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Arbeitsberichte des Lehrstuhls für Allgemeine
und Industrielle Betriebswirtschaftslehre an der
Technischen Universität München
Prof. Dr. Dr. h.c. Ralf Reichwald (Hrsg.)
Arbeitsbericht Nr. 33 (Okt. 2002) des Lehrstuhls für Allgemeine und Industrielle
Betriebswirtschaftslehre der Technischen Universität München
ISSN 0942-5098
© Copyright 2002 by N. Franke und F. Piller. Alle Rechte vorbehalten.
Nikolaus Franke and Frank T. Piller
Configuration Toolkits for
Mass Customization
Setting a Research Agenda
Configuration Toolkits for Mass Customization:
Setting a Research Agenda
Nikolaus Franke* and Frank T. Piller**
* Vienna University of Business Administration and Economics, Department of Entrepreneurship,
Augasse 2-6, 1090 Vienna, Austria, +43 / 1 / 31336-4582, nikolaus.franke@wu-wien.ac.at
** Technische Universität München, Dept. of General and Industrial Management, Leopoldstr. 139,
80804 Munich, Germany, Tel: +49 / 89 / 289-24820, piller@ws.tum.de
The idea of integrating users into the design and production process is a promising
strategy for companies being forced to react to the growing individualization of
demand. While there is a huge amount of managerial literature on mass customization,
empirical findings are scarce. Our intensive literature review shows that specifically the
core of a mass customization system, the toolkit and the users’ interaction with it, has
hardly been researched. The objective of this paper is to set a research agenda in the
field of user interaction with toolkits for mass customization. From the literature and 15
exploratory expert interviews with leading pioneering companies we deploy four key
research issues in this evolving field.
Note: An adapted version of this paper will be published in the International Journal of
Entrepreneurship and Innovation Management (IJEIM), 2003.
Franke/Piller: Configuration Toolkits for Mass Customization
2
1 Introduction
The idea of integrating users in the design and production process is a promising strategy
for companies being forced to react to the growing individualization of demand. In the mass
customization concept, goods and services are to meet individual customer’s needs produced
with near mass production efficiency (Tseng and Jiao 2001). While Toffler (1970) had
already anticipated the concept three decades ago, Davis coined the term mass customization
in 1987. The idea attained wide popularity with Pine's (1993) book.
Mass customization is often connected closely with the capabilities offered by new
manufacturing technologies (CIM, flexible manufacturing systems) reducing the trade-off
between variety and productivity. But the main distinctive principle of mass customization is
a mechanism for interacting with the customer and obtaining specific information in order to
define and translate the customer’s needs and desires into a concrete product or service
specification (Zipkin 2001). In this way, the customer is integrated into the value creation of
the supplier. “Consumers take part in activities and processes which used to be seen as the
domain of the companies” (Wikström 1996, p. 360). The result is a system of co-production,
i.e. a manufacturer-customer interaction and adaptation for the purpose of attaining added
value (Milgrom and Roberts 1990; Normann and Ramirez 1994).
The customer becomes a “co-producer” respectively “prosumer” (Toffler 1970). While this
view is not new (see Ramirez 1999 for an overview), it is only today that we see a broader
application of this principle in practice (in business-to-consumer as well as in business-to-
business markets). However, as the main part of the interaction with the customer takes place
during the configuration and therefore the design of a customer specific product, it seems
appropriate to call the customer rather a co-designer than a co-producer. Customer co-design
describes a process that allows customers to express their product requirements and carry out
product realization processes by mapping the requirements into the physical domain of the
product (Helander / Khalid, 1999, Tseng/Du 1998, von Hippel 1998). During these co-
designing processes, users sometimes even take over the role of being the innovators: the
“need-information” is converted into a solution at the locus of the user without costly shifts of
the information from user to the manufacturer (von Hippel 2001). Against this background,
the importance of an interaction and configuration toolkit that enables users to design the
product desired seems obvious.
The objective of this paper is to review the present research on configuration toolkits for
mass customization in order to set an agenda for future work. To provide a background, in
Franke/Piller: Configuration Toolkits for Mass Customization
3
section 2 we theoretically analyze the mass customization system and find support for our
conjunction that toolkits constitute a research field of supreme importance for the
understanding of success and failure of mass customization applications. We review empirical
studies on mass customization and find, on the contrary, that little knowledge on the use of
and the interaction with toolkits exists. We then identify four key research issues on the users’
interaction with these toolkits (section 3). The paper concludes with the outline of an
empirical research design to address these questions.
Our analysis and conclusions are not only based on literature. The field of user integration
and mass customization is evolving so rapidly that we found it important to include the
knowledge from industry experts as well. Therefore, we conducted a series of interviews with
experts from pioneering companies of mass customization (see table 1 for an overview). We
concentrated on firms that are reported to exhibit "best practice" within their industry or are
often quoted as a leading example, or to choose companies that have been carrying out mass
customization operations for a longer period of time. For each case, we interviewed managers
in charge of the customization program (which was often the CEO), and, if available, the
manager in charge of the web site and customer interaction or customer service. The
interviews were semi-structured and conducted in most cases face-to-face (otherwise by
telephone) between January 2001 and April 2002.1
<<< Insert table 1 about here >>>
2 Literature Review: Research on Mass Customization
2.1 The Importance of Toolkits for a Mass Customization System
The integration of the customer creates a collaboration between the supplier and the
customer which supersedes the traditional value chain. Companies successfully pursuing mass
customization build an integrated knowledge flow – that not only covers one transaction but
uses information gathered during the fulfillment of a customer-specific order to improve the
knowledge base of the whole company (Gilmore/Pine 2000; Piller 2001; Zipkin 2001).
During the whole process the interface between manufacturer and customer is crucial. Not
1 This approach of qualitative research is consistent with the "laddering approach" (Durgee 1986) and the
"narrative approach" (Mishler 1986) advocated by other qualitative researchers (Homburg et al. 2000). From
the field research, we tried to identify important key factors through an iterative process like recommended
by a number of qualitative researchers (e.g. Drumwright 1994; Workman et al. 1998).
