20112004 - Innovation Culture, Collaboration with external Partners.pdf

Innovation Culture, Collaborationwith External Partners andNPD Performancecaim_617 253..272

Malte Brettel and Nina J. Cleven

Researchers and managers have found that the use of external knowledge in the process ofnew product development (NPD) helps to sustain a firm’s competitiveness by strengtheningits innovative performance. However, little is known about why some firms use externalknowledge sources for NPD in an extensive manner while others hardly ever use them. Inaddition, there is disagreement about which external partners significantly contribute to theinnovative performance of a firm as valuable knowledge sources. Based on the resource-basedview (RBV) of the firm and Kitchell’s innovation adoption model, this paper expects a firm’sinnovation culture to have a significant impact on its openness to external knowledge –measured in terms of its collaborative behaviour with five different external partners – and forthat behaviour to influence the firm’s NPD performance. A sample of 254 technology-basedfirms across several industries is used to empirically test the research model with covariance-based structural equation modeling (SEM). The findings deepen our understanding of thediscrepancies between successful pioneering firms active in technology and knowledge sourc-ing and others being less successful.

Introduction

Technology-based firms operate in a com-petitive environment of companies and

organizations that invest heavily in new tech-nology and new product development (NPD),so each firm is surrounded by knowledgesources in the form of other firms. An increas-ing number of researchers from a range ofresearch areas (e.g., strategic management,technology and innovation management,strategic networking, organization manage-ment) have found that exploiting such knowl-edge resources for NPD can strengthen thecompetitiveness of firms that operate in adynamic environment (Hagedoorn & Duys-ters, 2002; Rothaermel & Hess, 2007; Almirall& Casadesus-Masanell, 2010). One of the mostprominent examples in this field of research isChesbrough who shaped the so-called openinnovation paradigm ‘that assumes that firmscan and should use external ideas as well asinternal ideas, and internal and external pathsto market, as the firms look to advance theirtechnology’ (Chesbrough, 2003, p. xxiv). Fol-lowing this approach, collaboration withexternal partners can result in new ideasand knowledge for new product innovations

(Lambe & Spekman, 1997; Ahuja, 2000) andcan give firms access to the complementaryassets needed to turn an innovation into acommercial success (Teece, 1986; Hagedoorn,1993). Inter-organizational collaboration alsospreads the cost of research and development(R&D) and allows firms to take advantageof synergies (Hagedoorn & Duysters, 2002;Howells, James & Malik, 2003; Kratzer,Gemunden & Lettl, 2011).

Although the advantages of collaborativeactivities in NPD are well known, there is littleresearch on the antecedents of a firm’s open-ness to using external knowledge sourcesor the antecedents of a firm’s more closedapproach to specific fields of R&D (Jones,Lanctot & Teegen, 2001; Calantone, Cavusgil &Zhao, 2002; Laursen & Salter, 2006; Lichtentha-ler, 2011). As a result, little is known about whysome firms intensively incorporate externalknowledge by co-operating with external part-ners in their NPD processes, while others stillfollow the ‘old’ closed-innovation approach.In this context, Knudsen (2007), for example,states that ‘future research could contribute byexamining what appropriate antecedents andmechanisms can make firms focus to a higherextent on complementary knowledge’ (p. 135).

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There is disagreement in the literature aboutwhich external knowledge sources (customers,suppliers, competitors, universities, etc.) con-tribute significantly to firms’ performance ininnovation and so are reasonable partners(Belderbos, Carree & Lokshin, 2004).

The aim of this research study is to add towhat is known about the answers to theseresearch questions. Building on the resource-based view (RBV) of the firm (Barney, 1991)and drawing on Kitchell’s (1995) ‘innovationadoption model’, the present study developsand empirically tests a theoretical model thatassumes that a firm’s innovation culture isan internal variable that either facilitates orimpedes collaborative activities in the contextof NPD.

The article proceeds as follows: the nextsection lays out the theoretical premises withan introduction to the RBV and the constructof innovation culture, followed by a brief over-view of the literature on collaboration withexternal partners. In the following section, theresearch model is described and hypothesesare generated. The fourth section presents theresearch design and the dataset. The fifthdescribes the methodology of analysis andthe results. The article closes with a detailedpresentation and discussion of the empiricalfindings.

Theoretical Background

Innovation Culture andthe Resource-Based View

The resource-based view (RBV) of the firmserves as the overarching theory for our study.It was first presented by Wernerfelt (1984),who suggested that firms that possess andstrategically exploit resources and capabilitiesthat are both valuable and rare will attain acompetitive advantage by capitalizing onthose assets. The RBV argues that thoseresources and capabilities that are also inimi-table and non-substitutable are particularlyimportant to the firm’s ability to improveits short-term and long-term performance(Barney, 1991). Current portrayals of the RBV,however, make clear that no direct resource–performance link exists and that strategicresources have only potential value in helpingthe firm take strategic actions (Ketchen, Hult &Slater, 2007). If actions that capitalize on theresources are taken, a competitive advantageresults that, in turn, enhances performance.

According to Barney (1986) and several otherresearchers (Deshpandé, Farley & Webster Jr,1993; Schein, 1996; O’Cass & Ngo, 2007), afirm’s culture, defined as ‘a complex set of

values, beliefs, assumptions, and symbols thatdefine the way in which a firm conducts itsbusiness’ (Barney, 1986, p. 657), can be a sourceof sustained competitive advantage, so it con-stitutes a strategic resource. Studies in the orga-nizational literature, for example, suggest thathaving a ‘strong’ corporate culture contributesto improved corporate performance as it playsa key role in determining the working climate,leadership style, strategy formulation, organi-zation behaviour and processes of the firm(Saffold III, 1988; de Brentani & Kleinschmidt,2004). Later, Hynes (2009) argues that corporateculture has a pervasive effect on an organiza-tion because the culture defines its employees,customers, competitors and suppliers and howthe organization interacts with each one.

The ‘innovation adoption model’ establishedby Kitchell supposes that ‘corporate culturefunctions as a transforming agent to ensuresystem survival’ (1995, p. 197); that is, whenthey are exposed to environmental dynamics(such as technology turbulence), successful,adaptive companies enact cultural norms thatstrengthen the firm’s capacity for outreach (tomarkets, innovation, information, etc.) and itsability to assimilate technologies. Accordingto Kitchell, ‘this may be achieved by fosteringcultural norms that emphasize flexibility tochange, openness in communication, and afuture orientation that focuses on staff devel-opment and strategic planning’ (1995, p. 197).In contrast, organizations that are less success-ful in fostering these cultural norms are lessable to change themselves or evolve with theirenvironments.

Building on the RBV and Kitchell’s (1995)innovation adoption model, the current studyfocuses on a firm’s innovation culture – as onefacet of corporate culture – and analyses itsimpact on the firm’s openness to externalknowledge. Later, we analyse how this open-ness contributes to NPD performance. In theliterature there are various definitions of afirm’s innovation culture. According to Kobergand Chusmir (1987) and Deshpandé, Farleyand Webster Jr (1993), the key aspects of inno-vativeness from a cultural perspective can bedescribed as creativity, openness, and recep-tiveness to new ideas, risk taking and entrepre-neurial mindset. Capon et al. (1992) and Guptaand Wilemon (1990) speak of a climate of open-ness and informal communication describinginnovation culture, while Kuczmarski (1998)describes a mindset that motivates employeesto endorse holistically a belief in creatingnewness.

