Key Research Issues in User Interaction with Configuration Toolkits in a Mass Customization System: The Foundation of the Idtown User Design Project Nikolaus Franke Vienna University of Business Administration and Economics, Department for Entrepreneurship, Augasse 2-6, 1090 Vienna, Austria, +43 / 1 / 31336-4582 E-mail:
[email protected]
Frank T. Piller Frank T. Piller, Technische Universität München, Dept. of General and Industrial Management, Leopoldstr. 139, 80804 Munich, Germany, Tel: +49 / 89 / 36078 216 E-mail:
[email protected] Abstract: While mass customization has already been discussed in the literature for more than a decade as a challenging and promising strategy, hardly any research has been done in the field of consumer behavior in mass customization environments. How do consumers face the challenge of designing new products using special mass customization toolkits? How can consumers fulfill tasks normally done by trained designers and product developers at the manufacturers? And how does this form of customer integration influence customer satisfaction? These questions are of paramount importance for the understanding of success and failure in mass customization applications. From an extensive literature review and 15 exploratory expert interviews with leading pioneering companies we deploy four key research issues in the evolving field of user interaction with mass customization toolkits. These questions are aimed to tackle in a large scale empirical project, the Idtown User Design project which research setting is described. Keywords: mass customization, configuration, user innovation, customer integration, toolkits, user co-design, customer satisfaction, flow experience, exploratory case studies Biographical notes: Prof. Dr. Nikolaus Franke is Professor of Entrepreneurship at the Vienna University of Business Administration and Economics and Director of the Research Center for Entrepreneurship and Innovation Management at the same institution. His research interests are Innovation Management, Toolkits for User Innovation, Communities and Innovation. Dr. Frank Piller is a senior lecturer within the MBA program at the Technische Universität München, Germany, and the Director of the TUM Research Group Mass Customization. His research interests are customer integration, mass customization and personalization, innovation and technology management, and strategic management.
Key Research Issues in User Interaction with Configuration Toolkits in a Mass Customization System: The Foundation of the Idtown User Design Project
Abstract: While mass customization has already been discussed in the literature for more than a decade as a challenging and promising strategy, hardly any research has been done in the field of consumer behavior in mass customization environments. How do consumers face the challenge of designing new products using special mass customization toolkits? How can consumers fulfill tasks normally done by trained designers and product developers at the manufacturers? And how does this form of customer integration influence customer satisfaction? These questions are of paramount importance for the understanding of success and failure in mass customization applications. From an extensive literature review and 15 exploratory expert interviews with leading pioneering companies we deploy four key research issues in the evolving field of user interaction with mass customization toolkits. These questions are aimed to tackle in a large scale empirical project, the Idtown User Design project which research setting is described. Keywords: mass customization, configuration, user innovation, customer integration, toolkits, user co-design, customer satisfaction, flow experience, exploratory case studies
1
1
Introduction
Enterprises in all branches of industry are being forced to react to the growing individualization of demand, yet, at the same time, increasing competitive pressure dictates that costs must also continue to decrease. Companies have to adopt strategies which embrace both cost efficiency and a closer reaction to customers’ needs. Mass customization meets this challenge by producing goods and services to meet individual customer’s needs with near mass production efficiency (Tseng and Jiao 1996). 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. While 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, 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 (and here especially in business-to-consumer markets and not only 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 more a co-designer than a co-producer. Customer codesign 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 (Du 2000, Helander / Khalid, 1999, Tseng/Du 1998, von Hippel 1998). These co-designing processes can go so far that the users 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. In this paper, we identify four key research issues in the users’ interaction with these toolkits (section 3). The basis is both by an extensive literature review and 15 exploratory expert interviews with leading pioneering companies. To provide a background, in section 2 we theoretically analyze the mass customization system and find support for our conjunction 2
that toolkits constitute a research field of paramount importance for the understanding of success and failure in 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. In section 4 we describe the research setting of a large scale empirical project that is aimed to tackle these questions, the Idtown user design project.
2 2.1
Literature Review: Mass Customization Research The Importance of Toolkits for Mass Customization
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 (Anderson 1997; Gilmore/Pine 2000; Piller 2001; Zipkin 2001). The representation of these processes in a knowledge loop model stresses the importance of an interconnected and integrated flow of knowledge (Figure 1, adapted from Reichwald/Piller/Moeslein 2000). The figure quotes the premier functions of a mass customization system. All these functionalities have to be delivered in an integrated, streamlined way, starting from the point of interaction where information required for individualization is surveyed, then on to processing this information for fulfillment and providing the customized offer, and finally activities that deepen the relationships and create customer loyalty. We will use this model to briefly discuss the most important work done on the different levels of a mass customization system: > 1. Design of solution space: Before a single customer can interact with a mass customization toolkit to customize an individual solution, a solution space has to be defined. Providing custom products and services with “mass production efficiency” is only achievable when a custom design falls within the pre-existing capability and degrees of freedom built into a given supplier’s fulfillment system (von Hippel 2001). The solution space relates to the basic product architectures, the number of customization possibilities offered for one component, the pricing mechanisms for each selection, but also the development of the fulfillment processes and the interaction tools. A rich body of research exists providing solutions and approaches for “design for mass customization” (e.g., Du 2000, Jiao 1998, Tseng/Jiao 1998, Tseng/Jiao/Su 1997). However, most of this research is based in engineering and has a some what technical rather than customer orientated approach.
3
2. Configuration means to transfer a customer’s wishes into concrete product specifications. This is one of the most critical parts of any mass customization business. It’s important to differentiate between old and new customers. For new customers, first a general profile of their desires and wishes has to be built up. This profile is transformed into a product specification. At this stage new technologies like recommendation engines provide help. For the configuration for regular customers the existing customer profile has to be used. The old configuration may be presented and customers just asked for variations. The second and all subsequent sales have to be as easy (time- and money-saving) as possible. This is one of the major ways of building up customer loyalty. 3. Fulfillment: After an order has been placed, it is transferred into specific manufacturing tasks. Scheduling activities follow. The production tasks are transferred to the responsible process units, whereby suppliers may be integrated in the customization of some parts, too. Often in a segmented production layout (one production segment is responsible for some modular product components) the order is fulfilled. During this step information management has to ensure that the right specifications of an order are at the right working place at the right time. As stated above, mass customization research originates in the manufacturing discipline (e.g., Ahlström / Westbrook 1999, Anderson 1997, Kotha 1995, Pine 1993, Rautenstrauch 1997, Schlie/Goldhar 1995, Victor / Boynton 1998; see Piller 2001 and Tseng/Jiao 2001 for a comprehensive overview about the research in this field). 4. Customer relationship management: After distribution, the relationship building that started with the configuration process continues. Increasing customer loyalty is one of the foremost objectives of companies going into mass customization (e.g., Peppers/Rogers 1997, Piller 2001, Pine/Peppers/Rogers 1995, Riemer / Totz 2001, Vandermerwe 2000, Wind / Rangaswamy 2001). The relationship potential of customization represents a valuable instrument for increasing customer loyalty. Direct switching costs rise due to established trust towards the supplier, vendor or service provider and its capabilities to meet promised quality levels. Additionally, if user satisfaction is high, then uncertainty and opportunity costs rise as a defecting customer risks losing the net-benefits of the current relationship. The customer’s relationship specific investments represent another source of loyalty. They can be based on the experience of a customer with a configuration toolkit, the knowledge about the modular structures of an offering, or the familiarity with a visualization tool. If customers can be persuaded to invest significantly into a specific relationship, then sunk costs increase (Riemer / Totz 2001). Even if a competitor possesses the same mass customization skills and offers a lower price, a switching customer would have to go through the procedure of supplying information for product customization again. Also, she is once more faced with uncertainties with regard to the quality and the producer’s behavior. However, the prerequisite for customer loyalty is the ability of mass customization to establish customer satisfaction.
