Are Consumers Ready to Adopt Highly Automated Passenger

Vehicles? Results from a Cross-national Survey in Europe

Ilias Panagiotopoulos

, George Dimitrakopoulos

, Gabrielė Keraitė

and Urte Steikuniene

Department of Informatics and Telematics, Harokopio University, Athens, Greece

Metis Baltic, Vilnus, Lithuania

Keywords: Highly Automated Passenger Vehicles, Consumer’s Intention to Adopt, Technology Acceptance, Perceived

Driving Enjoyment, Perceived Financial Cost, Perceived Reliability/Trust.

Abstract: Automated vehicles are currently being developed by major car manufacturers planning to be available in

market diffusion the next years. This disruptive technology is expected to provide an alternative type of

transportation services by positively affecting road safety, traffic congestion, more individual comfort and

convenience for drivers/users. However, besides the aforementioned societal benefits, researches on the

predictors influencing individuals’ attitudes and willingness to adopt automated vehicles in the future are

crucial requirements for their successful diffusion in international market. In this way, the current study aims

to investigate the factors that may hinder or facilitate consumers’ acceptance and adoption of Highly

Automated Passenger Vehicles (HAPVs). A research model through extending the original Unified Theory

of Acceptance and Use of Technology (UTAUT) was developed and accordingly an online survey was

conducted among the general public in Europe; 811 valid answers were collected and analyzed. The results

indicate that the constructs of perceived driving enjoyment, perceived financial cost, perceived reliability/trust,

social influence and performance expectancy were all useful predictors of behavioural intentions to drive/use

HAPVs. The findings derived from this study will contribute to car manufacturers towards HAPVs in order

not only to develop better driving automation technology systems for them, but also to develop proper

implementation strategies that will lead to widespread deployment in international market.

1 INTRODUCTION

Many innovations in vehicle technology and driver

assistance systems have been developed rapidly by

automotive and other related companies. The

individual and social demands for a safe, convenient,

efficient, and eco-friendly transportation are pushing to

fundamental changes in the transportation field like the

introduction of Automated Vehicles (AVs). AVs hold

much promise to significantly reduce road fatalities as

over 90% of road accidents come from human errors,

as well as social costs by positively affecting road

safety, traffic congestion, energy consumption,

people’s mobility, more individual comfort and

convenience for drivers (Piao et al., 2016).

Furthermore, new disruptive business models, such as

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car sharing mobility services, could also be developed

resulting to a strong decrease of car vehicles on the

roads (Fagnant and Kockelman, 2015).

Besides the numerous advantages with regard to

vehicle automation technologies, AVs may also face

numerous challenges before being introduced to the

market, ranging from vehicle performance

degradation due to unexpected situations (e.g., bad

weather conditions, driving automation system

failure, etc) to security breaches against malicious

attacks by cyber criminals (hackers), and legal

liability issues (Van Brummelen et al., 2018). These

concerns can be critical obstacles to the market

adoption and diffusion of AVs influencing

consumers’ intention to drive/use them (Kyriakidis et

al., 2017).

Panagiotopoulos, I., Dimitrakopoulos, G., Kerait

e, G. and Steikuniene, U.

Are Consumers Ready to Adopt Highly Automated Passenger Vehicles? Results from a Cross-national Survey in Europe.

DOI: 10.5220/0009398804730480

In Proceedings of the 6th International Conference on Vehicle Technology and Intelligent Transport Systems (VEHITS 2020), pages 473-480

ISBN: 978-989-758-419-0

473

Therefore, forecasting technology usage and

acceptance by the end users becomes fundamental in

order to understand aspects that are likely to minimize

consumer resistance and maximize adoption of

driving/using AVs. In this respect, the automotive

industry still lacks a widely accepted and used

framework to assess technology acceptance towards

AVs. As such, the goal of this study was twofold:

 First, to design and introduce an adapted version

of the original Unified Theory of Acceptance

and Use of Technology (UTAUT) social-

psychological model in predicting consumers’

intension to drive/use Highly Automated

Passenger Vehicles (HAPVs).

 Second, to investigate in what extent European

consumers intend to drive/use HAPVs in the

future, by identifying the factors that affect the

uptake of such vehicles.

It should be stated that the term HAPVs refers to

road passenger vehicles with a high level of

automation within their driving system, being capable

to execute all the elements of the dynamic driving

task in certain roadway and environmental conditions

(SAE International, 2018).

