NEW TECHNOLOGY-BASED FIRMS IN THE NEW MILLENNIUM VOLUME IV
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New Technology-Based Firms in the New Millennium Volume III (2004) Edited by Wim During, Ray Oakey and Saleema Kauser New Technology-Based Firms in the New Millennium Volume II (2002) Edited by Ray Oakey, Wim During and Saleema Kauser New Technology-Based Firms in the New Millennium Volume I (2001) Edited by Wim During, Ray Oakey and Saleema Kauser New Technology-Based Firms at the Turn of the Century New Technology-Based Firms at the Turn of the Century (2000) Edited by Ray Oakey, Wim During and Michelle Kipling New Technology-Based Firms in the 1990s New Technology-Based Firms in the 1990s Volume VI (1999) Edited by Ray Oakey, Wim During and Syeda-Masooda Mukhtar Previous titles in this series published by Paul Chapman Publishing New Technology-Based Firms in the 1990s Volume I (1994) Edited by Ray Oakey New Technology-Based Firms in the 1990s Volume II (1996) Edited by Ray Oakey New Technology-Based Firms in the 1990s Volume III (1997) Edited by Ray Oakey and Syeda-Masooda Mukhtar New Technology-Based Firms in the 1990s Volume IV (1998) Edited by Wim During and Ray Oakey New Technology-Based Firms in the 1990s Volume V (1998) Edited by Ray Oakey and Wim During Other titles of interest from Elsevier Management of Technology, the Key to Prosperity in the Third Millennium Edited by T. Khalil Management of Technology, Growth through Business Innovation and Entrepreneurship Edited by M. Von Zedtwitz Management of Technology, Sustainable Development and Eco-Efficiency Edited by Louis A. Lefebvre, Robert M. Mason and Tarek Khalil Silicon Valley North: A High-Tech Cluster of Innovation and Entrepreneurship Edited by Larisa V. Shavinina Related Journals—Sample copies available on request Journal of Business Venturing Journal of Engineering and Technology Management Journal of High Technology Management Research Technological Forecasting and Social Change Technovation
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NEW TECHNOLOGY-BASED FIRMS IN THE NEW MILLENNIUM VOLUME IV EDITED BY
WIM DURING University of Twente, Enschede, The Netherlands
RAY OAKEY Manchester Business School, Manchester, UK
SALEEMA KAUSER Manchester Business School, Manchester, UK
Amsterdam — Boston — Heidelberg — London — New York — Oxford Paris — San Diego — San Francisco — Singapore — Sydney — Tokyo iii
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Contents
Contributors 1.
Introduction Wim During, Ray Oakey and Saleema Kauser
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Part I. Theory 2.
3.
4.
5.
Knowledge Creation, Its Transfer, and the Role of Science Enterprise Education: A Research Agenda Elizabeth Chell and Ray Oakey
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Developing Entrepreneurial Capability to Facilitate Academic Entrepreneurship and Technology Commercialisation Sarah Cooper and Lesley Hetherington
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The Territorial Development of Innovation Support Assets Through University/Business Interactions: Towards a Dynamic Model Paul Benneworth and Stuart Dawley
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Towards a Model of Opportunity Recognition and Development in Entrepreneurial Hi-Tech Start-Ups John S. Park
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Part II. Strategy 6.
Entrepreneurial Orientation and Entrepreneurs’ Intentions and Objectives Thelma Quince and Hugh Whittaker
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7.
Strategic Operation Concerns of UK Biotechnology Companies Yuen Yoong Leong and Yongjiang Shi
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8.
High-Technology Small Firms: Instilling Innovation and Creativity Nicholas O’Regan and Abby Ghobadian v
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Contents
9. Venture Leasing as an Alternative Source of Financing for High Technology Small Firms—An Explorative Empirical Study Among German High Technological, Leasing and Venture Capital Companies Stephan Golla, Stefanie Kramer and Heinz Klandt 10. Small- and Medium-Sized Enterprise in the E-Business and Knowledge Management Era S.C.L. Koh and S. Maguire
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Part III. Clustering and Spin-off Firms 11. Clustered High-Technology Small Firms and Innovation Networks: The Case of Post-Production in London Gary A. S. Cook and Naresh R. Pandit
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12. HTSFs in Peripheral, Knowledge Intensive Areas: An In-Depth Analysis in Pisa Alberto Di Minin, Michela Lazzeroni and Andrea Piccaluga
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13. Alliance Business Models for University Start-Up Technology Ventures: A Resource-Based Perspective Tim Minshall
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14. Needs of Start-Up Technology-Driven Enterprises: Starting Point for Governmental Policy Aard Groen, Ineke Jenniskens and Peter van der Sijde
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15. The Role of International Co-operation in University Technology Transfer—The Case of The Ural State Technical University Dina Akhmetova and Galina Lekhova
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16. The Impact of Science Park and Incubator Location on the Finance Opportunities of Technology-Based Small Firms Farid Ullah and Peter Taylor
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Contributors
Dina Akhmetova
Manchester Business School, Booth St West, Manchester, UK
Paul Benneworth
Centre for Urban and Regional Development Studies,University of Newcastle, Newcastle-upon-Tyne, UK
Elizabeth Chell
Institute for Entrepreneurship, School of Management, University of Southampton, Highfield, Southampton, UK
Gary A.S. Cook
School of Management, University of Liverpool, Chatham Street, Liverpool, UK
Sarah Cooper
Hunter Centre for Entrepreneurship, University of Strathclyde, Glasgow, UK
Stuart Dawley
Centre for Urban and Regional Development Studies, University of Newcastle, Newcastle-upon-Tyne, UK
Alberto Di Minin
University of California, Berkeley (USA) and In-Sat Lab, Scuola Superiore Sant’ Anna, Piazza Martini della Liberta, Pisa, Italy
Wim During
Nikos, University of Twente, Enschede, The Netherlands
Abby Ghobadian
Centre for Interdisciplinary Strategic Management Research, Middlesex University, London, UK
Stephan Golla
Kf W-Endowed Chair for Entrepreneurship, EUROPEAN BUSINESS SCHOOL, International University Schloß Reichartshausen, Oestrich-Winkel, Germany
Aard Groen
Nikos, University of Twente, Enschede, The Netherlands
Lesley Hetherington
Hunter Centre for Entrepreneurship, University of Strathclyde, Glasgow, UK
Ineke Jenniskens
Nikos, University of Twente, Enschede, The Netherlands vii
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Contributors
Saleema Kauser
Marketing International Business and Strategy Division, Manchester Business School, Booth St West, Manchester, UK
Heinz Klandt
KfW-Endowed Chair for Entrepreneurship, EUROPEAN BUSINESS SCHOOL, International University Schloß Reichartshausen, Oestrich-Winkel, Germany
Lenny Koh
Management School, University of Sheffield, Sheffield, UK
Stefanie Kramer
KfW-Endowed Chair for Entrepreneurship, EUROPEAN BUSINESS SCHOOL, International University Schloß Reichartshausen, Oestrich-Winkel, Germany
Michela Lazzeroni
University of Pisa and In-Sat Lab, Scuola Superiore Sant’ Anna, Piazza Martini della Liberta, Pisa, Italy
Galina Lekhova
Centre for Innovative Business, Ural State Technical University, Ekaterinburg, Russia
Yuen Yoong Leong
Institute for Manufacturing, University of Cambridge, Mill Lane, Cambridge, UK
Stuart Maguire
Management School, University of Sheffield, Sheffield, UK
Tim Minshall
Centre for Technology Management, Institute for Manufacturing, University of Cambridge, Mill Lane, Cambridge, UK
Ray Oakey
Operations Technology & Innovation Management Division, Manchester Business School, Booth St West, Manchester, UK
Nicholas O’Regan
Centre for Interdisciplinary Strategic Management Research, Middlesex University, London, UK
Naresh R. Pandit
Marketing International Business & Strategy Division, Manchester Business School, Booth St West, Manchester, UK
John S. Park
Charles P Skene Centre for Enterpreneurship, Aberdeen Business School, The Robert Gordon University, Aberdeen, UK
Andrea Piccaluga
University of Lecce and In-Sat Lab, Scuola Superiore Sant’ Anna, Piazza Martini della Liberta, Pisa, Italy
Thelma Quince
ESRC Centre for Business Research, University of Cambridge, Trumpington Street, Cambridge, UK
Contributors
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Yongjiang Shi
Institute for Manufacturing, University of Cambridge, Mill Lane, Cambridge, UK
Peter Taylor
Accounting & Finance Division, School of Management, University of Liverpool, Chatham Street, Liverpool, UK
Farid Ullah
Accounting & Finance Division, School of Management, University of Liverpool, Chatham Street, Liverpool, UK
Peter van der Sijde
Nikos, University of Twente, Enschede, The Netherlands
Hugh Whittaker
Doshisha University, Kamigyo-Ku, Kyoto 602-8580 Japan
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Chapter 1
Introduction Wim During, Ray Oakey and Saleema Kauser
This 11th volume in the series of edited books emanating from the annual international High Technology Small Firms (HTSFs) conference is an evolving testimony to the progress of academic research in this specialist area of study. The conference and book series was originally devised to give a higher profile to research on HTSFs at a time, in the early 1990s, when interest in this size and type of firm was in decline. Moreover, it was also noted in an introductory chapter from an earlier volume of this series (Oakey, During, & Mukhtar, 1997) that, at its best, the research contained in this series should lead government policy in the form of a virtuous circle, where policy is both initially triggered by research, and then refined by subsequent research evidence on the performance of the initial policy measure. However, it was also regretfully noted in this introduction that governments in developed economies have a tendency to be seduced by policy “fads”, in circumstances where a policy solution to national and regional development problems is adopted, without any detailed consideration of the theoretical validity or logistical feasibility involved in such an action. An example of this behaviour was the popularity of science parks in the United Kingdom (and other European nations) during the 1980s (Oakey, 1985; Massey, Quintas, & Weild, 1992; Westhead, Batstone, & Martin, 2000). A large number of science parks were established in advance of any evidence on their efficiency in nurturing the birth and growth of HTSFs, which, when it subsequently became available, proved that science parks were, at best, neutral in terms of additionality (Westhead & Cowling, 1995). However, in the recent past, the United Kingdom Government has been keen to emphasise that policies adopted (e.g. those by the Department of Trade and Industry) should be strongly evidence-based (e.g. DTI, 2004). In principle, this assertion would appear a sign of progress, in areas of policy closely relevant to HTSFs. However, the development of policy from a strong research-based theoretical knowledge of how things work remains difficult to identify. Two areas of concern relevant to the papers included below might be mentioned. First, although there is a long tradition of multi-disciplinary research into clustering (or agglomeration economies) (Martin, 1999), the haste with which the United Kingdom and other European governments have rushed into a broadly cluster-based industrial development policy has been unnerving. Such concern partly derives from government policy becoming excessively dependent on the work of Michael Porter (Porter, 1998). New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 1
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While the academic quality of Porter’s, mainly United States derived, work is not in question, the rather crude use of this work as a basis for policy by the United Kingdom government, particularly in a United Kingdom regional development context, has been oversimplistic in approach. The vigour with which cluster policy has been pursued at a United Kingdom regional level has resulted in many Regional Development Agencies (RDAs) creating a cluster for virtually every economic activity within the region. Apart from such an approach ignoring the key sectoral targeting ethos of the cluster development approach, it has resulted in insufficient staff numbers with which to conduct policy, either by managing individual clusters, or performing the important task of integrating the activities of individual clusters into regional “super-clusters” in order that they might compliment each other. Second, recent government initiatives to encourage academic enterprise have exhibited similar flaws. Although a desire by the United Kingdom Government to increase the level of academic entrepreneurship, and subsequent “spin off ” of academic enterprises, is laudable, current goals for the Science Enterprise Programme through the vehicle of Science Enterprise Centres (SECs) are neither guided by relevant theory nor empirical evidence on what is (or should be) realistically achievable (Chell & Oakey, 2004). Again, this policy, in keeping with the clusters example above, has been hastily put into action without any real conceptual understanding of what mechanisms are at work in the academic enterprise process, and what can be most efficiently done to ameliorate the problem of a relatively low level of United Kingdom academic entrepreneurship. While it is obviously preferable to have industrial policies based on research evidence rather than political dogma, the main problem with governments of any political persuasion is that, often due to the need for haste within a short guaranteed term of office (e.g. 4 or 5 years), the rapidly assembled evidence on which policy is based is often neither of high enough quality, nor obtained in sufficient quantity. Governments, even after adopting an “evidence-based” approach in theory, often have considerable licence to adopt politically expedient policies that are based on sympathetic partial evidence, in practice, often provided by their own (non-independent) consultants. Put simply, there is so much “evidence” of varying quality available, that virtually any policy could be justified on “evidential” grounds. However, although the above observations might appear rather demotivating to serious academic researchers, an argument can, nonetheless, be made for good quality independent academic research to be continued, despite the tendency for government in many developed economies to ignore important academic expert opinion, and the results on which they have been based. It is most important for academics to continue to be independent, and more valuable for them to be driven by ethical research than judged on the extent to which they have influence on the government. If, after pursuing a totally independent course of action, academics are consulted by the government, their views will be of enhanced value due to such independence, both to the government, and the wider public at large who would be able to use this information when deciding which governments to elect. As in the case of previous volumes in this series, the current collection of papers inform many issues important to governments as they seek to promote technological progress, in this case through promoting the formation and growth of HTSFs. Indeed, there are specific papers that address the two examples of government policy noted above (i.e. clusters and academic enterprise). However, all these papers offer a detailed and evidence-based insight into the experiences of an international group of researchers working on HTSFs. In this
Introduction
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volume, individual papers are grouped into three main sections (in order of appearance); these are Theory, Strategy and Clustering and “spin off” firms. While the sections on theory and strategy are long-standing research areas within this series, an equally long-standing concern for academic “spin offs” has been more recently augmented by a growing interest in clustering, reflected in the “clustering and spin off” section of this volume which, through the related topics of incubation, science park formation and high technology clustering (e.g. Silicon Valley; Route 128) has created linked research and subsequent policy on the formation and growth of HTSFs in specialist locations of increasing geographical scale within regions. Individual papers are introduced under their relevant section headings below.
Theory Of the four papers comprising this section on theory, the first three deal with the currently very relevant topic of academic enterprise. In Chapter 2, through their paper on “Knowledge creation, its transfer, and the role of science enterprise education: A research agenda”, Elizabeth Chell and Ray Oakey develop a number of conceptual themes from a consideration of what contributions relevant existing theories make to explaining academic enterprise, and how a more specific theoretical approach might be developed. In Chapter 3 with a contribution, entitled “Developing entrepreneurial capability to facilitate academic entrepreneurship and technology commercialisation” Sarah Cooper and Lesley Hetherington consider the HTSF development responsibilities of the university to the region in which it is located. This trio of papers on academic impacts is concluded in Chapter 4 by Paul Benneworth and Stuart Dawley with a paper entitled “The territorial development of innovation support assets through university/business interactions: Towards a dynamic model”, an investigation that further examines the role of the university in its local business environment. Chapter 5 is the last in this section and is contributed by John S. Park entitled “Towards a model of opportunity recognition and development in entrepreneurial hi-tech start-ups”. This paper explores the important issue of how entrepreneurs identify opportunity, which is clearly a key juncture in the formation and growth process of entrepreneurial HTSFs.
Strategy Strategy although, in theory, a key factor in the HTSF growth process (and often recorded in a business plan), is often not adhered to in practice. Especially in circumstances where a HTSF technical entrepreneur-based firm formation is concerned, instinct often plays a greater strategic role than formal planning. In Chapter 6, Thelma Quince and Hugh Whittaker further explore this relationship between entrepreneurship and strategy in a paper entitled “Entrepreneurial orientation and entrepreneurs’ intentions and objectives”. In Chapter 7, Yuen Yoong Leong and Yongjiang Shi, investigate strategy in a particular sectoral context where a high level of leading-edge new firms are involved. Their paper investigates “Strategic operation concerns of UK biotechnology companies”.
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Chapter 8 explores the relevance of a strategy, when Nicholas O’Regan and Abby Ghobadian contribute their paper on “High technology small firms: Instilling innovation and creativity”. In Chapter 9, Stephan Golla, Stefanie Kramer and Heinz Klandt examine a financial aspect of strategy in their paper “Venture leasing as an alternative source of financing for high technology small firms — An explorative empirical study among German high technological, leasing and venture capital companies”. This section is concluded by Lenny Koh and Stuart Maguire in Chapter 10 where they examine the influence of e-business technology on strategy in “SMEs in the e-business and knowledge management era”.
Clustering and Spin off Firms The key factor determining advantage in both HTSF clusters and cases of university “spin off ” is local specialisation in high technology in general, and/or in particular branches of high technology (e.g. biosciences). This final section begins with two papers on specialist clustering in differing national and industrial environments. First, Chapter 11, entitled “Clustered high technology small firms and innovation networks: The case of post-production in London” by Gary Cook and Naresh Pandit explores the highly specialized skills of film and television production, lying close in the heart of Central London. Second, in Chapter 12, Alberto Di Minin, Michela Lazzeroni and Andrea Piccaluga in their paper “HTSFs in peripheral, knowledge intensive areas: An in-depth analysis in Pisa” map the gradual evolution of a high technology industrial cluster over a substantial period of time in a relatively peripheral region of Italy, a process that has offered change through the development of a high technology cluster around Pisa. The remaining papers in this section are directly concerned with university “spin offs”. In Chapter 13, Tim Minshall explores “Alliance business models for university start-up technology ventures: A resource based perspective” in and around Cambridge. This specific study investigates how university derived start-up firms were able to collaborate with much large enterprises, without jeopardising their independence. In Chapter 14, the potential role of the public sector in promoting university-derived HTSFs is considered in “Needs of start-up technology-driven enterprises: A starting point for governmental policy”, explored by Aard Groen, Ineke Jenniskens and Peter van der Sijde. In Chapter 15, Dina Akhmetova and Galina Lekhova investigate “The role of international co-operation in university technology transfer — The case of The Ural State Technical University”, an instance where the previous Soviet regime continues to influence attitudes towards HTSF “spin off” and subsequent HTSF growth. Finally, in Chapter 16, “The impact of science park and incubator locations on the financial opportunities of technology-based small firms”, is investigated by Farid Ullah and Peter Taylor, in a paper that explores whether the ability to raise capital is related to the location of HTSF “spin offs”.
References Chell, E., & Oakey, R. P. (2004). Knowledge creation, its transfer, and the role of science enterprise education. Innovation, Management, Policy and Practice, 6(3), 445–457.
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Department of Trade and Industry (2004). A government action plan for small business. Making the UK the best place in the world to start and grow an business: The evidence base. DTI/Pub 7078/10k/01/04/NP. Martin, R. (1999). The new geographical turn in economics: Some critical reflections. Cambridge Journal of Economics, 23(1), 65–91. Massey, D., Quintas, P., & Weild, D. (1992). High tech fantasies: Science parks in society. London: Routledge Publishers. Oakey, R. P. (1985). British university science parks and high technology small firms: A comment on the potential for sustained economic growth. International Journal of Small Business, 4(1), 58–67. Oakey, R. P., During, W., & Mukhtar, S. (1997). High technology small firm development: Bridging the academic-practitioner gap. In: R. P. Oakey, W. During & S. Mukhtar (Eds), New technology-based firms in the 1990s, Vol. III. London: Paul Chapman Publishing. Porter, M. (1998). On competition. Harvard Business Review Book. USA, Boston: Harvard Business School Press. Westhead, P., Batstone, S., & Martin, F. (2000). Technology-based firms located on science parks: The applicability of Bullock’s sift-hard model. Enterprise and Innovation Management, 1(2), 107–139. Westhead, P., & Cowling, M. (1995). Employment change in independent owner-managed high technology firms in Great Britain. Small Business Economics, 7(2), 111–140.
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Part I Theory
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Chapter 2
Knowledge Creation, Its Transfer, and the Role of Science Enterprise Education: A Research Agenda* Elizabeth Chell and Ray Oakey
Introduction A crucial part of the Government’s industrial and innovation policy is the support of entrepreneurship and the creation of new business through various programmes. The White Paper (2001) on enterprise, skills and innovation set out how Government should work with individuals, communities and business to help them create opportunity through change. This builds upon previous policy, which in 1993 identified the importance of utilising the UK’s science and research base (Cm 2250) and applying knowledge to produce higher value-added goods and services (Cm 4176). The 1998 White Paper (Cm 4176) had identified the importance of innovation and enterprise for the growth, competitiveness and development of the UK economy in several key industrial sectors. Nevertheless, the UK’s economy still lags behind that of its major European counterparts (HM Treasury, 2000). Thus, the challenge remains to address these barriers to growth. At one level the Government is keen to address what it considers to be a lack of enterprise culture within the education and training system, while at another level it seeks to improve technology transfer from universities into industry. To this end, significant investment is being targeted at an incubator fund, designed to support academic small business “spin-offs”. There is also increased funding for the exploitation of technologies (identified by the Foresight exercise) by new high technology-based firms. Additionally, the education and support of UK scientists and engineers expert in enterprise and business skills is being tackled by the creation of 13 Science Enterprise Centres (SECs). These Centres have been established at universities where there is evidence of academic excellence in science, engineering and technology (SET). Their remit is to work with scientists, engineers and technologists, to foster and promote new scientific ideas from academia * Reprinted with permission from eContent Management Pty Ltd. Article entitled: Knowledge Creation, its Transfer and the Role of Science Enterprise Education: A Research Agenda, from journal: Innovation: Management, Policy and Practice, ISSN 1440-1266, Volume 6, Issue 3 (Dec) 2004, pages 444–457. New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 9
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through to commercialisation. Moreover, while regionally focussed, the SECs, in partnership with the universities, seek to educate engineers and scientists to become entrepreneurially focused individuals interested in commercialising their personal ideas, university research, or other products and services. Many of these programmes depart from the traditional content of management courses, where the student remains at “arms-length” from the reality of the business environment. Instead, such enterprise courses are directly concerned with real life patent issues, IP, manufacturing and product evaluation difficulties. However, in the case of enterprise, as in other instances (e.g. the current government enthusiasm for clusters as a regional policy instrument), there is a tendency for government policy to forge ahead, beyond our research-based knowledge of the phenomenon in question. Thus, as noted above, although SECs are a reality, many of the mechanisms that drive the process of academic entrepreneurship (and which might be enhanced through policy to create additional activity in this area) are not well understood. Although it would have been better for policy to follow sequentially a clear theoretical and practical understanding of this phenomenon, the following proposed research agenda, nonetheless, seeks to promote a better understanding by suggesting investigations into the academic entrepreneurial process in parallel with the practical development of SECs. Consequently, such work would make it possible to “fine tune” the process of stimulating new enterprises derived from academic origins. Nonetheless, currently there remains a dearth of useful theory pertaining to early- stage, nascent entrepreneurial ventures. Indeed, there is debate over how a nascent entrepreneurial venture should be defined. Reynolds (1994) quoted in Aldrich (1999, p. 77) takes the definition back to the individual when he defines a nascent entrepreneur as “someone who initiates serious activities that are intended to culminate in a viable business start-up”. Those “serious activities” include “networking, resource mobilisation and entrepreneurial enactment” (ibid.). However, from the emergence of an idea, the gestation period for a realised firm formation may be a lengthy process. Further “intention” is, arguably, necessary but not sufficient. Other theorists (e.g. Howard Stevenson) would argue for the importance of opportunity, nonetheless, this leaves open the question of the triggers that prime individuals to engage in entrepreneurial behaviour. Furthermore, the dearth of strong conceptualisation also extends to Organisation Theory which does not yet indicate definitively how a Nascent Entrepreneurial Unit (NEU) organises itself, or what form it should ideally take. Moreover, there is a paucity of analytical research to explain how the NEU should best learn and develop, how it should garner resources, how it might capture new knowledge and skills, or how it can develop an effective vision of the new venture process (Honig, 2001). Organisation Behaviourists, have nonetheless, begun to theorise about how the NEU organises thought processes and relations, through research focussing on the importance of intention (Bird, 1989), the pursuit of opportunity (Stevenson & Jarillo, 1990) and the development of entrepreneurial teams (Kamm, Shuman, Nurick, & Aaron, 1990). There is also a paucity of analytical research to explain how the NEU learns and develops, how it garners resources, how it captures new knowledge and skills and develops a vision of new venture emergence (Honig, 2001). Furthermore, the process of Entrepreneurship education raises a number of issues that emerge from the academic literature. These include crossprofessional communication, collaboration and co-operation (Amabile et al., 2001), the joint production of knowledge (Gibbons et al., 1994) and the problem of bridging the “great divide”
Knowledge Creation, Its Transfer, and the Role of Science Enterprise Education
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by generating and disseminating knowledge across academic and business cultural boundaries (Rynes, Bartunek, & Daft, 2001). However, there are related theoretical frameworks that might be adapted to inform these questions such as Population Ecology (Aldrich, 1999), Resource Based View (RBV) Bergmann Lichtenstein, & Brush (2001), Human Capital Theory (Honig, 2001) and Networking Theory (Bloodgood, Sapienza & Carsrud, 1995). Contrary to the approach of Aldrich (1999), that adopts a Population Ecology theoretical position, this paper assumes primarily an RBV. The RBV assumes that a firm’s resources are both tangible and intangible, the latter being unique to each firm. Such resources will encompass human capital such as experience and education, but more particularly the development of tacit knowledge. From a strategic perspective, regarding the choice of product-markets for exploitation, how resources are combined is also important. Such unique and crucial behaviour patterns form the core competencies and capabilities of a firm, nascent or otherwise. In this way NEUs are able to differentiate themselves in the market place and it is this that enables them to develop a unique competitive advantage. Further, from a wider economic perspective, it has been argued that the RBV is more compatible with the Schumpeterian view of competition (i.e. “creative destruction”) where firms are actively competing against each other for technological supremacy. It is the process by which firms create new products, processes and markets that destroys the competitive advantage of firms reliant on the, by then obsolete technologies (Mazzucato, 2002, p. 4). Moreover, the inclusion of the concept of intangible resources is compatible with the notion of tacit skills, patterns of co-operation and intangible assets that take time to learn and develop (Whittington, 2001). This theoretical stance enables a focus to be maintained on knowledge capture and its development as a key intangible asset of the NEU and clearly fundamental to the process of Science Enterprise.1
The Evolution of Science Enterprise Knowledge The nature and sociology of knowledge creation and transfer is fundamental to the science enterprise process. Scientific knowledge is usually created in the laboratory through the work of a “critical mass” of scientists under the leadership of a senior scientist. The knowledge development process is ordered according to the ethics of the scientific establishment in circumstances where new knowledge is only accepted as proven after appropriate experimentation and testing has been scrutinised by the wider scientific community. This gives authority to the knowledge that has been created. Knowledge at this stage is not necessarily for anything other than the development of the academic discipline concerned, and the expansion of the boundaries of “what is known”. This purity of the scientific process is respected and protected for its demonstrable value to Homo sapiens, and for that fundamental natural curiosity that 1
Nonaka, Toyama, and Nagata (2000, p. 8) criticise the RBV because they say “it fails to address the dynamism in which the firm continuously builds … resources”. Both RBV and KBV may be criticised for their focus on the extant established firm. While in the case of RBV this has been addressed to a degree by Garnsey (1998), the KBV espoused by Nonaka appears to focus exclusively on the medium to large corporation and its R&D department. Thus, in this paper we attempt to address these criticisms by applying RBV and KBV to the nascent entrepreneurial venture creation process.
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separates humankind from other forms of life. Any consideration of the financial value of such advances is incidental and must come later. This knowledge development process structures the operation of the scientific community, since sub-groups of scientists often compete to produce new scientific knowledge in particular fields of endeavour. The scientist’s career usually follows a linear path that is both bureaucratic and professional and so unlike the entrepreneurial career path (Kanter, 1995). However, when the value and purpose of science is questioned, judgement and subjectivity are involved. In particular, if scientific knowledge is to be applied to solve a particular problem, the views of various stakeholders need to be taken into consideration. Science enterprise thus concerns the application of science and technology to the benefit of stakeholders and customers. However, this imperative does not explain the above evolution of science enterprise knowledge. Even before commercial considerations come into play, there is a need for those concerned with the development of the concept of science enterprise to understand how novel ideas are generated. This suggests, following Polanyi (1967), that initial knowledge development and transfer (i.e. exploitation in a practical sense) is a tacit (silent and unspoken) process. However, such statements need to be developed theoretically. Nonaka et al. (2000, p. 2) explain that in their knowledge-based view (KBV), “knowledge is essentially related to human action”. Knowledge is both context-specific and generated through interaction between people. It is communal, highly distributed and difficult to capture. What the NEU is about is the exploitation of knowledge, by creating new knowledge, i.e. an innovation. This knowledge is a key resource or asset to the NEU, but in order to exploit such knowledge the NEU needs to access other sources of knowledge and understanding (known as tacit knowledge). This knowledge context concerns all aspects of venture development, and commercialisation processes to the extent that: Knowledge-creating processes are necessarily context specific, in terms of who participates and how they participate in the process (ibid. p. 8). Moreover, knowledge is created dynamically in time and space. Individuals that participate in the knowledge creation process are required to be trustful in sharing knowledge. This requires “continuous exchanges” that “strengthen relationships”. Furthermore, in this KBV there is hypothesised a “knowledge creating function” referred to as the Socialisation– Externalisation–Combination–Internalisation (SECI) spiral. This spiral describes the conversion of knowledge from tacit through to explicit forms, thus, (1) Socialisation (tacit to tacit), (2) Externalisation (tacit to explicit), (3) Combination (explicit to explicit), (4) Internalisation (explicit to tacit). Applying this theory to the NEU in a science enterprise context suggests the following hypothesised sequence. Socialisation may occur at different points for example, in respect of developing scientific understanding vis a vis an R&D group in the laboratory. From an enterprise point of view, the establishment of Venture Clubs may be an informal means of socialising the NEU into understanding the business creation process. Externalisation will be required as NEUs relate to others, such as mentors, venture capitalists or clients. Combination it is suggested will occur as the NEU widens its external network to include “weak ties”. Internalisation ultimately will involve the development of an understanding of the processes of knowledge transference and exploitation, and the processes that the NEU will have experienced in creating a vehicle for knowledge transfer.
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Since knowledge transfer is an embedded process i.e. it is both culturally bounded and context dependent, Science Enterprise should seek to understand, not only this production process, but also the nature of the end-user of the products. This ambition is another part of the seamless knowledge generation and knowledge exploitation process, whether the enduser is an individual sponsor of, or a potential new market for, an innovation or scientific discovery. Knowledge of industrial markets is developed through contact with marketing and R&D specialists within companies, intermediate suppliers of technology, and other equipment and external agents with specific expertise and relevant skills. This network of contacts supplies knowledge that the enterprising scientist will glean and use later, often in conjunction with his or her own “invention push” generated ideas, through the brokering of deals, the negotiation of contracts, and sourcing of venture capital. Such knowledge of industrial markets and services (e.g. venture capital provision) is also partly tacit. The development of “face-to-face” derived information enables the enterprising scientist to make more efficient judgements about how to further develop his or her innovation, both in terms of technical imperatives and the resources needed to develop them. This process must essentially include, not only a knowledge requirement, but also the know-how that permits the post invention/innovation enterprise stages of the process. A model that encapsulates this enterprise process is shown in Figure 1. Ideally, Science Enterprise utilises the science knowledge generation process in the following ways. First it seeks to understand if there are practical applications for new advancements in science, and given a particular proposed use, how the technology in question might be transferred from the scientific realm into a commercially successful product. This may involve the modification of existing scientific knowledge of a particular (e.g. dye laser) technology in order to produce a better technological solution. Incremental innovations of this sort are common, and the process of their development is relatively well understood. However, the exploitation of the radical or totally novel invention, where a paradigm shift that challenges our understanding of the technology concerned, is often much more difficult to integrate into existing industrial and commercial practices. Such inertia
End-User Requirement
Scientific & Enterprise Knowledge
NEU
Scientific/ Enterprise Understanding
Scientific Enterprise Venture
Industrial Market Knowledge
Figure 1: Schematic diagram of the science enterprise knowledge transfer process.
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often occurs because large industrial firms have entrenched investments in an existing technological paradigm (e.g. the inhibited development of a battery driven car; resistance by IBM to the desktop computer) (Oakey, 1993). The development of scientific knowledge by the enterprising scientist alone, or within an R&D group, in response to end-user requirements and industrial market knowledge, results in new knowledge, termed scientific enterprise understanding. Ultimately the outcome of this understanding may result in the development and launch (i.e. founding) of a new venture. However, not all technology developed in this manner is necessarily exploited through a new venture. In particular, many academic inventors will seek to surrender their intellectual property ownership of a new technology or business idea that they have developed at an early stage as an alternative to founding a business by selling out to a large established corporation. However, it has been argued (Oakey, 1995) that many inventors of new technologies will seek to form a science enterprise venture in order to maintain control over the development of the technology they have invented, while also seeking to add value to this intellectual capital. Such a strategy keeps their “options open” in that development of their new business concept will (hopefully), improve the new idea as it becomes more credible and less risky, which also allows them to either sell the company at an enhanced value, or grow it further as a separate entity. In both instances, adding value to the new enterprise through growth and success increases the scope and value of options.
The Development of Science Enterprise Capability A key question that has exercised many academics in business schools is ‘can Entrepreneurship/entrepreneurial skills be taught’? Sceptics have pointed to untutored enterprising individuals as models of personified entrepreneurial behaviour, and to aspects of business venturing attributes that they suggest involve special inherent personal qualities (i.e. nature) that, by inference, cannot be taught (Fiet, 2000a,b). If these assumptions were true, they would imply a limit to the pool of individuals in a population that could be entrepreneurs. Moreover, such a view would clearly confound government agendas (based on training) that wish to increase entrepreneurially led innovative activity and wealth creation for the benefit of the economy and ultimately the populace (e.g. Cm 4176, 1998). However, Science Enterprise (or indeed entrepreneurship generally), construed as a process of knowledge acquisition, rejects the assumption that entrepreneurs (or enterprising scientists) are “born”. Rather it asserts that entrepreneurial skills and behaviours may be acquired or enhanced through, (a) developing understanding and (b) learning through experience in a supportive environment. The success (or failure) of the science enterprise programmes, now in place, will test these conflicting assertions. The RBV theorises that the nascent entrepreneur mobilises resources that vary according to the stage of development of the venture concerned (Bergmann Lichtenstein et al., 2001; Garnsey, 1998). However, from a learning perspective, what these core resources might be and how the NEU might access them are key questions. Nascent entrepreneurs usually rely upon their experience, which is a resource that involves investment in the business (Becker, 1964; Honig, 2001). Less tangible resources that give reputation (Shane, 2004), external network development of significant others (Hansen, 1995) and internal
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entrepreneurial team development have been identified as key ingredients of success (Kamm et al., 1990). Early stage outside assistance is also important for both survival and growth (Chrisman, 1989; Chrisman & McMullan, 2000). Moreover, these resources are not simply ingredients. They imply a complex process by which they may be leveraged. This process, it is suggested, involves the learning and development of understanding by the NEU founder, from explicit knowledge to tacit understanding. Knowledge and know-how that is gleaned from practice and engagement with practitioners becomes embedded and ingrained. This acquired expertise then becomes virtuous as the NEU gains confidence, demonstrated capability and reputation. Such a process attracts further resource and becomes selfreinforcing. However, the different types of knowledge that the nascent entrepreneur/scientist must acquire further complicate the process of resourcing a business proposition. Knowledge may be categorised as: ●
●
●
●
Experiential knowledge assets comprise shared tacit knowledge, built up through shared, hands-on experience as the NEU develops skill and know-how; Conceptual knowledge assets consist of explicit knowledge articulated through images; symbols and language. Scientific knowledge, the product concept, and the development of a brand, are all examples; Systemic knowledge assets are systematised and packaged explicit knowledge that may be contained in documents, specifications, manuals, a database, a patent or license; Routine knowledge assets comprise tacit knowledge that is routinised and embedded in actions and practises, sometimes referred to as “recipe knowledge”. These may be the norms that the NEU develops that become a part of its culture.
Such knowledge assets determine the boundaries of the NEU. The process of knowledge creation ensures that these boundaries are likely to expand if the spiral of knowledge creation is positive (i.e. increasing). However, there is likely to be a cost to knowledge acquisition and this will ultimately determine whether the boundaries expand and the NEU, and its business proposition, grows and develops. The human capital approach allows for a consideration of the human resource element of the NEU. Founding teams are more common than the lone, heroic entrepreneur that much folklore implies (Kamm et al., 1990). Indeed, there is some evidence to indicate that in SET-based ventures, the entrepreneurial founding team predominates over the solo founder (Obermayer, 1980; Teach, Tarpley, & Schwartz, 1986; Cooper & Daily, 1997). Moreover, teams often have a positive impact on venture performance and growth (Chell & Baines, 2000), although it has been argued that friendship is likely to be a salient ingredient (Frances & Sandberg, 2000). However, unknown factors include, for example, what functional and team roles do different team members adopt and why? Do teams emerge from friendships or working relations in the laboratory? Or are they based on specialist skills (e.g. typically an engineer, financial expert and a marketeer)? Just how compatible are team members in respect of motives for starting a business and their personal values? How is conflict resolved? How does the team develop over time, and in particular, in relation to the science base and external network relations? What is the distribution of ownership within the team and how does that affect subsequent venture development? Finally, does the business idea or the team come first, and does it matter?
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In sum, the future research agenda should test the model that posits the salience of resources, the development of the knowledge base and human capital in respect of the prospects — viability, sustainability and growth — of the NEU. In addition this research agenda should examine the relative effectiveness of team as opposed to individual entrepreneurial ventures.
The Process of Knowledge Transfer Thus far, the agenda for Science Enterprise has principally focussed upon technology transfer of an “untutored” body of technology and entrepreneurship out of universities. However, this debate has tended to underplay the importance of science enterprise education as a means of increasing the stock of ideas and entrepreneuring through this process (Mukhtar, Oakey, & Kipling, 1999; Oakey, Mukhtar, & Kipling, 2002), and how science enterprise might be best taught to achieve such an increase. ‘Technology transfer’ is problematic as a core principal because of its susceptibility to reification, often involving simplification. Technology is not a “knowledge parcel” that can be passed from laboratory to marketplace, almost as a “free good”. Confidentiality as a result of the high cost involved in exploiting basic scientific ideas may act as a cause of friction to the free flow of technological information in all “technology” transactions. Indeed, this friction can be particularly acute in the case of university-based “transfers”, where the “leading edge” nature of much of the technology involved heightens the levels of both R&D costs and risk (Roberts, 1991; Oakey, 1995). It is the further extension of scientific knowledge to solve a particular problem and produce an (often new) technical solution to a problem (frequently, as noted above, with no commercial objective in mind — e.g. a new medical advance) that drives the university “spinout” process. Hence, a more appropriate label is knowledge transfer between interested parties (i.e. from the university to the new firm) under conditions of confidentiality. The process is likely to involve the successful capture of relevant but distributed knowledge and know-how from a variety of sources that are transdisciplinary (Gibbons et al., 1994). By transdisciplinary we mean that the knowledge is across disciplinary boundaries and its location is also distributed within both public and private sectors.
The Venture Creation Process It is assumed that the NEU in a higher education context (Aldrich, 1999; Reynolds, 1994) is located within or between the public and private sector space (e.g. from private study bedroom to university laboratory to incubator then to Science Park). In the context of science enterprise and incubation, ideas may start in the public sector but at some stage if successful they may be moved into the private sector. Likewise ideas within the private domain may be dropped into the public domain, and nurtured in a university-based incubator before re-emerging as a virgin commercial enterprise. However, the process by which nascent enterprises originate and evolve is problematic. Enterprise and incubation managers attempt to capture knowledge from a variety of sources to facilitate the venture development
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(Autio & Klofsten, 1998). However, little is systematically known; knowledge development and capture appears to be ad hoc and on a “need-to-know” basis. Fourteen discrete behaviours have been identified that characterise nascent entrepreneurship (Aldrich, 1999, p. 78). They are not, however, specific to Science Enterprise. They include, for example, “serious thought about the business”, “looked for facilities and equipment”, “invested own money in new firm”, “organised start-up team”, “wrote business plan”, etc. They are intended to indicate typical activities of nascent entrepreneurs during the “gestation period”. This phase concerns the garnering of resources with a view to the creation and exploitation of opportunities by developing competitive advantage. However, although lists are indicative of the type of activity involved, what they do not convey is the intangibility of the resources involved, what specific resource “bundles” are needed and when and how they are to be configured (Bergmann Lichtenstein et al., 2001). Furthermore, when considering the science enterprise academic, these are likely to be alien behaviour patterns, associated with a commercial orientation, that are distinctly different from the realms of academe (Druilhe & Garnsey, 2001). This difference is particularly opposite when considering appropriate processes of learning and knowledge development (Argyris & Schon, 1978; Blackler, 1995; Kolb, 1984). Initially the nascent entrepreneur may be dimly aware of the steps that need to be taken. Mistakes may be made, for example, in making public one’s scientific ideas. Support environments are likely to be crucial in enabling the nascent entrepreneur to explore extant information and knowledge, to recognise what is important to know, and to be able to process such information such that the idea may be developed into an opportunity. During this process, knowledge will be assimilated as the nascent entrepreneur develops the opportunity further. To achieve this, the NEU should involve relevant outside agents such as financiers, suppliers and in particular the first vital customer/client. This is the point at which knowledge transference takes place. It has been characterised as the process of the conversion of the knowledge in the context of application (Revans, 1980).
Conclusions and Suggested Research Agenda The knowledge production process is extremely complex and its efficiency is subject to many factors that may act as inhibitors. For example the NEU may lack awareness, or there may be social, economic, technological, strategic, organisational, legal and security issues that damage this knowledge production system within the venture creation process. What appears to be key to the success of this process is: (a) the development of an understanding of contingent and contextual information and the shift from tacit to explicit and codified knowledge such that ideas and opportunities may be articulated to significant others; (b) the rapid interaction between different modes of knowledge development in a purposive, non-linear fashion involving progress through iteration. This process is insufficiently well understood in that knowledge is neither predictive nor causal nor prescriptive, and at best it is heuristic. Moreover, because the NEU is such a
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fragile “organisation”, the fundamental problem is developing robust knowledge of what needs to be known, how it might be done, when and with whom. Hence a future research agenda should aim to take account of this following: ●
●
●
●
●
Increase our understanding of different knowledge resource requirements of the nascent entrepreneurial units at different stages in their development; The NEU throughout its development phase will receive inputs of knowledge — factual and process knowledge — from a wide variety of sources. They include academics, practitioners, mentors and the wider support environment (Bolton, 1997). These interventions will include knowledge that is transmitted in codified form to aid the development of understanding and knowledge of processes — know how — that is more difficult to codify and may be transmitted by means of stories, metaphors, insights gleaned from case studies, analogies and models. There is clearly a learning issue of how such tacit knowledge — the difficult to interpret and transmit know-how — can be conveyed to the NEU. This process we see as being particularly problematic at each and every point in the NEU’s development (i.e. whether it is in the pre-incubation, incubation or product development and selling stages). Hence the research agenda should aim to address this problem by examining knowledge development at the different stages in specific contexts such as — the student entrepreneur (virginal NEU), the incubating NEU, and the practising NEU. The contexts will be HE enterprise programmes, university-based incubators, Science Parks respectively. Put succinctly, any future research agenda should seek to; Develop a depth of evidence-based knowledge in order that it can be used to fine tune policy and benefit the work of the Science Enterprise Centres, Incubators and Science Parks; Investigate how both tacit and explicit knowledge is captured and utilised by the NEU in a Science Enterprise context; Consider how policy interventions may facilitate knowledge capture and development within the NEU.
The individual enterprise student NEU is likely to select a programme of study designed to develop his/her knowledge of the development of a business proposition from idea to launch. Such a context conforms to the definition of a nascent entrepreneur. However, in order to explore the issue of “intention” and “opportunity” thoroughly it is necessary to consider the likelihood that some virginal student entrepreneurs may progressively choose to participate in “awareness-raising” classes, business plan competitions and so forth. This possibility suggests a gap in our present knowledge of the choices that may be exercised to ensure exposure to relevant knowledge and know-how. Further, where the student NEU is on a programme of study that includes or is predominantly about the development of a business proposition from idea to business plan (and potential launch), the NEU may include a business mentor, enterprise supervisor, academic, and external sources of support/interventions such as a venture capitalist, sponsor, customer, business angel and other student members of an enterprise start team. A future research agenda should also aim to: ●
Develop theoretical and practical understanding of knowledge capture and development processes at the level of the NEU.
These should include both personal and team development processes.
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Such a research agenda, however, should include the acquisition of knowledge that may be either generic or contextually specific. Both are problematic in respect of the evolution of enterprise knowledge and know-how. Generic knowledge still requires the translation and adaptation to the individual and/or team’s knowledge base. Contextually, specific knowledge requires the development of knowledge of the specifics of the situation and environment for enterprise development. Here tacit knowledge is likely to be considerable. It is important therefore to develop an understanding of NEU and sector specifics; for example, the key factors influencing enterprise development in bioscience, ICT, textiles etc. Hence the research agenda should aim to: ●
Carry out comparative work across specific SET sectors.
The NEU is assumed to commence with an idea underpinned by scientific knowledge but have little else by way of resources. Indeed social capital that the NEU brings to bear on the venture creation process (VCP) is likely to comprise scientific knowledge but little by way of enterprise knowledge or skill. They will need to develop business knowledge and skill and where they lack experience draw on that of their mentor or significant others. The latter are likely to arise from networking activity — a skill posited to be crucial to successful business venturing (Dubini & Aldrich, 1991; Chell & Baines, 2000). Other resources include the use of proxies for reputation (for example, through association with particular sponsors, venture capital providers, etc.). This raises the question of differences in environment, some being resource-rich relative to others. This suggests two further issues: ●
●
First, what are the sources of resource for the VCP, and how are those resources captured and managed? Second, what do different environments tell us about the knowledge creation and transfer process?
These two issues underscore the importance for the research to examine ways in which it can address the distribution of knowledge that is both multidisciplinary (across disciplinary boundaries in SET and entrepreneurship) and transdisciplinary (encompasses sources of knowledge outside academe — in particular that of practitioners). Hence our final item for a proposed research agenda is that it should: ●
Develop a methodology whereby distributed knowledge that is both explicit and tacit is captured, understood and assimilated in order to assure research relevance, rigour and quality.
While the keenness of the current government for the encouragement of academic entrepreneurship should be applauded, such enthusiasm is no substitute for a clear understanding of the problem, and perhaps more importantly, ameliorative policies to effectively achieve a solution. The above proposed research agenda is not an attempt to over complicate an otherwise straightforward problem (a shortcoming for which academics are often culpable), but is the bare minimum necessary in order to allow us to progress to sensible long-term policies to provide better entrepreneurship education, and the subsequent encouragement of additional nascent academic entrepreneurs. The fact that there are major theoretical and practical areas where we remain substantially ignorant is the strongest rationale for not allowing practice to “lose touch” with our conceptual understanding of this subject area. To proceed in undue
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haste might eventually mean that we will be forced to repent at leisure, as hasty and inappropriate policies lead to failure. This would be extremely unfortunate since it would damage the legitimate, and much delayed, emergence of entrepreneurship in general, and academic entrepreneurship education in particular, as the key explanatory variable in the modern industrial innovation growth equation.
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Fiet, J. O. (2000b). The theoretical side of teaching Entrepreneurship. Journal of Business Venturing, 16, 11–14. Fowles, S. (2001). The problem with knowledge management. PREST Working Paper. Frances, D. H., & Sandberg, W. (2000). Friendship within entrepreneurial teams and its association with team and venture performance. Entrepreneurship Theory and Practice, 25(2), 5–25. Garnsey, E. (1998). A theory of the early growth of the firm. Industrial and Corporate Change, 3, 523–556. Gibbons, M., Limoges, C., Nowotny, H., Schwartzman, S., Scott, P., & Trow, M. (1994). The new production of knowledge. London: Sage. Hansen, E. L. (1995). Entrepreneurial networks and new organisation growth. Entrepreneurship Theory and Practice, 19(4), 7–19. Honig, B. (2001). Learning strategies and resources for entrepreneurs and intrapreneurs. Entrepreneurship Theory and Practice, 26(1), 21–35. Kamm, J. B., Shuman, J. C., Nurick, J. A., & Aaron, J. (1990). Entrepreneurial teams in new venture creation: A research agenda. Entrepreneurship Theory and Practice, 14(4), 7–18. Kolb, D. A. (1984). Experiential learning. Englewood Cliffs, NJ: Prentice-Hall. Mazzucato, M. (2002). Introduction. In: M. Mazzucato (Ed.), Strategy for business. A Reader. London: Sage. Mukhtar, S.-M., Oakey, R. P., & Kipling, M. (1999). Utilisation of science and technology graduates by the small and medium sized enterprise sector. Education and Training, 41(8), 425–436. Nonaka, I., Toyama, R., & Nagata, A. (2000). A firm as a knowledge-creating entity: A new perspective on the theory of the firm. Industrial and Corporate Change, 9(1), 1–20. Oakey, R. P. (1993). Predatory networking: The role of small firms in the development of the British biotechnology industry. International Small Business Journal, 11(4), 9–22. Oakey, R. P. (1995). High technology new firms: Variable barriers to growth. London: Paul Chapman Publishing. Oakey, R. P., Mukhtar, S.-M., & Kipling, M. (2002). Student perspectives on entrepreneurship: Observations on their propensity for entrepreneurial behaviour. International Journal of Entrepreneurship and Innovation Management, 2(4/5), 308–322. Obermayer, J. H. (1980). Case studies examining the role of R&D contract funding in the early history of high technology companies. Cambridge MA: Research & Planning Institute. Polanyi, M. (1967). The tacit dimension. London: Routledge. Revans, R. (1980). Action learning: New techniques for managers. London: Blond & Briggs. Roberts, E. B. (1991). Entrepreneurs in high technology: Lessons from mit and beyond. Oxford: Oxford University Press. Rynes, S. L., Bartunek, J. M., & Daft, R. L. (2001). Across the great divide: Knowledge creation and transfer between practitioners and academics. Academy of Management Journal, 44(2), 340–355. Shane, S. A. (2004). Academic Entrepreneurship: University spinoffs and wealth creation. Aldershot: Edward Elgar. Stevenson, H. H., & Jarillo, J. C. (1990). A paradigm of entrepreneurship: Entrepreneurial management. Strategic Management Journal, 11, 17–27. Teach, R. D., Tarpley, F. A., Jr., & Schwartz, R. G. (1986). Software venture teams. In: R. Ronstadt, J. Hornaday, R. Peterson & K. Vesper (Eds), Frontiers of entrepreneurship research. Wellesley, MA: Babson College. White Paper (2001). Enterprise skills and innovation. www.dti.gov.uk/opportunity/forall/pages/ Whittington, R. (2001). What is strategy and does it matter? (2nd ed.). London: International Thomson Business Press.