Franke/Piller: Configuration Toolkits for Mass Customization
4
only does it comprise the solution space of the production facilities, but it is also the design
instrument both for new and existing customers, the core communication tool, and supposed
to be the main origin of customer loyalty (e.g., Pine/Peppers/Rogers 1995; Riemer / Totz
2001; Vandermerwe 2000). This mechanism was mentioned in most of our interviews as
being a premier success factor of mass customization, even if almost half of the interviewees
admitted that their implemented system does not fulfill this task properly.
Additionally, the interaction system is the prime instrument for reducing the user’s costs
arising from a principal-agent constellation that is inevitable in mass customization. From the
customer’s perspective the co-design is connected with additional costs (Huffman/Kahn 1998;
Gilmore/Pine 2000). Users often have no clear knowledge of what solution might correspond
to their needs, sometimes they still have to find out what their needs are. As a result,
customers may experience uncertainty during the design process. Uncertainty about the
behavior of the supplier exists, too. The newer and more complex the individualization
possibilities are, the more information gaps increase. These processes are characterized by an
asymmetrical distribution of information − a typical principal agent constellation
(Fama/Jensen 1983): A customer (principal) orders from the supplier (agent) − and often pays
in advance − for a product she can only evaluate in a virtual form and has to wait days or even
weeks to receive it. These uncertainties can be interpreted as additional transaction costs of a
customer arising from individualization. One of the most important tasks of the supplier is to
ensure that the customer’s expenditure is kept as low as possible, while the benefit she
experiences has to be clearly perceptible.
Interaction systems for mass customization are the premier instrument to reduce these
costs. Known as configurators, choice boards, design systems, toolkits, or co-design-
platforms, these systems are responsible for guiding the user through the configuration
process. Different variations are represented, visualized, assessed and priced which starts a
learning-by-doing process for the user. While the term “configurator” or “configuration
system” is quoted rather often in literature, it is used for the most part in a technical sense
addressing a software tool. The success of such an interaction system is, however, by no
means not only defined by its technological capabilities, but also by its integration in the
whole sale environment, its ability to allow for learning by doing, to provide experience and
process satisfaction, and its integration into the brand concept. Tools for user integration in a
mass customization system have to contain much more than arithmetic algorithms to combine
modular components. Using an expression from von Hippel (2001), we will therefore use the
term “toolkit” in the following.
Franke/Piller: Configuration Toolkits for Mass Customization
5
While toolkits theoretically do not have to be based on software, all known mass
customizers are using a system which is at least to some extent IT based, Despite a huge
variation, mass customization toolkits consist of three main components (Bourke 2000;
Weston 1997; Piller 2001):
• The core configuration software presents the possible variations, and guides the user
through the configuration process, asking questions or providing design options.
Consistency and manufacturability are also checked at this stage.
• A feedback tool is responsible for presenting the configuration. Feedback information
for a design variant can be given as a visualization and in other forms (e.g. price
information, functionality test etc.) and is the basis for the trial-and error learning of the
user.
• Analyzing tools finally translate a customer specific order into lists of material,
construction plans, and work schedules. They further transmit the configuration to
manufacturing or other departments.
There is a broad spectrum of toolkits for customer driven product development and
configuration. On one end the continuum there are simple toolkits where users are just
allowed to choose from different options (color, size, etc.) – a good example is Dell
Computers. In such systems, the degree of innovativeness possible is rather limited. On the
other end of the scale, there are toolkits that assign the user a much more active role. Here, the
user actually creates (and not chooses) which allows for radical innovations. An example for
these more extreme toolkits is open source software where the users are (almost) free to
program whatever comes to their mind. But although toolkits thus can be quite heterogeneous:
the user’s interaction with it is of premier importance for the success of the respective user
integration system.
2.2 Empirical Work in the Field of Mass Customization
In 1996 Lampel/Mintzberg (1996) had already identified more than 2000 articles written
on Mass Customization. A recent exploratory study by the authors of this paper in two
bibliographic databases leads to more than 3500 articles on the subject. Although both figures
are probably misleading since they include many articles from non-scientific sources, they
indicate the growing amount of research in the field. But surprisingly, many authors tend to
build their work on rather shallow case studies or fail to include any empirical research at all.
Here, a large research deficit emerges. Table 2 lists the extant (“serious”) empirical work the
authors could identify in the field of mass customization.
Franke/Piller: Configuration Toolkits for Mass Customization
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The empirical research can be differentiated into three clusters: The first and largest group
is formed by work structuring the field of mass customization and customer interaction in
general (Ahlström/Westbrook 1999, Duray et al. 2000, Feitzinger/Lee 1999, Franke/Mertens
2001, Kotha 1996, MacCarthy/Bramham/Brabazon 2002, Piller 2001, Piller/Schoder 1999,
Strauss/Schoder 2000, Vickery/Droge/Germain 1999; for more literature see Da Silveira /
Borenstein / Fogliatto 2001; Piller 2001; Tseng/Jiao 2001). Typical research questions are the
state of art within different branches of industry, structural approaches of mass customization,
and the identification of best practices. This work is driven by the objective to show that mass
customization is a specific form of value creation and to illustrate how it differs from (craft)
customization and mass production.
The second group is related to specific questions of customer (user) driven innovation
(Franke/von Hippel 2002, Gruner/Homburg 2000, Thomke/von Hippel 2002, von Hippel
1998 and 2001). Here, in the focus of the research is not to customize goods or services, but to
integrate customers or users into new product development. The objective of the studies in
this group is to analyze how user driven innovation works, how users perform their innovating
activities (by the means of innovation toolkits), and what success can be achieved.
