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

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, Industry-Academic Links Paper type: Research paper Introduction The increasingly networked nature of innovation is associated with firms linking with other firms and organisations and can be seen as part of a more permeable (Pisano, 1990) and open (Chesbrough, 2003a; 2003b; 2003c) and distributed (Coombs et al., 2003; Ramlogan et al., 2007) process within industry Firms very rarely innovate solely on their basis of their in-house knowledge capabilities, but increasingly depend on external sources of knowledge and research As such, there has been a continued expansion of more traditional forms of R&D collaboration and partnerships (Powell, 1998; Tapon and Thong, 1999; Orsenigo et al., 2001; Hagerdoorn, 2002) in parallel with the development of newer forms of partnership and 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 in earlier times (Graham, 1985; see also Sanderson, 1972; Meyer-Thurow, 1982; Liebenau, 1984; Homburg, 1992), but has re-emerged and transmogrified into a different type of interactive regime Increasingly, firms have extensive external research and innovation linkages, forming complex distributed innovation networks (Coombs and Metcalfe, 2002; Chang, 2003) with greater levels of R&D and innovation 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 by Chesbrough (2003a; 2003b) as part of the ‘open innovation’ model The open innovation model has focused around the context of the firm and has taken the 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 a wider context by, firstly, taking the open innovation model and using it as a lens to look at the implications of this new paradigm on the role of universities and higher education institutions (HEIs1) their interactions with firms Secondly, the paper seeks to analyse the implications for firms in this more open framework by analysing the impact of their collaborations with different types of actor within the innovation system (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 of the open innovation model, but also how it relates to the systems of innovation approach and, on a practical level, the implications of this for firms and policymakers 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 the market uptake of innovations once produced (Chesbrough, 2006) In this sense the model or approach has two main elements (Chesbrough, 2003a; 2006): ‘Inbound’ Open Innovation associated with the establishment and management of knowledge links associated with scientific and technical competences between firms and external organisations linked to improving the innovative performance of the firm; and ‘Outbound’ Open Innovation associated with establishing and managing links to commercially exploit technological knowledge There have, however, been two basic weaknesses of the open innovation model which are relevant to this study Firstly, there has been very little discussion within the open innovation literature about the implications 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 significant limiting factor in the development of the model Secondly, the approach has taken a simple firm perspective based on it as the (single) unit of observation with simple dyadic 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 of intellectual 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) have reviewed university-industry interactions within an open innovation context, although the value of the paper is not in intrinsically about using an open innovation framework but rather in outlining future research in the field They raise the important issue that open innovation and university-industry relations should not be viewed simply as some generalised links, but rather about deeper, more fundamental relationships within a network (Perkmann and Walsh, 2007, pp 273-5) Openness has also been discussed in the systems of innovation approach, although more systemically through increasing internationalisation of linkages and the breaking down of boundaries The role of the university as an actor within these increasingly 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 university linkages with firms, but rather their overall influence and institutional arrangements More recent studies have also sought to explore such relationships within a more specific evolutionary context, although remaining at a fairly abstract and conceptual level (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 is likely 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 innovation debates that ‘openness is good for you’, i.e that for firms or organisations pursuing an interdependent strategy of collaborating and networking in innovation will experience 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 of universities and other actors within a more open innovation process and especially in terms of the impact of such interactions on innovative performance Taking such an approach the paper focuses mainly on the ‘inbound’ aspects of the open innovation framework (although the research does relate to certain elements of the ‘outbound’ open innovation process in the paper) The paper, more specifically, focuses on two key aspects: 1) Firstly, a priori, how 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? Methodological Framework and Data Before discussing the methodological framework and survey data in more detail it is worth noting two methodological assumptions behind the analysis Firstly, this approach implicitly adopts a modified and interactive chain linked model of innovation (Kline and Rosenberg, 1986; Varma, 1993; Malecki, 1997; Godin, 2006) which involves a set of feedback loops and external linkages in the process of innovation 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 of different types of firms within the innovation process and effective co-ordination among these and other actors as essential to creation and development of viable ‘innovation chains for the design, production, and marketing of new products and services (Kline and Rosenberg 1986, pp 303-304) Secondly, and associated with the interactive chain linked model, this study implies causality in the effects between the relationships of the different partners between R&D and technological interaction and outcomes in terms of innovative performance (Section 3) As such, there is an assumption that there is a causal connection between collaboration effects and innovative performance on a conceptual level (and longer term output and employment and profitability levels), and that what is measured is a temporal precedence for the cause (‘cause must proceed effect’) (Cook and Campbell, 1979) by taking two datum points 2002 and 2007 There are deep fundamental questions about what is a ‘cause’ (Holland, 1986, pp 984-5) and discussions surrounding the 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 further stimulate innovation, i.e there may some evidence of reverse causality (although this opens 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 questionnaire survey of firms in three standard regions of the UK (the East of England, the North West and Wales) that took place between June 2008 and February 2009 The selection of the three regions was to provide a range of different regional environments in terms per capita incomes and employment levels and growth together with innovation and productivity levels On this basis, the East of England is categorised