Antecedents of innovation impacts in publicly funded collaborativeR&D projects

Yiannis E. Spanos a,n, Nicholas S. Vonortas b,c, Irini Voudouris a,1

a Athens University of Economics and Business, Management Science and Technology Department, 76, Patission Street, GR10434 Athens, Greeceb Department of Economics, The George Washington University, 1957 E Street, N.W., Suite 403, Washington, DC 20052, United Statesc Center for International Science and Technology Policy, The George Washington University, 1957 E Street, N.W., Suite 403, Washington,DC 20052, United States

a r t i c l e i n f o

Available online 1 October 2014

Keywords:Collaborative R&DInnovation impactsPublic support for R&D

a b s t r a c t

This study investigates antecedents of innovation impacts derived by firms participating in publiclyfunded collaborative R&D projects. Innovation impacts are reflected in product and process innovation,and inimitability of the resulting technology. Project and firm-specific factors are considered. The datasetfor our analysis is based on survey responses from 694 firms collected through an extensive datacollection effort of pan-European scale. The general picture emerging from our results is that firmsengaging in such projects can gain in terms of innovation, conditional on their superior in-housecapabilities and the nature of the project itself.

& 2014 Elsevier Ltd. All rights reserved.

1. Introduction

It is widely understood that firms do not rely exclusively ontheir internal R&D activities to maintain their technologicalcompetitiveness (Powell and Grodal, 2005; Malerba andVonortas, 2009). Technological inter-firm alliances constitute aprominent complementary vehicle for the creation and exploita-tion of new knowledge, a process upon which economic and socialdevelopment is based. Since the mid-eighties the number of R&Dalliances has grown rapidly (Hagedoorn, 2002), and so has theacademic and policy interest in the phenomenon. Reflecting thelatter, direct subsidies for collaborative research have become acentral element in research and technology policy in advancedeconomies.

Our aim in this study is to identify determinants of innovationimpacts obtained by firms that participate in publicly fundedcooperative R&D projects; specifically, projects funded by the fifthand sixth European Union (EU) Framework Programme (FP) forResearch, Technological Development and Demonstration (1998–2006). Public funding of research and innovation is typicallyjustified in terms of market failures related with problems ofincomplete appropriability of the returns to R&D resulting fromknowledge spillover mechanisms (Nelson, 1959; Arrow, 1962),

failures that lead to serious private sector underinvestment inR&D. Public intervention is also needed to address systemic fail-ures that block the functioning of innovation systems as a result ofconflicting incentives between enterprises and public sector orga-nizations, institutional rigidities stemming from narrow speciali-zation, asymmetric information, and lack of networking (OECD,1999). Seen under this light, the FP represent a key instrument ofthe European research policy towards the (admittedly ambitious)goal of transforming the EU into the most competitive anddynamic knowledge-based economy in the world (i.e. the “Lisbonobjectives”).

At the EU level, there exists convincing evidence from a largenumber of evaluation studies that the FP do indeed play asignificant role in the European R&D landscape. At the nationallevel of analysis, the potentially positive effects of publicly fundedcollaborative research projects on participating organizations'innovative activities and performance have been fairly well estab-lished in the literature (see, for example, Hagedoorn et al. (2000),Link et al. (2002), Hemphill and Vonortas (2003), Busom (2000),Wallsten (2000), Lach (2002), Almus and Czarnitzki (2003),Czarnitzki et al. (2007)). We know little, however, about thefactors that contribute to the innovation impacts obtained byparticipants in such projects. Our study is an attempt to provideanswers towards this direction.

For present purposes, innovation impacts are conceived tocomprise two basic dimensions: first, new/improved products(goods and services) and processes achieved by participating firmsand second, the “inimitability” of the resulting technology. Project

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Technovation

http://dx.doi.org/10.1016/j.technovation.2014.07.0100166-4972/& 2014 Elsevier Ltd. All rights reserved.

n Corresponding author. Tel.: þ30 210 8203561.E-mail addresses: spanos@aueb.gr (Y.E. Spanos),

vonortas@gwu.edu (N.S. Vonortas), ivoudour@aueb.gr (I. Voudouris).1 Tel.: þ30 210 8203559.

Technovation 36-37 (2015) 53–64

and firm-specific factors are considered, together with a numberof controls. The dataset for our analysis is based on surveyresponses regarding collaborative R&D behavior and outcomescollected through an extensive data collection effort of pan-European scale. The field survey targeted exclusively organizations(companies, universities, research institutes, etc.) that were knownto have been involved in the fifth and/or sixth FrameworkProgramme (FP5, FP6). As such, ours is not a study about thecausal impact of (publicly funded) collaborative R&D per se, as thiswould require an entirely different research design (notably thepresence of an additional comparable sample of firms not engagedin cooperative research, or perhaps a comparable sample of firmsparticipating in privately-funded research consortia); neither dowe adopt an additionality perspective in an attempt to isolate theimpact of public support on the innovative behavior of participat-ing firms (i.e. how the firmwould carry out its innovative activitiesabsent public funding) (see, for example, Wanzenböck et al.(2013), Hsu et al. (2009), Lee (2011)), as this would require thatwe observe our sample firms before and after participation in thefocal project. Instead, we seek to identify project- and firm-levelcorrelates of innovation impacts derived by companies participat-ing in FP research projects. The general picture emerging from ouranalyses is that such antecedents concern superior in-houseinnovation-related capabilities and the nature of the project itself.

The remainder of the paper is organized as follows. The nextsection presents the theoretical background and hypothesesdevelopment. Section 3 describes the data collection methodologyand the measures used in our analyses. Section 4 presents theresults, followed by a discussion of the findings. The final sectionconcludes.

2. Background and hypotheses

Given the central role the FP play within the wider context ofEuropean science and technology policy, it is not surprising that avast and diverse body of policy and professional literature hassought to evaluate their impacts; in contrast, to our knowledge,there is very little research about the specific factors contributingto firms' ability to derive innovation impacts from their participa-tion in public support schemes.

A number of evaluation studies over the past couple of decadeshave cumulated evidence that the FP support scientific excellenceby attracting top scientists and leading research institutions;contribute significantly to long-term economic growth; deliverpositive social and environmental impacts; generate crowding-ineffects (i.e. have a positive effect on the total availability of R&Dfunding and on the level of companies' R&D investments); andappear to produce large numbers of patents, innovations, andother types of micro-economic benefits for the participatingenterprises, even though firms take part in such projects toachieve knowledge and technology-related objectives rather thanto develop commercial products and services. (For a detailedreview of these impacts, see “Impact Assessment Accompanyingthe Communication from the Commission ‘Horizon 2020 – TheFramework Programme for Research and Innovation’”, 2011.)

The academic literature, on the other hand, conducted mainly,but not exclusively, at the national level of analysis and basicallylooking at issues of additionality of public support for innovation,at least partially confirms the idea that public funding (including,but not limited to, support for collaborative R&D) has a positiveeffect on innovation activities and outputs. For instance, Massoand Vahter (2008), using Estonian data from CIS4 (2002–2004),find that public innovation funding positively affects product butnot process innovation (using CIS3 data, covering 1998–2000, theimpact is insignificant). Using cross-country data from CIS3, an

OECD study (Jaumotte and Pain, 2005) finds that an increase of1 percentage point in the proportion of firms receiving publicsupport corresponds with a rise of 0.4 percentage points in thelikelihood of being a successful innovator, a statistically significantassociation. In addition, it is found that this positive effectconcerns “true innovators” but not imitators, and firms in manu-facturing but not in services. In a study of UK firms using data fromthe CIS4, Battisti and Stoneman (2010) find that the percentage offirms that received public support increases with the intensity ofinnovative activity.

