Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles

doi:10.1016/j.trc.2019.08.020

Transportation Research Part C: Emerging Technologies

Volume 107, October 2019, Pages 411-422

https://doi.org/10.1016/j.trc.2019.08.020 Get rights and content

Highlights

•
This study examines influential factors for intention to use autonomous vehicles.
•
System and psychology level factors are identified by literature reviews.
•
Perceived risk affects intention to use without any effects of antecedents.
•
Self-efficacy is presented to affect perceived ease of use and intention to use.
•
Psychological ownership significantly affects intention to use.

Abstract

Autonomous vehicles are expected to be commercialized within a few years, and researchers have investigated various factors that influence their adoption. However, only a few studies have considered comparative and psychological perspectives that can affect user-vehicle relationships. Focusing on this limitation, this study investigates influential factors on the use of autonomous vehicles in terms of a technology acceptance model (which considers perceived ease of use, perceived usefulness, and intention to use) and factors for autonomous vehicle use (e.g., perceived risk, relative advantage, self-efficacy, and psychological ownership (i.e., feeling of ownership)). Our results show that self-efficacy positively affects the perceived ease of use and intention to use, while the relative advantage affects perceived usefulness. Psychological ownership affects the intention to use but not the perceived usefulness. This implies that encouraging a consumer to form a psychological bond (i.e., psychological ownership) with an autonomous vehicle may be an effective strategy for promoting the use of autonomous vehicles.

Introduction

Autonomous vehicles have great potential for improving the safety and efficiency of transportation. With minimized human intervention and optimized traffic control systems, autonomous vehicles can lead to a new transportation environment with less traffic and safer driving. Over the last decades, autonomous vehicles have been a conceptual idea. However, advances in artificial intelligence and real-time data processing technologies have enabled the development of practical autonomous vehicles. Early autonomous vehicles were only able to recognize and handle some driving situations, but these limitations have been removed due to continued technological advances. Several vehicle companies, including Ford, Honda, Toyota, Nissan, Volvo, Hyundai, Daimler, Fiat-Chrysler, and BMW, are developing autonomous vehicles and have plans to release full-automation vehicles (i.e., vehicles that can drive without any human intervention) starting in 2021 (Walker, 2018).

With ongoing technological advances in autonomous vehicles, several researchers have investigated influential factors for vehicle adoption. Merat et al. (2012) suggested that the reliability of autonomous vehicles may be a crucial factor affecting adoption. Verberne et al. (2012) suggested that trust is a crucial psychological factor when considering the acceptability of automation technologies in automobiles. Choi and Ji (2015) showed that trust was a major construct determining users’ willingness to adopt autonomous vehicles. Finally, Ward et al. (2017) found that perceptions of risk and benefit, knowledge, and trust were related to the intention to use automated vehicles.

Although these studies have suggested various factors that influence the potential adoption of autonomous vehicles, influential factors for autonomous vehicle adoption still need to be investigated further from two perspectives. First, factors related to the technology acceptance model need to be reexamined by considering the context of autonomous vehicles in more detail. Many previous studies have addressed factors that influence the technology acceptance model, but these reports are often inconsistent due to the different considerations of underlying structures. Reviewing the existing studies of autonomous vehicles and reorganizing the relationships of the factors can yield meaningful insights related to the use of autonomous vehicles. Second, psychological factors influencing the use of autonomous vehicles also need to be examined accordingly. When reconstructing influential factors in the technology acceptance model, several psychological factors related to the use of autonomous vehicles can be examined together, thereby extending the understanding of users’ perceptions of autonomous vehicles.

Based on these issues, this study reviews previous studies related to autonomous vehicles and identifies the relationships between factors that need to be examined. By reviewing the identified factors in detail, we design a structural equation model and examine how users perceive the properties of autonomous vehicles when determining their potential use of autonomous vehicles. Based on the findings from existing studies and this study, we aim to provide more detailed information related to users’ perceptions of autonomous vehicles, as well as the influential factors determining an individual’s intention to use autonomous vehicles.

