*The authors would like to thank Steffen Wiedemann, Martin Schreier, Marion Pötz, and the students of the E&I Research course (2002–2003) for their valuable assistance. We also are indebted to Helmut Strasser (chair of experimental mathematics and statistics, Vienna University of Economics and Business Administration) for his contributions regarding the concept of entropy. Also, we would like to express our gratitude to Abbie Griffin and the two anonymous reviewers, whose comments helped us to improve the article significantly. Finally, the authors would like to thank the Jubilaeumsfonds der Oesterreichischen Nationalbank for funding this research project.
Address correspondence to Nikolaus Franke, Vienna University of Business Administration and Economics, Department of Entrepreneurship, Augasse 2-6, 1090 Vienna, Austria; Tel: +43-1-31336-4582; E-mail: firstname.lastname@example.org.
This study analyzes the value created by so-called “toolkits for user innovation and design,” a new method of integrating customers into new product development and design. Toolkits allow customers to create their own product, which in turn is produced by the manufacturer. In the present study, questions asked were (1) if customers actually make use of the solution space offered by toolkits, and, if so, (2) how much value the self-design actually creates. In this study, a relatively simple, design-focused toolkit was used for a set of four experiments with a total of 717 participants, 267 of whom actually created their own watches. The heterogeneity of the resulting design solutions was calculated using the entropy concept, and willingness to pay (WTP) was measured by the contingent valuation method and Vickrey auctions. Entropy coefficients showed that self-designed watches vary quite widely. On the other hand, significant patterns still are visible despite this high level of entropy, meaning that customer preferences are highly heterogeneous and diverse in style but not completely random. It also was found that consumers are willing to pay a considerable price premium. Their WTP for a self-designed watch exceeds the WTP for standard watches by far, even for the best-selling standard watches of the same technical quality. On average, a 100% value increment was found for watches designed by users with the help of the toolkit. Taken together, these findings suggest that the toolkit's ability to allow customers to customize products to suit their individual preferences creates value for them in a business-to-consumer (B2C) setting even when only a simple toolkit is employed. Alternative explanations, implications, and necessary future research are discussed.
A toolkit is a design interface that enables trial-and-error experimentation and gives simulated feedback on the outcome. In this way, users are enabled to learn their preferences iteratively until the optimum product design is achieved (von Hippel and Katz, 2002). The manufacturer, in turn, produces the product to the customer's specifications. Toolkits exist in various fields, ranging from computer chips (ASICS) to individualized athletic shoes. Depending on the type of toolkit, the outcome is an individualized product (Park et al., 2000) or even an innovation (Thomke and von Hippel, 2002). The rationale underlying the toolkit, however, is the same: it allows the customer to take an active part in product development.
Any new concept must be analyzed thoroughly for its actual value. While several authors advocate the merits of the toolkit concept, others recently have discussed its limitations (e.g., Agrawal et al., 2001; Zipkin, 2001). They argue that the broad solution space offered by toolkits is of limited value because for most users the cost of actively designing might exceed the benefits of getting an individualized product. Particularly in business-to-consumer (B2C) applications, the value creation potential of toolkits is questioned, and this notion has received some endorsement by the recent shutdown of Mattel's “MyDesign Barbie” and Levi's “Original Spin” site.1 On the other hand, a considerable number of successful applications have been reported anecdotally. A recent literature review, however, shows that only very few academic studies have dealt with this new phenomenon. Among them, merely anecdotal case studies are the rule, and none have attempted a quantitative analysis of the value such toolkits deliver to customers (Franke and Piller, 2003).
The purpose of this study is to advance the understanding of the new concept of toolkits for user innovation and design by analyzing their actual value creation from the customer's perspective. In a quantitative study of a design-focused watch toolkit in a B2C setting, the questions posed were (1) if customers actually make use of the solution space offered by toolkits, and, if so, (2) how much value the process of self-design actually creates.
This article is organized as follows: section 2 presents a literature review of the concept of toolkits for user innovation and design, and section 3 describes the methods applied. The findings then are summarized in section 4. The final section discusses the implications of the study as well as alternative explanations and future research.
Toolkits for User Innovation and Design
Von Hippel (2001) defines toolkits for user innovation as a technology that (1) allows users to design a novel product by trial-and-error experimentation and (2) delivers immediate (simulated) feedback on the potential outcome of their design ideas. This idea of outsourcing design-related tasks in product development to customers stands in sharp contrast to the traditional practice of new product market research. The traditional method of obtaining customer input is to gather data meticulously from representative customers in a chosen market sector and then to use this (need-related) information in order to create ideas for new products (e.g., Lonsdale et al., 1996; Rangaswamy and Lilien, 1997). In order to reduce the risk of failure, need-related information from customers is integrated iteratively at many points in the new product development (NPD) process (e.g., Cusumano and Selby, 1995; Dahan and Hauser, 2002; Holmes, 1999). After many time-consuming iterations between the customer and manufacturer, a new or adapted product is found, usually at high cost. However, although enormous market research expenditure is the norm, the flop rates for new products are still relatively high (Cooper, 1999; Crawford, 1979; Griffin et al., 2002).
