Proximity as a Resource Base for Competitive Advantage: University–Industry Links for Technology Transfer

Proximity as a Resource Base for Competitive

Advantage: University–Industry Links for

Technology TransferPeter Lindelof 1

Hans Lofsten2

ABSTRACT. One of the important arguments in favor of

Science Parks is the claimed networking benefit. A total of 273

new technology-based firms (NTBFs) were surveyed. The

assessing of academic knowledge and expertise by businesses

located on site is a key principle of Science Parks. Science Park

NTBFs stand out as a special group of small firms in terms of

performance (Growth: sales and employment). The arguments

presented in this paper recognize the complex nature of co-

operative resources. The level of interaction in the innovation

process between firms located on Science Parks and local

universities is generally low, but it is higher than the level of

interaction exhibited by firms that are not Science Park firms.

The underlying premise of our research propositions (P1 and

P2) is that the NTBF-specific co-operative resources will

provide the firm with a competitive advantage. This paper,

building on the resource-based theory and empirical evidence,

argues that NTBFs working with universities that have more

proximity achieve certain advantages. Proximity between

NTBFs and universities promote the exchange of ideas

through both formal and informal networks. Statistically

significant differences between Science Park NTBFs and off-

Park NTBFs were recorded with regard to product

development in the last three years.

JEL Classification: proximity, resources, Technology Transfer,

links, new-technology-based firms

1. Introduction

In today’s technologically intensive industries,firms are finding it harder to retain sources ofcompetitive advantage. Competitors respond veryrapidly to new products, even if product develop-ment time is shortened. Strategy as a means forachieving competitive advantage (rate of product

development, price competition, technology devel-opment, competitor behavior etc.) takes intoaccount both the positioning analysis of whatbusiness to be in, where to compete and theresource-based analysis of how to compete(Grant, 1991). The competitive advantage is theresult of a thorough understanding of the externaland internal forces that strongly affect anorganization. However, in competitive environ-ments profitability is more likely to be associatedwith resource and capability-based advantagesthan with positioning advantages resulting frommarket segment selection and competitive posi-tions based upon some form of generic strategy(Grant, 1996). This study especially applies tosmall new technology-based firms (NTBFs)—those most likely to face the challenges ofincreasing scale, market scope, product innova-tion and research links.

Research links may take many forms, fromformal contracts for research to more informalcontracts as well as the transfer of personnelbetween academia and industry (Quintas et al.,1992). According to Quintas et al. (1992), themodel of technological development which lies atthe core of the Science Park is essentially a linearmodel. The model suggests that the outputs frombasic research provide a knowledge-base whichcan be drawn upon by bodies undertaking appliedresearch and experimental development. SciencePark NTBFs, being generally very small andsensitive to commercial pressures, are not in aposition to undertake long-term R&D. Theproblem is thus two-edged; academic basicresearch is too long-term for these small firms,whilst academia is not equipped to meet therequirement for immediate problem-solving activ-ities which commercial pressures demand. Theassessing of academic knowledge and expertise by

1Chalmers University of Technology

Department of Industrial Dynamics

SE-412 96 Goteborg, Sweden

E-mail: [email protected] University of Technology

Department of Industrial Dynamics

SE-412 96 Goteborg, Sweden

E-mail: [email protected]

Journal of Technology Transfer, 29, 311–326, 2004

# 2004 Kluwer Academic Publishers. Manufactured in The Netherlands.

businesses located on site is a key principle ofScience Parks. Westhead (1997) claims thatScience Parks reflect an assumption that techno-logical innovation stems from scientific research,and that Science Parks can provide the catalyticincubator environment for the transformation of‘‘pure’’ research into production.

NTBFs working with universities that are morein proximity may achieve certain advantages.Proximity between firms and universities promotesthe natural exchange of ideas through both formaland informal networks (Deeds et al., 2000).According to Deeds et al. (2000), formal methodsinclude licensing and co-operative alliances (Laneand Lubatkin, 1998), while informal methodsinclude mobility of scientists and engineers, socialmeetings and discussions (Pouder and St. John,1996). Second, formal and informal exchangesprovide information not only regarding formalprojects, but also about on-going research amongother firms and organizations. Our key relation inthis paper is between the university and the NTBF,and includes the proximity between the firm andits university and technology transfer activities.According to Williams (1985), there are five mainways in which universities may contribute to thedevelopment of NTBFs: (i) by providing oppor-tunities for students to acquire skills and attitudeswhich could be used to create and promote thesuccess of NTBFs, (ii) by promoting research inhigh technology which may create opportunitiesfor innovation by small firms, (iii) by encouragingstaff to provide advice and consultancy services inthe field of high technology, (iv) by allowing staffto create or take part in the creation of firms toexploit high technology, and (v) by creating firmsto exploit the research and development activitiesof staff in fields of high technology.

Resource-based theory is used to argue that co-operative competencies are complementary totechnical competencies and may serve as a sourceof competitive advantage (Barney, 1991, 1992;Reed and DeFillippi, 1990). The arguments pre-sented in this paper recognize the complex natureof co-operative resources. In Section 2, there willbe a brief review of the resource-based theory, andwe present the conceptual model and researchpropositions in the study. Section 3 presents thesample, means and frequencies in our study.Section 4 shows the empirical results and the

patterns of the linkages between university-indus-try and technological innovation. Finally, Section5 contains a discussion of the results and outlinesthe direction of future research.

2. Analytical framework

Entrepreneurial firms and resource-based theory

A firm’s strategic posture can be established alonga spectrum ranging from conservative to entrepre-neurial (Covin and Slevin, 1989; Miller andFriesen, 1982). Conservative firms tend to berisk-adverse, non-innovative and reactive. Entre-preneurial firms tend to be risk-takers, innovativeand proactive. The conservative-entrepreneurialdichotomy also shares similarities with some of thedichotomies developed in the NTBF literature.Findings demonstrate that small firms are gener-ally expected to favor differentiation strategies,since they will only rarely be able to utilizeeconomies of scale. Small firms may possessvarious bundles of resources that serve as thefoundations for development. According to theresource-based view (Penrose, 1959), differences inresources should be utilized and lead to differencesin sustainable competitive advantage. During thelast decade, management researchers have emi-grated to, or extended, the scope of their intereststo entrepreneurship issues. The popularity of theresource-based view of the firm in strategicmanagement has been paralleled in entrepreneur-ship research (Davidsson and Wiklund, 2001).