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Chapter 3
Developing Entrepreneurial Capability to Facilitate Academic Entrepreneurship and Technology Commercialisation Sarah Cooper and Lesley Hetherington
Introduction Local, regional and national development agencies are looking to a wide range of organisations to contribute to new venture creation and growth, as they seek entrepreneurial capital and new venture opportunities to support through the commercialisation process. With government research laboratories, universities have been linked to the emergence and growth of innovative regions in the United Kingdom (UK) (Buswell & Lewis, 1970; Morphet, 1987; Keeble, 1987). As sources of new knowledge and developers of intellectual human capital, universities contribute through the creation of technology, and associated intellectual property (IP), with the potential to form the basis of new ventures or fuel innovation in existing ventures. Universities play varying roles in both encouraging recognition of technology with commercial potential and supporting incubation of business ventures. Universities also develop human capital in the form of skilled graduates and technical experts to meet the labour requirements of innovative ventures and cultivate future entrepreneurial capital. Some of those attracted to work or study at a university remain in the locality once they leave, working for other organisations or in their own business (Cooper, 1996). There are notable examples of universities playing active roles in stimulating the growth of technology agglomerations, such as Route 128 (Massachusetts Institute of Technology (MIT)), Silicon Valley (Stanford and Berkeley) and Silicon Fen (University of Cambridge). MIT has not only been responsible for generating technology around which local firms have been built, but also many MIT academics are actively involved with local technology organisations (BankBoston, 1997). Some of its academics span the academia–industry divide, maintaining their university post and commercial associations through, for example, holding company positions. Most universities do not have such marked effects on regional development: there are many universities but “a much smaller number of Silicon Valleys and Route 128s” (Storper, 1995).
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Whether a university plays a role in local, regional or national economic development will be influenced in part by its propensity to encourage the commercial exploitation of its IP and its ability to create a culture supportive of enterprise in its staff and students. This paper focuses on the role of universities in developing technology and entrepreneurial capital. It includes discussion of university–industry interaction and considers universities as entrepreneurial incubators. While university-developed entrepreneurs may benefit from the environment in which they have evolved, a number of areas are identified in which their academic origins may leave them lacking certain skills, knowledge and understanding, which are advantageous for entrepreneurship. In this paper evidence is presented from a technology entrepreneurship education programme developed by the Hunter Centre for Entrepreneurship at the University of Strathclyde. Some outcomes of the programme are discussed before the paper concludes with issues for policy.
The University: Environment and Function Neither of the two main university functions of research and education (British Council, 1978) should be viewed as at odds with each other; the challenge is to achieve an acceptable balance. In looking to universities to play a role in developing regional areas of specialisation and generation of skilled human and entrepreneurial capital, it is important to understand aspects such as the university environment, culture and systems which impact upon their ability to meet such expectations. UK universities receive government funding for research and teaching, however, over the last 15–20 years, the amount received has fallen significantly in relative terms. At the same time, the government has been exerting pressure to raise university participation rates to 50% of school leavers. Universities are seeking essential funding from other sources. Many are turning to commercially-oriented avenues to help plug the funding gap. These include research contracts from industry, research council grants requiring industrial collaboration and commercialisation of university generated technology via licensing, outright sale of the technology or an equity holding in any resulting spin-off. An Increasingly Commercial Research and IP Focus Universities are the home of much leading-edge research with the potential to make fundamental breakthroughs in knowledge, some with scope for exploitation by industry or academic spin-outs. Applied research tends to be the major focus of research and development within both large and small commercial organisations; however, universities are increasingly focusing at least some of their work towards areas with a greater focus on realities of the market. More and more academics are working on projects partially or wholly funded by industry. In other cases, universities are engaging in collaborative arrangements with commercial ventures to undertake programmes of joint research, benefiting from the expertise of leading researchers and market awareness of the commercial partner. Where innovations with commercial applications emerge from collaborative projects, the commercial partner is well placed to develop and launch viable products and services into the marketplace, using its market knowledge and
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established distribution channels. Participation in high-level, leading-edge research should help to enhance teaching quality. Where academic staff have strong collaborative commercial research links, they are well placed to understand industry needs and integrate into the student curriculum elements to develop the knowledge and skills sought by employers. In addition to seeking funding from commercial organisations, universities are also becoming aware of the value of their IP, much of which has, in the past, gone unexploited. Many institutions have formalised commercialisation and technology transfer activities, with specialist departments responsible for aspects including IP identification and valuation, contract negotiations and support of academics establishing new ventures or commercialising technology via another route. Academics have to be careful regarding IP and be aware that in order to be able to commercialise their research they must not disclose results in such a way as to invalidate any ownership claim. Undergraduate and postgraduate students, predominantly, own their own IP, although growing numbers of universities are asking enrolling students to sign away their automatic IP rights, in favour of a distribution of funds in the event of subsequent commercial exploitation of know-how which they developed. Most UK academics do not own their own IP where it relates to employment. Ownership rests with the university although many have agreements whereby inventors receive a share of income resulting from commercial exploitation of IP which they created. Universities must recognise the appropriate level of involvement in the ownership and management of spin-outs. Too proactive a role may stifle business formation and growth, particularly the ability to raise external venture funding, while foregoing ownership rights may result in a university losing a valuable revenue stream should the company or technology be successful. Mobilising and Developing Human and “Potential” Entrepreneurial Capital Universities play an important role in attracting spatially mobile undergraduate and postgraduate students and staff (Cooper, 1996; Cooper et al., 2001). A university with a reputation for excellence in specific disciplines will attract human capital from elsewhere in the UK and overseas. This magnet effect attracts highly capable individuals to study and work within educational institutions. During a university education students develop specialist knowledge in their field of study and specific and transferable skills, utilised during their studies and highly valuable in their future careers. There is an increasing emphasis on the skills dimension as prospective employers seek evidence of abilities such as communication and team-building skills. Professional engineering bodies require science and engineering students to be exposed to management and enterprise issues, and government initiatives such as Science Enterprise Challenge should result in more enterprise and entrepreneurship education for science and engineering students, making them better placed to understand the commercial dimensions of their subject. Such initiatives are also looking to achieve culture change within universities, which will result in increased levels of commercialisation’ by research and teaching staff. A university, which generates technology and develops human and entrepreneurial capital, may contribute to local levels of economic activity in a number of ways. Where quality employment opportunities exist local to the university, a sizeable number of students may seek permanent employment in the vicinity, aiding the development of innovative technology firms. Students who have identified new venture-creation opportunities may set
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up companies locally, possibly with assistance from university commercialisation staff. Staff may be attracted out of universities by high-level employment opportunities or to establish a venture to exploit technology originating from their own research or developed elsewhere in the university or beyond. A Culture of Enterprise? Creating a culture supportive of enterprising behaviour within large organisations can bring performance improvements, which increase the feeling of achievement in staff and encourage individuals or groups to consider exploiting their capabilities through entrepreneurship. The decision to establish a business in the outside world can, in turn, improve the prestige and social and professional standing of individuals. For this reason, the culture, both within the source organisation and in the local area, is important in influencing the likelihood of entrepreneurial action (Cooper, 2000). In 1983, the Scottish Council for Development and Industry (1983) recognised the distinctive culture within universities, perceived as remote from the commercial world, although it acknowledged that increased collaboration with industry and a growing focus on technology commercialisation was realising change. Howells et al. (1998) identify a number of institutional reasons for commercialising research: “to generate additional income … to participate in local economic development … to gain prestige for the institution … to fulfil obligations to sponsors … to motivate and reward staff”. They also identify individual motivations such as financial benefits, recognition and the achievement of entrepreneurial status and the wish to make a contribution to society. External processes including research assessment and the development of league tables, factoring in linkages with industry, also encourage universities to demonstrate how well connected they are in commercial terms. Some university systems may militate against commercialisation. Traditional promotion systems favoured the generation of academic papers with emphasis upon the volume of papers rather than their “qualitative merit” which led “scientists [to] publish increasing numbers of papers, frequently overlooking the needs of industry” (British Council, 1978). Early research assessment exercises also placed little value on evidence of commercial involvement. Promotion systems and research evaluation criteria are changing to reward commerciallyoriented activities. Before commercially oriented work was common, some colleagues who were not interacting with external players resented time spent by others working away from the university. This did little to create an environment supportive of enterprise or entrepreneurship. Today, colleagues such as David Lane, formerly of Cyclacel (Dundee), provide examples of academic entrepreneurship; still there are few academic role models.
Technology Entrepreneurship and Academia Cooper (1973) identified a number of influences on the entrepreneurial process including the founder himself, the organisation for which he worked previously and external factors
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relating to the locality of the new firm. Elements interact to create a climate more or less favourable to entrepreneurial activity. He identified that the propensity for firms to generate spin-offs varied markedly; some of the same concepts can be applied to universities, particularly that relating to role models. Firms generating high levels of entrepreneurial spin-outs were located in areas of high entrepreneurship, where the presence of role models stimulated prospective entrepreneurs to find new ventures. Most technical entrepreneurs share key traits such as a high degree of personal drive, a willingness to accept risk and uncertainty, possession of “technical” and business acumen and a determination to succeed. Given that high degrees of uncertainty surround technology development in many sectors, especially those with a scientific base such as biotechnology, the technical entrepreneur has to tolerate high degrees of risk. Those within academia are not used to weighing up or bearing risk in a commercial sense or in terms of the security of their employment. Research among new technology-based (NTB) firms indicates that the majority emanate from commercial firms (Oakey et al., 1990; Oakey, 1995; Cooper, 1996; Lindholm Dahlstrand, 1999), although many universities are looking at ways to increase the flow. A relatively large number of new firms are indirect university spin-outs as their founders worked for other organisations within the locality following graduation or resignation, before themselves setting up in business (Lindholm Dahlstrand, 1999). Technology entrepreneurs follow a number of contrasting routes to entrepreneurship, differing in their educational, employment and skills base (Cooper, 1998). The occupational/work background is important for a number of reasons; the founder gains important sectoral and market knowledge and his occupation influences the extent of commercial and business experience which he acquires (Cooper, 2000). A number of studies of technology entrepreneurship have focussed on the entrepreneurial incubator, the organisation in which the entrepreneur worked prior to starting his or her new venture. While universities are not major incubators of technology companies, variations occur between sectors with evidence of more academics starting biotechnology than electronics firms (Oakey et al., 1988; Oakey et al., 1990; Cooper, 1996). However, work on the wider entrepreneurial career (Cooper et al., 2001) indicates that universities play a key role in shaping entrepreneurial careers, as they are where embryonic entrepreneurs develop technical competences and skills which shape their entrepreneurial futures. Besides taking a first degree, many technical entrepreneurs study for a masters or doctorate, which enables them to specialise or broaden their education. Some graduates remain in the university as researchers or lecturers, from where a minority leave to establish their own venture or work in that of others. In defining a typology of role models, Jones-Evans (1995) identifies academic entrepreneurs as one of four groups, the others being producer, user and opportunist entrepreneurs. The organisational setting within which an entrepreneur has worked influences a number of features of the venture creation process. Typically, science or engineering academics work in research groups, with colleagues drawn mainly from the same disciplines. They may have research contacts and friends outside the workplace who work in industry. The concept of teamwork will be familiar to such an academic and if leader of a research group, he or she is likely to have some management experience. Most aspects of the new venture creation process are likely to be new to the academic. More junior or doctoral researchers will posses a much lower awareness of commercial issues and lack leadership and management experience. The impact of this background can usefully be conceptualised within Timmons’
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(1999) venture creation framework of opportunity, team and resources, in order to appraise the extent to which the university acts as an effective entrepreneurial incubator. Opportunity: For the purposes of debate two aspects of opportunity are relevant, identification and seizure. An entrepreneur needs to understand the environment within which his business operates, a process which will help when he establishes his firm in a field with which he is familiar. The ability to identify opportunities is a function of experience and the broader the experience of an individual the easier it will be to spot possible applications of technological developments (Timmons, 1999; Chandler, 1996). The majority of new business ideas and opportunities are identified through the previous workplace, and studies report as many as 80% of founders setting-up within a similar market or using similar technology (Roberts, 1991; Cooper, 1998). Opportunity spotting is more likely to occur in a commercial setting where, for example, observation of the market and customer or supplier feedback/comments sow the seeds of new business ideas. Lack of a commercial background may render the task of opportunity recognition relatively problematic. Arguably, enhancement of opportunity recognition skills and learning of tools to aid the process and then raising awareness of the resources which require to be accessed is beneficial, given the genesis of many academic entrepreneurs. The stimulus, or trigger event, is important in encouraging the entrepreneur to seize the opportunity. A pull factor such as a market opportunity or push factor such as frustration or threat of redundancy are less frequent occurrences in academia than industry. There are likely to be fewer occurrences within the academic environment to stimulate the entrepreneurial process. Some founders start their firm as a part-time activity while still employed; this provides the opportunity to judge whether the business is viable. Some technology areas are characterised by long lead-times, meaning that significant time and resources are invested in research before any saleable result is produced. The part-time model is one which a number of academics will adopt. The decision to seize an opportunity may be influenced by the presence of role models, others who have successfully become entrepreneurs. Cooper (1973) identified high spin-out rates in areas characterised by high levels of entrepreneurial activity. The attraction of academics to entrepreneurship will be dependent upon creating a culture of entrepreneurial action. Team: The majority of technology ventures are the product of collective entrepreneurship by teams of individuals. Many NTB sectors are highly competitive and a firm’s competitive advantage is usually based upon the knowledge and expertise of its founder. The level of education among technical entrepreneurs is generally higher than that of nontechnical entrepreneurs (Storey, 1982; Monck et al., 1988). Indeed, Roberts (1991) attributes the generally high 70–80% survival rate of NTB firms to the level of education of their founders; many academic entrepreneurs will have high level qualifications, bringing high levels of technical expertise to their team. Different team members contribute complementary functional specialisms so that a weakness in one team member’s portfolio of capabilities is compensated for by other members’ strengths. Evidence indicates that in many instances teams comprise former work colleagues who bring contrasting skills and experience to the team (Cooper et al., 2001). If an academic has spent much or all of his or her career in university, the pool of prospective entrepreneurial partners is limited by the diversity within the professional network. If the academic has worked on collaborative industry projects, that network will be richer, but the breadth of functional areas represented may still be constrained.
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Resource: The level of financial resource required to establish a technology venture varies between and within sectors. For example, the start-up and on-going capital requirements of a biotechnology venture are much more significant than those of a software start-up. Given the pay level of UK academics, their ability to build up personal wealth through employment is limited. Their ability to raise external funding will be influenced by factors such as an ability to develop a persuasive business case, technical credibility and reputation in the field in which the business is to be established, and being able to convince investors that the entrepreneurial team has the ability to succeed. A commercial investor may be forthcoming depending upon the nature of the innovation. The new venture may be established in a university-owned incubator, meeting real estate requirements in the early stage of the business. Members of the entrepreneur’s old research team may become some of the first employees. The broader entrepreneurship literature also identifies certain personal characteristics, motivations and behaviours, which are important in the entrepreneurial process. Entrepreneurs may be viewed as motivated by the need for achievement, power and affiliation (McClelland, 1962). Timmons (1999) highlights six dominant themes, which are important in influencing entrepreneurial success. These are commitment and determination which are seen as more important than any other factor; leadership, which includes being a selfstarter and having an internal locus of control with high standards; opportunity obsession, value creation and enhancement to serve the market place; tolerance of risk, ambiguity and uncertainty, with the ability to resolve problems and integrate solutions; creativity, self reliance and the ability to adapt, which includes lack of fear of failure; and motivation to excel, i.e. having a goal and results orientation, with high but realistic goals. Social skills, which help entrepreneurs interact effectively, are also identified as important in the new venture creation process (Baron & Markham, 2000). The facets which have been identified as possibly contributing to venture success are social perception, the accuracy in perceiving others leading to more effective interactions; impression management, producing positive reactions in others, especially first impressions; persuasion and influence, including the ability to change others’ attitudes and/or behaviour in desired directions; and social adaptability, the ability to adapt to or feel comfortable in a wide range of social situations. While a prospective entrepreneur may not possess some of these attributes and skills, the following discussion will explore whether, and if so how, they may be developed through education, if they are lacking as a result of past experience.
Developing Academic Entrepreneurial Capabilities The Context and Approach Historically, Scotland has possessed a powerful science and technology base, spawning inventions including anaesthesia, antiseptic surgery, Insulin, Penicillin, Interferon and Dolly the sheep. Scotland is home to leading research institutions, such as the Alba Centre and the Institute for System Level Integration, many of which involve collaborations between leading universities. National government and European Union (EU) agencies support some initiatives aimed at commercialising the technology base and fostering
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greater entrepreneurial awareness and readiness in the graduate science and technology community. Key players include the Office of Science and Technology (1999) through the Scottish Institute of Enterprise, the EU through the European Regional Development Fund (ERDF) and Scottish Enterprise (SE), Scotland’s main economic development agency. Other bodies including the Entrepreneurial Exchange, a networking organisation for Scotland’s leading entrepreneurs, provide support for the development and delivery of enterprise education at school, college and university level. In the longer term students attending university should benefit from a sound understanding of the nature of enterprise. The Commercialisation Enquiry (Scottish Enterprise/Royal Society of Edinburgh, 1996) recognised that while commercialising research made an important and varied contribution to the Scottish economy it could be much greater. Universities were identified as well positioned to play a key role as sites of leading-edge research and the source of numerous innovative opportunities. Failure to exploit internal ideas and entrepreneurial capabilities represents a loss to the institution and wider economy, but the right cultural environment and support infrastructure can facilitate commercialisation. The above discussion on industry versus academic incubation indicates that many academics and students have little or no commercial experience on which to draw when deciding which route to pursue to commercialise their technology. They are not familiar with the process of new firm formation, nor are academics alert to the alternative scenarios for commercialisation which would enable them to remain within their university post while having varying levels of involvement in the new business started by others, or a technical consultant role with the organisation which licenses or buys the technology. Once students and academics understand the steps involved in company formation, the skills and expertise required, and the demands of managing a venture and a growing team, it is possible for them to identify which role they wish to play. The Technology Entrepreneurship Programme The Hunter Centre for Entrepreneurship @ Strathclyde (HCE@S) was established at the University of Strathclyde in 1996 as the Strathclyde Entrepreneurship Initiative, and renamed in 2000 following a £5 m donation from Tom Hunter, a Strathclyde alumnus. The Centre’s offering includes nine undergraduate electives and a range of postgraduate courses, which includes the Postgraduate Technology Entrepreneurship Programme (PTEP), the focus of this paper. Exit routes from the Postgraduate Technology Programme include a Postgraduate Certificate, Diploma or MSc in Technology Entrepreneurship in association with the Engineering and Science faculties. The HCE@S works closely with the University’s Research and Consultancy Services, whose brief includes facilitating the commercialisation of the University-generated technology. The PTEP programme was launched in 1999 to assist the commercialisation of technology from the Universities of Strathclyde and Glasgow. The programme was initially 50% ERDF funded, a condition being that it was provided free to eligible students and researchers during the period of funding. The balance of funding was contributed by the two Universities, SE, Glasgow Development Agency and the MacRobert Trust. Now an accredited postgraduate programme within the University, it aims to be self-funding. The PTEP was developed to provide principally engineers and scientists with knowledge, tools and techniques, which would make them better placed to consider new venture
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creation or involvement, as a career option. It was targeted at MSc students, doctoral and post-doctoral researchers and other research staff within science and engineering departments, as well as individuals studying at other universities or working in technology-based businesses in the west of Scotland. It was recognised that some participants would establish ventures in the short or long term while others would realise that entrepreneurship might not be for them. For participants intending to remain in academia but undertake commercially-oriented research at the academia–industry interface, the programme would enable them to develop a critical appreciation of the issues and processes surrounding venture creation and management, which would facilitate future interactions with commercial partners, whether on collaborative research programmes or when considering technology transfer deals. The introduction of various personal development tools helps to unravel further the motivations of individuals and uncover their reasons for involvement, and assist them in selection of role, operating level, and industry environment that is most compatible with their natural orientation. The techniques also provide insights into the likely sustainability of their drive and enthusiasm. It is easier to help those who understand their own motivations and expectations through the commercialisation process. Programme Design and Delivery The programme was conceived as comprising four modules; Opportunity Recognition, Accessing Resources, Technology Venture Management and an In-company Placement. Its objective was the development of management skills in innovation and company formation, with modules covering key issues for technology commercialisation and new venture creation. Modules were designed to provide a highly practical overview of new venture creation and venture management issues of importance to potential science and technology entrepreneurs. The PTEP aims to create a learning environment simulating the real-life experience of a business start-up, using conceptual frameworks drawn from academic research and practical tools and techniques, supported by case studies. Assessment follows the process of developing an actual business start-up from opportunity exploration to business plan formulation. Guest entrepreneurs and professional advisors, including venture capitalists, bankers and IP lawyers support core class delivery. Students are encouraged to use these visitors as consultants and a source of expertise to enhance their projects. In Year 1, the first two modules were delivered, using largely case studies and group work methods common in MBA courses but unfamiliar to most scientists and engineers. At the outset of Opportunity Recognition, students formed groups of up to six members, to simulate start-up teams; students remained in these teams throughout the modules as they developed a business idea. Business opportunities were based on the technology know-how and commercial experience of group members. Teams researched potential markets and developed their idea into an “investor ready” business plan for the culmination of Accessing Resources, which saw evaluation of the final plan and presentation to a panel of external investors and entrepreneurs. The winning team was awarded a scholarship to a 3-week intensive Summer School at UCSD, California. In Year 2, the module in Technology Venture Management was added, with the fourth in the programme’s third year of operation (Table 1). A personal development dimension was added in Year 2, driven by three factors. First, the reluctance of two of the four company founders from Year 1 to go out and talk to potential
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Table 1: Student activity on the PTEP.
Module 1: Opportunity recognition Module 2: Accessing resources Module 3: Technology venture management Module 4: In-company placement Short-term commercialisation
Year 1
Year 2
Year 3
1999–2000
2000–2001
2001–2002*
42 25
29 29 21
47 18 9 4 4 companies 2 companies 4 companies (4 students) (7 students) (4 students)
Note: Student numbers: Enrolled students, not all completed the course. * Module 1 of 2001–2002 was the last module to be covered by ERDF funding.
customers; secondly, the addition of a staff member with experience in personal development applied to executive and senior management motivation; and thirdly, the public sector funding carried the expectation that commercialisation would be facilitated where appropriate. Issues of personal development and the value of self-awareness and selfreflection were made more prominent. Issues of team formation and the nature of effective teams were raised in the first session, with further information provided through direction to readings and self-assessment exercises. Little additional process support or facilitation was provided in Opportunity Recognition, while Accessing Resources was reshaped around the importance of developing social capital to access resources. The nature, structure and information richness of networks was explored as were the skills and mindset required for effective networking. Emphasis was placed on the value of understanding personal motivations and developing clear goals and objectives prior to networking. Concepts were reinforced through project group and individual tutorials. Personal effectiveness and development tools were selected on applicability to a variety of contexts, with emphasis on models, which build links between the separate skill areas of personal and interpersonal effectiveness. Covey’s (1992) “Seven Habits model” was used to link the skills and attitudes of excellence in individual application and performance with the requirement to work effectively with others in the entrepreneurial team. Issues of building social capital to facilitate access to resources were also emphasised within the programme. The SMART goals framework of project management was combined with the “well-formed outcomes” model of Neuro-Linguistic Programming (NLP) (James, 2000) to create high quality personal goals, which are important in developing strategically and ethically sound business relationships. Other NLP processes, such as values elicitation, were taught and practiced by students in class. The revised programme represented a blending of theory and frameworks relating to the new venture creation/management process and tools and techniques to help participants understand better their own entrepreneurial motivations and goals and enable them to build quality relationships to aid company development. In Year 3, the programme was opened up to business postgraduate students and fees were introduced from the second module, with scholarships available for science and engineering postgraduates and staff. The personal development tutorial element was omitted
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from the first two terms and only actively reintroduced for those students attending Summer School. The core personal development models introduced in Year 2 were maintained but the in-class demonstration and teaching of processes was omitted. Personal values were only addressed on one occasion, in group tutorials (10+ students). Much of the contact time was spent in dissemination of knowledge and theory rather than application. All students who won places on the Cambridge Entrepreneurship Summer School received at least one one-to-one tutorial using the modified SMART goal-setting model.
Results from the First ThreeYears of the Programme Data from 118 individuals are considered in developing the discussion for this paper. The faculty profile for participants reflects the concentration on science (37%) and engineering (43%) with business accounting for the minority (20%). Over three-quarters of participant were male (76%); broadening the student base from Year 2 onwards has attracted more female participants with higher proportions of females from business (35%) than science (29%) or engineering (18%). The profile of participants showed that the majority were students studying for doctorates, with the second most important group being masters students. The third group constituted postdoctoral researchers and academic lecturing staff. Evidence from the performance of students during the modules indicates that the PTEP has provided students with a sound understanding of the new venture creation and management process. The development of robust and well-formed business plans based around the technologies of group members, constitute significant advancement of these technologies towards commercialisation. In addition, participants in Years 2 and 3 have had the opportunity to develop processes for determining their levels of motivation and commitment to entrepreneurship, and appreciate other applications for their enhanced knowledge of commercialisation. Year 1: 42 students enrolled at the start of the programme (Table 1). Some students did not complete both modules due to other academic commitments but benefited from the following key elements. The groups generated five business plans. The programme engendered enthusiasm in participants and encouraged them to consider business and entrepreneurship as a career option. It opened up new horizons and stimulated students to evaluate, or re-evaluate, what they were doing professionally and why. Summer School scholarships were offered to the five members (all male) of the team with the winning business plan who attended the 3-week Entrepreneurship Summer School at UCSD. The winning opportunity emerged from a combination of two emergent technologies into a new application in an industry where a third team member had prior industrial experience. A market was identified in Scotland and there was the expectation that it could be expanded internationally. There were some very strong players in the team, including the student who had access to the main IP; however, during Summer School, the team disintegrated as conflicts of motivation, personality and the desire to develop different applications of the core technology came to light. Two of the winners have gone on to commercialise opportunities in their individual areas of interest and the third is focused on eventually starting his own business. The other two individuals expressed no benefit from the experience. Year 2: The most notable change between Years 1 and 2 results is found in the postprogramme performance of those attending Summer School. Summer School attendees from
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the second cycle reported having benefited greatly from the experience, in terms of learning and, more importantly, professional networking, development of social capital and enhanced personal business confidence. The tools and techniques learned during the PTEP programme enabled them to maximise the benefits from attending the Summer School compared with students from the first year of the programme. None of the students attending the Summer School were British, four were male and one female. The group comprised members from two teams, all three from the winning team and two from the runner-up team. All students received personal skills and presentation coaching, including reflection on their core motivation and values during the PTEP; in addition, Summer School attendees received at least one in-depth, one-to-one personal goal-setting coaching session prior to Summer School to clarify career-aligned reason for attending. From this cohort the SMAR team of three has formed a business; the opportunity was built around the Ph.D. research of an Italian team member who attended the PTEP during the final year of her Ph.D. She had developed software for use in the design of ships’ sails. Using the software it was possible to design a yacht sail, and rather than have to make the sail in order to be able to test it, the software allowed simulation of the sail under conditions at sea. The major benefit was the removal of the prototyping stage, which often resulted in returning to the drawing board for adjustments prior to developing a Mark II version and so on. The company continues its work and has secured a strategic alliance with a French company in its target market, which is interested in manufacturing using its software. SMAR has employed its first non-student member of staff and successfully negotiated disengagement of one founder whose personal circumstances conflicted with the company’s needs, while retaining the personal goodwill and friendships. The two from the other team are now pursuing separate entrepreneurial careers. One has secured $1 m of matched funding and found a technical partner for an internet-based retail opportunity; the other has completed his Ph.D. and accepted a marketing and sales position with a technology start-up. The educational and commercialisation outcomes were seen as successful. Year 3: The third cohort received less personal development support during their course (confined to the networking and accessing resources sections); however, all those awarded Cambridge Entrepreneurship Summer School places received one-to-one coaching, to establish clearly aligned objectives in the term prior to attendance. This coaching led to two of the original nominees withdrawing, with other students attending in their place. Of the four students who attended all were working on individual ideas rather than being part of a team. All four participants reported great benefit from the Summer School and are continuing to pursue entrepreneurial-based activity; two already had a business and these continue (although slowly), the remaining two are still developing the concepts explored during Summer School. Participant Reflections With respect to the three class-based modules, participants were asked to reflect on their contribution to the development of knowledge, skills and abilities. A recurrent theme with regard to Opportunity Recognition was its role in clarifying the concept of opportunity as distinct from an idea and the development of a more realistic (quality) perspective on business ideas. One respondent valued learning how to “evaluate ideas based on real facts and
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not wishful thinking … I am a bit more conservative now. That restricts creativity but saves you from going through a process that leads you nowhere. I tend to have more ideas with better potential”. The module provided frameworks for opportunity identification and analysis, improving the quality of the idea assessment process; a Ph.D. student in engineering acquired “knowledge of how to assess a business opportunity, with real life examples which I think was very valuable”. He also gained an appreciation of the dynamic nature of the market and of opportunities within it: “I learnt how an opportunity changes over time and how it is necessary to continuously re-evaluate the initial opportunity … I am better at spotting a business opportunity as well as assessing the potential of it”. The complexity of opportunities also featured in the reflective comments: “[The module] raised my antennae to identify and realise windows of opportunities. I am more aware of the commercialisation potential of different technologies. I am able to correlate many different things together to make better commercial ideas”. With respect to Accessing Resources, some respondents highlighted the knowledge and frameworks acquired to help with business planning while others noted the development/enhancement of skills associated with networking to develop social/entrepreneurial capital or pitch to prospective investors. The module highlighted that “it is not important to own the resources but to know what resources can be accessed, when and how … to use others to get the venture going”. One participant mentioned developing “a good understanding of the ins and outs of the key aspects of the business plan and actual tools to use to build an idea”. Another commented “there is a huge number of organisations out there to help. Although it may not seem so, everyone who is a potential investor is there to see your idea become successful. People are there to help not shoot you down and where one person or group may not be interested there are many more who will be”. Interfacing with the external environment is critical, as one participant put it: “I have a better understanding of the importance of networking and to meet people that might be able to open doors that will enable the business to become a reality, and perhaps also to further the opportunity itself”. Another commented on a skill developed to enhance the quality of communication with potential investors: “One of my biggest achievements in this module was how to become succinct in presenting any idea, any business idea. Because of the short time potential investors normally have, the need to develop an accurate speech about our business was key to generate the needed interest among investors. Initially, we had to overcome any shyness about approaching investors and to present our project. We found that in the same way as we seek investors, also investors seek for a good business opportunity”. Technology Venture Management helped participants to understand “the key elements of running the business”. “I got a taste of how and what a business person may face out there in real life … I have a better ability to cope with real life business management situations because I am better prepared now” as one respondent reflected, while another developed, “the realisation that management is the key to success. Bad management can make an excellent idea unsuccessful and vice versa. It is essential that the correct people are in place”. Issues associated with teams featured in a number of reflections including, “the importance of team work and member’s team selection”, and the attention to detail/stickability required within the group, a “much better appreciation of group dynamics/interaction, and a better appreciation of the “drilling down” process that’s required to develop an idea into something approaching a business opportunity, i.e. enthusiasm alone is not enough”.
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As a result of taking the programme some participants recognised that their original idea was not viable, others reshaped their ideas. Some realised that they required more experience before embarking on company formation while for others participation marked the turning point in providing the confidence to take their idea forward. As one participant put it “I would say that I am much more aware of what is required of starting a business and will be continually looking out for opportunities in order to do so. At the same time I now realise just how much work is required to make a new enterprise successful and find it a very daunting challenge. I would say that I am possibly more likely to start my own business but more cautiously and with my eyes wider open”. Of the overall experience a postdoctoral researcher in science commented: … going to the classes was always very stimulating … I loved the mix of students and met some interesting and nice people there and enjoyed the group dynamics. The hands-on practice focus I thought was essential in getting some messages across to me … I came to see Entrepreneurship as a way to create opportunity and make something — and not just “business” (in the financial sense) — happen. Some preconceptions about “business” or rather why entrepreneurs do what they do were changed. To me meeting other entrepreneurs (I was about to delete this first part of the sentence, i.e. the “other”, but decided to leave it as shows that I think about myself in this role) was in fact one of the best part; to learn about the people behind it. Initially my motivation to attend the course came from my current involvement with the potential commercialisation of technologies which we are developing with a “Proof of Concept” grant. This is still ongoing and what I have learned has certainly helped me to assess the potential of the technology as a business opportunity in a more realistic light, i.e. putting on the business hat instead of the “techie”. But it has also helped me with strategies/tools for how to explore the opportunity further, e.g. the “go out and find out about it” attitude … The course has changed my attitude to many things (I am looking at things from the “opportunity angle”). From my point of view the personal development angle was (strangely) the most important immediate benefit and I have “ongoing discussions with myself ” with regards to the issues that were raised during the course. To me my own “fit” in a venture and its alignment with my personal goals is critically important. I would want my students and children (those who I “educate”) to have an opportunity to partake in a similar course because it is very empowering. Meanwhile a lecturer in engineering reflected: The entrepreneurial course gave me the ability to unlock the potential of all those years of engineering education. The course is focused around the analysis of case studies from real life entrepreneurial activities and teaches skills that are invaluable for the development of a more market centric view of engineering research …
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Research is no good without a commercial outcome; otherwise it just remains research that consumes capital. If you always look at the people in the news and say I could do a better job than them, well do it now you have the opportunity. Since the course I have been attempting to build a research programme that addresses the commercial goals as well as the academic. In the next few years I hope to set up a small company to exploit this research using the techniques learnt at the Hunter Centre.
Conclusions for Education and Economic Development There are many reasons for raising awareness of enterprise and entrepreneurship within the academic staff and student community ranging from the general economic benefit of increasing levels of new firm formation and innovation within existing enterprises to personal benefits and financial advantage of commercialising technology and IP. Programmes such as the PTEP offered within Strathclyde are able to develop greater understanding of the commercial aspects associated with new venture creation and growth. Techniques to assist in developing new ideas and screening those ideas to identify viable opportunities complement the technical know-how and expertise possessed by many university staff and graduate level students. A much stronger awareness of the business issues is seen associated with including entrepreneurship education in the university curriculum from raising awareness of the nature of entrepreneurship and its role in economic and technological development to facilitating actively commercialisation of university IP. While the inclusion of personal development has the potential to benefit all students, it provides the opportunity to boost significantly the effectiveness of entrepreneurship education programmes where commercialisation and company start-ups are key objectives. Evidence from individual and group feedback indicates that the PTEP provides participants with a valued opportunity to develop understanding of new venture creation and management process (including the elements of opportunity, team and resources) as well as acquire some of the personal attributes and behaviours (through the use of personal development tools) identified by Timmons as central to entrepreneurship. Introduction of students from backgrounds other than science and engineering increased the functional richness of the group. It provided participants with an opportunity to work with those from different backgrounds, contributing contrasting but complementary skills and competences, and gave class-based venture teams a more realistic dimension. Many participants from an academic background lacked industry experience. Those who identified a desire to pursue an entrepreneurial pathway acquired knowledge of commercial dimensions relevant for exploitation of their technologies and developed abilities to work effectively with others. The inclusion of self-reflection and core personal development elements enabled students to make clear choices about the relevance of the programme to their immediate careers and future professional lives. For some this served to increase their motivation for entrepreneurship by bringing their entrepreneurial interests into alignment with other areas of their life, enhancing commitment to their entrepreneurial endeavour and enabling them to network and access resources more effectively. For others the programme identified
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conflicts between potential entrepreneurial interest and other areas of fundamental personal importance, enabling the student to make conscious choices about their allocation of time and effort. Early indications are that the active introduction of personal development concepts and processes to complement new venture creation and management, as happened in the second year of the programme, facilitates team formation and function. In this cohort Summer School attendees (the leading students from the year) remained as teams beyond the PTEP and Summer School programme. The SMAR team underwent several challenges within its team of three which it was able to resolve using personal development concepts. This is not to say that programmes with no personal development dimension will not result in commercialisation; two students from the first cohort who received no additional personal development performed outstandingly and were motivated to commercialise their technologies through forming companies alone. Findings from the PTEP suggests that the personal development dimension cultivates robust entrepreneurial capital which is more aware of the tangible and less tangible aspects of venture creation. With a current emphasis on supporting the commercialisation of university-generated technology, a programme of education such as that offered by the HCE@S through its PTEP provides one mechanism to help facilitate the transfer process. There is evidence that academics passing through the programme develop a stronger commercial awareness, which is likely to result in them engaging in research with a stronger commercial focus, generating technology for which they now understand alternative exploitation pathways. Some who remained in academia in the short term have an eye to entrepreneurship in the longer term. Doctoral and postdoctoral participants have identified personally aligned career and technology exploitation pathways. Some are spinning-out on completion of their studies while others have taken up employment in the high technology environment, some in start-ups, to provide more time to explore their opportunity, cultivate networks and accesses critical resources. The wider economic community gains through the outcomes from education programmes, which emphasise commercial and personal dimensions of technology entrepreneurship. Prospective entrepreneurs make better-informed decisions on their degree of readiness and the right timing to start a business, reducing the incidence of false starts, which could result in failure and possible stigmatisation of the second-time-around entrepreneur. For those who decide that the time is not yet right, the human capital developed through a technology entrepreneurship education programme is well placed to help them contribute as an employee in a technology venture, attuned to the nature and challenges of the business. Those who do spin-out increase the new venture creation pool and transfer the technology from the university to benefit both the university and wider economy. They also increase the pool of academic entrepreneurial role models, encouraging others to consider the opportunities. Finally, academics with a greater commercial focus who remain within the university are better placed to capitalise on technology transfer opportunities for their research outputs which they can exploit or transfer to Scottish or nonScottish companies, with benefits accruing to the wider economy. They are also ideally placed to inject a greater spirit of enterprise into the student educational experience to engender a more entrepreneurial culture within the university and enthuse our entrepreneurs of tomorrow.
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References BankBoston. (1997). MIT: The impact of innovation. Boston: BankBoston Economics Department. Baron, R. A., & Markham, G. D. (2000). Beyond social capital: How social skills can enhance entrepreneurs’ success. Academy of Management Executive, 14(1), 106–116. British Council. (1978). Academic/industrial collaboration in Britain and Germany. Proceedings of the British–German seminar on academic research and industry, Cologne: The British Council. Buswell, R. J., & Lewis, E. W. (1970). The geographical distribution of industrial research activity in the United Kingdom. Regional Studies, 4(3), 297–306. Chandler, G. (1996). Business similarity as a moderator of the relationship between pre-ownership experience and venture performance. Entrepreneurship: Theory and Practice, 20(3), 51–65. Cooper, A. C. (1973). Technical entrepreneurship: What do we know? R&D Management, 3(2), 59–64. Cooper, S. Y. (1996). Small high technology firms: A theoretical and empirical study of location issues. Unpublished Ph.D. thesis, Edinburgh, Heriot-Watt University, Department of Business Organisation. Cooper, S. Y. (1998). Entrepreneurship and the location of high technology small firms; implications for regional development. In: R. P. Oakey & W. During (Eds), New technology based firms in the 1990s (pp. 247–267). London: Paul Chapman. Cooper, S. Y. (2000). Technical entrepreneurship. In: S. Carter & D. Jones-Evans (Eds), Enterprise and small business: Principles, practice and policy (pp. 220–241). London: Pearson Education Limited. Cooper, S. Y., Harrison, R., & Mason, C. (2001). Entrepreneurial histories and geographies: A reappraisal of the role of the incubator organisation. Paper presented to the 21st Babson Entrepreneurship Research Conference. Jonkoping, Sweden, 14–16 June. Covey, S. (1992). The habits of highly effective people. New York: Simon & Schuster. Howells, J., Nedeva, M., & Georghiou, L. (1998). Academic–industry links in the UK. Manchester: PREST. James, T. (2000). NLP master practitioner certification. London: The Performance Partnership. Jones-Evans, D. (1995). A typology of technology-based entrepreneurs — a model based on previous occupational background. International Journal of Entrepreneurial Behaviour and Research, 1(1), 26–47. Keeble, D. (1987). Entrepreneurship, high-technology industry and regional development in the United Kingdom: The case of the Cambridge Phenomenon. Paper presented to the seminar Technology and territory: Innovation diffusion in the regional experience of Europe and the USA. Instituto Universitario Orientale, University of Naples. Lindholm Dahlstrand, Å. (1999). Technology-based SMEs in the Göteborg Region: Their origin and interaction with universities and large firms. Regional Studies, 33, 379–389. McClelland, D. C. (1962). Business drive and national achievement. Harvard Business Review, 40(4), 99–112. Monck, C. S. P., Quintas, P., Porter, R. P., Storey, D. J., & Wynarczyk, P. (1988). Science parks and the growth of high technology firms. London, Croom Helm. Morphet, C. S. (1987). Research, development and innovation in the segmented economy: Spatial implications. In: B. Van der Knaap & E. Wever. (Eds), New technology and regional development (pp. 45–62). Beckenham: Croom Helm. Oakey, R. P. (1995). High-technology new firms: Variable barriers to growth. London: Paul Chapman. Oakey, R. P., Faulkner, W., Cooper, S. Y., & Walsh, V. (1990). New firms in the biotechnology industry. London: Pinter Publishers. Oakey, R. P., Rothwell, R., & Cooper, S. Y. (1988). The management of innovation in high technology small firms. London: Pinter Publishers.
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Office of Science and Technology (1999). Science Enterprise Challenge, http://www.dti.gov.uk/ ost/ostbusiness/claysec.htm Roberts, E. B. (1991). Entrepreneurs in high technology. Oxford: Oxford University Press. Scottish Council Development and Industry (1983). Profit through partnership: Industry–academic collaboration in Scotland. Edinburgh: Scottish Council Development and Industry. Scottish Enterprise/Royal Society of Edinburgh (1996). Prosperity for Scotland: Commercialisation enquiry final report. Glasgow: Scottish Enterprise/Royal Society of Edinburgh. Storey, D. J. (1982). Entrepreneurship and the small firms. London: Croom Helm. Storper, M. (1995). The resurgence of regional economies, ten years later: The region as a nexus of untraded interdependencies. European Urban and Regional Studies, 2, 191–221. Timmons, J. A. (1999). New venture creation. Boston, MA: Irwin.
Chapter 4
The Territorial Development of Innovation Support Assets Through University/Business Interactions: Towards a Dynamic Model Paul Benneworth and Stuart Dawley
There was at the turn of the 1980s a watershed in the history of technology transfer in the universities of the United States and in Western Europe … what is occurring is not only an increase in the volume of activity, but a transformation of the practice of technology transfer itself … it cannot be any more understood as a transmission of knowledge from the university to the receiver easily and usually with almost no follow-up … technology transfer looks more like a game of soccer in which the university is a member of the team (Gibbons et al., 1994, p. 87).
Introduction We live in an age when learning, innovation and knowledge have become key drivers of economic development, and the institutions associated with promoting these attributes are regarded as vital to ensuring sustainable economic success (Robertson, 1999; Cooke, 2002; MacKinnon, Chapman, & Cumbers, 2002). Some writers have argued that the type of knowledge required by businesses and governments has recently undergone a significant shift, and the most effective institutions are those best positioned to participate in these new modes of knowledge production (Gibbons et al., 1994; Etzkowitz & Leyesdorff, 2000). This generally accepted belief is founded upon a wide range of examples of regions and economies where high levels of connectivity between universities, government laboratories, innovation agencies and firms has been associated with successful regional economic performance (cf. Angel, 1994; Storper & Salais, 1997; Saxenian, 2000; Wicksteed, 2000). Intuitively, universities seem ideally placed to play a co-ordinating role within these knowledge networks, as their teaching and research activities have great potential for engendering and supporting innovation, learning and knowledge promotion activities. New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 41
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Recently, however, concern has been raised over the gulf between studies concentrating on highlighting specific cases of successful places and arguments placing universities generally central to territorial development. Uncritical readings of these relationships have given rise to what Autio (1997) called the “growth myopia”, in which a limited number of atypical high science content, high economic benefit and high-profile case studies have obscured and overshadowed the mundane reality of the majority of University Business Interactions (UBIs) (Benneworth, 2004). This is a particular problem for less successful regions, who face the dual problem of frequently being poorly endowed with knowledge assets, while these assets they do have failed to produce the spectacular benefits seen in exemplar regions. In particular, there remain significant unanswered questions concerning how specific activities can produce general territorial benefits, raising competitiveness and productivity. It is clear that not all universities produce equal territorial benefits (Di Gregorio & Shane, 2003; Feldman & Desrocher, 2003), and questions have been asked more recently about the underlying processes by which these relationships between universities and businesses can produce more general territorial economic development benefits, especially in less successful regions. At a meso-scale, the regional innovation systems literature has stressed universities’ roles in engendering and supporting particular types of learning behaviours, while evolutionary economists have highlighted universities’ roles in creating territorial collective assets (Maskell & Malmberg, 1999; Cooke, 2002). At a micro-scale, writers such as Jones-Evans et al. (1999); Van der Sijde et al. (2002) and Klofsten and Jones-Evans (2000) have taxonomised the particular activities in universities which make knowledge more accessible to local businesses. The attention paid to UBIs is valuable because they can have a vital role to play at supporting economic development at a range of scales. What is lacking in these approaches is bridging between the two scales, to explore whether particular activities in less hospitable innovation environments can help these less successful places to close the gap with what Armstrong (2001) calls the “totemic sites of the new economy”. We have previously examined how UBIs improve regional environments for innovation, and have found that universities work with innovative firms in developing particular activities which have general research benefits (Benneworth & Charles, 2004; Benneworth & Dawley, 2004). That research focused on the different roles played by different types of firms in developing new technology transfer activities. In this chapter, we switch the focus away from the firm to look more explicitly at the issue of the universities’ sophistication in supporting innovation. We focus on universities’ own innovation processes, to ask if there might be management lessons, which potentially can be applied to universities to increase their territorial economic impacts. We begin by looking at recent theoretical developments regarding the territorial economic value of innovation, and note that the interactive innovation model proposed by Muller and Zenker (2001) intuitively fits with the mechanism for innovation producing territorial learning benefits. We then focus specifically on the university as a provider of knowledge-intensive business services (KIBS) to explore the university innovation process, and specifically how the development of technology transfer activities within universities can potentially create territorial “knowledge pools”. We then revisit a small study from which we drew the conclusions concerning the role of firms in this “territorial knowledgepool co-evolution” process and briefly précis the findings of this research. We then look at
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the specific collaborative activities between universities and firms in the study, which created these technology transfer mechanisms. From these findings, we hypothesise about the types of barriers, which universities might face in improving their own practises while developing these technology transfer activities. We conclude by speculating on a theoretical framework for evaluating universities technology transfer/“third strand” activities, beyond the currently in-use static, “travelling best-practise” approaches.