The remainder of the paper is structured as follows.

Section 2 takes a look at the relative research works on

user acceptance of vehicle automation. Section 3

describes our motivation for using UTAUT model in

this study and the hypotheses from a proposed

UTAUT-extended model concerning the acceptance

and use of HAPVs. In Section 4 the research approach

for empirically testing the hypotheses is presented. In

section 5 we report on the findings from a

questionnaire survey that investigates the extent to

which the constructs of the proposed UTAUT model

explain the acceptance and driving/usage of HAPVs.

Section 6 discusses the research findings, in line with

literature, whereas in section 7 the concluding part

reflects on the outcomes of the entire study and

recommendations for further research.

2 RELATED WORK

With the advanced and dynamic growth of

technologies, how fast the consumers are accepting

these technologies depends on a number of factors such

as availability of technology, convenience, consumers’

need, trust, etc. A variety of well-known theories and

technology acceptance models have been widely used

to assess and gauge consumers’ behavioral intentions

and determine the factors which most positively

influence potential users’ likelihood to adopt new

vehicle technologies (Park and Kim, 2014).

In addition, some studies in the existing literature

have applied various models into understanding of

user acceptance towards vehicle technology and

autonomous driving. Cho et al. (2017) applied an

expanded UTAUT acceptance model about the

advanced driver assistance systems (ADAS), where

the determinants anxiety, self-efficacy, perceived

safety, trust and affective satisfaction were included

as direct predictors of behavioral intention to use

ADAS, in addition to the basic factors of the original

UTAUT model. In addition, Madigan et al. (2016)

assessed user acceptance of automated road transport

systems (ARTS) using the original UTAUT

framework. They found that performance expectancy

had the strongest impact on consumers’ behavioral

intentions to use ARTS. Furthermore, Madigan et al.

(2017) investigated consumers’ intension to use

ARTS by extending UTAUT model to include the

effects of facilitating conditions and hedonic

motivation. The results of this study indicated that

hedonic motivation was the strongest predictor on

consumers’ intention to use ARTS. Moreover,

Nordhoff et al. (2017) investigated user acceptance of

driverless shuttles in public transport in an open and

mixed traffic environment by using the original

UTAUT framework. Results show that the

acceptance and use of such shuttles is predominantly

influenced by their performance expectancy, effort

expectancy and social influence.

3 CONCEPTUAL MODEL

Although the aforementioned studies in the existing

literature have investigated and replicated the original

UTAUT model and agreed that it is valid in predicting

end users’ acceptance towards vehicle technology

and autonomous driving, further extensions are

needed, in most of cases, to fully explore the

predictors influencing consumers’ attitudes and

willingness to use/accept innovative vehicle

technologies.

Figure 1: The proposed theoretical UTAUT-extended

model.

In this direction, the present analysis takes the

initial UTAUT model with the main three

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474

determinants, Performance Expectancy (PE), Effort

Expectancy (EE), and Social Influence (SI), as a

starting point, by incorporating three further direct

determinants, namely "Perceived Driving Enjoyment

(PDE)", "Perceived Financial Cost (PFC)" and

"Perceived Reliability/Trust (PRT)" (see Figure 1).

3.1 Perceived Driving Enjoyment

With the expected widespread availability of vehicles

with advanced driving automation technology

becoming ever-closer, the way we travel is set to be

revolutionized. In this context, driving pleasure is

also likely to play a role in such a new and innovative

environment. This specific kind of hedonic

motivation is called as Perceived Driving Enjoyment

(PDE), and is defined as "the degree to which

individuals perceive enjoyment and pleasure derived

from driving/using HAPVs".

As found in the literature, hedonic motivation has

been shown to be one of the most important factors

influencing consumers’ acceptance of technology

across a variety of sectors (Venkatesh et al., 2012). In

the context of automation in vehicles, according to the

study of Madigan et al. (2017), hedonic motivation,

or users’ enjoyment of the system, was the strongest

predictor on consumers’ behavioral intensions to use

ARTS.

3.2 Perceived Financial Cost

AVs adoption is highly encouraged by economic

factors such as operating costs, maintenance costs,

insurance costs, fuel costs, etc. (Zmud and Sener,

2017). Due to the fact that the present study focus on

HAPVs, perceived financial cost is also likely to play

an important role in consumers’ willingness to adopt

and purchase/use such vehicles in the future. In this

way, the factor Perceived Financial Cost (PFC) is

defined as "the degree to which individuals perceive

financial costs derived from purchasing and

driving/using HAPVs".

