Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
Available online 23 February 2024
2590-1982/© 2024 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
Modelling public attitude towards air taxis in Germany
Hamid Mostofi
*
, Tobias Biehle , Robin Kellermann, Hans-Liudger Dienel
Technische Universit¨
at Berlin, Department for Work, Technology and Participation, Mobility Research Cluster, Marchstraße 23, MAR 1-1, Berlin 10587, Germany
ARTICLE INFO
Keywords:
Air Taxis
eVTOL
Urban air mobility
Public attitude
Acceptance
SEM modelling
ABSTRACT
Urban air mobility (UAM) holds great promise as an expansion of the transportation system in cities. Despite the
progress in UAM technology, there remains significant uncertainty surrounding how the public will accept and
react to these mobility services.
This study employed a structural equation model (SEM) to construct a comprehensive framework that delves
into the factors influencing public attitudes toward air taxis. Data for the model is derived from a sequential
exploratory mixed methods approach. The initial phase involved identifying acceptance factors towards air taxis
through five focus groups, laying the foundation for the subsequent structural model development. A survey
involving 819 participants was conducted in Germany in the next phase. The latent variables in this model are
the expected benefits, expected risks, and the personal level of technophilia. The results show that rising stress
levels through new air traffic flows, noise, and blocking sky views affect negative attitudes toward air taxis in
public spaces. In contrast, the user expectation of avoiding traffic jams and achieving time savings contributes
positively. Additionally, people who are more technophilic tend to have a more positive attitude toward air taxis.
However, the perceived negative consequences of air taxis exert more substantial and stronger influences on
public attitude than the expected benefits. By introducing the acceptance factors and relevant dimensions of a
public attitude, this study provides insights to shape the design of UAM in accordance with the common good.
1. Introduction
1.1. Background
In the 1950s, various protagonists had expected helicopters to form a
new means of public transport for rapid transit in and between metro-
polises. However, high costs, lack of infrastructure, safety and security
risks, as well as public rejection due to noise emissions prevented this
vision from realisation (Dienel, 1997; Cohen et al., 2021). Meanwhile,
technological progress has led to the development of new electric ver-
tical take-off and landing (eVTOL) aircrafts (Shamieyeh 2017) that own
quieter electric propulsion systems and, at least prospectively, cost-
saving automated vehicle control. Considering these developments,
the vision of an Urban Air Mobility (UAM) is revived.
In expectation of a relevant market share, investments in the six
leading North American and European eVTOL producers and prospected
UAM operators (Joby Aviation, Lilium, Paragon, Archer Aviation, Beta
Technologies, and Volocopter) accounted for around US$ 4.6 billion
between 2020 and 2021 alone (Shaposhnikov et al., 2021). The business
models of these companies are primarily aimed at urban and regional
passenger transport. Depending on increasing battery capacities, an
expansion to inter-city transport is envisaged (Garrow, n.d.). While
market analysts predict that air taxis will not become economically
viable before 2030 (Grandl et al., 2021) manufactures are pushing for an
early market launch in the first half of this decade and are supported by a
growing political consensus that air taxis shall play a role in future
passenger transit (European Commission, 2020). If recent developments
continue to materialize they mark not just a historical turning point in
aviation, but the beginning of a new era in which low level airspace may
become the ‘third dimension’ of transportation (Kellermann et al.,
2020).
Despite optimistic investment forecasts and technological as well as
regulatory frameworks for UAM operations advancing steadily, there is
a high degree of uncertainty regarding the public response toward such
services. Contemporary literature has already provided insights on fac-
tors affecting the adoption of air taxi services by potential customers,
ranging from target customer definitions (Goyal et al., 2021, Ahmed
et al., 2021) to vehicle design considerations (Stolz et al., 2021; Edwards
and Price, 2020), to design guidelines for passenger handling and
transport service provision (Han et al., 2019; Rice et al., 2022).
* Corresponding author.
E-mail address: [email protected] (H. Mostofi).
Contents lists available at ScienceDirect
Transportation Research Interdisciplinary Perspectives
journal homepage: www.sciencedirect.com/journal/transportation-
research-interdisciplinary-perspectives
https://doi.org/10.1016/j.trip.2024.101045
Received 22 November 2022; Received in revised form 9 September 2023; Accepted 14 February 2024
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
2
However, the technology also entails an inherently public dimension. It
will be a characteristic of UAM that far fewer people will benefit from its
services than those who will be exposed to its potential negative exter-
nalities (Clothier et al., 2015). As a recent studies for a relatively small
European metropolis with 1.5 million inhabitants shows, even an ex-
pected market share of 1 % on the overall modal split will account for
45.000 flights per day (Ploetner et al., 2020). Thus, the potential ben-
efits, e.g. of increased mobility and exclusive travelling experiences may
stand against the potential impacts of UAM on the urban sound- and
landscape, environmental and sustainability concerns, as well as secu-
rity issues in air or in proximity to ground infrastructure components
(Straubinger et al., 2021).
By outlining which dimensions have a significant, positive or nega-
tive influence on attitudes towards UAM, social science attitude research
can contribute to a better handling of this friction and help policy
makers, urban planners and business developer to design services in
accordance with the public’s expectation. Against this backdrop, this
article presents and tests a model to explain the attitude toward air taxis
from a societal point of view. The model examines the influence of ex-
pected benefits, expected risks, and the individual level of technophilia
on the attitude. The relevant data for the model was received using a
sequential exploratory mixed methods design. In a first step, factors
affecting attitudes toward air taxis were identified within the scope of
five focus groups conducted in three German state capitals. In a second
step, these qualitative derived factors were converted into the devel-
opment of a questionnaire and the realization of a population-
representative telephone survey in Germany, which provided quantita-
tive data. The measurement model of these variables is tested using a
confirmatory factor analysis, and the overall structure of the SEM is
assessed on a sample size of 819 entities.