4
We see from this brief description of the mass customization system that the interface between manufacturer and customer is crucial. Not 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 the main origin of customer loyalty. Additionally, it is the prime instrument for reducing the user’s costs arising from a principalagent constellation that is inevitable in mass customization. From the customer’s perspective the co-design (or elicitation, as Zipkin (2001) terms it) 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:1 A customer (principal) orders from the supplier (agent) − and often pays in advance − for a product she can’t evaluate and has to wait days or even weeks to receive it. Without a clear reference point for the definition of optimal performance it is also difficult to judge whether a case of warranty arises. This is much less difficult when purchasing standardized goods or services. These uncertainties and factor transfers 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. Configuration systems for mass customization are the premier instrument to reduce these costs. Known as configurators, choice boards, design systems, 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 configuration systems theoretically don’t have to be based on software, all known mass customizers are using a system which is at least to some extent IT based. They may include physical measurement tools like 3D-scanners or visualization tools like 3D screens. Despite a huge variation, configuration systems consist of three main components (Anderson 1997; Bourke 2000; McHugh 1996; Weston 1997; Piller): • 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.
1
Principal-agent theory tries to explain how different actors behave in situations that are typically characterised through asymmetrically distributed information and uncertainties about the occurrence of certain environmental situations as well as the partner’s behaviour (Eisenhardt 1989; Fama/Jensen 1983; Wigand/Reichwald/Picot 1997).
5
• A visualization tool is responsible for presenting the configuration. This is one of the most important tools, as, in many mass customization systems, visualization substitutes the possibility of seeing a real product before the purchasing transaction, due to the made-to-order concept. • 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. Here a customized price might also be calculated. The term “configurator” or “configuration system” is used in a technical sense and addresses a software tool. The success of a configurator 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. Using an expression from von Hippel (2001), we will therefore use the term “toolkit” in the following. 2.2
Previous empirical work in the field of mass customization and user driven innovation
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 leading bibliographic databases lead 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 1 lists most of the previous (“serious”) empirical work the authors could identify in the field. 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 (Davenport / Harris / Kohli 2001, 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 6
development. We have shown above the close relation between toolkits for mass customization and toolkits for user innovation. The objective of the studies in this group is to demonstrate that user driven innovation pays, and to define how such an approach should be developed. 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, Dellaerta 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. > While these papers 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 into how users interact with these tools, and how the design of a toolkit influences purchasing decisions and customer satisfaction. Here, the research of the third group starts. However, 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.
7
3
Key Research Issues on Toolkits for Mass Customization
3.1
Methodological comments and field of research
In this section we will outline four key issues in the area of customer interaction with toolkits in the mass customization environment. Our means of identifying these issues are twofold: Firstly, our research is based on an intensive literature analysis trying to identify the state of the research on mass customization. Secondly, we conducted a series of interviews with experts from pioneering companies of mass customization to cover a field characterized by a heterogeneous population of firms and a system of value creation that is just at the start of its “s-curve”. The objective was to identify urgent problems and research questions not yet mentioned in the literature. Firms were selected from a large database of examples of mass customization by the authors from a range of industry sectors from Europe and the United States (see table 2 for an overview). We tried to identify 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 2002.2 We grouped the interview topics (after homogenizing different terminology of the informants for the same issue) around the topics we identified as most relevant from our literature research. > From both sources, four key issues appear of paramount 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?
We 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 2
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; Schouten / McAlexander 1995; Workman et al. 1998).
8
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; Fulkerson / Shank 2000; Lee / Barua / Whinston 2000; May 2001; Zerdick et al. 2000). 3.2
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 (Totz/Riemer 2001). 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 – they are also very 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 a configuration 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. Anderson 1997; Bourke 2000; McHugh 1996; Weston 1997). But, as our literature review has shown above, hardly any empirical analysis exists 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 (Alba / Marmorstein 1987; Betts 1995; Park et al. 1994; Stabell/Fjedlstadt 1998). To address these questions empirically for the field of mass customization, a premier source of information for such an analysis 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 IPaddress, the URL of all pages actually requested, detailed information on time spent on a page, and documentation of other actions. Accordingly, in the first phase of the project we asked several actors in the field of mass customization 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 9
frequently emphasizes the potential value of permanent learning from user behavior (e.g., Fowler et al. 2000; Kotha 1995; Pine/Peppers/Roger 1995; Peppers/Roger 1997; 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 configurators are coded in “Flash”, a programming language, and this particular software does not create log files easily. In other cases the log files were either not produced or not stored, a strong clue as to the lack of understanding of even the management of mass customization firms about the importance of gaining insights as to how users interact with their configuration site. 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. We can conclude from that, that there is little knowledge on user interaction patterns with toolkits for mass customization not only in research but also among practitioners. From our exploratory interviews we received the impression that very crucial decisions regarding the design of the configurator are often based on relatively simple rules of thumb that were never tested empirically. 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. Kim 1997, Nielsen 2001, Sano 1996, Vora 1998) that are deduced from such research also apply to mass customization configurators 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 as the customer takes the role of a co-innovator in a mass customization system. Although it’s debatable how far the individualized products are innovations from an objective perspective, the outcome might presumably be innovative from the customer’s point of view. Thus, it might be helpful to study how 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 10
recommends the implementation of immediate feedback tools for mass customization toolkits. On the other hand, it can be argued that typical mass customization sites do not allow for very innovative products – thus it might well be possible that users have a relatively clear perception of the intended outcome of the design process. In conclusion we would like to 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?
•
3.3
How “targeted” as opposed to a pure trial and error procedure is the design process?
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; Hufman / Kahn 1998; Sahin 2000; 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; Zipkin 2001). Large assortments and choice are often supposed to be perceived as negative by consumers. Instead of offering possibilities and choice, they seem monumental and frustrating.3 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 (circa 2*1011) were displayed in a shop, this shop 3
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 (Bawa /Landwehr/Krishna 1989; Kahn 1998; Miller 1956).
11
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. These numbers show 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 fashion. 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. Also, 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, 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 12
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 configurators. To exaggerate slightly, we have no idea if mass customization configurators are perceived as a bewildering menace or as a still insufficient remedy. Hence, we have to gain answers to questions such as
3.4
•
Do users fell 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?
•
Are there great differences between different customer groups? Which factors cause these differences? 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 configuration kit 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, Anderson/Weitz 1995, Patterson/Johnson/Spreng 1997, Rust/Zahorik 1993). Manufacturers therefore often strive for shelf-space in high-level outlets. In a mass customization system, the physical store is replaced by the configuration 13
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 / Riemer (2001) deliver an extensive contingency model but do not offer any empirical insights so far. Some evidence is given by Oon/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 the configurators (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. 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 1996, 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 configurator’s characteristics 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 mass customization configurators. To conclude we suggest that research on process and product satisfaction, flow experience, their interrelation, determinants and consequences regarding mass customization configurators 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? 14
• 3.5
Which user characteristics cause differences likely to be observed? 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 codesign 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 (Chamberlin 1962; Du/Tseng 1999).4 As the latter might be more enduring, this points to the utmost significance of the value of individualization.5 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 (Glazer 1999, Kotler 1989, Pine 1993, van Hoek/Peelen/Commandeur 1999).6
4
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).