A third, rather small group of research tries to understand personalization, customization
and customer integration from the perspective of the customer or user (Bauer/Grether/Leach
1999, Dellaert et al. 2001, Huffman/Kahn 1998, Khalid/Helander 2001, Meuter et al. 2000,
Ng 2000, Oon /Khalid 2001). The basic research questions are how consumers handle choice
and experience the integration into configuration, and what are the factors of (customer)
satisfaction which are related to customization. Additional, explicitly methodological work
comes from Liechty/Ramaswamy/Cohen (2001) and Tian / Bearden / Hunter (2001). We will
discuss these findings together with other methodological issues later in this paper.
<<< Insert Table 2 about here >>>
While the papers presented in table 2 are strongly differentiated with regard to their
methodology, research field, focus, and even their findings, they all stress the importance of
the toolkit for customer interaction. Configuration tools are identified by the studies of the
first group as a distinguished part of mass customization systems, being an important enabler
of the cost position of mass customization. Similarly, the papers of the second group are based
on the fact that technology enables the use of toolkits for user innovation without high
transaction costs. However, the papers of groups 1 and 2 just state the importance of an
interaction tool and discuss some of their generic characteristics, but do not provided insight
Franke/Piller: Configuration Toolkits for Mass Customization
7
into how users interact with these tools, and how the design of a toolkit influences purchasing
decisions and customer satisfaction. This is the focus of the researchers of the third group. But
despite a few exceptions which will be discussed in further detail in the next section, the
studies do not address the characteristics of toolkits in mass customization environments in
particular.
Thus, while the transfer of the findings of other areas of customer interaction to mass
customization provides some interesting insight, we feel that there is the need for more
specific research. While there is plenty of research on the design of retail stores, shop layouts
and retailing environments, there is practically no comparable (user directed) research on the
design of mass customization toolkits. The transfer of studies of web sites for online selling is
difficult as traditional online shops are much more related to print catalogs than to a modern
toolkit for customer interaction in a mass customization environment. In conclusion we state
that there is an immense gap between the canonical importance of configuration toolkits and
the state of the art regarding the empirical findings. As mentioned above, our exploratory
interviews confirmed this gap as well for acting mass customizers.
3 Key Research Issues on Toolkits for Mass Customization
From both our exploratory interviews and the literature review, four key issues appear of
supreme importance for the development of our understanding of the phenomenon of mass
customization:
• Process pattern of user interaction: How do users interact on a mass customization web
site?
• Reception of complexity: Does "mass confusion" exist?
• What drives user satisfaction concerning toolkits?
• Value of individualization: Does mass customization pay?
This chapter will briefly explain these key issues, review the related literature, present
reasons why empirical insights in these questions are important and discuss what kind of
empirical information appears pertinent in the light of the status quo of the literature. We will
focus our discussion in the following on mass customization systems that are Internet based,
and thus on toolkits for mass customization that are integrated within a web site. While mass
customization is not connected per-se with electronic business, its growth is related widely
with the upcoming Internet economy. The use of the Internet as a communication medium
facilitates the efficient production of customized goods as well as the personalization of
customer relationships (Duray et al. 2000; Lee / Barua / Whinston 2000).
Franke/Piller: Configuration Toolkits for Mass Customization
8
3.1 Process Pattern of User Interaction
As already discussed, the configuration toolkit takes the role of the central interface
between the mass customization company and the customer. All other points of contact like
shipping, the product itself, and the company’s reaction to possible complaints occur later – or
do not occur at all if the interaction with the tool was so unpleasant that the user terminated
the design process. But it’s not only that user satisfaction with the toolkits is critical for the
success of mass customization applications – toolkits are also costly to develop, implement,
operate, and change (Investments for recent web based toolkits for mass customization start at
100,000 US$, and most companies have invested at least ten times this sum, according to our
exploratory survey). Hence, the programming of such a tool is both a risky and important
investment for a mass customization company.
One would expect a rich research literature and ample empirical insights in this apparently
important issue. There is a fair amount of literature on technical aspects of product
configurators and how to integrate them with the other elements of a mass customization
system (e.g. Bourke 2000; Weston 1997). But out of our literature review we could hardly
identify any empirical analysis on the actual interaction patterns of customers with toolkits for
mass customization. Thus, before turning to specific topics such as the reception of
complexity, determinants of satisfaction, and economic consequences of the interaction we
have to gain an understanding of how users actually interact with extant mass customization
configurators, i.e. how they proceed while designing a product and which patterns are visible
in the discovery of one’s own needs (Park et al. 1994; Stabell/Fjedlstadt 1998). From our
exploratory interviews we received the impression that very crucial decisions regarding the
design of the configuration toolkit are often based on relatively simple rules of thumb that
were never tested empirically. It seems that there is little knowledge on user interaction
patterns with toolkits for mass customization not only in research but also among
practitioners.
In the following we therefore consider what insights related research areas offer. Research
into user interaction with data bases (e.g. Canter/Rivers/Storrs 1985) and web sites resp. the
Internet in general (e.g. Chen/Rada 1996, Nielsen 1995) provides only limited insights.
Naturally, rules such as “make the site structure easily understandable” or “avoid long
downloading time” (e.g. Nielsen 2001, Vora 1998) that are deduced from such research also
apply to mass customization toolkits as they are normally integrated into web sites. But the
task of actively designing a product goes far beyond usual browsing behavior.
Studies in innovation processes might reveal more insights. Also in a mass customization
system, the customer takes the role of a co-innovator. Thus, it might be helpful to study how
Franke/Piller: Configuration Toolkits for Mass Customization
9
innovations, specifically user innovation processes are conducted. For example, it is often
found that novel products are developed by ‘learning by doing’ processes (von Hippel / Tyre
1995, Thomke/von Hippel/Franke 1998) resp. ‘trial and error’ processes (Ishii/Takaya 1992,
Polley/Van de Ven 1996). The underlying rationale is that it is difficult or even impossible to
know what one wants at the outset. Therefore, a targeted design process is very unlikely when
the outcome has innovative characteristics. The innovator has to learn what is possible, try
different possibilities, learn from errors, compare different solutions, and thus conduct a time
consuming, iterative learning process. Von Hippel (2001) therefore strongly recommends the
implementation of immediate feedback tools for mass customization toolkits.
