Higher Education Institutions in an Open Innovation System A UK Perspective.DOC

Higher Education Institutions in an Open Innovation System: AUK Perspective Jeremy Howells¹̕², Ronnie Ramlogan², Shu-Li Cheng², ¹Faculty of Business and Law, University of Southampto

Higher Education Institutions in an Open Innovation System: A

UK Perspective

Jeremy Howells¹̕², Ronnie Ramlogan², Shu-Li Cheng²,

¹Faculty of Business and Law, University of Southampton; ²Manchester Institute of Innovation Research, Manchester Business School, University of Manchester, Oxford Road, Manchester, M13 9PL,

UK

Abstract

Purpose: The paper explores the nature and impact of Higher Education Institution (HEI) in a

distributed, open innovation system using a survey of some 600 firms in the UK.

Design/methodology/approach: Primary data is used from a postal questionnaire survey of 600 firms

across three United Kingdom (UK) regions: Wales, the North West and the East of England.

Findings: The analysis reveals significant differences in firm collaboration with HEIs across the UK and

the value and impact that such collaborations have on firm development The nature and effects of such collaboration vary significantly between the type of firm involved and their location and the analysis investigates this in relation to various aspects of innovative activity and firm performance.

Originality/value: Although much of the nature and effects of such collaboration are as one would

expect, some of the results are counter-intuitive and highlight the care we should place on assessing the role of universities and other HEIs in open innovation systems.

Keywords: Open innovation, Higher Education Institutions, Networks, Economic impact, Collaboration,

partnerships (Powell, 1998; Tapon and Thong, 1999; Orsenigo et al., 2001;

Hagerdoorn, 2002) in parallel with the development of newer forms of partnershipand interchange (Chen, 1997) This should not be viewed as a necessarily new

 Email: j.howells@southampton.ac.uk

phenomenon and indeed may be seen as reverting to a model that was dominant inearlier times (Graham, 1985; see also Sanderson, 1972; Meyer-Thurow, 1982;Liebenau, 1984; Homburg, 1992), but has re-emerged and transmogrified into adifferent type of interactive regime Increasingly, firms have extensive externalresearch and innovation linkages, forming complex distributed innovation networks(Coombs and Metcalfe, 2002; Chang, 2003) with greater levels of R&D andinnovation outsourcing activity (Veugelers, 1997; Howells, 1999; Hones, 2000;

Howells et al., 2003) This process has led to many industries with much more open

research and technical systems This process has collectively been characterised byChesbrough (2003a; 2003b) as part of the ‘open innovation’ model

The open innovation model has focused around the context of the firm and has takenthe perspective of the firm in terms of how innovation is shaped and developed(Chesbrough, 2003c; 2006) This paper seeks to view the open innovation model in awider context by, firstly, taking the open innovation model and using it as a lens tolook at the implications of this new paradigm on the role of universities and highereducation institutions (HEIs1) their interactions with firms Secondly, the paper seeks

to analyse the implications for firms in this more open framework by analysing theimpact of their collaborations with different types of actor within the innovationsystem (mainly, though not solely, from an ‘inbound’ perspective; see below) Lastly,the paper will seek to draw implications of the analysis for both the development ofthe open innovation model, but also how it relates to the systems of innovationapproach and, on a practical level, the implications of this for firms and policymakers

2 Open Innovation and the Role of Universities

The open innovation model has centred on the firm and the R&D process in relation

to the inflows and outflows of knowledge to accelerate internal innovation and themarket uptake of innovations once produced (Chesbrough, 2006) In this sense themodel or approach has two main elements (Chesbrough, 2003a; 2006): ‘Inbound’Open Innovation associated with the establishment and management of knowledgelinks associated with scientific and technical competences between firms andexternal organisations linked to improving the innovative performance of the firm;and ‘Outbound’ Open Innovation associated with establishing and managing links tocommercially exploit technological knowledge There have, however, been two basicweaknesses of the open innovation model which are relevant to this study Firstly,there has been very little discussion within the open innovation literature about theimplications of this more open, networked world for innovation actors other than

firms This has now recently been acknowledged by Gassmann et al (2010) in their

review of the open innovation literature but this omission remains a significantlimiting factor in the development of the model Secondly, the approach has taken a

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simple firm perspective based on it as the (single) unit of observation with simpledyadic relationships (see, Anderson, 1994) rather than viewing firms in distributed

forms, networks (Vanhaverbeke, 2006; Vanhaverbeke and Cloodt, 2006; Maula et al.

