Technology transfer organizations: Services and business models

Technology transfer organizations: Services and business models

Réjean Landry, Nabil Amara n, Jean-Samuel Cloutier, Norrin HalilemDepartment of Management, Faculty of Business, Laval University, Québec City, Que., Canada G1V 0A6

a r t i c l e i n f o

Keywords:Technology transferIntermediary organizationsServices provided to firmsSurveyRegressions

a b s t r a c t

Knowledge and technology transfer organizations (KTTOs) are crucial nodes connecting suppliers andusers of knowledge that support the endogenous potential of innovation in firms. Prior studies on theservices provided to firms by KTTOs tend to have weak theoretical foundations, to rely on case studyapproaches, and to focus attention on one service or a few services provided by a single organization.This study extends and integrates elements from a conceptual knowledge value chain and businessmodel frameworks. The value chain perspective allows integrating the services offered by KTTOs in thevalue chain of firms. As for the business model perspective, it allows developing hypotheses about howKTTOs create and deliver value for client firms. To test these hypotheses, we collected and analyzed adata set of 281 publicly supported KTTOs located in Canada. The empirical results show that differenttypes of KTTOs tend to specialize in the provision of services at different stages of the value chain offirms, and to benefit from complementarity effects between service offerings. Our analysis also showsthat different types of KTTOs devise different types of business models that are centered on serviceslinked to different stages of the value chain. Overall, these results suggest that managers of KTTOs couldimprove their business models and increase value to client firms by increasing the degree of customizationof solutions offered to clients which, in turn, would also increase revenues from clients, and hence reduceKTTOs′ vulnerability to reductions in government funding.

& 2013 Elsevier Ltd. All rights reserved.

1. Introduction

Knowledge is recognized as a fundamental asset for firms andorganizations (Teece, 1998), as the main resource upon whichcompetitive advantage is founded (Albino et al., 1999; Kogut andZander, 1992; Nonaka, 1994; Reisman, 2005), and its transfer as acritical factor necessary to improve productivity (Janis, 2003;Martyniuk et al., 2003) and to innovate (Albino et al., 2004;Cohen and Levinthal, 1990; Reisman, 2005). Moreover, as studieson determinants of innovation have shown the importance ofexternal sources of knowledge for the development and improve-ment of product and process innovations (Spithoven et al., 2011;Vega-Jurado et al., 2008; Amara et al., 2008; Laursen and Salter2006; von Hippel, 1988), more attention has been paid to thevarious types of actors who act as knowledge and technologytransfer intermediaries in the innovation process (Howells, 2006).These intermediaries are considered by many as “crucial nodes”connecting the suppliers to the users of knowledge (Bessant andRush, 1995; Howells, 2006; Howard Partners, 2007; Matt andSchaeffer, 2009; Spithoven and Knockaert, 2009; Theodorakopoulos

et al., in press; Hewitt-Dundas, 2012) in order to support theendogenous potential of innovation in firms (Hassink, 1997; Northet al., 2001). As a consequence, governments have come to relyheavily on knowledge and technology transfer intermediary organi-zations (KTTOs) as instruments of innovation policies.

Several types of knowledge and technology transfer interme-diaries are involved in the innovation process of firms. Hence,publicly funded technology transfer offices housed in universities,community colleges and public research organizations, publiclyfunded regional economic development agencies, knowledge-intensive business service (KIBS) firms, professional associations,advisory bodies and knowledge workers could all be considered asintermediaries that facilitate the transfer of knowledge supportingthe innovation process in firms. Each type of intermediary hasa different form and achieves a different role in the innovationprocess of firms, but the specificities of their roles are not wellunderstood because they tend to be conflated in the literature.In this paper, we argue for a more nuanced understanding of thedifferent roles of different types of KTTOs by looking at theirdifferent points of insertion in the value chain of firms. Such aperspective led us to uncover a great deal of variations withrespect to the contribution of different types of KTTOs in theinnovation process of firms.

Moreover, we assume that approaching KTTOs as a singleclass of organization is likely to improve our understanding of

Contents lists available at ScienceDirect

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

Technovation

0166-4972/$ - see front matter & 2013 Elsevier Ltd. All rights reserved.http://dx.doi.org/10.1016/j.technovation.2013.09.008

n Corresponding author. Tel.: þ1 418 656 2131x4382; fax: þ1 418 656 2624.E-mail addresses: [email protected] (R. Landry),

[email protected] (N. Amara), [email protected](J.-S. Cloutier), [email protected] (N. Halilem).

Please cite this article as: Landry, R., et al., Technology transfer organizations: Services and business models. Technovation (2013), http://dx.doi.org/10.1016/j.technovation.2013.09.008i

Technovation ∎ (∎∎∎∎) ∎∎∎–∎∎∎

www.sciencedirect.com/science/journal/01664972

www.elsevier.com/locate/technovation

http://dx.doi.org/10.1016/j.technovation.2013.09.008



mailto:[email protected]










innovation for two important reasons (Dalziel (2010): First, thispaper lays stress on this class of organization that is largely under-documented and under-studied in empirical studies on innovation(Lundvall, 1992; Cooke et al., 2004; Malerba, 2002), in conceptualmodels (Etzkowitz and Leydesdorff, 2000; Howells, 2006), and inthe data collection on innovation (Dalziel, 2006). Yet, KTTOs areintermediary organizations that are too numerous and too impor-tant as innovation policy instruments to be ignored. Second,approaching empirically and conceptually these organizationalintermediaries as a single class of organization will facilitate theproduction of generalizations which, in turn, will make theorydevelopment on intermediary organizations easier. Dalziel (2010)points out that such results will require working with a populationof organizations that is sufficiently large and sufficiently hetero-geneous to allow the derivation of generalizations from thespecificities of particular types of organizations.

In this paper, we focus attention on publicly supported KTTOs thatgovernments use as instruments of innovation policy. First, we identifyservices that KTTOs offer to firms. Then, we argue that different typesof KTTOs offer different services at different stages of the value chainof firms. Third, we argue that when managers of KTTOs have to figureout how to create value for firms, how to relate to firms, throughwhich resources, with what strategies, and how to make money, theyare devising business models. Fourth, we argue that different types ofKTTOs devise different types of business models that are centered onservices linked to different stages of the value chain. Finally, we arguethat managers of KTTOs could improve their business models andincrease value to clients by increasing the degree of customization ofsolutions offered to clients which, in turn, would also increaserevenues from clients, and hence reduce KTTOs′ vulnerability toreductions in government funding.

This paper builds and extends earlier studies that have inves-tigated KTTOs from three perspectives that we integrate into asingle and more comprehensive conceptual framework. First, thestudies that have investigated the services offered by KTTOs (Diaz-Puente et al., 2009; Howells, 2006; Janis, 2003; Lee and Win,2003; Rasmussen et al., 2006; Reamer et al., 2003; Seitzer, 1999;Spithoven and Knockaert, 2009) have generally focused on one ora few services. Until now, most studies of this research streamhave been based on case study approaches, thus making it difficultto generalize research results to other organizations and otherservices (Bramwell and Wolfe, 2008; Howells, 2006; Sharma et al.,2006). Our analysis builds and extends conceptually and empiri-cally these studies by integrating, in a single study, 21 serviceslinked to different stages of the value chain of firms.

Second, most prior studies on knowledge and technology trans-fer intermediaries have dealt with university technology transferoffices (UTTOs) and public research organizations (PROs) focusingtheir attention on patents and spin-offs (Agrawal, 2001; Hanel,2006; McAdam et al., 2012; Hewitt-Dundas, 2012), especially onhow much revenue patents and spin-offs generate for the researchinstitutions that house these technology transfer intermediaries(Debackere and Veugelers, 2005; Jensen et al., 2003; Siegel et al.,2003, 2004, 2007). UTTOs and PROs primarily offer services linkedto the exploration stage of the knowledge value chain of firms. Ouranalysis builds and extends these earlier studies by also consideringtwo other types of KTTOs (community college technology transferoffices and regional economic development institutions) that aremore focused on the offering of services linked to the exploitationstage of the value chain of firms. Furthermore, we integrate, as aconstitutive element of the conceptual business model framework,the issue of revenue generation dealt with in studies on patents andspin-offs. Such an approach allows us to integrate, in a singlerevenue perspective, that KTTOs generate revenue not only frompatents and spin-offs, but also revenue generated from the otherservices provided to firms.

A third and dominant stream of earlier studies focuses itsattention on linkages forged between KTTOs and firms (Arundeland Geuna, 2004; Geuna et al., 2006; Kodama, 2008; Laursen andSalter, 2004; Sharma et al., 2006; Wright et al., 2008; Yusuf, 2008).Our analysis builds and extends from this research stream by usingthe linkages forged between KTTOs and firms as one of theconstitutive elements of the business conceptual model frame-work. As we will see in the conceptual and empirical sections ofthis study, different types of KTTOs devise different typesof business models characterized by differences in the strengthof ties they forged with client firms.

This paper makes three contributions by extending our under-standing of the services provided by KTTOs, while taking intoaccount important criticisms addressed to KTTOs. First, by com-parison to prior studies in the field, this paper contributes toadvance knowledge on KTTOs by considering a larger variety ofservices in order to take into account both technology and marketservices, and by relying on the data of a population of organiza-tions that is sufficiently large and heterogeneous to allow thederivation of generalizations from the specificities of particulartypes of services and organizations. Based on such an approach,this paper shows that different types of KTTOs provide services atdifferent stages of the value chain of firms. Showing that differenttypes of KTTOs help firms at different stages in the innovationprocess carries important policy implications regarding the roleof KTTOs.

Second, the value chain concept suggests paying more atten-tion to better coordinating the linkages between services (Porter,1985). By looking at the services that KTTOs offer at the differentstages of the value chain, the statistical results of this studyadvance knowledge by uncovering complementarity effectsamong multiple types of services that are provided to firms byKTTOs. The presence of such complementarity effects carriesimportant implications for policy makers and managers of KTTOs.Third, we also contribute to the literature on KTTOs through therefinement of concepts and measures for operationalizing andconnecting elements of the value chain concept with elements ofthe business model concept. Such a conceptual integration wasinstrumental in developing and testing hypotheses that show howdifferent types of KTTOs implement different types of businessmodels that are centered on different types of services linked tothe different stages of the value chain of firms. Such results carryimportant theoretical, managerial, and policy implications.

In order to achieve these contributions, this paper will address insequence two conceptual questions and four empirical questions:

� Why do KTTOs emerge as intermediation organizations?� Why do we have different types of KTTOs?� Why do different types of KTTOs provide services at different

stages of the value chain of firms?� Are the services provided to firms complementary, substitute

or independent from each other?� What are the relations between the services provided to firms

and elements of KTTOs′ business models?� Finally, do different types of KTTOs develop different business

models?

The rest of the paper is organized as follows. First, we willreview prior studies to discuss these questions and derive workinghypotheses (Section 2). In Section 3, we will present the surveydata collected from different types of Canadian KTTOs. Theanalytical plan and presentation of the results will make upSections 4 and 5 of the paper. Then, in Section 6, we will discusshow the statistical results answer the four questions of this study.Finally, in the conclusion section of the paper, we will summarizethe major results, derive policy and managerial implications, and

R. Landry et al. / Technovation ∎ (∎∎∎∎) ∎∎∎–∎∎∎2






address the limitations of results and directions for future studieson KTTOs.

2. Conceptualizing the emergence and differentiationof KTTOs as intermediation organizations

In this section, we first provide a rationale to explain theemergence of KTTOs as intermediation organizations. Second, wedifferentiate types of KTTOs according to their knowledge base.Then, we identify services that KTTOs may provide to firms; weintegrate them into the conceptual value chain framework andformulate a working hypothesis regarding the provision of servicesby KTTOS. Fourth, we outline arguments concerning complemen-tarities, substitution, and independence among services providedto firms. Finally, we discuss how services are linked to otherelements of business models, thus creating differentiated businessmodels that singularize the different types of KTTOs.

2.1. Why do KTTOs emerge as intermediation organizations?

There is a large body of empirical studies pointing to theimportance of external sources of knowledge, resources, andtechnology in the development of innovation by firms (Becheikhet al., 2006). However, the acquisition and absorption of externalknowledge, resources, and technology are made difficult due tothe fact that the producers and users of knowledge and otherresources belong to different communities separated by a “valleyof death” (Landry and Amara, 2012a; Branscomb and Auerswald,2002), as well as governed by different sets of incentives. On theone hand, universities and government laboratories, as producersof knowledge, respond primarily to incentives related to themaximization of measurable research results. On the other hand,the behavior of firms, as users of knowledge, is primarily governedby incentives related to commercial measurable business results.And, as pointed out by Dasgupta and David (1994), there are noeconomic incentives inducing producers and users of knowledge towork jointly on a continuous basis. This issue has been addressed withmore or less success by the innovation policies of most developedcountries. It is plausible to assume that the lack of convergencebetween these two sets of incentives has stimulated the emergence oforganizations, referred to as intermediation organizations, that aim toenhance the endogenous potential of innovation of firms in under-taking activities that “other actors are reluctant to undertake” (Dalziel,2010, p. 12).

2.2. Why do we have different types of KTTOs?

Firms are highly heterogeneous with respect to their resources,capabilities of innovation, and capabilities to create value fromknowledge. A single type of intermediation organization couldlikely not embody all the specialized resources and capabilitiesrequired to help firms explore, validate, and exploit the knowledgeopportunities that a highly heterogeneous population of firms face.Following Dalziel (2010), we assume that a highly heterogeneouspopulation of firms stimulate the emergence of a highly heteroge-neous population of KTTOs acting as intermediation organizations.

