Computers in Industry 64 (2013) 436–447
Contents lists available at SciVerse ScienceDirect
Computers in Industry
journal homepage: www.elsevier.com/locate/compind
Sales configurator capabilities to avoid the product variety paradox:
Construct development and validation
Alessio Trentin *, Elisa Perin, Cipriano Forza
Università di Padova, Dipartimento di Tecnica e Gestione dei sistemi ind.li, Stradella S. Nicola 3, 36100 Vicenza, Italy
A R T I C L E I N F O
A B S T R A C T
Article history:
Received 27 April 2012
Accepted 4 February 2013
Available online 7 March 2013
Sales configurators are applications designed to support potential customers in choosing, within a
company’s product offer, the product solution that best fits their needs. These applications can help firms
avoid the risk that offering more product variety and customization in an attempt to increase sales,
paradoxically results in a loss of sales. Relatively few studies, however, have focused on the
characteristics sales configurators should have so as to avoid this paradox. Furthermore, empirical
investigation on the effectiveness of the recommendations made by these studies has been hindered by
the lack of psychometrically sound measurement items and scales. This paper conceptualizes, develops
and validates five capabilities that sales configurators should deploy in order to avoid the product variety
paradox: namely, focused navigation, flexible navigation, easy comparison, benefit-cost communication,
and user-friendly product-space description capabilities. It is hoped that this study will provide a
parsimonious measurement instrument to advance theory testing in the field. Moreover, this instrument
may be a useful diagnostic and benchmarking tool for companies seeking to assess and/or improve sales
configurators they use or develop.
ß 2013 Elsevier B.V. All rights reserved.
Keywords:
Product configuration
Software capabilities
Measurement development
Mass customization
1. Introduction
A trend toward an increase in product variety and customization has been observed worldwide in many diverse industries [1–
4]. The promise of increased product variety and customization is
that by offering customers exactly what they want, or at least
something closer to their ideal product solutions, companies will
be able to charge higher prices and/or to gain higher market shares
[5–7], thereby increasing revenues.
There is a risk, however, that a strategy of product proliferation
and customization backfires, leading to lower rather than greater
revenues, as increasingly suggested in literature [7–13]. Potential
customers, for example, may feel so confused and overwhelmed by
the number of product configurations offered by a company that
they choose not to make a choice at all [8] and the company loses
potential sales. Firms offering product variety and customization
may therefore experience what has been termed the ‘‘product
variety paradox’’ [14]: offering more product variety and
customization in an attempt to increase sales paradoxically results
in a loss of sales.
* Corresponding author. Tel.: +39 0444 998742; fax: +39 0444 998884.
E-mail addresses: alessio.trentin@unipd.it, alextren@tiscalinet.it (A. Trentin),
perin@gest.unipd.it (E. Perin), cipriano.forza@unipd.it (C. Forza).
0166-3615/$ – see front matter ß 2013 Elsevier B.V. All rights reserved.
http://dx.doi.org/10.1016/j.compind.2013.02.006
This risk is exacerbated by the fact that the Web has made
immense product choice and a significant amount of productrelated information potentially available to customers [15,16].
Confronted with this information explosion, customers face no
easy task searching for their ideal product configurations without
salespeople’s support [16].
An important role in alleviating the risk of experiencing the
product variety paradox can be played by sales configurators
[14,17,18]. A sales configurator is a subtype of software-based
expert systems (or knowledge-based systems) with a focus on the
translation of each customer’s idiosyncratic needs into complete
and valid sales specifications of the product solution that best fits
those needs within a company’s product offer [19,20]. The
fundamental functions of a sales configurator include presenting
a company’s product space, meant as the set of product solutions
that a firm offers [21], and guiding customers in the generation or
selection of a product variant within that space, thus preventing
inconsistent or unfeasible product characteristics from being
defined [18,22]. Additional functionalities of a sales configurator
may include providing real-time information on price and/or
delivery terms of a product variant, making quotations [23,24] and
recommending a product solution that can be further altered [17].
Sales configurators may be stand-alone applications or modules of
other applications, known as product configurators, which support
not only translation of customer needs into sales specifications, but
also translation of sales specifications into the product data
A. Trentin et al. / Computers in Industry 64 (2013) 436–447
necessary to build the product variant requested by the customer,
such as bill of materials, production sequence, etc. [25].
Many studies on sales configurators and, more generally, on
product configurators have investigated technical or application
development issues, such as the modeling of configuration
knowledge or the algorithms to make configurators faster and
more accurate [e.g., 26–32]. Many other studies have provided
detailed accounts of the introduction and use of a configurator in a
single company, focusing mainly on implementation challenges
and operational performance outcomes from the company
perspective [e.g., 23,24,33–36]. In this vein, large-scale hypothesis-testing studies on the effects of product configurator use on a
firm’s operational performance have recently appeared as well
[37,38].
Instead, less attention has been given in literature to which
characteristics of sales configurators reduce the effort involved in
the specification process and drive users’ satisfaction with this
process [18], thereby alleviating the risk that companies experience the product variety paradox [14]. Huffman and Kahn [8],
Kamis et al. [39], and Valenzuela et al. [11] find that enabling
choice by product attributes, rather than among complete product
alternatives, eases the customer decision process, especially as the
number of attributes and their values increase. Randall et al. [40]
suggest that inexperienced customers should be allowed to specify
the relative importance of their needs, rather than the values of
design parameters of the product, whereas expert customers
should be allowed to directly specify design parameters. Dellaert
and Stremersch [41] find that customer perceived effort is reduced
if only the price of the configured product, and not also the price of
each choice option, is presented. A broader set of recommendations is made by Randall et al. [17] and Salvador and Forza [14],
such as providing an initial configuration that the customer can
subsequently alter, supporting incremental refinement, and
structuring customer–company interaction. However, the empirical study of how sales configurators should be designed to ease the
customer decision process and to increase configuration processrelated value for the customer is still in its infancy [18,42].
To help narrow this research gap, the present paper conceptualizes, develops and validates five sales configurator capabilities that are expected to motivate and facilitate further
empirical investigation in the field. Moreover, by distilling the
capabilities implied by prior research recommendations, the
present paper enhances understanding of when and why sales
configurators generate value for customers and thus support mass
customization strategies.
2. Literature review and construct definitions
2.1. The product variety paradox
Prior research suggests several mechanisms that explain why a
company’s strategy of product proliferation and customization
might prove detrimental, rather than beneficial to the company’s
revenues [13]. In particular, four inter-related mechanisms link
product variety and customization to the difficulty experienced by
potential customers in configuring the product solutions that best
fit their needs within a company’s product space. The experience of
difficulty accompanying a potential customer’s decision process
may become an input to his/her evaluation of the decision outcome
itself [11,13,43,44]. Consequently, greater decision difficulty for
potential customers may translate into lower satisfaction with the
configured products and, eventually, into reduced willingness to
make a purchase [11,13].
A first explanation for the product variety paradox relies on
choice complexity, defined as the amount of information processing necessary to make a decision [11]. As product variety and
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customization increase, so too does choice complexity, since more
alternatives have to be processed in order for a potential customer
to make a decision based on rational optimization. The amount of
information processing is a widely acknowledged source of
decision difficulty [45]. If potential customers are provided with
‘‘too much’’ information at a given time, such that it exceeds their
processing limits, information overload occurs [46]. Information
overload, in turn, may lead potential customers to choose from
competing brands that do not require such cognitive effort [7] thus
reducing the company’s revenues.
