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Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
Available online 13 March 2023
1369-8478/© 2023 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY-NC-ND license
(http://creativecommons.org/licenses/by-nc-nd/4.0/).
Acceptance of self-driving cars among the university community:
Effects of gender, previous experience, technology adoption
propensity, and attitudes toward autonomous vehicles
´
Agnes H˝
ogye-Nagy
a
,
*
, G´
abor Kov´
acs
b
, Gy˝
oz˝
o Kurucz
c
a
University of Debrecen, Institute of Psychology, Department of Social and Work Psychology, Egyetem t´
er 1, 4032 Debrecen, Hungary
b
Sz´
echenyi Istv´
an University, Department of Criminal Sciences, Egyetem t´
er 1, 9026 Gy˝
or, Hungary
c
University of Debrecen, Institute of Psychology, Department of Social and Work Psychology, Egyetem t´
er 1, 4032 Debrecen, Hungary
ARTICLE INFO
Keywords:
Acceptance of self-driving cars
Technology Adoption Propensity
Conditional process modeling
Attitudes toward autonomous vehicles
ABSTRACT
This article investigates the acceptance of autonomous cars based on the role of attitudes toward
autonomous vehicles, acceptance of technology, previous experiences, and gender. Using an
online survey, which includes the Acceptance of Autonomous Vehicles (AVAS) and Technology
Adoption Propensity scale (TAP), a sample of 1273 members of a university community was
collected. Acceptance of using autonomous cars in a test drive and ordinary, real trafc scenarios,
as well as the intention to buy one were measured via self-administered items. We used condi-
tional process modeling to get a more detailed insight into the connections among these factors.
The ndings revealed that all four factors of attitudes towards autonomous vehicles (benets in
usefulness, benets in situations, commonalities concerns, system concerns) but only the opti-
mism factor of technology adaption propensity affected the acceptance. Dependency seemed to
affect benets in usefulness and the two concern variables. Gender differences are almost entirely
explained away by the effects of attitudes. Previous experience had no signicant effect in the
model.
1. Introduction
Concerns about autonomous vehicles (AVs) are one of the major challenges of the present and future technological developments.
There are increasing expectations that AVs will help solve safety issues in trafc, transportation problems of the elderly, and will
enhance the quality of public transport. On the other hand, there are serious concerns about information and software safety, legal
aspects, and economic consequences, as well.
Attitudes regarding AVs and the willingness to use self-driving cars are the focus of this current research. Researchers are often
interested in individuals’ acceptance of AVs, willingness to use and intention to buy them. Behavioral intention, generally, can be
predicted by attitudes, which was proven in AV research. Respondents with positive attitudes to AVs are more willing to use them (e.g.,
Dai et al., 2021; Payre et al., 2014).
Studies on gender differences regarding intention to use and attitudes often focus on self-driving cars, not on AVs generally. Abraham
et al. (2017) showed that a higher level of comfort of in-vehicle automation was observed in the answers of male respondents compared
* Corresponding author.
E-mail addresses: hogye-nagy.agnes@arts.unideb.hu (´
A. H˝
ogye-Nagy), gkovacs@sze.hu (G. Kov´
acs), kurucz.gyozo@arts.unideb.hu (G. Kurucz).
Contents lists available at ScienceDirect
Transportation Research Part F:
Psychology and Behaviour
journal homepage: www.elsevier.com/locate/trf
https://doi.org/10.1016/j.trf.2023.03.005
Received 22 February 2022; Received in revised form 2 March 2023; Accepted 5 March 2023
Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
354
to females, in general. Males are usually more likely to use and buy self-driving cars and have more positive attitudes compared to
females (e.g., Charness et al., 2018; Hand & Lee, 2018;Hohenberger, et al., 2016; K¨
onig & Neumayr, 2017; Kyriakidis et al., 2015;
Payre et al., 2014; Qu et al., 2019). The difference also holds in the acceptance of automated public transport (Bernhard et al., 2020).