In this paper, KTTOs refer to a class of organization thatoperates as bridges between research and business knowledge,and firms. As demonstrated by Howells (2006), studies on innova-tion intermediation have considered intermediation from a largediversity of perspectives, notably as relationships, processes, andservices. In this paper, we adopt the services perspective anddefine KTTOs as intermediation organizations who provide ser-vices to firms to help them extract value from knowledge, inorder to enhance their endogenous potential of innovation. These

services may be developed to help firms in traditional industries aswell as firms in science-based industries. Focusing on servicesprovided to firms makes it possible to clearly circumscribe theclass of intermediation organization who is helping firms at thedifferent stages of their value chain. Such a focus makes it possibleto exclude a class of organization that may be too broad to have ameaningful impact on the innovation process of firms.

Though such KTTOs are becoming increasingly more diversi-fied, one may differentiate four emblematic types of organizationswhich, in the OECD countries, rely on very different knowledgebases: a first group includes university technology transfer offices(UTTOs) which have the mandate to manage and commercializethe knowledge generated by researchers in academic depart-ments. UTTOs can rely on a knowledge base closer to the explora-tion than to the validation and exploitation stages of the valuechain. A second group includes public research organizations(PROs) which are government-funded research organizations inwhich the management, exploitation, and transfer of knowledge tofirms are becoming more and more important. By comparisonwith UTTOs, which cover research knowledge encompassinga large spectrum of scientific disciplines, PROs are usually specia-lized in areas like biotechnology, advanced materials, agriculture,etc. PROs can usually rely on a knowledge base closer to theexploration and validation stages than to the exploitation stage ofthe value chain. In Canada and in the United States, a third group,made up of newer organizations where research is not verydeveloped, includes community college technology transfer offices(CTTOs) which are technology transfer units within government-funded teaching institutions with 2 or 3-year teaching programs.CTTOs′ employees have expertise and access to equipment theycan rely on to help firms at the validation and exploitation stagesof the value chain. Hence, CTTOs can usually rely on a knowledgebase closer to the validation and exploitation stages than to theexploration stage of the value chain.

UTTOs, PROs, and CTTOs are usually public organizations.By comparison, a fourth group, generally made up of regionalorganizations, includes semi-public nonprofit knowledge and tech-nology transfer organizations (NPOs) which, in OECD countries, arepublicly funded to variable extents and are governed by boardsdominated by representatives of firms. NPO employees usually havetraining and experience in engineering and management. Thegovernment subsidies received by these organizations are designedto help these intermediaries provide services that complement theservices provided by the other types of KTTOs. There are veryimportant differences from one NPO to another, given differences inregional, economic, and political characteristics. Thus, NPOs canusually rely on a knowledge base closer to the market exploitationof knowledge than to the other stages of the value chain. Hence,they tend to help firms at the commercialization stage of the valuechain. The differentiated knowledge base of these four emblematictypes of KTTOs is likely to influence the types of services that theyprovide to firms. Let us now consider the types of services thatKTTOs might offer to firms.

2.3. Why do different types of KTTOs provide services at differentstages of the value chain of firms?

There are no consolidated directories of services provided tofirms by KTTOs. To identify potential services, we relied on twocomplementary approaches. First, we relied on technical reports andstudies on technology intermediation, and knowledge and techno-logy transfer to develop a first list with regard to the services offeredto firms by intermediary organizations (Diaz-Puente et al., 2009;Janis, 2003; Lee and Win, 2003; Rasmussen et al., 2006; Reameret al., 2003; Seitzer, 1999; Spithoven and Knockaert, 2009). Then, inorder to identify the actual services provided to firms by KTTOs, we

R. Landry et al. / Technovation ∎ (∎∎∎∎) ∎∎∎–∎∎∎ 3






developed a second list based on the web site contents of the 416Canadian KTTOs. Based on these two sources of data, we set up anexhaustive list of service identifiers that we merged according totheir thematic similarity. This procedure of conceptual reduction ledus to progressively differentiate 24 services. We submitted this listof services to an advisory committee composed of the executivedirectors or CEOs of nine KTTOs. The wording used to describe someservices was modified, and then pretested with ten other KTTOs.A final version of the list of services was developed after the pretest.Such a list is made up of a sufficient number of heterogeneousservices to allow the production of generalizations from the study ofthe specificities of the services provided to firms by particular typesof KTTOs. Moreover, such a list is also made up of services that aresufficiently concrete to measure, in a valid and reliable way, whatservices KTTOs actually provide to firms rather than measuringwhat services they claim to provide to firms in their public relations.

The delivery of these different services occurs within the knowl-edge value chains of firms (Landry and Amara, 2012a; Lundquist,2003; Phan and Siegel, 2006). We suggest that such a conceptualperspective involves a value chain of value-added services thatcomprises three primary stages (see Fig. 1): (1) the exploration ofknowledge-based opportunities which consists of services aimed athelping firms to specify research and technological needs, andaccess to relevant technologies, equipment, and patents; (2) thetechnical validation of knowledge-based opportunities refers toservices aimed at helping firms with prototypes, scaling up, patent-ing, and certification, and; (3) the exploitation of knowledge-basedopportunities consists of services aimed at helping firms on legalissues, access to capital, and commercialization. Needless to pointout that like any conceptual framework, the conceptual value chainframework should be viewed as a simplified representation of thereal world of innovation. In the real world, the innovation process ischaracterized by many feed-back loops and feed-forward loopswhere KTTOs and firms are more concerned with solving problemsthan with aligning their actions with well-defined stages of aconceptual value chain framework. However, such a simplifiedrepresentation of reality is useful to help analyze a crucial challengefor KTTOs, which is about figuring out where to position theirservice offerings in the knowledge value chain. Hence, based on therationale developed in the two previous sections, we hypothesizethat:

Hypothesis 1. The differentiated knowledge base of the differenttypes of KTTOs will induce them to specialize with respect to howthey help firms to enhance their endogenous innovation capabi-lities. More specifically, we hypothesize that UTTOs and PROs willspecialize in the provision of services at the exploration stage ofthe value chain, CCTOs will specialize in the provision of services

at the validation and exploitation stages and, finally, NPOs willspecialize in the delivery of services at the exploitation stage of thevalue chain.

2.4. Are the services provided to firms complementary, substitute orindependent from each other?

As pointed out by Porter (1985), the value chain frameworksuggests paying attention to the coordination among the elementsthat are included in the value chain. Such a perspective is all themore justified since, with the exception of PROs, the other KTTOsare usually small organizations operating with very limitedresources. Therefore, they cannot afford to produce and providetoo many services at the same time, except if there are comple-mentarity effects among services. According to Milgrom andRoberts (1995, p. 181), complementarities arise when “doing moreof one thing increases the returns to doing more of another”.According to Roberts (2004), Tzabbar et al. (2008), and Ennen andRichter (2010), complementarities generate system effects such asthat “the whole becomes more (or less) than the sum of its parts”.Thus, the benefits that may arise from complementarities are dueto economies of scope. Hence, KTTOs might generate economies ofscope when they can share the same resources, expert knowledge,and skills for the production and delivery of multiple services.Landry and Amara (2012b) showed the existence of complemen-tary effects between adjacent stages of a value chain linking theexploration, validation, and exploitation of knowledge. Hence,based on this rationale, we hypothesize that:

Hypothesis 2. On the one hand, complementarity effects willemerge between services offered at the exploration and validationstages, as well as between services offered at the validation andexploitation stages. On the other hand, we expect either substitu-tion effects or independence between the services of the explora-tion and those of the exploitation stages of the value chain.

2.5. What are the relations between the services provided to firmsand elements of KTTOs′ business models?

Once a KTTO manager has chosen the services his organizationshould offer to firms, the next question is to figure out how tocreate value for firms, what types of firms to reach, how to relateto firms, through which resources, with what strategies andfinally, how to make money (Pries and Guild, 2011). Each of thesechoices involves explanatory variables linked to different elementsof business models. According to Osterwaldeer and Pigneur (2010,p. 14), a business model “describes the rationale of how an

Legal issues: Preparation of patent applications Spin-off creation in order to exploit inventions Contractual agreements negotiation & management Capital access: Commercial bank loans Angel investors or angel network Venture capital Commercialization: Product positioning Business case development Design and implementation of business processes Advertising and promotion of new products Access to markets/distribution channels Access to international markets/distribution channels

Needs specification related to technologies, production equipment and patents Needs specification related to research Assistance to access pertinent research

Prototype design and technical feasibility testing Product and process safety certification Manufacturing practices, processes and technology improvement Access to specialized equipment or facilities to scale up production Access to expertise to scale up production

EXPLORATION OF KNOWLEDGE-BASED

OPPORTUNITIES

EXPLOITATION OF KNOWLEDGE-BASED OPPORTUNITIES

TECHNICAL VALIDATION OF KNOWLEDGE-BASED

OPPORTUNITIES

Fig. 1. The knowledge and technology transfer value chain.







organization creates, delivers, and captures value”. For KTTOmanagers, the formulation of a business model is a key decisionbecause once the model is set, the services are in place, theresources have been committed, it becomes difficult to change thebusiness model due to forces of inertia and resistance to change(Zott and Amit, 2010).

Although the business model concept constitutes a conceptualframework, not a theory (Teece, 2010), it helps to develop hypothesesabout factors that could influence the choices to be made by KTTOmanagers. Osterwaldeer et al. (2005) have reviewed the mostcommon building blocks of business models. In this paper, wepropose to rely on the Chesbrough (2007, 2010) approach to thebusiness model concept because it provides generic explanatoryvariables to analyze the different sources of value rather than specificsources of value for particular types of organizations. In this paper,following Landry and Amara′s (2012a) theoretical paper on knowl-edge and technology transfer business models, we have integrated,into a business model framework, six explanatory variables likely toinfluence decisions regarding KTTOs′ service offerings: customervalue proposition, market segment, revenue generation mechan-isms, positioning within the value network, strategies, and keyresources. Three control variables were added: size of organizations,size of urban agglomerations where KTTOs operate, and finally, thetypes of organizations.

2.5.1. Customer value propositionIn the conceptual business model framework, the starting point is

the development of a value proposition. Firms do not want KTTOs′services; they want services that help them to solve problems, getjobs done more effectively, conveniently, and affordably (Teece,2010). However, KTTOs cannot expect to meet firms′ needs andrequirements with standard services because of the large variety oftheir situations in terms of industry, resources, capabilities, etc. Thevalue created by the services provided by KTTOs is likely to varyaccording to the degree of customization of the service solutionsprovided to firms (Gwinner et al., 2005; Vargo, 2008). Hence, thebest customer value proposition that KTTOs may offer is to tailorcustom-made services for a single client firm. It is likely an excessiveexpectation, given that most KTTOs are small, and therefore havevery limited financial and intellectual resources. A possible compro-mise for KTTOs is to offer services that provide half-customizedsolutions. Thus, we suggest that services which correspond mainly oralmost only to basic research results create less value for client firmsthan services which correspond to problem-specific solutions custo-mized for the needs and requirements of a single client firm. Hence,we hypothesize that:

Hypothesis 3. KTTOs′ service offerings will be negatively asso-ciated with the provision of non-customized solutions and posi-tively associated with the provision of mainly or partly customizedsolutions.

2.5.2. Market segmentKTTOs cannot avoid identifying a market segment (Chesbrough,

2007, 2010). They must ask themselves for what groups of firmsthe services provided are useful and create value. One mighthypothesize that large firms are less likely than SMEs to sufferfrom a lack of the resources required to explore, validate, andexploit external knowledge-based opportunities for the develop-ment or improvement of their products and production processes.Moreover, SMEs are less likely to have the absorptive capacityrequired to derive benefits from services linked to the explorationof knowledge-based opportunities than large firms. Hence, wehypothesize that:

Hypothesis 4. KTTOs′ service offerings will be positively asso-ciated with the provision of services to SMEs at the validation and

exploitation stages, while positively associated with the provisionof services to large firms at the exploration stage of knowledge-based opportunities.

2.5.3. Revenue generation mechanismsHow are KTTOs compensated for their service offerings? The

sustainability of KTTOs depends on the revenue they can capturefrom their provision of services to firms at the different stages ofthe knowledge value chain. The KTTOs′ revenue streams comeprimarily in the form of government subsidies and the sale ofservices to firms (McAdam et al., 2012). One might hypothesizethat the willingness of firms to pay for the services they acquirefrom KTTOs measures, at least in part, the value created for firms.Hence, we hypothesize that:

Hypothesis 5. KTTOs′ service offerings will be positively asso-ciated with high or moderate revenue streams from client firmsfor services linked to the validation and exploitation stages, whilenot associated with a revenue stream from client firms for servicesprovided at the exploration stage of the value chain.

2.5.4. Positioning in the value networkAre working relationships forged between KTTOs and their

clients? Do KTTOs and their clients forge very close workingrelationships, practically like if they were in the same work group?Conversely, at the other extreme of the continuum, do KTTOs andtheir clients forge very distant working relationships, practicallylike people that KTTOs do not know well (Gwinner et al., 2005)?Based on studies on networks, we hypothesize that:

Hypothesis 6. Increasing the strength of ties (closeness) betweenKTTOs and their clients generates a common understanding that ispositively associated with the KTTOs′ service offerings at all threestages of the knowledge value chain.

2.5.5. StrategiesStrategy refers to a set of decisions and actions that aims to give

the KTTO a superior performance and ultimately a competitiveadvantage over rival organizations (Porter, 1996, 2008). Develop-ing a strategy helps the KTTO to understand what to do, what tobecome, and how to plan to get there. A strategy defines the scopeof a KTTO’s intentions, in particular in relation to how it willmobilize professional knowledge in order to develop and improveits services. KTTOs without strategy likely react to short termopportunities without achieving medium and long term goals(Miles and Snow, 1978). Based on this rationale, we hypothesizethat:

Hypothesis 7. KTTOs which have developed, to a greater extent,market strategies, knowledge management strategies, and strate-gies related to the promotion of their services, are more likely tobe more active in the KTTOs’ service offerings at all stages of thevalue chain.