(2006), or more aggregated forms, such as industries (Dittrich and Duyster, 2007;

Chiaroni et al., 2010; Enkel and Gassmann, 2010) Thus, a number of different

perspectives have started to be developed, although still largely from the perspective

of the firm (see Gassmann, 2006; West et al., 2006), and coverage, even within this

range, is limited with a focus on certain sectors or firm types, in particular high

technology sectors (Chiaroni et al., 2010, p 223) or large, R&D intensive firms (Gassmann et al., 2010)

This is now changing with open innovation studies examining the implications ofintellectual property rights (West and Gallagher, 2006), low technology industries

(Chesbrough and Crowther, 2006; Chiaroni et al., 2010; Lli et al., 2010) or

undertaking cross-industry analysis (Enkel and Gassmann, 2010) However, from the

most recent review of the literature (Gassmann et al., 2010) gaps remain and one has been in respect of the role of universities (Gassmann et al., 2010, p 216) in what

might be termed the new open innovation landscape Perkmann and Walsh (2007)2have reviewed university-industry interactions within an open innovation context,although the value of the paper is not in intrinsically about using an open innovationframework but rather in outlining future research in the field They raise theimportant issue that open innovation and university-industry relations should not beviewed simply as some generalised links, but rather about deeper, more fundamentalrelationships within a network (Perkmann and Walsh, 2007, pp 273-5)

Openness has also been discussed in the systems of innovation approach, althoughmore systemically through increasing internationalisation of linkages and thebreaking down of boundaries The role of the university as an actor within theseincreasingly dense set of interrelationships has also been examined within local,

regional or national innovation system (see, for example, Boucher et al., 2003;

Gunasekara, 2006a; 2006b; Coenen, 2007; Drabenstott, 2008; Uyarra, 2010).However, here the emphasis has not been on the specific evaluation of universitylinkages with firms, but rather their overall influence and institutional arrangements.More recent studies have also sought to explore such relationships within a morespecific evolutionary context, although remaining at a fairly abstract and conceptuallevel (McKelvey, 1997; van der Steen and Enders, 2008) Moreover, West et al (2006,

p 300) admit that since the open innovation model was framed very much within a

US context, the applicability of the model to other National Systems of Innovation islikely to be at least constrained and remains to be fully tested in other national

contexts

Lastly, there has been the ongoing assumption in virtually all open innovationdebates that ‘openness is good for you’, i.e that for firms or organisations pursuing

an interdependent strategy of collaborating and networking in innovation willexperience a net benefit from so doing (see Gerstenfeld, 1977 for an early scepticism

on this) However, this has had little empirical analysis or verification In short, is

open innovation and collaboration actually good for you?

This paper, therefore, seeks to explore this research gap by analysing the role ofuniversities and other actors within a more open innovation process and especially interms of the impact of such interactions on innovative performance Taking such anapproach the paper focuses mainly on the ‘inbound’ aspects of the open innovationframework (although the research does relate to certain elements of the ‘outbound’open innovation process in the paper) The paper, more specifically, focuses on twokey aspects: 1) Firstly, a priori, how do firms view working in a more open,collaborative context with universities? 2) Secondly, are there any impacts in terms

of collaborating with universities in terms of influencing the innovative performance

of firms collaborating with them?

