Erasmus University Rotterdam
Erasmus School of Economics
Supervisor: Prof. B.G.C. Dellaert
Master Thesis Marketing
“The drivers for consumers to participate in the
online mass customization of cars”
Student:
M.J. Overbeek
Student number: 402629
E-mail:
402629mo@student.eur.nl
Date of delivery: 21-05-2015
Online Mass Customization of Cars
M.J. Overbeek - 402629
Executive
Summary
This master thesis studies the incentives for consumers to participate in the online mass
customization of cars. Recent research proves that 94% of the car buyers start their shopping
experiences online. Online mass customization of cars implies that people are able to
customize their preferred car in an online setting with a clear visualization of the modifications
they have implemented. Theory shows that drivers to participate in online mass customization
could
be
‘system
characteristics
feature-related’
of
or
could
the
relate
to
consumer
personal
involved.
The conceptual framework that has been applied was partly inspired by the study from
Dellaert & Dabholkar (2009). The specific system feature we research is the range of
customization choices. We apply the method of a survey in order to collect data for this
research. In the questionnaire, two different scenarios will be applied. Respondents are sent
to an online car customization system with a relatively small range of customization choices.
After that, respondents are sent to a second scenario with a wider range of customization
choices. After working on both external online mass customization systems, respondents have
to answer the same set of questions related to the just performed customization
assignment.
In
total
103
respondents
completed
the
online
questionnaire.
Results show that more customization choices increase the intention to use an online mass
customization system of cars in a direct way. The indirect effect of this system feature is even
larger while more choices increase the perceived enjoyment of the consumer involved. This
variable is in fact the only personal characteristic from our study with a positive, significant
effect on the intention to use such a system. That is the main outcome of this research ,
perceived enjoyment positively effects the intention to use an online mass customization
system of cars. This result supports the so-called ‘I designed It myself’ effect where enjoyment
during
engage
2
the
customization
in
process
is
online
the
main
motivation
mass
in
order
to
customization.
Online Mass Customization of Cars
M.J. Overbeek - 402629
Table of content
1.Introduction ............................................................................................................................ 5
2. Theoretical Background......................................................................................................... 7
2.1. What are online mass customization programs? ............................................................ 7
2.2. Creative consumer behaviour ......................................................................................... 9
3. Conceptual framework ....................................................................................................... 11
3.1 The impact of consumers’ perceptions of creativity, enjoyment and control on the
intention to use an OMCS ..................................................................................................... 11
3.1.1. Creativity............................................................................................................. 11
3.1.2. Enjoyment........................................................................................................... 12
3.1.3. Control ................................................................................................................ 13
3.2.The impact of range of mass customization choices on consumer perceptions of
creativity, enjoyment and control ........................................................................................ 14
3.2.1. Creativity............................................................................................................. 15
3.2.2. Enjoyment........................................................................................................... 16
3.2.3. Control ................................................................................................................ 16
3.3. Moderating variable ‘product expertise’....................................................................... 17
3.4. Overview of the hypotheses ......................................................................................... 20
4. Methodology ....................................................................................................................... 21
4.1 Data collection ................................................................................................................ 21
4.2. The Questionnaire ........................................................................................................ 21
4.3. Measurements of the questionnaire ........................................................................... 22
4.3.1. Range of mass customization choices ............................................................... 22
4.3.2. Intention to use an OMCS ................................................................................. 22
4.3.3. Consumer Perceptions of creativity .................................................................. 23
4.3.4. Consumer Perceptions of enjoyment ................................................................ 23
4.3.5. Consumer Perceptions of control ...................................................................... 23
4.3.6. Product expertise .............................................................................................. 24
4.4. Control variables ........................................................................................................... 26
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5. Analysis and Results ........................................................................................................... 27
5.1 Factor analysis............................................................................................................. 28
5.1.1. Scenario ‘0’ with the small range of choices ..................................................... 28
5.1.2. Scenario ‘1’ with the wide range of choices ...................................................... 29
5.2. Reliability test ............................................................................................................ 29
5.3. Hypotheses testing ................................................................................................... 30
5.3.1. Regression analysis for intention to use an OMCS ............................................ 31
5.3.2. Regression analysis for perceived creativity ..................................................... 31
5.3.3. Regression analysis for perceived enjoyment ................................................... 32
5.3.4. Regression analysis for perceived control ......................................................... 33
5.3.5. Regression analysis for moderating effect of product expertise on perceived
creativity ...................................................................................................................... 33
5.3.6. Regression analysis for moderating effect of product expertise on perceived
enjoyment .................................................................................................................... 34
5.3.7. Regression analysis for moderating effect of product expertise on perceived
control .......................................................................................................................... 35
5.3.8. Overview results of hypotheses testing ............................................................ 37
5.4. Mediation analysis .................................................................................................... 38
5.5. Regression analysis with control variables ............................................................... 41
6. Conclusion ........................................................................................................................... 44
6.1. Theoretical implications ............................................................................................ 44
6.2. Managerial implications ............................................................................................ 45
6.3. Limitations and further research ............................................................................ 46
References ............................................................................................................................... 49
Appendix ................................................................................................................................. 55
4
Online Mass Customization of Cars
M.J. Overbeek - 402629
1.
Introduction
The online sales of cars are increasing and the future looks even better according to Foy (2013)
in the Financial Times. Online showrooms and digital dealerships will totally revolutionise car
buying. Car buying is going to change with this upcoming movement. Online car sales are
increasing, but consumers can also just use the internet to orient towards looking for a new
car. This has been proven by a research from Capgemini (2013) which states that 94% of the
car buyers start their shopping experience online. This trend is strengthened by the recent
news that half of the Dutch car showrooms will disappear in the upcoming years (De
Volkskrant, 2015). A possible reason for this movement might be the consumer’s ability to
configure
a
phenomenon
car
to
is
their
own
called
preferences
online
in
an
online
mass
setting.
This
customization.
The automobile industry is a very innovative industry which is mainly caused by the high level
of competition (Hashmi & Van Biesebroeck, 2010). There are many carmakers which target
markets all over the world so that it is crucial for them to stay innovative. These firms need to
outperform their competitors on a particular field. Whereas a firm such as Tata tries to focus
on price, Tesla anticipates on the green movement and was one of the first companies that
introduced a full electric vehicle (Vance, 2013). What all these firms do have in common is that
they offer possibilities to customize their cars online. Customization of cars was for a long time
only possible at your local car dealer without a precise visualization. Nowadays, consumers
interested in a model can customize their preferred car in a online setting with a clear
visualization of the modifications they have implemented (Herrmann et al., 2011).
But what drives people to use these kinds of modern techniques instead of browsing through
car models in traditional showrooms? Car manufacturers already offer the possibility for
online mass customization of their cars so their should be a certain interest. Our problem
statement is as follows: what are the main drivers for consumers to participate in the online
mass customization systems of cars? These drivers could be related to the features of the
online mass customization system or could relate to personal characteristics of the consumer
involved. Both sides will be highlighted by using research questions, such as: how could
system-related features increase the consumer’s intention to use online mass customization
5
Online Mass Customization of Cars
M.J. Overbeek - 402629
systems? Does existing knowledge about cars influence the intention to use these
customization systems? What effect do consumer perceptions of the customization process
have on the intention to use such a customization system? Do more customization
choices
also
increase
the
enjoyment
of
the
consumers
involved?
The relevance of this research is demonstrated in the overall upcoming movement of online
mass customization, a phenomenon earlier researched by Franke & Piller (2004) and Dellaert
& Dabholkar (2009). More and more firms offer their consumers these kinds of systems in
order to generate sales. Our research has an addition compared with the research from
Dellaert & Dabholkar (2009) by studying the perceived creativity of consumers. There exists
evidence from Costello & Keane (2000) and Moreau and Dahl (2005) which reveals that more
choices could inhibit consumer’s creativity. A finding that up tot now has not been studied in
an online environment. Moreover there has never been an extensive research about online
mass customization in the car industry. Knowing that online mass customization is an overall
upcoming movement, it is interesting to study this phenomenon in this specific product group.
2.
2.1.
6
Theoretical
What
are
online
mass
background
customization
programs?
Online Mass Customization of Cars
M.J. Overbeek - 402629
The product offering of traditional retailers is mainly focused on offering a broad assortment
of a product group. In a perfect situation, this wide variety of items has an attraction to every
consumer searching in that product group (Huffman & Kahn, 1998). According to Berger et al.
(2005) the retailer’s function is being a buffer or intermediate between manufacturers and
consumers. Retailers sell the standardized products which are produced in cost-efficient mass
production.
seen
In
as
this
product
the
centric
situation
buying
the
side
of
consumers
the
were
only
market.
Besides this product centric approach there exists an upcoming movement of a consumeroriented approach, known as mass customization. Aichner and Coletti (2013) state that mass
customization is the tool in order to offer affordable services and goods combined with a wide
range of personalisation options. To be precise, mass customization tries to combine two
business practices: craft and mass production (Pine, 1993). With this consumers are actively
involved in the design-stage of the product. Mass customization is therefore often defined as
the process of co-design, emphasizing the interaction between consumers and companies
involved in mass customization (Piller et al., 2005). The strength of mass customization seems
to be personalization for an affordable price while ‘pure customization’ is much more
expensive. Therefore, mass customization is used in more and more different
product
groups
(Bernhardt
et
al.,
2007).
When looking at the history, cars seem to be a typical example of a product customization
according to Aichner and Coletti (2013). At the start of car production in the nineteenth
century, cars were almost completely hand-built. By this human touch consumers were able
to indicate what kind of personalisation they preferred. However, costs were high because of
the small volumes and the amount of labour involved. Mass production of cars came at the
end of this century together with the introduction of the Ford Model T. This first relatively
affordable car resulted in a cost line going down and an increasing market demand. Together
with this movement, the product customization faded away while black was the only colour
possible. The consumer choice disappeared and this lasted until around 1960 with the
returning choice of car colour. After this evolution there came choice of engines and in the
1980s way more customization options were available for car buyers. Later on, car buyers
could even choose a contrasting colour of their roofs or mirrors (Aichner & Coletti, 2013).
7
Online Mass Customization of Cars
M.J. Overbeek - 402629
What is discussed previously all took place in a traditional, offline setting. Together with the
overall internet movement car customization also moved towards an online setting. While
customization in the car industry has known many phases it is interesting to study its newest
phase, the one of online mass customization. The relevance of studying online car
customization is emphasized by Hermann et al. (2011) who mention that in the German car
industry, more than 70% of the car buyers configured an automobile on the internet in the
year 2011. This finding is being supported by the statement from Capgemini (2013) which
shows that 94% of the car buyers start their shopping experience online. This last statement
does not necessarily stress online car configuration but it does highlight that the internet is a
very
important
medium
for
consumers
interested
in
cars.
Online configurators require particular software in order to be operational on the internet.
Therefore, experts often talk about online mass customization systems (OMCS). This term is
being used while an online system now facilitates the interaction between consumer and firm
instead of a salesperson or catalogue (Franke & Piller, 2004). Consumers involved in online
mass customization are able to design a product in an online environment according to preset options. For instance, a consumer can choose out of given set of colour options with regard
to the product feature ‘car colour’ (Park et al., 2000). One of the main features of OMCS that
car manufacturers apply is visualization. This feature matches the main benefit of physical
stores where people can see the product before they buy it (Dellaert & Dabholkar, 2009). By
changing
immediately
particular
see
features
the
of
the
changes
product,
they
have
consumers
can
implemented.
