![The Unified Theory of Acceptance and Use of Technology (UTAUT) and definitions of the constructs [23] The model is based on an extensive literature review and empirical comparison of the Theory of Reasoned Action, the Technology Acceptance Model, the Theory of Planned Behaviour, a model combining the Technology Acceptance Model and the Theory of Planned Behaviour, the Model of PC Utilization, the Motivational Model, the Social Cognitive Theory and the Innovation Diffusion Theory, including their extensions [23]. The key element in all these models is the behaviour, i.e., use of the new technology. In a validation of the acceptance and use of computer software by workers in the USA, the UTAUT model outperformed the eight individual models, accounting for 70 percent of the variance (adjusted R2) in use [23]. The model postulates two direct determinants of use: ‘intention to use’ and ‘facilitating conditions’. ‘Intention to use’ is in turn influenced by ‘performance expectancy’, ‘effort expectancy’ and ‘social influence’. Gender, age, experience and voluntariness of use act as moderators, see Figure 1. The UTAUT model has also been utilized in other areas such as adoption of mobile services among consumers [24] and in the health sector (e.g. [25], [26], [27] and [28]). The studies largely support the appropriateness of the UTAUT model in these areas. However, the social influence was not found to be as strong a predictor as suggested by the model when investigating information/communication technologies and decision support in the health](https://www.researchgate.net/profile/Emeli-Adell/publication/229049067/figure/fig1/AS:300815312015364@1448731367946/The-Unified-Theory-of-Acceptance-and-Use-of-Technology-UTAUT-and-definitions-of-the_Q320.jpg)
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Drivers’ needs and acceptance of assistance functions
475
ACCEPTANCE OF DRIVER SUPPORT SYSTEMS
Emeli Adell
Lund University
P.O. Box 118, SE-221 00 Lund, Sweden
e-mail: emeli.adell@tft.lth.se
ABSTRACT: Driver support systems aimed at improving traffic safety
have undergone considerable development of late, but these
technological systems obviously have to be used by drivers if they are
to be successful in reducing fatalities and trauma. For this, driver
acceptance of the system is vital. The recognized importance of
acceptance notwithstanding, there is no common definition of what it is
in terms of driver support systems. This paper examines the definition
of acceptance of driver support systems. It also makes reference to
previous experiences from the information technology area and to a
pilot test, using data from a field trial with a driver support system, of
whether the Unified Theory of Acceptance and Use of Technology
(UTAUT) may also be used as a framework for understanding the
acceptance of driver support systems is carried out. The results support
to some extent the use of UTAUT as a model for acceptance of driver
support systems.
1 INTRODUCTION
Recent years have seen substantial development of different driver support
systems aimed at improving traffic safety. For instance, Intelligent Speed
Adaptation, (ISA), Forward Collision Warning (FCW), Automotive Collision
Avoidance System (ACAS), Fatigue Monitoring, Road-Departure Crash
Warning System (RDCW) and Lane Departure Warning (LDW) have been
developed and tested (see e.g. [1], [2], [3], [4], [5] and [6]). If these technologies
are to be successful in reducing fatalities and trauma, they have to be used by
drivers. For this, driver acceptance of the system is vital. This is recognised by
many, e.g. Najm et al [4], who state that “driver acceptance is the precondition
that will permit new automotive technologies to achieve their forecasted benefit
levels”, and van der Laan et al. [7] see acceptance as the link to use, thereby
materializing the potential safety effects, and conclude that “it is unproductive to
invest effort in designing and building an intelligent co-driver if the system is
never switched on, or even disabled”.
It is the driver who makes the decision to use or not use a system. Since
acceptance is individual, it can only be based on an individual’s personal
attitudes, expectations, experiences and subjective evaluation of the system
and the effects of using it [8]. The effects of the system (e.g. reduction in
accident risk) can only influence acceptance if they are known, understood and
believed in by the driver. A misunderstanding of the system will influence
acceptance as much as a correct conception. To achieve the acceptance and
use of new systems, personal importance to the users has to be valued more
highly than degree of innovation (see e.g. [9]). However, the technology push is
high and policies and political goals are often confused with the driver’s
Human Centred Design for Intelligent Transport Systems
476
personal goals. Societal goals and individuals goals do not necessarily coincide.
