Consumer innovativeness and intentioned autonomous car adoption

Leicht, Thomas; Chtourou, Anis; Youssef, Kamel Ben

doi:10.1016/j.hitech.2018.04.001

Cited by 147 publications

(94 citation statements)

References 26 publications

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“…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: 97%

“…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%

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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: Exposure To Avs: Knowledge and Experiencementioning

confidence: 97%

Section: Introductionmentioning

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

View full text Add to dashboard Cite

show abstract

“…When individuals are highly innovative, they can bear the high degree of uncertainty and have a more positive intention to use the innovation. In other words, they are less likely to perceive risks and more receptive to technological innovation [65]. Adeiza et al held that innovation is a basic feature of human beings, which reflects the degree of interest of users in a new field [66].…”

Section: User Innovativenessmentioning

confidence: 99%

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

et al. 2019

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Along with the development of Fintech, many scholars have studied how information technology is applied to financial services with a focus on extended methods for application. Few scholars have studied the influence mechanism behind the adoption of Fintech services. This paper proposes an improved technology acceptance model (TAM) that incorporates user innovativeness, government support, brand image, and perceived risk as determinants of trust to investigate how users adopt Fintech services. We designed a questionnaire, sent it to active customers of the Hefei Science and Technology Rural Commercial Bank, and obtained 387 eligible responses. We analyzed the data with a structural equation model (SEM) to test the hypotheses, including the relationships of all latent variables. The results reveal that users’ trust in Fintech services has a very significant influence on users’ attitudes for adoption. In addition, perceived ease of use and perceived risk does not affect users’ attitudes toward the adoption regarding Fintech services. This study contributes to the literature of the adoption of Fintech services by providing a more comprehensive view of the determinants of users’ attitudes by combining trust of Fintech services with TAM.

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“…Social norm is a key variable in the TPB, which can be defined as the person's perception that other important influencers believe that he should or should not accept an innovation [99]. It is also named subjective norm [52] or social influence [51] in a few studies. When a certain kind of innovation is widely consumed, the impact of social norm on purchase intention can be stronger than that of the innovation that is being privately consumed [108].…”

Section: Social Normmentioning

confidence: 99%

“…Safety is the most frequently occurring word in all of the collected literature. A large number of consumers expressed safety concerns in their reports [37,51,71]. Safety is one of the main factors that people consider when choosing a certain car [119].…”

Section: Safetymentioning

confidence: 99%

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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Consumer innovativeness and intentioned autonomous car adoption

Cited by 147 publications

References 26 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

Adoption Intention of Fintech Services for Bank Users: An Empirical Examination with an Extended Technology Acceptance Model

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

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