Evidence map›Paper›PMID 41293105›Full record

ArticleFrontiers in psychology2025

Factors influencing older adults' adoption of AI voice assistants: extending the UTAUT model.

Haoran Li, Xin Wei

Abstract read
In one paragraph

Article in Frontiers in psychology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
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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

2 authors.

Haoran LiDepartment of Philosophy, Autonomous University of Barcelona, Barcelona, Spain.
Xin WeiFaculty of Geography and History, University of Barcelona, Barcelona, Spain.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: With the acceleration of global population aging and the digitalization process, the potential application of AI voice assistants among the elderly has become increasingly apparent. However, the adoption of this technology by older adults remains relatively low. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT), this study extends the model by introducing two variables, perceived AI experience and perceived AI trustworthiness, to explore the key factors influencing older adults' use of AI voice assistants. Methods: Data were collected through a structured survey, with participants consisting of 413 elderly users from Shanxi Province, China, using a convenience sampling method. The gender distribution was 53.1% male and 46.9% female, with ages ranging from 60 to 75 years and older. The data were analyzed using Structural Equation Modeling (SEM). Results: The results showed that performance expectancy, facilitating conditions, perceived AI trustworthiness, and perceived AI experience all had a significant positive effect on the elderly's intention to use AI voice assistants, while effort expectancy negatively influenced the intention. Additionally, although social influence significantly affected perceived AI trustworthiness, its impact on the intention to use was not significant. Furthermore, intention to use played an important mediating role in the actual behavior of older adults using AI voice assistants. Discussion: This study enriches the application of the UTAUT model in technology adoption research among older populations by incorporating perceived AI experience and perceived AI trustworthiness. The findings provide practical guidance for optimizing the design and promotion strategies of age-friendly AI voice assistants, highlighting the importance of enhancing user trust and experience to improve technology adoption among the elderly.

Indexed as

AI voice assistantselderly userstechnology acceptanceuser experienceUTAUT model

Identifiers

PMID41293105
PMCPMC12641254

What Socratic 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 Socratic graph.