An OMCS can be quite basic but also very elaborative. An online car configurator could be
more extensive than the other with regard to the range of consumer choices. This will be a
key area of research, which aims at whether the range of mass customization choices
influences consumer perceptions of OMCS and thereby the intention to use these kinds of
systems. The consumer perception which has our main focus will be a relatively new consumer
perception in this research field of mass customization: consumer’s perceived
creativity,
8
which
will
be
discussed
in
the
next
section.
Online Mass Customization of Cars
M.J. Overbeek - 402629
Earlier research from Dellaert & Dabholkar (2009) studied the intention to use an OMCS with
usage of the consumer perceptions: enjoyment, control, complexity and product outcome.
Only their first two perceptions will be used in this new research while these perceptions are
relatively new in researching online mass customization systems. The influence of the last two
perceptions, complexity and product outcome, has already been proven in several studies.
According to the research from Dellaert & Dabholkar (2009), their two added consumer
perceptions turned out to have a strong impact on the intention to use an OMCS. Although
another product setting (car customization) will be applied compared to their research about
jeans, it is interesting to test whether their statements also prove valuable in
the
online
2.2.
configuration
Creative
of
cars.
consumer
behaviour
There are hundreds of different meanings of creativity in existing literature. However when it
comes to consumer’s creativity, experts often talk about problem-solving creativity. Creativity
is part of the daily survival of consumers and emphasizes the usefulness of creative thinking
when people face issues. Issues can be solved in a familiar way, but when people face new
problems, a greater need arises for new ways of doing things and therefore, the need for
creativity arises (Ackoff & Vergara, 1981). A clear definition of creativity is the one of Lubart &
Sternberg (1995) which states that creativity means the person’s capacity to produce work
that is original in the first place and in the second place is adapted to the
constraints
faced
in
that
situation.
When discussing creative consumers they will be defined as individuals or a group of
individuals which adapts, modifies or transforms a proprietary offering. When the earlier
discussed Ford Model T came out, farmers did not only use them as a means of transportation.
In fact, they modified this automobile in order to use it as a power source for driving
generators, mills, and lathes (Berthon et al., 2007). These famers apparently had a problem
with the propulsion of these tools and through creative thinking they tackled this problem. So
it is not only possible to modify the look of your car in a creative way, there are
also
creative
methods
of
using
these
cars.
Many acts of creative consumer behavior have, however, little to do with problem solving per
9
Online Mass Customization of Cars
M.J. Overbeek - 402629
se. Holt (1997) states that people modify or compose their clothes, houses or cars in their own
specific ways depending on their personal tastes. When this is the case, creativity can be
perceived as self-expression with social communication towards others. The utilitarian
function of creativity is then not crucial; it is about aesthetics. An expression that refers to
refinement or beauty of a product or any other outcome of a creative process, while not
keeping the utilitarian function in mind (Holt, 1997). One of the difficulties when dealing with
creativity is how to evaluate it. In a professional context, creativity could be evaluated by socalled design experts. These experts evaluate student’s projects made in a design studio.
These design experts are expected to share similar opinions about creativity levels applied in
a certain design project (Casakin & Kreitler, 2006). Even so how precise are
these
shared
opinions
and
are
they
shared
in
the
first
place?
Instead of evaluating creativity by outsiders or ‘design-experts’, there is also evidence
emphasizing the self-assessment of creativity. This could be considered as a self-judgement of
one’s perceived skills and imaginative abilities to generate bright solutions, behaviours and
ideas (Kaufman & Sternberg, 2010). Casakin and Kreitler (2006) show in their research
evidence that individuals’ self-assessed creativity turns out to be a legitimate and valid tool
for evaluating their own designs. Although other researchers such as Paulhus and John (1998)
doubt the validity of self-assessed creativity because of the assumed biases, the self-assessed
creativity will be applied in this research. Nevertheless this will be done while mentioning that
perceived creativity is not a fixed aspect. This view has been popularized while the creative
customer is demanding a more prominent role in product development and innovation (The
Economist, 2005).
3.
Figure
10
Conceptual
1:
The
framework
conceptual
framework
Online Mass Customization of Cars
M.J. Overbeek - 402629
Product expertise
System feature
Consumer perceptions
Range of mass
Creativity
customization choices
Enjoyment
Intention to
use OMCS
Control
3.1 The impact of consumer perceptions of creativity, enjoyment and control on the
intention
to
use
an
OMCS
This conceptual framework is partly inspired by the research from Dellaert & Dabholkar (2009)
who studied the drivers for consumer’s intention to use an OMCS. In their conceptual
framework they implemented a separation between system features, consumer perceptions
of cost-benefits and the intention to use an OMCS. Their manner of classification is used but
by implementing composed variables which will be further discussed in this chapter.
3.1.1.
Creativity
Whereas the concept of creativity was discussed earlier, its influence on the intention to use
mass customization programs will now be discussed. A phenomenon which often goes along
with creativity is daring to take risks. In order to be creative, individuals have to be willing to
take the risk with a possibility of failure (Tesluk et al., 1997). Hirschman (1980) found out that
consumers’ creativity is positively correlated with inter alia, looking for new experiences and
“modernity’ which implies the openness towards new ideas. Both correlations indicate that
creative consumers are open to new ideas and are often looking for them. An OMCS could
therefore be an attractive option for them while this target group do not just go along
with
11
the
crowd.
Online Mass Customization of Cars
M.J. Overbeek - 402629
Another interesting finding is the 'I designed it myself' effect in mass customization (Franke
et. al, 2010). When people really believe that they are the creator of a product, their utility
increases significantly and so does their willingness to pay for the product. Füller et. al. (2009)
state that an individual’s differences with regard to their creativity level will cause differences
in behaviour within an OMCS. A further study proved that creative consumers are more open
to co-creation of product and therefore more likely to participate in an OMCS compared to
their so called “less-creative counterparts” (Füller et al., 2011, p. 269). When consumers
perceive that they became more creative, it will be more likely that they will
participate
again
in
an
OMCS.
H1a: Greater perceived creativity in online mass customization increases intention to use.
3.1.2.
Enjoyment
According to Dellaert & Dabholkar (2009), perceived enjoyment is defined as “the consumer’s
perception of the pleasure associated with the experience of using online MC” (p. 46). At the
time when only traditional retail stores existed, there was evidence that enjoyment during the
shopping experience provided value for the consumer involved (Hirschman, 1983). This
hedonic shopping value could, according to Maclnnis & Price (1987), even increase without
actually buying the product you are looking for. It is about the enjoyment during the shopping
activity. Knowing the high prices cars usually have, consumers involved in the mass
customization of cars should definitely not also have to buy that car. An average citizen could
for
instance
not
afford
a
Ferrari,
but
someone
could
just
enjoy the customization process of that car (Holbrook & Hirschman, 1982).
Nowadays, the use of internet has emerged and so do the online shopping numbers.
Researchers such as Babin et al. (1994) have demonstrated the existence of hedonic and
utilitarian shopping value. These two values also seem to exist in the online environment.
According to Wolfinbarger and Gilly (2001) a separation exists between experiential, for
enjoyment, and the known utilitarian, with a goal, function of shopping. Although their
research proves that online consumers are mainly goal-oriented, it is interesting to test
whether enjoyment is a prominent consumer value. Their research is thirteen years old and
in
12
that
period
of
time
the
usage
of
internet
increased
enormously.
Online Mass Customization of Cars
M.J. Overbeek - 402629
With regard to technology-based services there is no clear evidence that perceived enjoyment
could increase the intention to use these technologies (Dabholkar & Bagozzi, 2002). Franke et
al. (2010) emphasize the mentioned the "I designed It myself" effect where fun during the
process of customization plays an important motivation to engage in an OMCS. In concrete
terms the research from Dellaert & Dabholkar (2009) proved that there is a positive effect of
enjoyment on the intention to use an OMCS in the setting of online jeans customization.
H1b: Greater perceived enjoyment in online mass customization increases intention to use.
3.1.3.
Control
Besides adding enjoyment as relatively new consumer perception, Dellaert & Dabholkar
(2009) also added ´control´ in their conceptual model as main consumer perceptions.
Perceived control is by them defined as “the extent to which consumers believe they are able
to determine the outcome of the MC process” (p. 45-46). With regard to personal interaction,
it is proven that a sense of control is desired by individuals. Schutz (1966) states that a feeling
of control is even necessary in order to interact in a satisfying manner with other individuals.
An example from Gilmore and Pine (1996) shows that Levi Straus adapted mass customization
in an early stage but with one main shortcoming. Although Levi Straus offered consumers
jeans which where based on consumers’ preferred sizes and styles, consumers were not that
satisfied. They only became satisfied if they were able to try the jeans in one of the design
stages. After providing feedback they got a feeling of control over the final product and their
satisfaction
with
the
customization
process
increased
as
well.
The earlier discussed research from Wolfinbarger and Gilly (2001) discovered that goalseeking consumer are predominantly active on the internet. It was noted that these
consumers prefer a feeling of freedom and/or control. The desire for this notion of control
could increase the incentive to involve in an OMCS where the consumer is in control when
making all kinds of product-specific decisions. The research from Pavlou & Fygenson (2006)
provides findings that consumers with a sense of control also have more intention to adopt ecommerce activities. An OMCS could be such an activity and Dellaert & Dabholkar (2009)
therefore implemented the consumer perceptions of control in their conceptual model. Their
13
Online Mass Customization of Cars
M.J. Overbeek - 402629
outcome was that consumers’ perceived control has a significant, positive effect on the
intention
to
use
an
OMCS.
H1c: Greater perceived control in online mass customization increases intention to use.
3.2. The impact of the range of mass customization choices on consumer
perceptions of creativity, enjoyment and control
The OMCS feature which will be highlighted in this study is the range of customization
choices available for the user. Online customization is in fact an interaction between the
company and the user. Whereas the company makes decisions about what kind of options
are available to the consumers, they also have to decide whether the range of customization
choices is extensive or relatively restricted (Piller et al., 2005). Different theories are
available which state whether consumers appreciate an extensive range of choices or not.
According to this literature there seems to exist a gap between the earlier mentioned 'I
designed it myself' effect (Franke et. al., 2010) and the ‘overchoice’ effect from Gourville &
Soman (2005).
The 'I designed it myself' effect (Franke et. al., 2010) states that consumers appreciate a high
level of control over the customization process. By providing a high amount of choices, an
OMCS could satisfy these consumers, while more choices mean more control instruments
for these individuals (Aichner & Coletti, 2013). It is interesting to test this finding with regard
to the three consumer perceptions. The other way of thinking emphazises the ‘overchoice’
effect, also known as the burden of choice (Gourville & Soman, 2005; Huffman & Kahn,
1998; Piller et al., 2005). This view states that consumer could become overwhelmed when
they are faced with too many options. A clear example from Piller et al. (2005) is the
situation when you are in a restaurant with too many choices on the menu card. Consumers
could become uncomfortable with an extreme number of choices. Therefore it seems to be
interesting to investigate whether and how the range of choices in an OMCS affects the
consumer perceptions.
3.2.1.
Creativity
Literature that actually describes the influence of the range of online customization choices
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on the consumers’ creativity does not exist but there is evidence available from a traditional,
offline environment. These researches used the concept of constraints with regard to the
influence on someone’s creativity. A type of constraint which has often been used in that
context are time constraints. When individuals are involved in an creative activity Stokes
(2001) found out that individuals become more creative when facing time constraints. This
finding has a side note that these constraints should be not too extreme, a statement Kelly &
Karau (1993) indicate as well. Ridgeway and Price (1994) composed a concept that moved
from time constraints towards input constraints. A person who wants to prepare a meal but
is facing input constraints such as restricted access to markets and products, actually
becomes
more
creative
in
his
cooking
activity.
