Content uploaded by Yue Wang
Author content
All content in this area was uploaded by Yue Wang on Jun 20, 2018
Content may be subject to copyright.
The Effect of Default Options on Consumer Decisions in
the Product Configuration Process
Yue Wang1* and Daniel Yiu-Wing Mo1
Abstract.
1
Product configurators have been accepted as an
important enabling toolkit to bridge customer needs and company
offerings. In the configuration process, customers choose from a
set of predefined attributes and their options. The combination of
choices forms the desired product configuration. It is observed that
some online configurators provide default options for each attribute.
Although previous studies show that the default option
significantly affects customers’ choices during the product
configuration process, it is not clear how other factors mediate this
impact. In this paper, we investigate how product types, number of
choices, customers’ degree of expertise, the importance of the
attributes and the configuring sequence affect consumers’ decisions
in the configuration process when default options are presented.
Based on a series of empirical experiments, we find that customers’
degree of expertise, the rating of the attribute importance, and the
number of attribute choices have a significant effect on customers’
choices for utilitarian products. For hedonic products, the
importance of the attributes and the configuring sequence are
significant factors.
Keywords: status quo effect, configurator, default option,
customisation
1 INTRODUCTION
Due to the rapid growth of the Internet and e-commerce over the
past ten years, online choice configurators have become an
important toolkit for customisation by customers. A product
configurator consists of a set of predefined components or
attributes. Customers are presented with this set of valid attribute
choices, and they specify the attribute options that meet their
requirements. In this case, the complicated customised product
design task can be reduced to a set of selection tasks from existing
attributes or a components pool. This configure-to-order-based
mechanism has been widely used in industry. Successful cases
include Dell computers, Adidas, and Nike. By using configuration
systems, firms can increase their profit through better sales and
higher flexibility. Greater customer involvement in the choice
configurator also increases customer satisfaction [1]. Thus,
companies can improve their competitive advantage and position
by using these toolkits [2].
However, some challenges persist. One of the major challenges
is to provide a more user-friendly interface to facilitate choice
navigation and decision making in the configuration process. Some
effort has been devoted to this research direction. For example,
1
Department of Supply Chain and Information Management, School of
Decision Sciences, Hang Seng Management College, Hong Kong, China
*correspondence author, email: yuewang@hsmc.edu.hk
Wang et al. proposed information theory and game theory based
method to elicit customer needs adaptively [3] [4]. The
configuration sequence is also customised based on the active
customer’s previous specifications during the configuration process.
In this way, the customers’ choice navigation process is more
efficient and more user friendly. Customers can get what they want
quickly and with less burden of cognitive load. Studies have
proposed needs-based configuration systems facilitate consumer
decision making, particularly for customers without much domain
knowledge [5]. The needs-based configurators show a series of
product descriptions to customers. Customers then just need to
indicate importance or relevance of the descriptions and use
semantic words (e.g., ‘cheaper’ or ‘larger’) to modify an existing
reference product. This can greatly reduce the semantic gap
between customer needs and the company’s offerings, although the
needs in natural language is still not supported.
Recently, it has been observed that some online configurators
provide default options. If a customer makes no choice on the
attribute, the default option is selected in the final product, as can
be found in the Mini Cooper’s configurator in Figure 1.
Figure 1. Screenshot of the Mini Cooper’s online product configurator
with default choices (accessed May 2017)
In the study of economics and psychology, it has been
acknowledged that the current situation (status quo) is often
considered a reference point from the decision makers’ point of
view. Deviation from the status quo is considered a loss, a
phenomenon called ‘status quo bias’. According to Mandl and
Felfernig [6], status quo bias exists in product configurators,
meaning that consumers’ decisions are affected by the default
options. However, it is not clear how the default options affect
consumers’ decisions or which factors are significant in the
selection of default choices. Therefore, this paper addresses these
questions through empirical experiments.
2 RELEVANT RESEARCH
A substantial amount of relevant work has addressed similar
research questions, particularly in cognitive bias in consumer
decisions. In this section, we briefly review the relevant studies on
default options, the anchoring effect and the salience effect.
2.1 Default options
Default options have been studied in the marketing literature. They
are considered a type of decision-making heuristic through which
cognitive load can be significantly reduced [7]. Through empirical
experiments, Johnson et al. also noticed that a lack of cognitive
attention leads customers to select default choices. Customers may
be paying little or no attention when they choose the default option
[7]. This type of default is considered an attention-based default.