A related explanation for the product variety paradox relies on
anticipation of post-decisional regret, which is a cognitively
determined negative emotion that individuals experience when
realizing or imagining that their present situation would have been
better, had they acted differently [47]. When choice complexity
becomes excessive, potential customers may become unable to
invest the requisite time and effort in seeking the best option for
them based on rational optimization and may turn from
compensatory decision strategies, which process all of the
available information, to non-compensatory heuristics, which
reduce information processing demands by ignoring potentially
relevant information [45,48,49]. Furthermore, potential customers
may have uncertain preferences because of poorly developed
preferences or poor insight into their preferences [49–51], so that
their wants at the time of choice can have low correlations with
their likes at the time of consumption [10]. When potential
customers are unable to engage in rational optimization and/or
have uncertain preferences, they may anticipate the possibility of
post-decisional regret due to poor fit between the selected product
configuration and the customer’s preferences [9,10,52]. If this is
the case, potential customers take into account this possibility into
their decision processes, seeking to avoid or minimize postdecisional regret [10,52]. The goal of minimizing post-decisional
regret makes potential customers’ decision processes more
difficult [9] and may lead them to delay their purchase decisions
[9,52] or to prefer a standard product to a customized one [10].
A third related explanation for the product variety paradox
relies on responsibility felt by potential customers for making a
good decision. As product variety and customization increase,
potential customers feel more responsible for their choices, given
the greater opportunity of finding the very best option for them
[9,13]. These enhanced feelings of responsibility promote anticipated regret, as subjectively important decisions, for which
individuals feel more responsible, will result in more intense
post-decisional regret when things go awry [47,52]. By amplifying
anticipated regret and the resulting decision difficulty, responsibility for making a good decision magnifies the negative impact of
choice complexity on customers’ willingness to make a purchase.
Finally, a fourth mechanism relating product variety and
customization to decision difficulty relies on conflict between
product attributes that are linked to highly valued goals for
potential customers [7,11,45,53]. To increase product variety and
customization, companies need to broaden the range of product
attributes on which they allow their potential customers to make a
choice [54]. As the number of product-differentiation attributes
increases, so too does the likelihood that potential customers have
to make trade-offs among attractive attributes. This happens
because offering all the possible combinations of all the different
levels of all the product-differentiation attributes may be
economically unfeasible, owing to insufficient manufacturing
process flexibility and limited product modularity [55]. Explicit
trade-offs among attractive attributes not only increase the
cognitive effort required of potential customers to process all of
the available information [7], but also cause potential customers to
experience negative emotions such as anticipated regret [7]. This
happens because trade-off resolution involves consideration of
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potential unwanted consequences and threatens one’s reputation
of self-esteem as a decision maker [56]. The negative emotions
associated with between-attribute trade-offs are another mechanism that links increased product variety and customization to
greater subjective experience of choice task difficulty [11] and
decreased satisfaction with the chosen product [13], thus
explaining the product variety paradox.
2.2. Sales configurator capabilities to avoid the product variety
paradox
In the following subsections, we propose five sales configurator
capabilities that help companies avoid the product variety paradox
by hindering operation of at least one of the mechanisms outlined
in the previous section. For each proposed capability: (i) we
provide its conceptual definition as well as empirical illustration
by means of one existent Web-based sales configurator, (ii) we link
development of that capability with a number of recommendations made by relevant previous research and, finally (iii) we
explain why that capability is expected to alleviate the risk of
experiencing the product variety paradox.
2.2.1. Focused navigation capability
We define focused navigation capability as the ability to quickly
focus a potential customer’s search on a product space subset that
contains the product configuration that best matches his/her
idiosyncratic needs. An example of Web-based sales configurator
deploying this capability is Toshiba’s laptop customization site,
which offers potential customers a laptop finder to narrow their
search down to a specific color, processor, and/or any other desired
product-differentiation attribute value (Fig. B1).
Indeed, a fundamental way of improving focused navigation
capability is to allow potential customers to sequence their choices
concerning the value of each product-differentiation attribute
from the least uncertain choice to the most uncertain one [14]. This
is because, according to the attribute being considered, a
customer’s preferences may be more or less uncertain [50] and
preference uncertainty is an antecedent of anticipated regret
[10,57]. If the early choices a potential customer is required to
make are those for which his/her preferences are best developed,
then he/she is enabled to narrow down search more quickly, as
anticipated regret associated with those choices is lower.
Noteworthy, a prerequisite for this way of structuring the
customer–company interaction is the by-attribute presentation
of the company’s product space, meaning that the customer is
asked which value he/she prefers for each product-differentiation
attribute instead of being required to choose from among a set of
fully specified product configurations, as happens with the byalternative presentation [8]. Another option to enhance focused
navigation capability is to provide one or more starting points,
where a starting point is defined as an initial product configuration
that is close to the customer’s ideal solution and that may be
further altered [17]. Starting points can be recommended with
little or no effort on the customer’s part, based on his/her past
purchases and/or customer input concerning simple demographics, intended product usage and his/her best developed
preferences [30,58]. Noteworthy, this solution requires complementing the by-attribute presentation of the product space with
the by-alternative presentation. The same applies to another way
of improving focused navigation capability, that is to allow a
potential customer to completely exclude certain product solutions from consideration if he/she does not wish for them [59].
Focused navigation capability helps to avoid the product variety
paradox by reducing choice complexity and by mitigating
anticipated regret. A sales configurator with this capability does
not force potential customers to go through and evaluate a number
of product options that they regard as certainly inappropriate for
themselves. Therefore, this capability reduces the amount of
information processing necessary to make a decision without
potential customers experiencing anticipated regret [10,47,52,57].
Furthermore, by quickly reducing the size of the search problem,
this capability enables potential customers to invest more time and
effort in exploring the product options for which their preferences
are less certain. Potential customers can learn more about both
these options and the value they would derive from them,
especially when focused navigation capability is complemented
with the capabilities discussed in the subsequent sections. In
addition, potential customers can rely on more time-consuming,
compensatory decision strategies that enable rational resolution of
between-attribute conflicts [49], if any. As a consequence, once a
potential customer has selected his/her most preferred product
configuration, he/she is more confident that the chosen solution is
the one that best fits his/her needs within the company’s product
space. Reduced uncertainty on the superior fit of the selected
product configuration with the customer’s preferences, in turn,
translates into less anticipated regret [52].
2.2.2. Benefit-cost communication capability
We define benefit-cost communication capability as the ability
to effectively communicate the consequences of the available
choice options both in terms of what the customer gets (benefits)
and in terms of what the customer gives (monetary and
nonmonetary costs). An example of Web-based sales configurator
deploying this capability is Dell’s laptop customization site. At each
step of the configuration process, this site gives potential
customers the possibility to click on the ‘‘Help Me Choose’’ button,
which opens up a page with a list of recommendations suggesting
the advantages of every choice option (Fig. B2a). Moreover, the site
communicates the price variation that selecting each of the
available options would cause with respect to the price of the
current configuration (Fig. B2b).
Indeed, a fundamental way of improving benefit-cost communication capability is to explain what potential needs a given
choice option contributes to fulfill and to what extent it does so
[14]. This explanation is especially important when choice options
involve design parameters of the product, such as specifications of
product components, because potential customers are often
unable to relate design parameters to satisfaction of user needs
[17]. According to the product attribute being considered, this
explanation may be more effectively provided by means of
different media, including texts, photos, animations or other
simulations of the real product on a computer [60]. Besides the
benefits, it is also important to communicate monetary and
nonmonetary costs of each option, for example by displaying the
prices of the individual product components from among which
potential customers can choose or by warning potential customers
that certain options imply longer delivery lead-times [14].
Benefit-cost communication capability helps avoid the product
variety paradox by mitigating anticipated regret. During the sales
configuration process, potential customers seek to anticipate the
value they will perceive from consumption of the product being
configured [61]. Perceived product value is defined as the
customer’s ‘‘overall assessment of the utility of a product based
on perceptions of what is received and what is given’’ [14,62]. By
delivering clear prepurchase feedback on the effects of the
available choice options, a sales configurator with high benefitcost communication capability fosters potential customers’
learning about the value they would derive from these options
[63,64]. This learning process makes a potential customer more
confident that the product configuration he/she has selected is the
one that best fits his/her needs within the company’s product
space. Reduced uncertainty on the superior fit of the chosen
A. Trentin et al. / Computers in Industry 64 (2013) 436–447
product configuration with the customer’s preferences, in turn,
translates into less anticipated regret [52], thus lowering choice
task difficulty [9].
