how can external relationships enhance innovation in smes? new evidence for europe

How Can External RelationshipsEnhance Innovation in SMEs? New Evidencefor Europe*jsbm_355 310..339

by Andrea Lasagni

This paper investigates the role of external relationships as key drivers of smallbusiness innovation. An empirical analysis is based on data for approximately 500small and medium-sized enterprises (SMEs) in six European countries. The resultsindicate that innovation performance is higher in SMEs that are proactive in strength-ening their relationships with innovative suppliers, users, and customers. Further-more, the findings of this paper support the view that SMEs will have better newproduct development results if they improve their relationships with laboratories andresearch institutes.

IntroductionInnovation in firms has been one of

the primary themes in economic researchfor many years. Beginning with Schum-peter’s (1942) intuition on the role of the“innovative” entrepreneur, the field hasconsiderably widened and now includesnumerous different aspects. The meaningof the word “innovation,” however, hasaltered significantly in recent decades.This word was originally linked to therole of research and development (R&D),whereas currently, it tends to be associ-ated with the knowledge used in theprocess of generating new ideas.

There is now a significant amount ofliterature that supports the idea thatinnovation results are favored by thepresence of relationships, networks, alli-ances, and other different forms ofinteraction with external sources ofknowledge (among others, Powell andGrodal 2005; Tether 2002). At the sametime, “open innovation” has become oneof the topics that is most debated bymanagement scholars (Chesbrough 2003;Chesbrough, Vanhaverbeke, and West2006; Dahlander and Gann 2010;Huizingh 2011; Lichtenthaler 2011). Theuse of external relationships is increas-ingly interpreted as a key factor in

Andrea Lasagni is lecturer of Microeconomics at University of Parma.Address correspondence to: Andrea Lasagni, Dipartimento di Economia, Università di

Parma, Via Kennedy 6 43100 Parma. E-mail: [email protected].*The author would like to thank JSBM Special Issue Guest Editors and three anonymousreviewers for their insightful comments on earlier drafts of this manuscript. The author is alsograteful to I3SME project partners and to CNA Emilia Romagna and CNA Innovazione forproviding the access to I3SME survey data. The usual disclaimers apply.

Journal of Small Business Management 2012 50(2), pp. 310–339

JOURNAL OF SMALL BUSINESS MANAGEMENT310

enhancing the innovation performance ofmodern enterprises. Several worksconfirm that network ties can be valuabletools for fostering innovation perfor-mance (e.g., Chen, Chen, and Vanhaver-beke 2011; Freeman 1991; Love andRoper 2001; Nieto and Santamaria 2007;Rammer, Czarnitzki, and Spielkamp2009; Rogers 2004; Zeng, Xie, and Tam2010). Indeed, these knowledge linkscan offer firms easier access to new ideasand can enhance the transfer of knowl-edge from university and research unitsto business activities.

Aside from this increasing attention tothe approaches to “open” innovation,technological and market transformationshave made competition everywhereincreasingly stiff, and small and mediumenterprises (SMEs) have come to dependincreasingly on innovation, sometimes fortheir survival (Verhees and Meulenberg2004). Although there has been wide-spread recognition that SMEs can achieveimportant advantages by “opening” theirinnovation strategies and investing inexternal network development (Freel2000; Nieto and Santamaria 2007; Rogers2004; Romijn and Albaladejo 2002), thedebate appears to be relatively fluid(Hoffman et al. 1998; Tether 1998).

The main focus of this paper is oninnovation performance in SMEs. Twoprincipal research questions areaddressed in the empirical analysis. First,this study asks whether SMEs that areproactive in strengthening their relation-ships with innovative suppliers and cus-tomers are more likely to achievepositive results in the innovation of prod-ucts or services. The second question iswhether innovative SMEs are more likelythan other SMEs to take advantage oflinkages with R&D laboratories and uni-versities. The motivation behind thechoice of focusing on these types of link-ages can be explained as follows. First,results from the European CommunityInnovation Survey and other nationalinnovation surveys show that suppliers

(together with clients or customers) arethe most sought-after innovation part-ners (Organisation for EconomicCo-operation and Development [OECD]2010). It is more likely for firms to sourceexternal knowledge along the supplychain because competences have prob-ably to be complementary. Second, inEuropean countries with available data,large firms report more cooperation withhigher education or government institu-tions than SMEs. Thus, it is important tounderstand whether SMEs that are notengaged in partnerships with universitiesor other scientific research units are alsoassociated with a lower number of inno-vative products.

The empirical support used to answerthe two questions is based on a largesample of approximately 500 SMEslocated in six European countries(Austria, Germany, Italy, Hungary,Poland, and Slovenia). To control for thedifferent dimensions of innovation, man-agers were asked to indicate the degree ofnovelty (in the range of products or in thecontribution to turnover) that their SMEshave achieved in the past two years.

Studies focusing on external sources ofknowledge as “innovation gateways” forSMEs are relatively scarce. Moreover,there is a limited amount of empiricalresearch on the innovation practices ofSMEs located in Eastern and WesternEuropean countries. This paper aims toaddress these research gaps. It is a con-tribution that enhances existing empiricalevidence with a multivariate econometricexercise using a large-scale data sample.

The paper is structured as follows. Thefollowing section reviews the literatureon open innovation, networking, andexternal relationships, and illustrates theresearch hypotheses. The Data and Meth-odology section describes the data andthe empirical methodology. The Empiri-cal Results section provides the findings,whereas the Discussion section containsthe discussion of results. The final sectionformulates the concluding remarks.

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Conceptual Frameworkand Hypotheses

There is now a large body of knowl-edge1 that shows the importance ofinnovation capabilities for a firm’s com-petitiveness and success. For the pur-poses of the paper, this section aims tosummarize and review developments ininnovation research. Clearly, a compre-hensive and complete discussion of theargument is not attainable within thislimited space. The main objective here isto focus attention on the theoretical andempirical studies that are relevant tosupport the following points. First, whenfirms embrace “open innovation” strate-gies (i.e., internal uses of external knowl-edge to innovate), the use of externalrelationships can represent a key factor inenhancing their innovation performance.Second, as “open innovation” emerged asa new paradigm, several studies focusedon large firms; however “open innova-tion” strategies could also be effectivelyadopted in smaller organizations. In fact,a number of recent works have analyzedhow external relationships can supportsmall business innovation results. Thissecond point is relevant for the specificsubject of this paper’s analysis: the role ofexternal relationships for the innovationperformance of SMEs in Europe.

After having discussed these essentialaspects of the theoretical background,the hypotheses are introduced and dis-cussed in the final part of the section.The goal is to link this literature debateto the empirical framework of the paper.

Open innovation, Networking, andExternal Relationships: AnOverview of the Literature

It is well documented in both theeconomics and the management litera-

ture (Chesbrough 2003; Chesbrough,Vanhaverbeke, and West 2006; Powelland Grodal 2005; Tether 2002) that theinnovation activities of firms are notexclusively “internal” processes. Fewfirms are actually able to “go it alone” insupporting innovation investments.There is also a broad consensus on theimportance of external collaboration forthe innovation performance of firms. AsTether (2002) recalls,2 innovation is pro-gressively seen by analysts as an “inter-active” and “distributed” process. Anincreasing number of studies (e.g., Chen,Chen, and Vanhaverbeke 2011; Freeman1991; Love and Roper 2001; Nieto andSantamaria 2007; Rammer, Czarnitzki,and Spielkamp 2009; Rogers 2004; Zeng,Xie, and Tam 2010) show that firms mustincrease their number of interactionswith other market base actors (i.e., cus-tomers and suppliers) or research insti-tutions (i.e., universities and researchcenters). The relevance of these twochannels can be summarized with twomain aspects: the lack of internal knowl-edge resources and a desire to minimizethe risks associated with innovation.

