market novelty, competence-seeking and innovation networking

12
Market novelty, competence-seeking and innovation networking Mark Freel a, , Jeroen P.J. de Jong b,c a Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa, ON, Canada K1N 6N5 b EIM Business and Policy Research, P.O. Box 7001, 2701 AA Zoetermer, The Netherlands c Rotterdam School of Management, Erasmus University Rotterdam, Burg. Oudlaan 50, 3000 DR Rotterdam, The Netherlands article info Keywords: Innovation networking Competences Novelty Tie strength Network roles Tie specificity Relational embeddedness Small firms abstract Studies of innovation networking have frequently been concerned with the occurrence of dyadic relationships and with their apparent impact on simple measures of firm-level innovation outputs. This paper takes a more detailed look by analyzing the connection between different types of innovation and forms of networking. Based on the market novelty of innovation outcomes and the extent to which innovation activities require new competences, four types of innovation are identified. It is proposed that these types correlate with various innovation network dimensions, including the volume of networks, the strength and content of ties, and the specificity of ties. Drawing on survey data of 594 innovations realized by Dutch small firms, it is observed that the requirement to access new competences for innovation correlates positively with the number of network partners involved. We also note more subtle connections between types of innovation and networking, including that novel innovation outputs correlate with using network partners as a source of inspiration, whilst new competences associate with networking for knowledge capital. In the latter case, these activities also draw on new and intended ties relatively often, i.e. network partners which are actively sought out for the specific contributions they may make to the innovation process. Finally, innovation which is simultaneously new-to-the market and requires new competences uses strong ties relatively often. Implications for innovation policy and practice are discussed. & 2009 Elsevier Ltd. All rights reserved. 1. Introduction Over the last 25 years, the innovation studies literature has devoted considerable attention towards understanding the role of external relationships in explaining the relative innovation performance of firms, regions and countries (Alm and McKelvey, 2000). For instance, over 15 years ago, Freeman (1991) noted that ‘‘yboth empirical and theoretical research has long since demonstrated the importance [for innovation] of both external and internal networks of information and collaboration’’ (p. 501). Without doubt, the linear model of innovation, with its attendant implications of atomistic endeavour, has gradually given way to a view of innovation as an iterative, cumulative and cooperative phenomenon, fundamentally driven by processes of interactive learning (Lundvall, 1992). A variety of, more and less, convincing arguments has been given for this shifting emphasis. Most call attention to increasing specialisation pressures and the consequent focus upon core competences (Robertson and Langlois, 1995; Freel and Harrison, 2006). In such circumstances, as firms become more adept at doing particular things, they become correspondingly less capable of doing other things. In general terms, they run the risk of failing to notice opportunities and threats arising beyond their immedi- ate expertise. More specifically, since innovation intrinsically involves doing something different, successful innovation is increasingly likely to require sources of complementary compe- tence that lie outside of the innovating firm. Nooteboom (1999) calls this the ‘principle of external economy of cognitive scope’: ‘‘yone needs complementary, outside sources of cognition: cognition by others which is relevant but also different’’ (p. 795). Few, particularly small, firms are likely to be repositories of sufficient resource (competence or cognition) to innovate independently (Tether, 2002). Rather, innovations are increasingly viewed as the product of networks of firms (and other organiza- tions). Though there is a general appreciation of the value of ‘‘networks’’ for innovation, our understanding of how variations in innovation network structure and content may relate to differences in innovation outcomes is perhaps less well developed (Gilsing and Duysters, 2008). Clearly, innovation networking (and collaboration) may be more or less enduring and intensive; it may involve more or less resource sharing or commitment; and, it may be driven by more or less intention, direction and specificity. Yet, in much of the innovation studies literature, research on ARTICLE IN PRESS Contents lists available at ScienceDirect journal homepage: www.elsevier.com/locate/technovation Technovation 0166-4972/$ - see front matter & 2009 Elsevier Ltd. All rights reserved. doi:10.1016/j.technovation.2009.07.005 Corresponding author. Tel.: +1613 562 5800x4733; fax: +1613 562 5164. E-mail address: [email protected] (M. Freel). Technovation 29 (2009) 873–884

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ARTICLE IN PRESS

Technovation 29 (2009) 873–884

Contents lists available at ScienceDirect

Technovation

0166-49

doi:10.1

� Corr

E-m

journal homepage: www.elsevier.com/locate/technovation

Market novelty, competence-seeking and innovation networking

Mark Freel a,�, Jeroen P.J. de Jong b,c

a Telfer School of Management, University of Ottawa, 55 Laurier East, Ottawa, ON, Canada K1N 6N5b EIM Business and Policy Research, P.O. Box 7001, 2701 AA Zoetermer, The Netherlandsc Rotterdam School of Management, Erasmus University Rotterdam, Burg. Oudlaan 50, 3000 DR Rotterdam, The Netherlands

a r t i c l e i n f o

Keywords:

Innovation networking

Competences

Novelty

Tie strength

Network roles

Tie specificity

Relational embeddedness

Small firms

72/$ - see front matter & 2009 Elsevier Ltd. A

016/j.technovation.2009.07.005

esponding author. Tel.: +1613 562 5800x4733

ail address: [email protected] (M. Freel).

a b s t r a c t

Studies of innovation networking have frequently been concerned with the occurrence of dyadic

relationships and with their apparent impact on simple measures of firm-level innovation outputs. This

paper takes a more detailed look by analyzing the connection between different types of innovation and

forms of networking. Based on the market novelty of innovation outcomes and the extent to which

innovation activities require new competences, four types of innovation are identified. It is proposed

that these types correlate with various innovation network dimensions, including the volume of

networks, the strength and content of ties, and the specificity of ties. Drawing on survey data of 594

innovations realized by Dutch small firms, it is observed that the requirement to access new

competences for innovation correlates positively with the number of network partners involved. We

also note more subtle connections between types of innovation and networking, including that novel

innovation outputs correlate with using network partners as a source of inspiration, whilst new

competences associate with networking for knowledge capital. In the latter case, these activities also

draw on new and intended ties relatively often, i.e. network partners which are actively sought out for

the specific contributions they may make to the innovation process. Finally, innovation which is

simultaneously new-to-the market and requires new competences uses strong ties relatively often.

Implications for innovation policy and practice are discussed.

& 2009 Elsevier Ltd. All rights reserved.

1. Introduction

Over the last 25 years, the innovation studies literature hasdevoted considerable attention towards understanding the role ofexternal relationships in explaining the relative innovationperformance of firms, regions and countries (Alm and McKelvey,2000). For instance, over 15 years ago, Freeman (1991) noted that‘‘yboth empirical and theoretical research has long sincedemonstrated the importance [for innovation] of both externaland internal networks of information and collaboration’’ (p. 501).Without doubt, the linear model of innovation, with its attendantimplications of atomistic endeavour, has gradually given way to aview of innovation as an iterative, cumulative and cooperativephenomenon, fundamentally driven by processes of interactive

learning (Lundvall, 1992).A variety of, more and less, convincing arguments has been

given for this shifting emphasis. Most call attention to increasingspecialisation pressures and the consequent focus upon corecompetences (Robertson and Langlois, 1995; Freel and Harrison,2006). In such circumstances, as firms become more adept at

ll rights reserved.