A rigid definition of constraints is that they are “limitations or barriers imposed on the
individual that may lead to decreased or non-participation in an activity” (Patterson, 2001, p.
1). The research from Moreau and Dahl (2005) consists of both types of constraints, time and
input constraints. Their findings suggest that consumers turn out to become more creative
when they face a more constraint environment with regard to input constrains and when time
constraints are lacking. These input constraints reduce their customization options and
therefore the consumers’ level of creative processes increases. This is in line with earlier
cognitive psychology research from Stokes (2001) and Costello & Keane (2000). By restricting
the consumers’ options they are prevented of following their path-of-least-resistance (Ward,
1994). The input constraints could in this research be considered as the range of customization
choices
of
in
an
customization
OMCS
available
choices
means
for
a
consumers,
relatively
low
whereas
level
of
a
input
wide
range
constraints.
H2a: When the range of online mass customization choices decreases, perceived creativity
increases.
3.2.2.
Enjoyment
Section 3.1.2. showed that enjoyment is a prominent consumer value in shopping experiences.
During a shopping experience, consumers prefer to be in charge while they decide what to
purchase. With regard to customization, the ‘I designed It myself’ effect (Franke et al., 2010)
could indicate that consumers prefer many customization choices in order to like the
customization process. These persons want to get involved with the customization process
15
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and with a small number of customization choices, the probability of having fun during the
task will decrease. When facing constraints, individuals could according to Jackson (2000),
limit
the
enjoyment
and
participation
in
a
leisure-related
activity.
Findings from motivational researchers Ryan & Deci (2000) suggest that constraints, such as a
particular set of instructions, will decrease a individual’s perception of autonomy during a
task. The feeling of having freedom during a task could probably increase the enjoyment
perceived. Dellaert & Dabholkar (2009) actually investigated the influence from range of
customization choices on consumer perceptions of enjoyment in the process of an OMCS.
Their outcomes show that there exists a significant, positive relationship between both
concepts. The system feature ‘choices’ is able to influence the consumer side of perceived
enjoyment. The more customization choices available, so less perceived constraints, the
more
enjoyment
a
consumer
will
perceive.
H2b: When the range of online mass customization choices increases, perceived enjoyment
increases.
3.2.3.
Control
Research from Veitch & Gifford (1996) provides evidence that individuals prefer to have
choices in order to have a sense of personal control. Personal control means, according to
Ward and Barnes (2001), that a person has the perception that the choices he or she makes
actually matter and thus determine an outcome. Providing a wide range of choices could
increase the level of perceived control. These findings all come from a phychological point of
view. In the field of consumer research Hui & Bateson (1991) provide evidence that perceivedcontrol exists, which means that providing more choice to consumers will create a more
pleasant service experience for them. According to these findings it is plausible that a larger
range of OMCS choices will also cause a higher level of perceived control.
H2c: When the range of online mass customization choices increases, perceived control
increases.
16
Online Mass Customization of Cars
3.3.
Moderating
M.J. Overbeek - 402629
variable
‘product
expertise’
According to several researches, consumers involved in mass customization could face an
‘overchoice’ effect (Gourville & Soman, 2005; Huffman & Kahn, 1998; Piller et al., 2005). This
means that consumers could become confused by a wide range of choices. Other researches
emphasize this phenomenom as well, but they make a distinction between consumers with
and consumers without product expertise. According to Chase and Simon (1973) experts are
better able to process a high number of options. Furthermore, they are less subject to the
‘overchoice’ effect or the information overload like the researchers refer to this. Consumers
with product expertise know what their preferences are with regard to the product
attributes
they
have
to
make
decisions
about.
Dellaert and Stremersch (2005) found out that consumers with product expertise considered
mass customization systems less complex than consumers with low levels of product
expertise. Consumers that lack product expertise could have problems with figuring out what
they prefer. A huge range of options could, especially for them, be overwhelming instead of
enriching (Huffman & Kahn, 1998). This problem could frustrate consumers and decrease sales
of the firms which offer the wide range of choices. In order to overcome the problem of
consumers lacking product expertise, some retailers actively guide these consumers through
the shopping experience. They do this by explicitly asking these consumers for their withinattribute preferences. When retailers know this, they are better able to offer options that best
match
the
needs
of
these
specific
consumers
(Huffman
&
Kahn,
1998).
According to this evidence, it is plausible that product expertise effects the influence of range
of mass customization options. The moderating role of ‘product expertise’ will therefore be
placed in the relationship between range of customization choices and the three consumer
perceptions, see figure 1. The presence of this moderator will influence the effects of
consumers with product expertise (Chase & Simon, 1973; Huffmann & Kahn, 1998). These
consumers are better able to process a wide range of choices compared to consumers with
less product expertise. With this knowledge it is plausible that product expertise will decrease
the negative effect of range of choices on perceived creativity, hypothesis H2a. Product
expertise makes consumer more capable in the customization assignment. With regard to
hypotheses H2b and H2c, the influence of product expertise will probably be positive of
17
Online Mass Customization of Cars
M.J. Overbeek - 402629
nature. If consumers prove to be more capable, they will better enjoy the assignment because
of the familiarity. The same will probably hold for the perceived control which will increase
more for consumers with product expertise. The hypotheses related to the moderating
variable
‘product
expertise’
are
therefore
as
follows:
H3a: Product expertise decreases the effect of range of online mass customization choices
on
perceived
creativity
H3b: Product expertise increases the effect of range of online mass customization choices on
perceived
enjoyment
H3c: Product expertise increases the effect of range of mass customization choices on
perceived
Figure
control
2:
The
conceptual
framework
with
hypotheses
implemented
Product expertise
H3a H3b +
H3c +
Range of mass
Perceived creativity
H2a H1b +
customization
choices
Perceived enjoyment
Intention to
use OMCS
H2b +
H2c +
18
H1a +
Perceived control
H1c +
Online Mass Customization of Cars
M.J. Overbeek - 402629
3.4. Overview of the hypotheses
Table 1: Overview of the hypotheses
Hypotheses
H1a
Greater perceived creativity in online mass customization increases
intention to use
H1b
Greater perceived enjoyment in online mass customization increases
intention to use
H1c
Greater perceived control in online mass customization increases intention
to use
H2a
When the range of online mass customization choices decreases, perceived
creativity increases.
H2b
When the range of online mass customization choices increases, perceived
enjoyment increases
H2c
When the range of online mass customization choices increases, perceived
control increases
19
Online Mass Customization of Cars
H3a
M.J. Overbeek - 402629
Product expertise decreases the effect of range of online mass
customization choices on perceived creativity
H3b
Product expertise increases the effect of range of online mass
customization choices on perceived enjoyment
H3c
Product expertise increases the effect of range of mass customization
choices on perceived control
4. Methodology
4.1.
Data
collection
The method of a survey will be applied in order to collect data for this research. According to
Malhotra et al. (2006) the main advantages of surveys are the simplicity, ease and reliability.
Surveys collect data from its respondents by asking questions about what they do, how they
think and who they are (Balnaves & Caputi, 2001). Andrews (1984) doubts the validity of data
derived from surveys because respondents often seem to give socially desirable answers. Piko
(2006) recognises this issue and his advice is to stress the anonymity in the survey,
consequently the results will be as close to the truth as possible. At the beginning of the survey
is stated that people who fill in the survey completely, have a chance to win a gift. Giving a
reward is a recommendation of Piko (2006) to increase the response rate of a survey.
The data will be collected in an online environment by using the online survey tool ‘Qualtrics’
(2015). Malhotra et at. (2006) found out that surveys on the internet have as main advantages
that respondents can fill in the survey at ease and the collection of data is relatively cheap and
20
Online Mass Customization of Cars
M.J. Overbeek - 402629
easy to gather. Wright (2005) recognizes the speed of gathering data on the internet and also
states that the analysis could be faster than hand-filled surveys since computers do not make
counting mistakes. Respondents will in the first instance also be approached on the internet,
with
communication
mails
asking
tools
such
for
as
Facebook,
participation
4.2.
in
LinkedIn
the
The
and
by
online
sending
e-
survey.
Questionnaire
In the English questionnaire, two different scenarios will be applied. These two scenarios are
related to the range of customization chices. In both scenarios, respondents get the same
assignment to customize a car website towards their own preferences on an external, existing.
When they have done this, the respondents have to return to the survey and need to answer
questions related to the just fulfilled customization assignment. In the first scenario,
respondents will be send to a car customization website with a relatively small range of
customization choices. After answering the first scenario-related questions, respondents are
send to the second scenario with many more customization choices. This OMCS is less
constrained compared to the program from the first scenario but in both scenarios the same
type of car is being used, a Renault Twingo. The website from the first scenario is Justlease.nl
(Justlease, 2015) and the second website is from Renault itself (Renault, 2015).
The questions related to the scenarios are all fixed-response questions and the data collected
in this research is therefore of a quantitative sort. This type of data has as main advantage
that it makes analysis, coding and interpretation of the data more easy. This data is also
useable for comparing while the fixed-response aspect reduces the variability of the survey
results (Malhotra et al., 2006). According to Babbie (2004) this research could also be
considered a cross-sectional study, while the data arises from a sample at one time period. In
order to answer the survey questions, respondents do not need prior knowledge about cars
or mass customization in general. The survey is relatively easy to understand and so
appropriate for a large target group.
4.3.
Measurements
of
the
questionnaire
In this section the content of the questionnaire will be explained. A complete overview of the
scale items could be found table 2.
21
Online Mass Customization of Cars
4.3.1.
Range
M.J. Overbeek - 402629
of
mass
customization
choices
No direct survey questions are attached to this variable while it is included in the two different
scenarios of the survey. With regard to the analysis, the customization program with the small
range of choices gets the label ‘0’ and the more extensive program will receive the label ‘1’.
4.3.2.
Intention
to
use
an
OMCS
In line with the research from Dellaert & Dabholkar (2009), their scale items will be applied
which correspond with the variables from our conceptual framework. With regard to the
intention to use an OMCS, the question they used in order to measure this variable is: “If a
mass customization option, as described in the scenario, was available to you, would you make
use of it?” (p. 69). This question will be modified slightly while this research does not offer a
fictitious scenario in the survey like they did. The question in order to measure ‘intention to
use an OMCS’ will therefore be: if an online mass customization system, like you just worked
with, was available to you, would you make use of it? The corresponding scale of this variable
is a 100% percentage scale, derived from the Ruler and Option scale (Michell, 1997). A 100%
percentage scale starts at 0% and ends at 100%, in between are 100 markers which could be
selected. The usage of a 100% percentage scale will provide a sensitive measurement of
someone’s
attitude,
4.3.3.
in
this
case
Consumer
their
intention
perceptions
to
use
an
of
OMCS.
creativity
This variable, as well as the variables in sections 4.3.4., 4.3.5. and 4.3.6., will be assessed
according to the 7-point Likert scale. Where ‘1’ means strongly agree and ‘7’ means strongly
disagree, this agreement scale is an advice of Braunsberger and Gates (2009). The two
questions with regard to the variable ‘perceived creativity’ are inspired by the research from
Füller
et
al.
(2009),
these
two
questions
are:
- I considered myself as an inventive kind of person during the customization process.
- I considered myself as creative and original in my thinking and behaviour during the
customization
4.3.4.
process.
Consumer
perceptions
of
enjoyment
With regard to this variable, the questions from the research by Füller et al. (2009) will be
22
Online Mass Customization of Cars
used,
M.J. Overbeek - 402629
these
-
three
questions
“Participation
-
was
Participation
-
Participation
4.3.5.
are:
fun
was
was
Consumer
enjoyable
exiting”
(p.
perceptions
85)
of
control
The scale items of this variable are derived from the research by Dellaert & Dabholkar
(2009).
