A longitudinal analysis of user acceptance of electric car-sharing services

Electric car-sharing services (ECS), which have been expected to address the negative externalities of owning and using private cars, remain relatively small in the mobility market. Existing research on ECS adoption has relied on cross-sectional designs and models. This study marks the first longitudinal extension of the unified theory of acceptance and use of technology (UTAUT) to analyze the dynamic process of ECS adoption. With a two-wave online panel survey, results from autoregressive cross-lagged structural equation modeling confirm that psychological factors and behavioral intention to adopt ECS show stable autoregressive effects. It is found that the behavioral intention established earlier significantly strengthens psychological factors afterwards, revealing a feedback mechanism and cross-lagged effects. The relative importance of social influence grows over time and ultimately surpasses performance expectancy, reflecting a shift in the power of the influencing factors. These findings demonstrate the value of longitudinal perspectives in adoption research. The extended model also reveals a critical role of attitudes toward sharing, innovativeness, and mobility-related factors. Policymakers and ECS operators are recommended to continuously monitor user motivations and apply dynamic adjustments to strategies to promote durable shifts toward on-demand and climate-neutral mobility.

Authors

Institutions

Publication Details

Journal
Transportation Research Part A Policy and Practice
Published
2026-08-25
DOI
https://doi.org/10.1016/j.tra.2026.105231
Primary Topic
Transportation and Mobility Innovations
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

A longitudinal analysis of user acceptance of electric car-sharing services

Senkai Xie, Feixiong Liao
Transportation Research Part A Policy and Practice
Transportation and Mobility Innovations
article

A longitudinal analysis of user acceptance of electric car-sharing services

Senkai Xie, Feixiong Liao
article en

Abstract

Electric car-sharing services (ECS), which have been expected to address the negative externalities of owning and using private cars, remain relatively small in the mobility market. Existing research on ECS adoption has relied on cross-sectional designs and models. This study marks the first longitudinal extension of the unified theory of acceptance and use of technology (UTAUT) to analyze the dynamic process of ECS adoption. With a two-wave online panel survey, results from autoregressive cross-lagged structural equation modeling confirm that psychological factors and behavioral intention to adopt ECS show stable autoregressive effects. It is found that the behavioral intention established earlier significantly strengthens psychological factors afterwards, revealing a feedback mechanism and cross-lagged effects. The relative importance of social influence grows over time and ultimately surpasses performance expectancy, reflecting a shift in the power of the influencing factors. These findings demonstrate the value of longitudinal perspectives in adoption research. The extended model also reveals a critical role of attitudes toward sharing, innovativeness, and mobility-related factors. Policymakers and ECS operators are recommended to continuously monitor user motivations and apply dynamic adjustments to strategies to promote durable shifts toward on-demand and climate-neutral mobility.

Transportation Research Part A Policy and PracticeVol. 213
Eindhoven University of Technology (NL)
Openalex Percentile: Top 17%
Transportation and Mobility Innovations
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.