2.5.6. Key resourcesKey resources needed by KTTOs, which are fundamental to the

production and delivery of knowledge and technology transferservices to firms, are people and technical resources (Walsh et al.,2008). KTTOs need to rely on the highly specific knowledge oftheir employees in order to successfully produce and deliverservices to their client firms (Theodorakopoulos et al., 2012).The production and delivery of the services provided by KTTOsrequire employees with scientific and business expertise (Gwinneret al., 2005; Neu and Brown, 2005; Walsh et al., 2008). A scientificbackground is necessary for an understanding of the underlyingscience and technology issues involved in the services beingprovided. Such a background allows for a deeper understandingof the needs, requirements, and expectations of firms with respectto the services acquired from KTTOs. Likewise, training in business







helps KTTOs’ employees to understand the business issuesinvolved in knowledge and technology transfer, and to developand provide the business components of knowledge and technol-ogy transfer services. Based on this rationale, we hypothesize that:

Hypothesis 8. As the number of scientists and engineers employedby KTTOs increases, so does the KTTOs’ service offerings provided atthe exploratory and technical validation stages.

Hypothesis 9. As the number of KTTOs’ employees with businesstraining increases, so does the KTTOs’ service offerings provided at theexploitation stage linked to access to capital and commercialization.

The development and improvement of the services provided byKTTOs also depend on investments in technical resources linkedto various ways of generating and finding knowledge, as well asto ways of storing and spreading knowledge. Hence, we herehypothesize that:

Hypothesis 10. Increasing the use of technical resources in KTTOscontributes to enhance the KTTOs’ service offerings offered at allthree stages of the value chain.

2.5.7. Control variablesThree control variables are introduced in this study. Organiza-

tional size (number of employees) is introduced because it likelyinfluences the organizational capability of KTTOs to produce anddeliver services. The size of the urban agglomeration where theKTTO is located is also considered as a control variable because itcaptures differences in opportunities to provide services that likelydiffer from agglomeration to agglomeration. The type of KTTO, inour case university technology transfer offices (UTTOs), collegetechnology transfer offices (CTTOs), public research organizations(PROs), and nonprofit organizations (NPOs), is the third controlvariable introduced because one might hypothesize that each typeof KTTO differs significantly with respect to its knowledge base atthe various stages of the knowledge value chain.

The operational definitions of the business model elements arepresented in Appendix A.

2.6. Do different types of KTTOs develop different business models?

The above hypotheses concern how KTTOs’ might be posi-tioned on each element of a business model. Additional hypoth-eses are required in order to explain how different configurationsof elements’ positioning are connected and centered on a dom-inating theme. We hypothesize that the design of KTTOs businessmodels is predominantly centered on the services provided tofirms, more specifically to services linked to the exploration,validation, and exploitation stages of the knowledge value chain.This choice is appropriate for KTTOs because it relates to theirfundamental “raison d'être” which is to provide services thatsupport the innovation process in firms. KTTOs can provideservices at all three stages of the value chain, thus generating alarge number of possible configurations. However, for the purposeof illustrations, we hereafter derive three emblematic businessmodels which hypothesize that KTTOs predominantly provideservices at one stage of the value chain. Hence,

Hypothesis 11. Exploration-centered business models are likely tobe implemented by research organizations such as universitiesand PROs that would predominantly: provide services linked tothe exploration stage of the value chain, do not customize servicesfor firms, target large firms, generate a small fraction of theirrevenue from client firms, forge weak ties with client firms, do notdevelop well defined market strategies, and rely on researchers astheir key resources to develop their offer to firms.

Hypothesis 12. Validation-centered business models are likely to beimplemented by intermediaries such as technology transfer officesof community colleges that would predominantly: provideservices linked to the validation stage of the value chain, custo-mize services for firms, target SMEs, generate a large fraction oftheir revenue from client firms, forge strong ties with client firms,develop well-defined market strategies, and rely on employeeswith technical training and experience to develop their offerto firms.

Hypothesis 13. Exploitation-centered business models are likely tobe implemented by economic development intermediaries such asNPOs that would predominantly: provide services linked to theexploitation stage of the value chain, customize services, target themarket of SMEs, generate a large fraction of their revenue fromclient firms, forge strong ties with client firms, develop welldefined market strategies, and rely on employees with engineeringand business training and experience to develop their offer tofirms.

3. Data collection and data coding

3.1. Data

3.1.1. Studied populationsListings are available for the identification of the university

technology transfer offices (UTTOs) and the community collegetechnology transfer offices (CTTOs). However, there are no direc-tories for the other types of KTTOs targeted in this study: thepublic research organizations (PROs), and the nonprofit knowledgeand technology transfer organizations (NPOs). To identify thepopulation of the PROs and NPOs, we relied on the web site ofvarious Canadian and provincial government agencies to develop alist of KTTO organizations. We also used a snowball strategy, usingreferences to other websites, to identify additional organizations.These complementary search approaches allowed us to identifymore than 900 organizations. This procedure was taken up bythree research assistants who worked independently from oneanother. It generated results that matched at more than 95%.A subset of 416 organizations was kept and considered to be partof the population defined in this study, following the use of aninclusion criterion with regard to the offer of services to firms.Thus, all the organizations that did not offer services to firms wereexcluded from the population of organizations to be surveyed.

3.1.2. Questionnaire development and data collectionThe questionnaire was developed by using technical reports,

the academic literature on KTTOs, information available on thewebsites of 416 Canadian KTTOs, and comments and suggestionsfrom an advisory committee composed of the CEO or executivedirectors of nine KTTOs. The questionnaire was pretested with tenother organizations. The questionnaire was administered, byphone, by a survey firm, to the executive directors of the KTTOs.The interviews were conducted between November 19, 2008 andFebruary 21, 2009. The survey firm tried to contact the executivedirectors of the 416 organizations included in this population. Theexecutive directors of 91 organizations were unreachable duringthe survey period, even after many recalls. This difficulty isprobably due to the small size of several of these organizations.In all, the survey firmwas able to reach 325 of these organizations.Of this number, 51 organizations refused to participate, and 62organizations were not interviewed after being contacted becausethey did not offer services to firms and thus were not part of thepopulation targeted by this survey. In short, by February 21, 2009,212 interviews had been completed, for a possible population of263 organizations. The response rate is thus 80.6% (212/263







organizations). Such a number of interviews generated data on asufficiently large number of organizations to capture the hetero-geneity of intermediation organizations, while allowing to gen-erate general explanations from the analysis of the specificities ofthe different types of organizations.

3.2. Data coding

3.2.1. Dependent variablesThere are five dependent variables considered in this study.

These variables capture the offering of services related to thedifferent stages of the knowledge value chain presented pre-viously. To measure the offering of services to firms, KTTOs wereasked to qualify, on a 5-point Likert scale, the frequency withwhich they had offered 24 services to firms during the 3 yearspreceding the survey (1¼Never to 5¼Very often). To identify theunderlying dimensions assessed by this measuring instrument ofservice offerings, we firstly performed an exploratory factoranalysis (EFA) with a principal axis factoring with the entire setof services listed in the measurement instrument (24 services).This EFA permitted to discard three services that were weaklycorrelated with the overall set of services (i.e., those whosecommunalities are lower than .50).

Then, a second exploratory factor analysis with the remaining 21services was conducted, using the same extraction method. Table 1shows the results of this second EFAwith a Varimax rotation method.Factors with an eigenvalue greater than 1.0 were retained and thecut-off of factors loading was greater than .5. These results reveal fivedistinctive factors underlying the stages of service offerings by KTTOsto private firms that we labeled as follows: (1) exploration ofknowledge-based opportunities (4 services); (2) technical validationof knowledge-based opportunities (5 services); (3) legal issues linked

to the exploitation of knowledge-based opportunities (3 services); (4)issues regarding access to capital linked to the exploitation ofknowledge-based opportunities (3 services); and (5) commercializa-tion issues linked to the exploitation of knowledge-based opportu-nities (6 services). These five factors explain 72.11% of the originalvariance of the studied phenomenon. We also assessed the statisticalreliability of these five factors. As can be seen at the bottom ofTable 1, the Cronbach’s alphas range from .662 for the legal issuesfactor to .886 for the commercialization factor, indicating that theitems forming each factor are reliable. However, we have to point outthat although there is no strict threshold for high reliability, Ahireand Devaray (2001) and Nunally (1978) recommended relying on athreshold of .50 for emerging construct scales like the ones tested inthis study and .70 for maturing constructs.

The factors underlying the stages of service offerings by KTTOswill be used as dependent variables in our econometric models.More precisely, we used the weighted sum of items correspondingto each factor to construct an index. Therefore, the mean scores ofa particular stage of service offerings can take on non-integervalues from 1 to 5 (Thornhill and White, 2007, p. 556).

To compare the level of service offerings across the stagesof the knowledge value chain, we used Duncan’s post hoc test thatcompares the means for groups of organizations in homogeneoussubsets. The results of Duncan’s post hoc test for each stage ofservice offerings are reported in Table 2. For the index of the level ofservices linked to the exploration of knowledge-based opportunities,the results indicate no significant differences between the four typesof organizations (Table 2a). The results of this test also show thatCTTOs and NPOs offer a significantly higher level of services thanUTTOs and PROs at the stage of the services included in the index ofthe technical validation of knowledge-based opportunities(Table 2b). Likewise, for the index of services regarding legal issues

Table 1PFA results underlying the knowledge value chain stages of service offerings.

Factors

1 2 3 4 5

Services linked to exploration of knowledge-based opportunitiesHelp firms to specify their needs related to technologies, production equipment, and patents .595Help firms to specify their needs related to research .795Help firms to access ideas and information on relevant technologies, equipment, and patents .815Help firms to access pertinent research .832Services linked to technical validation of knowledge-based opportunitiesHelp firms with prototype design and tests of technical feasibility (product testing) .561Help firms with product and process safety certification .606Help businesses to scale up their manufacturing practices, processes, and technology .698Help businesses to access specialized equipment or facilities to scale up production (e.g., testing specialized equipments, etc.) .700Help businesses to access expertise to scale up production (e.g., student interns, engineers, faculty, experts of the industry, etc.) .644Services linked to exploitation of knowledge-based opportunities: Legal issuesHelp firms to prepare patent applications .800Help to create spin-offs in order to exploit inventions .550Help firms regarding negotiation and management of contractual agreements .681Services linked to exploitation of knowledge-based opportunities: Access to capitalHelp firms to access commercial bank loans linked to the development of new or improved products and processes .579Help firms to access angel investors or angel networks linked to the development of new or improved products and processes .802Help firms to access venture capital linked to the development of new or improved products and processes .839Services linked to exploitation of knowledge-based opportunities: CommercializationProvide assistance related to product positioning (first on the market, creating a niche, etc.) .682Help firms to develop a business case .557Help firms in the design and implementation of business processes (customer needs assessment service, inventory management) .609Help firms regarding advertising and promotion of new products .831Help client firms access markets/distribution channels .839Help client firms access international markets/distribution channels .761Eigenvalue 2.783 1.525 1.074 1.258 7.22Variance explained 14.254 9.261 7.113 6.088 35.393Cronbach’s Alpha .812 .776 .662 .859 .886(a) Total variance extracted by the five factors: 72.11%; KMO¼ .870; Bartlett’s test¼2261.3 (p-value¼ .000)(b) Extraction method: principal components(c) Rotation method: varimax with Kaiser normalization.







linked to the exploitation of knowledge-based opportunities, theresults presented in Table 2c indicate that the organization types canbe grouped into two different levels of service offerings: UTTOs andNPOs constitute a sub-group of organizations that provide a higherlevel of services than the other sub-group composed of CTTOs andPROs. For the index of services regarding access to capital linked tothe exploitation of knowledge-based opportunities, the resultsreported in Table 2d show that NPOs offer a significantly higherlevel of these services than the other three types of organization,whereas PROs offer the lowest level of these services linked toaccess to capital. Finally, as can be seen in Table 2e, NPOs scorehigher than the other three types of organization on the level ofservices regarding commercialization issues linked to the exploita-tion of knowledge-based opportunities.

3.2.2. Independent variablesThe explanatory variables considered in this study were

regrouped into seven categories that capture different buildingblocks of business models: (1) customer value proposition (i.e.,non-customized solutions, mixed solutions, customized solutions),(2) market segment (i.e., size of private firms that receivedservices), (3) revenue generation mechanisms (i.e., importance ofrevenue from sales of services in the total budget of KTTOs),(4) positioning within the value network (i.e., the strength of tiesbetween the KTTOs and their partners), (5) strategies (i.e., marketstrategies, knowledge-management strategies, promotion of servicesstrategies), (6) key resources (i.e., technical resources, knowledgeresources), (7) control variables (i.e., size of organization, size ofurban agglomeration where organization is located, types of orga-nization). The operational definitions and descriptive statistics ofthese explanatory variables are presented in Appendix A.

For the six independent variables, based on multiple-itemscales and included in the econometric models (LnTIES, MARKET,KNOWMNG, PROMO, GENFIND, STOSPRE), we conducted a princi-pal components factor analysis (PCFA) on the construct scales toassess their unidimensionality (Ahire and Devaray, 2001). More-over, the values of Cronbach’s α indicate that the items formingeach index are reliable (see Appendix A). Finally, the correlationmatrix between the continuous predictors used in the econo-metric models (Appendix B), and the tolerance statistic values forthese predictors that are all much higher than .2, ensure that thereis no multicollinearity concern (Menard, 1995; Field, 2006).