At the same time, however, higher benefit-cost communication
capability may lead to greater choice complexity, with negative
effects on decision difficulty. For instance, individual pricing of the
available choice options may make cost-benefit trade-offs more
salient and, hence, may increase information processing demands
[41]. To fully realize the potential advantages of benefit-cost
communication capability, therefore, this capability needs to be
complemented with the focused navigation one, which lowers
choice complexity by quickly reducing the size of the search
problem for potential customers. As a result, the learning process
enabled by benefit-cost communication capability focuses only on
those choice options for which potential customers’ preferences
are less certain and, thus, the possible negative effects of this
capability on choice complexity are mitigated.
2.2.3. Flexible navigation capability
We define flexible navigation capability as the ability to
minimize the effort required of a potential customer to modify a
product configuration that he/she has previously created or is
currently creating. An example of Web-based sales configurator
deploying such capability is Converse’s site, which allows potential
customers to customize sport shoes through a multiple step
configuration process, where each step corresponds to one
customizable feature of the product. This configuration process
is depicted by a progress bar made up of as many boxes as are
configuration steps. Potential customers can modify any previously selected feature by simply clicking on the related box, without
losing any other choice that they have previously made (Fig. B3).
Indeed, a fundamental way of improving flexible navigation
capability is to allow sales configurator users to change the
choice made at any previous step of the configuration process
without having to start it over again [17]. Furthermore, after
changing the choice made at a given step, potential customers
should not be required to go through all the subsequent steps up
to the current one. Instead, they should be asked to revise only
those choices, if any, that are no longer valid because of the
change they have just made [65]. Another option to enhance
flexible navigation capability is to allow potential customers
engaged in configuring their products to bookmark their work
[17]. Bookmarks enable potential customers who are exploring
alternative product configurations to immediately recover a
previous configuration in the case that they decide to reject the
newly created one.
Flexible navigation capability helps to avoid the product variety
paradox by mitigating anticipated regret. A sales configurator with
this capability enables potential customers to quickly make and
undo changes to previously created product configurations.
Consequently, the number of product solutions a potential
customer can explore in the time span he/she is willing to devote
to the sales configuration task is larger. Stated otherwise, potential
customers can conduct more trial-and-error tests to evaluate the
effects of initial choices made and to improve upon them. Trialand-error experimentation promotes potential customers’ learning about the value they would derive from consumption of the
product being configured [63,64], especially when flexible
navigation capability is complemented with the benefit-cost
communication one as well as those discussed in the subsequent
sections. This learning process makes potential customers more
confident that the product configuration they have selected is the
one that best fits their needs within the company’s product space.
Reduced uncertainty on the superior fit of the chosen product
configuration with the customer’s preferences, in turn, translates
into less anticipated regret [52].
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2.2.4. Easy comparison capability
We define easy comparison capability as the ability to minimize
the effort required of a potential customer to compare previously
created product configurations. An example of Web-based sales
configurator deploying this capability is K SWISS’s site, which
enables potential customers to save configured shoes in their ‘‘My
Account’’ areas and, subsequently, access them at any time
(Fig. B4). Once logged in, the site user finds all his/her previously
saved shoes portrayed on the same Webpage, so that they can be
easily compared.
Indeed, a fundamental way of improving easy comparison
capability is to allow potential customers to save a product
configuration they have just created and, then, to compare
previously saved configurations side-by-side in the same screen
[17]. The advantages of providing an overview of previous
configurations can be enhanced by highlighting commonalities
and differences among them, especially if the sales configuration
process involves many choices. In this manner, a potential
customer can immediately understand, for example, which
configuration choices have caused the price or weight difference
between two configurations he/she is comparing. Another solution
to enhance easy comparison capability is to rank-order previously
created configurations in terms of fit to the customer’s preferences
or profile [50]. This can be accomplished with little or no effort on
the customer’s part, based on his/her past purchases and/or
customer input concerning simple demographics, intended
product usage and his/her best developed preferences [30,58].
Easy comparison capability helps avoid the product variety
paradox by reducing choice complexity and by mitigating
anticipated regret. A sales configurator with this capability fosters
potential customers’ learning about the value they would derive
from consumption of the product being configured. This happens
because, in assessing the value of a particular product solution,
customers tend to rely on comparisons with other alternatives that
are currently available or that have been encountered in the past
[50,66]. In particular, the possibility of easily comparing complete
product configurations is of greatest assistance when global
performance characteristics, which arise from the physical
properties of most if not all of the product components [55], are
important to potential customers. In brief, easy comparison
capability gives potential customers practice at evaluating
alternative configurations and provides anchors for the evaluative
process [8]. Consequently, potential customers improve their
confidence that the configuration they have eventually selected is
the one that best fits their needs within the company’s product
space. In turn, reduced uncertainty on the superior fit of the chosen
product configuration with the customer’s preferences translates
into less anticipated regret [52]. A sales configurator with high easy
comparison capability also alleviates choice complexity, by
reducing information processing necessary to make comparisons.
Potential customers do not need to rely on their limited working
memory to recover configurations they have previously created.
Moreover, potential customers do not need to rely on their limited
computational abilities to decompose the configurations they
want to compare to find out similarities and differences among
them.
2.2.5. User-friendly product-space description capability
We define user-friendly product-space description capability as
the ability to adapt the product space description to the needs and
abilities of different potential customers, as well as to different
contexts of use. An example of Web-based sales configurator
deploying such capability is Volkswagen’s site, which allows
potential customers to customize Polo utility cars. For each
available choice option, users are allowed to opt for a brief
description or a detailed one with more technical information by
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selecting the ‘‘More Info’’ button (Fig. B5a). In addition, choice
options affecting the esthetics of the car are described using both
text and product images, with the latter changing automatically as
the potential customer selects different options (Fig. B5b).
Indeed, one way of improving user-friendly product-space
description capability is to employ content adaptation techniques
(cf. [67]) to provide optional detailed information pertaining to the
available choice options. In this manner, potential customers with
higher involvement for the product, who are more interested in
acquiring product information [68], are allowed to learn more
about the choice options for which their preferences are less
developed. Conversely, customers with lower involvement, who
feel less responsible for making a good decision [52], are not forced
to process product information they are not interested in. In this
respect, a promising approach is to design multimedia-based
interfaces that enable potential customers to retrieve rich
information and explanations about specific product parts/
features while looking at an illustration of the product and
without breaking the continuity of their product evaluation
processes [69]. Another option to enhance user-friendly product-space description capability is to adapt information content
presented to potential customers according to their prior
knowledge about the product [17,59]. Particularly, novice customers should be allowed to use a needs-based interface, where the
available choice options involve desired product performance and
functions, while expert customers should be enabled to employ a
parameter-based interface, where the available choice options
include design parameters such as specifications of product
components [14,17]. User-friendly product-space description
capability can also be improved by presenting the same information content by means of different media, so as to streamline
human–computer interaction based on potential customers’
characteristics, such as cognitive abilities, age, motivation, cultural
background, etc. [70,71].
User-friendly product-space description capability helps avoid
the product variety paradox by reducing choice complexity and by
mitigating anticipated regret. A sales configurator deploying this
capability provides potential customers with the information
content they value most according to their individual characteristics or usage contexts and does not bother users with
communications they do not need [59]. In addition, a sales
configurator with this capability augments or switches modalities
of presentation of the same information content in such a way that
each individual user’s information processing is enhanced [72]. By
tailoring both information content and information format, this
capability reduces information overload and eases the customer
decision process [73–75]. In particular, this capability allows for
aligning the way in which the product space is presented to a
potential customer with the way in which he/she is able or willing
to express his/her requirements [63,64]. As potential customers
interact with a sales configurator in their customary language, they
become able to assess the fit of the configured product with their
needs more easily and in less time [40]. This means that, once a
potential customer has selected his/her most preferred product
configuration, he/she is more confident that the chosen solution is
the one that best fits his/her needs within the company’s product
space. Reduced uncertainty on the superior fit of the selected
product configuration with the customer’s preferences, in turn,
translates into less anticipated regret [52].
