Lead User Identification in Conjoint Analysis Based Product Design

Lead User Identification in Conjoint Analysis Based Product Design Alexander Sänn and Daniel Baier Abstract Nowadays, the lead user method [von Hippel, Manag Sci 32(7):791 805, 1986; Lüthje et al. (Res Pol 34(6):951 965, 2005)] and conjoint analysis [Green and Rao (J Market Res 8(3):355 363, 1971), Baier and Brusch (Conjointanalyse: Methoden - Anwendungen - Praxisbeispiele, Springer, Heidelberg, 2009)] are widely used methods for (new) product design. Both methods collect and analyze customers preferences and use them for (optimal) product design. However, whereas the lead user method primarily creates breakthrough innovations [see von Hippel et al. (Harv Bus Rev 77(5):47 57, 1999)], conjoint analysis is more capable for incremental innovations [Helm et al. (Int J Manag Decis Making 9(3):242 26, 2008), Baier and Brusch (Conjointanalyse: Methoden - Anwendungen - Praxisbeispiele, Springer, Heidelberg, 2009)]. In this paper we extend conjoint analysis by lead user identification for the design of breakthrough innovations. The new procedure is compared to standard conjoint analysis in an empirical setting. 1 Introduction The main goal of this paper is to measure the performance of traditional conjoint analysis against a lead user extended one. The preferences of lead users and non lead users are compared and the effect of the difference to the results of standard conjoint analysis is described. As a consequence of these observations conjoint analysis and the lead user method are combined in a new approach to increase the performances of both methods. The field of mountain biking is used as an application example. Lead users are expected to decline innovations not solving A. Sänn ( ) D. Baier Institute of Business Administration and Economics, Brandenburg University of Technology Cottbus, Postbox 101344, 03013 Cottbus, Germany e-mail: alexander.saenn@tu-cottbus.de; daniel.baier@tu-cottbus.de W. Gaul et al. (eds.), Challenges at the Interface of Data Analysis, Computer Science, and Optimization, Studies in Classification, Data Analysis, and Knowledge Organization, DOI 10.1007/978-3-642-24466-7 53, Springer-Verlag Berlin Heidelberg 2012 521

522 A. Sänn and D. Baier their individual needs unlike non lead users preferring (incremental innovations) in general. Section 2 of the paper provides the theoretical background. Section 3 describes the empirical study, including survey preparation and accomplishment, and summarizes the results to prove the expected differences. In Sect. 4 the results of the study are discussed and the characteristics of lead user and non lead user groups are compared based on the empirical findings. Further an outlook to future research is given. 2 Known and New Approaches for Product Design 2.1 History The historical background of currently used methods, schemes and techniques for (new) product design and development is characterized by a huge amount of innovation flops and high financial risks for the innovative company. Cooper and Kleinschmidt described new products as high-risk endeavors binding most of the company s resources to product development and commercialization. Approximately 46% of these resources go to unsuccessful projects and further 35% to products failing the commercialization (Cooper and Kleinschmidt 1987). The traditional scheme of the manufacturer active paradigm (MAP) classified the customer as a passive stakeholder, responding to new developments by rejection or acceptance. Von Hippel noted that the need information itself is a very complex construct and therefore gathering information about customers needs is a hard process. Thus conventional market research techniques fail and a... whole new approach is needed to produce products and services that accurately respond to users needs (von Hippel 2001). The customer active paradigm (CAP) dealed with the customer as an innovation supplier, developing own solutions based on common products or developing even new products. Today, there are several known methods to measure customers preferences, e.g. conjoint analysis (Green and Rao 1971), as well as the customer integration in the innovation process itself, e.g. the lead user method introduced in 1986 (von Hippel 1986). On the one hand conjoint analysis is a widely used method and a state of the art technique for incremental innovations according to the MAP (Baier and Brusch 2009), but on the other hand the lead user method is the state of the art technique to generate breakthrough innovations according to the CAP (von Hippel et al. 1999; von Hippel 2005). 2.2 Conjoint Analysis Conjoint analysis is a state of the art technique to measure customers preferences (see Green and Rao 1971). Since it was developed in 1971 by Green and Rao the method has been improved and extended to several variants (e.g.