1.2. Literature review
The advancing market maturity for eVTOLs seems to have not reach
the awareness of the broader public yet. Drawing on the USA and its
ambitions industry, authors surveyed 23 % (Shaheen et al., 2018) and
19 % (Aydin, 2019) awareness towards advanced air mobility concepts
in 2018 and 2019 respectively. Still in a 2022 online survey conducted
for NASA with a sample size of 1500 citizen from Los Angeles and Ohio,
only around 15 % of respondents stated some familiarity with the air taxi
concept (MAVEN, 2022). In addition to that, firsthand experiences with
the technology are scare as only a very few people have used an eVTOL
nor experienced its operation from a resident’s point of view. Apart from
simulations (Stolz and Laudien, 2022) and ad-hoc surveys at demon-
stration events (Behme and Planing, 2020), qualitative and quantitative
attitude research must therefore draw on the respondent’s contempo-
rary knowledge of future air transportation scenarios and results must be
understood as time and location specific. However, overarching trends
can be outlined. For example, Tepylo et al. (2023) analysed several
studies in the USA between 2011 and 2018 and show how the willing-
ness to fly in such an autonomous aircraft increased from almost 10 % to
over 50 % (Tepylo et al.). In contrast to that, a German population-
representative telephone survey from 2020 shows that only a minority
of 18 % of respondents somewhat or fully agree to use air taxis for their
personal mobility (Dannenberger et al., 2020), while a representative
online study two years later found that 22 % of respondents would be
willing to use air taxis for inner-city commuting (Verband Unbemannte
Luftfahrt VUL, 2022).
The intention to use air taxi services has been attracted broad
research interest. Especially the perceived usefulness of service has been
outlined of central importance by statistical analysis (Winter et al.,
2020). For passenger UAM authors show that this hope is strongly
associated to a reduction of travel times (Al Haddad et al., 2020). It is
therefore more comprehensible, that a representative study with par-
ticipants from six European cities commissioned by EASA concludes that
an average of 49 percent would try out and pay more for an air taxi if the
trip in question can be done in half the time it takes with a roadside taxi
service (EASA, 2021). Regarding the risk perception of UAM, Han et al.
(2019) outlined that the respondents risks perception to life and limb
has a significant negative influence on the attitude to board electric-
powered aircrafts, which should be particularly true during early op-
erations and low familiarity with the technology (Han et al., 2019). In
addition to these findings, research indicates that participants’ feelings
of safety about eVTOLs strongly depends on how they are piloted.
Chancey and Politovich show that remotely piloted eVTOLs are trusted
less compared to services with a pilot on board (Chancey and Politowicz,
2020). What is more, the perceived service reliability of air taxi services,
e.g. on-time performance (Al Haddad et al., 2020) and low performance
risk (Han et al., 2019) are important for the adoption of air taxis and the
desire to use all-electric passenger aircraft respectively.
In addition to the relevance of potentials and risks for forming public
attitude, individual dispositions of technological openness or techno-
philia are shown to play a role in the formation of attitude toward air
taxis, as already suggested by the adoption of delivery drones (Yoo et al.,
2018), electric vehicles (Schlüter and Weyer, 2019) or driving support
systems (Ntasiou et al., 2021). Winter et al. (2020) point out that people
have different basic attitudes toward new technologies. Within the
framework of their empirical study, they concluded that a greater
openness toward new technologies generally also affects the willingness
to book an autonomously operating air taxi positively. Al Haddad et al.
(2020) have the same assessment for automated air taxis. Furthermore,
both groups of authors show that the willingness to fly with a passenger
drone decreases the smaller is the understanding of the technology that
controls the vehicle (Winter et al., 2020; Al Haddad et al., 2020).
While the willingness to use air taxi services has a direct impact on
the economic feasibility of UAM business models, the development of
urban airspace as a new transport level is also a societal and political
decision. After all, traffic externalities and questions of social equality
may hamper the technologies’ expansion into the public realm (Biehle,
2022). Thus, research should contribute to understanding the central
factors influencing public attitudes towards passenger UAM. Aiming to
illuminate public acceptance levels of UAM services, survey data
revealed air taxis to be perceived rather critically. The mentioned cross-
European study conducted by EASA (2021) shows that among various
application scenarios of drone technologies, the use of air taxis in urban
areas for inner city point to point travel was considered among the least
useful application. In contrast, the perceived benefits of automated
drones are primarily seen in deployment scenarios with clear added
value for society. These include, in particular, the rapid delivery of
medicines or the use of eVTOLs to transport patients. In general, com-
mercial applications are considered to be of secondary importance
(EASA, 2021).
Regarding the impact of passenger UAM on public space, the afore-
mentioned population representative study from Germany shows that
43 % of respondents believe that air taxis would make urban areas less
liveable, while only 22 % of respondents are sure that passenger trans-
port with air taxis would have a positive impact on the quality of life in
cities. When asked about a future in which many people would use air
taxis, 61 % of respondents rated it as very or fairly bad if air taxis
blocked the currently unobstructed view of the sky (Dannenberger et al.,
2020). In a statistical model, authors already confirmed the significance
of these both factors for the formation of public attitudes towards de-
livery drones in urban areas (Kellermann et al., 2023). Further concerns
from various surveys in the field of passenger UAM are summarized in
Çetin et al. (2022) whereby possible negative environmental impacts
form a large thematic cluster. Especially, noise emissions of air taxis are
identified as an overarching obstacle to community acceptance (Çetin
et al., 2022). Regarding the necessary ground infrastructure for air taxis,
so-called vertiports, the mentioned EASA study asked respondents to
identify their top three concerns related to a potential vertiport in vi-
cinity to their place of living. Noise (48 %) and safety concerns (41 %)
were feared most. In addition, concerns about visual pollution (32 %),
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
3
increasing inbound and outbound traffic (29 %) and taking up land
better used for living or recreation (28 %) rank highest (EASA, 2021) In
a survey from the UAM model region Ingolstadt, Germany, the authors
show that air taxis can also affect citizens’ sense of security in the vi-
cinity of vertiport infrastructures or along flight routes. Risks of misuse,
for example, through attacks on weak points in the IT infrastructure, are
considered even more relevant than the threat of technical faults or the
danger of collisions and wildlife strikes (Janotta et al., 2021).
