Towards a ComprehensiveUnderstanding of Lead Userness:The Search for Individual Creativitycaim_626 76..92

Rita Faullant, Erich J. Schwarz, Ines Krajger andRobert J. Breitenecker

In innovation research the identification of lead users has attracted considerable researcheffort. While lead user research has made important advances, there is still a significant lack interms of understanding antecedents to lead userness. Therefore the aim of this paper is to offera framework which is rooted in creativity psychology in order to provide a more comprehen-sive understanding of who leading-edge users are. It will allow for a systematic investigationand detection of innovative users. We conducted an empirical study in the field of smallkitchen appliances in co-operation with Philips Consumer Lifestyle, a field which lacks someof the typical characteristics that have been emphasized in markets traditionally studied inlead user research. With our research we show that (1) lead userness is fundamentally linkedto individual creativity; (2) particularly creativity- and domain-relevant skills (cognitive style,product knowledge and use experience) are related to lead userness; (3) creativity-relevantskills can be explained by personal characteristics, such as education, gender and openness toexperience.

Introduction

The use of external ideas for value creation isa key principle in the design approach

called ‘democratized innovation’, which spe-cifically considers users as an important sourceof innovation. Its basic assumption is that usershave the necessary skills and expertise tomodify existing products or develop newproducts on their own (Chesbrough, 2003; vonHippel, 2005). Within this research field, it isespecially the identification of ‘lead users’ –who tend to be innovative – that has attractedconsiderable research effort (von Hippel, 1988;Morrison, Roberts & von Hippel, 2000; Franke& Shah, 2003; Lüthje, Herstatt & von Hippel,2005). Lead users show certain characteristics:(a) they are ahead on important market trendsand (b) they stand to gain substantial benefitsfrom developing new solutions as they expe-rience significant dissatisfaction with existingsolutions (von Hippel, 1986). Beyond thesetwo hallmark features, however, little isknown about the personal characteristics of‘leading-edge users’, and Franke, von Hippel

and Schreier (2006) have called for a morein-depth investigation of this user type. In aninitial study, Schreier and Prügl (2008) inves-tigated possible antecedents of lead userness,using both field-related and field-independentvariables. While we acknowledge the signifi-cance of Schreier and Prügl’s contribution tothe field, we feel that their work could beextended by the elaboration of a more system-atic theoretical framework which can guidethe selection of possible antecedent variables.To this end, we aim to offer a framework forthe antecedents of lead userness which isrooted in creativity psychology, a field that hasbeen relatively neglected in lead user research.This will enable the development of a moredifferentiated picture of the personal charac-teristics of lead users.

In the following we will first argue that leaduserness is fundamentally linked to individualcreativity. Using the framework we havedeveloped, we will link antecedents of per-sonal creativity to the hallmark characteristicsof lead userness. We will then present theresults of an empirical study in which we test

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whether skills relevant to creativity are able toexplain lead userness, and thus whether theproposed framework will enable a deeperunderstanding of the personal characteristicsof leading-edge users. The study has beenexecuted in a consumer goods research settingwhich is not a typical one for lead user identi-fication, namely the field of kitchen appliances.Previous lead user identification in consumergoods markets has been undertaken mainly inthe markets for sports equipment such as thatfor kite surfing or technical diving. Thesefields are markets where (a) users rely heavilyon technical equipment to practise their hobby,(b) the majority of users have evolved theirskills in line with a common market trend, and(c) where leading-edge status is relativelyeasily observable and users are able to reporttheir own status relative to others (Schreier,Oberhauser & Prügl, 2007; Schreier & Prügl,2008). The kitchen appliance sector lacks thesecharacteristics, but users in this field mightstill possess the ability to modify and developnew solutions. Our framework has been ableto shed additional light on the personal char-acteristics of users, going beyond the standardhallmark characteristics of lead userness,thereby also enabling a reliable identificationof leading edge status also for these othertypes of market.

The Link between Lead Usernessand Creativity

The primary goal of user integration in newproduct development is the creation of newand promising problem solutions which willbe appreciated by a firm’s potential customers.Previous research has shown that userspossess the ability to develop such new solu-tions in co-operation with a firm in a series ofworkshops (Lilien et al., 2002), or that they areeven able to develop new marketable solu-tions on their own (Füller, Jawecki & Mühl-bacher, 2007). The capability to develop newsolutions is strongly related to individual cre-ativity (Mumford, 2000; Carayannis & Gonza-lez, 2003; Simonton, 2003). Although leadusers demonstrate creativity by modifying ordeveloping new products, individual creativ-ity remains a widely neglected issue withinlead user research and has only recentlyreceived some attention in the area of con-sumer research (Burroughs & Mick, 2004;Moreau & Dahl, 2005; Krajger, 2009). Creativ-ity can be defined as ‘the production of novel,useful ideas or problem solutions’ (Amabile etal., 2005, p. 368). This widely shared under-standing of creativity highlights two essentialaspects of creative outcomes: first, the product

or outcome has to be novel, which can bedescribed as the extent to which an idea isoriginal or unexpected (Sternberg & Lubart,1999). The second aspect requires an outcometo be useful, which means it has to be of prac-tical value and be appropriate in terms of thetask constraints (Amabile, 1983; Sternberg &Lubart, 1999). This latter dimension is espe-cially important in new product development.The success of new products is determined bythe degree of acceptance shown by customers.New products which have a high novelty valuebut lack appropriateness might be perceivedas being bizarre and fail to gain customeracceptance (Im & Workman, 2004). Previousstudies have shown that lead users (or leadingedge users) are able to develop both novel anduseful problem solutions that have proved tobe highly successful on the market (Lilienet al., 2002). Thus it can be assumed that reli-ably identified leading edge users are creativeindividuals.

The systematic search for leading edge userstherefore inevitably leads to the detection ofthe creative capacity and its antecedents. Inlead user research, however, the creativityaspect has so far not been adequatelyaddressed. One study which considered therole of users’ creativity in new product devel-opment was conducted by Kristensson,Gustafsson and Archer (2004), who demon-strated that ordinary users were able toproduce more original and more valuableproduct ideas than professional developers. Ifone accepts the idea that lead users do indeeddemonstrate creativity when successfully inte-grated in new product development, thedegree of lead userness can be linked to deter-minants of individual creativity. Thus thecentral proposition of this paper is: An indi-vidual’s degree of lead userness can be explainedby determinants of individual creativity.