Lead User Identification in Conjoint Analysis Based Product Design 523 Wittink and Cattin 1989). Furthermore, conjoint analysis is still an actual topic in practice and science (Teichert and Shehu 2010). Traditional conjoint analysis itself follows an approach for measuring preferences on complete stimuli (a set of attributes) by decomposition. The stimuli are generated by predefined attributes and attribute-levels (Baier and Brusch 2009). Further there are limitations for using traditional conjoint analysis in the new product development process, e.g. because of the limitation of attribute numbers and attribute levels as well as the excessive demand from the customer itself. The lack of knowledge and experience of ordinary customers influences the reliability of conjoint analysis, because of the fact that consumers cannot fully express their needs, have latent needs and change their mind frequently (Slater and Narver 1998; Jeppesen 2005). Proceeding problems of conjoint analysis are the separation of high- and low-involved respondents as shown and the issue of a long-range forecast throughout a survey (Jeppesen 2005). The traditional conjoint analysis was chosen as a dominant methodical foundation since former empirical studies used it too (Helm et al. 2008). 2.3 Lead User Method The lead user method itself was developed as a market-oriented technique to integrate customers in the development process according to the CAP (von Hippel 1986). A company focuses on special customers so-called lead user and collaborates with the lead user group within several development steps to generate breakthrough innovations (Lilien et al. 2002). Breakthrough innovations are described as discontinuous innovations leading to advanced technological capabilities or enhanced product capabilities by combining knowledge from different fields. Further, radical innovations are described by combining both attributes (e.g. Veryzer 1998). The lead user is describes as a customers that is ahead of the market trends and have needs that cannot be satisfied by current market-available products. The lead user is basically described by: They are at the leading edge of an important market trend and...are currently experiencing needs that will be experienced by many users in that market (von Hippel 1986; von Hippel 2005). In addition, lead user are said to posses competencies for solution generation, do research on problem related information, have a wide-ranged market overview and face extreme situations. Further, a lead user might be able to develop a personal need solution and may innovate on its own. As opposed to a random set of customers and typical respondents, dealing with real-world experiences and trying to integrate the innovation into a new usage context that does not even exist yet with common experiences, lead users are able to overcome this issue and think out of their experiences (or out of the box) (Lüthje and Herstatt 2004). Along with these main characteristics the lead user itself reveals his idea to the company and receives a needed solution as his individual benefit from co-operation. Nowadays the lead user method is the state of the art technique to generate breakthrough innovations with minor risks of commercialization (see Herstatt and von Hippel (1992),

524 A. Sänn and D. Baier Schreier and Prügl (2008)). A lead user project follows four steps (von Hippel 1986; Lilien et al. 2002; von Hippel 2005): 1. Goal generation and team foundation 2. Market trends and needs research 3. Lead user identification 4. Concept workshop 2.4 Implementing Lead User Identification in Conjoint Analysis The new approach describes the extension of traditional conjoint analysis by a lead user identification part. Instead of using the results of all respondents for designing products, the results of the identified lead users are analyzed separately. The extended approach is applied within the mountain biking field, where user innovations are quite frequent (Franke and Shah 2003). Preliminary research was done by Helm et al. (2008) using traditional conjoint analysis and Lüthje et al. (2005) applying the lead user method in the field of mountain biking (see Helm et al. (2008), Lüthje et al. 2005). The usability of conjoint analysis for (breakthrough) innovations is restricted, so it is the main objective of the extension to identify leading customers by their use experience, their technical skills and their own idea development. The concatenation of conjoint analysis and lead user identification in the field of mountain biking may increases the performance of the measurement for generating breakthrough innovations throughout a survey. 3 An Application to Mountain Bike Product Design 3.1 Application Outline The study started with trend analysis by gaining information from (scene) magazines (e.g. bike, mountain bike, mtb and Fahrrad news ), commercial catalogs, discussion boards, dealers and professional mountain bike sportsmen as a proper foundation for the upcoming lead user identification process. Further, historical literature was screened for an overview of nineteenth and twentieth century patents in the bicycle market (see Herzog 1991). The captured innovations were aggregated in three runs into five innovation fields (e.g. transmission), shown in Table 1. With the help of experts, bicycle dealers and (semi-)professionals the trends were categorized in innovation levels and aggregated to five attributes for stimuli card rendering. These cards consist of three triple leveled attributes (suspension, transmission, wheels & tires) and two double leveled attributes (ebike concept and the pedal