; fax: +1613 562 5164.

doing particular things, they become correspondingly less capableof doing other things. In general terms, they run the risk of failingto notice opportunities and threats arising beyond their immedi-ate expertise. More specifically, since innovation intrinsicallyinvolves doing something different, successful innovation isincreasingly likely to require sources of complementary compe-tence that lie outside of the innovating firm. Nooteboom (1999)calls this the ‘principle of external economy of cognitive scope’:‘‘yone needs complementary, outside sources of cognition:cognition by others which is relevant but also different’’(p. 795). Few, particularly small, firms are likely to be repositoriesof sufficient resource (competence or cognition) to innovateindependently (Tether, 2002). Rather, innovations are increasinglyviewed as the product of networks of firms (and other organiza-tions).

Though there is a general appreciation of the value of‘‘networks’’ for innovation, our understanding of how variationsin innovation network structure and content may relate todifferences in innovation outcomes is perhaps less well developed(Gilsing and Duysters, 2008). Clearly, innovation networking (andcollaboration) may be more or less enduring and intensive; it mayinvolve more or less resource sharing or commitment; and, it maybe driven by more or less intention, direction and specificity. Yet,in much of the innovation studies literature, research on

ARTICLE IN PRESS

Deg

ree

of e

xter

nal ‘

new

ness

’N

ew to

the

firm

onl

y

N

ew to

the

mar

ket

Existing skills/competences New skills/competencesDegree of internal ‘newness’

MarketDeveloping

CompetenceDeveloping

Incremental

Radical

Fig. 1. An innovation schema.

M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884874

innovation networking has tended to be concerned with thesimple occurrence or existence of external linkages (typicalexamples include, Oerlemans et al., 1998; Bougrain and Haude-ville, 2002; Freel, 2003). That is, firms are observed to have eithernetworked/collaborated or not. A prominent exemplar is providedby successive [European] Community Innovation Surveys (CIS).These surveys have asked firms whether they cooperated forinnovation (with a variety of partners and at various spatialscales). This tells us little about the content or structure of thenoted networks. Given the broad scope of the CIS, such abstractionmay be inevitable, but nevertheless it limits our view of what kindof networking is related to specific types of innovation.

Similarly, in innovation policymaking the view of ‘more isbetter’ is reflected in much attention to public–private collabora-tion, and, to a lesser extent, the development of incumbent firms’networking and collaboration capabilities (e.g., Guy, 2007). Thatdifferent types of innovation may correlate with different types ofnetworks, is often overlooked. In contrast, detailed case studies(e.g. Gilsing and Nooteboom, 2004, 2006) and work on smaller-scale, highly specific samples (e.g. Powell et al., 1996) have tendedto suggest a more complex relationship between innovation andnetworking when researchers adopt a more nuanced definition ofinnovation, and when consideration is given to both the relationalcharacteristics and the content of innovation networks.

The current paper is concerned with building upon this latterwork. Drawing upon data from a sample of 594 innovations insmall firms, based in the Netherlands, the paper explores therelationship between types of innovation and networking. In theformer concept, innovation is defined relative to both internal andexternal considerations. In the latter, we are able to explore bothrelational characteristics (strong, weak and intended ties) andnetwork content (in terms of the flow of knowledge andresources). In so doing, it is hoped that the analysis will contributeto a greater understanding of the connection between differentdegrees of newness and forms of networking. Such understandingmay inform the development of management practices and moresophisticated policy measures in support of innovation network-ing.

1 This argument may be made in reverse with respect to incremental

innovation.

2. Theory and propositions

2.1. Conceptualising innovation

The starting point for this paper rests in the conceptualisationof innovation. Recent evidence suggests that different types ofinnovation may rely on different kinds of knowledge inputs(Todtling et al., 2008). Yet, too often, measures of innovation areunnecessarily crude. For example, a common observation in theempirical literature is that networking is positively related, notonly to the introduction of innovations but, to the novelty ofinnovations (Freel, 2003; Nieto and Santamaria, 2007), wherenovel innovations are frequently defined as ‘‘new to the market’’,and, in this sense, novelty is concerned solely with outputs.Relatedly, a parallel argument posits a positive relationshipbetween networking and the complexity of innovation processes.Here, innovation is concerned with interactive learning and it issuggested that ‘‘ythe more complex the learning process, themore interactions it probably requires’’ (Johnson and Lundvall,1993, p. 75). More specifically, ‘‘more complex processes increasethe probability of problems in the innovation process. Confrontedwith these problems, innovator firms are forced to enter theirexternal environment to gain access to and obtain necessarycomplementary resources’’ (Oerlemans et al., 2001, p. 345). Thedirection of these dual arguments has often led to the conflationof ‘novelty’ and ‘complexity’ in empirical studies. However, whilst

the former is usually referenced externally (given the focus onoutputs), the latter may, more sensibly, draw its referenceinternally—in relation to the existing skills and competences ofthe firm.

This separation of the complexity of innovation activities andthe novelty of innovation outputs (along internal competence andexternal output dimensions of ‘newness’) is illustrated in Fig. 1.The innovation schema proposed is not revolutionary, andrepresents a play on many standard textbook accounts.However, it serves to clearly lay out the various patterns ofinternal and external newness, resulting in four types ofinnovation. In this, our view of externally referenced novelty isconsistent with standard practice—innovations are more or lessnovel relative to existing market offerings. Relative complexity, incontrast, is a function of the extent to which generation of theinnovation requires the acquisition of new skills or competences.In this very direct sense, complexity is not concerned withproduct architectures, but with skill acquisition. Moreover, thoughthe dimension is competence-anchored and independent from theexternal newness dimension, it is not a simple analogue of theclassic distinction between competence enhancing andcompetence destroying innovations (Tushman and Anderson,1986). Rather, our concern is with competence acquisition,which may be either destroying or enhancing, but is thought toseparately influence innovation outcomes (Gatignon et al., 2002).

Following this, innovations may be incremental, marketdeveloping, competence developing or radical; depending uponthe dual degree of output novelty and competence acquisition.Incremental innovations are those which are neither new in themarket nor require the development of new competences. Incontrast, radical innovations are regarded here as both new in themarket and require the development of new skills. Since the latterare likely to be marked by higher perceived uncertainty, bothmarket and technical, they are likely to be ‘radical’ from the firm’svantage.1 Crucially, our innovation scheme is drawn ‘‘from theperspective of the firm’’.

Hereafter we develop propositions to link the four types ofinnovation with a variety of forms of networking. This isimportant not just for scientific purposes, but also to guide

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M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884 875

management thinking and for policy making. The more innova-tions contain elements of ‘‘newness’’ (internal and/or external),the more likely they are to be characterised by knowledgespillovers between innovating actors (by either adopting externalknowledge and/or by new offerings to the market). Besides,innovations with a higher degree of newness, along eitherdimension, are more likely to induce breakthroughs from whichthe broader society will benefit. Such arguments are at the heartof standard rationale used to justify innovation policies (cf. Arrow,1962; Nelson, 1959), and in fact, the European Commission (2006)has used similar reasoning to legitimize interventions in marketprocesses by offering policies to support innovation activities,including networking and collaboration.

2.2. Dimensions of relational networks

It has been long noted by scholars and researchers thatnetworks play an important role in the performance of organiza-tions. Organizations are part of a value chain and dependent uponexternal actors to accommodate changes in their operatingenvironments. Firms’ ability to build and maintain an inter-organizational network of relationships is viewed as key tosustained performance (e.g. Kogut, 2000). More specifically,networks are considered important for the development ofinnovations—as most innovations are rooted, not exclusivelywithin the organization, but instead at the intersections withactors outside the firm, such as competitors, universities andbusiness partners (Pisano, 1990).

