Evidence map›Paper›PMID 40426474›Full record

ArticleBehavioral sciences (Basel, Switzerland)2025

Investigating Elderly Individuals' Acceptance of Artificial Intelligence (AI)-Powered Companion Robots: The Influence of Individual Characteristics.

Jing Liu, Xingang Wang, Jiaqi Zhang

Abstract read
In one paragraph

Article in Behavioral sciences (Basel, Switzerland), 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 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.

Jing LiuCollege of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Xingang WangCollege of Mechanical and Electrical Engineering, Guangdong University of Petrochemical Technology, Maoming 525000, China.
Jiaqi ZhangDepartment of Industrial Art, Graduate School of System Design, Tokyo Metropolitan University, Tokyo 1910065, Japan.

Funding

Guangdong Provincial Department of Science and Technology 2022ZDZX3013Guangdong Provincial Department of Science and Technology 2024A1515011777Guangdong University of Petrochemical Technology 2020rc034Guangdong University of Petrochemical Technology 2023rcyj2011
6 · The paper itself

Abstract

The emergence of AI companion robots is transforming the landscape of elderly care, offering numerous conveniences to senior citizens when their children are not around. This trend is particularly pertinent in ageing societies such as China. Against this backdrop, the present study aims to explore the acceptance of AI companion robots among the elderly from a user-centric perspective. By leveraging insights from existing studies in the literature, we identified three individual characteristic variables-technology optimism, innovativeness, and familiarity-to extend the Artificial Intelligence Device Use Acceptance (AIDUA) model. Subsequently, we developed a conceptual model which was empirically tested through structural equation modelling (SEM) analysis. Our dataset comprised responses from 452 elderly individuals in China. The results revealed that technology optimism and innovativeness were positively associated with performance expectancy and effort expectancy, whereas familiarity inversely predicted perceived risk. Furthermore, emotion was found to be positively influenced by performance expectancy and effort expectancy but negatively impacted by perceived risk. This research extends the AIDUA model within the context of AI companion robots by integrating individual characteristic variables. These findings offer valuable insights for the design and development of companion robots and enrich the domain of Human-Robot Interaction (HRI).

Indexed as

artificial intelligencebehaviour intentionscompanion robotelderly individualtechnology acceptance

Identifiers

PMID40426474
PMCPMC12109019

What OpenQuestion holds

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LicenceCC BY
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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.