An analysis of early versus late respondents’ answers to keyvariables of the study was also performed. The rationale forcomparing early and late respondents is the assumption that laterespondents might approximate non-respondents, because if theinterviewer had not made extra efforts to reach them, they tooprobably would have been non-respondents (Miller and Smith,1983; Radhakrishna and Doamekpor, 2008). More specifically,we performed a comparison between the first and last 10% ofrespondents (the latter being used as a proxy for the non-respondents) according to the five indices measuring the depen-dent variables, organization’s size, and the main indices measuringthe explanatory variables used in the econometric models. Theresults indicate no significant differences in responses to any ofthese key variables of the study. Hence, we can conclude that non-respondents are likely similar to late respondents, and thus thenon-response bias is not a major concern in our sample.

4. Analytical plan

The analytical plan is structured in three sequential steps.Firstly, we used Mplus 3.13 — a structural equation-modeling

Table 2Means of the variables referring to stages of service offerings for groups of KTTOs in homogeneous subsets according to types of organization: Duncan’s testa.

Types of organization N Subset for α¼ .05 Types of organization N Subset for α¼ .05

1 1 2

(2a) Exploration of knowledge-based opportunitiesa (2b) Technical validation of knowledge-based opportunitiesUniversity technological transfer office (UTTO) 42 3.45 Public research organization (PRO) 39 2.28Public research organization (PRO) 39 3.57 University technological transfer office (UTTO) 42 2.35College technological transfer office (CTTO) 54 3.71 Nonprofit organization (NPO) 77 2.74Nonprofit organization (NPO) 77 3.72 College technological transfer office (CTTO) 54 2.90Significanceb .181 Significance b .683 .335

Types of organization N Subset for α¼ .05 Types of organization N Subset for α¼ .05

1 2 1 2 3

(2c) Exploitation of knowledge-based opportunities: Legal issues (2d) Exploitation of knowledge-based opportunities: Access to capitalCollege technological transfer office (CTTO) 54 1.70 Public research organization (PRO) 39 1.29Public research organization (PRO) 39 1.85 College technological transfer office (CTTO) 54 1.38 1.38Nonprofit organization (NPO) 77 2.33 University technological transfer office (UTTO) 42 1.72University technological transfer office (UTTO) 42 2.48 Nonprofit organization (NPO) 77 2.32Significance b .358 .364 Significance b .595 .052 1.000

Types of organization N Subset for α¼ .05

1 2

(2e) Exploitation of knowledge-based opportunities: CommercializationPublic research organization (PRO) 39 1.70University technological transfer office (UTTO) 42 1.99College technological transfer office (CTTO) 54 2.06Nonprofit organization (NPO) 77 2.57Significance b .058 1.000

a We performed the homogeneity of variance test. The values of the Levene statistic obtained indicate that, in the five situations, we can reject the null hypothesis ofequality of variance between the groups of organization.

b Duncan’s test compares means for groups in homogeneous subsets when equal variances are assumed. When the significance test is above the threshold¼ .05, the nullhypothesis (nondifferences of means) cannot be rejected.







package by Muthén and Muthén (1998–2004) — to estimate asaturated path model which allows to simultaneously estimatefive OLS regressions to explore the correlates of the stages ofservice offerings previously identified with the exploratory factoranalysis, namely, Exploration of knowledge-based opportunities(EXPLOR), Technical validation of knowledge-based opportunities(TECVALID), and the three sub-stages of the exploitation ofknowledge-based opportunities: legal issues (LEGAL), access tocapital (ACESSK), and commercialization (COMMER).

This path model was fitted to the data using a maximumlikelihood (ML) of a multivariate normal density function, as alldependent variables considered in this study are continuous andnormally distributed. Technical details about the ML estimator areprovided in Muthén (1998–2004, pp. 17–20) and in Golob (2003,pp. 8–9). The path model used in this study allows for jointlyestimating five OLS regressions while controlling for the existenceof mutual covariances between their disturbances (Amara et al.,2008; Galia and Legros, 2004). The major issue raised from the useof separate models is related to the possibility of getting inefficientestimators if some equations’ disturbances are correlated (Belderboset al., 2004). Therefore, for each of the five dependent variables, wedeveloped the following ordinary least squares model:

Yi ¼ βXiþεi

where Yi is the dependent variable referring to the stage of serviceofferings for organization i, Xi is the vector of K explanatory variablesfor organization i, β is a vector of parameters to be estimated fororganization i, and εi is the error term for organization i.

Secondly, as the fit of the saturated path model estimated inthe first stage cannot be assessed (Saturated models always fitperfectly as they typically have 0 degree of freedom.), the samemodel was estimated, but by fixing insignificant parameters (i.e.,those with p4 .10, two-tailed) at 0. This second unsaturated pathmodel can be assessed for model fit as its degree of freedom isdifferent from 0. As mentioned in Ouimet et al. (2007), Golob andRegan recommend fixing insignificant parameters, as “saturatedmodels are difficult to interpret, because statistically significanteffects can be diminished due to multicollinearity with insignif-icant effects” (2002, p. 217).

Thirdly, we also estimated the same unsaturated path model,but with the covariances between the equations’ error-terms fixedat 0. The comparison of this constrained unsaturated path modelwith the unsaturated one with free error-terms permits to assess ifthe simultaneous estimation of the five OLS regressions is moreappropriate than the use of separate regression models. If this isthe case, the free error-term covariances will serve as proxies ofthe complementarity, substitution or independence effects betweenthe stages of service offerings.

5. Results

The results of the saturated (i.e., with 0 degree of freedom) andunsaturated path models (which take into account only thesignificant coefficients) are summarized in Tables 3 and 4 respec-tively. The results regarding error-term covariances betweenstages of service offerings are summarized in the lower part ofTable 4. Finally, the results of the comparison of the constrainedunsaturated path model with the unsaturated one with free error-terms are also reported in the lower part of Table 4.

5.1. Overall model fit, R-squares and error-term covariances

As mentioned in Section 4, the saturated path model estimatedin the first step could not be assessed for model fit as it typicallyhas zero degree of freedom. We therefore only present the fit of

the unsaturated model (Table 4), which excludes the insignificantparameters found in the saturated model estimated in step 1. Theunsaturated path model had 44 degrees of freedom and aninsignificant χ2 statistic of 33.719 (p-value¼ .869). The insignificantχ2 indicates that the final unsaturated path model has a very goodfit. The R2 estimates that are listed on the lower part of Table 4show that access to capital (ACESSK) and commercialization(COMMER) are the stages of service offerings that are the mosteffectively explained.

We also estimated the same unsaturated path model, but with thecovariances between the equations’ error-terms fixed at 0. Thecomputed value of the Likelihood Ratio Index (LR index), thatcompares the Log Likelihoods’ values related to the unsaturated modeland to the model forcing the covariances between the equations’error-terms to be equal to zero, is significant at the1% level (χ2¼655.101; p-value¼ .000). This suggests that the nullhypothesis, that all the error-term covariances are all zeros, is stronglyrejected. This last result provides evidence, at least for our data, thatthe use of the separate standard OLS models is inappropriate toestimate the determinant of the stages of service offerings.

5.2. Complementarities among value chain stages of service offerings

The estimates of the error-term covariances of the five regres-sion equations are listed at the bottom of Table 4. All of thesecovariances are significant and positive, except for the covariancebetween service offerings linked to the exploration of knowledge-based opportunities, and service offerings linked to the access tocapital. These last two stages of service offerings seem to beindependent from each other, as indicated by the correspondinginsignificant covariances between the estimated disturbances.Overall, these results support the hypothesis of interdependencebetween the different stages of service offerings. However, somecovariances between pairs of stages of service offerings are higherthan others, suggesting the presence of higher complementaritiesbetween some pairs of stages of service offerings than others.

More specifically, the highest covariances are between serviceofferings linked to the access to capital and service offerings linkedto commercialization (.262), and service offerings linked to thetechnical validation of knowledge-based opportunities and serviceofferings linked to legal issues (.251). At the other extreme, thelowest covariances are between service offerings linked to theexploration of knowledge-based opportunities and service offer-ings linked to legal issues (.100), and service offerings linked to theexploration of knowledge-based opportunities and service offer-ings linked to commercialization (.101). Hence, service offeringslinked to the exploration of knowledge-based opportunitiesappear to be more related to the service offerings linked to thetechnical validation of knowledge-opportunities than to any of theother three stages of service offerings.

5.3. Effects of explanatory variables on value chain stages of serviceofferings

As for the extent to which explanatory variables explain thevarious stages of service offerings, results show that anywherefrom nine to twelve variables are significant at levels varying from1% to 10% in each of the five equations. Let us first consider thecapacity of the different variables to explain stages of serviceofferings. Developing mixed solutions for client firms (MIXED)(half-customized solutions and half-basic research) rather thancustomized solutions (CUSTOM) (almost only customized solutionsand mainly customized solutions) had a significant and positiveimpact on three stages of service offerings, namely, exploration ofknowledge-based opportunities, technical validation of knowledge-based opportunities, and commercialization. Conversely, developing







non-customized solutions for client firms (NOCUSTOM) (mainly basicresearch and almost only basic research) had a negative and significantimpact on all the five stages of service offerings. Likewise, the marketsegment, as measured by the percentage of private firms with lessthan 10 employees that were targeted by KTTOs’ services (SEG-MENT), is significantly and positively related to all stages of serviceofferings, except exploration of knowledge-based opportunities.

Moreover, the KTTOs that generated no revenues (NOREV)or moderate revenues (MODREV) from sale of services weremore present in services linked to the exploration of knowledge-based opportunities than those that generated important revenues(IMPOREV) from the sale of services. However, the KTTOs thatgenerated no revenues from the sale of services were more present

in services regarding legal issues linked to the exploitation ofknowledge-based opportunities than those that generated importantrevenues from the sale of services. The strength of ties between theKTTOs and their clients (TIES) had a significant and negative impacton three stages of service offerings: exploration of knowledge-basedopportunities, access to capital, and commercialization. More pre-cisely, for these three stages, an increase in the distance between theKTTOs and their clients decreases the capacity of the KTTOs to offerthe services related to these stages. With regard to the three indicesmeasuring the KTTOs’ strategies, market strategies (MARKET) wasfound to have a significant impact on the services linked to thetechnical validation and legal issues stages. Knowledge managementstrategies (KNOWMNG) had a significant and positive impact on

Table 3Saturated multivariate path model results explaining the stages of service offerings.

Independent variables Exploration ofknowledge-basedopportunities

Technical validationof knowledge-basedopportunitiesl

Exploitation ofknowledge-basedopportunities:Legal issues

Exploitation ofknowledge-basedopportunities:Access to capital

Exploitation ofknowledge-basedopportunities:Commercialization

Coeff. (β) Tstatistics

Coeff. (β) T statistics Coeff. (β) Tstatistics



Intercept 1.891nnn 4.105 .620 1.123 .411 .897 .942nn 2.073 .942nn 2.075CUSTOMER VALUE PROPOSITIONMixed solutions (MIXED)a .214n 1.806 .101 .903 .187n 1.782 .097 .827 .161n 1.776Non-Customized solutions (NOCUSTOM) a � .254n �1.791 � .224n 1.805 � .325nn �2.149 � .339nn �2.256 � .308nn �2.060MARKET SEGMENTPercentage of private firms with less than 10 employeesthat received services (SEGMENT)

.002 .822 .004n 1.836 .005nn 2.282 .006nnn 3.095 .006nnn 2.893

REVENUE GENERATION MECHANISMSNo revenues from sale of services (NOREV) b � .320nn �2.098 � .119 � .822 .326nn 2.146 � .033 � .217 � .084 � .559Moderate revenues from sale of services (MODREV) b � .362nn �2.275 � .122 � .811 .128 .804 � .051 � .326 .101 .645POSITIONING WITHIN THE VALUE NETWORK:Strength of ties (TIES) � .447nn �2.278 � .231 �1,244 � .105 � .539 � .385nn �1.980 � .299n �1.764STRATEGIESMarket strategies (MARKET) .080 1.135 .254nnn 3.828 .147nn 2.105 � .017 � .242 .071 1.024Knowledge management strategies (KNOWMNG) .281nnn 3.827 .019 .271 � .036 � .500 .059 .818 .146nn 2.018Promotion of services strategies (PROMO) .091n 1.803 .010 .161 .039 .601 .185nnn 2.850 .196nnn 3.031KEY RESOURCESTechnical resourcesGeneration & Finding of knowledge (GENFIND) � .014 � .168 .166nn 2.155 .079 .981 .137n 1.809 .192nn 2.399Storing & Spreading of knowledge (STOSPRE) .027 .370 � .072 �1.058 .016 .218 � .105n �1.764 � .168nn �2.367Knowledge resourcesNumber of employees with scientific or engineeringtraining (LnSCENGIN)c

� .042 � .695 .002 .043 � .006 � .104 � .086n 1.748 � .062 �1.047

Number of employees with management training(LnMNG)c

.067 .883 � .095nn �1.926 � .016 � .216 .188nn 2.488 .113n 1.750

CONTROL VARIABLESSize (LnSIZE) c .150nnn 2.742 .209nnn 3.584 .142nn 2.323 .022 .365 � .028 � .467Size of urban agglomerationsSmall (SMALL) d � .186n �1.789 � .023 � .169 � .222n �1.763 � .308nn �2.141 � .291nn �2.032Medium (MEDIUM) d .031 .246 .142 1.176 .023 .179 � .185n �1.704 � .010 � .077Types of organizationsNonprofit organization (NPO)e .147 .674 .761nnn 3.689 .123 .567 � .126 � .584 � .021 � .097College technological transfer office (CTTO)e .248 1.250 .589nnn 3.139 .552nnn 2.992 .578nnn 2.939 .284n 1.865University technological transfer office (UTTO)e .320nn 1.924 .629nnn 3.098 .974nnn 4.561 .339n 1.779 .100 .472

Covariances between disturbances ε1 ε2 ε3 ε4

ε2 .199nnn

ε3 .093nn .233nnn

ε4 .005 .111nnn .143nnn

ε5 .088nn .178 .198nnn .258nnn

Number of observations 212R2 .350 .353 .309 .401 .390

n Indicate that the coefficient is significant, respectively, at the 10% thresholds.nn Indicate that the coefficient is significant, respectively, at the 5% thresholds.nnn Indicate that the coefficient is significant, respectively, at the 1% thresholds.a The reference category is Customized Solutions (CUSTOM).b The reference category is Important Revenues from Sale of Services (IMPOREV).c Ln indicates a logarithmic transformation.d The reference category is large urban agglomerations.e The reference category is Public Research Organization (PRO).







service offerings regarding exploration and commercialization.Finally, strategies regarding the promotion of services (PROMO)exerted a positive impact on three stages of service offerings:exploration, access to capital, and commercialization. Likewise, theindex measuring the frequency of use by the KTTO of technical toolsfor generating and finding knowledge (GENFIND) had a positiveimpact on three stages of service offerings, namely, technical valida-tion, access to capital, and commercialization.