Since an innovation culture is an intangibleresource that cannot be measured directly(Godfrey & Hill, 1995), we conceptualize it as alatent factor that is reflected in the four dimen-

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sions: orientation toward new technologies,learning orientation, willingness to take risks,and future market orientation. Thereby, wedraw on de Brentani’s and Kleinschmidt’s(2004) definition of innovative culture whichencompasses most of the dimensions of inno-vation culture mentioned in the literature. Ingeneral terms, then, we define an innovationculture as ‘the degree to which organizationsare predisposed to learn continuously andto develop knowledge with the intention todetect and fill gaps between what the marketdesires and what the firm currently offers’.

Collaboration with External Partners

Researchers observe that many firms havecome to rely more extensively than ever onexternal linkages – becoming part of networksin which resources, knowledge and informa-tion circulate (Teece, 1992); providing knowl-edge in areas where internal sources areinadequate (Leonard-Barton, 1992) and copingmore effectively with the increasing speed,risks and costs of NPD (Vanhaverbeke, Duys-ters & Noorderhaven, 2002). The synergisticeffects of complementary resources andknowledge can help firms accelerate NPD pro-cesses (Faems, Van Looy & Debackere, 2005;Knudsen, 2007; Laursen & Salter, 2006) andreact to changing environments in moredynamic and flexible ways.

A review of the existing literature revealsa multitude of potential external sources forfirms that want to use them. While the benefitsof competitor involvement are controversial

(McGill, 2007), customer involvement inNPD, which has been analysed intensively, istypically associated with positive interactions(Voss, 1985; Von Hippel, 1986; Tomes, Arm-strong & Clark, 1996; Brockhoff, 2003; Salomo,Steinhoff & Trommsdorff, 2003). Studies havealso found the involvement of suppliers (Dyer,1996; Ragatz, Handfield & Scannell, 1997;Primo & Amundson, 2002; Van Echtelt et al.,2008) and universities or research institutes tobe useful in developing successful new prod-ucts (Hise, Futrell & Snyder, 1980; Santoro &Betts, 2002). Another source that has attractedthe notice of researchers is consultants andindependent research institutes (Bessant &Rush, 1995; Knudsen, 2007; Tether & Tajar,2008). In a case study with a manufacturerof industrial products, Alam (2003) found thatconsulting engineering firms are a viableexternal source for NPD. In pursuit of a com-prehensive picture, we considered all of theseexternal partners – customers, suppliers, com-petitors, universities and independent expertssuch as public research institutes and consult-ants – as potential partners for NPD projects.Our study focuses on collaborations whoseobjective is to gain input for new productsand technologies (for a review, see Powell,Koput & SmithDoerr, 1996). The followingsection describes the conceptual frameworkand derives the hypotheses.

Research Model and Hypotheses

The research model underlying the presentstudy, shown in Figure 1, is based on Kitchell’s

Strategic Action Competitive AdvantageStrategic Resource

Collaboration with External Performance Innovation CulturePartners in the NPD Process Outcome

H5-eH1aOrientation towardstechn. innovation

Customers

Suppliers

Competitors

Universities

Indep. experts

NPDperformance

H6

H7

H9

H8

-eH2 aLearning orientation

-eH3 aWillingness to take risks

-eH4 aFuture marketorientation

Figure 1. Research Model and Research Hypotheses



(1995) innovation adoption model, whichargues that a firm’s cultural norms strengthenits capacity for outreach and its ability toassimilate technologies; these abilities play akey role in the firm’s survival because theydetermine how the firm fosters or fails to fosterthe flexible and outward-looking behaviourrequired to handle environmental exigencies.The model observes how an innovative culture(a strategic resource), as measured by its fourlatent variables, effectuates the firm’s collabo-ration with external partners (strategic action)to enhance NPD performance (competitiveadvantage). The model is used to analyse thefirm’s innovation culture as an antecedent tothe firm’s openness toward collaboration withexternal partners (Hurley & Hult, 1998).

An orientation towards technological innovationreflects one important dimension of the inno-vation culture of a firm (Hurley & Hult, 1998;Herrmann, Gassmann & Eisert, 2007). Accord-ing to Berthon, Hulbert and Pitt (1999), ‘firmsthat enact a technological innovation orienta-tion devote their energy towards inventing andrefining superior products’ (p. 37), wherebytwo components, ‘openness to innovate’ and‘capacity to innovate’, are called into action.Atuahene-Gima and Ko (2001) see innovationorientation as ‘human resource practices thatfoster support for innovative and risky behav-ior and that enable employees to keep up withchanging technologies’ (p. 61). Thus, the ratio-nale for an innovation orientation is that tech-nology has the potential to create new marketsand customers. Worren, Moore and Cardona(2002) further argue that an orientation towardsinnovation is an intentional and calculated planor strategic intent that provides directiontowards an organization-wide commitment tomore and faster innovations.

Our study adopts the definition of orienta-tion towards technological innovation pro-vided by Herrmann, Gassmann and Eisert(2007, p. 99):

[A]n openness to new types of technologies,the ability to search for these technologiesproactively, being able to recognize themearly on, and reacting to them appropri-ately, as well as an attempt to use thesetechnologies purposefully for innovationto develop technologically first-class pro-ducts that are superior to those of thecompetitors.

Since firms are usually unable to developevery new technology on their own and aretherefore dependent on external knowledgesources, we assume that a firm’s orientationtowards technology innovation has a signifi-cant impact on the firm’s collaborative activi-

ties for NPD with diverse external partners.Therefore, we formulate the following explor-ative hypotheses:

Hypotheses 1a–e. Orientation towards techno-logical innovation is positively associated withcollaborative activities with customers (H1a),suppliers (H1b), competitors (H1c), universities(H1d), and independent experts (H1e).

Another factor proposed in the literature asan important dimension of a firm’s innovationculture is learning orientation (Baker & Sinkula,1999; Calantone, Cavusgil & Zhao, 2002).Organizations in which management consid-ers learning central to survival embed a learn-ing orientation in their corporate cultures thatencourages their employees to question theirassumptions about the company, its environ-ment, and their existing knowledge, and tofind new ways of learning (Argyris & Schön,1978; Fiol & Lyles, 1985; Herrmann, Gassmann& Eisert, 2007).

In the context of product innovation, learn-ing orientation is key to a successful productinnovation process. Learning orientation indi-cates the degree to which firms are committedto challenging systematically the fundamentalbeliefs and practices that define the innova-tion process itself (Day, 1994). A learningculture encourages firms to question theinformation they process as well as whethertheir particular approach to innovation isapplicable to processing that information.More specifically, firms with a strong learningorientation question whether the core beliefsabout customers, competitors and suppliersthat provided the basis for their past actionscontinue to be applicable. Since sources ofknowledge for NPD do not reside exclusivelyinside the firm but are also often found in theinterstices between the firm and universities,research laboratories, suppliers and cust-omers (Powell, Koput & SmithDoerr, 1996;Kessler, Bierly & Gopalakrishnan, 2000;Faems, Van Looy & Debackere, 2005), thesefirms scan the external environment for newtechnological paradigms that may offer abetter means by which to deliver core benefits(Baker & Sinkula, 1999). Consequently, weexpect that the firm’s learning orientation has apositive impact on collaborative activitieswith external partners, and we formulate thefollowing explorative hypotheses:

Hypothesis 2a–e. Learning orientation is posi-tively associated with collaborative activitieswith customers (H2a), suppliers (H2b), com-petitors (H2c), universities (H2d) and indepen-dent experts (H2e).

