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Journal of Manufacturing Technology ManagementEmerald Article: Complementarity between innovation activities and innovation performance: Evidence from Spanish innovative firmsAna Ma Serrano-Bedia, Ma Concepción López-Fernández, Gema García-Piqueres
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To cite this document: Ana Ma Serrano-Bedia, Ma Concepción López-Fernández, Gema García-Piqueres, (2012),"Complementarity between innovation activities and innovation performance: Evidence from Spanish innovative firms", Journal of Manufacturing Technology Management, Vol. 23 Iss: 5 pp. 557 - 577
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Ana Ma Serrano-Bedia, Ma Concepción López-Fernández, Gema García-Piqueres, (2012),"Complementarity between innovation activities and innovation performance: Evidence from Spanish innovative firms", Journal of Manufacturing Technology Management, Vol. 23 Iss: 5 pp. 557 - 577http://dx.doi.org/10.1108/17410381211234408
Ana Ma Serrano-Bedia, Ma Concepción López-Fernández, Gema García-Piqueres, (2012),"Complementarity between innovation activities and innovation performance: Evidence from Spanish innovative firms", Journal of Manufacturing Technology Management, Vol. 23 Iss: 5 pp. 557 - 577http://dx.doi.org/10.1108/17410381211234408
Ana Ma Serrano-Bedia, Ma Concepción López-Fernández, Gema García-Piqueres, (2012),"Complementarity between innovation activities and innovation performance: Evidence from Spanish innovative firms", Journal of Manufacturing Technology Management, Vol. 23 Iss: 5 pp. 557 - 577http://dx.doi.org/10.1108/17410381211234408
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Complementarity betweeninnovation activities andinnovation performance
Evidence from Spanish innovative firms
Ana Ma Serrano-Bedia, Ma Concepcion Lopez-Fernandez andGema Garcıa-Piqueres
Departamento de Administracion de Empresas (Organizacion de Empresas),Universidad de Cantabria, Santander, Spain
Abstract
Purpose – The purpose of this paper is to analyze the existence of complementarity betweeninnovation activities (internal innovation, external innovation and cooperative R&D), as well as theirimpact on firms’ innovation performance.
Design/methodology/approach – Drawing on the Third Community Innovation Survey (CIS-3) forSpain, a multiple regression model is used to study the existence of complementarity betweeninnovation activities and their impact on innovation performance. The sample for the study is3,964 innovative firms.
Findings – First of all, the empirical results propose that the complementarity appears only betweeninternal innovation and either external or cooperative innovation – but not with both together, whichis in-line with the “absorption capacity” notion. Second, the use of external and cooperation innovationin isolation does not yield positive effects on innovation performance. This finding contradicts thesubstitution argument and supports the absorptive capacity argument. Finally, innovation strategiesdo not seem to be dissimilar between industries.
Research limitations/implications – The main limitation of the paper is the use of cross-sectiondata, which implies less robust results as an empirical test.
Practical implications – The empirical results allow the authors to recommend company managersand public administration officials to improve and support internal innovation. These activities shouldbe combined with the high levels of external acquisitions that Spanish firms have in order to increasetheir innovation performance as the absorption capacity theory and this paper’s empirical resultssuggest.
Originality/value – The first contribution of the paper is the inclusion of the third form of innovation:cooperation. The second contribution refers to the inclusion of the service sector in the authors’ sample.
Keywords Spain, Manufacturing industries, Manufacturing technology, Service sector,Organizational innovation, Innovation activities, Innovation strategies, Complementarity,Innovation performance
Paper type Research paper
1. IntroductionInnovation has long been acknowledged as one of the critical driving forces in enhancingsocial welfare, as well as being crucial for the long-term survival and growth of the firm(Schumpeter, 1939; Baumol, 2002). Indeed, faced with increasing internationalcompetition, innovation has become a central focus in the long term strategies of firms(Veugelers and Cassiman, 1999). However, managing innovation is not a straightforwardexercise (Tushman et al., 1997; Van de Ven et al., 1999), but becomes a complex process
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Received December 2008Revised October 2009
Accepted June 2011
Journal of Manufacturing TechnologyManagement
Vol. 23 No. 5, 2012pp. 557-577
q Emerald Group Publishing Limited1741-038X
DOI 10.1108/17410381211234408
when designing and implementing an innovation strategy directly related to themultitude of objectives such a strategy implies (Faems et al., 2005).
Traditionally, the literature has identified two main innovation activities: “internal”innovation and “external” knowledge acquisition (Veugelers and Cassiman, 1999;Cassiman, 2004; Cassiman and Veugelers, 2006), which has been called in the literaturethe “make or buy” decision (Perrons and Platts, 2005; Chang, 2003; Cassiman andVeugelers, 2006). More recently, a body of literature has identified the existence of a thirdinnovation activity, the cooperation with other partners for the development ofinnovations (Navarro Arancegui, 2002; Hull, 2003; Chen and Yuan, 2007), which can beconsidered a hybrid form between internal and external innovation (Pisano, 1990).
Today, concurrent with performing internal innovation, firms tend not only to “buy”but also to “cooperate” to obtain all the capabilities they need, ranging from research anddesign, manufacturing, and marketing to after sale service, in order to profit from theirinnovations (Teece, 1986; Hartung and MacPherson, 2000; Rigby and Zook, 2002). In thiscontext, strategic selection and utilization of resources in the innovation network hasturned into a key factor for firm competitiveness and survival. However, deciding on anoptimal balance between innovation activities is a complicated issue (Chen and Yuan,2007). Consequently, when deciding which innovation strategy is the optimal one, firmstend to combine the three innovation activities, a fact which suggests that thesealternatives represent complements rather than substitutes. From a theoretical point ofview a relevant factor that supports the hypothesis of complementarity is the existenceof what Cohen and Levinthal (1989, 1990) called “absorptive capacity”. According to thisconcept, external knowledge is more useful when a company engages in its own R&D, or,when a firm has a stock of prior knowledge at its disposal (Kamien and Zang, 2000).Therefore, we can explain that firms that tend to combine internal external andcooperation will be in the best condition to succeed in its innovative processes (Freeman,1991; Leiponen, 2005; Tether, 2005). Attempts to empirically compare these ideas haveled to conflicting results, because while some studies confirm the existence ofcomplementary relationships between some of the sources of innovationimplementation (Becker and Peters, 2000; Casisman and Veugelers, 2002, 2006;Beneito, 2006; Schmiedeberg, 2008), other studies have found them to be substitutes(Veugelers, 1997; Jirjahn and Kraft, 2006; Laursen and Salter, 2006). It is thereforenecessary to provide a more detailed examination of the effects of different innovationactivities, and the effects that these strategies have on their innovation performance.