as a core UK region, the North West has an intermediate status, whilst Wales is classified as a peripheral UK region This framework has been employed by numerous studies over the last thirty years (see, for example, Keeble, 1980) and remains robust in terms of recent economic and innovation data (Hollanders, 2007) Using the Office of National Statistics (ONS) publication ‘UK Businesses: Activity, Size and Location’ (Wetherill, 2008) to determine the distribution of firms by size and economic 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 a selection of around 2,400 firms each from North West and East of England and 1,200 firms from Wales The survey instrument was focused around three main areas: firm characteristics 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 mainly structured to elicit closed binary/multiple choice responses in the expectation that this would facilitate a good response rate The final analysis was based on valid responses (postal or web based response modes) received from 371 firms although response rates varied depending on the specific question Table shows the distribution of valid returns by region and the relative response rates, whilst Table A! provides a variable description list Table shows some selected descriptive statistics Overall, just over 30 percent of firms responding to the survey indicated that they engaged in either product/service, process or organisational innovation between 2002 and 2007 Around 70 percent classified themselves as mainly service related firms with the remainder being primarily engaged in manufacturing activities Most of the responding firms were relatively 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 the calculations, this falls to 11 Responding firms appear to be well established as their average time in operation was 18 years Just 11 percent of firms were recorded as being engaged in university collaborations with those on average being marginally younger than the sample average Lastly, Table provides a set of summary descriptions for the key variables used for logistic estimations 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 other collaborative partners in general (Section 2) The study confirms that universities remain poor status providers as sources for information on innovation and as collaborative 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 11 th out of 12 as information sources on innovation (Table 4) Confirming previous studies, customers and clients, followed by suppliers were the most important sources of information about 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 major source, perhaps not surprisingly, was in-house knowledge followed by standards and professional and industry associations By contrast, at the bottom were public research establishments and then universities (and other forms of HEIs) In short, firms see universities as being poor sources for innovation information More importantly, in terms of the open innovation and networking agenda, we may infer from this that universities are seen as low priority, low-order partners for forming collaborations and in the development of network architectures More generally, perceived barriers to using universities by firms are various and depend on whether you take a firm or university perspective, but seem to centre on differences in the research and financial objectives of the two sets of organisations as well as information 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 very positive and significant effect on innovation for all firms who responded to this section of the survey Thus, using separate logistic regression models, the odds of a firm being innovative for new product and service innovations and process innovations were increased by 6.0 and 5.1 times, respectively, if they were collaborating with a university, the second (product) and highest (process) effects of all types of collaborators The effect on organisational innovation was less, but still significant 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) whose odds ratio for product innovation was 8.6 times, 4.2 for process innovations and 4.6 for organisational innovation and new business methods (Table 5) Thus, universities are ranked, respectively, first and second best partners in terms of successful innovation outcomes in relation to process innovation and new product and service innovations It is only in the category of organisational innovation and new business methods where universities and HEIs perform less well; indeed having the lowest impact on innovation outcomes of all partner types Suppliers and customers, as vertical collaborators, follow PREs and universities as having the greatest impact on innovation performances, whilst horizontal collaborators, covering partner companies and competitors have the lowest impacts on innovative performance As such, although universities may not be the initial favoured collaborators in numerical terms for firms, when collaboration does occur with a university it has a significant and very appreciable influence on innovative performance How can we explain this turnaround? One way could be to view firms as going through a ‘hierarchy of engagement’ with universities and overcoming perceived or actual barriers to such contact Developing a collaborative, open innovation strategy can incur high scanning, coordination and learning costs associated with establishing and maintaining a collaborative link or network (and this is indeed reflected in the wider debate 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 with search costs which may not be insignificant if a specific and detailed piece of information is required from a university (for example, data relating to metallurgical failure rates at various temperatures or tribology issues associated with wear rates under extreme conditions) At this level, there are firms who have no contact whatsoever with universities and those that in terms of using them as information sources The second stage would then involve moving on to more resource and time commitment levels by entering into some of kind of collaborative engagement with a university or HEI This level of engagement would involve not only higher barriers in terms of search costs, but also in terms of resource allocation and the risks and uncertainty of such collaborations failing Obviously within this latter category of collaborators 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 in terms of using universities as innovative partners One would expect on this basis that novice firms in terms of collaborator patterns would show a high resistance to using universities as innovation partners (for whatever, in their case, pre-conceived reasons noted above) By contrast, firms who had used universities as collaborators as a sub-set of firms overall, given the generally successful outcome of collaboration with universities would not hold the general negative views that firms possess of universities These hypotheses are, however, not fully borne out by the data Those firms who were collaborators were partitioned from those who were not and an analysis of how both groups perceived the importance of universities as information sources was done In the survey, firms were asked to rank the importance as being either low, medium, high or not used Figure shows the comparative positions of the two groups with non-collaborators taking an overall dimmer view of the importance of universities than collaborators