Toolkits offer potential advantages compared to the traditional method of new product development in that they enable an individual user to state or specify his or her preferences precisely. Moreover, the interaction between user and toolkit can be easier than the alternative of costly interaction between the user and manufacturer in the process of market research. Most notably, the information obtained with the help of a toolkit is located at the individual level: the manufacturer then can produce and can deliver a designated product to suit the individual user. The resulting—potentially far closer—fit between user preferences and the product itself should yield a higher level of satisfaction with the new product and subsequently should increase the customer's willingness to pay (WTP).
Obviously, there are variations in the types of available toolkits. Some very complex toolkits offer a large solution space and cannot be employed without a precise technical understanding [e.g., toolkits for designing application-specific integrated circuits, as described by von Hippel and Katz (2002)]. They depend upon the customer taking on a very active role as designer and allow substantial innovations. Most of them are employed in business-to-business (B2B) settings where the economic benefits of toolkits are apparent in many situations. Other toolkits, particularly in consumer markets, only offer a small solution space and only allow users to combine relatively few options [e.g., toolkits for designing eyeglasses, as described by von Hippel (2001)]. Although the underlying principle is the same, the latter toolkits focus on individuality and customization rather than on innovation. The present study therefore suggests using the enhanced term toolkits for user innovation and design, as it describes this new concept's entire range of applications.
Why and When Toolkits Make Sense
Two lines of argumentation have been brought forth to explain the potential benefits of toolkits for innovation and design: (1) the heterogeneity of customer preferences; and (2) the problems associated with shifting preference information from the customer to the manufacturer.
It is common knowledge that customer preferences are heterogeneous and change quickly in many markets. The need for economies of scale, however, has forced manufacturers either to satisfy the general needs and preferences of a customer segment with a standard product (thus leaving many customers or potential customers dissatisfied, if only to a certain degree) or to offer a custom-made product at a very high price. Recently, new production technologies dramatically have reduced the fixed costs of tooling in manufacturing. These “mass customization” methods have enabled custom goods to be produced with near mass production efficiency (Pine, 1993; Tseng and Jiao, 2001; Tseng and Piller, 2003; Wind and Rangaswamy, 2001).
To date, only few studies have attempted to quantify the heterogeneity of user preferences. In an empirical study on Apache's security software, Franke and von Hippel (2003a) show that users in fact do have very unique needs, leaving many displeased with standard products. Users even claimed that they were willing to pay a considerable premium for improvements that satisfy their individual needs. In a meta-analysis of published cluster analyses, Franke and Reisinger (2003) find evidence that this dissatisfaction is not an exception. Current practice in market segmentation generally leads to high levels of total variance left over as in-segment variation (approximately 50% on average). This means that a major group of customers remains somewhat dissatisfied with standard offerings, even in seemingly mature markets. Another indicator for the heterogeneity of user needs is the fact that many users take the time to innovate or to modify existing products. Franke and von Hippel (2003b) present an overview of several studies and show that in the fields sampled to date, 10 to nearly 40% of users report having modified or developed a product for in-house use (in the case of industrial products) or for personal use (in the case of consumer products). This would lead to the expectation that at least in some markets customers would value the opportunity to tailor a product to their specific needs and thus would make use of the solution space offered by a toolkit.
The second line of argumentation focuses on the problem of shifting preference information from the customer to the manufacturer. Such information is known to be difficult to encode, to transfer, and to decode (Cooper, 1979; Dougherty, 1990; Leonard-Barton, 1995; Poolton and Barclay, 1998). If a manufacturer conducted a market study on the need for new products, the most frequent answer received probably would be, “I want the same product, only better and cheaper.” One theoretical explanation for this phenomenon is described by the concept of information stickiness.
The stickiness of a given unit of information is defined as the incremental expenditure required to transfer that unit from one place to another in a form that can be accessed by a given information seeker. When this expenditure is low, information stickiness is low; when it is high, stickiness is high. The definition of sticky information is broader and also incorporates tacit knowledge (Polanyi, 1958) as one of several possible causes of stickiness. Thus, information stickiness may be rooted in the characteristics inherent to the information itself (e.g., tacitness), and/or it may be due to the individual characteristics of an information seeker or provider and that provider's style of interaction (von Hippel, 1994).
Studies have shown that the stickiness of information can be very high in innovation-related matters (Ogawa, 1998; von Hippel, 1998). Many users truly are not aware of their needs when it comes to new products, and even if they are, they often are not able to formulate them explicitly. A toolkit can be a means of “unsticking” such information. It often is found that novel products are developed through “learning-by-doing” processes (von Hippel and Tyre, 1995; Thomke et al., 1998) or by “trial and error” (Ishii and Takaya, 1992; Polley and Van de Ven, 1996). In order to find a solution, the innovator needs to be informed about all of the possibilities at her or his disposal—must try out various possibilities, learn from errors, compare different solutions, and thus engage in a time-consuming, step-by-step learning process. Toolkits provide just such a setting for trial-and-error learning. Obviously, they make sense when information stickiness is high and can be unstuck by trial-and-error learning.