University/Business Interactions: From Transactions to Relationships A common feature of universities in recent years has been the development of a range of institutional mechanisms to support their contribution to their regional economies (JonesEvans et al., 1999; Heydebreck, Klofsten, & Maier, 2000). SME Innovation and the Co-Evolution of Territorial Innovation Knowledge Assets In this chapter, our focus is university/business interactions, primarily with small- and medium-sized entrepreneurs (SMEs) in less successful regions, for two reasons. Firstly, SMEs have distinct innovation needs, which can be met under certain circumstances by university activities. A range of writers concur that SMEs are special cases of innovators, because they have few internal resources for innovation, making them uniquely vulnerable, and also heavily reliant on accessing external resources in dealing with the problems that arise in the course of innovation. As young, small and technology-oriented organisations, NTBFs have a number of limitations, particularly with regard to the level of resources and breadth of skills that can hinder the performance of these roles (Fontes & Coombs, 2001, p. 81). [T]here are certain size-related characteristics of SMEs that have important implications both for their support needs and for the way in which those needs are addressed. (North, Smallbone, & Vickers, 2001, p. 304). Tödtling and Kaufmann (2001) argue that the absence of particular resources in SMEs leaves them overly reactive to external changes (cf. Jones & Stevens, 1999). Martin and Scott (2000) highlight that SMEs are therefore more exposed to the risks of innovation, as they are often involved in fewer projects, and the failure of any one project is potentially disastrous for them. Second, we are interested in SMEs in peripheral regions, regions which are typically poorly endowed with the types of external resources which SMEs can use to augment their internal resources in solving innovation problems. In environments replete with innovation resources, there are fewer incentives for SMEs to expend effort in helping universities to be more open, because universities already tend to be adept at interacting with firms. If universities in these places are not effective partners, SMEs can access these resources through
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other firms, R&D laboratories, peer networks and other mechanisms (cf. Wicksteed, 2000; Lawton Smith, Glasson, Simmie, Chadwick, & Clark, 2003). Fontes and Coombs (2001) argue that SMEs tend only to drive technological change where the resources they require are not already readily available, sparse innovation environments, which largely correspond to less successful regions (Gomes-Cassares, 1997). Fontes and Coombs (2001) refer to the process of “densification of the techno-economic network” in which SMEs work with universities, and this co-operation process helps to reconfigure the university to be more externally oriented. They further argue that high-technology SMEs are potential sources of expertise in working with universities in “adapting external technology for local use”. The Co-Evolutionary Model of Innovation This suggests that university business interactions can have a territorial benefit arising from the relationships arising from SMEs working with universities, such as by improving the university’s openness to external partners. Firms working with universities also derive benefits from the relationship in that it helps them to solve their innovation problems. Although the collaboration does not intend to produce territorial knowledge assets, these benefits arise as a side effect; to theorise this side effect, we argue that the benefit arises through the collaboration creating something more durable than the transaction itself. An analogy to this can be seen in the way consultants build their competitive advantage. Wood (2002) argues that technical consultancies build problem-solving knowledge which they sell to client firms, and which complement the clients’ greater technical expertise (cf. Creplet, Dupouet, Kern, Mehmanpazir, & Munier, 2001). If universities build a knowledge base, which they sell locally, and which benefits those local firms to which they sell it, then this would suggest that a general territorial benefit had been created. What Muller and Zenker (2001) argue is that these consultancy transactions represent two separate innovations; one by the consultancy and the other by the client firm. These innovations represent a process of co-evolution where each party to the transaction is solving their own problems, but to solve those problems they create, and then draw on, a common resource. This relationship is thus a hybrid asset which offers benefits to each party, but can be considered to exist in its own right. Because of the importance of tacit knowledge and interpersonal interaction to the particular relationships, there is a strong territoriality to these shared knowledge pools. This process of co-evolution is shown in Figure 1. Within this above analysis, universities appear as relatively weakly autonomous players, certainly much weaker than the private sector KIBS firms. Fontes and Coombs are quite explicit about this weakness; their analyses envisage SMEs in less-favoured regions driving change and improvements to the regional innovation environment in general, and local universities in particular. However, returning to Muller and Zenker’s model of territorial innovation, overemphasis on SMEs in each case seems highly asymmetric, given that their model suggests that both SME and KIBS innovations are important to the development of the territorial learning pool. This suggests that there is an equal need to focus on the innovation in the KIBS provider, in this case the university. Moreover, it suggests that the way the innovation within the university takes place will be important in shaping the formation of the territorial knowledge pool, and hence producing regional economic benefit.
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KIBS
Knowledge service innovation
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SMES
Knowledge pool development
SME (product) innovation
Figure 1: The dual innovation spin-off loop in knowledge intensive business services. Source: Research Policy, Volume 30, Issue 9, pp 1501–1516, Business Services as Actors of Knowledge Transformation, Muller and Zenker (2001). Reprinted with permission. University Technology Transfer Activities: Institution or Innovation? Universities throughout the OECD are under increasing pressure to raise their level of regional engagement, as public policy makers seek to leverage past investments in science and technology to produce current and future economic benefits (Robertson, 1999; Dahlstrand & Jacobsson, 2003). The last 25 years has witnessed a plethora of new institutional forms which universities have adopted to produce these benefits (Jones-Evans et al., 1999). The US has historically blazed a trail with the establishment of university technology transfer offices, and the 1980 Bayh–Dole Act was a further incentive to universities and academics to drive forward the commercialisation of their knowledge (Charles, Perry, & Benneworth, 2003). A University’s capacity to support technology transfer can build up over time in ways that can be regarded as institutionally (if not territorially!) embedded. Heydebreck et al. (2000) argue that, as a consequence of this, universities have “service bundles” of latent capacity which are partially activated in response to specific demands (p. 94). We would stress once more that each partner in the relationship has very different reasons for the collaboration, although this can lead to stable and evolving collaborations, which extend between particular specific projects. Benneworth (2001) extends this idea of a relationship to offer a taxonomy of the different types of UBIs, which can co-exist in a single relationship between individuals in universities and firms. Benneworth suggests an eightfold typology of interactions including collaborative research, technical services, academic entrepreneurship, student placement, curriculum development, strategy development/leadership, professional development and academic spin-off. Service bundles (cf. Heydebreck et al., 2000) coalesce when individuals and teams in universities build up capacities and a shared infrastructure, which can be transferred during particular transactions. The link between these novel institutional forms, shared infrastructure and regional economic development is not automatic, although there is a tendency to assume that particular
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institutional forms can produce particular economic benefits. These various approaches all focus on the specificities of the particular transaction or institution, and conceptually assume a direct link between the activity and its territorial outputs (e.g. Van der Sijde et al., 2002). This offers no immediate bridge to an understanding how the particular activities correspond to more systemic changes, which can be regarded as regional economic developments. There is clearly an issue here of how it is possible to argue that meso-level changes have taken place (i.e. that activities are of regional benefit) on the basis of a set of micro-scale activities and outcomes. In the “universities” literature, this question has been finessed to a degree by arguing that universities have broader impacts on the socio-economic and cultural systems of their regions, changes which are transmitted through, but not contained by, particular activities and interactions (Robertson, 1999). As a first step towards bridging between regional and institutional scales is to look at what endures in relationships between particular transactions, and then consider if these enduring features can be regarded as territorially embedded (Benneworth & Dawley, 2004). The model of co-evolution of territorial knowledge pools suggests a mechanism for exploring this issue. University research, commercialisation and technology transfer are — by all reasonable measures — knowledge-intensive business services in their own right (Wood, 2002). Our hypothesis is therefore that tacit and regional specific knowledge may be created by this co-evolution process between firms and universities. We have already tested one half of this thesis, that is, the role played by firms in the co-innovation process (Benneworth & Dawley, 2004). The limit to this approach was that it focused exclusively on one half of the co-evolution process (the firm-based element), by assuming that the universities responded functionally to meet the needs of their partner firms. There seem intuitively to be further insights to be derived from exploring the other side of Muller and Zenker’s model, the service innovation process. In this sense, we are referring to the way that the institutional innovation within the university supports the co-evolution between firm and university. Our guiding question is therefore “how can universities introduce these changes which benefit the local innovation environment, and what are the features of those institutional innovations which have the greatest degree of success”?
Universities as Institutional Innovators There are a number of “ideal” institutional types suggested by which universities could improve their external engagement, such as industrial liaison office, graduate entrepreneurship programmes, spin-off promotion, science parks and incubators (Jones-Evans et al., 1999; Rappert, Webster, & Charles, 1999; Oakey, During, & Kauser, 2002; Pirnay, Surlemont, & Nlemvo, 2003). These are all undoubtedly useful activities, but not all these activities will necessarily benefit host territories, or be equally valuable. Clarysse, Wright, Lockett, Velde, and Vorhora (2004) note the differences between what they term “low selectivity” and “incubation” spin-offs in terms of the policy resources required, the link to the university technology base and the economic growth expectations of each approach. Hulsink and Elfring (2003) note that different types of incubator have very different characteristics and
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benefits, particularly at a territorial level. These papers are both much more recent examples of what Massey, Quintas, and Wield (1992) noted as the “hi-tech fantasy” underlying science parks, and Autio (1997) hints at with her idea of growth myopia (qv), namely, the assumption that spin-off firms are all high-technology growth-oriented activities which are enough to promote growth across all regions. The new institutional forms adopted by universities for the promotion of technology transfer intuitively seem important in making sense of increasing interactions, as they have been created at a time when there is extensive evidence that there has been a qualitative increase in the volume of this “third strand” activity. As Cooper (2001) notes: The ability of [universities] to contribute to economic development will vary depending on their mix of specialisation … each institution will have to develop its own approach, dependent on its own context (p. 203). This suggests that Heydebreck et al. (2000) service bundles are neither innate nor spontaneous capacities of universities, but are deliberately and deliberatively constructed to achieve a set of ends. This in turn suggests that improving the quality of the innovation process by which these institutions are created could also increase the territorial benefit they provide (Feldman & Desrochers, 2003). Rather than focus on the institutional forms which universities adopt, we consider here the institutional development processes through which universities proceed in seeking to develop these “outward-facing” activities (cf. Benner, 2003). A first step might be to identify different types of behaviour and provision, and consider how universities create new types of institution, which contribute positively to the regional situation. In the previous work, we have developed a benchmarking approach for universities (Charles & Benneworth, 2002) in one part of which we highlight the characteristics of universities which are good, average or poorly performing in terms of regional engagement. In Figure 2, we offer these as a potential first step in considering institutional innovation for technology transfer and regional development. The weakness with Figure 2 is that it is just a list of characteristics of universities rather than a classification per se. Indeed, the benchmarking approach seeks to develop a “balanced scorecard” for institutions, highlighting areas of weakness which require a specialised attention, rather than creating a single synoptic score for an institution. One strength of the firms sophistication classification identified in our past research was that firms in the same class have similar and predictable ways of organising, conducting and reflecting on their innovation processes (Charles, Benneworth, & Clouston, 1998; Charles & Benneworth, 2001; Benneworth & Charles, 2004). There is no suggestion in Figure 2 that there is any such coherence at the level of the institutions. While one institution may well be similarly rated for its graduate entrepreneurship and graduate support activities, there is no reason for graduate entrepreneurship, student placements and “work with inwards investors” to be similarly rated within institutions. Figure 2 cannot therefore be regarded directly as a sophistication classification, although it might hint at some of the issues on which a classification could be based. Although Figure 2 does not meet this sophistication need, it is intuitive to ask whether it is possible to classify or segment universities in similar terms to firms, by relating to managing the development of these programmes, activities and strategies that promote
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Low
Average
High
Strategic plan for business support
No strategic plan. Ad hoc approach to business support/
Strategic plan developed, but only partially implemented
Strategic plan developed in inclusive consultation across university, and widely embedded in all departments
Creation of spin-offs
Less than one spin-off created per year
Average of 2 spin-offs created per year
More than 5 spin-offs created per year
Engagement in inward investment
Listed as regional research resource in attracting new investors, but no central research excellence database.
Project-specific, tactical collaboration with RDAs in winning new inward investment
University works pro-actively with the RDA in targeting new investors and embedding existing firms.
Promoting graduate entrepreneurship
Advice limited to careers service and ad hoc encouragement
‘Islands of provision’: specialised courses/ modules in certain departments relying on short-term funding
Institution wide support mechanisms acknowledged by local partners as key contributors.
Monitoring graduate entrepreneurship
No post hoc support or monitoring of outcomes
Support for 20-50 students hitting 50% start-up rate.
Support for 30-100 students hitting 50% start-up rate
Availability of entrepreneurship modules
Isolated provision in specialist departments and business schools
Generally available in business faculties and as initiative of careers service
Widespread availability across institution especially in non scientific departments
Student placements
Only offered where required by external validation bodies e.g. teaching
Placements are an option for all vocational students and most of these students do a placement
Placements are an option for all students; most of these students do a placement
Incentives to staff to work with business
Barriers outweigh incentives; working with business is seen as ‘dead weight’ and undermining teaching and research
Some incentives in place; supportive policy focused on research but business reach out not fully recognised (e.g. promotions)
Strong signals to staff at all levels to collaborate underpinned by effective incentive procedures well applied.
Figure 2: The characteristics of different types of university in developing new activities for regional innovation support. Source: HEFCE (2002).
engagement with firms. Under our approach, innovation has two important characteristics. The first is that innovators use external knowledge sources to solve problems where the hypothesis is that the relationships with firms provide them with access to external innovation resources for dealing with the problems they face in innovating. The second is that innovation can be managed better, and a key element of learning concerns learning to innovate better. Returning to our earlier observation that universities do not have to be regarded as passive actors in this process. Rather, the changes take place as part of a more complex and interactive process in which universities actively shape their responses to firms needs in ways, which meet their own institutional needs. Thus, the way the university manages this business engagement with regional partners influences its eventual impact. This hypothesis
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is not contentious at the level of institutional management; there is much literature that suggests that universities can be managed in order to increase their positive territorial contributions (Gray, 1999; Boucher, Conway, & Van der Meer, 2003). What is lacking in these arguments is a link between particular institutional management practises and the economic growth benefits which later arise from firms working with the university. We therefore look inwards on universities’ own practises, and to ask: (i) How can we characterise the different ways in which firms work with universities to create regional learning assets? (ii) Can these approaches be developed into a model of the “densification process” for less successful regions? (iii) What are the implications of this for the management of universities so their relationships with local businesses have the greatest territorial impact?
Research Method, Analytic Approach and Study Background This paper does not directly present findings from a novel research, rather it returns to an investigation research project from which several elements have already been published. The reason for this arose from the dissemination of the original research project, in which we had argued that universities must discriminate in developing technology transfer activities, and concentrate on creating new institutional forms by working with the most strategic and sophisticated innovators. The feedback from conference presentations (cf. Benneworth & Dawley, 2003a, b) was that we were challenged as to whether or not it was possible to also explore the innovation process within universities, and also how universities improved their own innovation practises. Our critics argued that this would produce a more robust set of recommendations for universities, providing specific and practical recommendations for the implementation of new technology transfer projects and activities. Thus, this paper is a response to these challenges. We have returned to our original data set and reanalysing five examples where universities developed new activities for technology transfer. Our aim has been to characterise how universities developed new technology transfer mechanisms, and begin to identify the two areas we had previously explored for firms. These two areas were the different levels of effectiveness in university practise, and the barriers universities faced in moving between these different levels. The initial research project was a study for the UK Small Business Service, on how innovating firms used different sources of information in solving their innovation problems. That project involved 43 interviews, which provided an insight into some of the key areas where universities had previously been involved. However, only a few of those interviews were with university actors, which made it hard to draw conclusions on the university innovation process. In response to the comments on the early versions of the research, we therefore complemented the initial four university interviews with a number of further interviews. These interviews sought to build a bigger picture of what was taking place in universities, including nine additional interviews with university representatives, two more interviews with collaborating firms, two interviews with the centres of excellence and a presentation from the Regional Development Agency. The aim of these interviews was to explore whether the
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Table 1: The size of firms and formation date interviewed in the sample. Description (employees)
Firms
Formation date
Firms
Micro-businesses (1–10) Minibusinesses (11–30) Small (31–50) Small/medium (51–100)
16 6 2 2
Before 1988 1988–1992 1993–1997 1998–2002
4 3 5 14
Table 2: The reason for the company’s formation and company sector. Description Liquidation/bankruptcy spin-off “Intrapreneurial” start-up Technology-based start-up Professional services start-up Hostile spin-off Other
Firms
Sector
Firms
7 5 5 3 3 3
Engineering, R&D/architecture Web design and computer services Analytic software design Light engineering and manufacturing Plastics, chemicals, pharma/biotech Retail Construction
6 5 4 4 4 2 1
changes that the universities went through could be regarded as innovation processes. These interviews took place from May 2003 to April 2004. We chose the Northeast of England because of its economic situation as a less-favoured region with problematically low levels of business innovation. The Northeast is the poorest and least populated of the English regions, although Tyne & Wear (the study location) is the richest of its sub-regions, at 82% of UK average GDP (1998). Although the region has a high level of manufacturing, Tyne & Wear is predominantly a service-led economy, with only 18% of employment in manufacturing, 15% in business services and 28% working in public sector service provision. Much employment in the private service sector is in business service back-offices, call centres and customer relationship management activities. Tables 1 and 2 provide some details of the sample of companies in the initial sample, drawn from companies which had sought public sector support for their innovation. Consequently, the sample does not reflect the business structure of the region, with a predominance of manufacturing companies and a relatively high number of spin-off firms (Table 1).
Universities’ Business Interaction Innovation Processes The research did not initially attempt to develop a framework for understanding how universities develop innovation support services. However, the response to Benneworth and Dawley (2003a, b) suggested that there was a pressing need to develop a classification for universities; in that paper, we could fairly be criticised for assuming that universities were all homogeneously effective innovators who unproblematically developed responses to
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innovation. However, analysing the survey interviews revealed how firms and universities had co-evolved these services, the universities using their relationships with firms to help them address the problems they faced on innovating. We identified five main services (see below) offered to universities, which had clearly been developed through a process to which an innovation analysis could be applied. The two criteria for the selection of these five were that for each of these cases, (i) firms and universities we interviewed had co-developed the services, and (ii) other firms we interviewed had used the activities to help meet their own innovation requirements. 1. Although the Teaching Company Scheme (TCS) is now well established nationally, and Durham University houses the region’s TCS centre, the idea of placing graduates into businesses as a bridge into universities is comparatively recent. One of the firms interviewed had participated in the original TCS (national) pilot scheme with a great degree of success, and followed that up with three more schemes. Their involvement extended to working with the local TCS centre to ensure that the interests of all three parties are explicitly stated at the start of each TCS project in a formal framework. Although that might seem counterintuitive and seemingly bureaucratic, the framework is a useful way of explicitly managing parties within a TCS with divergent interests, that can easily lead to the collapse of the scheme; 2. The Smart award programme (a government scheme promoting innovation in SMEs) was refined to meet the needs of local companies. Although the programme was changed at a national level, with Small Firms Merit Award for Research and Technology (SMART) and Support for Products Under Research (SPUR) amalgamated into a single multi-level programme, there were also regional refinements of the programme and the universities were active in this. The government office was granted dispensation to encourage the number of applications because of low levels from the Northeast. The Government Office for the North East (GONE) team developed a specialism in assisting firms assemble applications, respected by those interviewed firms who had held awards. A number of very successful innovators had been through a series of meetings with GONE including screening, modifications and acceptance to assist with the refinement of this approach. The universities in the region were an important source of ideas to go into these programmes, and university-based innovators were frequently recipients of these awards. In 2003, for example, nine of the awards went to firms linked to the University of Newcastle, who had decided to begin tracking success with Smart awards as part of their broader efforts to increase commercialisation; 3. Centres of excellence emerged in the Northeast as part of the evolving thinking of a team within the Regional Development Agency (RDA) who were developing a single innovation action plan for the region. This team commissioned the Massachusettsbased consultant Arthur D. Little to review the linkages and potential for growth in the research base in the Northeast. In the course of this, they observed that there were very different science and technology bases underpinning the nine different clusters which had been identified already in the region (Arthur, 2001). The report identified that there were five areas in which the region had or could reasonably build commercially viable research strengths. The report recommended that the existing assets be combined into five regional centres of excellence. The universities were centrally involved in the
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research base mapping process, and their research strengths were vital to the five areas identified. The universities chose to engage with this process strategically, although what emerged at the end of the process was not entirely to the universities’ advantage. For the universities to access any of the novel resources through the Centres of Excellence programme, a number of universities in the region had to reconfigure their business development work to face these five centres more explicitly. In one case, a university business development manager went to work for a spin-off company, which subsequently won a Smart award; 4. University commercialisation activities is a fourth area where there was considerable service innovation. In the Northeast, the regional universities had put a large amount of effort into the development of “Knowledge House” as a single point of contact for regional firms to access university consultancy services. Knowledge House had originally been funded by European Regional Development Funding, and that limited both the size and geographical location of firms able to access the services it offered. The key challenges for Knowledge House at the time of the research included moving from being a project to an organisation, while at the same time increasing the revenues to the six participating regional universities. Knowledge House operated under the umbrella of the regional university association. This organisation recruited one local university’s Deputy Vice Chancellor (DVC) with a responsibility and experience in regional development, to take executive responsibility for among other things, the success of Knowledge House. Thus, the DVC’s experiences in one institution were transferred and magnified to the regional scale; 5. Collaborative research projects is a final area where there was significant university/ business interaction between universities and firms, particularly those where problems in the industrial partners were driving the academic research agenda. These operated very differently to the linear model of innovation where ideas are generated in the universities and diffused into firms. This was particularly clear in the case of European Commission Framework Programmes involving both local firms and universities where, on occasion, each took the role of advocate/animateur and partner. A number of the firms worked closely with the university commercialisation organisations to help them improve the delivery of their services and increase their spin-off rates. One of the business support organisations we interviewed was itself a (antipathetic) university spin-out company exploiting proprietary knowledge of “hot-housing” to encourage entrepreneurs. The interesting feature of that firm is that the knowledge generated in the co-evolution was directly commercialisable, suggesting that this co-evolution does create tangible assets with independent commercial value. In each case, the relationship between university and firm is far more complex than a linear model of innovation would suggest.
The Firms’ Contribution to the Universities’ Innovation Processes Just as consultancies are continually innovating by “providing consultancy knowledge”, universities must innovate to provide commercialisation services, which may involve profound organisational innovation within the university, alongside the development of particular
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technical services (Jones-Evans et al., 1999). We therefore consider the contributions which investigated firms made to the universities’ innovation processes. Firstly, we found that there were a range of reasons that universities engaged with firms. These are summarised in Figure 3. Interestingly, Figure 3 does not explain why the universities developed the particular programmes and activities listed, because the outputs shown as significant to the collaborating universities and academics were not (with the exception of the funds that they brought in) the types of outputs that came out of the above five services. There are clear reasons for the universities to develop the services, but they are not necessarily relevant to firms. This is because all the benefits arose from having relationships with the firms, rather than developing those services in conjunction with the firms. It is important to note that not all innovating firms were equally useful in this regard, and it was primarily the better innovators which provided the real incentives for universities to collaborate. A second important feature was that the resources that this provided to the universities were predominantly final outputs, rather than helping them to develop new and improve existing technology transfer services. In Figure 4, we separate out those resources which universities used specifically in developing new technology transfer services and those that the university actors accessed through the UBIs. What Figure 4 shows is that universities do work with firms in developing new services for firms, and that they do obtain different benefits, depending on how good those firms are themselves at innovating. As a first-cut analysis, this suggests that for any innovation support service provided by a university, the university needs to organise its innovation process (i.e. in which the university learns how to offer its expertise as commercial services), to ensure that firms are only involved when they have something positive to contribute to that development process. Just as elsewhere, we have identified that naïve SMEs can see their innovation “derailed” by large and demanding partner firms. Similarly, universities that are learning about technology transfer need to work with the kinds of firms able to give them constructive feedback. The best innovating firms are most able to give the best feedback, and while novice innovators might have the most to gain from services, early involvement might _____________________________________________________________________ F inancial
Organisational
Prestige
Consultancy fees
Status of department within university
Industrial collaborator
Studentship fees
Dealing with succession of senior staff
Referee
Matched time
Undergraduate student placement
Reference site
Collaborative research income
Papers ‘Research excellence’
_____________________________________________________________________
Figure 3: The ways in which local universities benefited from their linkages to all innovators.
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Paul Benneworth and Stuart Dawley
Mode of
Expert firm
Experienced
Inexperienced
Strategic adviser
Laboratory
Laboratory rats
activity
Novice Mice waiting for crumbs
Nature of
Articulating to the
A good source of
Willing to buy
Willing to buy any
demand
university a vision
sophisticated
services already
service to solve any
of potential
demand
created and
problem; very
technological
encouraging
approach university
limited comfort
services
universities to
with new problems
zone to absorb new
‘raise their game’
to solve
ideas.
Scope of
Willing to pay for
Long term
Willing to try and
Occasional/ erratic
involvement
high quality R&D
collaborations
comment critically
users of innovation
including academic
between key
on new services to
services.
outputs
innovators and
increase their
academics
effectiveness
Territorial
Shapes the
Provides a set of
Use the services to
Potentially negative
impact of
university to
sophisticated
improve their own
if the novice firms
collaboration
support its own
industrial users to
innovation
use the services
high quality
help the university
performance and
badly or waste
research activities
win external funds
hence the territorial
money and
to support new
outcomes
underperform.
commercialisation
Figure 4: The relationship between firms and “universities” by firm sophistication. Source: after Benneworth and Dawley (2004).
significantly hinder the development of the particular innovation support service from the university.
Universities as Institutional Innovators: Towards a Barriers Approach? Alongside this development process, there were a number of bottlenecks that university managers appeared to face in each step of moving the development process forward as follows. ●
Idea generation. There are a number of channels for idea generation which can be mutually contradictory: regional/strategic documents, the university’s own corporate plan, the availability of (e.g. ERDF) funding, existing customers, market research and the expertises of existing staff members. External connections helped the universities to prioritise their own actions, as with the Centres of Excellence;
The Territorial Development of Innovation Support Assets ●
●
●
55
Project development and design. It can be difficult to sensibly design a new service which meets the needs of conflicting parameters: customer needs, staff capability, funding output targets, funding timings. Good links with firms helped the universities to establish potentially useful activities as one-off projects, then if they were successful, with a handful of good partner firms, to seek to consolidate them into self-sustaining activities; Programme development and piloting. Not all the programmes which were attempted were successful, and good feedback from partners was essential in deciding which activities to persevere with; Launch and evaluation. Public sector business support is subject to changes which undermine the business model of particular programmes; if funding expires and the programme has not been embedded within the core practises of the university, then it is unlikely to survive.
Although not all of the different activities demonstrated that they passed through all the stages of an innovation project, the fact that the formation process was progressive suggests that it makes sense to use the innovation model for at least understanding what is taking place. A number of the interviewees stressed that, in introducing new programmes and ideas, they frequently faced a number of uncertainties, which were very difficult to reconcile. Returning to the analogy with the business development process, there appeared to be a missing stage in a number of the support schemes available, which could correspond to the prototype phase of product innovation, and which were on occasion filled by pilot schemes. If pilot schemes are a key element in the development of better innovation support services, this issue has a number of implications for the way such pilot programmes are evaluated: ●
●
●
●
Part of the success of a pilot programme is the degree to which it improves the accessibility of the service to less-sophisticated firms, which may include educating the service providers; Related to this is the fact that the evaluation of the pilot needs to consider whether it is going to be easier to deliver the service to a wider range of (less experienced) companies at the end of the trial; Because firms do not always have a good sense of how useful a service has been, effective evaluation requires evaluation of how firms have used a service, rather than the firm’s own thoughts on that service; Pilot programmes can play a big role in developing the skills of university staff by giving them a chance to test their ideas and learn to deal more generally with different classes of firms; the knowledge created in the support organisations need not be overlooked in pilots.
Classifying Universities’ Institutional Innovation Processes We have so far argued that university technology transfer services have two aspects. Firstly, that university technology transfer services are being developed in processes where problems are encountered, the university draws on internal and external resources to solve these problems and that the relationships they have with firms which facilitate problem solving. Secondly, universities seek to improve their own innovation performance by changing their internal structures to better access external resources (as in the case of Knowledge House),
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Paul Benneworth and Stuart Dawley
or to ensure better knowledge flow in the technology transfer process, such as with the creation of business development managers. The key question is whether it is possible to segment universities as robustly as we have already done with firms, and clearly, given the scope of the research undertaken, and with the limit of the five examples of university new service development from an opportunistic interview sample, there is not the volume of evidence to confidently develop a fully worked through model. However, two features appear clearest in determining the success that universities’ reach-out activities in terms of increasing the total volume of innovation done in the region. These two features are certainly interrelated. ●
●
Strategic commitment: this was important to ensure that the particular relationship deals were structured in sustainable ways; where relationships were ad hoc, then there was a risk that the interests of academic and firms were not properly aligned. Conversely, when universities were engaged with particular activities in a strategic and corporate manner, then these activities were not threatened by changes in academics working environment (such as the Centres of Excellence); Culture for collaboration: where the universities were most successful is where particular activities fitted with their existing interests. The Teaching Company Scheme (for example) sat well within the Schools of Engineering in a number of the regional universities, which were the main university partners in the scheme.
In Figure 5, we set out a potential framework for the characteristics of universities at each of the sophistication levels, in terms of the strategic vision of the university, the culture of collaboration and the implications of this for the way the university works with strategic partners. At the lowest level might be those universities who have placed no strategic priority on regional business collaboration; although collaboration might take place, it is out of the sight of university managers, and thus tends to be reactive to the demands of local partners. It is
Univ level
The strategic approach of the institution to business interactions
The ‘new service generation’ process working with businesses
The ‘organisational style’ of the university’s business interactions
1
No strategic plan.
The university only offers routine technical services in response to client needs (e.g. rendering drawings electronically).
Reactive/ ad-hoc
2
Strategic plan for reachout developed centrally, with limited internal embedding
The university develops new technologies and ideas in demand to requests from SMEs
Collaborative/ technological
3
Strategic plan for reachout developed centrally, with greater internal embedding
The university introduces and develops new ideas with a limited/ selected range of clients
Collaborative/ strategic
4
Strategic plan developed in inclusive consultation across university
The university disseminates its new ideas to a wider range of clients and stimulates their own innovative processes.
Positional/ strategic
Figure 5: Key barriers to improvement in the institutional innovation process.
The Territorial Development of Innovation Support Assets
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also very vulnerable to unforeseen consequences of changes within the university to teaching or research activities and organisation. At the other end of the spectrum are those universities which are exemplars of regionally engaged universities, with strong strategies for regional business support, which are widely implemented within the institution. Extending the Sophistication Approach: Barriers to Improvement The rationale for using a sophistication approach for innovation management is that it allows analysis of organisations’ attempts to improve their innovation performance. Newcastle University, for example, has over the last decade taken a series of quite explicit steps to improve regional engagement both in strategic terms and in encouraging a culture of collaboration — appointing a new Vice Chancellor, reorganising academic and service universities and prioritising bidding for funding streams which support regional engagement. These steps and this process have not proceeded entirely smoothly; it has been a learning process for the institution. This is part of an explicit mission to create a surplus for the university, which can be invested in the wider institutional development, but that investment programme potentially will create regional development benefits. In terms of establishing a future research agenda for exploring the detail of how universities support innovation in less successful territories, we can identify some of the barriers which universities might face in improving their own ability to contribute to territorial innovation outcomes. These barriers are summarised in Figure 6.
Business support The interaction between the firm and the organisation (BSO) BSO (cf. Fontes & Coombs, 2001) sophistication 1
The BSO only offers routine technical services in response to client needs (e.g. rendering drawings electronically).
Key barriers to improvement
Lack of sophisticated clientele Management structure compartmentalises tasks Closely tied to programme funding
2
The BSO sources new technologies and ideas in demand to requests from SMEs
Few incentives to be creative in the organisation (risk averse culture) Weak demand from clients means not challenge to improve service
3
The BSO introduces and develops new ideas with a limited/ selected range of clients
Difficulty in building up internal expertise because of need to chase external funding Lock-in to existing projects because not designed to be opened up to a wider clientele.
4
The BSO disseminates its new ideas to a wider range of clients and stimulates their own innovative processes.
[barriers to remaining at level 4] External disruption: reorganisation creates climate of uncertainty and leads to staff loss. BSO becomes associated with high profile failure and unable to organise sophisticated network activities
Figure 6: Possible barriers to move between the different sophistication levels for universities.
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Concluding Discussion: Latency and Enactment in University/Business Interactions In this paper, we have been concerned how universities contribute to supporting innovative companies by developing supportive institutions collaboratively with a range of partner firms. We concentrated on SMEs because of the distinctive needs which SMEs have, which if met by universities, in turn make the universities a better source of support for more firms. We argue that this means that improvement in universities’ reach-out activities can be regarded as a meso-scale change as long as it can be demonstrated that new capacities have been created. This requires doing as we did in the original research project, to demonstrate that other firms have drawn on what emerged from the institutional innovation project to solve their innovation problems, such as using the Teaching Company Scheme, working with a centre of excellence or accessing consultancy through Knowledge House. A second institutional dimension to this point is the way in which universities themselves improve their “innovation sophistication”. Although none of the improvements such as the Centres of Excellence were consciously developed and managed as innovation projects, there appears to be scope for managing the creation of novel knowledge-brokerage institutions as innovation projects. Heydebreck et al. note that service bundles evolve, and there may well be capacity to rationally manage the innovation process to drive forward that innovation process within the universities. Figure 6 suggests the different stages at which it makes sense to involve firms as universities develop novel institutions for supporting firm-based innovation process. If there is a genuine innovation process (as part of the co-evolution between firms and universities), then there may be value in exploring whether different types of institutions are differently sophisticated. This would suggest investigating the barriers that universities at different levels experience in innovating, and possible strategies for improving the sophistication levels of universities. This is the first of the avenues for future research, to examine at a micro-scale the innovation process by which universities create technology transfer activities, and how universities improve their capacity to create new support activities. Treating universities as KIBS — even as a temporary analytic device — offers interesting possibilities for understanding the creation and transfer of best practice ideas between institutions. This is potentially of great interest to policy-makers; in the UK, there is a mix of reactive and prescriptive approaches at the centre, between allowing universities to propose their own solutions and imposing programmes to which the universities must conform. If universities’ technology transfer activities are analogous to KIBS, then their innovation processes might well follow Wood’s hierarchy of knowledge transfer. Best practice would generally be derived from universities working with multinational companies (MNCs), but there would be a role for universities oriented to national and local firms. The case of the Centres of Excellence followed this description perfectly, of a framework developed by a multi-national (Arthur D. Little) working with clients globally, but which only made sense when worked through by the regional universities. This reflects Wood’s (2002) observation that “national and regional systems also possess technological, organisational and cultural knowledge that is vital for economic success” (p. 79). Particular universities may be able to specialise in technology transfer in particular sectoral or technological areas, rather than having research expertise in these areas that they seek to
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commercialise. However, there remains a need for much more empirical work to explore the interesting theoretical issues raised by exploring universities as commercial-type innovators, and considering the territorial consequences of universities’ attempts to improve their own performance in developing technology transfer institutions.
Acknowledgements This paper draws on research funded by the English Small Business Service Research and Evaluation Service, from a small project in 2002, entitled “Targeted Innovation Support for SMEs”. Early versions of this paper were presented at the 11th High technology small firms conference, in Manchester, 12th–13th July and the The business of higher education conference at Newcastle Business School, 2nd October 2003. The final revisions to the paper were undertaken as part of the ESRC-funded project “Bringing Cambridge to Consett? University spin-offs in the periphery”, RES-000-22-0659. Thanks are also due to Professor David Charles, Lars Coenen, Catherine Hodgson and Fiona Whitehurst for their support, advice and participation in the follow-up interviews. Particular thanks is also due to those firms, academics and technology transfer staff who gave their time during the interviews, although any errors remain the sole responsibility of the authors.
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Kaufmann, A., & Tödtling, F. (2001). Science-industry interaction in the process of innovation: The importance of boundary-crossing systems. Research Policy, 30, 791–804. Klofsten, M., & Jones-Evans, D. (2000). Comparing academic entrepreneurship in Europe: The case of Sweden and Ireland. Small Business Economics, 14(2), 299–309. Lawton Smith, H., Glasson, J., Simmie, J., Chadwick, A., & Clark, G. (2003). Enterprising Oxford: The growth of the Oxfordshire high technology economy. Oxford: Oxfordshire Economic Observatory, ISBN 1 874370 33 8, £20 (pbk). MacKinnon, D., Chapman, K., & Cumbers, A. (2002). Learning, innovation and regional development: A critical appraisal of recent debates. Progress in Human Geography, 26(3), 293–311. Martin, S., & Scott, J. T. (2000). The nature of innovation markets failure and the design of public support for private innovation. Research police, 29, 437–447. Maskell, P., & Malmberg, P. (1999). Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23(1), 167–185. Massey, D., Quintas, P., & Wield, D. (1992). Hi-technology fantasies. London: Routledge. Muller, E., & Zenker, A. (2001). Business services as actors of knowledge transformation: The role of KIBS in regional and national innovation systems. Research Policy, 30, 1501–1516. North, D., Smallbone, D., & Vickers, I. (2001). Public sector support for innovating SMEs. Small Business Economics, 16(2), 303–317. Oakey, R., During, W., & Kauser, S. (2002). A continuing research agenda. In: R. Oakey, W. During & S. Kauser (Eds), New technology based firms in the millennium (Vol. 2). London: Pergamon. Pirnay, F., Surlemont, B., & Nlemvo, F. (2003). Towards a typology of university spin-offs. Small Business Economics, 21, 355–369. Rappert, B., Webster, A., & Charles, D. R. (1999). Making sense of diversity and reluctance: Academic relations for USOs and SMEs. Research Policy, 28, 873–890. Robertson, D. (1999). Knowledge societies, intellectual capital and economic growth. In: H. Gray (Ed.), Universities and the creation of wealth. Buckingham: Open University Press. Saxenian, A.-L. (2000). Networks of immigrant entrepreneurs. In: C. M. Lee, W. F. Miller, M. G. Hancock & H. S. Rowen (Eds). The Silicon valley edge: A habitat for innovation and entrepreneurship. Stanford, CA: Stanford University Press. Storper, M., & Salais, R. (1997). Regional worlds of production: The action frameworks of the economy. Cambridge, MA: Harvard University Press. Van der Sijde, P., Karnebeck, S., & Benthem, J. V. (2002). The impact of a university spin off programme: The case of HTSFs established through TOP. In: R. Oakey, W. During & S. Kauser (Eds), New technology based firms in the millennium (Vol. 2). London: Pergamon. Wicksteed, B. (2000). The Cambridge phenomenon revisited. Cambridge, UK: Segal Quince Wicksteed. Wood, P. (2002). Innovation and the growth of business consultancy: Parallel but associated trends. In: P. Wood (Ed.), Consultancy and innovation: The business service revolution in Europe. London: Routledge.
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Chapter 5
Towards a Model of Opportunity Recognition and Development in Entrepreneurial Hi-Tech Start-Ups John S. Park
Introduction The field of opportunity recognition is relatively under-developed in entrepreneurship research. Many authors have proposed this as a rich and relevant research topic (Bygrave & Hofer, 1991; Shaver & Scott, 1991; Gaglio, 1997; Venkataraman, 1997; Shane & Venkataraman, 2000). However, despite an abundance of ideas at various stages of development which have been presented at conferences (Hills & Shrader, 1998; Hills, Shrader, & Lumpkin, 1999; Koen & Kohli, 1998; Singh, Hills, Hybels, & Lumpkin, 1999; Zietsma, 1999; Craig & Lindsay, 2001; Shepherd & De Tienne, 2001), the formal literature remains comparatively sparse (Shane, 2000; Colarelli O’Connor & Rice, 2001; Ardichivili, Cardonzo, & Sourav, 2003). The unique contribution of this work is to utilise the existing literature to both synthesise a holistic model of opportunity recognition and to identify the gaps in the current state of opportunity recognition theory with specific reference to the field of high-tech start-ups. Opportunity recognition is such a loosely defined field, it is perhaps beneficial to configure the topic more precisely. In this paper, the focus remains on the entrepreneurial firm, but with specific reference to high-technology start-ups. The rationale behind this is twofold. First, from an economic standpoint these firms are the potential corporate giants of the future so a better understanding of how best to grow acorns into oak trees would benefit both entrepreneurs and society. Second, these firms work in an extreme environment where the technology challenges are high, and the resources generally scarce (Julien, 1995). In the technology sector business survival, let alone growth, is dependent on finding and exploiting a reliable innovation strategy quickly, and before other entrepreneurs. The lessons learned in the high-technology environment can be transferable to less extreme sectors.
New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 63
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There is a significant body of literature on high-technology firms (Shan, 1990; Feldman & Ronzio, 2001; Thierstein & Beate, 2001; Bruton & Rubanick, 2002; Maxwell & Westerfield, 2002). Therefore, it is necessary to provide a definition of a high-technology firm to avoid confusion with other definitions in the literature. In simple terms, it is a firm that uses rapidly emerging or evolving technology as a key part of its product development, production or marketing strategy. These firms are important because they are seen by many governments as having an increasing role to play in the regeneration and growth of strong national economies. Many view technology firms as having extremely high growth potential, mainly due to the global export potential for these firms’ unique technologies and the positive impact on the economy (Scottish Executive, 2001; OECD, 2003). As a result, better understanding of effective opportunity recognition processes used in such technology sectors would have obvious benefits in helping government develop and refine appropriate development policies and support programmes. Clearly, technology-related innovation itself is an immense field of study. There are several highly developed themes, which have become established in the literature with many angles covered. Common themes include: organisational structure (Lee-Kelley, 2002), innovation and product development strategies (Drejer, 2000), ways to enhance creativity and improve idea generation in organisations (Coates, Cook, & Robinson, 1996; Hall, 1995), quality management systems (Lockamy & Khurana, 1995; Shen, Tan, & Xie, 2000), reducing cycle times (Smith & Reinertsen, 1991) and developing an organisational culture that promotes new product innovation (Nemeth, 1997). However, by far the majority of the literature has focussed on the large organisation mainly due to the extent of research material available on large firms. Even the champion of entrepreneurial innovation (Drucker, 1985) cites almost exclusively large firm examples in his text. It is the intent of this work to try to start to fill this gap by reviewing the current relevance of the opportunity recognition literature for high-tech start-up firms. The issue with the focus on large organisations is that the important role of the individual can become lost. For the small entrepreneurial firm it has long been recognised that the individual entrepreneur has a large role to play in the evolution of the company and its products (Herron & Sapienza, 1992). The endless hunt for the “ideal” entrepreneurial personality in the academic literature is at least in part testimony to the importance of the individual represented by an endless quest to identify the role the individual personality traits of the entrepreneur in the development of new ventures plays (Gartner, 1988; Bygrave & Hofer, 1991; Herron & Sapienza, 1992). It is the purpose of this paper to better understand the role of the individual in developing the high-tech business opportunity. This paper starts to explore the importance of three key elements in the small hightechnology start-up — the entrepreneur, the collective knowledge and experience of the firm and technology. Experience also plays a key role, particularly in technology start-ups. In such environments, the barriers to entry into a particular technology field or market are extremely high. To enter them it requires a combination of relevant scientific technical expertise and market knowledge. However, experience is not simply related to technology. A missing link in the current literature is how the entrepreneur interacts with the rest of the firm’s people and processes and more specifically, how the entrepreneurial experience combines with the managerial and technical experience to produce marketable technology innovations.
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The role of technology is pivotal but has been largely ignored in the literature. Technology is the foundation upon which many entrepreneurs’ products are built. Key questions to be answered are where does the technology originate and how does it evolve in combination with the other factors in the model? An invention from the mind of the entrepreneur, recent external technology developments, acquisition and external literature such as patent applications, are just a few of the potential sources of technology leads. But, these are of little value unless there is a vehicle to recognise their opportunity and translate it into winning products. It takes people to turn ideas into profitable opportunities, a point made by some of the earliest writers in the field who proposed that champions of innovation could transform or even destroy existing market structures (Schumpeter, 1934, 2002). The objective of this paper is to synthesise the available literature with specific relevance to high-technology start-ups. The paper will be split into three sections: (1) the entrepreneur and their role in driving the formation and development of business venture, (2) experience, both of technology and running a business and finally (3) the role of the technology.
The Entrepreneur Although, there are many recent high-profile examples of innovation or innovators transforming markets, for example Bill Gates and James Dyson, in the fields of computer operating systems and vacuum cleaners, respectively, this is not a new phenomenon. Innovation was proposed as a means of transforming markets in the early part of the 20th century (Schumpeter, 1934). Schumpeter later described the process of “creative destruction”, where technology (new products, new methods of production) would transform markets, essentially destroying the status quo and creating a whole new wave of innovation (Schumpeter, 2002). The entrepreneurial innovator only has a short-term advantage until the large firms climb on board and the innovation becomes the market norm. Schumpeter does not actually outline how the innovation happens, describing innovation more as an outcome based on extraordinary individual ability than a definable process. A better understanding of the interaction of inanimate technologies with people to create profitable market innovation is a highly desirable outcome for both large and small companies alike. Although, the search for an ideal type of entrepreneurial personality has been much maligned, as being misguided or inappropriate (Gartner, 1988), the important role of “people” in the foundation of any venture cannot be under-estimated. Many scholars have tried to test the hypothesis that entrepreneurs possess “special” personality characteristics, with little success. This is not only due to limitations in the trait theory itself, but also the methodology used to identify personality traits (Gartner, 1988; Chen, Greene, & Crick, 1998; Chell, 1999). An obvious flaw in the personality theory is the assumption that the variables characterising the entrepreneur and the environment are static. The reality is that the environment is changing constantly and traits or characteristics alone have little ability to explain behaviour (Delamar, 2000). It also fails to recognise that much of the personality trait research was done after the entrepreneurial event, so the causality is difficult to establish (Gartner, 1988). Were the traits present to begin with or did they evolve during the entrepreneurial process? More recent authors have proposed a more in-depth study of the
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process and actions of entrepreneurs as being a more effective way to better understand their contribution to entrepreneurial success (Shane & Venkataraman, 2000). Whatever the driving force, personality or otherwise, someone has to take the decision to start the venture. This paper draws together and synthesises the key themes about individuals from earlier literature that are relevant to opportunity recognition in new technology start-ups. Alertness One of the earliest writers in the field in the 18th century recognised that some individuals could recognise differences in value of goods across different market sectors and that this could be exploited for profit (Cantillon, 1931). In the 18th century case, the merchant bought goods at a fixed price and then assumed the risk of finding a market in which they could be sold at a higher price and yield significant future profit. This risk and reward model was also proposed in a revised form over 200 years later (Knight, 1921). Such a transfer can involve some form of product or process transformation. This is highly relevant in the field of technology where some huge product innovations have largely involved the transfer of a “lowvalue” technology from one business sector to another where it becomes “high-value” (Christensen, 1997). This is still relevant where many scientists have been constantly exposed to a technology on a daily basis yet failed to realise its enormous potential in new markets. Many microbiologists use standard techniques to grow bacteria on a daily basis and many know that bacterial growth can be inhibited or accelerated by the presence or absence of certain poisons or nutrients. However, one Aberdeen microbiologist known to the author used this simple technique, and a few obliging bugs, to develop a land remediation business. These simple organisms and techniques to measure their growth could be used to assess the state of contamination of land and as an ongoing tool to monitor clean-up efforts. The result was a multi-million pound business in cleaning polluted land with a few bugs and a wellestablished laboratory test technique. Kirzner (1973) had a similar view of the ability of entrepreneurs to recognise the different value of commodities in different markets. His view was that entrepreneurs had a higher level of alertness for opportunities and would actively seek them out. This is a point reinforced in later studies (Kaish & Gilad, 1991; Busenitz, 1996), where entrepreneurs were shown to be more active in seeking opportunity than corporate managers. Hills and Shrader (1998) and Zietsma (1999) found that the successful entrepreneurs in their surveys had a high level of entrepreneurial alertness. Timmons (1999) also makes a point that successful entrepreneurs have the capacity to see what others do not. He cites two scientists, Edison and Einstein, who between them wrongly predicted that the nickel battery would replace gasoline and that nuclear energy would never be obtainable. This suggests that even the most brilliant scientific minds are not always fully tuned to business opportunity. Understanding how successful entrepreneurs successfully manage the opportunity recognition process is even more relevant today with so much new technology available. This can take the form of large technology firms putting proprietary technologies on show on technology licensing websites such as Yet2.com. Here the corporate intent is to find others outside of the organisation who will recognise the potential value in pastures new,
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and allow them to gain access to the technology in return for a share of the profits. However, having the technology on show and available is just one part of the equation. It also requires an entrepreneur to be alert to its potential reapplication, willing to take the risk of starting a business to exploit it and even more extreme, a Schumpeterian champion to engage in creative destruction of an existing market. In the earlier microbiologist example, the technology was there for all to see, but it just needed someone to find the profitable application. The fact that examples such as this exist may suggest the possibility that scientists and engineers opportunity recognition radars are not effectively tuned to spot real opportunities. The alertness theme has also been picked up in more recent research with researchers studying how social networking could help identify opportunities and the ability of entrepreneurs to both foster and activate weak network ties when new opportunities are presented (Singh et al., 1999). As the problems associated with attributing causality and the success of the entrepreneurial process have shown that we need to expand from the isolated individual and place them in a wider context. Networking is clearly a highly relevant part of the alertness theme. Networking is one way to share information on opportunities and build and develop a knowledge network that will enable the entrepreneur to exploit opportunities of which they have little practical knowledge (Singh et al., 1999). There have been many recent works on various networking themes. This includes basic network building processes, e.g. building long-term trust and respect (Anderson & Jack, 2002), the positive impact of inter-firm co-operation on the innovation process (Feldman & Ronzio, 2001; De Propris, 2002), through to the importance of external networks in providing resources critical to the long-term growth and competitiveness of the firm (Lechner & Dowling, 2003). A Need for Achievement or Desire to Control One’s Destiny McClelland (1961) introduced the idea of need for achievement as being a common factor among entrepreneurs. However, in the world of technology innovation many of the players are often already high achievers with many qualifications and business achievements already under their belt. So what inspires them to leave the comfort of the existing job? A desire for increased locus of control may offer partial explanation (Rotter, 1966). The desire for control and individual achievement may manifest itself in the desire to escape the shackles of the bureaucratic multinational organisation, slow to stop pace of academia or to simply get rich out of their own technologies! Environmental Influences One of the main issues of debate in the personality approach is that it is highly dependent on the impact of external environmental influences. People’s behaviour often changes as they gain experience and knowledge through interaction with the world around them. Moore (Reproduced in Bygrave, 1997) proposed that the start-up process can involve the interaction of external environmental factors with the individuals or collective personalities involved in starting up the firm.
