How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle

Adnan, Nadia; Nordin, Shahrina Md; Bahruddin, Mohamad Ariff bin; Ali, Murad

doi:10.1016/j.tra.2018.10.019

Cited by 162 publications

(96 citation statements)

References 37 publications

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“…Fleury et al [20] concluded that performance expectancy is a critical factor for predicting users' intention to adopt a corporate carsharing system. This relationship has also been confirmed by studies on user acceptance of autonomous vehicles [42,45,49] and electric vehicles [41,50]. Therefore, we hypothesize: Hypothesis 1 (H1).…”

Section: Performance Expectancy Of Ecsupporting

confidence: 66%

“…However, in the context of a corporate carsharing system, Fleury et al [20] found that facilitating condition only partially mediated the effect of effort expectancy on behavioral intention. Therefore, similar to previous studies [44][45][46], we decided not to examine the effect of facilitating condition on behavioral intention to use EC. The proposed research model is given in Figure 3.…”

Section: Research Model and Hypothesesmentioning

confidence: 99%

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Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

et al. 2019

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Emerging electric carsharing (EC) systems have demonstrated their advantages and attracted public attention. The number of EC systems is growing throughout the world, especially in metropolitan areas in developing countries. For successful implementation, developers need to understand the public acceptance of EC services. In this study, we sought to determine the factors that affect EC acceptance in the context of developing countries. The study involved 437 individuals, aged between 18 and 65 years, who were randomly sampled from an EC service area in China. The determinants of EC acceptance were investigated based on an extended version of the Unified Theory of Acceptance and Use of Technology (UTAUT) and tested by using Structural Equation Modeling. The results indicated that hedonic motivation (HM) has a powerful effect on behavioral intention (BI) to use the EC service in the future. Performance expectancy (PE), effort expectancy (EE), and familiarity with the carsharing concept (FM) also influenced EC’s acceptance. However, the impact of social influence (SI) did not emerge from this study. The results also revealed that gender moderates the effects of EE and FM on BI. Age moderated the effect of FM on BI and unexpectedly moderated the impact of HM on BI. The present study confirmed the validity of the UTAUT research model in predicting the intention to use an EC system in developing countries. Implications and recommendations for government and EC developers are also discussed.

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Section: Performance Expectancy Of Ecsupporting

confidence: 66%

Section: Research Model and Hypothesesmentioning

confidence: 99%

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

et al. 2019

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“…Cavoli, Phillips, Cohen, and Jones (2017) pointed out that the long-term effects of automated vehicles are currently unclear. Adnan, Nordin, Bahruddin, and Ali (2018) assumed that ethical questions have not been sufficiently and transparently discussed, and that ethical issues related to accidents are still hypothetical, given that highly or fully automated vehicles are not yet available on the market. Following the recommendations of Adnan et al (2018), future research should more closely investigate the relationship between the ethical implications of automated driving and user acceptance of automated driving technology.…”

Section: Risk and Benefit Perception And Travel Purposementioning

confidence: 99%

What impressions do users have after a ride in an automated shuttle? An interview study

Nordhoff

Winter

Payre

et al. 2019

Transportation Research Part F: Traffic Psychology and Behaviou

117

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“…Bansal and Kockelman 2018;Bloom et al 2017;Brinkley et al 2017; Fraedrich & Lenz 2016; Gilbert & Daily 2018; Greaves et al 2018;Jardim et al 2013;Lavieri et al 2017;Lilijamo et al 2018; Medina and Jenkins 2017; Menon 2015; Pakusch & Bossauer 2017; Pettigrew et al 2018, 2019; Portouli et al 2017; Regan et al 2017; Sanaullah et al 2016; Sauders & Charness 2016; Shabanpour et al 2017, 2018; Shin & Shunsuke 2017; Ulahannan et al 2018; Wang and Ankar 2019; Woisetschläger 2016; Woldemanuel and Nguyen 2018). Benefits encompass improvements in productivity due to the engagement in non-driving related tasks, benefits for the environment (e.g., reduction of fuel consumption, emissions and traffic congestion, lower vehicle ownership), increased mobility independence and freedom for the physically-impaired and elderly, no need to spend resources on learning how to drive, lower insurance rates, lower repair costs (in case of less accidents), and easier and quicker and less expensive parking(Adnan et al 2018;Bazilinskyy et al 2015;Bennett et al 2019;Dai & Howard 2013;Daziano et al 2017;Hulse et al 2018;Kelkel 2015;Lustgarten & Le Vine 2018;Medina and Jenkins 2017;Portouli et al 2017;Sauders & Charness 2016;Shin & Shunsuke 2017;Tennant et al 2016;Yap et al 2016).…”

mentioning

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

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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How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle

Cited by 162 publications

References 37 publications

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

Travelers’ Acceptance of Electric Carsharing Systems in Developing Countries: The Case of China

What impressions do users have after a ride in an automated shuttle? An interview study

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

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