Along these lines, the objective of this paper is to analyze the existence ofcomplementarity between these three innovation activities (internal innovation, externalknowledge acquisition and cooperative R&D), as well as their impact on firms’innovation performance. The existing empirical literature has mainly focused on thecomplementarity between internal and external innovation activities. Our empiricalstudy, however, also includes the third, or hybrid, form of innovation: cooperation. Theanalysis of this innovation activity is one of the contributions of our paper to the existingliterature. The interest of the inclusion of the cooperation activity in the analysis isjustified for the Spanish case because it is used by a significant percentage of firms.Along this line, although the external innovation activity is the most used activity(70 percent) followed by the internal one (47 percent), a total of 19 percent of the firms usecooperation innovation activities. This percentage is high enough to consider the studyof cooperation as a third innovation activity and to include it in our analysis.
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The other contribution of this study is the inclusion of the service sector in our sample.This is in contrast to the bulk of the research carried out to date which has centred almostexclusively on the manufacturing sector. We study the existence of complementarity bycarrying out a regression of innovation performance on innovation strategies(combinations of innovation activities) following the “Productivity Approach”.To accomplish these objectives the empirical research used firm level data from theThird Community Innovation Survey (CIS-3) for Spain. The rest of the paper isorganized as follows: in Section 2 we review the literature on complementarity ininnovation strategies. In Section 3 we explain the empirical analysis through thepresentation of the data sources, the methodology and the variables used. The empiricalfindings are presented in Section 4, and the final section contains the conclusions,implications and limitations of the research.
2. Literature review of complementarity in innovation activitiesIn this section we review the literature that has focused on the study of thecomplementarities between innovation activities. Traditionally, the literature hasidentified two main innovation activities: “internal” and “external” (Veugelers andCassiman, 1999; Cassiman, 2004; Cassiman and Veugelers, 2006). As far as internalinnovation is concerned, it represents a traditional innovation activity that mainlyconsists on the development of innovation activities based on the use of firm’s internalcapabilities (Vega-Jurado et al., 2009) where the own-generation of knowledge is fullyinternalized (Frenz and Ietto-Gillies, 2007) and can be defined as formal expendituresinside the firm (Beneito, 2006). The external innovation activities are related to the accessto knowledge external sources trough licensing, R&D outsourcing, companyacquisition, or the hiring of qualified researchers with relevant knowledge (Arora andGambardella, 1990). More recently, cooperation on innovation activities has grownsignificantly (Navarro Arancegui, 2002; Hull, 2003; Chen and Yuan, 2007). This is aresult of both the importance for companies to developing a body of knowledge in acontext of innovation processes, and the fact that this has become increasinglyuncertain, costly and complex (Chang, 2003). This type of the development of innovativeactivities is considered a hybrid form (Pisano, 1990) between internal and externalsources. The strategic alliance literature stresses that it can contribute for a firm’scompetitive advantage allowing firms to gain access to knowledge and capabilities theydo not possess (Al-Laham et al., 2008).
In more precise terms, this literature review has focused on the effects on performanceof these different possible innovation activities (internal, external and cooperation), anddistinguishes between those that occur when these sources are pursued exclusively,on the one hand and combined, on the other.
2.1 Effects on performance resulting from the use of individual sources of innovationdevelopment activitiesFirst, as far as the use of internal innovation activities in isolation is concerned,companies invest a great deal of time and resources in the search for innovationopportunities that will increase their ability to create, use and recombine knowledge, andto enable them to operate at the forefront of existing knowledge and develop newresources and capabilities that will be difficult to imitate. All this may place them in abetter position to both obtain and maintain a long-term competitive advantage
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(Chen and Yuan, 2007), and to increase the chances of a return on their innovationactivities over time (Roberts and Amit, 2003). From this point of view, the choice ofinternal innovation activities can significantly influence innovation performance(Katila, 2002; Katila and Ahuja, 2002). A positive relationship is confirmed by, forexample, Love and Roper (1999), Mairesse and Mohnen (2001) and Frenz and Ietto-Gillies(2007). However, and according to March (1991) organizations have to make choicesbetween exploration of new alternatives, for example, invention of new technologies,and exploitation of existing ones, and maintaining an appropriate balance betweenexploration and exploitation is a crucial factor in firm survival and prosperity. This isbecause firms that engage in exploration to the exclusion of exploitation are likely to findthat they suffer the costs of experimentation without gaining many of its benefits,whereas firms that engage in exploitation to the exclusion of exploration are likelyto become effective in the short run but self-destructive in the long run. Consequently,an incorrect balance between exploration and exploitation could have a negative effecton organizational performance.
Based on these arguments regarding the use of internal innovation activities, andaccording to the existing empirical evidence we propose the following hypothesis:
H1. The use of internal innovation activities in isolation will have a positiveimpact on innovation performance.
Second, according to the absorptive capacity notion the use of external and cooperationactivities in isolation will have a negative impact in terms of innovation performance.From this point of view, knowledge from outside the company cannot become an inputfor internal innovation processes unless it develops at the same time its own internalinnovation activities, thus allowing the company to obtain a stock of knowledge thatenables it to absorb, evaluate and use that outside knowledge (Cohen and Levinthal,1989; Cohen and Levin, 1989). Therefore, based on this position we formulate thefollowing hypotheses:
H2. The use of external innovation activities in isolation will have a negativeimpact on innovation performance.
H3. The use of cooperation innovation activities in isolation will have a negativeimpact on innovation performance.