However, the index measuring the frequency of use by the KTTOof technical tools regarding the storing and spreading of knowledge(STOSPRE) exerted a negative impact on services linked to theaccess to capital and commercialization. With regard to knowledgeresources embodied in employees of KTTOs, the number of

employees with scientific or engineering training (LnSCENGIN) isfound significant and exerting a negative impact on the serviceslinked to the access to capital stage, whereas the number ofemployees with business training (LnMNG) is found significantand exerting a positive impact on the services linked to access tocapital and commercialization, and exerting a negative impact onservices regarding the technical validation stage of the value chain.

As for the control variables considered in this study, the numberof employees (LnSIZE) is found to have a positive and significanteffect on three stages of service offerings: exploration, technicalvalidation, and legal issues. With regard to the location effect, theresults show that being located in small agglomerations (SMALL),instead of being located in large agglomerations (LARGE), exerted a

Table 4Unsaturated multivariate path model results explaining the stages of service offerings.

Independent variables Exploration ofknowledge-basedopportunities

Technicalvalidation ofknowledge-basedopportunities

Exploitation ofknowledge-basedopportunities:Legal issues

Exploitation ofknowledge-basedopportunities:Access to capital

Exploitation ofknowledge-basedopportunities:Commercialization






Intercept 2.240nnn 6.858 .592nn 2.332 .700nnn 2.778 .938nnn 2.823 .860nnn 2.904CUSTOMER VALUE PROPOSITIONMixed solutions (MIXED)a .154n 1.779 .133nn 1.980 .069n 1.769Non-Customized solutions (NOCUSTOM) a � .358nn �2.449 � .270nn �2.055 � .355nn �2.435 � .373nnn �2.975 � .377nn �2.527MARKET SEGMENTPercentage of private firms with less than 10 employeesthat received services (SEGMENT)

.003n 1.777 .005nn 2.392 .006nnn 3.120 .006nnn 3.044

REVENUE GENERATION MECHANISMSNo revenues from sale of services (NOREV) b � .264nn �2.160 .271nnn 3.046Moderate revenues from sale of services (MODREV) b � .300nn �2.275POSITIONING WITHIN THE VALUE NETWORKStrength of ties (TIES) � .377nn �2.134 � .347nn �1.899 � .167n �2.197STRATEGIESMarket strategies (MARKET) .235nnn 4.567 .150nn 2.729Knowledge management strategies (KNOWMNG) .300nnn 5.185 .143nn 2.431Promotion of services strategies (PROMO) .134nn 2.384 .169nnn 3.019 .231nnn 4.257KEY RESOURCESTechnical resourcesGeneration & Finding of knowledge (GENFIND) .110nn 2.091 .142nn 1.959 .186nn 2.584Storing & Spreading of knowledge (STOSPRE) �� .076nn �2.054 � .160nn �2.470Knowledge resourcesNumber of employees with scientific or engineeringtraining (LnSCENGIN)c

� .068nn �2.719

Number of employees with business training (LnMNG)c � .130nnn �2.661 .153nn 2.267 .075n 1.875CONTROL VARIABLESSize (LnSIZE)c .124nnn 3.295 .233nnn 5.225 .141nnn 3.894Size of urban agglomerationsSmall (SMALL)d � .210nn �1.971 � .189n �1.720 � .290nn �2.118 � .223nn �1.955Medium (MEDIUM)d � .173n �1.814Types of organizationsNonprofit organization (NPO)e .783nnn 5.103College technological transfer office (CTTO)e .548nnn 3.655 .464nnn 3.770 .677nnn 5.213 .307nn 2.546University technological transfer office (UTTO)e .082n 1.824 .549nnn 3.120 .865nnn 5.847 .371nnn 2.793

Covariances between disturbances ε1 ε2 ε3 ε4

ε2 .203nnn

ε3 .100nnn .251nnn

ε4 .007 .117nnn .145nnn

ε5 .101nnn .188nnn .207nnn .262nnn

Number of observations 212R2 .306 .289 .271 .376 .333Unsaturated path model with free error-terms χ2(44)¼33.72, p-value¼ .869Constrained unsaturated path model with error-terms fixed at 0.: χ2(54)¼207.93, p-value¼ .000

n Indicate that the coefficient is significant, respectively, at the 10% thresholds.nn Indicate that the coefficient is significant, respectively, at the 5% thresholds.nnn Indicate that the coefficient is significant, respectively, at the 1% thresholds.a The reference category is Customized Solutions (CUSTOM).b The reference category is Important Revenues from Sale of Services (IMPOREV).c Ln indicates a logarithmic transformation.d The reference category is Large urban agglomerations.e The reference category is Public Research Organization (PRO).







negative impact on all stages of service offerings except thetechnical validation stage. Being located in medium agglomerations(MEDIUM), instead of being located in large agglomerations, had anegative impact on the services linked to the access to capital.Likewise, with regard to types of KTTOs, the results show thatUniversity Technology Transfer Offices (UTTOs) were more activethan Public Research Offices (PROs) in the offering of services at allstages of the value chain, except for the commercialization stage.Moreover, Community College Technological Transfer Offices(CTTOs) were more active than PROs in the offering of services atall stages of the value chain, except for the exploration stage. Finally,Nonprofit Organizations (NPOs) were more active than PROs in theoffering of services related to the technical validation stage.

5.4. Differentiating types of business models according to typesof KTTOs

To compare the different elements of business models acrossKTTOs, we used Duncan’s post hoc test (if equal variances areassumed) or Tamhane’s post hoc test (if equal variances are notassumed), to compare the means for groups in homogeneoussubsets. The null hypothesis tested is the equality of means forthe variables referring to the six elements of business modelsconsidered in this study, namely, (1) customer value proposition, (2)market segment, (3) revenue generation mechanisms, (4) positioningwith the value network, (5) strategies, and (6) key resources (seeAppendix 1 for the operational definitions of these variables).

The results of post hoc tests are reported in Table 5. For thevariable referring to the customer value proposition measured on aLikert scale indicating the level of customization of solutions thatKTTOs provided to firms, the results indicate that there are nostatistically significant differences between CTTOs, NPOs, and UTTOs,nor between UTTOs and PROs. However, NPOs and CTTOs are morelikely to provide customized solutions to their clients than PROs. Forthe variable market segment measured as the percentage of firmswith less than 10 employees that received services from KTTOs, theresults indicate that these firms received more services from NPOsand CTTOs than from PROs and UTTOs. Likewise, for the variable

revenue generation mechanisms, the results indicate that the per-centage of sale of services in the organization’s total budget is higherin CTTOs than in the other three types of organizations. For thepositioning of the KTTOs within their value network, the resultsshow no significant differences between the four types of organiza-tions considered in this study.

The four types of organizations were also compared with regardto their engagement in three strategies that they implement in theirday-to-day management, namely, market strategies, knowledge man-agement strategies, and promotion of service strategies. The results ofthe post hoc tests for each of these strategies indicate that:

� CTTOs and NPOs were more engaged in market strategies thanUTTOs and PROs;

� There are no significant differences between the four types oforganizations with regard to knowledge management strategies;

� PROs were less engaged in the promotion of service strategiesthan UTTOs, CTTOs, and NPOs.

Finally, the four types of organizations were compared withregard to two categories of key resources they retained:(i) technical resources captured with two variables referring respec-tively to generation and finding of knowledge, and storing andspreading of knowledge; and (ii) knowledge resources also measuredwith two variables, namely, number of employees with scientific orengineering training, and number of employees with business training.The results indicate that:

� The level of the organizations’ engagement in the generation andfinding of knowledge is higher in PROs than in CTTOs, whereasthere are no significant differences on this matter between CTTOs,UTTOs, and NPOs, nor between UTTOs, NPOs, and PROs;

� PROs are more engaged in the activities related to the spreadand the storage of knowledge than the other three types oforganizations;

� On average, the number of employees with scientific or engi-neering training, and the number of employees with business

Table 5Means of the variables referring to elements of business models for groups of KTTOs.

Types of organization All KTTOs Nonprofitorganization(NPO) [a]

College technologicaltransfer office (CTTO)[b]

Universitytechnologicaltransfer office(UTTO) [c]

Public researchorganization(PRO) [d]

Mean (SD) Mean (SD) Mean (SD) Mean (SD) Mean (SD)

CUSTOMER VALUE PROPOSITION† 101.50 (55.28) 92.38¼b¼c-d (51.30) 91.12¼a¼c-d (51.35) 113.97¼a¼b¼d (63.77) 120.97þaþb¼c (53.32)MARKET SEGMENT 27.12 (21.97) 51.50¼bþcþd (23.25) 41.78¼aþcþd (24.27) 27.93�a-b¼d (22.97) 27.31�a-b¼c (14.94)REVENUE GENERATION MECHANISMS 18.42 (18.08) 18.56�b¼c¼d (17.74) 31.04þaþcþd (23.29) 10.90¼a-b¼d (13.20) 8.79¼a-b¼c (18.15)POSITIONING WITHIN THE VALUENETWORK

1.82 (.54) 1.76¼b¼c¼d (.51) 1.81¼a¼c¼d (.59) 1.91¼a¼b¼d (.56) 1.86¼a¼b¼c (.48)

STRATEGIESMarket strategies 2.94 (.93) 3.08¼bþcþd (.82) 3.23¼aþcþd (.83) 2.62�a-b¼d (1.04) 2.63�a-b¼c (1.07)Knowledge management strategies 3.39 (.91) 3.41¼b¼c¼d (.76) 3.31¼a¼c¼d (.88) 3.28¼a¼b¼d (1.04) 3.61¼a¼b¼c (1.03)Promotion of services strategies 2.91 (.95) 3.13¼b¼cþd (.89) 3.09¼a¼cþd (.82) 2.88¼a¼bþd (.96) 2.28�a�b�c (.99)KEY RESOURCESTechnical resourcesGeneration & finding of knowledge 2.28 (.87) 2.23¼b¼c¼d (.82) 2.18¼a¼c-d (.85) 2.21¼a¼b¼d (.89) 2.56¼aþb¼c (.96)Storing & spreading of knowledge 2.86 (.98) 2.66¼b¼c-d (1.00) 2.85¼a¼c-d (.88) 2.86¼a¼b-d (.98) 3.29þaþbþc (.94)Knowledge resourcesNumber of employees with scientific orengineering training

26.19 (25.27) 16.82¼b¼c�d (21.86) 12.74¼a¼c�d (18.23) 10.64¼a¼b�d (14.42) 80.05þaþbþc (41.86)

Number of employees with business training 4.87 (8.47) 4.64¼b¼c�d (7.78) 3.22¼a¼c�d (7.16) 3.69¼a¼b�d (5.27) 8.90þaþbþc (12.40)

“a”, “b”, “c” and “d” refer to the four types of organization. The signs “þ” and “–” indicate that the mean of the homogeneous subset of organizations is statisticallysignificantly (po .05) greater or smaller than another homogeneous subset of organizations according to Duncan’s or Tamhane’s post hoc test that compares the means forgroups of organizations in homogeneous subsets. Duncan’s test is used if equal variances are assumed and Tamhane’s test is used if equal variances are not assumed. The sign“¼” indicates that no significant differences exist between the compared subsets of organizations.

† The post hoc test was performed on ranked data. Therefore, numbers in row corresponding to customer value proposition are mean rank.







training are higher in PROs than in the other three groupsof KTTOs.

6. Discussion

How do these statistical results contribute to answer the fourempirical questions addressed at the beginning of this paper?

6.1. Do different types of KTTOs provide services at different stagesof the value chain of firms?

We hypothesized that the differentiated knowledge base of thedifferent types of KTTOs would induce them to specialize withrespect to how they help firms enhance their endogenous innova-tion capabilities. More specifically, we hypothesized that UTTOsand PROs would specialize in the provision of services at theexploration stage of the value chain, CCTOs at the validation andexploitation stages, and NPOs at the exploitation stage of the valuechain. However, contrary to our expectations, the results of thisstudy show that the involvement of UTTOs, CTTOs, PROs, and NPOsdoes not differ in the provision of services at the exploratory stageof the value chain. We were expecting to observe higher levels ofservice offerings from research-intensive organizations such asUTTOs and PROs than from CTTOs and NPOs which are much lessresearch-intensive organizations. Furthermore, as hypothesized,the results of this study show that NPOs and CTTOs are moreinvolved than UTTOs and PROs regarding the services offered atthe validation stage of the value chain. Contrary to our hypothesis,NPOs are also more involved than UTTOs and PROs in serviceoffering at the validation stage. Finally, as expected, the results ofthe statistical analyses suggest that NPOs are generally moreinvolved in the provision of services linked to the exploitationstage of the value chain than PROs and UTTOs (see Table 2).

