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Modeling User Acceptance of In-Vehicle Applications for Safer Road Environment

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Driver acceptance studies are vital from the manufacturer’s perspective as well as the driver’s perspective. Most empirical investigations are limited to populations in the United States and Europe. Asian communities, particularly in Southeast Asia, which make for a large proportion of global car users, are underrepresented. To better understand the user acceptance toward in-vehicle applications, additional factors need to be included in order to complement the existing constructs in the Technology Acceptance Model (TAM). Hypotheses were developed and survey items were designed to validate the constructs in the research model. A total of 308 responses were received among Malaysians via convenience sampling and analyzed using linear and non-linear regression analyses. Apart from that, a mediating effect analysis was also performed to assess the indirect effect a variable has on another associated variable. We extended the TAM by including personal characteristics, system characteristics, social influence and trust, which could influence users’ intention to use the in-vehicle applications. We found that users from Malaysia are more likely to accept in-vehicle applications when they have the information about the system and believe that the applications are reliable and give an advantage in their driving experience. Without addressing the user acceptance, the adoption of the applications may progress more slowly, with the additional unfortunate result that potentially avoidable crashes will continue to occur.
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Citation: Razak, S.F.A.; Yogarayan, S.;
Abdullah, M.F.A.; Azman, A.
Modeling User Acceptance of
In-Vehicle Applications for Safer
Road Environment. Future Internet
2022,14, 148. https://doi.org/
10.3390/fi14050148
Academic Editor: Marco Fiore
Received: 30 March 2022
Accepted: 28 April 2022
Published: 11 May 2022
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future internet
Article
Modeling User Acceptance of In-Vehicle Applications for Safer
Road Environment
Siti Fatimah Abdul Razak 1, *, Sumendra Yogarayan 1, Mohd Fikri Azli Abdullah 1and Afizan Azman 2
1Faculty of Information Science and Technology, Multimedia University, Melaka 75450, Malaysia;
mastersumen@gmail.com (S.Y.); mfikriazli.abdullah@mmu.edu.my (M.F.A.A.)
2Faculty of Digital Technology and Media, Universiti Melaka, Melaka 78200, Malaysia; afizan@kuim.edu.my
*Correspondence: fatimah.razak@mmu.edu.my
Abstract:
Driver acceptance studies are vital from the manufacturer’s perspective as well as the
driver’s perspective. Most empirical investigations are limited to populations in the United States
and Europe. Asian communities, particularly in Southeast Asia, which make for a large proportion
of global car users, are underrepresented. To better understand the user acceptance toward in-
vehicle applications, additional factors need to be included in order to complement the existing
constructs in the Technology Acceptance Model (TAM). Hypotheses were developed and survey
items were designed to validate the constructs in the research model. A total of 308 responses were
received among Malaysians via convenience sampling and analyzed using linear and non-linear
regression analyses. Apart from that, a mediating effect analysis was also performed to assess the
indirect effect a variable has on another associated variable. We extended the TAM by including
personal characteristics, system characteristics, social influence and trust, which could influence users’
intention to use the in-vehicle applications. We found that users from Malaysia are more likely to
accept in-vehicle applications when they have the information about the system and believe that
the applications are reliable and give an advantage in their driving experience. Without addressing
the user acceptance, the adoption of the applications may progress more slowly, with the additional
unfortunate result that potentially avoidable crashes will continue to occur.
Keywords:
in-vehicle application; driver assistance; technology acceptance; regression analysis;
statistical evaluation; significance tests
1. Introduction
Car manufacturers, throughout recent years, have been focusing on delivering new
car models equipped with in-vehicle or pre-install applications [
1
] which support car
navigation, maneuver and stabilization. Typically, the applications involve either a camera,
sensors or a millimeter wave radar, or a combination of these, to support the driver and
ease the driver’s driving experience rather than annoy [
2
,
3
] which may cause the system
to be turned off or its disuse. Human limitations in assessing the current driving context
may be lifted via an extension of these in-vehicle applications (see Table 1). Generally, these
applications were designed for the global market. However, users’ acceptance toward
technology has been known to be influenced by cultural differentiation. Overlooking this
factor may cause the technology to receive poor attitudes and acceptance rates from the
users which eventually may lead to poor or nonactual use of the application [2].
Future Internet 2022,14, 148. https://doi.org/10.3390/fi14050148 https://www.mdpi.com/journal/futureinternet
Future Internet 2022,14, 148 2 of 21
Table 1. In-vehicle applications.
Features Description
Lane Departure Alert/
Warning (LDW)
Vibrates the steering wheel or emits a warning
sound when the car strays off its lane.
Lane-Keep Assist (LKAS)
Applies gentle steering correction when the car
is veering off its lane.
360-Degree-Parking Assist
(360 cam)
Provides a “bird’s eye” view of the car’s
surroundings.
Rear Cross Traffic Alert (RCTA)
Used when reversing out into the busy street to
alert the driver of the approaching vehicle’s
direction.
Forward Collision
Warning (FCW)
Gives a warning buzzer if a frontal collision is
imminent. No braking actions.
Autonomous Emergency
Braking (AEB)
Applies maximum braking pressure if driver
does not respond after warning. Range, speed
and detection ability vary.
Adaptive Cruise Controls (ACC)
Maintains a preset highway cruising speed.
Brakes and accelerates automatically to
maintain a preset safe distance. Some models
allow limited (less than 30 s) hands-free driving
Low-Speed Follow/ Traffic
Jam Assist (TJA)
Assists in stop–go driving.
Follows the vehicle ahead, automatically
braking/accelerating.
Driver maintains control of steering wheel.
Auto Parking (A-Park)
Automatic steering for parking.
Driver maintains control of gear selector (drive
or reverse), braking and accelerating.
Depending on the model, it may work on both
parallel and perpendicular parking.
Head-up Display (HUD) Projects core driving-related information to
driver’s view or windscreen.
Blind Spot Monitor (BSM) Lights up warning on the side mirrors when a
vehicle is in the blind spot.
Auto Hold/Brake Hold (A-Hold)
For use in traffic jam/red light.
Maintains brake pressure even when driver
takes the foot off the brake pedal.
Automatically releases when a driver
accelerates.
Hill-Start Assist (HSA)
Maintains brake pressure to prevent the vehicle
from rolling backward as the driver prepares to
drive uphill.
Hill Descent Control (HDC)
Typically used for 4 ×4 vehicles.
Maintains safe speed when driving downhill
on muddy terrain.
Pedal Misapplication
Control (PED)
Prevents accidental reversing/acceleration in
the wrong direction, i.e., driver wrongly
selected drive instead of reverse.
Auto High Beam (A-BEAM)
Forward-oriented lights that turn brighter and
dimmer automatically, depending on the other
vehicles and available light on the road.
Driver acceptance studies are vital from the manufacturer’s perspective as well as the
driver’s perspective. Generally, these vehicle manufacturers have included these systems
Future Internet 2022,14, 148 3 of 21
in their manufactured cars to promise an improved and safer driving experience for both
vehicle drivers and passengers. Understanding consumers’ acceptance is key for effective
implementation and the actual use of advanced driver-assistance systems. There is limited
research on understanding the acceptance process of in-vehicle applications from the
driver’s viewpoint [4].