Section snippets

A brief review of previous studies

Numerous studies have investigated factors that influence the potential use of autonomous vehicles and suggested that safety, environmental concerns, relative advantage, compatibility, subjective norms, and self-efficacy can be considered influential factors for intention to use of autonomous vehicles (Gkartzonikas and Gkritza, 2019). We reviewed previous studies which addressed influential factors for autonomous vehicles based on a theoretical framework of technology acceptance and provided

Perceived ease of use and perceived usefulness

Previous studies of TAM have considered three core factors: perceived ease of use, perceived usefulness, and intention to use. However, these reports have been inconsistent. For example, some studies reported that all of the relationships were significant (e.g., Panagiotopoulos and Dimitrakopoulos, 2018, Wu et al., 2019) but other studies reported that only some relationships were significant (e.g., Buckley et al., 2018, Choi and Ji, 2015, Hein et al., 2018). Choi and Ji (2015) found that

Data collection

To examine the hypotheses of the developed research model, we designed a questionnaire consisting of 28 items to measure seven constructs. The items were adapted from previous studies and modified according to the context of autonomous vehicles. Table 2 shows the constructs and questionnaire items considered in this study.

The questionnaire was provided to 313 Korean respondents who were recruited from the research panel of Macromill Embrain, an online survey company with the largest panel in

Structural equation modeling

To identify the underlying structure of the items, we conducted a factor analysis based on respondent data. Because the examined items were designed to measure seven constructs in previous studies (i.e., PEOU, PU, ITU, PR, RA, SE, and PO), we considered that the number of constructs in factor analysis should be seven (Matsunaga, 2010). Also, through iterative checking of communalities and factor scores for seven constructs, we excluded 5 items from the analysis (PEOU4, PU3, ITU2, PR4, PO2) and

Discussion

It is an autonomous vehicle. The matter is how useful it is rather than how easy it is to use.

The original technology acceptance model assumed that the perceived ease of use affected the perceived usefulness, and both factors affect the intention to use (Davis, 1989, Davis et al., 1989). However, among previous studies looking into autonomous vehicles, which examined all of these relationships, only Panagiotopoulos and Dimitrakopoulos (2018) reported that all of the relationships were

Limitations of this study

To help grasp the results and findings of this study in more detail, three limitations need to be stated. First, although statistical tests showed that the examined constructs were acceptable in terms of reliability and validity, some of the constructs were measured by items less than three (i.e., intention to use) and presented high correlations. Thus, future studies can consider different sets of items for the constructs and further improve the reliability and validity of the findings in this

Conclusion

In this study, we examined influential factors for intention to use autonomous vehicles. Relationships between seven factors (i.e., perceived usefulness, perceived ease of use, intention to use, perceived risk, relative advantage, self-efficacy, and psychological ownership) were examined using structural equation modeling. Our results showed that perceived usefulness, self-efficacy, perceived risk, and psychological ownership can be significant factors that affect intention to use autonomous

Acknowledgements

This work was supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea (NRF-2019S1A5A8035093).

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2017R1C 1B 1003650).

References (61)

I. Ajzen
The theory of planned behavior
Organ. Behav. Hum. Decis. Process.
(1991)
L. Buckley et al.
Psychosocial factors associated with intended use of automated vehicles: a simulated driving study
Accid. Anal. Prev.
(2018)
F.D. Davis et al.
A critical assessment of potential measurement biases in the technology acceptance model: three experiments
Int. J. Hum Comput. Stud.
(1996)
D. Efthymiou et al.
Factors affecting the adoption of vehicle sharing systems by young drivers
Transp. Pol.
(2013)
M.S. Featherman et al.
Predicting e-services adoption: a perceived risk facets perspective
Int. J. Hum. Comput. Stud.
(2003)
C. Gkartzonikas et al.
What have we learned? A review of stated preference and choice studies on autonomous vehicles
Transport. Res. Part C: Emerg. Technol.
(2019)
C. Hohenberger et al.
Not fearless, but self-enhanced: the effects of anxiety on the willingness to use autonomous cars depend on individual levels of self-enhancement
Technol. Forecast. Soc. Chang.
(2017)
W.R. King et al.
A meta-analysis of the technology acceptance model
Inform. Manage.
(2006)
M. König et al.
Users’ resistance towards radical innovations: the case of the self-driving car
Transport. Res. Part F: Traffic Psychol. Behav.
(2017)
M. Kyriakidis et al.
Public opinion on automated driving: results of an international questionnaire among 5000 respondents
Transport. Res. Part F: Traffic Psychol. Behav.
(2015)

P. Liu et al.

Road tests of self-driving vehicles: affective and cognitive pathways in acceptance formation

Transport. Res. Part A: Pol. Pract.