Lead User Identification in Conjoint Analysis Based Product Design 525 Table 1 Selected attributes and attribute-levels for conjoint measurement Attribute Standard level Incremental level Breakthrough level Suspension No suspension Hardtail suspension Full suspension Transmission Standard drive train Carbon chain guide Gear belt Wheels & tires Standard wheel Runflat tires Fiberglass wheel ebike concept No, w/o power assistance Yes, with individual assistance Safety No, w/o pedal lock Yes, with pedal lock lock as the breakthrough innovations), given in Table 1. The orthogonal design was applied to reduce the combinations to a set of sixteen stimuli cards and to lower the excessive demand from the customer. Based on previous research and experience in mountain biking a two-parted survey was further rendered. The first part covered customers use experience along with the technical skills and the evaluation of predefined bicycle innovations (e.g. runflat tires) for check of reliability. The second part asked about the self-made innovation, the innovation depth (idea, concept, prototype or market status), the basic problems the user addresses and the used sources of solution information. In preparation of the survey accomplishment information of mountain biking hot spots, cycling clubs, bicycle dealers and local professional bikers were gathered as the target market. Since the lead user method concentrates on analog markets and foreign markets too, different cyclists (e.g. racing, bmx etc.) were interviewed as well. We expect that the separation of lead user respondents, by extending the conjoint analysis with a lead user identification part, will result in breakthrough innovations even within traditional conjoint measurement. Further we assume that the overall non lead users preference differs from lead users preference and influences the overall conjoint measurement resulting in more incremental innovations. According to previous research reports, user-innovators in the mountain biking field (about 38%) pretend to be individual riders with no major financial resources (Lüthje et al. 2005; Helm et al. 2008). Since lead users are pragmatists (Slater and Narver 1998), we predict that they decline the given innovations, which are not solving their individual needs leading to breakthrough innovations. 3.2 Data Collection The survey accomplishment itself was divided into three general steps. The faceto-face interviewed respondents had to sort and rank the stimuli cards according to their individual preferences in the first step, fill out the questionnaire in the second step and rate the holdout cards at last. From time to time the order of the first and second step was changed to avoid order effects. The study itself was located in different cities to avoid a local search bias. Users of the target market of mountain biking, of analogue markets (e.g. bicycle racing) and of foreign market segments were identified according to the lead user method. The interviewers concentrated