R&D is no doubt a major resource forinnovation. The problem of insufficientor inadequate internal competences hasalready received significant attention inthe case of large R&d-intensive firms, inrelation to their absorptive capacityframework (Spithoven, Clarysse, andKnockaert 2011). In their influentialstudy, Cohen and Levinthal 1990) pointout that internal activities, such as R&D,not only generate new information, butcan also improve the firm’s ability toassimilate and exploit existing informa-tion. As Zahra and George (2002)emphasize, the amount of externalknowledge that a firm observes is anincreasing function of its absorptive

1For a review, see Pavitt 2005.2Many other scholars have contributed: for instance, see Teece 1992, Freeman 1991, and Cooke2005.


capacity. At the same time, for a givenquantity of identified external knowl-edge flows, the degree by which the firmderives benefits also depends on itsabsorptive capacity (Caloghirou and Kas-telli 2004).

The debate on the openness of inno-vation has gradually also influenced themanagement literature. In earlier contri-butions, the willingness to take on exter-nal ideas was included in the list ofimportant success factors for industrialinnovation (Rothwell 1994). Morerecently, the so-called open inno-vation paradigm (Chesbrough 2003;Chesbrough, Vanhaverbeke, and West2006) has emerged as the strategicorientation toward capturing externalknowledge to commercially exploit itsprofitable innovative potential. If firmsintroduce new technologies with thehelp of other actors in the innovationsystems, their innovation strategy mustbe “open” toward more collaborationand external sourcing of knowledge (see,e.g., Katila and Ahuja 2002; Laursen andSalter 2006). In brief, open innovation isa paradigm that assumes that firms canand should use internal and externalideas, as well as internal and externalpaths to market, as they look to advancetheir technology (Chesbrough 2003).

A burgeoning number of studies arecurrently available in the open innova-tion literature.3 In the existing surveys, itis possible to recognize different view-points. According to Huizingh (2011)and Lichtenthaler (2011), this moderntheoretical contribution is a useful foun-dation for conducting further empiricalresearch on how firms innovate. In fact,the concept of “open innovation strat-egy” introduced by Chesbrough (2003)allows inward and outward knowledgetransfer to be integrated, and it bringsideas from technology management(e.g., external technology acquisition)together with those from new productdevelopment. Dahlander and Gann(2010) highlighted that researchers tendto use different definitions and focustheir research on different aspects.Because openness is not a binaryconcept (open versus closed), to addressthe continuum of the various dimensionsof “open innovation,” it is worth refer-ring to a taxonomy proposed byDahlander and Gann (2010) (Table 1).

In particular, they define “sourcing” asthe inbound innovation—nonpecuniaryoption, whereas “acquiring” is theinbound innovation—pecuniary choice.In addition, they define outboundinnovation-revealing (firms reveal inter-

3This topic has stimulated the publication of various special issues in international scientificjournals, such as Technovation, R&D Management, and European Journal of InnovationManagement.

Table 1Different Types of Openness for Innovation

Inbound Open Innovation Outbound Open Innovation

Pecuniary Acquiring SellingNonpecuniary Sourcing Revealing

Source: Adapted on the basis of Dahlander and Gann (2010).

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nal resources without immediate finan-cial rewards, seeking indirect benefits tothe focal firm) and outbound innovation-selling (firms commercialize their inven-tions and technologies through selling orlicensing out resources that were deve-loped in other organizations). The fourcells in the matrix may be a good startingpoint for empirical research. It is pos-sible to focus on activities comprisingeach of the four strategies, and toexamine their effectiveness for differentorganizations and in different contexts.

For the purpose of this paper, it isappropriate to limit the discussion to“inbound” strategies, that is, efforts toenrich the company’s own knowledgebase through the integration of suppliers,customers, and external knowledgesourcing. There are several channelsthrough which firms can strategicallyaccess external sources of know-how(Bodas-Freitas et al. 2011; Kang and Kang2009; Rothaermel and Hess 2007). In otherwords, open innovation strategy can beimplemented with the support of differentinnovation partnerships. One of the mostwidely studied subjects in this area is theso-called external networking, that is “afirm’s set of relationships with otherorganizations” (Pittaway et al. 2004).According to Powell and Grodal (2005),interorganizational networks are a meansby which organizations can pool orexchange resources and jointly developnew ideas and skills. Many empiricalstudies (Freeman 1991; Love and Roper2001; Rogers 2004) show that networkscontribute significantly to the innovativecapabilities of firms by exposing them tonovel sources of ideas, improving access toinputs and enhancing the transfer ofknowledge (Nieto and Santamaria 2007;Zeng, Xie, and Tam 2010). The heteroge-neity of resources and resource mobiliza-tion are the key concepts whereinnovation networks are concerned. Alli-

ances can also be referred to as networkrelationships. According to Street andCameron (2007), alliances and networksare instances of external relationshipsbecause they both involve commerciallyoriented interorganizational connections.

In the literature, it appears that con-cepts such as networks and networkingare more popular today than ever before.However, as Pittaway et al. (2004) empha-size, the term “networking” can be intrin-sically ambiguous, and different typesof partnerships have been labeled as“network relationships” in empiricalanalyses. When a firm is able to efficientlyuse external sources of innovation andintegrate them into the internal innovationprocesses, this is recognized as “networkcompetence” (Rammer, Czarnitzki, andSpielkamp 2009). In addition, Lechnerand Dowling (2003) claim that externalrelationships are founded to serve diverseobjectives and that each firm has a uniquerelational mix that changes along thedevelopment path of the firm.

To address the complexity of theseissues, this research chooses to concen-trate the discourse by examining twotypes of external relationships that can beexpected to be relevant to the “inboundopen innovation” strategies of firms.

First of all, firms could invite users orcustomers to participate in the innovationprocess directly to quickly obtain newproduct definitions. Firms can also accel-erate innovation and reduce costs byfacilitating the participation of suppliersin the design and development process.These are networking relationships in themarket space between each single enter-prise and its business partners.

The supply chain literature (since theearlier studies in the 1970s and ’80s)emphasizes the importance of these ver-tical network relationships in the productdevelopment process.4 In particular, theconcept of lead user innovation is based

4See von Hippel 1988, and Lundvall 1985, among others.


on the research of von Hippel (1988),who found that many commerciallyimportant products are initially concep-tualized and prototyped by lead usersrather than by manufacturers. Recently,the number of these studies has grownconsiderably.5 Through close contactwith innovative users, manufacturingfirms can absorb radically new productconcepts and select the most promisingprototype versions. Through thismethod, manufacturers can improve theefficiency of the innovation process,accelerate the innovation process, andreduce the risks associated with marketintroduction (see also Clark 1989; Clarkand Fujimoto 1991). Furthermore, userinteraction enables companies to acquirenew technological skills, learn about rel-evant technological trends, and extendtheir innovation and technology-related networks (Lettl, Herstatt, andGemuenden 2006; Petersen, Handfield,and Ragatz 2003).

The second type of external linkage isthe relationship with universities, con-sultants, research institutes, researchand technology organizations, and otherassociations. Industry-science collabora-tions provide firms with access to newknowledge and increase their under-standing of emerging scientific develop-ments. Universities and governmentresearch institutes can be important part-ners that bring new scientific and tech-nological knowledge into the firm(Lundvall 1992). Perkmann and Walsh(2007) claim that university–industryrelationships are extensively practiced,although differences exist across indus-tries and scientific disciplines. Theresearch on the role of firm–university

linkages for innovation is rich with stud-ies,6 but some mixed results make theconclusions on this subject still relativelyprovisional. For example, close collabo-ration with universities appears to facili-tate new breakthrough innovations andproducts (Belderbos et al. 2004a, 2004b).Laursen and Salter (2004) found thatfirms who adopt “open” search strategiesand invest in R&D are more likely thanother firms to draw from universities,indicating that managerial choice mattersin shaping the propensity of firms todraw from universities.

At the same time, several studies dem-onstrate that cooperation between firmsand universities or research institutes isdifficult and complex to manage, andsignificant national and industry differ-ences emerge with respect to this type ofnetworking activity (see Pavitt 2005).Vega-Jurado et al. (2008) investigated theeffect of external and internal elementsto the firm on product innovationnovelty in Spain. Their results suggestthat cooperation with universities andpublic research organizations might havea limited role in the competitiveness ofSpanish manufacturing firms.