For example, the policy goal behind ISA could be to increase traffic safety or to
increase speed limit compliance. These goals might not be relevant to some
drivers e.g. due to their feeling that safety measures are redundant because of
their own personal driving skills [10], or that speeding is not seen as a “real
crime” [11]. Nevertheless, they might find that the system helps them to avoid
speeding tickets or that they have an interest in innovative systems.
Despite the recognized importance of acceptance, there is no prevailing
definition of what it is or how to measure it in terms of driver support systems.
The many different ways of assessing acceptance may cause confusion and
lead to incorrect conclusions or interpretations. In a literature review, reported in
[12], 9 different approaches to measure acceptance were found. Most
researchers measure acceptance without defining it, thereby defining it implicitly
by the measurements whey use. This makes the validation of the
measurements impossible.
The present situation is troublesome. If acceptance is not defined, then we
cannot be sure that the tool we use to measure it will give valid results. And
without knowing how acceptance is defined it is impossible to understand how
drivers’ experiences influence it. The inconstancy of acceptance definitions
(implicitly defined or not), and of measurements and thereby the diversity of
results – even though collected in the same experiment (see e.g. [13] and [14])
present a breeding ground for misinterpretations and misuse of the results.
What is more, it makes comparisons between systems and settings almost
impossible.
2 DEFINITION OF ACCEPTANCE
2.1 Present definitions of driver acceptance
Definitions of acceptance were found through a literature review in the
databases Transport and Elin (Lund University’s Electronic Library Information
Navigator), supplemented with relevant papers, reports, presentations etc. (for
more details about the literature review see [12].
The definitions were classified into five categories. The first category uses the
word “accept” to define acceptance. The second category is concerned with the
needs and requirements of users (and other stakeholders). This may be
interpreted as the usefulness of the system. The third category of definition
sees acceptance as the sum of all attitudes, implying that other, for example,
more emotionally formed attitudes are added to the more “rational” evaluation of
the usefulness of the system (as in category 2). The fourth category focuses on
the will to use the system. This definition of acceptance aims for a behavioural
change and may be seen as being based on the earlier categories, in that the
will to use a system is based on drivers’ assessment of the usefulness of the
system (as in category 2) as well as all other attitudes to the system and its
effects (as in category 3). This fourth category stresses the will to act as a
consequence. The fifth category of acceptance emphasizes the actual use of
the system, which presumably is influenced by the will to use it (as in category
4).
Drivers’ needs and acceptance of assistance functions
477
Viewing the categories like this, they may to some extent be seen as a
progression from assessing the usefulness of a system towards the actual use
of that system, the later categories including the earlier ones. This progression
perspective, however, cannot include category 1, which uses the word “accept”
to define acceptance, but does not provide any information about what is
implied by acceptance or accept.
There are also different types of acceptances described in the literature.
Authors have made distinctions between attitudinal and behavioural acceptance
[15], [16], between social and practical acceptance [17] and between different
levels of problem awareness of the individual [18]. There is also discussion
about ‘conditional’ [19] and ‘contextual’ [20] acceptance in the literature.
Goldenbeld [21] makes a distinction between acceptance and support, where
acceptance is the willingness to be subjected to something (e.g. pay taxes)
while support is the liking for doing so and some stress the importance of
making a distinction between acceptability and acceptance (e.g. [22]).
2.2 Proposal for a new definition of acceptance
The use of the system is vital in striving to improve traffic safety by deploying
driver support systems. It is the use of the system that will materialise its
potential and hopefully produce benefits for the driver and the society. Neither
attitudinal acceptance [16] nor support [21] requires any impact on the actual
use of a system. Hence, the main aim and focus should be on behavioural
acceptance [16], the utilization level as described by Kollmann [15] or the
acceptance definition category 5 – actual use, which emphasizes the use of the
system. From this perspective, the second and third categories of acceptance
definitions (usefulness and all attitudes), attitudinal acceptance [16] and the
attitude level described by Kollmann [15] influence the will to use and the actual
usage, but are not to be seen as acceptance.