2.2 Effects on performance resulting from the use of joint sources for the development ofinnovation activitiesAs we have commented in the introduction section, in the literature the analysis of therelationship between innovation performance and the use of several sources for thedevelopment of innovation activities reveals the existence of various arguments andempirical evidence. On the one hand, some authors give significant support to thehypothesis of a positive effect on performance, which would indicate that these activitiesare complementary (Becker and Peters, 2000; Cassiman and Veugelers, 2002, 2006;Schmiedeberg, 2008; Frenz and Ietto-Gillies, 2007). On the other hand, a negative andtherefore substitutive relationship is found by other scholars (Love and Roper, 2001;Jirjahn and Kraft, 2006; Laursen and Salter, 2006; Vega-Jurado et al., 2009). These mixedempirical results indicate that we consider how to adjust our models of innovation toresolve these conflicting findings (Table I).
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Starting with the arguments supporting the hypothesis of complementarity between themeans of innovation, a relevant factor is the existence of what Cohen and Levinthal(1989, 1990) called “absorptive capacity”. According to this concept, knowledge fromoutside the company cannot become an input for internal innovation processes unless itdevelops into an internal investigation, thus allowing the company to obtain a stock ofknowledge that enables it to absorb, evaluate and use that outside knowledge (Cohenand Levinthal, 1990; Rosenberg, 1990; Kamien and Zang, 2000). First, under thisapproach, the combined use of internal innovation activities with external and/orcooperation can positively contribute to innovative performance in a number of ways: byallowing the company to better assess the quality of potential partners in innovationwhile reinforcing its attractiveness as a partner (Veugelers and Cassiman, 1999), byassisting the company in attaining more profitable innovation projects through greateraccess to resources and knowledge through the various sources (Haour, 1992; Arora andGambardella, 1994), by improving communication and coordination between internaland external activities, thereby increasing the likelihood of successfully completingcollaboration projects (Bougrain and Haudeville, 2002), and in the case of thecombination of external innovation and cooperation, by maximizing incomingspillovers[1]. Along this line, March (1991) suggests that organizations that developeffective instruments of coordination and communication can be expected to have betterperformance and to become more reliable, less likely to deviate significantly from themean of their performance distributions. Second, according to the absorptive capacitynotion the combined use of external and cooperation activities will have a negative
Author Data source Sample Results
Becker andPeters (2000)
MannheimInnovation Panel(MIP)
Manufacturing firms(Germany)
Internal and cooperation arecomplements
Love and Roper(2001)
ProductDevelopmentSurvey (PDS)
Manufacturing firms(UK, Germany andIreland)
Internal and external arecomplements, and internal andcooperation are substitutes
Cassiman andVeugelers (2002)
CommunityInnovation Survey(CIS)
Manufacturing firms(Belgium)
Internal and external, and internal,external and cooperation arecomplements
Beneito (2006) Survey ofEntrepreneurialStrategies (ESEE)
Manufacturing firms(Spain)
Internal and external are complements
Cassiman andVeugelers (2006)
CIS Manufacturing firms(Belgium)
Internal and external are complements
Jirjahn andKraft (2006)
Hannover Panel Manufacturing firms(Germany)
Internal and cooperation aresubstitutes
Laursen andSalter (2006)
CIS Manufacturing firms(UK)
Internal and external are substitutes
Schmiedeberg(2008)
MIP Manufacturing firms(Germany)
Internal and cooperation arecomplements
Vega-Juradoetal.(2009)
CIS Manufacturing firms(Spain)
Internal and cooperation arecomplements
Frenz and Ietto-Gillies (2007)
CIS Manufacturing andservice firms (UK)
Internal and external and internal andcooperation are complements (withoutstatistical confirmation)
Table I.Summary of the empirical
literature aboutcomplementarity
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impact in terms of innovation performance unless firm develops at the same time its owninternal innovation activities. As far as empirical evidence is concerned, the existence ofa complementary relationship has been confirmed empirically for the case of internalinnovation and cooperation in Schmiedeberg (2008), Becker and Peters (2000), Beckerand Dietz (2004) and Tsai and Wang (2009); for internal and external innovation inBeneito (2006) and Cassiman and Veugelers (2002, 2006); and for the three activitiestogether in Cassiman and Veugelers (2002).
Against these theoretical and empirical arguments in favor of complementarityTransaction Cost Economics (Coase, 1937; Williamson, 1985) considers that external andinternal innovation activities are substitutes (Arrow, 1962; Pisano, 1990). This literaturehas focused particularly on the choice between internal and external development,which is known as “Make or buy decision” (Veugelers and Cassiman, 1999; Beneito,2003). According to this theory, on the one hand, the external innovation enablescompanies to eliminate the costs and risks associated with internal development(Chen and Yuan, 2007; Huang et al., 2009), which are generally higher than those derivedfrom acquisition (Beneito, 2003) as well as to access externally available specialistknow-how, and to attain the economies of scale associated with specialization (Veugelersand Cassiman, 1999; Chen and Yuan, 2007). On the other hand, the presence of high levelsof complexity, specificity and uncertainty associated with R&D, and the possibility ofopportunistic behaviour in transactions, reduce the potential benefits of the externalinnovation, making the source of internal hierarchy to the market more efficient(Williamson, 1985). These arguments support the hypothesis of a substitutabilityrelationship between internal and external activities (Vega-Jurado et al., 2009), althoughthey could also suggest the use of cooperation to achieve the best balance between costand risk. According to this, innovation can be understood like other activity that costsless when firms develop it through a relational contract. Spillovers also strengthen theargument for the existence of substitutive innovation activities. In the case of incomingspillovers, companies that put great effort into R&D and therefore occupy a position atthe frontier of knowledge may not gain any advantage from the possibility of havinggreater external knowledge. In the case of outgoing spillovers, the problem is thedifficulty for the company in maintaining the control and ownership of its R&D results(Cassiman et al., 2002; Amir et al., 2003; Belderbos et al., 2004a, b).