The resource-based theory resources are classi-fied as tangible, intangible and personnel-based(Grant, 1991). Tangible resources include, forexample, plant and equipment. Intangibleresources include reputation, technology andhuman resources include the training and expertizeof employees. As these resources are not produc-tive on their own, the analysis also needs toconsider a NTBFs organizational capabilities.Christensen (1995) has proposed a differentiatedframework that distinguishes several generic cate-gories of innovative assets: Scientific researchassets involve both basic research of a pre-competitive nature and applied and/or industrialresearch that provide direct inputs into processdevelopment and new product application. Processinnovative assets comprise both resources and

312 Lofsten and Lindelof

capabilities for ‘‘hardware’’ process innovation.Product innovative application assets are theresources and capabilities required to produceproduct development. Technical application dealswith purely technical assets in order to reducetechnical uncertainty, while functional applicationis directed towards reducing functional uncertaintywith respect to the user-interface. Aesthetic assetsare mostly thought of as a part of the marketingattributes of the product.

Firm resources include all assets, capabilities,organizational processes, information, knowledge,firm attributes etc., controlled by a firm (Daft,1983). These firm-specific heterogeneous resourcescan be classified into three general categories: (i)physical capital resources (plant and equipment),human capital resources (skills and know-how)and organizational capital resources (capabilitiesassociated with formal and informal planning,controlling and coordinating) (Barney, 1991).Distinctions may be drawn between static anddynamic resources. Barney (1991) has emphasizedthat a major assumption of the resource-basedtheory is that the link between resources andcompetitive advantage is opaque, particularly tothose outside the firm. Priem and Butler (2001)and Bacharach (1989) reduce it to the statementthat only valuable and rare resources can besources of competitive advantage. Barney (1991)argued that for a resource to be a source ofsustained competitive advantage there must be nostrategically equivalent valuable resources that arethemselves either not rare or imitable. Theliterature on external co-operative relationshipsbetween firms suggests that choosing an externalpartner with complementary technologies andstrategies, and building a co-operative relationshipbased on trust and mutual respect can be proble-matic (Dodgson, 1992).

Firm or capital resources are often referred toas capabilities and Tyler (2001) refers to physical,human or organizational assets that surpasssimilar assets of most competing firms. Further-more, technological capabilities are the technicalassets of the firm. According to Teece (1986, p. 288)‘‘an innovation consists of certain technical knowl-edge about how to do things better than theexisting state of the art’’. As noted by Teece(1989), the complementary relationship betweenco-operative resources and technological innova-

tion is also evident between the firms. Teece (1989)suggested that modern communications equip-ment serves to facilitate the timely technologytransfer necessary in parallel product develop-ment. We underline that R&D equipment, as aphysical capital resource, can also be purchased bycompetitors, and any competitive advantage willat best be temporary.

Our paper will focus on the subset of humanand organizational capital resources of a firmidentified as co-operative or technologicalresources. It is generally recognized that intechnologically intensive industries competitivefirms need a set of core resources in R&D.Successful commercialization may depend onother organizational resources to support andcomplement new products emanating from R&D.Resource-based theory extends the product mar-ket view to include factor markets, and suggeststhat firms wishing to obtain expected abovenormal returns from implementing factor marketstrategies must be consistently better informedabout the future value of those strategies thanother firms in the same market (Barney, 1986).

Research propositions

By providing a Science Park location that isproximal to important customers, suppliers,researchers and other businesses/organizations, itis assumed that the NTBFs will be able to buildnetworks that support their development. Mostaccessing of academic resources relates to low-levelcontacts based on recruiting university graduatesor informal contacts. Certain institutes or depart-ments, in different universities, strongly relates toindustry. According to Quintas et al. (1992),analysis of research links is possible only at thelevel of the firm involved. There are two principalforms of academic-Science Park links at the levelof the individual Park NTBF:

. The establishment of spin-off firms, formed byacademic staff taking research out of thelaboratory and onto the Science Park, startingtheir own commercial firms.

. The occurrence of research links facilitatingtechnology and knowledge transfers.

Proximity as a Resource Base for Competitive Advantage 313

Universities and other higher education institu-tions are an important source of new scientificknowledge. Industry can gain access to thisknowledge or resources by developing formaland informal links with higher education institutes(OECD, 1993). Therefore, the development ofhigher education institute links is assumed toencourage technology innovation and production(Westhead and Storey, 1994). Hence locales withhighly interlinked higher education institutes areexpected to have enhanced levels of wealthcreation and job generation (Malecki, 1991). Thelinkage between Science Park NTBFs and theuniversity is fundamental to the concept of ScienceParks. The Science Park can be considered as ageographically distinct environment in whichsocial and institutional processes can emerge, andthe Science Park environment is expected tobecome more integrated through a texture ofsocial and institutional networks taking placeover time (Johannisson, 1998).

In common with other empirical evidence(Massey et al., 1992; MacDonald, 1987), the levelof interaction between firms located on ScienceParks and local universities is generally low.Felsenstein (1994) underlines that it is higherthan the level of interaction exhibited by firmsthat are not Science Park tenants. The notion thatnetworks are important in innovation, and thatfirms will build networks if they are close, seems tosatisfy some need for rational arguments. Theamount and timing of organizational innovation isbecoming increasingly important (Damanpour,1991; Eisenhardt and Schoonhoven, 1990). Thus,there is a need to understand how strategies mayaffect a firm’s ability to innovate, even thoughthose strategies are not designed to affect the firm’sinnovativeness. Science Park managers have animportant role not only in establishing links, butalso in encouraging the development of moreformal links over time. Westhead and Storey(1995) believe higher education institutions shouldappreciate the necessity of having an effectivemanagerial structure designed to ‘‘add value’’ totenant firms.