526 A. Sänn and D. Baier Table 2 Selected respondent groups in the target market, analog markets, and foreign market segments Extreme sports (e.g. downhill, stunt cyclists) Professional sports (e.g. competing cyclists) Semi-professional sports (e.g. club cyclists) Business background (e.g. bike messengers) Ordinary use context (e.g. freetime cyclists) Table 3 Preferred attribute-levels across all respondents and for each user group Attribute Overall Lead user Non lead user Suspension Hardtail Hardtail Hardtail Transmission Carbon Standard Carbon Wheels & tires Runflat Standard Runflat ebike concept No No No Safety Yes No Yes on cyclists in extreme, in professional and in business surroundings as well as on ordinary cyclists as assumed non lead users, given in Table 2. 3.3 Data Quality The empirical research generated n D 123 complete surveys out of 140 interviews (87%). Among all completed surveys 96 were considered to work with for further research in reason of external quality requirements, leading to a response rate of 68% (Lüthje et al. (2005) with 42% response rate and Helm et al. (2008) with 94% response rate). The average age of all respondents was 26 years, categorized by 78% male and 22% female interview partners. The average time for the conjoint measurement was about 25 minutes per respondent. Overall 30 innovative ideas were gathered. Among all ideas 60% were shifted to further development status (4 ideas currently being on the market) leading to an idea ratio of 30% (Lüthje et al. (2005): 38%). The average Pearson r D 0:986 and Spearman r D 0:816 indicate a satisfactory internal and external validity (Helm et al. (2008): Spearman r D 0:85). A first comparison of lead user group and non lead user group shows differences in the validity measurement, as lead user seem to answer in a less consistent way (Pearson r D 0:961, Spearman r D 0:736 lead user group; Pearson r D 0:989, Spearman r D 0:835 non lead user group). Survey surroundings might caused this effect. 3.4 Results Among lead user and non lead user groups the preferences of suspension and ebike concept differ in a decent significant way as transmission differs in a less significant way. Along with this issue the overall conjoint analysis is influenced by the higher amount of non lead users. The preferences are given in Table 3.

Lead User Identification in Conjoint Analysis Based Product Design 527 Table 4 Part-worths of attribute-levels for all respondents and for each user group Part-worths Overall.n D 96/ Lead user.n D 18/ Non lead user.n D 78/ Attribute-level Mean (Std. dev.) Mean (Std. dev.) Mean (Std. dev.) No suspension 1:546.3:416/ :116.3:075/ 1:930.3:394/ Hardtail 1:097.1:834/ :914.1:736/ 1:139.1:864/ Full suspension :449.2:945/ 1:030.2:762/ :790.2:896/ Standard :076.1:198/ :569.1:389/ :224.1:106/ Carbon :376.1:313/ :139.1:335/ :431.1:311/ Gear belt :301.1:218/ :708.1:284/ :207.1:191/ Standard :220.1:220/ :144.1:295/ :303.1:195/ Runflat :171.1:451/ :190.1:358/ :254.1:467/ Fiberglass :049.1:375/ :046.1:282/ :049.1:404/ No ebike :583.1:790/ 1:483.1:908/ :375.1:707/ Yes, ebike :583.1:790/ 1:483.1:908/ :375.1:707/ No safety :193.1:322/ :267.1:294/ :300.1:313/ Yes, safety :193.1:322/ :267.1:294/ :300.1:313/ Significance: *p 0:1, **p <0:05, ***p <0:01 Further was expected that the separation of lead user and non lead user groups could reveal breakthrough innovations within conjoint analysis. As the study showed the extension led indeed to 3 of 5 different preferred attribute-levels (see Table 3). This provides valuable information for the product design process. As assumed the non lead user group prefers innovations in general in contrast to the pragmatical lead user group. The accepted attribute-levels need further investigation within a lead user workshop. Along with the expected results the survey provides 30 new ideas for additional research, among them 16 innovations by lead users. Table 4 indicates the differences in preferences of the lead user segment and the non lead user segment in contrast to standard (overall) conjoint analysis. 4 Conclusions and Outlook The aspect of different preferences from the lead user group to the non lead user group was shown in this paper. In contrast to the standard conjoint analysis the separation of lead user and non lead user group leads to different results and might reduces the risk of innovations. The lead user group is able to express their needs in a more concrete and reliable way (Jeppesen 2005). Further, the group seems to rely on basic developments and decline innovations (improvements as well as breakthrough innovations) not fitting own needs. Although they are said to have a wide-ranged market overview and to collect use experience with new products first, but they tend to be pragmatists (Slater and Narver 1998; Lüthje et al. 2005). Since this conjoint measurement used often market-available innovations, bad use experiences might caused the result. As could be seen, both breakthrough innovations (ebike and

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