These results suggest that the knowledge base of KTTOs is notsufficient to explain the differences of service offering at thedifferent stages of the value chain. These results might imply, assuggested by Suvinen et al. (2010) that KTTOs are mainly situatedlocally (even for those situated in metropolitan areas) and thatlocal conditions regarding the heterogeneities of firms should betaken into account to explain the provision of services to firms.

6.2. Are the services provided to firms complementary, substituteor independent from each other?

We hypothesized the emergence of complementary effectsbetween services offered at the exploration and validation stages,as well as between services offered at the validation and exploita-tion stages. In a complementary manner, we hypothesized eithersubstitution effects or independence would be observed betweenthe services of the exploration and those of the exploitation stagesof the value chain. The results of the study suggest the presence ofvarious patterns of complementarities between many packages ofservice offerings. Hence, the complementarities between theservices provided at the exploration and validation stages arehigher than the complementarities between the services offered atthe exploration and exploitation stages. Furthermore, the servicesoffered at the validation stage exhibit higher degrees of comple-mentarity with those offered at the exploitation stage thanbetween the services provided at the exploration and exploitationstages. The highest degrees of complementarity were foundbetween the exploitation services linked to the access to capitaland the exploitation services linked to the commercialization ofknowledge-based opportunities. The statistical analysis did notsuggest the presence of any substitution effects among services.However, as expected, the results revealed that the service offering

between the services of the exploration stage of the value chainand those of the exploitation stage concerning access to capitalwere independent from each other.

These results suggest that there are much more complemen-tarity effects among service offerings than expected. It might meanthat there are more synergetic effects between services of the valuechain than we expected. These synergetic effects might be deve-loped over time as KTTOs’ personnel develop expertise in additionalservices in response to their goal of helping firms to enhance theirendogenous innovation capabilities. Furthermore, the innovationprocess of firms cannot likely be as easily compartmentalized thanassumed in the conceptual value chain framework.

6.3. What are the relations between the services provided to firmsand elements of KTTOs’ business models?

The results of the statistical analysis confirm Hypothesis 3pointing to the fact that developing non-customized rather thancustomized solutions for client firms is associated with a decreasein the level of service offerings to firms at all stages in the valuechain. Interestingly, developing mixed solutions (half-customizedsolutions and half-basic research) rather than customized solu-tions is related to increases in the level of service offerings at theexploration stage, and at two sub-stages of the exploitation stage:services linked to legal issues and services linked to commercia-lization issues. These results suggest that KTTOs transformresearch knowledge and technology into customized or semi-customized solutions for client firms.

As expected in Hypothesis 4, the results show that the higherthe propensity of KTTOs to serve firms of less than 10 employees,the higher their level of service offerings at the validation andexploitation stages of the value chain. These results suggest thatKTTOs which offer higher levels of services at these stages tend toserve a distinct customer group characterized by a lack ofresources to validate and exploit external knowledge-based oppor-tunities for the development and improvement of their productsand production processes: the very small firms.

We hypothesized (H5) that KTTOs’ service offerings would bepositively associated with high or moderate revenue streams fromclient firms for services linked to the validation and exploitationstages, while not associated with a revenue stream from clientfirms for services provided at the exploration stage of the valuechain. However, the results indicate that the KTTOs’ businessmodel does not primarily depend on revenues from the sale ofservices, but that it relies on government subsidies to finance thedevelopment and delivery of their services. As a consequence, itmeans that KTTOs are highly vulnerable to changes in theinnovation policies of their governmental sponsors.

Hypothesis 6 is partly confirmed by the results of the statisticalanalysis. Indeed, the results show that the KTTOs and their clients areengaged in close relationships that generate a common understandingbetween KTTOs and firms that may facilitate the production anddelivery of services at the exploration stage, and at sub-stages of theaccess to capital and commercialization of the exploitation stage.Unexpectedly, the hypothesis is not confirmed for the results regard-ing the service offerings of the validation stage. This unexpected resultmight mean that the technical services offered at the validation stageare partly or entirely produced at the KTTOs’ facilities rather than inthe facilities of client firms. Overall, one might nevertheless concludethat the KTTOs’ business model relies more often on strong ties thanon weak ties.

We expected that KTTOs would develop, to a greater extent,market strategies, knowledge management strategies, and strate-gies related to the promotion of their services, would be morelikely to be more active in the offering of services at all stages ofthe value chain. The results of the statistical analyses suggest that







KTTO managers devise and implement: market strategies whendealing with services provided at the validation stage and whendealing with legal issues of the exploitation stage; knowledgemanagement strategies when dealing with services provided atthe exploration stage and when dealing with commercializationissues of the exploitation stage; and finally, promotion of servicesstrategies when dealing with services provided at the explorationstage and when dealing with issues linked to the access to capitaland commercialization of the exploitation stage of the value chain.Hence, contrary to Hypothesis 7, KTTO managers appear to face acomplex world in which they need to rely on differentiatedstrategies to deal with the services provided to firms at differentstages of the value chain.

We also hypothesized that increasing the number of scientistsand engineers employed by KTTOs would increase the offering ofservices at the exploratory and technical validation stages (H8),whereas increasing the number of KTTOs’ employees with busi-ness training would increase the offering of services at theexploitation stage linked to the access to capital and commercia-lization (H9). Hypothesis 9 is confirmed by the empirical results.However, as for Hypothesis 8, the results show that increases in thenumber of scientists and engineers are associated with decreases inthe helping of firms to access to capital, while increases in thenumber of employees with business training decrease the level ofservices at the validation stage. Overall, these results suggest thatKTTO managers must dispose of different types of human resourcesfor different stages of the value chain to make their business modelwork but that the relations between human resources and serviceofferings at the different stages of the value chain are far morecomplex than hypothesized.

Finally, we hypothesized that increasing the technical resourcesthat KTTOs could mobilize in helping firms would augment theservices offered to firms at all three stages of the value chain.In reality, the results show that technical resources regarding thegeneration and finding of knowledge are positively related to thevalidation and exploitation stages of the value chain, whereasthe technical resources regarding the storing and spreading ofknowledge are negatively related to the exploitation stages of thevalue chain. Hence, these results suggest that different types oftechnical resources induce KTTO managers to develop servicesthat are offered at different stages of the value chain.

6.4. Do different types of KTTOs develop different business models?

Although, the knowledge base of KTTOs is not sufficient toexplain the differences of service offering at the different stages ofthe value chain, the results of the statistical analyses show, ashypothesized, that different types of organizations predominantlycenter their business models on services linked to one stage of thevalue chain. However, the configurations observed regarding theother elements of business models appear far more complex thanassumed in the three hypotheses (H11, H12, and H13) regardingthe emblematic business models. Hence, the results of the statis-tical analyses suggest that four very different types of businessmodels emerge by positioning the different types of KTTOs on thedifferent building blocks defining business models:

6.4.1. Type 1: The CTTOs’ validation-centered business modelThe community college knowledge and technology transfer

organizations (CTTOs) are more likely than PROs to providecustomized solutions for single client firms, to have a well definedmarket segment which targets firms of less than 10 employees, togenerate higher revenue from the sales of services to firms thanthe other types of KTTOs, to implement well defined market strate-gies, and to pay more attention to the validation of knowledge-based

opportunities than UTTOs and PROs. However, the CTTOs’ businessmodel relies on fewer resources than PROs, while CTTOs are less likelyto have well defined strategies than PROs regarding the promotion oftheir services, and they are less involved than NPOs in the provision ofservices at the exploitation stage of the value chain.

6.4.2. Type 2: The NPOs’ exploitation-centered business modelLike CTTOs, the nonprofit knowledge and technology transfer

organizations (NPOs) are more likely than PROs to providecustomized solutions for single client firms, to have a well definedmarket segment which targets firms of less than 10 employees,and to implement well defined market strategies. However, theyare more involved than CTTOs in the provision of services at thethree sub-stages of the exploitation of knowledge-based opportu-nities: solving legal issues, facilitating access to capital, andfostering the commercialization of innovation.

The NPOs may lack the knowledge resources required to becomeleaders in providing services at the exploration and validation stagesof the value chain. However, and surprisingly, although the boardsof NPOs are dominated by representatives of firms, and that theirservice offerings are linked to the commercial exploitation ofknowledge, they appear to generate less revenue from the sales ofservices to firms than CTTOs do. Thus, NPOs appear to rely moreheavily on government subsidies than on revenue from the sales ofservices. This business model is highly vulnerable to changes ingovernment innovation policies. NPO managers should try reducingtheir vulnerability to government subsidies by attempting to derivemore financial benefits from their ties with firms.

6.4.3. Type 3: The PROs’ government dependent exploration-centeredbusiness model

The public research organizations (PROs) can rely on moretechnical resources and more personnel with a training in engi-neering and business than any of the other types of KTTOs. ThePROs are also more likely than the other types of KTTOs toimplement well defined strategies regarding the promotion oftheir services. Furthermore, by comparison with the other types ofKTTOs, they are less involved in the provision of services at thethree stages of the knowledge value chain, less likely to targetfirms of less than 10 employees, and less likely to generaterevenue from the sales of services to firms.

By comparison with CTTOs and NPOs that provide services toSMEs located within their regions, the PROs are more likely toprovide services to large firms located outside their regions andoutside their province. They rely on government funding to createvalue by searching for and exploring knowledge-based opportunities.However, the PROs’ business model appears to be weaker than thatof CTTOs and NPOs in many aspects, especially the provision ofservices at the different stages of the value chain, the value it wantsto create for single client firms, the types of firms it chooses to serve,and its revenue mechanisms. The PROs’ knowledge and technologytransfer activities are embedded in larger organizations and have toanswer to the strategy of corporate management. The PROs are morelikely to earn revenue by licensing intellectual property rights tofirms rather than by selling technical and business services that helpfirms enhance their innovation potential at the different stages of thevalue chain.

6.4.4. Type 4: The UTTOs’ undifferentiated exploration-centeredbusiness model

Like the PROs, the university knowledge and technology transferorganizations (UTTOs) are less likely than the other types oforganizations to provide customized solutions for single client firms,to serve a well defined market segment of firms, to generaterevenue from the sales of services to firms than the other types of







KTTOs, to implement well defined strategies, and to pay attentionto the validation of knowledge-based opportunities than UTTOsand PROs. However, the UTTOs’ business model relies on lowerresources than PROs, while UTTOs are more involved than PROs andCTTOs regarding services linked to legal issues at the exploitationstage of the value chain, and more involved than PROs regardingthe provision of services linked to issues of access to capitalat the exploitation stage of the value chain. Hence, by comparisonwith the other types of organizations, UTTOs do not appear tohave a distinct business model. One may describe this model asan undifferentiated knowledge and technology transfer businessmodel.

Like the PROs, the UTTOs are embedded in larger organizationshaving the mandate to advance, validate, and exploit research-based opportunities. Their managers are induced to offer servicesthat are related to the large research knowledge-based opportu-nities they have access to and to find market opportunities toexploit them. Such incentives lead them to rely on an inside-outbusiness model where they are more involved in helping uni-versity researchers to resolve legal issues and issues linked to theaccess to capital in order to commercialize innovations protectedby well defined intellectual property rights. The UTTOs may lackthe types of technical and human resources required to develop anexternally aware business model, where external relationshipswith firms would help them to identify firms’ needs and require-ments, in order to be able to provide services that would contri-bute to enhance the endogenous innovation potential of firms.Furthermore, like the PROs, the UTTOs are embedded in organiza-tions where owning intellectual property rights is necessary inorder to be able to capture the value of discoveries and inventionsmade by their researchers.

7. Conclusion, implications, and limitations

7.1. Summary of the results

Overall, four major results emerge from this study: first,although the knowledge base of KTTOs is not sufficient to explainthe differences of service offering at the different stages of thevalue chain, we found that different types of KTTOs are specializedin differently emphasizing the provision of services at the differentstages of the knowledge value chain; second, different patterns ofcomplementarities were found between many packages of serviceofferings; third, with few exceptions, the KTTOs’ general businessmodel is built around the following building blocks: they offermixed or customized solutions to their clients, target very smallfirms, do not primarily depend on revenues from the sale ofservices, develop strong ties with their partners and clients,formulate differentiated strategies for the different stages ofthe value chain, and dispose of differentiated types of humanresources for different stages of the value chain; finally, four verydifferent types of business models emerge by positioning thedifferent types of KTTOs on the different building blocks definingbusiness models: the CTTOs’ validation-centered business model;the NPOs’ exploitation-centered business model; the PROs’ gov-ernment dependent exploration-centered business model; and theUTTOs’ undifferentiated exploration-centered business model.

7.2. Managerial implications

The results of this study carry important managerial implica-tions. First, the criticisms pointing to confusion and overlapping asto the services provided to firms by KTTOs appear to be exagger-ated, given that different types of KTTOs are specialized inemphasizing the provision of services differently at the different

stages of the knowledge value chain. These results suggest thatentrepreneurs and policy-makers suffer from a lack of under-standing and information regarding the contribution of KTTOs inthe innovation process of firms. Hence, KTTO managers shouldcollaborate with policy-makers and industrial associations toimprove their marketing strategies and the quality of the informa-tion they disseminate regarding the contribution of their servicesat the different stages of the innovation process of firms.