A network can be seen as a resource in itself andthrough the network the entrepreneur acquiresaccess for resources and capabilities such as capitalinnovation and advice (Zukin and DiMaggio,1990; Uzzi, 1996, 1997; Gulati et al., 2000).

Entrepreneurial networks can be categorized intoformal and informal networks (Birley, 1985).Informal networks are recognized to includepersonal (friendship) relations, family ties andbusiness partners. Formal networks consists ofsuppliers of capital such as venture capitalists,banks, creditors and professionals such as accoun-tants, lawyers and trade associations (Das andTeng, 1997). Studies done by Sahlin-Andersson(1990), Quintas et al. (1992), and Johannisson(1998) show that co-operation between firms wasless than one might expect. Sahlin-Andersson(1990) argues that the reason for location in aScience Park was not to establish new contacts butto preserve old ones. Lowegren-Williams (2000)argues that the lack of formal co-operation andnetworking is due to the heterogeneity of thelocated firms.

The network is created through a path depen-dent process and is therefore, idiosyncratic anddifficult to imitate as well as being a subject ofimmobility, immitablity and non-substitutional(Grant, 1991; Gulati, 1999). Other barriers tomobility exist where resources are firm-specific,where property rights are so called co-specialized(Peteraf, 1993). There are barriers that hinder theduplication of resources such as complexity,tacitness and specificity (Reed and DeFillippi,1990), economics of scale, producer learning andinformation impactedness (Rumelt, 1984, 1987).This could explain why NTBFs on Science Parksregard close proximity between firms as a crucialfactor for their ability to develop networks.

Research proposition 1:

P1: Independent Science Parks (NTBFs) will usethe nearby academic facility for research networkswith universities to a larger extent than independentoff-Park sample (NTBFs). The higher degree ofnetworking activity is dependent on the geographicalproximity.

Innovative resources are required to producetechnological innovation, but they are rarelysufficient to assure commercial benefits fromtechnological innovation. Successful commercialexploitation of technological innovation mainlyrequires access to assets that are complementary toinnovative assets (Teece, 1986). Teece (1986) takesan ex-post perspective and, on technological


innovation, asks what assets will be required toreap commercial value from a given productinnovation. The complementary assets are con-ceived in terms of the broad functional categoriesof the firm: manufacturing, distribution, market-ing etc.

Organizational research suggests that firms indynamic environments with higher levels ofinformation processing, communication andknowledge transfer are more likely to developcompetencies which will result in successfultechnology innovation than firms in these environ-ments with lower levels of co-operative resources(Coff, 1997; Henderson and Cockburn, 1994).Technological capabilities represent an importantsource of competitive advantage in technologicallycompetitive markets (Nelson, 1991). Intellectualproperty rights, patents and launches of newproducts are a major consideration in university-industry collaborative ventures. One measure ofoutput is the level of patenting activity in firms.However, for the majority of NTBFs undertakingR&D, the ultimate purpose is the launch of newproducts, although some firms undertake contractR&D for client firms. The typical developmentpattern for new firms has been typified as an initialheavy dependence on contract research and devel-opment activities. Santoro and Gopalakrishnan(2001) showed that trust, geographic proximityand flexible university policies for intellectualproperty rights, patents and licences were stronglyassociated with greater technology transfer activ-ities. However, Santoro and Gopalakrishnan(2001) underline that while this has importantimplications for future university-industry alli-ances, one must be mindful of trust’s temporalnature and the consequential role of both theinternal and external context (Mayer et al., 1995).

Since NTBFs and universities both view patentsand new products as an opportunity to increaserevenues, establish competitive advantage, compe-tition over these rights is often contentious(Phillips, 1991). If universities want to encouragelinks with industry to advance new technologies,then greater flexibility over these rights is needed(Bower, 1993). How does activity on a universityScience Park affect dimensions of universitytechnology transfer? (Siegel et al., 2003). For themajority of Science Parks firms undertaking R&D,the ultimate purpose is the launch of new products

and markets. Another key role of NTBFs is toaccelerate diffusion of technology, and in so doingenhance the competitive position of users.

Research proposition 2:

P2: Independent Science Parks (NTBFs) willrecord higher levels of technology innovation(product development) than independent off-Parksample (NTBFs).

The domain of this study is restricted to industrieswhere (i) the technological environment is dynamicor hostile, (ii) perceived uncertainty is high, and(iii) a resulting high need exists for obtaining andprocessing new information. In such industriestechnological innovations can be expected to rangefrom dynamically continuous to discontinuous(Lin and Zaltman, 1973). According to Barney etal. (2001), Eisenhardt and Martin (2000), and Fiol(2001), competitive advantage cannot be sustainedin dynamic and rapidly changing markets. Theseenvironments evolve so quickly that no sustainedcompetitive advantage is possible. However,Eisenhardt and Martin (2000) specifically identifythe conditions under which sustained competitiveadvantage is possible in these settings—when afirm applies its dynamic capabilities in making itsstrategic choices.

The research propositions relate specifically tothe potential for performance associated with co-operation with universities: communication,knowledge transfer, information processing,R&D equipment, basic and applied research etcFurthermore, it is argued that NTBFs with co-operative resources in several of the capabilitycategories (in our case—technological innova-tion—product development) discussed can beexpected to have a more sustainable competitiveadvantage. Strategy as a means for achievingcompetitive advantage takes into account boththe positioning analysis of what business to be in,and hence, performance (growth and profitability).According to Christensen (1996), in the resource-based theory, there is no common and unambig-uous definition of, and distinction between, thenotions of resources, assets, capabilities andcompetencies. In this paper, we propose a distinc-tion between resources and capabilities (see alsoChristensen, 1995, 1996). Capabilities are capa-cities for structuring and orienting different


resources that potentially provide the firm with acompetitive advantage. The underlying premise ofour research propositions (P1 and P2) is that theNTBF-specific co-operative resources will providethe firm with a temporary competitive advantage.This paper, building on the resource-based viewand empirical evidence, argues that resources(University-industry links and co-operation withuniversities) can enhance technological innova-tion.