Males also would pay more for automation (Kyriakidis et al., 2015). If we consider the different levels of automation, we can see that
men prefer non-autonomous vehicles or fully autonomous ones while women prefer a medium level of automation (advanced driver
assistance systems) (R¨
odel et al., 2014).
Age also seems to have an effect. Younger respondents have more positive attitudes, fewer concerns, fewer worries (K¨
onig &
Neumayr, 2017), and higher acceptance (e.g. Deb et al., 2017; Hulse et al., 2018) regarding AVs. Consequently, the elderly prefer
conventional vehicles (Wicki, 2021). Though, another study concluded, that older drivers have fewer concerns about AVs (Qu et al.,
2019).
Our (economic) decision-making is affected by familiarity, not simply the experience, but rather the objective information we can
gain (LaRiviere et al., 2014). Familiarity with AVs can reduce worries and have a positive effect on attitudes toward them, as shown in
some studies (e.g. Dai et al., 2021). Drivers with prior knowledge of AVs are less concerned with AVs and more willing to relinquish
driving control (Charness et al., 2018). Nees (2016) concluded that respondents who are familiar with self-driving technology and are
exposed to articles on the topic had a greater acceptance of self-driving cars. However, Wicki (2021) found no effect of familiarity on
acceptance or concerns in their research.
A few studies have investigated the relationship between technology adoption and attitudes toward advanced driving assistance
systems and AVs. Rahman et al. (2017) found that perceived usefulness showed a stronger effect on the intention to use advanced
driving assistance systems than perceived ease of use. According to Koul and Eydgahi (2018), there are signicant positive re-
lationships between perceived usefulness and perceived ease of use of autonomous car technology and intention to use driverless cars.
Müller (2019) investigated the acceptance of autonomous vehicles and found that perceived usefulness and perceived ease of use are
positive predictors of the intention to use these vehicles. Positive attitudes towards new technology were positively correlated with
attitudes to AVs in a study in 11 countries (Tennant et al., 2019), and also in Hungary (Kov´
acs & Lukovics, 2022). Generally, males tend
to think that technology is favorable (Amin et al., 2015; Cai et al., 2017; Park et al., 2019; Venkatesh et al., 2003). Ratchford and
Barnhart (2012) introduced a conceptual model of Technology Adoption Propensity (TAP) according to which the use of technology can
be described by two supportive and two inhibitory factors. Optimism and prociency are positive predictors of technology use, while
dependence and vulnerability are rather negative predictors. Martos et al. (2019) found gender differences in prociency; males had
higher values than females. Their ndings also show that men have more positive attitudes to technology than women.
The objective of this paper was to investigate the effects of attitudes toward AVs and technology adoption propensity on the
acceptance of autonomous cars. Technology adoption propensity is considered as an attitude connected to the attitudes toward AVs,
but is broader and more general. We also examined gender differences and previous experience (or knowledge) of AVs. Based on
previous results we anticipated that men rather than women would be more positive towards self-driving cars, more apt to adopt new
technology, and also have stronger intentions to use a self-driving car. We hypothesized that the attitudes toward self-driving vehicles,
previous experience with them, and the acceptance of technology generally help predict acceptance. Furthermore, we wanted to
investigate if attitude towards self-driving vehicles is able to mediate between acceptance of new technology and acceptance of self-
driving cars, and if gender moderates these relationships. We expected that positive attitudes towards new technology and self-driving
vehicles, and previous experience predict a higher level of acceptance of self-driving cars; these effects were expected to be lower in
case of females. To be able to test mediator as well as moderator effects besides the main effects, we used conditional process modeling
(Hayes, 2018), which is a statistical model used in several areas to study mediator and moderator effects (e.g. Gully et al., 2013; Levant
et al., 2015). The conceptual diagram of the hypothesized relationships is shown in Fig. 1. When conceptualizing attitudes towards self-
driving vehicles, we used the four-factor concept of Qu et al. (2019) in which benets and concerns are distinguished, so it would give a
Fig. 1. Conceptual diagram of the effects tested in the current study.
´
A. H˝
ogye-Nagy et al.
Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
355
more detailed picture of the attitudes examined.