Focusing more narrowly on subsidized cooperative research,Czarnitzki and Fier (2003) find that publicly supported networksin Germany tend to patent more compared to privately financedconsortia. Mora-Valentin et al. (2004) examined a sample ofpublicly supported cooperative research agreements in Spain andfound that the factors with the highest effect on firm's perceptionsof success are commitment, previous links, definition of objectivesand, negatively, the degree of conflict. Using the second and thirdwaves of the CIS from Germany and Finland, Czarnitzki et al.(2007) find that both public R&D funding and collaborativeresearch positively affect patenting activities of the participatingorganizations. In Japan, Branstetter and Sakakibara (2002) foundthat participants in publicly funded research consortia increasedtheir patenting activities over time. There exists, therefore, fairlystrong evidence that public support is positively related torecipient firms' innovative behavior and performance, and – moreclosely related to our purposes here – that participation in publiclyfunded collaborative research schemes confers a positive impact interms of innovation outputs. But as noted above, we know verylittle as to what drives these impacts for firms participating in suchprojects, and this is what we now turn to.

2.1. Hypotheses

The conceptual framework guiding this study is simple andintuitive. At the center of our analysis lie the innovation impacts afirm can derive from its participation in a publicly fundedcollaborative R&D project (i.e. product and/or process innovation,and the inimitability of the resulting technology). We considerdeterminants of these impacts along two basic levels of analysis:project and firm. Project-related factors refer to the intrinsiccharacter (i.e. novelty and complexity) of the technology pursued;firm-level characteristics refer to those internal attributes (i.e.innovation-related capabilities and experiences) that enable aparticipating firm to benefit from cooperative R&D. We expectthat both types of factors will have a bearing on the likelihood ofinnovation, as explained below.

2.1.1. Project-specific effects on innovation impactsInnovation impacts derived by a firm participating in an R&D

consortium necessarily will be influenced by the intrinsic character-istics of the explored technology. Publicly financed R&D programmes,such as the EU FP, seek to support projects that otherwise would notbe undertaken by the private sector precisely because of the high riskand complexity associated with the research.

Novelty here refers to the degree of change in the technologyrelative to prior technologies and the extent of familiarity with it(Stock and Tatikonda, 2000). The degree of novelty manifests itselfnot only in terms of the technical risk of actually developing thebasic knowledge underlying the technology in question as well asthe required functionality, but also in terms of the commercialrisks associated with any new technology. Complexity refers to thedegree of interdependence between the subcomponents in thetechnology, the degree of interdependence between the technol-ogy and elements external to it, as well as to the scope of the

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–6454

technology (Stock and Tatikonda, 2000). Collectively, novelty andcomplexity define inherent characteristics of the project, repre-senting what might be called “project uncertainty” (Stock andTatikonda, 2000; Tatikonda and Rosenthal, 2000). Excessive levelsof project uncertainty will affect negatively the likelihood ofsuccess.

Intuitively, innovation is especially difficult if partners in aconsortium have limited knowledge and experience with theunderlying technology. Project uncertainty imposes significantdemands for successful collaboration, including effective coordi-nation and integration mechanisms (Gulati and Singh, 1998). Asproject uncertainty increases, so do information-processing needsthat, in turn, require greater information-processing capabilities(e.g., Daft and Lengel, 1986; Bensaou and Venkatraman, 1995). Inaddition, superior capabilities will be demanded on the part of theparticipating firm aiming at concrete innovation impacts when theproject, on which these impacts are based, is highly uncertain.

Hypothesis 1. Project uncertainty will be positively related toinnovation impacts, but the relationship will be non-linear(inverse U-shaped). As uncertainty becomes excessively high, thereturns to the participating firm will begin to diminish.

Uncertainty will be lower, however, when the project buildsupon prior R&D efforts. Irrespective of whether these past researchefforts were carried out in the context of an earlier FP project, anational R&D programme, a privately funded collaborative project,or an in-house project, accumulated learning and experience inthe specific technological area will provide consortium memberswith better a understanding of the underlying technology, and willenable them to anticipate and more effectively respond to thetechnical and managerial challenges related to project implemen-tation. Consequently, it will be more likely that the participatingfirm will gain significantly from the project.

Hypothesis 2. When a given project builds on related past R&Dactivities, a participating firm will be more likely to obtaininnovation impacts from that project.

2.1.2. Firm-specific effects on innovation impacts. An extensiveliterature emphasizes the importance of internal resources andcapabilities for innovation (e.g. Rothaermel and Hess, 2007;Coombs and Metcalfe, 2000) and competitive advantage (Barney,1991; Rumelt, 1991; Wernefelt, 1984). More recent research hasextended this line of reasoning to inter-organizational alliances. Ithas been argued that key motives for collaborating in R&Dalliances are gaining access to new, complementary skills andcapabilities from partners (see Powell and Brantley (1992), Khanna(1998), Varadarajan and Cunningham (1995)), and learning fromeach other so as to enhance their own competencies in newproduct development (Rothaermel, 2001).

A participating firm's ability to assimilate and further developresults from collaborative R&D into concrete innovation for its ownadvantage is a function of its absorptive capacity (Cohen andLevinthal, 1990). The problem faced by a participant firm is thateven if a new technology is successfully developed, this technol-ogy is usually just one piece of knowledge that needs to becomplemented with other developments (i.e. components, sub-systems, process innovations) so as to be embodied in a complexproduct that delivers the functionality required for commercialapplication. The effectiveness of a participating firm's attempt toappropriate and, importantly, further develop knowledge spil-lovers generated within the consortium depends upon sufficientlevels of pre-existing in-house innovative capabilities and accu-mulated investment in own R&D. Conversely, if the focal firm doesnot have adequate absorptive capacity, then new knowledgegenerated within the consortium, no matter how important may

be, is not likely to be beneficial. And this is all the more importantin publicly funded research consortia, which are supposed to beprimarily exploratory in nature, dealing with generic rather thanproduct-specific development research.

Within this line of reasoning, the firm's record (or “history”) ofinnovation-related activities, as reflected in past R&D and priorinnovation performance will, in principle, influence its capacity toderive impacts from collaborative R&D projects (Kleinknecht andReijnen, 1992; Colombo and Garrone, 1996).

Another important element of the firm's capacity to absorb andinternalize knowledge spillovers from its cooperative R&D activitiesrelates to its competence in integrating knowledge from multiple,distributed sources within its own organizational boundaries andbeyond, a process referred to as “integrative capabilities ” (Kogutand Zander, 1992; Nadler et al., 1997; Verona, 1999). The developmentof a new product or a new process innovation is a challenging task.The ability to integrate the various functions, activities, and informa-tion flows within and between organizations required for developingand bringing a new technology into the market (Souder and Jenssen,1999; Lorenzoni and Lipparini, 1999) (or implement a process innova-tion in its own activities) act as an adhesive (Verona, 1999), byabsorbing knowledge from external sources and by blending thedifferent technical competencies required for the task.

Hypothesis 3a. Firms with a strong innovation history, asreflected in R&D related activities and past innovation perfor-mance, will tend to gain more in terms of innovation from theirparticipation in cooperative R&D projects.

Hypothesis 3b. Firms with strong integrative capabilities willtend to gain more in terms of innovation from their participationin cooperative R&D projects.

A firm's ability to protect, through legal and/or “competitive”means, its innovative position from rivals also constitutes animportant capability related to innovation (Ziedonis, 2004). Thisis because the incentives to innovate and, importantly, to coop-erate for innovation will depend on the extent to which the resultsfrom innovative activities can be protected and appropriated(Veugelers and Cassiman, 1999, Pyka and Saviotti, 2001). Besidesusing formal mechanisms such as patenting and trademarks, thereare other informal ways of defending critical knowledge fromimitation that do not depend on legal protection. A firm canstrategically protect its information through secrecy, the complex-ity of the technology, by obtaining lead-time over competitors, orby tying the innovation together with complementary servicessuch that rivals are forced to compete against the bundle ofservices offered by a firm. It follows that participating firms withsuperior appropriation capabilities (either through strong legalprotection or though informal means) will be able to enforceintellectual property rights and thus protect themselves fromunintended knowledge leakages to partners and thus more likelyto obtain significant innovation impacts (Dachs et al., 2004;Khanna et al., 1998).