3. Sample and survey

Matching criteria, Science Parks and NTBFs inSweden

One logical way to assess the technologicalinnovation (product development) of ScienceParks is to compare the performance of theirtenants to similar firms not located there. Previousresearch has shown this approach has its limita-tions (Mian, 1991): (1) there is no reliable and cost-effective way to identify a comparison groupbecause of poor data sources on small start-upfirms, (2) there is no reliable way to identify acomparison group because of a strong selectionbias of university technology business incubator,(3) lack of control on firm variables, and (4) theeffects of university technology business incuba-tors are not limited to their tenant firms. Weiss(1972) has suggested three conditions that experi-ence suggests particularly recommend the use ofcomparative evaluations: (1) when the issues arereal and policy-makers are faced with vitaldecisions among alternative strategies for action,(2) when the alternative programs are relativelywell-defined having substantially similar aims, and(3) when there is a preliminary evidence thatprograms have the viability and strength to offersome likelihood of success. However, the use of‘‘matched samples’’ has become widely appre-ciated and utilized in the small firms andentrepreneurship research fields (according toPeck, 1985; O’Farrell and Hitchens, 1988; West-head, 1995).

Local authorities in Sweden have developed arange of local economic initiatives designed tocreate new employment opportunities. One ele-ment has been the encouragement of small hightechnology-based firms to achieve high rates of

growth. Local authorities have also played a keyrole in encouraging universities to take a moreactive role in the revival of local economies.Several financial institutions have made commit-ments to Swedish Science Parks. There is nouniformly accepted definition of a Science Park,and there are several similar terms used to describesimilar developments, such as ‘‘Research Park’’,‘‘Technology Park’’, ‘‘Business Park’’, ‘‘Innova-tion Center’’ etc. (Monck et al., 1988). Currie(1985) and Eul (1985) have attempted to distin-guish between Innovation Centers, Science Parksand Research Parks. MacDonald (1987) says thateach of these terms are used interchangeably todescribe the following package: (1) a property-based initiative close to a place of learning and (2)one which provides high quality units in a pleasantenvironment.

The total number of Science Parks in Sweden,in 1999, was 23 (see Swedepark; the SwedishScience Park Association). We initially chose tolimit our study to 10 Science Parks. The mainparticipants establishing Science Parks in Sweden,such as universities, local authorities and devel-opment agencies have encouraged the formationof a heterogeneous group of parks. We excluded13 of the parks in this study because they werebrand new or acting as a ‘‘firm hotel’’. TheUnited Kingdom Science Park Association(UKSPA) distinguishes between ‘‘managed’’ and‘‘non-managed’’ Science Parks. A managedScience Park has a full-time on-site manager(Westhead and Storey, 1994). Siegel et al. (2003)also underline that it may be important todistinguish between ‘‘managed’’ and ‘‘non-mana-ged’’ parks. This was our primary selectioncriterion for incorporating or excluding ScienceParks. Those Science Parks that where non-managed were excluded and regarded as moreof a ‘‘business hotel’’ than a facility that couldprovide assistance and resources for locatedNTBFs (Ambrosio, 1991). The total number offirms with a technological base in the 10 parkswas 477. However, defining what is, and what isnot, high technology is problematic.

From the selection criteria, the following 13Science Parks were excluded: Atrium 21 (Kalmar),Berzelius Science Park (Linkoping), Centek(Lulea), Chalmers Innovation (Goteborg), Sahl-grenska Biomedicinska (Goteborg), Innova (Karl-


stad), Creative Center Skaraborg (Skovde), Sunds-valls Utvecklingscentrum, Teknikbyn (Vasteras),Teknikdalen (Borlange), and Videum (Vaxjo). Theremaining 10 Science Parks were: Aurorum(Lulea), Electrum/Kista (Stockholm), Ideon(Lund), Mjardevi (Linkoping), Novum (Stock-holm), Ronneby Softcenter (Ronneby), Stuns/Uppsala (Uppsala), Teknocenter (Halmstad),Teknikhojden (Stockholm), and Uminova(Umea).

Science Parks contain not only independent,entrepreneurially managed firms but also firmswhich may be part of a group where the ultimateownership is outside the park. The independencecriterion ensures that effects of key customerrelationships are not mixed with those of firmparents. In order to make valid comparisons bothbetween this study and other studies, only single-plant independent firms are included (joint-stockfirms, trading companies, limited partnershipcompanies etc.). As expected, the new and emer-ging technologies such as information and soft-ware technology and electronics, dominated thepopulation. The Science Park sample (N ¼265NTBFs) is a random sample of 477 independentNTBFs located on Science Parks in Sweden, andwas drawn on a stratified basis from the totalnumber of on-Park firms (industrial categories,according to weightings from Science Park firms,step by step).

To identify the off-park NTBFs CD-rombusiness data bases were used, and a database ofnew Swedish technology-based firms that weredeveloped within the CREATE group at theDepartment of Industrial Dynamics at ChalmersUniversity of Technology. The database includesall Swedish firms that fulfill certain criteria of size,year of foundation, independence at start andindustry (Rickne and Jacobsson, 1999). 1,126 firmswere identified as fulfilling these criteria. The off-Park random sample consists of 500 independentfirms and were drawn on a stratified basis(branches, according to weightings from SciencePark firms, step by step). 200 firms were excludedaccording to technology base etc. The identifica-tion of comparable off-Park firms was a time-consuming project, and off-Park selection pro-blems were compounded due to name changes,changes of location and business closures. SciencePark firms were then ‘‘matched’’ with a similar

group of off-Park firms based on the selectioncriteria (Westhead and Storey, 1994): industry,ownership type of the firm, age of the firm andlocation of the firm.