Most empirical investigations are limited to populations in the United States, Europe,
Korea, China and Taiwan, with a different culture than Malaysia [
5
]. The user perspective
on new and unfamiliar technology will vary and will normally be driven by personal
characteristics and locality. A technology developed based on European culture may
not directly fit to users residing in Asian countries [
6
]. As a result, it is critical to assess
the responses of people from a certain background as part of the global market user
acceptance studies of the technology [
7
,
8
] to acquire the full potential of innovations that
cannot be optimally reached until they are well-received by society. Asian communities,
particularly in Southeast Asia, which make for a large proportion of global car users,
are underrepresented. Even though most modern vehicles nowadays are equipped with
in-vehicle applications (see Table 2), these applications are considered new among local
drivers. Furthermore, Malaysians generally prefer vehicle-resident features that help them
avoid accidents [
9
], such as collision warnings with auto-braking systems and blind-spot
information systems. These vehicle safety features could influence the vehicle buying
behavior of urban buyers in Malaysia [
10
]. However, not all drivers have a propensity
or desire to implement the technology. Hence, we intend to explore their acceptance and
intention to use the applications should they be made available.
Table 2. Available advanced driver-assistance systems based on vehicle models.
MODEL
LDW
LKAS
360 cam
RCTA
FCW
AEB
ACC
BSM
HUD
A-HOLD
HSA
HDC
PED
A-BEAM
TJA
A-PARK
Perodua MyVi - - - - / / v- - - - / - / - - -
Toyota Rush - - / / / / vp - / - - - - / - - -
Perodua Aruz - - - - / / vp - - - - / - / - / /
Hyundai Ioniq / / - / / / vp / / - / / - - - - /
Proton X70 / - / - / / v/ / - / / / - / / -
Honda CR-V / / - - / / vpc / ** /#- / / - - - - /
Mazda CX-5 / / / / * / / - / / / / - - /%- -
Nissan X-Trail / - / / - - - - - / / / - - / /
Toyota Hilux - / - - - / - - - / - - / -
Mitsubishi Triton / - - / / / - / - - / / / / - -
Ford Ranger / / - - / / vp / - - - / / - - / -
Honda Accord / / - - / / vpc //#- / / - - - - -
Mazda 3 / / / / * / / / / / / / - - /%- -
Mazda 6 / / / / * / / - / / / / - - /%- /
Toyota Camry / / - / / / / / / / / - - / - /
/ = available; - = not available; ** = with low speed;
#
= lane watch camera;
vpc
= vehicle, pedestrian, child above
1 m tall; vp = vehicle and pedestrian; v= vehicle only; * = with braking; % = adaptive.
Using the results of this study, along with other available information, the decision-
makers may decide how to proceed with additional activities involving in-vehicle technolo-
gies. To our best knowledge, we have not found any further study investigating drivers’
acceptance, specifically addressing Malaysian drivers. The output of this study may also
provide input to the implementation of the Malaysia Intelligent Transport System (ITS)
Blueprint, which aims to help drivers make informed decisions by outlining three Focus Ar-
eas, namely Automated Enforcement, Weigh-in-Motion and Emergency Management [
11
].
The remainder of this paper is organized as follows. Section 2describes road safety and
advanced driver systems applications focused on Malaysia. The works directly related to
this study are discussed in Section 3. Section 4discusses the methodology approach used
for this study. The findings and discussions for this study are provided in Section 5. Finally,
Section 6presents the conclusions.
Future Internet 2022,14, 148 4 of 21
2. Related Works
User perspectives, attitudes and their actual use of the technology are critical for
the continuous development of any new technology. Models and frameworks have been
established to explain new technology adoption and to include aspects that can influence the
user acceptance [
12
,
13
]. This includes the Motivation Model, Innovation Diffusion Theory
(IDT), Uses and Gratification Theory, Social Cognitive Theory, Theory of Reasoned Action
(TRA), Model of PC Utilization, Unified Theory of Acceptance and Use of Technology
(UTAUT) and hybrid models. A summary of the existing ones is illustrated in Figure 1.
Figure 1. Summary of existing technology adoption models.
2.1. Motivational Models
Motivational models consider intrinsic and extrinsic factors that motivate a user to
use a technology. For instance, if the user perceives that the technology will help them to
perform better, they will use the technology. This is classified as an extrinsic factor or also
referred to as perceived usefulness. Furthermore, if a technology is perceived as easy to
use or an entertaining technology, users may also adopt the technology; this is also known
as an intrinsic factor or the perceived ease of use (enjoyment) [
4
]. According to this model,
the perceived usefulness and perceived enjoyment directly influence a user’s intention to
use a technology. When a user perceives a technology as useful and easy to be used, they
will use the technology. Previous research has applied this model in various domains to
assess the adoption and usage of new technologies. However, these models may require
other factors to be customized to specific types of technology.
2.2. Innovation Diffusion Theory (IDT)
The IDT is a generic model proposed by Everett Rogers in 1992 to assess innovation
adoption factor rates [
14
] that focuses on system characteristics and organization based
on three core components, i.e., the adopter, innovation characteristics and decision pro-
cess. Moreover, four factors create technology awareness involving time, communication
channels, innovation or social influence. Technology is most adopted when a user sees the
technological relative advantages, its compatibility with current technology and that it is
less complex and allows trials and observation. The decision to use a technology may be
influenced by user classification, i.e., early adopters, the early or late majority, innovators
or laggards [
13
]. A study on the user acceptance of two types of in-vehicle applications
Future Internet 2022,14, 148 5 of 21
which are retro-fit and integrated assumed their respondents are early adopters of the
technology based on their high education level, high income and positive attitude toward
new technology [
15
]. However, because this model does not explain the impact of attitude
on the user acceptance or the adoption of the innovation, this model is less practical for
outcome-based predictions [
14
]. The authors of [
16
] integrated the IDT and TAM in their
investigation related to autonomous vehicle acceptance.
2.3. Uses and Gratification Theory (U&G)
This model focuses on the user’s motivation and satisfaction to use specific communi-
cation technologies which are influenced by social and psychological factors. Three main
components include satisfaction, behavioral usage and motivations. The behavioral usage
component refers to the amount, duration and type of usage. Nevertheless, this model is
not only limited to assessing communication media technology. The model may be utilized
when the media is used as a part of the work process [
13
]. No recent publications reported
on the use of the U&G model for addressing the user acceptance of in-vehicle applications.
This offers a new opportunity which can be explored by future researchers.
2.4. Social Cognitive Theory (SCT)
The SCT predicts information technology usage based on the bi-directional relationship
of three factors which reliably and commonly influence one another—behavior, personal
and environment. The behavioral factor includes technology usage, performance and
adoption issues, while the personal factor considers a user’s personal characteristics. Ad-
ditionally, environmental factors consider external factors such as facilities and social
influence. The constructs include self-efficacy, anxiety, outcome expectancy (performance
and personal) and preference toward the technology [
13
,
17
]. Similar to the U&G, we
were not able to find recent publications about the SCT applied for investigating the user
acceptance of in-vehicle applications.