(2019)

R. Madigan et al.

What influences the decision to use automated public transport? Using UTAUT to understand public acceptance of automated road transport systems

Transport. Res. Part F: Traffic Psychol. Behav.

(2017)

D. Milakis et al.

Policy and society related implications of automated driving: a review of literature and directions for future research

J. Intell. Transport. Syst.

(2017)

L. Moták et al.

Antecedent variables of intentions to use an autonomous shuttle: moving beyond TAM and TPB?

Revue Européenne de Psychologie Appliquée/Europ Rev Appl Psychol

(2017)

I. Panagiotopoulos et al.

An empirical investigation on consumers’ intentions towards autonomous driving

Transport. Res. Part C: Emerg. Technol.

(2018)

W. Payre et al.

Intention to use a fully automated car: attitudes and a priori acceptability

Transport. Res. Part F: Traffic Psychol. Behav.

(2014)

J. Schlüter et al.

Car sharing as a means to raise acceptance of electric vehicles: an empirical study on regime change in automobility

Transport. Res. Part F: Traffic Psychol. Behav.

(2019)

A. Waytz et al.

The mind in the machine: anthropomorphism increases trust in an autonomous vehicle

J. Exp. Soc. Psychol.

(2014)

J. Wu et al.

The role of environmental concern in the public acceptance of autonomous electric vehicles: a survey from China

Transport. Res. Part F: Traffic Psychol. Behav.

(2019)

Z. Xu et al.

What drives people to accept automated vehicles? Findings from a field experiment

Transport. Res. Part C: Emerg. Technol.

(2018)

A. Bandura

Self-efficacy mechanism in human agency

Am. Psychol.

(1982)

A. Bandura

Self-efficacy conception of anxiety

Anxiety Res.

(1988)

F. Becker et al.

Literature review on surveys investigating the acceptance of automated vehicles

Transportation

(2017)

T. Brell et al.

sCARy! Risk perceptions in autonomous driving: the influence of experience on perceived benefits and barriers

Risk Anal.

(2018)

D. Child

The Essentials of Factor Analysis

(1990)

W.W. Chin

Commentary: issues and opinion on structural equation modeling

MIS Quarterly

(1998)

J.K. Choi et al.

Investigating the importance of trust on adopting an autonomous vehicle

Int. J. Human-Comput. Interact.

(2015)

D.R. Compeau et al.

Computer self-efficacy: development of a measure and initial test

MIS Quarterly

(1995)

F.D. Davis

Perceived usefulness, perceived ease of use, and user acceptance of information technology

MIS Quarterly

(1989)

F.D. Davis et al.

User acceptance of computer technology: a comparison of two theoretical models

Manage. Sci.

(1989)

Cited by (141)