A questionnaire was sent to the managingdirectors of these firms in January 1999. Thequestionnaire had been thoroughly pretested andmodified as a result of discussions with six firms.Questionnaire responses were collected fromindependent organizations (respondent: man-ager/director) during early 1999, and in themiddle of 1999. By then 134 firms had respondedto the survey, after two reminders (and onereminder by telephone) in the spring of 1999.The response rate of 50% compares favorablywith similar mail surveys of entrepreneurial firms(Yli-Renko et al., 2001, 24%; McDougall et al.,1994, 11%; Chandler and Hanks, 1995, 19%). Ofthe firms that had not responded to the survey,some firms could not be located or had noactivity, and some firms said they did not havetime to answer the questionnaire. The question-naire included questions about, for example,strategies, importance of location, co-operationwith other firms, networks, business advice,financing etc.

It is difficult to say if the combined on and off-Park sample can be regarded as a representativesample of all NTBFs in Sweden. The Science Parksample, however, was reasonably representative ofall NTBFS located on parks. When comparingdifferences between on and off-Park NTBFs,observed differences could reflect the motivationsof the NTBFs as well as the added value of aScience Park location. Studies of new technology-based industry include a section intended to definehigh technology. These indicators fall into twogroups (Monck et al., 1988): measures of resourceinputs to high technology activity, i.e., R&Deffort, R&D expenditure and the employment ofqualified personnel; and secondly, measures ofoutput or performance of high technology firms,such as growth rates, patent records and techno-logical innovations. A range of questions in oursurvey were intended to provide an indication ofthe technological capability of the NTBFs. Theseinclude information on the inputs to R&D,percentage of staff employed (and founders) byfirms that have qualified scientists and researchlinks with universities.


Means and frequencies

A total of 273 NTBFs were surveyed, of which 134were on a Science Park and 139 were not on apark. It will be recalled that the objective of theoff-Park sample was to identify primarily high-tech independent firms. The tracking of NTBFs onSwedish Science Park organizations was success-fully achieved because information was moreextensive from Science Park managers surround-ing organization name changes and/or organiza-tion changes. The branches are software/information technology, technology consultants,electronics/electrical, pharmacology and pharma-ceutical preparation, mechanics and industrialchemistry/plastics industry (see Table I).

Science Parks NTBFs stand out as a specialgroup of small firms in terms of performance, that isemployment (on-park: 27.94%, off-park: 4.20%)and sales growth (on-park: 38.75%, off-park:11.19%). Profitability measures (on-park: 2.97%,off-park: 6.51%, no statistical difference) of perfor-mance do not follow the same pattern. Table Iindicates that despite the efforts tomatch—in termsof employees and turnovers—the firms on and off-Parks, it was not possible to achieve this perfectly.Most of the businesses are rather young (mean; 7.19and 9.00 years), probably reflecting the fact thatsmall firms tend to be younger than medium orlarge-sized firms.When comparing the performanceof small firms, it is of importance to recognize theimportance of age. It is easy to observe illusorydifferences in growth amongst a group of smallfirms over time if one group is younger than theother, since (in proportionate terms), younger firmsgrow faster than older firms. This section has shownthat Science Parks have catered for a high propor-tion of somewhat younger and smaller NTBFs. Themain difference between our groups (Science Parkand off-Park) could be apparent with regard tostated technology structure and R&D intensity. Anumber of firms are leading edge firms undertakingR&D. Others are less sophisticated, undertakinglittle R&D, and are essentially involved in down-stream commercial activities.

Measures

The Pearson correlation is used to predict theinitial factorability using visual examination,

identifying those variables (items) that are statis-tically significant. The correlation analyzes presentthe simple relationships among items (Pearson-correlation, � 1 – 1). This provides an adequatebasis for proceeding to the next level of examina-tion—factor analysis. Tables III and VI reportcorrelations between variables used in the study.Exploratory factor analyzes were conducted todetermine the underlying construct of networksrelated to universities and geographical proximitywere of a unidimensional or a multi-dimensionalconstruct, and to examine the validity for thehypothesized construct of technological innova-tion. The factor analysis used a principal compo-nent method with a varimax rotation. The varimaxrotation is orthogonal, and is uncorrelatedthroughout the rotation process and obtainstheoretically meaningful factors. Factor loadingsare considered significant for differing samplesizes.

In our case a sample size of 134 needs a factorloading exceeding 0.50–0.45 to be consideredsignificant at 0.05–level (Hair et al., 1995). Thecumulate variance should exceed 50% of the totalvariance for the latent variable to be consideredvalid (Gerbing and Andersson, 1987). For testingthe reliability of the latent constructs we com-puted the Cronbach’s alpha, where the moreconservative value of 0.5 was used as a thresholdvalue (Cohen, 1977) and this is sufficient forexploratory studies (Hair et al., 1995). All thevariables in our study were significant and thevariables loaded on one factor. The purpose ofthis study is to identify underlying patterns ofresearch network activities, geographical proxi-mity and technological innovation, for exampleanalyze and identify latent constructs. You mightwant to pursue this analysis with a structuralequation modeling approach where the linearrelations of latent constructs are more explicitlyinvestigated. But, in this paper, we identify theunderlying patterns.

The following section presents the results of thecorrelation matrixes and factor analyses. Theresults of the factor analyses and the Cronbach’salpha are reported in Tables IV and VII. Eachvariable is named and linked with a factor; a factorloading. The stage involves estimation of themeasurement model using factor analysis. Thisstage tests whether or not the variables selected to


measure each construct exhibit sufficient conver-gent and discriminate validity.

Resources—networks related to universities. Allmeasures were five-point Likert-type scales (Co-operation with universities, Yes ¼ 1, No ¼ 0).

Factor 1, Co-operation with universities,importance of location ða ¼ 0.95Þ, whichcaptures the extent to which a firm’s activitieswith the university were influenced by itsproximity to the university. The 12 variablesmeasure the variety of highly-related activitiesfrom a wider range of collaborative activities

Table I

Means and frequencies of surveyed high technology organizations over the 1996–1998 period.