From an empirical point of view the substitutive relation between internal andexternal innovation finds support in Veugelers (1997), Love and Roper (2001) andLaursen and Salter (2006). Jirjahn and Kraft (2006) for their part, empiricallydemonstrated that internal innovation and cooperation are substitutes.
Assuming the proposals of the absorptive capacity notion and taking into accountthat the majority of the available empirical evidence supports the hypothesis ofcomplementarity between internal and external, internal and cooperation and for thethree activities together we propose the following hypotheses:
H4. The use of internal and external innovation activities together will have apositive impact on innovation performance.
H5. The use of internal and cooperation innovation activities together will have apositive impact on innovation performance.
H6. The use of external and cooperation innovation activities together will have anegative impact on innovation performance.
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H7. The use of internal, external and cooperation innovation activities togetherwill have a positive impact on innovation performance.
Once the hypotheses have been formulated we turn into the empirical study of thepaper in order to test them.
3. Empirical studyThis section presents the data sources and the methodology that includes theexplanation of the variables and the empirical model.
Data sourcesThe empirical analysis was carried out by using firm level data contained in the Survey onTechnological Innovation in Firms conducted by the National Statistics Institute of Spain(INE) as part of the CIS-3. The Community Innovation Surveys (CISs) are one of the maininstruments developed by the EU in order to obtain data on innovation indicators and toaccess national innovation performance. Results were gathered via a postal questionnaireasking questions on topics such as “effects of innovation”, “factors hampering innovation”and “innovation-related expenditure”. The survey goes out to a sample of enterprises ineach country. Within each country the sample is designed to be representative of allregions, all industrial sectors and all enterprise sizes. The survey was carried out inaccordance with the methodological directives defined in the Oslo Manual (OECD, 1997).
The survey includes data on 11,778 firms belonging to both the manufacturing andservice sectors which represents a response rate of 73 percent of the total sample.However, the sample used in this study is restricted to firms that innovate. These firmsare differentiated from those that do not innovate based on their answer to the surveyquestion of whether they had actively engaged in innovation (by introducing new orimproved products or processes) in the period comprising 1998-2000. In summary, oursample consists of 3,964 manufacturing and service firms that responded that they hadinnovated in products or processes during this period. Regarding the differencesbetween innovating and non-innovating firms our data show that innovating firms aremore specialized in services activities, smaller and operate more in international marketsthan non-innovating firms.
MethodologyVariables.
Dependent variable. Our phenomenon of interest is the effect of different innovationstrategies on innovation performance in order to determine if these strategies arecomplementary. Therefore, our dependent variable, TURNOVER, is an innovationperformance measure that indicates the percentage of the firm’s turnover generated bynew or substantially improved products during the period 1998-2000. This variable issimilar to the ones used in numerous other papers (Klomp and van Leeuwen, 2001;Criscuolo and Haskel, 2003; Monjon and Waelbroeck, 2003; Faems et al., 2005;Cassiman and Veugelers, 2006; Cetindamar and Ulusoy, 2008).
Independent variables. The independent variables indicate different innovationstrategies utilized by the firm. The construction of these variables has been determinedfor two main reasons. On the one hand, we have followed procedures employed inseveral other papers (Veugelers, 1997; Belderbos et al., 2004a, 2006; Veugelers andCassiman, 2005; Cassiman and Veugelers, 2006). On the other hand, the construction has
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been conditioned for the kind of the available data. In the survey firms answered if theyhave developed or not innovation through internal, external or cooperation activities in acategorical way. Thus, we have constructed the following dummy variables:
. ONLY_INTERNAL. A dummy variable that takes value 1 if firms innovated onlythrough internal innovation activities in the period 1998-2000, and 0 otherwise.
. ONLY_EXTERNAL. A dummy variable that takes value 1 if firms innovatedonly through at least one of the following external activities in the period1998-2000, and 0 otherwise: acquiring R&D services, acquiring machines orequipment, or acquiring intangible technology (licenses, know-how, etc).
. ONLY_COOPERATION. A dummy variable that takes value 1 if firmsinnovated only through cooperation with other partners in R&D and innovationin the period 1998-2000, and 0 otherwise.
. INTERNAL&EXTERNAL. A dummy variable that takes value 1 if firmsinnovated by combining internal and external innovation activities, and 0otherwise.
. INTERNAL&COOPERATION. A dummy variable that takes value 1 if firmsinnovated by combining internal and cooperation innovation activities, and 0otherwise.
. EXTERNAL&COOPERATION. A dummy variable that takes value 1 if firmsinnovated by combining external and cooperation innovation activities, and 0otherwise.
. INTERNAL&EXTERNAL&COOPERATION. A dummy variable that takesvalue 1 if firms innovated by combining internal, external and cooperationinnovation activities, and 0 otherwise.
. OTHER. For some innovation activities the Innovation Technological Survey didnot differentiate if they are internal or external, and then, it would be impossible toassign them as either purely internal or purely external activities. For this reason,we have created the variable OTHER for the empirical study as a referencecategory which is a dummy variable that takes value 1 in this case and 0 otherwise.
Control variables. Furthermore, we have included two additional variables in ouranalysis to control for firm characteristics that have been identified in the empiricalliterature: firm size (Veugelers, 1997; Faems et al., 2005; Cassiman and Veugelers, 2006;Arbussa and Coenders, 2007) and industry dummies (Veugelers, 1997; Beneito, 2003;Belderbos et al., 2006; Faems et al., 2005; Cassiman and Veugelers, 2006; Arbussa andCoenders, 2007; Chen and Yuan, 2007):
. SIZE. This variable measures de size of the firm and has been used traditionally asa control variable (Beneito, 2006; Jirjahn and Kraft, 2006; Laursen and Salter, 2006;Schmiedeberg, 2008; Frenz and Ietto-Gillies, 2007) since the work of Schumpeter(1943). In the paper the author proposed that the size of a company can be adeterminant of its innovative activity. However, approaches to this theme in theliterature have not demonstrated concurrence. On the one hand, small firms havemore flexible structures, which can be an advantage to innovation (Damanpour,1992), so we should expect greater innovative performance in these firms.However, large companies can achieve economies of scale in their innovationactivities (Cockburn et al., 2008), and therefore greater innovative performance.