2. Methodology
2.1. Participants
Mainly the communities of the University of Debrecen and Sz´
echenyi Istv´
an University in Gy˝
or participated in the study. The call
for participation was shared on Facebook sites of the universities and the universities’ online educational platforms. Respondents could
sign up for a rafe in which one person would win a book purchase voucher worth 20,000 HUF. Convenience sampling was used.
There were 1350 participants in total. We discarded n =35 participants because of inconsistent answers or because they were
below 18 years of age. An additional 42 participants didn’t have a driving license–we decided to omit them during the analysis. The
nal sample size was N =1273.
There were n =490 (38.5%) females and n =783 (61.5%) males in the sample. The age ranged from 18 to 74 years and the majority
of the sample was young adults (M =27.2, SD =9.15). Most of the respondents were students of a university (59.7%), or members of
non-teaching staff at the university (29.3%). A minority of the sample was members of the teaching staff (0.4%). Some of the par-
ticipants reported a job status that may be, but is not necessarily, related to the university (11%). A participant could be assigned one or
several of the above statuses based on his/her answer to an open-ended question.
The sample was diverse in terms of how many years ago they acquired their driving license (range: 0–47 years, M =8.3, SD =8.35).
There also was a large variability in the amount of weekly driving–38% (n =488) of the sample was driving up to 50 km per week, 27%
(n =345) 51 to 150 km, and 35% (n =440) more than 200 km per week.
3. Materials and methods
We used a battery of questionnaires and psychological scales through Google Forms. The battery of questionnaires consisted of a
custom-made questionnaire that included the Hungarian adaptation of the Autonomous Vehicle Acceptability Scale (AVAS; Qu et al.,
2019), the Hungarian adaptation of the Technology Adoption Propensity scale (TAP; Ratchford & Barnhart, 2012) and other scales
measuring some driving-related behavioral tendencies and personality traits that we ignored in the present study.
In our custom questionnaire, respondents provided basic demographic information (gender, age, current job status), some driving-
related experience (what kind of driving licenses they have, how long ago they have had their driving license, how much do they drive
regularly, how many [if any] trafc accidents they been in), the knowledge of and type of experience with self-driving cars, and the
intention of trying out or buying a self-driving car. The latter was measured with three questions, each of which could be answered on a
seven-point Likert scale ranging from 1 (“I certainly don’t”) to 7 (“I certainly do”). The questions were about trying out a self-driving
car (driving during a test drive or in ordinary trafc) and buying a self-driving car if nancial conditions weren’t a concern. These
questions were used to measure the acceptance of self-driving cars.
Attitudes to AVs were measured using the Autonomous Vehicle Acceptability Scale (Qu et al., 2019). The original scale consists of 18
items in 4 factors. In the Hungarian adaptation, there are 15 items in 4 factors (Kurucz et al., 2022). Each of the items is judged on a
seven-point Likert scale ranging from 1 (“I totally disagree”/”I don’t worry at all”) to 7 (“I totally agree”/”I’m very worried”). The scale
has two benecial factors, benets in usefulness (BiU) and benets in situations (BiS), and two anxious factors, commonalities con-
cerns (CoC) and system concerns (SyC).
The general acceptance of technological innovations was measured using the Technology Adoption Propensity scale (Ratchford &
Barnhart, 2012), adapted to Hungarian by Martos et al. (2019). The Hungarian adaptation of the scale contains three factors, namely
prociency, dependency, and optimism. All items were rated on a seven-point Likert scale ranging from 1 (“I totally disagree”) to 7 (“I
totally agree”).
4. Results
We checked participants’ answers for inconsistencies and out-of-the-ordinary answers to exclude those which potentially
compromise the quality of our data. One participant reported having 1000 accidents, and another 9 participants reported previously
having a trafc accident, but later on, mentioned having 0 accidents. 25 participants stated that they obtained their driving license
before the age of 16, which contradicts Hungarian regulations. One of the participants also reported to be below 18 years old. In sum,
35 participants were excluded from the analysis because of the above reasons. Another 42 participants were excluded because they
didn’t have a driving license at the time of the survey.