Concluding this brief review on attitude and acceptance research in
the field of UAM, a broad body of studies engages into forecasting
customer adoption by investigating factors that increase the willingness
of defined customer segments to use air taxi services. In contrast, only a
few studies tried to analyse the societal perception of air taxis’ deploy-
ment in public airspace and the factors that influence public attitudes.
Within this field, survey data prevails over more robust modelling ap-
proaches that aim to outline, which acceptance factors have a statistical
significance on attitudes. This article addresses this research gap,
providing orientation for design option for passenger UAM in accor-
dance with public norms and expectations.
2. Methodology & data
2.1. Sequential exploratory mixed methods
Basis for this research forms a sequential exploratory mixed methods
design (Tashakkori & Teddlie, 2009; Leech & Onwuegbuzie, 2009). In
this methodological approach, qualitative data on the attitude towards
air taxis was collected first, which was seen highly relevant because, as
shown above, the awareness and knowledge towards the technology is
still limited. Thus, instead of drawing on acceptance factors from the
literature and conducting a survey, in a first step, focus groups were
conducted to deliberate on the topic of air taxis. Second, to develop a
model structure, results of the focus groups were evaluated using con-
tent analysis within the theoretical framework of technology acceptance
research. Quantitative data for the variables was than gathered in a
second step by conducting a telephone survey with a standardized
questionnaire, allowing to build and test the structural equation model
(Fig. 1).
2.2. Structural equation model (SEM)
This paper applies SEM (structural equation model) to analyze
different attitudinal factors related to air taxis, including latent and
observable variables. SEM contains different statistical approaches:
analysis of variance, multiple regression, factor analysis, and path
analysis (Bowen and Guo, 2011). SEM measures and estimates the as-
sociations among observed and latent variables. The integrated analyt-
ical methods in SEM enclose between-group and within-group variance
comparisons through the ANOVA method. Therefore, SEM analyzes
linear associations among variables while, at the same time, it accounts
for measurement errors, which is one of the most splendid limitations of
other statistical methods. Hence, SEM includes Path analysis and factor
analysis together. Path analysis examines the hypothesized associations
among variables. Factor analysis studies how latent variables are
calculated from observed variables (measurables). These analyses are
usually performed by using data in the form of means or correlations and
covariances (i.e., unstandardized correlations). The maximum likeli-
hood function is applied to estimate coefficients and parameters.
Factor analysis (measurement model) evaluates how well sets of
observed variables measure latent variables. These latent constructs
cannot be measured directly and are related to psychological concepts
such as attitudes and emotions (Bowen and Guo, 2011). The Cronbach
test was applied to examine the reliability of the measurements. A higher
alpha suggests that correlation among observed variables is acceptable
to be representative of a latent variable. Many studies indicated that the
values of Cronbach’s alpha should be above 0.70 to assure the reliability
of the constructs (Netemeyer et al., 2003).
2.3. Data set
First, qualitative data were collected by conducting a set of focus
groups to guide the development of a model structure by identifying and
classifying relevant variables that could influence perceptions and atti-
tudes toward air taxis. Building on that qualitatively derived set of
variables, quantitative data were gathered in a second step by con-
ducting a telephone survey.
2.3.1. Focus groups
The five focus groups took place in the German capital Berlin and in
the two state capitals of Stuttgart and Erfurt in autumn 2019. The aim
was to qualitatively investigate attitudes but also concerns and expec-
tations toward fully automated, remotely operated air taxis. Participants
were chosen according to a pre-screening questionnaire aiming to
exclude participants who had never heard about drones before and those
who worked in the drone industry.
Since respondents’ attitudes toward new technologies are often
related to age (Arning and Ziefle, 2007; Jakobs et al., 2008; Niehaves
and Plattfaut, 2014) and gender (Gefen and Straub, 1997; Venkatesh
and Morris, 2000), two focus groups were conducted with older (45–65)
and two with younger (18–44) participants while gender balance was
always ensured. In addition, the representation of different levels of
education, income, and household sizes were ensured to avoid selection
biases (Hollis et al., 2002).
The implementation of the focus groups followed the methodological
procedure proposed by Benighaus and Benighaus (2012). First, partici-
pants were introduced to different concepts and applications of civilian
drones in a ten-minute presentation. Afterward, a commercial video
clip
1
was shown, which demonstrated what an automated passenger
transport by drone within an urban transport system might look like.
Following this, the participants discussed under the guidance of a pro-
fessional moderator based on a pre-developed discussion guideline
about air taxis as a possible new transport mode in the context of their
everyday life and living environment (Benighaus and Benighaus, 2012).
All focus groups were recorded, transcribed and analyzed within the
theoretical framework of technology acceptance theory (Lucke, 1995;
Sch¨
afer and Keppler, n.d) through qualitative content analysis (Mayring,
2012) and the help of the qualitative data analysis software Atlas.ti
(Version 8). For coding, first quotations were sorted into the theory-
based categories: attitudes, behavioural intentions, object- subject-
and context-related acceptance factors (Crabtree and Miller, 1992). The
different attitudes and acceptance factors, however, were generated
inductively throughout the analysis of the transcripts to assure the
identification and contextualization of acceptance factors, which have
not been previously investigated by other studies (Boyatzis, 1998). The
weighting of the factors was derived from the frequency with which they
were coded (Kellermann and Fischer, 2020).
2.3.2. Survey
The survey with 1000 respondents from Germany was conducted in
early 2020 using fully structured computer-assisted telephone in-
terviews (CATI). The aim was to investigate attitude, willingness to use
quantitatively, and the most frequently discussed concerns and expec-
tations about air taxis based on factors derived from the focus group
discussions. The survey sample was representative of the German pop-
ulation older than 18 years.
2
As an introduction to the survey, respondents were informed that it
1
The video is available under: https://www.youtube.com/watch?v=44bSw
-wPW4c.