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Moore (Reproduced in Bygrave, 1997) describes a series of start-up stages, innovation, triggering event, implementation and growth, and outlines how both individual personality and external environmental factors can influence each of the proposed key stages. The most important factor from an entrepreneurship standpoint is the triggering event as this is the factor, which turns entrepreneurial dreams into reality. Understanding the triggering process is key because it is from this point that the “scientist” turns entrepreneur and their thought paths change. Davidsson (1995) proposed a similar model, which involves interaction of personal and domain attitudes coming together to create a situation where entrepreneurial conviction is translated into entrepreneurial intent. Yet, people do play an important role, and without them no business can hope to develop or survive. However, as in general entrepreneurship research, personality factors in isolation cannot result in the development of a technology start-up business. A technology business generally does not form around one individual, their desire to work for themselves and a triggering event to provoke them into action. It also requires additional elements, complementary experience necessary to develop and refine the technology, run the business and most important the actual technology to sell. Scientific discipline teaches a thorough and methodical process of continually developing and testing hypotheses about the world and shaping them through interactions with other academics. This process if followed in business would have chaotic consequences both in terms of missing the window of opportunity and exposing the idea to competitors before the venture is even launched. This leads us on to the next part of the equation, the combination of existing technical expertise with the knowledge necessary to initiate and run an entrepreneurial firm.
Developing and Growing “Entrepreneurial” Experience Entrepreneurial self-efficacy and entrepreneurial intent Entrepreneurial self-efficacy is a term used with increased frequency and revolves around the idea that entrepreneurs need to develop a perception of self-confidence in their entrepreneurial abilities before they are willing to start up in business (Bandura, 1986). Boyd and Vozikis (1994) suggest that self-efficacy plays an important role in the development of entrepreneurial intentions and actions. Chen et al. (1998) suggest that it is entrepreneurial self-efficacy in five key skill areas: marketing, innovation, management, risk-taking and financial control, which are key differentiators between people actively interested in setting up in business or those who have set up in business. In other words, entrepreneurship students and successful small business founders are much better equipped with such skills than those who follow a more traditional management career path, e.g. general management students or business executives. It is argued that self-efficacy is a perceived confidence barrier and that many people who have the skill to run a business, do not, because they perceive themselves as not having the necessary skills. More recent authors build on this further by suggesting that actual intent to start up in business, possibly fuelled by entrepreneurial self-efficacy, is the best predictor of eventual entrepreneurial behaviour (Krueger, Reilly, & Carsrud, 2000). This is often signalled by the fact that many entrepreneurs set up in business in advance of finding the actual opportunity
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(Zietsma, 1999). They are by definition more receptive to opportunities as they simply need to find one to generate their flow of income. Developing a “feel” for the market It is a common theme that success breeds success with studies confirming that many entrepreneurs are serial in nature with a number of business start-ups under their belt. Hill’s (1995) pilot study surveyed a number of successful entrepreneurs and over 79% of his sample had pursued more than three opportunities in the previous 5 years. It is also becoming common for researchers to identify and to develop an opportunity recognition process based on “gut-feelings” rather than formal market research (Hills, 1995; Hills & Shrader, 1998; Craig & Lindsay, 2001). Learning to manage risk and uncertainty on a daily basis Recently, the search for an ideal entrepreneurial personality has matured. Researchers are focussing on how entrepreneurs behave rather than personality traits with particular focus on defining the cognitive mechanisms used by entrepreneurs to process information (Shaver & Scott, 1991). This is a deliberate attempt to identify in what ways they function differently to corporate managers (Palich & Bagby, 1995; Busenitz & Barney, 1997). One such study focussed on entrepreneurs’ attitudes and approaches to processing and managing risk (Palich & Bagby, 1995). Unexpectedly, in this study entrepreneurs did not regard themselves as being more willing to take risks than managers in large corporations, which on face value would seem to suggest the earlier risk and reward theories of Cantillon (1931) and Knight (1921) do not hold true. However, one should interpret these results with caution. Just because entrepreneurs do not perceive themselves to be more willing to take calculated risks, does not mean that they are risk averse. This only implies that they simply do not perceive themselves that way, the actual reality may be very different. Maybe they are so comfortable and experienced at handling risk that they do not regard risk as being risky anymore? The strategic management literature also suggests that strategic decision makers adopt unique decision-making processes as opposed to managers (Dutton & Jackson, 1987). Palich & Bagby (1995) provided additional support for this theory by citing previous research that suggested when presented with unequivocal data, managers and entrepreneurs would process the data in exactly the same way and come up with the same conclusions. However, when presented with equivocal data the entrepreneurs viewed it in a consistently positive manner. Palich and Bagby (1995) confirmed this in a follow-up study specifically conducted among entrepreneurs and managers. They also demonstrated that entrepreneurs interpret data perceiving strengths versus weaknesses, and opportunities versus threats. Entrepreneurs also used heuristic thinking processes more often than managers. One example of this is using experience to interpret uncertain situations by comparing it to a previous frame of reference. This type of result has been replicated in other studies (Busenitz & Barney, 1997; Baron, 1998). The use of bias in heuristics in decision-making processes used by entrepreneurs has been studied further (Baron, 1998). Entrepreneurs live in a world surrounded by uncertainty, which means that comprehensive and cautious decision making is simply impossible. In situations where there is information overload, high uncertainty, high novelty, strong emotions, high time pressures or fatigue there is increased susceptibility to use cognitive biases or heuristic thinking processes. Simply stated the brain suffers from an excessive
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amount of information to process and reach a conclusion about and in these situations has to find ways to ease the burden. It has also been shown that repeated use of these processes can lead to a degree of overconfidence exhibited by entrepreneurs versus managers (Busenitz & Barney, 1997). This can be easily explained as entrepreneurs repeatedly encounter situations of uncertainty and thus become proficient in the use of heuristics to make reliable decisions with limited information in short time frames. These system simplification processes manifest themselves in the form of a number of cognitive biases (Barron, 1998). Such cognitive biases include: counterfactual thinking — the effects of imagining what might have been; affect infusion — the influence of current states of thinking on decisions and judgements; attributional style — tendencies by individuals to attribute various outcomes to internal/external causes; the planning fallacy — strong tendencies to underestimate the time or effort required to complete a piece of work and selfjustification — the tendency to justify decisions even if they result in negative outcomes. Similar simplification processes have been observed in strategic decision makers in the corporate environment, indicating that anyone at the cutting edge of business can learn to be proficient in such techniques (Krabuanrat & Phelps, 1998). It also leads to the intriguing conclusion that these thinking processes can be learned and even taught to prospective entrepreneurs. It also starts to help explain why simply being a clever scientist or engineer does not guarantee entrepreneurial success. They use a logical inclusive thinking process that almost has to be unlearned in the real world of business or complemented with an entrepreneurial partner. Do cognitive biases generate innovative solutions? This raises the question of whether this results in entrepreneurs being comfortable launching themselves into ventures with many gaps only filled in their own mind. It is almost as if the entrepreneur decides a vision of the future and figures out the problems that have to be tackled as they go along, with these problem solutions turning into the actual innovations. Maxwell and Westerfield (2002) reinforce this point of view demonstrating that the use of innovative technology is significantly higher in firms managed by “opportunistic” entrepreneurs described as founders who have a high level of education and business experience. Baker, Miner, and Eesley (2001) cited many previous authors in support of the theory that improvisation is a key part of business start-up. They propose that the start of the design process often coincides with the commencement of the business venture and that improvisation in the face of changing customer expectations could be a key organisational skill.
Experience Existing Technical and Managerial Experience This experience factor is prominent in recent studies mainly suggesting that prior experience of markets, not technology, plays a prominent role in successful opportunity recognition processes (Shane, 2000; Ardchivili et al., 2003). Venkataraman (1997) helps to explain why this is so important in that each person develops a knowledge corridor, which enables them to see the potential benefit in an opportunity because they have an existing frame of
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reference points with which to interpret it. Hills et al. (1999) suggest that based on previous research between 50 and 90% of start-up ideas come from prior work experience. This fits well with some earlier theories on learning proposed by Cohen and Levinthal (1990) who argued that the level of prior knowledge is a key factor in enabling a firm to take in and understand new market opportunities. They propose that there is a knowledgebased barrier to entry where a certain level of knowledge is a prerequisite to being able to recognise and interpret new external information. Some of this contradicts reality, as it implies that the broader the R&D knowledge base of the firm, the more effective it will be at recognising market developments and providing innovative solutions. However, the emergence of Microsoft, Apple and Intel from mere minnows to corporate giants of the 1990s does much to dispel the myth that only large firms can amass sufficient knowledge to innovate. Vesper (1996) and Bygrave (1997) have described the importance of prior experience in the development of the entrepreneurial venture. Hills and Shrader’s (1998) survey of successful entrepreneurs indicated that most business ideas among those surveyed stemmed from prior experience, knowledge of customers and markets or as a response to a specific issue in the marketplace. The importance of knowledge of customer problems is again apparent in the research by Shepherd and De Tiennes (2001). There is no significant research where technology experience is identified as being an important factor, even though many of the businesses studied in the literature were involved in the technology sector. Does this mean that market pull is more of a success driver than technology push? It might explain the mixed results of University Spin-outs, who are often rich in technology but poor in business management experience — particularly when it comes to estimating potential future value on their scientific endeavours (Howard, 2002). Most research demonstrates a strong focus on the market-driven opportunities proposed by Timmons (1999) as being the most fruitful focus areas of idea generation. This theme is paralleled by research into opportunity recognition in large firms (Colarelli O’Connor & Rice, 2001) where it is often the senior business manager not the lab scientist who spots the true value of technology. A supporting quote Colarelli O’Connor and Rice (2001, p. 104) provided is: Although I didn’t do much with the business unit that would ultimately market this technology, I knew something about the field. It did seem important to me. If you look at the history of innovation in this field, there hasn’t really been much. This … really had the potential to change the game. This seems to suggest that the scientific minds need to be complemented with a partner with a big picture approach. That is to say, someone who can take an overview of the market place and find a business application for the technology. Based on this premise the author hypothesises that technology entrepreneurs with a scientific training complemented by business experience gained in an industrial background will have a better chance of success than those from a purely academia-based environment. The former will have picked up important knowledge (albeit tacit in some cases) on both the technologies they have developed and the markets in which the products are sold.
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Technology The final piece of the jigsaw is the actual technology that provides the competitive advantage for the business. This is almost impossible to define but recent technology development in the fields of electronics, computers, software, biotechnology and of course the internet, have provided the competitive edge for many new high-growth entrepreneurial firms. Technology itself is not an innovation. Innovation is the combination of technology with market needs to create a profitable opportunity. Consequently, the technology is not the end; it is simply the means of opportunity recognition. For entrepreneurial firms, innovation may manifest itself in many forms. Most were well summed up by Schumpeter (2002) in his concept of creative destruction: new products, new markets, new ways to make products and new ways of selling products. The latter is supported by an intriguing example (Schumpeter, 2002, p. 85), “In the case of the retail trade the competition that matters arises not from additional shops of the same type, but from the mail-order house and the supermarket which are bound to destroy those pyramids sooner or later”. This is striking, in that he correctly predicted the imminent demise of the current state of the retail trade, but how ironic that his innovative retails sales techniques are now being threatened by the next retail evolution e.g. the Internet, which is threatening to cannibalise many of these outlets. However, some are actually doing very well by embracing the technology and changing the way they do business to their advantage. The innovation is new ways of serving markets, and not a computer, software or whatever else. The innovation provides a better way of serving customers. New technology is just one means of doing this and will always be replaced by the next generation technology. Do Some High-Tech Spin-Outs have the Model in Reverse? The relevance for the high-tech start-ups, particularly spin- outs, is that spin-out companies often start with a technology and attempt to find a market. With so much in the way of technology so readily accessible in patent databases, on-line licensing sites etc, one wonders if the technical community might be more effectively utilised by engaging entrepreneurs to provide them with market opportunities. They could then focus their creative and technical brains to find the innovative solutions that probably already exist in the many diverse fields of scientific research.
The Current State Opportunity Recognition Theory Although some authors have tried to map the opportunity recognition process, the literature is still largely undeveloped (Hills et al., 1999; Shane, 2000; Colarelli O’Connor & Rice, 2001). The most comprehensive review is by Gaglio (1997) in which she draws upon the earlier research of Long and McMullan (1984) who proposed opportunity recognition as a long deliberate process. Long and McMullan (1984) propose a four-stage process involving prevision, vision, elaboration and eventual launch decision. However, this model fails to include the extremely important refinement stage — a critical part of any market entry.
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The most recent study of opportunity recognition processes used by entrepreneurs is directly based upon technology businesses. Shane (2000) demonstrates that just one technology can spawn multiple business opportunities. He conducted detailed surveys among eight entrepreneurs, who had all exploited very different market manifestations originating from just one MIT technology patent. He proposes a simple model of opportunity recognition based on two key components of technological invention and prior experience. Shane (2000) argues that the ability of an entrepreneur to recognise the market value of a particular technological innovation is based on their ability to recognise the value of the invention in the market based on the previous experience they have in solving customer problems in related markets. This again builds much on the many works cited earlier proposing previous experience as a key factor in opportunity recognition processes. There has been an abundance of recent conference papers on various themes of opportunity recognition in various stages of development (Hills & Shrader, 1998; Hills et al., 1999; Koen & Kohli, 1999; Singh et al., 1999; Zietsma, 1999; Craig & Lindsay, 2001; Shepherd & De Tienne, 2001). The themes explored are: (i) Building understanding of the sources of information used by entrepreneurs to identify opportunities (Hills, 1995); (ii) The importance of social networks in opportunity recognition (Julien, 1995; Singh et al., l999); (iii) The structure of the opportunity recognition process (Hills et al., 1999); (iv) The role of personal intuition in the conception, development and execution of opportunities (Baker et al., 2001; Craig & Lindsay, 2001); (v) The specific role of prior knowledge and the importance of knowledge of customers and markets (Shepherd & De Tienne, 2001). The journal arena remains largely under-developed; with focus again on the importance of prior knowledge, personality traits and social networks as potential antecedents of entrepreneurial alertness to business opportunities (Shane, 2000; Ardichivili et al., 2003). The theme has also been explored in the large firm context, with particular focus on the case studies on how large firms have successfully matched emerging market needs with the innovative technologies required to deliver them (Colarelli O’Connor & Rice, 2001). There is a striking theme flowing through most of this literature. This is the importance of understanding markets and customers and the need to not only place this at the heart of any successful product development strategy, but also the need for products to evolve with changes in the external market and technology evolution.
Towards a Model of Opportunity Recognition We now propose the following three-component model to describe the combination of the three essential components that come together during the process of opportunity recognition in high-tech start-ups (see Figure 1). This model proposes that new product innovation results from an interaction between three individual components; the founding entrepreneur, collective experience within the
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Technology
Experience
INNOVATION
Figure 1: Conceptual model of opportunity recognition process in high-tech start-ups. firm and technology. It is how these three components interact and combine that will ultimately define the final output of the innovation process. While all of these factors have been identified in the literature the actual interaction of the components has yet to be the focus of any real detailed empirical study. The one striking omission in all of the current literature is the complete lack of any real in-depth analysis of the contribution of the technology component to the entrepreneurial venture. This would have been easily explained if most of the organisations studied had not been in the technology sector, but the opposite is true, a number of these studies have had technology firms as the focus of their study (Shane, 2000). Alvarez and Busenitz (2001) propose an investigation into how firm’s resources come together in entrepreneurial ventures, but the technology element and its interaction are not investigated in any real depth. This could lead to two opposite conclusions. First, that technology is not important. The argument here could be that in a technology-rich world, technology is a commodity raw material just like any other. In today’s world, technology is continually developing all around us. With technology in such abundance it could be argued that one would always be able to find a technology to fit the need once an appropriate market opportunity had been identified. The contrary conclusion is that it does play a role, but the importance of that role has not been fully recognised. This is not surprising given the complete lack of any real detailed study of the sources of technology in entrepreneurial firms and its interaction with the other components of the proposed model. Even in established fields of innovation research it is hard to identify the true meaning of innovation. Innovative companies are often defined and measured by R&D spending levels, patents or turnover (Avermaete, Viaene, Morgan, & Crawford, 2003). Unfortunately, none of these measures in isolation is a good barometer of market innovation. Other works make the point that the development of effective product development strategies in big firms is still far from an exact science, and is in fact quite the opposite, as failure seems to be the rule and not the exception (Nichols, 1994; Cooper, 1999). In fact, the latter strongly suggests that the three main reasons large firms’ product development efforts fail are, lack of customer understanding, up front homework not being done or that products enter the development stage without clear enough definition. The other striking point is that a significant body of the opportunity recognition literature reviewed to date seems to suggest entrepreneurs start by defining and understanding
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the customer and then making the rest up as they go (Hills, 1995; Hills & Shrader, 1998; Craig & Lindsay, 2001). This seems to indicate that the big firms may be failing by trying to institutionalise the process, with product development becoming a production line. In such a system it is possible that the firm may lose sight of its customers and their unique requirements. This is supported in part by Barney (1995), who demonstrated how some large firms have utilised their unique internal assets, e.g. knowledge and people to respond competitively to external market threats, while other companies have succumbed to the external pressure. Maybe it is the lack of personalised input in the opportunity recognition process in some large firms that is to blame for frequent failure and that by re-establishing the personality and experience factors they will make product development a less exact but more successful science! The need to complement scientific brains with entrepreneurial and business ones is becoming more apparent. Many high-tech start-ups particularly in the biotechnology sector are increasingly using partnership arrangements to find the best home for the technology and there is also evidence about increasing strategic focus in the business activities of the small technology firm (Shan, 1990; Sutton & Kelley, 1997; Smith, 1998). The model outlined in Figure 1 proposes that new technology development is a key component in the innovation process for all high-tech firms, large or small. However, the innovation is not based on technology alone. It proposes that innovation is the end result of a complex interaction of the inanimate technology with the living components of the model, in other words, with the personalities driving the foundation of the venture and the collective experience, both technical and managerial. It is these interactions that will be studied in future work in the form of case studies focussing on recent technology start-ups. The objective will be to uncover the complex interaction of three separate factors and try to better understand how they are combined to produce a successful high-tech start-up or university spin-out company. Understanding the key elements and their interaction will build on other recent works to further help government policy makers produce development policies and assistance programmes that will truly facilitate the process (Thierstein & Beate, 2001). Perhaps this work has shown the need to better understand the three elements, (the entrepreneur, experience and technology), but not, however, in isolation. Rather, it has shown that their dynamics, synergy and interactive roles must be appreciated. This new model enables us to probe a number of important theoretical questions. One very interesting field of study will be how cognitive biases and heuristics impact on the innovation process. There is no doubt these short cuts are beneficial as they enable entrepreneurs to make speedy and reliable decisions about the potential viability of the technology, but there is more to understand. If we can better understand these processes it will help to demonstrate whether there is a path to entrepreneurial enlightenment. If so we can start to help individuals learn effective entrepreneurial decision-making processes. If this is true it will further highlight the need for universities to be better able to tap into the external entrepreneurial efforts in order that they might more effectively define the potential markets for their new technology developments. Some other key questions need to be researched. How do the various experience factors contribute to the model? Of particular interest will be the combination of technical experience and management experience and the role they play in identifying and refining the
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technology to make it fit for market exploitation. It will also help to probe whether and in what ways new technology developments or accidental discoveries reap the benefit of previous market experience to develop new product opportunities. However, at this point this theory is speculative and this paper is only the beginning of the journey to better understand the innovation process. The theories proposed will be rigorously tested with follow on qualitative studies in fresh technology start-ups, observing the process first hand to obtain a better feel and understanding for the actual processes taking place. This will help develop and refine the theory with the ultimate objective of developing a robust model that can be quantitatively tested across various technology sectors. The ultimate aim is to build a more complete picture of the components of entrepreneurial success in the high-tech sector. The intent is to better define the process of effective product innovation for the theoretician, policy maker and practitioner alike. Hopefully, the defining of effective processes will improve market exploitation of future technology developments across many sectors and increase the chance of the technology-driven economic well-being sought by government policy makers. Hopefully, the development and refinement of this model of opportunity recognition will not only help better understand the process of setting up a technology or spin-out venture, it will also help entrepreneurs in larger organisations. The hope is that it will help these corporations identify the key elements of an entrepreneurial innovation process and use them to create a more entrepreneurial spirit among their R&D communities. It is not mere conjecture that entrepreneurs in large firms can learn from entrepreneurs in small ones. Many recent studies make it even more apparent that entrepreneurs in small firms are increasingly more proficient at developing innovative new technologies, than are large businesses (Maxwell & Westerfield, 2002).
Conclusions The objective of this paper has been to analyse the literature to date to establish a better theoretical frame of reference to study the opportunity recognition processes used in high-tech start-up companies. The model proposed in this work is that new product innovation results from an interaction between three individual components: the entrepreneur, the experience within the firm and technology. While all of these factors have been identified in the literature the actual interaction of the components has yet to be the focus of any real detailed empirical study. The literature shows these three components, but often in isolation. Research is now moving towards a more dynamic and contextual appreciation of these elements but this literature review still highlights an apparent need to synthesise additional elements and their interactions into the model of opportunity recognition in high-tech start-up firms. As a result the presence of a driving entrepreneur is not enough in the technology sector. Certain kinds of knowledge are required for technology to be fully exploited. The importance of prior experience and market knowledge has become a recurring theme in recent entrepreneurship research, but this focus on market knowledge has meant that technical
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knowledge and its role in transforming embryonic technical breakthroughs into real products and processes has been largely ignored. This new model proposes that technology innovation requires a mix of technical, entrepreneurial and managerial experience to turn an embryonic new technology into a market success. A key part of this is matching the logical thinking processes of the scientific world with the ability to learn as you go that is so often a feature of the entrepreneurial venture. The recent attempts to develop better understanding of the unique cognitive thinking processes that can occur in the high-pressure environment are extremely relevant and it may even be that cognitive biases and errors can play a role in the innovation process itself. The contribution of this model is a new way to look at technology innovation — a market-driven approach with a partnership between those who are really attuned to the opportunity. It also reflects a need to better understand the detailed interaction of the three components of the model. In simple terms, we need to understand how the inanimate technology evolves with the interaction between the business-driven entrepreneur who seeks out the opportunity, and the scientist and managers who shape the technology into a form that is capable of delivering the required market innovation.
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Julien, P. (1995). New technologies and technological information in small businesses. Journal of Business Venturing, 10(6), 459–475. Kaish, S., & Gilad, B. (1991). Characteristics of opportunities search of entrepreneurs versus executives: Sources, interests, general alertness. Journal of Business Venturing, 6(1), 45–61. Kirzner, I. (1973). Competition and entrepreneurship. Chicago, IL: University of Chicago Press. Knight, F. H. (1921). Risk, uncertainty and profit. Boston, MA: Houghton Mifflin. Koen, P. A., & Kohli, P. (1998). Idea generation: Who comes up with the most profitable products? In: P. D. Reynolds, W. D. Bygrave, N. M. Carter, S. Manigart, C. M. Mason, D. Meyers & K. G. Shaver (Eds), Frontiers of entrepreneurship research. Boston, MA: Wellesley. Krabuanrat, K., & Phelps, R. (1998). Heuristics and rationality in strategic decision making: An exploratory study. Journal of Business Research, 41(1), 83–93. Krueger, N. F., Reilly, M. D., & Carsrud, A. L. (2000). Competing models of entrepreneurial intentions. Journal of Business Venturing, 15(5–6), 411–432. Lechner, C., & Dowling, M. (2003). Firm networks: External relationships as sources for the growth and competitiveness of entrepreneurial firms. Entrepreneurship & Regional Development, 15(1), 1–26. Lee-Kelley, L. (2002). Situational leadership: Managing the virtual project team. Journal of Management Development, 21(6), 461–476. Lockamy, A. L., & Khurana, A. (1995). Quality function deployment: Total quality management for new product design. International Journal of Quality and Reliability Management, 12(6), 73–84. Long, W., & McMullan, W. E. (1984). Mapping the new venture opportunity identification process. In: J. A. Hornaday, F. A. Tardley, J. A. Timmons & K. H. Vesper (Eds), Frontiers of entrepreneurship research. Boston, MA: Wellesley. Maxwell, J. R., & Westerfield, D. L. (2002). Technological entrepreneurism: Characteristics related to the adoption of innovative technology. SAM Advanced Management Journal, 67(1), 9–21. McClelland, D. (1961). The achieving society. Princeton, NJ: D. Van Nostrand. Nemeth, C. J. (1997). Managing innovation: When less is more. California Management Review, 40(1), 59–74. Nichols, K. (1994). Developing with the best. World Class Design to Manufacture, 1(2), 7–12. OECD (2003). The policy agenda for growth. Paris, France: OECD Publications. Palich, L. E., & Bagby, D. R. (1995). Using cognitive theory to explain entrepreneurial risk-taking: Challenging conventional wisdom. Journal of Business Venturing, 10(6), 425–438. Rotter, J. (1966). Generalised experiences for internal versus external control of reinforcement. Psychological Monographs, 80(1), 609. Schumpeter, J. (1934). Theory of economic development. Cambridge, MA: Harvard University Press. Schumpeter, J. A. (2002). The process of creative destruction. In: S. Shane (Ed.), The foundations of entrepreneurship (pp. 179–184). Cheltenham, UK: Edward Elgar Publishing Ltd. Scottish Executive (2001). A smart successful Scotland: Ambitions for the enterprise networks. Edinburgh, UK: HM Stationary Office. Shan, W. (1990). An empirical analysis of organisational strategies by entrepreneurial high-technology firms. Strategic Management Journal, 11, 129–139. Shane, S. (2000). Prior knowledge and the discovery of entrepreneurial opportunities. Organizational Science, 11(4), 448–469. Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. Academy of Management Review, 25(1), 217–226. Shaver, K. G., & Scott, L. R. (1991). Person, process, choice: The psychology of new venture creation. Entrepreneurship Theory and Practice, 16(2), 23–45. Shen, X. X., Tan, K. C., & Xie, M. (2000). An integrated approach to innovative product development using Kano’s model and QFD. European Journal of Innovation Management, 3(2), 91–99.
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Shepherd, D. A., & De Tienne, D. R. (2001). Discovery of opportunities: Anomalies, accumulation and alertness. In: W. D. Bygrave, E. Autio, C. D. Brush, P. Davidsson, P. G. Greene, P. D. Reynolds & H. J. Sapienza (Eds), Frontiers of entrepreneurship research. Boston, MA: Wellesley. Singh, R. P., Hills, G. E., Hybels, R. P., & Lumpkin, G. T. (1999). Opportunity recognition through social network characteristics of entrepreneurs. In: P. D. Reynolds (Ed.), Frontiers of entrepreneurship research. Boston, MA: Wellesley. Smith, J. A. (1998). Strategies for start-ups. Long Range Planning, 31(6), 857–872. Smith, P. G., & Reinertsen, D. G. (1991). Producing products in half the time. New York, NY: Van Norstrand Reinhold. Sutton, R. I., & Kelley, T. (1997). Creativity doesn’t require isolation: Why product designers bring visitors “Backstage”. California Management Review, 40(1), 75–91. Thierstein, A., & Beate, W. (2001). Incubator technology and innovation centres in Switzerland: Features and policy implications. Entrepreneurship & Regional Development, 13(4), 315–331. Timmons, J. A. (1999). New venture creation: Entrepreneurship for the 21st century. Singapore: McGraw-Hill Book Co. Venkataraman, S. (1997). The distinctive domain of entrepreneurship research. Advances in Entrepreneurship, Firm Emergence and Growth, 3, 119–138. Vesper, K. H. (1996). New venture experience. Seattle, Washington: Vector Books. Zietsma, C. (1999). Opportunity knocks or does it hide. In: P. D. Reynolds (Ed.), Frontiers of entrepreneurship research. Boston, MA: Wellesley.
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Chapter 6
Entrepreneurial Orientation and Entrepreneurs’ Intentions and Objectives Thelma Quince and Hugh Whittaker
Introduction Recognition of the economic significance of small firms, and high-tech small firms in particular, grew during the last quarter of the 20th century (Loveman & Sengenberger, 1991; Acs & Audretsch, 1993; Acs et al., 1999). In the UK, several strands of policy have coalesced into a general focus on ‘competitiveness’ (Wren, 2001; DTI, 2001). At the same time, organisational theory has come to view entrepreneurship as a firm-level phenomenon. In particular, increasing attention has been paid to ‘entrepreneurial orientation’ seen as process reflected in recurring organisational behaviour (Covin & Slevin, 1991) rather than the actions of individuals possessing certain attributes or characteristics. Behaviour important in both policy and organisational theory contexts includes willingness to take risk, innovativeness, technological leadership and a pro-active stance towards competition (Khandwalla, 1977; Covin & Slevin, 1991; Lumpkin & Dess, 1996; DTI, 2001). In small high-technology firms, however, organisational outcomes are likely to be profoundly influenced by founders (Hannan, Burton, & Baron, 1996). What characteristics or attributes of individual entrepreneurs influence the types of organisational behaviours outlined above? Situational variables such as employment status and individual variables such as personality traits and demographic characteristics have been found to be relatively poor predictors of even survival and success let alone of specific organisational behaviour such as innovativeness (Cooper & Gascon, 1992). Drawing upon work in social psychology, entrepreneurship has been studied as intentional, planned behaviour by individuals (Bagozzi, Baumgartner, & Yi, 1989; Ajzen, 1991; Krueger, Reilly, & Carsrud, 2000). However, one area that has not been widely studied is the extent to which the more general objectives and intentions held by entrepreneurs shape specific organisational actions and behaviour. The aim of this paper is to examine the innovativeness, pro-activeness and openness to risk displayed by a group of small high-technology firms and to try to assess the extent to which such behaviour was related to the objectives held by their CEOs. The paper draws New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 83
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upon two surveys of high-tech entrepreneurs conducted in the UK: the first in 1998 and the second in 2001. The former examined information about family and educational background, and antecedents to and motivations for setting up business. The same CEOs were included in a second survey, which addressed ownership structures, market characteristics, innovative behaviour, personal objectives and recent performance. The paper begins by outlining the main characteristics of the CEOs and their businesses and the recent performance of these firms. A discussion of the indicators of entrepreneurial orientation used follows. The final sections examine first, the extent to which displayed entrepreneurial orientation was found to be related to recent performance and second, the three types of objectives expressed by the CEOs: motivations for and intentions when setting up business, personal objectives for the business and growth objectives.
The Study Over 500 businesses in high-tech sectors,1 located in various parts of the UK, responded to the first survey (1998). Of these, 153 (29%) responded to the second survey. In 133 cases (87%), the same individual responded to both surveys, but in 20 cases it was clear that the person responding in 2001 was not the same person who had responded in 1998. The criteria adopted were that the person responding in 2001 should be familiar with the objectives held by the 1998 respondent and that the 1998 respondent was likely to play an active role in the company in 2001 (see tables A1 to A4 in the appendix). The effective number of businesses considered was 142. Background Characteristics of the CEOs and their Businesses The CEOs CEOs taking part in both surveys were predominantly male with an average age of 53 in 2001. Two-thirds of the businesses were collaborative ventures involving more than one owner/founder. These were evenly divided between those in which ownership was shared wholly internally and those in which some ownership was held externally (Table 1).2 The firms Two-thirds of the firms were in manufacturing, with instrument manufacturers accounting for more than half of these. Service sector firms were roughly evenly divided between those engaged in telecommunications and computer software3 (CST) and technical testing and R&D firms (RDT). Three-quarters of the firms had been founded before 1990, over 80% had turnover of less than £5 million in 2001, and almost 80% employed fewer than 50 people. Median firm age was 18.5 years, median turnover size £1.6 million and median employment size was 27 employees. There were significant differences 1
These were defined according to Butchart (1987). Sole founders were situations where the respondent alone held 100% of the equity, proxy sole founders were businesses in which the only other shareholder was the respondent’s spouse. External owners were those not actively involved in running the businesses. 3 Firms engaged in ‘routine computer bureau services’ were not included in the study. 2
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Table 1: Characteristics of CEOs.
Age groups in years 40 40 50 50 60 60 Total Gender Male Female Ownership group No current ownership Sole props and proxy sole props Internally shared (no external ownership) Externally and internally shared Total
Frequency
Per cent
9 36 63 32 140
6.4 25.7 45.0 22.9 100.0
129 13
90.8 9.2
7 38 48 43 136
5.1 27.9 35.3 31.6 100.0
between firms in different sectors: manufacturing firms tended to be older and larger, in terms of employment, but RDT firms were smaller in terms of revenue (Table 2).4 Recent performance Respondents were asked to give the percentage change in real turnover and employment, and average pre-tax profits recorded during the 2 years prior to the 2001 survey. Just over half of the firms recorded growth in turnover in the period and just under half recorded growth in employment. Median turnover growth during the 2 years prior to the study was 20% and the comparable figure for employment was 15%.5 Over 40% of the firms recorded average pre-tax profits of more than 10%; median profit rate was 9.2%. Eleven firms recorded losses. To differentiate firms on the basis of performance, account was taken of size and sector. Accordingly, firms were allocated to relative performance groups in respect of turnover and employment growth within size bands.6 Size was not important in respect of profits, but sector was. Significant differences were found in average levels of profitability between manufacturing and service sector firms. (Average pre-tax profits in manufacturing were 4
5
Median
Age
Manufacturing CST RDT
22 13 14
Turnover (£ million) 2.0 1.3 0.4
Employment 32 17 9
Mean annual growth: 34.9%, SD 49.7% and mean employment growth 21.1%, SD 14.7. The average annual growth in turnover recorded by firms with turnovers of less than £1 million was 55% compared to 32% for firms with turnovers over £1 million. Similarly, firms employing less than 20 had expanded employment by an average 32% as compared to between 20% and 16% in larger firms. 6
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Table 2: Characteristics of the businesses. Activity Manufacturing Instruments Computers, electrical and electronics Other manufacturing Services Computer activities and telecommunications R&D and technical testing Total Date of founding Since 1990 1989–1980 Before 1980 Total Turnover size group £1 million £1 m £5 m £5 m Total Employment size group 20 employees 20 50 employees 50 employees Total
Frequency
Per cent
95 52 18 25 47 20 27 142
66.9 36.6 12.7 17.5 33.1 14.1 19.0 100.0
26 52 64 142
18.3 36.6 45.1 100.0
44 61 20 125 Frequency 45 62 31 138
35.2 48.8 16.0 100.0 Valid Per cent 32.6 44.9 22.5 100.0
10.7% compared to 27.5% and 30.5%, respectively, for firms in the two service sectors.) Relative performance of profits was judged in relation to sector (Table 3).
Entrepreneurial Orientation (EO) Although later extended by Lumpkin and Dess (1996), the dimensions of entrepreneurial orientation with which this paper is concerned are innovativeness, pro-activeness and openness towards risk (Miller, 1983; Covin & Slevin, 1986, 1989). Lumpkin and Dess (1996) suggest that EO represents key entrepreneurial processes and focuses on how new ventures are undertaken, whereas entrepreneurship refers to the content of the decisions taken, and for what it is undertaken. Most empirical studies of entrepreneurial orientation have utilised the instrument developed by Miller (1983) and extended by Covin and Slevin (1986, 1989, 1991). This instrument has been criticised on the grounds that it mixes perceptual or subjective evaluations, namely current attitudes held by CEOs, with self reporting of arguably more objective information relating to past organisational behaviour (Brown, Davidsson, & Wiklund, 2001). The approach adopted in this paper was first to focus on reported organisational
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Table 3: Performance 1999–2001.
Turnover growth (real) Decreased Unchanged Increased Total Employment growth Decreased Unchanged Increased Total Average pre-tax profits Loss/negligible (2%) 2–5% 5–10% or less 10% Total
Frequency
Per cent
31 30 75 134
22.8 22.1 55.1 100.0
25 47 59 131
19.1 35.9 45.0 100.0
19 16 29 47 111
17.1 14.4 26.1 42.3 100.0
behaviour as a demonstration of EO and second to set the subjective intentions and objectives held by the CEOs in relation to the levels of EO demonstrated. Pro-activeness Pro-activeness is concerned with ‘first mover’ and other actions aimed at seeking to secure and protect market share and with a forward-looking perspective reflected in actions taken in anticipation of future demand (Miller, 1983; Covin & Slevin, 1989; Lumpkin & Dess, 2001). The indicators of pro-activeness used here comprised collaboration, incidence and extent, innovations (in particular the incidence of ‘novel’ innovations), activities designed to protect intellectual property (IP) and market structure. Collaboration Information was sought about formal or informal collaboration or alliances with other organisations during the 2 years prior to the 2001 study and the purpose of collaboration. Over half of the firms reporting collaboration had relationships with at least one of the following: ‘firms in the same line of business’, ‘customers’ and ‘suppliers’. The majority (more than two-thirds) reporting collaborative partnerships gave more than one purpose. However, regardless of the type of organisation collaborated with, the purpose of collaboration was dominated by market-related issues. Of 74 CEOs giving reasons for collaboration, 62 mentioned either ‘to expand the range of products/services’ and/or ‘to provide access to new markets’. Half of the remaining CEOs gave ‘meeting current customer/client needs’ as the purpose of collaboration. Given that market-related issues dominated reasons for collaboration, firms were assessed for pro-activeness in terms of the diversity of organisations with which they had collaborated.
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The incidence of innovation Pro-activeness is concerned with ‘first mover’ actions. The 2001 survey asked about innovation in product/service, logistics and/or delivery and whether innovations constituted changes ‘new to both the firm and the industry’ or changes ‘new only to the firm’. The former were termed ‘novel innovation’ and can be seen to demonstrate pro-activeness. The level of intellectual property (IP) activity Over 60% of firms undertook no activity concerned with protecting IP in the 2 years prior to the 2001 survey. Among the minority of firms that were active, there was a clear distinction between those applying for such protection in relation to an individual development and those firms in which this was a more generalised activity and often combined with licensing. Market structure: Niche-market effect In common with other studies of small businesses (Kitson & Wilkinson, 2000), the majority of firms (60%) could be classified as operating in ‘niche markets’: confronting five or fewer serious competitors. Although striving to establish dominance in a niche market may demonstrate pro-activeness, account also needs to be taken of the extent to which that market is dominated by one or two customers, on which the firm is highly dependent. Few firms (13%) were dependent on a single customer for more than 50% of turnover, however, in more than a third, the top three customers accounted for more than 50% of turnover. It can be suggested that the ideal ‘niche market’ sought is where customer dependence is relatively low and serious rivals few. Such situations were considered to have a ‘positive’ niche-market effect. The reverse situation — relatively high customer dependence combined with higher numbers of serious rivals — was considered to have a ‘negative’ effect and the intermediate situation a ‘neutral’ effect. On the basis of the above indicators, the firms were ‘scored’ in terms of their level of pro-activeness (Table 4). Innovativeness Innovativeness is concerned with supporting and encouraging new ideas, experimentation and creativity likely to result in new products, services or processes (Miller & Friesen, 1983). The indicators used to assess innovativeness comprised the level of involvement in R&D, the extent of innovation and qualifications of the workforce. Level of involvement in R&D Firms were asked to indicate their level of involvement in R&D. For a small number of firms (13), R&D was their primary activity. In total, almost 60% claimed to be highly involved in R&D, although a quarter undertook no R&D. Extent of innovation The 2001 survey asked the nature of innovations, classifying them as product/service, process and logistics or delivery of service. Some firms recorded innovative activity across a broad spectrum, whereas others recorded innovations only in respect of product or service. The diversity or extent of innovative activity was considered an important indicator of innovativeness (Table 5).
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Table 4: Pro-activeness indicators.
Collaboration No collaboration Collaboration with only one type of organization Collaboration with more than one type of organisation Total Innovation Non innovator Low level (non-novel) High level (novel) Total Level of IP activity None undertaken Less active More active Total Number of serious competitors 2 or fewer 3–5 More than 5 Total Dependence on top three customers 25% of turnover 25% 50% of turnover 50% of turnover Total Niche-market effect Negative: high dependence/many rivals Neutral: even dependence and rivals Positive: low dependence/few rivals Total
Frequency
Per cent
63 29 48 140
45.0 20.7 34.3 100.0
26 39 75 140
18.6 27.9 53.6 100.0
82 19 31 132
62.1 14.4 23.5 100.0
36 45 54 135
26.7 33.3 40.0 100.0
48 40 49 137
35.0 29.2 35.8 100.0
46 53 36 135
34.1 39.3 26.7 100.0
Qualifications of the workforce Firms in the study operated in technology sectors. It has been argued that ‘individual intellectual capacity’ influences innovation in technologybased firms (Briggs & Keogh, 1999). In this context qualification of the workforce, and in particular the proportion qualified to degree level or above, was considered an important factor likely to influence innovativeness. In almost 40% of firms, full-time employees qualified to first-degree level or above comprised more than 25% of the workforce. Again firms were compared on the basis of their employment size, since in many firms employing less than 20, employees with a degree accounted for more than 50% of the total workforce, such percentages were rare in firms employing more than 50.
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Thelma Quince and Hugh Whittaker Table 5: Innovativeness indicators.
Engaged in R&D Yes our primary activity Yes continuously Yes occasionally No Total Extent of innovation (score max 3) No innovation 1 2 3 Total Full-time employees with degrees None 10% 10% 25% 25% 50% 50% 75% 75% Total
Frequency
Per cent
13 68 24 35 140
9.3 48.6 17.1 25.0 100.0
26 41 32 41 140
18.6 29.3 22.8 29.3 100.0
29 33 22 21 15 15 135
21.4 24.4 16.3 15.6 11.1 11.1 100.0
As with pro-activeness, the above indicators were used to ‘score’ the firms in terms of innovativeness. Risk-taking Measuring the extent to which individuals differ in their willingness to take risk is contentious. Early work in small business research tended to focus on various psychological characteristics such as locus of control (Rotter, 1966) and tolerance of ambiguity (Gasse, 1982). The CEOs’ subjective evaluation of their approach towards risk is also fraught with difficulty since what one person regards as a ‘calculated’ approach, another may regard as ‘aversion’. Others have suggested that the differentiating factor is the way risks are calculated (Norton & Moore, 2002). This study focused on behaviour that might indicate willingness to take risks, namely seeking venture capital, and two indicators of willingness to invest with uncertain returns: level of spending on R&D and investment in training as indicated by the level of off-job training for full-time employees. R&D spending Just over half of the firms undertaking R&D spent an average of less than 10% of the turnover on R&D in the 2 years prior to the 2001 study but a quarter recorded levels of more than 10% of turnover (Table 6).
Entrepreneurial Orientation and Entrepreneurs’ Intentions and Objectives
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Table 6: Indicators of risk taking behaviour.
R&D spending No R&D undertaken 10% of turnover 10% of turnover Total Seeking venture capital Not sought Sought Total Percentage of full-time employees given off-job training None 10% or less 10% 20% 20% Total
Frequency
Per cent
35 54 31 120
29.2 45.0 25.8 100.0
110 28 138
79.7 20.3 100.0
42 22 26 29 119
35.3 18.5 21.8 24.4 100.0
Venture capital Few firms (28) had sought venture capital and those that had were evenly divided in terms of success. Seeking venture funding was regarded as indicative of a willingness to take risks. Off-job training The impact of involvement in technology sectors was evident from the incidence and extent of training undertaken. Many studies of small firms suggest that they do not train (Voss, Blackman, Caglian, Hanson, & Wilson, 1998). However, like other recent studies (Barnett & Storey, 2000), the incidence of training in the firms in this study was high. Almost two-thirds provided some off-job training for full-time employees, and in a quarter over 20% of full-time employees were given such training. As for employment growth and qualification of the workforce, relative training performance was assessed in respect of employment size. Indicators were combined to derive a risk-taking behaviour ‘score’; however, in this case greater weight was given to seeking venture capital. On the basis of the raw scores derived from the indicators outlined above, the dimensions were positively related to each other. Innovativeness and pro-activeness were particularly strongly related and risk-taking was slightly more strongly related to innovativeness than pro-activeness (Table 7).
Entrepreneurial Orientation and Recent Performance For the purposes of analysis, the firms were allocated to simple high:low dichotomous groups for each dimension on the basis of the median score. For pro-activeness and innovativeness, this resulted in roughly equal-sized groups but less so in the case of risk-taking. When
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Thelma Quince and Hugh Whittaker Table 7: Relationship between EO dimension scores. Spearman’s rho (using raw scores)
Risk
Pro-activeness Correlation coefficient Sig. (2-tailed) N Innovativeness Correlation coefficient Sig. (2-tailed) N
Pro-activeness
0.244 0.005 130 0.497 0.000 136
135
Table 8: EO dimensions and recent performance. Pro-activeness
Innovativeness
Risk taking
Low (%) High (%) Low (%) High (%) Low (%) High (%) Turnover growth No growth Low growth High growth Total Employment growth No growth Low growth High growth Total Profits Loss/insignificant Low High Total *
48.5 26.5 25.0 68 * 59.4 26.6 14.1 64 ** 30.9 32.7 36.4 55
40.6 26.6 32.8 64 * 49.2 20.0 30.8 65 ** 48.2 28.6 23.2 56
** 55.2 22.4 22.4 67 * 60.3 25.4 14.3 63
** 34.8 30.3 34.8 66 * 49.3 20.9 29.9 67
31.4 33.3 35.3 51
46.7 28.3 25.0 60
41.1 31.5 27.4 73
50.9 16.4 32.7 55
54.2 26.4 19.4 72
54.5 20.0 25.5 55
30.9 36.4 32.7 55
49.1 24.5 26.4 53
p 0.1. p 0.05.
**
conventionally measured, EO is regarded as having a positive impact on firm performance. However, empirical tests of this relationship using a variety of performance measures have yielded inconsistent results (Covin & Slevin, 1986, 1989; Zahra, 1991; Smart & Conant, 1994; Covin, Slevin, & Schultz, 1994; Zahra & Covin, 1995). Furthermore, studies of the impact on growth of innovation in small firms have pointed to ‘temporal asymmetry’ arising out of the lagged nature of the impact of innovation (Cosh, Hughes, & Wood, 1996). Hence, given the extremely short time period, no strong relationships were expected, nevertheless, weak relationships did emerge (Table 8).
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93
Both pro-activeness and innovativeness were weakly positively related to employment growth, and innovativeness was positively related to turnover growth. In particular, over half of the firms, judged to have shown relatively low levels of innovativeness, had experienced no growth in turnover between 1999 and 2001 compared to about a third of their more innovative counterparts. A weak, negative relationship emerged between proactiveness and profits. About half of the firms judged to be highly pro-active made losses or recorded low pre-tax profits compared to 30% of less pro-active firms. Personal Characteristics and Experiences of CEOs Early research on entrepreneurship focused on the characteristics and experiences of individual entrepreneurs as determinants of survival and success. The 1998 survey focused on the antecedents to entrepreneurship including previous work experience, qualifications, family background and first and subsequent experiences of entrepreneurship. Confirming the findings of others, such factors appeared to have little impact on organisational behaviours associated with EO (Cooper & Gascon, 1992). The only exceptions to this were the level of educational attainment of the CEO and whether the firm’s founding team had included someone who had previously worked in a research base organisation. The latter experience was weakly positively related to innovative behaviour. Firms headed by CEOs with no tertiary educational qualifications were likely to have been less open to risk and less innovative (see the appendix). Entrepreneurs’ Objectives and Intentions With the exceptions noted above, there was little evidence that the background of the CEO had a marked impact on the pro-active, innovative or risk-taking behaviour displayed by his/her firm. Does this point to EO being outside the influence of the CEO in small hightech businesses? While background may not be important, the objectives and intentions that entrepreneurs hold may have a greater influence. Three types of objectives held by entrepreneurs are likely to shape the actions of their organizations: 1. Reasons for starting a business; 2. Personal objectives for the self and the business; 3. Growth objectives. Starting the business Respondents to the 1998 survey were asked to rate 16 reasons for starting a business in terms of their importance using a 3-point Likert scale: ‘insignificant’, ‘significant’ and ‘crucial’. Factor analysis suggested five underlying groups (Table 9): 1. 2. 3. 4. 5.
Autonomy and material advancement; Personal and family enjoyment; Perception of opportunities; Exercise and development of skills; Altruism.
The three most important individual reasons for starting the business given were ‘a strong future seen for this’ (perception of opportunity), ‘to be my own boss’ (autonomy/advancement)
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Table 9: Reasons for starting the business. Reasons for starting the business Autonomy and material advancement Personal and family enjoyment Perception of opportunity Exercise and development of skills Altruism
N
Mean scores
SD
113 109 114 110 109
2.14 2.00 1.92 1.77 1.75
0.636 0.672 0.604 0.564 0.484
Table 10: EO dimensions and reasons for starting the business. Reasons for starting the business (Mean Scores) Personal enjoyment To enjoy my life For the happiness of my family Perception of opportunity Saw strong future in this work To provide a new service To commercialise existing technology To commercialise a new technology Altruistic reasons To do something worthwhile investing my energy in To do something useful for society For the development of science and technology
Pro-activeness Innovativeness
Risk-taking
Low
High
2.03
1.78*
1.58 1.43 1.63
1.86* 2.02**** 1.86**
1.16
****
1.57
Low
High
Low High
1.71 2.27 1.63 1.46 1.48 1.61
2.08*** 2.57** 1.91* 1.96*** 1.93*** 1.88***
1.78 2.09*** 2.31 2.54* 1.60 1.93**
1.37 1.17
1.64** 1.55***
1.48 2.00*** 1.68 1.84*
1.28 1.49*
p 0.1. p 0.05. *** p 0.01. **** p 0.001. *
**
and ‘something worthwhile investing my energy in’ (altruism). Higher levels of support for reasons of increasing income, getting on in the world and managing a business meant that overall autonomy and advancement reasons were the most strongly supported. Such reasons for starting a business are universal. Together with reasons related to personal and family happiness and the exercise and development of personal skills, they are likely to be equally strongly held by lifestyle entrepreneurs as those seeking to create growth-oriented businesses. By and large, this was reflected in the results. No marked differences were found between CEOs of businesses judged to differ in levels of EO in respect of reasons for starting the business related to autonomy and advancement or the exercise of personal skills and only minor differences in respect of reasons relating to personal and family happiness (Table 10).