and in fact only a small proportion (0.35%) identified universities as being of high importance However, about 9% felt that they were of medium importance and just under 40% thought they were of low importance Of those firms that collaborated, only around 26% thought universities were of high importance whilst over 60% rated universities as of medium or low importance This seems to confirm the relatively biased view that firms generally have of universities (and seems to be at variance with the results of the earlier logistic regression analysis) A more formal analysis was also conducted to try to gain a deeper understanding of such behaviour For this exercise a proportional odds model (ordinal logistic regression) was adopted to unearth some of the attributes of firms rating university as the most important (‘high’) source of information relative to those that rated university as medium, low or not used Three explanatory variables were considered: size, proxied by log employment, sector (service or manufacturing) and age (young firms being defined as those operating years or less) The results are presented in Table Firm size and the sector where they are based turn out to be the 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 held constant 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 more highly than smaller firms as information sources Thus, as firms get larger they are 1.42 times more likely to rate universities as high important information and knowledge sources4 Perceptions about university importance are also affected by sectoral origin When comparing service firms to manufacturing, given other variables, the odds of rating universities as highly important versus the combined medium, low and not used are likely to be 50 percent (0.456) lower than for manufacturing firms, given the other variables being held constant Manufacturing firms, therefore, are more likely to hold universities in high regard as important knowledge sources than service related firms This, in turn, may be linked to the fact that service firms have not been well linked into the general science base of national systems 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 to identify the key types of collaborations they engaged with universities Training and continuing professional development (CPD) was ranked first Also of note were such issues as use of research facility, research projects, and student internships Surprisingly, co-patenting and licensing activities which feature highly in the literature on university firm interactions lie outside this list (12 th and 15th respectively) By contrast, informal collaborations are highly rated by firms Logistic regression was also applied to analyse a more detailed model in which the probability of innovation was then regressed on university collaboration and firm characteristics (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 of working 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 of working with universities is in the development of new methods, skills and techniques An explanation for this could be that such methods and techniques are associated with new product and service innovations or proves innovations rather than just involving organisational innovations However, there were also important impacts arising from such collaboration and this is notably improved profitability and market share Universities relationships also acted as wider conduits for firms to networking relations and in terms of enhanced productivity, but these associations varied quite considerably depending on regional location Perhaps more significant here are the perceived barriers that firms hold regarding university engagement reasons for, despite all the efforts made on the part of universities and policy support for such types of engagement, there appear to remain significant barriers for firms interacting with universities The key barriers ranked by firms are provided in Figure and centre on relevance (ranked st) and differences in objectives and expectations between the two realms of industry and academia (4 th), resource costs (3rd) and time horizons (5th) (c.f university perspectives on this; for example, Howells, et al 1998, p 22) Issues around alignment between firms and universities and resource constraints appear to have a significant impact on limiting the level of interaction between these two actor sets However, a major barrier is the lack of knowledge that firms possess around what HEIs can offer to firms in relation to innovation collaboration (ranked 2nd) Firms, therefore, still appear to lack the knowledge about what HEIs can offer them in terms of expertise associated with research and innovation Conclusions What has this study revealed in terms of industry-industry collaboration within an increasingly open framework for innovation? On one level, the answer is relatively simple Universities bring clear observable benefits to firms in terms of innovative performance as collaborative partners in a range of different collaborative activities Indeed, universities represent one of the best types of collaborative partners for firms 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 firms that held this perception, whereas collaborating firms with experience of working with firms would hold universities in higher esteem with respect to their merits as collaborative partners for innovation The management and policy implication for this interpretation, if found true, would be that by fostering contact with universities will lead firms to develop familiarity with their culture and operational environment and thereby improve their perceptions of the value of university interaction and willingness 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 effect was found to be only marginal Even once a firm had collaborated with a university and 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 with expectations surrounding a particular collaboration or wider collaborative network may therefore not be met, as partners have different objectives and means of 10 Homburg, E (1992), “The emergence of research laboratories in the dyestuffs industry, 1870-1900”, The British Journal for the History of Science, Vol 25, No 1, pp 91-111 Howells, J (1999), “Research and technology outsourcing”, Technology Analysis & Strategic Management, Vol.11, pp 17-29 Howells, J Gagliardi, D and Malik, K (2008) “The growth and management of R&D outsourcing: evidence from UK pharmaceuticals” R&D Management, Vol 38, pp 205-219 Howells, J James, A and Malik, K (2003), “The sourcing of technological knowledge: distributed innovation processes and dynamic change”, R&D Management, Vol 33, pp 395-409 Howells, J Nedeva, M and Georghiou, L (1998) Industry-Academic Links in the UK Final Report to the Higher Education Funding Council for England (HEFCE), Bristol Jack, S L (2005), “The role, use and activation of strong and weak ties: a qualitative analysis” Journal of Management Studies, Vol 42, No 6, pp 1233-1258 Justman, M and Teubal, M (1995), “Technological infrastructure policy (TIP): creating capabilities and building markets”, Research Policy, Vol 24, pp 259-281 Keeble, D E (1980), "Industrial decline, regional policy and the urban - rural manufacturing shift in the United Kingdom", Environment and Planning A, Vol 12, pp 945 – 962 Kline, S and Rosenberg, N (1986), ‘An overview of innovation’, In: Landau, R and Rosenberg, N (Eds.) 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