2
The data set is publicly available under: https://data.gesis.org/sharing/#!
Detail/doi.org/10.7802/2155.
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
4
focused on the future of urban transport, in which drone technologies for
passenger transportation might play a role. Respondents were then
asked to agree or disagree with various statements on a five-point Likert
scale for each question. In addition, a “do not know/no answer”
response option was offered as a choice. To minimize a response bias due
to a fixed order of questions, the corresponding batteries of questions
were randomized (Chaudhuri and Mukerjee, 2020). Moreover, to
minimize Acquiescence Response Bias (Bogner and Landrock, 2016), the
questionnaire followed a query of factors by an alternation of positively
and negatively formulated items.
2.3.3. Sample
After removing entries with missing values from the original dataset,
the sample size used for the statistical analysis in this paper consisted of
819 respondents. Table 1 indicates the characteristics of the sample,
which, despite its reduced size, still corresponded strongly to the socio-
demographics of Germany. However, with 54.3 % in the sample, men
are slightly overrepresented in the model sample (Statistisches Bunde-
samt, 2020). About 29 % of respondents were aged 18–39. The age
group, 40–59, was represented by about 40 %. Around 31 % of re-
spondents were 60 years or older. Most respondents reported a monthly
net household income of 4,500 euros or more (about 27 %). Over two-
thirds of respondents stated to be employed. Around 21.1 % of re-
spondents live in major cities of 500,000 or more inhabitants. About 50
% of the respondents originated from smaller towns with a population
between 5.000 and 100,000 inhabitants.
2.4. Model and hypotheses
The proposed attitudinal model consists of 16 observed variables
that are entirely based on the qualitative results of the five focus group
discussions (Kellermann and Fischer, 2020). Based on these results and
informed by key concepts from technology acceptance theories, the four
latent constructs of expected benefits, expected risks, technophilia, and
attitude were defined. Fig. 2 shows the structural model.
2.4.1. Public attitude
Within the classical technology acceptance models of TAM (Davis,
1989) and Theory of Planned Behavior (TPB, Ajzen, 1991), a person’s
attitude toward an object determines, among other factors, an in-
dividual’s behavioral intention to use it. Referencing drone related
acceptance research, studies have already utilized the concept of atti-
tude as a dependent variable to draw on the usage of drones and related
services (Clothier et al., 2015; Chamata and Winterton, 2018; Han et al.,
2019; Yoo et al., 2018). As this study tries to shed light on the perception
of citizens toward air taxis services, attitude, and not the usage intention
represents the target variable. Hence, hypotheses in this research test the
formation of attitudes toward air taxis.
In alliance to Lee (2009), attitude can be understood as a person’s
positive or negative thoughts concerning the performance of a behavior
(Lee, 2009). From the focus group discussions, four dimensions of pos-
itive and negative concerns regarding air taxis emerged: their implica-
tions on the quality of life, the safety of related services, their utility, and
their environmental impact. For example participants worried that it
would increase the stress level if air taxis “buzz around all the time”, that
they “can be hacked”, that it would make sense “to get from one city to the
other, when they are not connected through public transportation” or that
they would only be willing to accept the use of the technology if it was
“environmentally sound in any way” (Kellermann und Fischer 2020).
As these four dimensions mainly reflect broader public interest
concerns, the concept of public attitude was created. Legitimizing the
selection of these qualitatively derived attitudinal dimensions, they also
align to basic constructs of behavioral and acceptance research that have
been conceptualized across various other domains. While e.g., the
construct of environmental attitudes has been prominently used as a
predictor to explain behaviors in the context of individual travel pat-
terns (Susilo et al. 2012; Murray et al. 2010), the construct of safety
attitudes has been used to explain and predict safety-related issues in
road traffic (Ram & Chand 2016) or public health sector (Lee et al.
2010).
Accordingly, in order to receive quantitative data, the four qualita-
tively derived attitudinal dimensions were measured in the survey by
asking respondents how much they would agree to the statements of air
taxis (1) bringing advantages in the respondents’ everyday life, (2) being
safe, (3) having a positive effect on the quality of life in cities, and (4)
being more environmentally friendly than a regular taxi.
2.4.2. Expected risks
In the body of acceptance theory, the concept of risk was introduced
as a variable to influence attitude formation or decision making when
the consequences of an action or the circumstances surrounding it cause
uncertainty or anxiety (Bauer, 1960). In drone-related research, the
Fig. 1. Sequential mixed-methods approach.
Table 1
Socio-demographic distributions of the model sample.
Count %
Gender male 445 54.3 %
female 373 45.5 %
divers 1 0.1 %
Age 18–29 years 84 10.3 %
30–39 years 152 18.6 %
40–49 years 157 19.2 %
50–59 years 173 21.1 %
60 +years 253 30.9 %
Household monthly income below 500 EUR 4 0.5 %
500 until below 1.000 EUR 21 2.6 %
1.000 until below 1.500 EUR 53 6.5 %
1.500 until below 2.000 EUR 58 7.1 %
2.000 until below 2.500 EUR 87 10.6 %
2.500 until below 3.000 EUR 74 9.0 %
3.000 until below 3.500 EUR 72 8.8 %
3.500 until below 4.000 EUR 76 9.3 %
4.000 until below 4.500 EUR 80 9.8 %
4.500 and more 222 27.1 %
No indication 72 8.8 %
Employment employed 519 63.4 %
unemployed 300 36.6 %
City Size below 5.000 EW 119 14.5 %
between 5.000–20.000 177 21.6 %
between 20.000–100.000 227 27.7 %
between 100.000–500.000 123 15 %
more than 500.000 173 21.1 %
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
5
variable of (perceived) risk has already been conceptualized (Clothier
et al., 2015) and served as a relevant factor on attitude research on in
empirical models (Han et al., 2019). In this research, expected risks are
understood as concerns in respect to a future introduction of air taxis in
urban areas.