External Relationships andInnovation in SMEs

Taking the earlier discussion intoaccount, a relevant question emerges: “Isthere any convincing literature that sup-ports the hypothesis that external rela-tionships are also beneficial for theinnovation performance of SMEs?” Morethan a few studies argue that SMEs cangain by “opening” their innovation strat-egies and investing in external networkdevelopment (Freel 2000; Nieto and

5The evidence on the role of vertical network relationships is abundant. See Mudambi 1996;Johnsen et al. 2006; Lettl, Herstatt, and Gemuenden 2006; Reichstein and Salter 2006; Piller andWalcher 2006, Lau, Tang, and Yam 2010; Tomlinson 2010; Hernández-Espallardo, Sánchez-Pérez, and Segovia-López 2011.6Other recent works are Arza and López (2011); Monjon and Waelbroeck (2003); Segarra-Blascoand Arauzo-Carod (2008); Bekkers and Bodas Frejas (2008).

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Santamaria 2007; Rogers 2004). Tosupport this argument, scholars refer tosome stylized facts. For example, inno-vation in SMEs is frequently hamperedby a lack of resources and opportunitiesto recruit researchers. Second, family andindividual objectives often limit deci-sions inside of SMEs. This family–firmeffect might persuade entrepreneurs toavoid radical and innovative changes.Some recent studies (Hadjimanolis 2000;Lee et al. 2010; Romijn and Albaladejo2002; van de Vrande et al. 2009) havebegun to analyze open innovation insmaller organizations, to test whether theinnovation performance of SMEs can beenhanced by the strategic use of externalrelationships. For example, Lee et al.(2010) placed the concept of open inno-vation in the context of SMEs. Their find-ings support the potential of openinnovation for SMEs and suggest thatvarious networking models can facilitateopen innovation among small Koreanfirms. Hadjimanolis (2000), using casestudies, investigates the relationshipbetween the capacity of top managers tobuild and maintain external links and theinnovativeness of small Cypriot firms.Romijn and Albaladejo (2002) analyze alarge set of determinants but includeonly small electronics and software firmsin their sample. In a recent study, van deVrande et al. (2009) found that SMEsengage in many open innovation prac-tices. Interestingly, they found thatmedium-sized enterprises (100–499employees) are more likely to embraceopen innovation strategies. Finally, thereis recent evidence showing that the intro-duction of different types of innovationis, indeed, associated with the utilizationof different types of information sourcesand collaborative relationships (DeJong and Vermeulen 2006; Todtling,Lehner and Kaufmann 2009; Varis andLittunen 2010).

According to Karo and Kattel (2010),it is noteworthy that the most compre-hensive accounts on the open innovation

paradigm have been developed primarilyusing the experience of leading U.S. mul-tinational firms. Due to this peculiardimension of the open innovationconcept, applying its principles andmethods to assess innovation practices inSMEs could require some additionalassumptions. In addition, in the surveyprovided by Hoffman et al. (1998), thetopic of external linkages and SME’s“innovative performance” is classifiedamong “contradictory findings.”

In his seminal contribution, Rothwell(1991) claimed that one area in whichSMEs can be at a disadvantage in com-parison with their larger counterparts isin addressing knowledge from externalsources. In fact, SMEs might have rele-vant difficulties because of limitedopportunities to identify practices,minimal capabilities for screeningmarket and technology trends, and pos-sible problems in measuring the businessimpact of innovation (Forsman andRantanen 2011). At the same time, thereis some consensus around the fact thatmany SMEs are flexible and have strongrelationships with customers, enablingrapid response to technical and marketshifts. Small firms usually have effectiveand rapid internal communication (littlebureaucracy), and a dynamic manage-ment style (Rothwell 1994). Freeman andSoete (1997) argue that flexibility is alsoa big advantage for SMEs in terms ofinnovative activities. The capacity ofsmall firms for innovation is, therefore,found to depend, to a great extent, onthe stimulation provided by the context,which may or may not encourage thefirms to interact with regard to innova-tion (Acs and Audretsch 1988;Kleinknecht et al. 1989; Rothwell andDodgson 1994).

Several studies investigated thestrengths and weaknesses of SMEs intheir organization of innovation pro-cesses (e.g., Acs and Audretsch 1988;Vossen 1998). The relevant novelty ofthese recent contributions appears


related to the fact that SMEs shouldcount more heavily on external knowl-edge as an input to innovation than largefirms (see Feldman 1994; Hagedoorn andDuysters 2002; Malecki and Tootle 1996).Most of the empirical studies focus onU.S. economy (e.g., Hagedoorn 1993;Vossen 1998). Almeida and Kogut (1997)found that small U.S. semiconductorfirms had a closer link with regionalknowledge networks than large firms (asevidenced by patent citation data). TheCommunity Innovation Survey 2008(Eurostat 2010) shows that large compa-nies are more likely to collaborate usingan outside-in process than small andmedium-sized companies. Finally, Ebers-berger et al. 2012) investigate the openinnovation strategies with CIS-3 (1998–2000) data for Austria, Denmark,Belgium, and Norway. They found thatSMEs exhibit a lower tendency to col-laborate with any type of collaborationpartner.

In summary, it is worth noting that theexistence of such a large literature on theinnovativeness of SMEs is indicative ofboth the importance of the issue and theopenness of the debate (Tether 1998).The ability to utilize external networks isconsidered high in SMEs by someauthors and low by others.

External Relationships andInnovation in SMEs: ResearchHypotheses

The review of literature in this sectionhas documented that (1) external rela-tionships (with suppliers and customersor with universities and research insti-tutes) are positively associated with suc-cessful innovation for firms, and (2) theuse of external knowledge sources canalso be valuable in the case of SMEs. Atthis stage, it is possible to identify theanalytical framework supporting thispaper investigation. Street and Cameron(2007) have delivered an in-depth litera-ture review of small business and exter-nal relationships research. Figure 1

displays their description of a “concep-tual model of external relationship influ-ences, management processes, andeffects in small business.”

The original reference is McGrath(1964). The model describes inputs, pro-cesses, outputs, and the associationsbetween them. Link “A” in Figure 1 rep-resents the effect of antecedents on aprocess. Street and Cameron (2007)emphasized that environmental charac-teristics, such as industry or geographiclocation, have an effect on external rela-tionships and relationship formation.Link “C” refers to direct relationshipsbetween antecedents and outcomes,such as those drawn between a smallbusiness characteristic (e.g., ownershipof a key resource) and certain outcomes(e.g., enhanced competitive position).Small firms that have past experiencewith external partners have been foundto be more capable of developing exter-nal collaborations (Das and Teng 1998),suggesting a reciprocal, or feedback,condition. This feedback relationship isrepresented by link “D” in Figure 1.Finally, the link denoted by “B” refers toexternal relationship activities and theirassociated outcomes.

This final link, “B,” is the focus of thispaper’s investigations. Street andCameron (2007) argue that it is possibleto refer to the resource-based view(RBV) theory to support the idea thatexternal relationships are value-creatingresources. In a nutshell, RBV theory con-siders strategic capabilities as a pool ofinternal resources that are strategicallyrelevant for achieving a competitiveadvantage (Barney 1991; Penrose 1959).The RBV also suggests that the combina-tion of a unique collection of resourceswithin a single firm will create synergiesleading to sources of sustained competi-tive advantage (Barney 1991).

Adopting the RBV perspective, Streetand Cameron (2007) highlighted thatsmall firms have to be proactive in select-ing, developing, and maintaining these

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resources, which can be represented astangible assets (such as proprietary tech-nologies) as well as intangible assets(such as patents or licensing agree-ments). Hence, “the RBV provides aresource-orientated explanation to thequestion of how small businesses derivevalue from an external relationship”(Street and Cameron 2007). In otherwords, external relationships are factorsthat can provide a competitive advan-tage, namely resources in the parlance ofthis theory.

In the recent literature, differentinsights have been proposed as support-ing arguments for this interpretation ofexternal linkages as value-creatingfactors. First of all, some scholars havefocused on extending RBV approach inorder to consider also network resources

that enlarge the opportunity set of thefirm (see Verona, 1999 and Lavie 2006).For example, Lavie (2006) illustratedhow firm-, relation-, and partner-specificfactors determine the contribution ofnetwork resources to the rents extractedfrom alliances. Verona (1999) argues thatfirms should develop internal and exter-nal integrative capabilities, which arenecessary for absorbing critical knowl-edge from external sources and facilitat-ing new product development.

