Evidence map›Paper›PMID 41487640›Full record

ArticleFrontiers in public health2025

Exploring the determinants of online purchase intention for aging-friendly products: evidence from senior consumers in urban Chinese context.

Liqian Yang, Ruitian Xie, Xuyun Zhao, Guanhui Zheng, Shengxiang She

Abstract read
In one paragraph

Article in Frontiers in public health, 2025. 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

5 authors.

Liqian YangGuangzhou College of Technology and Business, Guangzhou, China.
Ruitian XiePuning People's Hospital, Puning, China.
Xuyun ZhaoRajapark Institute, Bangkok, Thailand.
Guanhui ZhengGuangzhou College of Technology and Business, Guangzhou, China.
Shengxiang SheGuangzhou College of Technology and Business, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

This study examines the determinants of older adults' intention to purchase aging-friendly products via online shopping platforms, focusing on senior consumers in Guangzhou, China. Drawing on the Technology Acceptance Model and the Theory of Planned Behavior, it addresses the growing need to understand digital consumption behaviors among aging populations and the factors shaping their engagement in online marketplaces. A structural model was developed to evaluate the effects of digital literacy, trusting beliefs in online shopping, technology anxiety, economic cost, and subjective norms on purchase intentions, using data collected through self-administered questionnaires from 546 adults aged 60 and above. Structural equation modeling was employed to test the hypothesized relationships. The analysis identified cognitive, emotional, social, and economic dimensions as significant contributors to intention formation, with trusting beliefs further reinforcing older adults' willingness to engage in online purchasing. Overall, the study refines technology-acceptance frameworks for aging populations and underscores the need for digital inclusion strategies supported by public institutions, online retailers, aging-friendly manufacturers, and family caregivers.

Indexed as

AgingCommerceConsumer BehaviorIntentionInternetUrban PopulationAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedSurveys and Questionnairesaging-friendly productsdigital literacyolder adultsonline shopping behaviortechnology anxietytrusting beliefs in online shopping

Identifiers

PMID41487640
PMCPMC12758152

What OpenQuestion holds

Textmetadata
LicenceCC BY
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Registered trials

None linked

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.