In conclusion we suggest that studies on the actual design behavior of mass customization
users are decisive. So far and somewhat surprisingly this important task is still a black box.
We have to gain answers to questions such as
• Do users follow specific patterns while interacting on a mass customization web site?
• How many variants are explored and changed before making a final decision?
• Do individual users have distinct “styles” in customizing products?
• Can we observe “learning effects” of users interacting on a mass customization web
site during the course of configuration?
• Do users have a relatively clear perception of the intended outcome of the design
process? How “targeted” as opposed to a pure trial and error procedure is the design
process?
• In how far are these findings impacted by different user types (e.g. lead user vs.
average user) and toolkits types (e.g. simple choice board vs. toolkits allowing for
radical innovations)?
3.2 Reception of Complexity: Does “Mass Confusion” Exist?
While mass customization is often addressed in the literature as a promising and beneficial
approach to meet today’s market demands, some authors have recently discussed its limits
and concerns (e.g., Agrawal / Kumaresh / Mercer 2001; Zipkin 2001). One limit of mass
customization often quoted is that excess variety may result in an external complexity that
Pine termed as “mass confusion” (in: Teresko 1994). Customers can be overwhelmed by the
number of choices during product configuration (Friesen 2001; Huffman / Kahn 1998). Large
assortments and choice are often supposed to be perceived as negative by consumers. Instead
Franke/Piller: Configuration Toolkits for Mass Customization
10
of offering possibilities and choice, they seem monumental and frustrating.2 Everyone who
has experienced decision situations in the face of numerous choice possibilities – e.g. in a
super market in a foreign country trying to figure out which of the 200 detergents to choose or
in a restaurant facing a menu with 500 meals – knows that to equate a high number of
possibilities with high customer satisfaction would be starry-eyed optimism.
The number of choices on typical mass customization sites exceeds these well-known
decision problems by far. In fact, one has to convert the choice numbers into a familiar area to
get an adequate understanding of how many choices the customer has. For example, if all the
possible variations of Idtown.com watches, one of our interviewees, (circa 2*1011) were
displayed in a shop, this shop would need to be the size of Luxembourg. If one wanted to
build a shop large enough to display all variants of Customatix.com sport shoes (circa 3*1021)
the surface of the whole earth would be scarce – in fact one would need 7,000 planets of the
size of the earth, each completely covered with a shop. Toolkits allowing for innovation offer
yet endless possibilities. This shows that the premonitions mentioned above are justified
beyond question. The burden of choice may simply lead to information overload (Maes 1994;
Neumann 1955), resulting from the limitations of the human capacity to process information
(Miller 1956).
In the field of mass customization, there is only one empirical study that addresses these
points directly. Huffman and Kahn (1998) conducted two surveys with 60-80 participants
each on the customization of a sofa and a hotel package, respectively. They used an
experiential research setting and asked students of a marketing class to evaluate the two
product configurators. An important finding is that satisfaction with the configuration
processes is related to the degree of user input in an inverted u-shaped pattern. This means
that there is a point of “mass confusion” after a specific degree of variety, but also, that
variety has to have some extent to address the needs of a mass customization customer.
Huffman and Kahn state that attribute based presentation is preferred to alternative based
presentation of customization items. This finding also is an indicator of “mass confusion”:
Users tend to prefer not to choose from a long list of options for a customization possibility
but rather express a need or preference. A fitting option should be then determined
automatically by the configurator. While Huffman and Kahn (1998) provide some early and
important insight, their research is limited by the fact that subjects were not in a real-life
purchasing decision and thus did not have to bear the consequences from their decision,
2 It has been found that in some cases very large assortments may make consumers more promotion sensitive
than they might be when faced with smaller assortments. Possibly this is because the promotion information
is used to screen out unacceptable alternatives from the large assortment into smaller manageable
consideration sets (Kahn 1998; Miller 1956).
Franke/Piller: Configuration Toolkits for Mass Customization
11
resulting in possibly biased data. Also, the research was not done with a modern web based
toolkit, resulting in a limited comparability with today’s advanced toolkits.
Research in user innovation (von Hippel 1988), on the other hand, has shown that despite
large assortments and the great variety of offerings in most product fields users are often
dissatisfied with existing products and often take over the task of innovating. Originally
focusing on industrial markets, recent studies demonstrated that this pattern also holds for
consumer markets. Among end-users, too, there is a high rate of innovative activities (Shah
2000, Lüthje 2002, Franke/Shah 2002). For example, Lüthje (2002) found that in a
representative sample of outdoor athletes, ten percent built a prototype of new sport
equipment. Franke/Shah (2002) found even higher proportions of innovators in four samples
of snowboarders, canyonists, handicapped cyclists, and sailplaners. The manufacturer-active
and customer-passive paradigm (von Hippel 1978) that has been dominating consumer
marketing for decades seems no longer consistent with these findings. Of course not all
consumers in all product categories are willing to play such an active role. The proportions
found by Lüthje (2002) and Franke/Shah (2002), however, are notably high, suggesting that at
least a large minority of consumers in every product field is likely to be eager to gain more
choices and a more active role in the design of products – thus acclaiming the offering of
mass customization. It might even be that the variety of choices of typical mass customization
configurators is considered too low by some users. As we depicted, permutations of choice
options quickly reach an immense number of possible products. But to some degree, these
numbers are misleading. Notwithstanding the seemingly endless options, the role of the user
in most cases is still rather passive: she is just enabled to (passively) choose from lists, not to
(actively) create as von Hippel (2001) suggests in his conceptual analysis of toolkits for user
innovation. Of course active creation would augment the levels of complexity and user
endeavor required.
To conclude, we have to state that there is almost no empirical insight on how customers
actually respond to the complexity created by mass customization toolkits.
Hence, we have to gain answers to questions such as
• Do users feel overloaded by the information on mass customization sites?