The proposed acceptance definition, postulates that acceptance is “the degree
to which an individual intends to use a system and, when available,
incorporates the system in his/her driving”. This definition establishes the
relationship between acceptance and use, implied by many researchers.
Further, it stresses the importance of user centred view and the importance of
manifesting the intention to use the system in actual behaviour. By this
definition a driver does not have to like to use the system to demonstrate
acceptance. It is enough that he/she ‘tolerates’ the use. The definition also
implies that there are different degrees of acceptance, that it is not limited to
acceptance/no acceptance but to be of a more continuous nature, and could of
course also be zero.
3 A PILOT TEST OF THE UTAUT MODEL IN THE
CONTEXT OF DRIVER SUPPORT SYSTEMS
3.1 The Unified Theory of Acceptance and Use of
Technology
Following the rapid development of new technologies and software in computer
science, interest in the acceptance and use of these technologies has increased
Human Centred Design for Intelligent Transport Systems
478
significantly. A number of different models are used in the information
technology area, which today includes one of the most comprehensive research
bodies on the acceptance and use of new technology. In 2003, Venkatesh et al.
[23] integrated eight of the most significant models of individual acceptance into
one comprehensive model - The Unified Theory of Acceptance and Use of
Technology (UTAUT), see Figure 1.
Figure 1: The Unified Theory of Acceptance and Use of Technology (UTAUT)
and definitions of the constructs [23]
The model is based on an extensive literature review and empirical comparison
of the Theory of Reasoned Action, the Technology Acceptance Model, the
Theory of Planned Behaviour, a model combining the Technology Acceptance
Model and the Theory of Planned Behaviour, the Model of PC Utilization, the
Motivational Model, the Social Cognitive Theory and the Innovation Diffusion
Theory, including their extensions [23]. The key element in all these models is
the behaviour, i.e., use of the new technology. In a validation of the acceptance
and use of computer software by workers in the USA, the UTAUT model
outperformed the eight individual models, accounting for 70 percent of the
variance (adjusted R2) in use [23].
The model postulates two direct determinants of use: ‘intention to use’ and
‘facilitating conditions’. ‘Intention to use’ is in turn influenced by ‘performance
expectancy’, ‘effort expectancy’ and ‘social influence’. Gender, age, experience
and voluntariness of use act as moderators, see Figure 1.
The UTAUT model has also been utilized in other areas such as adoption of
mobile services among consumers [24] and in the health sector (e.g. [25], [26],
[27] and [28]). The studies largely support the appropriateness of the UTAUT
model in these areas. However, the social influence was not found to be as
strong a predictor as suggested by the model when investigating
information/communication technologies and decision support in the health
Drivers’ needs and acceptance of assistance functions
479
sector [26] and [27]. Extensions/modifications of the model were recommended
both in the adoption of mobile services area and within the health sector [24]
and [25].
3.2 Using the UTAUT for a driver support system – a pilot
test
To investigate the UTAUT model in the context of driver support systems, a pilot
test was carried out in 2008. Data for this pilot test was collected in 2006 and
2007 during field trials to evaluate a prototype driver support system
(SASPENCE). The original model was applied as far as possible. However, the
experimental design of the field trials could not be modified for the evaluation of
UTAUT. Nevertheless, additional questions to the already planned
questionnaires allowed data collection for examination of the inter-relationships
of ‘performance expectancy’, ‘effort expectancy’, ‘social influence’ and ‘intention
to use’, including gender and age as moderators. A summary of the trial is given
below, more details about the trial are reported in [6] and [12].