3 Methodological Framework and Data

Before discussing the methodological framework and survey data in more detail it isworth noting two methodological assumptions behind the analysis Firstly, thisapproach implicitly adopts a modified and interactive chain linked model ofinnovation (Kline and Rosenberg, 1986; Varma, 1993; Malecki, 1997; Godin, 2006)which involves a set of feedback loops and external linkages in the process ofinnovation and more recently has been adopted within the notion of the innovation

value chain developed from Kline and Rosenberg (Roper et al., 2008) The strength of

the value chain is the acknowledgment of the complementary involvement ofdifferent types of firms within the innovation process and effective co-ordinationamong these and other actors as essential to creation and development of viable

‘innovation chains for the design, production, and marketing of new products andservices (Kline and Rosenberg 1986, pp 303-304) Secondly, and associated with theinteractive chain linked model, this study implies causality in the effects between therelationships of the different partners between R&D and technological interactionand outcomes in terms of innovative performance (Section 3) As such, there is anassumption that there is a causal connection between collaboration effects andinnovative performance on a conceptual level (and longer term output andemployment and profitability levels), and that what is measured is a temporalprecedence for the cause (‘cause must proceed effect’) (Cook and Campbell, 1979)

by taking two datum points 2002 and 2007 There are deep fundamental questionsabout what is a ‘cause’ (Holland, 1986, pp 984-5) and discussions surrounding the

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issue of causality are non-trivial (see, for example, Granger, 1969; Pearl, 2000;Goldthorpe, 2001) It is acknowledged over the longer term that growth can furtherstimulate innovation, i.e there may some evidence of reverse causality (although thisopens up the wider debate of whether organisational ‘slack’ or ‘crisis’ is more likely

to stimulate change and innovation in terms of next rounds of investment; see, for

example, Nohira and Gulati, 1996; Mone, et al., 1998) Nevertheless, the analysis

assumes the overall and main ‘drift’ of causality over this time period is moving in

the direction of the effects of collaboration in time period n¹ influencing innovation performance n².

In terms of the research framework the study is based on a large scale questionnairesurvey of firms in three standard regions of the UK (the East of England, the NorthWest and Wales) that took place between June 2008 and February 2009 Theselection of the three regions was to provide a range of different regionalenvironments in terms per capita incomes and employment levels and growthtogether with innovation and productivity levels On this basis, the East of England iscategorised as a core UK region, the North West has an intermediate status, whilstWales is classified as a peripheral UK region This framework has been employed bynumerous studies over the last thirty years (see, for example, Keeble, 1980) andremains robust in terms of recent economic and innovation data (Hollanders, 2007).Using the Office of National Statistics (ONS) publication ‘UK Businesses: Activity, Sizeand Location’ (Wetherill, 2008) to determine the distribution of firms by size andeconomic activity in each region, a stratified sample of one percent of firms was then

drawn using the Financial Analysis Made Easy (FAME) database3 This resulted in aselection of around 2,400 firms each from North West and East of England and 1,200firms from Wales The survey instrument was focused around three main areas: firmcharacteristics such as age ownership, size and type of business; innovation activities

of the firm, including information regarding sources of knowledge for innovation;and, specific questions related to university collaborations Questions were mainlystructured to elicit closed binary/multiple choice responses in the expectation thatthis would facilitate a good response rate The final analysis was based on validresponses (postal or web based response modes) received from 371 firms althoughresponse rates varied depending on the specific question Table 1 shows thedistribution of valid returns by region and the relative response rates, whilst Table A!provides a variable description list

Table 2 shows some selected descriptive statistics Overall, just over 30 percent offirms responding to the survey indicated that they engaged in either product/service,process or organisational innovation between 2002 and 2007 Around 70 percentclassified themselves as mainly service related firms with the remainder beingprimarily engaged in manufacturing activities Most of the responding firms wererelatively small in relation to employment although the largest firm responding

employed just under 6,000 employees Average employment, however, was around

40 and if large firms (those with 400 or more employees) were excluded from thecalculations, this falls to 11 Responding firms appear to be well established as theiraverage time in operation was 18 years Just 11 percent of firms were recorded asbeing engaged in university collaborations with those on average being marginallyyounger than the sample average Lastly, Table 3 provides a set of summarydescriptions for the key variables used for logistic estimations