3. Instrument development and validation
Consistent with prior studies [e.g., 76–78], our instrument
development and validation is based upon a two-stage approach.
In the first phase, the measurement items for each construct are
generated and tentative indications of reliability and validity are
provided. In the second phase, we further refine and validate our
multi-item scales using large-scale sample data and confirmatory
analyses to derive stronger assessments of the psychometric
properties of our measures.
3.1. Instrument development and preliminary assessment
The items for the five sales configurator capabilities were
generated based upon the relevant literature and extensive
interviews with practitioners involved with the development
and use of sales configurators. All the items were measured by
means of a 7-point Likert scale (7 = completely agree, . . .,
1 = completely disagree). Additionally, we used only positive
statements, as negatively worded questions with an agree–
disagree response format are often cognitively complex [79] and
may be a source of method bias [80].
The original set of items was vetted through three steps in order
to remove potential for measurement error from the new scales.
First, the items were reviewed by a group of six people with
different experiences and perceptions relative to sales configuration, who were questioned about the appropriateness and
completeness of the instrument. Second, to replicate as closely
as possible data collection procedures to be used in our large-scale
study, we pretested the instrument with 20 engineering students
from our university, who were asked to comment on any problems
encountered while responding, such as interpretation difficulties,
faulty instructions, typos, item redundancies, etc. Based on the
feedback from the focus group and field pretesting, redundant and
ambiguous items were either modified or eliminated. Finally, the
resulting instrument was evaluated through a Q-sort procedure for
establishing tentative indications of construct validity and
reliability [77,81]. Each of 10 practitioners who are experienced
in developing or using sales configurators was given a questionnaire containing short descriptions of the proposed capabilities,
together with a randomized list of the items. Subsequently, these
expert judges were asked to assign each item to one or none of the
defined capabilities. All the items were placed in the target
construct by at least 75% of the judges and, therefore, were retained
for our large-scale study [61]. The final instrument consisted of 17
items and is reported in Appendix A.
3.2. Large-scale refinement and validation
Each of the proposed sales configurator capabilities indicates a
fundamental benefit potential customers should experience and
perceive during the sales configuration process if the product
variety paradox is to be avoided, regardless of how such benefits
are delivered. This is consistent with the capability perspective of
routines, which tends to treat routines as a ‘‘black box’’ and is
mainly interested in the purpose or motivation for routines [82].
Notice that sales configurators embody rules and procedures to
generate or select the product variant that best fits each customer’s
idiosyncratic needs within a company’s product space and, as such,
are repositories of organizational procedural knowledge: namely,
they are software-embedded routines [83].
Accordingly, to measure the proposed sales configurator
capabilities, we needed to collect data on sales configurations
experiences made by potential customers using sales configurators. Specifically, data for our large-scale study were gathered on a
sample of 630 sales configuration experiences made by 63
engineering students at the authors’ university (age range: 24–
27; 29% females) using Web-based sales configurators for
consumer goods relevant to the participants. As a result, our data
are biased in favor of young, male, and fairly adept persons who are
familiar with the Internet. At the same time, however, young
people adept at using Internet also represent the majority of
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Table 1
Discriminant validity.
Square root
of AVE (%)
Benefit-cost communication capability
Easy comparison capability
User-friendly product-space
description capability
Flexible navigation capability
Focused navigation capability
***
Correlations
Benefit-cost
communication
capability
Easy comparison
capability
User-friendly
product-space
description
capability
Flexible
navigation
capability
Focused
navigation
capability
0.854
0.853
0.861
–
0.237***
0.727***
0.237***
–
0.358***
0.727***
0.358***
–
0.338***
0.356***
0.486***
0.760***
0.334***
0.772***
0.822
0.834
0.338***
0.760***
0.356***
0.334***
0.486***
0.772***
–
0.393***
0.393***
–
Significant at p < 0.001.
business-to-consumer sales configurator users [42,84]. Each
participant was asked to configure a product according to his/
her individual preferences on 10 different, preassigned, Web-based
sales configurators and to fill out a questionnaire for each of these
configuration experiences. We decided to control for possible
effects of participants’ characteristics before assessing the
psychometric properties of our measurement scales. Consequently, consistent with prior studies [38,85], we regressed our 17
indicators on 63 dummies representing the participants in our
study and used the standardized residuals from this linear,
ordinary least square regression model as our data in all the
subsequent analyses.
Prior to conducting confirmatory analyses, we also evaluated
common method bias, which is often mentioned as a major source
of concern in studies involving self-report measures [86]. Our
analyses suggest that any common method bias that exists in our
study is unlikely to be problematic. First, we conducted Harman’s
single-factor test, using confirmatory factor analysis (CFA) to test
the hypothesis that a single factor can account for all of the
variance in our data [87], and we found a very poor fit of the model
testing such hypothesis with our data (RMSEA (90% CI) = 0.194
(0.188; 0.200), x2/df (df) = 24.57 (119)). Furthermore, we controlled for the effects of a single, unmeasured latent method factor
[87], and found that, on average, only 7.55% of the variance of our
indicators is due to the method factor [cf. 88,89].
Subsequently, CFA was employed to assess unidimensionality,
convergent validity, discriminant validity, and reliability of our
measurement scales. We used a variance-covariance matrix of the
17 indicators to input data, maximum likelihood method to
estimate the model, and LISREL 8.80 to conduct the analysis.
Unidimensionality and convergent validity were assessed by
estimating an a priori measurement model, where the empirical
indicators were restricted to load on the latent factor they were
intended to measure. The model showed a good fit to the data:
RMSEA (90% CI) = 0.047 (0.040; 0.054), x2/df (df) = 2.39 (109),
CFI = 0.991, NFI = 0.984. Furthermore, inspection of the standardized factor loadings (see Appendix A) indicated that each was in its
anticipated direction (i.e., positive correspondences between
latent constructs and their posited indicators), was greater than
0.50, and was statistically significant at p < 0.001. Altogether,
these results suggested that every item was significantly
associated with one and only one latent factor and that, for each
scale, all items in the scale were convergent [78,90–92].
Discriminant validity was tested using [93] procedure. For each
latent construct, the square root of the average variance extracted
(AVE) exceeded the correlation with each of the other latent
variables (see Table 1), thereby suggesting that our measurement
scales represent distinct latent variables [93].
Reliability was assessed using both AVE and the Werts, Linn,
and Joreskog (WLJ) composite reliability method [94]. All the WLJ
composite reliability values were greater than 0.70 and all the AVE
scores exceeded 0.50, indicating that a large amount of the
variance is captured by each latent construct rather than due to
measurement error [93,95].
Finally, we examined the predictive validity of our constructs
by investigating whether they exhibit relationships with other
constructs in accordance with theory [76]. Our proposed sales
configurator capabilities are posited to help firms avoid the risk
that offering more product variety and customization to increase
sales, paradoxically results in a loss of sales. Accordingly, these
capabilities are hypothesized to positively influence both choice
satisfaction and purchase intention. In the same way as Valenzuela
et al. [11], we measured choice satisfaction as follows: ‘‘How
satisfied or dissatisfied are you with the product that you have
customized? (seven-point scale: very dissatisfied/very satisfied)’’.