Brown and Krishna argued that the default options can contain
information about the product and thus affect consumer decision
making, i.e., they can be considered information-based defaults [8].
For example, they found that low (less expensive) defaults
sometimes have more positive effects than high (more expensive)
defaults in the case of information-based defaults. In addition, they
may create negative effects when customers already know that the
default option is the best choice. In this case, customers may be
less likely to choose the default choice than the non-default choice.
Compared with expert customers, novice customers more easily
accept the default options [9]. Because the complexity of custom
decision-making tasks decreases the willingness of customers to
participate and reduces the perceived value of the products, novice
customers are more affected. This means that when customers are
less familiar or have little knowledge of the product, the default
options have a greater impact [10].
Many studies show that different framing or a different default
model can bring more profit to the enterprise and create greater
interest. For example, the option-framing method can affect
consumers’ choices, guiding consumers to choose high-price
attributes [11].
2.2 Anchoring effect
The anchoring effect is a cognitive bias that affects the tendency to
rely heavily on the first message received in the decision. The
pictures of the default product may lead to a certain result that
affects the choice of the customer. People tend to rely on the first
piece of information received in the decision-making process,
called the anchor, even if this information has nothing to do with
the decision [12]. People tend to use the earliest pieces of
information to make quick decisions.
The anchoring effect can be divided into high-value and low-
value anchor points as the basis of comparison. If the customer
starts with a lower-value anchor, the final estimation will be lower
than when starting with a high-value anchor. There is often a gap
between the final estimate and the actual value. Lopes argued that
this is one of the possible sources of anchoring bias or anchoring
effects [13]. In studies, the anchorage value has had a significant
effect on the subsequent reaction.
Loosely speaking, default options can be considered anchors.
Park [11] argued that the anchoring effect of default options can be
explained by endowment or loss aversion. Default options can also
be considered reference options. The endowment effect means that
subjects are less willing to deviate from the reference options. Thus,
people tend to select the default in their decision-making process.
2.3 Salience effect
Salience is a term that highlights a comparison to the surroundings,
including memory, attention, pragmatic, perceptual, emotional and
cognitive surroundings. Salience is usually produced by novelty or
unexpectedness, but it can also be generated by shifting attention to
a feature. Salience usually depends on the context. The ability of
human beings to process information is limited. People cannot
adapt to all aspects of any given situation. Salience determines
which information is most likely to draw attention and have the
greatest impact on the world’s perception. People usually do not
consciously realise the extent to which they are affected by
salience.
The salience effect indicates that people tend to emphasise the
most noticeable or salient information. Salient options gain more
attention, which makes customers more likely to choose these
options. In view of this, for e-commerce website design, salient
information helps to change shoppers’ ways of thinking or
perception. The default options are salient in their context.
According to findings about the salience effect, default options
may change people’s choices when they are configuring a product.
3 RESEARCH METHODOLOGY
In response to the research question, we conduct empirical
experiments to identify the significant factors in customer
decisions when default options are presented. In addition, we
compare customer satisfaction with the configuration result and
process to their satisfaction with traditional product configurators.
The literature suggests that default options affect customers’
decisions. However, the process and context of product
configuration are different from the product selection process
studied in previous research. More factors are involved in the
configuration process.
Product type - Products can be classified into two categories:
utilitarian products and hedonic products [14]. For utilitarian
products, customer choices are based purely on the functional
requirements. A certain domain knowledge or expertise is needed
to finish the configuring task. For hedonic products, customers’
choices are made based their subjective preferences. For example,
the corresponding attributes may be colour, shape or design.
Customers’ preferences for these attributes are subjective. In our
research, we ask whether product type mediates customers’
selection of default options.
Expertise - Experts have more experience and knowledge of the
product, and therefore they may not be affected by the default
option because they know what they want to purchase. Unlike
experts, novice customers have less knowledge about the product,
so they are easily affected by the default option. For utilitarian
products, the customers may not choose the specialist features or
components because of their complexity.
Number of choices - In studies of consumer decision making, it
has been acknowledged that the number of choices may also affect
consumers’ decisions. For example, if an attribute has a large
number of choices, the cost of evaluating them may be very high.
In this case, customers may use the default options to save effort in
the configuring process. Therefore, we also explore the effect of
the number of choices on selection decisions.
Order of the attributes – Levav showed that the order of the
attributes also affects customers’ decisions in product
customisation [15]. In the present study, the order of the attributes
in configurators is considered as a potentially significant factor in
customers’ choices when they face flexible option configurators.