Previous studies have shown that PFC is one of

the most important concerns influencing consumers’

acceptance of autonomous and self-driving

technology. More in detail, according to the study of

Howard and Dai (2015), which public perceptions

towards self-driving cars were explored, the cost

factor was one of the least attractive features.

Furthermore, Ahmed (2018) investigated automotive

engineers' perspectives on the awareness, demand

and trust on AVs in the current sharing infrastructure

with conventional vehicles. Results show that the cost

factor was one of the most important customer

requirements for the successful deployment of AVs.

3.3 Perceived Reliability/Trust

Several previous studies shown that trust is a crucial

contributor to an individual’s acceptability in the

context of e-services and e-government applications

(Mou et al., 2017) as well as in consumers’ intentions

towards driving automation technology and AVs

(Choi and Ji, 2015; Körber et al., 2018). In the present

study, PRT is defined as "the degree to which

individuals believe that HAPVs will ensure safe and

reliable travels by protecting them from potential

misuse and problems".

As found in the literature, according to the study of

Zmud and Sener (2017), most of the people surveyed

(82%) are not at all or only somewhat concerned that

their data would not be kept private when using self-

driving cars. Furthermore, the study by Choi and Ji

(2015) supports the claim that trust is a major

determinant to predicting the reliance on and adoption

of AVs. In addition, the study of Kaur and Rampersad

(2018) found that the ability of the driverless cars to

meet performance expectations and their reliability

were important adoption determinants.

3.4 Moderating Effects and Hypotheses

In the above extended UTAUT-model, as depicted in

Fig. 1, the moderating effects of age, gender, income

and culture were also considered. It must be noted

that while the original UTAUT model also included

voluntariness of use and experience as moderators,

these variables were not included in the present

analysis, in light of the fact that the vast majority of

potential consumers in Europe have no concrete and

real experience with HAPVs.

In addition, six hypotheses have been totally

formulated to better reflect the potentials of the

proposed theoretical UTAUT-extended model, as

follows:

 H1:"PE significantly affects individual BI to

accept and drive/use HAPVs"

 H2: "EE significantly affects individual BI to

accept and drive/use HAPVs"

 H3: "SI significantly affects individual BI to

accept and drive/use HAPVs"

 H4 "PDE significantly affects individual BI to

accept and drive/use HAPVs"

 H5: "PFC significantly affects individual BI to

accept and drive/use HAPVs"

 H6: "PRT significantly affects individual BI to

accept and drive/use HAPVs".

Are Consumers Ready to Adopt Highly Automated Passenger Vehicles? Results from a Cross-national Survey in Europe

475

4 METHOD OF ANALYSIS

4.1 Questionnaire and Measures

Similar to previous research in technology acceptance

towards autonomous and self-driving vehicles, the

present study employed a quantitative research

approach to test the proposed UTAUT-extended

model. In this direction, a questionnaire survey was

developed with three main parts.

The first part aimed to identify the socio-

demographic characteristics of the data sample, as well

as the general attributes of the respondents about car

vehicles and transportation mobility. The second part

of the questionnaire assessed the general experience

and concerns of the participants towards automation

technologies with multiple choice questions. In the

third part of the questionnaire a 26-item measurement

scale was administrated with appropriate measures for

each construct of the proposed research model (PE, EE,

SI, PDE, PFC, PRT) to assess consumers’ BI in

driving/using HAPVs. All items measured using a

five-point Likert scale, ranging from 1 (strongly

disagree) to 5 (strongly agree).

Items selection was based on relative statements

in the existing literature (Venkatesh et al., 2012; Choi

and Ji, 2015; Madigan et al., 2017) on each of the

aforementioned constructs. These items were

modified in a suitable manner due to the fact that the

present paper focus on HAPVs, which can perform

and control all critical driving functions in certain

traffic and environmental conditions.

4.2 Participants

The assessment towards HAPVs, which are expected

to be utilized in transportation activities the next

years, was conducted among adults in Europe

currently more than 18 years old. The research was

advertised via online means of communication (i.e.

websites, social media) inviting European people to

take part in a questionnaire survey, in order to explore

the knowledge, attitudes, perceptions and behaviours

in relation to HAPVs. The questionnaire was

disseminated between February and May 2018. All

respondents responded in English language.

