positively associated with perceived efficiency, whereas in the non-megacity region, both AI knowledge and AI application capability were positively associated with perceived efficiency and, in turn, the intention to use AI. These findings suggest that AI-related decision-making is associated not only with individual technical proficiency but also with contextual access to AI. Accordingly, policies should account for contextual disparities and consider strategies that strengthen AI knowledge and application capabilities in non-megacity regions to support more inclusive digital transformation.

Indexed as

AI application capabilityAI concernsAI knowledgecognitive pathwaysintention to use AIperceived efficiency

Identifiers

PMID42245597
PMCPMC13230043

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

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.