A Framework for Creativity

Various approaches can be identified in cre-ativity research (Sternberg & Lubart, 1999; foran overview, see El-Murad & West, 2004). The‘mystical’ approach considers creativity to be aspiritual force, one which becomes manifest inthe ‘Muse’ of poets. The ‘psycho-dynamic’approach is based on the belief that creativityis the result of tension between the uncon-scious self and conscious reality (e.g., Freud,1908/1959 cited in Sternberg & Lubart, 1999).For a long time the ‘traits’ approach dominatedcreativity research (e.g., Guilford, 1950). Thisresearch school sees creativity as rooted in thepersonality disposition of an individual and asbeing describable in terms of certain patterns

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of traits. A central concern has been the iden-tification of such trait patterns in order to beable to distinguish between creative and non-creative persons.

Interestingly, there are some parallelsbetween lead user(ness) research and creativ-ity research. As in lead user research, in manytheories creativity has been implicitly assumedto be a dichotomous trait, thereby distinguish-ing between creative and non-creative indi-viduals and outcomes. As in lead userresearch, the current common understandingof creativity is characterized by the assump-tion of a continuous underlying cline, allowingfor various degrees of creativity (Amabile,1983; for lead userness, see Morrison, Roberts& Midgley, 2004; Schreier & Prügl, 2008; Faul-lant et al., 2009). Amabile (1983) was also oneof the first to consider the social environmentof a person as a relevant determinant of cre-ativity. Her social-psychological conceptual-ization of creativity has been one of the mostinfluential theories in contemporary creativitystudies (Rickards & Moger, 2006), and has alsoinspired scholars in consumer research (Bur-roughs & Mick, 2004; Moreau & Dahl, 2005).Her model of creativity will serve as a frame-work for this study. Amabile’s (1996) concep-tualization of creativity can be considered as a‘confluence approach’, which hypothesizesthat multiple components must converge forcreativity to occur. Amabile identifies threecomponents necessary for creativity: domain-relevant skills, creativity-relevant skills and

task motivation. These components can belinked to various antecedents, thereby alsooffering an understanding of the roots of cre-ativity and lead userness from a conceptualpoint of view. Figure 1 displays the frameworkrelating components of creativity to lead user-ness, as well as personality-related variableswhich influence the components of creativity.

Domain-Relevant Skills

Domain-relevant skills include the entire setof available response possibilities from whicha new solution can be generated. The largerthe set, the more numerous are the availablealternatives. Domain-relevant skills compriseknowledge of both the domain itself and alsorelevant technical skills. Together these deter-mine how appropriate and correct a responseis. Knowledge of the domain includes facts,principles and opinions, as well as knowl-edge of paradigms and performance scriptspertaining to the domain (Amabile, 1983).Clearly, in order to be creative in pharmaceu-ticals, one must know a lot about chemistryand medicine. (For example, AlexanderFleming, who discovered penicillin, was abacteriologist in a hospital.) Domain-relevantskills also include the technical skills neces-sary for practising in the domain. For com-posers, for example, this might include theskill of playing an instrument (e.g., Mozartwas an excellent pianist as well as a

Figure 1. Research Framework for Explaining the Antecedents of Lead Userness

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composer). The importance of knowledge in adomain has been highlighted by other schol-ars: Feldman (1999), for example, states thatcreativity in its most powerful form has beenpreceded by a period of preparation, with theindividual spending a decade or so masteringthe domain. During this mastering period(the time of extreme use), creative peopleoften encounter a significant asynchronybetween mind and domain; they experiencedissatisfaction with what the domain cur-rently offers (Feldman, 1999). This spurs themon to search for new solutions. There has alsobeen a debate about the ‘right amount’ ofknowledge, and some have suggested thattoo much knowledge might restrict creativityand inhibit an individual from going beyondknown boundaries (e.g., De Bono, 1968;Sternberg, O’Hara & Lubart, 1997). Somestudies therefore suggest an inverseU-shaped relation between knowledge andcreativity (e.g., Weisberg, 1999), while othersclaim that there is no such thing as too muchknowledge, but rather only too many algo-rithms (Amabile, 1983), in the sense that theknowledge might be organized in too specificways, not usable in generalized settings.Increased knowledge per se should raise thethreshold from which an outstanding solu-tion can emerge. Knowledge of and skills in adomain do not by themselves necessarily leadto creativity and thus can be seen as a neces-sary, but not a sufficient, condition for cre-ative outputs (Nickerson, 1999).

In lead user research, the role of domain-relevant knowledge has also been recognized.It has been suggested that high lead usernesscan be found in individuals who are experi-enced users of particular products and arerecognized experts in their field. Due to theirextreme intensity of use, lead users placehigh demands on materials and products,and as a result they often experience dissatis-faction with existing products (von Hippel,1986). In lead user research, product-relatedknowledge is broken down into use experi-ence and consumer knowledge by Lüthje(2000). He found that while consumer knowl-edge does increase the probability of userinnovation, use experience can be particularlyimportant for the creation of innovativeoutputs as it enhances the effects of product-related knowledge and cognitive structures.Alba and Hutchinson (1987) proposed that,with greater use experience (they term it ‘usefamiliarity’), cognitive structures becomemore refined, which improves the user’sability to differentiate, analyse and elaborateproduct information. Furthermore, Schreierand Prügl (2008) showed that product knowl-edge and use experience seem to influence

lead userness. Based on creativity theory andsupported by initial findings in user innova-tion research, we propose that product-related knowledge and use experience areantecedents of lead userness.

H1a: The level of product-related knowledge ispositively related to an individual’s degree oflead userness.H1b: The extent of use experience is positivelyrelated to an individual’s degree of leaduserness.

Antecedents of Domain-RelevantSkills

Amabile (1983) proposes that, among otherthings, domain-relevant skills depend uponformal and informal education. There is littlediscussion about the fact that proper prepara-tion is crucial. Feldman (1999) states that, inextreme cases of creativity, the importance ofappropriate teachers, mentors and educationalprovision is obvious. Product-related knowl-edge as one aspect of domain-relevant skills istherefore assumed to be dependent on formaleducation (Amabile, 1996). Use experience, onthe other hand, results from the intense use ofa product: it is described as the number ofproduct-related experiences that have beenaccumulated by the individual user andincludes the use of the product in various situ-ations (Alba & Hutchinson, 1987).