During the focus groups, risks related to a growing number of air
taxis in the sky were discussed mainly from the perspective of being a
passively exposed resident rather than being a passenger. Concerns of
noise emissions (“We already have such a massive noise pollution in the city,
mostly through traffic and then that on top. That would be really loud for
sure”) of cognitive stress from the movement of many vehicles and a
blocked view to the sky have been stated most persistently as expected
negative consequences (Kellermann and Fischer, 2020). Interesting to
note is that the participants associated automation as a relevant risk, e.
g., for taxi drivers to lose their job. In contrast to studies on user
acceptance, the way in which air taxis are controlled was not treated as a
relevant criterion for safety in the focus groups (e.g., Winter et al., 2020;
Al Haddad et al., 2020). Accordingly, the following hypothesis is
proposed:
•H1: There is a significant association between a subject’s expectation
of risks and its attitude to air taxis.
The latent variable of expected risks was measured in the survey by
asking how bad for the respondent were (1) the noise from passenger
drones, (2) that automation would bring job losses to taxi drivers, (3) the
stress caused by air taxis flying around to passively exposed persons, (4)
that passenger drones would block the free view toward the sky.
2.4.2. Expected benefits
As a fundamental assumption of the TAM, the perceived usefulness of
a new technology has a direct correlation to respondents’ attitudes to-
ward it. In its original understanding, the perceived usefulness concerns
actual users and the benefits that information systems might bring them
personally (Davis, 1989). In this research, however, expected benefits
describe attributes of passenger UAM, that are deliberately recognized
by the public to address common issues and enhance welfare, never-
theless individuals might or might not intend to personally use such
services. This is in line with attitude research, e.g. on mobility policy
preferences, suggesting that general support for investments traffic-
reducing measures is strongly predicted by environmental and air
pollution perception variables (Schmitz et al. 2019) Thus, a correlation
between expected benefits and the formation of the public attitude to-
wards air taxis is suggested. In drone-related attitude research this
conceptual approach is novel as prior research aimed to explain the
adoption of the technology from a user or consumer perspective only
(Clarke, 2014; Chamata and Winterton, 2018; Al Haddad et al., 2020).
In the focus groups, the expected benefits of air taxis were seen by
some participants as alleviating common transport problems and, for
example, relieving road traffic for all. Other participants were sceptical,
pointing to the large number of vehicles in the air that would be needed
to achieve such an effect (“Maybe we would no longer have congestion on
the ground but then it would really be crowded in the air. I am not sure that
would improve the situation”). The question of whether electric automated
air taxis would become a more sustainable and possibly even cheaper
alternative to existing modes of transport was also discussed contro-
versially but partly seen as possible. More agreement was found on
potential individual benefits of air taxis. The idea of escaping congested
Fig. 2. Structural Model and Hypotheses.
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
6
streets and saving travel time seemed highly attractive to many (“That
really is an enormous time saving, especially at rush hour through the inner-
city here, you need half an hour or 45 min from one place to the other and
with [air taxi] it would be over within 5 min”). Finally, the participants felt
that it would be a great advantage if passengers in the future could
determine the drop-off area of an air taxi (Kellermann and Fischer,
2020). Accordingly, we propose the following hypothesis:
•H2: There is a significant association between a subject’s expectation
of benefits and its attitude to air taxis.
As predictors for the latent variable of expected benefits in the sur-
vey, it was asked how important it would be for the respondent that
passenger drones would (1) take one exactly to a place of one’s choice,
(2) be environmentally friendly, (3) be inexpensive, (4) that one would
not have to stand in a traffic jam with the air taxi and that (5) one would
save time with an air taxi.
2.4.3. Technophilia
The Diffusion of Innovation Theory (DOI) proposed by Rogers (1962)
provides a theoretical explanation for explaining why people do or do
not embrace new technologies. DOI suggests an innovation to slowly
move through different social groups, which also represent differing
levels of personal innovativeness. In drone-related research the open-
ness toward air taxis has already been shown to impact the willingness
to use an autonomously (Winter et al., 2020) or automated air taxi (Al
Haddad et al., 2020). In this research, the perceived individual affinity
toward new technologies, as we define technophilia, is introduced as a
predictor for the public attitude of air taxis.
This decision draws foremost from the focus groups. As limited ex-
periences and a rather high contingency on the expected effects of air
taxis prevailed among participants, their general disposition towards
novel technologies was emphasised for the deployment of air taxis in
urban space. As the content analysis shows, this was often independently
of individuals intending to personally use such services in future nor not.
On the one hand, participants expressed an intrinsic interest in new
technologies and argued positively for passenger drones (“I mean I just
really love it and it would be fine if it came true”). In stark contrast to this
was the scepticism (“Somehow this is all very strange to me. I’m already
afraid of this little helicopter and the cat is too”) or even hostility by other
participants to this and other technology (Kellermann and Fischer,
2020). Accordingly, we propose a third hypothesis:
•H3: There is a significant association between a subject’s techno-
philia and its attitude to air taxis.
The survey design drew on established questionnaires in the field,
which already measured technophilia as a latent variable (acatech and
K¨
orber-Stiftung, 2018). Accordingly, to measure technophilia, (1) the
respondents’ subjective level of information about technologies, (2)
their ability to get easily enthusiastic for new technology, and (3) their
general interest in technology were surveyed.
3. Data analysis and results
The sample size of the survey was 819. All the values of Cronbach’s
alpha were above the threshold (0.7), which confirmed the high reli-
ability of the measurement model. The result of the Cronbach test is
indicated in Table 2. The multicollinearity assumption by using the
value of the variance inflation factor (VIF). All the constructs were
considered as predictors of one of the constructs and calculated the VIF
scores. The VIF scores are less than 2.00, which are less than the rec-
ommended value of 10, indicating there is no high risk of multi-
collinearity (Hair et al., 1998). Structural equation modelling (SEM) was
utilized to estimate the hypothesized relationships. The analyses pro-
vided acceptable fit indices for the structural model.