One major network research stream deals with the concept ofrelational embeddedness—i.e. the dyadic relationships or tiesbetween the actor and each of its partners (Scholten, 2006).2 Thedimension of relational embeddedness that is studied most oftenis the strength of ties (Scholten, 2006). Strong ties are char-acterised by frequent contacts, are usually long-term, reciprocaland involve a strong degree of trust and emotional closeness. Incontrast weak ties are temporary, transient and normally involvelittle emotional investment. Ties of both types can generate value;strong ties may enable the transfer of complex knowledge, whilstweak ties provide new perspectives.

Recent research on networks has also emphasized theimportance of tie content (Adler and Kwon, 2002). Althoughstudying tie strength may improve understanding of how networkpartners contribute, assessing the content of those relations is animportant adjunct. In this vein, Podolny and Baron (1997) stressedthat ‘‘the network structure most conducive to organizationaladvancement depends significantly on the content of the social tieinvolved’’ (p. 674). In the context of innovative entrepreneurship,network partners have been found to perform various roles,including the discovery of opportunities, the mobilizing ofresources such as knowledge, finance and physical capital, andthe securing of organizational legitimacy (Elfring and Hulsink,2003; Nicolaou and Birley, 2003).

Beyond issues of tie strength and content, one further aspect ofrelational networks needs consideration: the specificity of ties. Tothis end, a ‘‘specific tie’’ is one in which the partner has beensought and included (in one’s network) for a specific purpose(Ruef, 2002). Clearly, there are parallels here to the concept of‘directed ties’, as it appears in the social network literature (e.g.Ruef, 2002). However, whilst directed ties entail unilateralmonitoring on the part of the focal firm (i.e. a lack of reciprocity),this is clearly not implied here. Rather, our specific ties involve

2 Another perspective is structural embeddedness, referring to the actor’s

position in a full network of relationships (Scholten, 2006). This perspective is very

demanding in terms of empirical data and not the focus here.

relationships. However, these are new relationships, where therehad been no prior ties between partners; either weak or strong.Examples could include the delivery of machinery by specialisedsuppliers; collaborating with representatives from knowledgeinstitutes to gain access to new scientific knowledge; and,consulting engineers to contribute to the development of newproducts. As innovation in small firms is frequently characterisedby proactive, intentional activities to obtain missing innovationresources (Winborg and Landstrom, 2001), the specificity of tiesalso warrants attention.

The following section develops propositions on the strength,content and specificity of network ties that contribute to the fourtypes of innovation. It first however elaborates on the volume ofrelational networks and how this may connect with our innova-tion schema.

2.3. Volume of networks

Until recently, empirical work on innovation and networks hadtypically been couched in terms of ‘more is better’. Studiescharacteristically focused on the volume of networks by connect-ing (usually dichotomous) indicators of the involvement ofnetwork partners with innovation measures such as new productintroductions or R&D (e.g. Oerlemans et al., 1998; Tether, 2002).Although there has been recent evidence of diminishing returns tonetwork experience (Powell et al., 1999), even these authors (intheir ongoing study of dedicated biotechnology firms) show thefundamental attachment bias to be towards multiconnectivity3

(Powell et al., 2005). This recent work suggests important nuanceto the ‘more is better’ dictum. However, in the context of a sampleof technology firms, it is still likely to provide a usefulapproximation.

Following this, we anticipate that the number of ties involvedwill vary with the degree of internal newness of innovations.Incremental innovations—here defined as innovations relying onfirms’ current skills and competences and not new to themarket—are likely to draw most heavily on information andknowledge that is available in-house (Maillat, 1991; cf. Oerlemanset al., 1998), implying less need to mobilize external sources. Inconsequence, one would anticipate that incremental innovationswould be marked by the involvement of fewer network partners.A similar reasoning may be applied to market developinginnovations. Although new to the market, such innovations donot demand the acquisition of new skills or competences. Incontrast, competence developing and radical innovations arelikely to require relatively more external ties. Here, we citeRycroft’s (2007, p. 567) paraphrasing of Ashby’s law of requisitevariety: ‘‘only variety generated in collective learning processes(within and between organizations) can absorb the varietygenerated by environmental uncertainty’’. In other words, and inthe context of technological innovation, resolution of the greatertechnical uncertainty attendant upon competence developing andradical innovations is likely to require more network partnersthan required by innovations that do not require new skills. Allother things being equal, multiple partners increase the likelihoodof finding solutions to apparently complex problems. Accordingly,the following proposition is forwarded:

P1. There is a positive association between the internal newness

requirements of innovation activities and the number of network

partners.

3 To the extent that multiconnectivity is concerned with ‘‘the extent to which

firms are connected by multiple independent pathways’’ (Powell et al., 2005, p.

1171) it appears to imply a bias towards more partners.

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M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884876

2.4. Strength and content of ties

In some senses, the internal newness dimension in Fig. 1 issimilar to the distinction between ‘exploration’ and ‘exploitation’(e.g. Powell et al., 1996; Rothaermel and Deeds, 2004). As March(1991, p. 85) summarizes, ‘‘ythe essence of exploitation is therefinement and extension of existing competences, technologiesand paradigmsy [the] essence of exploration is the experimenta-tion with new alternatives’’. In this light, one might view the left-hand side of Fig. 1 as composed of exploitation activities, and theright-hand side as composed of exploration activities. Admittedly,an either/or characterisation seems unlikely. Rather, one mightreasonably suppose that much explorative innovation simulta-neously exploits existing competencies. Nevertheless, framing Fig.1 in terms of, more and less, exploration and exploitation is auseful mechanism for connecting to an existing body of work oninnovation networking.

As Gilsing and Nooteboom (2004, p. 3) suggest, ‘‘an importantissue, in the network literature, is whether in networks forinnovation ties should be sparse and weak, to allow for variety,flexibility and low cost explorationyory‘cohesive’, to facilitatetrust and collaboration’’. Drawing on Granovetter’s (1973) classicessay, weak ties are more likely to be the source of non-redundantinformation: ‘‘ythose to whom we are weakly tied are morelikely to move in circles different from our own and will haveaccess to information different from we receive’’ (Granovetter,1973, p. 1371). In contrast, strong ties (based on enduring relationsand common interests) tend to reinforce existing views. Thecorollary for developing innovations seems clear. In the uni-dimensional view of innovation commonly adopted in surveyresearch, the use of weak ties is likely to correlate with higherlevels of external novelty in innovations. In other words, marketdeveloping innovations are likely to draw on the different ideasand new information more often provided by networks of weakties.

However, whilst the reach of weak ties may be great, it is likelythat such ties are capable of communicating relatively simpleinformation and ideas only (Uzzi, 1997). For instance, Granovetter(1973) gave the example of weak ties as a valuable source ofinformation on new employment opportunities. In this vein,Powell and Grodal (2005) talk in terms of ‘bandwidth’: ‘‘Weak tieshave a longer reach, but a much narrower bandwidth than strongties’’ (p. 61). Weak ties are likely to be thinner and less durable,and involve less commitment. As such, one may envisage weakties providing inspiration, advice or feedback, i.e. as a source ofopportunity, but less obviously as a source for joint problemsolving through resource or knowledge sharing. Innovations thatexpands market offerings (the upper quadrants in Fig. 1) areproposed to benefit most from such ties—small amounts of non-redundant information may be crucial. Indeed, the relationalembeddedness literature has identified weak ties as a source ofnovel information and opportunities (Hansen, 1999). This pro-vides the basis for the following propositions:

P2. The external newness of innovation outputs is associated with

the use of weak ties, i.e. market developing and radical innovations

use relatively many weak ties.