2.5. Theory of Reasoned Action (TRA)
The TRA was applied to assess the impact of attitude on technology adoption by
Davis et a
l. in 1989. It was first proposed for social psychology and later used to predict the
user acceptance of e-shopping [
18
]. Besides attitude, the model’s core components are the
subjective norms/social influence and intention to use the technology. The model does not
consider other factors that may influence user attention [
19
] such as knowledge, experience
or likeability, habits or morale consciousness [
14
]. For instance, the TPB model was devel-
oped to explain human behavior in general, whereas the TRA model was first proposed for
social psychology and later used to predict the user acceptance of e-shopping [18].
Three well known models, i.e., the Theory of Interpersonal Behavior (TIB), Technology
Acceptance Model (TAM) and Theory of Planned Behavior (TPB), are derived from the
TRA. A less general model compared to the TRA and TPB is the TAM [
20
]. In relation to our
study, the TAM was applied in [
21
] to assess the user acceptance of the smartphone-based
driver assistance in Brasov, Romania. The model removed the original subjective norms
component from the TRA. The perceived ease of use and perceived usefulness are proposed
as factors influencing a user’s attitude toward the technology [
20
]. If a user perceives that
a technology is useful and has a positive attitude toward the technology, the user will
have an intention to use the technology which will lead to the actual use of the technology.
However, the model does not include an information recency and completeness component
which may influence a user’s perceived usefulness and the perceived ease of use. On the
other hand, the TPB model was developed to explain human behavior in general [
18
] with
the assumption that all actions or behavior are planned. Hence, other factors do not have
an impact on user intention and the actual use of a technology [
14
]. The TPB extends the
TRA by adding attitude, subjective norms and a perceived behavioral control. In [
20
], the
authors performed a simulation study to assess the impact of an audio in-vehicle warning
for railway level crossings using the TPB.
Future Internet 2022,14, 148 6 of 21
In addition, the TIB model incorporates the TRA and TPB. The model adds habits,
facilitating conditions and affect or emotion to predict the user’s actual use of a technology.
The TIB assumed that personal characteristics are shaped by personal beliefs, attitudes,
social factors and past experiences. The complexity of the TIB allows only limited previous
work compared to the TRA and TAM [
13
]. The authors of [
22
] proposed a model of
commuter travel behavior performance based on the TIB. The model includes attitude,
social factor, affect and habit as the main factors.
2.6. Model of PC Utilization (MPCU)
The MPCU was proposed to predict the actual use of a personal computer (PC). The
most influential factors on the actual use of a PC are the user perceptions of the technology
complexity and consequences, social influence and job-fit (i.e., if the technology can improve
work performance or not). Two other factors, which are facilitating conditions and user
affective (i.e., how a user feels about the technology), do not have a strong influence on the
actual use of the PC [
17
]. The model is effective for assessing the voluntary user adoption
of a technology. Even so, user adoption may be influenced by how the user perceives the
short-term consequences of using the technology [14].
2.7. Unified Theory of Acceptance and Use of Technology (UTAUT)
The UTAUT model was created primarily to explain technology acceptance. The model
integrates 32 constructs in accordance with eight well-known adoption theories, including
the Theory of Reasoned Action (TRA), the Theory of Planned Behavior (TPB), the Technol-
ogy Acceptance Model (TAM), the combined form of the TAM and TPB (
C-TAM-TPB
), the
Model of PC Utilization (MPCU), the Innovation Diffusion Theory (IDT), the Motivational
Model (MM) and the Social Cognitive Theory (SCT) [
14
]. The model highlighted that the
actual behavior is highly influenced by user intentions. If a user has a positive intention
toward a technology, the user will eventually use or adopt the technology [
4
,
23
,
24
]. The
core constructs are performance expectancy, effort expectancy, social influence, facilitating
conditions and motivational controls. Users are assumed to have control of their behavior
and decisions over the use of technologies [
20
]. The research on in-vehicle applications that
applied the UTAUT includes [20,25,26].
Moreover, in-vehicle applications can be grouped based on the three driving automa-
tion standards described by the Society of Automotive Engineers (SAE), the NHTSA in the
U.S. and the BASt in Germany [27], as in Table 3. The three main challenges for in-vehicle
applications are the user acceptance, safety and security and the development time and
cost. In this study, we focused on in-vehicle applications that support driver’s driving
experience, i.e., level 0 to level 2 to address the user acceptance.
Recognizing drivers’ expectations and acceptability of in-vehicle applications would
thus help to pave the path for future development, allowing elements that influence
drivers’ acceptance or rejection of the technology to be addressed. A driver’s decision
or performance on the road may be influenced by the use of in-vehicle applications [
21
].
A quantitative study was conducted in Czech Republic to investigate the influence of
subjective factors, including the feeling of increased safety, ease of use, increased comfort,
trust in the system, previous experience with the system and other aspects such as price,
good references, system reliability and functions of the driver-assistance systems [
28
]. A
similar study among Finnish people is presented by [
29
]. Both studies did not provide any
theoretical models for the research design.
We believe that applying a theoretical model is necessary to limit the scope of our
study. Even though existing models and frameworks may be considered to explain the
drivers’ adoption and introduce factors that may affect the drivers’ acceptance, these
available models and frameworks were not specifically developed to address in-vehicle
applications. Previous research by [
20
] compared the TAM, TPB and UTAUT to assess
the validity of these theories in modeling the user acceptance of in-vehicle applications,
i.e., a fatigue monitoring system or an adaptive cruise control system combined with a
Future Internet 2022,14, 148 7 of 21
lane-keeping system among users in Boston, MA. The authors concluded that, among the
three models, the original TAM performance is the best. Moreover, a few studies apply the
TAM to investigate user technology acceptance factors [
30
], demonstrating that this model
can be used to investigate in-vehicle technology adoption. Because the applications are
pre-installed in manufactured vehicles, assessing whether drivers accept the applications or
not is a relatively new research area [
1
,
31
]. In addition, these studies use a few extra study-
specific elements (e.g., trust) to better understand the adoption process [
30
] and investigate
public opinion and the willingness to use in-vehicle applications. There are also studies
which are limited to a specific age group or criteria [
32
]. In this study, our respondents
are not focused on driver characteristics. Instead, users from various sociodemographic
characteristics were included in this study. We reviewed related research that addresses the
acceptance of any technology related to in-vehicle applications during the years 2015–2021.
A part of our findings is summarized in Table 4.
Table 3. In-vehicle technology applications based on driving automation standards.
Level SAE NHTSA BASt In-Vehicle Applications
0 No Automation No Automation Driver Only
Collision warning, navigation system,
lane departure warning, lighting and
visibility system.
1 Driver Assistance Function-specific
Automation Driver Assistance
Night-view assist, blind-spot assist,
parking sensors, driver drowsiness
detection, adaptive cruise control or
lane-keep technology.
2Partial
Automation
Combined Function
Automation Partial Automation
Adaptive cruise control, active
lane-keep assist or automatic
emergency braking.
3Conditional
Automation
Limited Self-Driving
Automation
A vehicle that can manage itself on a
freeway journey, excluding on- and
off-ramps and city driving, but driver
must be alert.
4 High Automation
Full Self-Driving
Automation
High Automation A vehicle that can complete an entire
journey without driver intervention
may be confined to a certain
geographical area (i.e., geofenced) or
could be prohibited from operating
beyond a certain speed.