Additionally, the decision for a smallfirm to form and strengthen externallinkages is justified by the opportunity ofobtaining access to assets that createvalue, are not available for purchase infactor markets, and require time to buildup. As explained in the work of Ahuja(2008), by collaborating with firms that

Figure 1External Linkages and Innovation: Conceptual Framework

Processes - Strategy planning- Relationship management: relationship formation and building, information sharing, and knowledge transfer

Outcomes - Organization development: access to resources, business development - Competitive advantage: ability to compete - Performance: innovation

Antecedents - Small firm characteristics - Relationship characteristics - Environment characteristics

A

B C

D

Source: Adapted on the basis of Street and Cameron (2007).


have developed stocks of the relevantassets, and sharing those existing assets,SMEs can resolve the problem of accu-mulation of assets across time. Further,as the shared assets can be accessedwithout separating them from the origi-nal firm, the problem of tradability canalso be bypassed. Thus, if a small firmlacks competitive resources, it can takeadvantage of external relationships toovercome this deficiency. The greater thefirm’s competitive resource shortage, thelarger its incentives to form linkages(Ahuja 2008).

Furthermore, Lorenzoni and Lipparini(1999) argue that the ability to interactand share knowledge with other compa-nies (they called “relational capability”)is a distinctive organizational compe-tence for firms that are transactionallyintensive in nature. Analyzing three leadfirm–network relationships, Lorenzoniand Lipparini (1999) found that the cre-ation and development of tightly inte-grated production networks emerge as asource of competitive advantage overcompetitors who miss out on intenserelational activity.

In sum, according to the RBV frame-work, access to more resources, competi-tive advantages, and increased economicvalue (e.g., product innovation) wouldbe expected outcomes from externalrelationships (Street and Cameron 2007).To link the conceptual framework dis-cussed earlier with this paper’s empiricalevidence, the final part of this section isdedicated to clarify the main researchquestions. The main focus of thisresearch is represented by two specifictypes of SMEs external relationships: thatwith suppliers and consumers, and thatwith laboratories and research institutes.

The relevance of the former is sup-ported by a large number of studiesfocusing on the innovation performancealong the supply chain (see Hernández-Espallardo, Sánchez-Pérez, and Segovia-López 2011; Lau, Tang, and Yam 2010;Sako 1994; Tomlinson 2010). Suppliers

and customers are often assumed to beengaged in partnerships with SMEs forthe reason that competences are mostlikely to be complementary. Thus, it ispossible to postulate that SMEs focusingon specific strategies for suppliers andcustomers involvement in new productdevelopment activities (i.e., informationsharing and product codesign) have agreater innovation performance thanSMEs that forgo this strategic orientation.The first hypothesis can be outlined asfollows:

H1: Innovation performance is higher inSMEs that are proactive in strengthen-ing their relationships with innova-tive suppliers, users, and customers.

Aside from supply chain relationships,knowledge can be sourced externally bySMEs also when linkages are developedwith universities, research institutes, andlaboratories. In this case, the literatureon hi-tech SMEs supports the fact thatinnovations are frequently application-oriented, therefore SMEs depend onexternal know-how for fundamentalresearch. Therefore, it is possible toclaim that SMEs can derive value fromtheir linkages with university andresearch units because these knowledgerelationships can offer them an easieraccess to new ideas and new technologi-cal capabilities. The second hypothesiscan be outlined as follows:

H2: Innovation performance is higher inSMEs that are proactive in strengthen-ing their relationships with relation-ships with laboratories and researchinstitutes.

Overall, it is expected that, for SMEs,customer and supplier relationships, onthe one hand, and science partnerships(universities, research institutes, etc.), onthe other hand, should play an importantrole in terms of obtaining innovativeresults.

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Data and MethodologyData were collected using a survey of

managers working in SMEs7 that werelocated in six European countries (in 2008and 2009). The survey was conducted bya consortium of innovation promotionagencies and centers in Austria, Germany,Italy, Hungary, Poland, and Slovenia.8

Approximately 600 SMEs were identifiedand interviewed with the help of facilita-tors and representatives from intermedi-ary bodies or other institutions. Thepractices included in the survey wereidentified from the literature and fromdiscussions with industry professionals.9

All respondents were asked to use a five-point Likert scale (1 = lowest relevance ofthe option/strategy, 5 = highest relevanceof the option/strategy) to measure theposition of the firm with respect to differ-ent managerial practices. A large numberof respondents did not answer theinnovation-related questions, whereasSMEs that were classified in two sectors(distributive trade and construction) wereexcluded because they did not providecomplete information on patents or trade-marks (registered or acquired). After con-trolling for missing values and othercross-checking, the final sample consisted

of 490 SMEs. In Table 2, the characteris-tics of the sample are displayed.

The percentage of firms located inItaly and Hungary is relatively higherthan expected, whereas the sample israther limited for the number of firms inGermany (Table 2). As for the size distri-bution, the sample appears skewedtoward medium-sized firms, whereasmicroenterprises (1–9 employees) areunderrepresented. This distribution isthe case in almost all of the countriessampled. At the same time, in thesample, there is a large share of SMEs forthe industry sector (approximately56 percent) compared with the servicesector (approximately 44 percent). Thisevidence for some overrepresentation ofindustrial SMEs is the result of work per-formed by the I3SME consortium. Thesampling frame adopted does not explic-itly state that it represents a highly com-parable measure of the population ofSMEs in these countries. The paneldesign was created to find valuable datafor broad industry trends across Easternand Western European markets. Thevalidity of the following analysis shouldnot be excessively compromised bysome imbalances in the distributionacross national boundaries.10

7These are firms with less than 250 employees, following the standard definition of SMEs asused by the European Commission.8The project “I3SME” was aimed at contributing to the micro and SME’s performance enhance-ment in the research and innovation fields. The specific objectives were to analyze the differentapproaches and services already utilized in the involved areas individuating good practices andexcellence, considering also the differences in terms of economic and territorial features,entrepreneurial and business activities; to define common methodologies to be applied,introducing the benchmarking approach and the ICT platform as tools for assessing andimproving the microenterprise and SMEs performances; and to train and support a communityof facilitators and advisors who have the task of supporting SMEs in self-analysis, learning,creation, and transfer of knowledge processes. The project website is http://www.i3sme.eu.9Some of the items extracted from the questionnaire are reported in the Appendix.10To take into account some underrepresentation effects with respect to the official Europeanfigures (see Table A1, in the Appendix) in the empirical analysis, this paper introduced a setof control variables that allow us to detect some specific patterns in the responses to thequestionnaire.


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of

Sm

all

and

Med

ium

-Siz

edEnte

rpri

ses

by

Siz

eC

lass

(Abso

lute

Val

ues

and

Per

centa

ges

)

Siz

e—U

pto

9Siz

e—10–1

9Siz

e—20–4

9Siz

e—50–9

9Siz

e—100–2

49

Tota

lP

erce

nt

ove

rTota

l

Countr

ies

Aust

ria

3810

86

769

14

.1G

erm

any

1420

199

971

14.5

Ital

y65

3829

80

140

28.6

Pola

nd

104

72

124

4.9

Hunga

ry60

1923

106

118

24.1

Slove

nia

527

45

068

13.9

Tota

l23

998

9040

2349

010

0.0

Sect

ors

Indust

ry85

6869

3718

277

56.5

Serv

ices

143

2818

34

196

40.0

Oth

erSe

rvic

es11

23

01

173.

5Tota

l23

998

9040

2349

010

0.0

Sourc

e:Ela

bora

tion

on

dat

afr

om

I3SM

Epro

ject

(see

det

ails

inth

ete

xt).