• What is an appropriate number of choices from the user’s perceptive?
• Do different process designs and experiences of toolkits make it possible to handle
different degrees of variety from the user’s perceptive?
• To what extent is the role of a more active designer rather than a more passive chooser
desirable?
Franke/Piller: Configuration Toolkits for Mass Customization
12
• Are there great differences between different customer groups? Which factors cause
these differences?
3.3 User Satisfaction: What Drives User Satisfaction with a Toolkit?
Both of the preceding research issues we discussed earlier lead to the same question: How
satisfied are users of mass customization toolkits and what are the drivers of their
satisfaction? The importance of this question is evident. Supposedly, only users who have a
particular minimum level of satisfaction with the toolkit will finalize the design process and
purchase the product, recommend the site to their acquaintances, and come back themselves –
always assuming that the satisfaction with the product designed is sufficiently high. Research
in customer satisfaction confirms the importance of this construct (Johnson/Gustafsson 2000).
It also seems conceivable that the satisfaction with the process has a large impact upon the
satisfaction with the product in mass customization (Riemer/Totz 2001). First, it has been
shown that the perception of product quality and that of a retail outlet are closely related
(Anderson/Sullivan 1993, Patterson/Johnson/Spreng 1997). Manufacturers therefore often
strive for shelf-space in high-level outlets. In a mass customization system, the physical store
is replaced by the toolkit. It has to deliver experience and meet the high customer expectations
connected with customization. This goes hand in hand with the demand for a steady quality of
service. Companies have to implement strong instruments to build trust and reliability in order
to reduce the risk seen by prospective customers in an individualization process. Secondly,
and even more important is the fact that in mass customization the individual product is the
direct result of the process. A mass customizer is offering a solution capability, not a product.
A felicitous and successful process will therefore have an impact on both process and product
satisfaction.
Few studies exist that tackle this important area in the domain of mass customization. Totz
and Riemer (2001) deliver an extensive contingency model but do not offer any empirical
insights so far. Some evidence is given by Oon and Khalid (2001). In two small surveys they
compare the perception of three mass customization web sites. For this, they measure user
satisfaction of different aspects of configuration toolkits (such as like quality of guidelines,
number of choices etc.). However, they hardly offer any explanation as to which factors cause
different satisfaction levels or data on the relative importance of the aspects investigated.
Bauer/Grether/Leach (1999) survey the relationship between user satisfaction and
customization on the Internet quite generally and find a positive correlation. However, their
research is based on interviews with managers about the perception of satisfaction of their
customers, and thus only offers indirect insights.
Franke/Piller: Configuration Toolkits for Mass Customization
13
Recently, the flow construct (Csikszentmihalyi 1977, 1990) has been discussed as a useful
variable for understanding consumer behavior on the World Wide Web
(Novak/Hoffman/Yung 2000, Bauer/Grether/Borrmann 2001). Flow, defined as the sum of
skill and challenge experienced is found to be positively related to users’ online search and
purchasing activities. It seems plausible that taken as a moderator variable between the
individual user’s or resp. the toolkit’s cha racteristics and user satisfaction, it will offer fruitful
insights. The peculiarities of user design with a mass customization toolkit as compared to
“normal” browsing and even online purchasing behavior, however, limit a direct transmission
of the findings. Empirical insights in this matter are therefore prerequisite.
According to the available literature mentioned above, we hypothesize that personal
characteristics such as creativity, innovativeness, need for individuality have an impact upon
the experience of flow and user satisfaction with a toolkits. To conclude we suggest that
research on process and product satisfaction, flow experience, their interrelation, determinants
and consequences regarding mass customization toolkits is a key issue in the advancement of
our understanding of the phenomenon in question.
Future projects should tackle questions such as
• Which factors cause user satisfaction with toolkits for mass customization?
• What is the interrelation between process and product satisfaction?
• Which user characteristics cause the satisfaction differences likely to be observed?
• Which usability characteristics of a toolkit cause the satisfaction differences likely to
be observed?
3.4 Value of Individualization: Does Mass Customization Pay?
For users, the decision to buy individualized products is basically the result of a simple
economic equation: if the (expected) returns exceed the (expected) costs the likelihood that
she employs mass customization will increase. Costs are, for example, the price of the product
(resp. the price premium if the individualized product has a higher price than a standard
offering) and the drawbacks of the user’s integration into value creation during the
configuration process we discussed earlier (such as risk, information overload, time and effort
required, demand for trust, delivery time etc.). Returns are twofold: firstly possible rewards
from the design process such as flow experience or satisfaction with the fulfillment of a co-
design task, and secondly the value of customization, i.e. the increment of utility a customer
gains from a product that fits better to her needs than the best standard product attainable
Franke/Piller: Configuration Toolkits for Mass Customization
14
(Chamberlin 1962; Du/Tseng 1999).3 As the latter might be more enduring, this points to the
utmost significance of the value of individualization.4 Only if users value this increment of
utility highly enough, they are likely to design their own products via mass customization
sites and may be willing to pay a price premium. Our exploratory interviews let us presume
that consumers are indeed willing to pay price premiums for individualized solutions. This
important hypothesis, however, needs to be tested in large-scale research.
From a manufacturers point of view price premiums are not the only motive to employ
mass customization solutions. The chance for sustainable differentiation from its competitors
is also of high importance. Today’s market heterogeneity, increasing variety, steadily
declining product life cycles, decreasing customer loyalty, and the escalating price
competition in many branches of industry are the main motivators for firms going into mass
customization (Pine 1993). Thus, the sheer willingness of consumers to interact within a mass
customization system and to try a toolkit for mass customization is obligatory. In other words:
mass customization will be a perpetual phenomenon only if, respectively only in markets
where the value of individualization exceeds a minimum level.5 To our knowledge hardly any
attempt exists to explicitly measure (i.e. quantify in economic terms) the users’ need for
individualization or to quantify the value of customization from a user’s perspective. In a
recent study, Franke and von Hippel (2002) show that users’ needs for security in the field of
web server software is highly heterogeneous, suggesting that individualized solutions (like
open source software which offers virtually unlimited individualization) can be interpreted as
a market reaction to the high value of individualization in this field (and unfulfilled needs by
standard products).