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95
Marked and statistically significant differences were found in the importance placed on perception of opportunity as a reason for starting the business between CEOs of businesses demonstrating more innovative, pro-active and risk-taking behaviour. In particular, businesses judged to have been highly pro-active were far more likely to have been founded by people seeking to commercialise new technology. Interestingly, differences also emerged in the importance placed on altruistic reasons, however, this was related to the evaluation of ‘contributing to science and technology’. Personal objectives Respondents to the 2001 survey were asked to rate the importance of 12 personal objectives on a 5-point Likert scale ranging from ‘not at all’ to ‘crucial’. Factor analysis suggested four main groups for ten of the objectives: reflecting an ‘enterprise focus’, concerned with ‘personal satisfaction’, concerned with ‘financial returns’ and two objectives that received little support but may reflect an ‘idealised technology business focus’ (see the appendix). The top five individual objectives receiving most support were ‘to build a business with a reputation for excellence’ (enterprise focus), ‘conduct business on the basis of fairness and moral integrity’ and ‘maximise my enjoyment and satisfaction from work’ (both personal satisfaction), ‘build a business which can exist without me’ and ‘provide a positive and stable environment for employees’ (both enterprise focus). Overall, the strongest level of support was found for objectives reflecting an enterprise focus followed by those relating to personal satisfaction (Table 11). Differences were found in the importance ascribed to personal objectives by CEOs of business differing in EO. As indicated, objectives that can be seen as reflecting concern for the enterprise and in particular those relating to the environment for employees and the existence of the business beyond the CEO’s personal involvement were among the most strongly held. But CEOs of businesses that had demonstrated high levels of innovativeness and more pro-activeness ascribed significantly higher levels of support to these objectives. This stronger enterprise as opposed to purely personal focus may also be reflected in the finding that the ownership of more innovative businesses was more dispersed to include external owners. The CEOs of businesses demonstrating higher levels of EO were also more likely to give higher levels of support for financial objectives. However, the largest differences were found in respect of the objectives contributing to science and technology and preparing the business for initial public offering (IPO).7 Businesses that had been more innovative, pro-active and more Table 11: Personal objectives. Personal objectives Enterprise focus Personal satisfaction focus Financial focus Ídealised technology business focus
7
N
Mean score
SD
129 130 127 129
4.26 4.01 3.49 2.13
0.662 0.656 1.043 0.934
Although not a realistic option today, it was at the time of the 2001 survey.
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open to risk appeared to be headed by CEOs more committed to advancing science and technology and to a lesser extent who held, as a long-term goal, the objective of stock exchange floatation. Interest in and commitment to new technology appeared to carry over from being a reason for founding to current objectives for CEOs of more innovative and pro-active businesses. The same can be said of the differential found in respect of altruistic concerns (Table 12). Table 12: EO dimensions and personal objectives. Personal objectives (Mean scores) Enterprise focus Build a business with a reputation for excellence Provide a positive/stable environment for employees Build a business that can exist without me Financial focus Maximise current and future returns for the owners Increase the value of the business for potential capital gains Ídealised technology business focus To contribute to the development of science and technology To prepare the business for future stock exchange/ AIM listing Build a business that contributes to the well-being of society Ownership group Sole/proxy sole proprietor Internal ownership Some external ownership Total p 0.1. p 0.05. *** p 0.01. **** p 0.001. *
**
Pro-activeness
Innovativeness
Low
Low
High
4.13
4.39**
3.93
4.26**
3.93
4.26*
3.34
3.65*
3.90
High
4.30**
Risk-taking Low
High
3.37 3.27
3.70* 3.63*
3.23
3.64**
3.24
3.59*
1.80
2.53****
1.7
2.58****
1.94
2.37***
2.01
3.14****
1.94
3.14****
2.28
2.86***
1.58
1.94**
1.45
2.04***
2.96
3.46***
%
%
%
%
** 35.5 43.5 21.0 62
22.7 31.8 45.5 66
%
%
Entrepreneurial Orientation and Entrepreneurs’ Intentions and Objectives
97
Growth objective In contrast to much that has been written about small firms in general, the majority of the high-tech small businesses in this study sought growth (Storey, 1994). The principal difference lay in whether ‘moderate’ or ‘substantial’ growth was sought (Table 13). As can be seen, the desired level of growth sought by CEOs was very strongly positively related to the innovative and pro-active behaviour displayed by their businesses. In particular, very few CEOs of businesses judged to have been highly pro-active were aiming for no growth (Table 14).
Conclusions This paper sought to examine the innovativeness, pro-activeness and willingness to take risk demonstrated by a group of high-tech small firms and to assess the extent to which such behaviour was related to the objectives and intentions held by their CEOs. The approach adopted deviated from that normally used to assess EO by focusing on reported behaviours. Questions for further research remain as to the extent to which the normal instruments used are reflected in activities such as innovation, R&D and collaboration. The personal characteristics and experiences of CEOs appeared to have little impact on how their businesses behaved, but their intentions and objectives did. The findings were generally stronger in respect of innovative and pro-active, than risk-taking behaviour. Innovative and pro-active businesses were more likely to have been founded by entrepreneurs with a clear perception of an opportunity related to the commercialisation of a new or existing technology, who held personal objectives reflecting an enterprise focus, Table 13: Growth objective. Growth objective
Frequency
Per cent
No growth Moderate growth Substantial growth Total
23 64 53 140
16.4 45.7 37.9 100.0
Table 14: EO dimensions and growth objective. Pro-activeness
Innovativeness
Risk-taking
Low (%) High (%) Low (%) High (%) Low (%) High (%) Growth objective No growth Moderate growth Substantial growth Total *
p 0.01. p 0.001.
**
** 27.1 51.4 21.4 70
** 4.4 39.7 55.9 68
26.1 50.7 23.2 69
* 7.1 40.0 52.9 70
22.1 41.6 36.4 73
7.1 50.0 42.9 55
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who had concerns for employees and a perception of the business as an entity existing outside of and beyond themselves and who aimed for substantial growth. Both in respect of reasons for starting the business and what they were currently seeking to do, the CEOs of more entrepreneurially oriented businesses regarded technology as more important than their counterparts in less entrepreneurially oriented businesses. The findings provide support for the notion of ‘technology policy’ as being important in relation to innovativeness in particular. Zahra and Covin (1993) expressed this as the firm’s ‘commitment to acquiring, developing and employing technology’. Pro-activeness in competition, innovativeness and willingness to take risks are increasingly seen as crucial activities in the development of competitiveness by all types of business but for technology and high value-added businesses, they are arguably even more important. Not all firms are equally innovative, pro-active or open to risk. This paper sought to shed some light on the possible factors influencing these activities within the context of small high-tech businesses in which founder(s) strongly shape business activity.
Appendix Table A1: Types of respondent. Same person Yes No: other founder No: family member No: promoted non family No: recruited non family No: MBO/MBI Total Effective total
Frequency
Per cent
133 3 6 5 2 4 153 142
86.9 2.0 3.9 3.3 1.3 2.6 100.0
MBO, Management Buy Out; MBI, Management Buy In.
Table A2: EO dimensions and CEO characteristics and experiences.
Age of CEO Age when started first business Number of businesses founded Parent entrepreneur Yes Total Diversity of prior experience Prior sales/marketing experience Prior R&D work experience
Pro-activeness
Innovativeness
Risk-taking
Low (%)
Low (%)
Low (%)
High (%)
*
*
High (%)
High (%)
No differences No differences No differences ** ** 31.3 47.0 67 66 No differences No differences
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99
Table A2: Continued. Pro-activeness
Innovativeness
Low (%)
Low (%)
High (%)
Yes Total Worked in Research Base Yes Total Qualifications No tertiary Degree level Postgraduate qualification Total
Risk-taking
High (%)
** 17.4 46 ** 45.5 27.3 27.3 55
** 36.6 52 ** 24.2 33.9 41.9 62
Low (%)
High (%)
27.9 61
45.0 40
** 40.9 31.8 27.3 66
** 21.7 30.4 47.8 46
* p 0.1. ** p 0.05.
Table A3: Reasons for starting the business. Reasons Autonomy/get on reasons (mean scores) To be my own boss To increase my income To get on in the world To manage a business Enjoyment reasons (mean score) To enjoy my life For the happiness of my family Opportunity reasons (mean score) Saw a strong future in this work To provide a new service To commercialise existing technology To commercialise a new technology Skills including technical (mean score) To use own technical skills To use own marketing skills To use own management skills Society reasons (mean score) To do something worthwhile To do something useful for society For the development of science and technology
N
Mean
SD
113 118 112 110 109 109 111 113 114 116 109 107 111 110 111 109 109 109 112 109 110
2.14 2.39 2.07 2.02 2.01 2.00 2.13 1.92 1.92 2.43 1.78 1.73 1.73 1.77 1.88 1.72 1.70 1.75 2.39 1.50 1.38
0.6360 0.74 0.78 0.87 0.80 0.6718 0.76 0.76 0.6038 0.68 0.82 0.80 0.84 0.5635 0.74 0.71 0.73 0.4836 0.68 0.65 0.61
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Table A4: Personal objectives. Personal objectives Enterprise focus Build a business with a reputation for excellence Provide a positive/stable environment for employees Build a business that can exist without me Personal satisfaction focus Conduct business on the basis of fairness and moral integrity To maximize my enjoyment and satisfaction from work To maintain my personal involvement Financial focus Maximise current and future returns for the owners Increase the value of the business for potential capital gains Ídealised technology business focus To contribute to the development of science and technology To prepare the business for future stock exchange/AIM listing Enable people to maintain a balance between work and home Build a business that contributes to the well-being of society
N 129 130 131 132 131 130 127 129 131 129 129 131
Mean
SD
4.26 4.60 4.08 4.09 4.01 4.32 4.22 3.51 3.49 3.60 3.40 2.13 2.58 1.72 3.81 3.21
0.662 0.68 0.82 1.05 0.656 0.77 0.83 1.02 1.043 1.21 1.21 0.934 1.29 1.06 0.83 1.13
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Chapter 7
Strategic Operation Concerns of UK Biotechnology Companies Yuen Yoong Leong and Yongjiang Shi
Introduction The biotechnology industry has been recognised as one of the most critical sectors by many developed and developing countries. The roots of biotechnology are fundamentally linked to the basic natural sciences and thus form an important component of the knowledge-based economy and generate sustainable economic power. In addition, biorelated developments hold the promise of a new economic growth engine for developed countries and it is indeed also a new opportunity for many developing countries to capture a new wave of technology development (DTI Report, 1999). Besides contributing to more employment, the industry is poised to have a strategic impact on the entire national economy because of the interactive relationship between biotechnology development, innovation and entrepreneurship. The biopharmaceutical industry, which consists of two very different types of players — the giant pharmaceutical companies and the more entrepreneur-based biopharmaceutical companies — is characterised by strong regulation and very expensive research and development (R&D). Every new drug has to pass through the strictly monitored and time-consuming pipeline, which includes pre-clinical trial, three phases of clinical trials and regulatory approval stages, which could cost about $800 million (Tufts University estimate, 2001) and take more than 10 years. Another peculiar phenomenon in the pharmaceutical industry seems to be that, while rich, giant pharmaceutical companies seem to wield a decreasing amount of innovative power, research institutions that are relatively less financially endowed seem to be spinning off many innovative ideas. Multinational pharmaceutical companies are more inclined to spend a larger proportion of their R&D money in the later stages of development in order to reduce risk. Their enthusiasm for early stage exploration and discovery is much less. However, in the past two decades, breakthroughs in the biosciences and technology have not only generated enormous potential for new drugs and treatments for various types of New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 103
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diseases, but also stimulated the growth of entrepreneurial biotechnology start-up companies that spend much less in R&D (Table 1), but yet seem to understand the secrets of innovation better than their larger counterparts. A very interesting question might be how these entrepreneurial biopharmaceutical companies with such modest funding could go through the extremely expensive and timeconsuming channel to take their innovation to market. Must they co-operate with the big pharmaceutical companies, or can they survive independently? Have they created a new paradigm for their survival and growth? From a theoretical perspective, besides the traditional options of market and firm mechanisms, the inter-firm business network has become an emerging arena for innovation and running business, as well as for researching new business models and management skills (Gulati et al., 2000). Currently, it is no longer a secret that a company may own a very small portion of a supply chain and yet be able to co-ordinate or integrate the whole supply chain to deliver a competitive product to its targeted market. It has become very popular for all types of companies to downsize and outsource their non-core business tasks and to set up inter-firm collaborations (Lamming et al., 2000). It is equally interesting to notice that there are increasing observations about geographic clustering emerging worldwide (Piroe & Sabel, 1984; Porter, 1998). The authors thought the biopharmaceutical sector might be an attractive industry for observing and understanding how small and medium enterprises (SMEs) in the biopharmaceutical industry function as a complex value chain with few resources. This paper seeks to address two basic issues — identify key concerns of biopharmaceutical managers and clarify the research foci of developing an inter-firm business network in the biopharmaceutical industry. The paper consists of four main sections. After a brief
Table 1: R&D expenditure and growth of the top five pharmaceutical and biotech companies in 2001. Different types of companies
R&D expenditure 2001 ($m)
R&D expenditure in sales in 2001 (%)
4847 3818 3591 3116 2773
19 12 11 15 17
9.3 4.9 15.7 0.1 3.8
865 526 344 315 309
25 30 69 32 137
2.4 7.4 15.3 3.9 17.2
Pharmaceutical Pfizer Inc. (US) GlaxoSmithKline (UK) Johnson & Johnson (US) Aventis (US) AstraZeneca (US) Biotechnology Amgen (US) Genentech (US) Chiron (US) Biogen (US) Serono (Switzerland) Source: Reuters Business Insight (2002).
Growth of R&D 2000–2001 (%)
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description of the research design, it reviews the biopharmaceutical industry by introducing two industrial case studies that represent two ends of the British biopharmaceutical companies’ size spectrum. The paper then summarises the key challenges that manufacturing or operations managers are facing. After reviewing the existing literatures in the areas related to the industry issues, the paper highlights the research gaps and demands, and outlines a further research plan to gain detailed understanding of biomanufacturing networks.
Research Design The aims of this research are to investigate how biopharmaceutical SMEs operate and understand what their strategic operation challenges are (Figure 1). The study started with a literature search to find out as much as possible about the biopharmaceutical sector. Interviews are then conducted at six biopharmaceutical companies and one contract manufacturer located in the Cambridge area to gain further understanding of their operations. Key industrial issues and challenges are identified from the interviews and sector literature search. A review of the theoretical literature is then undertaken to clarify the gaps between the industry and theories. Future research aims and design are then defined.
Case Studies This section describes two cases representing two ends of the UK biopharmaceutical industry spectrum in terms of their business models. Case A is a virtual company and Case B is more vertically integrated. A Virtual Drug Company
Research curiosity in: - bio-business model for growth - inter-firm business network
Company A is a drug development company founded by people with no background in the pharmaceutical business. The company aims to tackle diseases with a high unmet medical
Biopharma firm case studies in the Cambridge area
UK and global biopharma sector review
Identifying key industrial issues and challenges
Clarifying gaps between industry and theories: research aims and design
This paper is focused on these stages of the research work
Theoretical reviews on inter-firm business network, evolutionary industrial development, new product development, and P/OM
Figure 1: Research design.
Further detailed case studies and research
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need and substantial market opportunities. They focus on two therapy areas. To date, the company has been successful in building up its capabilities and position by in-licensing and developing several promising product opportunities. Such diversification reduces the risk of both technical and clinical failure of an underlying platform technology. Company A employs less than 20 people to conduct the company’s virtual business network operations. The company employs additional staff on a flexible contract basis to reflect the immediate needs of the business including the type and development stage of the individual products in the portfolio. The company outsources pre-clinical and clinical development activities thereby avoiding costly research teams and laboratories, and keeping fixed overhead to a minimum. The company’s competitive advantage, according to the CEO, lies in its efficient decisionmaking process and expertise in designing clinical trial experiments. Unlike in established pharmaceutical companies that are more bureaucratic, there is less pushing decisions around departments due to the inclination to avoid responsibility. This is an important reason why Company A wants to remain virtual instead of becoming more vertically integrated. The company also places great emphasis on experimental design for all studies (toxicology, efficacy, safety, etc.) because it will help the company to make important decisions on investment or whether they have a potential product. “In Biology, the ‘shades are grey’ and good decisions depend heavily on well-designed experiments”, said the CEO. One of the key players in the biopharmaceutical industry is the contract research organisations (CROs). Big pharmaceutical companies used to own their in-house clinical research teams, but this has proven very costly because utilisation is not optimum. Since the 1960s, the CRO phenomenon has grown significantly, transforming what was once a cottage industry of consultants and independent laboratories into an industry of its own. An important trigger of this is the incorporation of the Kefauvner-Harris Act into the American legal system in 1962 after the Thalidomide disaster (Colburn et al., 1997). The legislation required drug development companies to provide proof of efficacy for their new drug applications (NDAs) in addition to the already mandatory proof of safety. This resulted in the need to meet more sophisticated and time-consuming clinical endpoints, which meant significantly more Phase III testing. The amount of work necessary for supporting an NDA increased significantly and this accentuated the need for expanded contracting, which included the increased use of CROs to manage multicentre programmes (Piachaud, 2000). A typical CRO’s service can range from “manufacturing and quality control to preclinical pharmacokinetic, pharmacology and toxicity studies; from the design, conduct and analysis of sophisticated Phase I and Phase II pharmacokinetic/pharmacodynamics decision making studies to managed multi-centre studies; or from quality assurance to database management, statistical analysis and reporting” (Colburn et al., 1997). The CEO of Company A expressed dissatisfaction with the quality of CROs’ service. The CROs may have personnel who can run the machines, but they may not have the management and control ability to solve problems and ensure quality of the data that is so vitally needed for regulatory purposes. It is vital that a CRO is able to provide middle management, i.e. someone who observes anomalies in the laboratory and makes suggestions to the sponsor for further tests to be carried out. Unfortunately, many CROs cannot do that, which often leads to incomplete information for decision-making, lost time and extra cost. CROs generally use templates and do not add in the significance of the data and words. The ability to
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interpret data in the context of the overall project and stage of development is vital. CROs operate in a unit cost economy. The more reports or trials they can put through, the more money they will make. Although CROs often claim that they offer project management services, in reality they offer a project administration service. CROs treat young biopharmaceutical companies and giant pharmaceutical companies differently. For the former, a lot of guidance and leading is usually necessary and it is vital for a CRO to send someone who has a broad knowledge of the entire drug development process to sit in meetings with the client. The reason being that in a typical meeting with the client, the client’s managers would be throwing various ideas around. It is then of paramount importance for the CRO representative to be able to quickly synthesise these ideas and give guidance accordingly. In the case of Company A, due to the extensive experience of the CEO, this is less applicable because he has the expertise to design the experiments himself and give clear instructions to the CRO to execute his plans. In relation to big pharmaceutical companies, CROs would usually take a less leading role because the established companies would usually have their set ways of doing things.
A More Integrated Company Company B is an integrated, international venture, researching, developing and marketing ethical pharmaceutical products in Europe and the US. The company is listed both on the London Stock Exchange (LSE) and the New York Stock Exchange. It has a strong financial profile with an extensive late-stage product pipeline providing good prospects for sustained growth, driving its goal of becoming a global biotechnology leader. The company was founded as the first biotechnology company in the UK in 1980, with the National Enterprise Board as its major shareholder, to exploit British discoveries in the biological sciences. The company was initially active in many fields including the production of proteins in cell culture, medical diagnostics, antibody engineering, food processing technology and human therapeutics. Today, the company focuses on developing treatments for autoimmune and inflammatory disorders. At the same time, the company is also expanding into the areas of oncology products and antibodies for treating osteoporosis. The major events that have shaped Company B are summarised as shown in Table 2. Having sold the production facilities in 1996, Company B depends on a few selected contract manufacturing organisations (CMOs) in the UK and Europe to satisfy its production needs. CMOs are companies that provide production solutions qualified for clinical testing and commercialisation; they could also undertake pre-clinical production process development. A 1-year lead-time is usually required to obtain production slots with a CMO. It is also necessary for them to estimate the quantity of materials needed before reserving manufacturing capacity. In Company B’s interactions with the CMOs, concerns related to intellectual property rights (IPR) are present. For instance, the Global Manufacturing Director of the company is consciously wary of CMOs incorporating some of their own technologies into the production process, which could then lead to IPR disputes, even though this has not happened before in the company’s history. Besides this, there may also be other issues in production development such as the environmental differences between their laboratories and a
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Table 2: Historical time line of company B. 1980 1987
1990
1993 1996 1999 2000 2001
Founding of company Following a substantial private placing,* the company divests itself of peripheral businesses and concentrates on the discovery, development and production of novel therapeutic products. • New management reorganised the company into two operating groups responsible for drug discovery and production, respectively; • Implements a strategy of collaborating with big pharmaceutical companies. Obtains a full listing on the LSE, followed by a secondary offering in 1996 to support its R&D activities. The production division of the business was sold to a major CMO enabling the company to concentrate on the discovery and development of novel drugs. Merger with a predominantly chemistry-based company.† Merger with the second company mentioned in footnote 1. Company divested itself of non-core business, including the vaccine production business. • Acquires another company, which provides access to a novel antibody programme; • Acquires a German company, which provides sizable pharmaceutical sales and marketing organisation in Germany, the largest market within the EU.
* The direct selling of shares in a private company necessary to investors, often without the intermediary of a stockbroker. In the USA it is called a private placement. † There were three motivations for the merger. First, the merger helped Company B to expand its research base into the world of small molecules. Second, there were strong synergies between Company B and the other company because the latter had a profitable marketing division, which could be divested and bring in substantial cash; 18 months before the merger, the other company had acquired a gene-based biotechnology company in the US. Company B could thus have access to novel genomics drug discovery programmes. Third, the merger will give Company B the critical mass to acquire another company, which was largely UK-based and focused on developing, producing and selling vaccines and has an outstanding record in pharmaceutical marketing. Source: Company website and interview.
contractor’s production facilities, which could make the transfer of production processes difficult and possibly push the project off schedule. The number of CMOs used by Company B is few, in the range off one to two. Hence, the company does have the concern of becoming overly dependent on their CMOs. As biologics production capacity is limited, there is keen competition for the service of CMOs. Company B has found it necessary to present an attractive long-term business plan to CMOs to secure a strategic alliance relationship. In addition, they also keep an eye on whether competitors might acquire their CMO. There may be IPR complications if such an acquisition took place. For a company that aspires to become a fully integrated biopharmaceutical company, whether to develop one’s own production capability is an issue on the mind of senior management. “We know that production is important. Bringing it in house is just a matter of timing,” said the Director of Global Manufacturing. Company B understands that process innovation is a critical capability for product innovation in a high-tech market like biopharmaceuticals. However, he also acknowledged that even if the company started having
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Figure 2: EU specialist biotechnology companies 1999 (Ernest & Young, 1999). its own production plant, it is unlikely that all production will be brought in-house. Hence, the new situation would be a combination of in-house and contract production. The other concerns of Company B include how to set priorities among their potential projects and allocate resources to meet stakeholder expectations. Which project should they pursue and how should they pursue them (full development, or licence, or both)? Which type of partner should they pick and when? Which current projects to put aside? How many of the projects could the company effectively handle? What development strategy would leave the company in the best position in the long term, and which strategies could result in the firm losing precious capital?
Industry Review Having seen two specific cases of biopharmaceutical companies, this section proceeds to provide a general overview of the UK biotechnology industry. The UK currently leads Europe in biotechnology (Figure 2), although it is still some way behind the US (DTI Report, 1999). The picture is one of strong entrepreneurship and a sophisticated venture capital market whereby over £1 billion was invested over the last 10 years. There are 270 specialist biotechnology companies employing 14,000 people, with a further 200 companies with 25,000 employees providing related services (DTI Report, 1999). Figure 3 shows that these companies are largely concentrated in Cambridge, London, Oxford and Southern Scotland. Collaborating is a critical capability for biotechnology companies because of the vast resources required to translate new discoveries into marketable products. These resources are
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Figure 3: Location of UK specialist biotechnology companies (Ernest & Young, 1999). so broadly distributed that no single firm has all the capabilities necessary for success. Despite their relatively small size and lack of resources, biopharmaceutical SMEs could still perform a role in highly competitive drug development activity. This is possible because an elaborate division of labour between companies has evolved in biopharmaceuticals. A whole range of contract service providers such as CROs, CMOs and other specialist firms have emerged to carry out specialist roles (Table 3). Many SMEs also find it necessary to co-operate with established pharmaceutical firms in late stage clinical development and subsequent commercialisation. This phenomenon is particularly true when forward integration is difficult and capital is scarce.
Industrial Challenges Do Biopharmaceutical Start-Ups Understand their Living Environment? The way biopharmaceutical companies function is fundamentally different from the traditional pharmaceutical companies. The former function within a network of companies whereas the latter is used to vertical integration. The difference in operation mode is due to difference in the amount of resources at the companies’ disposal. Different kinds of companies constitute the network, as shown in Table 3. In a web of such complex relationships, it could be a daunting task for new biopharmaceutical entrepreneurs to visualise the entire
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Table 3: Different players in the biopharmaceutical sector. Different players
Role
Academic institutions, Biopharmaceutical SMEs, and Technology specialists
Basic science research, understanding nature and mechanisms of life, developing knowledge and theory, exploitation of scientific findings, and drug discovery
Contract research organisations CROs
Data management — clinical data management solutions Bio-statistics, statistical planning, evaluation and medical report Toxicology trials monitoring
Contract service organisations
Provides pre-clinical testing, development and production services Regulatory and ethical procedures
CMOs
Provides production solutions qualified for clinical testing and commercialisation Could undertake pre-clinical production process development as well Filling Packaging
Contract sales organisations
Distribution and outbound logistics Advertisement and publicity Export and import
Pharmaceutical companies
Late stage development partners to biopharmaceutical SMEs Sales and marketing
network that is necessary for them to develop their potential molecule or new chemical entity. For leaders of more established biopharmaceutical companies, can they see what the trend of contract manufacturing is and respond creatively to it? Have they completely understood why such networks exhibit stronger innovation power than the vertically integrated pharmaceutical companies? How Might an Individual Bio-SME Survive and Grow in the Network? How to balance the dynamics of vertical integration and virtual collaboration? Both types of business models have their advantages and disadvantages. The corporate business model that is most suited for a firm at a particular time depends on the resources available, firm growth strategy and opportunities that present themselves. In fact, what matters for start-ups is not, in
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abstract, what some business model is or is not able to do; it is what the people are interested in doing, want to do and are capable of doing. Either model works. However, if the people from one company were put in to run the other company, they may fail because it is not their way of doing things (Platts, 2003). How to raise money from venture capitalists (VCs) or the stock market? It usually takes more than 10 years to bring a drug from the laboratory to the market. Young biopharmaceutical SMEs that still do not own revenue-generating resources will have to depend on external funding to sustain themselves through the non-profit generating drug development periods. Where should one position oneself on the value chain in order to capture the most value? Different parts of the value chain would have different concentrations of competition and profit margins. Given the limited resources that biopharmaceutical SMEs have at a particular point in time, it is critical for them to assess how to position themselves so that they may generate or attract enough cash to ensure their continual survival and nurture future expansion potential. How might one respond creatively to the evolving environment? Roles of the players in the biopharmaceutical industry evolve with time due to expansion or contraction of business models. This means that the services available in the market are constantly changing as well. Being able to keep aware of the changes that are taking place in the environment is the first step towards developing a creative response that would improve one’s survival and growth opportunities. How Might a Focal Bio-SME Manage Networks? How should one manage a product development project that involves the co-operation of many other players? Formal contracts provide the ground rules for inter-firm relationships. More importantly, however, whether a relationship is going to work depends on how the project manager of a biopharmaceutical SME goes about engaging the contractors and partners. More fundamentally, it is important that the personal chemistry works and the cultures fit. Managers need to know what pitfalls to avoid and good practices to emphasise in navigating the complex web of inter-firm relationships. How can one create a more efficient network configuration? As a firm’s product portfolio expands, the complexity of its inter-firm networks would increase correspondingly. The maintenance of each relationship demands skills. There would come a tipping point whereby the firm would need to think of the efficiency question when all the networks are being put together. The objective would be to generate more diverse information and capabilities per relationship, and thus produce desired benefits with minimum costs in terms of redundancy, conflict and complexity.
Theoretical Challenges Evolution of Manufacturing Systems Over the past two decades, companies have gone down the path of internationalisation, on to outsourcing and then into network-based relationships. Manufacturing systems have
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evolved from the traditional input-output transformation model into a network-based model (Flaherty, 1986; Ferdows, 1989, 1997; Dubois, 1993; Shi et al., 2003). The internationalisation of manufacturing operations once implied internalising business activities (Rugman, 1980). More recently, in almost every sector, the meaning of internationalisation has become the externalising of business activities through outsourcing of non-core business activities (Lamming et al., 2000). Even more recently, it is observed that companies may own a very small part of the supply chain, but the development and retention of strategic skills enables them to enthuse, inspire, integrate and co-ordinate the entire supply chain (Platts, 2003). In short, internationalisation and externalisation are the two basic trends in the development of manufacturing systems. Manufacturing systems in the biotechnology industry are a combination of the two trends identified above. Like other R&D intensive industries, for instance new materials and information technology, firms rely on a range of external contracts and internal competencies to innovate (Hagedoorn & Schackenraad, 1994). External contacts and internal competencies are important in determining the long-term viability and growth of these firms because these lead to new technologies and innovations. This observation bears much resemblance to the concept of global manufacturing virtual networks (GMVN) that is proposed to capture manufacturing operations that are dispersed by geographical locations and ownership (Li et al., 2000; Shi & Gregory, 2002). Inter-Firm Relationships and Outsourcing Existing literature on inter-firm relationships and outsourcing does not adequately explore the area of internationalisation combined with externalisation. The rationale of forming interfirm relationships, ranging from economics to behavioural, has been addressed by many (Teece, 1992; Dyer & Singh, 1998; Gulati, 1998; Barringer et al., 2000). While studies like this are useful in grasping the “why” of inter-firm relationships, they do not throw light on what characteristics makes one relationship more effective than another. What are the key factors in the design of relationships that work, what are the competencies and skills that make the networking work and how are deals structured so that contractual agreements support the necessary atmosphere and empower the relationship?1 These are interesting topics that deserve further research because start-ups that configure more efficient networks gain a significant and sustaining competitive advantage in the biopharmaceutical industry (Baum et al., 2000). The literature of outsourcing focuses primarily on how outsourcing decisions should be made (Welch & Nayak, 1992; Venkatesan, 1992; Probert, 1997; Cox, 1997; Fine, 1998; Baines et al., 1999). Various value considerations like cost, core competence, supplier availability and technology have been accounted in existing outsourcing decision processes. Some research has also been done on how to overcome the potential problems when managing outsourcing relationships (Bendor-Samuel, 2000; Quinn, 2000). Unlike in many industries where firms outsource activities because they are less valuable, biopharmaceutical SMEs usually engage with contractors because they do not have the physical facilities such as laboratories or 1
Thanks to input from M. J. Platts.
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production plants to carry out the work. In fact, many of biotechnology firms aspire to become more vertically integrated when their financial capability permits. The rationale lies in the fact that work such as pre-clinical process development and production are knowledge generating and the accumulation of such knowledge has a far-reaching impact on a firm’s competitiveness. Due to the difference in outsourcing rationale, existing work that guides outsourcing decisions will not be too helpful to biopharmaceutical entrepreneurs. New Product Development and Project Management Network relationships are increasingly seen as important for technological innovations (Jones et al., 1998; Senker & Faulkner, 1992). Analysis of innovation networks has been informed by particular perspectives such as institutional and evolutionary economics and politics (McLoughlin, 2001). However, these positions are diverse and lack synthesis. Existing literature gives much guidance to new product development (NPD) process conducted within single firms (Wheelwright & Clark, 1992, 1995), but less within a network of firms. In the sector of biopharmaceuticals, research has been carried out from the perspective of production, which includes the challenges of process development (Pisano, 1997) and on the innovative nature of biopharmaceutical manufacturing (Feldman & Ronzio, 2001). There are good processes in the design field that will guide a creative person through the NPD process (Platts, 2003). Recent work has highlighted some organisational aspects of restructuring in the pharmaceutical industry to support the emergence of informal intra-firm and inter-firm relations in NPD projects (Zeller, 2002). Due to a growing inter-firm division of labour, cross-functional and cross-firm project teams have emerged as an important mechanism for managing NPD (Sydow & Windeler, 1999). Much work has been directed at how and why cross-functional teams work the way they do (Jassawalla & Sashittal, 1999). Less research is found in the area of inter-firm project management. Resource-Based Theory and Evolutionary Economics Observations of how biotechnology companies grow and organise resources to compete suggest relevance of the resource-based theory (RBT). The RBT in its original Penrosean form (1959) aimed to show how the entrepreneurial firm could grow by identifying “productive opportunities”, which could be exploited on the basis of resources which the firm had developed or could acquire (Best & Garnsey, 1999). More recent RBT has emphasised that when strategies are based on firm-specific resources, they are not easily duplicable by competing firms (Barney, 1991). This is particularly true in a network-based environment as the competencies that make networks work come from specific individuals working in a company and such competencies are often tacit knowledge. The paradigm of RBT contrasts with the market power paradigm put forward by Porter (1980) and Shapiro (1989). Porter’s approach emphasises industry structure and positioning within that structure as determinants of competitive advantage. Shapiro’s strategic conflict approach uses game theory and shows competitive outcomes to be a function of the effectiveness with which firms keep their competitors off balance through pricing strategies,
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signalling, control over information and strategic investments. However, he did not explain how this could be done. Networking activities of a company can make it possible not merely to adapt to an existing industrial environment, but to create a favourable environment that would support the corporate business model and improve a company’s selection position. This position is not static and could not be attained by looking at industry structure or national institutions; a broader theory such as that provided by evolutionary theory is required to encompass the selection conditions of the marketplace, regulations and global networks. To show that a firm does not adapt passively to the global environment, but is proactive in creating favourable selection conditions, it is necessary to examine how resources were accessed, created and leveraged by the firm — in Penrosean mode. There are analogies with the case of Hercules Powder described by Penrose, where the expanding firm detected new opportunities and set out to build on its resources and acquire further resources in order to exploit these opportunities (Best & Garnsey, 1999).2
Implications to Industry and Academia Having identified the operation concerns of biopharmaceutical SMEs and the relevant theoretical challenges in previous sections, we have teased out three particular concerns that we aim to address in future research. Table 4 summarises the comparison between existing theories and industrial issues. The research gaps that we will aim to address are circled3,4 in Table 4. First,3 existing literature on inter-firm relationships does not give sufficient guidance to biopharmaceutical entrepreneurs in network configuration and operation. Our forthcoming work to characterise how successful biotech companies design, operate and improve their manufacturing networks could enrich existing theories. Table 4: Industrial issues and theory matrix. Related Theories Inter-firm Relationships/ Outsourcing Network Configuration (What?) Network Industrial Operation Issues (How?) Resource building/Growth (How?)
Project Management
Strategy (Porter and RBT)
Evolutionary Theory See 4
N
Y
N
N
N
Y
Y
Y
Y
Y
N
Y
N
Y
Y
Y
N
N
N
N
Y
Y
N
N
N
N
Y
Y
Y
See 3
Left column = Might theory help practice?
Y = Yes N = No
2
New Product Development/ Introduction
Thanks to input from E. W. Garnsey.
Y See 4
Right column = Might practice contribute to new theories?
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Second,4 existing internal–external analysis of a firm provided by RBT combined with Porterian approach is incomprehensive for this unique industry. We need a new conceptual base to explain the networking phenomenon in the industry. This can be achieved by tapping into evolutionary theory, which emphasises historical contingencies, broad principles and patterns of change. Entrepreneurs may also gain a fresh perspective of their networking activities by viewing them through the eyepiece of evolution. Several biopharmaceutical products whose development process involves networking with multiple partners have been identified and are in the process of being studied. The unit of analysis will be the physical flow of a particular product within the total manufacturing network. The products will be defined from the perspective of the focal firm. Semistructured interviews are conducted with personnel from the focal and node companies and they are invited to map their manufacturing networks for the specific products chosen for the study. The interviews explore decision processes, collaboration mechanisms and dynamics of interactive relationships. Such understanding is necessary for the development of practical guidelines for biotechnology managers.
Conclusions In the biopharmaceutical environment, each company has its own expertise and through a combination of mutual need, repeated interaction and membership in a common technological community, networks of collaborative ventures are formed. It is clear that young biopharmaceutical companies are facing a considerable learning curve in relation to understanding and managing their networks more effectively and efficiently. The key operation challenges could be summarised as network configuration, operation and building necessary skills. Observing the complex biopharmaceutical environment through the eyepiece of evolution could potentially yield a better understanding of the great multitude of networking activities it encompasses. Future work to extend existing theories in inter-firm relationships and outsourcing has also been designed.
References Baines, T. S., Whitney, E. W., & Fine, C. (1999). Industrial practices in manufacturing technology sourcing. The International Journal of Production Research, 37(4), 939–956. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99–120. Baum, J. A. C., Calabrese, T., & Silverman, S. B. (2000). Don’t go it alone: Alliance network composition and startups’ performance in Canadian biotechnology. Strategic Management Journal, 21, 267–294. Bendor-Samuel, P. (2000). Turning lead into gold: The demystification of outsourcing. Provo, UT: Executive Excellence Publishing. Best, M., & Garnsey, E. W. (1999). Edith Penrose 1914–1996. Economic Journal, 109(453), 187–201.
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Senker, J., & Faulkner, W. (1992). Networks, tacit knowledge and innovation. In: R. Coombs, A. Richard, P. P. Saviotti & V. Walsh (Eds), Technological collaboration: The dynamics of cooperation in industrial innovation. Cheltenham: Edward Elgar. Shapiro, C. (1989). The theory of business strategy. Rand Journal of Economics, 20(1), 125–137. Shi, Y., & Gregory, M. (2002). Global manufacturing virtual network (GMVN): A new manufacturing system for market agility and global mobility. Unpublished paper. Shi, Y., Fleet, D., Gregory, M. J. (2003). Global manufacturing virtual network (GMVN): Revisiting the concept after three years. EurOMA 2003, Cuomo, Italy. Sydow, J., & Windeler, A. (1999). Projektnetzwerke: Management von (mehr als) temporaren Systemen. Kooperation im Wettbewerb Neue Formen und Gestaltungskonzepte im Zeichen von Globalisierung und Informationstechnologie. E. J. and S. E. J. Wiesbaden, Gabler, pp. 211–235. Teece, D. J. (1992). Competition, cooperation and innovation. Journal of Economic Behaviour and Organisation, 18, 1–25. Venkatesan, R. (1992). Strategic sourcing: to make or not to make. Harvard Business Review, 70(6), 98–107. Welch, J. A., & Nayak, P. R. (1992). Strategic sourcing: A progressive approach to the make-or-buy decision. Academy of Management Executive, 6(1), 23–31. Wheelwright, S. C., & Clark, K. B. (1992). Revolutionising product development. New York: The Free Press. Wheelwright, S. C., & Clark, K. B. (1995). Leading product development: The senior manager’s guide to creating and shaping the enterprise. New York: The Free Press. Zeller, C. (2002). Project teams as means of restructuring research and development in the pharmaceutical industry. Regional Studies, 36(3), 275–289.
Chapter 8
High-Technology Small Firms: Instilling Innovation and Creativity Nicholas O’Regan and Abby Ghobadian
Introduction Technological developments are rapidly advancing and are a key determinant of overall business performance (Hitt, Ireland, Camp, & Sexton, 2001; Scott, 2000). Technology is also a key driver of change in the business environment. Consequently, in industries characterised by rapid technological change and intense global competition, firms need a range of resources and expertise to develop and market timely and cost-effective products or new technologies (O’Brien, 2003). The rapid and unprecedented rate of change means that many high-technology small firms (HTSF) are re-evaluating their strategic plans in order to ensure that they can gain, attain or retain competitive advantage in the face of growing competition. However, it is becoming increasingly difficult for firms to compete successfully, particularly in fast-cycle markets (Bettis & Hitt, 1995; Kuratko, Ireland, & Hornsby, 2001). The volatility of the business environment is an important factor in explaining the firm performance (Eisenhardt & Martin, 2000). The literature suggests that innovation is needed to combat the shortening of product life cycles, and to take advantage of new opportunities (Barkema, Baum, & Mannix, 2002). For example, Cohen and Levinthal (1990) found that firms with significant R&D experience tend to have greater success in deploying it commercially compared with firms with minimal experience. However, achieving effective innovation is a complex and formidable task, with many organisational obstacles hindering the process. Indeed, Ahmed (1999) contends that while virtually all firms talk about innovation and many invest significantly in R&D, few actually succeed. He suggests that many firms are reluctant to make the necessary investment and commitment in innovation due to the significant risk element involved. While resource allocation and in particular, investment in R&D is important, organisational behavioural aspects are also critical in converting R&D expenditure into effective innovation. This aspect is emphasised by Ahuja and Katila (2001), who contend that ‘a long tradition of research in technology suggests that new innovative outputs are often the result of combining existing New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 119
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elements of knowledge into new syntheses. This implies that a ‘fusing’ of organisational behaviour and innovation capabilities is needed.
Aims of the Research The ability of HTSFs to meet growing consumer expectations is largely based on their capability to invest in R&D and their subsequent effectiveness in achieving innovation to deliver new products at competitive prices. Accordingly, R&D is a vital activity (Barkema et al., 2002). The failure of many HTSFs to successfully convert R&D into innovation indicates that there are many hurdles to be overcome in the innovation process. For example, Kim and Mauborgne (2000) suggest that such hurdles ‘make or break the commercial viability of even the most powerful innovative ideas’. What can HTSF managers do about these hurdles, and what determines the level and effectiveness of the innovation strategies adopted? In this paper, we adopt an organizational stance and contend that there are three main drivers of R&D and its subsequent conversion into effective innovation: strategic management, culture and leadership as outlined in Figure 1. An understanding of these dimensions is critical in order to explain the deployment of R&D/innovation strategies.
A Framework Approach This paper examines R&D/innovation in the context of the resource-based view. Barney (2001) defines resources in terms of tangible and intangible assets used by the firm to implement a value-creating strategy. The resource-based view of strategy suggests that strategic
Performance
Innovation
Research and Development Expenditure
Leadership
Culture
Strategic Planning
Figure 1: A framework approach — drivers of innovation.
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management emphasises the configuration and reconfiguration of resources in order to ensure a ‘fit’ between the external and internal environments (Teece, Pisano, & Shuen, 1997). Zott, 2003, p. 99 suggests that ‘there is increasing evidence that firm performance is affected by firms’ ability to integrate, build and reconfigure their resources and competencies’. This study examines the factors that drive the deployment of innovation. We adopted a framework approach based on three major influences: the firm’s strategic plan, its ability to lead the process and its ability to nurture the innovation process. In addition, we examined the degree of investment in R&D as a percentage of overall sales for firms based on their primary emphasis on each of the drivers and contrasted the firms in the upper quartiles with firms in the lower quartiles. This study focuses on HTSFs—an area that is under researched to date. The following sections outline an evaluation of strategic planning as a means of gaining competitive advantage, leadership, organisational culture and R&D/innovation.
Strategic Planning Rumelt, Schendel, and Teece (1991) state that the central debate in strategy is to explain why some firms have greater performance than others. For many firms, the right approach to strategy formulation and deployment can lead to distinct competitive advantage. The essence of strategy is to consider the ‘various forms of advantages that a dominant firm may be able to develop and hold over its competitors for some period of time’ (Shamsie, 2003). In a reference to the need for innovation and creativity in the context of strategic planning, Shamsie (2003) suggests that ‘existing competitors or new competitors are usually able to find ways of matching or improving upon most types of advantages that are commonly developed’. The key is therefore to maintain a leading position for a significant period of time. The importance of strategic planning is well documented in the literature (Larsen, Tonge, & Ito, 1998). A number of research studies indicate that small firms using strategic planning performed better than non-strategic planning firms (Kargar & Parnell, 1996; Naffziger & Mueller, 1999). In an earlier study, Delbecq and Mills (1995) identified four main characteristics that apply to highly innovative firms compared with less innovative firms: ● ● ● ●
the commitment of financial and other support for innovation; an assessment of the external markets; management and employee support for innovation at all levels of the organisation, and the use of a structured methodology to ensure that potential innovation is thoroughly evaluated.
Researchers have adopted a number of independent characteristics to delineate strategic processes (Ramanujam, Venkatraman, & Camillus, 1986; Ramanujam & Venkatraman, 1987; Veliyath & Shortell, 1993; Kargar & Parnell, 1996). Each of the characteristics is supported by the literature and all are consistent with the characteristics applicable to highly innovative firms described by Mills and Delbecq (1996). Following those researchers, we adopted the following characteristics to describe the strategic process: external orientation, internal orientation, departmental co-operation, the use of analytical techniques, resources for strategy and strategy as a control mechanism.
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Leadership Dess and Lumpkin (2003) define leadership as ‘the process of transforming organizations from what they are to what the leader would have them become’. Accordingly, leadership implies a significant degree of innovation. Indeed, Teece (2000) suggests that the success of a leader can be ascertained from their ability to manage the innovation processes. This is consistent with the contention by Maccoby (2001) that the real test of leadership is to sustain enhanced performance in the medium to long term. Maccoby (2001) describes the five interrelated aspects of leadership: ‘foresight, systems thinking, visioning, motivating, and partnering’. Arguably, the first three aspects are strongly linked to innovation. The positive association between leadership and performance is well documented. Miller and Shamsie (2001) state that a ‘growing body of literature has identified important effects that leaders’ characteristics can have on the strategies and performance of their organisations’. Others such as Daily, McDougall, Covin, and Dalton (2002), and Arvonen and Pettersson (2002) suggest that effective strategic leadership is a prerequisite for strategic success. Wilderom and van den Berg (1997) in an empirical study of small firms derived, tested and validated the following leadership styles: transformational, transactional, human resource orientation and laissez faire.
Organisational Culture Culture is a term that has received widespread attention over the past decade. However, the understanding of what culture really is, is still unclear. Lounsbury and Glynn (2001) define culture as ‘an interpretive framework through which individuals make sense of their own behaviour, as well as collectivists in their society’. However, Verbeke, Volgering, and Hessels (1998) found 54 different types of organisational culture — their analysis suggested that the common core of culture is the way things are done in the organisation. Research on culture and what comprises the various dimensions of culture is limited. Indeed, Cooke and Szumal (2000) suggest that the determination of new initiatives by leaders to propagate controls, results in a ‘cultural bypass’ and has an adverse impact on the motivation and loyalty of employees. Culture is often seen as the conduit through which management can influence this process (Harris & Ogbonna, 1999). In addition, culture is considered as a major obstacle in the implementation of new ideas, processes and systems (Morgan, 1989). The literature suggests that innovation depends on the existence of shared values (culture) (Wenger & Snyder, 2000). Indeed, Buckler (1997) suggests that innovation is ‘a culture — an almost spiritual force — that exists in a company’. Accordingly, it is no surprise that Deshpande, Farley, and Webster (1993) suggest that a firm’s prevailing culture type is closely linked to innovation. However, they qualify this by saying that some culture types are more likely to result in effective innovation compared with others. Culture was operationalised based on the following dimensions tested and validated by Wilderom and van den Berg (1997) on small firms: human resources, external orientation, empowerment, internal orientation and intergroup orientation.
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R&D and Innovation Harrison (2003) describes R&D as ‘a primary activity for developing new products and new technologies’. In essence, R&D is the forerunner of innovation. Rogers (1995, p. 11) defines innovation as ‘any idea, practice or object that is perceived to be new by an individual or other unit of adoption’. Innovation involves firms adopting new products and/or processes to increase their competitiveness and overall profitability. It involves new ways of identifying the needs of new and existing clients. Innovation is one of the principal challenges to the management of HTSFs. ‘Innovate or fall behind, the competitive imperative for virtually all businesses today is that simple’, is how Leonard and Straus (1997, p. 111) see it. Hitt et al. (2001, p. 484) state that innovation is critical to enable smaller firms to compete in domestic and global markets. The importance of innovation for HTSFs and start-up firms is encapsulated by Lee, Lee, and Pennings (2001) when they state that innovation is the key to competition as ‘competitors cannot easily mimic innovativeness … since it depends on quality and quantity of R&D personnel and the complex social relationships’. The literature suggests that HTSFs have a higher degree of creativity and new product development than larger firms (Vossen, 1998). Failure to pursue a creative approach is summed up by Hamel and Prahalad (1994), as the route to becoming ‘a laggard’. Clearly, the capability to innovate quickly is a key factor in the sustainable competitive advantage of any firm. The importance of innovation as a driver of sustainable competitive advantage is well documented (Shoham & Fieganbaum, 2002; Roberts, 1999). Kanter (1999) stresses that ‘winning in business today demands innovation’. The limited number of existing studies on innovation relate to resource availability and cost factors (Dougherty & Hardy, 1996; Hitt, Hoskisson, Johnson, Richard, & Moesel, 1996). This narrow focus prompted Shoham and Fieganbaum (2002) to suggest the ‘need for additional theoretical integration to link organizational context with industry-level dynamics’. Accordingly, this study addresses this specific suggestion.