Hypothesis 4. Firms with strong appropriation capabilities will bemore likely to derive innovation impacts from their participationin cooperative R&D projects.

3. Methodology

3.1. Sample

The results reported in this study are obtained from a large,pan-European sample of industrial firms having participated in thefifth and sixth FP. We derived the sampling framework from theCORDIS database, which maintains up-to-date information on all

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–64 55

FP-funded projects and participating organizations. From CORDISwe extracted the relevant project information on all FP5 and FP6projects, as well as data on participating organizations (organiza-tion name, type, address, person in charge of the project, contactdetails, etc.). Table 1 shows some key facts about the population ofFP5 and FP6 projects from which we drew our sample.

The total population at our disposal was 121,600 participatingorganizations (including both private enterprises and researchorganizations, such as Universities and research institutes). Thisis split in 80,397 for FP5 (66%) and 41,263 for FP6 (34%). From thispopulation, we randomly selected a representative sample of54,492 (i.e. 70.4% for FP5 and the remaining 29.6% for FP6). Theseorganizations received an invitation to participate in the surveyassuring anonymity.

Questionnaires in English measuring various aspects of theproject and participating organizations' characteristics were admi-nistered electronically. It is important to note that the invitationwas directed to the person that had the primary responsibility fora given project in the respective participating organization, asidentified in the CORDIS database. In addition, to enhance thesurvey response rate, a call center was contracted to call potentialrespondents for a period of 6 weeks. In total 15,500 calls have beenregistered and 2650 follow up e-mails were sent with the detailsof the survey. The entire data collection exercise lasted 10 weeks(March–May, 2006). Of the 54,492 organizations which receivedthe invitation to participate in the survey, 7098 (3379 enterprisesand 3719 research organizations) completed the electronic ques-tionnaire for an overall response rate of 13.03%. The response rateachieved is normal for this kind of research.

Extensive descriptive analyses established that the original dataset collected was representative of the population. For example,the distribution of the received questionnaires is 70.2% for FP5 and29.8% for FP6 projects, which is almost identical to the distributionin the original sampling frame. Similarly, the breakdown of surveyresponses per programme (FP5 and FP6) for thematic area,funding “instruments” and types of participating organizationsshowed that the data are representative of the aggregate numbersin the sample. The same holds for the response per country andper FP: our analysis shows that for the vast majority of countriesthe ratio of the number of responses actually obtained to thenumber expected (based on the sampling framework) rangesbetween 0.75 and 1.25, which indicates little bias in the responsein terms of geography.

Because we are interested in the determinants of impactsobtained by firms participating in FP projects, in this study weuse a sub-sample of the original data set, which contains informa-tion collected from enterprises (i.e. research organizations, such asUniversities and institutes, were excluded) representing projects

that were completed prior to the survey. This reduced our sampleto 1870 observations; unfortunately, due to a large number ofmissing values in variables of interest to this study, the effectivesample size used for substantive analyses is much lower (i.e. theestimation sample includes 694 complete observations). It isimportant to emphasize that our sampling framework made itpossible to obtain multiple observations per project and/or perfirm. It was therefore possible to obtain data from differentpartners for the same project as well as data from the same firmfor different projects. Hence our sample consists of firm-projectdata points; below we provide more details about the methodo-logical issues entailed by such clustering of the data.

Even though the original sample has been found to be repre-sentative of the population of FP projects, whether this extends tothe estimation sample used here as well is a crucial question. Weconducted a series of analyses to check for potential non-responsebias. The results (available in the online Supplementary materialaccompanying the paper) provide reasonable confidence that non-response bias is not a serious problem in our study.

3.2. Variables and measures validation

Measurement items developed and tested by previous studieswere used as much as possible in our survey questionnaire; forexample, the wording of the questions we used to measureinnovation history and past performance, and appropriation cap-abilities, were adopted from the harmonized survey questionnaireof the Community Innovation Surveys (CIS). The constructs ofinterest to the study were tapped using both multiple question-naire items (scales) and categorical (dummy-coded) variables.With respect to the former, respondents rated each item on5-point Likert-type scales in which higher values were alwaysassociated with higher levels of the construct. Following confir-matory factor analyses, which verified the psychometric propertiesof our measures (available upon request from the authors), weconstructed composite measures of these constructs by averagingthe respective individual items. (The only exception to this is theconstruction of the dependent variable measuring inimitability ofthe resulting technology – see below.) Table 2 provides an over-view (descriptive statistics and the data source) of the variablesused in our study for the estimation sample, as well as their meandifferences between sub-samples created on the basis of (a) first-time participants vs. firms having participated more than once inthe FP; (b) SMEs vs. large firms; and (c) firms with a significantrole in the project vs. firms with a secondary role. As will beexplained below, these are control variables that are potentiallyendogenous to the innovation impacts (our dependent variables).

Table 1Key statistics on the FP5 and FP6.

FP5 (1998–2002) FP6 (2002–2006)

No. of participants 80,397 41,263No. of signed contracts 15,700 10,058Total EC financial contribution (EUR million) 13,065 16,669Proposal success rate 26% 18%Average number of participants per contracta 6.1 12.0Average EC contribution per contract (EUR million)a 0.95 2.73

Types of participantsHigher education 31% 36%Industrial organizations (engaged in manufacturing activities and/or industrial services) 10% 19%Research organizations (private or public) 29% 28%Other 30% 17%

a This figure excludes the FP5 Human Potential Specific Programme (IHP) and the FP6 Human Resources and Mobility activity (HRM).Source: FP6 final review: subscription, implementation, participation. EC, Research Directorate-General, 2008.

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–6456

3.2.1. Dependent variablesAs already noted, innovation impacts derived by project parti-

cipants are conceptualized to comprise two dimensions: productand/or process innovation, on the one hand, and inimitability of theresulting technology, on the other. We measured innovation withtwo dummy variables, asking respondents to indicate whethertheir organization has produced new or significantly improvedgoods/services and process innovation as a result of their partici-pation in the specific project. Inimitability of the resultingtechnology is measured using two Likert-type questions, adaptedfrom Steensma and Corley (2000), which asked respondents toindicate the degree of difficulty for competitors to (a) “reverseengineer” and (b) copy the technology resulting from the focalproject. We constructed the composite of the two questions byaveraging individual responses and then re-expressed the mea-surement as an ordinal variable ranging from 0 (not at all) to 3(very difficult).

3.2.2. Independent variablesProject uncertainty. As defined in this study, uncertainty consistsof two dimensions: novelty and complexity. We measured projectnovelty with Likert-type items tapping the degree to which thefocal project was perceived, as compared to an “average” project,to be scientifically and commercially risky and distant from thefirm's core area of technological expertise. We used similar Likert-type questions to gauge project complexity, asking respondents toindicate the extent to which they perceived the focal project asbeing scientifically and technically complex and long-term (asopposed to short-term) term. Because we hypothesize a non-linearrelationship between project uncertainty and innovation impact,we include square terms of both project novelty and complexity toaccount for the suggested inverse U-shaped relationship.

We have also predicted that uncertainty will be lower forprojects that are building on past research activities. We thereforeasked respondents to indicate whether the focal project was

Table 2Descriptive statistics and source of the variables used in the study.