“Indust

ry”

incl

udes

man

ufa

cturi

ng

(NA

CE

Rev

.2

code

“C”)

and

wat

ersu

pply

;se

wer

age,

was

tem

anag

emen

t(N

ACE

Rev

.2

code

“E”)

.“S

ervi

ces”

incl

ude

acco

mm

odat

ion

and

food

serv

ice

activi

ties

(NA

CE

Rev

.2

code

“I”)

,in

form

atio

nan

dco

mm

unic

atio

n(N

ACE

Rev

.2

code

“J”)

,pro

fess

ional

,sc

ientific,

and

tech

nic

alac

tivi

ties

(NA

CE

Rev

.2

code

“M”)

,an

dad

min

istr

ativ

ean

dsu

pport

serv

ice

activi

ties

(NA

CE

Rev

.2

code

“N”)

.“O

ther

serv

ices

”in

clude

educa

tion

(NA

CE

Rev

.2

code

“P”)

,hum

anhea

lth

and

soci

alw

ork

activi

ties

(NA

CE

Rev

.2

code

“Q”)

,an

doth

erse

rvic

eac

tivi

ties

(NA

CE

Rev

.2

code

“S”)

.

LASAGNI 321

The rest of this section is dedicated todescribing the dependent variables, theindependent variables, and other con-trols, together with the econometricmodel used to test the empirical rel-evance of the hypotheses introduced inthe Conceptual Framework and Hypoth-eses section.

The Dependent Variable:Innovation Performance

As anticipated earlier, this paper aimsto investigate the impact of differentexternal knowledge linkages on theinnovation performance of SMEs. In theliterature, there are several possible waysto measure innovation: radical versusincremental innovation, product orprocess innovation, etc. As illustrated byAmara and Landry (2005), a multiscaleindicator is often a suitable choice toreveal the degree of novelty in the inno-vation and to more reliably differentiateinnovators from noninnovators. In thiswork, to assess the various dimensionsof innovation performance, surveyrespondents were asked to indicate thedegree of innovativeness (in the range ofproduct or in the share of turnover) thatis the result of their management proce-dures. This choice appears quite consis-tent with the basic suggestions anddefinitions of the Oslo Manual (OECD2005) about innovation being the “imple-mentation of a new or significantlyimproved product (good or service).” Inother terms, the responses to these ques-tions form the basis for the identificationof “innovators,” and this is a standardapproach in innovation survey method-ology (see Mairesse and Mohnen 2010;Tether 2002).

More specifically, two indexes havebeen expanded to measure the level ofinnovative efforts in every SME:

• Range innovativeness: If therespondent provides a score of 1,this indicates “No change in exist-ing products” in the last twoyears, whereas a score of 5 wouldsuggest “Marked changes in theproduct’s range”;

• Turnover from new products: Ifthe respondent provides a scoreof 1, this indicates “No turnoverfrom products conceived in thelast 2 years,” whereas a score of 5means that “More than 30% ofturnover is from new products.”

Interviewed managers11 were asked torefer to these innovative activities if theywere carried out over the previous twoyears. The Cronbach’s alpha (a) statisticcoefficient for the two items is 0.709,which represents a high degree of inter-nal consistency. Based on the scores pro-vided by the respondents for the twovariables, it is possible to define “innova-tive” SMEs as those enterprises that haveprovided a score of 4 or 5 for both thefirst and the second item. This identifica-tion procedure allowed us to distinguish124 active innovators and 366 noninno-vators in the sample. Thus, this two-group classification scheme was used inall subsequent analysis regarding the roleof external relationships. This approachis more focused on product innovative-ness than on other form of innovation(process or organizational changes)examined in the literature. Nonetheless,because there is, as yet, no absolute con-sensus regarding how to conceptualizethe degree of novelty for the products orprocesses in surveys on innovation(Amara and Landry 2005), this definitionis a reasonable compromise; this defini-tion also simplifies comprehension forthe respondents in the SMEs.

11The project “I3SME” was particularly oriented to very small firms, therefore additionalclarifications from internal technical documents show that the respondents are frequentlyentrepreneurs or the firm’s owners.


As expected, the group of innovativeSMEs shows significantly higher averagescores than the other firms (Table 3) forboth items.

Independent Variables: ExternalRelationships for Innovation

Key independent variables in thisstudy evaluate whether the firms have astrategy for the outside-in process ofinteracting with suppliers, customers,and other science partners (universities,research institutes, etc.). In other words,the role of open innovation strategieswas assessed by asking respondents toevaluate the involvement of their cus-tomers and suppliers in terms of theirinnovative collaboration experiences.Similarly, to evaluate the importance oflinkages with universities and other sci-entific institutes, managers were asked ifthe relationships between the firm andlaboratories or research institutes wereongoing and active.

To test the empirical relevance of H1(the role of knowledge relationshipswith customers and suppliers), five vari-ables were taken into account:

• Involvement _ in _design _ process:If the respondent provides ascore of 1, this indicates “Nointeraction with suppliers andcustomers,” whereas a score of 5means that “Partner suppliersand customers are fully involvedand supported by informativesystem”;

• Innovative_key_customers: If therespondent provides a score of 1,this indicates “Strategy not identi-fied,” whereas a score of 5 means“Strategy to strengthen the rela-tionship”;

• Machinery _ innovative _ key _suppliers: If the respondent pro-vides a score of 1, this indicates“Strategy not identified,” whereas

Table 3Means, Standard Deviations (S.D.) for the “InnovationPerformance” Items Included in the Cluster Analysis

RangeInnovativeness

Turnover fromNew Products

Noninnovative firmsNumber of Firms 366 366Mean 2.66 2.52S.D. 1.20 1.36

Innovative FirmsNumber of Firms 124 124Mean 4.47 4.42S.D. 0.50 0.50

TotalNumber of firms 490 490Mean 3.12 3.00S.D. 1.32 1.46

Note: For each item, respondents were asked to assign scores on a five-point ordinalscale. A detailed description of each item is reported in the Appendix.Source: Elaboration on data from I3SME project (see details in the text).

LASAGNI 323

a score of 5 means “Strategy tostrengthen the relationship”;

• Materials_innovative_key_suppli-ers: If the respondent provides ascore of 1, this indicates “Strategynot identified,” whereas a score of5 means “Strategy to strengthenthe relationship”;

• Information_system_innovative_key_suppliers: If the respondentprovides a score of 1, this indicates“Strategy not identified,” whereasa score of 5 means “Strategy tostrengthen the relationship.”

It is worth to draw some attention tothe fact that, during the interview, respon-dents were asked to “consider if the keysuppliers/customers are clearly identifiedand if a strategy to facilitate the introduc-tion of the innovative solutions in thefirm exists.” Although this is not a propercriteria for judging the validity and reli-ability of these self-reported constructs,the presence of some additional clarifi-cations should reduce the variance ofscores reported by respondents.

As far as H2 (the role of relationshipswith external research units or bodies) isconcerned, only one specific questionwas available in the questionnaire: Rela-tionship_with_laboratories_researchinstitutes: If the respondent provided ascore of 1, this indicates “Not existing,”whereas a score of 5 means that there isa “Close ongoing relationship with morethan a single institute.”

Other Control VariablesBecause the data set covers approxi-

mately 490 SMEs located in six Europeancountries, it is reasonable to assume thatvarious unobservable effects could beassociated with each individual firm’sresults. This assumption is the motiva-tion for controlling the analysis for anumber of other factors that might affecta firm’s innovation performance.

First, it is necessary to control forinternal R&D activities. This decision

allows us to consider that some SMEs canachieve innovation through internal R&Defforts (Cassiman and Veugelers 2006).No quantitative data on actual R&Dexpenditures are available from the ques-tionnaire. As a consequence, two otherproxies for internal R&D resources werechosen. The first is a variable related tothe capacity for producing research out-comes internally. This variable is basedon the question on the number ofpatents registered by the firm (Patents_or_trademarks_registered): If the respon-dent provides a score of 1, this indicates“No patent or trademark registered,”whereas a score of 5 means that “Devel-opment of patents is a typical activity.”The second variable is related to thecapacity to acquire research outcomesfrom the market (Trademarks_licences_patents_bought). This variable is basedon the question regarding the number oftrademarks, licenses, and patents boughtfrom research institutes, laboratories, orother companies: If the respondent pro-vides a score of 1, this indicates “Neveracquired,” whereas a score of 5 indicatesthat the “Company holds specific trade-marks, licences or patents in a logic ofexclusivity.”