5 Full Automation Full Automation
Table 4. Summary of previous work related to user acceptance of in-vehicle applications.
Context/Focus Type of Study Main Findings
Smartphone-based navigation application with a collision
warning system [21]Real-traffic experiment
Driver’s acceptance is attributed to
user attitude and
perceived usefulness.
Lane-change collision avoidance system using a haptic
feedback force [33]Driving simulator
Driver’s acceptance is influenced by
corresponding system design
with expectations.
Low emission zone and school zone alert system [15]Real-traffic experiment and questionnaire
via email
Experienced drivers have higher
satisfaction level and positivity
regarding system usefulness.
Adaptive cruise control and lane centering [34]Controlled road experiment and
post-drive survey
Driver acceptance is influenced by
system functionalities.
Collision/Risk Alerts (CR); Collision Mitigation (CM);
Automatic Driving Tasks (AT); Lighting and Visibility (LV);
and Miscellaneous Driving Aids (MA) [32]
Structured live survey
Female drivers are more positive
toward collision avoidance features.
Features promoting safety are
underutilized by drivers.
Future Internet 2022,14, 148 8 of 21
Table 4. Cont.
Context/Focus Type of Study Main Findings
Automatic lane-change system [35]Experimental design with 1823
lane-change events
Driver acceptance of the system was
evaluated using performance index.
Parking assistance systems [36] Survey
Driver acceptance is influenced by the
system reliability.
Trust in technology, effect on driving skills and behavior
and technology preferences among teens [37]
Standard focus group methodology and
purposive sampling methods
Driver acceptance is influenced by
trust and reliability of the
vehicle technology.
ACC, FCW, LDW, blind-spot monitoring, driver
drowsiness detection system, traffic sign recognition
system, automatic high beam [29]
Survey
Driver acceptance is influenced by
perceived safety benefit of
the systems.
Forward collision warning and mitigation (FCWM) [38] Online survey
Driver acceptance is influenced by
knowledge regarding system
automation level.
Forward collision warning and lane departure warning [
39
]
Questionnaire
Driver acceptance is influenced by
attitude, perceived usefulness and
subjective norms.
Fatigue monitoring system or an adaptive cruise control
system combined with a lane-keeping system [19]Driving simulator and online survey Driver acceptance can be modeled
using TAM and TPB.
Adaptive cruise control (ACC) and lane=keeping assistance
(LKA) [40]Survey
Driver owners have different
understanding of ACC and LKA
systems and tend to over-estimate the
system capabilities.
There is no relationship between trust
and frequent usage of the systems.
Night-view assist, blind-spot assist, parking sensors, driver
drowsiness detection, emergency-brake assist, cruise
control and emergency stop system [41]
Online survey
Driver acceptance is influenced by
system usefulness, reassurance and
trust as well as system level
of autonomy.
Vehicle system related to driving convenience and safety [
4
]
Online survey
Driver acceptance is positively
influenced by factors related to driver
convenience and trust.
3. Research Model and Hypotheses Development
According to the literature, Technology Acceptance Model (TAM) is a reliable model
for exploring the acceptability of new technologies [
20
,
39
] and describing user behavior
and technology usage [
42
]. However, because TAM was commonly explored in the context
of other technologies that are different to in-vehicle applications, we aim to investigate
other constructs related to driver acceptance by extending the basic TAM model.
The basic independent variables, i.e., perceived usefulness (PU) and perceived ease of
use (PE), were first selected from the basic TAM model to represent users’ intention to use
the in-vehicle applications. We assumed that if user-perceived in-vehicle applications are
useful, the user would also perceive that the applications are easy to use and eventually
will intend to use the applications. AT and PU are the most important factors influencing
the user acceptance [
21
]. Moreover, if a user perceives that the in-vehicle applications are
easy to be used, i.e., users can easily activate the application when they are in the driver’s
seat, and if the interaction process through the human–machine interface is simple and useful
for a driving experience, the user will indicate a positive attitude (AT) and a high probability
of accepting the applications. However, we removed the actual use variable because the
in-vehicle applications are momentarily not essential components of all vehicle models, and it
is beyond the scope of this study. The intention to use in-vehicle applications (BI) is set as the
target variable of the model to represent users’ or drivers’ actual system use.
Three other variables relevant to the context of drivers were included, including trust
(T), system characteristics (SCs) and social influence (SI). SC refers to the preferable design
or features of the in-vehicle applications, whereas SI represents social factors that influence
a user’s opinion on aspects of the in-vehicle applications. A study among licensed drivers
Future Internet 2022,14, 148 9 of 21
from United States and Canada revealed that drivers who are not vehicle owners but have
better knowledge of system capabilities have lower trust level. However, the trust level
of drivers who are also vehicle owners does not seem to be influenced by the knowledge
of system capabilities [
40
]. In another study, Chan et al. studied the effect of trust in the
user acceptance of 5G-connected autonomous vehicles. Authors concluded that trust has
mediating effect on PU, PE and SI with BI [
43
]. Apart from that, ref. [
35
] mentioned that
users are more accepting toward the technology if they are certain of the recommendation
provided by the system. This may be reflected using T and PU.
In addition, personal characteristics (PCs) of users refer to gender, age, prior knowl-
edge about in-vehicle applications, self-capabilities and involvement in accidents as well as
driving distance per week which may influence a user’s trust, the expectation of system
characteristics and impact of social influence toward the in-vehicle applications. Individ-
uals who are highly educated and earn a good paycheck may be more willing to use the
in-vehicle applications [
15
]. The older a driver is, the more concerned they are regarding the
PU, T and SC [
41
]. Furthermore, ref. [
39
] concluded that drivers in Jakarta community are
positively and significantly influenced by only AT, PU and subjective norms to adopt FCW
and LDW. Hence, based on our literature, the research model is designed as in Figure 2and
the research questions of this study are listed in Table 5with corresponding hypotheses.
Table 5. Research questions and hypotheses.
Research Questions Hypothesis
Q1: What relationship exists between the PU and EU variables
of the research model?
H1: User-perceived ease of use (EU) of in-vehicle applications
positively affects perceived usefulness (PU) of the applications.
Q2: What influences exist between PU and EU with the
mediating variable (AT) in the research model?
H2: User-perceived usefulness (PU) of in-vehicle applications
positively affects their attitude toward the applications (AT).
H3: User-perceived ease of use (EU) of in-vehicle applications
positively affects their attitude toward the applications (AT).
Q3: How does user attitude (AT) impact the user intention to
use the in-vehicle applications (BI)?
H4: User attitude (AT) toward the in-vehicle applications
positively affects their intention to use the application (BI).
Q4: What influences exist between PU and EU with the target
variable (BI) in the research model?
H5: User-perceived usefulness (PU) of in-vehicle applications
positively affects their intention to use the applications (BI).
H6: User-perceived ease of use (EU) of in-vehicle applications
positively affects their intention to use the applications (BI).
Q5: What is the impact of social influence (SI) on user-perceived
usefulness (PU) and perceived ease of use (EU) of the
in-vehicle applications?
H7: Social influence (SI) positively influences drivers’ perceived
usefulness (PU) of the applications.
H8: Social influence (SI) positively influences drivers’ perceived
ease of use (EU) of the applications.
