Beyond habit: how perceived risk and personalised adaptation influence autonomous vehicle acceptance in the near-elderly group

With the ageing population's growing demand for mobility, this study examines acceptance of automated vehicles (AVs) among the near-elderly. By integrating perceived risk, behavioural inertia, and personalised adaptation into the Unified Technology Acceptance and Use Theory (UTAUT) framework, a new model was estimated using Partial Least Squares Structural Equation Modelling (PLS-SEM). Data were collected from 352 licenced drivers aged 55-64 in mainland China through a national online survey. Findings reveal that behavioural inertia is not critical, whereas perceived risk is the strongest negative driver of acceptance intention. Conversely, personalised adaptation and performance expectancy are the primary positive drivers. Mediation analysis shows that personalised adaptation significantly influences intention through effort expectancy, though not through behavioural inertia. Results suggest that AV design should minimise perceived risk and provide adaptive functions, such as frequent-route memory, driving-habit adaptation, and clearer system communication, to enhance adoption among this demographic.

Authors

Institutions

Publication Details

Journal
Ergonomics
Published
2026-10-29
DOI
https://doi.org/10.1080/00140139.2026.2750452
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Beyond habit: how perceived risk and personalised adaptation influence autonomous vehicle acceptance in the near-elderly group

Qingchuan Li, Xin Chen, Nengmin Guo
3 links
Ergonomics
Human-Automation Interaction and Safety
article

Beyond habit: how perceived risk and personalised adaptation influence autonomous vehicle acceptance in the near-elderly group

Qingchuan Li, Xin Chen, Nengmin Guo
article en

Abstract

With the ageing population's growing demand for mobility, this study examines acceptance of automated vehicles (AVs) among the near-elderly. By integrating perceived risk, behavioural inertia, and personalised adaptation into the Unified Technology Acceptance and Use Theory (UTAUT) framework, a new model was estimated using Partial Least Squares Structural Equation Modelling (PLS-SEM). Data were collected from 352 licenced drivers aged 55-64 in mainland China through a national online survey. Findings reveal that behavioural inertia is not critical, whereas perceived risk is the strongest negative driver of acceptance intention. Conversely, personalised adaptation and performance expectancy are the primary positive drivers. Mediation analysis shows that personalised adaptation significantly influences intention through effort expectancy, though not through behavioural inertia. Results suggest that AV design should minimise perceived risk and provide adaptive functions, such as frequent-route memory, driving-habit adaptation, and clearer system communication, to enhance adoption among this demographic.

Ergonomics
Shenzhen University (CN), Harbin Institute of Technology (CN)
Natural Science Foundation of Guangdong Province
Good health and well-being, Sustainable cities and communities, Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Human-Automation Interaction and Safety
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.

Laika access coming soon
Beyond habit: how perceived risk and personalised adaptation influence autonomous vehicle acceptance in the near-elderly group — Qingchuan Li, Xin Chen, et al. · Ergonomics (2026) | TGRS Research Map | TGRS