The development of new or significantlymodified products is always related to risk,



such as the risk of failure caused by innovativeproduct technologies or by new products notbeing adopted by the market as originallyexpected. If external partners are added tothe NPD process, the potential for risks onlyincreases, for example, in terms of the riskinherent in giving away sensitive knowledge(Grindley & Teece, 1997; Veugelers, 1998) orbecoming too dependent on outside knowl-edge (Doz, 1996). However, without takingrisks, firms cannot gain the experience from thesuccesses and failures that are required in orderto develop more competitive repertoires(Sitkin & Pablo, 1992) and the new productsthat will keep up with market trends.

For that reason, Kohli and Jaworski (1990)argue that only if the top management demon-strates a willingness to take risks and acceptsoccasional failures as being natural are juniormanagers likely to introduce new proposalsin response to changes in customer needs.Following Herrmann, Gassmann and Eisert(2007), a firm’s willingness to take risks is ‘acharacteristic of the enterprise culture andexpresses the extent to which a company isprepared to accept the risks brought about bythe high uncertainty of innovation, to viewoccasional setbacks as normal, and to investsufficient resources in new products eventhough the chances for success are uncertain’(p. 100). We adopt this definition in our studyand propose that the firm’s willingness to takerisks enhances the propensity to co-operatewith external partners in the process of NPD.Hence, we formulate the following explorativehypotheses:

Hypothesis 3a–e. A firm’s willingness to takerisks is positively associated with collaborativeactivities with external partners, meaningcollaboration with customers (H3a), suppliers(H3b), competitors (H3c), universities (H3d)and independent experts (H3e).

The marketing literature has traditionallyemphasized that firms need to be market-oriented by placing the highest priority on theprofitable creation and maintenance of supe-rior value for customers (Kohli & Jaworski,1990; Narver & Slater, 1990; Deshpandé, Farley& Webster Jr, 1993). However, some authorsposit that a strong market orientation alsoleads to incremental and application-orientedinnovations, rather than to truly breakthroughinnovations (Bonner & Walker, 2004; Knudsen,2007). For example, Gatignon and Xuereb(1997) found that a strong customer orientationleads to less radical innovation. To prevent thisdevelopment, firms are urged to search for andstrategically invest in new products and tech-nologies that are geared to satisfying the needsof future customers. Chandy and Tellis call

this emphasis a future-market focus and defineit as ‘the extent to which a firm emphasizesfuture customers and competitors relative tocurrent customers and competitors’ (1998: 479).In order to identify these future needs andactions, firms must search in various directions,both inside the company and especiallyoutside. Hence, we hypothesize:

Hypothesis 4a–e. Future market orientationis positively associated with collaborativeactivities with customers (H4a), suppliers(H4b), competitors (H4c), universities (H4d)and independent experts (H4e).

Collaboration with External Partners and itsImpact on NPD Performance

Collaboration with customers is one of themost intensively discussed relationships inthe context of NPD. Customer involvement inthe process of NPD is often associated withpositive NPD performance because customerinput helps firms gain new ideas about pro-ducts and solutions (Urban & Von Hippel,1988), understand customers’ needs, and iden-tify new market trends early (Li & Calantone,1998). Customer input in the early stages ofNPD can also allay problems related to poordesign (Knudsen, 2007) and poorly conceivedmarket introductions (Tether, 2002; Belderboset al., 2004). Consequently, customer involve-ment may lead to better product innovations(Souder, Buisson & Garrett, 1997; Brockhoff,2003) and increase the chance of NPD success.A number of researchers confirm theseassumptions (Freel, 2003; Faems, Van Looy &Debackere, 2005), but others find negative(Lööf & Heshmati, 2002; Knudsen, 2007) orinsignificant relationships between collabora-tion with customers and NPD performance(Monjon & Waelbroeck, 2003; Belderbos,Carree & Lokshin, 2004). For example, Bonnerand Walker (2004) note that customer involve-ment can lead to negative and counterproduc-tive effects if the focus lies only on the needsof a single type of customer group andproduct features do not satisfy the needs of abroad range of customers. Further, Knudsenobserves that ‘the average customer is unableto conceptualize ideas or improvementsbeyond the realm of their own experience’(2007, p. 133), a perspective that does not servethe development of major product innovationsthat address a wide range of customers. Inaddition, Nieto and Santamaria (2007) remarkthat firms that concentrate too heavily on cus-tomers run the risk of losing access to impor-tant new technological developments and mayfall behind. Nevertheless, we assume that the



positive aspects of customer involvement inthe NPD process outweigh the negative onesand hypothesize that:

Hypothesis 5. Collaboration with customers inthe NPD process has a positive effect on NPDperformance.

Supplier involvement in the NPD process is alogical consequence of a focus on efficiency.Because of their regular contact with the firmsthat buy from them, suppliers are familiar withthe processes, demands and conceptions ofthose firms and tend to be easy to communicatewith concerning NPD projects. Suppliershave specific knowledge and competencies,can be a source of innovative ideas and criticaltechnologies (Håkansson & Eriksson, 1993),and are interested in strengthening their rela-tionships with their clients. Van Echtelt et al.(2008) argue that companies that are constantlyunder pressure to deliver superior value totheir customers – and, thus, to enhance productdevelopment in terms of productivity, speedand product quality – may choose to strengthentheir co-operation with their suppliers concern-ing NPD processes in order to get relief (Primo& Amundson, 2002; Ragatz, Handfield &Petersen, 2002). Further, greater collaborativeactivity with suppliers enables firms to concen-trate on their core competencies and to com-plement their R&D with that of the supplier(Tether, 2002; Belderbos, Carree & Lokshin,2004). In addition to increasing the efficiencyand effectiveness of NPD efforts, supplierinvolvement helps firms identify technicalproblems and ensure that product innovationsare portable in the production process(Knudsen, 2007).

Risks related to collaboration with suppliersin NPD may include an increase in depen-dency and the danger that jointly developedand otherwise secret knowledge is dissemi-nated to the firm’s competitors who may alsobe clients of the supplier. However, contractscan be negotiated to minimize this danger.Therefore, we hypothesize that:

Hypothesis 6. Collaboration with suppliers inthe NPD process has a positive effect on NPDperformance.

While competitor involvement in NPD pro-cesses may seem counterintuitive, collabora-tion with competitors can help to consolidatesupplementary knowledge and to generatesynergistic effects that can accelerate capabilitydevelopment and reduce the time and costsneeded for NPD for both firms (Belderbos,Carree & Lokshin, 2004; Tsai, 2009). This kindof collaboration also allows firms to benchmarktheir own technological capabilities and, as aresult, to differentiate themselves from each

other more clearly (Linn, 1994). In this regard,Tether (2002) argues that it makes more sensefor competitors to find areas where strengthsare complementary and to develop jointly anew range of products, rather than to replicatethe other firms’ strengths. Further, collabora-tion can help competitors implement standardsin the market, compete successfully againstthird-party competitors (Perks & Easton, 2000),and influence the regulatory environment.