Q6: How does trust (T) influence perceived usefulness (PU) and
perceived ease of use (EU) of the in-vehicle applications
among users?
H9: Trust (T) positively influences drivers’ perceived usefulness
(PU) of the applications.
H10: Trust (T) positively influences drivers’ perceived ease of
use (EU) of the applications.
Q7: How do system characteristics (SCs) influence perceived
usefulness (PU) and perceived ease of use (EU) of the in-vehicle
applications among users?
H11: System characteristics (SCs) positively influence users’
perceived usefulness (PU) of the in-vehicle applications.
H12: System characteristics (SCs) positively influence users’
perceived ease of use (EU) of the in-vehicle applications.
Q8: How do personal characteristics (PCs) influence trust (T),
social influence (SI) and system characteristics (SCs) of the
in-vehicle applications among users?
H13: Personal characteristics (PCs) positively influence system
characteristics (SCs) of the in-vehicle applications.
H14: Personal characteristics (PCs) positively influence trust (T)
toward the in-vehicle applications.
H15: Personal characteristics (PCs) positively influence social
influence (SI) toward the in-vehicle applications.
Future Internet 2022,14, 148 10 of 21
Figure 2. Research model.
4. Questionnaire Design and Data Collection
We collected data from 308 respondents using a questionnaire which was designed
based on previous studies. The items in the questionnaire or survey for each construct
are shown in Table 6. Respondents are required to rate the items on a scale of 1 (totally
disagree) to 7 (totally agree).
Table 6. Survey items.
Construct Items
Perceived Usefulness (PU)
In-vehicle application features make driving more convenient.
In-vehicle application features would enable me to reach my destination quickly and safely.
In-vehicle application features would enable me to reach my destination cost-efficiently.
Using in-vehicle application features means extensive internet connectivity is required.
Using in-vehicle application features in my vehicle is meaningless if other vehicles are not equipped with
in-vehicle application features as well.
Perceived Ease of Use (EU)
I do not need special training to learn how to use in-vehicle application features.
I require in-vehicle application features instruction manual to be able to use the features perfectly.
It is easy to become skilful in using in-vehicle application features.
In-vehicle application features are easy and simple to understand.
Attitude (AT)
I think using in-vehicle application features would be a good idea.
I think in-vehicle application features would make my driving experience more interesting and fun.
When I drive a vehicle with in-vehicle application features, I feel satisfied.
Overall, available in-vehicle application features in my vehicle meet my expectations.
I will recommend in-vehicle application features to others.
Intention to Use (BI)
I am willing to use in-vehicle application features in the future.
I am willing to use in-vehicle application features frequently and consistently if given the opportunity.
If the vehicle with in-vehicle application features becomes available to me, I plan to obtain and use it.
I will use in-vehicle application features if required.
Trust (T)
I believe in-vehicle application features are verified professionally.
I believe the in-vehicle application features are reliable.
I believe in-vehicle application features will perform better as an add-on to my vehicle.
I believe my driving experience will be safer with in-vehicle application features.
I am worried about using in-vehicle application features.
System Characteristics (SCs)
I am afraid that a mounted dashcam to display alerts from in-vehicle application features will distract my driving.
Using in-vehicle application features do not really bother me to drive.
I will only use in-vehicle application features with audio when I drive.
In-vehicle application features with visuals on the vehicle dashboard will not affect my driving.
I prefer in-vehicle application features integrated into a mounted car dashcam.
Social Influence (SI)
I would be proud to show the vehicle with in-vehicle application features to people who are close to me.
I would feel more inclined to use in-vehicle application features if it was widely used by others.
I would prefer to have someone else as a passenger when I drive a car with in-vehicle application features.
Other people will encourage me when I use in-vehicle application features.
Other people will think I am wasting money when I purchase a vehicle with in-vehicle application features.
Future Internet 2022,14, 148 11 of 21
The sociodemographic details of each respondent include their gender and age as other
previous work. In addition, we require information whether they are licensed driver or not,
involvement in road accidents, driving distance per week, locality as well as knowledge
about in-vehicle applications. Respondents were also required to self-declare their hearing,
vision and motor skills. The survey items categorized under the personal characteristics’
variable are presented in Table 7.
Table 7. Details of respondents (n= 308).
Personal Characteristics (PCs) Response Category (n)
Gender Male (113); Female (195)
Age
18–25 years old (152), 26–34 years old (38),
35–54 years old (82), 55–64 years old (25),
above 64 years old (11)
Driver’s License Yes (264), No (44)
Accident Experience Yes (155), No (153)
Locality Rural (53), Suburban (110),
Urban (145)
Knowledge about in-vehicle applications No (79), Yes (229)
Self-reported capabilities Limited (141), Not Limited (167)
Driving distance per week
less 100 km (195), 100–200 km (62), 201–300 km
(23), 301–400 km (3), more than 400 km (25)
5. Data Analysis and Results
The user responses are made available in Zenodo, an open-access repository under the
Creative Commons Attribution 4.0 International license. The data analysis was performed
using the Real Statistics Resource Pack software (Release 7.6) for MS Excel [
44
], including
items consistency, correlation analysis, variance inflating factor, regression analysis and
mediation analysis. Moreover, the results are presented either in tabular form or figures,
and findings are discussed accordingly.
5.1. Construct Items
In this study, a total of seven constructs, i.e., the SC, SI, T, PU, EU, AT and BI, were
involved, and the responses were collected based on the survey items as in Table 6. The
Cronbach’s Alpha tests were performed to assess the reliability of the multiple-question
Likert scale used to measure the latent variable structure of psychological measures as
single items derived from the respondents. The Cronbach’s Alpha value presents how
reliable a set of test items are to validate and authenticate the response for the TAM
constructs, i.e., 0.91–1.00 (excellent), 0.81–0.90 (good), 0.71–0.80 (good and acceptable),
0.61–0.70 (acceptable) and 0.01–0.60 (not acceptable). We found that the scale system is
highly reliable for all constructs where the average is 0.7872. All variables have a value
of Cronbach’s Alpha higher than 0.7 except for the target variable, i.e., BI, which is 0.6294
(Table 8). Nevertheless, the items for the BI are still acceptable. Increasing the number of
items under this construct may increase the internal consistency.
Table 8. Reliability and validity analysis on variables.
Variables T SC SI PU EU AT BI
Cronbach’s α0.7100 0.8161 0.9733 0.8586 0.7522 0.7712 0.6294
The Kaiser–Meyer–Olkin (KMO) test value of 0.791 indicates that the sample is ad-
equate and has sufficient information to estimate factor solutions. In addition, Bartlett’s
Future Internet 2022,14, 148 12 of 21
test p-value is less than 0.001 which is significant to reject the null hypothesis. Hence,
there exists some level of correlation among the items to estimate the factor loadings. A
Confirmatory Factor Analysis was performed using JASP 0.16.1. The factor loadings of
each item are shown in Figure 3.
Figure 3. Factor loadings.
In addition, we calculated the Average Variance Extracted (AVE) and determined
the convergent validity as well as the discriminant validity. We conclude that there is a
convergent validity when the AVE is 0.5 and above. Moreover, the discriminant validity
exists when the square root of AVE is more that the correlation value. The results are shown
in Table 9. Evidence of both the convergent and discriminant validity demonstrates the
constructs’ validity.