We checked the adequacy of the questionnaires’ factor structure using polychoric correlations, as these are better suited to the
ordinal nature of the answers, according to Li (2016), Yang-Wallentin, J¨
oreskog, and Lou (2010). In conrmatory factor analysis (CFA)
we used the robust diagonally weighted least squares (WLSMV) method for estimation, using the R lavaan package (Rosseel, 2012). In
the case of TAP, we found a marginally acceptable t of the proposed factor model by Martos et al. (2019), X
2
(51) =532.9, p <.001
CFI =0.91, TLI =0.88, RMSEA =0.086 (90% CI[0.080-0.093]). In the case of the Hungarian version of AVAS a good t to the data was
found, X
2
(98) =317.41, p <.001, CFI =0.98, TLI =0.98, RMSEA =0.047 (90% CI[0.041-0.052]).
When conducting a principal components analysis on the three items measuring acceptance of self-driving cars, the rst component
exhibited a high variance, in contrast to the remaining components. This initial component explained a substantial 74% of the variance
in the input variables. We viewed this as strong evidence for the aggregation of the answers to these questions on a single scale.
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Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
356
We used the average of the answers to calculate the scores of the subscales of the questionnaires AVAS and TAP and the acceptance
of self-driving cars scale. Descriptive statistics, reliability measures, as well as inter-scale correlations are shown in Table 1. With one
exception (system concerns), the scales show acceptable or good internal consistency according to Cronbach’s
α
measure of reliability.
All positive aspects of attitudes toward AVs, dependency, and prociency showed a positive correlation, whereas negative aspects of
attitudes toward AVs showed negative correlations with acceptance of AVs.
We tested the differences between men and women in their attitudes toward AVs, the acceptance of technology in general, and the
acceptance of self-driving cars. We used Mann-Whitney U test because we found signicant deviations from the normal distributions in
the case of every variable under consideration among both men and women.
Regarding the attitudes toward AVs, men seemed to respect somewhat more the benets of self-driving vehicles and showed less
concern than women (see Fig. 2.a). Men also judged themselves as more procient in using modern technology and were more
optimistic about it than women (see Fig. 2.b). However, the differences are mostly modest and are remarkable only in the case of
commonalities concerns and prociency. Also, men showed higher acceptance of self-driving cars (M =5.41, SD =1.52) than women
(M =4.87, SD =1.47) with the difference being signicant (Mann-Whitney U =235760, p <.001).
A signicant difference in acceptance of self-driving cars was found between those with and without previous experience with self-
driving vehicles (Mann-Whitney U =1619732, p <.001). Respondents with previous experience showed somewhat higher acceptance
(M =5.65, SD =1.48) than respondents without such experiences (M =5.14, SD =1.52).
We used conditional process modeling–coined by Andrew Hayes in the rst edition of Hayes (2018)–to test the independent effects
of the attitudes toward AVs on the acceptance of self-driving cars, and the mediating role of attitudes toward AVs between the adoption
of technology and the acceptance of self-driving cars. We also tested the moderating effect of gender and the effect of previous
experience with self-driving cars. We used a modied version of model 15 proposed by Hayes (2018), which is suitable for testing the
direct effect of the predictor variable as well as the mediating effect of several mediator variables and the moderating effect of one
additional variable. For the analysis, we used version 4.1 of the R script provided on https://www.processmacro.org. We used the TAP
scales as predictor variables. Since the model allows only one predictor variable, we tted the model to each scale of the questionnaire
separately–the other scales of the questionnaire were included in the model as covariates. We also used gender and previous experience
as covariates. The mediator variables were the four scales of the AVAS questionnaire. To investigate the potential moderator effects of
previous experience, TAP and AVAS scales on the acceptance of self-driving cars, we utilized gender as a moderator variable.
Before tting the models, the gender and previous experience variables were dummy coded and the attitude scales were
centralized. In the regression models, heteroscedasticity consistent standard errors (HC3) were used, as proposed by Hayes and Cai
(2007). 50,000 bootstrap samples were used to estimate the standard errors and condence intervals of the indirect effects. To acquire
the bootstrap samples, the same random seed was used across all models. The parameters and model t measures are shown in Table 2.