1. Response rate:

On-park sample Off-park sample

N 265 300

n 134 139

No valid firms 5 5

Response rate (%) 52.1 48.0

2. Variables—means and frequencies:


Response No response Response No response

Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation Mean

Standard

deviation

Growth (%)

Sales 38.75 66.62 31.31 43.35 11.19 31.01 12.54 31.11

Employment 27.94 66.19 25.76 65.06 4.20 25.21 6.84 20.60

Profitability 2.97 21.99 1.56 24.85 6.51 12.71 6.07 15.26

Start

Salesa 9,292.4 16,360.7 10,658.8 16,540.2 11,532.7 16,054.5 12,101.0 13,023.4

Employment 10.37 19.19 10.35 13.96 11.72 14.67 11.49 12.77

Branchb 3.29 2.06 3.31 2.00 3.42 2.02 3.28 2.02

Age 7.19 2.94 8.37 2.19 9.00 1.83 9.17 1.55

Regionc 120.40 256.77 101.13 224.89 123.94 217.06 74.55 140.26

3. Branch—frequencies (%)


Response No response Response No response

Software/information

technology

34.3 30.0 29.7 30.7

Technology consultants 25.4 23.6 26.1 24.8

Electronics/electrical 11.9 16.4 13.0 13.9

Pharmacology and

pharmaceutical preparation

14.2 15.5 13.0 13.9

Mechanics 9.7 10.9 11.6 11.9

Industrial chemistry/

plastics industry

4.5 3.6 6.5 5.0

Sum 100.0 100.0 100.0 100.0

Notes: a1000 SEK; bBranch (six branches), different weightings; cRegion (10 regions), different weightings.

Source: Lindelof and Lofsten (2002, p. 147)


between NTBFs and universities (Yes, No, Likert-type scales and factor loadings). The scales areLikert scales (1–5), however, some of the managershave answered 0 in the questionnaire (i.e. R&Ddepart., off-Park: mean 0.98). Two variables aredichotomous (Yes or No): Co-operation withuniversities and patents. Here, resources fortechnological innovation refer to the resourcesrequired for producing new or improvedtechnologies and ultimately new products.

Technological innovation—product development.Technological innovation ða ¼ 0.60Þ variableswere recorded with regard to productdevelopment (Change of products and timeinterval between changes of products) in the lastthree years (Likert-type scale 1–5, Months andYes ¼ 1, No ¼ 0).

4. Analysis

Resources—networks related to universities (P1)

This section reports the responses of firms toquestions about the types of research networks(research networks in the innovation processwhich are committed by NTBFs—in terms ofR&D equipment, facilities and human resourceswhich NTBFs invest in the R&D process) relatedto universities and information about the factorswhich may explain any differences in technologyinnovation (product development). We are inter-ested in finding any differences between NTBFsthat have chosen to locate on Science Parkscompared to those that have chosen to locateelsewhere. By providing a Science Park locationthat is proximal to important customers, suppliers,researchers and other businesses/organizations it isassumed that the NTBFs will be able to buildnetworks that support their development. Indifferent universities certain institutes or depart-ments strongly relate to industry. Table II showsthe nature and extent of linkages between firmsand the university/higher education institute. Thesecond is that the survey makes it clear that theproportion of NTBFs on Science Parks with linkswith universities is comparatively high. However,

most accessing of academic resources relates tolow-level contacts based on recruiting universitygraduates or informal contacts.

Some significant differences can be seenbetween Science Park firms and the off-Park firms(see Table II). There were expected differencesbetween Park and off-Park firms—in some cases,significance at the 1% level. Park-based firmsappear to place greater emphasis upon access toequipment, R&D and personnel categories. Thereare formal and informal contacts with academics,that is student project links, employment ofgraduates, research projects, etc. Science Parkfirms tend to be more involved in co-operationwith universities. In 1999, firms located on aScience Park were significantly more likely to havea link with a local university. Science Parkmanagers have an important role not only inestablishing links, but also in encouraging thedevelopment of more formal links over time.

Table II shows networks related to universities,and the tables focus on links primarily related toR&D, personnel and technology transfer. On-Parkfirms place a greater emphasis on co-operationwith universities and formal contacts with aca-demics in the university. These contacts with theuniversities may be channels of communicationfacilitating information transfer. On-Park firmsalso appear to place greater emphasis upon accessto R&D equipment, R&D documents, recruit-ment, basic and applied research. The use ofequipment and facilities is perhaps not available tothose outside the university and park complex.Earlier observed levels of university—NTBFsinteraction have lead to suggestions that NTBFsare more dependent on linkages and informationflows from other similar firms than on interactionwith universities (MacDonald, 1987).

On-Park firms rate basic research and appliedresearch more highly. Applied research may berequired to solve problems with productionengineering after the prototype stage. Someperception of a market is implicit in that appliedresearch which takes place prior to the emergenceof a clear technological opportunity (Monck et al.,1988). Table III reports correlations between thesignificant variables, and there are strong correla-tions between network activities and the proximityto the university. Table IV reports the factoranalysis (co-operation with universities, network


activities and proximity to the university). TheCronbach’s a for the latent construct was 0.95exceeding the minimum value of 0.50 and the itemsare therefore considered to be reliable predictors ofthat latent construct.

Science Park proponents have claimed thatScience Parks offer NTBFs an environment thatsupports network formation and meets the opera-tional needs of NTBFs. There may be park-basedfirms which have the majority of their formal linkswith universities. Science Parks are a particularly

suitable location for new businesses, and oppor-tunities exist for Park managers to developtraining and business placing programs to assistpotential entrepreneurs. Opportunities also existfor park managers to develop ‘‘added value’’networks with similar organizations. Complement-ing this picture is the empirical evidence pointingto the generally low level of collaboration. Somequestions can be raised about the extent ofresearch links, and the basis for the Science Parkstrategy as a means to achieve linkage.