Following Schlosser et al. [96], we measured purchase intention by
means of three items, each rating the same statement (‘‘If I needed
this type of product, I do think I would buy the product that I have
just configured’’) on a different seven-point scale (‘‘Unlikely/likely’’
( 3 to +3), ‘‘Impossible/possible’’ ( 3 to +3), ‘‘Improbable/
Table 2
Predictive validity.
Choice satisfaction
Path coefficient
Benefit-cost communication capability
Easy comparison capability
User-friendly product-space description capability
Flexible navigation capability
Focused navigation capability
***
**
*
y
Significant at p < 0.001.
Significant at p < 0.01.
Significant at p < 0.05.
Significant at p < 0.10.
0.277
0.071
0.133
0.101
0.302
Purchase intention
t-Value
***
4.322
2.198*
1.972*
3.048**
4.631***
Path coefficient
t-Value
0.137
0.103
0.131
0.095
0.375
2.189*
2.941**
1.904y
2.590**
5.267***
442
A. Trentin et al. / Computers in Industry 64 (2013) 436–447
probable’’ ( 3 to +3), respectively). This measure of purchase
intention proved to be both reliable and valid (WLJ composite
reliability: 0.972; AVE = 0.920; square root of AVE exceeded the
correlation with each of the other latent variables). The structural
model testing the hypotheses that the proposed sales configurator
capabilities positively influence both choice satisfaction and
purchase intention, showed a good fit to the data: RMSEA (90%
CI) = 0.0432 (0.0372; 0.0493), x2/df (df) = 2.18 (169), CFI = 0.993,
NFI = 0.987. All the path coefficients are positive and statistically
significant (see Table 2), indicating that each of the five sales
configurator capabilities has a significant positive effect on both
choice satisfaction and purchase intention and thus establishing
the predictive validity of our constructs.
4. Discussion, managerial implications and future research
directions
In the last few years, literature has repeatedly warned against
the risk that companies offering more product variety and
customization in an attempt to increase sales, paradoxically
suffer from a loss of sales [7,10–13]. Sales configurators can play
an important role in avoiding this product variety paradox, yet
relatively few studies have focused on the characteristics these
applications should have so as to yield this benefit [18]. Drawing
upon prior research concerning sales configurators and the
customer decision process, the present paper conceptualizes five
capabilities that sales configurators should deploy in order to help
avoid the product variety paradox: namely, focused navigation,
flexible navigation, easy comparison, benefit-cost communication, and user-friendly product-space description capabilities.
Overall, these capabilities support personalization of the sales
configuration experience according to each individual user’s
characteristics and context of usage. Benefit-cost communication
capability combined with user-friendly product-space description capability supports personalization on the content and
presentation levels [cf. 97], while focused navigation, flexible
navigation, and easy comparison capabilities support personalization on the interaction level [cf. 97]. Personalization of the sales
configuration experience is essential to build successful sales
configurators, which improve fit between selected product
configuration and customer needs while limiting search effort
[cf. 97,98]. The ultimate goal would be to simulate the adaptive
and heuristic behavior that makes salespeople effective and aids
in improving both the shopping experience and the final product
choice [99,100].
By conceptualizing the abovementioned sales configurator
capabilities, our paper also enhances understanding of how
information technology may help achieve mass customization
[e.g., 19,101–103]. As recently acknowledged in literature
[42,61,84,104,105], mass customization involves not only improving compatibility between product customization and the firm’s
operational performance, but also augmenting the customer
perceived value of both the customized product and customization
experience. Both product utility and customer satisfaction with the
customization experience are negatively affected by the complexity and effort involved in the sales configuration process [8,41,104].
The present paper provides additional insight into the mechanisms
through which sales configurators may prevent these negative
effects: namely, reduction of choice complexity and mitigation of
anticipated regret through user-friendly description of the
company’s product space, focused and flexible navigation of this
space, easy comparison among alternative points of this space, and
effective communication of the benefits and costs of the available
choice options. By illuminating these mechanisms, the present
paper enhances understanding of when and why sales configurators generate value for customers and, therefore, support mass
customization strategies.
Consistent with the capability perspective of routines [82], the
proposed capabilities indicate five fundamental benefits that sales
configurator users should experience and perceive during the
customization experience, regardless of how such benefits are
delivered. While the solutions to develop the proposed capabilities
are likely to be context-specific and subject to variation due to
technological changes, the proposed set of capabilities is expected
to provide more general indications to companies selecting or
building a sales configurator. Developing and/or implementing
such a system involves costs and, for these costs to make economic
sense, the system must yield benefits, otherwise spectacular
failures are possible [42]. The capabilities proposed in this paper
point to five fundamental requirements that any sales configurator
should meet, regardless of the specific design solutions adopted, in
order to ease customers’ decision processes and, consequently,
generate value for them.
Another contribution of this study is the development and
validation of an instrument to measure the proposed set of
capabilities. The instrument was rigorously tested for content
validity, unidimensionality, convergent validity, discriminant
validity, predictive validity, and reliability. In particular, we found
that each of the proposed capabilities significantly predicts both
choice satisfaction and purchase intention, in accord with the
theoretical argument that these capabilities help avoid the product
variety paradox. Admittedly, our large-scale validation study
involved hypothetical rather than real purchase experiences, only
focused on sales configurators for consumer goods, and used
students as subjects for research. Therefore, future studies should
strengthen the proposed instrument through a series of further
refinements and tests across different populations and settings,
including truly representative samples of potential customers,
sales configurators for industrial goods, etc. Though conscious that
development of a measurement instrument is an ongoing process
[106], we believe our instrument will be a useful diagnostic and
benchmarking tool for companies seeking to assess and/or
improve their sales configurators. Further, we believe the
instrument developed in this paper will be of use to researchers
not only as a basis for refinement and extension, but also directly.
Future studies could develop and test hypotheses linking the
proposed capabilities to the various dimensions of the value of
customization that have been discussed in literature
[42,61,84,104,105]. In particular, further research is needed to
empirically investigate complementarities among the proposed
capabilities, meaning that the effects of one capability on the
customer perceived value of customization is reinforced by
another capability, as our paper suggests. Besides the direct and
joint effects of the proposed capabilities on the value of
customization, future empirical studies could also examine the
antecedents of these capabilities. Experimental studies, for
instance, could be designed to understand how the proposed
capabilities are influenced by specific technical solutions such as
the use of virtual reality, which is gaining increasing utility for a
variety of applications in product development [107] and could
therefore aid customers in designing their own products through
Web-based sales configurators.
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A. Trentin et al. / Computers in Industry 64 (2013) 436–447
Appendix A. Measurement instrument
Benefit-cost communication capability (WLJ composite reliability: 0.890; AVE: 0.729)
Thanks to this system, I understood how the various choice options influence the value that this
BCC1
product has for me
Thanks to this system, I realized the advantages and drawbacks of each of the options I had to
BCC2
choose from
This system made me exactly understand what value the product I was configuring had for me
BCC3
Easy comparison capability (WLJ composite reliability: 0.913; AVE: 0.727)
The system enables easy comparison of product configurations previously created by the user
EC1
EC2
The system lets you easily understand what previously created configurations have in common
EC3
The system enables side-by-side comparison of the details of previously saved configurations
The systems lets you easily understand the differences between previously created configurations
EC4
User-friendly product-space description capability (WLJ composite reliability: 0.896; AVE: 0.742)
UFDC1
The system gives an adequate presentation of the choice options for when you are in a hurry,
as well as when you have enough time to go into the details
UFDC2
The product features are adequately presented for the user who just wants to find out about them,
as well as for the user who wants to go into specific details
The choice options are adequately presented for both the expert and inexpert user of the product
UFDC3
Flexible navigation capability (WLJ composite reliability: 0.861; AVE: 0.675)
FlexN1
The system enables you to change some of the choices you have previously made during the
configuration process without having to start it over again
With this system, it takes very little effort to modify the choices you have previously made during
FlexN2
the configuration process
Once you have completed the configuration process, this system enables you to quickly change any
FlexN3
choice made during that process
Focused navigation capability (WLJ composite reliability: 0.901; AVE: 0.696)
FocN1
The system made me immediately understand which way to go to find what I needed
The system enabled me to quickly eliminate from further consideration everything that was not
FocN2
interesting to me at all
The system immediately led me to what was more interesting to me
FocN3
FocN4
This system quickly leads the user to those solutions that best meet his/her requirements
a
Standardized
factor loadinga
Measurement error variancea
0.888
0.211
0.828
0.314
0.844
0.288
0.879
0.938
0.679
0.891
0.227
0.120
0.538
0.207
0.842
0.291
0.918
0.157
0.821
0.326
0.730
0.468
0.872
0.239
0.856
0.267
0.785
0.752
0.384
0.435
0.879
0.911
0.228
0.169
All standardized factor loadings and measurement error variances are significant at p < 0.001.