P3. The external newness of innovation outputs is associated with

less intensive roles for network partners, i.e. market developing and

radical innovations are more likely to draw on network partners as a

source of inspiration.

In contrast, in innovation activities requiring knowledge thatextends beyond existing competences, strong ties are likely toprovide a surer foundation for the transfer of new knowledge(Hansen, 1999). In much of the relational embeddedness literature

there is an emphasis on durable relationships (Rycroft, 2007). It isargued that, to be effective, network linkages need to be morethan temporary experiments. This is likely to particularly applywhen (perceived) uncertainty is high—such as when knowledgerequirements are beyond current expertise. In such cases, theefficacy of network relationship is likely to be higher when firmshave confidence in partner integrity and a belief in commoninterest. Whilst strong ties may constrain access to (cognitively)distant ideas and information (Moran, 2005), the information thatis transferred is thicker. As relationships deepen, trust lessensconcerns over opportunism, and firms are able to share morefinely grained information and tacit knowledge (Suarez-Villa,1998). Tsai and Ghoshal (1998), for instance, report that trustincreased resource exchange and combination between partners,contributing to product innovation. This is the situation envisagedwhen internal newness is high. Though the innovation outputmay not be new, from the perspective of the marketplace, itrequires that new skills and competences be developed within thefirm. This, in turn, suggests a higher degree of technicaluncertainty and transaction specific investment. In such circum-stances, joint ventures, based on enduring relationships, andcharacterised by a joint commitment of resources are thought tobe an appropriate and likely outcome (Kogut, 1988; Love andRoper, 2004). Relatedly, past research has shown explorativecollaboration to be instrumental in developing new competences,with joint learning process and experimentation central tosuccessful innovation (e.g. Koza and Lewin, 1998). Drawing onthe above we propose:

P4. The internal newness of innovation activities is associated with

the use of strong ties, i.e. competence developing and radical

innovations use relatively many strong ties.

P5. The internal newness of innovation activities is associated with

the use of network partners for knowledge capital, i.e. competence

developing and radical innovations are likely to use network partners

as a source of knowledge.

In essence, P2–P5 imply an elaboration and test of Gilsing andNooteboom (2004). Their central hypothesis is that (p. 6) ‘‘forreasons of both competence and governance, in exploration tiesneed to be dense and strongy while in exploitation ties need tobe more sparse’’.

As previously indicated, the simple analogy to exploration andexploitation may be too limited for current purposes. Innovationswhich are both new to the market and require the acquisition ofnew competences may be the apogee of exploration. Yet, strongties alone are liable to be insufficient. When the scope ofinnovation is limited (to products or processes which alreadyexist in the market), the range of necessary partners may berestricted to a small and easily identifiable group. In thesecircumstances (i.e. competence developing) knowledge transferwill be easiest, and sufficient, through existing (strong) ties(Robertson and Langlois, 1995). However, over time, strong tiesmay provide only redundant information and result in inertia(Hoang and Rothaermel, 2005). Radical innovations (the top right-hand quadrant in Fig. 1) are likely to also require the diversityaccessed through looser, more distant, weak ties. In this vein, Ruef(2002) noted that individuals located in heterogeneous networks,encompassing both strong and weak ties, were more likely to beregarded as innovative by their peers. In the words of Scholten(2006): ‘‘A strong tie may constrain the search for novelinformation, whereas a weak tie may hamper the transfer ofcomplex knowledge and reliable resources’’ (p. 41). In otherwords, a mixture of weak and strong ties may present the correctblend of novelty and trust necessary for radical innovations (thosewhich involve both target and technical uncertainty). This dual

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Table 1Overview of propositions.

Type of innovation Proposition

1—Network

size

2—Weak

ties

3—Role

inspiration

4—Strong

ties

5—Role

knowledge

6—Role physical

capital

7—Role human

capital

8—Specific

ties

Incremental � � � � � � � �

Market developing � + + � � o o �

Competence

developing

+ � � + + o o +

Radical + + + + + + + +

+: Proposed score above average, o: on average, �: below average.

4 The issue of availability is likely to be, at least partially, driven by

perceptions. If the resources required are familiar, it seems likely that the firm

will know where they can be easily found. In this sense, they will appear to be

relatively available and will require limited search.

M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884 877

requirement is contained in the second and fourth propositions:radical innovation is likely to be associated with relatively highuse of both weak and strong ties.

Beyond this general statement, however, one wonders at thelikely resource implications of the confluence of internal andexternal newness. As a starting point, one might take Ahuja’s(2000) distinction between the ‘‘resource sharing’’ and ‘‘knowl-edge spillover’’ benefits of interfirm networks. The former, heasserted, primarily relate to the transfer and sharing of informa-tion (largely codified) and physical assets, while the latter arelikely to revolve around the transfer of tacit know-how (largelynot codified). The relationships between innovation type and theuse of networks for either information or know-how wereaddressed in P3 and P5, respectively. Beyond these, the assump-tion we make here is that the use of networks as a source of eitherphysical assets or manpower is an indication of the need forgreater ‘‘scale’’ in innovation projects. In the context of small firmsfor example, pooling of resources is frequently concerned withobtaining economies of scale or scope (Oughton and Whittam,1997). When innovating such organizations are regarded to bebehaviorally advantaged, but materially constrained (Freel, 2000).In relation to our innovation schema, we propose that radicalinnovations are most likely to embody greater ‘‘scale’’ (bothliterally and in terms of ambition) and require greater ‘‘scope’’,ceteris paribus, than either market or competence developinginnovations alone. More specifically, to the extent that radicalinnovation must resolve both technical and target uncertainties, itis likely to require greater resources to both expand markets andextend competences. For incremental innovation, the oppositewould apply. Accordingly, the following propositions are sug-gested:

P6. The confluence of external and internal newness is associated

with the use of networks as a source of physical capital, i.e. radical

innovations are most likely to use networks for physical capital, and

incremental innovations are less likely to do so.

P7. The confluence of external and internal newness is associated

with the use of networks as a source of human capital, i.e. radical

innovations are most likely to use networks for manpower, and

incremental innovations are less likely to do so.

2.5. Specificity

Past work suggests that the intentionality (or its analogue: thedirectedness) of network ties is another dimension that maycorrelate with innovations’ degree of newness. Ruef (2002), forexample, found that business start-ups composed exclusively offamily, friends, or work colleagues (no specific ties) were lessinnovative than teams of entrepreneurs involving no prior

relationships (specific ties). For the specificity of ties, weanticipate that this will often vary with the extent to whichknowledge requirements can be met by existing competences.Past innovation networking studies have shown that it is unlikelyfor current network partners to provide all missing know-how(Scholten, 2006). The specific knowledge required to develop new,and particular, innovation skills and competences is liable to beless broadly distributed and is expected to require moreintentionality. Likewise, entrepreneurship research has demon-strated that bootstrapping activities (which are by definitionproactive, intentional and specific) are not solely characteristics ofenterprise creation, but also enable the later development ofinnovations (Winborg and Landstrom, 2001). In contrast, firmsengaged in new to the market innovations will draw inspiration oraccess advice and feedback externally. Such early-stage contribu-tions of network partners typically occur serendipitously andgenerally rather than intentionally or specifically (DeTienne andChandler, 2004). Even where market developing innovationsrequire more formal cooperation, required resources are likely tobe relatively familiar and available.4 In other words, competencedeveloping and radical innovations are more likely to make use ofspecific ties. The following is proposed:

P8. There is a positive association between the internal newness of

innovation activities and the use of networks formed for a specific

purpose, i.e. competence developing and radical innovations are more

likely to draw on specific ties.