5
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.
6
It is important to note that the value position of mass customization is not based on price premiums. Traditionally, the cost position of mass customization (demanding near mass production efficiency) is attributed to the combination of economies of scale and scope (e.g., Cox / Alm 1999; Duray et al. 2000; Kahn 1998; Pine 1993; Sahin 2000). Piller and Tseng (2001) show that these economies are becoming supplemented by additional cost saving potentials based on the integration of the customer in the value creation process, economies of interaction and economies of relationship. These cost saving potentials can counterbalance the additional costs connected with mass customization (like higher switching costs, additional logistic and information handling costs, and additional transaction costs).
15
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.7 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 advantages of customization highly enough is mass customization likely to become a mass phenomenon, too. Thus, research is needed to tackle questions such as
4
•
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?
The Idtown User Design Project
The key issues in consumer interaction with mass customization configurators outlined above are tackled in an ongoing research project, the Idtown User Design Project. In the following section, we will describe the design of this study in detail by presenting the underlying research site, the method of data collection, and the measures used. This research project is, as far as we know, the first large-scale and integrated effort to learn more about mass customization from the perspective of consumer behavior and to evaluate the value drivers and constraints of mass customization from the users’ point of view. The objective of this paper is not to discuss results from the empirical data but to present the innovative research design that may help to broaden our knowledge of the research field of mass customization.
7
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.
16
4.1
Research Site: Idtown.com
In order to analyze customer interaction with a mass customization configurator we decided to focus on a specific web site. Compared to the possible research design of investigating the interactions on several web sites our approach has the advantage of gaining in-depth insights (high internal validity). A multiple web site approach always bears the risk of an apples-and-oranges problem. We have to concede, however, that the external validity of our approach is limited, i.e. we always have to discuss the matter of to what extent our results are generalizable. Yet we are confident that in the early stage of development of this research area profoundness is more important than broadness (Bettis 1991, Kotha 1995). The web site we chose is a spin off company founded by the Advanced Manufacturing Institute of the Hong Kong University of Science and Technology (HKUST) in 1997 and operates as an independent business under the umbrella company Global Customization Ltd. in Hong Kong. Idtown is one of the most established web sites in the field and is quoted as a famous example of business-to-consumer mass customization in many publications (e.g., Cairncross 2000, Piller 2001, Pine/Gilmore 1997, Tseng/Jiao 2001). While the web site (www.Idtown.com) was in operation continuously between October 1997 and March 2002, it was shut-down for a major renovation in April 2002.8 The site is scheduled to re-open at the end of 2002, and will serve as a test field for further studies carried out by the authors while again being in full commercial operation. Idtown offers watches that can be customized by the users within three basic product lines (young plastic watches like the famous “Swatch” watch, more elegant metal watches, and smaller ladies watches). In all product lines, the consumer can choose between a large number of variants for the watch case, face, the straps, and the hands. Additionally, users can place their own wording and messages on the watch and upload digital pictures to be placed on the watch face (the later option was excluded from our empirical study). The total number of possible variations is around 200 billion. In our assessment the configurator is relatively easy to use. It offers a broad variety of design possibilities. Really innovative design solutions, however, are not possible and the role of the user comprises more “choosing” than “designing” behavior. The watches are priced between 40 and 55 USD. Delivery time is between one to two weeks after placement of the order (to the USA, Europe and most Asian countries). The watches are produced in a flexible manufacturing plant in Hong Kong. The consumer interface is exclusively on the web, most sales are done via its own web site, additional sales come from some web sites featuring the products within an online shop-in-shop approach. A screen shot of the web site is shown in figure 2. The user starts at the homepage by choosing
8
Readers wanting to experience the web site may obtain a pass word for a test site showing the state of Idtown during the course of our research and data gathering by writing an e-mail message to the authors.
17
one of the basic product categories. She can follow a top-down approach going through the different levels of components. Alternatively, the user can choose the sequence of selecting options freely. It is always possible to go one or more steps back, or to restart the design process by going back to the homepage. Also, a user does not have to make a decision for every component but can choose a pre-configured option. During the whole process of configuration, the toolkit visualizes the actual selection with a picture of the watch. Placing a custom watch in the shopping cart and proceeding to check out is similar to any other known online shopping web sites. >
4.2
Design of the empirical study and data collection
To explore the consumers’ interaction with mass customization, we drew a sample of 165 users (see below for a more detailed discussion of the sample). All were given a token (a prepared credit card) for a watch designed by themselves, manufactured by Idtown. They were informed that the project dealt with mass customization, and they would get the real watch according to their own design after the project. The design process was fulfilled independently on four remote PCs provided by the authors. We collected data from four sources over two different time periods: First, we gave the subjects a short (written) questionnaire that they had to fill out before they started the design process (referred to as “questionnaire I” in the following). The questionnaire mainly contained questions concerning the attitude towards mass customization. In addition to the data obtained, this questionnaire had the function of drawing the probands’ attention to the data collection form of questionnaires. Our purpose was to distract them from our data source described in the following. Secondly, the PCs were prepared with a “spy program”9 that tracked each step of the individual design process (what probands did, how long it took them, how often decisions were abandoned etc.). The program works hidden in the background and only users with exceptionally good knowledge of software are able to detect it. We are convinced that the probands in our study did not suspect that their behavior was being monitored.10 Thus, the design behavior is presumably free of biases that normally occur if probands know that they are being observed. The output of the program is much more powerful than usual log files as 9
The spy program is called “123 PC&Internet Monitor” and can be downloaded at www.iopus.com.
10
Of course the subjects were informed about this monitoring after completion of the design process (resp. after completion of the second questionnaire). We explicitly asked whether they agree that we use the (anonymized) data for our analysis and made clear that they would get the watch independently from their decision. All subjects agreed. We are confident that our procedure meets ethical standards and that no subject was interfered by our monitoring.