Table 9. Convergent and discriminant validity.
No. of Indicator AVE AVE/Indicator
PU 5 0.7295 0.8541
EU 4 0.5473 0.7398
AT 5 0.7809 0.8837
T 5 0.7778 0.8818
SC 5 0.8808 0.9385
SI 5 0.5859 0.7655
PC 6 0.5537 0.7441
BI 4 0.6717 0.8195
5.2. Correlation Analysis
The Pearson product–moment correlation coefficient rwas calculated between the
variables. The r value is interpreted as 0.0–0.1 (negligible), 0.10–0.39 (weak), 0.40–0.69
(moderate), 0.70–0.89 (strong) and 0.90–1.00 (very strong). Based on Table 10, in general,
positive relationships exist among the TAM variables. Strong relationships exist between
the PU and T and also the SC and SI with r values more than 0.7. In addition, strong
Future Internet 2022,14, 148 13 of 21
relationships also exist between the AT and BI as well as the SI and T. However, the EU
and BI are the only variables with weak relationships compared to the other variables with
moderate relationships.
Table 10. Correlation between TAM variables.
PU EU AT T SC SI BI
PU 1
EU 0.584278 1
AT 0.597966 0.400629 1
T0.739867 0.603682 0.549366 1
SC 0.720569 0.664573 0.579474 0.686981 1
SI 0.749924 0.474516 0.555729 0.84442 0.597722 1
BI 0.507614 0.334304 0.896483 0.469818 0.416398 0.445437 1
Furthermore, the relationship among the personal characteristic (PC) variables was
also assessed and illustrated in Figure 4. There is no correlation (negligible) among most
of the variables. However, weak relationships exist between a few variables. This shows
that although the response of one variable may change the other correlated variables, the
relationship is not strong and can be ignored.
Figure 4. Relationship among PC variables.
5.3. Multicollinearity
When there is a relationship among the exploratory or control variables, there is a pos-
sibility of multicollinearity. In regression analysis, the first step is to detect multicollinearity.
Generally, a variance inflating factor (VIF) above four indicates that multicollinearity might
exist, and further investigation is required. When the VIF is higher than 10, there is sig-
nificant multicollinearity that needs to be corrected. Because only the VIF for trust (T) is
slightly above four, it can be safely ignored without suffering from multicollinearity. The
regression coefficients are not impacted and only exist in the control variable but not in the
variables of interest (PU, PE and BI). The VIF of each variable is shown in Table 11.
Future Internet 2022,14, 148 14 of 21
Table 11. Variance Inflating Factor (VIF).
PC Variables VIF Exploratory
Variables VIF
Gender 1.1208 Trust 4.2503
Age 1.1637
System characteristics
1.8976
Driving license 1.0660 Social influence 3.4920
Accidents 1.0405 Perceived usefulness 1.5183
Locality 1.1249 Perceived ease of use 1.5183
Knowledge 1.1070
Self-reporting capabilities 1.1239
Driving distance per week (km) 1.0926
5.4. Causal Relationship
The causal relationships between the constructs PU, EU, AT, BI, DC, SC and SI are
investigated using regression analysis. The results are shown in Table 12. The hypotheses are
validated at 95% confidence level. If the null hypothesis is rejected, it is statistically significant
that there is a non-zero correlation among the variables, and it can be modeled with the
regression equation. Furthermore, the correlation between the predicted value of Y generated
in the equation and the actual Y value for each unit refers to the multiple R. The coefficient
values provide the impact or weight of a variable toward the entire regression model.
Table 12. ANOVA results.
XY Multiple R Coefficient Std. Error t Stat p-Value Hypothesis
EU PU 0.58428 0.48622 0.03861 12.59397 1.38E-29 H1 rejected
PU AT 0.59797 0.32618 0.04264 7.64880 2.65E-13 H2 rejected
EU AT 0.40063 0.32618 0.04264 7.64880 2.65E-13 H3 rejected
AT BI 0.89648 0.90591 0.02560 35.39342 3.40E-110 H4 rejected
PU BI 0.50761 0.50186 0.04870 10.30616 1.41E-21 H5 rejected
EU BI 0.33430 0.27504 0.04433 6.20492 1.78E-09 H6 rejected
TPU 0.73987 0.75133 0.03906 19.23795 1.29E-54 H9 rejected
SI PU 0.74992 0.62271 0.03140 19.83043 7.41E-57 H7 rejected
SC PU 0.72057 0.69049 0.03798 18.17872 1.37E-50 H11 rejected
TEU 0.60368 0.73669 0.05562 13.24611 5.74E-32 H10 rejected
SI EU 0.47452 0.473489 0.05021 9.42990 1.06E-18 H8 rejected
SC EU 0.66457 0.765272 0.04919 15.5580 1.29E-40 H12 rejected
The assumption is that the combination of independent variables will generate a
larger multiple R or correlation than any single variable used as a predictor variable.
When single predictors were used (T, SC, SI) to predict the PU, the multiple R values
were 0.73987, 0.74992 and 0.72057, respectively. The multiple R value increased to 0.82550
when the predictors were combined. Moreover, the Adjusted R2 indicates the amount of
variability being explained by the regression model. Any field that attempts to predict
human behavior, such as psychology, typically has R-squared values lower than 0.5. For
instance, the value of the Adjusted R2 is 0.6783 when the T, SC and SI were assigned to
predict the PU. This shows that the explanatory power of trust, system characteristics and
social influence to perceived usefulness is 67.83%. Because the value is more than 0.5,
the variables are a good fit to predict the perceived usefulness of in-vehicle applications.
This means that the combination of the variables has a significant positive effect on the
user-perceived usefulness of in-vehicle applications. When trust, system characteristics
and social influence have high values, users are much more likely to perceive that an
in-vehicle application is useful. The multiple regression analysis results of different models
are summarized in Table 13.
Future Internet 2022,14, 148 15 of 21
Table 13. Multiple Regression Analysis Results.
Model Multiple R Adjusted R2 F p-Value Sig.
T, SC, SI PU 0.82550 0.678298 216.7664 3.52E-75 Yes
T, SC, SI EU 0.69949 0.484246 97.08173 4.3E-44 Yes
T, SC, SI AT 0.63593 0.398535 68.8067 5.52E-34 Yes
T, SC, SI BI 0.494563 0.237138 32.81056 2.12E-18 Yes
PU, EU AT 0.601292 0.357365 86.36036 1.91E-30 Yes
PU, EU BI 0.509737 0.254979 53.53434 1.18E-20 Yes
PU, EU, AT
BI
0.897264 0.803159 418.5442 1.4E-107 Yes
5.5. Mediating Effect Analysis
This section uses the hierarchical regression method as suggested by [
44
] to verify the
mediating effect of attitude between different factors. First, we analyzed the mediating
effect when (i) AT is the mediator between the PU and BI and (ii) AT is the mediator
between the EU and BI. The results are shown in Table 14. When the partial correlation
analysis is performed i.e., without the AT as the mediator, the correlation value between
the PU and BI dropped to
0.0355, and the correlation value between the EU and BI also
dropped to 0.0271.