The eight propositions are summarized in Table 1. They providethe basis for investigating network content and structure issues ina sample of innovations. In very general terms, we suggest aconnection between the internal and external newness ofinnovations and the types of networks firms engage in. In thefollowing sections we construct models which allow us to testthese presuppositions.

3. Data

We employ a database developed from a survey undertaken byEIM Business and Policy Research, a research institute specialisedin small business, entrepreneurship and innovation. Commis-sioned by the Dutch Ministry of Economic Affairs, this surveyaimed to explore how small- and medium-sized enterprises(SMEs) use their networks to support the initiation and imple-mentation of innovations (EIM, 2005). Although the survey was

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M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884878

not specifically executed for the current paper, its data are wellsuited to empirically test our propositions.

Following the Dutch definition of SMEs, the survey includedfirms with less than 100 employees (EIM, 2006). The samplingframe consisted of the Chambers of Commerce database contain-ing data on all Dutch firms. All data were collected in Septemberand October 2004, over a period of 4 weeks, by means ofcomputer-assisted telephone interviewing. Respondents weremanagers responsible for day-to-day business processes—usuallythe owner/entrepreneur, and otherwise a general manager.

The sample was disproportionally stratified across four typesof industries and two size classes. The survey addressed bothmanufacturing and services firms. The initial sample consisted of1934 firms. Responses were obtained from 1004 respondents whohad been willing to co-operate (52%). A comparison of thedistribution of respondents and non-respondents by type ofindustry indicated that there was no non-response bias present.A w2-test between the distributions revealed no significantdifferences at the 5% level (p ¼ 0.43). For size classes a similarresult was found: p(w2) ¼ 0.67.

As the study aimed to explore how SMEs use their networks tosupport innovation, the survey started with screening questions toidentify those firms who had implemented at least one innovationin the past 3 years. Following Europe’s Community InnovationSurvey, innovations were assumed to include both new productsand processes (OECD, 2005). Product innovation was defined ashaving introduced to the market a product whose characteristicsor intended uses differed significantly from those of previouslyproduced products, or an existing product whose performancehad been significantly enhanced or upgraded. Process innovationwas defined as the adoption of new or significantly improvedproduction methods, including new methods of distribution. Inall, 594 respondents satisfied the criterion of having realized atleast one innovation during the period covered by the survey.Table 2 details the distribution of respondents across groups ofindustries and size classes. Groups of industries have beenidentified using the OECD’s (2001) classification of high-techand knowledge-intensive industries.

One distinctive feature of our data is that it also captures firmswith less than 10 employees, a group of firms that most publiclyavailable data sources tend to discard. We should, however, stressthat some types of firms will probably be under-represented. Forexample, firms with no more than 10 employees cover 83% of theDutch business population (EIM, 2006). Although such small firmsare generally less innovative than their medium-sized counter-parts, this group is probably under-represented. In the survey ithad been chosen to over-represent larger firms in order to enablecomparisons between size classes (EIM, 2005). Because the Dutchversion of the Community Innovation Survey does not include

Table 2Distribution of respondents across industries and size classes.

Sector NACE codes Examples of industries

High-tech

manufacturing

23–25, 29–34 Chemicals, rubber and plastics, ma

medical instruments

Low-tech manufacturing 15–22, 26–28, 35–37 Food, beverages, textiles, leather, p

Knowledge-intensive

services

72, 73, 742 Computer and related services, com

Other services 50–71, 74 (excl. 742),

90–93

Wholesale, retail trade, hotels and

services

these smallest firms, population figures of innovative SMEs arenot available for the Netherlands. Thus, we are unable to formallygauge the ‘representativeness’ of our sample.

For types of industries a similar caveat is appropriate. For‘political’ reasons, it had been decided to collect a larger share ofrespondents from manufacturing industries. These industriesreceive most attention from policy makers and are ‘heavy users’of Dutch innovation policy instruments. In 2006 manufacturingfirms represented 10% of the Dutch business population(EIM, 2006) while in our data they represent 42%. Although thegiven stratification of the sample inevitably distorts the validity ofour descriptive statistics and aggregated observations, we do notexpect that the legitimacy of the correlation analyses presented inthe next sections has been seriously compromised.

3.1. Variables

Data on relational networks were collected for specificinnovations that respondents had first been asked to identify. Ifrespondents had realized multiple innovations in the past 3 years,they were asked to select the most recent case. Next, eachrespondent was asked to describe what the innovation was about,and why it was new in comparison with previous products orprocesses. In marketing research surveys this technique is widelyapplied to obtain random samples of research objects, e.g.innovations within firms (Churchill, 1999). As an additionaladvantage, respondents were providing details on those innova-tions which were freshest in their minds, and their answers werethus anticipated to be most reliable. Table 3 presents the variablesthat we used to empirically test our propositions. Most variablesare constructed by combining various questions from the survey.One should note that all variables relate to specific innovations,with the exception of size class and type of industry. In this way,our research is object (rather than subject) oriented. Mostcomparable research takes the firm as the unit of analysis (e.g.Oerlemans et al., 1998; Tether, 2002). In directing our attention tothe innovation, we offer a less common perspective.

For their most recent innovation, respondents were first askedto assess the degree of newness. This was measured with twoperceptual questions: if the innovation was new in their market orsimply new to their firm; and, if the innovation required theacquisition of new skills or competences (yes or no). We combinethese indicators to obtain the degree of external and internalnewness of all surveyed innovations (recall Fig. 1). Happily, froman analytical perspective, sample innovations were relativelyevenly distributed across the typology.

Next, respondents were asked to provide a full inventory of thenetwork partners that, in their perception, had contributed to theinnovation process. We summed the number of mentioned

Size class

1–9

employees

10–100

employees

chinery, office-, electrical-, communication-, 36 79

aper, wood, metals, furniture 56 76

mercial R&D, consultancy, engineering 50 86

restaurants, personal services, transport, financial 56 155

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Table 3List of variables and descriptives.

Variable Description Descriptives (n ¼ 594)

Type of innovation Type of innovation, coded 0 (incremental innovation), 1 (market

developing innovation), 2 (competence developing innovation) or 3

(radical innovation)

Incremental ¼ 28%, novel ¼ 24%, complex ¼ 20%,

radical ¼ 28%

Network size Number of network partners involved in the innovation process Mean ¼ 2.43, SD ¼ 1.69

Role inspiration Number of network partners involved as a source of inspiration Mean ¼ 0.27, SD ¼ 0.57

Role knowledge Number of network partners involved as a source of knowledge Mean ¼ 0.38, SD ¼ 0.75

Role physical capital Number of network partners involved as a source of physical capital

(including finance)

Mean ¼ 0.54, SD ¼ 0.86

Role human capital Number of network partners involved as a source of human capital/

manpower

Mean ¼ 0.58, SD ¼ 0.92

Directed ties Number of directed ties involved in the innovation process (i.e. partners

which were previously no part of the firm’s network, and that were

approached proactively to contribute to the innovation process)

Mean ¼ 0.53, SD ¼ 0.97

Strong ties Number of strong ties involved in the innovation process (i.e. partners

characterised by regular or almost continuous contact, and regular private

conversations)

Mean ¼ 0.48, SD ¼ 0.91

Weak ties Number of weak ties involved in the innovation process (i.e. partners

characterised by no or incidental contacts, and no or incidental private

conversations)

Mean ¼ 0.78, SD ¼ 1.15

Object of innovation Object of innovation, coded 0 (process innovation) or 1 (product

innovation)

Process ¼ 62%, Product ¼ 38%

Firm size Number of employees in full-time equivalents Mean ¼ 23.2, SD ¼ 24.7

Industry Type of industry, coded 1 (low-tech manufacturing), 2 (high-tech

manufacturing), 3 (knowledge-intensive business services) and 4 (other

services)

Low-tech ¼ 22%, high-tech ¼ 19%, knowledge-

intensive ¼ 23%, other services ¼ 36%

M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884 879

partners to obtain a proxy for the size of the network. Thismeasure must be regarded as a proxy as respondents weredrawing on their immediate recollection and may have over-looked some partners. On average, respondents recalled 2.43network partners. Only 9% indicated that no external partners hadbeen involved, 25% mentioned one partner, 25% mentioned two,17% mentioned three, and 24% give details of four or morepartners (one respondent even listed 10 contributing networkpartners).