Second, the introduction of a controlvariable for the level of human resourcesthat are available in the firm’s labor forceis opportune and consistent with theliterature on the absorptive capacityframework (Cohen and Levinthal 1990).The only available proxy is anotherscale variable that uses the itemIn-house_training, which refers toinvestments in training for workers witha specific budget in the balance sheet. Ifthe respondent provides a score of 1, thisindicates “Does not invest in training,”whereas a score of 5 means that “Morethan 5% of its turnover is dedicated totraining.”

In addition, this study controls for thesize of the firm because larger firms canhave specialized personnel dedicated toinnovation, and are thus more likely to


innovate (see, among others, Cohen andKlepper 1996; Leiponen 2005). Follow-ing previous work, size is measured bydummies for employment classes (e.g.,Belderbos et al. 2004; Tether 2002).

Another important aspect thatdeserves a specific control is the interna-tional exposure of the SMEs. A positiverelationship between innovation andinternational openness is frequentlyexpected (D’Angelo 2010; Ebersbergeret al. 2012). Unfortunately, no data onexport or foreign investment activitieswere available. Thus, a variable measur-ing the capacity of the firm to reach newmarkets was identified as a proxyfor these characteristics. In particular,Turnover_new_geographical_markets isthe utilized variable: If the respondentprovides a score of 1, this indicates “Anyturnover is from new geographical,”whereas a score of 5 means that “Morethan 30% of turnover is from new geo-graphical markets.”

Finally, to limit the omitted variablesbias, sector (according to classificationNACE Rev. 2, one digit codes) andcountry dummies are included in someregression models.

The Econometric ModelThe empirical approach in this study

is to test H1 and H2 regarding externalrelationships and innovation with aregression model. A multivariate analysiswas elaborated using the variables andother controls described earlier. Specifi-cally, a logistic regression is applied toidentify the differences between innova-tive (firms identified based on the rangeof innovativeness and turnover from newproducts, as described earlier) and otherfirms. This method allows to estimate

what the determinants of the probabilityare for an SME to be classified as “inno-vative” or not.

The baseline equation to be esti-mated, thus, becomes as follows:

Pr Innov XF XCi F i C=[ ] = + +1 Φ ( )β β ε(1)

where F(·) is the cumulative standardnormal distribution, and bF and bC are thevectors of the parameters to be esti-mated. The parameters of the model areestimated via maximum likelihood (ML)estimation. Considering the analysis, acaveat should be noted. Although a logitmodel is able to reveal the relationshipor association between variables, withcross-sectional data, it is hard to confirmthe existence of a causal relationship orits direction. It should be taken intoaccount that retrospective reporting isemployed for innovation measures. Nev-ertheless, it is only possible to state theexistence of statistical associations, orthe lack of such (Freel and Robson2004).12

Empirical ResultsBefore presenting the pattern of

empirical results obtained by applyingeconometric techniques, it is useful toassess the matrix of correlation coeffi-cients and its corresponding significancelevels between all explanatory variables(control dummies are excluded). Asanticipated earlier, all indicators are mea-sured on a five-point Likert scale(1 = lowest relevance of the option/strategy, 5 = highest relevance of theoption/strategy). Due to this measure-ment approach, the more robust Spear-

12An alternative option would be to rely on an instrumental variables approach. In this case,however, this approach is difficult to implement because the cross-sectional nature of the datadoes not allow the introduction of a lagged variable as an instrument. In addition, a large partof the questionnaire is based on Likert-scale questions, and the structural information on firmsis very limited (country, size, and sector). Therefore, the likelihood of finding a viableinstrument is very low.

LASAGNI 325

man rank correlation coefficients arereported in Table 4. Many pairwise cor-relation coefficients are relatively low(below 0.3).

A possible hint toward multicollinear-ity issues emerges when we look at thedegree of correlation for the three vari-ables that are connected to the roleof innovative suppliers (Machinery_innovative_key_suppliers, Materials_innovative_key_suppliers, andInformation_system_innovative_key_sup-pliers). However, the computed meanvariance inflation factor (VIF) value isonly 1.22, whereas the commonly usedrule of thumb suggests that a VIF of 10 orgreater is a possible cause of concern.Though the correlation of coefficients ispositive and significantly different fromzero for most of the explanatory vari-ables included in Table 4, the correlationis low. This evidence supports the viewthat it is possible to capture differentinformation about management strate-gies in the sample firms with theseindexes.

The overall scenario that emergesfrom the logit regressions (Table 5) isfairly consistent with what is found inother similar studies. The different esti-mated models show an acceptable pre-dictive power, with more than 75 percentof predictions correct. The value of thepseudo R2 is approximately 0.2 in morerobust specifications, which is quite rea-sonable for qualitative dependent vari-able regressions (Amara and Landry2005). In columns (1) and (2), the base-line models (Logit_1A and Logit_1B)are reported. Because of the high degreeof correlation between the twovariables that measure the capacity forproducing or acquiring R&D (Patents_or_trademarks_registered and Trademarks_licences_patents_bought), two differentspecifications were estimated using thetwo indexes alternatively (in specifica-tions A and B, respectively). The robust-ness of the baseline results is checkedwith the use of various controls (country,

sector, and size dummies), and thesenew findings (models Logit_2A andLogit_2B) are reported in columns (3)and (4).

In the first place, the results forcontrol variables are largely as expectedand consistent with previous studies. Infact, it is found that the coefficient forinternal research activities (Patents_or_trademarks_registered) is positive andsignificant. This finding is also confirmedwhen the variable used is that referringto acquired R&D (Trademarks_licences-_patents_bought), even though, in thiscase, the coefficient is significant at only10 percent. In other words, even thoughit is frequently found that the SMEs showa lower propensity than larger organiza-tions to produce internal R&D, wheninnovative versus noninnovative SMEsare examined, this factor emerges as arelevant one. Furthermore, the presenceof a qualified labor force is positivelyassociated with the probability of beingan innovative firm because the coeffi-cient for the variable In-house_trainingis positive and significant (at 10 percent).

Examining the results for the firstcentral topic for this paper (H1), the vari-able Involvement_in_design_process wassignificant with a positive sign. Thisresult suggests that when the formula-tion and design stage of new products isaccompanied by interaction with suppli-ers and customers, the innovation perfor-mance of SMEs can be improved. As inthe literature discussed in the earliersection, these results could offer empiri-cal support for H1. However, none of theother proxies for the innovative involve-ment of suppliers and customers show asignificant association with the probabi-lity of being an “innovative” firm. It islikely that the insignificance of thesevariables is related to the behavior ofmicrofirms (1–9 employees). In fact,some scholars note that the smallestenterprises tend to be less able to exerteffective control on information flowsto and from suppliers and customers


Tab

le4

Mea

ns

and

Corr

elat

ion

bet

wee

nExpla

nat

ory

Var

iable

s(M

easu

res

are

Sco

res

in1–5

scal

e)

Mea

n[1

][2

][3

][4

][5

]

Var

[1]

Invo

lvem

ent

of

cust

om

ers/

supplier

sin

des

ign

pro

cess

3.37

1[2

]M

achin

ery_

innova

tive

_key

_supplier

s2.

850.

1554

*1

[3]

Mat

eria

ls_i

nnova

tive

_key

_supplier

s3.

170.

2092

*0.

6129

*1

[4]

Info

rmat

ion_

syst

em_i

nnova

tive

_key

_supplier

s2.

890.

1898

*0.

4382

*0.

3228

*1

[5]

Innova

tive

key

cust

om

ers

3.43

0.35

48*

0.21

55*

0.26

60*

0.23

83*

1[6

]Rel

atio

nsh

ipw

ith

labs

and

univ

ersi

ties

2.74

0.20

82*

0.23

84*

0.21

39*

0.19

10*

0.24

24*

[7]

Pat

ents

or

trad

emar

ks

regi

ster

ed1.

770.

1787

*0.

2265

*0.

2358

*0.

1270

*0.

1702

*[8

]In

-house

trai

nin

g1.

900.

2112

*0.

2175

*0.

1644

*0.

2047

*0.

2225

*[9

]Turn

ove

rfr

om

new

geogr

aphic

alm

arket

s3.

150.

1564

*0.

1310

*0.

1321

*0.

1277

*0.

2249

*[1

0]Tra

dem

arks,

lice

nse

s,or

pat

ents

bough

t2.

510.

088

0.08

880.

1922

*0.