Full estimation sample Breakdown by Source

Mean Min Max SD First-time participation SME ROLE in project

No Yes No Yes No Major

Innovation impactsProduct innovation 0.49 0 1 0.50 0.45 0.53 0.45 0.50 0.34 0.534;nnn SurveyProcess innovation 0.36 0 1 0.48 0.34 0.38 0.41 0.34 0.25 0.394;nnn SurveyInimitability (of technology) 1.11 0 3 1.19 1.07 1.15 1.12 1.10 0.79 1.204;nnn Survey

Project effectsProject novelty 3.18 1 5 0.85 3.24 3.102;n 3.08 3.212;n 3.03 3.224;nnn SurveyProject novelty squared 10.79 1 25 5.25 11.14 10.402;n 10.21 10.99 9.88 11.074;nnn SurveyProject complexity 3.67 1 5 0.78 3.69 3.66 3.79 3.632;n 3.54 3.714;nnn SurveyProject complexity squared 14.09 1 25 5.54 14.17 14.01 14.90 13.822;n 13.24 14.353;nn SurveyProject builds on past R&D 0.75 0 1 0.43 0.77 0.724;nnn 0.79 0.732;n 0.72 0.75 Survey

Firm effectsPast intramural R&D 0.91 0 1 0.28 0.94 0.892;n 0.94 0.912;n 0.88 0.932;n SurveyPast extramural R&D 0.66 0 1 0.47 0.68 0.65 0.81 0.614;nnn 0.59 0.694;nnn SurveyPast new to market product innovation 0.79 0 1 0.41 0.81 0.76 0.84 0.773;nn 0.78 0.79 SurveyPast new to firm product innovation 0.61 0 1 0.49 0.65 0.563;nn 0.72 0.574;nnn 0.59 0.61 SurveyPast process innovation 0.58 0 1 0.49 0.60 0.55 0.72 0.534;nnn 0.58 0.58 SurveyIntegrative capabilities 3.46 1 5 0.80 3.56 3.353;nn 3.70 3.384;nnn 3.50 3.45 SurveyFormal appropriation capabilities 3.39 1 5 1.03 3.46 3.312;n 3.73 3.284;nnn 3.28 3.42 SurveyInformal appropriation capabilities 3.90 1 5 0.81 3.91 3.89 3.96 3.883;nn 3.79 3.93 Survey

Project controlsConsortium size (in log) 2.03 0 5 0.63 2.01 2.05 2.12 2.004;nnn 2.09 2.01 CORDISConsortium size (in level) 9.35 1 119 8.16 CORDISConsortium size squared (in log) 4.51 0 23 2.63 4.44 4.60 4.79 4.423;nn 4.80 4.42 CORDISProject duration 31.29 2 78 11.27 32.40 30.024;nnn 35.24 29.954;nnn 29.56 31.812;n CORDISPartners from industry (%) 0.26 0 1 0.25 0.23 0.293;nn 0.26 0.26 0.23 0.26 CORDISLeader from industry 0.20 0 1 0.40 0.19 0.20 0.14 0.21 0.15 0.21 CORDISIdea from industry 0.61 0 1 0.49 0.64 0.563;nn 0.62 0.60 0.63 0.60 SurveyEmerging market 0.44 0 1 0.50 0.46 0.411;þ 0.38 0.46 0.46 0.43 SurveyEarly stage of market development 0.32 0 1 0.47 0.29 0.35 0.29 0.33 0.25 0.34 SurveyFast growth 0.11 0 1 0.32 0.10 0.13 0.12 0.11 0.19 0.092;n SurveyFP6 0.04 0 1 0.21 0.05 0.04 0.02 0.05 0.08 0.03 CORDIS

Firm controlsRole in the project 0.77 0 1 0.42 0.78 0.76 0.85 0.743;nn 0.00 1.00a SurveyFirm size (SME vs. large) 0.75 0 1 0.44 0.67 0.844;nnn 0.00 1.00a 0.83 0.723;nn SurveyFirst time participation 0.46 0 1 0.50 0.00 1.00a 0.30 0.524;nnn 0.49 0.46 SurveyFamiliarity with partners 0.79 0 1 0.41 0.86 0.714;nnn 0.84 0.772;n 0.75 0.802;n SurveyFirm age 26.45 1 58 18.15 28.88 23.653;nn 38.82 22.244;nnn 23.83 27.233;nn SurveyManufacturing 0.74 0 1 0.44 0.72 0.783;nn 0.83 0.724;nnn 0.62 0.784;nnn Survey/CORDISObservations 694 372 322 176 518 160 534

þ Difference significant at po0.10.n Difference significant at po0.05.nn Difference significant at po0.01.nnn Difference significant at po0.001.a Test not applicable.

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–64 57

building on past R&D, and created a dummy variable (project buildson past R&D) that equals one when the project continues pastresearch (either conducted in-house, in another framework pro-gramme, in a national programme, or in a past cooperativeproject), and equals zero otherwise.

Absorptive capacity. This construct refers to the participating firm'sability to assimilate and exploit knowledge generated in thecontext of collaborative R&D for its own advantage. As discussedabove, it is reflected in the firm's history of prior innovationactivities and in its integrative capabilities. Following priorempirical literature (see for example, Cohen and Levinthal(1990), Veugelers (1997), Cockburn and Henderson (1998)), wemeasure innovation history using two dummy variables (pastintramural R&D and past extramural R&D) asking respondents toindicate whether (a) the firm has engaged continuously inintramural (in-house) and (b) extramural R&D activities (i.e. theacquisition of R&D services from external parties) in the three yearperiod prior to the project. In addition, we used three dummyvariables (past new to market product innovation; past new to firmproduct innovation, and past process innovation) to measureinnovation performance during the same time period:(a) introduction of new or significantly improved goods orservices that were new to the market; (b) introduction ofproduct innovation new to the firm; and (c) introduction of newor significantly improved process innovation. Integrativecapabilities, on the other hand, is measured using four Likert-type questions measuring the firm's ability to integrate crossfunctional innovative activities within and across organizationalboundaries, to timely introduce new products to the market, anddevelop strategies that integrate market needs into their offerings(Helfat and Raubitschek, 2000).

Appropriation capabilities. This construct refers to the firm's abilityto protect innovation through formal legal means, such aspatenting, and through informal means, such as secrecy,complexity and lead-time. We asked respondents to indicate theimportance of various tactics for legal (formal appropriationcapabilities) or informal (informal appropriation capabilities)protection of their firm's innovative position. High score onthese items imply high appropriation capabilities. Theconfirmatory factor analysis mentioned above established thetwo-dimensional nature of the scale.

Control variables. We controlled for several factors at both theproject and firm levels that might affect innovation impactsobtained to participating firms. Unless otherwise noted,information for project controls was extracted from the CORDISdatabase. Beginning with project characteristics, consortium size isan obvious starting point. A large consortium would, in principle,significantly affect team dynamics and be strongly associated withperformance (Ancona and Caldwell, 1992; Jehn, 1995). Wemeasure consortium size with the number of partners in thefocal project. Because this variable is heavily skewed, as iscommon in the literature we take the natural log of the numberof partners to compensate for skewness. We include both size andsize squared to accommodate the possibility of a non-linearrelation (i.e. inverse U-shaped) between consortium size andinnovation impacts.

We also control for project duration. The length of the timespan during which project participants work together will gen-erally have a positive impact on performance (Katz, 1982; Gibson,1999; Hoang and Rothaermel, 2005). Project duration is measuredin months.

Another important characteristic of a project is the extent towhich the consortium consists of organizations coming from theindustry (vs. academia). Even though, as already noted, our datacomprises firms, not research organizations, the composition of theconsortia to which our sample firms belong to conveys importantinformation for our purposes. Specifically, it is reasonable toexpect that consortia in which there is a greater number ofparticipants coming from the industry there will also be anincreased tendency towards producing product/process innova-tion. We control for this by taking the percentage of members inthe consortium that are private enterprises (% partners fromindustry).

Within this same line of reasoning, if the leader of the projectcomes from the industry we would expect greater motivation andefforts towards concrete innovation impacts. Similarly, in projectswhere the original research idea originates from an industrialpartner, it will be more likely to result in concrete innovationoutputs. We employ two dummy variables to control for theseeffects. The first (leader from industry), based on informationextracted from the CORDIS database, equals one when the projectleader is an industrial firm and zero otherwise. The second dummyvariable (idea from industry) takes the value of one when theresearch idea originates from an industrial partner in the con-sortium (including the responding firm) and zero otherwise. It isbased on a question in our survey questionnaire, which askedrespondents to indicate “who [responding firm/industrial partner/University or research institute partner] generated the specific FPproject idea”.