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Other authors, however, suggest that the relationship between size and innovationis not necessarily linear (Kamien and Schwartz, 1982; Cohen and Levin, 1989;Evangelista et al., 1997) and may be affected by other characteristics of thecompany and sector (Veugelers and Cassiman, 1999). To measure firm size weconstruct a variable that takes value 1 if the number of employees of the firm isunder 45, value 2 if this number is between 45 and 500, and value 3 if the firm hasover 500 employees. This way of constructing the variable is determined by thetype of data supplied in the survey.
. INDUSTRY_DUMMIES. These variables indicate the industrial sector of thefirm, which is considered to reflect, among other things, the technologicalopportunity and the appropriability conditions that the firm faces (Beneito, 2003).The use of industry dummies was introduced by Scherer (1965) and they areusually used to eliminate industry differences (Chen and Yuan, 2007).We construct our industry dummies at two-digit NACE, following Beneito(2003) and Belderbos et al. (2004a, 2006). The NACE code system is the Europeanstandard for industry classifications and stands for “General Name for EconomicActivities in the European Union”. This renders a total of 21 sectors, as labelled inTable IV. Therefore, 21 dummy variables have been constructed with the sector“other manufacturing” taken as the category of reference.
Empirical model. To evaluate the existence of complementary relationships betweeninnovation activities we follow the “Productivity Approach”[2], as have several otherresearchers on innovation (Cassiman and Veugelers, 2002, 2006; Belderbos et al., 2006;Schmiedeberg, 2008). According to the “Productivity Approach”, the existence ofcomplementarity can be tested by carrying out a regression of a measure of innovationperformance on exclusive combinations of innovation activities (innovation strategies).If innovation activities are complementary, the effect of their complementarity willshow up in measures of innovation performance.
The equation:
Pi Ai1;Ai
2;Ai3;Xi; u;b
� �¼ 1 2 Ai
1
� �1 2 Ai
2
� �1 2 Ai
3
� �u000 þ Ai
1ð1 2 Ai2Þ
ð1 2 Ai3Þu100 þ 1 2 Ai
1
� �Ai
2 1 2 Ai3
� �u010
þ 1 2 Ai1
� �1 2 Ai
2
� �Ai
3u001 þ Ai1Ai
2 1 2 Ai3
� �u110
þ Ai1 1 2 Ai
2
� �Ai
3u101 þ 1 2 Ai1
� �Ai
2Ai3u011
þ Ai1Ai
2Ai3u111 þ Xibþ 1i
ð1Þ
shows the relation between innovation performance and innovation strategies, andwhere:
i refers to firm i.
Aij [ {0; 1};j ¼ 1; 2; 3 indicates the innovation activity choices of firm i.
ukl are the coefficients on the firm’s innovation strategy choice.
Xi is a vector that includes other firm characteristics as control variables.
1i is the error term.
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and the conditions for complementarity between the practices A1, A2 and A3. Theseconditions imply that higher returns are achieved when the practices are used togethercompared to a situation when they are used in isolation (Belderbos et al., 2006; Cassimanand Veugelers, 2006).
We have that if ONLY_INTERNAL is A1¼1, ONLY_EXTERNAL is A2¼1, andONLY_COOPERATION is A3¼1, then INTERNAL&EXTERNAL¼ A1A2(1 2 A3),INTERNAL&COOPERATION ¼ A1(1 2 A2)A3, EXTERNAL&COOPERATION¼ (1 2 A1)A2A3, INTERNAL&EXTERNAL&COOPERATION¼ A1A2A3 andNOINTERNAL&NOEXTERNAL&NOCOOPERATION¼ (1 2 A1)(1 2 A2)(1 2 A3).
To test the existence of complementarity by analyzing how differentinnovation strategies (combination of innovation activities) affect the performance ofthe innovation process following the productivity approach, we use a a linearregression by OLS over the dependent variable. Our empirical model has been specifiedas follows:
TURNOVER ¼ aþ bXi þ d1 ONLY_INTERNAL þ d2 ONLY_EXTERNAL
þ d3 ONLY_COOPERATION þ j1 INTERNAL&EXTERNAL
þ j2 INTERNAL&COOPERATION
þ j3 EXTERNAL&COOPERATION
þ j4 INTERNAL&EXTERNAL&COOPERATION
þ j5 NOINTERNAL&NOEXTERNAL&NOCOOPERATION þ 1i
where the X-vector consists of the firm-level control variables included in our analysis(firm size and industry dummies at the two-digit NACE level).
4. Empirical resultsFrequencies of innovation strategiesTable II presents data showing the frequency of the various innovation strategies. Itshows that the most common innovation strategy selected by firms is the use of externalactivities alone to seek innovations (33.22 percent of firms use this strategy), followed bythe strategy that combines internal and external activities (21.97 percent). Theinnovation strategies employed by the least number of firms are those that containcooperation activities (ONLY_COOPERATION, INTERNAL&COOPERATION and
Innovation strategy Frequency of innovation strategy Share (%)
ONLY_INTERNAL 412 10.39ONLY_EXTERNAL 1,317 33.22ONLY_COOPERATION 35 0.88INTERNAL&EXTERNAL 871 21.97INTERNAL&COOPERATION 127 3.20EXTERNAL&COOPERATION 132 3.33INTERNAL&EXTERNAL&COOPERATION 468 11.81OTHER 602 15.19Total 3,964 100.00
Table II.Frequencies ofinnovation strategies
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EXTERNAL&COOPERATION). Furthermore, in Table II we find that 15.19 percentof firms develop some kind of innovative activity that cannot be classified as internal orexternal.
Descriptive statistics and correlations of innovation strategiesIn the first place, Table III shows that strategies combining internal innovation with anyother innovation activity are positively correlated with the measure of innovationperformance as expected. This result is in line with the notion of absorptive capacity(Cohen and Levinthal, 1989). Second of all, we can see that other kinds of innovationstrategies that do not include internal innovation are either negatively correlated withthe turnover variable (ONLY_EXTERNAL and ONLY_COOPERATION strategies),or not correlated with it (EXTERNAL&COOPERATION strategy).
