Utility intention and acceptance evaluation of self-driving cars and its technology in the Philippines

The rapid growth of technology on self-driving cars brings opportunities and challenges for transportation systems and populations, especially in developing countries such as the Philippines. This study focused on the understanding of factors influencing Filipino consumers' acceptance and intention to adopt self-driving cars. Despite being widely available among developing countries, this has yet been evidently available in the Philippines as only limited number of self-driving cars are seen among the streets. Utilizing a sample of 439 respondents, this research employed a Partial Least Squares Structural Equation Modeling approach to examine behavioral intention and actual usage through an integrated framework combining the Extended Technology Acceptance Model and the Valence Framework of Behavioral Beliefs. The key variables to measure perceptions such as perceived usefulness, perceived benefits, willingness to use, relevance, ubiquity, cost, and risk were assessed simultaneously. The findings showed that behavioral intention is the strongest predictor of actual use while perceived benefits, relevance, and usefulness are statistically significant predictors of intention. In contrast, perceived risk, cost, and social influence were not statistically significant. The results provided information on an important crossover between self-driving car technologies and the perceived mobility needs and perceived benefits for Filipino consumers. It was also found that strategic interventions, such as public awareness, infrastructure, and policy support are necessary for people to feel comfortable with autonomous mobility.

Institutions

Publication Details

Journal
Acta Psychologica
Published
2025-12-19
DOI
https://doi.org/10.1016/j.actpsy.2025.106133
Primary Topic
Human-Automation Interaction and Safety
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Utility intention and acceptance evaluation of self-driving cars and its technology in the Philippines

Acta Psychologica
Human-Automation Interaction and Safety
article

Utility intention and acceptance evaluation of self-driving cars and its technology in the Philippines

article en

Abstract

The rapid growth of technology on self-driving cars brings opportunities and challenges for transportation systems and populations, especially in developing countries such as the Philippines. This study focused on the understanding of factors influencing Filipino consumers' acceptance and intention to adopt self-driving cars. Despite being widely available among developing countries, this has yet been evidently available in the Philippines as only limited number of self-driving cars are seen among the streets. Utilizing a sample of 439 respondents, this research employed a Partial Least Squares Structural Equation Modeling approach to examine behavioral intention and actual usage through an integrated framework combining the Extended Technology Acceptance Model and the Valence Framework of Behavioral Beliefs. The key variables to measure perceptions such as perceived usefulness, perceived benefits, willingness to use, relevance, ubiquity, cost, and risk were assessed simultaneously. The findings showed that behavioral intention is the strongest predictor of actual use while perceived benefits, relevance, and usefulness are statistically significant predictors of intention. In contrast, perceived risk, cost, and social influence were not statistically significant. The results provided information on an important crossover between self-driving car technologies and the perceived mobility needs and perceived benefits for Filipino consumers. It was also found that strategic interventions, such as public awareness, infrastructure, and policy support are necessary for people to feel comfortable with autonomous mobility.

Acta PsychologicaVol. 262
Mapúa University (PH)
Openalex Percentile: Top 30%
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.