Recently, some authors have emphasized the downside of toolkits for user innovation and design. Pine argues in an interview that the active learning role of the user–designer may lead to “mass confusion” instead of “mass customization” (Teresko, 1994). Users might be overwhelmed by the number of possibilities at their disposal (Huffman and Kahn, 1998; Kamali and Loker, 2002; Stump et al., 2002; Wind and Rangaswamy, 2001; Zipkin, 2001). Anyone who has been forced to choose from a very wide selection—for example, in a restaurant that offers 500 entrées—knows that equating a large number of possibilities with high customer satisfaction would be blind optimism. The human capacity to process information is limited (Miller, 1956). The burden of having to choose from too many options may lead simply to information overload (Maes, 1994; Neumann, 1955). Consequently, users may turn away from the liberty to choose and may decide for the standard (or starting) solution offered by a toolkit (Dellaert and Stremersch, 2003; Hill, 2003)—or they even may frown and turn their backs completely.
Empirical Studies on Toolkits
The number of firms operating with toolkits is growing steadily in industrial as well as in consumer markets. Many examples can be found easily on the Internet. A recent literature review revealed that empirical studies on toolkits, however, are scarce (Franke and Piller, 2003). In short, the evolving literature on mass customization concentrates on technical and production aspects instead of on the interface between user and producer, that is, the toolkit itself. The literature that directly addresses toolkits mostly supplies only anecdotal studies and describes toolkit cases in a narrative style. Furthermore, publications focus on firms implementing and using toolkits, not on users interacting with them. The present study will review the most recent exceptions here [For an exhaustive overview, see Franke and Piller (2003)].
Jeppesen (2002) analyzes 78 computer games and finds that toolkits, although well accepted by users, may increase the need for manufacturer support. This drawback is alleviated in many cases by user-to-user support systems. Franke and von Hippel (2003a) analyze the users of Apache security software, which is “open source” (i.e., it can be modified by skilled users). They find that users who introduce their own software modifications are significantly more satisfied than noninnovating users; thus, they come to the conclusion that toolkits create value for users. Estimates reveal that the average (i.e., less-skilled) Apache user is willing to pay a considerable amount (over $5,000 per user) in order to ensure that his or her individual security needs in Apache are met to full satisfaction. This indicates that the individual adjustment of a product to a user's needs potentially constitutes an enormous value increment. It has to be noted, however, that the respondents made those indications on the basis of hypothetical products (and not based on a concrete product or concept).
In an experimental study with 72 research subjects, Kamali and Loker (2002) examine the involvement of consumers who designed a T-shirt using a toolkit. The results point to an overall interest in designing as well as higher satisfaction with the toolkit as involvement increased. The higher level of interactivity also increases the customers' willingness to purchase. When asked about their willingness to pay more for truly customized T-shirts, participants responded affirmatively. However, the authors point out that future research should investigate this matter more thoroughly.
Dellaert and Stremersch (2003) study consumer interaction with a design toolkit for personal computers (PCs). They find a trade-off between product utility (i.e., the utility of a customized product better fitting a user's needs) and process complexity as perceived by the user. If perceived process complexity is high, perceived product utility decreases. The study also points to the fact that toolkits appeal more to expert consumers.
Park et al. (2000) and Levin et al. (2002) compare the effect of using either a subtractive or an additive option-framing method on the user perception of a customizable product. Both studies show that subtracting yields increased willingness to pay.
In summary, it can be said that in the literature an early an understanding of the value and potential drawbacks of toolkits for user innovation and design is evolving. One piece that is still missing, however, is a quantification of such toolkit-generated value compared to offering standard, noncustomized products. It is not known whether customers are willing and able to make use of the possibilities toolkits offer. The present research aims to contribute to answering these open questions.
Research Object: A Watch Toolkit
The decision was made for this study to focus on a single, relatively simple toolkit in a B2C setting that only allows design (and not innovation) activities. Compared to the possible research design of investigating the interactions with multiple toolkits, this approach has the advantage of providing deeper insight (i.e., high internal validity). A multiple-case approach always involves the risk of an apples-and-oranges solution. It must be admitted, however, that the external validity of this approach is limited; that is, the extent to which these results can be generalized always must be questioned. Certainly, they will not apply to sophisticated B2B toolkits that allow real innovation.
A product area was agreed upon that can be characterized by high heterogeneity of preferences, imagining that relatively simple “Swatch”-type watches in the 25-to-100-euro price range would be a promising selection. This study's investigations were based on 16 expert interviews with industry specialists (retailers, manufacturers, trade specialists), and it was found that at least 2,000 different models of these watches are offered on the (local) market where the study took place (Austria). Most producers frequently change their product range at least twice a year. In a five-year period, this would lead to more than 20,000 different “standard” designs. This was interpreted as a clear indication of high heterogeneity and dynamic preferences in this market.