Concern about the attribute - it has been acknowledged that if a
customer cares more about one particular attribute, he or she will
be more motivated in the information processing task. [16]. To
evaluate and appraise a substitute, consumers have to allocate the
weight of the decision-related criteria and prioritise them. Often,
consumers do not have enough mental capacity to evaluate all of
the attribute levels for all of the attributes offered [17]. Consumers
usually start with the most important attribute and proceed based
on the order of the attributes’ importance [18]. In the context of
product configurators, concern about each product attribute is
potentially a significant factor in customers’ choices. When
configuring a product, customers may not be concerned with all of
the attributes. They may pay attention to only a few attributes. For
example, when a customer wants to configure a laptop, he or she
may only be concerned about the CPU and hard disk size, while
other attributes are less important. In studies of consumer decision
making, it has been acknowledged that consumers usually use
different heuristics to make decisions, including elimination by
aspect, compensation, conjunctive, disjunctive and so on [16]. All
of these heuristics can be traced back to the fact that the customer
may only be concerned with certain attributes. In the context of
product configurators, concern about each product attribute is
potentially a deciding factor in customers’ choices.
4 EXPERIMENT DESIGN
We develop configurators for a watch and a laptop, which are a
hedonic product and a utilitarian product, respectively. Screenshots
of the watch and laptop configurators are shown in Figure 2. All of
the attributes of the watch can be considered hedonic attributes,
meaning that customer choices are based purely on their subjective
preferences. No expertise in watches is needed to finish the
configuring task. The laptop’s attributes are utilitarian. The choices
are determined by customers’ functional requirements. A certain
amount of background knowledge is needed to finish the
configuring task. Because the purpose of this paper is to study
which factors affect customer decisions when default choices are
presented and customers’ satisfaction with the configured product
and the configuring process, the comparative study is conducted
using a traditional single choice-based configurator. Thus, the four
types of configurators used in this paper are developed as shown in
Table 1. For each product, the base configurator is the normal
version without default options. This is the configurator used as the
control group. For the other versions, each attribute has a default
option. To eliminate the effect of option difference on customers’
choices, we randomly assign the default options for each
experiment participant. It means that for difference customers, the
default options encountered in the configuration tasks are different
as well. This configurator is used to investigate consumers’
decision behaviour. The default option for each attribute is also
randomly selected for each experiment subject. This could offset
the influence of choice on consumers’ selections.
In the experiment, a participant is randomly assigned to one of
the four configurators. After the configuring task, the participant is
directed to another configurator with a different product type and
configurator type. For example, if the first randomly assigned
configurator is configurator III, which is a single-choice watch
configurator, then the next configurator the participant encounters
is configurator II, which has different product type and
configurator type. Before and after each configuring task, the
participant completes a pre-experiment survey and a post-
experiment survey for each product. The pre-experiment survey is
used mainly to determine the relative importance that customers
accord to each attribute and their degree of expertise with the
utilitarian product. The experiment can be summarised as in Figure
3.
Figure 2(a). Screenshot of the watch configurators, with default options
Figure 2 (b). Screenshot of the PC configurators, with default options
Figure 3. Experiment process
Table 1. Configurators used in the experiment.
Base
configurator w/o
default options
Configurator w/
default options
Laptop (utilitarian
product)
I
II
Watch (hedonic
product)
III
IV
5 EXPERIMENT RESULT AND
DISCUSSION
5.1 Basic statistics
One hundred forty participants are recruited from a university in
Hong Kong. Each experiment subject receives 30 Hong Kong
dollars as compensation for his or her time and effort. We check
customers’ choice distribution with and without default choices.
The purpose is to see whether the default choices lead to a
significant difference in consumers’ behaviour.
The statistics on the choice distribution are shown in the
following table. If the default options have no effect on customers’
decisions, the distribution of customers’ choices should not be
significantly different for each attribute. A chi-square test is used to
check the difference between the distributions. The p-value of the
test result is shown in the last column.