The
questions
are
the
following:
- I am satisfied with the amount of control I have over the customization process provided in
the
scenario
- The customization process, portrayed in the scenario, will give me control over designing
my
4.3.6.
own
car
Product
expertise
The questions related to the variable ‘product expertise’ are based on the research from
Siemens et al. (2006), the questions to measure ‘product expertise’ are therefore as follows:
-
I
-
23
am
I
I
experienced
perceive
am
myself
knowledgeable
in
the
as
with
field
a
regard
of
car
cars
expert
to
cars
Online Mass Customization of Cars
Table
2:
The
M.J. Overbeek - 402629
scale
items
of
the
variables
Variable
Questions
Scale
Intention to use
If an online mass customization system, like
100% percentage
you just worked with, was available to you,
would you make use of it?
(Dellaert & Dabholkar, 2009)
Consumer perception
- I considered myself as an inventive kind of
of creativity
person during the customization process
7-point Likert
- I considered myself as creative and original
in my thinking and behaviour during the
customization process
(Füller et al., 2009)
Consumer perception
- Participation was fun
of enjoyment
- Participation was enjoyable
7-point Likert
- Participation was exiting
(Füller et al., 2009)
Consumer perception
- I am satisfied with the amount of control I
of control
have over the customization process
provided in the scenario
24
7-point Likert
Online Mass Customization of Cars
M.J. Overbeek - 402629
- The customization process, portrayed in
the scenario, will give me control over
designing my own car
(Dellaert & Dabholkar, 2009)
Product expertise
- I am experienced in the field of cars
7-point Likert
- I perceive myself as a car expert
- I am knowledgeable with regard to cars
(Siemens et al., 2006)
4.4.
Control
variables
Some of the chosen control variables are of demographic nature, such as age and gender. It
could be possible that younger people are more known with the internet and therefore more
likely to use an OMCS in general. Besides this data, some questions will be about the
respondent’s familiarity with online mass customization and mass customization in a
traditional retail setting. Other control variables are more car-related such as whether
respondents have a driving licence or own a car. While the customization program has a car
theme, it is conceivable that respondents which are more familiar with cars have a higher
intention
to
use
this
specific
OMCS.
A control variable specificially related to the amount of choices will test how respondents
perceive the amount of choices. Reutskaja and Hogarth (2005) state that choice satisfaction
has an inverted U-shaped relationship with the amount of alternatives in the choice set.
Having too few choices is not preferable, but having too many options is not either. The survey
question to test this phenomenom will be: “Do you feel you had the right amount of options
to choose from?” (Reutskaja & Hogarth , 2005, p. 12). This question could be answered
according a 9-point Likert scale, where “1 = “No, I had too few choice options,” 5 = “Yes, I had
just the right number of choice options,” and 9 = “No, I had too many choice options” “
(Reutskaja & Hogarth , 2005, p. 12). The variable ‘range of customization choices’ that will be
25
Online Mass Customization of Cars
M.J. Overbeek - 402629
used in the hypotheses testing is scenario-given, but this control variable will measure the
respondent’s opinion about the range of customization choices.
5. Analysis and Results
The questionnaire was online for thirty days and 147 persons filled in the questionnaire in that
time period . After studying the data it turned out that 44 questionnaires contained missing
values. These persons abandoned the questionnaire in an early stage, mainly at the point
where the respondent had to visit one of the two external customization websites. It seems
that some people are not willing to put some effort in filling in a questionnaire. So when these
people noticed that they had to visit an external website as part of the questionnaire, the
required effort was apparently too much to ask. Therefore, these 44 responses were deleted
from the dataset which led to a final total of 103 respondents (N= 103). So 70,07% of the
respondents
that
started
the
online
questionnaire
actually
completed
it.
With regard to the demographics of the final sample, it turned out that 62,14% of the
respondents was male and 37,86% was female. The majority of the respondents had the Dutch
nationality, to be precise 89,32% of them. Other more car-related data shows that 76,69% of
the respondents has a driving license and 47,57% of the respondents owns a car. The
percentage that owns a car seems to be relatively low, but this is probably caused by the age
of our sample. The mean age of the sample is 25,55 years, but even more interesting is that
75,73% of the respondents is 25 years old or younger. Respondents had on average more
experience with online mass customization than with mass customization in a traditional retail
setting, 43,69% and 34,95% respectively. This outcome is probably also caused by the age of
26
Online Mass Customization of Cars
our
sample,
instead
of
younger
that
people
more
M.J. Overbeek - 402629
have
less
online
offline
experience
shopping
(Wan
experience
et
al.,
but
2012).
The first step of analyzing the sample data will be the application of a factor analysis. This is
an useful method in order to reduce the large amount of survey questions. This factor analysis
will first test the validity and after that a reliability test will be performed on the questions
which, according to the factor analysis, belong to one variable. The Cronbach alpha will then
be studied and when all these findings are appropriate, a regression analysis
will
be
performed
5.1.
to
test
the
hypotheses.
Factor
analysis
While two different scenarios were used in the questionnaire, the factor analysis will be
divided per scenario. The outcomes of the factor analysis will then also be less extensive and
therefore easier to interpret. Like shown in table 2, four factors are expected to emerge in
the factor analysis. These factors are the four variables which consist of more than one survey
question: product expertise, perceived creativity, perceived control and perceived enjoyment.
The factor analysis of scenario ‘0’ will first be performed, the scenario with the small range of
choices. In both scenarios the variable ‘product expertise’ is constant while
the questions
5.1.1.
of this
Scenario
variable are not
‘0’
with
the
attached to one of the scenarios.
small
range
of
choices
In this first factor analysis the variables Experience (1,2,3), Creativity_0 (1,2), Enjoyment_0
(1,2,3) and Control_0 (1,2) are implemented, these variables could be found in Appendix B.1.
A fixed number of four factors is set while this is given by the theoretical framework. For
results of the factor analysis, see appendix C.1. To check the reliability of the performed factor
analysis, the Kaiser-Meyer-Olkin measure of sampling adequacy and the Bartlett’s test of
sphericity will be studied. The Kaiser-Meyer-Olkin measure of sampling adequacy turns out to
be 0,82. This is an appropriate value while Field (2013) states that values above 0,50 are
acceptable and values between 0,80 and 0,90 are even great. The Bartlett’s test of sphericity
seems to be good as well, this value is highly significant with a value of 0,00.
Now both indicators show that the factor analysis is appropriate, the factor loadings from the
27
Online Mass Customization of Cars
M.J. Overbeek - 402629
rotated component matrix will be highlighted. In order to make interpretation and comparison
more easily loadings below 0,50 are suppressed. Field (2013) states that these loadings do not
provide sufficient content about the underlying dimension. The four factors explain 91,06% of
the cumulative variance and the factor loadings score high on their own factor and lower then
0,50 on the other factors. This confirms the statements from the methodology, the lowest
loading is 0,70 and the highest loading is 0,94.. This scenario seems to pass the factor analysis.
The same method of analysis will now be applied on the other scenario with the wide range
of customization choices.
5.1.2.
Scenario
‘1’
with
the
wide
range
of
choices
In this second factor analysis the variables Experience (1,2,3, Creativity_1 (1,2), Enjoyment_1
(1,2,3) and Control_1 (1,2) are implemented, which could be found in Appendix B.1. The same
methods are being used like discussed in the previous section, for results see Appendix C.2.
The Kaiser-Meyer-Olkin measure of sampling adequacy is 0,75, slightly lower compared to the
first factor analysis. Values between 0,70 and 0,80 are perceived as good (Field, 2013) so the
Bartlett’s test of sphericity will now be highlighted. The significance level is equal to the first
analysis, a level of 0,00. The rotated component matrix shows good outcomes as well, all
factor loadings score again high on their own factor and lower then 0,50 on the other factors.
The four factors explain 86,80% of the cumulative variance. Both scenarios
seem
to
pass
5.2.
the
factor
analysis.
Reliability
test
The factor analyses showed existing constructs in the dataset. At this section the reliability of
the questions that form a construct will studied by using the Cronbach’s Alpha. According to
Field (2013) the Cronbach’s Alpha could have a score between 0 and 1, scores above 0,70 are
generally perceived as reliable and the closer to 1, the more reliable these questions are. For
instance, the following questions form the construct ‘product expertise’: I am experienced in
the field of cars, I perceive myself as a car expert and I am knowledgeable with regard to cars.
The Cronbach’s Alpha of these three questions measured is 0,93. This value is larger than 0,70
and close to 1 so there could be stated that these questions are reliable in testing the same
construct.
28
This
analysis
is
applied
to
the
six
other
constructs
as
Online Mass Customization of Cars
well
and
the
results
can
M.J. Overbeek - 402629
be
seen
in
table
3
on
the
next
page.
Table 3: The Cronbach’s Alpha outcomes
Construct
Cronbach’s alpha
N of items
Experience
0,93
3
Creativity_0
0,94
2
Enjoyment_0
0,96
3
Control_0
0,89
2
Creativity_1
0,84
2
Enjoyment_1
0,90
3
Control_1
0,84
2
All Cronbach‘s alpha values turn out to be high with values between 0,84 and 0,96. The seven
constructs can therefore be perceived as relatively reliable with regard to the related survey
questions. Before starting with the regression analysis, the survey questions that belong to
one construct will first be joined together. Since all corresponding questions have the same 7point Likert scale, the new variables could be computed by summing up the corresponding
questions and after that dividing by the same number of questions. Here some examples:
‘Product
expertise
‘Creativity_0
5.3.
=
=
(Expertise1
+
(Creativity1_0
Expertise2
+
Hypotheses
+
Expertise3)
Creativity2_0)
/
3’
/
and
2’.
testing
A linear regression analysis will now be applied in order to test the hypotheses from the
conceptual framework, like shown in table 1. Before starting with these regressions, the
29
Online Mass Customization of Cars
M.J. Overbeek - 402629
variables of the two scenarios need to be merged. This is done by creating a dummy variable
called ‘scenario’ which has the label ‘0’ or ‘1’ where ‘0’ applies to the customization program
with the small range of choices and ‘1’ to the more extensive customization program. By
implementing this dummy variable, the N of our dataset doubles because the scenariodependent variables of each respondent gets separated. Now each respondent appears twice
in the dataset, once with variables concerning scenario ‘0’ and once with scenario ‘1’
variables. The descriptive statistics of the adapted dataset can be seen in appendix B.2. It is
now possible to study the difference in average intention to use between the two scenarios.
The mean intention to use of scenario ‘0’ turns out to be 51,00% and the mean intention to
use of scenario ‘1’ is 74,18%, see appendix B.3. The intention to use the online mass
customization program with the wide range of customization choices is on average higher
compared
5.3.1.
to
Regression
the
one
analysis
with
for
the
the
small
intention
range
to
use
of
choices.
an
OMCS
The usage of this regression analysis will test the hypotheses H1a, H1b and H1c. The
dependent variable is ‘intention to use’ which is measured by a 100% percentage scale . The
independent variables are ‘perceived creativity, enjoyment and control’, all measured by a 7point Likert scale. The R² value of the model is 0,472 which implies that 47,20% of the variance
between these variables is explained by this model. The model is further significant as a whole
with F= 62,15 and p=0,00. The outcomes of the regression analysis can be seen in
table
4
below.