18
virtually every action carried out by the PC user is recorded. In doing so, we could also solve the problem of availability of lock-file data that we have described above in section 3.2. Thirdly, having finished the design process, the probands were asked to complete an online questionnaire (“questionnaire II”). The questionnaire concentrated on the following main topics: (1) perception of and satisfaction with the product designed, (2) perception of and satisfaction with the design process, (3) general attitudes and characteristics of the respective consumer. Fourthly, we sent e-mails to the participants after we had received notice from Idtown that the watches had been shipped (approximately three weeks after the experiment), asking them to fill out another short online questionnaire (“questionnaire III”). This third questionnaire again dealt with the attitude towards mass customization and the satisfaction with the selfdesigned product. In the data collection process we made sure that the information from the different sources could be assigned to the individual proband. Thus, we are able to observe changes, make comparisons, and correlate variables of the different data sources on the individual level. For our questionnaires we drew upon existing scales from previous research (table 3). In some instances we had to reduce the excessive number of items of the original scales in order to facilitate the filling out of the questionnaire. For this, we conducted a pilot study with 35 students. The reduction was based on classic Cronbach’s Alpha analysis, and we obtained very satisfactory Alphas from 0.79 to 0.91. All questionnaires were successfully pretested. > Probands were acquired from students who were on campus during the days of research. In our design of data collection a completely random sample was not really possible. Due to the highly exploratory research question and the shortness of any data at present this is also not necessary. Thus, our data is biased in favor of young, highly educated people who have at least a minimum level of experience with the internet. However, according to the management of Idtown, this group also represents the major group of customers of the actual sales. We asked approximately 300 students in order to obtain a large enough sample of n = 165 participants. Nearly all denials were due to a lack of time because of the start of courses. If we take the people who agreed to join the experiment as a basis, we enjoyed response rates of 71% to 100% in the different stages of data collection (table 4). We assign this exceptionally high rate both to the high incentive (probands received the watch for free) and probably also to the fun factor of the experiment. The data contained very few missing values. We conjecture that several interesting findings can be found in the data set to close the knowledge gaps in the field of mass customization toolkits. However, the objective of this paper is not to 19
discuss the outcomes of this project, but to present the research design as a possible way of addressing the key research issues identified above. >
5
Conclusion
In this paper we explored the research field of mass customization and identified four key research issues that seem prerequisite to enhance our understanding of this important phenomenon. We described the Idtown User Design Project, an elaborate effort with which we hope to be able to tackle some of the questions identified. Due to the early stage of the project we are not able to display results so far. We are confident, however, that we yield some valuable insights from the data set. Due to the methods employed, our results will bear some limitations which future endeavors might avoid. For example, it might be fruitful to enhance the number of participants of such surveys or experiments and strive for representative samples. It would be worthwhile to obtain international data sets to detect cultural differences of interaction patterns (Khalid/Helander 2001). Furthermore different methods of data collection (e.g., eye tracking) might reveal interesting insights into the interaction of the user with the configurator. Longitudinal studies could enhance our understanding of possible habituation effects and the satisfaction in the long run. Of course it is important to gain insights from other product fields, different types of configurators (e.g. offline configurators or configurators that assign the user a more active role in the design process). Compared to other product categories, consumers have less knowledge on mass customization in general. Consumers seldom have enough past experiences with customization to relate a specific purchasing or design process to a former incident. This makes consumer based research much more difficult than in other fields. This bias has to taken into account when transferring findings from empirical mass customization research into practice. We have tried to overcome this limitation at least partly by not only surveying motivations of consumers but by observing probands in an (almost) real design and buying situation. However, here more longitudinal studies are also needed. Technological options are also expanding rapidly. From our exploratory interviews we learned that practitioners see a real-life visualization of the results from the co-design process as a crucial success factor. Here, the increase of band-width and new visualization technologies like advanced 3D modeling will provide new possibilities to design toolkits for mass customization. The same is true for recommendation tools that may allow much greater degrees of self-design and customer-co-design in the future (Ansari / Essegaier / Kohli 2000; Kwak 2001). The implementation of these instruments in a mass customization toolkit in 20
connection with other forms of online consultancy like call-back buttons, auto-customized FAQ lists or chat robots make it possible to simulate real-life interaction processes between a customer and a human expert much better within a web based toolkit than is today’s state of the art. The advent of a new generation of toolkits based on these and other emerging technologies offers not only new possibilities to expand the use of mass customization approaches to larger customer groups, but also provides new fields for research. However, as mentioned before, before exploring the effects of new technologies on customer interaction in detail, we have to find an answer to more basic questions like the sheer willingness of consumers to interact within a mass customization system and to use the toolkit. We have to find measures to explicitly measure a users’ need for individualization and to quantify the value of customization from a customer’s perspective. Compared to the overwhelming amount of research in other areas of consumer behavior and consumer satisfaction, research on these basic topics is still very limited in the growing field of mass customization. We invite other researchers to join us in the fascinating exploration of this new and important research area.
References 1
Agrawal, M., Kumaresh, T.V. and Mercer, G.A. (2001) ‘The False Promise of Mass Customization’, The McKinsey Quarterly, Vol. 38, No. 3, pp. 62-71.
2
Ahlström, P. and Westbrook, R. (1999) ‘Implications of mass customization for operations management: an exploratory survey’, International Journal of Operations & Production Management, Vol. 19, pp. 262-274.
3
Alba, J. et al. (1997) ‘Interactive Home Shopping: Consumer, Retailer, and Manufacturer Incentives to Participate’, Journal of Marketing, Vol. 61, pp. 38-53.
4
Anderson, David M. (1997) Agile Product Development for Mass Customization, Chicago: Wiley.
5
Anderson, E. and Sullivan, M. (1993) ‘The Accedents and Consequences of Customer Saticfaction for Firms’, Marketing Science, Vol. 12, pp. 125-143.
6
Anderson, E. and Weitz, B. (1992) ‘The Use of Pledges to Build and Sustain Commitment in Distribution Channels’, Journal of Marketing Research, Vol. 29, pp. 62-74.
7