3.2.1 Method
The SASPENCE system is a driver support system which assists the driver to
keep a safe speed (according to road and traffic conditions) and a safe distance
to the vehicle ahead. The “Safe Speed and Safe Distance” function
informs/warns the driver when a) the car is too close to the vehicle in front, b) a
collision is likely due to a positive relative speed, c) the speed is too high
considering the road layout and d) the car is exceeding the speed limit. The
driver receives information and feedback from the system by means of an
external speedometer display located on the instrument panel, haptic feedback
in the accelerator pedal or in the seat belt and an auditory message when a too
short headway could lead to imminent danger.
Two different test routes were used to evaluate the system, one in Turin, Italy,
and one in Valladolid, Spain. Both routes were approximately 50 km long and
contained both urban and rural road stretches and a motorway section. The test
drivers drove the test route twice, once with the system on and once with the
system off, thus serving as their own controls. The order of driving was altered
to minimize bias due to learning effects.
At each site, 20 randomly selected inhabitants, balanced according to age
groups (18-24, 25-44, 45-64 and 65-69) and gender, participated in the trial.
Unfortunately, the data for one test driver was lost due to system failure in Italy,
and one of the test drives in Spain was cancelled for safety reasons.
Before the drivers used the SASPENCE system, they were given a brief
explanation of the system. The questions regarding the UTAUT assessment
were given to the drivers as part of the questionnaire after the second drive.
The items for assessing ‘behavioural intention’, ‘performance expectancy’,
‘effort expectancy’ and ‘social influence’ were adopted from Venkatesh et al.
[23]. Some of the items had however to be adapted to fit the context of driver
assistance systems, see Table 1. Each item was measured using a seven-point
scale, ranging from “strongly disagree” (1) to “strongly agree” (7) (identical to
[23]).
Human Centred Design for Intelligent Transport Systems
480
Table 1: The original UTAUT items and the modified items used in this
study to assess acceptance of driver support systems.
Original items [23] Modified items
Behavioural intention to use the system (BI):
Imagine that the system was on the
market and you could get the system in
you own car.
BI1 I intend to use the system in the next <n>
months
I would intend to use the system in the
next 6 months
BI2 I predict I would use the system in the
next <n> months
I would predict I would use the system
in the next 6 months
BI3 I plan to use the system in the next <n>
months
I would plan to use the system in the
next 6 months
Performance expectancy (PE):
PE1 I would find the system useful in my job I would find the system useful in my
driving
PE2 Using the system enables me to
accomplish tasks more quickly
Using the system enables me to react to
the situation more quickly
PE3 Using the system increases my
productivity
Using the system increases my driving
performance
PE4 If I use the system, I will increase my
chances of getting a raise
If I use the system, I will decrease my
risk of being involved in an accident
Effort expectancy (EE):
EE1 My interaction with the system would be
clear and understandable
My interaction with the system would be
clear and understandable
EE2 It would be easy for me to become skilful
at using the system
It would be easy for me to become
skilful at using the system
EE3 I would find the system easy to use I would find the system easy to use
EE4 Learning to operate the system is easy
for me
Learning to operate the system is easy
for me
Social influence (SI):
Imagine that the system was on the
market and you could get the system in
you own car.
SI1
People who influence my behaviour
would think that I should use the
system
People who influence my behaviour
would think that I should use the
system
SI2 People who are important to me would
think that I should use the system
People who are important to me would
think that I should use the system
SI3
The senior management of this business
has been helpful in the use of the
system
The authority would be helpful in the
use of the system
SI4 In general, the organization has
supported the use of the system
In general, the authority would support
the use of the system
3.2.2 Results
Factor analysis confirmed on the whole the similarity of the items within the four
constructs. However, items PE3 and PE4 did not show high loadings on
performance expectancy. Item PE3 showed more resemblance to social
influence while item PE4 did not show any clear resemblance to any of the four
constructs. One explanation for this might be that the transformation of items
Drivers’ needs and acceptance of assistance functions
481
PE3 and PE4 from the context of IT to driver support system brought about a
different meaning. Together with the low loadings on ‘performance expectancy’,
it was decided to exclude both these items from further analysis, which
increased the content validity. The remaining items were represented by four
summated scale variables (averages of item scores).