Of course, there are risks related to collabo-rative activities among competitors, such as theunintended transfer of sensitive knowledge.Therefore, a firm must weigh carefully the ben-efits and risks of collaboration with competi-tors, safeguard its own knowledge base fromunintended appropriation and imitation, andensure that areas of application of the jointlydeveloped knowledge are clearly defined.

Empirical studies on competitor involve-ment in NPD processes have resulted in con-flicting findings. For example, while Lööfand Heshmati (2002) found that collaboratingwith competitors is positively related to newproduct sales, others have found negativeor insignificant relationships between thiskind of collaboration and sales (Cassiman &Veugelers, 2002). Miotti and Sachwald discov-ered in their research study that collaborationwith competitors is both rare and not signifi-cant to NPD performance (Miotti & Sachwald,2003). Because of the conflicting findings in theliterature and the mainly positive argumentsconcerning collaboration among competitors,we hypothesize:

Hypothesis 7. Collaboration with competitors inthe NPD process has a positive but insignificanteffect on NPD performance.

Universities and their research institutes area constant source of scientific knowledge cre-ation and innovation. As research acceleratesand grows increasingly expensive, industrytries to leverage academic knowledge specificto specialized technical support and equip-ment to complement internal R&D and gainaccess to emerging technologies (Tidd & Tre-whella, 2002). According to Spencer (2003),firms that do not acquire technological knowl-edge from universities may fall behind andbe less likely to make technological break-throughs that lead to viable commercial prod-ucts. Knowledge exchange with universitiesmay be in the form of formal agreementsbased on stable relationships, like joint R&Dprojects, or they may be in the form of informalagreements that are more like sporadicinterchanges (Cassiman & Veugelers, 2006;Segarra-Blasco & Arauzo-Carod, 2008). Uni-versities are seen as low-risk sources ofinformation that is especially useful for basic



and long-term strategic research, particularlyin pre-competitive technologies. Moreover,Tether (2002) argues that co-operation withuniversities is likely to cost less than co-operation with other external partners, such assuppliers. However, while Tijssen (2002) andNarin, Hamilton and Olivastro (1997) find thatNPD increasingly depends on the results ofbasic university research, Laursen and Salter(2004) find only a limited number of firms thatdraw directly from universities as a source ofinformation or knowledge for their innovativeactivities. Therefore, we hypothesize that:

Hypothesis 8. Collaboration with universities inthe NPD process has a positive effect on NPDperformance.

Independent experts, such as engineeringconsultancies, specialized start-up firms andpublic research institutes, are alternativeknowledge sources that employ a large pool ofdistinguished technology specialists capable ofgenerating new product ideas. These expertsprovide fundamental scientific and/or techno-logical knowledge, applied knowledge andspecialist skills (Tether, 2002). Referring todesign consultants, Bruce and Morris (1998)argue that internal designers often becomecomplacent and too familiar with the com-pany’s approaches and products and fail toprovide innovative ideas, while external designconsultants can provide fresh ideas. Bessantand Rush (1995) comment that consultants canprovide a variety of inputs to the innovationprocess in roles that go beyond the traditionalprovision of expertise; they provide technicalknowledge for tailored projects by acting as anadditional workforce or by advising the firm onthe product development process in general,and contribute to NPD by providing informa-tion about customer requirements, assistingin development and testing of prototypes,designing the new products, and assisting indiffusion of the new products (Alam, 2003;Knudsen, 2007). Even though the role of inde-pendent experts in NPD processes is stillunclear and has been researched only to alimited degree, we hypothesize that:

Hypothesis 9. Collaboration with independentexperts has a positive effect on NPDperformance.

Research Design

Sample and Data Collection

To validate the theoretical model empirically,we administered a survey to a sample oftechnology-based and knowledge-intensiveGerman companies during the period from

March to May 2009. We chose technology-based firms as our sample since they areconsidered more important in creating newwealth for society than are low-tech firms, andthey better manifest the influence of internalcapabilities and strategic networking. Theindustries represented in the sample includethe mechanical engineering and tool-makingindustry (29%); the electrical (14%), biotech-nology and medical engineering (9%), auto-motive (7%), pharmaceutical and health care(5%) industries; and several other knowledge-intensive industries. The mailing list for thesurvey was obtained from company listings onthe German Chamber for Industry and Com-merce database. Since it is widely believed thattop managers provide the best informationabout environmental and organizational char-acteristics, all surveys were directed to thechief executive officer (CEO) of the firm and tothe manager of the R&D or marketing divi-sion. Each was given the alternative to partici-pate via an online version of the survey or totake the pdf print-out version of the surveyand send it back by post or fax. Yu and Cooper(1983) show that non-monetary incentivesincrease response rates, so we offered respon-dents the opportunity to receive a report onthe findings. One week after the first mailingwas dispatched, reminder mail was sent tothose from whom no reply had been received,and a second and final reminder was sentagain one and a half weeks later. Altogether,263 completed surveys were sent back, ofwhich only nine responding firms had to bedeleted because of missing information. Thismeant that 254 evaluable surveys remained.Most questionnaires (almost 60%) were com-pleted by the CEO, followed by division anddepartmental managers of the marketing anddistribution department and the R&D depart-ment. Some 23 per cent of the respondentsanswered by fax or post, and the rest partici-pated in the online version of the survey. Mostof the participating companies were estab-lished, internationally oriented companieswith a focus on the business-to-business (B2B)customer segment. Further, 48 per cent weremedium-sized firms (50–500 employees), 34per cent were small (1–50 employees), and 18per cent were large (>500 employees).

Threats to Reliability and Countermeasures

We undertook additional steps to check thereliability and validity of our data. First, weused scale reordering, as suggested by Salancikand Pfeffer (1977), which reduces the sequenc-ing effects of consistency by arranging theitems on a self-reported questionnaire so thatmeasures of the dependent variables follow,



rather than precede, those of the independentvariables. We adopted this method by placingthe NPD performance measure after all otherlatent variables and in front of the controlvariables.

To analyse the data for non-response bias,online-offline bias, and informant-bias, weapplied the Mann–Whitney and Kolomogorov–Smirnov tests, as recommended by Arm-strong and Overton (1977). In the case of thenon-response bias, we sorted the sample byresponse time and divided it into three groups(early, middle and late) to determine whetherthere are significant differences between theaverage values of each indicator of the early andlate respondents. t-Tests indicated no signifi-cant differences (p > 0.05). To test the datafor online-offline bias, we compared the twogroups of online and offline replies. To identifythe presence of informant bias, we comparedthe responses of CEOs with those of all otherstaff members and again detected no evidenceof potential biases. The results indicated noevidence for any of the three potential biases.Since the data for this study were obtained froma single survey, common method variance waspossible (Crampton & Wagner, 1994; Lindell &Whitney, 2001), but Harmans’s single-factorshowed that (a) no single factor emerged froman exploratory factor analysis, and (b) no onegeneral factor accounted for the majority of thecovariance among the measures in the sample.Thus, there is sufficient reason to believe thatthere is no strong common method bias presentin the data (Podsakoff & Organ, 1986; Venaik,Midgley & Devinney, 2005). A second testdeveloped by Liang et al. (2007) was also usedto seek out evidence of common method bias.Results demonstrated that the average substan-tively explained variance of the indicators is0.811, while the average method-based vari-ance is 0.003, making the ratio of substantivevariance to method variance about 270:1.Further, most method factor loadings are notsignificant so, given the small magnitude ofmethod variance, there is no serious concernabout a common method bias in this study(Williams, Edwards & Vandenberg, 2003; Lianget al., 2007).