Ansari, A., Essegaier, S. and Kohli, R. (2000) ‘Internet recommendation systems’, Journal of Marketing Research, Vol. 37, pp. 363-375.
8
Bauer, H.H., Grether, M. and Borrmann, U. (2001) ‘Die Erklärung des Nutzerverhaltens in elektronischen Medien mit Hilfe der Flow-Theorie’, Marketing ZFP, Vol. 23, pp. 17-30.
9
Bauer, H.H., Grether, M. and Leach, M. (1999) ‘Relationship Marketing im Internet’, Jahrbuch der Absatz- und Verbrauchsforschung, Vol. 45, pp. 284-301. 21
10 Bawa, K., Landwehr, J.T. and Krishna, A. (1989) ‘Consumer Response to Retailers' Marketing Environments’, Journal of Retailing, Vol. 65, pp. 471-495. 11 Bettis, R. (1991) ‘Strategic management and the straightjacket’, Organizational Science, Vol. 2, pp. 315-319. 12 Betts, M. (1995) ‘Preference vs. Performance’, Computerworld, Vol. 30, No. 1, pp. 75-76. 13 Bourke, R. (2000) ‘Product Configurators: Key Enabler for Mass Customization - An Overview’, Midrange Enterprise, August 2000. 14 Cairncross, F. (2000) ‘All Yours: The dream of mass customization’, The Economist, April 1st - 7th 2000, pp. 59-61. 15 Canter, D., Rivers, R. and Storrs, G. (1985) ‘Characterizing User Navigation through Complex Data Structures, Behavior and Information’, Technology, Vol. 24, pp. 93-102. 16 Chamberlin, E.H. (1962) The Theory of Monopolistic Competition, 8. ed., Cambridge: Harvard University Press. 17 Chen, C. and Rada, R. (1996) ‘Interacting with Hypertext: A Meta-Analysis of Experimental Studies’, Human Computer Interaction, Vol. 11, pp. 125-156. 18 Cox, M. and Alm, R. (1999) The Right Stuff: America’s Move to Mass Customization, Policy Report No. 225, Washington: National Center for Policy Analysis. 19 Csikszentmihalyi, M. (1977) Beyond Boredom and Anxiety, San Francisco: Jossey-Bass. 20 Csikszentmihalyi, M. (1990) Flow: The Psychology of Optimal Experience, New York: Harper & Row. 21 Da Silveira, G., Borenstein, D., Fogliatto, F.S. (2001) ‘Mass Customization: Literature Review and Research Directions’, Int. Journal of Production Economics, Vol. 72, pp. 1-13. 22 Davenport, T., Harris, J.G. and Kohli, A. (2001) ‘How do they know their customers so well?’, Sloan Management Review, Vol. 42, No. 2 (Winter), pp. 63-73. 23 Dellaert, B.G. (2001) Using Conjoint Choice Experiments to Model Consumer Choices of Product Component Packages, Working Paper, Department of Marketing, Tilbourg University. 24 Dewan, R., Jing, B. and Seidmann, A. (2000) ‘Adoption of Internet-based product customization and pricing strategies’, Journal of Management Information Systems, Vol. 17, No. 2, pp. 9-28. 25 Drumwright, M. (1994) ‘Socially responsible organization buying’, Journal of Marketing, Vol. 58, pp. 1-19. 26 Du, X. and Tseng, M.M. (1999) ‘Characterizing Customer Value for Product Customization’, Proceedings of the 1999 ASME Design Engineering Technical Conference, Las Vegas. 27 Du, X. (2000) Architecture of Product Family for Mass Customization, PhD thesis, Department of Industrial Engineering and Engineering Management, The Hong Kong University of Science and Technology, Hong Kong. 22
28 Duray, R. et al. (2000) ‘Approaches to Mass Customization: Configurations and Empirical Validation’, Journal of Operations Managements, Vol. 18, pp. 605-625. 29 Durgee, J. (1986) ‘Depth-Interview Techniques for creative advertising’, Journal of Advertising Research, Vol. 26, pp. 29-37. 30 Eisenhardt, K.M. (1989) ‘Agency Theory: An Assessment and Review’, Academy of Management Review, Vol. 14, pp. 57-74. 31 Fama, E.F. and Jensen, M.C. (1983) ‘Separation of ownership and control’, Journal of Law and Economics, Vol. 26, pp. 301-25. 32 Feitzinger, E. and Lee, H. (1997) ‘Mass Customization at Hewlett-Packard: the Power of Postponement’, Harvard Business Review, Vol. 75, No. 1, pp. 116-121. 33 Fowler, S. et al. (2000) ‘Beyond Products: New Strategic Imperatives for Developing Competencies in Dynamic Environments’, Journal of Engineering and Technology Management, Vol. 17, pp. 357-377. 34 Franke, N. and Shah, S. (2002) ‘How Communities Support Innovative Activities: An Exploration of Assistance and Sharing Among End-Users’, Research Policy (forthcoming). 35 Franke, N. and von Hippel, E. (2002) Satisfying Heterogeneous User Needs via Innovation Toolkits: The Case of Apache Security Software, MIT Sloan School of Management Working Paper # 4341-02. 36 Franke, T. and Mertens, P. (2001) ‘User Modeling and Personalization - Some Experiences in German Industry and Public Administration’, in M.M. Tseng and F.T. Piller (Eds.) Proceedings of the World Congress on Mass Customization and Personalization MCPC 2001, Hong Kong. 37 Friesen, G.B. (2001) ‘Co-creation: When 1 and 1 make 11’, Consulting to Management, Vol. 12, No. 1, pp. 28-31. 38 Fulkerson, B. and Shank, M. (2000) ‘The New Economy Electronic Commerce − and the Rise of Mass Customization’, in M. Shaw et al. (Eds.) Handbook on Electronic Commerce, Berlin: Springer, pp. 411-430. 39 Gilmore, J.H. and Pine, B.J. (2000) ‘Customization That Counts’, in J.H. Gilmore and B.J. Pine (Eds.) Markets of One, Boston: Harvard Business School Press, pp. vii-xxv. 40 Glazer, R. (1999) ‘Winning in Smart Markets’, Sloan Management Review, Vol. 40, Summer, pp. 59-69. 41 Gruner, K. and Homburg, C. (2000) ‘Does customer interaction enhance new product success?’, Journal of Business Research, Vol. 49, pp. 1-14. 42 Helander, M. and Khalid, H. (1999) ‘Customer Needs in Web-Based Do-It-Yourself Product Design’, in J. Abeysekera et al. (Eds.) Proceedings of the 10th Anniversary of M.Sc. Ergonomics International Conference, Lulea, Sweden, pp. 9-14.
23
43 Homburg, C., Workman, J.P. and Jensen, O. (2000) ‘Fundamental changes in marketing organization: the movement toward a customer-focused organizational structure’, Journal of the Academy of Marketing Science, Vol. 28, pp. 459-478. 44 Huffman, C. and Kahn, B. (1998) ‘Variety for Sale: Mass Customization or Mass Confusion’, Journal of Retailing, Vol. 74, pp. 491-513. 45 Ishii, K. and Takaya, I. (1992) ‘A Process Design Approach Based on the Fusion Model’, Technovation, Vol. 12, pp. 499-508. 46 Jiao, J. (1998) Design for Mass Customization by Developing Product Family Architectures, PhD thesis, Department of Industrial Engineering and Engineering Management, The Hong Kong University of Science and Technology, Hong Kong. 47 Johnson, C (1998) ‘On the problems of validating DesktopVR’, in H. Johnson, L. Nigay and C. Roast (Eds.) People and Computers XIII, London: Springer, pp. 327-338. 48 Johnson, M.D. and Gustafsson, A. (2000) Improving customer satisfaction, loyalty, and profit: an integrated measurement and management system, San Francisco: Jossey-Bass. 49 Kahn, B.E. (1998) ‘Dynamic Relationships with Customers: High-Variety Strategies’, Journal of the Academy of Marketing Science, Vol. 26, Winter, pp. 45-53. 50 Khalid, H.M. and Helander, MG. (2001) ‘Facilitating Mass Customization and Web-based Do-It-Yourself Product Design’, in M.M. Tseng and F.T. Piller (Eds.) Proceedings of the World Congress on Mass Customization and Personalization MCPC 2001, Hong Kong. 51 Kim, J. (1997) ‘Towards the Construction of Customer Interfaces for Cyber Shopping Malls’, Electronic Markets, Vol. 7, No. 2, pp. 12-15. 52 Kotha, S. (1995) ‘Mass Customization: Implementing the Emerging Paradigm for Competitive Advantage’, Strategic Management Journal, Vol. 16, Special Issue ‘Technological transformation and the new competitive landscape’, pp. 21-42. 53 Kotha, S. (1996) ‘From Mass Production to Mass Customization: The Case of the National Industrial Bicycle Company of Japan’, European Management Journal, Vol. 14, pp. 442450. 54 Kotler, P. (1989) ‘From Mass Marketing to Mass Customization’, Planning Review, Vol. 18, No. 5, pp. 10-13, 47. 55 Kwak, M. (2001) ‘Web Sites Learn to Make Smarter Suggestions’, Sloan Management Review, Vol. 42., Summer, p. 17. 56 Lampel, J. and Mintzberg, H. (1996) ‘Customizing Customization’, Sloan Management Review, Vol. 37, pp. 21-30. 57 Leavitt, C. and Walton, J. R. (1988) Openness of Information Processing as a Moderator of Message Effekts on Behavior, Faculty Working Paper, College of Business of Administration, Ohio State University.