Appendix B. Illustrative sales configurators
Figs. B1–B5.
Fig. B1. Example of sales configurator deploying focused navigation capability.
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A. Trentin et al. / Computers in Industry 64 (2013) 436–447
Fig. B2. (a) Example of sales configurator deploying benefit-cost communication capability. (b) Example of sales configurator deploying benefit-cost communication
capability.
A. Trentin et al. / Computers in Industry 64 (2013) 436–447
445
Fig. B4. Example of sales configurator deploying easy comparison capability.
Fig. B3. Example of sales configurator deploying flexible navigation capability.
Fig. B5. (a) Example of sales configurator with user-friendly product-space description capability. (b) Example of sales configurator with user-friendly product-space
description capability.
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A. Trentin et al. / Computers in Industry 64 (2013) 436–447
References
[1] B.J.I. Pine, Mass Customization: The New Frontier in Business Competition,
Harvard Business School Press, Boston, MA, 1993.
[2] L.F. Scavarda, A. Reichhart, S. Hamacher, M. Holweg, Managing product variety in
emerging markets, International Journal of Operations & Production Management 30 (2) (2010) 205–224.
[3] M. Bils, P.J. Klenow, The acceleration in variety growth, The American Economic
Review 91 (2) (2001) 274–280.
[4] W.M. Cox, R. Alm, The right stuff: America’s move to mass customization, Annual
Report-Federal Reserve Bank of Dallas, 1998, pp. 3–26.
[5] S. Kekre, K. Srinivasan, Broader product line: a necessity to achieve success?
Management Science 36 (10) (1990) 1216–1231.
[6] W.L. Berry, M.C. Cooper, Manufacturing flexibility: methods for measuring the
impact of product variety on performance in process industries, Journal of
Operations Management 17 (2) (1999) 163–178.
[7] J.T. Gourville, D. Soman, Overchoice and assortment type: when and why variety
backfires, Marketing Science 24 (3) (2005) 382–395.
[8] C. Huffman, B.E. Kahn, Variety for sale: mass customization or mass confusion?
Journal of Retailing 74 (4) (1998) 491–513.
[9] S.S. Iyengar, M.R. Lepper, When choice is demotivating: can one desire too
much of a good thing? Journal of Personality and Social Psychology 79 (6)
(2000) 995–1006.
[10] N. Syam, P. Krishnamurthy, J.D. Hess, That’s what I thought I wanted? Miswanting and regret for a standard good in a mass-customized world, Marketing
Science 27 (3) (2008) 379–397.
[11] A. Valenzuela, R. Dhar, F. Zettelmeyer, Contingent response to self-customization
procedures: implications for decision satisfaction and choice, Journal of Marketing Research 46 (6) (2009) 754–763.
[12] X. Wan, P.T. Evers, M.E. Dresner, Too much of a good thing: the impact of product
variety on operations and sales performance, Journal of Operations Management
30 (4) (2012) 316–324.
[13] K. Diehl, C. Poynor, Great expectations?! Assortment size, expectations, and
satisfaction, Journal of Marketing Research 47 (2) (2010) 312–322.
[14] F. Salvador, C. Forza, Principles for efficient and effective sales configuration
design, International Journal of Mass Customisation 2 (1–2) (2007) 114–127.
[15] J. Alba, J. Lynch, B. Weitz, C. Janiszewski, R. Lutz, A. Sawyer, S. Wood, Interactive
home shopping: consumer, retailer, and manufacturer incentives to participate
in electronic marketplaces, Journal of Marketing 61 (3) (1997) 38–53.
[16] B. Xiao, I. Benbasat, E-commerce product recommendation agents: use, characteristics, and impact, MIS Quarterly 31 (1) (2007) 137–209.
[17] T. Randall, C. Terwiesch, K.T. Ulrich, Principles for user design of customized
products, California Management Review 47 (4) (2005) 68–85.
[18] M. Heiskala, J. Tiihonen, K.-S. Paloheimo, T. Soininen, Mass customization with
configurable products and configurators: a review of benefits and challenges, in:
T. Blecker, G. Friedrich (Eds.), Mass Customization Information Systems in
Business, IGI Global, London, UK, 2007, pp. 1–32.
[19] C. Forza, F. Salvador, Application support to product variety management,
International Journal of Production Research 46 (3) (2008) 817–836.
[20] A. Haug, L. Hvam, N.H. Mortensen, Definition and evaluation of product configurator development strategies, Computers in Industry 63 (5) (2012) 471–481.
[21] M.M. Tseng, T.F. Piller, The Customer Centric Enterprise: Advances in Mass
Customization and Personalization, Springer Verlag, Berlin, Germany, 2003.
[22] N. Franke, F.T. Piller, Key research issues in user interaction with user toolkits in a
mass customization system, International Journal of Technology Management
26 (5/6) (2003) 578–599.
[23] J. Vanwelkenhuysen, The tender support system, Knowledge-based Systems 11
(5–6) (1998) 363–372.
[24] L. Hvam, S. Pape, M.K. Nielsen, Improving the quotation process with product
configuration, Computers in Industry 57 (7) (2006) 607–621.
[25] C. Forza, F. Salvador, Product Information Management for Mass Customization,
Palgrave Macmillan, London, UK, 2007.
[26] S.M. Fohn, J.S. Liau, A.R. Greef, R.E. Young, P.J. O’Grady, Configuring computer
systems through constraint-based modeling and interactive constraint satisfaction, Computers in Industry 27 (1) (1995) 3–21.
[27] T. Soininen, J. Tiihonen, T. Männistö, R. Sulonen, Towards a general ontology of
configuration, Artificial Intelligence for Engineering, Design, Analysis &
Manufacturing 12 (4) (1998) 357–372.
[28] A. Felfernig, G. Friedrich, D. Jannach, Conceptual modeling for configuration of
mass-customizable products, Artificial Intelligence in Engineering 15 (2) (2001)
165–176.
[29] S.K. Ong, Q. Lin, A.Y.C. Nee, Web-based configuration design system for product
customization, International Journal of Production Research 44 (2) (2006)
351–382.
[30] X. Luo, Y. Tu, J. Tang, C.K. Kwong, Optimizing customer’s selection for configurable product in B2C e-commerce application, Computers in Industry 59 (8)
(2008) 767–776.
[31] P.T. Helo, Q.L. Xu, S.J. Kyllönen, R.J. Jiao, Integrated vehicle configuration system –
connecting the domains of mass customization, Computers in Industry 61 (1)
(2010) 44–52.
[32] G. Hong, D. Xue, Y. Tu, Rapid identification of the optimal product configuration
and its parameters based on customer-centric modeling for one-of-a-kind
production, Computers in Industry 61 (3) (2010) 270–279.
[33] J.R. Wright, E.S. Weixelbaum, G.T. Vesonder, K.E. Brown, S.R. Palmer, J.I. Berman,
H.H. Moore, A knowledge-based configurator that supports sales, engineering,
and manufacturing at AT&T network systems, AI Magazine 14 (3) (1993) 69–80.