Shared versus pooled automated vehicles: Understanding behavioral intentions towards adopting on-demand automated vehicles
2024, Travel Behaviour and Society
The success of future, on-demand AVs depends on whether and how the public adopts them as regular travel modes. To this end, this study has four objectives: (a) understand the effect of current travel behavior on adopting shared automated vehicles (SAVs) and pooled automated vehicles (PooledAVs), and accordingly, propose a theoretical model for behavioral intention to use on-demand AVs based on a mediation analysis; (b) identify the commonalities and differences between the characteristics of SAVs’ and PooledAVs’ early adopters; (c) investigate Canadian residents’ intentions to adopt on-demand AVs; and (d) differentiate between shared mobility-oriented and AV technology-oriented predictors to determine whether behavioral intentions are driven by enthusiasm for AV technology or linked to shared mobility. This study uses data from an online survey administered in fall 2021 to residents of six major Canadian metropolitan areas. Workers and students aged 18 to 75 were selected for analysis (n = 3622). The study employs a full structural equation model (SEM) and a partial mediation analysis. The SEM uses seven affective motivations: attitude towards a behavior, subjective norms, perceived behavioral control, driving enthusiasm, sensation seeking, ample knowledge about AVs, and eco-friendliness. The findings of this paper paint a complex picture with respect to the application of constructs from common technology adoption models to the study of on-demand AVs. Also, results suggest that although the characteristics of early adopters differ slightly among the examined service types, individuals with positive attitudes who are current users of ride-hailing services are likely to use both on-demand service types.
The impact of people's subjective perception on their acceptance of automated vehicles: A meta-analysis
2024, Transportation Research Part F: Traffic Psychology and Behaviour
The automated vehicles (AV) is an important driving force for intelligent transportation technologies. Previous studies have found significant influence of people’s subjective perception on their acceptance of AVs, but how people’s perception impose influence their acceptance has not reached consensus. This study reviewed published papers addressing this topic to provide some insights of this issue. The preferred reporting items for systematic reviews and meta-analysis method (PRISMA) is used to select qualified papers across major databases. The impacts of nine subjective perception factors on public acceptance of AVs revealed by the papers are reanalysed following a rigorous meta-analytic process. The results show that people’s perceived usefulness of AVs and attitude toward using strongly affect their acceptance of AVs. The increase of perceived ease of use could slightly increase the acceptance. Lower perceived risk often leads to higher acceptance of AVs. Social influence hardly affects people accepting AVs. Males and females had different degrees of trust, perceived benefit and effort expectancy on AVs. Peoples from different countries perceived benefits and effort expectancy of AVs in their own ways. Based on paper reviewing results, some suggestions for survey design, data collection and analysis are proposed for future studies.
Pricing strategies of a battery swapping servicefor electric vehicles
2024, Transportation Research Part D: Transport and Environment
Battery swapping services are attracting attention as an alternative method to charge electric vehicles due to their benefits in charging time. However, previous studies rarely addressed consumers’ preference of the services and how service providers can determine service prices. This study investigated consumers’ willingness to pay for the services using a contingent valuation method and showed the effects of individual characteristics on the willingness to pay. According to the results, consumers’ mean willingness to pay was KRW 107.4 thousand (USD 78.8). Potential electric vehicle consumers who felt more uncomfortable about long charging time had higher willingness to pay. Consumers’ psychological ownership and symbolic motives regarding their cars had negative and positive impacts on the willingness to pay, respectively. Moreover, service providers can encourage consumers to participate in the services and increase service revenue, applying price discrimination based on driving distance and the number of service uses.
Private vehicle drivers’ acceptance of autonomous vehicles: The role of trait mindfulness
2024, Transport Policy
Autonomous vehicles (AVs) are stepping into our daily life. The acceptance of AVs by people, especially private vehicle drivers (PVDs), would significantly influence the deployment of AVs and the future shape of our transportation. Trait mindfulness, characterized by novel distinction-drawing that results in being sensitive to the novelty, is an enduring mindset that may, though not certain, facilitate the acceptance of AVs. To provide an answer, a questionnaire survey was conducted and the 14-item Langer Mindfulness Scale is adopted to measure participants' trait mindfulness. A structural equation model is established based on the Technology Acceptance Model to explore the relationship between trait mindfulness and PVDs' AV acceptance. Results show that trait mindfulness is a positive predictor of PVDs' AV acceptance, and mindful drivers tend to be open to accept AVs and would become early users of AVs. Mindfulness could improve PVDs’ AV perceptions to promote AV acceptance. This finding contributes to the early promotion of AVs in practice, e.g., targeting AV advertisements to the mindful PVDs would be particularly effective.
Driving forward together: The common intention of Indonesians in different residential areas to use autonomous vehicles
2024, Transportation Research Interdisciplinary Perspectives
Despite the significant benefits of Autonomous Vehicles (AVs) for global transportation, Indonesia's diverse geographical landscape encounter unique adoption challenges due to infrastructural shortcomings and economic limitations. This study explores the adoption of AVs in Indonesia, considering its potential market and crucial role in AV and Electric Vehicle supply chains. Drawing upon the Technology Acceptance Model (TAM) and the Unified Theory of Acceptance and Use of Technology (UTAUT), we assessed AV acceptance across Indonesia's Metropolitan Cities, frontier regions (“3T”), and the New National Capital City (IKN) areas. Using a cross-sectional study design, we distributed an online questionnaire, focusing on demographics, perceived safety, transport mode changes, and behavioral intention towards AVs, based on TAM and UTAUT factors. From 1,255 valid responses, we found significant influences of gender (t (1253) = 4.22), safety perceptions (F (2,1252) = 52.373), and transport mode frequency (F (4, 1250) = 6.662) on AVs adoption intentions. Both TAM and UTAUT models were moderately effective in explaining willingness to use AVs (R² = 34% and 48%, respectively). TAM highlighted perceived usefulness (β = 0.421) and ease of use (β = 0.540), while UTAUT emphasized effort expectancy (β = 0.317) and social influence (β = 0.240). However, the findings from the multigroup analysis did not corroborate the role of residential areas in determining the intention to use AVs. These findings offer crucial insights for developing AV promotion strategies, creating user-friendly designs, and formulating supportive policies for AV adoption in diverse Indonesian regions.
Exploring university students’ acceptability of autonomous vehicles and urban air mobility
2024, Journal of Air Transport Management
AVs and UAM will provide many benefits. However, lack of user acceptance is the predominant barrier to the adoption of automated transport technologies. Yet, little is known about AV and UAM acceptability in developing countries. To this end, this study investigates the factors affecting the acceptability of AVs and UAM in Türkiye. The sample was drawn from 369 university students. An extended UTAUT model was established with additional hedonic motivation, perceived safety, and personal innovativeness constructs. Findings revealed that perceived safety and performance expectancy were the strongest determinants of behavioral intention to use AVs. On the other hand, social influence and hedonic motivation were the main drivers of UAM acceptability. Furthermore, personal innovativeness was a strong factor when estimating the utilitarian value in both models. The findings could guide the politicians, scholars, and stakeholders of the AV and the UAM market.