Table 4: Regression analysis for the intention to use an OMCS
Variable
Unstandardized coefficients
Significance
(Constant)
9,68
0,02
Creativity
1,61
0,14
Enjoyment
8,86
0,00
Control
1,76
0,16
According to these results, only hypothesis H1b could be supported. The variable perceived
enjoyment has a significant (p= 0,00) and positive (B= 8,86) effect on intention to use. This
30
Online Mass Customization of Cars
M.J. Overbeek - 402629
implies that if the 7-point Likert scale of perceived enjoyment increases with ‘1’, the intention
to use the mass customization system increases with 8,86%, ceteris paribus. This positive
influence corresponds with the direction of hypothesis H1b. Hypotheses H1a and H1c will be
rejected while both variables proved to have an insignificant effect on intention
to
use
5.3.2.
(respectively
Regression
p=0,14
analysis
for
and
p=0,16).
perceived
creativity
With the regression analysis below, hypothesis H2a ‘when the range of online mass
customization choices decreases, perceived creativity increases’ will be tested. In this model
the variable ‘perceived creativity’ is the dependent variable and ‘scenario’ is the independent
variable. This variable indicates whether a respondent had a limited or extensive
customization program. The R² value is 0,130 which implies that 13,00% of the variance
between these variables is explained by this model. The model is significant as a whole with
F= 30,46 and p=0,00. The results of the regression analysis can be seen in table 5 below.
Table 5: Regression analysis for perceived creativity
Variable
Unstandardized coefficients
Significance
(Constant)
3,55
0,00
Scenario
1,09
0,00
The results of this regression analysis do not support hypothesis H2a. The variable ‘scenario’
has a significant (p= 0,00) and positive (B= 1,09) effect on perceived creativity. The significance
is thus good, but the direction of the effect does not correspond with the hypothesis. There
was stated that a negative effect should occur, but apparently perceived creativity increases
when
5.3.3.
faced
to
a
customization
Regression
program
analysis
with
for
a
wide
range
perceived
of
choices.
enjoyment
This regression analysis will test hypothesis H2b ‘when the range of online mass customization
choices increases, perceived enjoyment increases’. In this model the variable ‘perceived
enjoyment’ is the dependent variable and ‘scenario’ is the independent variable. The R² value
is 0,289 which implies that 28,90% of the variance between these variables is explained by this
31
Online Mass Customization of Cars
M.J. Overbeek - 402629
model. The model is further significant as a whole with F= 83,02 and p=0,00. The outcomes of
the
regression
analysis
can
be
seen
in
the
table
below.
Table 6: Regression analysis for perceived enjoyment
Variable
Unstandardized coefficients
Significance
(Constant)
3,59
0,00
Scenario
1,57
0,00
The outcomes of this regression analysis support hypothesis H2a. The variable ‘scenario’ has
a significant (p= 0,00) and positive (B= 1,57) effect on perceived creativity. The overall
perceived enjoyment is thus a mean of 1,57 point higher when faced to an extensive
customization program instead to a customization program with a small range of choices,
ceteris paribus. This corresponds with the findings from Ryan & Deci (2000) who stated that a
feeling of freedom during a task, in this case by having a wide range of choices, increases
the
perceived
5.3.4.
Regression
analysis
enjoyment.
for
perceived
control
The usage of this regression analysis will test hypothesis H2c ‘when the range of online mass
customization choices increases, perceived control increases’. In this model the variable
‘perceived control’ is the dependent variable and ‘scenario’ is the only independent variable.
The R² value is 0,204 which implies that 20,40% of the variance between these variables is
explained by this model. The model is significant as a whole with F=52,33 and p=0,00. The
results
of
the
regression
analysis
can
be
seen
in
table
7
below.
Table 7: Regression analysis for perceived control
Variable
Unstandardized coefficients
Significance
(Constant)
3,55
0,00
Scenario
1,47
0,00
The results of this regression analysis support hypothesis H2c. The variable ‘scenario’ has a
32
Online Mass Customization of Cars
M.J. Overbeek - 402629
significant (p= 0,00) and positive (B= 1,47) effect on perceived control. The overall perceived
control is thus a mean of 1,47 point higher when faced to an extensive customization program
instead of a customization program with a small range of choices, ceteris paribus. More
choices apparently increase the sense of having control over performing the customization
task.
5.3.5. Regression analysis for moderating effect of product expertise on perceived creativity
The following three regression analyses will test the interaction effect of product expertise
like stated in hypothesis H3a, H3b and H3c. First hypothesis H3a will be tested, in this case
‘product expertise’ should have an interaction effect with ‘scenario’ which has a negative
influence on perceived creativity. ‘Product expertise’ is in the model implemented as a
separate variable and as an interaction variable with ‘scenario’. The R² value of the model is
0,164 which implies that 16,40% of the variance between these variables is explained by this
model. The model is further significant as a whole with F=13,16 and p=0,00. The outcomes of
the
regression
analysis
can
be
seen
in
table
8
below.
Table 8: Regression analysis for perceived creativity
Variable
Unstandardized coefficients
Significance
(Constant)
3,20
0,00
Scenario
0,69
0,13
Product expertise
0,03
0,23
Scenario x Product expertise
0,04
0,33
The results of this regression analysis are obvious, none of the variables implemented has a
significant effect. The variable ‘product expertise’ has no significant effect on perceived
creativity at itself (p= 0,23) and no significant effect as an interaction variable together with
scenario (p= 0,33). The level of product expertise has apparently no effect on perceived
creativity in a direct or indirect way.
5.3.6. Regression analysis for moderating effect of product expertise on perceived
33
Online Mass Customization of Cars
M.J. Overbeek - 402629
enjoyment
This regression analysis will test hypothesis H3b ‘product expertise increases the effect of
range of online mass customization choices on perceived enjoyment’. The independent
variables of this model are the same like the variables of table 8 and the dependent variable
is now ‘perceived enjoyment’. The R² value of this model turns out to be 0,302 which implies
that 30,20% of the variance between these variables is explained by this model. The model is
significant as a whole with F=29,17 and p=0,00. The results of this regression analysis can be
seen
in
table
9
on
the
next
page.
Table 9: Regression analysis for perceived enjoyment
Variable
Unstandardized coefficients
Significance
(Constant)
3,99
0,00
Scenario
0,88
0,03
Product expertise
-0,04
0,12
Scenario x Product Expertise
0,07
0,06
The results of this regression analysis do not support hypothesis. The interaction variable has
the right positive direction (B= 0,07) but is just not significant (p= 0,06). The variable ‘scenario’
at itself is now significant (p=0,03) and has a positive effect (B= 0,88). This is in line with the
supported outcome of hypothesis H2b which showed the same result. Product expertise is as
an independent variable not significant (p= 0,12), but this is not surprising since we only
assumed an indirect effect of product expertise. The indirect effect of product expertise (p=
0,06) is in fact more significant compared to the direct effect of product expertise (p= 0,12),
so this is in line with that assumption. However hypothesis H3b still needs to be rejected while
the significance level is slightly too low given a significance level of 0,05.
5.3.7. Regression analysis for moderating effect of product expertise on perceived control
At this section hypothesis H3c ‘product expertise increases the effect of range of online mass
customization choices on perceived control’ will be tested. Hypothesis H3a and H3b proved to
be insignificant, knowing that, it is likely that ‘product expertise’ does not have an indirect
effect at hypothesis H3c as well. The R² value of this model is 0,21 which means that 21,00%
34
Online Mass Customization of Cars
M.J. Overbeek - 402629
of the variance between these variables is explained by the model. This model is further
significant as a whole with F=17,99 and p=0,00. The outcomes of the regression analysis can
be
seen
in
table
10.
Table 10: Outcomes regression analysis for perceived control
Variable
Unstandardized coefficients
Significance
(Constant)
3,91
0,00
Scenario
0,95
0,05
Product expertise
-0,03
0,23
Scenario x Product expertise
0,05
0,23
The results of this regression analysis do not support hypothesis H3c. The interaction variable
‘scenario x product expertise’ has the right positive direction (B= 0,05), but is not significant
(p= 0,23). Product expertise does not have a significant indirect effect on perceived control
(p= 0,23) and does not have a direct effect on perceived control (also p= 0,23). The variable
‘scenario’ is again significant (p=0,05) and has a positive effect (B= 0,95). This is in line with
the supported outcome of hypothesis H2c that showed the same result. Now with the
presence of other independent variables, ‘scenario’ has the same positive effect on perceived
control. More choices increase the sense of having control over a customization task, but the
existence
35
of
product
expertise
does
not
influence
this
relationship.
Online Mass Customization of Cars
M.J. Overbeek - 402629
5.3.8. Overview results of hypotheses testing
Table 10: The results of the hypotheses testing
Hypotheses
H1a
Result
Greater perceived creativity in online mass customization
Rejected
increases intention to use
H1b
Greater perceived enjoyment in online mass customization
Supported
increases intention to use
H1c
Greater perceived control in online mass customization
Rejected
increases intention to use
H2a
When the range of online mass customization choices
Rejected
decreases, perceived creativity increases.
H2b
When the range of online mass customization choices
Supported
increases, perceived enjoyment increases
H2c
When the range of online mass customization choices
Supported
increases, perceived control increases
H3a
Product expertise decreases the effect of range of online
Rejected
mass customization choices on perceived creativity
H3b
Product expertise increases the effect of range of online
Rejected
mass customization choices on perceived enjoyment
H3c
Product expertise increases the effect of range of mass
customization choices on perceived control
36
Rejected
Online Mass Customization of Cars
5.4.
M.J. Overbeek - 402629
Mediation
analysis
According to the conceptual framework from figure 2, there could probably exist a mediation
effect while the range of customization choices could influence the dependent variable
‘intention to use’ indirectly. This could be facilitated by the variables ‘perceived creativity,
enjoyment and control’. In order to test this, a mediation analysis will be performed according
to the method from Preacher and Hayes (2008). Their analysis offers the possibility to perform
a test with multiple mediators implemented. Since this frameworks deals with three possible
mediators, this seems to be an appropriate method of analysis. Preacher and Hayes (2008)
provide a SPSS software tool on their website (Hayes, 2015) with which the analysis could be
performed. ‘Intention to use’ is implemented as the dependent variable (DV), ‘scenario’ as the
independent variable (IV) and perceived creativity, enjoyment and control as the proposed
mediators (MEDS). The complete outcome of the analysis could be found in appendix E.8., in
the tables below a summary of the results. Note that in this analysis the constant is taken into
account, but is not shown in the output of this external SPSS software tool.
Table 11: Outcomes of mediating analysis, IV to Mediators (a paths)
Variable
Coefficient
Significance
Creativity
1,09
0,0000
Enjoyment
1,57
0,0000
Control
1,47
0,0000
Table 12: Outcomes of mediating analysis, Direct Effects of Mediators on DV (b paths)
37
Variable
Coefficient
Significance
Creativity
1,55
0,15
Enjoyment
7,76
0,00
Control
1,60
0,20
Online Mass Customization of Cars
38
M.J. Overbeek - 402629
Online Mass Customization of Cars
M.J. Overbeek - 402629
Table 13: Outcomes of mediating analysis, Total Effect of IV on DV (c path)
Variable
Coefficient
Significance
Scenario
23,18
0,0000
Table 14: Outcomes of mediating analysis, Direct Effect of IV on DV (c' path)
Variable
Coefficient
Significance
Scenario
6,95
0,02
The R² value of the total model (Appendix E.8) turned out to be 0,4942 which implies that
49,42% of the variance between these variables is explained by this model. The model is
further significant as a whole with F=49,11 and p=0,00. The results show that scenario (IV) has
a significant influence on all three mediators. However, the mediators do not all have a
significant effect on intention to use (DV), in fact only enjoyment has a positive (B= 7,76) and
a significant influence (p= 0,00). When focussing on the effect of scenario (IV) on intention to
use (DV) there could be noticed that the total effect (B= 23,18 and p= 0,00) and the direct
effect
(B=
6,95
and
p=
0,02)
both
are
significant.