For an overview of the signicant effects in the model, see Fig. 3. In terms of direct effects, we found that attitudes toward
autonomous vehicles signicantly inuenced acceptance, with benecial factors (BiU, BiS) showing positive, and anxious factors (CoC,
SyC) showing negative effects. Apart from these, only optimism regarding technological innovations had a signicant impact, with a
strength similar to the previous effects. The only moderating effect of gender was for the CoC scale where women’s concerns about
public transport issues were a stronger predictor of the acceptance of self-driving cars than men’s. Neither gender nor previous
experience with self-driving cars was found to have a signicant direct effect on the acceptance of self-driving cars.
We found multiple indirect effects of the adoption of technology on the acceptance of self-driving cars, several of which included a
moderating effect of gender (see Table 3). We found a signicant indirect effect of optimism, mainly through the benecial factors of
attitudes toward self-driving vehicles (BiU and BiS), and through CoC–in the latter case, the effect was found to be slightly stronger for
women than for men. Dependency was related to acceptance of self-driving cars mainly through the anxious factors of AVAS, and
additionally through BiU. Among these effects, we also found a moderating effect of gender on the effect of CoC–the effect was stronger
for women than men. The direction of the indirect effects corresponds to the direction of the direct effects of TAP scales. We found no
signicant indirect effects of previous experience with self-driving cars for either men or women.
Table 1
Descriptive statistics, internal consistency measures (Cronbach-
α
) of the scales, and correlation between scales (Pearson-correlation coefcients).
inter-scale correlations
min max M SD
α
2. 3. 4. 5. 6. 7. 8.
AVAS
1. benets in usefulness (BiU) 1 7 4.17 1.45 0.85 0.37
***
−0.33
***
−0.11
**
0.23
***
0.13
***
−0.02 0.36
***
2. benets in situations (BiS) 1 7 5.11 1.42 0.76 −0.37
***
0.02 0.35
***
0.20
***
0.07* 0.36
***
3. commonalities concerns (CoC) 1 7 3.38 1.58 0.89 0.21
***
−0.18
***
−0.19
***
0.19
***
-0.43
***
4. system concerns (SyC) 1 7 5.73 1.33 0.61 0.05 0.02 0.24
***
−0.22
***
TAP
5. dependency 1 7 5.1 1.07 0.78 0.47
***
0.20
***
0.33
***
6. prociency 1 7 4.69 1.42 0.83 0.14
***
0.24
***
7. optimism 1 7 4.53 1.12 0.70 −0.05
8. acceptance of AVs 1 7 5.2 1.52 0.81
Note. * p <.05;
**
p <.01;
***
p <.001.
´
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ogye-Nagy et al.
Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
357
5. Discussion
The aim of this study was to investigate how gender, previous experience, attitudes toward autonomous vehicles (AVs), and
technology adoption propensity affect the acceptance and use of self-driving cars.
Consistent with previous studies, we found signicant differences between men and women in their willingness to use autonomous
vehicles and attitudes toward them. Males reported a higher level of willingness to use self-driving cars (consistent with e.g Hand &
Lee, 2018; Hohenberger et al., 2016; Payre et al., 2014) and tended to respect the benets of self-driving vehicles more and show fewer
concerns than females. However, differences were remarkable only in the case of commonalities concerns, while Qu et al. (2019) found
signicant differences only in the factor benets in usefulness. While several studies showed gender differences in both perceived
usefulness and concerns, as well (e.g.Acheampong & Cugurullo, 2019; Hilgarter & Granig 2020). Similarly, males tended to judge
themselves as more procient in using modern technology and were more optimistic about this than women. This is consistent with the
ndings on attitudes towards technology (e.g. Amin et al., 2015; Cai et al., 2017; Park et al. 2019; Venkatesh et al. 2003), and is partly
consistent with Martos et al. (2019), who reported gender differences only in prociency.
We also found small but signicant differences in the acceptance of self-driving between those with and without previous expe-
rience with autonomous vehicles, which was also shown by Charness et al. (2018); Nees (2016); Pakusch & Bossauer (2017); Wang
et al. (2020).