In general, the determinants of domain-relevant skills (i.e., both domain-relevantknowledge and use experience) seem to behighly context-dependent (for creative work inthe film industry, an arts education would behelpful; for creative work in Haute Cuisine,some training in cookery). In our specific case,product knowledge of small kitchen appli-ances would require some form of technicaleducation. In our study, gender might be acontext-specific characteristic relevant to thedistribution of use experience, but that mightnot be the case in other situations. Also, in hertheoretical framework of creativity, Amabile(1996) states that domain-relevant skills areinfluenced by context-specific variables. Fol-lowing this reasoning, and in order to increasethe generalization of our hypothesis, we willspecify the concrete predictors of domain-relevant skills in the methods section, and wepropose:

H2a: Product-related knowledge depends uponcontext-specific personal characteristics.H2b: Differences in use experience depend uponcontext-specific personal characteristics.

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Creativity-Relevant Skills

According to Amabile’s (1996) model,creativity-relevant skills include an appropri-ate cognitive style, which, in general, ‘refers toa person’s consistent pattern of processinginformation and organising it into a system ofthought which influences behaviour’ (Foxall &Haskins, 1987, p. 65). There is broad agreementamong researchers that some operations of thehuman cognitive system are more likely tolead to original problem solving than others(Barron & Harrington, 1981; Matherly & Gold-smith, 1985; Mellou, 1996; Sternberg & Lubart,1999). Differences in cognitive styles mightexplain the ‘something extra’ that character-izes creative work (Amabile, 1996; Sternberg,O’Hara & Lubart, 1997). An appropriate cog-nitive style for creative problem solving ischaracterized by the ability to understandcomplexity, to break through prevailingthought patterns, and to try new pathways inproblem solving when old sets do not work(Amabile, 1996). Furthermore, persistence,self-discipline and the ability to abandonunproductive search strategies are also condu-cive to creative work (see also Mumford,2000). It is suggested that creative outputs sig-nificantly depend upon the quality of theinitial problem formulation (Weisberg, 1999).Experiments in creativity research, forexample, have shown that novice studentswere able to replicate Nobel Prize-winningdiscoveries once introduced to the problemand the experiments (Dunbar, 1995). Thus, theproblem definition is of utmost importance tothe quality of new products and for creativework. Individual cognitive styles determinenot only the extent to which users are able todefine a problem but also how well they areable to go beyond known thought and percep-tion patterns and to think ‘outside the box’. Assuch creative individuals have a more differ-entiated perception of how things can be inter-preted, they are more able to search for and tryout novel solutions.

In research on user integration into newproduct development, the central role of cog-nitive styles has only been recognized in animplicit sense. The new product developmentprocess starts with the phase of problem rec-ognition, which, in most instances, is vagueand fuzzy (Kim & Wilemon, 2002). A series ofmethods and techniques has been proposed torefine the initial problem formulation phase innew product development. User integrationhas been proposed as one promising means toimprove this stage of new product develop-ment, but the cognitive style of the specificindividuals to be integrated has not yet beenconsidered. The ability to go beyond known

solutions has also been emphasized in newproduct development. In the practice of userintegration, the phenomenon of ‘functionalfixedness’ has often been encountered, i.e.,integrated users lack the ability to imaginenon-existing solutions, or they are locked intocurrently known thought and solution pat-terns (Ulwick, 2002). Individuals with anappropriate cognitive style are able to over-come this fixedness and go beyond existingpatterns of solutions. In the literature this lattertype of thinking has also been described as‘divergent thinking’: the tendency to generatedifferent approaches to problem solutions, incontrast to ‘convergent thinking’, whichendeavours to identify the single correctresponse to a given problem (Simonton, 1999).Divergent thinking has consistently been asso-ciated with innovative problem solving andcreativity (Kirton, 1976; Foxall, 1995). There-fore we propose:

H3: An individual’s degree of lead userness isinfluenced by cognitive style. The more pro-nounced the predisposition to divergent think-ing, the higher the lead userness.

Antecedents of Creativity-RelevantSkills

While Amabile argues in her model thatcreativity-relevant skills can be taught andlearned using creativity techniques as sug-gested by Osborn (1953), she also clearly statesthat some skills might be rooted in stable per-sonality traits. Indeed, since its beginnings,personality psychology has constantly soughtto study the individual differences manifestedby outstanding personalities and to exploretheir uniqueness in terms of personality traits(Feist, 1998), whereas in the innovation litera-ture only a few studies have pursued this lineof research (e.g., Puccio & Grivas, 2009). Inpersonality psychology, considerable agree-ment has been achieved on the theory that fivehigher order factors can account for the patternof traits across individuals: neuroticism, extra-version, agreeableness, openness and con-sciousness (Costa & McCrae, 1992; McCrae &John, 1992). As a consequence, the Five FactorModel (also known as the Big Five) has beenemployed in and linked to essential areas ofapplied psychology and social science, e.g.,emotions and well-being (Watson & Clark,1984; Larsen & Ketelaar, 1991), life satisfaction(Costa & McCrae, 1980), marketing and con-sumer behaviour (Mooradian, 1996), andmotives and values (Roccas et al., 2002).

Feist (1998) conducted a meta-analysis on 50years of research into personality and creativ-

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ity. His findings suggest that, above all, open-ness to experience is the one dimension ofpersonality that has strongly and consistentlybeen linked to creativity. Openness to experi-ence describes the extent to which individualsare open-minded, curious, original and imagi-native (Costa & McCrae, 1992). Openness canbe assessed in the areas of fantasies, feelings,aesthetics, actions and values. People withhigh scores in this dimension tend to be flex-ible, intelligent and sensitive; they have wideinterests and tend to question existing normscritically. In contrast, individuals with lowopenness scores are described as conventional,uncritical, realistic and are assumed to preferthe old rather than the new (Feist, 1998).Although Feist’s (1998) meta-analytical studyconfirms that openness to experience posi-tively affects creativity, it is questionablewhether this influence is direct.