Using data collected in our survey, we also control for the stageof the life-cycle of the relevant market at the time the project wasinitiated. We tentatively expect that firms participating in projectsinitiated when the relevant market is at early stages of itsdevelopment would be more likely to obtain innovation impacts.We used an indicator variable with four categories to gauge thestage of the life-cycle: (a) emerging market; (b) early stage ofmarket development; (c) fast growth; and (d) mature market. The“mature market” category acted as the referent category. The finalproject-level control (again taken from our survey) is a binaryvariable measuring whether the project belongs to FP6 (vs. FP5).

Turning to firm-specific controls, role in project is a dummyvariable denoting whether the respondent firm played a major rolein the project. We asked respondents to indicate whether the firmwas acting as project coordinator and/or user and/or technologyproducer; the dummy was coded one in such case and zerootherwise. We would expect that firms playing a dominant role inthe project to be more likely to gain in terms of innovation. Firm sizeis one of the most widely used variables in the innovation literature.We approximate size using a dummy variable coded one if the firmhas up to 250 employees and zero otherwise. Essentially this variablecontrasts small- or medium-sized firms to large enterprises.

Another potentially important firm characteristic is its generalalliance experience as well as its familiarity with one or morepartners in the focal project (Hoang and Rothaermel, 2005).Following prior research, we expect that firms having participatedrepeatedly in prior collaborative R&D projects will be more likelyto gain in terms of innovation in a subsequent project (Hoang andRothaermel, 2005). We measure general alliance experience andfamiliarity with partners in the consortium with two dummyvariables. The first references whether the focal project is theresponding firm's first time participation in an FP. The secondbinary variable (familiarity with partners) measures whether thefirm has previously collaborated with any of the partners in theproject.

We also control for firm age. Generally, we would expect olderfirms, owing to their accumulated experience, to be better attransforming an emerging new technology into concrete product

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–6458

or process innovation. We measure firm age in years since date ofestablishment. The final firm specific control concerns whetherthe responding firm is in manufacturing (vs. services). In theanalyses reported below, we also use country dummies to controlfor country-specific characteristics that may affect our results.

3.5. Analytical strategy

Because our three dependent variables (i.e. the two dummyvariables for product and process innovation, and an ordinal variablefor inimitability of the resulting technology) are clearly not indepen-dent of one another, we employed two modeling approaches thatexplicitly take into account this non-independence to test thehypotheses. The first approach uses the familiar bivariate probitspecification for modeling the effects of our independent variables onthe two innovation dummies jointly, that is, by taking into accountthe correlation between product and process innovation.

The second approach was a two-part model (Greene andHensher, 2010) to examine determinants of inimitability. Accord-ing to Greene and Hensher (2010), two-part models describesituations where an ordered outcome is part of a two-stageprocess. In our case, a firm may or may not succeed in developinga product and/or process innovation, and then, conditional onhaving innovated, it achieves a certain level of inimitability inthe resulting technology. The first stage is therefore a binaryoutcome; this is represented by a “participation” (probit) equation,which models the determinants of a participating firm's success ininnovating (i.e. product and/or process innovation). The secondstage concerns an ordered outcome, represented by an ordinalprobit equation, which models the effects of our independentvariables on the (degree) of inimitability of the resultingtechnology.

Following Harris and Zhao (2007), we specify a zero inflatedordered probit with correlated errors (ZIOPC) model to take intoaccount the situation where a firm has to overcome two hurdles:(a) to innovate in the first place, and (b) conditional on innovation,to develop a product and/or process that is difficult to be imitatedby competitors. Note that the second stage includes the possibilitythat the firm does innovate, but the resulting technology is easilyimitable. We therefore observe two types of “zeros” in our data(hence the term, zero inflated): one resulting from the firm'sinability to innovate in the first place, and the second from itsinability to produce an innovation difficult to imitate.

Recall that inimitability [of the resulting technology] is anordinal variable, ranging from 0 (not at all) to 3 (very difficultfor competitors to imitate). Obviously, those firms indicating noinnovation (i.e. no product and no process innovation) havemissing values in this variable. But these are not actually missingvalues; they represent “genuine zeros” (Jones, 2000) in that theoutcome of interest is in fact not “feasible”. Contrast this with thesituation where the outcome, conditional on non-participation, isnot observable (but could be potentially feasible), in which case asample selection model would be appropriate (Greene andHensher, 2010).

Our model is as follows:

Stage 1: “Participation” equation (i.e. to produce a product and/or process innovation)

Innovationn¼ x'βþu; u`�N½0;1�Innovation¼ 1 if Innovationn40; 0 otherwise;

ProbðInnovation¼ 1jxÞ ¼Φðx'βÞ

Stage 2: “Ordered outcome” equation

Inimitabilityn¼ x'γþε; ε�Ν½0; 1�;Inimitability¼ j if μj�1 o Inimitabilityn r μj;

j¼ 0; 1; 2; J ¼ 3;μ�1 ¼ �1; μ0 ¼ 0; μj ¼ þ1

where Innovation is a dummy variable coded 1 when the firmreports product and/or process innovation, 0 otherwise; Inimit-ability is as defined earlier; x is a vector of independent variables;β and γ are a set of unknown parameters to be estimated by thedata; μj are threshold parameters for the ordered probit (stage2 equation); and Φð:Þ is the cumulative density function of thestandard normal distribution.

Furthermore, because the two stages are likely notindependent of one another, we allow unobserved firm effectsin stage 1 and 2 equations to be correlated; we specify:u

ε

� ��Ν

00

� �;

1;ρρ;1

!" #, where ρ is an additional parameter to

be estimated, indicating the degree of relatedness between thetwo stages. We estimate this model by maximum likelihood, usingthe “cmp” routine in STATA, which accommodates a variety ofconditional mixed processes such as ours (Roodman, 2011).

4. Results

The descriptive statistics and zero-order correlations betweenthe dependent and independent variables are presented in theonline Supplementary materials file accompanying the paper.Table 3 reports the results for (a) the bivariate probit model forproduct and process innovation (Model 1) and (b) the ZIOPC modelfor innovation and inimitability (Model 2). As noted earlier, our dataare clustered since, for a fraction of our estimation sample, wehave multiple observations for the same project. More specifically,the distribution of respondents per project of the 694 observationsused for estimation is as follows: 104 respondents for 52 projects(i.e. two observations per project), and 21 observations for7 projects (i.e. three per project). The remaining 569 observationsare unique (i.e. one observation per project). Given this clusteringwithin projects, we requested robust standard errors (Hubert/White “sandwich” estimator) for all estimated models to correctfor the non-independence of observations pertaining to the sameproject. The results from the collinearity test indicate no multi-collinearity problem for the key constructs of interest in this study.Except of those variables for which we include both linear andquadratic terms in the models, VIF ranges between 1.06 and 2.65.As already noted, after listwise deletion of missing data, ourestimation sample contains 694 observations.

We have modeled the product-process innovation outcomesjointly as two dependent binary variables (Model 1) in a way thateach outcome depends on the same set of regressors and isaffected through the error structure by the other outcome. Theresults show the correlation between the error terms to be highlysignificant ðρ¼ 0:41; p¼ 0:00Þ. If ρ was zero, then the log like-lihood of the bivariate probit would be equal to the sum of the loglikelihoods of two separate univariate probits. A Wald testðχ2

1ð Þ ¼ 36:5; p¼ 0:00Þ strongly rejects the null hypothesis ofρ¼0, thus supporting our choice of a bivariate probit model.Looking at the ZIOPC model (Model 2), we also find that thecorrelation between the error terms of stage 1 (innovation) and 2(inimitability) equations is strong and highly significantðρ¼ 0:76; p¼ 0:00Þ. This suggests that the two processes are notindependent of one another and should be specified by way of a

Y.E. Spanos et al. / Technovation 36-37 (2015) 53–64 59

joint model; after all, the two equations describe innovationprocesses corresponding to the same firm.