Table 2. Consumers’ choice distribution for watch attributes
Attribute
Number
of
Attribute
choices
Attribute choice
distribution
(with default
option, 40
subjects)
Proportion of
choices for each
attribute (w/o
default option,
52 subjects)
P-value
of chi-
square
test
Frame
3
(15, 14, 11)
(21, 14, 17)
0.501
Band
6
(3, 9, 3, 11, 4,
10)
(2, 6, 0, 21, 3,
20)
0.004
Calibre
2
(10, 30)
(21, 31)
0.047
Outer
8
(8, 5, 6, 2, 8, 2,
5, 4)
(9, 9, 6, 2, 10, 8,
7, 1)
0.014
Arm
2
(19,21)
(15, 37)
0.009
Table 3. Consumers’ choice distribution for laptop attributes
Attribute
Number
of
Attribute
choices
Attribute choice
distribution
(with default
option, 49
subjects)
Proportion of
choices for each
attribute (w/o
default option,
47 subjects)
P-value
of chi-
square
test
Monitor
5
(19, 19, 8, 2, 1)
(6, 27, 9, 5, 0)
0.000
Resolution
3
(6, 35, 8)
(8, 31, 8)
0.64
Screen
2
(12, 37)
(20, 27)
0.011
Operating
System
4
(16,12, 13, 8)
(18, 5, 17, 7)
0.014
CPU
6
(4, 17, 16, 4, 5, 3)
(2, 7, 18, 12, 6,
2)
0.001
RAM
9
(4, 4, 10, 1, 10, 8,
2, 5, 5)
(3, 3, 8, 6, 10, 4,
8, 1, 4)
0.000
Graphics
Card
5
(11, 18, 13, 2, 5)
(8, 11, 20, 4, 4)
0.066
Hard disk
7
(7, 11, 4, 8, 8, 5,
6)
(7, 7, 10, 5, 8, 6,
4)
0.210
Battery
6
(5, 8, 12, 8, 5, 11)
(10, 4, 8, 11, 3,
11)
0.071
Based on the tables, we can see that for most attributes, the
distributions of customer choices are significantly different, as the
corresponding p-value is small. This means that default options
affect customers’ decisions during the configuring process. We
notice that only the watch frame in watch, screen resolution and
hard disk in laptop don’t have significant difference between the
base configurators and the default option-based configurators.
After further investigation, we found that the choices for these
three attributes either have very strong dominance relationship in
terms of customer preferences (screen resolution or hard disk), or
very heterogeneous customer preferences (watch frame, the
choices can be found in Figure 2). For the former case, customers
tend to choose the clearly superior choices regardless of the default
options. For the latter case, customers’ choices are purely
determined by the preferences. Default options can hardly change
their intrinsic preferences.
5.2 Which factors affect customers’ decisions?
Because we want to study the effects of different factors on the
selection of default options, it is natural to use a binary variable as
an indicator that indicates whether the participant selects the
default option in the configuring task for configurators II and IV,
as mentioned in the previous section. The independent variables
are the number of choices, the order of the attributes, the concern
about each attribute and the customers’ expertise (only for the
laptop, the utilitarian product). The numbers of choices for the two
types of products are shown in the second column of Tables 1 and
2. The relative importance that customers accord to each attribute
is elicited from the pre-experiment survey. We use the pre-test
survey to elicit information about the customers’ concern about
each attribute. A Likert scale ranging from 1 to 7 is used to allow
customers to specify their degree of concern. ‘1’ corresponds to the
least degree of concern, and a larger number means a higher degree
of concern. A sample question for the watch configurator is ‘How
concerned are you with the calibre compared to other parts of a
watch?’ Regarding expertise, we designed a basic knowledge test
for laptops containing 10 multiple-choice questions. The number of
correctly answered questions is used as the measure of the
customer’s degree of expertise.
Because the responses are binary variables, logistic regression is
used to identify the relationship between independent variables and
responses. The result is shown in Tables 3 and 4. For the laptop,
the utilitarian product, expertise is an independent variable. For the
watch, the hedonic product, the selection of attributes does not
depend on customers’ expertise; only subjective preferences matter.
Thus, expertise is not considered in the regression model of the
watch. Model 1 includes all of the independent variables and all of
the first-order interactions between independent variables. A
stepwise procedure is then conducted to remove the insignificant
factors one by one from the model according to the p-value in the
regression until only the significant variables remain.