Critics of the Five-Factor Model claim thatthe five dimensions are too broad to allowany prediction of concrete human behaviour(e.g., McAdam, 2004). Some studies in cre-ativity research concluded that there is nodirect influence of openness on creativity(George & Zhou, 2001) but instead only amoderating effect (e.g., Baer & Oldham,2006). McCrae (1987) established a causalchain in which openness to experience wasrelated to divergent thinking, which in turnpositively influenced creativity. Similarly,Taggar (2002) found only a weak correlationbetween openness and creativity but a muchstronger correlation between openness andcreativity-relevant processes. McCrae (1987)offers various theoretical explanations forwhy open people have more creativity-relevant skills: first, open people mightsimply be fascinated by open-ended, creativetasks and therefore score more highly on suchtasks. Second, they may have developed intel-lectual and divergent thinking abilities overtheir life-time, and third, their appreciation ofnovelty and interest in a varied range of expe-riences may serve as a basis for the develop-ment of thinking abilities, which in turnreinforce their interest in seeking out novelexperiences. Therefore we propose:

H4: Openness to experience is positively relatedto creativity-relevant skills. The higher theopenness scores, the more likely an individual isto possess an appropriate cognitive style that isconducive to creativity.

Task Motivation

The third component in Amabile’s (1983, 1996)model of creative performance is task motiva-

tion. It is the one component that operates atthe most specific level and accounts for thedifference between what an individual can doand what he or she will in fact do. Motivationis typically distinguished as either intrinsic orextrinsic. According to Deci (1975), people areintrinsically motivated when they engage in atask for no apparent reward other than the taskitself. In contrast, a task is characterized byexternal motivation if it is accomplished as ameans to another end (Crutchfield, 1962). Atask of this type is instrumental to achievingsome valued outcome external to the task, forexample money and recognition. Such exter-nal outcomes are assumed to deflect an indi-vidual’s attention from the task to the expectedreward. In contrast, intrinsic motivation is ableto set free an individual’s full positive poten-tial because it entails the inherent tendency toseek novelty and challenges, to extend andexercise one’s own capacities, and to learnand explore (Ryan & Deci, 2000). Research oncreativity has shown that people who producehighly creative work have a deep immer-sion in and exceptional devotion to theirchosen domain and invest significant energyin their creative endeavour (Sternberg, O’Hara& Lubart, 1997; Simonton, 1999): in otherwords, they are intrinsically fascinated by thetask.

Csikszentmihalyi (1990) has described thisstate of deep immersion, where nothing elseseems to matter, as the ‘flow experience’. It ischaracterized by a match between the indi-vidual’s skills and the difficulty of the task. Asskills grow, increasingly difficult challengesare sought – a phenomenon also known inlead user research. It has been shown thatexternal motivation can undermine intrinsicmotivation and can therefore be detrimental tocreativity (for a review, see Collins & Amabile,1999). Recent research on consumer creativity,however, has shown that some external con-straints (e.g., time constraints) can lead tomore creative results than unconstrained situ-ations (Burroughs & Mick, 2004; Moreau &Dahl, 2005). Indeed, it is acknowledged thatmost highly creative individuals also have astrong need to achieve recognition, whichco-exists with their deep intrinsic motivation(Collins & Amabile, 1999). Nevertheless, mostcreativity researchers agree that intrinsic moti-vation is a more effective determinant forcreative work than external motivation (Nick-erson, 1999). Ryan and Deci (2000) have pro-posed that there exist various types of extrinsicmotivational styles which to some degree arenot detrimental to creativity. Intrinsic motiva-tion, however, is seen as the one type of moti-vation that is most firmly linked to creativeoutput. Therefore we propose:

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H5: Intrinsic motivation is more positivelyrelated to lead userness than extrinsicmotivation.

Empirical Study

Methodology and Measurement

We conducted an empirical study in the fieldof small kitchen appliances in co-operationwith Philips Consumer Lifestyle Klagenfurt, amajor manufacturer of consumer electricalappliances. As mentioned in the introduction,our aim is to extend knowledge in lead userresearch as well as knowledge about the kindsof fields to which such research applies. Leaduser research has been pioneered in industrialgoods markets, and has been extended inrecent years also to selected consumer goodsmarkets, which evidence particular character-istics. Most of the markets studied are nottypical mass markets, but rather representvery specialized niche markets such as techni-cal diving or kite surfing. The current studywas conducted in the field of small kitchenappliances, a consumer goods mass marketwhose customers and products can be foundin practically every household. As with manyother mass-market consumer products, smallkitchen appliances are often used withoutspecial interest or personal meaning. But theremight well still be consumers who are ableand willing to be creative in this domain.(For example, Soll (2006) showed that usershave been creative in idea competitions fordishwashers.)

Like many other producers of mass-marketconsumer goods, Philips runs an applicationtest centre with around 3,000 registeredtesters, who regularly test prototypes of prod-ucts scheduled to be launched in the nearfuture. While prototype testing is an importantand valuable task in the new product develop-ment (NPD) process, such tester databasesmay well include product testers withincreased creative potential that might also beuseful in earlier phases in the NPD cycle.Therefore, we believe our research field is onethat not only has particular relevance for theo-retical research but also has major practicalimplications. The study was piloted in Novem-ber 2007. The final data collection was carriedout in January 2008.

All 3,000 product testers registered at thePhilips Application Research Center Klagen-furt received a letter inviting them to partici-pate in the study. The first 150 to respond wereinvited to take part, out of which four did notshow up, so that the final sample consisted of146 participants. We are aware that this process

of sample selection leads to some extent toa self-selection bias, although the overallnumber of people showing interest in partici-pating in the study was not significantlygreater than the number of people who wereactually invited to take part. Indeed theselected sample proved to be representative ofthe overall pool of registered testers in termsof age and education, but there is some differ-ence in gender representation as 65 per cent ofthe tester pool are female versus 76.7 per centin our sample. Age ranges from 16 to 70, withan average age of 43.4 years. The sample, whilepredominantly female, was fairly balanced interms of levels of formal education: 8.2 percent of the sample had completed compulsoryschooling 20.5 per cent had completed anapprenticeship, 29.5 per cent had attended avocational high school, 30.8 per cent hadattended an academic secondary school, and11 per cent of the sample hold a universitydegree.