The internal consistency reliabilities of the summated scale variables were
tested with Cronbach’s Alpha coefficient (). All constructs demonstrated an
internal consistency higher than 0.70, (BI: 0.862, PE (PE3 and PE4 excluded):
0.728, EE: 0.764, SI: 0.721).
The relationships between the independent constructs (PE, EE, SI) and
intention to use the SASPENCE system (BI) were examined by applying linear
regression analysis. First, the unadjusted effects, i.e. crude effects (meaning
that there was only one independent variable in the model) and then the
adjusted effects of variables (by simultaneously entering other independent
variables into the model) were analysed. The results obtained by the analyses
are shown in Table 2.
Table 2: Effects of independent variables on the dependent variable
‘behavioural intention’ (BI), based on linear regression models
Independent variable
in the model
Coefficient
(βstandardized)
p-value R2adjusted Model
Performance expectancy
PE 0.41** 0.011 0.15 BI = a + β*PE
PE, EE 0.38** 0.025 0.13 BI = a + β *PE + c*EE
PE, SI 0.37** 0.015 0.22 BI = a + β *PE + c*SI
PE, EE, SI 0.36** 0.027 0.20 BI = a + β *PE + c*EE + d*SI
Effort expectancy
EE 0.22 0.186 0.02 BI = a + β *EE
EE, PE 0.10 0.522 0.13 BI = a + β *EE + c*PE
EE, SI 0.16 0.306 0.10 BI = a + β *EE + c*SI
EE, PE, SI 0.06 0.704 0.20 BI = a + β *EE + c*PE + d*SI
Social influence
SI 0.35** 0.030 0.10 BI = a + β *SI
SI, PE 0.31** 0.042 0.22 BI = a + β *SI + c*PE
SI, EE 0.32** 0.048 0.10 BI = a + β *SI + c*EE
SI, PE, EE 0.30* 0.053 0.20 BI = a + β *SI + c*PE + d*EE
PE: performance expectancy, EE: effort expectancy, SI: social influence ** p<0.05; *
p<0.10
‘Performance expectancy’, i.e., the expected benefits gained by using the
system, had a significant positive effect on intention to use the system. It had a
significant crude effect, and only small changes in the coefficient of
determination were observed when other independent variables (EE and SI)
were added to the model.
The same pattern was observed for ‘social influence’, which also demonstrated
a significant positive crude effect on intention to use the system and only small
changes in the coefficient of determination when the other independent
variables (PE and EE) were added to the model.
Human Centred Design for Intelligent Transport Systems
482
However, ‘effort expectancy’ showed no significant direct relation to intention to
use the system. No significant effects could be found together with
‘performance expectancy’ and ‘social influence’. Further, when including the
effort expectancy in the model, the adjusted explanatory power decreased.
The explanatory power of the UTAUT model for intention to use the
SASPENCE system (BI) was 20 % when all independent variables were
included (PE, EE and SI). ‘Performance expectancy’ (PE) and ‘social influence’
(SI) had a significant impact on ‘behavioural intention’ (BI). The standardised
beta coefficient revealed that the impact of ‘performance expectancy’ was
slightly more significant than that of ‘social influence’. In this data material the
‘effort expectancy’ (EE) did not show any correlations to ‘behavioural intention’.
The inclusion of the moderators ‘gender’ and ‘age’ did not affect the results,
regardless of whether ‘effort expectancy’ was included in the analysis or not.
4 DISCUSSION
Good academic practice emphasises the importance of being clear and distinct
to minimize ambiguity and to facilitate comprehension and revision of the
scientific work presented. However, in ITS research the definition of
‘acceptance’ is usually taken for granted and most researchers assess
acceptance without defining it. This is one of the fundamental problems in
acceptance research today. The different ways of measuring acceptance makes
comparisons between different studies and different systems difficult.
The proposed acceptance definition postulates that acceptance is “the degree
to which an individual intends to use a system and, when available, to
incorporate the system in his/her driving”.