Table 4. Relationship between response and different variables - laptop
Independent Variables
Model 1 (logistics
regression)
Model 2 (logistics
regression, stepwise
result based on
model 1)
Expertise
0.693*
(0.384)
0.715**
(0.321)
Concern about attribute
-0.339
(0.425)
-0.234***
(0.0802)
Sequence of
configurator
-0.353
(0.614)
Number of choices
0.198
(0.424)
0.402*
(0.227)
Expertise * Concern
0.0022
(0.0483)
Expertise * Sequence
0.0082
(0.0478)
Expertise * Number of
Choices
-0.1053**
(0.0515)
-0.1028**
(0.0504)
Concern * Sequence
0.0074
(0.0494)
Concern * Number of
Choices
0.0093
(0.0547)
Sequence * Number of
Choices
0.0613
(0.0991)
*: p-value<0.1; **: p-value<0.05; ***: p-value<0.01
Remark: the numbers represent the coefficients of the corresponding
independent variables in the logistics regression. The numbers in the
parentheses are the standard deviation of the corresponding coefficients.
Based on the result shown in Table 4, we find that the degree of
expertise is moderately significant in affecting customers’
decisions about default choices. The interaction of degree of
expertise and number of choices is significant in affecting
customers’ decisions to choose the default options. Through a
stepwise procedure, we can eliminate the insignificant independent
variables one at a time. This leads to model 2, which consists only
of the significant independent variables. We find that the degree of
expertise, degree of concern about each attribute, and the
interaction between degree of expertise and number of choices are
significant in affecting customers’ decisions. In particular, the
coefficient of expertise is positive. This means that if a customer’s
expertise is greater, he or she is more likely to choose the default
options. This finding seems different from previous study in [9]. It
should be noted that we use logistics regression to identify the
relationship between the independent variables and the choice of
default options. In [9], the authors study the relationship between
the number of selected default options and the expertise degree.
Thus the research questions are different. This can explain the
difference of the experiment findings.
The sign of the coefficient of degree of concern is negative,
indicating that if a customer is more concerned with an attribute,
then he or she is less likely to choose the default options. The
coefficient of number of choice is positive, meaning that if an
attribute has more choices, customers are more likely to choose the
default option. It has been acknowledged that when more choices
are presented, the burden of choice is much higher. In this situation,
customers may stay with the default option to save time and effort
in product configuration.
Table 5. Relationship between response and different variables - watch
Independent Variables
Model 1 (logistics
regression)
Model 2 (logistics
regression, stepwise
result based on model 1)
Concern about attribute
0.17
(0.337)
-0.218*
(0.129)
Number of Choices
0.078
(0.145)
Sequence
-0.333
(0.446)
-0.334***
(0.112)
Concern * Number of
Choices
-0.0182
(0.017)
-0.016***
(0.00422)
Concern * Sequence
0.021
(0.089)
Number of Choices *
Sequence
-0.0315
(0.053)
*: p-value<0.1; **: p-value<0.05; ***: p-value<0.01
Remark: the numbers represent the coefficients of the corresponding
independent variables in the logistics regression. The numbers in the
parentheses are the standard deviation of the corresponding coefficients.
For the watch configurator, the attributes are not technical. The
selection is based purely on appearance, and no knowledge is
required for the configuring task. Therefore, there is no individual
variable to quantify the degree of expertise. Based on model 1, we
find that none of the individual variables are significant. Through a
stepwise procedure, the original regression model can be modified
to model 2, in which all of the variables are significant. The degree
of concern is moderately significant. Configuring sequence and the
interaction of concern with number of choices are significant in
affecting customers’ decisions to choose the default options. We
also notice that all of the signs of the coefficients are negative.
Therefore, when customers are more concerned with the attribute,
they do not choose the default option. This finding is identical to
the case of the laptop. However, in contrast to the laptop
configurator, the sequence of the attribute in the configuring
process is significant. We think the reason is that for the laptop
configurator, the numbers of choices for different attributes are
quite similar. However, for the watch configurator, the number of
choices ranges from 2 to 24. Thus, the sequence is significant in
the customer’s decision. In addition, it is observed that customers
tend to choose the default options that are presented early. We also
find that the interaction between concern and number of choices is
also significant in affecting the choices.
6 CONCLUSION
Product configurator design has been widely studied in the area of
engineering. Very little work investigates the effect of default
options on consumer decision making during the configuring
process. This paper studies whether default options have a
significant effect on people’s decisions in the context of product
customisation. In the settings of product configurators, a default
choice is highlighted for each product attribute. During the
experiment, we find that some respondents accept the default
choices and others reject them. It is of primary interest to study
which kinds of products and what type of attributes are influenced
most by the default options. Through a set of empirical
experiments, we show that customers’ choices are significantly
influenced by default options. For utilitarian products, we also note
that expertise, concern for the product attribute, number of choices
and the interaction between expertise and number of choices
significantly mediate the default options’ effect on customers’
choices. However, for hedonic products, concern about the product
attribute, order of configuration and the interaction between
concern and number of choices are significant factors. From
companies’ perspective, customers are more likely to select the
default options. This could potentially benefit customisers and
improve the operations of the company.