Measures

To test the hypotheses suggested by theresearch model, measures of each constructwere developed using multiple items andLikert-type scales (1 = ‘strongly disagree’ to7 = ‘strongly agree’). A comprehensive litera-ture review helped to identify relevant con-cepts and previously operationalized scaleitems. Although some items were derivedfrom existing validated scales, the items that

measure collaborative activities with externalpartners were developed specifically for thisstudy. After several iterations of item editingand refinement, we conducted pre-test inter-views with researchers and practitioners inorder to identify any problems with questionwording and questionnaire layout. These inter-views yielded many useful suggestions thatstrengthened the content and validity of theresearch tools.

We measured firms’ innovation culture usingfour latent variables: orientation towardstechnological innovation, learning orientation,willingness to take risks, and orientationtowards future markets. Following Herrmann,Gassmann and Eisert (2007), we measured thefirst variable, orientation towards technologi-cal innovation (a = 0.84), using six items thatreflect the willingness and proactivity of thefirm in adopting and using new technologiesin NPD. To capture the second variable, learn-ing orientation (a = 0.86), we used four itemsfrom Hult, Ketchen and Arrfelt (2007) thatdetect the general view of the firm concerningthe importance of learning and the willingnessto learn continuously. The third variable, will-ingness to take risks (a = 0.89), was measuredusing five items from Jaworski and Kohli thatreflect the firm’s approach to risk taking inNPD (Jaworski & Kohli, 1993). Finally, orienta-tion towards future markets and future cus-tomers (a = 0.78) was measured by five itemsfrom Jaworski and Kohli (1993).

Because of data limitations, Faems, Van Looyand Debackere (2005), Laursen and Salter(2006) and Tsai and Wang (2009) – who,among others, investigated the open innova-tion phenomenon by analysing innovationsurvey data sets (CIS and TTIS) – measured theinvolvement of external partners in the NPDprocess using single-item dummy variables.However, Laursen and Salter commentedsubsequently that ‘future research shouldexamine this issue by developing several fine-grained items for each of the knowledgesources’ (2006, p. 147). Tsai and Wang con-cluded that ‘from a collaboration point of view,it would be more convincing to analyse thedepth or closeness of partner collaboration’(2009, p. 525). We followed these calls in ourstudy and developed five multi-item measuresfor five external partners: customers, suppli-ers, competitors, universities and independentexperts. These external partners are well rep-resented in a variety of innovation surveys(e.g., CIS and TTIS) and are also discussedindividually in several research studies. Abroad literature review (Gruner & Homburg,2000; Jones, Lanctot & Teegen, 2001; Swan &Allred, 2003; Atuahene-Gima, 2005; Fey, 2005;Koufteros, Vonderembse & Jayaram, 2005;



Laursen & Salter, 2006; Tsai & Wang, 2009)and several interviews with practitioners andresearchers resulted in five constructs, eachwith three to four items, that capture sepa-rately collaboration with customers (a = 0.85),suppliers (a = 0.90), competitors (a = 0.87),universities (a = 0.95) and independentexperts (a = 0.93). All measures query howoften and intensively the firm involves theexternal partner in its NPD process and howimportant the inter-exchange and integrationis to the firm. The measurement scale for theseitems was a 7-point Likert-type scale from1 = ‘fully disagree’ to 7 = ‘fully agree’.

Following Griffin and Page (1993) and Zahraand Covin (1993), we operationalized productinnovation performance (a = 0.91) by measuringthe frequency of market launches of new prod-ucts, the innovation publicity generated by thefirm, the degree of novelty of new products andthe percentage that new products represent inthe product portfolio and in the sales volume.Respondents were asked to draw a comparisonwith major competitors. Unlike a numberof researchers (Shan, Walker & Kogut, 1994;Ahuja, 2000; Stuart, 2000; Nicholls-Nixon &Woo, 2003), we decided against measuringinnovative outcome via patent output since, inour view, patents do not measure a firm’sability to bring new products (innovations) tomarket successfully but only to invent productsand technologies (Mansfield, 1986; Deeds &Hill, 1996). All the scales used are listed in theAppendix.

We controlled for several variables that mayinfluence the overall research model: firm size,firm age, market, industry type and strategy.The size of the firm is measured by the numberof full-time employees, which has been a proxyfor size in previous studies related to innova-tion and firm performance (Caloghirou et al.,2004; Schoenmakers & Duysters, 2006). Wecontrolled for firm size since previous studieshave shown that the number of employees ishighly correlated with the propensity of a firmto co-operate with external partners in R&D(Bayona, Garcia-Marco & Huerta, 2001; Fritsch& Lukas, 2001). Firm age was measured inyears since foundation. We controlled for firmage since the age of a company may predict theexperience it has in collaborating with externalpartners. The control variable ‘market’ mea-sures whether the largest market of the firm islocal, regional, national or international (Kitch-ell, 1995; Laursen & Salter, 2006). The variabletakes values from 1 to 4, with 1 correspondingto ‘local’ and 4 corresponding to ‘international’.Further, we included 13 industry controls toaccount for differences in the propensity toinnovate across industries. Finally, the surveybriefly described the cost leadership and

differentiation strategies and respondentswere asked to verify the dominant strategytype of their firms (Govindarajan & Fisher,1990). We controlled for the firms’ leading strat-egy type since it may prevent or benefit firms’co-operation with external partners (Miotti &Sachwald, 2003).

Measurement Reliability and Validity

Discriminant validity was tested by running aconfirmatory factor analysis for each constructin the framework. To stabilize the indicatorreliability, all items with factor loadings of lessthan 0.7 were dropped (Fornell & Larcker,1981), although in two exceptional cases (will-ingness to take risks, future market orientation)the threshold of 0.6 was used to drop items(Hulland, 1999). To assess construct reliability,we calculated and analysed the composite reli-ability (CR) and the average variance extracted(AVE) of each construct; Bagozzi and Yi (1988)recommend threshold values of 0.7 for CR and0.5 for AVE. We also examined the Cronbach’salpha for each construct based on Nunnally’s(1979) recommendation of a threshold alphavalue of 0.7. The factor loadings, CR, AVE andCronbach’s alpha of each construct met therequirements. Table 1 summarizes the results.

Finally, the Fornell–Larcker Criterion wastested by analysing the squared correlationbetween each pair of factors to determinewhether they are below the average varianceextracted (AVE), and results support discrimi-nant validity. Several positive and statisticallysignificant correlations between collaborationindicators and internal capabilities suggestthat internal capabilities can help the collabo-rative activities and vice versa. Also notableare positive and statistically significant corre-lations among collaboration indicators. Table 2provides the correlations, means and standarddeviations of all variables.

Results

Goodness of Fit Measures

Our hypotheses were tested using causalmodelling by means of the covariance-basedstructural equation method AMOS. Theoverall goodness of fit indices indicate thatthe hypothesized models are good repre-sentations of the structures underlying thedata (Baumgartner & Homburg, 1996). The fitindices are as follows: relative chi-square (c2/df) = 1.64, comparative fit index (CFI) = 0.96,normalized fit index (NFI) = 0.90, Tucker–Lewis Coefficient (TLI) = 0.95, goodness-of-fit index (GFI) = 0.86, adjusted goodness offit (AGFI) = 0.82, root mean square error of



approximation (RMSEA) = 0.05, standardizedroot mean square residual (SRMR) = 0.08.

