Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors

Liu, Peng; Guo, Qianru; Ren, Fei; Wang, Lin; Xu, Zhijie

doi:10.1016/j.trc.2019.01.022

Cited by 179 publications

(110 citation statements)

References 53 publications

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“…The correlation between trust and other external variables is nothing new [111], and the above discoveries prove this. Moreover, two groups reported in their studies that elderly have stronger trust in AVs than young [30,68,69], whereas Bansal et al [12] got the opposite conclusion. Bansal et al [12] added that the elderly are less willing to pay for AVs because of their low trust.…”

Section: Trustmentioning

confidence: 93%

“…The significance of perceived risk on acceptance intention was surveyed in a total of seven literatures we collected. Two of them reported that perceived risk is a strong direct predictor of acceptance intention and, perceived risk has a strong negative impact on intention [37,69]. In other words, people's willingness to accept AVs declines with the increase of perceived risk.…”

Section: Perceived Riskmentioning

confidence: 99%

“…Quite a few significant factors affecting the acceptance of AVs were founded too. For example, Hulse et al and Liu [57,69] both deeply researched people's perception of various aspects of risk in AVs, and concluded that the perceived risk has a significant negative impact on the acceptance intention. Some of their findings are similar to those that cited behavior theories that contain perceived risk.…”

Section: Theoretical Issuesmentioning

confidence: 99%

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The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

et al. 2020

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Excessive dependence on autonomous vehicles (AVs) may exacerbate traffic congestion and increase exhaust emissions in the future. The diffusion of AVs may be significantly affected by the public’s acceptance. A few factors that may affect people’s acceptance of AVs have been researched in the existing studies, one-third of which cited behavioral theories, while the rest did not. A total of seven factors with behavior theories are screened out that significantly affect the acceptance intention, including perceived ease of use, attitude, social norm, trust, perceived usefulness, perceived risk, and compatibility. Six factors without behavior theories are summed up that affect AV acceptance, namely safety, performance-to-price value, mobility, value of travel time, symbolic value, and environmentally friendly. We found that people in Europe and Asia have substantial differences in attitudes toward AVs and that safety is one of the most concerned factors of AVs by scholars and respondents. Public acceptance of the different types of AVs and consumers’ dynamic preferences for AVs are highlighted in the review too. The quality of literature is systematically assessed based on previously established instruments and tailored for the current review. The results of the assessment show potential opportunities for future research, such as the citation of behavior theories and access to longitudinal data. Additionally, the experimental methods and the utilization of mathematical and theoretical methods could be optimized.

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Section: Trustmentioning

confidence: 93%

Section: Perceived Riskmentioning

confidence: 99%

Section: Theoretical Issuesmentioning

confidence: 99%

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The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

et al. 2020

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“…Besides age and gender, Liu, Guo, Ren, Wang, and Xu (2019) and Kyriakidis, Happee, and De Winter (2015) observed a positive relationship between income and willingness to pay for vehicle automation. Hardman, Berliner, and Tal (2019) revealed that 'Pioneers' and 'Pro-automated consumers' had the highest incomes, while 'Driverless sceptics' and 'Laggards' had the lowest incomes.…”

Section: Socio-demographicsmentioning

confidence: 99%

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

157

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Automated vehicle acceptance (AVA) is a necessary condition for the realisation of higher-level objectives such as improvements in road safety, reductions in traffic congestion and environmental pollution. On the basis of a systematic literature review of 124 empirical studies, the present study proposes MAVA, a multi-level model to predict AVA. It incorporates a process-oriented view on AVA, considering acceptance as the result of a four-stage decision-making process that ranges from the exposure of the individual to automated vehicles (AVs) in Stage 1, the formation of favourable or unfavourable attitudes towards AVs in Stage 2, making the decision to adopt or reject AVs in Stage 3, to the implementation of AVs into practice in Stage 4. MAVA incorporates 28 acceptance factors that represent seven main acceptance classes. The acceptance factors are located at two levels, i.e., micro and meso. Factors at the micro-level constitute individual difference factors (i.e., sociodemographics, personality and travel behaviour). The meso-level captures the exposure of individuals to AVs, instrumental domain-specific, symbolic-affective and moral-normative factors of AVA. The literature review revealed that 6% of the studies investigated the exposure of individuals to AVs (i.e., knowledge and experience). 22% of the studies investigated domain-specific factors (i.e., performance and effort expectancy, safety, facilitating conditions, and service and vehicle characteristics), 4% symbolic-affective factors (i.e., hedonic motivation and social influence), and 12% moral-normative factors (i.e., perceived benefits and risks). Factors related to a person's socio-demographic profile, travel behaviour and personality were investigated by 28%, 15% and 14% of the studies, respectively. We recommend that future studies empirically verify MAVA using longitudinal or experimental studies.

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“…It also results in risks and challenges with regard to safety, security, legal liability, and regulation (P. Liu & Xu, 2020). In previous studies, large parts of the public, especially younger and highly educated participants with higher income, were willing to accept automated driving, although some people had negative attitudes toward AVs (P. P. Liu, Guo, Ren, Wang, & Xu, 2019).…”

Section: Introductionmentioning

confidence: 99%

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

Niu

Sun

et al. 2020

Hum Ftrs & Erg Mfg Svc

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A high degree of automated driving distracts drivers more easily, resulting in slow recognition of critical events during driving and slow responses to emergencies. Automated driving and manual switching processes are also prone to erroneous decisions. We conducted a simulated automated driving experiment to study participants' visual perception characteristics during the takeover of vehicle control. The present study used dynamic videos to imitate the driving situations when drivers returned their gaze from the distractive source to the road. We collected the drivers' eye movement data to analyze the search strategy and physiological characteristics of the drivers after the takeover reminder. The results showed that the instant information search method of the drivers was scanning of the driving scene. When the degree of distraction deepened and the hazard level of scenes increased, the pupil diameter of the drivers increased and the fixation duration became longer. These findings can help to design takeover warnings and support more intelligent automated driving systems to judge whether measures should be taken to interfere with the driver's operation to avoid collisions. Furthermore, the drivers' fixation point distribution focused on the left side and the lower side of the scene. We suggest that the takeover warning is displayed in the head-up display. This study provides a better understanding of drivers' visual perception characteristics when drivers' eyesight returns from other distractors to the driving scene and a good theoretical basis for the design of hazard warning information for automated driving.

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Willingness to pay for self-driving vehicles: Influences of demographic and psychological factors

Cited by 179 publications

References 53 publications

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

The Determinants behind the Acceptance of Autonomous Vehicles: A Systematic Review

A multi-level model on automated vehicle acceptance (MAVA): a review-based study

Study on drivers' visual perception characteristics during the take‐over of vehicle control in automated driving

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