The Complementary Role of Survey-Based and Neuroscience Measurements in Modeling Customer Acceptance of Self-Driving Technology

Lukovics, Miklós; Prónay, Szabolcs; Majó-Petri, Zoltán; Kovács, Péter; Ujházi, Tamás; Volosin, Márta; Palatinus, Zsolt; Keszey, Tamara

doi:10.2139/ssrn.4233946

Cited by 3 publications

(5 citation statements)

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“…First of all, the respondents must be provided with the experience of traveling in a self-driving vehicle during the study, thus we suggest the application of experimental practices. Second, completing it with the use of cognitive neuroscience instruments, through empirical procedures, we could identify emotions such as anxiety, excitement, or overall emotional engagement, which have been proven to influence consumer acceptance (Lukovics et al, 2023). Third, it can also be useful to adapt research methods which are specifically suited to study consumer preferences related to products still under development, such as the different versions of conjoint analysis (Ujházi, 2023).…”

Section: Discussionmentioning

confidence: 99%

“…This is reflected in the fact that there are already several cities in the world where, even though on a testing basis, selfdriving vehicles already travel the roads without a control of safety drivers, performing all the dynamic functions of driving completely autonomously (Cochen et al, 2018). On the other hand, there is no full social acceptance yet as many worry about how safe the use of AVs will be, who will be held legally responsible for an occasional accident, and what additional economic, societal, and ethical challenges the spread of AVs entails (Lukovics et al, 2023). Consequently, it is particularly important to explore the consumer acceptance of AVs, more specifically, its barriers (KPMG, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Analyzing the models of consumer acceptance of technology from the perspective of preparedness for autonomous vehicles

Ujházi,

Lukovics,

Majó-Petri

et al. 2024

Green and Digital Transitions

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In our study, we review the framework of questionnaire research methods suitable for analyzing the consumer acceptance of vehicle industry innovations, more specifically, of autonomous vehicles. Our aim is to identify the most widely used research models and examine which variables affect consumer acceptance of self-driving technologies to the greatest extent. The various modified versions of the TAM and UTAUT models are the most commonly used models to address the topic in the literature. Both models are characterized by the attempt to predict a consumer’s behavioral intention based on the specificities of a technology. Due to the difficulties of prior testing of self-driving technologies, it is worth reviewing these methods from the aspect of how suitable they are for capturing the consumer acceptance of this technology and through what adaptation measures. We do not aim to question the validity of researching the topic by questionnaire surveys, but we find it important to emphasize the significance of cautious adaptation. In the present paper, we intend to provide a methodological basis for future research related to autonomous technology by revising the TAM and UTAUT methods.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Analyzing the models of consumer acceptance of technology from the perspective of preparedness for autonomous vehicles

Ujházi,

Lukovics,

Majó-Petri

et al. 2024

Green and Digital Transitions

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“…A fifth and a more general limitation of the survey studies regarding AV acceptance is that most of the respondents had no prior experience with autonomous driving systems; therefore, their responses can be regarded as mostly hypothetical. It was demonstrated that being a passenger in an autonomous vehicle changed attitudes toward AVs [22,90,96], and increased the explanatory power of psychological models of acceptance [97,98].…”

Section: Plos Onementioning

confidence: 99%

“…First, based on the results of the present study, not only the direct effects of predictors, but hypothesis-driven mediation or moderation analysis could be used. Second, combining survey data with allowing participants to experience AVs in a controlled experimental design could also contribute to better reliability of results of self-reported questionnaires [98]. Third, qualitative methods such as interviews or narrative texts could provide essential information on reasons for mapping consumers' concerns and motivations regarding AV from further different perspectives as well.…”

Section: Plos Onementioning

confidence: 99%

The leading role of personality in concerns about autonomous vehicles

Volosin,

Kálnay,

Bánffi

et al. 2024

PLoS ONE

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Development of autonomous vehicles (AVs) is growing in a rapid rate, however, the most dominant barriers in their adoption seem to be rather psychological than technical. The present online survey study aimed to investigate which demographical and personality dimensions predict attitudes towards AVs on a Hungarian sample (N = 328). Data was collected by convenience and snowball sampling. Three-level hierarchical regression models were applied: in the first level, demographical variables, then general personality traits and third, attitude-like personality factors were entered. We demonstrated that the predictive effect of age, gender and education disappeared when personality dimensions were included into the models. Importantly, more positive general attitudes towards technology and higher optimism regarding innovations predicted eagerness to adopt AVs. On the other hand, individuals with more negative attitudes and higher dependence on technology as well as those with lower level of Sensory Sensation Seeking and higher level of Conscientiousness were more concerned about AVs. Our results suggest that AV acceptance cannot be regarded as a one-dimensional construct and that certain personality traits might be stronger predictors of AV acceptance than demographical factors.

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“…[11,12]. These factors to this day are commonly measured using questionnaires or interviews [13,14]. However, from a more biological or ecological perspective perception refers to awareness of factors of the environment that have signi cance with respect to our goals, actions, and wellbeing.…”

Section: Introductionmentioning

confidence: 99%

Physiological measurements of passengers in self-driving cars encountering unexpected road events

Palatinus,

Volosin,

Dudás

et al. 2023

Preprint

Self Cite

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In a preliminary analysis investigating the EEG and eye movement patterns of car passengers’ significant differences were reported in human driven and self-driving trials [15]. The differences suggested a preference and lower levels of anxiety in human driven conditions. The aim of the study reported here was to relate these differences to unexpected road events in real life passenger experience. These events were quick path corrections due to unforeseen obstacles on the path (deer and human shaped dummies). Every passenger went through both human and self-driving trials. The order of trials was balanced. Besides EEG and eye movements head movements and blinking frequencies were also recorded. Overall EEG and eye-tracking results were comparable to the preliminary findings showing the same overall differences between conditions. Analyses targeting the unexpected events showed moderate affective preferences for human drivers in the EEG data. Analyses of eye movements and head movements revealed larger multifractal spectrum differences for events vs smooth travel compared to human vs self-driving conditions. Blinking frequencies during the trip were lower during unexpected events, indicating higher levels of alertness.

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The Complementary Role of Survey-Based and Neuroscience Measurements in Modeling Customer Acceptance of Self-Driving Technology

Cited by 3 publications

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Analyzing the models of consumer acceptance of technology from the perspective of preparedness for autonomous vehicles

Analyzing the models of consumer acceptance of technology from the perspective of preparedness for autonomous vehicles

The leading role of personality in concerns about autonomous vehicles

Physiological measurements of passengers in self-driving cars encountering unexpected road events

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