3.1. Fitness of model
The comparative fit index (CFI), the Tucker–Lewis index (TLI), and
the root mean square error of approximation (RMSEA) are used to check
the fitness of the model.
The comparative fit index (CFI) indicates the model fit by checking
the discrepancy between the data and the hypothesized model. The CFI
value is in the range from 0 to 1, and if its value is close to 1, it indicates
better fit. The calculated CFI in this model is 0.984, which is larger than
0.9, indicating an acceptable model fit. Tucker–Lewis index (TLI) is an
incremental fit index, that TLI >0.90 indicates an acceptable fit (Bentler
and Bonett, 1980). In this model TLI is 0.980.
The root mean square error of approximation (RMSEA) is one of the
most widely used measures of misfit/fit of structural equation model-
ling. RMSEA indicates how well the model, with unknown but optimally
chosen parameter would fit the populations covariance matrix (Byrne,
2013). It is ‘one of the most informative fit indices’ (Diamantopoulos
and Siguaw, n.d.) due to its sensitivity to the number of estimated pa-
rameters in the model. The values of 0.01, 0.05 and 0.08 indicate
excellent, good and mediocre fit respectively. In this model, RMSEA is
around 0.035 which indicates a good fit.
Table 2
Results of Cronbach Test.
Latent
Variable
Observed Variable Variable names Cronbach
alpha
Technophilia How much would you agree to the following statement:
In general, I am well
informed about new
technologies.
Well informed 0.798
I get easily enthusiastic
about new technologies.
Enthusiastic
I am always interested in
new technologies.
Interested
Expected Risks How bad would be for you…
…the noise from air taxis. Noise 0.821
…the stress caused by air
taxis.
Stress
…job losses of taxi drivers
because of air taxis.
Job loss
…blocked free view of the
sky by air taxis.
Blocked sky view
Expected
Benefits
How important would be for you…
…that air taxis generate
time savings.
Time saving 0.846
…that air taxis avoid traffic
jams.
Avoiding traffic
jams
…that air taxis are
environmentally friendly.
Environmentally
friendly
…that air taxis take you
exactly to a place of your
choice.
Determinability
…that air taxis create price
advantages.
Price Advantage
Attitude How much would you agree to the following statement:
…that passenger transport
with air taxis is more
environmentally friendly
than a regular taxi.
Environment 0.851
…that air taxis are safe. Safety
…that passenger transport
with air taxis would have a
positive effect on the quality
of life in cities.
Quality of life
…that passenger transport
with air taxis would bring
me advantages in my
everyday life.
Utility
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
7
3.2. Factor loadings
The measurement model indicates how latent variables are measured
by observed variables. The Results are the measurement model are
shown in Table 3 and also in Fig. 3.
The latent variable of “expected risk” in this model is measured by
the five observable variables implying significantly different load fac-
tors. While the concern of stress through movement in the sky and noise
from air taxis have the highest load factor by (0.86) and (0.81),
respectively, Job loss is still significant but has the lowest factor load by
0.49.
The latent variable “expected benefits” was defined by 5 observed
variables. While the observed variable avoiding traffic jams has the
highest load factor by 0.85, environmentally friendly has the lowest load
factor by 0.5. The variables of time saving, and determinability have close
load factors by 0.79 and 0.77, respectively.
The measurement model of technophilia includes three observed
variables. The variable of easily feeling enthusiastic has the highest load
factor by 0.86 and the variable of usually well informed about new
technologies has the lowest load factor by 0.61.
The standardized estimates between the latent variable public atti-
tude and its measurement show that the observed variable of quality of
life, meaning that Passenger transport with air taxis would have a pos-
itive effect on the quality of life in cities, has the highest load factors by
0.85. Environment, meaning that transporting people by air taxi is less
environmentally friendly than driving a normal taxi, has the lowest
standardized estimates (0.49).
3.3. Structural model & hypothesis testing
Table 4 indicates the results of the associations between “Attitude”
and the other three latent variables. The model suggests significant as-
sociations of attitude with expected risks (H1) by (β = − 0.52, p <0.001)
and expected benefits (H2) by (β =0.38, p <0.001). Moreover, the
standardized estimates of technophilia is 0.11 with the p-value <0.001,
indicating a significant association with attitude. Therefore, all three
latent variables have significant associations with attitude (see Fig. 3).
4. Discussion
The relevance of all 16 observed variables that were derived from
qualitative research and applied in the SEM model via the four latent
variables (expected risks, expected benefits, technophilia, and attitude)
is confirmed by significant factor loadings. An overall acceptable fitness
of the structural equation model is given as per the relevant indices CFI
(0.984), TLI (0.980), and root mean square error of approximation
RMSEA (0.035). Furthermore, the presented structural model supports a
significant association between expected risks (-0,52), expected benefits
(0.38) from air taxis as well as of respondents’ level of technophilia
(0.11) on the attitude in the dataset (n =819).
The public attitude toward air taxis was considered as the latent
dependent variable in this model. It is reliably represented by a
composition of the following four observed variables that were extracted
from the focus group discussions and reflect central attitudinal di-
mensions of broader public interest: i) concerns about safety, ii) impacts
on the quality of life in cities, iii) the environmental dimension, and iv)
general utility of air taxis. In the measurement model of “attitude”, the
estimator “quality life” has the highest load factor of 0.846, which means
this factor extracts a high variance of the variable “attitude”.