View all citing articles on Scopus

View full text

Autonomous vehicles can be shared, but a feeling of ownership is important: Examination of the influential factors for intention to use autonomous vehicles

Highlights

Abstract

Introduction

Section snippets

A brief review of previous studies

Perceived ease of use and perceived usefulness

Data collection

Structural equation modeling

Discussion

Limitations of this study

Conclusion

Acknowledgements

Organ. Behav. Hum. Decis. Process.

Accid. Anal. Prev.

Int. J. Hum Comput. Stud.

Transp. Pol.

Int. J. Hum. Comput. Stud.

Transport. Res. Part C: Emerg. Technol.

Technol. Forecast. Soc. Chang.

Inform. Manage.

Transport. Res. Part F: Traffic Psychol. Behav.

Transport. Res. Part F: Traffic Psychol. Behav.

Transport. Res. Part A: Pol. Pract.

Transport. Res. Part F: Traffic Psychol. Behav.

J. Intell. Transport. Syst.

Revue Européenne de Psychologie Appliquée/Europ Rev Appl Psychol

Transport. Res. Part C: Emerg. Technol.

Transport. Res. Part F: Traffic Psychol. Behav.

Transport. Res. Part F: Traffic Psychol. Behav.

J. Exp. Soc. Psychol.

Transport. Res. Part F: Traffic Psychol. Behav.

Transport. Res. Part C: Emerg. Technol.

Self-efficacy mechanism in human agency

Am. Psychol.

Self-efficacy conception of anxiety

Anxiety Res.

Literature review on surveys investigating the acceptance of automated vehicles

Transportation

sCARy! Risk perceptions in autonomous driving: the influence of experience on perceived benefits and barriers

Risk Anal.

The Essentials of Factor Analysis

Commentary: issues and opinion on structural equation modeling

MIS Quarterly

Investigating the importance of trust on adopting an autonomous vehicle

Int. J. Human-Comput. Interact.

Computer self-efficacy: development of a measure and initial test

MIS Quarterly

Perceived usefulness, perceived ease of use, and user acceptance of information technology

MIS Quarterly

User acceptance of computer technology: a comparison of two theoretical models

Manage. Sci.