Second, the results regarding complementarity effects betweenthe services suggest that managers and policy-makers who fail torecognize complementarities between services linked to theaccess to capital and commercialization may lead to the under-exploitation of synergies, and therefore KTTOs’ revenues andperformance. Similarly, a failure to recognize complementaritiesbetween services offered at the validation stage and serviceslinked to the legal issues of the exploitation stage may lead tothe under-exploitation of synergies and therefore KTTOs’ revenuesand performance. Hence, managers of KTTOS and public policysupporting KTTOs should attempt to take into account howdifferent services reinforce each other, instead of attempting toprevent the entry of KTTOs in the provision of complementaryservices.

Third, although KTTOs cannot meet firms’ needs with standardservices, industrial associations and policy-makers cannot expectKTTOs to develop the best customer value proposition based oncustomized services for single client firms in a context where, withthe exception of PROs, most KTTOs can count only on a smallnumber of employees. Thus, the best value proposition that oneshould expect from KTTOs are services that provide half-customized solutions for clients’ firms. These results mean thatpolicy-makers could improve KTTOs’ value proposition only byincreasing their resources.

Overall, the results of this study suggest that managers ofKTTOs could improve their business model in increasing the valuecreated for their clients by augmenting the degree of customiza-tion of the solutions offered to their clients, and by increasing therevenues they generate from sales of services in order to reducetheir vulnerability to reductions in government subsidies. Policy-makers should recognize the existence of complementaritiesbetween the services provided at the different stages of the valuechain, and thus refrain from attempting to rely on a concept ofKTTOs that would specialize in the provision of a limited numberof services at a single stage of the value chain.

7.3. Limitations and future research

This study has limitations in context and methods that informthe interpretations of results and suggest further research. Hence,the findings of this study convincingly establish that there arecomplementarities and independence between the services pro-vided to firms by KTTOs. Further research should investigate, bothat the theoretical and empirical level, how the virtuous circleamong multiple knowledge transfer services may emerge andmay become sustainable over time. Further research should alsoconsider the dynamics of joint coordination of multiple knowledgetransfer services over mid and long time periods.

Second, this study adopted a supply side perspective thatshould be complemented by a demand side perspective focusingon the use and appreciation of the services acquired by the KTTOs’client firms. Third, there are, as we pointed out at the beginning ofthis paper, many other categories of intermediaries who provideservices to help firms improve their innovation process, and otherstudies should focus their attention on additional categories ofknowledge and technology intermediaries. Fourth, this studyfocused its attention on Canadian KTTOs. Although this studydeals with organizational structures that are present in other







Table A1

Measure Sub-items Mean (SD) Percentage (number) Cronbach’salpha

CUSTOMER VALUEPROPOSITION

Three dichotomous variables constructed with respect to the responses of the organization to the following question:Which of the following statements best describes the services you offered to private firms over the last three years?� Non-customized solutions (NOCUSTOM): (Mainly basic research & Almost only basic research¼1; Else¼0) 19.3

� Mixed solutions (MIXED): (Half-customized solutions and half-basic research¼1; Else¼0) 38.7

� Customized solutions (CUSTOM): (Almost only customized solutions & Mainly customized solutions¼1; Else¼0) 42.0

CUSTOM is the reference category.MARKET SEGMENT[SEGMENT]

� Measured as the percentage of private firms with less than 10 employees that received services from the KTTO 39.90 (28.29)

REVENUE GENERATIONMECHANISMS

Three dichotomous variables constructed with respect to the responses of the organization to the following question:Please estimate (as best you can) the percentage of sale of services in your organization’s total budget over the last threeyears?� Non-revenue from sale of services (NOREV): (0% of organization’s total budget from sale of services¼1; Else¼0) 47.6

� Moderate revenue from sale of services (MODREV): (from 1% to 25% of organization’s total budget from sale ofservices¼1; Else¼0)

24.1

� Important revenue from sale of services (IMPOREV): (more than 25% of organization’s total budget from sale ofservices¼1; Else¼0)

28.3

IMPOREV is the reference category.POSITIONING WITHINTHE VALUE NETWORK[LnTIES]

Measured as an index on a 5-point Likert scale (1¼Veryclose: practically like being in the same work group;2¼Somewhat close: like discussing and solving problemstogether; 3¼Somewhat distant: like with people that youdo not know well; 4¼Distant: like a working group withwhich you can only have a quick exchange of information;5¼Very distant: practically like with people that you do notknow at all) describing the working relationship betweenorganizations and their clients in the following sectors.

� Industry� Public sector organizations, government departments /

agencies� Universities� Colleges

1.82 (.54) .692

The strength of ties index is thus the sum of the scores ofthe items corresponding to the KTTO’s responses. Therespondents’ scores, which initially ranged from 4 to 20,were weighted in order to take into account “does notapply” answers. Thus, for each respondent, the sum of thescores was divided by the number of applicable item(s).Even though the initial index ranges from 1 to 5, onceweighted, it can take on non-integer values. This variablewas matched with the normal distribution using alogarithmic transformation

STRATEGIESMarket strategies[MARKET]

Measured as a weighted index on a Likert scale of frequencyranging from 1¼Never to 5¼Very often of the engagementof the organization, over the last three years preceding thesurvey, in the following four activities in order to provideservices to private firms:

� Seeking new geographic markets for your services� Extending your current services to new categories of

clients� Developing new services� Developing and exploiting niches or specialized markets

2.94 (.93) .772

Knowledge managementstrategies [KNOWMNG]

Measured as a weighted index on a Likert scale of frequencyranging from 1¼Never to 5¼Very often of the engagementof the organization, over the last three years preceding the

� Using and updating scientific information databases� Developing measures favoring knowledge sharing

between your employees

3.40 (.91) .793

R.Landry

etal./

Technovation∎(∎∎∎∎)

∎∎∎–∎∎∎

16Pleasecite

thisarticle

as:Lan

dry,R

.,etal.,Tech

nology

transfer

organization

s:Services

andbu

siness

mod

els.Technovation

(2013),http

://d

x.doi.org/10.1016/j.tech

novation

.2013.09.008i





survey, in the following four activities in order to provideservices to private firms:

� Capturing and using knowledge obtained from otherindustry sources (e.g., industry associations, competitors,clients and suppliers)

� Capturing and using knowledge obtained from publicresearch institutions including universities andgovernment laboratories

Promotion of servicestrategies [PROMO]

Measured as a weighted index on a Likert scale of frequency ranging from 1¼Never to 5¼Very often of theengagement of the organization, over the last three years preceding the survey, in the following three activities inorder to provide services to private firms:

� Promoting your services through advertising(advertising campaign, websites, etc.)

� Promoting your services through direct marketing(prospectus, email marketing, technicalreports, etc.)

� Promoting your services through public relations(seminars, press conferences, etc.)

2.91(.95)

.719

KEY RESOURCES RELATED TO TECHNICAL RESOURCESGeneration & Finding ofknowledge [GENFIND]

Measured as a weighted index on a Likert scale of frequency ranging from 1¼Never to 5¼Very often of the use bythe organizations, over the last three years preceding the survey, of the following five tools for the developmentand improvement of their services offered to private firms:

� Data mining/ text retrieval software� Expert or decision support software� Automatic e-mail alerts� Intelligent agent or artificial intelligence� Mind/knowledge mapping software

2.28(.98)

.770

Storing & Spreading ofknowledge [STOSPRE]

Measured as a weighted index on a Likert scale of frequency ranging from 1¼Never to 5¼Very often of the use bythe organizations, over the last three years preceding the survey, of the following four tools for the developmentand improvement of their services offered to private firms:

� Content/document management software� Intranet or enterprise information portal� Knowledge repository or digital archive� Workflow/process management software

2.86(.98)

.738

KEY RESOURCES RELATED TO KNOWLEDGE RESOURCESEmployees with scientific orengineering training[LnSCENGIN]

Measured as number of employees with scientific or engineering training. This variable was matched with the normal distribution using a logarithmic transformation 26.19(55.27)

Employees with businesstraining [LnMNG]

Measured as number of employees with business training. This variable was matched with the normal distribution using a logarithmic transformation 4.87(8.47)

CONTROL VARIABLESSize of KTTOs [LnSIZE] Measured by the total number of full-time employees (equivalent full time) in 2008. This variable was matched with the normal distribution using a logarithmic

transformation46.57(85.68)

Size of urban agglomerations A series of dichotomous variables defined as follows:� Large agglomerations [LARGE] is a binary variable coded 1 if the organization is based in an agglomeration of more than 1 million people, and coded 0 otherwise.� Medium agglomerations [MEDIUM] is a binary variable coded 1 if the organization is based in an agglomeration between 100 000 and 1 million people, and coded

0 otherwise.� Small agglomerations [SMALL] is a binary variable coded 1 if the organization is based in an agglomeration of less than 100 000 people, and coded 0 otherwise.

The reference category is Large agglomerations.

30.742.926.4

Types of organizations A series of dichotomous variables indicating the types of organization. The organizations are regrouped in four types:� Nonprofit organization (NPO)� College technological transfer office (CTTO)� University technological transfer office (UTTO)� Public research organization (PRO)

The reference category is Public Research Organization (PRO).

36.325.519.818.4

R.Landry

etal./

Technovation∎(∎∎∎∎)

∎∎∎–∎∎∎

17

Pleasecite

thisarticle

as:Lan

dry,R

.,etal.,Tech

nology

transfer

organization

s:Services

andbu

siness

mod

els.Technovation

(2013),http

://d

x.doi.org/10.1016/j.tech

novation

.2013.09.008i





OECD countries, its results could potentially reflect peculiarities ofthe Canadian institutional infrastructure of organizations thatsupport the innovation process of firms. This potential short-coming could be compensated by multi-country studies thatwould attempt to neutralize the impact of national peculiarities.

Finally, further research is also required at the measurementlevel. Most prior quantitative studies on KTTOs focused theirattention on easily measurable variables like patents and spin-offcreation. This study attempted to measure services provided byKTTOs in order to help firms enhance their innovation potential atdifferent stages of their value chain. To the extent of our know-ledge, this study also appears to be the first to measure theelements composing the conceptual business model frameworkin a quantitative study. Clearly, further studies should aim to betterunderstand, at the theoretical level, why KTTOs provide services tofirms, as well as to better measure the peculiarities of the variouselements composing the KTTOs’ business models by complement-ing cross sectional studies with longitudinal investigations.

These limitations notwithstanding, we believe that our resultscontribute to establish a more complex system of mutually reinforcingknowledge transfer services than prior studies based on the study ofpatents and spin-offs. The results of this paper also show that theKTTOs’ business models matter, but further studies will be necessaryto better understand how to nurture the KTTOs’ business models.

Acknowledgments

The authors would like to gratefully acknowledge the financialsupport for this research from the Social Sciences and HumanitiesResearch Council of Canada, and the contribution of ten CEO andexecutive directors of knowledge and technology transfer organi-zations who provided comments and suggestions at differentstages of this research. We also wish to acknowledge the helpfulcomments made by the two anonymous reviewers.

Appendix A. Definitions of independent variables anddescriptive statistics

See Table A1.

Appendix B. Correlations between continuous explanatoryvariables

See Table B1.

References

Agrawal, A., 2001. University–industry knowledge transfer: literature review andunanswered questions. International Journal of Management Reviews 3 (4),285–302.

Ahire, S.L., Devaray, S., 2001. An empirical comparison of statistical constructvalidation approaches. IEEE Transactions on Engineering Management 48 (3),319–329.

Albino, V., Garavelli, A.C., Gorgoglione, M., 2004. Organization and technology inknowledge transfer. Benchmarking, Bradford 11 (6), 584.

Albino, V., Garavelli, A.C., Schiuma, G., 1999. Knowledge transfer and inter-firmrelationships in industrial districts: the role of the leader firm. Technovation 19(1), 53–63.

Amara, N., Landry, R., Traoré, N., 2008. Managing the protection of innovations inknowledge-intensive business services. Research Policy 37 (9), 1530–1547.

Arundel, A., Geuna, A., 2004. Proximity and the use of public science by innovativeEuropean firms. Economics of Innovation and New Technology 13 (6), 559–580.

Becheikh, N., Landry, R., Amara, N., 2006. Lessons from innovation empirical studiesin the manufacturing sector: a systematic review of the literature from 1993–2003. Technovation 26 (5–6), 644–664.

Belderbos, R., Carree, M., Diederen, B., Lokshin, B., 2004. Heterogeneity in R&Dcooperation strategies. International Journal of Industrial Organization 22 (8–9),1237–1263.

Bessant, J., Rush, H., 1995. Building bridges for innovation; the role of consultants intechnology transfer. Research Policy 24 (1), 97–114.

Bramwell, A., Wolfe, D.A., 2008. Universities and regional economic development:the entrepreneurial University of Waterloo. Research Policy 37 (8), 1175–1187.

Branscomb, L.M., Auerswald, P.E., 2002. Between Invention and Innovation: AnAnalysis of Funding for Early Stage Technology Development. Report preparedfor the Economic Assessment Office of the Advanced Technology Program ofthe US National Institute of Standards and Technology.

Chesbrough, H., 2010. Business model innovation: opportunities and barriers. LongRange Planning 43 (2–3), 354–363.

Chesbrough, H., 2007. Business model innovation: it’s not just about technologyanymore. Strategy & Leadership 35 (6), 12–17.

Cohen, W.M., Levinthal, D.A., 1990. Absorptive capacity: a new perspective onlearning and innovation. Administrative Science Quarterly 35 (1), 128–152.

Cooke, P., Heidenreich, M., Braczyk, H.-J., 2004. Regional Innovation Systems.Routledge, London.

Dalziel, M., 2006. The impact of industry associations. Innovation: Management,Policy & Practice 8 (3), 296–306.

Dalziel, M., 2010. Why do innovation intermediaries exist? Paper presented that theDRUID Summer Conference, Imperial College London Business School, June16–18, 2010.