Having identified network partners, the survey asked a seriesof questions prompting respondents to elaborate on each of theidentified ties. Respondents first indicated how partners hadcontributed.5 Various roles were suggested, including being asource of inspiration, knowledge, physical capital (includingfinance) and human capital, and partners may have taken onmore than one role. To obtain relevant measures we summed upthe number of network partners with each specific roles. Onaverage, respondents indicated that 0.27 ties had been a source ofinspiration to initiate or design the innovation process, 0.38partners served a source of knowledge, 0.54 were used as a sourceof physical capital, and 0.58 provided human capital.6

To measure the specificity of ties, following Ruef (2002),respondents were asked to indicate whether a partner was alreadypart of the network before the innovation process started or if thepartner had been searched for purposefully to make a specificcontribution. A negative answer to the former and a positiveanswer to the latter were required to be classified as a specific tie;respondents on average had 0.53 specific ties. To measure thestrength of ties, respondents indicated if they maintained contactswith partners on a regular basis (with answers ‘barely’, ‘inciden-tally’, ‘regularly’ and ‘almost continuously’) and if they everdiscussed private matters with them (‘never’, ‘incidentally’ or

5 Strictly, network contributions are a matter of firms’ perceptions.6 The survey identified more roles, including network partners being a source

of advice, and an open-ended category (‘otherwise, namelyy’) which captured

miscellaneous roles such as providing permits. These data were of no use for the

current paper and discarded from our analysis.

‘regularly’). Both questions were combined to obtain a proxy fortie strength. We presupposed that tie strength is a continuum andthat there is a ‘grey’ area in which individual ties cannot beregarded as decidedly strong or weak. To qualify as a decidedlystrong tie, we required that partners were characterised by at leastregular contact and regular discussions of private matters(echoing Granovetter, 1995). On average, this applied to 0.48 ofthe identified network partners. To be classified as weak ties, werequired that partners were characterised by at most incidentalcontacts and also at most incidental private conversations. Usingthese criteria, on average 0.78 partners were marked as weak ties.We also analyzed if our findings as presented hereafter weresensitive to these definitions. Using a more relaxed demarcation(only using the frequency-of-contacts indicator) resulted insimilar findings. These results are not presented here, butavailable from the authors on request.

The dataset also contained various background characteristics,including the object of innovation (product versus processinnovation) and the sector and number of employees of theinnovative firm. These variables were all used as control variablesin the analyses to be presented hereafter.

4. Results

This section sets up a range of multivariate analysis of variancemodels to test our propositions. Analysis of variance is used touncover the effects of categorical-independent variables on aninterval-dependent variable (Turner and Thayer, 2001). Keyassumptions in analysis of variance are that the groups formedare relatively equal in size and have similar variances on thedependent variable. Further, the dependents are assumed to benormally distributed for each value category of the independentvariable. Earlier, we noted that our sample of innovations is fairlyevenly distributed across the four types. Although Levene testsindicated that the null hypothesis of homogeneous variancesought to be rejected, analysis of variance has been shown to berobust against departures from homogeneity as long as the ratio

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M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884880

of largest and smallest variances between groups does not exceed4:1 (Turner and Thayer, 2001). Additional descriptive statistics(not shown here) demonstrated that this criterion was satisfied.Finally, we note that results can be expected to be robust againstviolations of normality and homogeneity of variance assumptionswhen the groups formed are relatively equal in size (Turner andThayer, 2001).

To test our propositions we estimated eight models with typeof innovation (incremental, market developing, competencedeveloping or radical) as the independent variable, and variousaspects of relational networks as dependent variables. Each modelcontrolled for broad industry classification, firm size and object ofinnovation. Previous work has demonstrated important sectoralvariations in the nature and source of innovations by firms (e.g.Pavitt, 1984; Evangelista, 2000). Accordingly, one might anticipatebroad sectoral variations among respondent firms in the use ofnetworks to support innovation processes. In the case of firm sizethere is a considerable body of evidence indicating a positiverelationship between firm size and resource acquisition, riskspreading, and the recruitment and retention of specialisedworkers (e.g. Vossen, 1998; Nooteboom, 1994). From this, weanticipated that firms of different sizes may need, and use,networks differently. We used the log transformed number ofemployees as a covariate, reasoning that one additional employeeprobably has a greater impact on the innovation practices andnetwork needs of smaller firms than on those of larger ones.Finally, for object of innovation we entered a dummy variablerepresenting product innovation (with process innovation as thereference group) to control for the possibility that firms may usetheir networks differently to support product rather than processdevelopment.

Correlations between the key variables in our analyses arepresented in the Appendix to this paper. No correlations exceededan absolute value of 0.50. In our tests of P2–P8 however, weentered the size of networks as an additional control variable. Our

Table 4Multivariate analysis of variance models on network variables.

Model

I Network

size

II Weak

ties

III Role

inspiration

IV Strong

ties

Descriptive statistics

Incremental (n ¼ 166) 1.83 0.66 0.22 0.40

Market developing (n ¼ 144) 2.19 0.67 0.37 0.46

Competence developing

(n ¼ 116)2.82 0.97 0.20 0.35

Radical (n ¼ 168) 2.95 0.88 0.28 0.67

Total (n ¼ 594) 2.43 0.78 0.27 0.48

Tests of differences

F-value 15.1�� 0.2 3.14 2.94

Df(x;y) 3; 585 3; 584 3; 584 3; 584

Partial e2 0.072 0.001 0.025 0.02

F-value of contrast test 38.9�� 0.4 4.24 1.7

Control variablesc

Industry type 1.3 1.2 0.8 1.9

Log firm size 0.9 0.0 0.5 5.54

Product innovation 0.1 3.8 0.0 0.1

Network size – 185.7�� 7.3�� 124.2�

�� po0.001.� po0.01.4 po0.05.a Contrast test on presupposed low score for incremental innovations.b Contrast test on presupposed high score for radical innovations.c F-value of significance tests of control variable.

measures for network roles (inspiration, knowledge, physical andhuman capital), tie strength and directed ties all depend on thenumber of involved partners—for example, the more partnersinvolved the higher the probability of having weak ties. Thecorrelation matrix (see Appendix) indeed confirmed theseartificial relationships. By controlling for the total number ofinvolved partners, we avoided the possibility that significancetests would (indirectly) reflect our first proposition.

All analyses employed the Unianova procedure in SPSS. Resultsare presented in Table 4.