4 Results and Analysis: The Benefits of Collaboration and Openness

On the basis of analysing the survey data, a number of important observations can

be made about the nature of industry collaboration with universities and othercollaborative partners in general (Section 2) The study confirms that universitiesremain poor status providers as sources for information on innovation and ascollaborative partners in the innovation process, confirming studies from the Europe

and North America (Gerstenfeld, 1977; Cosh et al., 2006; Abreu, et al 2008; Freel, et

al 2009; Cosh and Hughes, 2010) Thus universities were ranked 11th out of 12 asinformation sources on innovation (Table 4) Confirming previous studies, customersand clients, followed by suppliers were the most important sources of informationabout innovation suggesting that firms seem to place a great deal on their vertical

forward and backward linkage networks (see also Roper et al., 2008, p 965) in terms

of access points for knowledge and information about innovation The next majorsource, perhaps not surprisingly, was in-house knowledge followed by standards andprofessional and industry associations By contrast, at the bottom were publicresearch establishments and then universities (and other forms of HEIs) In short,firms see universities as being poor sources for innovation information Moreimportantly, in terms of the open innovation and networking agenda, we may inferfrom this that universities are seen as low priority, low-order partners for formingcollaborations and in the development of network architectures More generally,perceived barriers to using universities by firms are various and depend on whetheryou take a firm or university perspective, but seem to centre on differences in theresearch and financial objectives of the two sets of organisations as well asinformation and communication problems around establishing and maintaining such

links (Howells et al., 1998; Charles and Conway, 2001; Schartinger et al., 2001; Decter

et al., 2008)

However once established, this study reveals that collaborations by firms with

universities and other higher education institutions were found to have a verypositive and significant effect on innovation for all firms who responded to thissection of the survey Thus, using separate logistic regression models, the odds of afirm being innovative for new product and service innovations and process

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innovations were increased by 6.0 and 5.1 times, respectively, if they werecollaborating with a university, the second (product) and highest (process) effects ofall types of collaborators The effect on organisational innovation was less, but stillsignificant with an odds ratio of 2.8 By contrast, the only other actor type which had

a bigger probability on innovation were public research establishments (PREs) whoseodds ratio for product innovation was 8.6 times, 4.2 for process innovations and 4.6for organisational innovation and new business methods (Table 5) Thus, universitiesare ranked, respectively, first and second best partners in terms of successfulinnovation outcomes in relation to process innovation and new product and serviceinnovations It is only in the category of organisational innovation and new businessmethods where universities and HEIs perform less well; indeed having the lowestimpact on innovation outcomes of all partner types Suppliers and customers, asvertical collaborators, follow PREs and universities as having the greatest impact oninnovation performances, whilst horizontal collaborators, covering partnercompanies and competitors have the lowest impacts on innovative performance

As such, although universities may not be the initial favoured collaborators innumerical terms for firms, when collaboration does occur with a university it has asignificant and very appreciable influence on innovative performance How can weexplain this turnaround? One way could be to view firms as going through a

‘hierarchy of engagement’ with universities and overcoming perceived or actualbarriers to such contact Developing a collaborative, open innovation strategy canincur high scanning, coordination and learning costs associated with establishing andmaintaining a collaborative link or network (and this is indeed reflected in the widerdebate surrounding strong and weak ties in maintaining wider business relationships;Jack, 2005, p 1254) In addition, it is not just the cost of building these networks; it

is their maintenance that can pose a heavy burden to firms, especially SMEs (Howells

et al., 2008) With the first stage we may therefore envisage firms using universities

as potential information sources for innovation This would involve low commitment

in terms of resources and time on either side, although would involve firms withsearch costs which may not be insignificant if a specific and detailed piece ofinformation is required from a university (for example, data relating to metallurgicalfailure rates at various temperatures or tribology issues associated with wear ratesunder extreme conditions) At this level, there are firms who have no contactwhatsoever with universities and those that do in terms of using them as informationsources The second stage would then involve moving on to more resource and timecommitment levels by entering into some of kind of collaborative engagement with auniversity or HEI This level of engagement would involve not only higher barriers interms of search costs, but also in terms of resource allocation and the risks anduncertainty of such collaborations failing Obviously within this latter category ofcollaborators there will, in turn, be those firms who will be more novice collaborators

(who may have not used university before) and those who are more experienced interms of using universities as innovative partners.