To test the theoretical model, participantshad to complete a self-administered question-naire. The constructs used were based on exist-ing scales from the published literature. Leaduserness was measured in terms of the typicallead user characteristics following an opera-tionalization proposed by Lüthje (2000). Leaduserness was assessed using two subscales:‘ahead of the trend’ measured by four items(Cronbach’s alpha = 0.836); ‘high expectedbenefit’ measured by four items (Cronbach’salpha = 0.671). Because the Cronbach alphavalue for high expected benefit fell below theappropriate threshold of 0.7, we tested forimprovements to the scale by deleting oneitem. This procedure led to only minorchanges in the alpha coefficient (0.679); there-fore we decided to retain the original scale forour analysis.

From the two sub-scales, the individual leaduserness scores were calculated by meanscomputation. For the measurement of domain-relevant skills, including the variables‘product-related knowledge’ and ‘use experi-ence’ (single item), scales were adopted fromLüthje (2000). Product-related knowledge wasmeasured by three items and yielded a Cron-bach’s alpha of 0.781. The cognitive style wasassessed using the scale from Kreuzig (1981).This scale measures divergent thinking, whichis the ability to analyse problems and to gobeyond existing patterns when finding newsolutions. A Cronbach’s alpha value of 0.784was reached after the initial eight items werereduced to seven items. The personality trait of‘openness to experience’ was measured by theGerman version of the NEO PersonalityInventory (Borkenau & Ostendorf, 1993), astandard measurement instrument in person-

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ality research (Hossiep, Paschen & Mühlhaus,2000; Ziegler, 2002). Two items were removedfrom the original scale and a Cronbach’s alphaof 0.727 was reached. The ‘task motivation’construct measures users’ intrinsic and extrin-sic motivation. For the assessment of users’motivation to participate in the product devel-opment process, single items were applied.1All items except ‘use experience’ wereanswered on a five-point Likert-type scaleranging from strongly disagree to stronglyagree. Use experience was measured on a five-point ordinal scale ranging from ‘never’ to‘several times a week’.2 In the case of multi-item scales, the mean value of correspondingitems represents the scale values in the furtheranalysis. All items are listed in Table 1. Inorder to assess the context-specific personalcharacteristics, which we assumed wouldinfluence product-related knowledge and useexperience, we included the following per-sonal data: level of technical education wasused to account for product-related knowl-edge (knowledge about materials, perfor-mance, etc., of kitchen appliances); age(measured in years) and gender were used toindicate differences in the level of use experi-ence (gender was included in the models in theform of a dummy variable, with ‘female’ = 0and ‘male’ = 1).

Analysis and Results

We applied three multiple ordinary leastsquares (OLS) regression models and oneordinal regression model to test our hypoth-eses. Because the categories of use experienceare not equidistant and the empirical distribu-tion of use experience is asymmetric andskewed to the left, the assumption of an OLSregression does not hold. As the estimation ofan OLS regression model for use experienceled to non-normally distributed residuals, weapplied a proportional odds model for ordinalresponses (cumulative link model with logitlink; Agresti, 2002).3 We controlled for both ageand gender in the models for divergent think-ing and product-related knowledge and forage only in the model for use experience. Wedid not consider age and gender in the modelfor lead userness because users’ personal char-acteristics are used as determinants in the firststage of our theoretical model. Table 2 lists thedescriptive statistics and correlations of all thevariables in our models.

Scores for individual lead userness were cal-culated from the means of two subscales:‘being ahead of the trend’ and ‘high expectedbenefit’. Thus the theoretical boundaries ofthis scale have a minimum of 1 and amaximum of 5. Figure 2 displays the histo-

Table 1. Measurement and Items

Items

Lead userness: ahead of the trend (Cronbach’salpha = 0.836)

I usually find out information about kitchenappliances before others do.

I have benefited significantly from the earlyadoption and use of new kitchen appliances.

I am regarded as being on the ‘cutting edge’ inthe field of kitchen appliances.

I have a comprehensive knowledge of the kitchenappliances available on the market.

Lead userness: high expected benefit (Cronbach’salpha = 0.671)

I have often noticed technical problems withkitchen appliances (e.g., changing the differentattachments, preparing dough, cleaning theappliance, noise during use).

I am dissatisfied with the design (e.g., colour,shape) of kitchen appliances.

I have new needs which are not satisfied byexisting kitchen appliances.

I am dissatisfied with the existing equipmentoffered on the market.

Divergent thinking (Cronbach’s alpha = 0.784)I am interested in everything.Logic puzzles fascinate me.When I come across a problem, I think it through

thoroughly.It is easy for me to regard things from several

completely different points of view.When I come across a problem that I cannot solve

immediately, I try to find out more information.When faced with something I do not understand, I

tend to analyse the facts.I want to know about everything in great detail.Product-related knowledge (Cronbach’s

alpha = 0.781)I have a good technical knowledge concerning

kitchen appliances (e.g., electric motors, cuttingmethods).

I have a good knowledge of the materials that areused for kitchen appliances (e.g., plastics,metal).

I am able to repair kitchen appliances by myself.Openness (Cronbach’s alpha = 0.727)10 items (NEO-FFI scale)Motivation: Reasons for registering as a test

person/for testing Philips products (singleitems)

I am testing new products for Philips to earnmoney (extrinsic)

I am testing new products for Philips to get toknow about new products (intrinsic)

Use experience (single item)How often do you use kitchen appliances?

TOWARDS A COMPREHENSIVE UNDERSTANDING OF LEAD USERNESS 83

Volume 21 Number 1 2012© 2012 Blackwell Publishing Ltd

Tab

le2.

Des

crip

tive

Stat

isti

csan

dC

orre

lati

ons

for

allM

easu

rem

ent

Scal

es

Mea

nS

td.d

ev.

min

max

(1)

(2)

(3)

(4)

(5)

(6)

(7)

(8)

(9)

(10)

(1)

Lea

dus

erne

ss2.

771

0.75

41

4.38

(2)

Ahe

adof

the

tren

d2.

914

0.98

61

50.

