Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

Kohl, Christopher; Knigge, Marlene; Baader, Galina; Böhm, Markus; Krcmar, Helmut

doi:10.1007/s11573-018-0897-5

Cited by 38 publications

(14 citation statements)

References 86 publications

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“…In line with Kohl et al (2018) and Raue et al 2019, we argue that the perception of risks and benefits are important factors predicting AVA. Similar to Milakis, Van Arem, and Van Wee (2017) in their ripple effect of automated driving model, the moral-normative perception of risks and benefits in our model represents higher-order implications of AVs on energy consumption, air pollution, economy, public health and social equity.…”

Section: Moral-normative Aspects Of Ava: Risk-benefit Perceptionsupporting

confidence: 89%

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

154

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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: Moral-normative Aspects Of Ava: Risk-benefit Perceptionsupporting

confidence: 89%

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

154

View full text Add to dashboard Cite

show abstract

“…However, these issues might not have become salient in the public discourse. Kohl et al (2018) showed that on Twitter, the discussion around driverless vehicles typically centers around accidents involving self-driving cars, concerns for safety and privacy, and personal benefits such as saving time, with relatively little reference to this technology’s larger socioeconomic implications. Nevertheless, as self-driving cars enter the political discourse, future research should still track the influence of economic conservatism.…”

Section: Discussionmentioning

confidence: 99%

The ideological divide in public perceptions of self-driving cars

Peng

2020

Public Underst Sci

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Applications in artificial intelligence such as self-driving cars may profoundly transform our society, yet emerging technologies are frequently faced with suspicion or even hostility. Meanwhile, public opinions about scientific issues are increasingly polarized along the ideological line. By analyzing a nationally representative panel in the United States, we reveal an emerging ideological divide in public reactions to self-driving cars. Compared with liberals and Democrats, conservatives and Republicans express more concern about autonomous vehicles and more support for restrictively regulating autonomous vehicles. This ideological gap is largely driven by social conservatism. Moreover, both familiarity with driverless vehicles and scientific literacy reduce respondents’ concerns over driverless vehicles and support for regulation policies. Still, the effects of familiarity and scientific literacy are weaker among social conservatives, indicating that people may assimilate new information in a biased manner that promotes their worldviews.

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“…Perceived benefits and perceived risks are the key determinants of public acceptance of, intention to use, and willingness to pay for, AVs [15,16,111,114], despite some existing contradictory reports regarding no contribution between perceived risks and intention to use AVs [114]. As for Payre et al, [82], such inconsistency stems from dissimilar AV deployment contexts-e.g., "different road types, driving environments, and/or physical/mental status" ([56], p.414).…”

mentioning

confidence: 99%

Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature

Golbabaei

Yiğitcanlar

Paz

et al. 2020

Journal of Open Innovation: Technology, Market, and Complexity

149

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Fully autonomous vehicles (AV) would potentially be one of the most disruptive technologies of our time. The extent of the prospective benefits of AVs is strongly linked to how widely they will be accepted and adopted. Monitoring and tracking of individuals’ reactions and intentions to use AVs are critical. The current study aims to explore and classify individual predictors (i.e., influential factors or determinants) of public acceptance of, and intention to use AVs, by conducting a systematic literature review and developing a conceptual framework to map out the individual influential factors that shape public attitudes towards AVs, which influence user acceptance and adoption preferences. This framework contains the key factors identified in the systematic review—i.e., demographic, psychological, and mobility behavior characteristics. The findings of the review disclose that public perceptions and adoption intentions vary significantly among different socio-demographic cohorts. Commuters value different aspects concerning AVs, which shape their intentions on acceptance and adoption. Thus, direct experience with AVs along with education and communication would be helpful to change people’s attitudes towards AVs in a positive way. The study informs urban and transport policymakers, managers, and planners, and helps in planning for a healthy AV adoption process with minimal societal disruption.

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Anticipating acceptance of emerging technologies using twitter: the case of self-driving cars

Cited by 38 publications

References 86 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 ideological divide in public perceptions of self-driving cars

Individual Predictors of Autonomous Vehicle Public Acceptance and Intention to Use: A Systematic Review of the Literature

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