However, based on the analysis of the mediation and moderation effects between the variables in question, we can conclude that
the differences between men and women in this acceptance can be fully explained by the differences in the attitudes toward AVs and
technological innovations. Contrary to previous ndings, we found no direct effect of gender nor previous experience on the intention
to use self-driving cars. Wicki (2021) also found similar results investigating the effect of familiarity. Attitudes toward AVs proved to be
effective predictors of intention, concerns had a negative effect, and benets had a positive effect. Only one aspect of technology
acceptance propensity was an effective predictor, namely the factor of optimism, which had a direct effect on acceptance, and also an
indirect effect through benets and commonalities concerns. The factor dependency had an indirect effect through benets in use-
fulness and concern variables. Gender affected benets in usefulness (men see more benets) and commonalities concerns (women
show a higher level of worry) and also moderates the effect of commonalities concerns on acceptance (the effect is more negative in the
case of women). Previous experience with AVs didn’t seem to have any effect in this complex model of relationships.
Results suggest that males tend to be more willing to use self-driving cars because they worry less about commonalities issues and
see more benets of AVs. Technology adoption propensity, namely optimism, and dependency seem to inuence attitudes to AVs.
Optimism can directly affect the acceptance of self-driving cars. Attitudes both on general (toward technological innovations) and
specic (toward AVs) levels may inuence the willingness to use self-driving cars.
6. Conclusions
This study is, to our knowledge, the rst to examine the effect of attitudes toward self-driving vehicles, previous experience with
them, and the acceptance of technology generally to predict acceptance, while considering the mediator effect of attitudes towards
self-driving vehicles (as a specic level of attitudes), between acceptance of new technology (as a general level of attitudes) and
acceptance of self-driving cars, and also considering the moderator effect of gender. Measuring the attitudes both generally and
specically can be benecial in understanding the phenomenon of accepting autonomous cars since attitudes (both to AVs and
technological innovations) seem to have a larger impact than previous experience with them. Moreover, using a multivariate design,
we might present a more accurate picture of contributing factors. Our results can be informative for a better understanding of why
people accept or do not accept self-driving cars, and how acceptance can be enhanced through attitude change. Industries and gov-
ernments can gain a detailed, gender-specic, understanding of perceived benets and concerns toward self-driving vehicles, and how
Fig. 2. Means of attitudes toward AVs and acceptance of technology by gender. Error bars represent 95% condence intervals around means, p
values show the results of Mann-Whitney U tests.
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Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
358
Table 2
Parameters and t measures of the tted regression models.
Dependent variables
acceptance of AVs BiU BiS CoC SyC
independent variable Coeff SE
a
Coeff SE
a
Coeff SE
a
Coeff SE
a
Coeff SE
a
gender
b
−0.004 0.083 −0.066 0.087 −0.369
***
0.088 0.727
***
0.096 −0.105 0.087
prev. experience
c
0.209 0.119 0.089 0.140 0.033 0.115 −0.136 0.136 −0.004 0.117
dependency −0.086 0.047 −0.084* 0.039 0.015 0.035 0.326
***
0.038 0.285
***
0.037
prociency 0.057 0.047 0.023 0.034 −0.008 0.035 −0.062 0.038 −0.032 0.034
optimism 0.225
***
0.058 0.307
***
0.045 0.430
***
0.044 .−0.228
***
0.047 0.017 0.041
BiU 0.162
***
0.038 – – – – – – – –
BiS 0.177
***
0.046 – – – – – – – –
CoC −0.176
***
0.038 – – – – – – – –
SyC −0.220
***
0.038 – – – – – – – –
gender*previous experience −0.010 0.253 – – – – – – – –
gender * dependency 0.135 0.069 – – – – – – – –
gender * prociency 0.012 0.064 – – – – – – – –
gender * optimism 0.060 0.087 – – – – – – – –
gender * BiU 0.048 0.059 – – – – – – – –
gender * BiS −0.070 0.062 – – – – – – – –
gender * CoC −0.131* 0.056 – – – – – – – –
gender * SyC 0.077 0.061 – – – – – – – –
Constant 5.209
***
0.055 0.016 0.057 0.138
**
0.053 −0.265
***
0.056 0.041 0.054
R
2
=0.337F
(5,1267) =41.036
***
R
2
=0.06F
(5,1267) =14.134
***
R
2
=0.135F
(5,1267) =38.652
***
R
2
=0.147F
(5,1267) =46.93
***
R
2
=0.058F
(5,1267) =12.878
***
Note.