The toolkit chosen for this study is operated under the brand name “Idtown” by Global Customization Ltd., Hong Kong, a spin-off company founded by the Advanced Manufacturing Institute of the Hong Kong University of Science and Technology (HKUST). Idtown was one of the most established websites in the field and is referred to as being famous for business-to-consumer mass customization in many publications (e.g., Cairncross, 2000; Khalid and Helander, 2003; Piller, 2003; Tseng and Jiao, 2001). The website http://www.idtown.com operated continuously between October 1997 and March 2003. It was shut down due to management problems and for a major renovation in April 2003. The site is scheduled to reopen in 2005 and then will serve as a testing area for future studies. A similar toolkit in operation is Factory1to1.com, which is operated by a Swiss watchmaker.
The toolkit of Idtown is relatively simple. The problem-solving activities in which users engage consist only of the visual aspects of watch design (functional aspects of “what a watch does” are known to users and consistent across the design space). Users designing a watch can engage in learning by doing because they can look immediately at a simulation that incorporates each design decision made. The toolkit thus allows for trial-and-error learning with an immediate feedback function (design). It contains a module library and opens up an immense solution space of at least 650 million different possible product designs. In the present study's assessment, the toolkit is relatively easy to use. It offers a wide variety of design possibilities: selecting and combining predefined options for the strap (80 alternatives), case (60 alternatives), face (150 alternatives), the hour/minute hands (30 alternatives), and the second hand (30 alternatives) of a watch. Truly innovative solutions, however, are not possible, and the role of the user merely consists in “designing” instead of “innovating.” A screen shot of the website is shown in Figure 1.
Users start on the home page by choosing one of the basic product categories. They can follow a top-down approach and can go through the different levels of the components, or they can choose the sequence of selecting options freely. It always is possible to go back one or more steps or to begin the design process over by returning to the home page. Moreover, users do not have to make a decision for every component but can choose a preconfigured option. During the entire process of configuration, the toolkit depicts the current selection with a full picture of the self-designed watch. Placing a customized watch in the shopping cart and proceeding to check out is similar to other online shopping websites.
Design of Experiment A
A sampling of 165 users was taken, and they were presented with a token for a self-designed watch fabricated by Idtown. The design process was carried out independently on four remote PCs provided by the authors.
Empirical data were collected at different stages for each subject. First, the individual design solution each participant came up with was stored. Second, once they had finished the design process, the participants were asked to fill out a questionnaire that asked about their willingness to pay for the self-designed watch compared to selected standard (i.e., not user-designed) models.
Participants were recruited from among (graduate-level) management students who were on campus at the time of this study. In the data collection design, a completely random sample was not entirely possible, which, however, is not mandatory at this stage due to the exploratory nature of the research questions and the lack of sufficient data. Therefore, these data are biased in favor of young and adept persons who are familiar with the Internet. According to the management of Idtown, it is worth noting that this group represented their major target group for actual sales. In order to obtain a sufficiently large sample of n=165 participants, 300 students were asked, “Would you be interested in taking part in a short research experiment? You will get a watch in return.” The acceptance rate thus came to 55%. Most refusals were due to a lack of time because courses were beginning. The high acceptance rate can be explained by both the high incentive and probably also the fun such experiments entail.
Design of Supplementary Experiments B, C, and D
Three more experiments were conducted in order to intensify understanding of the findings from Experiment A (Table 1). In Experiment B, another sample of students (who had not designed a watch themselves) were asked about their WTP for the user-designed watches (from Experiment A) and for their WTP for comparable standard types. The watches were displayed on two large posters showing the products in actual size. This was done in order to validate the WTP for the standards and to analyze whether the value increment of a self-designed watch is designer specific or general. Both experiments were repeated with a different method of measuring WTP, that is, Vickrey auctions (VAs) (Vickrey, 1961) (see next section). In all supplementary auctions, the study's sampling approach and acceptance rate was similar to Experiment A.
Table 1. Overview of Experiments
Acceptance Rate (%)
Method of Measuring WTP
Design of individual watch
Inspection of user-designed watches (and standards) displayed on a poster
Design of individual watch
Inspection of user-designed watches (and standards) displayed on a poster
Measuring Willingness to Pay
The core variable in this study is willingness to pay. Estimating a user's WTP is known to be a difficult task. Prior research offers several concepts for measuring WTP, ranging from actual transaction data to simulated auctions and survey data (Wertenbroich and Skiera, 2002). In the present study, the decision was made to use two different methods in order to cross-validate the results: the contingent valuation method (CVM) and the Vickrey auction.