Before turning to substantive results, it is worth noting that thecoefficients of the stage 1 equation in Model 2 (i.e. innovation) arenot particularly informative when compared to the respectivecoefficients for product and process innovation in Model 1. Recallthat innovation (Model 2, stage 1) is constructed as a dummyvariable measuring whether a firm has indicated a product and/orprocess innovation as a result of project participation. As such,stage 1 coefficients do not allow a distinction between product andprocess innovation when either one (but not both) outcomes areobserved; when both outcomes are observed, these coefficientscapture a crude, “average” effect on product and process innova-tion. As noted in Section 3, the real importance of the stage1 equation (in Model 2) is that it allows us to model innovation (inproduct and/or process) as an outcome that is both conceptuallyand pragmatically distinct from (albeit related to) the inimitabilityof the resulting technology (stage 2, Model 2).

Beginning with project-specific effects, the results in Table 3offer some support to the idea that project uncertainty will becurvilinearly related to innovation impacts (Hypothesis 1). Morespecifically, in Model 1 we find that novel projects are more likely

to result in product innovation (i.e. the coefficient for the linearterm is 1.00, po0.01), but these results are beginning to diminishwhen projects become too risky (i.e. the coefficient for thequadratic term is �0.12, po0.05). The respective coefficients ofproject novelty on process innovation are insignificant. Still inModel 1, project complexity is linearly and positively related withprocess innovation (the coefficients for the linear and quadraticterms are 0.86, po0.10, and �0.10, p40.10, respectively), and isfound insignificant with respect to product innovation.

Looking at Model 2, we observe a similar inverse U-shapedeffect of project novelty on both the probability of (stage 1)innovation (linear term: 0.80, po0.05; quadratic term: �0.11,po0.10) and an even stronger effect on the (stage 2) inimitabilityof the resulting technology (linear term: 1.17, po0.001; quadraticterm: �0.16, po0.01).

Hypothesis 2 states that projects building on past researchwould be more likely to obtain innovation impacts. The results inTable 3 provide full support to this hypothesis. Of the fourcoefficients of interest here (Models 1 and 2), all are of the correctsign and all are significant at the 0.05 level or lower.

With regards to firm-specific determinants, we find that atleast some aspects of the firm's past innovation “history” and

Table 3Results of bivariate probit and ZIOPC analyses on innovation impacts.

Model 1 Model 2Bivariate probit ZIOPC

Product innovation Process innovation Innovation Inimitability

Project effectsProject novelty 1.00** (2.66) 0.15 (0.40) 0.80* (2.23) 1.17*** (3.33)Project novelty squared �0.12* (�1.97) �0.01 (�0.16) �0.111;þ (�1.84) �0.16** (�2.83)Project complexity 0.12 (0.27) 0.861;þ (1.67) 0.37 (0.77) 0.32 (0.65)Project complexity squared �0.03 (�0.41) �0.10 (�1.41) �0.05 (�0.72) �0.01 (�0.14)Project builds on past R&D 0.28* (2.24) 0.57*** (4.35) 0.47*** (3.54) 0.37** (2.89)

Firm effectsPast intramural R&D 0.44* (2.33) �0.26 (�1.32) 0.18 (0.92) 0.04 (0.23)Past extramural R&D 0.201;þ (1.84) 0.15 (1.25) 0.04 (0.35) �0.04 (�0.33)Past new to market product innovation 0.16 (1.08) �0.00 (�0.01) 0.05 (0.33) 0.261;þ (1.84)Past new to firm product innovation �0.03 (�0.23) 0.05 (0.46) �0.01 (�0.04) 0.04 (0.38)Past process innovation 0.02 (0.23) 0.56*** (4.96) 0.18 (1.58) 0.14 (1.27)Integrative capabilities 0.25*** (3.40) 0.141;þ (1.88) 0.15* (1.97) 0.141;þ (1.90)Formal appropriation capabilities 0.02 (0.31) 0.01 (0.10) 0.07 (1.15) 0.02 (0.29)Informal appropriation capabilities 0.04 (0.53) 0.04 (0.50) 0.11 (1.46) 0.08 (1.10)

Project controlsConsortium size 0.12 (0.49) �0.32 (�1.25) �0.24 (�0.92) �0.471;þ (�1.78)Consortium size squared �0.01 (�0.21) 0.07 (1.15) 0.04 (0.63) 0.101;þ (1.73)Project duration 0.01 (1.42) �0.00 (�0.16) 0.01 (1.57) 0.011;þ (1.73)Partners from industry (%) 0.31 (1.22) �0.19 (�0.77) 0.06 (0.24) 0.15 (0.66)Leader from industry �0.10 (�0.68) �0.10 (�0.65) �0.10 (�0.63) 0.02 (0.17)Idea from industry 0.30** (2.72) 0.11 (0.95) 0.26* (2.25) 0.16 (1.47)Emerging market �0.05 (�0.30) 0.05 (0.27) �0.14 (�0.79) 0.08 (0.45)Early stage of market development 0.21 (1.14) �0.04 (�0.21) 0.06 (0.33) 0.321;þ (1.82)Fast growth 0.03 (0.14) 0.13 (0.58) �0.05 (�0.21) 0.15 (0.70)FP6 0.11 (0.43) 0.66* (2.47) 0.26 (1.00) �0.08 (�0.34)Firm controlsRole in the project 0.45*** (3.41) 0.40** (2.90) 0.42** (3.05) 0.28* (2.12)Firm size (SME vs. large) 0.241;þ (1.65) �0.02 (�0.12) 0.16 (1.07) �0.02 (�0.11)First time participation 0.37** (3.18) 0.34** (2.98) 0.26* (2.16) 0.191;þ (1.71)Familiarity with partners 0.23 (1.63) 0.21 (1.54) 0.28* (2.02) 0.18 (1.31)Firm age �0.00 (�0.60) 0.00 (0.80) �0.00 (�0.70) �0.00 (�1.45)Manufacturing 0.15 (1.05) 0.01 (0.09) 0.11 (0.75) 0.03 (0.24)Country dummies Yes Yes Yes YesConstant �5.64*** (�5.58) �4.21*** (�3.71) �4.59*** (�4.14)

N¼694 and t statistics in parentheses.þ po0.10.n po0.05.nn po0.01.nnn po0.001.

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performance are, as expected, significantly and positively asso-ciated with innovation impacts (Hypothesis 3a). In Model 1, bothintramural and extramural R&D are positively associated withproduct innovation (β¼0.44, po0.05; β¼0.20, po0.10, respec-tively), but not with process innovation. In contrast, firmsseeking process innovation are those that have a history ofintroducing process innovation in the past (Model 1: β¼0.56,po0.001). In Model 2, new-to-the-market product innovation,an indicator of past success in commercializing radical innova-tion, is found to increase the probability (β¼0.26, po0.10) ofproducing a highly inimitable technology as a result of projectparticipation.

The results in Table 3 also show a strong positive effect ofintegrative capabilities on all outcomes examined. Integrativecapabilities increase the likelihood of succeeding in product(β¼0.25, po0.001) and process innovation (β¼0.14, po0.10)(Model 1); they are also positively associated with innova-tion (stage 1, Model 2: β¼0.15, po0.05) and increase the prob-ability of coming up with an inimitable technology (stage 2,Model 2: β¼0.14, po0.10). Our results therefore fully supportHypothesis 3b.

Hypothesis 4 states that respondent firms with superior appro-priation capabilities, both formal (patenting) and informal (leadtime advantages, secrecy, complementary services, etc.), would bemore likely to obtain innovation impacts. The results in Table 3provide no support to this hypothesis; the coefficients of bothvariables are insignificant in Models 1 and 2, a finding that clearlywarrants further exploration. (In fact, in models not reported hereand where we do not include the country dummies, we findinnovation protection by informal means to positively affect boththe probability of innovation (stage 1, Model 2) and inimitability(stage 2, Model 2)).