24
58 Lee, C.-H., Barua, A. and Whinston, A. (2000) ‘The Complementarity of Mass Customization and Electronic Commerce’, Economics of Innovation & New Technology, Vol. 9, pp. 81-110. 59 Liechty, J., Ramaswamy, V. and Cohen, S. H. (2001) ‘Choice Menus for Mass Customization’, Journal of Marketing Research, Vol. 39, pp. 183-196. 60 Lüthje, C. (2002) ‘Characteristics of Innovating Users in a Consumer Goods Field’, Technovation, forthcoming. 61 MacCarthy, B.M., Bramham, J. and Brabazon, P.G. (2002) ‘Mass Customization Operations Modes’, in S. Beckman and K.K. Sinha (Eds.) Proceedings of the POMS 2002 Conference, San Francisco. 62 Maes, P. (1994) ‘Agents that Reduce Work and Information Overload’, Communications of the ACM, Vol. 37, No. 7, pp. 31-40, 146. 63 May, M. (2001) ‘Fake It, Don’t Make It: Use Mock Mass Customization as a Customer Relationship Tool’, Jupiter Research, Concept Report, January 2001. 64 McHugh, J. (1996) ‘Holy cow, no one's done this!’, Forbes, No. 6/1996 pp. 54-56. 65 Meuter, M. L. et al. (2000) ‘Self-service technologies: Understanding customer satisfaction with technology-based service encounters’, Journal of Marketing, Vol. 64, No. 3, pp. 50-64. 66 Milgrom, P. and Roberts, J. (1990) ‘The Economics of Modern Manufacturing: Technology, Strategy, and Organization’, The American Economic Review, Vol. 80, pp. 511-528. 67 Miller, G. A. (1956) ‘The Magic Number Seven, Plus or Minus Two: Some Limits on our Capacity for Processing Information’, The Psychological Review, Vol. 63, pp. 81-97. 68 Mishler, E. (1986) Research Interviewing: context and narrative, Cambridge: Harvard University Press. 69 Neumann, J. (1955) ‘Can We Survive Technology?’, Fortune, Vol. 91, No. 6, p. 106. 70 Ng, K.Y.M. (2000) Customizable 3D Virtual Objects: A Breakthrough in Electronic Catalogs in Internet Business, MPhil thesis, Dept. of Industrial Engineering and Engineering Management, The Hong Kong University of Science and Technology, Hong Kong. 71 Nicholas, S., Haldane, C. and Wilson, J.R. (2000) ‘Measurement of presence and its consequences in virtual environment’, International Journal of Human-Computer Studies, Vol. 52, pp. 471-491. 72 Nielsen, J. (1995) Multimedia and Hypertext: The Internet and Beyond, Boston: AP Professional. 73 Nielsen, J. (2001) ‘Top Ten Mistakes in Web Design’, in Karwowski, W. (Ed.) International Encyclopedia of Ergonomics and Human Factors, New York: Taylor & Francis, Ltd., pp. 738-740. 74 Normann, R. and Raminez, R. (1994) ‘From value chain to value constellation’, Harvard Business Review, Vol. 71, No. 4, pp. 65-77. 25
75 Novak, T.P. and Hoffmann, D.L. (1996) ‘Marketing in Hypermedia Computer-Mediated Environments: Conceptual Foundations’, Journal of Marketing, Vol. 60, July, pp. 50-68. 76 Novak, T.P., Hoffmann, D.L. and Yung, Y.-F. (2000) ‘Measuring the Customer Experience in Online Environments: A Structural Modeling Approach’, Marketing Science, Vol. 19, pp. 22-42. 77 Oon, Y. B. and Khalid, H. M. (2001) ‘Usability of Design by Customer Web Sites for Mass Customization’, M.M. Tseng and F.T. Piller (Eds.) Proceedings of the World Congress on Mass Customization and Personalization MCPC 2001, Hong Kong. 78 Park, C.W., Mothersbaugh, D.L. and Feick, L. (1994) ‘Consumer knowledge assessment’, Journal of Consumer Research, Vol. 21, pp. 71 – 82. 79 Patterson, P., Johnson, L. and Spreng, R. (1997) ‘Modeling the determinants of customer Satisfaction for Business-to-Business Professional Services’, Journal of the Academy of Marketing Science, Vol. 25, pp. 4-17. 80 Peppers, D. and Rogers, M. (1997) Enterprise One to One, New York: Doubleday. 81 Piller, F. (2001) Mass Customization, 2nd edition, Wiesbaden: Gabler. 82 Piller, F. and Schoder, D. (1999) ‘Mass Customization and Electronic Commerce’, Zeitschrift fuer Betriebswirtschaft, Vol. 69, pp. 1111-1136. 83 Piller, F. and Tseng, M.M. (2001) The Economies of Mass Customization, IEEM-Working Paper 08/2001, Hong Kong University of Science and Technology, Hong Kong. 84 Piller, F., Schaller, C. and Reichwald, R. (2002) ‘Building customer loyalty with collaboration nets’, in Q. Mills et al (Eds.), Collaborative Customer Relationship Management, New York: Wiley 2002, chapter 4 (forthcoming). 85 Pine, B.J. (1993) Mass Customization, Boston: Harvard Business School Press. 86 Pine, B.J. and Gilmore, J.H. (1997) ‘The Four Faces of Mass Customization’, Harvard Business Review, Vol. 75, No. 1, pp. 91-101. 87 Pine, B.J., Peppers, D. and Rogers, M. (1995) ‘Do you want to keep your customers forever?’, Harvard Business Review, Vol. 73, No. 2, pp. 103-114. 88 Polley, D. and Van de Ven, A.H. (1995) ‘Learning by Discovery during Innovation Development’, International Journal of Technology Management, Vol. 11, pp. 871-883. 89 Price, L. L. and Ridgeway, N. M. (1983) ‘Development of a Scale to Measure Use Innovativeness’, in R. P. Bagozzi and A. M. Tybout (Eds.) Advances in Consumer Research, Vol. 10, Ann Arbor, MI: Association for Consumer Research, 1983, pp. 679-684. 90 Ramirez, R. (1999) ‘Value Co-Production: Intellectual Origins and Implications for Practice and Research’, Strategic Management Journal, Vol. 20, pp. 49-65. 91 Rautenstrauch, C. (1997) ‘An Alternative Concept to MRP II for Mass Customization’, Proceedings of the International Conference of Manufacture Automation 1997, The University of Hong Kong, pp. 401-406. 26
92 Reichwald, R., Piller, F. and Möslein, K. (2000) ‘Information as a Critical Success Factor for Mass Customization’, Proceedings of the ASAC-IFSAM 2000 Conference, Montreal. 93 Riemer, K.,Totz, C.(2001) ‘The Many Faces of Personalization - an Integrative Economic Overview’, in M.M. Tseng and F.T. Piller (Eds.) Proceedings of the World Congress on Mass Customization and Personalization MCPC 2001, Hong Kong. 94 Rust, R., Zahorik, A and Keininhgham, T. (1995) ‘Customer Satisfaction, Customer Retention, and Market Share’, Journal of Retailing, Vol. 69, pp. 193-215. 95 Sahin, F. (2000) ‘Manufacturing Competitiveness: Different Systems to Achieve the Same Results’, Production and Inventory Management Journal, Vol. 42, pp. 56-65. 96 Sano, D. (1996) Designing Large Scale Web sites: a virtual Design Methodology, New York: Wiley. 97 Schlie, T.W. and Goldhar, J.D. (1995) ‘Advanced Manufacturing and New Directions for Competitive Strategy’, Journal of Business Research, Vol. 33, pp. 103-114. 98 Schouten, J. and McAlexander, J.H. (1995) ‘Subcultures of consumption: an ethnography of the new bikers’, Journal of Consumer Research, Vol. 22, pp. 43-61. 99 Shah, S. (2000) Sources and Patterns of Innovation in a Consumer Products Field: Innovations in Sporting Equipment, MIT Sloan Working Paper #4105, Cambridge, MA. 100 Stabell, C. B. and O. D. Fjeldstad (1998) ‘Configuring value for competitive advantage: on chains, shops, and networks’, Strategic Management Journal, Vol. 19, pp. 413–37. 101 Strauß, R. and Schoder, D. (2000) e-Reality 2000, Frankfurt: Consulting Partner Group. 