In the CVM, respondents are asked directly how much they are willing to pay for a product or service (Mitchell and Carson, 1989). This approach is relatively easy to use but is said to overestimate actual WTP. Studies that compare CVM WTP with actual cash payments have shown actual spending behavior to be only 15–20% of expressed WTP (Franke and von Hippel, 2003a).
Therefore, in order to validate this study's findings, two more experiments (C and D) were conducted by means of a Vickrey auction, in which participants' bids are sealed and in which no bidder knows about the others. The item is sold to the highest bidder at a price equal to the second-highest bid; thus, the winner pays less than the highest bid (Vickrey, 1961).
It can be shown both empirically and using game theory that the dominant strategy of a bidder is to bid the same as the actual maximum WTP (e.g., Cox et al., 1982; Hoffman et al., 1993). In Experiments C and D, real auctions were conducted; that is, participants submitted real, binding bids for actual watches, and the respective winner actually purchased a watch. In contrast to CVM, the VA results are biased downward because respondents were not selected based on an actual desire to purchase a watch. Taken together, both measures (CVM and VA) might enhance understanding of user WTP for individualized products in the watch market, although they both are biased.
Heterogeneity of Resulting User Designs
The purpose of toolkits is to address heterogeneous user preferences. If preferences are actually heterogeneous and if the toolkit offers different design solutions, heterogeneous user designs should be expected as the outcome of these processes. In this case, a certain increase in WTP also would be expected.
The heterogeneity of user designs is displayed in two ways. First, the study shows how many standard products would be necessary to meet user preferences (expressed in their individual designs). Second, entropy coefficients are calculated in order to express heterogeneity using a familiar ratio.
Necessary Standard Watches
In this part of the analysis, an investigation is undertaken of how many standard products would be necessary in order to meet customer preferences as well as the toolkit does. As the simple watch toolkit in this study only allows for configuration, an omniscient manufacturer also could (in theory) offer the corresponding set of standard watches. Table 2 shows the number of standard watches the manufacturer would have to offer in order to meet the preferences of the sample of 165 customers. The assumptions of this study's simulation are discussed subsequently. For the manufacturer, there are two decisions involved: (1) the intended proportion of customers aimed to reach with that manufacturer's standard watches; and (2) the degree of satisfaction the manufacturer believes is necessary.
Cells: Number of standard watches necessary to achieve both objectives. Source: Experiment A.
Percentage of customers whose preferences are meant to be met (n=165).
Minimum satisfaction for the individual customer (100%=customer gets exactly the face, strap, case, hour/minute hand and second hand desired; 80%=customer gets a watch that meets customer's preferences in only four of the five dimensions; 60%=customer gets a watch that meets customer's preferences in only three of the five dimensions, and so forth).
Of course it is desirable for a manufacturer to offer standard products that are appealing to as many potential buyers as possible. Unfortunately, this also makes it necessary to offer many standard products. In this study's sample, the manufacturer would need 159 different standard watches to meet the preferences of the entire sample of 165 users. If the manufacturer settled for meeting the preferences of 80% of users (or, in this sample, 132 participants), the number of standard products would drop to 126.
The manufacturer also could give up the ambition of meeting every customer's preferences. For example, the manufacturer might think it would be enough to meet customers' preferences at the level of 80% (i.e., the customer would get what she wants in exactly 4 of the 5 design dimensions, while her preferences in the fifth dimension would not be met). In this case, the number of standards necessary would be 134 for 100% market coverage and 101 if the manufacturer settled for attaining this 80% satisfaction level for only 80% of the customers.
The results show that a relatively large number of standard designs are necessary for this study's small sample. Only in cases where manufacturers accept a relatively small fraction of customers as a target group and believe that low levels of individual customer satisfaction are sufficient will the resulting number of necessary standard designs drop to manageable levels. One must not forget that in reality the market does not consist of 165 customers but of several million potential watch buyers. Although it is not possible simply to extrapolate the numbers, it becomes obvious that indeed a huge number of standard watches would be necessary to meet the preferences of a sufficiently large target group at a satisfactory level.
Finally, the assumptions underlying this study's analysis are not to be overlooked:
1It is assumed that a manufacturer has perfect knowledge of customer preferences and thus of the “optimum” standard designs. Of course, this is a huge overestimate of the capabilities of marketing research.
2It is assumed that any deviation in such a standard watch from the “ideal” watch (i.e., the one the individual created) is equally negative. This is a simplification, as some differences may not matter very much to a user, while others might be perceived as a huge setback.
3It is assumed that the toolkit-designed watch satisfies the user–designer completely; that is, the self-designed watch is treated as the “ideal” product (which again is a simplification, as it is not likely that the simple configuration toolkit will allow the manufacturer to meet every customer's needs in their entirety).
Entropy of User Designs
Originally derived from the physical sciences (thermodynamics), the concept of entropy was introduced in information theory by Claude Shannon (Shannon and Weaver, 1949). Entropy is a measure of the degree of disorder, uncertainty, or randomness of a probabilistic system. In management sciences, it has been used to measure diversification (Vachani, 1991), individual decision-making strategies (Gensch and Soofi, 1995), and brand purchasing behavior (Herniter, 1973), among other things.