The indirect effect that implies the mediation effect could be calculated by subtracting the
direct effect from the total effect, thus the indirect effect has an impact of ‘16,23’ (23,18 6,95). The indirect effect is more than two times as large as the direct effect of scenario (IV)
on intention to use (DV). A strong partial mediation exists where the mediated (indirect) effect
is larger compared to the direct effect. This mediation is according to table 12 only facilitated
by the variable ‘perceived enjoyment’ while this is the only mediator with a significant effect
on intention to use (DV). All of these results are shown in figure 3. Here the outcomes of the
mediation
‘N.S.’
39
analysis
are
implemented
means
in
the
conceptual
not
framework,
where
significant.
Online Mass Customization of Cars
M.J. Overbeek - 402629
Figure 3: The outcomes of the mediation analysis displayed in the conceptual framework
Perceived creativity
N.S.
+
Range of mass
+
customization
Intention to
Perceived enjoyment
choices
use OMCS
+
(Scenario)
+
Perceived control
+
40
N.S.
Online Mass Customization of Cars
5.5.
Regression
analysis
M.J. Overbeek - 402629
with
control
variables
This regression analysis will test whether the measurable control variables from our
questionnaire have an influence on the intention to use an OMCS. These control variables are:
gender, age, experience_traditional, experience_online, drivinglicence, carowner and
amountchoices. With these variables implemented in the regression on intention to use, the
R² of the model is 0,532, which implies that 53,20% of the variance between these variables
is explained by the model. This model is significant as a whole with F=18,27 and p=0,00. The
results of the regression analysis can be seen in table 15 below.
Table 15: Regression analysis for the intention to use with control variables implemented
Variable
Unstandardized coefficients
Significance
(Constant)
27,55
0,00
Scenario
7,02
0,02
Gender
-6,89
0,02
Age
-0,35
0,02
Experience_traditional
2,94
0,33
Experience_online
-0,71
0,80
Drivinglicense
3,45
0,24
Carowner
0,02
0,99
Amountchoices
0,14
0,87
Product Expertise
-0,69
0,02
Creativity
1,54
0,16
Enjoyment
8,10
0,00
Control
1,08
0,44
The outcomes of the regression analysis show that two out of the seven control variables have
a significant influence on the dependent variable ‘intention to use’. Gender (male= 1, female=
0) is the control variable that is significant (p= 0,02) and has a high coefficient (B=
-6,89). This implies that if a respondent is a male, the overall intention to use is a mean of
6,89% lower than when a respondent is a female, ceteris paribus. The other significant control
variable is age (p= 0,02); this variable has a negative influence on intention to use (B= -0,35).
41
Online Mass Customization of Cars
M.J. Overbeek - 402629
This implies that if a respondent is older, the intention to use an online customization program
will be lower in general. This outcome seems to be quite logical, while younger people are
generally more used to the internet compared to older people. The familiarity with internet
could increase the intention to use an OMCS. Another notable outcome of this regression
analysis is the effect of product expertise on intention to use an OMCS (B= -0,69 and p= 0,02).
This implies that a higher amount of product expertise decreases the intention to use an
OMCS, ceteris paribus. Product expertise could apparently have an effect on the dependent
variable ‘intention to use’ on itself, but not while interacting with ‘scenario’.
It is still unclear whether the two significant control variables (age and gender) do have an
interaction effect with ‘scenario’ on the intention to use an OMCS. Therefore, two interaction
variables are composed: ‘Gender x Scenario’ and ‘Age x Scenario’. After the addition of these
two variables to the previous regression analysis , the R² of the model becomes 0,535 which
implies that 53,50% of the variance between all these variables is explained by the model. This
model is significant as a whole with F=15,67 and p=0,00. The outcomes of the regression
analysis can be seen in table 16 on the next page.
42
Online Mass Customization of Cars
M.J. Overbeek - 402629
Table 16: Regression analysis for the intention to use with ‘Gender_Scenario’ &
‘Age_Scenario’
Variable
Unstandardized coefficients
Significance
(Constant)
31,14
0,00
Scenario
-0,57
0,94
Gender
-6,53
0,09
Age
-0,50
0,02
Experience_traditional
2,87
0,34
Experience_online
-0,70
0,80
Drivinglicense
3,44
0,25
Carowner
0,10
0,97
Amountchoices
0,03
0,98
Product Expertise
-0,69
0,02
Creativity
1,63
0,14
Enjoyment
8,12
0,00
Control
1,16
0,41
Gender x Scenario
-0,75
0,88
Age x Scenario
0,30
0,29
None of the composed interaction variables turn out to be significant (p=0,88 and p=0,29). By
the addition of these two variables, ‘gender’ at itself turns from a significant effect into an
insignificant effect on ‘intention to use’. The same applies to ‘scenario’ and that is especially
striking while its previous effect on ‘intention to use’ was significant. This finding supports the
result at section 5.4 which showed a strong partial mediation with regard to the effect of
‘scenario’ on the intention to use an OMCS. This mediation is facilitated by ‘perceived
enjoyment’, but there could exist more hidden variables that mediate this relationship. The
high p-value of ‘scenario’ (p=0,94) indicates that the direct effect on ‘intention to use’ is not
that
43
large
when
several
variables
are
included
in
the
regression
analysis.
Online Mass Customization of Cars
M.J. Overbeek - 402629
6.
Conclusion
The main question of this research was ‘what are the main drivers for consumers to participate
in online mass customization systems of cars?’. There exists one obvious driver according to
the outcomes and that is the level of perceived enjoyment in the customization process. When
consumers enjoy the online customization process of a car more, they have a higher intention
to use such an online mass customization program. According to our hypotheses the same
holds for the role of perceived control and perceived creativity, but both effects proved to be
insignificant in the regression analyses. Besides the role of these consumer perceptions, the
system features have an influence as well. In this study ‘the range of mass customization
choices’ was the only system-feature related variable and this variable seems to have a
positive, direct effect on the intention to use an OMCS. But its indirect effect proves to be
even larger, mediated by the perceived enjoyment of the respondents involved.
6.1.
Theoretical
implications
The main outcome of this research is the finding that perceived enjoyment positively effects
intention to use an OMCS. This is in line with the findings from Dellaert & Dabholkar (2009)
who investigated the intention to use an online jeans mass customization system. Although
cars are a completely different product group, the same results were obtained in this research.
This outcome supports the statements from Franke et al. (2010) who emphasized the ‘I
designed It myself’ effect where enjoyment during the customization process is an important
motivation in order to engage in online mass customization. Another finding from Dellaert &
Dabholkar (2009) did not get supported in this research. Perceived control did positively
influence the intention to use an OMCS, but this effect was not significant. The same applied
to the relationship between perceived creativity and intention to use an OMCS. Here a positive
influence
expected,
of
perceived
but
creativity
this
on
effect
the
intention
proved
to
to
use
be
an
OMCS
was
insignificant.
With regard to the relationship between the system feature ‘range of mass customization
choices’ and the three consumer perceptions, all three relationship turned out to be
significant and positively related. Consequently, when the range of online mass customization
choices increases, perceived creativity, enjoyment and control also increase. This conclusion
44
Online Mass Customization of Cars
M.J. Overbeek - 402629
is, with regard to the last two consumer perceptions, in line with the assumptions. However,
a negative relationship between the range of customization choices and perceived creativity
was expected. Evidence from Moreau and Dahl (2005) and Ridgeway and Price (1994) states
that consumers become more creative when they face a more constrained environment. This
concept does not hold in this study with its car setting. This could be caused by the fact that
performing
task
of
an
preparing
online
a
meal
car
(Ridgeway
customization
task
&
are
Price,
1994)
and
quite
the
different.
Perceived control increases with a wider range of customization choices, This conclusion is in
line with earlier research from Veitch & Gifford (1996) which stated that individuals prefer to
have choices in order to have a sense of personal control. Personal control implies that a
person has the perception that the choices he or she makes do actually matter (Ward &
Barnes, 2001). The positive relationship between range of customization choices and
‘perceived enjoyment’ re-confirms the "I designed It myself" theory from Franke et. al. (2010).
This theory indicates that consumers prefer lots of customization choices in order to like the
customization process. Ryan & Deci (2000) also stated that a strict set of instructions
decreases an individual’s perception of autonomy during a task. The interaction effect of
product expertise with range of customization choices was not proven in any way. This
outcome is in contrast with evidence from Chase and Simon (1973) which state that
individuals with product expertise are less subject to the ‘overchoice’ effect.
6.2.
Managerial
implications
Based on the results of this study, several managerial implications could be named. With
regard to car manufacturers who offer these kind of OMCS on their corporate websites, they
need to be aware that enjoyment plays a major role in order to use such a system. When these
companies make use of a basic OMCS with, for instance, a main focus on the prices of car
options, the attractiveness to consumers decreases. In this regard a distinction should be
made between premium and less-premium car manufacturers. A less-premium car
manufacturer like Dacia should perhaps use a less fancy, more ‘price-focused system’ while
their cars are price-focused as well. But this system should also not be too basic, knowing the
role of enjoyment in the customization process. Porsche, in contrast, a more premium car
maker, would probably benefit from a more luxurious look with less attention for pricing
45
Online Mass Customization of Cars
M.J. Overbeek - 402629
features in the OMCS they offer. They have to provide an optimal online customization
experience with a main focus on creating enjoyment, just like their sport cars offer driving
enjoyment
for
their
owner.
What Porsche and Dacia do have in common is that they give the instructions on the content
of their OMCS to software developers. The car manufacturers decide, of course, the range of
customization choices offered in the system. This study proves that a wide range of
customization choices increases the intention to use an OMCS in a direct and indirect way. It
cannot be stated that more choices are always better but it seems to be clear that car
manufacturers should not offer too few choices. Especially since a small range of
customization choices decreases the enjoyment of the customization process. A wellconsidered decision of the range of customization choices seems to be crucial to the success
of an OMCS. A car manufacturer could carry out a market research in an early design stage
of
their
OMCS
in
order
to
make
this
decision
in
a
substantiated
way.
Although car manufacturers offer these customization systems on their corporate website,
software developers have to build these systems. Knowing that system features could
influence the enjoyment of the customization process. For software developers it is crucial to
know which other system features are able to influence the consumer’s perceived enjoyment
positively. These software developers could also apply market research in order to discover
which system features increase the customization experience as well. This market research
should be customer dependent knowing the ‘Dacia-Porsche’ example. When a software
developer implements the insights from their market research in an effective way in their
systems
6.3.
his
services
will
Limitations
be
better
and
appreciated
by
further
his
customers.
research
The first limitation of this research is the representativeness of our sample with regard to the
population involved. The average age of our sample was relatively low with a mean of 25,55
years and besides that, 75,73% of the sample was 25 years old or younger. In order to obtain
more reliable results, the sample could have used more older respondents. In fact now just
47,57% of the sample owns a car, with a higher mean age of the sample this percentage will
probably also increase. For further research it could especially be interesting to study in what
46
Online Mass Customization of Cars
M.J. Overbeek - 402629
degree older people respond to online customization systems. Younger people have more
online experience (Wan et al., 2012), so they are in general probably more intended to use
these kinds of systems. Car manufacturers could especially be interested in
how
to
approach
a
relatively
older
group
in
an
online
environment.