Methodology To identify potential respondents for participation in the study, sample criteria were established. While no one directory provides an entirely suitable sampling frame, a random sample was available from a reputable commercial firm. As there are nearly 15,000 electronic/ engineering small firms in the UK (DTI, 1996), a simple random sampling method was used. The literature states that innovation performance can be measured according to the inputs (budgets allocated to R&D) or outputs (number of patents issued) (Ahuja & Katila, 2001; Henderson & Cockburn, 1996). However, the exploratory interviews and discussions with Managing Directors of six HTSFs and employer federations suggested that, in general, it was not possible to obtain wide-ranging hard measures of innovation in HTSFs. Therefore, we adopted the notion of measurement against purpose (Steiner, 1979). This approach was adopted because it was felt that the sample, which comprises smaller firms, is unlikely to yield adequate and reliable financial data. Criticisms on the use of financial data from small firms were made by Powell and Dent-Micallef (1997), and Hillman and Keim (2001) on the basis of varying accounting conventions or managerial manipulation.
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Data were gathered by means of a self-reporting survey questionnaire, consisting of questions to ascertain the emphasis given to strategic planning, culture and leadership and the degree of perception of satisfaction with the results of the strategic planning process. Selecting a self-reporting respondent is a well-established approach in management research (Avolio, Yammarino, & Bass, 1991). In practice, we assessed the degree of success in innovation arising from individual factors such as strategic planning, culture and leadership. A similar approach to assessing the level of satisfaction arising from specific factors and actions was adopted by other researchers (Luo & Park, 2001). The questions in relation to strategic planning were largely based on a survey instrument devised and tested by Kargar and Parnell (1996). All questions used a five-point Likert scale, with a response of 1 indicating that an item received ‘no emphasis’ and 5 indicating that an item received ‘strong emphasis’. The questions in relation to culture and leadership were largely based on a survey instrument devised and tested by Wilderom and van den Berg (1997). Respondents were also asked to indicate, on a five-point scale ranging from ‘highly dissatisfied’ to ‘highly satisfied’, the extent to which they were satisfied with their firm’s success in innovation. The procedures used to analyse the responses included the determination of the reliability of the instrument. Internal consistency was established using Cronbach’s Alpha and factor analysis. Correlation analysis was used to indicate the relationship between organisational culture, leadership, strategic planning and innovation. In a review of the literature, Westphal and Frederickson (2001) found that top management has a significant impact on strategic direction and change. We chose to use Chief Executives as respondents in this study as they are seen as having a wide breadth of knowledge of all the organisation’s functions, activities and operating environment (Frost, Birkinshaw, & Ensign, 2002; Hillman & Keim, 2001).
Response Factors such as change in address, size and sector incompatibility reduced the effective size of the sample to 702 HTSFs. One hundred and ninety four valid responses were received — a response rate of 27%. The degree of non-response was measured to eliminate any source of bias within the sample. All HTSFs were contacted by telephone to ascertain the reasons for non-response. The demographics of non-responding firms were compared with that of responding firms. No discernible differences were detected. This points to the absence of any serious response bias.
Data Analysis An overview of the strategic plans deployed by HTSFs over the previous 3 years indicated that the most frequent strategy adopted relates to new product development. This is consistent with the views of Hamel (2000), who states that innovation is the most important component of a firm’s strategy. Over one-fifth of the firms introduced new products, which
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implies that the organisations in this sample have a reasonable degree of success in innovation and product development. The following sections will identify the attributes of leadership, culture and strategic planning that impact on the effectiveness of innovation.
Culture and Innovation Descriptive statistics were used to examine the relationship between the relative strength of each culture style and the degree of emphasis placed on innovation (Table 1). This was achieved by computing the aggregate score for each of the five culture styles. These scores were then used to generate a new set of variables by determining the lower and upper range of scores. We then placed each of the culture styles for each firm into the lower, two intermediate and upper quartiles of the scores. Comparing the two extreme points of a continuum is commonly used by management researchers in this type of situation (Lee et al., 2001). Finally, we compared the percentage of firms indicating the extent of their R&D expenditure with the degree of success in achieving their innovation objectives for each of the culture types. The analysis of Table 1 indicates that without exception and regardless of the culture style, organisations with styles classified to the upper quartile achieved higher levels of innovation compared with firms classified to the lower quartile. This suggests that the strength of the culture style positively influences the innovation process. However, the findings also indicate that while the degree of investment in R&D varies, firms with predominantly empowerment and human resource culture styles indicated that they fulfilled or entirely fulfilled in excess of 65% of their innovation objectives — significantly higher than other culture styles.
Table 1: Percentage of firms indicating fulfilled/entirely fulfilled innovation objectives according to culture styles and percentage of firms indicating the extent of R&D expenditure. Culture styles Empowerment
External orientation
Internal orientation
Intergroup
Human resource
quartile
quartile
quartile
quartile
quartile
Upper Lower Upper Lower Upper Lower Upper Lower Upper Lower Innovation
73.2
54.3
58.8
57.5
53.3
50.0
56.7
55.6
65.3
50.1
R&D expenditure (%) 0 5.8 11.3 1–3 11.7 31.2 3–5 44.5 32.6 5–10 29.9 22.8 10 8.1 2.1
6.4 25.3 51.2 11.5 5.6
7.9 53.2 26.1 12.8 –
7.3 46.5 37.0 5.6 3.6
7.6 47.2 33.5 5.8 5.9
9.1 45.3 34.6 7.8 3.2
9.8 48.5 32.3 6.6 2.8
8.4 23.1 44.5 22.0 2.0
10.5 46.7 23.3 16.7 2.8
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Accordingly, it can be concluded that empowerment and human resource culture styles are more conducive to innovation activities compared with other culture styles.
Leadership and Innovation In a similar manner to the previous section, correlation analysis was used to establish the relationship between the leadership styles and the degree of emphasis placed on innovation. Descriptive statistics were used to examine the relationship between the relative strength of each leadership style and the degree of emphasis placed on innovation (Table 2). We also compared the percentage of firms indicating the extent of their R&D expenditure with the degree of success in achieving their innovation objectives in each of the leadership categories. The analysis of Table 2 shows that firms in the upper quartiles of both transformational and human resources leadership styles have greater success in achieving innovation compared with firms in the lower quartiles. This finding was not unexpected as both styles are normally associated with a longer-term outlook. This indicates that firms intending to change or revise their existing strategies and eliminate barriers to implementation should emphasise a mixture of both transformational and human resource leadership styles. The findings also indicate that while the degree of investment in R&D varies, firms with predominantly transformational and human resource leadership styles indicated that they fulfilled or entirely fulfilled in excess of 60% of their innovation objectives. In contrast, less than 50% of transactional leadership style firms indicated that their innovation objectives were fulfilled/entirely fulfilled. Laissez faire firms had the highest percentage of firms with no investment in R&D. Accordingly, it can be concluded that transformational and human
Table 2: Percentage of firms indicating fulfilled/entirely fulfilled innovation objectives according to leadership styles and percentage of firms indicating the extent of R&D expenditure. Leadership style
Innovation
Transformational
Transactional
Human resources
Laissez faire
quartile
quartile
quartile
quartile
Upper
Lower
Upper
Lower
Upper
Lower
Upper
63.7
61.5
47.7
37.4
71.4
65.6
13.3
7.9
9.6 40.1 29.4 15.5 5.4
8.2 48.5 28.2 15.1 –
12.4 42.5 36.0 9.1 –
7.7 31.6 30.3 23.6 6.8
8.9 39.1 26.3 21.5 4.2
32.6 52.7 14.7 – –
35.7 53.0 11.3 – –
R&D expenditure (%) 0 7.5 1–3 28.6 3–5 24.9 5–10 30.5 10 8.5
Lower
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resource leadership styles are more conducive to innovation activities compared with other leadership styles.
Strategic Planning and Innovation In a similar manner to both culture and leadership in the previous sections, we used correlation analysis to establish the relationship between the characteristics of strategic planning and the degree of emphasis placed on innovation. Descriptive statistics were used to examine the relationship between the relative strength of each strategic planning characteristic and the degree of emphasis placed on innovation (Table 3). Finally, we compared the percentage of firms indicating the extent of their R&D expenditure with the degree of success in achieving their innovation objectives for each of the strategy characteristics. The analysis of Table 3 indicates that without exception and regardless of the strategic planning characteristic emphasised, organisations with strategic styles classified in the upper quartile achieved higher levels of innovation compared with firms classified in the lower quartile. This suggests that the strength of the strategic planning culture style positively influences the innovation process. The findings also indicate that while the degree of investment in R&D varies, firms with a strong emphasis on staff creativity characteristic of strategy indicated that they fulfilled or entirely fulfilled in excess of 74% of their innovation objectives. Firms emphasising the external orientation characteristic of strategy followed closely with nearly 65% of their innovation objectives fulfilled or fully fulfilled. In contrast, characteristics with a predominant internal orientation indicated that they achieved significantly lower rates of fulfilment of their innovation objectives. Accordingly, it can be concluded that staff creativity and to a lesser extent, external orientation strategic characteristics are more conducive to innovation activities compared with other strategy characteristics.
Practical Implications of the Findings In line with the contention of Kelemen and Bansal (2002); and Hodgkinson (2001), this section will relate the findings to contemporary management practice. This analysis provides a practical step by step guide (Figure 2) for managers to consider in the deployment of innovative initiatives. Our framework identifies the organisational attributes that are specifically associated with innovation. While each factor is associated with innovation, managers are advised to consider the associated attributes simultaneously, rather than in isolation. This will enable managers to avoid emphasis on attributes of leadership, culture and strategy that are not directly related to the achievement of innovation. This approach is consistent with the work of Hax and Majluf (1996, p. 35), which advocates using a series of incremental steps and integrating them into the entire organisation while aligning strategic planning with corporate goals. This approach provides scope for greater creativity and flexibility.
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Strategy External orientation
Internal orientation
Staff creativity
Strategy — a control mechanism
Departmental co-operation
quartile
quartile
quartile
quartile
quartile
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
Upper
Lower
64.9
44.3
51.8
32.5
74.2
49.0
32.7
25.6
43.3
36.1
R&D expenditure (%) 0 8.7 1–3 28.5 3–5 40.2 5–10 16.7 10 5.9
8.9 31.0 43.8 12.1 4.2
15.3 42.0 32.9 9.8 –
19.3 49.1 24.4 7.2 8.4
5.1 32.7 33.0 20.8 6.1
7.5 41.4 27.3 17.7 –
25.4 42.7 29.0 2.9 1.5
29.3 44.9 25.8 – –
17.7 40.1 32.2 8.5 –
18.6 48.7 27.8 4.9 –
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Table 3: Percentage of firms indicating fulfilled/entirely fulfilled innovation objectives according to strategy characteristics and percentage of firms indicating the extent of R&D expenditure.
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Culture
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Leadership
- Empowerment
- Transformational/HR
Strategy - Staff Creativity
Figure 2: Innovation pyramid.
Concluding Remarks In this study we empirically tested the relationship between strategic planning, culture and leadership and innovation. This study found that transformational and human resources leadership, empowerment culture and staff creativity strategy are associated with innovation. The analysis indicated that strong leadership and culture styles irrespective of the style itself, as well as strong strategic planning characteristics resulted in higher degrees of fulfilment of innovation objectives. Finally, the results were used to derive a framework to ‘fuse’ leadership style, culture types, characteristics of strategic planning and innovation together. The analysis suggests that managers would be well advised to give detailed consideration to their leadership and culture styles to ensure that they are aligned with their overall strategic plan. The results outlined provide a practical guide towards achieving this aim. The study also confirms the failure of many HTSFs to successfully convert R&D into innovation, which suggests that the risk element of R&D and its conversion into successful innovation is a serious challenge for many HTSFs. However, it must be stated that the sample was restricted to two different sector types: mature products and stable technology and, products with short life cycles and changing technology, respectively. Clearly the analysis applies primarily to these sectors. In addition, the study did not attempt to examine the differences at the more detailed subsectoral level. In other words, it assumed that the engineering and electronics sectors were internally homogeneous. This assumption should be tested in future studies. In addition, any future research should consider a more in-depth approach. It would have been beneficial to augment the quantitative data with qualitative in-depth case studies or an ethnographic approach. Further testing should be carried out to confirm the finding’s relevance to practice and in particular its effective operationalisation. This might entail the development of a diagnostic framework to assist HTSFs to identify the aspects necessary for the effective deployment of innovation using a checklist principle. Such a checklist would be of immense value to HTSF managers as a self-monitoring instrument.
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Chapter 9
Venture Leasing as an Alternative Source of Financing for High Technology Small Firms — An Explorative Empirical Study Among German High Technological, Leasing and Venture Capital Companies Stephan Golla, Stefanie Kramer and Heinz Klandt
Introduction Young technological companies contribute to the wealth of national economies by creating innovations and employment (Pleschak, Berteit, Ossenkopf, & Stummer, 2002). In order to do so, they need to access huge amounts of capital to be able to start and pursue their business. But what can young technological companies do when risk-averse financial institutions reject credit applications? What if they do not qualify for either venture capital financing or public subsidies? What if the internal and external sources of capital are not sufficient to cover the required investments? These are real, frequent and fatal problems young technological companies have to face in Germany. Given these financing problems, it becomes obvious that there is a lack of financial instruments that specifically respond to their needs. There is a financing concept called venture leasing, yet only applied in the USA, that is designed to specifically support young technological companies. In brief, the technological company leases equipment in return for regular lease payments and equity warrants in the company. However, this financial instrument is far from being literally and empirically exhausted, and thus many questions are left for research to clarify. So, whether venture leasing represents a serious financial alternative for young technological companies is yet uncertain and is intended to be answered in the course of this work. Given the lack of empirical data, the purpose of this study is to examine the concept of venture leasing in more detail.
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Theoretical Frameworks With reference to Perridon and Steiner (1999), financial research concepts primarily focus on explaining how principal–agent relationships can be optimally designed, and how financial instruments can be most efficiently structured. The New Institution Economy (NIE), represents a perspective trying to combine the unstructured view of the Classical Financial Theory and the incomplete Neo-Classical Financial Theory. The NIE overcomes the neo-classical assumption of a perfect capital market and takes into consideration the existence of financial intermediaries as well as different forms of financing and organizations that cannot be explained by the Neo-Classical Economic Theory. It further expands the Neo-Classical Economic Theory by incorporating property rights and transaction costs to explain economic behavior. However, the existence of different financing institutions can only be explained by the fact that they enable the markets to perform better even if they mean a loss of welfare due to information asymmetries between principals and agents (Perridon & Steiner, 1999). The supporters of the NIE emphasize the organizational problems of young technological companies as a result of bounded rationality and opportunistic behavior of the market actors. The Agency Theory is extremely suitable for illustrating special problems of young technological companies, because they are dependent on co-operations with other actors in the market, such as capital provider, supplier, customers, and employees. So, whenever there is asymmetric information between the principal — a financial institution, for instance — and the agent — the management of a young technological company, for instance — there is a potential for the risk of moral hazard (Werner, 2000). Moral hazard describes an increase in the risk that one party behaves opportunistically after contracts have been signed and so harms the other party involved in the transaction who is not able to observe or control the behavior of the first party (Schmidt, 1981). There are three different types of moral hazard. First, “hidden characteristics” describe the problem of the agent concealing negative information and thus increasing the potential of an adverse selection by the principal. Second, “hidden action” or “hidden information” imply that the principal is not able to observe or judge the behavior of the agent after a deal has been closed. The agent then has the incentive to perform to a lower standard than required. Last, “hidden intention” describes a situation where the principal recognizes opportunistic behavior by the agent but is unable to sanction it due to a unilateral dependency on the agent, such as dependency on specific investments. There are certain institutional arrangements that allow a reduction of these risks by either reducing the information asymmetries or by providing the agent with incentives to not behave opportunistically (Werner, 2000). The goal of Agency Theory is to define contracts that harmonize the interests of both principal and agent and thus reduce the risk of moral hazard. Therefore, the principal will put up monitoring costs in order to be better able to control the agent, and reversely, the agent will afford bonding costs to try to convince the principal of his trustworthiness (Perridon & Steiner, 1999). Furthermore, information can be collected either by the financial institutions themselves, what is referred to as “screening”, or by young technological companies providing purposely relevant information, what is called “signaling” (Werner, 2000). Engaging in active signaling activities to secure sources of funding should be of interest to entrepreneurs, in order to reduce the disadvantages capital providers experience due to asymmetric information
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(Schmidt, 1981). In order to minimize the costs associated with the reduction of asymmetric information, financial institutions may require securities, a right to cancel the contract, or additional privileges such as options or warrants that can be turned into equity. Furthermore, financial institutions may require minimum equity ratios or other financial ratios before they engage in business relationships with any company (Perridon & Steiner, 1999). The Neo-Classical Theory states that the two factors, risk and return, play a predominant role in investment decisions. However, the Markowitz Portfolio Selection Theory can be used to explain investors’ reservations of financing high technology small firms (HTSFs). In summary it is the potential mismatch of expected returns and risks or the standard deviation of future cash inflows. According to the NIE, risks can be described as either endogenous or exogenous. Endogenous risks result from asymmetric information allocation between the contract partners. They appear as adverse-selection, moral hazard or hold-up risks. Exogenous risks are relatively independent from asymmetric information. These are caused by an imperfect information level about economic or legal conditions. However, it is sometimes impossible to separate these different kinds of risk (Opitz, 2000).
Venture Leasing as an Alternative Source of Financing Venture leasing, or subprime leasing, represents a financing concept that is frequently used in the USA since the late 1980s. However, it is rarely known and practiced within Europe. So, the first venture leasing transaction in Europe took place in France in 1998. In Germany, the concept of venture leasing will soon be established (Mayer, 2001b). Therefore, the following description of venture leasing will predominantly be based on the concept applied in the USA. The financial concept of venture leasing describes asset-backed venture capital for startup and early-stage companies, predominantly in technological industries. Unlike venture capital firms that require equity stakes in the company for the capital they provide, venture leasing companies provide financing in return for lease payments, the equipment’s residual value, and equity warrants in the company. It represents a financial instrument with less equity dilution and fewer restrictions than other debt financing instruments (Kleiman, 2001). Venture leasing is a concept that has a downside protection since the ownership of the leased equipment remains as a security with the lessor, and that has an upside potential due to the warrants that can be turned into equity once the company goes public or is sold. However, venture leasing does not represent an alternative to traditional leasing, it rather “provides a less dilutive complement to conventional venture equity” (Author unknown, 1999a). The intention of this financing concept is to provide start-up companies with the opportunity to get access to necessary operating equipment without exhausting the expensive capital raised from venture capitalists (Mayer, 2001b).
Characteristics of Venture Leasing Objects Similar to conventional leasing methods, the venture lessor provides HTSFs with financing for general purpose equipment such as personal computers, office equipment, laboratory
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measurement devices, etc. (Kleiman, 2001). It may be beneficial for start-up companies to lease such equipment that is needed only until the product development phase has been completed, that is used for general purposes only, and that is subject to profound technological changes. That is because such equipment can easily be returned to, exchanged or modernized by the lessor (Mayer, 2001a). With the expiration of the lease term, the venture lessee typically has the option to purchase the equipment at market value, or return it to the venture lessor (Mayer, 2001b). As a consequence, venture lessors are primarily interested in providing finance for general purpose equipment that is of further use and value after it has been returned by the lessee (Kleiman, 2001). Since most venture leasing companies do not provide all of the equipment and assets required, a syndicate of several venture leasing companies may be necessary (Pofeldt, 2000).
Compensation for Risk Venture lessor companies focus on innovative technological companies that have a high growth potential and are in need of high volumes of financing. The more innovative the company’s products are, the higher the risks associated with the development, production, and market acceptance. The risk of default of the start-up company is compensated by the venture lessor receiving warrants in the company (Mayer, 2001b). Start-up companies are also associated with highlevels of credit risks, such as the risk of not being able to repay the credit line completely or in due-date time. Since the venture lessor is the legal owner of the equipment he does not require additional securities as commercial banks and leasing agencies require (Mayer, 2001a). However, if the start-up company has difficulties affording future lease payments, it may be unfavorable for the lessor to claim the equipment back. That is because with the retraction of the equipment the start-up company will loose all possibilities to still achieve success (Mayer, 2001b). However, venture leasing firms include a significant risk premium in the lease payments as a compensation for the risks. This premium may be as high as 40% of the common lease rate and may have a negative effect on the firm’s liquidity (Author unknown, 1999b). Furthermore, venture lessors have to face a tremendous obsolescence of the leased equipment. In the first years of the lease term, the equipment may loose up to 50% of its original value. If the start-up company goes bankrupt, the remaining value of the used equipment may not be able to compensate for the acquisition costs for the lessor (Mayer, 2001b). This again is a reason why venture lease providers are compensated with a risk premium such as the option to purchase warrants in an initial public offering at an ex ante determined purchase price (Mayer, 2001a, b). With reference to the above described endogenous and exogenous risks based on the NIE, the venture leasing concept contains risk-reducing elements. In contrast to venture capital companies or banks, the venture lessor receives the advantageous option of a secondary purchase of the equipment. Furthermore, venture lessors have more expertise in valuating equipment and it is possible that they can identify market trends earlier. In addition, endogenous risks, such as moral hazard, are reducible too by an active equipment monitoring. The leasing contract is often coupled with maintenance orders and lessors have the possibility to observe equipment usage (Opitz, 2000).
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Benefits of Venture Leasing This concept has substantial benefits for the technological company. First, the company is able to save enormous amounts of cash that it otherwise would have to spend to purchase the leased assets. Thus, the management team of the company can spend the saved money on core purposes such as research and development and marketing activities. Since venture leasing represents a less dilutive financing instrument than venture capital, the founders do not have to give up substantially more equity in return for the financing. Second, venture leasing represents a suitable source of financing in cases where technological companies face difficulties acquiring bank credits or leasing financing and further venture capital may be unavailable. Third, the leased asset represents the only collateral required by venture leasing firms in contrast to other financial institutions that require commitment of all of the company’s assets. And finally, skilful negotiation of the price and amount of company warrants may result in a reduction in lease payments, and thus a further saving of liquidity.
Risks and Drawbacks of Venture Leasing Although venture leasing has many benefits for young technological companies, there are also certain risks that have to be taken into consideration. First, since it is hard to make predictions about young technological companies and the environment they operate in, an unanticipated event might cause a company to lose irreplaceable liquidity and be unable to afford lease payments anymore. As a consequence, the venture lessor may claim his equipment back and leave the company without any possibility of making a recovery. Second, exercised warrants may also lead to a dilution of equity. Third, if the structure of the lease does not qualify for an operating lease or a financial lease being balanced on the accounts of the lessor, then the leased equipment may negatively affect a company’s valuation by increasing both the asset and liability side of the balance sheet. Last, one has to take into consideration that the concept of venture leasing is strongly influenced by the stock market. Since warrants are preferably exercisable in case of an IPO of a company, and since the value and frequency of IPOs depends on the stock market conditions, the availability of the venture leasing concept may be questionable in times of bad market conditions, and vice versa (Hollander, 2001).
Differences between Venture Leasing and Traditional Leasing The one key difference between traditional leasing and venture leasing is that traditional leasing companies focus on profitable and well-established companies, whereas venture leasing companies support start-up and early-stage companies. That means traditional lessors are most concerned about stable revenue streams, while venture lessors, just like venture capitalists, predominantly focus on the future growth potential of young
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technological companies. Since venture leasing is a high-risk activity, there are more equipment defaults and write-offs than in traditional leases. Furthermore, venture lessors have the possibility to capitalize on the growth potential if the lessee company goes public and the lessors are able to use their warrants as equity kickers. Another difference is that venture lessors typically do not require security deposits or restrictive covenants — such as a minimum debt to equity ratio — whereas traditional lessors often require performance covenants and security deposits for non-investment grade lessees. Also, since venture lease contracts contain a default potential, the ex ante yields on venture leases typically overstate the returns lessors expect to earn by a greater margin than do traditional leases (Kleiman, 2001). Venture lessors and traditional lessors also apply different valuation techniques. High-risk firms mostly require specialized equipment and are evaluated by venture leasing companies based on the capabilities of the management team and the dynamics in the market. In contrast, traditional lessors employ valuation techniques such as historical and pro forma ratio analysis, similar to the techniques of bank lending offices (Lerner, 2001).
Geographic Spread of Venture Leasing Having described the concept of venture leasing, the following paragraphs will give an overview of the use and application of this concept in the USA, Europe, and Germany. The origins of venture leasing in the USA date back to the late 1960s, when several venture capital firms began providing lease financing in addition to equity financing to start-up companies. That was because young start-up companies increasingly faced difficulties obtaining equity or debt financing. Commercial banks or leasing agencies had little interest in providing financing to companies without stable operating revenues and no securities. Even if they were willing to extend loans at that time, they demanded that start-up companies purchase equipment with 30–70% cash upfront, which was usually impossible to manage (Lerner, 2001). In recent years, venture lessees primarily stem from the Internet, telecommunications, and pharmaceutical industries (Kleiman, 2001). Dominion Ventures, Comdisco Ventures, and Phoenix Growth Inc. are major actors in the Venture Leasing industry, with a minimum of $250 million in assets under management at the beginning of 1998 (Lerner, 2001). Furthermore, big manufacturers such as Microsoft, Gateway, and Dell have a range of special direct leasing concepts for start-up companies to offer. For instance, Microsoft employs a licensing concept “OPEN Subscription License” that provides start-up companies with the opportunity to lease their software cost effectively. Gateway and Dell support start-up companies with the financing of their IT equipment. Nevertheless, the “E-Business Accelerator Leasing” concept of Gateway, and the “New Venture Leasing Program” of Dell require young companies to already have obtained financing from venture capital firms. Start-up companies herewith obtain not only the possibility to save costs when leasing the manufacturers’ equipment, they can also create contacts and establish networks with major players in the IT industry. In return, the manufacturers expect to have early access to the
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innovations of the companies and have their software and hardware implemented in potential high-growth companies (Hoffmann, 2000). In contrast to its frequent application and early introduction in the USA, the financing concept of venture leasing was only initially applied in Europe in August 1998. The initial leasing transaction was conducted by European Venture Partners — a joint venture between Dominion Ventures, US specialist in venture leasing transactions, and Dresdner Kleinwort Benson — as a member of an equity syndicate providing financing to the technological company Inside Technologies. However, since this concept is virtually unknown in Europe, “ … educating the wider market about the concept of venture leasing will be a huge task” (Author unknown, 1999b). In Germany, the establishment of the venture leasing industry has just begun. There are a few institutions that intend to fill the gap of venture leasing in the German market. Among these, for instance, is Gefi (“Gesellschaft für Expansionsförderung mbH”), which predominantly focuses on technological start-up companies and applies a concept of financing trademark rights and patents that also bear growth potential for young technological companies (Demberg, 2002). Furthermore, suretyship plays an important role in supporting especially young companies in easing the access to sources of debt financing. In addition to suretyship engagements of the Federal Government, the provincial governments, and the Kreditanstalt für Wiederaufbau (KfW) — the German bank for entrepreneurship and middle-class companies, there is an independent suretyship bank in each of Germany’s federal states, and many of the suretyship banks have their own venture capital company. The differences between these suretyship programs are the size of the surety and the percentage of deficiency suretyship. That means, the suretyship banks of the Federal States engage in bank or leasing guarantees of below 1 million Euro and take over 80% of the deficiency surety. In contrast, the Kreditanstalt für Wiederaufbau (KfW) engages in deals of above 1 million Euro, but takes over approximately 40–50% of the deficiency suretyship. Therefore, theoretically, the risk of banks and traditional leasing firms is reduced due to the existence of suretyship banks what may lead to the question of whether the concept of venture leasing is necessary in Germany. However, the eagerness of suretyship banks to engage in business relationships with young and risky technological companies depends on the economic situation and potential of each Federal State. Nevertheless, the concept of suretyship is in some Federal States unsatisfactorily known and used. In some Federal States, financial institutions such as banks avoid engaging in suretyship relationships since these are usually associated with high levels of risk. Furthermore, most suretyship banks are only prepared to support already established companies and rarely engage in relationships with start-up firms. Also, banks are most often unwilling to take over the remaining 20% of the credit risk that is not granted by suretyship banks, and thus avoid dealing with young companies. However, the above named institutions do not completely respond to the needs of young technological companies. Therefore, the following part of this paper will illustrate an empirical explorative study, trying to determine the extent of publicity of the concept of venture leasing among young technological companies, leasing firms, and venture capital firms, and the estimated potential of venture leasing to penetrate the German market as a financial innovation.
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Explorative Research Study1 Since venture leasing has so far mainly been applied in the USA, the next part of this work will investigate whether this concept also has potential in Germany. The approach and research terms of Creswell (1994) have been applied in order to structure the following empirical investigation. The purpose of this study is to clarify whether venture leasing represents a serious alternative to traditional financing instruments for young technological companies in Germany. Due to the fact that the concept of venture leasing has not been introduced to the German market yet, there is no empirical data available. And since the German market is in many characteristics different from the US market, the applied concept and its performance and acceptance in the USA are not completely translatable to German market conditions. Therefore, the objective of the next chapter is to fill this existing gap with the means of an exploratory research study. In order to collect data about the knowledge of venture leasing and its estimated potential in Germany, a quantitative approach has been applied. The method of a survey has been chosen with the intention to generalize the findings from a sample of respondents to the complete population and thus infer general statements about the potential of venture leasing in Germany. Therefore, three independent surveys have been conducted, questioning venture capital firms, leasing firms, and young technological companies in Germany to grasp their experience and make estimations about the potential of venture leasing. The surveys were carried out in January 2003. The self-designed questionnaire have been distributed via E-mail to the interviewees, by sending them a link to a web site hosting the survey to be answered online. A single stage sampling procedure has been applied, where people have been contacted directly based on the availability of contact information. Thus, the names and contact information of the selected leasing firms were taken from the web site of the German association of leasing companies (http://www.bdl-leasingverband.de/). Altogether, 159 leasing firms have been contacted. In 2002, the whole population of leasing companies accounted for 2087 firms in total (Wassermann, 2002). Accordingly, the venture capital firms selected for interrogation were taken out of the online member list of the German Venture Capital Association (http://www.bvk-ev.de/). Out of the 216 orderly members of the German Venture Capital Association (BVK, 2002), a total of 181 venture capital firms have been selected for interrogation based on the availability of contact information. It is difficult to determine the exact number of venture capital firms in Germany. However, the total population is estimated to be between 350 and 400 firms (Dams, 2002). In addition, 276 young technological companies were selected for the third survey. The companies chosen for this survey should not have been founded prior to the year 2000 so that these belong to the category of “young” companies. Furthermore, a primary focus was placed on young technological companies in the biotechnology and software industries. Thus, the companies were derived partly from the Internet, namely from the web site
1
Due to limited scope of this paper the appendix is missing. However, all calculations and questionnaires are receivable by mail from
[email protected].
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“Biotech Companies in Germany” (http://www.i-s-b.net/firmen/sme.htm), and “Live Linux Verband” (http://www.linux-verband.de/start.html). Furthermore, the chamber of industry and commerce in Wiesbaden/Germany (“Industrie- und Handelskammer Wiesbaden”, http://www.ihk-wiesbaden.de/) provided a list of young technological companies that were suitable for being contacted for the survey. The companies provided by the chamber of industry and commerce belong to the biotechnology, hardware, and software industry and are located within and around the federal state of Hessen. Furthermore, a number of companies were taken from a database including technological companies of the European Business School, Oestrich–Winkel.
Research Questions The questions that are intended to be answered with the analysis of the survey are the following: ●
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To what extent is the concept of venture leasing known among venture capital firms, leasing companies, and young technological companies in Germany? How attractive are engagements with young technological companies for traditional leasing companies and thus, is there a need for venture leasing? Why is venture leasing not yet established in Germany despite its acceptance and frequent application in the USA? Does venture leasing represent a serious alternative source of financing for young technological companies?
Although there are more questions with regard to the concept of venture leasing, this study is focused on answering the given set of questions given the limited extent of this work. The firms contacted for all surveys have not been selected randomly. Instead, the contact information of both leasing firms and venture capital firms have been taken from national associations and are chosen on the level of available information. Accordingly, the young technological companies are selected on the basis of the availability of data, in particular the availability of their E-mail addresses. In addition, the exact population of young technological companies in Germany is unknown, the companies selected are predominantly focused on the industries of biotechnology and software, and the majority of the young technological companies belong to the same geographic area. Thus, general statements of this study have to be inferred with caution.
Analysis of the Survey Data Of the 159 leasing firms contacted, 34 have completed the survey data analysis. This represents a return rate of 21.38%. Of the 181 venture capital firms contacted, 52 firms completed the survey representing a return rate of 28.73%. Of the 276 young technological companies contacted, 92 have completed the survey, representing a return rate of 33.33%. However, due to the fact that 20 companies that
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answered the survey were founded prior to the year 2000, only 72 firms are included in the analysis of the survey data, that represents a modified rate of return of 26.09%.
Spread of the Venture Leasing Concept? Among the traditional leasing companies, 50% (17 votes) indicate that they know about the concept of venture leasing. Of the five leasing firms that engage in relationships with young technological companies, 40% (2 votes) know about venture leasing. In contrast, only 15% (8 votes) of the venture capital firms indicate a full knowledge of venture leasing. Of the 25 venture firms that are prepared to invest in at least one technological area — such as biotechnology, software, hardware, Internet, telecommunications, 20% (5 votes) know about venture leasing. Also, of the 37 venture capital firms that are prepared to invest in companies in the seed, start-up, and/or market introduction phase, 18.9% (7 votes) know about venture leasing. With reference to young technological companies, 28% (20 votes) have already learned about this alternative financing instrument. Most often young technological companies indicate that they became informed about this concept by chance (17%), through press releases (11%), or by venture capital firms (7%). Of those 23 companies in the biotechnology industry, about 35% know about venture leasing, and 18.5% (5 votes) of the 27 companies in the software industry. In addition, the two companies in the hardware industry, the one in the IT industry, and the one in the telecommunications industry know about venture leasing, as well as 33.33% of the companies in the Internet industry. Given this statistical analysis, it seems that traditional leasing companies are more informed about the concept of venture leasing than young technological companies and venture capital firms. Furthermore, the few venture capital firms knowing about venture leasing seem to communicate this concept intensively, since they account for one of the primary sources of information about venture leasing for young technological companies. These results indicate further, that though this concept is rarely practiced in Germany, it is relatively well known across industry actors.
Market for Venture Leasing Firms? The second research question deals with the problem of identifying the attractiveness of engagements with young technological companies for traditional leasing companies, and thus conclude whether there is a market gap for venture leasing provider. Of the traditional leasing companies, 23.53% (8 votes) are specialized in leasing assets to at least one technological industry, such as biotechnology, software, hardware, Internet, telecommunications etc., and 87.5% (7 votes) of those are specialized in leasing specific assets, EDV equipment, and machines. Furthermore, 15% (5 votes) of the traditional leasing companies have young technological companies among their core clients. 83% of those leasing firms that do not engage with young technological companies indicate that the most predominant barrier is the risk associated with such a company. Further drawbacks are negative historical experiences with young technological companies (24%, 7 votes), missing re-financing
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possibilities for leasing companies (10%, 3 votes), and the lack of appropriate equipment for this business segment. The average probability of leasing firms incorporating venture leasing into their portfolios — on a scale starting from 1 to 6 with 6 indicating a very high probability — is 1.88, with a standard deviation of 1.16. Of those leasing firms that know about the concept of venture leasing, the average probability accounted for 2.19 with a standard deviation of 0.974. In addition, those leasing firms that are specialized in leasing assets to technological industries indicate an average probability of 2.5 and a standard deviation of 1.02. And those firms that have young technological companies as core clients indicate an average probability of including venture leasing into their portfolios of 3.2, with a standard deviation of 0.7. Thus, the conclusions can be drawn that traditional leasing companies do actively lease specific assets to technological industries. However, instead of engaging with young technological companies, they prefer to engage with medium to big-sized corporations in technological industries. The risk associated with young technological companies was found to be the most prevailing barrier of leasing firms engaging with young technological companies. However, the probability of leasing firms including venture leasing into their portfolios is very small. However, the probability increases when the field is narrowed down to only those who know about the concept, that are specialized in technological industries or that engage with young technological companies. However, the probability remains below the average of the given scale. In a nutshell, traditional leasing companies generally avoid the segment of young technological companies and are not eager to engage in venture leasing activities.
Venture Leasing in Germany? The following paragraph intends to provide answers to the question of why the concept of venture leasing is missing in Germany despite its acceptance and frequent application in the USA. In addition, the potential for venture leasing to successfully penetrate the German market will be investigated. Therefore, the experience of both traditional leasing companies and venture capital firms will be taken into consideration. With respect to the leasing companies, the most prevailing reason for there being no German venture leasing market is that traditional leasing companies strictly avoid engagements with risky business projects (71%, 24 votes). Of further consideration is that 44% (15 votes) indicate the lack of publicity of the concept to be the reason for its neglect. Further barriers are a dearth of support for innovative ideas in Germany (26%, 9 votes), want of exit channels such as a healthy stock exchange (18%, 6 votes), and that financial institutions have no possibility of re-financing themselves when engaging in these kind of projects (12%, 4 votes). The leasing companies estimate the probability of this concept successfully penetrating Germany — on a scale from 1 to 6 with 6 indicating a high probability — to be 2.41 with a standard deviation of 0.91. Isolating only those companies which know about the concept of venture leasing it becomes obvious that the indicated average probability accounts for 2.53 with a standard deviation of 0.65. On the other hand, those not knowing about the concept indicated an average probability of 2.29 with a standard deviation of 0.55 (1 to 6 with 6 indicating excellent knowledge).
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The two major reasons for the absence of a venture leasing market in Germany that venture capital firms indicate are that the concept is unknown (85%, 44 votes), and that there are no venture leasing firms in Germany yet (84%, 43 votes). Further important reasons are the lack of exit channels such as a healthy stock exchange (25%, 13 votes), young companies trying to avoid financial instruments that further dilute their ownership positions (21%, 11 votes), and the missing support for innovative concepts in Germany (17%, 9 votes). In total, venture capital firms estimate the probability of the successful establishment of the venture leasing concept to be 2.71 on average with a standard deviation of 0.99. Regarding those companies which know about the concept, the estimated probability is 2.38 with a standard deviation of 0.57. Isolating those that do not know about venture leasing, the calculated statistics are 2.77 and 0.69, respectively. Thus, it can be inferred from these results that the tendency of leasing companies to avoid risky businesses, the absence of venture leasing companies, and the absence of publicity for venture leasing are identified to be the major reasons for there being no venture leasing market in Germany. However, the indicated average probabilities are below average and thus industry experts have resignations about a potential success of venture leasing in Germany.
Financing Alternative Venture Leasing? This last research question intends to clarify whether venture leasing represents a serious alternative source of financing for young technological companies. Therefore, the analysis will provide a description of the financial instruments used by the sample and investigate whether the companies are satisfied with their sources of capital. This is in order to derive implications for venture leasing as an alternative source of financing for young technological companies. Own resources are the main source of capital for a young technological company in the seed-, start-up, and market introduction phase. However, the importance of own resources diminishes slightly with the development of the company. Then, public subsidies and venture capital are found to be important financial sources, especially in the seed and start-up phase of the company. Thereafter, their use declines significantly. Bank credits are used less than venture capital though more often than leasing. However, bank credits and leasing are used consistently throughout the different life phases of a company. Of the 72 young technological companies, 29.2% (21 votes) have used venture capital in at least one stage of development. The average satisfaction with this specific instrument is 3.79 with a standard deviation of 1.26. The most common reasons for technological companies not using venture capital are that they have failed to find an appropriate venture capitalist (45%, 23 votes), the avoidance of venture capitalists exerting influence over their company (33%, 17 votes), the dilution of ownership positions (27%, 14 votes), and no capital requirements (24%, 12 votes). Public subsidies have been used by 36.1% (26 votes) of the young technological companies, which indicate 3.32 as the average satisfaction with a standard deviation of 1.3. Major barriers for companies not using public subsidies are the complicated and timeconsuming process of application (59%, 27 votes), and the failure to qualify for public
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subsidies (35%, 16 votes). Furthermore, 26% (12 votes) indicate that the concept of public subsidies is unknown to them. Of the young technological companies, 25% (18 votes) have used bank credits in at least one stage of development. These indicate an average satisfaction of 2.96 with a standard deviation of 1.3. The two predominant barriers of those not using bank credits are that bank credits are too expensive (41%, 22 votes), and that banks have rejected credit applications (37%, 20 votes). Lastly, 16.7% (12 votes) of the young technological companies have used traditional leasing financing during their development. These indicate an average satisfaction of 4.06 with a standard deviation of 1.43. The reason most often indicated by companies for not using leasing was that they had capital instead of asset requirements (34%, 21 votes), that leasing financing is too expensive (18%, 11 votes), that the companies had no collateral or securities to back the leasing financing (16%, 10 votes), and that credit financing was preferred to leasing (15%, 9 votes). The young technological companies are most satisfied with leasing, followed by venture capital, public subsidies, and lastly bank credits. Therefore, venture leasing as a combination of leasing and venture capital may correspond with the needs of young technological companies. However, the overall satisfaction with the financial instruments used is relatively high. Nevertheless, 29% (21 votes) of all technological companies think that venture leasing is appropriate for further financing needs. And out of the 20 companies, which know about the concept, 35% (7 votes) think that venture leasing is appropriate for the further course of their financing. The one company that has already used venture lease financing ranks its satisfaction with this financial instrument at 5. The major barriers for companies not referring to venture lease financing are the following: avoidance of further dilution of equity (39%, 28 votes), avoidance of influence exerted by venture leasing firms (32%, 23 votes), and unknown concept (31%, 22 votes). Thus, venture leasing seems to have a moderate potential to be useful for further financing purposes. The next step will investigate, whether there are differences between the two chief industry branches of the sample, namely biotechnology (32%, 23 votes) and software (38%, 27 votes). With respect to the biotechnology industry, the average satisfaction with the overall financing of the business is 3.3 with a standard deviation of 1.0. Furthermore, 35% (8 votes) know about the concept of venture leasing and 61% (14 votes) think that venture leasing is appropriate for the further financing of the business. With regard to the software industry, the overall satisfaction with the financial instruments used is 3.0 with a standard deviation of 0.85. In addition, 18.5% (5 votes) of these know about the concept of venture leasing and 14.8% (4 votes) think it is appropriate for the further financing of the company. Although there are only slight differences concerning the satisfaction with the financial instruments used, there are immense differences with respect to venture leasing aspects. Therefore, more than twice as many companies in the biotechnology industry know about venture leasing than in the software industry, and furthermore, four times as many biotechnological companies think that venture leasing is appropriate for the further financing of their companies. As a consequence, venture leasing may be more required and appropriate for companies in the biotechnology industry.
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Synthesis and Outlook This explorative research study has revealed that the concept of venture leasing is little known among industry actors such as leasing companies, venture capital firms, and young technological companies. However, traditional leasing companies avoid business engagements with young technological companies and are not eager to include venture leasing activities into their portfolios. Thus, there would be a need for independent venture leasing companies to be established. Leasing companies as well as venture capital firms estimate the probability of venture leasing successfully penetrating the German market to be low. The concept of venture leasing as it has been illustrated in the context of this work represents a potential opportunity for young technological companies to further access their required resources. However, given the American concept of venture leasing that usually requires companies to have qualified for at least one round of venture capital financing before venture leasing may be used, the extent of German young technological companies benefiting from venture leasing may be extremely limited. However, venture leasing may come in different forms and thus does not necessarily require venture capital financing in advance. Syndicates with venture capital firms or other venture leasing companies may be obsolete when engaging in co-operations with large leasing corporations for instance. Thus, the concept as it is applied in the USA may not have to be exactly transferred to Germany. Furthermore, equity financing strongly depends on market conditions, and thus, forecasts about the adaptability of venture leasing are difficult to make. In any case, venture leasing will develop in line with the venture capital industry. Thus, since there is currently not much capital available and investors have lost their trust in the financial markets, it may take some time before venture leasing may successfully be introduced in Germany. A magnitude of questions remain unanswered, such as when the concept of venture leasing will officially come to Germany, how it will be accepted by the market actors, and what benefit it will actually provide young technological companies. The scope of this work has included the demonstration that the concept has been noticed by market actors, that there is a need for further financing instruments, and that venture leasing may represent the solution for certain young technological companies wanting to pursue their business concepts. Therefore, since the concept of venture leasing has not been introduced to Germany yet, there is a need for market experts to actively communicate the idea of this concept in order to make young technological companies, as well as financial institutions and leasing companies aware of it and thus accelerate its introduction to Germany.
References Author unknown. (1999a). Venture leasing arrives in Europe. UK Venture Capital Journal, 92, 14. Author unknown. (1999b). Geleaste starthilfe. Süddeutsche Zeitung 10/08/1999, V2–V6. p. V2/6. Baier, W., & Pleschak, F. (1996). Marketing und Finanzierung junger Technologieunternehmen. Wiesbaden: Gabler Verlag. Bender, H. J., & Fiedler, H. (2002). Leasing — Chancen und Risiken für Existenzgründer. Gründerzeiten, 13, 1–6. Betsch, O., Groh, A. P., & Schmidt, K. (2000). Gründungs-und Wachstumsfinanzierung innovativer Unternehmen. München: R. Oldenbourg Verlag.
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BMBF Bundesministerium für Bildung und Forschung. (2002). Mittelstand Innovativ: kleine und mittlere Unternehmen im Fokus der Bildungs- und Forschungspolitik. Bonn: Referat Öffentlichkeitsarbeit. Brealey, R. A., & Myers, S. C. (2000). Principles of corporate finance. (6th ed.). Boston, MA: McGraw-Hill. BVK Bundesverband Deutscher Kapitalbeteiligungsgesellschaften e.V. (2002). BVK Statistik 2002 (vol. 3). Quartal, Berling: BVK. Creswell, J. W. (1994). Research design: Qualitative & quantitative approaches. Thousand Oaks, CA: Sage Publications. Daferner, S. (2000). Eigenkapitalausstattung von Existenzgründungen im Rahmen der Frühphasenfinanzierung. Sternenfels: Verlag Wissenschaft & Praxis. Dams, J. (2002). Wir erwarten wieder mehr Börsengänge. [On-line] retrieved February 13, 2003, from the World Wide Web: http://www.welt.de/daten/2002/02/04/0204un312084.htx. Demberg, G. (2002). Es muss nicht immer Leasing sein. Handelsblatt, 56, p. b12. Hoffmann, J. (2000). Liquidität für die Stars von morgen: Wie junge unternehmen mittels Leasing von IT–order Büroprodukten ihre Existenz aufbauen. Süddeutsche Zeitung 11/03/2000, p. V2/1. Hollander, J. (2001). Venture leasing: The other venture capital, [On-line], retrieved February 5, 2003, from the World Wide Web: http://www.gigalaw.com/articles/2001-all/hollander-2001-04-all.html. Kaufmann, F. (1997). Besonderheiten der Finanzierung kleiner und mittlerer Unternehmen: Ein Überblick über die Problemlage. Kredit und Kapital, 1, 140–155. Klandt, H. (1999). Gründungsmanagement: Der integrierte Unternehmensplan. München: R. Oldenbourg Verlag. Kleiman, R. T. (2001). The characteristics of venture lease financing. Journal of Equipment Lease Financing, 19, 2–10. Lerner, J. (2001). A note on the venture leasing industry. In: P. A. Gompers & W. Sahlmann (Eds), Entrepreneurial finance: A case book. New York, NY: Wiley. Mayer, M. D. (2001a). Leasing in der Unternehmensgründung — eine Alternative zur Fremdfinanzierung? In: H. Barske, A. Gerybadze, L. Hünninghausen & T. Sommerlatte (Eds), Das Innovative Unternehmen: Produkte, Prozesse, Dienstleistungen. Düsseldorf: Symposium Publishing. Mayer, M. D. (2001b). Venture Lease als Finanzinnovation in Deutschland: Zur Eignung von Venture-Leasing, der nicht innovationsspezifischen Geschäftsausstattung in wachstumsstarken Unternehmungen. Finanzierung Leasing Factoring, 5, 209–213. Nathusius, K. (2001). Grundlagen der Gründungsfinanzierung: Instrumente — Prozesse — Beispiele. Wiesbaden: Gabler Verlag. Opitz, C. (2000). Organisation der geteilten Nutzung. Wiesbaden: Gabler Verlag. Perridon, L., & Steiner, M. (1999). Finanzwirtschaft der Unternehmung. München: Verlag Franz Vahlen. Pleschak, F., Helmut, S., & Udo, W. (1994). Innovationsorientierte kleine Unternehmen: Wie Sie mit neuen Produkten neue Märkte erschließen. Wiesbaden: Gabler Verlag. Pleschak, F., Berteit, H., Ossenkopf, B., & Stummer, F. (2002). Gründung und Wachstum FuE-intensiver Unternehmen: Untersuchungen in Ostdeutschland. Heidelberg: Physica-Verlag. Pleschak, F., Sabisch, H., & Wupperfeld, U. (1994). Innovationsorientierte kleine Unternehmen: Wie Sie mit neuen Produkten neue Märkte erschließenGabler Verlag. Pofeldt, E. (2000). Venture leasing for the cash-poor, [On-line], retrieved November 5, 2002, from the World Wide Web: http://www.fortune.com/fortune/smallbusiness/offhours/articles/0,15114, 359803,00.html. Schmidt, R. H. (1981). Grundformen der Finanzierung — Eine Anwendung des neo-institutionalistischen Ansatzes der Finanzierungstheorie. Kredit und Kapital, 2, 186–221. Spittler, H. J. (1999). Leasing für die Praxis. Köln: Fachverlag Deutscher Wirtschaftsdienst. Wassermann, H. (2002). Leasing Gesellschaften in Deutschland 2002. Finanzierung Leasing Factoring, 6, 236–247. Werner, H. (2000). Junge Technologieunternehmen: Entwicklungsverläufe und Erfolgsfaktoren. Wiesbaden: Deutscher Universitäts-Verlag.