The entropy coefficient E of a system consisting of n possible states, with pi being the probability that the system will be in state i, can be calculated as follows:
The base of the algorithm is arbitrary; thus, the relative rather than the absolute entropy value is important. For example, if one threw a die six times and got the numbers (1, 2, 3, 4, 5, 6), the entropy of the system—the die—would be 2.58. Essentially, no concentration is visible; thus, the entropy coefficient is at its maximum. If a bogus die was used and the numbers (1, 3, 1, 5, 1, 1) were obtained, the entropy would be 1.25. The relative entropy coefficient (Eemp/Emax) for the second die thus would be 48.4%, indicating some pattern in the system. In this study's sample of user-designed watches, relatively high entropy was observed (Table 3). The relative entropy coefficients are fairly close to the maximum.
Empirical entropy coefficient divided by maximum entropy of system.
Test of null hypothesis: “there is no concentration in the data.”
The univariate model shows that only the “strap” dimension shows a somewhat lower entropy (73.7% of maximum entropy). Here, only 30 of the 80 design alternatives were chosen by the 165 users, with one single strap accounting for 64 designs chosen (38.8% of the sample). In the other dimensions, the entropy coefficient is higher. Naturally, the concentration declines as more dimensions are analyzed, meaning that entropy increases.
In the case of two dimensions, some combinations appear to be “natural” complements. The highest frequency could be observed with a specific strap/second-hand combination, which alone accounted for 34 user designs. When all five dimensions are analyzed simultaneously (i.e., the “complete” watch design is analyzed), entropy is 99.6% of its maximum possible value. The system is very close to maximum disorder, meaning that preferences at this level are quite heterogeneous.
On the other hand, the observable concentration pattern is clearly significant in all cases analyzed.2 Therefore, it can be concluded here with some certainty that although the heterogeneity of preferences is very high in the sample, there is still some tendency to cluster beyond pure chance. Preferences are not completely heterogeneous but follow some weak patterns.
Value of User Design
So far, this study only has assumed that deviations from the ideal design are relevant for users. Theoretically, it could be the case that the high observed heterogeneity of design solutions actually is random because users simply do not care about the design of watches. In this section, therefore, the value increment for self-designed watches is analyzed by checking whether people really care about their unique designs. Would they actually pay more to have their preferences met? As outlined in section 3, the value of the user design is measured by two means: CVM and VAs.
CVM. Two standard models of the same price and of equal quality were chosen as benchmarks for self-designed watches. They were identified as much-sold standard watches in a “quick-and-dirty” interrogation of two retailers. They are referred to as Standard Watches 1 and 2 henceforth (for a visual impression, see Figure 2). Another benchmark used in Experiment A was an imagined “ideal” watch in the same segment, which was user-designed by an imaginary “perfect” toolkit without any restrictions in the solution space (but still of equal and constant technical quality).
The results were remarkable (see Table 4). It was found that each user–designer's WTP for his or her self-designed watch was 48.5 euros, more than twice the WTP for the two standard types 1 and 2 with the same technical quality (21.5/21.5 euros). Differences are highly significant at p<.000 (paired t-tests). Of all participants, 87% and 85%, respectively, were willing to pay more for the self-designed watch than for Standard Watches 1 and 2. Obviously, the toolkit facilitated a high value increment for most of the respective user–designers.
Table 4. Results of the Willingness-to-Pay (WTP) Analysisa
Often-bought standard watches in same price and quality segment as user-designed watches.
“The ideal watch you can imagine designing with the perfect toolkit” (imaginary option to freely change form of cases, faces, colors, et cetera at the same technical standard and quality of product).
Most often sold standard products (“bestsellers”) within same price and quality segment.
All user–designers were asked about their WTP for their self-designed watch, for the standard watches (all displayed on the computer monitor), and for the imaginary “ideal” watch.
All subjects were asked about their WTP for 30 watches (displayed in full color and size on a poster). Beyond the standard watches (which were of course constant for each subject), subjects were asked about their WTP for a random selection from the 165 user-designed watches. Subjects were not aware of the different sources of the watch designs. The order of stimuli was random.
Each user–designer was asked for a bid (WTP) for the self-designed watch (displayed on the computer monitor) and knew that only the top 10% of the bids actually would get the watch.
All subjects were asked for their bids (WTP) for the standard watches (displayed on a poster) and knew that only the top 10% of the bids actually would get the watch.
Experiment C (n=102 user–designers in Vickrey auction)g
Experiment D (n=202 other subjects in Vickrey auction)h
Ratio of WTP measured by Vickrey auction and CVM
Having inquired about the WTP for the imagined “ideal” watch designed with the imaginary “ideal” toolkit, the WTP jumped to 92.0 euros—again an almost 100% increase (although the WTP for imaginary products certainly should not be taken literally). The differences concerning the WTP for the self-designed watch are highly significant at p<.000 (paired t-test).