With regards to project-specific controls, we expected aninverse U-shaped relationship between consortium size and suc-cess. In contrast, we find a U-shaped relationship with respect toinimitability (stage 2, Model 2: linear term: β¼�0.47, po0.10;quadratic term: β¼0.10, po0.10). Our prediction that participa-tion in projects with longer duration will more likely conferinnovation impacts is supported with regards to inimitability(stage 2, Model 2: β¼0.01, po0.10).

We also predicted that certain project characteristics, such asthe proportion of participants coming from industry, whether theproject leader comes from industry, and whether the project ideaoriginated from a firm (as opposed to a research organization), willbe positively associated with innovation impacts. We find onlylimited support to these predictions, specifically that firms inprojects where the idea stems from an industrial partner are morelikely to produce product innovation (Model 1: β¼0.30, po0.01)and are more likely to produce innovation in general (stage 1,Model 2: β¼0.26, po0.05).

We expected that participating firms would be more likely toobtain innovation impacts in projects initiated at early stages ofthe respective market's life cycle. We only find the coefficient ofearly stage market to be positive and significant for inimitability(stage 2, Model 2: β¼0.32, po0.10); no other coefficient for thestage of market life-cycle is found significant in both models.Finally, our results indicate that firms participating in FP6 (asopposed to FP5 projects) are more likely to produce processinnovation (Model 1).

Turning our attention to the effects of firm-specific controls, wefirst concentrate on three potentially endogenous variables,namely role in project, first time participation, and firm size (SMEvs. large-sized firms). If, for example, firms assuming a leading rolein collaborative projects (e.g. by being “coordinators”) are differentfrom “normal” participating enterprises in unobservable ways,ways which also influence innovation impacts, then role in project

would be an endogenous variable, and its (uncorrected) estimatedcoefficient would be biased.

We have tested for potential endogeneity in these variables andthe results obtained are equivocal. (An overview of the tests andresults is available in the online Supplemental material accom-panying the paper.) On the one hand, our tests of the jointsignificance of the correlations of cross-equation error terms rejectthe hypothesis of endogeneity in the “corrected-for-endogeneity”versions of both Models 1 and 2. On the other hand, some of theeffects of the suspected variables on innovation impacts turn outto be insignificant after correcting for endogeneity (while they arefound to be significant in the “uncorrected” bivariate probit[Model 1] and ZIOPC models [Model 2] in Table 3), somethingthat suggests that the variables in question may be endogenous.Given mixed results in these additional analyses, and acknowl-edging that arguments in support of endogeneity in these vari-ables are fairly strong, we prefer to err on the safe side andinterpret the findings below as merely suggestive. Clearly, furtherresearch would be needed before a conclusion can be drawn onthe issue.

The results in Table 3 show that the coefficient of role in projectis positive and significant in both Model 1 (β¼0.45, po0.001) andModel 2 (stage 2: β¼0.28, po0.05); the coefficient of firm size (i.e.SMEs vs. large firms) is found positive and significant only forproduct innovation (Model 1: β¼0.24, po0.10). First time partici-pation is used here as a proxy for general alliance experience (i.e.denoting minimal experience). Contrary to our expectations, thisvariable is also positively and significantly associated with innova-tion impacts in both Model 1 (β¼0.37, po0.01) and Model 2(stage 2: β¼0.19, po0.10).

With regards to the remaining firm-specific controls, we findthat when participating firms are familiar with other consortiumpartners from prior collaborative projects will be more likely toinnovate (stage 1, Model 2: β¼0.28, po0.05). The coefficients offirm age and of manufacturing (as opposed to services firms) arefound insignificant in both models.

We conducted two more tests to examine the robustness of thefindings reported above. As noted by one anonymous reviewer,besides clustering at the project level, we have an additional typeof clustering in our data. Given the research design, it is possiblethat a firm in our estimation sample has participated in more thanone project and thus is observed multiple times in our data.Indeed this was the case in our estimation sample. More specifi-cally: 31 firms are observed with 2 (different) projects; 14 firmswith 3 projects; 2 firms with 4 projects; and one firm with5 projects; each of the remaining 577 firms is observed with oneproject. For a firm observed more than once (i.e. with more thanone project) in the data set, it is entirely possible to obtaindifferent values for the same dependent variable. For example, afirm might have indicated a product innovation as a result of oneproject and no product innovation for the other project(s) in whichit has participated. To check whether this affects our findings, werun the same models as those in Table 3, this time correcting forclustering at the firm level, and obtained virtually the sameresults.

Moreover, following the suggestion of an anonymousreviewer, we have also tested whether the amount of publiccontribution on the project's budget has any effect on innovationperformance. We re-run analyses, this time including the naturallog of public contribution as an additional project-level controland found a significant negative effect on process innovation (theeffects on product innovation and on inimitability were insignif-icant). The results concerning the remaining variables werequalitatively similar to those reported in Table 3. (The results ofthese additional tests are available upon request from theauthors.)

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5. Discussion

5.1. Project-specific effects

We have predicted that the relationship between projectuncertainty (i.e. novelty and complexity) and innovation impactswill be inverse U-shaped, that is, at first positive, but whenuncertainty becomes excessively high the impacts will begin todiminish. Our findings indicate that project uncertainty has astrong effect on the innovation impacts obtained by participatingfirms, but the two dimensions of uncertainty we examinedinfluence differently our performance measures. Whereas projectnovelty affects in a curvilinear manner product innovation andinimitability of the technology, that is, positively at first and thennegatively when projects are excessively risky, complexity influ-ences linearly and positively process innovation. Process innova-tion, having to do with improvements in internal operations, maybe more complex than risky. Gopalakrishnan et al. (1999), forexample, found that process innovations were more tacit, moresystemic in nature, and thus more complex than product innova-tions. Because process innovation is inherently systemic, multipleorganizational actors may need to get involved, and this diversityof agents playing different roles may result in an innovationimplementation process that tends to develop without an overallcoherence (Francis and Bessant, 2005). In contrast, for productinnovation uncertainty is defined more as a function of the typesof knowledge required to successfully complete the task, knowl-edge that is usually unavailable or incomplete and of low relia-bility, resulting in a risky problem situation (Smith et al., 1991).Our results also suggest that such conditions tend to favor theadvent of a radically different technology, one that is difficult to becopied by competitors.

In addition, we find that projects building on past research aremore likely to result in innovation impacts. This is of course notsurprising; projects that build on past research activities will beinherently less uncertain given that the consortium will haveaccumulated important experience with the explored technology,experience that will promote the participating firm's efficacy todeal successfully with the project and obtain radically innovativeoutcomes.

5.2. Firm-specific effects

With regards to firm-specific determinants, we have hypothe-sized that a firm's absorptive capacity will be positively related toinnovation impacts obtained from participating in collaborativeR&D. We have also argued that absorptive capacity is reflected in afirm's past innovation history and performance, as well as on itsintegrative capabilities. The results show that product (but notprocess) innovation is associated with the systematic undertakingof R&D activities (both in-house and acquired by third parties), afinding well established in the extant literature. Process innova-tion impacts, in contrast, are associated with a past record ofprocess innovations. We also observe a clear positive associationbetween past and present achievements; experience in innovationapparently matters, as it breeds expertise for future endeavors.More generally, our findings substantiate the notion that two ofthe most important factors of success in R&D consortia may beparticipating firm's previous R&D experience and past innovationperformance (Fiol and Lyles, 1985; Child and Yan, 1999), as well asthe ability to integrate the various pieces of knowledge, bothinternally and externally generated, required for successful inno-vation (Zahra and George, 2002). Taken overall, our results clearlyindicate that absorptive capacity is an important determinant of aparticipating firm's ability to obtain innovation impacts from itsparticipation in collaborative R&D. Firm-specific absorptive

capacity appears to be a sine qua non for fully benefiting fromthe outcomes of collaborative R&D.