Table II

Significant variables (on-park and off-park). Resources (Networks related to universities)

Mean Standard deviation

Significant variables On-park Off-park F Significant p-value On-park Off-park Scalea

Co-operation with

Universities 0.67 0.51 15.81 0.00 0.01* 0.47 0.50 Yes/no (1/0)

R&D department 1.64 0.98 18.67 0.00 0.00* 1.67 1.30 1–5

Consultants 1.38 0.91 5.21 0.02 0.00* 1.45 1.24 1–5

Discussions 1.82 1.33 2.78 0.09 0.02* 1.80 1.63 1–5

R&D projects 1.34 0.97 2.80 0.09 0.03* 1.43 1.29 1–5

Transfer of R&D documents 1.26 0.86 4.35 0.04 0.01* 1.37 1.13 1–5

R&D equipment 1.43 0.94 7.24 0.01 0.01* 1.51 1.31 1–5

Recruitment 1.85 1.29 4.19 0.04 0.01* 1.82 1.59 1–5

Basic research 1.07 0.77 0.98 0.04 0.04* 1.19 1.12 1–5

Applied research 1.46 0.98 7.98 0.00 0.01* 1.57 1.32 1–5

General development 1.42 1.10 6.54 0.00 0.00* 1.48 1.43 1–5

Proximity to university 3.16 2.54 5.80 0.02 0.00* 1.37 1.59 1–5

Notes: *Significance at the 5%-level ðp-value < 0:05Þ, t-test, 2-sided; a1 ¼ very poor, 5 ¼ very high. Yes ¼ 1, No ¼ 0.

Table III

Correlation matrix: Resources (networks related to universities and geographical proximity)

Co-operationa 1 2 3 4 5 6 7 8 9 10 11 12

Co-operation with

1. Universities

2. R&D department 0.649**

3. Consultants 0.655** 0.816**

4. Discussions 0.686** 0.809** 0.749**

5. R&D projects 0.595** 0.713** 0.679** 0.751**

6. Transfer of R&D

documents

0.574** 0.641** 0.634** 0.791** 0.805**

7. R&D equipment 0.622** 0.712** 0.687** 0.761** 0.694** 0.703**

8. Recruitments 0.703** 0.548** 0.568** 0.632** 0.447** 0.528** 0.547**

9. Basic research 0.531** 0.661** 0.633** 0.655** 0.738** 0.731** 0.613** 0.547**

10. Applied research 0.589** 0.661** 0.651** 0.755** 0.816** 0.824** 0.737** 0.520** 0.734**

11. General development 0.605** 0.663** 0.635** 0.706** 0.676** 0.675** 0.713** 0.567** 0.589** 0.817**

12. Proximity to university 0.338** 0.455** 0.397** 0.542** 0.355** 0.423** 0.418** 0.446** 0.345** 0.396** 0.340**

Notes: **Correlation is significant (0.01 level), 2-tailed; *Correlation is significant (0.05-level), 2-tailed; aFactor 1: Co-operation with

universities, importance of location ða ¼ 0.95Þ.


Technological innovation—product development(P2)

Statistically significant differences between SciencePark NTBFs and off-Park NTBFs were recordedwith regard to product development (Change ofproducts and time interval between changes ofproducts) in the last three years (see Table V). Onefinding from this research is that Science ParkNTBFs are not able to channel investments intogreater R&D outputs (Patents) than comparableoff-Park firms. The number of patents registeredby a firm remains a widely used output measure ofthe level of technology diffusion. Monck et al.(1988) found that a larger proportion of SciencePark firms had lodged at least one patent in thelast two years than had off-Park firms (thesignificance was not statistically significant).Further research should investigate whether thelack of difference in R&D output levels (Patents) isdue to the management activities of Science Parkmanagers.

Here, the significant finding is that technologi-cal innovation (product development) was moreevident in off-Park firms (Change of products/services last 12 months and time interval betweenchanges of products). Perhaps Science Parksrepresent more centers of learning than innova-

tion, and it is difficult to say whether they can beeffective only in those areas in which innovation isscience-based, and less in areas in which innova-tion is based on the development of new productsand markets. Maybe those policy-makers seekingto support academic-industry links with a view topromoting technological innovation would betterachieve their objectives by looking beyond ScienceParks. Some of these results confirm the critique tothe efficiency of Science Parks and the difficulty ofmeasuring their benefits for innovation. However,we have some strong relations between change ofproducts/services last 12 months, time intervalbetween changes of products (months) and co-operation with universities (see Table VI).

Table VI reports correlations between thesignificant variables, and there are significantcorrelations between technological innovationand cooperation with the university. Table VIIreports the factor analysis (technological innova-tion—product development and co-operation withuniversities). The Cronbach’s a for the latentconstruct was 0.60 exceeding the minimum valueof 0.50 and the items are therefore considered to bereliable predictors of that latent construct.

5. Discussion

There has been an on going debate wherever theScience Parks contribute to the located firmscapability to innovate. Felsenstein (1994) andWesthead and Storey (1995) argue that theinnovative capability of the located firms weremerely a result of internal capability pre-existingfor the location and is influenced by the location.Felsenstein (1994), Westhead and Storey (1995)and Westhead (1997) draw as one conclusion thatthe network activities were higher for located firmswithin the Science Parks, and did not take intoconsideration that of proximity as a crucial factorfor creating and maintaining research networkswith the university. Our novel contribution to theliterature is that the NTBF-specific co-operativeresources will provide the firm with a competitiveadvantage. This paper, building on the resource-based theory and empirical evidence, argues thatNTBFs working with universities that are more inproximity achieve certain advantages. Proximitybetween NTBFs and universities promote the

Table IV

Factor analysis: Resources (networks related to universities and

geographical proximity)

Factors Factor 1

Factor names

Co-operation with

universities, importance of

location ða ¼ 0:95Þ

Co-operationa

Universities 0.771

R&D-Departments 0.859

Consultants 0.834

Discussions 0.911

R&D-projects 0.860

Transfer of R&D-documents 0.863

R&D equipment 0.843

Recruitments 0.707

Basic research 0.805

Applied research 0.882

General development 0.828

Proximity to university 0.534

Note: aCumulative variance� 66.25%. Values below 0.500 are

not included in the analysis. a (Cronbach a)> 0.500.


exchange of ideas through both formal andinformal networks. Statistically significant differ-ences between Science Park NTBFs and off-ParkNTBFs were recorded with regard to productdevelopment in the last three years.