[34] L. Hvam, Mass customisation in the electronics industry: based on modular
products and product configuration, International Journal of Mass Customisation 1 (4) (2006) 410–426.
[35] C. Forza, F. Salvador, Managing for variety in the order acquisition and fulfilment
process: the contribution of product configuration systems, International Journal of Production Economics 76 (1) (2002) 87–98.
[36] C. Forza, F. Salvador, Product configuration and inter-firm co-ordination: an
innovative solution from a small manufacturing enterprise, Computers in Industry 49 (1) (2002) 37–46.
[37] A. Trentin, E. Perin, C. Forza, Overcoming the customization–responsiveness
squeeze by using product configurators: beyond anecdotal evidence, Computers
in Industry 62 (3) (2011) 260–268.
[38] A. Trentin, C. Forza, E. Perin, Organisation design strategies for mass customisation: an information-processing-view perspective, International Journal of Production Research 50 (14) (2012) 3860–3877.
[39] A. Kamis, M. Koufaris, T. Stern, Using an attribute-based decision support system
for user-customized products online: an experimental investigation, MIS Quarterly 32 (1) (2008) 159–177.
[40] T. Randall, C. Terwiesch, K.T. Ulrich, User design of customized products,
Marketing Science 26 (2) (2007) 268–280.
[41] B.G.C. Dellaert, S. Stremersch, Marketing mass-customized products: striking a
balance between utility and complexity, Journal of Marketing Research 42 (2)
(2005) 219–227.
[42] N. Franke, M. Schreier, Why customers value self-designed products: the importance of process effort and enjoyment, Journal of Product Innovation Management 27 (7) (2010) 1020–1031.
[43] G.J. Fitzsimons, Consumer response to stockouts, Journal of Consumer Research
27 (2) (2000) 249–266.
[44] N. Novemsky, R. Dhar, N. Schwarz, I. Simonson, Preference fluency in choice,
Journal of Marketing Research 44 (3) (2007) 347–356.
[45] S. Chatterjee, T.B. Haeth, Conflict and loss aversion in multiattribute choice: the
effects of trade-off size and reference dependence on decision difficulty, Organizational Behavior & Human Decision Processes 67 (2) (1996) 144–155.
[46] N.K. Malhotra, Information load and consumer decision making, Journal of
Consumer Research 8 (4) (1982) 419–430.
[47] M. Zeelemberg, W.W. van Dijk, A.S.R. Manstead, Reconsidering the relation
between regret and responsibility, Organizational Behavior & Human Decision
Processes 74 (3) (1998) 254–272.
[48] J.W. Payne, J.R. Bettman, E.J. Johnson, Adaptive strategy selection in decision
making, Journal of Experimental Psychology: Learning, Memory, & Cognition 14
(3) (1988) 534–552.
[49] J.R. Bettman, M.F. Luce, J.W. Payne, Constructive consumer choice processes,
Journal of Consumer Research 25 (3) (1998) 187–217.
[50] I. Simonson, Determinants of customers’ responses to customized offers: conceptual framework and research propositions, Journal of Marketing 69 (1) (2005)
32–45.
[51] I. Simonson, Regarding inherent preferences, Journal of Consumer Psychology 18
(3) (2008) 191–196.
[52] M. Zeelemberg, Anticipated regret, expected feedback and behavioral decision
making, Journal of Behavioral Decision Making 12 (2) (1999) 93–106.
[53] R. Dhar, Consumer preference for a no-choice option, Journal of Consumer
Research 24 (1997) 215–231.
[54] F. Salvador, C. Forza, M. Rungtusanatham, Modularity, product variety, production volume, and component sourcing: theorizing beyond generic prescriptions,
Journal of Operations Management 20 (5) (2002) 549–575.
[55] K. Ulrich, The role of product architecture in the manufacturing firm, Research
Policy 24 (3) (1995) 419–440.
[56] M.F. Luce, Choosing to avoid: coping with negatively emotion-laden consumer
decisions, Journal of Consumer Research 24 (4) (1998) 409–433.
[57] J. Nasiry, I. Popescu, Advance selling when consumers regret, Management
Science 58 (6) (2012) 1160–1177.
[58] A. De Bruyn, J.C. Liechty, E.K.R.E. Huizingh, G.L. Lilien, Offering online recommendations with minimum customer input through conjoint-based decision
aids, Marketing Science 27 (3) (2008) 443–460.
[59] S. Spiekermann, C. Parashiv, Motivating human–agent interaction: transferring
insights from behavioral marketing to interface design, Electronic Commerce
Research 2 (3) (2002) 255–285.
[60] A.G. Sutcliffe, S. Kurniawan, J.-E. Shin, A method and advisor tool for multimedia
user interface design, International Journal of Human–Computer Studies 64 (4)
(2006) 375–392.
[61] A. Merle, J.-L. Chandon, E. Roux, F. Alizon, Perceived value of the mass-customized product and mass customization experience for individual consumers,
Production & Operations Management 19 (5) (2010) 503–514.
[62] V. Zeithaml, Consumer perceptions of price, quality, and value: a means-end
model and synthesis of evidence, Journal of Marketing 52 (3) (1988) 2–22.
[63] E. von Hippel, PERSPECTIVE: user toolkits for innovation, Journal of Product
Innovation Management 18 (4) (2001) 247–257.
[64] E. von Hippel, R. Katz, Shifting innovation to users via toolkits, Management
Science 48 (7) (2002) 821–833.
[65] B. Yu, J. Skovgaard, A configuration tool to increase product competitiveness,
IEEE Intelligent Systems 13 (4) (1998) 34–41.
[66] I. Simonson, A. Tversky, Choice in contexts: tradeoff contrasts and extremeness
aversion, Journal of Marketing Research 29 (3) (1992) 281–295.
[67] A. Kobsa, J. Koenemann, W. Pohl, Personalised hypermedia presentation techniques for improving online customer relationships, The Knowledge Engineering
Review 16 (2) (2001) 111–155.
A. Trentin et al. / Computers in Industry 64 (2013) 436–447
[68] J.L. Zaichkowsky, Measuring the involvement construct, Journal of Consumer
Research 12 (3) (1985) 341–352.
[69] Z. Jiang, W. Wang, I. Benbasat, Multimedia-based interactive advising technology for online consumer decision support, Communications of the ACM 48 (9)
(2005) 93–98.
[70] J.H. Gerlach, F.-Y. Kuo, Understanding human–computer interaction for information systems design, MIS Quarterly 15 (4) (1991) 527–549.
[71] L.M. Reeves, J. Lai, J.A. Larson, S. Oviatt, T.S. Balaji, S. Buisine, P. Collings, P. Cohen,
B. Kraal, J.-C. Martin, M. McTear, T. Raman, K.M. Stanney, H. Su, Q.-Y. Wang,
Guidelines for multimodal user interface design, Communications of the ACM 47
(1) (2004) 57–59.
[72] K. Stanney, S. Samman, L. Reeves, K. Hale, W. Buff, C. Bowers, B. Goldiez, D.
Nicholson, S. Lackey, A paradigm shift in interactive computing: deriving multimodal design principles from behavioral and neurological foundations, International Journal of Human–Computer Interaction 17 (2) (2004) 229–257.
[73] A. Ansari, C.F. Mela, E-customization, Journal of Marketing Research 40 (2)
(2003) 131–145.
[74] T.-P. Liang, H.-J. Lai, Y.-C. Ku, Personalized content recommendation and user
satisfaction: theoretical synthesis and empirical findings, Journal of Management Information Systems 23 (3) (2007) 45–70.
[75] H. Berghel, Cyberspace 2000: dealing with information overload, Communications of the ACM 40 (2) (1997) 19–24.
[76] S. Li, S.S. Rao, T.S. Ragu-Nathan, B. Ragu-Nathan, Development and validation of a
measurement instrument for studying supply chain management practices,
Journal of Operations Management 23 (6) (2005) 618–641.