Participation in the survey was completely

voluntary and no compensation was offered to

complete the relative questionnaire survey. To ensure

survey respondents had a clear understanding of the

different levels of driving automation technology,

respondents were required to read the simplified

definitions of vehicle automation, as defined in (SAE

International, 2018). Participants were not able to

continue to the next questions of the survey without

confirming a relative control question that they have

read and understood the definition regarding passenger

vehicles with a high level of automation within their

driving system, where the present study focus.

Out of the 847 on-line collected responses, 829

valid answers were used for final analysis, indicating

a 97.8 per cent acceptance rate. 18 respondents, who

had answered only the first four questions, as they

expressed their wish not to participate in the present

survey, were eliminated from the final sample. This

left a total number of 811 responses for final analysis.

5 RESULTS

5.1 Descriptive Statistics

Characteristics and background information of the

participants who filled out the questionnaire are

provided. 811 participants answered the survey. More

in detail, 353 respondents reside in Northern Europe

(43.5%) and 458 in Southern Europe (56.5%). The

gender split for the whole sample was 57.5% males

and 41.4% females, whereas 1.1% responded "I

prefer not to answer". With respect to age, 47.8% of

the respondents were between 18 and 30 years old,

29.1% between 31 and 40 years old, 22.7% more than

40 years old, whereas 0.4% responded "I prefer not to

answer". Regarding the educational level option,

most respondents were M.Sc. or/and Ph.D. holders

(53.9%), whereas 32.6% were university/college

diploma holders, 12.8% had secondary education or

less, and 0.7% responded "I prefer not to answer".

Moreover, among the respondents, 41.8% had a net

average monthly personal income below 1000€,

27.1% between 1000€ and 2000€, and 20.7% more

than 2000€, whereas 10.4% responded "I prefer not to

answer".

With respect to the respondents’ transportation

profile, 83.5% of them stated that they own a

passenger car, 49.4% indicated that they use

passenger cars as a daily commute transportation

mode, 54.5% responded that they are driving less than

5 hours per week (on average), and 49.8% stated that

the usual purpose of their travels with passenger cars

is professional (work, education, etc). Furthermore,

almost six to ten respondents (56.8%) stated that they

feel quite safe or extremely safe when they are

driving/using passenger vehicles today, whereas the

vast majority of respondents (79.2%) believe that

technology progress, until now, has extremely

improved (31.6%) or quite improved (47.6%) the

safety of their travels with passenger cars.

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Moreover, the vast majority of European

respondents (87.2%) had heard of AVs before their

participation in the present survey, whereas 9.7%

answered "No" and 3.1% responded "I do not know –

I’m not sure". Moreover, many respondents (almost

70%) had not any previous experience on

driving/using AVs before participating in this survey.

Regarding the respondents’ level of interest in AVs

before their participation in the survey, 52.7%

answered that they strongly interested (24.2%) or

somewhat interested (28.5%). In addition, 67.6% of

the people surveyed answered that they are strongly

interested or somewhat interested with the trends of

the global automotive community towards vehicle

automation. Finally, almost 65% of the total sample

had a positive general opinion regarding AVs

whereas only 7.9% had a negative general opinion

and 6.9% responded "I do not know – I’m not sure".

Figure 2: Responses on level of familiarity with ADAS.

5.2 Experiences with Automation

Technologies and ADAS

In this section, the experience of consumers towards

automation technologies and ADAS, is investigated.

Based on the survey results, almost three-to-four

respondents considered themselves, late adopters on

the technology adoption curve (e.g., they wait before

adopting a new automation technology).

Additionally, almost 40% of the respondents

stated that they are keeping up with the latest trends

in ADAS, and 60% indicated that they are strongly

agreed or somewhat agreed with the statement

"ADAS make easier my driving". Moreover, the

majority of the respondents (almost 70%) stated that

it is easy for them to use and apply ADAS in driving

and they do not waste too much time in driving with

their use.

Figure 3: Responses on "How important are the following

features for you regarding HAPVs?"

Furthermore, as depicted in Figure 2, the majority

of respondents indicated that only “seat belt warning

system” (81.6%) and “navigation system” (80.0%),

respectively, are the most quite/very familiar ADAS,

which are offered today on human-driven passenger

cars. On the other hand, it should be stated that the

level of respondents’ familiarity towards the other

available ADAS (e.g., cruise control system, park

assist system, etc) being offered by the automotive

industry is not exceed the threshold of 35%.