Distribution of firms across industries and innovation strategiesThe comparison across industries shown in Table IV indicates that the innovationstrategies selected are not dissimilar between services and manufacturing industries,and are in line with the results presented in Table III for the total of the sample. Thus,the ONLY_EXTERNAL strategy is the most frequent strategy used by all themanufacturing and services sectors at two-digit NACE level, in the same way as forthe whole sample. At the other extreme, and in line with the general results of Table III,the ONLY_COOPERATION strategy is the one followed by the fewest firms of eachsector at two-digit NACE level.
Regression resultsWith respect to the regression results, the significance of the F-value at the 0.01 levelindicates the existence of a linear relation between the dependent variable of the model(TURNOVER) and the independent variables, as can be seen in Table V.
The regression equation preserves 4.2 percent of the variance of the model. Thisrelatively low value can be explained for the use of cross-section data, because it hasbeen established that analyses carried out for temporal series of data obtain, in generalterms, higher values for R 2 (Uriel and Aldas, 2005).
Additionally, the last column of Table V presents the values for the variance inflationfactor (VIF) associated with each independent variable in the regression equation. Thisis an analysis typically used to deal with the issue of multicollinearity, especially whenexplanatory variables are strongly correlated (Dielman, 1991). The maximum VIF valueshown in Table III is 2.15, which falls far short of 10, the cut-off considered by Neter et al.(1983) or Hair et al. (1999) as a limit. Therefore, the multicollinearity problem does notseem to exist for our variables.
As far as the independent variables are concerned, all of them are statisticallysignificant with the exception of the SIZE control and the EXTERNAL&COOPERATIONinnovation strategy variables. And, with respect to the industry dummies, only thevariable for the “Machines and equipment” sector proves to be statistically significant( p , 0.1), and in this case, with a negative sign.
Beginning with the variables that prove to be statistically significant in the multipleregression equation, the Table V shows on the one hand, the significant andpositive effect that ONLY_INTERNAL, INTERNAL&EXTERNAL,INTERNAL&COOPERATION and INTERNAL&EXTERNAL&COOPERATION
Complementarity
567
Mea
nS
DV
1V
2V
3V
4V
5V
6V
7V
8V
9
V1:
SIZ
E1.
730.
462
1V
2:T
UR
NO
VE
R0.
220.
291
20.
007
1V
3:O
NL
Y_IN
TE
RN
AL
0.10
0.30
52
0.01
10.
044
**
1V
4:O
NL
Y_E
XT
ER
NA
L0.
330.
471
0.00
82
0.17
3*
*2
0.24
0*
*1
V5:
ON
LY
_C
OO
PE
RA
TIO
N0.
010.
094
0.00
32
0.03
7*
20.
032
*2
0.06
7*
*1
V6:
INT
ER
NA
L&
EX
TE
RN
AL
0.22
0.41
40.
000
0.10
2*
*2
0.18
1*
*2
0.37
4*
*2
0.05
01
V7:
INT
ER
NA
L&
CO
OP
ER
AT
ION
0.03
0.17
62
0.01
60.
05*
*2
0.06
2*
*2
0.12
8*
*2
0.01
72
0.09
7*
*1
V8:
EX
TE
RN
AL
&C
OO
PE
RA
TIO
N0.
030.
179
0.01
92
0.00
12
0.06
3*
*2
0.13
1*
*2
0.01
82
0.09
8*
*2
0.03
4*
1V
9:IN
TE
RN
AL
&E
XT
ER
NA
L&
CO
OP
ER
AT
ION
0.12
0.32
32
0.01
30.
051
**
20.
125
**
20.
258
**
20.
038
*0.
194
**
20.
067
**
20.
068
**
1V
10:O
TH
ER
0.15
0.35
90.
008
0.00
82
0.14
4*
*2
0.29
8*
20.
40*
20.
224
**
20.
077
**
20.
079
**
20.
155
**
Note:
Th
eco
rrel
atio
nis
sig
nifi
can
tat
:*
0.05
and
**
0.01
lev
els
Table III.Descriptive statistics andcorrelations of innovationstrategies
JMTM23,5
568
NA
CE
Sec
tor
No.
ofob
serv
atio
ns
insa
mp
le(%
)
ON
LY
_IN
TE
RN
AL
(%)a
ON
LY
_E
XT
ER
NA
L(%
)a
ON
LY
_C
OO
PE
RA
TIO
N(%
)aIN
T&
EX
T(%
)aIN
T&
CO
OP
(%)a
EX
T&
CO
OP
(%)a
INT
&E
XT
&C
OO
P(%
)aO
TH
ER
(%)a
Tot
al(%
)a
10-1
4M
inin
g38
(0.9
6)7.
9039
.47
0.00
21.0
52.
632.
6310
.53
15.7
910
015
,16
Foo
d30
0(7
.57)
10.3
233
.00
0.67
27.6
73.
673.
676.
0015
.00
100
17-1
9T
exti
le24
7(6
.23)
9.31
30.7
71.
2119
.84
4.05
4.05
17.0
013
.77
100
21P
aper
66(1
.66)
7.58
37.8
60.
0027
.27
1.52
4.55
1.52
19.7
010
022
Pri
nti
ng
and
pu
bli
shin
g13
4(3
.38)
12.6
929
.85
0.00
20.9
05.
222.
2413
.43
15.6
710
023
,24
Pet
role
um
and
chem
ical
s25
7(6
.48)
7.00
38.1
31.
5620
.23
2.33
1.95
7.00
21.8
010
025
Ru
bb
eran
dp
last
ic12
2(3
.08)
11.4
830
.33
1.63
25.4
13.
281.
646.
5619
.67
100
27M
etal
lurg
y10
8(2
.72)
12.0
434
.26
0.00
18.5
20.
934.
6311
.11
18.5
110
028
Met
alp
rod
uct
s21
5(5
.42)
11.1
631
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0.93
25.5
81.
863.