863

(3)

Hig

hex

pect

edbe

nefit

2.62

80.

825

14.

750.

797

0.38

3

(4)

Div

erge

ntth

inki

ng3.

929

0.55

52.

435

0.32

10.

280

0.25

2(5

)Pr

oduc

tre

late

dkn

owle

dge

2.23

51.

031

15

0.45

80.

430

0.32

30.

214

(6)

Use

expe

rien

ce4.

144

1.05

01

50.

450

0.40

00.

363

0.11

90.

139

(7)

Intr

insi

cm

otiv

atio

n4.

575

0.76

01

50.

159

0.27

0-0

.014

0.23

80.

183

0.14

3(8

)E

xtri

nsic

mot

ivat

ion

2.30

81.

273

15

-0.0

70-0

.139

0.03

4-0

.057

-0.1

64-0

.178

-0.1

55(9

)A

ge43

.390

12.9

9016

700.

269

0.22

60.

221

0.13

70.

204

0.28

50.

135

-0.3

29(1

0)Te

chni

cal

back

grou

nd1.

720

1.33

31

50.

127

0.12

90.

068

0.18

10.

156

-0.1

220.

109

0.00

3-0

.094

(11)

Gen

der

(mal

e)0.

233

01

-0.0

89-0

.085

-0.0

860.

189

0.33

8-0

.369

0.02

3-0

.048

-0.0

430.

326

Spea

rman

Ran

kco

rrel

atio

nco

effic

ient

for

corr

elat

ions

wit

h‘u

seex

peri

ence

’,‘in

trin

sic

mot

ivat

ion’

,‘ex

trin

sic

mot

ivat

ion’

,‘te

chni

cal

back

grou

nd’

and

‘gen

der

(mal

e)’;

Pear

son

prod

uct

corr

elat

ion

coef

ficie

ntfo

ral

loth

erco

rrel

atio

ns.

84 CREATIVITY AND INNOVATION MANAGEMENT

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gram of the sample distribution for lead user-ness. It indicates that the lower limit of the leaduserness scale was reached, but not the upperboundary of the scale. Thus the sample doesnot contain test users who would rate them-selves as advanced leading-edge users.

The significant F-statistics for each of thethree OLS models are significant. Thus, thenull hypothesis that all estimated coefficientsequal zero and that there is no relationshipbetween predictor and response variable hasto be rejected for all OLS regression models.Several tests were applied to test the linearregression assumptions. We checked that thedistributions of residuals are normal bymaking probability plots (Q-Q plots) andby applying statistical tests for normality(Shapiro–Wilk and Kolmogorov–Smirnovtest). We also tested the model specificationsby applying Ramsey’s Regression Specifica-tion Error Test (RESET) and Rainbow Test.Further, we tested the models for heterosk-edasticity by using the Breusch–Godfrey,Goldfeld–Quandt and Harrison–McCabe tests(Krämer & Sonnberger, 1986). None of thesetests for the OLS regression models shows sig-nificant results. Hence, these test results indi-cate that the assumptions of a linear regressionmodel are fulfilled in all three cases. In addi-tion, we tested our models for multicollinear-ity by calculating the variance inflation factor(vif). The vif scores for variables in the modelsare only slightly above 1 and therefore far fromthe upper threshold of approximately 10.

The outcome of the likelihood ratio testapplied to the ordinal regression model for useexperience, testing the null model (with onlythe intercept) against the full model, is signifi-cant, indicating that the specified model is an

improvement. The test for parallel lines, whichwas not significant, indicates that the slopesare the same across the response categoriesand that the proportional odds assumption forthe ordinal regression model is fulfilled.

The OLS regression model for lead usernessis displayed in Table 3. The model explains36.9 per cent of the variance of lead userness(adjusted R2 value). The coefficients ofproduct-related knowledge and use experi-ence are significantly positive at a level of 0.1per cent. These indicate that there is a strongpositive relationship between domain-relevantskills and lead userness. Therefore, hypoth-eses H1a and H1b can be accepted. The coeffi-cient of divergent thinking is also positive at asignificance level of 5 per cent. Hence, hypoth-esis H3 is supported. Both the estimatedparameters of intrinsic and extrinsic motiva-tion are not significant in our regressionmodel. Therefore hypothesis H5, in which westate that intrinsic motivation is more posi-tively related to lead userness than extrinsicmotivation, has to be rejected. We also testedseveral interaction effects of intrinsic andextrinsic motivation with product-relatedknowledge and use experience, but none wassignificant.

The regression model of divergent thinkingstyle (Table 4) results in an adjusted R2 value of0.201. The findings indicate a significant posi-tive relationship between openness to experi-ence and divergent thinking style. Thushypothesis H4 can be accepted. The dummyvariable for male users is significantly positive,indicating that men have a different thinkingstyle than women, and that the divergentthinking ability is more pronounced amongthe men than the women. The control variableof age is not significant.

The OLS regression model of product-related knowledge (Table 4) explains 17.5 percent of variance (adjusted R2). The suggestedparameter of a technical background in theuser’s job is not significant. Thus, hypothesisH2a is not supported and must be rejected. Wehave controlled for gender in this regressionmodel. The coefficient is significantly positive,indicating that the men have higher product-related knowledge than the women. Addition-ally, the regression results show that gendercan be used as a proxy variable for the techni-cal background of an individual. If gender isremoved from the regression formula, thecoefficient of a technical job becomes signifi-cantly positive. Hence, the men have a moretechnical education and background than thewomen. The control variable of age is also sig-nificantly positive. This finding indicates thatusers increase their product-related knowl-edge over the years.

Lead userness

Freq

uenc

y

1 2 3 4 5

0

5

10

15

20

25

30

35

Figure 2. Histogram of Lead Userness

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In the ordinal regression model for useexperience, a significant negative coefficient(0.1 per cent level) appears for the dummyvariable of men. Thus hypothesis H2b can beaccepted. The estimated parameters for age arepositive and significant (at the level of 0.1 percent). The probability of having higher useexperience increases with the age of thetesters. The findings show that male andyounger consumers have less use experiencethan the women (see Table 5).