[77] J.K. Stratman, A.V. Roth, Enterprise resource planning (ERP) competence constructs: two-stage multi-item scale development and validation, Decision
Sciences 33 (4) (2002) 601–628.
[78] L. Menor, A.V. Roth, New service development competence in retail banking:
construct development and measurement validation, Journal of Operations
Management 25 (4) (2007) 825–846.
[79] F.J. Fowler, Survey Research Methods, Sage Publications, Newbury Park, CA,
1993.
[80] H.W. Marsh, Positive and negative global self-esteem: a substantively meaningful distinction or artifactors? Journal of Personality & Social Psychology 70 (4)
(1996) 810–819.
[81] G.C. Moore, I. Benbasat, Development of an instrument to measure the perceptions of adopting an information technology innovation, Information Systems
Research 2 (3) (1991) 192–222.
[82] A. Parmigiani, J. Howard-Grenville, Routines revisited: exploring the capabilities
and practice perspectives, The Academy of Management Annals 5 (1) (2011)
413–453.
[83] L. D’Adderio, Configuring software, reconfiguring memories: the influence of
integrated systems on the reproduction of knowledge and routines, Industrial &
Corporate Change 12 (2) (2003) 321–350.
[84] N. Franke, M. Schreier, Product uniqueness as a driver of customer utility in mass
customization, Marketing Letters 19 (2) (2008) 93–107.
[85] G.J. Liu, R. Shah, R.G. Schroeder, Linking work design to mass customization: a
sociotechnical systems perspective, Decision Sciences 37 (4) (2006) 519–545.
[86] P.E. Spector, Method variance in organizational research, Organizational Research Methods 9 (2) (2006) 221–232.
[87] P. Podsakoff, S. MacKenzie, J. Lee, N. Podsakoff, Common method biases in
behavioral research: a critical review of the literature and recommended remedies, Journal of Applied Psychology 88 (5) (2003) 879–903.
[88] L.J. Williams, J.A. Cote, M.R. Buckley, Lack of method variance in self-reported
affect and perception at work: reality or artifact, Journal of Applied Psychology
74 (1989) 462–468.
[89] J.A. Cote, M.R. Buckley, Estimating trait, method, and error variance: generalizing
across 70 construct validation studies, Journal of Marketing Research 24 (3)
(1987) 315–318.
[90] D.W. Gerbing, J.C. Anderson, An updated paradigm for scale development
incorporating unidimensionality and its assessment, Journal of Marketing Research 25 (2) (1988) 186–192.
[91] J.C. Anderson, D.W. Gerbing, Structural equation modeling in practice: a review
and recommended two-step approach, Psychological Bulletin 103 (3) (1988)
411–423.
[92] J.F.J. Hair, R.E. Anderson, R.L. Tatham, Multivariate Data Analysis, third ed.,
Macmillan Publishing Company, New York, 1992.
[93] C. Fornell, D.F. Larcker, Evaluating structural equation models with unobservable
variables and measurement error, Journal of Marketing Research 18 (1) (1981)
39–50.
[94] C.E. Werts, R.L. Linn, K.G. Jöreskog, Intraclass reliability estimates: testing
structural assumptions, Educational & Psychological Measurement 34 (1)
(1974) 25–33.
[95] S.W. O’Leary-Kelly, R.J. Vokurka, The empirical assessment of construct validity,
Journal of Operations Management 16 (4) (1998) 387–405.
[96] A.E. Schlosser, T.B. White, S.M. Lloyd, Converting Web site visitors into buyers:
how Web site investment increases consumer trusting beliefs and online
purchase intentions, Journal of Marketing 70 (2) (2006) 133–148.
[97] G. Kreutler, D. Jannach, Personalized needs acquisition in Web-based configuration systems, in: T. Blecker, G. Friedrich (Eds.), Mass Customization,
[98]
[99]
[100]
[101]
[102]
[103]
[104]
[105]
[106]
[107]
447
Concepts – Tools – Realization, Proceedings of the International Mass Customization Meeting 2005 (IMCM’05), GITO-Verlag, Berlin, Germany, 2005 ,
pp. 293–302.
D. Jannach, A. Felfernig, G. Kreutler, M. Zanker, G. Friedrich, Research issues in
knowledge-based configuration, in: T. Blecker, G. Friedrich (Eds.), Mass
Customization Information Systems in Business, IGI Global, London, UK,
2007 , pp. 221–236.
D. Jannach, G. Kreutler, Rapid development of knowledge-based conversational
recommender applications with advisor suite, Journal of Web Engineering 6 (2)
(2007) 165–192.
A.V. Lukas, G. Lukas, D.L. Klencke, C. Nass, System and method for optimizing a
product configuration. Patent Number US 7,505,921 B1, Finali Corporation,
Westminster, CO (US), 2009.
T. Blecker, G. Friedrich, Mass Customization Information Systems in Business,
IGI Global, London, UK, 2007.
K. Steger-Jensen, C. Svensson, Issues of mass customisation and supporting ITsolutions, Computers in Industry 54 (1) (2004) 83–103.
J. Warschat, M. Kürümlüoglu, R. Nostdal, Enabling IT for mass customisation: the
IT architecture to support an extended enterprise offering mass-customised
products, International Journal of Mass Customisation 1 (2/3) (2006) 394–401.
N. Franke, M. Schreier, U. Kaiser, The ‘‘I designed it myself’’ effect in mass
customization, Management Science 56 (1) (2010) 125–140.
F.S. Fogliatto, G.J.C. da Silveira, D. Borenstein, The mass customization decade: an
updated review of the literature, International Journal of Production Economics
138 (1) (2012) 14–25.
R.L. Hensley, A review of operations management studies using scale development techniques, Journal of Operations Management 17 (3) (1999) 343–358.
C. Noon, R. Zhang, E. Winer, J. Oliver, B. Gilmore, J. Duncan, A system for rapid
creation and assessment of conceptual large vehicle designs using immersive
virtual reality, Computers in Industry 63 (5) (2012) 500–512.
Alessio Trentin is an assistant professor at the
Università di Padova (Italy), where he got a PhD in
operations management in 2006. In 2007–2008, he was
visiting assistant research professor at the Zaragoza
Logistics Center (Zaragoza, Spain), a joint research
center of MIT (USA) and Aragona government (Spain).
His research interests mainly concern form postponement, mass customization, build-to-order supply
chains, product configuration, and sustainable operations management. His work has been published in
Computers in Industry, the International Journal of
Operations & Production Management, the International
Journal of Production Economics, the International Journal
of Production Research, and the International Journal of
Mass Customisation.
Elisa Perin is a Ph.D. student in operations management at the Università di Padova (Italy). She holds an
MS in management engineering from the Università di
Padova (Italy). Her research interests are related to
mass customization, product configuration and sustainable operations management. Her work has been
published in Computers in Industry, the International
Journal of Production Economics, and the International
Journal of Production Research.
Cipriano Forza is a full professor of operations
management at the Università di Padova (Italy). He is
also on the faculty at the European Institute of
Advanced Studies in Management, where he teaches
research methods in operations management. He has
been visiting scholar at Minnesota University (USA),
London Business School (UK), and Arizona State
University (USA). His research focuses on product
variety management. Currently he serves as an associate editor for the Journal of Operations Management and
the Decision Sciences Journal. He is researching such
topics as mass customization, concurrent productprocess-supply chain design, and product configuration. He has been successfully assisting numerous companies in these topics. He has
published in the Journal of Operations Management, the International Journal of
Operations & Production Management, the International Journal of Production
Research, Computers in Industry, the International Journal of Production Economics,
Industrial Management & Data Systems, and other journals. In 2003 and 2007, he
published two books with McGraw-Hill and Palgrave Macmillan, respectively, on
product information management for mass customization.