One would expect on this basis that novice firms in terms of collaborator patternswould show a high resistance to using universities as innovation partners (forwhatever, in their case, pre-conceived reasons noted above) By contrast, firms whohad used universities as collaborators as a sub-set of firms overall, given thegenerally successful outcome of collaboration with universities would not hold thegeneral negative views that firms possess of universities These hypotheses are,however, not fully borne out by the data Those firms who were collaborators werepartitioned from those who were not and an analysis of how both groups perceivedthe importance of universities as information sources was done In the survey, firmswere asked to rank the importance as being either low, medium, high or not used.Figure 1 shows the comparative positions of the two groups with non-collaboratorstaking an overall dimmer view of the importance of universities than collaboratorsand in fact only a small proportion (0.35%) identified universities as being of highimportance However, about 9% felt that they were of medium importance and justunder 40% thought they were of low importance Of those firms that collaborated,only around 26% thought universities were of high importance whilst over 60% rateduniversities as of medium or low importance This seems to confirm the relativelybiased view that firms generally have of universities (and seems to be at variancewith the results of the earlier logistic regression analysis)

A more formal analysis was also conducted to try to gain a deeper understanding ofsuch behaviour For this exercise a proportional odds model (ordinal logisticregression) was adopted to unearth some of the attributes of firms rating university

as the most important (‘high’) source of information relative to those that rateduniversity as medium, low or not used Three explanatory variables wereconsidered: size, proxied by log employment, sector (service or manufacturing) andage (young firms being defined as those operating 5 years or less) The results arepresented in Table 6 Firm size and the sector where they are based turn out to bethe significant factors For a unit increase in log employees (size), universities are

1.42 times more likely to be rated as being of high importance relative to the

combined medium, low and not used categories, given the other variables being heldconstant in the model

Of course, the experience of firms as collaborators is closely linked to firm size Size

is therefore related to shaping perceptions, with larger firms rating universities morehighly than smaller firms as information sources Thus, as firms get larger they are1.42 times more likely to rate universities as high important information andknowledge sources4 Perceptions about university importance are also affected bysectoral origin When comparing service firms to manufacturing, given other

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variables, the odds of rating universities as highly important versus the combinedmedium, low and not used are likely to be 50 percent (0.456) lower than formanufacturing firms, given the other variables being held constant Manufacturingfirms, therefore, are more likely to hold universities in high regard as importantknowledge sources than service related firms This, in turn, may be linked to the factthat service firms have not been well linked into the general science base of nationalsystems of innovation up until recently and where collaborations with universities

have traditionally been weak (Miles et al., 2003)

Firms can collaborate with universities in any number of ways Firms were asked toidentify the key types of collaborations they engaged with universities Training andcontinuing professional development (CPD) was ranked first Also of note were suchissues as use of research facility, research projects, and student internships.Surprisingly, co-patenting and licensing activities which feature highly in theliterature on university firm interactions lie outside this list (12th and 15threspectively) By contrast, informal collaborations are highly rated by firms Logisticregression was also applied to analyse a more detailed model in which theprobability of innovation was then regressed on university collaboration and firmcharacteristics (including age, sector, size and various interaction effects) and region.There were found to be no significant differences by types of innovation However,firms with university collaboration are four times more likely to innovate compared

with those without A separate analysis that distinguishes between formal and informal (such as conferences, meetings and workshops) university collaborations

show that both are significant in terms of influencing innovation outcomes, with the

latter appearing to be equally important to the former

The survey firms were then asked more specific questions about the benefits ofworking with universities Surprisingly, given the impact on innovative performance

in relation to organisational innovations and new business methods noted above(Table 2), the results revealed that the most numerically important benefit ofworking with universities is in the development of new methods, skills andtechniques An explanation for this could be that such methods and techniques areassociated with new product and service innovations or proves innovations ratherthan just involving organisational innovations However, there were also importantimpacts arising from such collaboration and this is notably improved profitability andmarket share Universities relationships also acted as wider conduits for firms tonetworking relations and in terms of enhanced productivity, but these associationsvaried quite considerably depending on regional location