The theory that was explicitly proven in this research is the ‘I designed It myself’ effect from
Franke et. al. (2010). Fun during the customization process seems to be an important
motivation for consumers to engage in online mass customization of cars. According to the
results, this enjoyment could be increased by offering a wide range of customization choices.
There could, however, exist more system features that are able to increase the perceived
enjoyment. Think of features such as the use of video graphics, background music, colours or
the lay-out in general. This study was not able to test the influence of other system features
of an OMCS, so this could be of interest for further research. Elaborating on this, might
consumers appreciate a mobile application with which they are able to customize a car? All of
these considerations could be of interest for parties involved in the development of online
mass customization systems and could therefore pose interesting questions for further
research.
Another limitation of this research is the validity of the data. Validity can be defined as “the
extent to which an account accurately represents the social phenomena to which it refers”
(Silverman, 2011, p. 367). Data acquired from Likert-scales is from an ordinal sort, called
ordinal data. The main issue according to Jamieson (2004) is that the particular intervals
between each response category cannot be measured. Another validity aspect is the question
whether the respondents from the sample answered all questions in an honestly and
confidently way. There is evidence that people could answer survey questions in a unreliable
way. Research from Zaller and Feldman (1992) states that half of the people who were asked
the same question in two different interviews actually gave the same answer the second time.
These validity issues could probably be reduced in further research. One possible method to
increase the validity of a research is the implementation of triangulation, which implies using
multiple
methods
like
combining
field
research
with
surveys
(Jick,
1979).
A limitation that should be noticed as well, is the relatively large difference between the range
47
Online Mass Customization of Cars
M.J. Overbeek - 402629
of customization choices between the two scenarios. This research applied a customization
system with a few choices and one system with a wide range of choices. In order to get more
reliable results, a next research should apply more than two different scenarios with regard
to the range of customization choices. The difference in this study was that large so the results
could possibly be influenced improperly. A next study could place one or more scenarios in
between the two scenarios applied with regard to the range of customization choices. The
main reason this study only used two scenarios was the risk of respondents dropping out when
a survey is too time-consuming. It was expected that with offering three or more scenarios,
too many respondents would quit the survey before completing it. A next research might be
able
to
to
tackle
respondents
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this
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participating
by
offering
in
more
the
rewards
survey.
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Appendix
Appendix A: The questionnaire
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Page 1
Dear respondent,
I am a Master student at the Erasmus School of Economics and currently writing my thesis
about the online mass customization of cars. Mass customization is a marketing and
manufacturing technique that combines the flexibility and personalization of "custom-made"
with the low unit costs associated with mass production. A well-known example of online
mass customization is NIKEiD, at this website consumers are able to customize a pair of
sneakers towards their own preferences.
Filling in this questionnaire will not take more than ten minutes of your time. At the end of
the survey you can leave your email address. By doing this, you will enter a lottery to win a
€50 Coolblue gift card.
Thanks in advance!
Maarten Overbeek
Page 2
- Gender: male / female
- Age: free to fill in
- Highest achieved education level: primary school, high school, HBO bachelor, HBO master,
university bachelor or university master?
- Experience with mass customization in traditional retail?: yes/no
- Experience with online mass customization?: yes/no
- I have my driving licence: yes/no
- I own a car: yes/no
- Which car brand is most likely to become your next car?: free to fill in
- I am experienced in the field of cars: 7-point Likert
- I perceive myself as a car expert: 7-point Likert
- I am knowledgeable with regard to cars: 7-point Likert
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Page 3
Imagine Renault is your favourite car brand and you are looking for a relatively small car. The
new Renault Twingo seems to be the perfect car for you with its nice looks and compact
sizes.
Now please visit the link below in order to customize this car towards your own preferences.
After finishing the customization, please return to this questionnaire in order to continue.
http://www.renault.nl/showroom/personenautos/twingo/nieuwe-twingo/configurator/
Page 4
Could you please evaluate the customization program you just worked with by answering
the following questions:
Questions
Scale
If a mass customization program, like you just worked with, was
100% percentage
available to you, would you make use of it?
- I considered myself as an inventive kind of person during the
7-point Likert
customization process
- I considered myself as creative and original in my thinking and
behaviour during the customization process
- Participation was fun
7-point Likert
- Participation was enjoyable
- Participation was exiting
- I was satisfied with the amount of control I had over the
7-point Likert
customization process
- This customization process gave me control over designing my
own car
Do you feel you had the right amount of options to choose from?
9-point Likert
Page 5
Imagine again that you are seriously interested in a Renault Twingo. Renault is still your
number 1 car brand and you are really in love with the looks of this model.
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Please visit this other customization program with the link below, to customize this car also
towards your own preferences. After finishing the customization, please return to this
questionnaire in order to finish the last part.
https://leasen.nl/model/renault-twingo-sce70/configureer/
Page 6
Could you please evaluate the customization program you just worked with by answering
the following questions.
Questions
Scale
If a mass customization option, like you just worked with, was
100% percentage
available to you, would you make use of it?
- I considered myself as an inventive kind of person during the
7-point Likert
task
- I considered myself as creative and original in my thinking and
behaviour
- Participation was fun
7-point Likert
- Participation was enjoyable
- Participation was exiting
- I am satisfied with the amount of control I have over the
7-point Likert
customization process provided in the scenario
- The customization process, portrayed in the scenario, will give
me control over designing my own car
Do you feel you had the right amount of options to choose
9-point Likert
from?
Page 7
This was the survey, thanks for your participation!
If you want to enter the lottery to win an €50 Coolblue gift card, please leave your email
57
Online Mass Customization of Cars
address here:
Appendix B: Frequencies and descriptive Statistics
Appendix B.1: The initial dataset
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Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Gender
103
0
1
,38
,487
Age
103
12
64
25,55
8,599
Exp_traditional
103
0
1
,35
,479
Exp_online
103
0
1
,44
,498
Drivinglicence
103
0
1
,77
,425
Carowner
103
0
1
,48
,502
Expertise1
103
1
7
3,85
1,779
Expertise2
103
1
7
2,89
1,787
Experitise3
103
1
7
3,88
1,870
Intentiontouse_1
103
10
100
74,18
18,495
Creativity1_1
103
1
7
4,69
1,400
Creativity2_1
103
1
7
4,58
1,369
Enjoyment1_1
103
2
7
5,42
1,151
Enjoyment2_1
103
2
7
5,37
1,138
Enjoyment3_1
103
2
7
4,71
1,257
Control1_1
103
1
7
4,88
1,517
Control2_1
103
1
7
5,15
1,346
Amountchoices_1
103
2
8
5,02
1,578
Intentiontouse_0
103
1
92
51,00
24,415
Creativity1_0
103
1
7
3,59
1,593
Creativity2_0
103
1
7
3,50
1,558
Enjoyment1_0
103
1
7
3,76
1,431
Enjoyment2_0
103
1
7
3,68
1,416
Enjoyment3_0
103
1
7
3,34
1,472
Control1_0
103
1
7
3,57
1,649
Control2_0
103
1
7
3,52
1,656
Amountchoices_0
103
1
8
3,79
2,037
Valid N (listwise)
103
Appendix B.2: The final dataset with ‘scenario’ as dummy variable
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Respondent
206
1
103
52,00
29,805
Scenario
206
0
1
,50
,501
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Gender
206
0
1
,38
,486
Age
206
12
64
25,55
8,578
Exp_traditional
206
0
1
,35
,478
Exp_online
206
0
1
,44
,497
Drivinglicence
206
0
1
,77
,424
Carowner
206
0
1
,48
,501
Intentiontouse
206
1
100
62,59
24,532
Amountchoices
206
1
8
4,40
1,920
Expertise
206
3
21
10,63
5,063
Creativity
206
1,00
7,00
4,0922
1,51214
Enjoyment
206
1,00
7,00
4,3786
1,46582
Control
206
1,00
7,00
4,2816
1,62626
Valid N (listwise)
206
Appendix B.3: The compared means of ‘scenario’ and ‘intention to use’
Report
Intentiontouse
Scenario
Mean
N
Std. Deviation
small range of options
51,00
103
24,415
wide range of options
74,18
103
18,495
Total
62,59
206
24,532
Appendix C: Factor Analysis
Appendix C.1: Expertise, Creativity_0, Enjoyment_0 and Control_0
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KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
,823
Approx. Chi-Square
Bartlett's Test of Sphericity
1017,549
df
45
Sig.
,000
Total Variance Explained
Component
Initial Eigenvalues
Total
Extraction Sums of Squared Loadings
% of Variance
Cumulative %
Total
% of Variance
Cumulative %
Rotation Sums of Squared Loadings
Total
% of Variance
Cumulative %
1
5,195
51,947
51,947
5,195
51,947
51,947
2,641
26,407
26,407
2
2,691
26,905
78,852
2,691
26,905
78,852
2,265
22,649
49,057
3
,802
8,018
86,870
,802
8,018
86,870
2,118
21,181
70,238
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4
,419
4,188
91,059
5
,225
2,249
93,308
6
,217
2,166
95,474
7
,157
1,572
97,046
8
,130
1,301
98,347
9
,101
1,008
99,355
10
,065
,645
100,000
M.J. Overbeek - 402629
,419
4,188
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
Component
1
2
Expertise1
,942
Expertise2
,909
Expertise3
,937
3
4
Creativity1_0
,903
Creativity2_0
,885
Enjoyment1_0
,817
Enjoyment2_0
,815
Enjoyment3_0
,699
Control1_0
,828
Control2_0
,857
Extraction Method: Principal Component Analysis.
Rotation Method: Equamax with Kaiser Normalization.
a. Rotation converged in 7 iterations.
62
91,059
2,082
20,821
91,059
Online Mass Customization of Cars
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Appendix C.2: Expertise, Creativity_1, Enjoyment_1 and Control_1
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Approx. Chi-Square
Bartlett's Test of Sphericity
df
Sig.
63
,745
717,079
45
,000
Online Mass Customization of Cars
M.J. Overbeek - 402629
Total Variance Explained
Componen
t
Initial Eigenvalues
Total
Extraction Sums of Squared Loadings
% of
Cumulative
Variance
%
Total
% of
Cumulative
Variance
%
Rotation Sums of Squared Loadings
Total
% of
Cumulative
Variance
%
1
4,061
40,605
40,605
4,061
40,605
40,605
2,616
26,160
26,160
2
2,546
25,463
66,068
2,546
25,463
66,068
2,268
22,677
48,837
3
1,272
12,724
78,793
1,272
12,724
78,793
1,965
19,646
68,483
4
,801
8,006
86,799
,801
8,006
86,799
1,832
18,316
86,799
5
,396
3,964
90,763
6
,280
2,800
93,563
7
,237
2,369
95,932
8
,170
1,696
97,628
9
,129
1,289
98,917
10
,108
1,083
100,000
Extraction Method: Principal Component Analysis.
Rotated Component Matrixa
Component
1
2
Expertise1
,929
Expertise2
,912
Expertise3
,932
3
4
Creativity1_1
,880
Creativity2_1
,919
Enjoyment1_1
,918
Enjoyment2_1
,889
Enjoyment3_1
,643
Control1_1
,857
Control2_1
,899
Extraction Method: Principal Component Analysis.
Rotation Method: Equamax with Kaiser Normalization.
a. Rotation converged in 6 iterations.
