Which Factors Influence Attitudes Towards Using Autonomous Vehicles?

Böhm, Patricia; Kocur, Martin; Fırat, Murat; Isemann, Daniel

doi:10.1145/3131726.3131751

Cited by 16 publications

(7 citation statements)

References 6 publications

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“…Yet, the 13-factor-model in this case provided a better understanding of the research phenomenon by showing which factors are less influential in predicting the intention to use technology. It can also be argued that in previous AVA studies (Böhm et al 2017;Madigan et al 2016;Madigan et al 2017), instrumental domain-specific and symbolic-affective factors explained between 19.6% and 58.6% of the variance in individuals' behavioral intentions to use AVs. This implies that a substantial amount of variance has not been captured so far, which again justifies the development of MAVA that contains additional predictors of AVA.…”

Section: Discussionmentioning

confidence: 93%

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

158

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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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Section: Discussionmentioning

confidence: 93%

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

158

View full text Add to dashboard Cite

show abstract

“…Potential advantages of AVs (generally as well as taxis specifically) include the potential to reduce road accidents and produce a safer and more efficient and effective transport system [52,53]. Another economic and environmental based argument for AVs is that fuel consumption is claimed to be decreased due to more efficient driving [36,52,54].…”

Section: Autonomous Vehiclesmentioning

confidence: 99%

The Concept of Fairness in Relation to Women Transport Users

Hail

McQuaid

2021

Sustainability

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This paper discusses the concept of ‘fairness’ in transport, specifically regarding women using public transport, future autonomous vehicle taxis or bicycle sharing. Women generally have varying and complex mobility patterns compared to men and suffer disproportionate fairness issues when using transport. Different concepts of fairness are explored, including: equality of opportunity, equity and justice (including procedural, social and distributional justice). While each of these concepts has different implications for women using transport, it is also recognized that fairness principles should apply to all people (regardless of sex, gender or other characteristics). Analysis of the different forms of mobility, as represented by public transport, autonomous vehicles and bicycle sharing, illustrate a variety of specific fairness issues. Factors such as safety and security, cost, physical design of infrastructure and vehicles, and characteristics such as low-income or childcare responsibilities arise in each case. The three cases also indicate a range of both horizonal fairness factors (similar people being treated similarly) and vertical fairness factors (such as more disadvantaged people receiving greater support). Further research is required into setting frameworks for a more comprehensive inclusion of, and balance between, different concepts of fairness and their interactions in both transport policy and practice.

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“…Koul and Eydgahi 2018), some videos depicted a specific prototype rather than a close-to-reality usage situation. For example, to provide participants with a better understanding of the technology using a real-life example, Böhm et al (2017) included a stimulus video of the autonomous Mercedes F015 prototype in their survey.…”

Section: Videosmentioning

confidence: 99%

Making emergent technologies more tangible - Effects of presentation form on user perceptions in the context of automated mobility

Janotta¹

2023

SMR

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One of the main challenges of investigating consumers’ perceptions and acceptance of emergent technologies is that respondents have not had any experience with them. This posits the question of how such technologies can be effectively presented to consumers both in the context of research and in measures to foster acceptance. Using automated driving as a case study, this paper presents results from a comparative study of three presentation forms (vignette, real-world video, computer-generated VR) of a ride in an automated vehicle in an empirical study with 103 participants. Results from quantitative analyses show that both real-world videos and the VR simulation outperform textual descriptions in terms of visualisation capability and user experience. Unexpectedly, the VR simulation does not perform significantly better at creating mental images of automated driving than a vignette. Recommendations are offered regarding the choice of a suitable presentation form in empirical research in relation to study objectives.

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Which Factors Influence Attitudes Towards Using Autonomous Vehicles?

Cited by 16 publications

References 6 publications

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

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

The Concept of Fairness in Relation to Women Transport Users

Making emergent technologies more tangible - Effects of presentation form on user perceptions in the context of automated mobility

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