In Experiment B, another sample of subjects (who had not designed a watch themselves) was asked about (1) their WTP for the user-designed watches (from Experiment A); (2) their WTP for the two standard watches (1 and 2) used in Experiment A; and (3) their WTP for the four best-selling watches of the same quality. In order to identify these bestsellers, 16 retailers, manufacturers, and industry experts had been interviewed thoroughly. The bestsellers in the particular product and quality category all came from the Swiss brand “Swatch” (referred to as Standard Watches 3 to 6 henceforth). In the experiments, the “Swatch” label was concealed in order to isolate the design aspect.
The results clearly show the reliability of the WTP for Standard Watches 1 and 2 (Experiments A and B). The differences in both experiments were small (21.5 euros versus 22.4; 21.5 versus 23.1) and not significant. The differences between the WTP for the self-designed watch (Experiment A) and the best-selling Standard Watches 3 to 6 (Experiment B), however, are substantial (48.5 euros versus 18.5/20.0/25.8/32.7) and highly significant for all four pairs (p<.000). This confirms the value increment created by self-design, even when compared to the best-selling watches on the market.
It also was found that the mean WTP for the self-designed watches of other users (i.e., the WTP of nondesigners in Experiment B for user-designed watches from Experiment A) is notably different from that of the user–designer himself or herself. The mean WTP for one and the same watch decreases from 48.5 to 23.1 euros when nondesigners are asked about their WTP. The difference is highly significant at p<.000.
This denotes that the user-designed watches primarily are not designed better than standard watches but appear to be adapted better to the personal preferences of the user–designer. It may also point to another value-creating effect of self-design (see discussion section). It is interesting, however, that the designs amateurs make relying on a simple toolkit in a sketchy 13-minute design process bring about an even higher mean WTP in two cases (23.1 versus 18.5 euros for the best-selling Standard Watch 3; and 23.1 versus 20.0 euros for the best-selling Standard Watch 4; differences not significant). In two other cases, WTP comes relatively close to the bestsellers (23.1 versus 25.8 for Standard Watch 5; 32.7 euros for Standard Watch 6; the latter difference being significant at p<.000). Thus, when treated as potential standard watches, the amateurs' designs were attributed on average approximately the same value by the market as the best-selling standard models created by professional designers.
VAs. Experiments C and D were carried out in order to validate the findings regarding the relative differences in WTP for self-designed and standard watches with a more sophisticated method (Vickrey auctions). As discussed in the methods section, CVM is likely to overestimate actual WTP, while VAs are biased downward.
This study's results show that WTP measured by VA is, in fact, different from the CVM results. The “overestimation ratio,” however, is reasonably constant for most watches, ranging from 23.2 to 30.7%, with the sole exception being Standard Watch 6. This discovery generally confirms the analyses based on the CVM presented above, although it must be noted that the WTP for Standard Watch 6 comes very close to that of the self-designed watch (the difference not being significant). Thus, this study's analysis shows that on the watch market even a simple toolkit for user innovation and design yields a very high value increment compared to most best-selling standard products.
In this project, it was found that participating users attribute a high value increment to their own design activities, even in a B2C setting where the economic benefit of a customized solution is at first glance not as apparent as in B2B markets. The WTP for a self-designed watch is almost twice as high as for the best-selling standard model available on the market. Also, the product designs are indeed very heterogeneous. Thus, it would appear that offering individualized products by means of toolkits for user innovation and design is a promising way to exploit seemingly mature markets even further, although, of course, increased costs also must be taken into account.
In addition, it also was found that other potential customers (i.e., nondesigners) liked user-designed products. The other potential customers were not informed about the source of the design of those products, yet the mean WTP for “toolkit watches” is equal to the WTP for the bestsellers made by professional designers. It only can be surmised how attractive user-designed watches would be if lead users (instead of average users; von Hippel, 1986) generated new design solutions with a toolkit that allows real creative input (instead of the relatively simple toolkit in the present study). Therefore, in addition to employing toolkits as a means of individualizing products, manufacturers should consider using toolkits as a new market research method in order to introduce promising new standard products or product designs.
A question worth pursuing is why users are willing to pay such a high price premium for their self-designed products? Literature on toolkits to date emphasizes the functional benefit, that is, adapting a product to suit an individual preference or need. This study's finding that product designs are very heterogeneous gives rise to the interpretation that this factor indeed plays an important role. It is still merely an interpretation, and qualitative indicators from the experiments (such as statements from participants) lead to conjecture that other factors also have an impact on subjective value creation for user–designers. Specifically, the self-designed product not only has a well-adapted design; it is also an individual design.