In summary we may state that firstly, thefindings confirm that individual creativity andpersonality play an important role in the deter-mination of lead userness. Secondly, regres-sions show that domain-relevant skills(product-related knowledge and use experi-ence) and creativity-relevant skills (divergentthinking style) are related to lead userness.However, the third component, task motiva-tion, shows no significant impact. This mightbe attributed to the homogeneous nature of thesample or to the selection process, throughwhich the first 150 people to reply wereinvited to take part, of whom 146 in fact par-ticipated. Third, the components of individualcreativity can be explained by personality-

related characteristics. A summary of assumedand resulting relationships is presented inTable 6.

Discussion and Implications

With our research we have aimed to furtherthe understanding of who leading-edge usersare. As users demonstrate creative potentialwhen developing new and useful solutions forconsumer problems, we proposed to investi-gate antecedents of creativity as determinantsof lead userness. The results show that, indeed,a considerable proportion (37 per cent) of thevariance in lead userness can be explained bythe proposed theoretical framework: Theleading-edge status of users is significantlyinfluenced by individuals’ cognitive style, andtheir domain-relevant skills such as product-related knowledge and use experience. Thesevariables again depend upon the personalcharacteristics of users. On the one hand, theresults suggest that leading-edge users canindeed be identified by searching for the cre-ative potential of individuals in the domain.On the other hand, the level of variancedescribed leaves sufficient room for other

Table 3. OLS Regression Results for ‘Lead Userness’

Coefficent Estimate t-value p-value

Intercept -0.119 -0.250 0.803Divergent thinking 0.222 2.366 0.019*Product related knowledge 0.271 5.379 <0.001***Use experience 0.263 5.372 <0.001***Intrinsic motivation 0.061 0.913 0.363Extrinsic motivation 0.019 0.471 0.638Residual standard error 1.198F-statistic 17.980 <0.001***R2 0.391Adjusted R2 0.369max. vif 1.098Normality of residuals:Shapiro–Wilk 0.984 0.081Kolmogorov–Smirnov 0.069 0.483Test for linearity and functional form:RESET 1.496 0.195Rainbow 0.916 0.643Test for heteroscedasticity:Breusch–Godfrey 0.000 0.989Goldfeld–Quandt 1.327 0.125Harrison–McCabe 0.432 0.125

Significance level: * p < 0.05; ** p < 0.01; *** p < 0.001.

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Table 4. OLS Regression Results for ‘Divergent Thinking Style’ and ‘Product-Related Knowledge’

Divergent thinking style Product related knowledge

Estimate t-value p-value Estimate t-value p-value

Intercept 2.223 7.696 <0.001*** 1.098 3.673 <0.001***Gender (male) 0.227 2.339 0.021* 0.810 4.180 <0.001***Age 0.004 1.225 0.223 0.018 3.023 0.003**Technical background 0.093 1.505 0.134Openness 0.396 5.563 <0.001***Residual std. err. 0.496 0.937F-statistic 13.190 <0.001*** 11.280 <0.001***R2 0.218 0.193Adj. R2 0.201 0.175max. vif 1.018 1.125Normality of residuals:Shapiro–Wilk 0.984 0.089 0.988 0.216Kolmogorov–Smirnov 0.064 0.596 0.069 0.485Test for linearity and functional

form:RESET 1.123 0.328 1.352 0.266Rainbow 1.214 0.209 1.191 0.233Test for heteroskedasticity:Breusch–Godfrey 0.914 0.339 1.184 0.277Goldfeld–Quandt 0.929 0.619 1.052 0.416Harrison–McCabe 0.518 0.614 0.501 0.513

Significance level: * p < 0.05; ** p < 0.01; *** p < 0.001.

Table 5. Results of Ordinal Logistic Regression for ‘Use Experience’

Estimate Wald p-value

Threshold 1 (never) -2.303 11.044 <0.001***Threshold 2 (seldom) -1.241 4.281 0.039*Threshold 3 (several times a year) -0.101 0.032 0.858Threshold 4 (several times a month) 1.683 8.373 0.004**Age 0.043 11.523 <0.001***Gender (male) -1.638 18.339 <0.001***-2 logLik (intercept only) 363.298–2 logLik (final) 330.944LR-test 32.354 2d.f. <0.001***AIC 342.944Cox and Snell R2 0.199Nagelkerke R2 0.217Test of parallel lines 11.032 6d.f. 0.087

Cumulative logit model (Proportional odds model) (n = 146).Dependent variable: Use experience measured on a five-point ordinal scale.Baseline category is ‘several times a week’.Significance level: * p < 0.05; ** p < 0.01; *** p < 0.001.

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determinants of lead userness. Although a sig-nificant part of leading-edge status can beexplained by components of creativity, the twoconstructs are distinct. This leads to the ques-tion of what exactly are the specific featureswhich distinguish the two constructs, andwhat can be gained from this study for thepractice of real-world lead user identification.

We suggest that the introduction ofcreativity-relevant antecedents should be seenas complementary to the accepted hallmarkcharacteristics of lead userness. The latter arebased on users’ self-assessment of being aheadof the trend and having a high expectedbenefit from improved product solutions. Asargued in the introduction, many consumermass markets might lack the fundamental pre-requisite of leading-edge status being easilyobservable, and for mass-market consumerproducts users might find it difficult to assesstheir own level in terms of the two hallmarkcharacteristics of lead userness. Therefore,introducing further variables consistentlyassociated with creativity can increase the reli-ability of identifying the ‘right’ customers tobe integrated in new product development. Assuch, use experience and product-relatedknowledge seem to be important supplemen-tary criteria in the identification process asusers with high values on these scales can alsobe presumed to possess ‘sticky information’about problems and processes associated withcurrently available products. By explicitlytaking into account this dimension in the useridentification process, a company might wellimprove its chances of being able to access theimplicit knowledge of experienced users,which is otherwise very hard to tap but hasbeen shown to be a significant factor in newproduct development (von Hippel, 1994,1998).