This research still has some limitations. The number of subjects
can be larger. In addition, the methods on quantifying the expertise
degree of the subjects is very sensitive to the discrimination of the
questions in the pre-survey test. In our future work, we plan to
recruit more participants and further polish the questionnaire to
quantify the degree of expertise more accurately.
ACKNOWLEDGEMENTS
This research is supported by Hong Kong Research Grants Council
(Project No. UGC/FDS14/E02/15, for data collection) and (Project
No. UGC/FDS14/E07/17, for data analysis).
REFERENCES
[1] E. Garbarino and S. M. Johnson, ‘The Different Roles of Satisfaction,
Trust, and Commitment in Customer Relationships’, Journal of
Marketing, 63(2), 70–87, (1999).
[2] F. S. Fogliatto, G. J. Da Silveira and D. Borenstein, ‘The mass
customization decade: An updated review of the literature’,
International Journal of Production Economics 138(1), 14-25, (2012).
[3] Y. Wang, and M. M. Tseng, ‘Attribute selection for product
configurator design based on Gini index’, International Journal of
Production Research, 52(20), 6136-6145, 2014.
[4] Y. Wang, and M. M. Tseng, ‘Adaptive Attribute Selection for
Configurator Design via Shapley Value’, Artificial Intelligence for
Engineering Design, Analysis and Manufacturing, 25 (1), 189–199,
(2011).
[5] T. Randall, C. Terwiesch and K. T. Ulrich, ‘User Design of
Customized Products’, Marketing Science, 26(2), 268–280, 2007.
[6] M. Mandl, A. Felfernig, J. Tiihonen, and K. Isak, ‘Status Quo Bias in
Configuration Systems’, 24th International Conference on Industrial,
Engineering and Other Applications of Applied Intelligent Systems
(IEA/AIE 2010), Syracuse, New York, 105-114
[7] E. J. Johnson, S. Bellman, and G. L. Lohse, ‘Defaults, framing and
privacy: Why opting in-opting out’, Marketing Letters, 13, 5–15,
(2002).
[8] C. L. Brown, and A. Krishna, ‘The skeptical shopper: A
metacognitive account for the effects of default options on choice’,
Journal of Consumer Research, 31, 529–539, (2004).
[9] J. Wang, L. Cheng and W. Han, ‘The effect of default option on
customer decision behavior in product customization’, Proceedings of
10th International Conference on Service Systems and Service
Management, Hong Kong, 2013
[10] R. Thaler, and C. R. Sunstein, ‘Libertarian Paternalism’, American
Economic Review, 93 (2), 175-179, 2003.
[11] J. Park, and M. R. Banaji, ‘Mood and heuristics: The influence of
happy and sad states on sensitivity and bias in stereotyping’, Journal
of Personality and Social Psychology, 78, 1005–1023, (2000)..
[12] A. Tversky and D. Kahneman, ‘Judgment under Uncertainty:
Heuristics and Biases’, Science. 185(4157),1124-31, 1974.
[13] L. L. Lopes, ‘Decision making in the short run’, Journal of
Experimental Psychology, 7:377-85, (1981)
[14] R. Batra, O. T. Ahtola, ‘Measuring the hedonic and utilitarian sources
of consumer attitudes’, Marketing Letters 2(2), 159-170, (1991).
[15] J. Levav, M. Heitmann, A. Herrmann, and S. S. Iyengar, ‘Order in
Product Customization Decisions: Evidence from Field Experiments’,
Journal of Political Economy, 118(2), 274-299, 2010
[16] D. J. MacInnes, C. Moorman and B. J. Jaworski, ‘Enhancing and
measuring consumers’ motivation, opportunity and ability to process
brand information from ads’, Journal of Marketing, 55, 32-53, 1991.
[17] R. W. Olshavsky. ‘Towards a More Comprehensive Theory of
Choice’, in NA - Advances in Consumer Research Volume 12, eds.
Elizabeth C. Hirschman and Moris B. Holbrook, Provo, UT:
Association for Consumer Research, 465-470, (1985).
[18] A. Tversky,. ‘Elimination by aspects: A theory of choice’,
Psychological Review, 79(4), 281-299, (1972).