In conclusion, we state that research on the economic value of getting an individualized
product or service is an issue of vital importance. Only if enough customers value the
3 This benefit can be differentiated into customization in regard to exact fit (e.g., measurements of a product),
functionality (e.g., a customized interface or technological feature), and (aesthetic) design or taste (e.g.,
custom colors or patterns).
4 Specifically in the consumer goods field it is of high importance to distinguish between objective and
subjective individualization. It might in some instances be the case that the process of designing and thus the
“pride of authorship” creates the value for the customer and not so much the “real” individuality of the
product per se. One manager in our exploratory study attributed the success of the market introduction of
Dell Computers partly to the satisfaction of customers feeling “smarter” than their counterparts (co-workers,
neighbors, relatives) when they finished their configuration job and realized that they were able to co-design
a computer.
5 Dewan / Jing / Seidmann (2000) develop a theoretical framework to evaluate the optimal extent of
„customization“ with regard to possible pricing premiums. While the authors can show within their approach
that sellers tend to „over-customize“ despite the detriment to their profits (in the case of competing sellers in
a mass customization market), the authors fail to address this question with real data and do not take any of
the effects on consumer buying behavior into account.
Franke/Piller: Configuration Toolkits for Mass Customization
15
advantages of customization highly enough is mass customization likely to become a mass
phenomenon, too. Thus, research is needed to tackle questions such as
• How highly do actual and potential customers value individualization?
• Which factors have an impact on this valuation?
• What options of customization (fit, functionality, design) are valued most and in
which context?
• Are customized products objectively or merely subjectively individual?
• In how far are these findings impacted by different user and toolkits types?
4 Perspectives for further research
In this paper we explored the research field of mass customization. Focusing on toolkits for
integrating the user into the design process, we identified four key research issues. The
obvious next step would be to conduct empirical research to provide answers to these
questions. How can such projects look like?
We propose to concentrate primarily on actual interaction behavior of users with a specific
mass customization site. Information should be observed rather than asked as much as
possible (particularly regarding behavior as e.g. process and design patterns). The reason for
this is that we cannot expect much reflection and awareness regarding such processes – the
information is “tacit” (Polanyi 1958/1974). Accordingly, to rely (only) on self-reported
behavior is open to biases, errors and wild guesses.
To address these questions empirically in the field of web based toolkits, a premier source
of information should be log file data. Log files are the protocols of a specific user’s activities
on a web site. It usually contains information such as the IP-address, the URL of all pages
actually requested, detailed information on time spent on a page, and documentation of other
actions. Accordingly, we asked several leading mass customization firms in our exploratory
interviews for access to their log files. Our only concern was that this information is likely to
be of critical importance to the firm which might deter them from externalizing these
protocols. After all, the literature on mass customization frequently emphasizes the potential
value of permanent learning from user behavior (e.g., Kotha 1996; Piller/Schaller/Reichwald
2002). To our surprise, according to the companies we directly asked, non-disclosure is not
the problem – but these log file protocols simply do not exist! Many web based configuration
toolkits are coded in “Flash”, a programming language, and this particular software does not
create log files easily.
Franke/Piller: Configuration Toolkits for Mass Customization
16
But even if log files existed: For technical reasons they allow only limited insights into
user interaction. Proxy servers and browser caches are a common means of reducing the
amount of data transfer by buffering files already requested in a cache – thus if a user requests
a file several times or hits the “back” button, the log file registers only one request. It is
obvious that this leads to incomplete data protocols. “Spy programs”, installed on the PC of
the respective user, deliver a possibility to this problem: They track each command of a user
(and thus of the individual design process), the time etc. and work hidden in the background.
Only users with exceptionally good knowledge of software are able to detect it.
Other issues can only be tackled by employing questionnaires, such as judgments, mental
states etc. The solution to deliver a user who just finished a design process on a specific mass
customization website an online questionnaire seems obvious (e.g. via pop-up windows). The
problem is that response rates are usually very low which might result in serious biases.
Concluding, we propose that it makes sense to analyze user interaction with mass
customization toolkits in a controlled, experimental situation. A sample of users can be
invited to design a product on PCs which are prepared with a spy program. Directly after the
design process an (online) questionnaire is handed to them. It seems reasonable to conceal or
at least not fully reveal the specific method of the study in order to avoid biases – as long as
ethical standards are not violated (i.e. the respondents are harmed in any way). Results will
increase our understanding of the fascinating and important phenomenon of mass
customization and user design. Obviously, such insights are of practical value for this
evolving field, too.
Franke/Piller: Configuration Toolkits for Mass Customization
17
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Appendix
Table 1: Cases Covered in the Exploratory Phase of the Project
Company Products Markets
Cove (www.cove.com) men’s (formal) wear Germany
Creo (www.creo-shoes.com) fashion shoes world wide (but mainly Ge rmany and USA)
Customatix (www.customatix.com) fashion shoes USA
Dell Computers (www.dell.com) PCs world wide
Idtown (www.idtown.com) watches world wide (major markets are Japan,
Germany, UK, USA)
Interactive Custom Clothes Company
Designs (www.ic3d.com) jeans USA
Lands’ End (www.landsend.com) khakis (trousers) USA
Lego (www.lego.com) comics, special toy kits
(Mosaic product line) world wired (major markets are USA,
Canada and Germany)
miAdidas (www.miadidas.com) sport shoes (soccer,
running, basketball) Germany, UK, Netherlands, Italy, Japan,
Korea, USA
NikeID (www.nike.com) sport shoes (design) USA, Germany, Japan
Reflect.com (www.reflect.com) cosmetics and body care
USA
Selve AG (www.selve.net) women’s footwear Germany
Sovital (www.sovital.de) vitamin products Germany
Timbuk2 (www.timbuk2.com) bags and luggage USA, Canada (minor markets are Europe)
Westbury by C&A (www.CundA.de) men’s (formal) wear Germany
Franke/Piller: Configuration Toolkits for Mass Customization
25
Table 2 Empirical Research on Mass Customization and Related Fields
Research question Type Field Method Findi ngs
Ahlström/Westbrook
(1999) What are the implications
of mass customization for
operations management?