102 Teresko, J. (1994) ‘Mass Customization or Mass Confusion’, Industry Week, Vol. 243, No. 12 pp. 45-48. 103 Thomke, S. and von Hippel, E. (2002) ‘Customers as Innovators: A New Way to Create Value’, Harvard Business Review, Vol. 80, No. 2 (April). 104 Thomke, S., von Hippel, E. and Franke, R. (1998) ‘Modes of Experimentation: An Innovation Process - and Competitive - Variable’, Research Policy, Vol. 27, pp. 315-332. 105 Tian, K.T., Bearden, W. O. and Hunter, G. L. (2001) ‘Consumers' Need for Uniqueness: Scale Development and Validation’, Journal of Consumer Research, Vol. 28, June, pp. 5066. 106 Toffler, A. (1970) Future Shock, New York: Bantam Books. 107 Totz, C. and Riemer, K. (2001) ‘Usability of Design by Customer Web Sites for Mass Customization’, in M.M. Tseng and F.T. Piller (Eds.) Proceedings of the World Congress on Mass Customization and Personalization MCPC 2001, Hong Kong. 108 Tseng, M.M. and Du, X. (1998) ‘Design by Customers of Mass Customization Products’, CIRP Annals, Vol. 47, pp. 103-106. 109 Tseng, M.M. and Jiao, J. (1996) ‘Design for Mass Customization’, CIRP Annals, Vol. 45, pp. 153-156. 27
110 Tseng, M.M. and Jiao, J. (1998) ‘Concurrent Design for Mass Customization’, Business Process Management Journal, Vol. 4, pp. 10-24. 111 Tseng, M.M. and Jiao, J. (2001) ‘Mass Customization’, in G. Salvendy (Ed.) Handbook of Industrial Engineering, 3rd edition, New York: Wiley, pp. 684-709. 112 Tseng, M.M., Jiao, J. and Su, C.-J. (1997) ’Virtual Prototyping for Customized Product Development’, Integrated Manufacturing Systems, Vol. 8, pp. 334-343. 113 Van Hoek, R., Peelen, E. and Commandeur, H. (1999) ‘Achieving Mass Customization through Postponement’, Journal of Market Focused Management, Vol. 3, pp. 353-368. 114 Vandermerwe, S. (2000) ‘How Increasing Value to Customers Improves Business Results’, Sloan Management Review, Vol. 42 , pp. 27-37. 115 Vickery, S., Droge, C. and Germain, R. (1999) ‘The Relationship Between Product Customization and Organizational Structure’, Journal of Operations Management, Vol. 17, pp. 377-391. 116 Victor, B. and Boynton, A.C. (1998) Invented Here, Boston: Harvard Business School Press. 117 Von Hippel, E. (1978) ‘Successful Industrial Products from Customer Ideas’, Journal of Marketing, Vol. 42, pp. 39-52. 118 Von Hippel, E. (1988) The Sources of Innovation, New York: Oxford University Press. 119 Von Hippel, E. (1998) ‘Economics of Product Development by Users: The Impact of “Sticky” Local Information’, Management Science, Vol. 44, No. 5 (May) p. 629-644. 120 Von Hippel, E. (2001) ‘Perspective: User Toolkits for Innovation’, The Journal of Product Innovation Management Vol. 18, pp. 247-257. 121 Von Hippel, E. and Tyre, M. (1995) ‘How Learning is Done: Problem Identification in Novel Process Equipment’, Research Policy, Vol. 24, pp. 1-12. 122 Vora, P. (1998) ‘Human Factors Methodology for Designing Web sites’, in C. Forsythe, E. Grose and J. Ratner (Eds.) Human Factors and Web Development, New Jersey: Lawrence Erlbaum Associates. 123 Westland, J.C. and Au, G. (1998) ‘A Comparison of Shopping Experiences Across Three Competing Digital Retailing Interfaces’, International Journal of Electronic Commerce, Vol. 2, pp. 57-69. 124 Weston, R. (1997) ‘Web Automation’, PC Week, No. 32, p. 76. 125 Wigand, R., Picot, A. and Reichwald, R. (1997) Information, Organization and Management, Chichester / New York: Wiley. 126 Wikström, S. (1996) ‘Value Creation by Company-Consumer Interaction’, Journal of Marketing Management, Vol. 12, pp. 359-374. 127 Wind, J. and Rangaswamy, A. (2001) ‘Customerization’, Journal of Interactive Marketing, Vol. 15, pp. 13-32.
28
128 Workman, J., Homburg, C. and Gruner, K. (1998) ‘Marketing Organization: an Integrative Framework of Dimensions and Determinants’, Journal of Marketing, Vol. 62, pp. 21-41. 129 Zerdick, A., Picot, A. and Schrape, K. (2000) E-conomics, New York: Springer. 130 Zipkin, P. (2001) ‘The Limits of Mass Customization’, Sloan Management Review, Vol. 42, pp. 81-87.
29
What are the implications of mass customization for operations management?
Does customization / personalization influence customer relationship intensity?
How do companies gain customer knowledge by interacting with consumers?
How do consumers handle choice of modularized products?
How can mass customizers be classified?
Bauer/Grether/Leach (1999)
Davenport/Harris/Kohli (2001)
Dellaerta et al. (2001)
Duray et al. (2000)
Research question
Survey (n=126); subject of research: managers
Survey (n=728), simulation; subject of research: customers
Case studies (n=24)
Survey, (n=94); subject of research: managers
Survey; subject of research: machinery
Type
Findings
30
Various industries in the USA
Tourism: customization of travel packages
Exploratory Analysis, ANOVA
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
Two variables are key in classifying mass
Factor Development of a configurationally model for classifying mass customizers from the perspective of operations
Conjoint choice experiment, microsimulations
Various, mostly in the Interviews, Anecdotal evidence that process design is crucial “business to qualitative assessment for gaining market related knowledge by customer consumers” field interaction.
(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
descriptive statistics, Mass customization is seen as an interesting form correlation analysis of differentiation with specific patterns of design of operations. Study lacks clear differentiation between mass customization and traditional craft customization.
Method
US online brokers for Covariance Structure financial services, real Model (LISREL) estate, travel; online book and music sellers
various branches of industry, mostly b-tob
Field
Empirical Research on Mass Customization and Related Fields
Ahlström/Westbrook (1999)
Table 1
Appendix
How does a large electronics manufacturer deploy mass customization?
How do users perceive, handle and evaluate personalization within complex information systems?
Do “toolkits for user innovation” benefit users?
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?
Does complexity inherent
Feitzinger/Lee (1999)
Franke/Mertens (2001)
Franke/von Hippel (2002)
Gruner/Homburg (2000)
Huffman/Kahn (1998)
Research question
Survey /
Survey (n=310); subject of research: managers
Survey (n=138); subject of research: customers
Case studies and field experiments
Case study
Type
31
(a) Customization of
(German) machinery industry
Open Source Software
Evaluation of pilot platforms of the use of customization in management information systems (MIS), training and advising systems, tourism planning system
Electronics industry (Hewlett-Packard)
Field 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]
Findings
Regression analysis
Confirmatory factor analysis for measure validation, cluster and discriminant analysis
Cluster analysis, heterogeneity index, willingness to pay (WTP) scale
(1) Attribute based presentation is preferred to
(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.