Table 14. Mediation Analysis (PU affects the outcome of BI indirectly through mediator AT).
Coefficients Std Error t-Stat p-Value Correlation Semi-Part
PU AT 0.5850 0.0448 13.0504 3.00E-31 0.5980
AT BI 0.9059 0.0256 35.3934 3.38E-110 0.8965 0.7398
PU BI 0.5019 0.0487 10.3062 1.41E-21 0.5076 0.0355
PU 0.0438 0.0312 1.4037 0.1614
AT 0.9327 0.0319 29.2529 1.20E-90
EU AT 0.3262 0.0426 7.6488 2.65339E-13 0.4006
AT BI 0.9059 0.0256 35.3934 3.3791E-110 0.8965 0.8323
EU BI 0.2750 0.0443 6.2049 1.77517E-09 0.3343 0.0271
EU 0.0438 0.0312 1.4037 0.1614
AT 0.9327 0.0319 29.2529 1.20E-90
Moreover, to determine the significance of the relationship with the mediator AT,
the Sobel test was applied. Because the p-value is less than 0.05 (95% confidence level),
both paths, i.e., PU
AT
BI and EU
AT
BI, are significant and this confirms that
mediating relationships exist between the PU and EU with the BI where the AT is the
mediator variable. Additionally, we analyzed the mediating effects considering the EU
as the mediator variable between the SI, SC and T with the AT and PU as the mediator
variable between the SI, SC and T with the AT as in the research model. Similarly, the semi
correlation between the mediator variable and the target variable was weakened by the
direct variables. This indicates a mediating effect. We confirm the findings with the Sobel
Test as in Table 15. The p-values are significant at a 95% confidence level, ensuring that
the PU and EU are the mediator variables for the SC, SI and T, leading to the AT. A user
with high values of SC, SI and T will perceive the in-vehicle application as useful and easy
to use in their driving experience, which will lead to the user having a positive attitude
toward the application and eventually having a high intention to use the application.
Future Internet 2022,14, 148 16 of 21
Table 15. Results of Sobel Test.
Coefficients Std Error t-Stat p-Value
PU AT BI 0.5361 0.0438 12.2488 2.52E-28
EU AT BI 0.3592 0.0480 7.4791 8.03E-13
SI EU AT 0.190105 0.031894 5.960593 6.94E-09
SC EU AT 0.266247 0.038724 6.875561 3.49E-11
TEU AT 0.241852 0.036435 6.638004 1.45E-10
SI PU AT 0.448429 0.041098 10.91116 1.28E-23
SC PU AT 0.430876 0.040603 10.612 1.34E-22
TPU AT 0.241852 0.036435 6.638004 1.45E-10
5.6. Linear and Non-Linear Relationship
A correlation shows the relationship between two variables, while regression allows
us to see how one affects the other. Based on the correlation analysis results, we examine
the type of regression involving the personal characteristics variables, including gender,
age, locality, involvement in accidents, knowledge about in-vehicle applications, locality,
self-reported capabilities (vision, hearing, mobility and dexterity) and driving distance per
week as shown in Table 16. Non-linear regressions were found for age and intention to
use the in-vehicle application, self-reported capabilities and system characteristics, gender
and attitude. We compared the standard error values of linear and non-linear regression
models. A lower standard error value indicates a better fit model for the variables.
Table 16. Personal characteristics variable relationships.
Correlation Std Error (Linear) Std. Error (Non-Linear)
Locality PU 0.12525 0.05677 0.00184
Gender AT 0.15946 0.08532 0.00286
Age AT 0.1194 0.03490 0.00063
Knowledge AT 0.18236 0.09378 0.00372
Locality T0.10759 0.05602 0.00198
Knowledge T 0.14532 0.09498 0.00421
Knowledge SC 0.12307 0.10096 0.00424
Self-reported
capabilities SC 0.11057 0.10396 0.00425
Locality SI 0.14126 0.06822 0.00234
Knowledge SI 0.14740 0.11612 0.00490
Gender BI 0.14236 0.08645 0.00301
Age BI 0.13941 0.03518 0.00066
Knowledge BI 0.18607 0.09470 0.00389
Table 17 summarizes our findings when we further investigate the personal characteristics
variables, which may have a direct influence or moderate the investigated relationships.
Knowledge about in-vehicle applications positively influences trust, social influence and
system characteristics, which eventually fosters a positive attitude and higher intention to
use the application in their driving experience. Because the paper focuses on individual
acceptance, locality and knowledge contribute to social influence. Individuals who reside in
sub-urban and urban areas may have more exposure to the automotive changing landscape
and information, allowing them to react better toward their social cycle views and comments.
Future Internet 2022,14, 148 17 of 21
Table 17. Models with personal characteristics variable.
p-Value Regression Model X(Not Significant
p-Value)
Gender, age, knowledge BI 0.00027 Significant age (0.17926)
Gender, age, knowledge AT 0.00024 Significant age (0.36185)
Locality, knowledge SI 0.00226 Significant Significant
Self-reported capabilities,
knowledge SC 0.02361 Significant
knowledge (0.05479)
self-reported
capabilities (0.09237)
Locality, knowledge T 0.00845 Significant locality (0.08158)
6. Discussions
This study investigates the user acceptance of in-vehicle applications where recent
vehicle models are equipped with the application and automotive aftermarket service
vendors offer the application as a vehicle accessory that can provide advanced driver
assistance to users. Due to the variety of vehicle owners and users, this paper aims to
answer several research questions by modeling and quantifying the user acceptance of in-
vehicle applications based on survey responses. A research model based on the integration
of the TAM, trust, system characteristics, social influence and personal characteristics
is presented. In addition, the empirical results on the user intention to use in-vehicle
applications in a driving experience are provided. Trust and system characteristics are
important to Malaysian users. Users will simply turn-off or ignore the warnings from the
application which annoy them. They are keener on trusting their instincts then letting
technology influence their driving decisions. Their biggest worry is that the application
may be faulty and tempered by unauthorized personnel. However, if the users are getting
enough information and have seen or observed their close contact using the application
before, they have a more positive attitude toward the technology.
The results of this study are limited to users in Asian countries that are in the early
phase of implementing autonomous vehicles, specifically Malaysia. Because previous
studies focused mostly on European countries, which have a different culture, there may
be a variation of the results when it comes to investigating the user acceptance of the
technology. We assumed that in-vehicle applications would be made available to users
in recent vehicle models and be available as vehicle accessories to be installed in earlier
vehicle models. We did not consider the variety of in-vehicle applications and providers.
Hence, the respondents’ opinions are based on the general usage of in-vehicle applications.
Additionally, because attitude and behavior are important in marketing in-vehicle
applications, the TAM has been adopted to investigate the underlying relationship between
attitudes and the behavior or intention to use in-vehicle applications. The attitude–behavior
relation is not always straightforward and linear but may display non-linearities. Weak
attitude evaluations might not have much of an effect on the user’s intention to use the
in-vehicle applications. Additionally, an attitude change will not necessarily be followed
by an equal change in the user intention or actual application use. Thus, segmenting users
based on their attitude extremity before designing a marketing strategy can be valuable.