a
heteroscedasticity consistent standard errors;
b
coded as: 0 =male, 1 =female;
c
coded as: 0 =no experience, 1 =some experience; * p <.05;
**
p <.01;
***
p <.001.
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Transportation Research Part F: Psychology and Behaviour 94 (2023) 353–361
359
they affect acceptance.
6.1. Limitations
An important limitation of the study is the conned nature of the sample, as it contains only members of the university community,
mainly young students. A more heterogeneous sample might help to provide a more detailed pattern of attitudes among potential AV
users and owners.
Declaration of Competing Interest
The authors declare that they have no known competing nancial interests or personal relationships that could have appeared to
inuence the work reported in this paper.
Data availability
https://osf.io/kq7f5/?view_only=421edb20fa4f499abd6385f1b1389d0b.
Fig. 3. Diagram of signicant effects found with conditional process modeling (solid lines represent positive, dashed lines represent negative effects;
line width is proportional to effect size.
Table 3
Estimated indirect effects of TAP scales (with 95% bootstrap condence intervals inside parentheses).
Independent variables Mediator variables Indirect effects Index of moderated mediation
Males Females
prev. experience BiU 0.014 (−0.031; 0.062) 0.019 (−0.040; 0.080) 0.004 (−0.016; 0.033)
BiS 0.006 (−0.037; 0.046) 0.004 (−0.023; 0.031) 0.002 (−0.026; 0.020)
CoC 0.024 (−0.024; 0.074) 0.042 (−0.040; 0.125) 0.018 (−0.018; 0.065)
SyC 0.001 (−0.034; 0.037) 0.001 (−0.048; 0.055) 0.000 (−0.026; 0.023)
dependency BiU −0.014* (−0.029; −0.001 0.018* (0.037; −0.001) 0.004 (−0.017; 0.006)
BiS 0.003 (−0.010; 0.016) 0.002 (−0.006; 0.011) −0.001 (−0.009; 0.006)
CoC −0.057* (−0.085; −0.031) −0.100* (−0.136; −0.069) −0.043* (−0.081; −0.008)
SyC −0.063* (−0.090; −0.039) −0.041* (−0.071; −0.014) 0.022 (−0.011; 0.058)
prociency BiU 0.004 (−0.007; 0.016) 0.005 (−0.009; 0.021) 0.001 (−0.004; 0.009)
BiS −0.001 (−0.015; 0.011) −0.001 (−0.009; 0.007) 0.001 (−0.005; 0.009)
CoC 0.011 (−0.002; 0.026) 0.019 (−0.004; 0.044) 0.008 (−0.002; 0.023)
SyC 0.007 (−0.007; 0.022) 0.005 (−0.005; 0.016) −0.002 (−0.012; 0.004)
optimism BiU 0.050* (0.026; 0.079) 0.065* (0.034; 0.100) 0.015 (−0.020; 0.051)
BiS 0.076* (0.036; 0.120) 0.046* (0.010; 0.084) −0.030 (−0.085; 0.021)
CoC 0.040* (0.019; 0.066) 0.070* (0.039; 0.106) 0.030* (0.005; 0.060)
SyC −0.004 (−0.023; 0.014) −0.002 (−0.016; 0.010) 0.001 (−0.006; 0.012)
Note: * effects signicantly different from 0, based on 95% condence interval.
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ogye-Nagy et al.
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Acknowledgements
This work has been funded by the European Union through the project titled “Cooperation Centre for Higher Education and In-
dustry at the University of Gy˝
or” (GINOP-2.3.4-15-2016-00003).
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