5.3. Controls

The results concerning the control variables also merit somediscussion. Beginning with project-specific controls, the finding of aU-shaped effect of consortium size on the inimitability of technologyimplies that knowledge stemming from diversity of experiences –

presumably a consequence of bringing together a large number ofpartners – outweighs the associated transaction costs when itcomes to pursuing a novel and difficult to copy technology.Similarly, we find that projects of longer duration are more likelyto result in breakthrough (i.e. difficult to copy) technologies. Takentogether, these results suggest that large consortia, having ampletime to work together (the latter implying the reason why transac-tion costs may not be that important) are expected to result in noveltechnologies. Furthermore, it is obviously not surprising thatprojects in which the original idea stems from an industrial partnerare more likely to produce product innovation since in such projectsthe nature of the research work will tend to be more applied ratherthan exploratory.

Turning to firm-specific controls, our analysis has focused onrole in project, first time participation, and firm size. As alreadynoted, these variables may be endogenous in relation to innova-tion impacts. Assuming, however, that endogeneity is not aproblem with these variables, our findings suggest that playing acentral role in collaborative projects, in and of itself, leads tosuccess; that smaller firms tend to take better advantage of FPprojects to produce product innovations; and that, contrary to ourexpectations, first time participation increases the likelihood ofinnovation impacts. With regards to this latter result, we wouldexpect that firms with extensive alliance experience will be morecompetent in managing inter-organizational relationships (Dyerand Singh, 1998; Kale et al., 2002) and thus would be more likelyto obtain innovation impacts from their participation in collabora-tive R&D. Admittedly however, our proxy is a poor indicant of suchgeneral alliance experience. The finding of a positive associationbetween first time participation and innovation impacts couldimply that firms with no prior experience in FP projects are more“eager” to obtain results from their participation in such projects.

Finally, the positive effect of familiarity among partners oninnovation implies that such ties lead to greater commitment tomake the consortium work given the trust gained in the past, andthat it helps participants to build partner-specific routines ofcoordinating resources and tasks among themselves.

5.4. Implications to theory and practice

Increasingly, companies use technological inter-firm alliances forthe creation and exploitation of new knowledge, with participation inpublicly funded cooperative R&D projects being a particular case inpoint. While it is fairly well established in the extant literature thatparticipation in such collaborative research schemes confers positiveimpacts in terms of innovation outputs, we know very little aboutthe drivers of these impacts for participating firms. This is precisely thekey contribution of this study; it identifies project and firm-specificfactors that significantly influence innovation impacts, and in this way,represents a first step for future research along this line. For example,while we have examined some prominent factors influencing innova-tion impacts in publicly funded research consortia our model iscertainly not exhaustive. Essentially, we have treated consortiumdynamics as a “black box”, and thus we omitted from our analysesaspects related to project management and the effectiveness oflearning and knowledge sharing among consortium members. Eventhough project management and learning processes within the

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consortium may affect the likelihood of innovation impacts, we arguethat this influence may be indirect, and in the final analysis will bemediated by the participating firm's absorptive capacity. Furthermore,project uncertainty may also, at least partially, capture some of theindirect effects of project management and learning within theconsortium on innovation impacts. Modeling the effects of thosefactors using a causal model enabling the specification of such directand indirect effects on innovation impacts derived by participatingenterprises could be a promising area for future research.

Since our empirical context concerns publicly funded collabora-tive projects under the umbrella of the Framework Programme, theresults reported herein could also be seen from the wider perspectiveof European research policy. FP projects may indeed confer concretecommercial results besides the stated mandate of extending partici-pating firms’ technological knowledge. Our findings suggest thatlarge, diverse consortia, composed of partners who are familiar withone another and have significant innovation-related capabilities,having ample time to work on projects building on past researchand where the original idea stems from an industrial partner,projects that are risky (but not excessively risky) are more likely toresult in innovation outputs. Importantly, these “success factors”point to the nature of the selection criteria that should be consideredin the highly competitive process of project evaluation for financialsupport under the FP.

5.5. Limitations

As usual, the results reported here should be evaluated againstthe study's limitations. First, there is a potential problem of non-response bias; we have undertaken extensive analyses to check forthis and the results provide confidence that our estimation sampleis reasonably representative and that non-response does notcorrelate with a bias towards successful projects.

Another limitation concerns possible endogeneity of some of theexplanatory variables. We have presented reasons why role inproject, first time participation, and firm size may be endogenousto innovation impacts. Additional analyses provided mixed results,and therefore we cannot preclude the possibility of endogenous self-selection. Stronger tests of endogeneity bias are not possible giventhe data at hand. Recall, however, that the potentially endogenousvariables are not of primary interest to this study; they representfirm-specific controls, and as such are of secondary importance forpresent purposes. Future research may deal with this issue in a morecomprehensive manner than was possible here.

Furthermore, our dependent variables may contain some noise.Recall that we asked respondents to indicate whether their firmshave been able to derive new or significantly improved product orprocess innovation as a result of the project, and the extent towhich the resulting technology was difficult for competitors tocopy. There is a possible problem of attribution in these measures.Even though we explicitly asked respondents to indicate innova-tion impacts with reference to the specific project, we acknowl-edge that this is a limitation, which is, however, an inherentproblem in this type of research (Luukkonen, 2002). We believethere is no simple way to measure in precise and unambiguousterms the contribution of any given project in a subsequentinnovation. Recall, however, that our informants were individualsin charge of the project for their respective organizations, andhence would be – in principle, at least – in a position to provide areasonably accurate answer to these questions. In addition, asnoted earlier, our sample likely underestimates the innovationimpacts stemming from participation in these projects. If anything,our results are conservative rather than overly optimistic.

A final limitation pertains to the narrow scope of our depen-dent variables. We were interested to examine the “direct”impacts of participation in collaborative R&D on innovation. But

firms enter FP projects for a number of reasons, includingcontinuity of R&D effort, further development of technologicalcapabilities, keeping up with major technological developmentsand establishment of new relationships (Caloghirou and Vonortas,2000). Future research may examine more closely the determi-nants of these “indirect” impacts on project participants.

6. Conclusion

In this study we have attempted to identify antecedents ofinnovation impacts (i.e. product and process innovation, andinimitability of the resulting technology) derived by firms partici-pating in publicly funded collaborative R&D projects. We devel-oped hypotheses and tested for the effects of project and firm-specific factors on innovation performance in such projects.

Our results indicate that project novelty has a curvilinearrelationship with product innovation and inimitability of thetechnology; at moderate levels of risk the effects are positive,but begin to diminish as project risk becomes excessive. Projectcomplexity, another dimension of project uncertainty, affectspositively process innovation. Building on past R&D activitiesmakes it more likely that the project will result in important gainsin terms of innovation. With regards to firm-specific effects, ourresults indicate that a participating firm's prior R&D experienceand integrative capabilities, both of which reflect its absorptivecapacity are important determinants of innovation impacts.

Other factors that positively influence the likelihood ofsuccess include large consortium size and long project duration,familiarity with partners in the consortium, and project ideaoriginating from industry (rather than the academia). We havealso found that factors such as being a small firm and particularlybeing a first-time participant with a leading role in the projectare strong predictors of innovation impacts. However, we empha-size the need to consider these latter results with caution givenstrong arguments concerning potential endogeneity of thesevariables.

Acknowledgments

We would like to thank Robbert Fisher, Wolfgang Polt, AlfredKleinknecht, Ronald Dekker, Mireille Matt, Laurent Bach, EricSoderquist, Lucca Remoti and Gregory Prastacos for their contribu-tion in early stages of this research. We also thank Bart Noote-boom, Yannis Caloghirou, Charles Edquist and Georg Licht for theirvaluable comments. An early version of this study was presentedin the DIME Conference organized by the Maastricht University,in 2011.

Appendix A. Supplementary information

Supplementary data associated with this article can be found inthe online version at http://dx.doi.org/10.1016/j.technovation.2014.07.010.

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