In this paper we attempt to address the analysisof competitive advantage from a resource-basedperspective. Science Parks new technology-basedfirms stand out as a special group of small firms interms of performance (growth: sales and employ-ment). This paper proposes a conceptual model foranalyzing competitive advantage (resources andcapabilities) to produce technological innovations.The framework elaborates on contributions fromboth the more general resource-based perspectiveand the innovation and technology perspective ofthe firm. Our perspective on technological innova-tion raises the question as to what ‘‘innovativeresources’’ are required to produce a technologicalinnovation?

The resource-based theory has a strong focuson performance and second, the theory explicitlyrecognizes the importance of intangible concepts,

such as proximity and networks. These comple-mentarities offer an opportunity that highlightsthe technological innovation (product develop-ment), performance (growth and profitability) andnetworks. We explored technological innovationas a moderator of the relationship from increasedlinks with NTBFs and the universities. NTBFscollaborating with universities that are more inproximity may achieve certain advantages. IfNTBFs face a similar external environment, theresource-based theory suggests that those NTBFswith a similar initial resource endowment shoulddisplay similar patterns of firm behavior andperformance.

This research has explored the formal linkagesmade by high-technology-based firms located onand off Science Parks (Research proposition 1).Firms located on Science Parks were significantlymore likely to have a link with a local universitythan off-Park firms. The growth of NTBFs can beenhanced if managers, decision-makers as well asacademics in universities, appreciate the potential

Table V

Significant variables (on-park and off-park). Technological innovation—product development

Mean Standard deviation

Significant variables On-park Off-park F Significant p-value On-Park Off-Park Scalea

Change of products/services

last 12 months

2.59 1.99 0.20 0.89 0.00* 1.57 1.53 1–5

Time interval between

changes of products

12.24 14.93 4.03 0.05 0.03* 10.7 8.49 Months

Patentsb 0.32 0.36 0.76 0.38 0.56 0,47 0.48 Yes/no (1/0)

Notes: *Significance at the 5%-level ðp-value < 0:05Þ, 2-sided; a1 ¼ very poor, 5 ¼ very high. Yes ¼ 1, No ¼ 0; bPatents—no

significant difference between on-Park and off-Park firms.

Table VI

Correlation matrix: Technological innovation—product

development

1 2

1. Co-operation

with universities

2. Change of products/

services last 12 months

0.276**

3. Time interval between

changes of products (months)

0.263** 0.313**

Notes: **Correlation is significant (0.01-level), 2-tailed;

*Correlation is significant (0.05-level), 2-tailed.

Table VII

Factor analysis: Technological innovation—product

development

Factors Factor 1

Factor names Technological

innovation ða ¼ 0:60Þ1. Co-operation with

universities

0.529

2. Change of products/

services last 12 months

0.689

3. Time interval between

changes of products (months)

0.814

Notes: aCumulative variance ¼ > 54.57%. Values below 0.500

are not included in the analysis. a (Cronbach a)> 0.500.


benefits of university co-operation. It should benoted that even if local linkage structures areweak, this does not mean that the total impact ofthe Science Park on the local economy isnegligible. Universities are beginning to developinnovative new forms of relations with industrysuch as limited partnerships, R&D seed funds etc.A firm’s external networks, in this case theuniversities, may form a major contributor to thefirm’s performance (growth and profitability). Thefirm’s ability to mobilize resources, attractresearchers at the universities and identify entre-preneurial opportunities is conditional to externalnetworks, since social relations mediate transac-tions. The new technology-based firms shouldpursue strategies focusing on the development ofvaluable networks with external resource holders(such as the universities but also other firms etc.) inorder to succeed. In resource-based theory, tech-nological capabilities define the roots of a firm’ssustainable competitive advantage, since the cap-abilities comprise patents, new products etc.

Networks are vital to the discovery of oppor-tunities and to the testing of ideas. The collabora-tion with universities provides a means ofdeveloping technological knowledge. Universitiesand research centers also provide consultingassistance to new firms and opportunities forcontinuing education. No single university willprovide the full range of scientific or managementskills required by the park NTBFs. There may bepark-based firms which have the majority of theirformal links with universities. Certain institutes ordepartments, in different universities, stronglyrelate to industry. However, in our study, mostaccessing of academic resources relates to low-levelcontacts. The empirical analysis illustrates that theexploitation of linkages between university andindustry may provide the firm with resources thattend to be of strategic value to the firm. Like otherresources, ‘‘innovative resources’’ must comprise afit between the technology dimension (R&D-departments, R&D-staff, R&D equipment) andthe management dimension. The new technology-based firm must focus on how to get access tocomplementary resources to a given technologicalinnovation.

It is in our study difficult to say if the proximityand linkages to universities have main effects onthe new technology-based firm’s performance but

the on-Park NTBFs have stronger links andnetworks to universities, a higher level of techno-logical innovations and performance (growth).However, only NTBFs with internal resourcescan effectively absorb knowledge and technologiesthat are co-operatively developed with universities.The significant finding here is that technologicalinnovation (product development, research pro-position 2) was more evident in on-Park than off-Park firms (change of products/services last 12months and time interval between changes ofproducts). Another significant finding from thisresearch is that Science Park NTBFs are not ableto channel this resource investment into greaterR&D ‘‘outputs’’ (patents, etc.) than comparableoff-Park NTBFs. Further research should explorewhether the lack of difference in R&D ‘‘output’’levels is due to the management activities ofScience Park Managers. R&D activity is notor-iously difficult to measure. Small firms usually donot have clearly demarcated R&D departments orfunctionaries. Examples of other types of indica-tors are R&D effort and R&D expenditure. Here,additionally more refined research is needed toidentify R&D input and output differencesbetween independent firms located on and offScience Parks.

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Documents

Proximity as a Resource Base for Competitive Advantage: University–Industry Links for Technology Transfer