Hypothesis Testing

Although the model was calculated en blocusing covariance-based structural equationmodelling, we display the results in two tables(Tables 3 and 4) and discuss them in terms ofthe two parts of the model (innovation culture

to collaboration and collaboration to perfor-mance). Table 3 shows the results of hypoth-esis testing of the first part of the model, whichrelates innovation culture with the firm’s col-laboration activities with external partners inthe context of NPD: only nine of the 20 hypoth-esized relationships are supported. Hypoth-eses H1a–H1e suggest that orientation towardstechnological innovation is positively relatedto collaboration with all observed external

Table 1. Confirmatory Factor Analysis of Major Constructs

Factor Numberof items

Range ofparameterestimates

Cronbach’salpha

Compositereliability

(CR)

Averagevarianceextracted

(AVE)

Innovation cultureOrientation towards tech. inn. 6 0.73–0.86 0.84 0.91 0.76Learning orientation 4 0.72–0.92 0.86 0.91 0.78Willingness to take risks 5 0.66–0.93 0.89 0.93 0.76Future market orientation 5 0.64–0.81 0.78 0.87 0.69Collaboration with . . .. . . Customers 4 0.74–0.83 0.85 0.91 0.76. . . Suppliers 4 0.75–0.94 0.89 0.93 0.83. . . Competitors 3 0.87–0.89 0.87 0.94 0.89. . . Universities 4 0.93–0.97 0.96 0.98 0.93. . . Independent experts 4 0.87–0.94 0.95 0.96 0.87NPD performance 5 0.75–0.95 0.91 0.94 0.84

Table 2. Pearson Correlations, Means, Standard Deviations (N = 254)a

1 2 3 4 5 6 7 8 9 10

Collaboration with . . .1 . . . Customers 0.872 . . . Suppliers 0.34** 0.913 . . . Competitors -0.002 0.11 0.944 . . . Universities 0.33** 0.24** 0.11 0.965 . . . Independent experts 0.26** 0.32** 0.19** 0.52** 0.936 Orientation tow.

techn. innovation0.48** 0.26** 0.04 0.31** 0.20** 0.87

7 Learning orientation 0.42** 0.25** 0.01 0.25** 0.14* 0.56** 0.888 Willingness to take risks 0.35** 0.26** 0.09 0.25** 0.28** 0.50** 0.40** 0.879 Future market

orientation0.15* 0.22** 0.12 0.28** 0.26** 0.25** 0.25** 0.30** 0.83

10 NPD performance 0.42** 0.30** 0.00** 0.28** 0.20** 0.61** 0.49** 0.50** 0.32** 0.92

Means 5.18 4.53 3.59 3.67 3.32 5.06 5.67 4.81 3.69 4.24S.D. 1.32 1.51 2.29 2.08 1.71 1.31 1.16 1.33 1.29 1.45

a Square roots of AVE are listed on the matrix diagonal.* p < 0.05.** p < 0.01 (two-tailed).



Table 3. Results of Covariance-Based Structural Equation Modeling with AMOS, First Part of theResearch Model

Hypothesis Description of path Path coefficientand significance

Result

Collaboration with . . .H1a OtTIa → . . . Customers 0.431 **** SupportedH1b OtTI → . . . Suppliers 0.133 n.s. Not SupportedH1c OtTI → . . . Competitors 0.110 n.s. Not SupportedH1d OtTI → . . . Universities 0.606 *** SupportedH1e OtTI → . . . Independent experts 0.317 ** Supported

Collaboration with . . .H2a LOb → . . . Customers 0.207 ** SupportedH2b LO → . . . Suppliers 0.102 n.s. Not SupportedH2c LO → . . . Competitors -0.115 n.s. Not SupportedH2d LO → . . . Universities -0.126 n.s. Not SupportedH2e LO → . . . Independent experts -0.259 * Not Supported

Collaboration with..H3a WttRc → . . . Customers 0.115 * SupportedH3b WttR → . . . Suppliers 0.104 n.s. Not SupportedH3c WttR → . . . Competitors 0.016 n.s. Not SupportedH3d WttR → . . . Universities 0.016 n.s. Not SupportedH3e WttR → . . . Independent experts 0.201 * Supported

Collaboration with..H4a FMOd → . . . Customers 0.044 n.s. Not SupportedH4b FMO → . . . Suppliers 0.238 **** SupportedH4c FMO → . . . Competitors 0.112 n.s. Not SupportedH4d FMO → . . . Universities 0.601 **** SupportedH4e FMO → . . . Independent experts 0.493 **** Supported

* p < 0.10.** p < 0.05.*** p < 0.01.**** p < 0.001 (one-tailed).a OtTI = Orientation towards technological innovation, b LO = Learning orientation, c WttR =Willingness totake risks, d FMO = Future market orientation.

Table 4. Results of Covariance-based Structural Equation Modeling with AMOS, Second Part of theResearch Model

Hypothesis Despription of path Path coefficientand significance

Result

Collaboration with . . .H5 . . . Customers → NPD performance 0.455 **** SupportedH6 . . . Suppliers → NPD performance 0.148 ** SupportedH7 . . . Competitors → NPD performance 0.006 n.s. SupportedH8 . . . Universities → NPD performance 0.068 * SupportedH9 . . . Indep. Experts → NPD performance -0.001 n.s. Not supported

* p < 0.10.** p < 0.05.*** p < 0.01.**** p < 0.001 (one-tailed).



partners. With parameter estimates significantat the 0.001, 0.01 and 0.05 levels, hypothesesH1a, H1d and H1e – which posit that orienta-tion towards technological innovation is posi-tively related to collaboration with customers,universities and independent experts – aresupported, whereas hypotheses H1b and H1c– which relate orientation towards technolo-gical innovation with collaboration activitieswith suppliers and competitors – are not.Regarding hypotheses H2a–H2e, which pro-pose that learning orientation leads tocollaborative activities with all five externalpartners – only one hypothesis, H2a, whichrelates learning orientation with customer col-laboration, is supported. Regarding the firm’swillingness to take risks, two hypotheses,H3a and H3e – which suggest that a firm’swillingness to take risks leads to collaborativeactivities with customers and independentexperts – are supported by parameter esti-mates significant at the 0.1 level, but H3b–H3d are not. Finally, a firm’s future marketorientation turned out to be significantlyand positively (0.001) related to collaborationwith suppliers, universities and independentexperts, but not to collaboration with custom-ers and independent experts.

Table 4 displays the results of hypothesistesting of the second part of the model, whichrelates collaboration with external partners inthe context of NPD with NPD performance.Four of the five hypothesized relationshipsare supported. Hypotheses H5, H6 and H8suggest that collaboration with customers,suppliers and universities is positively relatedto NPD performance; with parameter esti-mates significant at the 0.001, 0.05, and 0.1levels, these three hypotheses are supportedand indicate that customer involvement influ-ences NPD performance most, followed by theinvolvement of suppliers and universities. Byyielding a positive but insignificant path coef-ficient for the relationship between collabora-tion with competitors and NPD performance,the results further verify H7. However, H9,which relates collaboration with independentexperts to NPD performance, is not supported;although parameter estimates are positive,they are not significant.

