Evidence map›Paper›PMID 42631053›Full record

ArticleInnovation in aging2026

Factors influencing older adults' intention to use social chatbots: examining technology acceptance, anthropomorphism, authenticity, and artificial intelligence social interaction.

Ka Lon Sou, Wei Quin Yow, Matthew Zhi Yuan Lau, Fuxi Ouyang

Abstract read
In one paragraph

Article in Innovation in aging, 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
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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

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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

4 authors.

Ka Lon SouHumanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore.
Wei Quin YowHumanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore.ORCID https://orcid.org/0000-0002-4066-7200
Matthew Zhi Yuan LauHumanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore.
Fuxi OuyangHumanities, Arts and Social Sciences, Singapore University of Technology and Design, Singapore, Singapore.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background and Objectives: Despite emerging evidence showing that older adults have begun engaging social chatbots for companionship in recent years, little is known about the factors influencing their adoption decisions. We developed an age-friendly social chatbot and examined older adults' intention to use it by integrating the Senior Technology Acceptance Model (STAM) with sociorelational factors relevant to human-artificial intelligence (AI) interaction. Research Design and Methods: A final sample of 140 community-dwelling older adults interacted with the chatbot for approximately 20 min and then completed questionnaires assessing their attitudes toward the chatbot, perceptions of the chatbot, and their intention to use it in daily life. Results: Higher control belief (e.g., perceived ease of use and facilitating conditions), one of the STAM factors, predicted higher intention to use the chatbot among older adults, whereas higher self-rated health predicted lower intention. Importantly, the stronger the sociorelational factors (e.g., authenticity; AI social interaction intensity), the higher the intention to use. However, higher levels of anthropomorphism of the chatbot predicted lower intention to use. Incorporating sociorelational factors into the STAM framework accounted for an additional proportion of variance, comparable to STAM factors alone. Discussion and Implications: Our findings suggest that control belief, authenticity, and social interaction intensity with social chatbots are key drivers of adoption intention among older adults. Unmet needs in life may also drive chatbot engagement. However, overly human-like chatbots may deter adoption. Taken together, integrating human-AI interaction factors with the STAM framework is essential when evaluating older adults' adoption of social chatbots.

Indexed as

Age-friendlyControl beliefConversational agentHuman-likeVirtual companionship

Identifiers

PMID42631053
PMCPMC13495109

What OpenQuestion holds

Textmetadata
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.