Perhaps more significant here are the perceived barriers that firms hold regardinguniversity engagement reasons for, despite all the efforts made on the part ofuniversities and policy support for such types of engagement, there appear to remain

significant barriers for firms interacting with universities The key barriers ranked byfirms are provided in Figure 2 and centre on relevance (ranked 1st) and differences inobjectives and expectations between the two realms of industry and academia (4th),resource costs (3rd) and time horizons (5th) (c.f university perspectives on this; forexample, Howells, et al 1998, p 22) Issues around alignment between firms anduniversities and resource constraints appear to have a significant impact on limitingthe level of interaction between these two actor sets However, a major barrier isthe lack of knowledge that firms possess around what HEIs can offer to firms inrelation to innovation collaboration (ranked 2nd) Firms, therefore, still appear to lackthe knowledge about what HEIs can offer them in terms of expertise associated withresearch and innovation

5 Conclusions

What has this study revealed in terms of industry-industry collaboration within anincreasingly open framework for innovation? On one level, the answer is relativelysimple Universities bring clear observable benefits to firms in terms of innovativeperformance as collaborative partners in a range of different collaborative activities.Indeed, universities represent one of the best types of collaborative partners forfirms in terms of innovative outcomes In short, innovation contact with universities

by firms dramatically helps their innovative performance However, this is clearly not

reflected in how firms view the desirability of universities as potential partners or

information sources, where they scored very poorly

One explanation for this dramatic discrepancy was that it was non-collaborating firmsthat held this perception, whereas collaborating firms with experience of workingwith firms would hold universities in higher esteem with respect to their merits ascollaborative partners for innovation The management and policy implication forthis interpretation, if found true, would be that by fostering contact with universitieswill lead firms to develop familiarity with their culture and operational environmentand thereby improve their perceptions of the value of university interaction andwillingness to enter into collaborative agreement with a university However,although the study found some marginal evidence of improved perception in relation

to their ranking of universities’ as an information source on innovation, this effectwas found to be only marginal Even once a firm had collaborated with a universityand the observed effect was likely to greatly increase the likelihood of that firm

successfully introducing an innovation, firms still retained a poor view of universities

as partners, even if the value and impact of such collaboration was actually very high.This may go back to more fundamental issues surrounding collaboration withexpectations surrounding a particular collaboration or wider collaborative networkmay therefore not be met, as partners have different objectives and means of

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collaborating or outsourcing(Lawton Smith et al., 1991; Powell, 1998) Thus, even if

a collaboration or open innovation network is successful in generating a newinnovation or commercial endeavour it may not benefit everyone in the network The study also suggests that there appear to remain significant barriers for firms,especially SMEs, to interacting with universities These, as noted above, appear to bearound relevance and time horizons, where alignment between industry andacademia, appears to be poor and resource issues where the costs associated withsuch interaction can be high, especially for small firms However, a major issueremains simple ignorance by firms of what universities do and what they couldprovide in terms of knowledge and support Firms in the UK, therefore, still appear

to lack the knowledge about what universities have to offer them in terms ofresearch and innovation

Acknowledgements:

This paper arises out of research funded by the UK Economic and Social ResearchCouncil (Grant Number ESRC RES-171-25-0038) as part of the Impact of higherEducation Institutions in Regional Economies Programme Special thanks go to allthe firms and universities who participated in the survey and to the two anonymousreferees who commented on an earlier version of the paper

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