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Appendix D: Reliability Analysis
Expertise (1,2,3)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,926
,926
3
Creativity_0 (1,2)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,939
,939
2
Enjoyment_0 (1,2,3)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,958
,958
3
Control_0 (1,2)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,890
65
,890
2
Online Mass Customization of Cars
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Creativity_1 (1,2)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,844
,844
2
Enjoyment_1 (1,2,3)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,895
,898
3
Control_1 (1,2)
Reliability Statistics
Cronbach's Alpha
Cronbach's Alpha
N of Items
Based on
Standardized Items
,840
66
,843
2
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Appendix E: Hypothesis testing
Appendix E.1: Regression analysis for ‘intention to use’
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,693a
1
,480
,472
17,822
a. Predictors: (Constant), Control, Creativity, Enjoyment
ANOVAa
Model
1
Sum of Squares
df
Mean Square
F
Regression
59215,912
3
19738,637
Residual
64157,836
202
317,613
123373,748
205
Total
Sig.
62,147
,000b
a. Dependent Variable: Intentiontouse
b. Predictors: (Constant), Control, Creativity, Enjoyment
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
Std. Error
(Constant)
9,678
4,163
Creativity
1,612
1,074
Enjoyment
8,859
Control
1,758
Beta
2,325
,021
,099
1,501
,135
1,553
,529
5,704
,000
1,241
,117
1,416
,158
1
a. Dependent Variable: Intentiontouse
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Appendix E.2: Regression analysis for ‘perceived creativity’
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,360a
1
,130
,126
1,41396
a. Predictors: (Constant), Scenario
ANOVAa
Model
Sum of Squares
Regression
1
df
Mean Square
F
60,893
1
60,893
Residual
407,854
204
1,999
Total
468,748
205
Sig.
30,457
,000b
a. Dependent Variable: Creativity
b. Predictors: (Constant), Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
Std. Error
Beta
(Constant)
3,549
,139
Scenario
1,087
,197
25,470
,000
5,519
,000
1
a. Dependent Variable: Creativity
68
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Appendix E.3: Regression analysis for ‘perceived enjoyment’
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,538a
1
,289
,286
1,23881
a. Predictors: (Constant), Scenario
ANOVAa
Model
1
Sum of Squares
df
Mean Square
F
Regression
127,398
1
127,398
Residual
313,068
204
1,535
Total
440,466
205
Sig.
83,015
,000b
a. Dependent Variable: Enjoyment
b. Predictors: (Constant), Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
Std. Error
Beta
(Constant)
3,592
,122
Scenario
1,573
,173
29,429
,000
9,111
,000
1
a. Dependent Variable: Enjoyment
69
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Appendix E.4: Regression analysis for ‘perceived control’
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,452a
1
,204
,200
1,45435
a. Predictors: (Constant), Scenario
ANOVAa
Model
1
Sum of Squares
df
Mean Square
F
Regression
110,684
1
110,684
Residual
431,485
204
2,115
Total
542,170
205
Sig.
52,330
,000b
a. Dependent Variable: Control
b. Predictors: (Constant), Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
Std. Error
Beta
(Constant)
3,549
,143
Scenario
1,466
,203
24,763
,000
7,234
,000
1
a. Dependent Variable: Control
70
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Appendix E.5: Regression analysis for moderating effect of product expertise on
perceived creativity
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,404a
1
,164
,151
1,39324
a. Predictors: (Constant), Scen_Exp, Expertise, Scenario
ANOVAa
Model
Sum of Squares
Regression
1
df
Mean Square
F
76,641
3
25,547
Residual
392,107
202
1,941
Total
468,748
205
Sig.
13,161
,000b
a. Dependent Variable: Creativity
b. Predictors: (Constant), Scen_Exp, Expertise, Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
(Constant)
Std. Error
Beta
3,203
,320
10,014
,000
Scenario
,686
,452
,227
1,516
,131
Expertise
,032
,027
,109
1,195
,234
Scen_Exp
,038
,038
,160
,983
,327
1
a. Dependent Variable: Creativity
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M.J. Overbeek - 402629
Appendix E.6: Regression analysis for moderating effect of product expertise on
perceived enjoyment
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,550a
1
,302
,292
1,23345
a. Predictors: (Constant), Scen_Exp, Expertise, Scenario
ANOVAa
Model
1
Sum of Squares
df
Mean Square
F
Regression
133,145
3
44,382
Residual
307,321
202
1,521
Total
440,466
205
Sig.
29,172
,000b
a. Dependent Variable: Enjoyment
b. Predictors: (Constant), Scen_Exp, Expertise, Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
(Constant)
Std. Error
Beta
3,989
,283
14,087
,000
Scenario
,877
,401
,300
2,189
,030
Expertise
-,037
,024
-,129
-1,552
,122
Scen_Exp
,065
,034
,287
1,925
,056
1
a. Dependent Variable: Enjoyment
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Appendix E.7: Regression analysis for moderating effect of product expertise on
perceived control
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,459a
1
,211
,199
1,45539
a. Predictors: (Constant), Scen_Exp, Expertise, Scenario
ANOVAa
Model
1
Sum of Squares
df
Mean Square
F
Regression
114,300
3
38,100
Residual
427,870
202
2,118
Total
542,170
205
Sig.
17,987
,000b
a. Dependent Variable: Control
b. Predictors: (Constant), Scen_Exp, Expertise, Scenario
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
(Constant)
Std. Error
Beta
3,913
,334
11,711
,000
Scenario
,951
,473
,293
2,013
,045
Expertise
-,034
,028
-,107
-1,208
,228
Scen_Exp
,048
,040
,191
1,206
,229
1
a. Dependent Variable: Control
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Appendix E.8: Mediation analysis
Run MATRIX procedure:
*****************************************************************
Preacher and Hayes (2008) SPSS Macro for Multiple Mediation
Written by Andrew F. Hayes, The Ohio State University
www.afhayes.com
For details, see Preacher, K. J., & Hayes, A. F. (2008). Asymptotic
and resampling strategies for assessing and comparing indirect effects
in multiple mediator models. Behavior Research Methods, 40, 879-891.
Also see Chapter 5 of Introduction to Mediation, Moderation, and
Conditional
Analysis. New York: The Guilford Press. http://www.guilford.com/p/hayes3
*****************************************************************
Dependent, Independent, and Proposed Mediator Variables:
DV =
Intentio
IV =
Scenario
MEDS = Creativi
Enjoymen
Control
Sample size
206
IV to Mediators (a paths)
Coeff
se
Creativi
1,0874
,1970
Enjoymen
1,5728
,1726
Control
1,4660
,2027
t
5,5188
9,1112
7,2339
p
,0000
,0000
,0000
Direct Effects of Mediators on DV (b paths)
Coeff
se
t
p
Creativi
1,5523
1,0620
1,4617
,1454
Enjoymen
7,7616
1,6031
4,8416
,0000
Control
1,5970
1,2290
1,2994
,1953
Total Effect of IV on DV (c path)
Coeff
se
t
Scenario
23,1845
3,0180
7,6821
p
,0000
Direct Effect of IV on DV (c' path)
Coeff
se
t
Scenario
6,9479
2,9174
2,3816
p
,0182
Model Summary for DV Model
R-sq Adj R-sq
F
,4942
,4842
49,1061
df1
4,0000
df2
201,0000
p
,0000
*****************************************************************
BOOTSTRAP RESULTS FOR INDIRECT EFFECTS
Indirect Effects of IV on DV through Proposed Mediators (ab paths)
Data
Boot
Bias
SE
TOTAL
16,2366
16,1503
-,0863
2,4425
Creativi
1,6879
1,7003
,0124
1,2616
Enjoymen
12,2075
11,9538
-,2537
2,8490
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Online Mass Customization of Cars
Control
2,3412
2,4962
M.J. Overbeek - 402629
,1550
2,2751
Bias Corrected Confidence Intervals
Lower
Upper
TOTAL
11,7824
21,4287
Creativi
-,3587
4,6323
Enjoymen
6,9836
18,4591
Control
-1,9219
7,1832
*****************************************************************
Level of Confidence for Confidence Intervals:
95
Number of Bootstrap Resamples:
1000
********************************* NOTES **********************************
------ END MATRIX -----
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Appendix E.9: Regression analysis for the intention to use with control variables
implemented
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,729a
1
,532
,503
17,300
a. Predictors: (Constant), Control, Carowner, Exp_online, Drivinglicence, Gender, Age,
Scenario, Exp_traditional, Expertise, Creativity, Amountchoices, Enjoyment
ANOVAa
Model
1
Sum of Squares
df
Mean Square
Regression
65610,054
12
5467,505
Residual
57763,693
193
299,294
123373,748
205
Total
F
Sig.
,000b
18,268
a. Dependent Variable: Intentiontouse
b. Predictors: (Constant), Control, Carowner, Exp_online, Drivinglicence, Gender, Age, Scenario, Exp_traditional, Expertise,
Creativity, Amountchoices, Enjoyment
Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
(Constant)
1
Beta
27,545
6,784
4,061
,000
Scenario
7,015
2,875
,143
2,440
,016
Gender
-6,886
2,858
-,136
-2,409
,017
Age
-,350
,150
-,123
-2,329
,021
Exp_traditional
2,943
2,989
,057
,985
,326
Exp_online
-,713
2,776
-,014
-,257
,798
Drivinglicence
3,452
2,941
,060
1,174
,242
Carowner
,020
2,677
,000
,008
,994
Amountchoices
,143
,895
,011
,160
,873
Expertise
-,685
,291
-,141
-2,355
,020
Creativity
1,537
1,088
,095
1,413
,159
Enjoyment
8,100
1,617
,484
5,008
,000
Control
1,079
1,401
,072
,770
,442
a. Dependent Variable: Intentiontouse
76
Std. Error
Online Mass Customization of Cars
M.J. Overbeek - 402629
Appendix E.10: Regression analysis of Appendix E.9 complemented with ‘Gender x
Scenario’ and ‘Age x Scenario’
Model Summary
Model
R
R Square
Adjusted R Square
Std. Error of the
Estimate
,731a
1
,535
,500
17,338
a. Predictors: (Constant), Age_Scenario, Gender, Exp_online, Drivinglicence, Carowner,
Amountchoices, Age, Creativity, Exp_traditional, Expertise, Control, Gender_Scenario,
Enjoyment, Scenario
ANOVAa
Model
1
Sum of Squares
df
Mean Square
Regression
65955,893
14
4711,135
Residual
57417,855
191
300,617
123373,748
205
Total
F
15,672
Sig.
,000b
a. Dependent Variable: Intentiontouse
b. Predictors: (Constant), Age_Scenario, Gender, Exp_online, Drivinglicence, Carowner, Amountchoices, Age, Creativity,
Exp_traditional, Expertise, Control, Gender_Scenario, Enjoyment, Scenario
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Coefficientsa
Model
Unstandardized Coefficients
Standardized
t
Sig.
Coefficients
B
(Constant)
1
Beta
31,135
7,766
4,009
,000
Scenario
-,573
8,077
-,012
-,071
,944
Gender
-6,531
3,776
-,129
-1,729
,085
Age
-,503
,208
-,176
-2,416
,017
Exp_traditional
2,873
2,996
,056
,959
,339
Exp_online
-,698
2,782
-,014
-,251
,802
Drivinglicence
3,436
2,947
,059
1,166
,245
Carowner
,103
2,684
,002
,038
,969
Amountchoices
,026
,905
,002
,028
,977
Expertise
-,693
,292
-,143
-2,377
,018
Creativity
1,630
1,094
,100
1,490
,138
Enjoyment
8,119
1,621
,485
5,009
,000
Control
1,162
1,412
,077
,823
,411
Gender_Scenario
-,749
5,020
-,012
-,149
,882
,304
,285
,176
1,065
,288
Age_Scenario
a. Dependent Variable: Intentiontouse
78
Std. Error