0909

0.16

14*

Mea

n[6

][7

][8

][9

][1

0]

[1]

Invo

lvem

ent

of

cust

om

ers/

supplier

sin

des

ign

pro

cess

3.37

[2]

Mac

hin

ery_

innova

tive

_key

_supplier

s2.

85[3

]M

ater

ials

_innova

tive

_key

_supplier

s3.

17[4

]In

form

atio

n_

syst

em_i

nnova

tive

_key

_supplier

s2.

89[5

]In

nova

tive

key

cust

om

ers

3.43

[6]

Rel

atio

nsh

ipw

ith

labs

and

univ

ersi

ties

2.74

1[7

]Pat

ents

or

trad

emar

ks

regi

ster

ed1.

770.

2711

*1

[8]

In-h

ouse

trai

nin

g1.

900.

2588

*0.

4842

*1

[9]

Turn

ove

rfr

om

new

geogr

aphic

alm

arket

s3.

150.

1997

*0.

1836

*0.

1537

*1

[10]

Tra

dem

arks,

lice

nse

s,or

pat

ents

bough

t2.

510.

1473

*0.

2139

*0.

1874

*0.

2065

*1

Note

:Sp

earm

anra

nk

corr

elat

ions

*=

*den

ote

sst

atis

tica

lly

sign

ifica

nt

at1

per

cent.

Sourc

e:Ela

bora

tion

on

dat

afr

om

I3SM

Epro

ject

(see

det

ails

inth

ete

xt).

LASAGNI 327

Tab

le5

Exte

rnal

Rel

atio

nsh

ipan

dIn

nova

tion:

Logit

Reg

ress

ions

on

the

Pro

bab

ilit

yof

Bei

ng

Innova

tive

SM

Es

Logit

_1A

Logit

_1B

Logit

_2A

Logit

_2B

(1)

(2)

(3)

(4)

Hyp

oth

esis

1In

volv

emen

tof

cust

om

ers/

supplier

sin

des

ign

pro

cess

0.04

1**

0.04

2**

0.05

0***

0.05

4***

(0.0

2)(0

.02)

(0.0

2)(0

.02)

Mac

hin

ery_

innova

tive

_key

_supplier

s-0

.001

0.00

30.

027

0.03

1*(0

.02)

(0.0

2)(0

.02)

(0.0

2)M

ater

ials

_innova

tive

_key

_supplier

s-0

.018

-0.0

13-0

.008

-0.0

06(0

.02)

(0.0

1)(0

.02)

(0.0

1)In

form

atio

n_s

yste

m_i

nnova

tive

_key

_supplier

s0.

016

0.01

20.

002

-0.0

02(0

.02)

(0.0

2)(0

.01)

(0.0

1)In

nova

tive

key

cust

om

ers

0.01

40.

014

0.01

50.

013

(0.0

2)(0

.02)

(0.0

2)(0

.02)

Hyp

oth

esis

2Rel

atio

nsh

ipw

ith

labs

and

univ

ersi

ties

0.02

9**

0.03

7**

0.03

7**

0.04

3***

(0.0

1)(0

.01)

(0.0

2)(0

.02)

Additio

nal

contr

ols

Pat

ents

or

trad

emar

ks

regi

ster

ed0.

059*

**0.

069*

**(0

.02)

(0.0

2)Tra

dem

arks,

lice

nse

sor

pat

ents

bough

t0.

028*

0.03

3**

(0.0

2)(0

.01)

In-h

ouse

trai

nin

g0.

042*

0.04

9**

0.03

40.

040*

(0.0

2)(0

.02)

(0.0

2)(0

.02)

Turn

ove

rfr

om

new

geogr

aphic

alm

arket

s0.

054*

**0.

056*

**0.

041*

**0.

044*

**(0

.01)

(0.0

1)(0

.01)

(0.0

1)Contr

ol

dum

mie

s(c

ountr

y,se

ctor,

size

clas

s)N

oN

oY

esY

esN

r.obse

rvat

ions

490

490

490

490

Log

L-2

38.3

***

-243

.8**

*-2

17.6

***

-224

.7**

*Pse

udo

R2

0.14

0.12

0.22

0.19

Ove

rall

per

centa

geof

corr

ecte

dpre

dic

tions

77.1

75.1

79.3

78.7

Note

s:The

dep

enden

tva

riab

leis

adum

my

vari

able

that

assu

mes

the

valu

e1

ifth

efirm

iscl

assi

fied

as“i

nnova

tive

,”an

d0

oth

erw

ise.

Mar

ginal

effe

cts

are

report

ed.M

argi

nal

effe

cts

are

eval

uat

edat

the

mea

ns

ofth

ein

dep

enden

tva

riab

les

for

the

unco

nditio

nal

expec

ted

valu

esofth

edep

enden

tva

riab

le;f

or

the

bin

ary

vari

able

s,w

ere

port

the

dis

cret

ech

ange

from

0to

1.St

andar

der

rors

inpar

enth

esis

.***

den

ote

sst

atis

tica

lly

sign

ifica

ntat

1per

cent;

**den

ote

sst

atis

tica

lly

sign

ifica

ntat

a5

per

cent;

*den

ote

sst

atis

tica

lly

sign

ifica

nt

ata

10per

cent.

Sourc

e:Ela

bora

tion

on

dat

afr

om

I3SM

Epro

ject

(see

det

ails

inth

ete

xt).


(Rothwell and Dodgson 1994). In short,it is possible to say that the role of ver-tical network relationships in the case ofthe innovation performance of EuropeanSMEs is only partially confirmed.

The presence of some proactivenessby SMEs in strengthening their relation-ships with laboratories or research insti-tutes seems to be an important factor forexplaining the probability of being aninnovative SME. The coefficient for thecorresponding variable is significant(1 percent level) in all models. This resultsuggests that, for innovative SMEs, theimportance of investing in R&D iscomplementary to sourcing knowledgefrom external relationships.

In summary, the overall scenario ofthese results supports the importance ofinternal, as much as external, knowledgeresources for innovation performance(Caloghirou and Kastelli 2004; Nieto andSantamaria 2007).

DiscussionThe fact that few prior studies on

innovation focus their attention on therole of open innovation strategies forSMEs calls for a discussion of the empiri-cal results presented in the previoussection. This paper contributes to thedebate on product innovation and exter-nal knowledge relationships in severalways.

First, as far as the theme of innovationsupported by relationships with custom-ers and suppliers (H1) is concerned, thefindings here appear to point to the sig-nificance of knowledge linkages insupply chain relationships. In particular,innovative SMEs involve their customersand suppliers in the design process morethan other firms. These results are rea-sonably consistent with those from pre-vious studies that focused on the role ofdownstream and upstream knowledgetransfers (e.g., Ebersberger et al. 2012;Hernández-Espallardo, Sánchez-Pérez,and Segovia-López 2011; Tomlinson2010).

Second, the results presented on theimportance of interactions with R&Dlaboratories and research institutes (H2)reveal similarities with the results foundby other previous works. For instance,Nieto and Santamarıa (2007) found thatcollaboration with research organiza-tions has a positive and significantimpact on the likelihood of achievingproduct innovations in the case ofSpanish manufacturing firms. In addi-tion, the results of Ebersberger et al.(2012) show that the percentage share ofSMEs that utilize R&D laboratories oruniversities for acquiring new ideas forinnovation in Austria is approximately10 percent.

Third, as remarked by Ebersbergeret al. (2012) and other scholars, a stronginternal capacity is an important comple-mentary factor for innovation success. Infact, the results of this paper confirm thathaving internal R&D resources (pro-duced or acquired) is a significant pointof strength for innovative SMEs.

Finally, the empirical evidence out-lined in this study appears coherent withthe general findings discussed in van deVrande et al (2009): currently, SMEs areengaged in many open innovation prac-tices, and popular strategies, such ascustomer and supplier involvement,coexist with science-driven externalrelationships.