2612
.56
13.0
210
029
Mac
hin
esan
deq
uip
men
t18
2(4
.59)
13.1
928
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0.55
24.1
83.
853.
3012
.64
14.2
910
030
-33
Ele
ctro
nic
s29
0(7
.32)
12.0
733
.79
1.38
22.7
63.
451.
3815
.52
9.65
100
34,
35C
ars
and
tran
spor
t18
6(4
.69)
9.68
28.4
90.
0024
.19
2.69
1.08
9.14
24.7
310
020
,26
,36
,37
Oth
erm
anu
fact
uri
ng
494
(12.
46)
9.31
40.0
80.
6117
.80
3.24
3.85
11.3
413
.77
100
40,
41U
tili
ties
36(0
.91)
8.33
44.4
40.
0019
.45
0.00
0.00
5.56
22.2
210
045
Bu
ild
ing
96(2
.42)
8.33
33.3
32.
0817
.71
1.05
6.25
14.5
816
.67
100
50-5
2R
epai
rof
mot
orv
ehic
les,
wh
oles
ale
and
reta
iltr
ade
229
(5.7
8)8.
7331
.44
0.87
19.6
53.
933.
9313
.55
17.9
010
055
Hot
els
and
rest
aura
nts
55(1
.39)
3.64
36.3
61.
8216
.36
3.64
9.09
12.7
316
.36
100
60-6
4T
ran
spor
t,st
orag
ean
dco
mm
un
icat
ion
176
(4.4
4)10
.23
31.2
51.
7026
.14
2.27
3.98
10.2
314
.20
100
65-6
7F
inan
cial
inte
rmed
iati
on11
8(2
.98)
10.1
729
.66
0.85
17.8
05.
081.
7019
.49
15.2
510
070
-74
Bu
sin
ess
serv
ices
447
(11.
28)
12.7
532
.44
0.45
23.2
63.
363.
8013
.65
10.2
910
090
-93
En
vir
onm
enta
lan
dot
her
serv
ices
168
(4.2
4)12
.50
27.9
81.
7920
.83
4.17
4.76
13.6
814
.29
100
No.
ofto
tal
obse
rvat
ion
sin
the
sam
ple
(%)
3,96
4(1
00)
Note:
aP
erce
nta
ges
ofsu
bse
ctor
sam
ple
Table IV.Distribution of firms
across industries andinnovation strategies
Complementarity
569
innovation strategies have on innovation performance. For this reason, we accept ourhypotheses H1, H4, H5 and H7 (Table VI). The positive effect of the three first variableson innovation performance, due to they are the combination of internal innovationactivities with external, cooperation, and external and cooperation activities theirsignificant and positive coefficients support the argument of the complementary natureof different innovation activities. These results suggest that the complementarity onlyoccurs when combining internal innovation activities with any other innovation activity(external, cooperation or both). This argument is supported by the “absorptive capacity”theory, as well as by empirical evidence (Rothwell et al., 1974; Freeman, 1991; Arora andGambardella, 1994; Veugelers, 1997; Cassiman and Veugelers, 2006). The positivecoefficient of the ONLY_INTERNAL innovation strategy indicates that the performanceof internal innovation activities by the firm positively affects the innovationperformance measure.
On the other hand, the coefficients of ONLY_EXTERNAL andONLY_COOPERATION are negative. Thus, our working hypotheses, H2 and H3,are accepted. These results mean that the use of these innovation strategies negativelyaffects the firm’s innovation performance. This negative relation could be due to thenecessity of internal innovation activity in firms in order to make external andcooperation activities successful in terms of innovation performance. This result issupported by the absorption capacity notion.
Finally, as far as the EXTERNAL&COOPERATION strategy is concerned its effecton innovation performance is negative but not significant, so H4 is rejected.
5. Conclusions, implications limitations and future researchThis study aims to find empirical evidence for the idea that various innovation activitiesare, by nature, complements rather than substitutes, and that the use of differentinnovation activities is related with innovation performance. We differentiate betweenthe type of innovation strategy (only internal innovation, only external innovation,only cooperation and all the possible combinations of them) and consider a performance
VariableB (no standardized
coefficients) SEb (standardized
coefficients) t-value VIF
SIZE 20.002 0.010 20.003 20.189 1.053ONLY_INTERNAL 0.033 * 0.018 0.034 1.783 2.146ONLY_EXTERNAL 20.077 * * * 0.014 20.125 25.469 1.053ONLY_COOPERATION 20.120 * * 0.050 20.039 22.418 1.925INTERNAL&EXTERNAL 0.051 * * * 0.015 0.073 3.355 1.179INTERNAL&COOPERATION 0.090 * * * 0.028 0.055 3.221 1.188EXTERNAL&COOPERATION 20.002 0.02 20.001 20.069 1.594INTERNAL&EXTERNAL&COOPERATION 0.037 * * 0.018 0.041 2.064 1.053INDUSTRY DUMMIES Included Included Included IncludedConstant 0.242 * * * 0.024 10.074R 2 0.042F 6.143 * * *
No. of observations 3,964
Note: Significant at: *p , 0.1, * *p , 0.05 and * * *p , 0.01
Table V.Regression results:dependent variable% turnover from newor substantiallyimproved products
JMTM23,5
570
measure: the percentage of the firm’s turnover generated by new or substantiallyimproved products in the period 1998-2000. The empirical study is carried out using dataon Spanish manufacturing and service firms from the CIS-3 for Spain.
Theoretical and research implicationsThe results of the empirical study confirm the proposals put forward by the absorptivecapacity notion. Therefore, our results, which support previous empirical literature,confirm the existence of complementarity between internal innovation and externalinnovation as well as between internal innovation and cooperation. However, thearguments from transaction cost theory that suggest a more substitutive relationship donot find empirical support in our analysis.