The key statistic in analysis of variance is the F-test ofdifference of group means. It tests if the means of the four typesof innovation are different enough not to have occurred by chance.Next, an estimate of the effect size is obtained with the partial e2-statistic. In Table 4 partial e2 indicates how much of the totalvariance in a specific aspect of relational networks is accountedfor by the four types of innovation, after the impact of the controlvariables has been partialed out (comparable with DR2 in OLSregression). Moreover, after the general F-test established anoverall relationship, we proceeded with contrast tests to test ourpropositions, i.e. trace differences between specific types ofinnovations. Finally, the bottom rows of Table 4 present theF-values of the control variables. This includes network size,which indeed was strongly related with all dependent variables inthe models II–VIII.

Model I shows that after controlling for type of industry, firmsize and object of innovation (i.e. dummy for product innova-tions), the degree of newness of innovation activities significantlyvaries with the number of involved network partners (F ¼ 15.1,po0.001). Partial e2 suggests that this relationship is rathersubstantial (e2

¼ 0.072). A contrast test that specifically comparesthe mean scores of incremental and market developing innova-tions versus competence developing and radical innovation givesa very significant result (F ¼ 38.9, po0.001). Our first propositionis supported: That is, there is a positive correlation between the

V Role

knowledge

VI Role physical

capital

VII Role human

capital

VIII Directed

ties

0.20 0.39 0.42 0.30

0.23 0.61 0.60 0.35

0.62 0.54 0.54 0.78

0.52 0.62 0.74 0.72

0.38 0.54 0.58 0.53

5.2� 1.4 1.6 4.3�

3; 584 3; 584 3; 584 3; 584

2 0.036 0.007 0.006 0.032

13.7��0.6a 0.3a

12.8��0.1b 1.0b

1.8 4.5� 0.7 0.4

0.3 0.7 0.1 4.54

0.5 2.5 1.1 1.1� 74.0�� 92.9�� 116.4�� 84.8��

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M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884 881

requirement to acquire new competences (i.e. the internal new-ness of innovations) and the number of network partners.

Model II tests our second proposition that market developingand radical innovation (i.e. externally referenced newness) userelatively many weak ties. The descriptives in Table 4 suggest thatthis proposition should be rejected, and indeed, the omnibusF-test and contrast test are non-significant. Note that the F-test forthe number of network partners is very significant (F ¼ 185.7,po0.001). This implies that the number of weak ties is stronglyconnected with the volume of networks, but after controlling forthis no significant connection remains between the type ofinnovation and the involvement of weak ties.

Model III tests our third proposition that the external newnessof innovation outputs is associated with drawing on networkpartners as a source of inspiration. The omnibus F-test suggestssignificant differences between the types of innovation (F ¼ 3.1,po0.05). Moreover, the follow-up contrast test reveals significantdifferences between market developing and radical versus incre-mental and competence developing innovations (F ¼ 4.2,po0.05). P3 is confirmed.

Model IV tests our fourth proposition that the need to acquirenew competences for innovation is associated with the use ofstrong ties. The omnibus F-test indicates significant differences atthe five percent level, but the subsequent test to comparecompetence developing and radical innovations with incrementaland market developing innovations remains non-significant(F ¼ 1.7, p40.05). A visual comparison of the descriptives in Table4 suggests that only radical innovations differ. Indeed, asuccessive contrast test which compares radical innovation withthe three other types is highly significant (F ¼ 4.0, po0.05). Wethus find only partial support for P4.

Model V investigates if innovation activities requiring newskills are connected with the use of network partners forknowledge capital. This proposition is strongly supported (omni-bus F significant at po0.01). The subsequent contrast analysisshows that competence developing and radical innovations usemore network partners as a source of knowledge, after controllingfor industry dummies, firm size, object of innovation and totalnumber of involved partners (F ¼ 13.7, po0.001).

Model VI deals with our sixth proposition that the confluenceof external and internal newness is associated with the use ofnetworks as a source of physical capital. Here we find nosignificant connections (omnibus F ¼ 1.4, p40.05). Proposition 6is rejected. Likewise, model VII tests P7 that the confluence ofinternal and external newness is connected with using networksfor human capital. This proposition is also rejected (omnibusF ¼ 1.6, p40.05).

Finally, model VIII tests our proposition on the connectionbetween competence acquisition and specific ties. It is confirmed,as we note a significant overall F-test and, from the subsequentcontrast test, we are able to conclude that competence developingand radical innovation use more (newly acquired) specific tiesthan incremental and market developing innovations (F ¼ 12.8,po0.001). Innovation activities with a high degree of internalnewness (i.e. which require new skills or competences) are morelikely to use ties specifically developed for the sake of theinnovation.

7 And that learning is self-reinforcing and path dependent (Cohen and

Levinthal, 1990).

5. Discussion

Much of the previous work on innovation networking hasexamined the impact of network involvement on the innovationpropensity of firms (and other organizations) (Provan et al., 2007).And, in so doing has been content with merely measuring theexistence of network links and innovation outcomes. This paper

has attempted to offer a more detailed view on the connectionbetween different types of innovation and relational networking.Firstly, in adopting a two-dimensional innovation schema, whichshould be broadly familiar to students and scholars in the field, weare able to more clearly differentiate between internal andexternal dimensions of innovation newness. From this weexplored how specific innovations developed by a sample of smallfirms draw on various aspects of networks, including tie strength,specificity of ties, and network content.

This paper first confirmed an important result of past empiricalnetwork studies, namely that the volume of networks matters torealize innovations. More specifically, the volume of networksvaries with the additional competence requirements of innova-tions: the more innovation requires new skills or competences,the more network ties are likely to be involved and vice versa.From this, one might usefully speculate that ‘creative abra-sion’—‘‘the synthesis that is developed from multiple points ofview’’ (Powell and Grodal, 2005, p. 59)—benefits competencedeveloping (and radical) innovations, given the greater technicaluncertainty that is likely. Innovations which appear more complexto the firm (in the sense of being beyond current competences) arelikely to require more complex solutions or, at least, morecomplex processes for generating solutions, and this complexityis likely to be a partial function of network size. In contrast, arelatively greater number of ties do not appear to be characteristicof innovations which build upon current skills to extend marketofferings.

Beyond the volume of networks, we find that various types ofinnovation are related to different forms of networking. Marketdeveloping innovations are correlated with using networkpartners as a source of inspiration; confirming our propositionthat new-to-the-market innovations particularly involve networkpartners for low commitment roles. In this light, one may betempted to suggest that market novelty (without technicaluncertainty) may more often be characterised by a greaterreliance on external inspiration and internal ingenuity. Compe-tence developing innovations proved to be associated with the useof network partners as a source of knowledge. Clearly, to theextent that innovation is concerned with searching (Nelson andWinter, 1982) and search is myopically constrained by existingcompetences,7 complex innovations (as we envisage them) arelikely to benefit from the acquisition of ‘other’ knowledge throughnetworking. Finally, we find strong support for an associationbetween competence developing innovations and tie specificity.In short, innovations that require the development of newcompetences appear to be characterised by a greater likelihoodof using specific ties. The opposite obviously holds: innovationsbuilding upon existing competences are less likely to utilizespecific ties. This appears consistent with the contrasting use ofnetworks for inspiration (market developing) and knowledge(competence developing). The former implies distance and, moreor less, serendipity; the latter may, more often, imply closenessand intention.

Of course, some of our propositions were not empiricallysupported. For instance, we found no unique connection betweenmarket developing innovations and weak ties. One may wonder ifsuch ties may usefully be leveraged for any innovation whichstrongly deviates from the state-of-the-art. It is, for example, nothard to imagine that competence developing innovations mayalso benefit from weak ties. And, indeed, that is what ourdescriptive results suggested (see Table 4). One can easily findinstances of new skills or competences that would not require the

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Table 5Correlation matrix (n ¼ 594).