Discussion and Implications

Based on the resource-based view (RBV) of thefirm and Kitchell’s (1995) innovation adoptionmodel, this study empirically investigates theeffect of a firm’s innovation culture on itsbehaviour to collaborate with external partnersin the process of NPD. We considered innova-tion culture an intangible strategic resource

and measured it using four variables: orienta-tion towards technological innovation, learn-ing orientation, willingness to take risks andfuture market orientation. We also analysedthe impact on NPD performance of collabora-tion with five different types of external part-ners. We empirically validated our researchmodel using a sample of 254 technology-basedfirms and covariance-based SEM with AMOS.Our analyses confirmed several of the hypoth-eses presented, but not all. The following dis-cussion addresses these results and outlinesthe academic and practical implications forresearchers and managers.

Our results indicate that a firm’s orientationtowards technological innovation has a signifi-cant and positive relationship to a firm’scollaborative activities with customers, univer-sities and independent experts. This result sug-gests that these external partners are importantsources of knowledge for firms that considertechnological innovation a strategically impor-tant task and that want to keep up with newtechnological trends by inventing and refiningsuperior products (Atuahene-Gima & Ko,2001). Collaboration with customers may helpfirms determine which kinds of technologiesare needed for future innovations, and collabo-ration with universities may allow firms tokeep up to date with the latest technologicaldevelopments. For their part, independentexperts such as engineering offices and inde-pendent research institutes may help firms toconceptualize and implement certain innova-tive product ideas. Although suppliers are dis-cussed in the literature as important sourcesof knowledge for product design, qualityimprovement, improved cycle time and costreduction (Ragatz, Handfield & Petersen, 2002;Song & Thieme, 2009), our analysis shows that afirm’s orientation towards technological inno-vation is not related to collaborative activitieswith suppliers. This result may be explainedif firms contact suppliers to implement tech-nological innovations and new ideas or toimprove the current status of products andproduct development processes, but do notexpect their suppliers to provide them withtechnological innovations.

The results of this study also reveal that afirm’s learning orientation and willingness totake risks have a marginal positive associationwith collaboration with customers and inde-pendent experts (0.1–0.05). This result suggeststhat firms consider customers and independentexperts the most important partners fromwhom to learn in the context of NPD, and thatfirms are willing to take on the risks associatedwith these collaborative activities. On the otherhand, learning orientation and willingness totake risks are not related to the willingness to



collaborate with suppliers, universities or com-petitors. Future research is recommended toverify this finding.

Finally, a firm’s orientation toward futuremarkets has a significant positive relationshipto collaboration with suppliers, universitiesand independent experts. This result suggeststhat firms that intend to invest in the develop-ment of products and technologies for futurecustomers expect important inputs from theinterchange with these external partners. Thisfinding is in line with several findings in theliterature (Bercovitz & Feldman, 2007; Tether& Tajar, 2008; Song & Thieme, 2009). WhileSong and Thieme (2009) find that suppliersare an important source from which to gathermarket intelligence, Spencer (2001) empha-sizes the relevance of published university-based research for corporate R&D in theUS, and Alam (2003) points to independentexperts, such as engineering consultants, as arich source of valuable new product ideas.However, a firm’s orientation toward futuremarkets is not related to customer and com-petitor involvement in NPD, suggesting thatfirms do not expect stimuli for new or signi-ficantly modified product ideas from colla-boration with their buyers and competingcompanies. Future research is necessary toinvestigate this finding further.

Our results reveal that firms that have a het-erogeneous network of collaborative partnersperform better in terms of sales turnover of newor improved products. However, not all of thefive external partners we observed significantlycontribute to NPD performance. Results showthat collaboration with customers is positivelyand significantly related to NPD performance,confirming that customers are an importantsource of knowledge for firms in the process ofNPD. This finding is in line with findings ofseveral other researchers (Von Hippel, 1986;Souder, Buisson & Garrett, 1997; Gruner &Homburg, 2000). Customer involvement helpsfirms to understand customers’ needs and toalign their products to those needs (Brockhoff,2003), reducing product inadequacies andmaking NPD processes more efficient. In com-parison with the involvement of other externalpartners, customer involvement turned out tohave the strongest effect on NPD performance.As a result, customer involvement can leadto more application-oriented developmentprojects with shorter time horizons, less riskand lower investments (Knudsen, 2007). Still,customers should not be the only externalpartner because the development of new prod-ucts that satisfy specialized customer needsmay represent the needs of only one particularand narrowly defined customer group (e.g.,lead users) and ignore the needs of other

current and potential customers (Bonner &Walker, 2004). In addition, Thomke and VonHippel (2002) remark that, even when custom-ers know precisely what they want, they oftencannot express that information to manufactur-ers clearly or completely. Consequently, NPDcollaboration with customers needs to becarried out carefully.

Collaboration with suppliers also has a sig-nificantly positive impact on NPD perfor-mance. Although they are the second mostimportant external partners, suppliers arehighly engaged in the enhancement of theirown products; however, because they hold alarge amount of technological knowledge intheir fields, collaborative activities with themhelp firms to update their products to thebest available technology and to ensure thatnew product ideas are producible (Primo& Amundson, 2002). Moreover, long-termmutual relationships, which are often coupledwith geographic proximity, favour supplierinvolvement (Wasti & Liker, 1997), so collabo-ration with suppliers provides a valuablesource of knowledge and can help technology-based firms improve their NPD performance.Nevertheless, firms must always bear in mindthe importance of protecting sensitive knowl-edge and avoid developing serious depen-dency when collaborating with suppliers(Doz, 1996).

Universities, particularly science and aca-demic research institutions, are an importantfactor in the development of major innovations(Rosenberg & Nelson, 1994), so it is not surpris-ing that collaboration with universities alsocontributes significantly to NPD performance.Cohen, Nelson and Walsh (2002) analyse thechannels of knowledge transfer between publicresearch organizations and innovative firmsand find that universities can play two particu-larly important roles: suggesting new ideas andhelping to complete foundering projects. Inte-grating universities into NPD processes pro-vides firms with access to the latest research,technological developments and advisoryservices in R&D. However, compared to col-laboration with suppliers, more effort is likelyto be required to ensure clear communicationthrough bureaucratic structures, so universitiesare best suited to strategic, long-term R&Dpartnerships.

While collaboration with customers, suppli-ers and universities (in that order) have posi-tive effects on NPD, our results show thatcollaboration with competitors and indepen-dent experts does not contribute significantlyto NPD performance. The risks associatedwith collaboration among competitors mayresult in the use of such partnerships in onlyrare and exceptional cases (e.g., Miotti & Sach-



wald, 2003). Collaboration with independentexperts, such as engineering consultants, isusually undertaken in order to solve highlyspecific and tangible problems (Bessant &Rush, 1995), so the involvement of expertsdoes not necessarily contribute to firms’general performance in innovation. However,since researchers have recently found thatindependent experts are a rich source of newproduct ideas of potential commercial value(Alam, 2003; Tether & Tajar, 2008), firms areadvised to exploit this knowledge source intheir NPD processes. Future research shouldmake further inquiries in this context.