Concluding RemarksIn the literature on innovation, a very

extensive consensus currently exists onthe importance of “openness” to newideas and solutions. Because organizingfor innovation with an “open” approachis a delicate process, several studies inthis research area have explored how thepresence of relationships, networks, alli-ances, and other different forms of inter-action with external sources ofknowledge could be crucial for innova-tion success (Powell and Grodal 2005;Tether 2002). Empirical evidence con-firms that firms that have introduced

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innovations with a higher degree ofnovelty are more likely than other inno-vative firms to have exploited externalsources of information during the deve-lopment of their new products or pro-cesses (Amara and Landry 2005; Inauenand Schenker-Wicki 2011; Tether 2002).

Aside from this stream of economicsresearch on ties and networks for inno-vation, a new surge of managementstudies has focused on the “open inno-vation” strategy (Chesbrough 2003; Ches-brough, Vanhaverbeke, and West 2006;Dahlander and Gann 2010; Huizingh2011; Lichtenthaler (2011), defined as aprocess in which organizations collabo-rate extensively with their environment,maximizing the efficiency of externalknowledge exploration and exploitation.The basic idea is that knowledge linkscan offer firms easier access to newideas, and can enhance the transfer ofcreative know-how from university andresearch units to business activities.

In an increasingly knowledge-intensive economy, SMEs are frequentlyforced to search for ways to exploitexternal sources of innovation (e.g.,Freel 2000; Nieto and Santamaria 2007;Rogers 2004; Zahra and George 2002).External relationships may well behelpful for innovation in SMEs becauseSMEs often suffer from a lack ofresources to invest in R&D. Moreover,SMEs are reluctant to accept the risksassociated with innovative projects.Therefore, it can be argued that theability to access external knowledgeresources efficiently can become a com-petitive factor for SMEs.

Using a large sample of EuropeanSMEs, this paper has contributed to theempirical literature on open innovationby seeking answers to two researchquestions. First, are strong relationshipsbetween SMEs and suppliers and/or cus-tomers connected with successfulproduct innovation (H1)? Second, areinnovative SMEs more likely than otherSMEs to take advantage of linkages

with R&D laboratories and universities(H2)?

According to the econometric modelsused in this work, it can be argued thatstronger relationships with customersand suppliers (H1) play a sizeable rolein supporting product innovation forEuropean SMEs. Regarding H2, thisstudy’s results confirm the positiveeffects of linkages with R&D laboratoriesand universities on the innovation per-formance of SMEs.

The findings of this research may haveimplications for the managers in SMEswho are responsible for the networkrelationships in their organizations. First,when small firms are able to strengthentheir customers–suppliers linkages,product innovation can acceleratebecause this type pf partnership allowsSMEs to reach new product developmentin shorter times. In particular, the foster-ing of closer relationships within thesupply chain can be regarded as impor-tant source of new ideas for the designstages. The role of suppliers in providinginnovative inputs (machinery, materials,and information) appears to be lessrobust, at least in this study.

Second, the empirical results supportthe view that SMEs will have better newproduct development results if theyimprove their collaborations and rela-tionships with laboratories and researchinstitutes. Therefore, additional policyefforts should be executed to increasethe number of small firms with a specificstrategy for fostering this knowledgechannel.

To conclude, the limitations of thispaper should be addressed. First, theempirical analysis has made use of quan-titative data for a sample of SMEs in sixEuropean countries, but no formal sam-pling procedure was performed duringthe collection. It is noteworthy that awide range of sectors and class sizes arerepresented in the data for Eastern andWestern European countries. However,results may not be generalizable to all


international small business scenarios,and some caution should be used wheninterpreting certain findings. Second, inthis study, the innovative firms wereidentified based on self-declared condi-tions, and this could generate a biastoward more self-confident SMEs.However, a large number of previousstudies have used self-reported data pro-vided by managers, and authors oftenhave to rely on the manager’s judgmentregarding the novelty of the innovationsthey have introduced.13

Finally, a further limitation concernsthe dynamic nature of interfirm networksversus the analysis thereof using cross-sectional databases. A cross-sectionalsample may or may not offer a significant“scenario” of a fast-changing process.However, the way questions were askedappears to suggest that the structural fea-tures of the product innovation strategieswere reasonably detected in the survey.

Future research is needed to examine towhat extent and how the process of intro-ducing innovation is affected by the rela-tionship between the firm and its externalenvironment, and by specific aspects of afirm’s organization. In technical terms, itwould be interesting to estimate the spe-cific impact of the presence of networks orother external knowledge sources on afirm’s innovative performance, controllingfor other (observed) factors and firmcharacteristics.

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AppendixTable A1

Proportion of Firms by Firm Size in the Sample andOfficial Eurostat Data

Employmentsize class

Austria Germany Italy Poland Hungary Slovenia

Sample Eurostat Sample Eurostat Sample Eurostat Sample Eurostat Sample Eurostat Sample Eurostat0–9 55.1 74.4 19.7 68.7 46.4 82.3 41.7 81.3 50.8 84.4 76.5 85.510–19 14.5 18.6 28.2 22.3 27.1 15.1 16.7 15.8 16.1 12.4 10.3 10.620–49 11.6 4.8 26.8 5.9 20.7 1.9 29.2 1.7 19.5 2.2 5.9 2.550–249 18.8 2.3 25.4 3.2 5.7 0.7 12.5 1.2 13.6 1.0 7.4 1.4Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0

Source: Elaboration on data from I3SME project (see details in the text) and Eurostat (year 2008)—Structural businessstatistics databases.

Table A2Items on I3SME Firm Questionnaire Related to Innovation

Performance

Item Description/Question Score = 1 Score = 5

Rangeinnovativeness

In the last two years, has the firmchanged in supplied product orprocess range? Have new productsor processes been introduced?

No change inexisting products

Marked changes inthe product’s range

Turnover fromnew products

This item refers to the capability ofthe firm to create income from theselling of new products.

No turnover fromproducts conceivedin the last two years

More than 30 percentof turnover fromnew products

Note: For each item, respondents were asked to assign scores on a five-point ordinal scale.

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Table A3Items on I3SME Firm Questionnaire Related to

Relationship with Suppliers and Customers


Involvement ofcustomers/suppliers indesign process

What is the level of customers’ andsuppliers’ involvement during theformulation and designing stage ofnew products?

No interactionwith suppliersand customers

Partner suppliers andcustomers are fullyinvolved and supportedby informative system

Innovative keycustomers

Customers are a vehicle forinnovating?

Strategy notidentified

Strategy to strengthen therelationship

Innovative key(machinery)suppliers

Machinery suppliers are a vehicle forinnovating.



Innovative key(materials)suppliers

Materials suppliers are a vehicle forinnovating.



Innovative key(informative)suppliers

Informative suppliers are a vehicle forinnovating.




Table A4Items on I3SME Firm Questionnaire Related to

Relationship with Laboratories or Research Institutes


Relationship withlaboratories or researchinstitutes

Has the firm activated acontinuous relationshipwith laboratories orresearch institutes?

Not existing Close ongoing relationshipwith more than a singleinstitute

Notes: For each item, respondents were asked to assign scores on a five-point ordinal scale. Additional textincluded for clarify the question “The evaluation should also consider if this relationship runs only with aspecific institute or if, vice versa, creating a relationship with institutes for developing innovative ideas canbe considered as a routine way to operate inside the company.”


Table A5Items on I3SME Firm Questionnaire Related to Other

Control Variables


Patents ortrademarksregistered

These items refer to the number ofpatents or trademarks registeredby the company. The variablealso considers if the registeringactivities are occasional, or belongto the company’s routine.

No patent ortrademarkregistered

Development of patentsis a typical activity.During the company’slife, more than 10,including both patentsand trademarks, havebeen developed

Patents, licenses,or trademarksbought

These items evaluate if thispotential source is considered andused by the company.

Never Company holds specifictrademarks, licenses, orpatents in a logic ofexclusivityFor a better evaluation, traditional

licenses like standard packagesfor the office automation havebeen set as a sector standard.

In-housetraining

These items refer to company’sinvestments in its employees’training with specific budget inthe balance sheet.

Does not investin training

More than 5 percent ofits turnover isdedicated to training

Turnover fromnewgeographicalmarkets

This item refers to the capability ofthe firm to create income fromthe selling of products in newgeographical markets.

Any turnoverfrom marketsdeveloped in thelast two years

More than 30 percent ofturnover from newgeographical markets.


LASAGNI 339

how can external relationships enhance innovation in smes? new evidence for europe

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