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Chapter 10
Small- and Medium-Sized Enterprise in the E-Business and Knowledge Management Era S.C.L. Koh and S. Maguire
Introduction Context This paper on e-business is concerned specifically with information systems solutions packages used by enterprises for e-business transaction purposes in order to meet customer requirements in Business-to-Business (B2B) and Business-to-Consumer (B2C) exchanges. B2B activities may include online purchase or procurement, respectively between customer and supplier, while B2C activities may include a consumer’s online inventory tracking behaviour prior to ordering a product. Popular among the e-business applications for B2B are Supply Chain Management (SCM) and Enterprise Resource Planning (ERP) systems. They usually take the form of “back-office” systems. An example of a B2C e-business application would be a Customer Relationship Management (CRM) system, and this is usually comprised of “front-office” systems. An “e-business suite” is a total solution approach, and consisting of SCM, ERP and CRM, and is designed to help enterprises to increase their operating efficiency, agility and responsiveness to change by exploiting new web-enabled technologies. The advent of information technology, expeditious data processing ability, configurable platforms, networking, the Internet and web-enabled systems has facilitated the rapid growth of the use of e-business in both public and private sector enterprises. This study has been carried out in small- and medium-sized enterprises (SMEs) in order to make comparisons between small- and-medium size firm performance and usage of e-business and knowledge management. We used the Department of Trade and Industry (DTI) definitions of size of enterprise to classify our sample. The Function of B2B Systems ERP can be defined as an accounting-oriented information system for identifying and planning the enterprise wide resources needed to take, make, ship and account for customer orders. New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 149
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Technically, an ERP system differs from the typical Manufacturing Resource Planning (MRPII) system like graphical user interface. There are various modules within an ERP system. A manufacturing enterprise is likely to use the production-planning module, while service enterprises may implement the financial accounting or fixed assets management module merely for costing and accounting purposes. One of the main drivers of the emerging trend for SMEs to implement ERP systems is the pressure from large business customers. ERP systems enable order processing to be computerised and performance to be monitored in real time. Many SMEs need to be equipped with this type of technology to provide a better and competitive service to their, often large, customers. This technology has become easily accessible due to the advent of the Internet, which can then lead to e-business applications for the entire supply and demand network. Most of the leading ERP vendors have penetrated the SMEs market with the development of mid-range ERP systems. For example, SAP released an all-in-one mid-range ERP system for the SMEs sector. Mid-range ERP systems are cheaper, and relatively less complex, and are developed to suit the business processes of SMEs. Some of the leading mid-range ERP systems include Alliance Manufacturing (Kewill), MFG/PRO (QAD) and WinMan (TTW). The big five ERP vendors — SAP, Oracle, People Soft, JD Edwards and Sage Group, have also started to move towards the e-business suite approach. The Boston-based Advanced Manufacturing Research (AMR) predicted the ERP market will reach US$69 billion by 2003 at an estimated compound annual growth rate of 32% (Angerosa, 1999). To achieve total business collaboration, many SMEs that are suppliers to large enterprise and the consumer market, will naturally move towards this culture. This is likely to further expand the application of these systems, and hence their market size. When this stage is reached, no enterprise will be able to afford not to be one of the systems users. Business competitiveness will be highly dependent on the role of these systems in an enterprise. It may be argued that, in a couple of years time, a basic-range system will emerge for very small enterprises. SCM can be defined as the combination of art and science that goes into improving the way an enterprise finds the raw materials it needs to make a product or service, manufacture that product or service and deliver it to customers (Koch, 2002). The five basic components for SCM are: plan, source, make, deliver and return. SCM software can be broadly categorised into two groups: Supply Chain Planning (SCP) and Supply Chain Execution (SCE). SCP uses high-level mathematical algorithms to improve the flow and efficiency of the supply chain and reduce inventory. This is similar to an information backbone or database of a system, which stores all relevant data and information on the product or service of an enterprise. The aim of SCE is to automate the different steps of the supply chain. This could be an electronic routing order from a manufacturing plant to a supplier. An ERP system may be required if you plan to implement SCP because it relies on the information stored by ERP. CRM is a strategy used to learn more about customers’ needs and behaviour patterns in order to develop stronger relationships with them (Deck, 2001). CRM is a process rather than a technological toolset, which helps to bring together information about customers, sales, marketing effectiveness, responsiveness and market trends.
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The most widely accepted and elaborated classification of CRM was that made by Nonaka (1994) by dividing knowledge into explicit and tacit. Explicit knowledge is precisely and formally articulated and codified in documents and databases defining corporate procedures and best practices (Alter, 2002). Tacit Knowledge is the practical wisdom possessed by experts that is difficult to capture, yet repeatedly demonstrated as important in contexts as varied as factory floors, research laboratories, army bases and corporate board rooms (Crowley, 2000). Another major type of knowledge is cultural knowledge (Blackler, 1995; Snowden, 2000; Choo, 2002), which is defined as the assumptions, beliefs and values of people. While many other knowledge classifications have also emerged, in this paper, we have focused on explicit, tacit and cultural knowledge. Large enterprises have a strong capability to make use of e-business and knowledge management due to their greater resource base and subsequent access to required skills and resources. However, many SMEs are not equipped with, or supported by, appropriate resources that would allow them to take advantage of the developing knowledge economy and move towards the e-business agenda. This problem is further amplified by the fact that many of the SMEs business customers and suppliers are moving towards the e-business and knowledge management solutions by applying a variety of information system packages for B2B and B2C transactions. The effect of these developments is that SMEs have to adopt new e-business solutions, otherwise the use of traditional business practices will prevent strategic customer–supplier partnership from being developed, hinder effective customer relationship management and disturb the responsiveness to uncertainty and diversity in their existing and future business environments. To this end, initial research has been carried out to examine, to what extent the above e-business and knowledge management approaches are being used by SMEs in the UK. However, this article first provides a literature review that will highlight the critical success factors related to e-business and the knowledge management of enterprises.
Literature Review Previous research has identified that the majority of tools, techniques and methodologies in the domain of Information and Communications Technology (ICT) have been developed with large firms in mind (Maguire & Magrys, 2001). This is true of support provided in the areas of project management; system development, risk management, benefits realisation, procurement and the formulation of Information systems (IS) and information technology (IT) strategies. However, in today’s business environment, the effective use of ICT can allow small firms to take advantage of ICT. Developing “intranets” and linking into “extranets” will allow small firms to exploit the business benefits of ICT, and this will allow them to forge strategic alliances with other organisations. Figure 1 shows, as one would expect, that SMEs have a wide range of applications at their disposal. However, although SMEs have been using ICT applications for many years (Maguire & Magrys, 2001), it is not clear whether SMEs have efficiently taken advantage of recent ICT and Knowledge Management initiatives. In some cases, they lack the resources and skills to do so. This lack of skills applies in both the technical and business areas, and often makes impossible the introduction of
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Figure 1: ICT applications in SMEs (from Maguire & Magrys, 2001).
e-business and knowledge management system. However, there are ways using which SMEs can overcome these constraints and some of these issues are discussed later in this section. E-business in its widest sense has been around for over 30 years. The adoption of e-business by SMEs, not surprisingly, varies by type of industry. The lowest levels of operational e-business services have been found to be the public, education and charitable sectors. The highest levels of e-business adoption were in the professional services sector (Daniel, Wilson, & Myers, 2002), where typical applications are online ordering and payment capabilities. The link between information and knowledge has been debated for many years. Churchman (1971) stressed that knowledge does not reside in a collection of information but in the user, and it is how the user reacts to a collection of information that decides whether the process is successful. In some instances, knowledge is seen as the capacity to recognise what information would be useful for making decisions. This allows organisations to utilise a knowledge base enabling managers to interpret information and use it in decision-making (Watson, 2002). This has led many organisations to view the archiving of best practices for later reference by other employees as a sensible means towards the end of efficient problem-solving. It is also hoped that effective knowledge management will prevent unnecessary resources being tied up undertaking inefficient search processes (Malhotra, 2000). Traditionally, data stored within company databases came from transactions and did not include knowledge that employees may have gained from working in the organisation (Simon, 2001). SMEs certainly should not be excluded from knowledge management initiatives since, when one considers the entrepreneurial input into these organisations, they are in a good position to exploit this potential benefit. Furthermore, knowledge management systems can be developed on universally available software at low cost to the organisation (Jessup & Valacich, 2003). However, it remains often difficult to justify the development of knowledge management systems on cost-benefit grounds (Skyrme, 1998). Most barriers to the development of knowledge management systems are non-technical (Awad & Ghaziri, 2003).
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It is generally accepted that, in the next few years, commerce based on knowledge will overtake that based on tangible products, and SMEs must position themselves firmly in this marketplace. This may entail SMEs transforming into successful learning organisations underpinned by effective, shared corporate knowledge bases (Wilson, 1997). It can be argued that small firms are more likely to introduce fundamentally new innovations as they have less commitment to existing practices and products (Storey, 1994). At the same time, they may be able to take advantage of existing software that has been thoroughly tested through its utilisation within other organizations (Perry, 1999). Similarly, small organisations may be less likely to be exposed to unproven technology (Gordon & Gordon, 1995). The personal computer has made computing accessible to businesses of all sizes. Technical advances have made computers more powerful and less expensive, giving small firms the potential to use software to make the business more competitive (Steinhoff & Burgess, 1993), while ICT has also advanced, enabling systems to be linked together within and across organisations and national boundaries (Alter, 2002). However, changes in technology, with implications for the number and skills of the workforce, pose a major challenge for most organisations and those responsible for the human resources of organizations (Bee & Bee, 1994). Small businesses may find it difficult to justify a substantial financial commitment in an area that they do not recognise as being core business. Moreover, an outsourcing relationship may be financially prohibitive as well as having the organisation reliant on third parties (Mylott, 1995). Thus, given the ultimate need for e-business competence in most SMEs, it may be necessary to recruit an expert, similar to a knowledge engineer, who is used to building expert systems (Watson, Houdeshel, & Rainer, 1997). Different authors have different perspectives on the relevance of knowledge management to SMEs. However, if one considers that it could include workflow, document control, e-mail, intranets, extranets, e-business, CRM, data mining and business intelligence (Regan & O’Connor, 2001), there is no reason why SMEs should be excluded on a technical or intellectual ground. A major constraint for small firms in the area of e-business and knowledge management may be their inability to make the necessary investment and/or to take advantage of the new concepts embodied within ICT. They may have to rely on outside consultants and significant knowledge transfers for ICT to make a viable contribution to their business. This is very problematic, since many small firms cannot afford to employ private consultants (Soriano, Roig, Sanchis, & Torcal, 2002). Thus, public assistance given to these SMEs should be of suitable quality, independent and proactive. Some authors question the level of specialist knowledge being provided by outside advisers (Bennett & Robson, 1999). The current public assistance offered to SMEs is fragmented, of variable quality, and in the case of certain providers, of suspect independence and motivation. Small companies even tend to rely on formal and informal networks rather than utilising publicly funded sources of support (Anderson & Boocock, 2002). To take advantage of current initiatives, it may be necessary to challenge the status quo within small firms (Mole, 2002). It may be difficult for small firms to see the role of knowledge management as part of a future business plan. Many entrepreneurial businesses view strategy formation as emergent rather than planned, even when they exist in complex and multifaceted sectors (Fletcher & Harris, 2002). Those outside consultants that view businesses
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as independent entities (as opposed to part of supply chains) are doing their clients a disservice. In future, they will need to advance their skills in managing under uncertainty (Sparrow & Bushell, 1997). It may also be difficult for small organisations to acquire the necessary technical skills alongside the in-depth understanding of their particular business (Foley & Green, 1989). However, it may in fact be the case that the new “information society”, heralded by the European Commission with its emphasis on networking, may produce a more conducive environment for encouraging growth orientation among autonomous entrepreneurial SMEs (Gray, 1997).
Results, Analysis and Discussions Combined structured questionnaire and semi-structured interview surveys were carried out to examine to what extent e-business and knowledge management approaches are being used by SMEs in the UK, particularly by small businesses in the manufacturing sector. The results were disaggregated into “small” and “medium” within the SME category, to provide a comparison between these sizes of organisation. A total of 126 questionnaires were sent to IT managers and system personnel (or equivalent), and 108 enterprises responded (86% response rate). Among the 108 respondents, 64 were SMEs. The 44 respondents identified as large firms were excluded from further analysis in this paper. Table 1 shows the distribution of relevant responses. ERP Applications Anticipating that the performance of survey enterprises might be affected by their use of e-business applications and/or knowledge management, we analysed both the types of e-business applications used, and whether knowledge management was being practised. We found that all these enterprises use ERP systems or ERP equivalent — Material Requirements Planning (MRP) or MRPII for production planning. This is not surprising because they are high-technology modern manufacturing enterprises that need to plan and schedule work using this type of system due to the volume and variety of parts and products that need to be handled simultaneously. However, not all of these enterprises use commercial systems. Figure 2 shows the difference between the use of commercial ERP packages and ERP systems that were developed in-house by SMEs and small enterprises. It can be seen that 70–80% of SMEs and small enterprises use commercial ERP systems, while only 20–30% of SMEs and small enterprises used ERP systems that were developed in-house. This result clearly indicated that enterprises tend to prefer commercial ERP systems, regardless of their size. This supports Gormley’s (1998) finding that more commercial ERP packages will be used in the future. Figure 3 shows the types of commercial ERP system used by survey enterprises. Among the commercial ERP systems identified, Tetra CS3, Caliach MRP and Alliance Manufacturing were widely used by SMEs and small enterprises. A total of 19 SMEs (38%) used these systems. This finding was not unexpected as these packages are developed for the SME market. However, we found five medium-sized enterprises (10%) use SAP R/3. This is an interesting finding because SAP R/3 is developed to suit large enterprises. A possible
SMEs in the E-Business and Knowledge Management Era Table 1: Relevant responses of questionnaire survey. Size
Frequency
Per cent
26 38 64
40.6 59.4 100.0
Percentages
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80 70 60 50 40 30 20 10 0 Developed in-house
Commercial package
Types of ERP system SMEs
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Figure 2: Types of ERP systems used by SMEs and small enterprises.
System 21 JBA
SMEs
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Types of commercial ERP system
BaaN IV Micross Kewill Efacs Exel JD Edwards WinMan TTW Mapics BPCS SSA Movex Intentia Impact Encore McGuffie Brunton MFG/PRO QAD SAP R/3 Alliance Manufacuring Caliach MRP Sage Tetra CS3
0.00
5.00
10.00
15.00
20.00
25.00
Percentages
Figure 3: Types of commercial ERP system used by SMEs and small enterprises.
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reason for this result may be due to the use of some modules of the SAP R/3 suite by these firms. It was envisaged that small enterprises would not use SAP R/3 and the findings confirmed this expectation. We also found that small enterprises do not use Impact Encore, Movex, JD Edwards, BaaN and System 21. Since these systems are developed for large enterprises, it was not surprising to find that they were not being used by small enterprises. SCM and CRM Applications Additionally, we also investigated whether SCM and CRM applications are used to support B2B and B2C activities in SMEs and small enterprises. The corresponding results are shown in Figure 4. Since the literature review showed that e-business suites are becoming increasingly advanced in the manufacturing sector, it would be interesting to find such applications in SMEs and small enterprises. The results clearly show that only the majority of SMEs and small enterprises do not use SCM and CRM applications, since a minority of 24 SMEs (37.5%) use such applications. Among these SMEs, seven are small enterprises. To further explore this finding, we interviewed respondents over the phone. The interview results successfully verified this finding. During the interviews, we also found that SCM and CRM concepts are being practised, such as building long-term partnership with suppliers, involving suppliers in new product development and inviting customers to enterprise events or product launches. SMEs and small enterprises were also aware of the importance of integrating SCM and CRM with ERP systems in order to provide total e-business. However, this sentiment does not imply that SMEs and small enterprises have effectively and efficiently implemented their SCM, CRM and ERP initiatives.
80 Percentages
70 60 50 40 30 20 10 0 Use SCM applications
Do not use SCM applications
Use CRM applications
Do not use CRM applications
SCM and CRM applications SMEs
Small enterprises
Figure 4: SCM and CRM applications in SMEs and small enterprises.
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Knowledge Management Here we further investigate the use of knowledge management in SMEs and small enterprises. We envisaged that knowledge management would be a relatively new concept for SMEs. Although it has been successfully applied in large enterprises, it is interesting to find out whether the SME community has accessed such techniques. Figure 5 shows the use of knowledge management in SMEs and small enterprises. The results show no difference between SMEs and small enterprises in the use of knowledge management. More than 90% of these enterprises do not use knowledge management. Further interviews with the respondents found that the core problem with knowledge management in the enterprises is that the expertise necessary for knowledge management is lost because a method to retain this expertise is not available. During the interviews, we also found that these enterprises do not create knowledge from existing information and business processes, and hence if an external expert is not available, no internal alternatives can be used to resolve the problem effectively. This finding showed that these enterprises are rigid, inflexible, non-agile and non-responsive to change and uncertainty in business and manufacturing environments. Although it may be argued that other studies (e.g. Bayraktar & Gozlu, 1994), have found SMEs and small enterprises to have the flexibility to handle change and uncertainty, our notion was that if these e-business-aware firms are to create competitive advantage for the long term, and to grow and advance into the e-business and knowledge management era, flexibility, agility and responsiveness to change and uncertainty are essential (Gunasekaran, 1998; Koh & Saad, 2002). Therefore, this result suggests that SMEs and small enterprises require support in knowledge management in order to achieve their goals. This support may be in terms of education and training, and developing new tools and methods to acquire and manage knowledge in SMEs.
Percentages
100 80 60 40 20 0 Use knowledge management
Do not use knowledge management
Use of knowledge management SMEs
Small enterprises
Figure 5: Knowledge management applications in SMEs and small enterprises.
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The interview results also showed that the claim of five SMEs (7.8%) that they use knowledge management is plausible. There were two small enterprises (7.7% of 26) out of these SMEs that use knowledge management, and that these enterprises create some explicit knowledge from existing information. The explicit knowledge created includes which suppliers to choose for a specific order, which machines to load for a specific order and which routing to monitor for specific manufacturing processes of parts and products. The evaluation and performance results from vendor rating exercises, machine breakdowns and service history and the number of rejects and reworking of parts and products are fundamental inputs to creating explicit knowledge. This explicit knowledge was created from the information derived from the ERP systems and the practices of SCM and CRM. We found that these small enterprises tend to create tacit knowledge although a method to capture and acquire such knowledge has not been used. The tacit knowledge was derived from personal experience and wisdom, organically created and shared among individuals in the relevant department. The tacit knowledge created includes practical approaches in dealing with certain tardy supplies of material, cutting tools substitution when a specific tool is not available and the appropriate manner to deal with certain types of customer. We also found no evidence that a method to capture and acquire cultural knowledge has been used by these enterprises. The interview result showed some indications that cultural knowledge is being created and managed by these small enterprises. This was reflected in the use of flexi-hours and profit sharing in some small enterprises. This could be considered as a type of justification for the appreciation of the value of people in an enterprise, and it was the only result to support the use of cultural knowledge. Reference Model Our overall results led to the conclusion that the use of knowledge acquisition, creation and management in enterprises is dependent upon the size of an enterprise. Figure 6 shows a conceptual model to represent the level of complexity in knowledge acquisition, creation and management with respect to the types of knowledge required for different sizes of enterprise. This research found that small enterprises are prone to use tacit and cultural knowledge due to the low level of complexity in acquiring, creating and managing such knowledge. In the manufacturing environment of high technology and highly e-business-aware small enterprises, many work and processes can be dealt with quickly using a combination of an authoritarian approach and an ad-hoc manner. Therefore, sharing of information and knowledge in such environments becomes easier and less complex. However, small enterprises will have more difficulty in acquiring, creating and managing explicit knowledge. Although explicit knowledge could easily be created from the information in ERP, SCM or CRM applications, small enterprises tend to use these applications independently and not in an integrated manner. Hence, to create useful explicit knowledge from these applications, they will have to develop third-party software to link the information together. Such efforts will make the entire process of acquiring and creating explicit knowledge more complex for small enterprises. Based on the analysis of small enterprises, it was axiomatic that large enterprises will be prone to an inverse level of complexity in acquiring, creating and managing knowledge. The model proposes that large enterprises will have less difficulty in explicit knowledge acquisition, creation and management due to the low level of complexity. Since large enterprises
SMEs in the E-Business and Knowledge Management Era
Level of complexity in knowledge acquisition, creation and management
Large
Low Level of new effort for e-business applications integration
High
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Medium
Small
High
Low Explicit
Tacit
Cultural
Types of knowledge
Figure 6: A conceptual model of level of complexity in knowledge acquisition, creation and management in different sizes of enterprise. tend to use integrated ERP, SCM and CRM applications, the requirement to link the information independently has been removed. This proposal was proved to be valid because of the many reported cases of successful knowledge management in large enterprises using mainly explicit knowledge. Nonetheless, it is very complicated to acquire, create and manage tacit and cultural knowledge in the manufacturing environment of large enterprises due to their highly structured and authoritarian approach in dealing with work and processes. Competition between peers in large enterprises is more evident. Thus, acquiring and creating tacit knowledge is likely to be very difficult. Additionally, cultural knowledge acquisition and creation for the total number of employees in large enterprises will prove to be complicated to manage owing to the diverse backgrounds and needs of different individuals, although this is relatively less complicated than acquiring and creating tacit knowledge. In acquiring, creating and managing explicit, tacit and cultural knowledge, mediumsized enterprises tend to have a steadily decreasing level of complexity. Figure 6 indicates that the level of complexity for acquiring and creating explicit knowledge in medium-sized enterprises is relatively lower than in small enterprises, but they will face similar problems due to the problem of e-business systems and applications integration. The slightly lower level of complexity in this is due to the tendency of more information regarding performance being recorded and available in medium-sized enterprises regarding performance. The level of complexity in tacit and cultural knowledge acquisition and creation in such mediumsized enterprises will be slightly higher than in small enterprises, yet they will face similar problems as discussed for large enterprises. The conceptual model aims to act as a reference framework to provide enterprises (and particularly SMEs), with knowledge references in terms of the level of complexity in acquiring,
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creating and managing explicit, tacit and cultural knowledge. The complexities were highlighted for all types of knowledge and sizes of enterprise. To reduce the level of complexity, we suggested that small enterprises should first examine ways to integrate the ERP, SCM and CRM applications (e.g. networking, and then creating useful explicit knowledge from the integrated e-business systems). This suggestion is also viable for medium-sized enterprises. To reduce the level of complexity in acquiring and creating tacit knowledge in mediumsized enterprises, we have suggested team building and the use of mobile technology. Team building will encourage employees to share knowledge in achieving the same goal, while the use of mobile technology will enable real-time B2B and B2C activities to be monitored, recorded and kept on the server. To reduce the level of complexity in acquiring and creating cultural knowledge, understanding of human behaviour and motivation will need to be understood by the senior management.
Conclusions and Further Work There appears to be an increasing use by SMEs of the new techniques, methods and methodologies put forward in this paper. However, what is most surprising is the significant use by small firms of the available techniques. It is very important that these organisations are able to adopt such approaches in an efficient and effective way. Generally, because the firms in the survey did not create knowledge from existing information and business processes, they were susceptible to problems if a key expert/knowledge-holder was unavailable. This is unfortunate as small firms, with employees who may be termed “entrepreneurial” may be in a better position than large organisations to take advantage of the new initiatives. However, where a “package” is modularised, special care must be taken by small firms in choosing the appropriate modules for their type of business. Furthermore, small firms should not adopt the new techniques if they do not have the specialist staff to oversee their adoption. Small- and medium-sized organisations must be able to put forward a sound business case for adopting these new modes of working. It is not a sufficient reason to argue that larger, more established organisations are using these techniques. Even large organisations, with copious amounts of resources, have had major problems in effectively implementing these approaches over the last 25 years. SMEs should be sceptical of vendors who claim that their products are appropriate for any size of organisation. Even though scaled-down versions of certain methodologies, i.e. PRINCE2, have been made available, this is no guarantee that they will be successful in small organisations. The next generation of organisations should be in a position to make use of information and extract knowledge from IS and the business environment to maximise their returns. However, next-generation manufacturing will be more uncertain owing to increasing variety of available products, and customer and consumer power. In line with the proposed reference model for using knowledge management and e-business integration by enterprises, the fundamental issues of uncertainty in an enterprise cannot be overlooked. Since more money will be spent on such e-business applications in the future, the treatment of uncertainty in an enterprise will become more important, because the system relies on accurate and up-todate data, and unexpected events will not be automatically countered by the system. Their
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effect on enterprise performance may include tardy delivery because information in the system was not used to create working knowledge.
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Clustered High-Technology Small Firms and Innovation Networks: The Case of Post-Production in London Gary A. S. Cook and Naresh R. Pandit
Introduction Clusters have been defined in many ways reflecting multidisciplinary interest and their varied forms, ranging from the weak, which do not confer significant advantages to incumbent companies to the strong, which enable high and sustained productivity (Markusen, 1996; Gordon & McCann, 2000; McCann, Arita, & Gordon, 2002). A general definition that captures the essence of the strong sustainable cluster is provided by the UK Department of Trade and Industry (DTI, 1998): A geographic concentration of competing, collaborating and interdependent companies and institutions which are connected by a system of market and non-market links. This quote emphasises the importance of interconnectedness between firms, which might appear to be unrelated in studies based on standard industrial classification categories. It also brings out the role of non-market linkages, which centre around trust, reputation and a common culture, which are known to be particularly important with respect to innovation (Camagni, 1991; Capello, 1999). Finally, the definition encourages us to think of clusters as complex systems of industrial organisation. It is this very complexity that makes them difficult to copy and therefore sustainable (Maskell & Malmberg, 1999). Recent empirical studies have shown that companies in strong clusters grow faster than average and that strong clusters attract a disproportionate amount of new entrants (Cook, Pandit, & Swann, 2001; Cook & Pandit, 2002; Pandit, Cook, & Swann, 2001, 2002). Also, productivity (Henderson, 1986) and innovation (Audretsch & Feldman, 1996; Baptista &
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Swann, 1998) are higher within strong clusters. The tendency for innovation to be highly clustered geographically has been noted in the United States (Jaffe, Trajtenberg, & Henderson, 1993; Audretsch & Feldman, 1996) as well as in Europe (Breschi, 2000; Paci & Usai, 2000). Productivity, innovation and entry effects may be mutually reinforcing mechanisms of cluster dynamism and the cumulative process by which clusters reproduce and strengthen their advantages relative to other locations. In particular, a history of successful innovation leaves firms well placed for further innovation and is likely to attract the start up of new innovative firms (Breschi, 2000). The mechanisms by which innovation has been facilitated, through geographic proximity is a main focus of this paper, addressing a gap between theoretical work on regional innovation systems and detailed empirical work on how they work in practice (Malmberg & Maskell, 2002; Sternberg, 1999). The central argument of this paper is that clusters of service firms may have much in common with clusters of manufacturing firms in respect to innovation. The focus of the paper is on innovation in the post-production sector of the broadcasting industry and its associated equipment suppliers. It reports a study based on 16 in-depth interviews with senior managers in addition to secondary sources going back to 1980.
Clusters and Global Innovation Networks Clustering and Innovation Saxenian (1994) provides a paradigm account of the link between clustering and innovation, with which the British broadcasting industry may be compared. Many of the general points she makes have been widely echoed in the literature, particularly those emphasising the advantages of flexible specialisation (Piore & Sabel, 1984; Scott, 1988; Best, 1990). Saxenian (1994) sets out to explain why Silicon Valley has recently prospered, whereas Route 128 has foundered in the wake of economic dislocation, which deeply affected both regions in the 1970s and 1980s. One feature of the “old competition” (to use Best’s (1990) terminology) is that oligopolistic firms are happy to compete on cost rather than innovation. The superior ability to innovate quickly, cheaply and constantly is the central reason why Silicon Valley has so comprehensively out-performed Route 128 in recent years. The importance of competition in innovation rather than cost is also stressed by Piore and Sabel (1984), Best (1990) and Porter (1990). Route 128 firms responded by squeezing suppliers to drive down costs precipitating a vicious circle of decline as the life was squeezed out of the regional system. A leitmotif running through Saxenian’s analysis is the importance of having a blend of competition and cooperation, a point also emphasised by Porter (1990) among others. As she notes (p. 46), “The paradox of Silicon Valley was that competition demanded continuous innovation, which in turn required cooperation among firms”. In this context Saxenian likens Silicon Valley to other notable industrial districts such as the Third Italy and BadenWurtemburg and indeed the Japanese keiretsu system. Prime importance is placed on the sharing of tacit knowledge. Saxenian also suggests that the development of a specialist supplier network is a key strength of Silicon Valley. This vibrant and highly specialised set of firms does not only offer static Marshallian externalities of higher productivity, but is also
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the wellspring of constant innovation (see also Scott, 1988), in part by allowing a greater diversity of technological paths to be pursued. The spinning-off of small firms from larger enterprises is seen as a principal method of knowledge diffusion throughout a region (Piore & Sabel, 1984; Best, 1990). Underpinning this is the notion that those who leave large firms to start up small firms often show the entrepreneurial zeal necessary to exploit new innovations in the spirit described by Schumpeter (1942) (Wever & Stam, 1999). These spin-offs themselves generate further spin-offs in the future (Saxenian, 1994; Keeble, Lawson, Moore, & Wilkinson, 1999). Spin-offs are easier in dense networks of small firms because the sense of collective identity makes the spin-off more acceptable rendering it easier for it to survive because it will be more easily integrated into supplier and customer networks. Successful spin-offs provide a set of role models and encourage others to follow suit. As the cluster grows, start-ups and specialisation increase, further reinforcing the competitive advantage of the cluster and so on. This is yet another illustration of the dynamic of cluster advantage. A classic means by which knowledge flows through the regional systems is labour mobility. The size and fluidity of the labour market in Silicon Valley compared to Route 128 is an essential component of its regional advantage. The development of pools of skilled labour, in both regions, had common causes: the existence of advanced seats of learning and research (Stanford for Silicon Valley, MIT for Route 128) and the existence of large firms. Once the region gains a critical mass (how one gauges such a point is opaque), then it enters a virtuous circle as it then becomes a magnet drawing the best labour. Indeed, Saxenian argues that once Silicon Valley forged ahead of Route 128 there was a “brain drain”. According to Capello (1999), high labour mobility within the cluster, but low labour mobility outside the cluster, is an important dynamic underpinning the accumulation of knowledge in a socialised way in networks of small firms. These social processes underpinning knowledge creation have embedded knowledge within the region and so provide regions with a hard-to-copy advantage (Maskell & Malmberg, 1999). Saxenian comments explicitly on the ways in which physical proximity contributes to the dynamism, cooperation and flexibility of Silicon Valley. She remarks (p. 4), “Network systems flourish in regional agglomerations where repeated interaction builds shared identities and mutual trust while at the same time intensifying competitive rivalries”. The timely exchange of information and the ability to do so face-to-face are of particular importance in the process of innovation. Geographical proximity encourages socialising, where information is exchanged in informal setting such as the Wagon Wheel bar in Mountain View. Demonstrating why proximity should matter to innovation has proved to be a gap in the literature on territorial innovation systems (Morgan, 2004). Morgan provides a cogent critique of the “geography is dead” thesis and advances some important reasons why proximity should matter. Virtual proximity by means of computer and communication technologies is no substitute for face-to-face interaction, where complex, tacit and ambiguous information has to be exchanged. Users and suppliers have to be able to communicate with each other. Morgan makes two other points germane to this paper. Even where local knowledge spillovers are important, the ecology of knowledge, of which the local system forms part, may operate at broader spatial scales. Nevertheless, he cites evidence that having highly tacit activities co-located may be important at early stages of major innovations. Malmberg and Maskell (2002) suggest that another advantage of proximity is the ability to monitor rivals,
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particularly their innovation attempts. Bode (2004) finds evidence that spillovers tend to decay with distance. In a study of technology intensive small firms, Keeble et al. (1998) find a relationship whereby firms with a stronger international orientation, either in terms of their export or innovation activity, also tend to have denser local interactions than firms with a lower international orientation. This is true for research collaboration with firms and universities and in links with other firms in the same line of business. They conclude that internationalisation, innovativeness and growth are strongly correlated and probably mutually reinforcing. They also find that the more technologically intensive firms tend to have fewer direct rivals. von Hippel (1988) highlights the importance of informal knowledge trading in the process of innovation and provides evidence on how and why such knowledge trading takes place. von Hippel argues that the best source of know-how will typically be in firms producing similar products and he observes that often such firms, even rivals, will be willing to share such information on request. This is most likely to occur, where the information is not a crucial part of the firm’s competitive advantage and there is some expectation that the firm to whom the knowledge is transferred will be in a position to reciprocate at some point in the future. In addition to knowledge flowing between rivals at the same stage in the value chain, von Hippel places considerable emphasis on the observation that innovation often takes place with users, even though the commercialisation of the innovation may be done by manufacturers. A particularly important group in von Hippel’s analysis of the source of innovations are so-called lead users. These are firms who are at the leading edge in terms of the demands they place on existing technology but also their own know-how. von Hippel suggests, and provides evidence for the fact that, these firms are able to develop their own solutions in terms of generating improvements to equipment and systems. Simmie, Sennett, Wood, and Hart (2002) provide important evidence on the location decisions of innovative firms. They conducted interviews with innovation award-winning firms in five of the 10 most innovative city regions in Europe — Amsterdam, London, Milan, Paris and Stuttgart. Firms were asked to rate 25 factors in terms of importance when choosing where to locate a new innovation. Professional and skilled labour combined with business services, transport and communications generally scored highly. Labour, premises and capital and production and consumption linkages and networks followed these. Specifically, in respect of innovation the most important relationships are with clients and customers. Simmie et al.’s data indicate that for the firms interviewed the crucial elements in their local learning innovation systems were highly qualified professional experts, skilled manual labour, external general and specialised business services, together with public universities, skills and training providers. Professional and skilled labour were judged as most important. Simmie et al. find, contrary to Porter (1990), that international competition drives innovation more than local rivalry. Nodes in Global Innovation Networks Amin and Thrift (1992) provide an important critique of models emphasising the local nature of systems of flexible specialisation. Among other things, such models do not recognise the importance of emerging global corporate networks (see also Castells, 2000; Sassen, 2001). Amin and Thrift argue that it is important to consider industrial districts and local
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complexes as the outgrowths of a world economy, which is still rapidly internationalising in a world of global corporate power. They identify important trends in the global economy whereby industries are increasingly organising on an integrated world scale and firms compete in oligopolies, with firms tending to “hollow-out” by an increasing use of subcontracting and strategic networking. The net result of these developments has been the growth of increasingly integrated global production filieres orchestrated and coordinated by large corporations. They argue that centres are needed to generate and disseminate discourses, collective beliefs, stories about what world production filieres are like. They are also needed as centres of sociability and to develop, track and test innovations. Contacts with numerous knowledgeable people helps spot gaps and find new uses for technologies. The internationalisation of R&D is a process of quite long standing, with multinational firms having moved in the direction of dispersing their R&D capability over a number of centres (Zander, 2002; Evenson, 1984). Cantwell and Iammarino (2000) argue that multinationals will form their innovation networks selectively with some locations, which are viewed as being more important being given greater priority in terms of the extent to which R&D capability will be dispersed to that location. They place emphasis on “… intangible assets, i.e. localized accumulated knowledge, skills and expertise, on which the MNC builds and reinforces it competitiveness on global markets”. (p. 319). Furthermore, they suggest that these choices by multinationals can set in motion both virtuous and vicious circles. They refer to a process of co-development between locally specialised fields of expertise and foreign centres of complementary technological specialisation. Both Cantwell and Iammarino and Bunnell and Coe (2001) acknowledge Dicken’s (1998) point that there is no simple distinction between the global and the local in terms of spatial scales of innovation, but rather there is a complex set of interdependencies between varying spatial scales. Cantwell and Iammarino suggest that a necessary condition for being a “principal actor” in a global innovation system is a “minimum threshold stock of technological competence” (p. 319) (although it is not clear how this could be objectively measured). To stay in the game a region must have an ability constantly to upgrade the quality of the local competence. Summary From this review of studies of generally high technology manufacturing, a number of factors emerge as being important in the innovation process. It is fundamental that innovation and product quality should be important dimensions of competition. Innovation is facilitated where there is dense interaction, supported by trust and common understanding, typically among dynamic and flexibly specialised concentrations of firms. Proximity is important both for creating trust and the ability to cooperate, and also supporting the density of interaction and the exchange of complex and tacit communication. Interaction between suppliers and users can be particularly important. Competition is important but needs to be combined with cooperation. New start-ups and spin-offs are important ways in which both competition and innovation are developed. The “mass” of the cluster contributes to facilitating not only new start-ups but also in fostering the development of a dense labour pool, which acts as a magnet to draw in more labour to the cluster. Labour mobility itself is an important means of knowledge diffusion. Linkages to external networks are also important
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and some regions acquire a high status in the strategic location of multinational firms because of their ability to create and guide leading-edge innovation.
An Overview of the Post-Production and Equipment Supply Sectors The British broadcasting industry is highly clustered, with an estimated 70% of employment in film and television concentrated in London. Within this, there is a very strong concentration on an area of approximately one square mile in Soho. The post-production sector has around 70% of its firms located in London, which account for 91% of total revenues (Film Council, 2003). One quarter of all firms are based in Soho (W1), which account for just under half of total revenues in the sector. While concentrated in Soho, these companies are also found in wider locations. Companies specialising on advertising spread down to the Covent Garden area. There are a number of companies in the West of London, close to the BBC in particular. There are also post-production companies located near the various studios, which exist in and near London, such as Pinewood, Shepperton and Ealing. Post-production in London is regarded internationally as being highly innovative and an early adopter of new technology (Film Council, 2003). Cook et al. (2001) find that some sub-sectors of broadcasting appear to be the most important in terms of cluster dynamics. In terms of growth dynamics, the strongest positive effects of cluster strength in a firm’s own line of activity appear in programme production and the manufacture of broadcasting systems. These are followed by post-production and programme distribution. The centrality of programme production and post-production in cluster dynamics is reinforced by entry dynamics (Figure 1). Programme production
TV broadcasting
Equipment supply
Radio broadcasting
Broadcasting systems
Programme production
Distribution
PostArtistes
production Key: 0 to 0.2
0.2 to 0.5
> 0.5
Figure 1: Patterns of entry attraction in British broadcasting. Source: Cook et al. (2001). (Figures in key refer to the size of the coefficients on entry attraction effects.)
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and post-production also exert strong positive entry attraction on each other. Thus, there is a powerful virtuous circle where cluster strength in each of these sub-sectors not only attracts entry but also leads to faster growth. Both of these sub-sectors attract entry from firms in other sub-sectors within the industry, and from specialist equipment suppliers. This is natural given the extent to which each sector relies on often highly specialist equipment, which they are likely to want to update, develop and keep in good repair. Before proceeding to analyse the nature of innovation and the interconnections between the post-production and equipment supply sectors, each will briefly be characterised. There are a number of important activities and processes, which are undertaken in the postproduction sector and a brief description will be given here to provide a basic orientation. ●
●
●
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Editing is a staple of post-production where the sequence of shots which will make up the final programme are selected. A highly important technological development in editing has been the development of digital non-linear editing techniques. Here the material is scanned into a computer and stored as digital code on a digitised hard disk. The editor is then able to assemble sequences very flexibly and also incorporate special effects. Lightworks and Avid were important rivals in non-linear technologies over the 1990s; Sound editing and track laying involves placing different sounds, e.g. speech, music, sound effects onto different tracks so that they can be moved around in relation to the image and in relation to each other. The final stage of the process is dubbing when the different sound sources are brought together and balanced against each other. Special sound effects may be incorporated at this stage; Graphic design runs a gamut from development of simple titles for a programme to sophisticated manipulation of images such as morphing. Technological innovation in this area has been led by companies such as Quantel, which introduced its Paintbox in 1981. Quantel vies with Discreet Logic, producer of high-end graphic effects systems such as Flame, Fire, Inferno and Smoke. This is an area in which the advertising industry has had a particularly strong influence; Telecine is a very important and “high-end” process, which involves tape to video transfers. This is a crucial process as it is the stage where the “look” of an image can be altered through colour correction, which changes the colour balance of the image to either make it look more natural or to create special effects such as creating a nightmarish quality. Leading manufacturers are Philips and Cintel.
There is a wide miscellany of other activities, which are brigaded under post-production, including outside broadcast units, studios and motion capture (a technique recording movement data from live actors and applying it to computer-animated characters) and motion control (a technique that uses a computer-controlled robotic arm which can be programmed to perform a sequence of precision moves). Post-production companies serve six main markets: broadcast television, broadcast radio, advertising, film, corporate video and pop music videos. The major segments, in all three of which London holds a leading world position, are commercials (36.4%), film (29%) and broadcast (18.9%) (Film Council, 2003). Companies tend to specialise on a certain type of work, but will often work across genres. Within this breakdown it has to be remembered that commercials are intimately connected with broadcasting and that film and broadcast are also strongly related. These three sectors use many of the same skills and technologies, however the
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economic nature of the markets differ. London is one of three main global centres for advertising, which is increasingly used as a hub for targeting pan-European and global markets. All three segments have significant exports, which stood at £774 million for advertising, £653 million for film and video and £440 million for television in 2000 (DCMS, 2001). Separate figures for the contribution of post-production within these overall export totals is not available. Film post-production is a volatile market in so far as there are a number of high-value projects linked to major films, which can cause surges and slumps in demand for the services of particular post-production houses. Only the largest houses have the equipment and personnel to bid for work on the top movies (Film Council, 2003). Film postproduction work is strongly international as big budget films emanate from Hollywood. Post-production companies are generally very small, with a median size of 15 employees. Even large Soho-based post-production companies have only around 150 employees, although they will typically also draw on a pool of freelance staff. There is a large number of small companies, with over 500 in London, and many freelance operators. The vast majority of firms in this industry are small-scale, stand-alone enterprises. Only 21% are subsidiaries of a larger company and only 10% of all firms have publicly traded shares (Film Council, 2003). The exception to the generally atomistic structure of post-production is the BBC, which is far larger than any other post-production facility. The BBC is the only UK post-production facility, which operates on a scale similar to the largest US facilities (Film Council, 2003). The activities of the BBC have been important in the development of the independent post-production sector in two respects. Firstly, the BBC has generated large positive externalities in the labour market as it has been an important trainer of many people who subsequently work for other companies. Secondly, under John Birt’s “Producer Choice” initiative the BBC outsourced post-production in the 1990s, which provided an important source of revenues for the independent sector. Equipment supply firms manufacture and/or supply the hardware and software, which is used by not only the post-production companies but also the broadcasters and production companies, some of whom also have in-house post-production sections. This sector consists of both manufacturers of broadcasting systems and equipment supply in terms of the sectors shown in Figure 1. The equipment manufacturers are typically larger. The median size of the equipment manufacturers is 39, but there are some firms, which are much larger. Some important multinational companies such as Sony, Panasonic and Philips have UK subsidiaries. These companies are based mainly to the South and East of London, particularly in the Thames Valley area and down into Hampshire. The prime reason for them being located outside London is their much larger floorplate. Almost three quarters of these firms are located in the South East. They are far fewer in number with only some 126 being listed in the 2002 edition of the Production Guide.