Limitations and Future ResearchOpportunities

Like every empirical study, this research hasseveral limitations that should be consideredwhen interpreting the results. First, the con-struct of innovation culture and its relationshipto firms’ innovation adoption behaviour needsto be investigated further. The current researchtook the first step in analysing the correlationbetween culture and openness, but there isstill much more in-depth research needed tounderstand this correlation fully. Therefore,the model developed for this study should betested in different settings (e.g., in additionalindustries and countries) as well as with othermeasurements of innovation culture. Second,we did not differentiate the type or phase ofproduct innovation, although it is reasonableto assume that different external partnerscontribute differently to NPD performancedepending on the type and phase of the inno-vation (e.g., idea generation, design, construc-tion, incremental vs. radical innovation, etc.).In order to elucidate the contribution of eachknowledge source to the process of NPD,future research should break the innovationside of the model into type and phase toanalyse this aspect of the relationship. Third,the literature based on dynamic capabilitytheory and organizational learning perspec-tives suggests that the form of collaborationundertaken by a firm depends on the difficultyof the learning environment and the complex-ity associated with knowledge developmentand transfer (Teece & Pisano, 1994; Nagarajan& Mitchell, 1998). Thus, different types of col-laborative activities (e.g., formal vs. informal,equity-based vs. non-equity based, long termvs. short term) may be appropriate in differentsituations, but we did not investigate thisaspect of the question. Fourth, we did not con-sider moderating effects in our research,although environmental influences (e.g., tech-nological, market and competitive dynamism)

may influence the causality of the model. Fifth,although the sample includes a wide rangeof high-technology industries, the data arelimited to one country and to technology-based industries. This limitation may putboundaries on the generalizability of theresults, so future research that considers therelationships examined here in terms of avariety of international firms and industriesin other kinds of settings is necessary beforeresults can be generalized with any confi-dence. Sixth, future research may extend thefive external knowledge sources investigatedin this study to include other classes, such astrade fairs, technology transfer centres or incu-bators. Finally, additional work and the appli-cation of other methods may validate the linearrelationship between collaborative activitiesand NPD performance (Pek et al., 2009).

Despite these limitations, we believe thatour research contributes to the present litera-ture on collaboration and provides severalpractical and actionable suggestions forresearchers and managers in the field of newproduct development.

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Malte Brettel is Professor of Entrepreneur-ship at the RWTH Aachen University,Germany. He researches in EntrepreneurialStrategies and Innovation, and has pub-lished in journals such as Strategic Manage-ment Journal, Journal of Product InnovationManagement, Journal of Business Research,Management Decision, and InternationalJournal of Strategic Management.

Nina J. Cleven ([email protected]) studied at the RWTH AachenUniversity, Germany, where she graduatedwith a Diploma in Mechanical Engineeringand Business Administration. Today she is aPhD student at the faculty of business andeconomics at the RWTH Aachen University.Her research interests are innovation man-agement, technology and knowledge trans-fer and strategic networks in new productdevelopment.



Appendix

Table A1. Measurement Scales

Innovation culture

Orientation towards technological innovation (adapted from Herrmann, Gassmann & Eisert,2007, a = 0.84)

1. We often use innovative technologies in the new product development.2. Our products always reflect state-of-the-art technology.*3. We are very proactive in the development and deployment of new technologies.4. Relative to our competitors, our development programs and new products are

technologically more ambitious.*5. We are very proactive in the construction of new technological solutions to serve the

customers’ needs.6. We always rank among the first to use a new technology for new product development.*Learning orientation (adapted from Hult, Ketchen & Arrfelt, 2007, a = 0.86)1. The willingness to learn is the key in the process of new product development.2. The basic requirement for static improvement of our new product development processes is

our willingness to learn.3. In our view employee learning is an investment not an expense.*4. General consensus in this company is that once we quit learning we endanger our future.Willingness to take risks (adapted from Jaworski & Kohli, 1993, a = 0.89)1. To bring innovative products on the market we do not avoid taking risks in the

development process.2. We are convinced that it makes sense to take financial risks to increase profits.*3. We favour and support the development of innovative products, knowing well that some

will fail.4. We agree that it is necessary to take risks in the context of new product development.5. We accept that new products occasionally fail.Future market orientation (adapted from Jaworski & Kohli, 1993, a = 0.78)1. Compared to our major competitors we place more emphasis on customers of the future, as

opposed to existing customers.2. We search purposefully for new areas of applications for our (new) products.*3. Our market research efforts are aimed at obtaining information about customers’ needs in

the future, relative to their current needs.4. It is easy for us to promptly detect shifts in our markets.*5. The search for new markets has a higher priority for us than the expansion of our business

activity in existing markets.(1 = ‘fully disagree’ and 7 = ‘fully agree’)

Collaboration with external partners in the NPD process

Involvement of customers (new scale, a = 0.85)1. We maintain regular communication with our customers with the aim to incorporate gained

information directly into our product development process.2. We involve customers directly in the innovation process.3. We assemble effective relationships with our customers, to better implement solutions for

their needs by using the most adequate technology.4. We regularly carry out market research (e.g., surveys, analyses of target groups etc.) to gain

more information about our (potential) customers.*



Table A1. continued

Collaboration with external partners in the NPD process

Involvement of suppliers (new scale, a = 0.89)1. We maintain regular communication with our suppliers to get in-depth knowledge about

ongoing technological developments.2. We involve suppliers actively in product development process.3. Our suppliers play an important role in our product development processes.4. We induce R&D co-operations with our suppliers to configure our product development

process more effectively.*Involvement of competitors (new scale, a = 0.87)1. Within the development of new products or technologies we align with selected

competitors to accelerate the development process and to share costs.2. We carry out R&D co-operations with competitors to profit from synergies as long as no

sensitive knowledge is concerned.3. We exchange information with competitors and accomplish benchmarks with them, to be

up-to-date concerning the latest technological developments and trends.*Involvement of universities (new scale, a = 0.96)1. We maintain R&D co-operations with universities to jointly develop and improve new

product technologies.2. We maintain regular communication with universities to be always up-to-date with the

latest technological inventions.3. In case of tangible technological problems concerning product development processes, we

regularly work closely with universities.4. The regular exchange with universities is important for our firm.*Involvement of independent experts (new scale, a =0.95)1. We work together with independent experts (e.g., public research institutes, engineering

consultants, specialized start-up firms, etc.) in terms of contractual agreements, to solvetechnological problems within new product development processes.

2. We regularly get in touch with independent experts within the process of new productinnovation.

3. In the context of new product development processes we work together with institutions(e.g., public research institutes, engineering consultants, specialized start-up firms, etc.) andpeople with special knowledge to benefit from their ideas.

4. The regular exchange with independent experts is important for our firm.(1 = ‘fully disagree,’ and 7 = ‘fully agree’)

Performance Measurement

New Product Development Performance (adapted from Griffin & Page, 1993; Zahra & Covin,1993, a =0.91)

1. We bring new and innovative products more often to market than other firms.*2. In our market we are known for our innovative products.3. Our new products differ substantially from their precursors.*4. The percentage of new and innovative products in the product portfolio is significantly

higher in comparison to our competitors.5. The percentage of sales generated through new and innovative products is significantly

higher in comparison to our competitors.(1 = ‘fully disagree’ and 7 = ‘fully agree’)

* eliminated items.



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