The results from the pilot test, applying the Unified Theory of Acceptance and
Use of Technology (UTAUT) in the area of driver support systems, supported to
some extent the use of this model as a framework to assess acceptance of a
driver support system, but the explanatory power of the model was only twenty
percent. The controlled experimental design led to similar experiences among
the drivers and hence a limited variance in the data. Additionally, the amount of
data was very limited; data was available for 38 drivers. Considering this, the
relatively small explanatory power is not surprising. Future studies, with more
participants and a targeted experimental design for the continued investigation
of the UTAUT model, have to be conducted to be able to possibly find a larger
explanatory power.
The pilot test highlighted the importance of ‘social influence’ for ‘behavioural
intention’ but did not verify the significance of ‘effort expectancy’ reported by
e.g. Venkatesh et al 2003 and Chang et al [64]. This may be a consequence of
the small amount of data that was available for the pilot test, or due to improper
assessment of the construct in the context of driver support systems. However,
the context of computer use, for which the UTAUT model was developed, differs
from the context of using driver support systems (driving). Driving demands
interactions with other road users and is therefore by its nature a task with a
strong social dimension. The importance of ‘social influence’ as a predictor of
‘behavioural intention’ in the context of a driver support system could be a
Drivers’ needs and acceptance of assistance functions
483
consequence of this. Further, the effort associated with the use of e.g. a
computer program and the use of a driver support system may be different.
Employing a computer program normally demands actions by the user, while a
driver support system normally runs without requiring input from the driver,
informing/warning the driver only when there is a need to do so.
The pilot test showed a methodological problem with the “translation” of the
items, which were used in information technology to assess constructs in the
UTAUT model, into the area of driver support systems. The items used were
adopted from Venkatesh et al. [23] as closely as possible. However, for two of
them (‘Using the system increases my productivity’, and ‘If I use the system, I
will increase my chances of getting a raise’) it did not make sense to keep the
original wording in the context of a driver support system. These two items also
showed validity problems and were excluded from the analysis after the factor
analysis.
In the pilot test, the item ‘Using the system increases my productivity’ was
replaced by ‘Using the system increases my driving performance’. This
appeared to be a too vague concept and the factor analysis indicated more
resemblance with ‘social influence’ than with ‘performance expectancy’. It is
possible that performance expectancy is better assessed through more direct
transportation-related effects like travel time and fuel consumption. The item ‘If I
use the system, I will increase my chances of getting a raise’ was translated into
‘If I use the system, I will decrease my risk of being involved in an accident’.
These two items have at least one major difference; while the original item
speaks of a reward (raise), the modified item speaks of a lack of negative
consequence (accidents). There are seldom rewards given for desired driving
behaviour. The analysis implies that traffic safety (absence of accidents) is
related to all four constructs (‘performance expectancy’, ‘effort expectancy’,
‘social influence’ and ‘intention to use’). This is likely to have its roots in its
fundamental importance for the driver, the people around the driver, and the
authority. It is possible that items dealing with avoiding fines or self appraised
rewards, such as enjoyment, comfort, image etc, may be more specifically
related to this dimension of ‘performance expectancy’.
The results indicate the need to investigate whether the items capture the
‘essence’ of the constructs when applied to driver support systems, both when
“translations” of the items are needed and when not. It seems that some of the
items are relevant, and that other items are ‘polluting’ the constructs with
irrelevant information. However, it is important to remember that these results
are based on one, quite small, data sample. It is possible that the results might
be different in another data set, suggesting other items to be the more relevant.
A construct, assessed by several items, is therefore likely to be more robust
than using single questions when modelling acceptance. The work on
identifying and assessing the constructs should be continued.
Further research is needed to continue the investigation of whether the UTAUT
could be a productive model through which to view acceptance of driver support
systems. This research should particularly address how the constructs should
be measured in the context of driver support systems, and special attention
should be given to ‘performance expectancy’. Further work is also needed to
Human Centred Design for Intelligent Transport Systems
484
examine the role of moderators in this context. To accomplish this, more
extensive studies, with significantly larger numbers of test subjects and targeted
experimental design, are necessary.
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