The roles of initial trust and perceived risk in public’s acceptance of automated vehicles

Zhang, Tingru; Tao, Da; Qu, Xingda; Lin, Rui Lin; Zhang, Wei

doi:10.1016/j.trc.2018.11.018

Cited by 517 publications

(359 citation statements)

References 65 publications

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“…In line with Ajzen and Fishbein (2005), Bagozzi and Lee (1999) and Venkatesh, Thong, and Xu (2016), we propose that these individual difference factors are generic AVA factors that influence the intention to use ADS-DVs by mediating or moderating the relationship between the factors at the meso-level. This is confirmed by Zhang et al (2019) who found that trust (individual difference factor at micro-level) mediated the correlation between perceived safety, usefulness and attitudes towards using AVs, which influenced the intention to use AVs (factors at meso-level). We also expect independent main effects of the individual difference factors on the factors at the meso-level, including the intention to use AVs.…”

Section: Micro-level: Individual Difference Factorsmentioning

confidence: 53%

“…UTAUT2, which follows from UTAUT1, suggests that an individual's behavioral intention to use information technology is influenced by three additional constructs in addition to the original UTAUT, i.e., hedonic motivation, price value and habit 4 (Venkatesh, Thong, and Xu 2012, 161). The suitability of the UTAUT1/2 to predict the acceptance of ADS-DVs has been supported by Leicht et al (2018), Kaur and Rampersad (2018), Moták et al (2017), Zhang et al (2019) and Zmud and Sener (2017) who revealed that performance expectancy (or its equivalent 'perceived usefulness') was significantly associated with the intention to use AVs. In , performance expectancy and effort expectancy measuring the component 'shuttle effectiveness' , which pertained to the performance of L4 ADS-DVs in comparison with respondents' existing travel, significantly correlated with the component 'intention to use' , which corresponds with the UTAUT construct 'behavioral intention' .…”

Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 96%

“…Bansal and Kockelman (2018) corroborated the effect of social influence on the willingness to adopt AVs by showing that some individuals were less likely to rely on the adoption rates of their friends (i.e., disabled, bachelor's degree holders and familiar with car-sharing) than others (i.e., elderly, single and Caucasian ethnicity). Acheampong and Cugurullo (2019) found a positive relationship between subjective norm (equivalent to social influence) and the perceived benefits (equivalent to performance expectancy, see Xu et al, 2018;Zhang et al, 2019), and ease of use of automated driving technology (equivalent to effort expectancy), and a positive relationship between subjective norm and perceived behavioral control (equivalent to facilitating conditions). Hartwich, Beggiato, and Krems (2018) postulate that affective variables are of growing importance for drivers' vehicle choices, and found a significant effect of enjoyment on automated driving.…”

Section: Symbolic-affective Aspects Of Ava: Hedonic Motivation and Somentioning

confidence: 99%

“…Current studies on AVA have not captured this multi-determination. Instead, they mainly investigate the influence of AVA factors in isolation, and through the lenses of technology acceptance (Hewitt et al 2019;Kaur and Rampersad 2018;Leicht Chtourou, and Youssef 2018;Madigan et al 2016;Madigan et al 2017;Wu et al 2019;Zhang et al 2019;Zmud and Sener 2017). The AVA factors identified by these studies encompass (1) individual characteristics (e.g., socio-demographics and differences in one's own personality; Alessandrini et al 2014;Haboucha et al 2017;Lavieri et al 2017; TNS Opinion and Social 2015), (2) instrumental domain-specific (e.g., performance and effort expectancy, physical vehicle and service characteristics; , (3) symbolic-affective (e.g., social influence and hedonic motivation; Bansal and Kockelman 2018;Madigan et al 2016), and (4) moral-normative factors of AVA (i.e., risk and benefit perception of AVs; Daziano, Sarrias, and Leard, 2017;Kyriakidis, Happee, and De Winter 2015;Moták et al 2017;Tennant et al 2016).…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

153

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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: Micro-level: Individual Difference Factorsmentioning

confidence: 53%

Section: Exposure To Avs: Knowledge and Experiencementioning

confidence: 96%

Section: Symbolic-affective Aspects Of Ava: Hedonic Motivation and Somentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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

Nordhoff

Kyriakidis

Arem

et al. 2019

Theoretical Issues in Ergonomics Science

153

View full text Add to dashboard Cite

show abstract

“…3.2.4 Trust. This construct makes reference to the confidence put by the individual on the other part [71] and plays an essential part in the disposition towards the use of AI-assisted technologies [72]. The use of this construct in TAM-based models designed for teaches is still limited but we can find examples of its use on other fields like banking [73] or organizational sciences [74] stablishing its effect on both PU and BI.…”

Section: Subjective Normmentioning

confidence: 99%

How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning

Prieto

Cruz-Benito

Therón

et al. 2019

Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality

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The use of AI is becoming a growing reality the educational field. One of the activities in which it is beginning to be implemented is the assessment of student achievement. This way, we can find in the literature an increasing number of investigations focused on the possibilities offered by the adoption of AI-driven assessment. However, the use of AI is also a source of concern that raises suspicions in some sectors of our society. In this context, knowing the position of the teachers towards this topic is critical to guarantee the successful development of the field. This paper intends to fill a research gap in the literature by offering a technology adoption model based on TAM to study the factors that condition the use of AI-driven assessment among teachers. To present this model we offer a background on the use of AI in education and the technology acceptance among teachers, as well as the definition of the eight constructs and the relational hypotheses included. Finally, the possibilities of the model and future lines of research are discussed.

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How does people's trust in automated vehicles change after automation errors occur? An empirical study on dynamic trust in automated driving

Tan

Hao

2023

Hum Ftrs & Erg Mfg Svc

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When errors of automated vehicles (AVs) occur, drivers' trust can easily be destroyed, resulting in the reduction of the use of AVs. This study aims to examine how error of AVs declines driver's trust by impacting their subjective perceptions. A driving simulator experiment is conducted, in which 104 participants (male = 58; female = 46) experienced automated driving with automation errors and rated their trust. The results indicate that automation error will affect the driver's perceived predictability, perceived reliability, and perceived safety, which will lead to the decline of trust and abandonment of automated driving. With the occurrence of automation error of AVs, perceived safety plays a more critical role in drivers' trust. In addition, when automation errors occur in specific tasks with low risk, the trust of drivers will drop faster than that in high‐risk tasks. This paper has explored the internal effects of the decline of driver's trust after automation errors of AVs, and further considers the influence of different external risks on these perception factors and trust. This study can help AVs manufacturers to formulate different degrees of trust repair strategies according to different driving tasks and accident severity.

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The roles of initial trust and perceived risk in public’s acceptance of automated vehicles

Cited by 517 publications

References 65 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

How to Measure Teachers' Acceptance of AI-driven Assessment in eLearning

How does people's trust in automated vehicles change after automation errors occur? An empirical study on dynamic trust in automated driving

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