(1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13) (14) (15) (16)

(1) Type:

Incremental

innovation

(2) Type: Market

developing

innovation

�0.35��

(3) Type:

Competence

developing

innovation

�0.31�� �0.28��

(4) Type: Radical

innovation

�0.39�� �0.36�� �0.31��

(5) Network size �0.22�� �0.08 0.11� 0.19��

(6) Role inspiration �0.06 0.104�0.06 0.03 0.19��

(7) Role knowledge �0.15�� �0.12� 0.16�� 0.12� 0.37�� �0.02

(8) Role physical

capital

�0.11� 0.05 0.00 0.06 0.38�� 0.07 �0.07

(9) Role human

capital

�0.114 0.01 �0.02 0.11� 0.42�� �0.11� �0.03 �0.02

(10) Directed ties �0.15�� �0.104 0.13� 0.13� 0.39�� 0.12� 0.13� 0.28�� 0.20��

(11) Strong ties �0.05 �0.01 �0.07 0.13� 0.41�� 0.16�� 0.11� 0.04 0.27�� 0.01

(12) Weak ties �0.07 �0.06 0.084 0.05 0.50�� 0.06 0.12� 0.29�� 0.19�� 0.47�� �0.104

(13) Object:

Product innovation

�0.22�� 0.17�� �0.15�� 0.18�� 0.04 0.04 0.03 0.08 �0.01 �0.02 0.04 �0.07

(14) Log firm size �0.15�� �0.02 0.14�� 0.05 0.094�0.02 0.07 0.00 0.02 �0.02 �0.07 0.05 �0.06

(15) Industry: Low-

tech

manufacturing

0.11� �0.05 �0.02 �0.05 �0.03 0.00 �0.03 0.07 0.00 �0.01 0.00 0.00 �0.06 �0.03

(16) Industry:

High-tech

manufacturing

�0.03 0.00 0.07 �0.03 0.03 0.06 �0.03 0.06 �0.04 0.02 �0.094 0.04 0.07 0.04 �0.26��

(17) Industry:

Knowledge-

intensive business

services

�0.12� 0.07 �0.03 0.07 �0.05 �0.02 0.07 �0.13 �0.02 �0.05 0.01 �0.104 0.104�0.06 �0.29�� �0.27��

�� po0.001.� po0.01.4 po0.05.

M. Freel, J.P.J. de Jong / Technovation 29 (2009) 873–884882

transfer of tacit knowledge, and thus be constrained by limitedbandwidth. One example might see experienced innovationworkers attending short courses in the use of a computerprogramme that would help to enable a specific process innova-tion.

Moreover, whilst we proposed that networking for competencedeveloping innovations was likely to be associated with the use ofstrong ties—as a way to mitigate opportunism and ease commu-nication—our proposition was only partially supported, i.e. radicalinnovations, characterised by a high degree of both external andinternal newness, appeared to involve relatively many strongties—though competence developing innovations, alone, did not.Perhaps this reflects the different degrees of opportunismsuggested by the degrees of market developing innovation. Asindicated by the support for our first proposition, both compe-tence developing and radical innovations appear to use multiplepartners to fill out their initial knowledge endowments. However,when an innovation already exists in the market or industry (i.e.the firm is strictly engaged in imitation8) it is likely that the scopefor economic opportunism will be less, even when the ‘‘imitation’’requires the acquisition of new skills.

Finally, though we found evidence of a link between radicalinnovation and the leveraging of knowledge through networks, wedo not find support for our proposed associations with networks

8 Here ‘‘imitation’’ is by no means intended pejoratively. Rather, many

successful innovations are likely to result from imperfect imitation (Alchian, 1950).

as sources of either physical or human capital. Certainly, thedescriptive statistics in Table 4 are suggestive—most especiallywith respect to the role of human capital. However, the evidenceis not compelling and, in contrast to knowledge, we are unable toconfidently assert that the sharing of simpler (embodied)resources marks out innovations which are both complex andnovel. Our initial propositions anticipated some scale (and scope)effects. Perhaps this was misplaced. Radical innovations may notrequire a greater volume of resources, simply a broader base fromwhich to search for opportunities or solutions (Alves et al., 2007).

6. Implications for future research

Obviously, the current study has its limitation; some of whichsuggest avenues for further research. For instance, our strongestempirical finding related to network size, the ‘usual suspect’ ininnovation networking analyses. We note, however, that theabsolute number of collaborations was small (with an average of2.43 partners and a maximum of 10). Our observations do notpreclude the possibility of diminishing (and, ultimately, negative)marginal returns to collaborations, as noted elsewhere (Deeds andHill, 1996). But, we suspect that the dangers of ‘malperformance’through excessive alliance building are likely to be very slight formost small firms. For instance, in Deeds and Hill’s (1996, p. 53)study, the authors note that ‘‘malperformance problems startbecoming serious when firms increase their number of alliances

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beyond about 25’’! Clearly, the firms in our sample have someroom to manœuvre. Nonetheless, we stress that our sampleincluded small firms only. It is uncertain if our empirical findingswould hold in samples of larger organizations. Small firms arerecognized for being more dependent on external parties torealize innovations (Vossen, 1998; Nooteboom, 1994), so futureresearch should also be done with samples of large-firminnovations.

In addition, our dataset is cross-sectional, allowing us toconfidently discuss ‘‘what’’ and ‘‘how many’’, but limiting whatwe can say about ‘‘why’’ and ‘‘how’’. For this reason we necessarilyframed our propositions in terms of associations rather thancausal inferences. Moreover, the data were collected from a singlesource, i.e. the owners/managers of innovating small firms. As aconsequence all variables were self-assessments. Although this isa feature that is shared with all publicly funded innovationsurveys (including the Community Innovation Survey), it limitsthe strength of conclusions.

Another limitation is that, though we are able to identifypartners’ roles, we are unable to discriminate partner types. Yet,just as different types of partners have been shown to contributedifferently in different domains of innovation (e.g. product orprocess), and in different sectors (see, for instance, much of thework inspired by Pavitt, 1984), so one might anticipate differentpartner types playing different network roles. For example, itseems reasonable to speculate that the inspirational role appar-ently important to market developing innovations would be bestfilled by customers, while suppliers more often comprise thestrong tie, knowledge sharing networks more characteristic ofcompetence developing innovations. Whilst such speculation maybe more or less fruitful, further research would be desirable here.

Similarly, as is common practice, this paper has treatedinnovation as an end rather than a means. Yet, in many waysthis is an unsatisfactory position. Whilst the intrinsic worth ofinnovation may hold in aggregate (at the level of the economy orthe industry), it does not follow that, even successful innovation,will inevitably confer a host of advantages upon the individualfirm. Or, specifically here, that innovations generated throughparticular forms of collaboration exhibit superior economicperformance. Before one rushes to recommend particular busi-ness strategies a better understanding of the implications forbusiness performance is warranted.

Notwithstanding these limitations, our empirical findingssuggest that external and internal dimensions of innovationnewness (and the implications for perceived target and technicaluncertainty) correlate with different networking challenges. Abetter understanding of the manner in which different networktypes associate with different innovation outcomes adds to theinnovation networking literature and informs future innovationpolicymaking. Further research in this direction is clearly appro-priate.

Appendix

Table 5 presents the correlations between the key variables inour analysis. Nominal variables (type of innovation, object ofinnovation and industry types) are presented as dummies.

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