Evidence map›Paper›PMID 40638598›Full record

ArticlePloS one2025

Understanding older adults' adoption of facial recognition payment: An integrated model of TAM and UXT.

Mingzhou Liu, Caixia Wang, Jing Hu

Abstract read
In one paragraph

Article in PloS one, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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.

Mingzhou LiuSchool of Mechanical Engineering, Hefei University of Technology, Hefei, China.ORCID https://orcid.org/0000-0001-6511-6758
Caixia WangSchool of Mechanical Engineering, Hefei University of Technology, Hefei, China.
Jing HuSchool of Mechanical Engineering, Hefei University of Technology, Hefei, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The dual challenges of digitalization and population aging highlight the need to improve the well-being of older adults. Facial recognition payment (FRP), an innovative "face-to-pay" technology, has gained widespread adoption in commercial applications due to its convenience. However, existing research has primarily focused on younger users, leaving a significant gap in understanding FRP adoption and user experience among older adults. To address this gap, this study integrates the Technology Acceptance Model (TAM) with User Experience Theory (UXT) to develop a comprehensive framework for examining FRP adoption among older users. By incorporating both cognitive and affective dimensions, the proposed model systematically investigates key determinants of FRP acceptance. Data were collected through a questionnaire survey of 387 older adults in China, and hypotheses were tested using structural equation modeling (SEM). The findings reveal that TAM-based factors, including perceived convenience, facilitating conditions, and technology anxiety, significantly influence perceived ease of use (PEOU), perceived usefulness (PU), and user attitudes, with perceived convenience identified as the most influential determinant. From a UXT perspective, perceived enjoyment, personal innovation, and privacy risk also play a crucial role in FRP adoption. Specifically, personal innovation and perceived enjoyment positively affect user attitudes, whereas privacy risk poses a barrier to adoption. Additionally, while technology self-efficacy does not directly impact user attitudes, it indirectly enhances PEOU and PU by increasing personal innovation. By integrating TAM and UXT, this study advances the understanding of older adults' technology acceptance behavior and offers theoretical and practical implications for the development of age-friendly FRP systems. The findings provide valuable guidance for technology developers, policymakers, and stakeholders in designing user-centered payment technologies that enhance usability, security, and overall user experience, ultimately improving the quality of life for aging populations.

Indexed as

Automated Facial RecognitionFacial RecognitionAgedAged, 80 and overChinaFemaleHumansMaleMiddle AgedSurveys and Questionnaires

Identifiers

PMID40638598
PMCPMC12244727

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.