Beginning with the innovation strategies based on the use of only one innovationactivity, our results indicate that internal innovation activity positively affectsinnovation performance, which is defined as the percentage of the firm’s turnover
Hypotheses Variable Empirical results
H1. The use of internalinnovation activities in isolationwill have a positive impact oninnovation performance
ONLY_INTERNAL (þ ) * Accepted
H2. The use of externalinnovation activities in isolationwill have a negative impact oninnovation performance
ONLY_EXTERNAL (2 ) * * * Accepted
H3. The use of cooperationinnovation activities in isolationwill have a negative impact oninnovation performance
ONLY_COOPERATION (2 ) * * * Accepted
H4. The use of internal andexternal innovation activitiestogether will have a positiveimpact on innovationperformance
INTERNAL&EXTERNAL (þ ) * * * Accepted
H5. The use of internal andcooperation innovation activitiestogether will have a positiveimpact on innovationperformance
INTERNAL&COOPERATION (þ ) * * * Accepted
H6. The use of external andcooperation innovation activitiestogether will have a negativeimpact on innovationperformance
EXTERNAL&COOPERATION (2 ) Rejected
H7. The use of internal, externaland cooperation innovationactivities together will havea positive impact on innovationperformance
INTERNAL&EXTERNAL&COOPERATION (þ ) * * * Accepted
Notes: Signigicant at: *p , 0.1, * *p , 0.05 and * * *p , 0.01; “(þ )” positive effect of the variable oninnovation performance; “(2 )” negative effect of the variable on innovation performance
Table VI.Hypotheses, variablesand empirical results
of OLS regression
Complementarity
571
generated by new or substantially improved products during the period 1998-2000.However, when firms seek to innovate by using only external or only cooperationactivities, innovation performance decreases. This result is supported by the “absorptivecapacity” notion. Along this line, the use of external innovation or cooperation does notyield positive effects in terms of innovation performance if firms do not simultaneouslyinvest in their own innovation activities that allow them to internalize and select externalknowledge.
Turning to the results obtained for the combination of innovation strategies ofdifferent innovation activities, they indicate complementarity for all combinations withthe exception of the EXTERNAL&COOPERATION strategy, for which wasnon-significant. Due to the fact that the remaining innovation strategies includeinternal innovation activity, we can conclude that complementarity appears onlybetween this innovation strategy and any other. Thus, our results confirm the existenceof complementarity between different innovation activities, when these include internalinnovation activity, which is in line with the absorption capacity point of view.
Management implicationsThese results have important implications for managers and public policy makers. Theresults suggest that innovation management requires the integration of various innovationactivities with the aim of maximizing the positive effects of each one. This managementapproach is in contrast with the profile of innovation strategies for Spanish firms, in whichthe most frequent strategy is the “only external” acquisition of innovations (33.22 percent),followed by the combination of internal and external activities (21.97 percent). Spanishfirms should invest more in creating their own internal innovation activities, which,combined with the high levels of external acquisitions characteristic of Spanish firms,would increase their innovation performance. This outcome is supported by both theabsorptive capacity theory and our empirical results. Along these lines, given that themajority of Spanish firms are small or medium-sized, with few resources to carry outinternal innovation, the design of concrete policies aimed at promoting internal innovationactivities in these types of firms, would have great relevance. Armed with this knowledge,public officials could draw up policies to assist small firms, e.g. through the granting offinancial assistance, the provision of employee training programs or supplying strongsupports for education of middle management in innovation which would increase thehuman and social capital of Spanish firms, and contribute positively to their innovativeness.
Limitations and future researchThe main limitation of the paper is the above mentioned use of cross-section data, whichyields less robust results in the empirical test. Therefore, it would be worthwhile to usethe methodology employed in this study using a temporal series of data. The use oftemporal series in the case of this study would be very interesting due to the dynamicnature of the innovation processes which made necessary to use this kind of data andbecause the use of panel data methodology could deal with the problems regarding to thecross-section estimations.
Furthermore, we propose to continue the study presented here by analyzing thedeterminants of firm selection of one innovation strategy over another. This researchwould be supported by the literature proposals presented in this paper.
JMTM23,5
572
Notes
1. Spillovers are the non-appropriable amount of knowledge that is produced by a firm’sinnovation efforts (Kaiser, 2002) and arise due to failures in the protection mechanism ofknowledge generated in an innovating firm. More precisely, the industrial organizationliterature analyzes the effect of two kinds of spillovers (Belderbos et al., 2004b): incomingspillovers (external information flows into the firm that increase the attractiveness ofcooperation for the firm) and outgoing spillovers (information flows out of the firm that limitthe appropriability of results from its innovation process).
2. This approach is based on the theory of supermodularity developed by Milgrom and Roberts(1990, 1995). This theory consists of a mathematical theory that states the necessaryconditions for activities to be complementary. These conditions can be summarized as follows(Cassiman and Veugelers, 2006): suppose that there are two activities, A1 y A2, and eachactivity can be performed by the firm (A1 ¼ 1) or not (A2 ¼ 0). The function P (A1, A2) issupermodular and A1 and A2 are complementary only if P(1, 1) 2 P(0, 1)$P(1, 0) 2 P(0, 0),i.e. adding an activity while the other activity is already been performed has a higherincremental effect on performance than when doing the activity in isolation.
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About the authorsAna Ma Serrano-Bedia received her PhD in Business Administration from the University ofCantabria, Spain, where she has been Assistant Professor of Operations Management since 1996at the Department of Business Administration. She has co-authored more than 35 journal articleson a variety of topics. Her primary interests are the effects of quality and environmentalmanagement systems on organizational strategies, and R&D and innovation management.Ana Ma Serrano-Bedia is the corresponding author and can be contacted at: [email protected]
Ma Concepcion Lopez-Fernandez is an Assistant Professor in Business Organization at theDepartment of Business Administration of the University of Cantabria, where she received herPhD. She has been the Dean of the Faculty of Business-Economics (1996-2004) and is currentlythe Vice-Rector for Academic Affairs. She has co-authored more than 50 journal articles relatedto business strategy and structure, innovation, and natural environment and tourism.
Gema Garcıa-Piqueres is a Teaching Assistant in Business Organization at the Department ofBusiness Administration of the University of Cantabria where she received her PhD. Her mainline of research is the study of the innovation processes from both firm’s and innovation systems’perspective, focusing on the relations between their partners as well as on sectoral differences.
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