Regarding the research hypotheses, the model confirms a significant
negative association of attitude with expected risks (H1). This finding is
consistent with previous technology acceptance studies from other do-
mains (Lee, 2009; Im et al., 2008; Vijayasarathy, 2004). Regarding the
varying influence of risk factors, our findings demonstrate that partic-
ularly people with a more negative attitude toward air taxis who expect
them to be a source for cognitive stress (0.863) and noise (0.814). While
stress generated by movements in the sky has not yet been considered as
a factor in previous studies, the central relevance of noise for the
acceptance of drones in urban spaces has been anticipated persistently
(Kellermann et al., 2020, Çetin et al., 2022). As the relevance of noise
concerns has already been confirmed for the case of delivery drones
(Kellermann et al., 2023), the present work now confirms this assump-
tion for the case of air taxis. Furthermore, the SEM revealed a negative
implication on the attitude of individuals expecting the extensive
implementation of air taxi services to create an impression of a blocked
sky (0.786), which has been suggested for drones, using psychological
experiments conducted by K¨
ahler et al. (2022). The study also identifies
the risk of job losses of taxi drivers as a controversial phenomenon of
automated air taxis with the lowest load factor of (0.49) among other
observed variables. This hints at the relevance of social implications
representing a previously untried dimension of UAM-related acceptance
studies, which could inform future research on innovative trans-
portation technologies.
Furthermore, the presented model reveals a significant positive as-
sociation of attitude with expected benefits (H2). This result aligns with
findings from technology acceptance research in various domains,
which have illustrated the relevance of beneficial factors in positively
affecting attitudes toward technology adoption (Davis, 1989, Lee, 2009,
Vijayasarathy, 2004). In relation to attitudes toward the specific case of
air taxis, our study provides evidence that particularly people form a
more positive attitude toward air taxis who expect them to be a transport
mode for avoiding traffic congestion (0.846), and to be a time saver
(0.793). This finding is consistent with a similar study by Al Haddad
et al. (2020) that showed reduced travel time to affect the willingness to
use air taxi services positively. Moreover, our results suggest that people
form more positive attitudes if they associate air taxis with the deter-
minability of mobility (0.774), and price advantages (0.691). However, the
observed variable of “environmentally friendly” has the lowest load factor
in this measurement model (0.497), indicating that this factor has a low
contribution on the measurement of the variable “expected benefits”.
The latter finding correspond to a study on attitudes toward delivery
drones, which also found the relative advantage of a more environ-
mentally friendly delivery to be a comparably weak but yet significant
predictor of creating a positive attitude or an intention to use delivery
drones (Kellermann et al., 2023). Moreover, Yoo et al. (2018) and
Mathew et al. (2021) also found the relative advantage of faster delivery
to be a significant predictor.
The standardized estimate of expected risks is −0.52 and for ex-
pected benefits is 0.38. The Wald test is applied to check the significant
difference between these two coefficients in the model. The results show
the significant difference between two estimates at 95 % confidence
level (Wald statistics 17.2, P-value <0.05). Therefore, the expected risks
of air taxis have a stronger association than the expected benefits (It
Table 3
Results are the measurement model.
The measurement model Estimate
Determinability <— Perceived_benefits 0.774
Avoiding traffic jams <— Perceived_benefits 0.846
Environmentally friendly <— Perceived_benefits 0.497
Time saving <— Perceived_benefits 0.793
Price advantage <— Perceived_benefits 0.691
Noise <— Perceived_Risk 0.814
Stress <— Perceived_Risk 0.863
Job loss <— Perceived_Risk 0.486
Blocked sky view <— Perceived_Risk 0.786
Enthusiastic <— Technophilia 0.845
Interested <— Technophilia 0.809
Well informed <— Technophilia 0.613
Environment <— Attitude 0.698
Quality of life <— Attitude 0.846
Safety <— Attitude 0.754
Utility <— Attitude 0.774
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
8
means that by one unit increase in expected risks, the value of attitude
decreases by 0.52 unit, while by one unit increase in expected benefit,
the attitude value increases by 0.38 unit. The relationship might, to
some extent, be explained by the lack of familiarity and experience with
air taxis. On the one hand, risks in the specific case of flying in urban
airspace may be perceived as more intuitive and tangible (e.g., stress,
noise). On the other hand, the potential benefits of air taxis might seem
unrealistic (e.g., affordability, environmental friendliness) or yet far
from reality (e.g., everyday commuting with an eVTOL and determin-
able drop-off locations). In this respect, behavioral and technology
adoption studies have postulated the relevance of factors such as fa-
miliarity and experience in forming attitudes (Davis, 1989; Karahanna
et al., 1999; Rogers, 2003), which is related to air taxi services that so far
have not been implemented.
Finally, the model suggests a significant positive association between
a person’s technophilia and attitude (H3) toward air taxis. Individuals
that consider themselves as being well informed (0.613), generally inter-
ested (0.809), or easily feeling enthusiastic about learning and trying new
technologies (0.845) have significantly more positive attitudes toward
air taxis. This finding can be interpreted as consistent with findings in
related fields. For example, the significance of technophilia was
confirmed by studies examining the user adoption of delivery drone
services, using the latent variables “personal innovativeness” (Yoo et al.,
2018) and “Cognitively Motivated Consumer Innovativeness” (Mathew
et al., 2021). The personal openness to new technologies and the risks
related to them were also found to affect the customer willingness to
book an autonomous (Winter et al., 2020) or automated (Al Haddad
et al., 2020) air taxi. However, Kellermann et al. (2023) did not capture
a significant positive association of technophilia and attitude in the case
of using delivery drones for commercial purposes. This discrepancy
might be explained by generally imagining it more thrilling to be
transported personally by an automated drone compared to receiving a
drone-delivered package.
Besides being confirmatory in nature, the presented results may
inform economic and political decision-makers toward the imple-
mentation of air taxis. First, concrete factors influencing public attitudes
were presented. Regarding negative impacts, a detailed exploration
should be made of how the perception of stress for people on the ground
is created by aircraft movements in the sky and how this perception can
be moderated, especially through prudent urban mobility planning and
stakeholder participation (Biehle 2023). Regarding the noise factor, a
similar research focus is already established (Vascik et al., 2018; Bauer,
2021), e.g., on technological means of noise mitigation or legal noise
emission limits. Regarding positive factors, fields of application for air
taxis should be identified in which traffic relief and time savings can be
credibly achieved. As studies show, both claims are not unconditional to
fulfil and strongly depend on the operational environment (Pukhova,
2021; BMVI, 2019; Kellermann et al., 2020).
Fig. 3. Structural Equation Model.