Survey;
subject of
research:
machinery
various branches of
industry, mostly b-to-
b
descriptive statistics,
correlation analysis Mass customization is seen as an interesting form
of differentiation with specific patterns of design
of operations. Study lacks clear differentiation
between mass customization and traditional craft
customization.
Bauer/Grether/Leach
(1999) Does customization /
personalization influence
customer relationship
intensity?
Survey,
(n=94);
subject of
research:
managers
US online brokers for
financial services, real
estate, travel; online
book and music
sellers
Covariance Structure
Model (LISREL) (1) Level of interaction is positively related with
all three measures of relationship intensity (user
satisfaction, commitment, trust; as perceived by
the management of the firms)
(2) commitment is showing the strongest
significance coefficient; user satisfaction is only
(weakly) significantly related
Dellaert (2001), Dellaert
et al. (2001) How do consumers handle
choice of modularized
products?
Survey
(n=728),
simulation;
subject of
research:
customers
Tourism:
customization of
travel packages
Conjoint choice
experiment, micro-
simulations
Under modularization, producers of products with
structural utility benefits are better off offering
their competitively weaker modules separately
while bundling their competitively stronger
modules with weaker modules
Duray et al. (2000) How can mass customizers
be classified? Survey
(n=126);
subject of
research:
managers
Various industries in
the USA Exploratory Factor
Analysis,
ANOVA
Development of a configurationally model for
classifying mass customizers from the perspective
of operations
Two variables are key in classifying mass
customizers:
(1) the point in the production cycle where the
customer is involved in specifying the product
[design/fabrication – assembly/use]
(2) the type of modularity used in the product
[design/fabrication – assembly/use]
Feitzinger/Lee (1999) How does a large
electronics manufacturer
deploy mass
customization?
Case study Electronics industry
(Hewlett-Packard) Interviews,
qualitative assessment
Postponement is identified and described as key
enabler of mass customization
Franke/Piller: Configuration Toolkits for Mass Customization
26
Research question Type Field Method Findi ngs
Franke/Mertens (2001) How do users perceive,
handle and evaluate
personalization within
complex information
systems?
Case studies
and field
experiments
Evaluation of pilot
platforms of the use
of customization in
management
information systems
(MIS), training and
advising systems,
tourism planning
system
Interviews,
qualitative assessment
(1) Privacy and acceptance to use this systems is
their largest hurdle for implementation
(2) Perception of usefulness and value-added is
major success factor of the use of these systems
Franke/von Hippel
(2002) Do “toolkits for user
innovation” benefit users? Survey
(n=138);
subject of
research:
customers
Open Source
Software Cluster analysis,
heterogeneity index,
willingness to pay
(WTP) scale
(1) Needs among users of web server software are
highly heterogeneous
(2) Dissatisfaction with standard offerings is high
(3) Users who used the toolkit and created their
own product are significantly more satisfied than
users who only used the standard products
Gruner/Homburg (2000) What is the impact on new
products’ success of (1) the
degree of consumer
interaction in different
stages of new product
development and (2) the
characteristics of the
involved customers?
Survey
(n=310);
subject of
research:
managers
(German) machinery
industry Confirmatory factor
analysis for measure
validation, cluster and
discriminant analysis
(1) Degree of customer interaction in early and late
stages of new product development process
increases new product success (but not in middle
stages of development of technical solution)
(2) customers with lead user characteristics,
financially attractive customers and close
customers are most attractive interaction partners.
Huffman/Kahn (1998) Does complexity inherent
with a wide number of
options lead to customers’
dissatisfaction “mass
confusion”?
Survey /
experiments
(n=79 and
n=65);
subject of
research:
customers
(a) Customization of
stay in hotels
(b) Customization of
sofa
Regression analysis (1) Attribute based presentation is preferred to
alternative based presentation of customization
items;
(2) Process satisfaction is related to degree of input
in an inverted u-shaped fashion
(3) Retailers should explicitly inquire customer’s
preferences and help consumers to learn their own
preferences
Khalid/Helander (2001) How does the cultural
background of a user
influence its use and
satisfaction of a
Survey
(n=137);
subject of
r
e
search:
Watch industry
(Idtown.com),
comparison of two
cultural backgrounds
Correlation analysis (1) Users follow top-down approach represented
by the product structure
(2) Malaysian users show larger enthusiasm
t
o
wards the idea of customization than Hong Kong
Franke/Piller: Configuration Toolkits for Mass Customization
27
Research question Type Field Method Findi ngs
configuration tool on the
Internet? customers of users within one
region (Hong Kong
versus Malaysia)
subjects
(2) Malaysian users evaluate the function “show
and manage time” as main benefit of a watch
much higher than Hong Kong users, who evaluate
aesthetics and style higher
Kotha (1996) What are the management
processes and
organizational structures of
an early mass
customization pioneering
company ?
Case study
with
longitudinal
data
Bicycle industry
(National Industrial
Bicycle Company of
Japan)
Interviews,
qualitative assessment
(1) The interaction of mass customization and
mass production systems can be an effective
source of knowledge creation and of
organizational learning;
(2) Identification of external and internal success
factors of mass customization
Liechty/Ramaswamy/Co
hen (2001) How can customizable
features on a choice board
be evaluated?
Survey and
experiment
(n=360);
subject of
research:
customers
Web-based
information services
(Internet Yellow
pages)
Bayesian approach
for menu-based
conjoint analysis,
fractional factorial
research design;
correlation analysis
Development and concept proof of experimental
choice menus for assessing customers' preferences
and price sensitivity for features offered on a
choice board
MacCarthy/Bramham/Br
abazon (2002) How can different
operations modes of mass
customization be
classified?