We have also shown that divergent thinkingis related to leading-edge status. One of thefeatures of this thinking style is the ability tothink ‘outside the box’, an ability that is highlyvalued in new product development as itpaves the way for currently unknown problemsolutions. This may help explain the phenom-enon reported in the findings of Kristenssonet al. (2002, 2004) that ordinary users producedmore original and valuable ideas than profes-sional developers or advanced users, whichmight be interpreted as being the result ofusers not being restricted by the functional fix-edness (of what is technically possible or not)but also as being the result of their thinkingstyle, i.e., they are predisposed to search forroutes outside the current solution patterns.Our results suggest that the identification ofcreative individuals for integration in newproduct development should be supple-mented by tried and tested scales from creativ-ity research, as proposed in our framework.Most of the variables in question are easilyobservable with a standardized questionnaire,which is especially important in the context ofconsumer goods markets.

The study has been executed in a consumergoods research setting which is not a typicalresearch context for lead user identification,namely the field of kitchen appliances, a con-sumer mass market that lacks certain charac-teristics which have been emphasized asstandard in previous studies on lead useridentification (Schreier, Oberhauser & Prügl,2007; Schreier & Prügl, 2008). Knowledge ofthe relationship between creativity and leaduserness might therefore be of particular helpto firms in industries where leading-edgestatus is difficult to observe. Our resultssuggest that the idea of user integration in newproduct development is also a valuable goal

Table 6. Summary of Hypothesized Relationships

Relationship Assumed Resulted

H1a Product-related knowledge – Lead userness + +H1b Use experience – Lead userness + +H2a Technical background – Product-related knowledge + n.s.H2b Gender (male) – Use experience + +H3 Divergent thinking – Lead userness + +H4 Openness – Divergent thinking + +H5 Intrinsic motivation (IM) – Lead userness (LU) + n.s.

Extrinsic motivation (EM) – Lead userness (LU) + n.s.(IM → LU) – (EM → LU)a + n.s.

+ positive significant; - negative significant; n.s. not significant.a Relationship between intrinsic motivation and lead userness is stronger than relationship between extrinsicmotivation and lead userness.

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for these mass-market product categories.With the understanding of what makes up acreative individual (or a leading-edge user)and the knowledge of appropriate scales foruser selection, opening up the innovationprocess towards user integration in such mass-market consumer goods could lead to similarsuccess stories as have been witnessed in pre-viously researched fields.

Knowledge of the drivers of lead usernesshas practical implications for the managementof companies which have their own commu-nity of product testers. These firms couldthemselves ‘generate and educate’ their ownlead users. All members of the tester commu-nity could be systematically screened forcreativity-relevant and domain-relevant skillsin order to identify the creative potential ofeach individual test person. On the basis ofthis, a score for creativity potential for eachmember of the tester community could be cal-culated, thereby facilitating a pre-selection ofthe most suitable product testers who could besigned up for a more intensive involvementin the new product development process.Instead of being employed only for prototypetesting, those individuals with the most prom-ising creative potential could be invited toinnovation workshops in the fuzzy front-end,and be integrated at earlier stages in the devel-opment phase. As lead userness is fundamen-tally dependent upon users’ domain-relevantskills, a firm could also increase product-related knowledge in a systematic way bydistributing product-related newsletters. Inaddition, the provision of new products fortesting and encouraging their heavy use couldbe a viable way to increase both product-related knowledge and use experience.

Limitations and Future Research

With our research we have shown, both theo-retically and empirically, that lead usernessand creativity are fundamentally interrelated.We have used standardized questionnaires todemonstrate this relationship – a last missinglink in the chain of evidence would be a work-shop where users selected for their leading-edge status and their creativity-relevantcharacteristics would be invited to generatesolutions for new products. Future researchmight address this. As regards personalitytraits, we have made use of ‘openness to expe-rience’ as it was most consistently found to berelated to creativity. Future research mightinvestigate other dimensions of the Big Fiveand their relation to leading-edge status. Thisis particularly relevant from a practical point ofview, as it would be desirable for lead userworkshops to integrate people with high

levels of extraversion (communicative indi-viduals) and conscientiousness (predisposi-tion to complete tasks carefully).

Our sample was selected from persons reg-istered with an existing company product testcentre. One must therefore be cautious aboutgeneralizing our findings by applying them toother consumer mass markets. A replication ofthe model with a randomized test samplewould be needed. We think that the motiva-tional component of the model especiallywould provide significantly more informationwith a heterogeneous sample of users. One ofthe main contributions of our study is thetransfer of a conceptual framework of creativ-ity to the field of innovation management.Some parts of the model, however, such as thedeterminants of domain-relevant skills, wouldhave to be specified by the particular contextbeing investigated. Therefore validation of ourfindings in other industrial contexts would bedesirable.

Notes

1. We used two different items to measure intrinsicmotivation, but the two items did not converge ina unique scale (Cronbach’s alpha <0.5, Spear-man’s rho = 0.455). We decided to use only one ofthese for our regression analysis but tested theother as well. The results are more or less thesame, with no significant effect from either.

2. The categories of the ordinal measurement scaleof ‘use experience’ are ‘never’, ‘seldom’, ‘severaltimes a year’, ‘several times a month’ and‘several times a week’.

3. We tested several model specifications for useexperience (OLS, ordinal regression with alterna-tive link functions: logit and complementary log-log link function). The results are stable in allmodel specifications with the same significanteffects. The ordinal regression model with logitlink (proportional odds model) fits the data bestwith regard to AIC statistics.

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Rita Faullant (rita.faullant@aau.at) is Assis-tant Professor at the Department of Innova-tion Management and Entrepreneurship atthe Alpen-Adria-Universität Klagenfurt.Her research centres on consumer co-creation and stakeholder integration innew product development, adoptionand diffusion processes of new productsand technologies, and on organizationalinnovativeness.

Erich J. Schwarz is Dean of the Faculty ofBusiness Administration at the Alpen-Adria-Universität Klagenfurt, and Full Pro-fessor for Innovation Management andEntrepreneurship. His research interestsfocus on entrepreneurial start-ups, userinnovation, innovative technologies andtechnology management.

Ines Krajger is Senior Scientist at theDepartment of Innovation Managementand Entrepreneurship at the Alpen-Adria-Universität Klagenfurt. In her PhD thesis,she focused on the creative potential ofinnovative users.

Robert J. Breitenecker is Assistant Profes-sor at the Department of Innovation Man-agement and Entrepreneurship at theAlpen-Adria-Universität Klagenfurt. Hisresearch centres on team heterogeneity inentrepreneurial start-ups.

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