Table 4
Results of the structural model.
Hypothesis Standardized
Estimates
P Results
H1: Attitude <— Expected_Risk −0.52 <0.001 Supported
H2: Attitude <— Expected
benefits
0.38 <0.001 Supported
H3: Attitude <— Technophilia 0.11 <0.001 Supported
H. Mostofi et al.
Transportation Research Interdisciplinary Perspectives 24 (2024) 101045
9
According to the logic of the model, it may be more advantageous in
terms of public attitude to effectively mitigate expected risks than
aiming to maximise the expected benefits identified in this paper.
Moreover, various studies indicate that the greatest public support for
small electric aircraft is, at least currently, not to be expected in their
deployment as commercial air taxis but in medical or humanitarian
applications (Nentwich and Horv´
ath, 2018; Sky Limits, 2021). This
suggests that the search for business models with clearer social added
values should be intensified (Straubinger et al., 2021). However, a sig-
nificant positive correlation between people with technophilic ten-
dencies and their attitude toward air taxis was confirmed in this
research. Therefore, it can be affirmed that UAM related mobility as a
service approach may count on innovators and early adopters as early
target group.
This study faces several limitations. First, focus group participants
were questioned about a technology that is not yet in service. People’s
evaluation criteria of air taxis may change after adoption because new
and previously unconsidered impacts may become apparent once the
technology is introduced. For example, most focus group participants
assumed that air taxis would not crash. If, however, critical incidents
were to occur in the future, safety concerns would likely affect public
attitudes stronger than they do today. Nevertheless, the qualitatively
derived attitudinal factors represent relevant factors at this early phase
of technology maturation and thus enrich the scientific and political
discourse, as their importance for forming public attitudes was
confirmed in the presented model.
Secondly, the survey participants were asked to assess possible
technology impacts of air taxis on society and urban space. Citizen’s
perceived risks and benefits of the technology depend on concrete
business models, i.e., the intensity of aircraft movements, the actual
noise impact of eVTOLs, and environmental impacts. Nevertheless, the
present study provides a clear understanding of which acceptance fac-
tors are particularly advantageous or disadvantageous features of air
taxis.
Thirdly, the data-gathering was conducted before the outbreak of the
COVID-19 pandemic. While several studies suggested the pandemic
slightly improve public attitudes toward automated drone-delivery
services (Thomas et al., 2021; Elavarasan & Pugazhendhi, 2020), the
effect of the virus on the perception of air taxis as a form of shared
mobility, in which passengers find themselves traveling in closed com-
partments, cannot be reflected within this study. Moreover, all research
data was gathered in Germany. A transferability of the results to other
world regions cannot be made without reservation. However, the im-
pacts of new mobility technologies on citizens’ perception and mobility
behaviors in different cities depend on the urban form, socioeconomic,
and cultural parameters (Mostofi, 2022), and sometimes they are con-
tradictory and opposite in different cities (Mostofi, 2021; Mostofi et al.,
2020a; Mostofi et al., 2020b). Moreover, a cross-national study on the
acceptance of various drone applications in Europe shows no severe
divergences between the examined member countries (EASA, 2021).
Therefore, validity can be assumed for the European context.
5. Conclusions
The implementation of urban air mobility (UAM) services, e.g., air
taxis, can be considered a disruptive development for the transport field.
However, apart from a potential opening of new transit and market
opportunities, air taxis’ disruptive character may become controversial
as they will operate in public spaces and represent a transport technol-
ogy of comparably high perceptibility.
Against this background, this study defines an understanding of
public attitudes toward air taxis. The structural equation model results
provide evidence of how this attitude is formed. Expected risks
(particularly cognitive stress from air traffic, noise emissions, and the
expectation of blocked skies) and expected benefits (particularly
avoiding traffic jams, saving time, and a location-flexible hop-on drop-
off) affect attitudes toward air taxis. The expectation of risks has
greater load factors in forming attitudes than the expectations of bene-
fits. In other words, the public attitude toward air taxis is stronger
associated with expected risks rather than expected benefits.
The results of this study may be of practical utility. Above all, the
currently expected risks from air taxis should be anticipated to be pre-
emptively minimized. From a traffic-psychological perspective, it
should be investigated how stress from traffic is formed and whether
urban air traffic can also reinforce similar dynamics. On the other hand,
aesthetic concerns should be considered regarding air taxi in-
frastructures, their routing, and route frequency.
What is more, a person’s technophile disposition positively affects
the formation of attitude. Like other consumer markets, urban air
transportation providers may count on the innovativeness of certain
consumer groups with high technophilia, e.g., innovators or early
adopters. However, based on our findings, we suggest political admin-
istrations harvest the benefits of innovative aviation technologies while
creating a regulatory framework that ensures the environmentally sus-
tainable, need-oriented, and aesthetic development of UAM, thus
responding to the central dimensions of public concerns.
Finally, the mixed methods approach of this study explored attitu-
dinal factors beyond the classical taxonomy of behavioral and accep-
tance research (e.g., social, environmental, and aesthetic concerns).
Consequently, this study advocates for future research to strongly
consider methodological approaches, including qualitative research, to
gain a more comprehensive understanding of potential users and the
passively exposed public.
CRediT authorship contribution statement
Hamid Mostofi: Conceptualization, Data curation, Formal analysis,
Methodology, Visualization, Validation, Writing – review & editing.
Tobias Biehle: Conceptualization, Formal analysis, Methodology,
Visualization, Writing – review & editing. Robin Kellermann: Project
administration, Writing – review & editing. Hans-Liudger Dienel:
Writing – review & editing.
Declaration of competing interest
The authors declare that they have no known competing financial
interests or personal relationships that could have appeared to influence
the work reported in this paper.
Data availability
The primary survey dataset (telephone survey) is publicly available
under: https://data.gesis.org/sharing/#!Detail/10.7802/2155.
Acknowledgement
We acknowledge support by the German Research Foundation and
the Open Access Publication Fund of TU Berlin.
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