Thus, there might be something like “pride of authorship” (Dabholkar and Bagozzi, 2002; Dittmar, 1992; Lea and Webley, 1997). The active role of designing the product oneself is likely to constitute a psychological benefit to users. Everyday examples of an analogous effect can be found in people who hang up 5,000-piece jigsaw puzzles they have completed themselves instead of hanging up pictures, although objectively jigsaw puzzles look less attractive than simple (and much cheaper) posters.
The self-designed product is also unique, and it has been found that people attribute greater value to products that are unique than to ones that are common, all other things—particularly the objective value—being equal (Brock, 1968; Fournier, 1991).
In addition to the output, the process itself might also be a source of subjective value. It is likely that users enjoy the design process due to a “flow” experience (Csikszentmihalyi, 1996) and the joy of performing an artistic and creative act. This would have strong implications for toolkit development (e.g., creating an entertaining process with larger solution spaces). Many examples can be found on the do-it-yourself market, where many activities would be incomprehensible if no specific benefits associated with either the process or the outcome outweighed the direct and opportunity costs (Banks, 1998; Toffler, 1989).
Finally, it must be noted that the sunk costs of time spent on designing, some notion of fairness (custom must be more difficult, so it is fair to pay more) or simple expectations (prior life experience tells us that individual products are more expensive), and other psychological explanations also might play a role. Future research should separate these effects both theoretically [See Schreier (2003) for a very recent attempt] and empirically. Knowledge about the sources of the WTP premium is crucial to understanding of the phenomenon of customer integration with toolkits for user innovation and design, as these sources constitute success factors for the toolkits themselves. Studies that aim to address this topic should measure WTP for both self-designed and standard products at the individual user's level. Otherwise it will not be possible to explain the increment using independent variables (such as the ones already mentioned) using a regression or structural equation model. As such methods are sensitive to outliers, this study proposes the use of VAs to measure WTP.
Other opportunities for further research can be derived from certain limitations of our study. For example, the present study uses students as subjects for research. While this is a common method (see Cooper et al., 1999; Höst et al., 2000), the overall population of watch buyers is much larger and far more diverse. Thus, it should be rewarding to replicate this study using a different sampling frame.
Furthermore, this study only analyzed the dyad of “user and toolkit.” However, Jeppesen (2002), Franke and Shah (2003), and Piller et al. (2003) discuss the benefits of user communities that cooperate in design and innovation activities. Thus, it would be interesting to examine the effects of collaborative design by users with regard to the final product, process satisfaction, heterogeneity, and WTP.
This study's empirical analysis focused on users designing watches. Though watches are a very common product and their market is characterized by high heterogeneity of demand, it would be worth investigating whether the findings drawn from this study also apply to other industries, such as automobiles, computers, clothing, footwear, or even self-service applications (Dabholkar and Bagozzi, 2002; Dellaert and Stremersch, 2003). Toolkits in these product areas are not limited to aesthetic variability but also allow an individualized fit (measurements) and functions. Some allow true innovation. Here, the WTP increment is likely to be even higher than in the case of this study's simple toolkit.
1Levi's closed its “Original Spin” (mass customization) operations in October 2003, after being in this business for almost 10 years. Customers received the program quite well and happily paid the premium of about 10 to 20% compared to the standard products. Analyses show that Levi's lacked, among other problems, a functioning toolkit to help users to capture the entire value for themselves, and also for the company. Thus, costly and error-prone interactions between the company and its customers were rather the norm than the exception, leading to a rather unstable business model (Pilter, 2004). Mattel, another pioneering company in the field, abandoned its customized “MyDesign Barbie” as well, though the company had a rather sophisticated toolkit for children users on the Internet. In an interview conducted by the authors, one manager said that the reason for stopping the program was indeed too much user feedback. The 39-dollar customized doll (a premium of about 100%) attracted so many orders that the supply chain and fulfillment system was not able to handle all orders in the promised time, leading to dissatisfied customers due to long delivery times. The company was not prepared to capture the customer value it was creating in its manufacturing system. Given the relatively small volume of sales of the customized dolls compared to overall sales volumes, Mattel decided not to invest in its manufacturing and logistics capabilities but merely to keep the toolkit online without the order button. Today, users still can configure and reconfigure dolls, but just for the fun of doing it.
2Probabilities were calculated using Monte-Carlo simulations with 10,000 iterations because the number of empty cells exceeded acceptable sizes and thus conventional chi-square tests are misleading. Corresponding simulations for multivariate concentrations (based on hierarchical log linear models) would involve so much programming (because of empty cells and the chi-square problem mentioned already) that the authors decided not to calculate them.
Nikolaus Franke is professor of entrepreneurship at the Vienna University of Economics and Business Administration and is director of the Research Center for Entrepreneurship and Innovation Management at the same institution. His research interests include innovation management, toolkits for user innovation, communities, and entrepreneurship.
Frank Piller is senior lecturer in the M.B.A. program at the Technische Universitaet Muenchen (TUM), Germany, and is director of the TUM Research Group Mass Customization. His research interests include customer integration, mass customization and personalization, innovation and technology management, as well as strategic management.