(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
Interviews, (1) Privacy and acceptance to use this systems is qualitative assessment their largest hurdle for implementation (2) Perception of usefulness and value-added is major success factor of the use of these systems
Interviews, Postponement is identified and described as key qualitative assessment enabler of mass customization
Method
What are the management processes and organizational structures of an early mass customization pioneering company ?
How can customizable features on a choice board be evaluated?
Kotha (1996)
Liechty/Ramaswamy/Co hen (2001)
MacCarthy/Bramham/Br How can different abazon (2002) operations modes of mass customization be classified?
How does the cultural background of a user influence its use and satisfaction of a configuration tool on the Internet?
5 case studies
Survey and experiment (n=360); subject of research: customers
Case study with longitudinal data
Survey (n=137); subject of research: customers
experiments (n=79 and n=65); subject of research: customers
with a wide number of options lead to customers’ dissatisfaction “mass confusion”?
Khalid/Helander (2001)
Type
Research question
32
Consumer goods, consumer electronics, electronic equipment, commercial vehicles
Web-based information services (Internet Yellow pages)
Bicycle industry (National Industrial Bicycle Company of Japan)
Watch industry (Idtown.com), comparison of two cultural backgrounds of users within one region (Hong Kong versus Malaysia)
stay in hotels (b) Customization of sofa
Field
(1) Users follow top-down approach represented by the product structure (2) Malaysian users show larger enthusiasm towards the idea of customization than Hong Kong 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
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
Findings
Interviews, qualitative assessment: classification of the case studies against the schemes identified in the
Bayesian approach for menu-based conjoint analysis, fractional factorial research design; correlation analysis
(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
Development and concept proof of experimental choice menus for assessing customers' preferences and price sensitivity for features offered on a choice board
Interviews, (1) The interaction of mass customization and qualitative assessment 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
Correlation analysis
Method
What are sources of satisfaction and dissatisfaction with selfservice technologies from the users’ perspective?
Does 3D visualization in Internet shopping lead to higher user satisfaction and higher propensity of purchase?
How does web site design and usability of online configurators influence user satisfaction and site efficiency in supporting design activity ?
What different process structures for mass customization exist, and what are best practices?
Meuter et al. (2000)
Ng (2000) (similar findings report Johnson 1998, Nicholas et al 2000, Westland / Au 1998)
Oon /Khalid (2001)
Piller (2001)
Research question
Case study research (n=120)
Survey (n=48); subject of research: customers
Survey (n=80); subject of research: customers
Survey (n = 823); subject of research: customers
Type
33
Various branches of industry (60% b-to-c; 40% b-to-b); (40% German, 40% US, 20% ROW companies)
Three mass customization web sites (clothes: squashblosson.com, watches: Idtown.com; bycicles: voodoocycles.com)
Consumer electronics (evaluation of three different electronic products in different presentation forms)
Various branches of industries using self service technologies (mostly ATM and online shopping sites)
Field
(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
(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
Interviews, Anecdotal evidence of success factors of mass qualitative assessment 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
One-way repeated measures ANOVA, factor analysis, principal component method
Experiment, correlation studies
(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 largest error-free functionality ("just did its job")
market fluctuations required and the strategic involvement of customers to meet existing modular product structure.
literature
Critical incident study, eliciting descriptions of memorable incidents by users, in addition quantitative methods like regression and correlation analysis between clusters
Findings
Method
How can consumers' need Two surveys for uniqueness be evaluated (n=273; (scale development)? n=621); subject of research: customers
What is the relationship between product customization and organizational structure?
What are the economics of product development by users?
Tian / Bearden / Hunter (2001)
Vickery/Droge/Germain (1999)
von Hippel (1998)
Case studies
Survey (n=217); subject of research: managers
Survey (interviews) (n=1308); subject of research: managers
What are the status, development, success factors and management implications of mass customization?
Strauß/Schoder (2000)
Survey (n=914); subject of research: managers
What is the state of art of connecting mass customization and customer relationship management?
Type
Piller/Schoder (1999)
Research question
Method
34
Application-specific integrated circuits (ASICs); computer telephony integration systems (CTI)
US manufacturers, various branches of industry
personal experiences of users (no specific fields)
Qualitative assessment
Covariance Structure Model (LISREL)
Validation studies with three-factor oblique model, measurement of factor loadings; validation studies
German, Austrian and Descriptive statistic Swiss companies of various industries
German companies; Descriptive statistics, various branches of correlation analysis industry, most companies (79%) are operating in the b-to-b market
Field
Anecdotal evidence that user-driven product development pays off (impact of “sticky” local information).
Customization associates with more formal control, fewer layers, narrower spans of control.
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.
(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)
(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
once order was placed (5) Closed “knowledge loop” (6) Top management support, clear governance structures concerning who owns the system
Findings
What are business models Case studies and strategy implications of toolkits for user innovation?
Thomke/Von Hippel (2002)
Case study
What are the benefits of toolkits for user innovation?
Type
von Hippel (2001)
Research question Qualitative assessment
Method
35
Flavor industry Qualitative (BBA), plastic assessment industry (GE Plastics)
Food Industry (Nestlé)
Field
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
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
Findings
Table 2:
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 Germany 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 jeans Designs (www.ic3d.com)
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
Table 3:
Germany
Measurement of Latent Constructs
Construct
Source
Original or modified?
Attitude towards mass customization Internet skills Innovativeness
n.a. Novak/Hoffman/Yung (2000) Leavitt/Walton (1988)
Creativity
Price/Ridgeway (1983)
Need for Individuality Flow
Tian/Bearden/Hunter (2001) Novak/Hoffman/Yung (2000)
Telepresence
Novak/Hoffman/Yung (2000)
Own development on the base of a group discussion Original Reduction of the number of items on the base of a pilot study (n =35) Reduction of the number of items on the base of a pilot study (n =35) Reduction of the number of items on the base of a pilot study (n =35) Original, text slightly shortened. Additional variable from log file Reduction of the number of items on the base of a group discussion
36
Number of items 5 6 6 6 7 2 2
Table 4:
Sample and Response Rate
Stage (1) Number of students we asked to participate (2) Number of students who agreed to participate Number of students who completed questionnaire I Number of students designed product Number of logged designed processes Number of students who completed questionnaire II Number of students who completed questionnaire III
n Circa 300 165 165 165 165 165 117
37
Response Rate I Base = (1)
Response Rate II Base = (2)
55 % 55 % 55 % 55 % 55 % 39 %
100 % 100 % 100 % 100 % 71 %
Figure 1:
The Knowledge Loop of Mass Customization basic design of solution space preliminary set of possible specifications; continuous improvement of capabilities based on user feedback and wishes
distribution and customer relationship management
configuration for existing customers
configuration for new customers
individual delivery; building long lasting customer relationships (learning relationships), customer knowledge
use of knowledge and feedback to streamline re-orders
gathering information needed for individualization (tools for customer interaction)
fulfillment configuration of solution, customer specific design, order preparation, price calculation; control of flexible manufacturing systems or content delivery systems, supplier integration
Figure 2:
The Web Site of Idtown.com
38