Dasgupta, P., David, P.A., 1994. Toward a new economics of science. Research Policy23 (5), 487–521.

Debackere, K., Veugelers, R., 2005. The role of academic technology transferorganizations in improving industry science links. Research Policy 34 (3),321–342.

Diaz-Puente, J.M., Cazorla, A., De Los Rios, I., 2009. Policy support for the diffusionof innovation among SMEs: an evaluation study in the Spanish region ofMadrid. European Planning Studies 17 (3), 365–387.

Ennen, E., Richter, A., 2010. The whole is more than the sum of its parts—Or is it? Areview of the empirical literature on complementarities in organizations.Journal of Management 36 (1), 207–233.

Etzkowitz, H., Leydesdorff, L., 2000. The dynamics of innovation: from nationalsystems and “mode 2” to a triple helix of university–industry–governmentrelations. Research Policy 29 (2), 109–123.

Field, A., 2006. Discovering Statistics Using SPSS, second ed. SAGE, Thousand Oaks,London.

Galia, F., Legros, D., 2004. Complementarities between obstacles to innovation:evidence from France. Research Policy 33 (8), 1185–1199.

Geuna, A., Fontana, R., Matt, M., 2006. Factors affecting university–industry R&Dprojects: the importance of searching, screening and signalling. Research Policy35 (2), 309–323.

Golob, T.F., 2003. Structural equation modeling for travel behavior research.Transportation Research Part B Methodological 37, 1–35.

Golob, T.F., Regan, A.C., 2002. Trucking industry adoption of information technol-ogy: a multivariate discrete choice model. Transportation Research Part C 10,205–228.

Gwinner, K.P., Bitner, M.J., Brown, S.W., Kumar, A., 2005. Service customizationthrough employee adaptiveness. Journal of Service Research 8 (2), 131–148.

Table B1

Variable Tolerance statisticsa SEGMENT LnTies MARKET KNWMNG PROMO GENFIND STOSPRE LnSIZE LnSCENGIN LnMNG

SEGMENT .806 1 � .130 .100 .029 .145 � .033 � .070 � .150 � .194 � .033LnTies .818 1 � .165 � .251 � .262 � .180 � .184 � .086 .006 � .006MARKET .606 1 .418 .414 .273 .237 � .004 � .067 .030KNWMNG .594 1 .219 .474 .426 .211 .105 .083PROMO .680 1 .059 � .009 � .151 � .174 � .025GENFIND .528 1 .5498 .297 .078 .115STOSPRE .544 1 .356 .227 .214LnSIZE .616 1 .523 .511LnSCENGIN .563 1 .561LnMNG .598 1

a Tolerance Statistic values are between parentheses.



http://refhub.elsevier.com/S0166-4972(13)00113-2/sbref1


























http://refhub.elsevier.com/S0166-4972(13)00113-2/othref0005




















































Hanel, P., 2006. Intellectual property rights business management practices: asurvey of the literature. Technovation 26 (8), 895–931.

Hassink, R., 1997. Technology transfer infrastructures: some lessons from experi-ences in Europe, the US and Japan. European Planning Studies 5 (3), 167–183.

Hewitt-Dundas, N., 2012. Research intensity and knowledge transfer activity in UKuniversity. Research Policy 41 (2), 262–275.

Howard Partners, 2007. Study of the Role of Intermediaries in Support of Innova-tion. Report prepared for the government of Australia, April 2007.

Howells, J., 2006. Intermediation and the role of intermediaries in innovation.Research Policy 35 (5), 715–728.

Janis, F.T., 2003. Technology transfer emerging issues high impact trends. Journal ofTechnology Transfer 28 (3–4), 241.

Jensen, R., Thursby, J.G., Thursby, M.C., 2003. The disclosure of licensing ofuniversity inventions: the best we can with the S**t we get to work with.International Journal of Industrial Organization 21 (9), 1271–1300.

Kodama, T., 2008. The role of intermediation and absorptive capacity in facilitatinguniversity–industry linkages – empirical study of TAMA in Japan. ResearchPolicy 37 (8), 1224–1240.

Kogut, B., Zander, U., 1992. Knowledge of the firm, combinative capabilities and thereplication of technology. Organization Science 3 (3), 383–397.

Landry, R., Amara, N., 2012a. Elucidation and enhancement of knowledge andtechnology transfer business models. VINE: Journal of Information and Know-ledge Management Systems 42 (1), 94–116.

Landry, R., Amara, N., 2012b. Why and how do academics bridge the gap betweeninvention and innovation? International Journal of Technology Management 58(3–4), 174–211.

Laursen, K., Salter, A., 2006. Open for innovation: the role of openness in explaininginnovative performance among UK manufacturing firms. Strategic Manage-ment Journal 27 (2), 131–150.

Laursen, L., Salter, A., 2004. Searching low and high: what types of firms useuniversities as a source of innovation. Research Policy 33 (8), 1201–1215.

Lundquist, G., 2003. A rich vision of technology transfer: technology value manage-ment. Journal of Technology Transfer 28 (3–4), 265–284.

Lee, J., Win, H.N., 2003. Technology transfer between university research centersand industry in Singapore. Technovation 24 (5), 433–442.

Lundvall, B. (Ed.), 1992. Pinter, London.Malerba, F., 2002. Sectoral systems of innovation and production. Research Policy

31 (2), 247–264.Martyniuk, A.O., Jain, R.K., Stone, H.J., 2003. Critical success factors and barriers to

technology transfer: case studies and implications. International Journal ofTechnology Transfer & Commercialisation 2 (3), 306.

Matt, M., Schaeffer, V., 2009. The role of technology transfer office of a large FrenchUniversity: the importance of internal and external strategies. Paper presentedat the XX ISPIM Conference, Vienna, June 20–24, 2009.

McAdam, R., Miller, K., McAdam, M., Teague, S., 2012. The development ofUniversity Technology Transfer stakeholder relationships at a regional level:lessons for the future. Technovation 32 (1), 57–67.

Menard, S., 1995. Applied logistic regression analysis. Sage University Paper Series onQuantitative Applications in the Social Sciences, 07-106, Sage, Thousand Oaks, CA.

Miles, R.E., Snow, C.C., 1978. Organisational Strategy, Structure and Process.McGraw-Hill, New York.

Milgrom, P., Roberts, J., 1995. Complementarities and fit strategy, structure, andorganizational change in manufacturing. Journal of Accounting & Economics 19(2–3), 179–208.

Miller, L.E., Smith, K., 1983. Handling non-response issues. Journal of Extension On-line, 21 (5). Retrieved from ⟨http://www.joe.org/joe/1983september/83-5-a7.pdf⟩.

Muthén, B.O., 1998–2004. Mplus Technical Appendices. Muthén & Muthén, LosAngeles, CA. Retrieved from URL: ⟨http://www.statmodel.com/download/techappen.pdf⟩.

Neu, W.A., Brown, S.W., 2005. Forming successful business-to-business services ingoods-dominant firms. Journal of Service Research 8 (1), 3–17.

Nonaka, I., 1994. A dynamic theory of organizational knowledge creation. Organi-zation Science 5 (1), 14–37.

North, D., Smallbone, D., Vickeers, I., 2001. Public sector support for innovatingSMEs. Small Business Economics 16 (4), 303–317.

Nunally, J.C., 1978. Psychometric Theory, second ed McGraw-Hill, New York.Osterwaldeer, A., Pigneur, Y., 2010. Business Model Generation. John Wiley & Sons,

Hoboken, New Jersey.Osterwaldeer, A., Pigneur, Y., Tucci, C.L., 2005. Clarifying business models: origins,

present, and future of the concept. Communications of the Association forInformation Systems (AIS) 15, 751–775.

Ouimet, M., Amara, N., Landry, R., Lavis, J., 2007. Direct interactions medical schoolfaculty members have with professionals and managers working in public andprivate sector organizations: a cross-sectional study. Scientometrics 72 (2), 307–323.

Phan, P.H., Siegel, D.S., 2006. The Effectiveness of University Technology Transfer:Lessons Learned from Quantitative and Qualitative Research in the U.S. and theU.K. Rensselaer Working Papers in Economics. Available at: ⟨http://www.economics.rpi.edu/workingpapers/rpi0609.pdf⟩.

Porter, M.E., 2008. On Competition, Updated and Expanded Edition. HarvardBusiness School Press, Boston.

Porter, M.E., 1996. What is strategy? Harvard Business Review November–Decem-ber, 61–78.

Porter, M., 1985. Technology and competitive advantage. The Journal of BusinessStrategy 5 (3), 60–78.

Pries, F., Guild, P., 2011. Commercializing inventions resulting from universityresearch: analyzing the impact of technology characteristics on subsequentbusiness models. Technovation 31 (4), 151–160.

Radhakrishna, R., Doamekpor, P., 2008. Strategies for generalizing findings insurvey research. Journal of Extension On-line 46(2). ⟨http://www.joe.org/joe/2008april/tt1.php⟩.

Rasmussen, E., Moen, O., Gulbrandsen, M., 2006. Initiatives to promote commer-cialization of university knowledge. Technovation 26 (4), 518–533.

Reamer, A., Icerman, L., Youtie, J., 2003. Technology Transfer and Commercializa-tion: Their Role in Economic Development. Economic Development Adminis-tration, US Department of Commerce.

Reisman, A., 2005. Transfer of technologies: a cross-disciplinary taxonomy. Omega33 (3), 189–202.

Roberts, J., 2004. The Modern Firm: Organizational Design for Performance andGrowth. Oxford University Press, Oxford, UK.

Seitzer, D., 1999. Technology transfer: a flexible link between researchuniversity and industry. European Journal of Engineering Education 24 (2),139–149.

Sharma, M., Kumar, U., Lalande, L., 2006. Role of university technology transferoffices in university technology commercialization: case study of the CarletonUniversity Foundry Program. Journal of Services Research 6, 109–139. (July).

Siegel, D.S., Veugelers, R., Wright, M., 2007. Technology transfer offices andcommercialization of university intellectual property performance and policyimplications. Oxford Review of Economic Policy 23 (4), 640–660.

Siegel, D.S., Waldman, D.A., Link, A.N., 2003. Assessing the impact of organizationalpractices on the relative productivity of university technology transfer offices:an exploratory study. Research Policy 32 (1), 27–48.

Siegel, D.S., Waldman, D.A., Atwater, L.E., Link, A.N., 2004. Toward a model of theeffective transfer of scientific knowledge from academicians to practitioners:qualitative evidence from the commercialization of university technologies.Journal of Engineering and Technology Management 21 (1–2), 115–142.

Spithoven, A., Clarysse, B., Knockaert, M., 2011. Building absorptive capacity toorganize inbound open innovation in traditional industries. Technovation 31 (1),10–21.

Spithoven, A., Knockaert, M., 2009. Determinants and effects of users’ searchstrategies in quasi internal technology transfer. Paper presented at the XXISPIM Conference, Vienna, June 20–24, 2009.

Suvinen, N., Konttinen, J, Nieminen, M., 2010. How necessary are intermediaryorganizations in the commercialization of research? Euripean Planning Studies18 (9), 1365–1389.

Teece, D., 2010. Business models, business strategy and innovation. Long RangePlanning 43 (2–3), 172–174.

Teece, D.J., 1998. Capturing value from knowledge assets: the new economy,markets for know-how, and intangible assets. California Management Review40 (3), 55–79.

Theodorakopoulos, N., SanchezPreciado, D.J., Bennett, D., 2013. Transferring tech-nology from university to rural industry within a developing economy context:theThe case for nurturing communities of practice. Technovation 32 (9-10),550–559.

Thornhill, S., White, R.E., 2007. Strategic purity: a multi-industry evaluation ofpure vs. hybrid business strategies. Strategic Management Journal 28 (5),553–561.

Tzabbar, D., Aharonson, B.S., Amburgey, T.L., Al-Laham, A., 2008. When is the wholebigger than the sum of its parts? Bundling knowledge stocks for innovativesuccess. Strategic Organization 6 (4), 375–406.

Vargo, S.L., 2008. Customer integration and value creation: paradigmatic traps andperspectives. Journal of Service Research 11 (2), 211–215.

Vega-Jurado, J., Gutiérrez-Gracia, A., Ferdandes-de-Lucio, I., Manjarrés-Henriquez,L.,. The effect of external and internal factors on firms’ product, Research Policy37 (4), 616-632.

von Hippel, E., 1988. The Sources of Innovation. Oxford University Press, New York.Walsh, K., Enz, C.A., Canina, L., 2008. The impact of strategic orientation on

intellectual capital investments in customer service firms. Journal of ServiceResearch 10 (4), 300–317.

Wright, M., Clarysse, B., Lockett, A., Knockaert, M., 2008. Mid-range universities’linkages with industry: knowledge types and the role of intermediaries.Research Policy 37 (8), 1205–1223.

Yusuf, S., 2008. Intermediating knowledge exchange between universities andbusinesses. Research Policy 37 (8), 1167–1174.

Zott, C., Amit, R., 2010. Business model design: an activity system perspective. LongRange Planning 43 (2–3), 216–226.




























































http://www.joe.org/joe/1983september/83-5-a7.pdf



http://www.statmodel.com/download/techappen.pdf

http://www.statmodel.com/download/techappen.pdf



















http://www.economics.rpi.edu/workingpapers/rpi0609.pdf

http://www.economics.rpi.edu/workingpapers/rpi0609.pdf











http://www.joe.org/joe/2008april/tt1.php

http://www.joe.org/joe/2008april/tt1.php






































































Date post:	20-Dec-2016
Category:	Documents
Upload:	norrin
View:	218 times
Download:	1 times

Technology transfer organizations: Services and business models

Documents