5.3 Intention to Drive/Use and Accept

HAPVs

In this section, the respondents’ intention to drive/use

and accept HAPVs is explored. The majority of

respondents indicated that “road safety” (78.8%),

“vehicle-environment interaction (59.2%), “safe

operation” (56.5%), “legal liability in case of

accidents and damages” (52.5%), and “vehicle

security and data privacy” (51.7%) were the most

extremely important features for the respondents

towards the driving/usage of a HAPV, as depicted in

Figure 3. On the other hand, a small portion of

respondents indicated that “employment with other

activities while driving” (19.4%), “community's

trends towards vehicle automation” (7.4%) and

“social influence” (7.3%) were the most attractive

features regarding the driving/usage of a HAPV.

Additionally, a multiple linear regression analysis

was conducted for predicting behavioral intentions

towards HAPVs taking into account the predictors of

PE, EE, SI, PDE, PFC and PRT. To test these effects,

the moderators (age, gender, income and culture) with

the aforementioned six independent predictors were

added to the model.

As demonstrated in Table 1, an examination of the

standardized beta weights (β) indicate that the

moderating variable "Culture" had a high significant

Are Consumers Ready to Adopt Highly Automated Passenger Vehicles? Results from a Cross-national Survey in Europe

477

positive effect influencing consumers’ BI to drive/use

HAPVs.

Table 1: Summary results of the regression analysis with

the presence of the four moderating effects.

H# Path

Modified UTAUT-

model (standardized

ath coefficients β)

Decision

PEBI

0.121** Supported

EEBI

-0.008# Rejected

SIBI

0.107*** Supported

PDEBI

0.215*** Supported

PFCBI

0.187*** Supported

PRTBI

0.107** Supported

AgeBI

-0.005#

GenderBI

0.053*

IncomeBI

0.045#

CultureBI

0.378***

Note: *p-value < 0.05, **p-value < 0.01, ***p-value < 0.001, # p-

value non-significant

In addition, hypothesis H4 states that PDE

significantly affects European consumers’ BI to

accept and drive/use HAPVs. Likewise, the results

supported hypotheses H5 and H1 which stipulates

that the determinants PFC and PE significantly affect

European consumers’ BI to accept and drive/use

HAPVs

Besides, hypotheses H3 and H6 which

hypothesized that the determinants SI and PRT

significantly affect European consumers’ BI to accept

and drive/use HAPVs, were also supported.

Meanwhile, the remaining construct EE is rejected

given that it was found to be statistically insignificant.

6 DISCUSSION

Although there is much excitement surrounding the

introduction of AVs, there are largely unknown at the

moment which determinants of user acceptance will

influence the uptake of HAPVs. The main purpose of

this study was to gain a more detailed understanding

of the factors that will affect potential end users’

future acceptance of HAPVs. In that framework, the

present work extends the original UTAUT model by

incorporating PDE, PFC and PRT constructs. Most of

the path coefficients in the proposed research model

were found statistically significant except the path

from EE to BI.

More specifically, five of the model’s predicted

relationships were supported, with PDE, PFC, SI,

PRT and PE all making significantly unique positive

contributions to users’ BI towards HAPVs. Similar to

Venkatesh et al. (2012), PDE was the strongest

predictor, suggesting that the most important factor

influencing positively consumers’ intentions to

drive/use HAPVs is how exciting, comfortable and

enjoyable will find them. The above confirm the

results of what Madigan et al. (2017) have studied

about the factor of hedonic motivation and its impact

on users’ acceptance towards ARTS vehicles. In a

similar manner, the aforementioned finding supports

the results of Nordhoff et al. (2018), where the

majority of survey respondents indicated that

driverless vehicles would take away the driving

pleasure or enjoyment.

Furthermore, our results show that PFC has a

positive influence on BI towards HAPVs, indicating

that the adoption of HAPVs is highly affected by

economic factors such as purchasing costs and

driving/usage operating costs (maintenance,

insurance, fuel, etc). It should be noted that a small

portion of respondents (almost three to ten) strongly

agreed or somewhat agreed that the cost of

purchasing PHAVs, as well as the operating cost of

driving/using PHAVs will be at reasonable prices

similar to currently used human-driven vehicles. The

above finding is also confirms the results of what

Howard and Dai (2015) have explored about the cost

factor and its impact on consumers’ perceptions

towards self-driving cars.