5 case studies Consumer goods,
consumer electronics,
electronic equipment,
commercial vehicles
Interviews,
qualitative
assessment:
classification of the
case studies against
the schemes
identified in the
literature
(1) Mass customizers differ from mass producers
and (craft) customizers in regard to the
environments in which the products are offered,
the customization strategy, and operational
practices and resources used.
(2) Basic enablers of mass customization (in
regard to customer integration) are the exposure to
market fluctuations required and the strategic
involvement of customers to meet existing
modular product structure.
Meuter et al. (2000) What are sources of
satisfaction and
dissatisfaction with self-
service technologies from
the users’ perspective?
Survey (n =
823);
subject of
research:
customers
Various branches of
industries using self
service technologies
(mostly ATM and
online shopping sites)
Critical incident
study, eliciting
descriptions of
memorable incidents
by users, in addition
quantitative methods
like regression and
correlation analysis
(1) Degree of user expectation when using self
service (configuration) is higher compared to
interpersonal interaction;
(2) degree of customization offered of by self
service technologies is positively correlated with
user satisfaction;
(3) largest factor of satisfaction was the degree of
perceived advantage of using technologies; second
Franke/Piller: Configuration Toolkits for Mass Customization
28
Research question Type Field Method Findi ngs
between clusters largest error-free functionality ("just did its job")
Ng (2000)
(similar findings report
Johnson 1998, Nicholas
et al 2000, Westland / Au
1998)
Does 3D visualization in
Internet shopping lead to
higher user satisfaction and
higher propensity of
purchase?
Survey (n=80);
subject of
research:
customers
Consumer electronics
(evaluation of three
different electronic
products in diffe rent
presentation forms)
Experiment,
correlation studies (1) 3D visualization increases user satisfaction
(compared to 2D images)
(2) 3D visualization increases propensity of
purchase (compared to 2D images), however
higher experience of sickness when site is not
performing technically
Oon /Khalid (2001) How does web site design
and usability of online
configurators influence user
satisfaction and site
efficiency in supporting
design activity ?
Survey
(n=48);
subject of
research:
customers
Three mass
customization web
sites (clothes: squash-
blosson.com,
watches: Idtown.com;
bycicles: voodoo-
cycles.com)
One-way repeated
measures ANOVA,
factor analysis,
principal component
method
(1) In comparison to other sites, Idtown was found
to be significantly flexible to navigate (during
configuration); however, users complained about
too little information.
(2) Highest willingness to purchase product at
Idtown side.
(3) Hierarchical structure of product components
allows users to complete the design (configuration)
task better
Piller (2001) What different process
structures for mass
customization exist, and
what are best practices?
Case study
research
(n=120)
Various branches of
industry (60% b-to-c;
40% b-to-b); (40%
German, 40% US,
20% ROW
companies)
Interviews,
qualitative assessment
Anecdotal evidence of success factors of mass
customization:
(1) Clear definition of “solution space”
(2) Translation of modular product/service
structures with configuration tool
(3) Smooth interfaces between product
configuration and order fulfillment
(4) No iterations between sales and fulfillment
once order was placed
(5) Closed “knowledge loop”
(6) Top management support, clear governance
structures concerning who owns the system
Piller/Schoder (1999) What is the state of art of
connecting mass
customization and customer
relationship management?
Survey
(n=914);
subject of
research:
managers
German companies;
various branches of
industry, most
companies (79%) are
operating in the b-to-b
market
Descriptive statistics,
correlation analysis (1) Companies are employing mass customization
to get stronger position of differentiation
(2) Lack of sufficient information management is
main hurdle
(3) Use of customer data for building customer
relationships is rather weak
Franke/Piller: Configuration Toolkits for Mass Customization
29
Research question Type Field Method Findi ngs
Strauss/Schoder (2000) What are the status,
development, success
factors and management
implications of mass
customization?
Survey
(interviews)
(n=1308);
subject of
research:
managers
German, Austrian and
Swiss companies of
various industries
Descriptive statistic (1) The strategy of mass customization is seen by a
third of the companies of increasing importance in
future
(2) financial services and utilities offer fewer
individual products
(3) mass customization is connected with more
customer satisfaction (from the perspective of
managers)
Tian / Bearden / Hunter
(2001) How can consumers' need
for uniqueness be evaluated
(scale development)?
Two surveys
(n=273;
n=621);
subject of
research:
customers
personal experiences
of users (no specific
fields)
Validation studies
with three-factor
oblique model,
measurement of
factor loadings;
validation studies
Development of a scale to evaluate consumers'
need for uniqueness (self perception of
uniqueness). Scale is defined by creative choice
counter conformity, unpopular choice counter
conformity, avoidance of similarity.
Vickery/Droge/Germain
(1999) What is the relationship
between product
customization and
organizational structure?
Survey
(n=217);
subject of
research:
managers
US manufacturers,
various branches of
industry
Covariance Structure
Model (LISREL) Customization associates with more formal
control, fewer layers, narrower spans of control.
von Hippel (1998) What are the economics of
product development by
users?
Case studies Application-specific
integrated circuits
(ASICs); computer
telephony integration
systems (CTI)
Qualitative
assessment Anecdotal evidence that user-driven product
development pays off (impact of “sticky” local
information).
von Hippel (2001) What are the benefits of
toolkits for user
innovation?
Case study Food Industry
(Nestlé) Qualitative
assessment By the use of a toolkit the normal time of 26
weeks for development of a novel food product for
an industrial customer was reduced to 3 weeks on
average
Thomke/Von Hippel
(2002) What are business models
and strategy implications of
toolkits for user
innovation?
Case studies Flavor industry
(BBA), plastic
industry (GE Plastics)
Qualitative
assessment Toolkits for user innovation demand
organizational changes, allow improved design
processes, make it to shift the design risks to the
customers, increase customer satisfaction, and help
to attract new customers