7. Conclusions
This study presents an implementation of the basic Technology Acceptance Model
(TAM) with additional constructs. In addition to the original TAM constructs, we added
three variables which are related to driver context, i.e., system characteristic (SC), trust
(T) and social influence (SI). We investigated the relationships of the SC, T and SI to the
perceived usefulness (PU) and ease of use (EU) which will influence user attitude and lead
to the intention to use the in-vehicle application. Moreover, we examined the relationship
between the personal characteristic (PC) variable and the SC, T and SI. Compared to a study
among Romanian licensed drivers [
21
] which highlighted perceived usefulness and attitude
Future Internet 2022,14, 148 18 of 21
as the main factors influencing the user acceptance of in-vehicle applications, our results
show that other factors such as knowledge about in-vehicle applications can significantly
affect trust and social influence, attitude and usage intentions among Malaysians. Similar
to the in-vehicle application users in Czech [
28
], our findings agree that a user will be
able to make an informed decision if they are aware of the advantages and limitations
of the applications. Applications which are promoted as safety applications will have a
higher acceptance rate. Moreover, gender and driving experience also have a moderating
effect on the BI [
4
]. However, in contrast to [
45
], which investigates user acceptance among
users in Rhodes Island, USA, our study shows age and gender do not have a significant
influence on the user acceptance. The driving distance per week or driving experience
also has no influence on the user acceptance. In addition, the findings show that trust,
system characteristics and social influence may also influence the perceived usefulness and
perceived ease of use, which in turn positively affects attitude toward using an in-vehicle
application, a significant predictor of usage intentions.
Thus, we summarized the findings from our work in relation to each research question
in Table 18. Our work fills the gap in the existing research by extending the basic TAM model
with variables which are influential on the user acceptance of the in-vehicle applications
in a Malaysia context. Even though our study is not designed to study specific in-vehicle
applications, vehicle marketers will generally benefit from understanding the factors which
positively influence the user acceptance of in-vehicle applications. Consideration of these
factors may provide better insight for the developers to ensure the positive response of users
toward the advantages of effectively utilizing the in-vehicle applications for a better and safer
driving environment. Communications can be strategically planned to educate the users on
the benefits of in-vehicle applications to increase the acceptance level in the nation.
Table 18. Findings based on research questions.
Research Question Findings
Q1: What relationship exists between the independent variables
(PU, EU) of the research model?
A user who perceives that the in-vehicle
application is easy to be used will also perceive that the
in-vehicle application is useful in their driving experience.
Q2: What influences exist between independent variables (PU,
EU) and the mediating variable (AT) in the research model?
The higher a user perceives that the in-vehicle application is
easy to be used and useful for their driving experience, the more
favorable the user attitude toward in-vehicle application.
Q3: How does driver ’s attitude (AT) impact the driver’s
intention to use the in-vehicle applications (BI)?
The more positive the attitude of a user
toward in-vehicle application, the higher the usage intention of
the application.
Q4: What influences exist between
independent variables (PU, EU) and the
target variable (BI) in the research model?
The higher a user perceives that the in-vehicle application is
easy to be used and useful for their driving experience, the
higher the usage intention of the application.
Q5: What is the impact of social influence (SI) on drivers’
perceived usefulness (PU) and perceived ease of use (EU) of the
in-vehicle applications?
The more positive social influence received by a user, the more
inclined the user is to perceive that the in-vehicle application is
useful and easy to be used.
Q6: How does trust (T) influence perceived usefulness (PU) and
perceived ease of use (EU) of the in-vehicle applications among users?
A user who believes that in-vehicle application is safe and
provides driving advantages will perceive that the application is
useful and easy to be used.
Q7: How do system characteristics (SCs) influence perceived
usefulness (PU) and perceived ease of use (EU) of the in-vehicle
applications among users?
The higher the perceived relative advantage of in-vehicle
applications, the greater the
perceived usefulness and ease of use of in-vehicle applications.
Future Internet 2022,14, 148 19 of 21
Table 18. Cont.
Research Question Findings
Q8: How do personal characteristics (PCs) influence trust (T), social
influence (SI) and system characteristics (SCs) of the in-vehicle
applications among users?
A user who has been involved in road
accidents has greater intention to use in-vehicle application.
A user who has limited self-reported
capabilities has greater intention to use
in-vehicle applications.
A user residing in urban or sub-urban area has greater impact
on social influence and trust which will influence their intention
to use in-vehicle application.
There is no sufficient evidence to conclude that age is a factor
which positively influences any of the other factors.
Author Contributions:
Conceptualization, S.F.A.R. and A.A.; data curation, S.F.A.R. and A.A.; formal
analysis, M.F.A.A.; investigation, S.F.A.R.; methodology, S.Y. and M.F.A.A.; resources, S.Y. and M.F.A.A.;
supervision, S.F.A.R.; validation, A.A.; writing—original draft, S.F.A.R. and S.Y.; writing—review and
editing, S.Y. All authors have read and agreed to the published version of the manuscript.
Funding:
This research was funded by the Fundamental Research Grant Scheme under the Ministry
of Education Malaysia (Grant Number: FRGS/1/2019/TK08/MMU/03/2).
Institutional Review Board Statement:
This research obtained ethical approval from the Technology
Transfer Office of the Multimedia University (Approval Number: EA0562021).
Informed Consent Statement:
Informed consent was obtained from all subjects involved in the study.
Data Availability Statement:
Abdul Razak, Siti Fatimah; Yogarayan, Sumendra; Abdullah, Mohd
Fikri Azli; Azman, Afizan In-vehicle Applications Among Malaysians 2022. https://doi.org/10.528
1/zenodo.6393708 (29 March 2022).
Acknowledgments:
The authors fully acknowledge the Ministry of Higher Education (MOHE) for
the approved fund which makes this important research viable and effective. The authors gratefully
acknowledge the use of services and facilities of the Connected Car Services Research Group, Centre
of Intelligent Cloud Computing at the Multimedia University. The authors would also like to thank
the anonymous reviewers for their constructive comments.
Conflicts of Interest: The authors declare no conflict of interest.
Abbreviations
360 cam 360-Degree-Parking Assist
ACC Adaptive Cruise Controls
AEB Autonomous Emergency Braking
A-Hold Auto Hold/Brake Hold
A-Park Auto Parking
AT Attitude
BI Intention to use the technology
BSM Blind-spot monitor
C-TAM-TPB Combination form of TAM and TPB
EU Perceived Ease of Use
FCW Forward Collision Warning
HDC Hill Descent Control
HAS Hill-Start Assist
HUD Head-up Display
IDT Innovation Diffusion Theory
LDW Lane Departure Alert/Warning
LKAS Lane-Keep Assist
MM Motivational Model
MPCU Model of PC Utilization
Future Internet 2022,14, 148 20 of 21
PED Pedal Misapplication Control
PU Perceived Usefulness
RCTA Rear Cross Traffic Alert
SC System Characteristics
SCT Social Cognitive Theory
SI Social Influence
T Trust
TAM Technology Acceptance Model
TIB Theory of Interpersonal Behavior
TJA Low-Speed Follow/Traffic Jam Assist
TPB Theory of Planned Behavior
TRA Theory of Reasoned Action
U&G User and Gratification Theory
UTAUT Unified Theory of Acceptance and Use of Technology
VIF Variance Inflating Factor
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