Regarding the PE, this study found that PE has a

statistically significant positive impact on BI towards

HAPVs, within the proposed research model,

suggesting that respondents are expected HAPVs will

provide significant potential benefits (road safety,

usefulness, etc) in performing their travel activities.

Our results support other findings of previous

research studies (Piao et al., 2016; Cho et al., 2017;

Madigan et al., 2017; Panagiotopoulos and

Dimitrakopoulos, 2018) where perceived usefulness

and performance expectancy were important

predictors in potential consumer’s intensions towards

autonomous vehicles and driving automation

technology.

Furthermore, our results show that SI has a

significant positive influence on BI, indicating that

the opinions of others will have an effect on

consumers’ likelihood on driving/using HAPVs when

they will become available on the international

market. The above finding supports previous research

studies, which found that social norms had a

significant impact on behavioral intentions towards

autonomous driving (Panagiotopoulos and

Dimitrakopoulos, 2018) and ARTS vehicles

(Madigan et al., 2017).

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In addition, this study found that PRT has a

significant positive influence on BI, indicating that

perceptions of how trusted the autonomous driving

system is to use will affect end users’ decision to

drive/use HAPVs for their travels. The above confirm

the results of other studies about the important role of

trust in autonomous and driverless vehicles along

with other determinants of acceptance (Kaur and

Rampersad, 2018; Nordhoff et al., 2018;

Panagiotopoulos and Dimitrakopoulos, 2018).

Furthermore, the factor EE failed to reach

significance in this study, suggesting that difficulty in

driving/using HAPVs is becoming less of a concern

for the potential consumers as they become more

user-friendly. The above result is in contrast to other

findings, where effort expectancy (Madigan et al.,

2016) and perceived easy to use (Panagiotopoulos

and Dimitrakopoulos, 2018) did have an impact on

behavioral intentions towards ARTS vehicles and

autonomous driving technology, respectively. On the

other hand, our result is similar to other related

studies (Choi and Ji, 2015) where effort expectancy

and perceived ease to use were insignificant

predictors of customers’ intention towards vehicle

automation.

Moreover, the relationship between the predictor

variables PDE, PFC, SI, PRT, PE, EE and BI was

found to be affected by moderating factors such as

gender and culture, contrary to age and income, with

the culture to be the strongest moderator. In regards

to culture, findings show that respondents which

reside in Northern Europe are more likely to drive/use

HAPVs, when they become available on the market,

contrary to the respondents from Southern Europe.

7 CONCLUSIONS

Gaining experiences with automation technologies in

vehicles over the coming years, could lead us to better

understand consumers’ willingness to drive/use AVs.

In this respect, the value of the contribution of this

paper lies in the utilisation of an adapted version of

the original UTAUT framework, through

investigating the factors that influence potential

European consumers’ willingness to drive/use

HAPVs, improving thus the overall understanding

towards public acceptance of such vehicles. In this

way, this paper is one of the few contributing to the

knowledge about predictors that will lead to

widespread adoption of HAPVs in the future by

European end users.

In particular, results show that PDE plays a big

part in consumers’ desire to drive/use HAPVs for

their travels. In this context, it is obvious that

consumers will still want to enjoy the driving/usage

of vehicles equipped with advanced driving

automation technologies. Furthermore, the financial

cost, the trust in automation technology, the social

popularity and the performance expectancy all appear

to be important deciding factors. Therefore, it is

hoped that in order to maximize HAPVs uptake,

designers and developers in the automotive field can

consider the above issues when implementing more

permanent versions of HAPVs.

Like any other studies, this research has some

limitations that should be considered before

interpreting the findings. In this manner, the

presented implications need to be evaluated in light

of the quite futuristic character of HAPVs at the time

of the survey. Highly automated vehicles are not yet

launched on mass consumer markets. Hence, our

respondents did not have any hands-on experience

with them and could only state their guesses based on

our description provided at the beginning of the

questionnaire as well as on information they might

have gathered on their own. Also, majority of our

sample individuals were relatively young (under 40

years old). Hence, differences might be found for

other age groups. In addition, our survey was

conducted via online means of communication

websites, social media) and, hence, excluded people

that do not use the Internet. Finally, since only

European people were surveyed, our results might not

hold true for non-European people as consumers’

opinions and preferences also vary among different

geographical regions.

ACKNOWLEDGEMENT

This paper has been partly supported by the

Electronic Component Systems for European

Leadership (ECSEL) Joint Undertaking under GA No

783190 (PRYSTINE) and GA No 737469

(AutoDrive).

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