High Technology and Innovation Innovation and Competition The way in which innovation occurs in both these sectors (i.e. post-production and manufacture) bears strong resemblance to the processes described by Saxenian (1994) in Silicon
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Valley. The post-production sector is characterised by strong competition on innovation among the leading firms. This is particularly true for those companies principally serving advertising and pop videos, where client companies (i.e. advertisers) are constantly searching for a new trick, which will make their product stand out from their competitors. Being first to be able to offer a new innovation allows a facility company, and indeed in turn the manufacturer, the ability to earn what is typically a short-lived innovation rent. Inevitably imitation quickly follows, and what was once fresh and striking very quickly becomes passé. Innovation depends on both having the right equipment and the personnel who can operate it. Moreover, advertising and pop promotions can afford to pay for premium post-production. In broadcasting too there is a limited demand for very high end post-production. The major equipment manufacturers are also in competition with each other, although here the nature of the competition is different from that in production and post-production. The degree of rivalry is intense, yet few of the firms in the Thames Valley cluster are in competition with each other. In most branches of technology, there are a very small number of firms, which compete with each other in global markets. In telecine, for example, Philips and Cintel lock horns in global competition. Pogle and Da Vinci vie for the market for colour correction add-ons. There are numerous companies, which seek to carve out specialist niches. The key to this competition is constant innovation. Rivalry in innovation is acute in the manufacturing sector, which results in a stream of innovations which the post-production sector has to evaluate and invest in. This rivalry displays a Schumpeterian dynamic of creative destruction (Schumpeter, 1942) and an evolutionary dynamic (Nelson & Winter, 1982). Table 1 is based on adoptions of new technologies by post-production firms in Soho reported in Broadcast, the leading industry trade periodical between 1980 and 2003. While only giving an incomplete impression, it does illustrate three important facets of competition in innovation in post-production. Firstly, particular companies vie very closely with one another, such as Cell and VTR and the Mill and MPC. Secondly, waves of adoption bear some correspondence with the incidence of labour poaching from other firms (Table 2). For example, many moves in the 1995–1997 period were associated with Flame and in 1999–2000 with Inferno. In the case of telecine, the spike in reported moves in 1995 is roughly synchronous with the arrival of the Rank Cintel Ursa Gold (not to mention improved Da Vinci and Pogle add-ons for Rank Cintel telecine machines) and in 2000 with the adoption of the advanced Philips Spirit Datacine and the Rank Cintel Ursa Diamond and C-Reality machines. There was a wave of moves of Henry operators between the end of 1993 and 1997. The third factor that the table hints at is the degree of rivalry between hardware/software manufacturers, well illustrated by waves of minor and major innovations associated with the fierce rivalry between Philips and Cintel in the 1980s and 1990s. The Labour Market Labour market pooling is highly important. High-quality labour is attracted to London because this offers the most likely source of continuity of employment. From the employer’s point of view, London is an attractive location, despite the obvious congestion, because it offers such an exceptional pool of creative talent relative to other areas of Britain. There is clearly a dynamic at work where the reputation of particular regional centres, above all London, attracts talent, which makes the centre a more desirable place to do business and so
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Table 1: Adoption of new technology by London post-production houses. Telecine machines Rank Cintel III Aug 81 Rushes Aug 81 TVA Jul 82 Thorn EMI Oct 82 TVi Jan 83 BBC Mar 83 Visions June 83 Humphries Feb 84 Complete Jul 85 Crow Feb 88 Visnews Jun 88 TVi Jul 88 VTR
Quantel graphics equipment Phillips Spirit Apr 86 VTR Apr 86 Cell
Spirit Datacine Apr 96 Cell Apr 96 VTR Apr 97 Cell Jul 97 Mill Aug 97 MPC Aug 98 Soho 601 Mar 99 Nats Nov 99 Blue Sept 01 Rushes Rank Cintel Ursa Gold Oct 01 Condor Jul 93 CAL Jan 94 Rushes Mar 94 Complete Aug 94 VTR Sept 94 The House Mar 96 Telecine Jun 93 Molinare
Harry Jan 87 Telecine Apr 87 TSI May 87 Molinare Jul 88 VTR Feb 89 Spitfire Jul 89 Rushes Aug 89 SVC Paintbox Jan 87 Telecine Jun 88 Capital TV 1988 625 1988 Framestore Feb 89 VTR Jan 91 Molinare Mar 92 Frontline TV Henry Feb 93 Cell Jun 93 VTR Early 93 MPC Early 93 Red Post Early 93 Framestore Mar 94 VTR Mar 94 Mill Jun 94 SVC Mar 96 Facilities R Us Jun 96 Molinare
Discreet Logic graphics software Flame Jun 93 CAL Jun 93 Rushes Jun 93 Peerless Camera Feb 94 MPC Sept 94 Framestore May 94 Mill Nov 94 Rushes July 95 Cinesite Aug 95 VTR Feb 96 Cinesite Apr 96 Mill Jun 96 MPC Fire Feb 96 Mill Feb 96 Cinesite Oct 96 SVC Sept 99 TSI Sept 99 Rushes Inferno Apr 96 Mill Nov 96 Electric Image Sept 98 MPC Apr 99 Framestore May 00 Soho 601 Aug 01 MPC
on (Nachum & Keeble, 1999). In this respect, the status of the broader media cluster in London is an important magnet for ambitious and highly skilled labour. Table 2 provides an indication of the importance of labour mobility in highly skilled grades. The figures were compiled by a “trawl” of Broadcast between 1983 and 2003. Clearly, not all staff moves are reported and changes in editorial policy mean relatively more or less space is devoted to the post-production sector in any given year. 1994 is a rogue year where there was one large movement of staff, but details of individuals’ job positions were
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Table 2: Number of staff moving company in Soho post-production by skill type. Year 1983–1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003
Editors
Telecine operators
13 7 2 1 5 7 4 1 5 4 1 2 2
4 2 3 0 7 2 3 2 0 7 0 4 6
Fire/Flame/ Inferno
Other graphics
Engineers
Other
3 1 3 2 7 4 2 7 2
4 2 7 5 4 12 9 6 2 9 2 9 5
0 2 0 1 3 1 0 1 4 2 0 0 0
11 1 3 14 5 3 3 5 4 11 4 6 10
Source: Broadcast Magazine.
not reported. Nevertheless, the figures illustrate that a high proportion of the reported staff moves were of highly skilled staff operating high-end equipment. Very often staff moves were linked with the acquisition of new equipment and software or major upgrades and flurries of staff moves often accompany waves of adoption of new technologies (Flame, Fire and Inferno are high-end graphics systems made by Discreet Logic). A good example of this is the case of Vince Narduzzo, doyen of telecine, who was lured to facilities house Telecine in May 1996 by their investment in the then leading machine, the Rank Ursa Gold. He was later lured to 4MC by the opportunity of developing a cutting edge high-definition telecine operation (with at least one move in-between to Pepper, a new start-up). Many of the job moves in the “Other Graphics” category in the 1990s were for operators of Quantel’s Henry high-end resolution and compositing tool. The table indicates that mobility of engineers between postproduction companies is a small but significant feature of job mobility. There is a modest amount of labour mobility between the manufacturing sector and the post-production sector. There are instances, for example, of engineers and software engineers moving from the manufacturing sector into the post-production sector specifically to develop an innovation, for example, a piece of plug-in software for a piece of equipment. There is also a certain degree of labour mobility between manufacturing firms, although it does not appear to be as high as in the post-production sector. Labour mobility has some interesting links with innovation. As already stated, when firms invest in new equipment it is often accompanied by the poaching of staff from other companies. Here there is a two-way connection and a link between technological and creative innovations. The technology is worthless without the creative talent to exploit it, and the top creative talent will only be attracted if it has the best and latest equipment with which to work. Therefore, the two types of innovation go hand in glove. London is able to attract
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top talent from around the world, which means there is a very well-developed labour pool of talented and energetic creative people, who are willing to experiment and drive innovation forward. These people are attracted by the ability to do the most demanding and creative work using the latest technology. What is more there is competition among these creative people for reputations, which provides a further competitive impulse to innovation. The density of the Soho cluster supports innovation through specialisation and the division of labour, an important advantage identified by Adam Smith (1937). There are very many highly specialised firms in the cluster who will be well placed to innovate in respect of very detailed aspects of post-production. If innovation were all left to a few large firms such innovations would not arise, as they do not have the resources to cover all the options on offer. A case in point is formatting adverts for transmission to mobile phones. This is the province of very specialised firms, not the major post-production houses, which do most of the work post producing the adverts in the first place. It is not uncommon for firms in the post-production sector to let or sub-let space in their premises to other firms, typically ones providing complementary skills with whom they will have strong and cooperative relationships. Spin-offs Labour mobility also makes it comparatively easy to set companies up and often forms an important element of spin-offs. New companies generally find it easy to lure staff away from other companies. A common scenario with spin-offs is that the entrepreneurs, who set up the spin-off, will typically take a number of key staff with them. Those key staff bring with them a ready-made client base. A very good case in point is the formation of Smoke and Mirrors by four leading “Flame” artists in the mid-1990s. These top creative people were able to start up and had an immediate customer base who were loyal to them. A very recent successful spin-off from Molinare in 1998, the Farm, was largely staffed at the outset by ex-Molinare people. Things are slightly more problematic with respect to venture capital. While there is specialist finance available and the start-up costs are often substantial enough to require the support of a venture capital firm, broadcasting in general has suffered over the years from being seen as a risky and unreliable sector with many firms not operating as normal commercial organisations. Some of these fears are well founded because some companies come and go quite quickly, only later to be resurrected, while former creditors are left with unpaid debts. It is difficult to generalise about the relationship between spin-offs and their parents. The overall impression is that relationships are not as cordial and supportive as the picture painted of Silicon Valley by Saxenian (1994). Table 3 shows details of numbers of spin-offs identified from a scrutiny of Broadcast magazine for the years 1980–2003. The figures represent spin-offs including management buy-outs but not including other new start-ups or formations of new divisions or subsidiaries by existing firms. Where there was a clear indication that the firm was being founded to exploit a new technology, either as creator or adopter, this has been indicated. Other typical reasons for spin-offs include personality clashes and exploitation of a new market opportunity. Since these numbers only relate to spin-offs, which were reported in Broadcast, they are not exhaustive and only indicate general trends. What Table 3 does indicate is that spin-offs are common within the London post-production sector and that they have an important direct link with technological innovation and a wider link
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Table 3: Numbers of spin-offs by reason for foundation. Years
To exploit new technology
Other
10 1 5 12 5 33
5 13 14 18 10 60
1980–1984 1985–1989 1990–1994 1995–1999 2000–2003 Total
Table 4: Spin-outs from major post–production houses. Moving picture company Rushes 1977 Computer FX 1981 Rushes The Mill 1989 Dean Street Post 1992 Space Productions 1993 Big Finish/Smoke & Mirrors 1995 First Art 1997 Red 2001
Tvi Independent Post-production Company 1992 Martyrs 1995 Two Plus One 1998 Molinare Electronic Images June 1993 AV Dept. 1987 Revolution 1989 Steve Turner Associates 1990 The Farm 1998
The Mill Big Finish/Smoke & Mirrors 1995 Lola 2000 Finish 2003
within innovation in so far as they bring new approaches to the market and increased competition. The broad similarity of the role of spin-offs in innovation and cluster dynamism to that of Silicon Valley, albeit at a far more modest scale, is underscored in Table 4. Particularly interesting is the influence of the Moving Picture Company. This is one of the most successful post-production houses in London, and a company at the forefront of both technological and creative innovations. Rushes, one of its direct progeny, boasts a similar status. Rushes, in its turn has spun-out highly important companies such as the Mill and Dean Street Post. The Mill, in turn, spun out Smoke and Mirrors. This is the evidence of the phenomenon identified by Saxenian (1994) of core companies being responsible for waves of important spin-offs, particularly the Shockley Transistor Corporation, and its direct spinout, Fairchild semi-conductor.
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Knowledge Sharing and Personal Networks There is also an important dynamic whereby people are able to keep abreast of important technological developments through word of mouth and the ability to “see for themselves” by visiting local post-production companies which either own, or are conducting a trial, of the latest designs. Soho is renowned for its clubs, bars and restaurants which provide important social spaces where those in the industry can meet and talk. People talk of good ideas picked up in the queue for the sandwich bar. Ideas “hang in the air” in the fashion described by Marshall (1920, 1927). Close proximity is important not only to be in touch with the flow of ideas from which innovation may spring but also to work out how ideas may be translated into products, which ultimately relies on pooling the know-how of a diverse set of (economic) agents. In the post-production sector there is an active network, particularly within Soho. There are particular networks which operate, an important type being among members of a particular craft. For example, there is a bar where animators tend to congregate and share ideas, reportedly talking in a shorthand intelligible only to members of that fraternity. There is extensive socialising between the production and post-production communities, where long hours of work drift into after-work socialising. Engineers form another highly important network. Particularly among engineers, there are examples of knowledge trading, which exemplify the processes analysed by von Hippel. Engineers in the post-production sector have two important professional affiliations. One is to their profession, the other is to the cluster. Both of these transcend narrow company loyalties and lead to cooperative behaviour patterns, which help sustain the cluster and drive its creative and innovative dynamism. Engineers will help each other out in practical ways. Equipment will be loaned and spare parts provided to make sure that work is delivered on time and to the highest level of quality. Herein lies an important cluster benefit in so far as there are many sources of help very close at hand, which can be crucial when solutions have to be found very quickly. Engineers are predisposed to do this by professional norms of doing everything possible to get the job done to the highest level and also an appreciation that the reputation of London post-production for providing the best work is a collective public good. Moreover, as discussed by von Hippel (1988) there is a recognition that a repeated game is being played and that tit-for-tat is a good strategy. People play cooperatively now in the expectation that their opposite numbers will do the same in the future. This norm of reciprocation is also deeply embedded within the creative community. Overarching professional and company rivalries is a belief that outstanding work done by any individual within the cluster boosts the cluster’s reputation to the benefit of all. There is active benchmarking against competitors in all aspects of the business. Even the entrepreneurs who own the post-production companies, who tend to be less disposed towards cooperation, nevertheless recognise that in some cases it is in their self-interest to do so. There is important knowledge sharing between manufacturers and post-production companies, with engineers often being the prime conduit through which the flows take place. Engineers within the post-production play a pivotal role. They understand the technical side, but also have great insight into the creative side of the business, therefore they can articulate what the customer is really after in a way it would be much more difficult to accomplish if engineers from the manufacturing companies were to attempt to talk to creative people direct. Post-production engineers are sometimes involved in client meetings with, for example
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advertisers, where their role is to advise on how far it is technically possible to realise the advertiser’s vision, and at what cost. The ability to solve problems face-to-face is a more general advantage of proximity. Firstly, the fact that people know each other personally means that they can discuss problems more candidly, rather than having to go through the niceties of normal communication channels. They are discussing problems engineer-to-engineer, rather than haggling company to company. The key advantage is that problems can be sorted out more quickly. This advantage is writ large where there are interface problems between different bits of equipment. These problems are easier to resolve if it is possible to get all the relevant engineers together in the same room to thrash it out. Proximity also helps in another way in sorting out gremlins with new products. Engineers within the post-production companies compare notes on any problems they are having with new equipment. This can lead to a solution, which someone else has devised being passed on. It can also help identify cases where there is a general problem which manufacturers are not being entirely candid about. All these trouble-shooting activities smooth the path of innovation. Important Customers and Lead Users The major equipment manufacturers make considerable efforts to keep abreast of what is happening in the market. They often maintain an engineering presence in Soho, part of which is to provide rapid trouble-shooting, but also to keep abreast of market trends. There is interaction with the engineers in the post-production houses in a number of ways. Engineers from the post-production houses are able to visit the R&D labs of the manufacturing companies which is beneficial to both sides. Firstly, the post-production engineers are able to see what developments are in progress before products are formally unveiled at trade exhibitions. Secondly, the manufacturing engineers are able to get feedback on their developments, without waiting for a potentially costly flop in the market place. Thirdly, such visits provide a forum for candid discussion, which can involve discussing problems which the manufacturing engineers are working on but cannot, at present, solve. Occasionally, this may lead to the post-production engineers going to help out the R&D team in the manufacturing firm. This they are willing to do because they end up getting a product which is more closely suited to their needs. Again, the manufacturing engineers gain insight into what the end user actually requires. In some instances, customers allow the engineers from one company to examine the products of their rivals. The incentive for the customer here is to try and develop a rival source of supply for a technology. Some customers are very demanding about the product they require and are not only willing to pay a premium for customisation but also work with the manufacturer to help develop an appropriate solution. This is a behaviour as described by von Hippel (1988) of lead users. Manufacturing companies also try to keep abreast of market trends through their sales force which keeps up more or less regular contact with the post-production houses that buy their equipment. The more important the client the more regular the contact. An important question is always “what more do you want”? One respondent described this as a feedback loop which was “going on non-stop”. When new equipment is being developed it is typically alpha- and beta-tested in a number of top post-production companies. Where a multinational company such as Sony is concerned, these test sites will be located in key centres around the world of which London is of
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prime importance. This on-site testing is important for two reasons, both salient to the innovation process. Firstly, very good feedback is obtained on how to develop the product ready for market. Secondly, the fact that a piece of equipment has been tested by a leading facility and endorsed by them is very important in driving sales for the product. Being a test site also has important advantages for the facility. They get “first go” with the latest equipment, before it is available to some of their rivals. Moreover, they have the opportunity to shape the development of the product closer to their own requirements. The ability to work on cutting-edge equipment also attracts top creative talent and helps retain them. External Networks and Knowledge Flows Both Soho post-production and the Thames Valley manufacturing clusters have the common distinction of being important nodes in a global industry. Indeed, the entire broadcasting complex forms a neo-Marshallian node in Amin and Thrift’s (1992) sense. The UK is the preferred manufacturing base for many broadcast equipment manufacturers not only because of the manufacturing cluster itself, but also because of the standing of the British broadcasting industry and above all the post-production sector in Soho. The latter is viewed as being globally pre-eminent in terms of its quality, innovativeness and creativity, ahead of the Hollywood area. In terms of advertising post-production London also has a high status, but not superior to New York. Hollywood, although larger and better endowed with highend equipment, is seen as being more of a production line, lacking the creative cutting edge of London. According to one respondent who works for a major non-UK multinational, the UK has a strong influence on the global thinking of his company because of its strength in broadcast, manufacture and above all post-production. This global pre-eminence is important not only in making London an important node in the global innovation system, it also serves to attract talent from an international market and also to bring high-level work into the cluster from around the world. The fact that London is a node in a network is a very important aspect of its own success in innovation and in the process of innovation globally. Both manufacturing companies themselves and many post-production companies, including some quite small operations, have bases in a range of countries. In the case of manufacturers this includes both production facilities and R&D facilities. The incentives for the post-production companies to maintain operations in other countries, most especially the USA, are three-fold (and by the same token these are reasons why post-production houses from the USA and elsewhere feel it is important to have a presence in Soho). Firstly, they are simply part of a growth strategy. Secondly, they enhance the reputation of the company. Thirdly, they expose the company to the latest market and technological trends. The way in which the local node interacts with the network can be illustrated by a sketch of an actual innovation which is discussed in very general terms for reasons of confidentiality. An R&D facility in the UK was pursuing a particular line of R&D effort. In the process, it was interacting with post-production in London to get feedback on whether the developments in progress were meeting the requirements of the market. In this dialogue a need was established for which the UK facility had no ready solution. This fact was fed back to the corporate HQ which then relayed the problem to all its other R&D facilities around the world. These facilities then contributed something to the solution which was ultimately pooled by
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the UK R&D lab which worked up the prototype. This was then referred back to the engineering department at corporate HQ to translate the design into production which was achieved in the multinational’s home company. This provides a simple example of how intensive knowledge can flow within a node of extensive knowledge flows as part of a global network to produce an innovation. The “Mass” of the Cluster London has other important advantages which help sustain the cluster and which contribute in their way to its dynamism, some of which manifest themselves in higher rates of technological innovation. The sheer volume and concentration of demand makes it feasible to buy and amortise the latest equipment. Other regional centres in the UK struggle to find the volume of work to make these very expensive investments pay. The existence of major international airports is particularly important to the manufacturing companies who need to exploit their innovations in global markets. Many products are highly specialised and customised, so it is imperative to be able to get to meet customers as easily as possible. It is also important in the post-production business for work which comes in from overseas, particularly the USA. London also benefits from its status as a major cultural centre. The international work this helps to attract is important to the place of London as a node in the international production and innovation network for two related reasons. It brings in some high-end work. It also keeps London “wired in” to sophisticated centres elsewhere, especially Hollywood and New York. Other Important Institutions There are number of other forums within which exchanges of ideas take place. There are a number of important trade exhibitions such as NAB in the USA and IBC in Europe. These are major showcases at which manufacturers display their latest equipment and often secure early orders. At these events there is an element of “cat and mouse”, with customers being invited to special demonstrations away from the prying eyes of rivals at which particularly innovative features are revealed. Another important dimension of these major trade shows is that they are also visited by component manufacturers who are trying to keep abreast of how their components are being used, and to inform their own innovative efforts in terms of what the market needs. There are important technical societies such as the Royal Television Society and the Moving Image Society. The latter organises demonstrations where manufacturers can show their wares to practitioners and get feedback. The Moving Image Society is part of a global network of knowledge flows as it exchanges information with cognate societies in other countries. The Moving Image Society also has a strong relationship with the British Academy of Film and Television Arts in order to promote exchange of ideas between technical and creative people. IEEE is a very important forum for engineers and there is a more specialised group, the Chief Engineers of Facilities Forum, covering engineering and technical matters in post-production. The trade press also plays a role both for engineers and others in keeping abreast of developments. In all this, the role of the broadcasters must not be neglected. It is a distinct advantage to be close to the engineering departments of the major broadcasters. This is so for two reasons.
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Firstly, whatever is produced by the production and post-production sectors, both programmes and advertisements, have to meet the technical standards of the broadcasters. Secondly, the engineers within the broadcasters, and especially within the BBC, have a reputation for expertise and thoroughness, which is a reservoir of know-how on which engineers both within post-production and within manufacturing companies can draw. These engineers talk both informally and also formally on a variety of industry steering groups. Manufacturers are keen to have their products adopted by major broadcasters, not only because it means a large volume of sales directly, but because it is a valuable endorsement for the product. What is more, manufacturers value the fact that engineers within the major broadcasters can play a valuable role in seeing that the equipment works properly.
Conclusions This paper suggests that some of the conventional wisdom emerging about high-technology clusters may also be applicable to service industries of which broadcasting is an example. Cluster dynamics in terms of growth and entry effects are very similar to a range of hightechnology sectors. Geographic proximity plays an important role in innovation. In this respect and others, the London broadcasting cluster bears comparison with Silicon Valley, in type if not in scale. Informal knowledge trading plays a crucial role, as also emphasised in a different treatment of innovation by von Hippel. By analogy with Saxenian’s account of Silicon Valley, engineers play a central role in the knowledge flows, which underpin innovation. Other factors, which have strong echoes of Silicon Valley are the importance of spinoffs, new firms’ start-ups, labour mobility and a dense concentration of demand, particularly sophisticated demand (again as stressed by von Hippel), and of flexibly specialised production. The importance of customers, skilled labour and “technologists” in the innovation story also agrees with the empirical findings of Simmie et al. The importance of the indigenous post-production sector to the overseas multinationals in manufacture adds an important qualification to Cantwell and Iammarino’s results. Their study misses these important spillovers between interconnected sectors, which appear quite separate when viewed through the lens of standard industrial classification systems. There is a very powerful positive feedback loop, which sustains cluster dynamism. London has a pre-eminent reputation in post-production and a first class reputation in broadcasting and engineering for the broadcast industry. This sustains a flow of talented labour and high-end work into the cluster, which further enhances its reputation. There are important lead users in the industry. The leading post-production companies have the ability to produce innovations and play an important role in helping manufacturers to innovate, both through formal testing and more informal dialogue. The post-production companies in their turn are being driven by other “lead users” who are above all the advertisers and pop video producers, who demand constant innovation. These processes of positive feedback appear to leave London well placed to sustain its position as a leading node in an increasingly global industry. The evidence is that there are strong positive externalities, which promote innovation and the concentration of innovation is not simply a reflection of clustering due primarily to externalities in production, although such production externalities are important also (Cook et al., 2001).
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Acknowledgement This research was partly funded by the Economic and Social Research Council (grant R000223258).
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Keeble, D., Lawson, C., Lawton Smith, H., Moore, B., & Wilkinson, F. (1998). Internationalisation processes, networking and local embeddedness in technology-intensive small firms. Small Business Economics, 11, 327–342. Keeble, D, Lawson, C., Moore, B., & Wilkinson, F. (1999). Collective learning processes, networking and ‘institutional thickness’ in the Cambridge region. Regional Studies, 33, 319–332. Malmberg, A., & Maskell, P. (2002). The elusive concept of localization economies: towards a knowledge-based theory of spatial clustering. Environment and Planning A, 34, 429–449. Markusen, A. (1996). Sticky places in slippery space: A typology of industrial districts. Economic Geography, 72, 293–313. Marshall, A. (1920). Principles of economics. London: Macmillan. Marshall, A. (1927). Industry and trade. London: Macmillan. Maskell, P., & Malmberg, A. (1999). Localised learning and industrial competitiveness. Cambridge Journal of Economics, 23, 167–185. McCann, P., Arita, T., & Gordon, I. R. (2002). Industrial clusters, transaction costs and the determinants of MNE location behaviour. International Business Review, 11, 647–663. Morgan, K. (2004). The exaggerated death of geography: Learning, proximity and territorial innovation systems. Journal of Economic Geography, 4, 3–21. Nachum, L., & Keeble, D. (1999). Neo-Marshallian nodes, global networks and firm competitiveness: The media cluster of central London. ESRC Centre for Business Research, University of Cambridge, Working Paper no. 138. Nelson. R., & Winter, S. (1982). An evolutionary theory of economic change. Cambridge, MA: Belknap. Paci, R., & Usai, S. (2000). Technological enclaves and industrial districts: An analysis of the regional distribution of innovative activity in Europe. Regional Studies, 34, 97–114. Pandit, N. R., Cook, G. A. S., & Swann, G. M. P. (2002). A comparison of clustering dynamics in the British broadcasting and financial services industries. International Journal of the Economics of Business, 9, 195–224. Pandit, N. R., Cook, G. A. S., & Swann, G. M. P. (2001). The dynamics of industrial clustering in British financial services. Service Industries Journal, 21, 33–61. Piore, M., & Sabel, C. (1984). The second industrial divide: Possibilities for prosperity. New York: Basic Books. Porter, M. E. (1990). The competitive advantage of nations. London: Macmillan. Sassen, S. (2001). The global city. Princeton, NJ: Princeton University Press. Saxenian, A. (1994). Regional advantage: Culture and competition in Silicon Valley and Route 128. Massachusetts: Harvard University Press. Schumpeter, J. A. (1942). Capitalism, socialism and democracy. New York: Harper & Row. Scott, A. (1988). New industrial spaces: Flexible production, organization and regional development in North America and Western Europe. London: Pion. Simmie, J., Sennett, J., Wood, P., & Hart, D. (2002). Innovation in Europe: A tale of networks, knowledge and trade in five cities. Regional Studies, 36, 47–64. Smith, A. (1937). The wealth of nations. London: J.M. Dent & Sons. Sternberg, R. (1999). Innovative linkages and proximity: Empirical results from recent surveys of small and medium-sized firms in German regions. Regional Studies, 33, 529–540. von Hippel, E. (1988). The sources of innovation. Oxford: Oxford University Press. Wever, E., & Stam, E. (1999). Clusters of high technology SMEs: The Dutch case. Regional Studies, 33, 391–400. Zander, I. (2002). The formation of international innovation networks in the multinational corporation: An evolutionary perspective. Industrial and Corporate Change, 11, 327–353.
Chapter 12
HTSFs in Peripheral, Knowledge Intensive Areas: An In-Depth Analysis in Pisa1 Alberto Di Minin, Michela Lazzeroni and Andrea Piccaluga
Introduction In this study we seek to describe a particular growth model for high-tech (HT) businesses in a local peripheral economy, characterized by the presence of a traditional manufacturing sector and important higher education and scientific research public structures. The model takes into account the clear distinction, which exists between traditional and HT sector and, within the HT sector, between the main sub-sectors and players in the HT arena. We argue that some of the characteristics of this model, such as the interactions between companies and local institutions, the knowledge and technology transfer process, and localization advantages, are only partially mapped by a purely quantitative approach. A more in-depth study is needed to understand the strengths and weaknesses of competitiveness and reasons for the expansion and decline of HT clusters. The subject of analysis is the HT cluster present in the province of Pisa, in central Italy, which well exemplifies the contradictions, problems and opportunities of a traditional economy that has evolved into an increasingly HT and knowledge-based economy. This paper is divided into three parts. In part one, a development model is presented and described in relation to other models discussed in the literature, and in particular, those dealing with a local HT-led growth process. Different approaches to the study of local technological transfer and concentration of knowledge intensive activities are also introduced and discussed. The second part deals with the analysis and interpretation of the HT sector in Pisa. Four different types of HT firms are identified and discussed in relation to three distinct 1
A previous version of this paper has been presented at the RSA conference on Reinventing Regions in the Global Economy, Pisa, 12–15 April 2003. We acknowledge useful comments from Phil Shapira and Nicola Bellini. We also thank Chiara Cavallo, Alessandra Patrono and Federica Vannelli for useful collaboration during the research. Most of the empirical data about HTSFs in Pisa have been collected within the Observatory on High-Tech companies in the Province of Pisa (http://osservatorio.sssup.it/), managed by In-Sat Lab and the Provincial Government of Pisa. The present work is the result of a common research effort; nonetheless, par. 1 can be attributed to Andrea Piccaluga, par. 2.1–2.3 to Michela Lazzeroni and par. 2.4 and 2.5 to Alberto Di Minin.
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stages of development of the local economy. The third part of the paper tries to identify general characters of the Pisa case with regard to both its strengths and weaknesses, and the potential for the application of this approach to the study of other HT-led growth areas.
HT Development in Italian Peripheral Regions HT-led growth has been among the most-studied topics by regional scientists and economists. The result is an impressive amount of models, methods of analysis and cases, with the objective of capturing the phenomenon and its crucial components. Political authorities in both developed and emerging economies have clearly identified the HT sector as one of the most strategically important to monitor and promote. Previous studies, such as Storey and Tether (1998), empirically supported the claim that HT sectors in advanced economies are likely to experience fastest employment and income growth rates. Also, dynamic small HT-based firms might play an important role by linking the sources of knowledge creation such as universities, and the industry, in a way not feasible for big corporations (Autio & Yli-Renko, 1998). In some cases, such as in biotech, this is explained by the nature and complexity of the technology and the research (Jones, 1992). In these cases, big companies integrate the presence of small companies, into their strategic behavior, in the early phases of commercialization (Arora & Gambardella, 1990). The nature and evolution of the HT sector governance structure therefore becomes an important element of academic analysis (Delapierre et al., 1998). Empirical findings, such as those of Carlino et al. (2001), have shown that HT firms are likely to concentrate in metropolitan areas, or in the proximity of public and private R&D centers (Bade & Nerlinger, 2000). However, these studies of agglomeration are not likely to fully explain the reasons behind this phenomenon. As in the traditional “low-tech” sectors, agglomeration is often a precondition for positive clustering dynamics. This study tries to contribute to that part of the economic literature, which has identified similarities and differences between the HT concentrations/clusters and industrial districts as known in the Italian experience, or clusters of firms in traditional sectors. The works of Piore and Sabel (1984) and Sabel (1993), among others, have shown to the international research community the particular nature of Italian industrial districts, and the “studied trust” present in non-market transactions, which helps explain the success and failure of industrial agglomerations. In the HT sector, it has empirically shown that local cooperation is also important for technology-based firms, an element that facilitates competitiveness and innovativeness (Shan, 1990). The social capital of the region was also found to be positively correlated with faster technical knowledge growth and competitiveness, for HT companies (Yli-Renko & Autio, 2001). By shifting the focus of the analysis from agglomerations to networks, researchers were able to show that the knowledge/awareness of resources available in a network became an important resource, to be coupled with the internal capabilities, of the HT firm (Lee et al., 2001). A more qualitative approach is therefore necessary to suggest important interactions between the firm and its environment. In particular, an analysis of the level of the agglomeration cannot clarify why some groups of firms or entire regions have an easier access to markets and technologies than others (Heydebreck, Klofsten, & Maier, 2000). Examples of these types of approaches focus on the advantages that spring from the agglomeration/clustering of activities in a particular sector, within the borders of a regional
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economy, such as biotech in Cambridge (Cooke, 2002, p. 146) or Silicon Valley (Saxenian, 1994). This emphasis on knowledge flows leads to labeling all sorts of clusters as “HT concentrations”, since it is misleading to talk about “low technology” (Porter, 1998), given that all technology is high, complex and critical to monitor, even in sectors dominated by the most traditional forms of production. Nevertheless, in an economy often characterized by the dominance of a successful “traditional” industry, the transition, so much desired, to a new HT-based excellence is anything but obvious. The emphasis of the literature shows that the attention of the researchers has to be focused on the appropriate combination of entrepreneurial culture with the capacity to attract or to endogenously create new technological competences. Some of the areas in transition are very peripheral, with respect to the traditional hubs of innovation and economic power. This represents both the incentive to use new technologies as a basis for a new centrality and growth, or policy makers need to use such transition to secure peripheral growth.2 Italy, despite its traditional inclusion among the most important players in the world economy, is a country which lags behind in R&D investments, mainly because of its large numbers of small or very small firms, and an overall lack of large R&D-based companies.3 Also, while the public research system has good scientific performance in terms of number and quality of publications, it does not generate a much needed intense technological transfer processes, which seems to be experienced elsewhere in both Northern Europe and the United States. With the objective of contributing to the already vast literature on this topic (Lawton Smith, 2000), we propose four categories of HT-based development in peripheral regions in Italy: 1. Non-metropolitan areas, often medium-sized university cities, where the public sector has heavily invested in scientific research. In this contexts, HT firms are likely to be set up, often as spin-off of public research centers. Established firms are also attracted to the area by the abundance of qualified human resources, often in the same sectors of specialization as the public laboratories. The case of Pisa falls into this first category; 2. Areas where significant private investment, usually by one or a few large technologyintensive companies, has promoted a more effective exploitation of existing areas of excellence in local public scientific research. The most typical case in Italy is Catania where, according to Schillaci et al. (2000), investments by Stm exploited and further strengthened significant positive externalities from the local public research effort in physics (University of Catania and the National Institute of Physics); 3. Areas which are not historically characterized by a relevant entrepreneurial or industrial tradition, and where previous public investment in research has combined with more recent private investment, to cause the development of an HT cluster. The relative importance of private and public investments in these cases is debatable, as for example, the development of the Cagliari ICT cluster. In this case, a public investment in the CRS4 project might have had some influence in the successive investment by Video on Line and the Tiscali phenomenon (Ferrucci & Porcheddu, 2002);
2
The CyberGeorgia project http://www.cherry.gatech.edu/cyberga/ is an example of such an effort to promote technological adoption and networiing in peripheral and mid-sized cities. 3 See, for exapmle, the Technology Achievement Index, developed by the UN in 2001. Italy is excluded from the group of 18 innovation leaders, and ranked among the second group of potential leaders.
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4. Areas where the concentration of firms in a new specific technology-based industrial sector has reached a critical mass without relevant contributions from public research owing to the action of a schumpeterian innovator and the involvement of larger firms from external areas or abroad. In these cases, high or mid-tech activities are established in areas, which were relatively poor in terms of scientific and technological know-how, but with a strong and established entrepreneurial culture. Examples are the biotech clusters in Mirandola (Lipparini & Lomi, 1999) and the packaging district around Bologna. In the first case, the entrepreneurial initiative of Mauro Veronesi in the 1960s initiated a process, which today has led to the creation of approximately 100 firms. In the second case, the leadership role of the firm Acma was critical. These typologies and cases show that HT-based development can be pursed in different ways in peripheral areas. For example, some peripheral locations offer advantages (or overcome disadvantages) that more traditional centers are not able to offer. At the same time, the dimensions of the local market, the business community, and the scarcity of some of the factors of production (such as qualified labor or capital) might represent bottlenecks for the development of an HT economy. Today, Italy is in an unusual international competitive position. On the one hand, the country faces the competition of advanced economies, which are investing heavily in R&D (both by public and private actors), and are able to impose their technological leadership on international markets. On the other hand, emerging economies are becoming good quality manufacturers themselves and also represent alternative locations for qualified private investments, due to the presence of a skilled and cheap labor force and political environments, which open up to foreign direct investment (and technologies). Betting on the HT development of Italian peripheral areas might indeed seem an interesting albeit risky alternative. The role that the public sector needs to play should overcome the disadvantages of a peripheral position, which might be an obstacle for private investors. This description of the case of Pisa shows the problems and opportunities that an economy based on traditional and small firms faces in the transition toward an HT-based economy, and the creation of new linkages between research and territorial development.
In Search of an HT Identity: The Case of Pisa Public Research and Advanced Education in Pisa Pisa is a mid-sized university city, characterized by an outstanding public research base and a well-developed education and training system. The city has three universities, including two schools of advanced studies. The University of Pisa is the largest of these, enrolling 7,000 students a year and employing about 1,500 researchers. The two superior schools — the Scuola Normale Superiore and the Scuola Superiore Sant’Anna — specialize in pure and applied sciences, respectively, employing overall about 260 researchers. Among the most significant research presences are ●
the National Research Council (CNR) specializing in computer science, physics, mathematics, geology, chemistry and natural sciences (800 researchers);
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Table 1: Composition of the university population in Pisa compared to Tuscany and the rest of Italy. Students (2000)
Students per 1,000 inhabitants
Graduated (2000)
109.6 32.2 27.0
3,657 10,674 139,108
Pisa 42,466 Tuscany 114,260 Italy 1,560,342
Graduated % of in scientific graduated technology in scientific studies technology studies 1,710 3,375 41,794
46.8 31.6 30.0
% of graduated in engineering
17.1 10.4 11.6
Source: MIUR, www.miur.it ●
● ● ●
the National Institute for Nuclear Physics (INFN), which employs 250 researchers in particles dynamics and fundamental and theoretical physics; a laboratory of the National Institute for Energy and Environment (ENEA); the Research Center for Experiences and Studies for Military Applications; a laboratory of the former electric power monopoly company ENEL (150 people).
The University of Pisa is the birthplace of IT in Italy. Here, in 1955, a team of professors built the first Italian computer, called CEP (Pisa Electronic Calculator). The machine, entirely assembled in Italy, was one of the most advanced computers of the time. After this project, the importance of the IT sector in the city increased. CNUCE, a university national institute for IT, and the first computer science faculty in Italy were set up in 1964 and 1968, respectively. The Pisa university system also has a remarkable capacity of attracting students. The number of students, for a city with less than 100,000 inhabitants, is impressive. Altogether 42,5004 people were enrolled in the academic year 2000–2001. These students are an important source of qualified labor, and represent an important locational advantage for the attraction of HT investments for the city, when compared to other peripheral areas. Almost half of the students graduate from scientific or engineering departments, with an average far above the national level (Table 1). Changes in the Economic Structure of Pisa Apart from a historical presence of the pharmaceuticals and chemicals sectors, the economy of the city was based on traditional industries, such as furniture, footwear, leather, textiles and the transport industry, with the presence of the Piaggio company (the manufacturer of world famous Vespa). These sectors still remain important for the local economy, in terms of employment and revenues, but have entered in a stage of consolidation and severe restructuring. The furniture sector is declining, and the transport industry (mainly Piaggio and its network of sub-suppliers) is on the edge of a serious crisis. Tourism continues to be a crucial resource for the local economy, since about 10 million people visit the city every 4
Source www.miur.it.
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year to see the world famous Leaning Tower. It is, however, mostly 1-day tourism, which has a relatively weak impact on the local economy. Overall the economic situation of the province lies between the restructuring of some of the big companies, and the slow but significant growth of some of the small firms. As a matter of fact, it is in the area of knowledge-based indicators that the city of Pisa shows performance indicators, which are clearly above national and European averages (Figure 1). Despite its small population, Pisa has more than 4% of total Italian researchers, more than 5% of total scientific-technological publications, more than 5% of Italian patents in the United States, about 4% of graduates in scientific-technological disciplines, more than 8% of total research spin-off firms and more than 6% of European projects where an Italian organization is project coordinator. For example, Pisa boasts some excellent examples of technological spin-off activities from the public research to private industry. Scuola Sant’Anna began a technology incubatorlike initiative in 1990, which has resulted in 12 spin-off firms headed by professors and/or students in robotics, mechatronics and IT. The Scuola Sant’Anna, the Piaggio company, and the provincial and city local administrations also promoted the launch of a new company, Pont-Tech, to encourage technology venture development in the area. A technology incubator in the town of Navacchio is located in a former factory renovated with local and EU funds. Initiatives such as the Pisa Research Consortium, seek to provide support for technology transfer projects to existing firms, or to promote quality management practices, as in the case of Qualital, established in 1988. Nonetheless, despite this presence, previous quantitative research (Counts & Di Minin, 2002) and political authorities agree on the fact that the quality and volume of the local public research effort is not fully exploited, and in particular, the city lacks the links between research and factors that could lead to more robust territorial development. The area is characterized by the presence of more than 200 HT companies, with a turnover of 1.4 billion Euros, with an employment of approximately 6,500 people. These firms Scientific-technological Publications
Researchers in scientifictechnological disciplines
Population
European projects (asco-ordinators)
9 8 7 6 5 4 3 2 1 0
US Patents
Graduates in scientifictechnological disciplines
Research Spin-offs
Figure 1: The relative weight of the Province of Pisa, compared to the rest of Italy (Lazzeroni, 2004).
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operate in a diverse variety of sectors, even if IT clearly dominates in terms of number of establishments. The Evolution of the HT Sector in Pisa Looking back at the economic development in the area of Pisa, it is possible to identify at least three distinct evolutionary phases. Birth and innovative growth (1955–1980) In this phase, Pisa was in the favorable position of being able to exploit the extraordinary investment in IT by the public and private research system. Large companies, such as Olivetti and IBM, decided to invest in Pisa in order to have privileged access to the results of the public research system. However, these investments, as well as the setting up of smaller IT companies, remained rather isolated and not linked to the rest of traditional local manufacturing activities, which at that time were far more important in terms of employment. Consolidation and stability (1980–1995) This phase is characterized both by a consolidation of competencies in the IT field and by a diversification of research and private industrial actors into other scientific and technological fields, such as mechatronics and life sciences. Attention on technology transfer activities greatly increased, despite the fact that various initiatives were launched without a single, widely accepted plan for the area. Tertiary activities were increasingly considered as possible complement or even substitutes, for more consolidated and declining manufacturing sectors. Spin-off companies, and small HT start-ups replicate the “dual system” of a few big firms and a large group of small companies already present in other traditional sectors in Pisa. Clustering and restart (1995–2003) In this phase, the area of Pisa is widely recognized as an important HT cluster, and exploitation of research results becomes a priority for local public research organizations. Awareness of the importance of knowledge-based development increases at both regional and local administrative levels. The HT community diversifies and opens up to sectors such as the medical-pharmaceutical, electronic, microelectronic, and telecommunication. Growth rates show rapid increase in both the number and the turnover of HT businesses. The funding of technology parks and incubators in the province of Pisa, leads to a spatial diffusion of what was before a mainly urban phenomenon. Nonetheless, the economic and sociopolitical weight of the HT community should further increase if the mission of a knowledge-based cluster is to be realized. At present (Table 2) more than half of the HT firms in Pisa operate in the IT sector. Table 2 shows the composition of the industry. According to the Observatory, the HT sector produces a turnover of 1.4 billion Euros, employing about 6,500 people, most of whom are highly qualified. The so-called “IT Diffusion” and the pharmaceutical sectors are the largest in terms of turnover, being responsible for 63% and 17% of the total revenues, respectively. Also, within the HT sector, it is therefore possible to observe the mentioned dualism between small firms and big companies. In fact, 70% of the HT companies in Pisa employ less than 30 people (see Table 3 for a sectoral
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Number of firms
IT Services IT R&D Innovation Management Mechanics & Electronics Energy & Environment Telecommunication services Microelectronics Pharmaceutical Telecommunication R&D Biomedical IT diffusion Other Total
% of Total firms
62 46 27 22 9 6 9 7 6 3 2 10 209
29.7 22.0 12.9 10.5 4.3 2.9 4.3 3.4 2.9 1.4 1.0 4.8 100
Source: Observatory on HT companies in the Province of Pisa, 2003.
Table 3: Pisa HT industries’ invoice and employment. Industry
IT Services IT R&D Innovation Management Mechanics & Electronics Energy & Environment Telecommunication services Microelectronics Pharmaceutical Telecommunication R&D Biomedical Other Total
Average turnover (valuation) (%) 5.7 7.7 0 4.2 12.5 5.0 0 2.2 12.0 N/A 14.1 6.2
Average number of employees (valuation)
20.4 16.1 5.2 17.7 8.5 5 5.1 179.1 40.5 8.5 25 28.0
Average employment growth rate (1998–2001) (%) 86.5 79.4 5.6 40.3 73.3 0 35.8 N/A N/A 98.2 62.3
Source: Observatory on High-Tech Companies in the Province of Pisa, 2003.
average of employment). The IT sector is dominated by firms, which employ an average of 12 people per company, while the average in the pharmaceutical sector is 297 employees per firm. During the 1998–2001 period, the HT sector experienced a significant and steady growth of more than 62% in employment. In particular, the Information Technology sector (both
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IT Services and IT R&D) has been the fastest growing sector, in terms of number of people employed (87% in IT Services and 79% in IT R&D). The number of HT companies has experienced a steady growth during the 1990s. Almost half of the HT companies localized in Pisa have been set up after 1995 (41.5%) (Table 4). The youngest firms usually operate in the IT sector (51.4%), telecommunication, energy and environment. This growth trend is confirmed by data from the Italian Central Bureau of Statistics (Istat), according to which in 1981 the HT sector in Pisa employed 3,859 people, while in 1991 this number increased by 35%. According to Istat data, in 1996 the HT sector in Pisa was employing 5,707 persons,5 with an average yearly growth of 3% over a 15 years period. Also, important financial and governance restructuring happened within the HT industry. The three main typologies are (1) merging and acquisitions of local companies on behalf of international corporations, (2) holding relationships with national companies and (3) the rise of local industrial groups. The pharmaceutical sector has been by far the most dynamic. Five out of the seven companies based in the province got access to foreign capital and radically changed their proprietary assets. In 1997, the Gentili Institute, one of the oldest chemistry firm in Pisa (set up in 1917), became a member of the Merck Sharp & Dhome Group. In the same year, the American Baxter acquired the Austrian Immuno, and the English financial group 3i took part in a family buyout operation for Farmigea in 2001; finally, two foreign companies recently invested in Pisa: the Spanish Grifols (1994) and US Abiogen Pharma (1997). Beyond the pharmaceutical sector, other important foreign groups invested in Pisa. In the aerospace sector, Alenia Marconi System, the result of a joint venture between British Aerospace and the Italian Finmeccanica, has a small research center. In mechanics and electronics, the Japanese multinational Mitsuba and the German company Siemens invested in Pisa, while in the IT sector Engisanità (previously part of Olivetti) was set up as a joint venture between the French group GFI Informatique, and the Italian Ingegneria Informatica; in 1997, ISL (a local IT company) was acquired by the French group Altran. Table 4: Foundation years of the HT companies in Pisa. Foundation year From 1910 to 1980 From 1981 to 1985 From 1986 to 1990 From 1991 to 1995 From 1996 to 2001 Total
Number of firms 13 10 19 37 56 135
% of Total firms
Number of IT firms
% of Total IT firms
9.6 7.4 14.1 27.4 41.5 100
4 7 8 16 37 72
5.6 9.7 11.1 22.2 51.4 100
Source: Observatory on HT companies in the Province of Pisa, 2003 (total sample: 135 companies). 5
According to estimates of the Technology Observatory this number is now beyond 6,500. The upcoming results of the official census will soon confirm this growth trend.
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Table 5: Equity relationships in the HT sector. Industries
Pharmaceutical Information Technology Total Industries Industries
Pharmaceutical Information Technology Total Industries
% of Companies held by an industrial group
Nature of the group Local group National Foreign group (%) group (%) (%)
60.0 28.3
– 52.9
33.3 35.3
66.7 11.8
31.3 % of Companies that hold shares in other companies 60.0 23.3
48.4
32.3
19.4
Local group – 78.6
National group – 14.3
Foreign group 100.0 7.1
27.8
63.0
22.2
14.8
Source: Observatory on HT companies in the Province of Pisa, 2003 (total sample: 135 companies).
Italian corporations have also invested in the area. This happened, either by means of buying local companies (such as the Espresso Group acquiring Ksolutions in IT, or Laboratori Guidotti becoming part of the Menarini Group in pharma), or by establishing local branches in the area, such as Netikos, part of the Telecom Italia, which absorbed a pre-existing Italian IT company. As a result, 28.3% of the sampled IT companies and 60% of the pharmaceutical firms are now part of larger business groups (Table 5). Strengths and Weaknesses of HT in Pisa Table 6 summarizes the results of the study on Pisa HT sector. This section expands the discussion about the main findings. R&D investment in the sector is quite relevant. Both the percentage of people employed in R&D activities and the expenditure in R&D over total revenues are quite high, by far above 10%, which is usually considered to be the minimum level for the HT sector. The level of people employed in R&D is 42% on average. These data might however overestimate the real importance of R&D in the sector, given the micro-dimensions of most of these firms, where it is quite common for R&D personnel to cover other roles in the organization. Nevertheless, the workforce is generally highly qualified, usually graduated from scientific and technical schools (72%), and 83% of the companies claim to have introduced technological innovations. R&D is most of the time carried out in house, rather than acquired from outside (the make/ buy ratio is 70:30), but there are significant differences among sectors. Outsourcing prevails in the telecommunication sector (with a make/buy ratio of 28:72), and the two channels have equal importance in the pharmaceutical sector (make/buy ratio of 51.5:58.5) (Table 7). The use of patents is still quite limited. Only 23% of the interviewed companies own a patent or have applied for a US, European or Italian patent.
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Table 6: Analysis of the main opportunities and obstacles for HT development in Pisa. Actors
Pros
Cons
University system
An outstanding public research base and well-developed systems for education and training Capacity of acting as catalyst to attract and retain students and potential entrepreneurs Several technology transfer organizations have been established and some technology poles are active in the area
Networking activities within the area appear low and this hinders firms growth and transfer of knowledge from public science A main challenge for the area is how to encourage a more positive attitude toward risk-taking and adaptation to new trends in the educational sector
High-tech firms
Fast and significant rise of new firms Emerging new corporate governance Steady growth trend of the sector: the number of high-tech companies has risen constantly during the 90s, including foreign multinationals, local companies, start-ups and spin-off companies Overall, good innovation capacity.
Lack of intense networking activities among firms Weakness of HT-led initiatives by local associations Absence of a formally agreed strategy and lack of a clear leadership in the hightech community Low capacity to enter extra-regional markets and low marketing skills of the technical entrepreneurs
Human resources
Highly qualified Rather abundant Less expensive than in the North of Italy and North of Europe
Low possibility of labor mobility inside the area and risk of losing talents Low attitude toward enterpreneurial risk
The area of Pisa
Good quality of life Presence of good transportation infrastructures (port, airport, highways, rail) Central geographical position in Italy Presence of some high-tech incubators infrastructures
Limited regional market Low presence of innovative finance instruments Lack of some knowledge business services Need for better organisation of networking institutions and infrastructures
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Table 7: Main R&D parameters. Industry
IT Services IT R&D Mechanics & Electronics Energy & Environment Microelectronics Pharmaceutical Telecommunication Innovation Management Biomedical Other Total
% of R&D employees in 2001
% of R&D expenditures in 2001
R&D make/ buy ratio
Number of patents
% of employees graduated in scientifictechnical subject
% of firms that have introduced technological innovations
38.1 34.4 36.1
21.9 26.2 14.2
69/31 61/39 90/10
2 3 6
65.6 58.4 68.2
92.9 77.3 77.8
56.7
18.3
87/13
4
94.5
75.5
75 12.3 36.2 12.5
38 4.99 16.25 10.0
73/27 52/48 28/72 N/A
2 2 0 1
95% 38.17 83.75 52.75
100 100 66.7 N/A
N/A 44.4 42
N/A 22.7 20
N/A 68/32 70/30
2 2 23
N/A 74.5 72
N/A 75 83
Source: Observatory on HT companies in the Province of Pisa, 2003 (total sample: 73 companies).
It is important to consider the interactions that take place within the HT sector. The presence of a particularly strong public research investment should lead to significant networking between HT companies and the university system. However, even if the number of companies that have interactions with the university is quite high (76%), the frequency and extent of these relationships are quite limited. Interactions between HT companies and universities are often related to partnerships over research projects and internships for students, but they are not the most common source for new technologies. Generally, companies invest in in-house R&D, for the generation of new technologies, only rarely using collaborations with universities or with other HT firms (Table 8). According to the survey, the interactions between the university system and HT firms are not based on systematically organized initiatives (like liaison offices or technology transfer partnership), but are often generated by personal contacts, and are informally organized. Difficulties in building up strong and lasting networking between university system and HT local companies concern different goals and time frames. Also, the different organizational sizes of the partners and the different desired scales of projects, generate significant difficulties. In particular, it is hard to set up the right incentives to tempt small companies to take part in university R&D projects. Usually, the small companies, which systematically interact with the university, have strong relationships with the academic environment before the firm’s start-up phase.
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Table 8: Sources used to develop new technologies. Ways to introduce new technologies
Frequencies Never
% of R&D inside of the company % of collaboration with university % of collaboration with companies % of R&D bought by public research system % of R&D bought by private research system % of technology license purchase
Sometimes Often
4.0 39.2 28.9 72.7 73.7 62.2
8.1 34.0 46.4 20.2 20.2 23.5
52.5 5.2 1.0 1.0 0.0 2.0
Always 35.4 21.7 23.7 6.1 6.1 12.2
Source: Observatory on HT companies in the Province of Pisa, 2003 (total sample: 99 companies).
Table 9: Geographic location of the markets and of the main competitor. Geographic area
Most important market (%)
Second market (%)
Location of the main competitor (%)
Province Tuscany Italy Europe Rest of the world
12.5 22.5 53.8 10.0 1.3
5.3 31.6 28.9 10.5 23.7
12.5 12.5 43.8 15.6 15.6
Source: Observatory on HT companies in the Province of Pisa, 2003.
Generally, HT entrepreneurs recognize that the university system plays a crucial role. They demand local universities to be involved in basic research projects, to be internationally competitive, and to attract world-class excellence. Local entrepreneurs feel that universities should also be able to produce transferable results, and should be more sensible to specific local needs. Networking within the industrial HT community is also quite limited. What seems to be lacking is an agreed strategy to foster the development of the sector. There are no strong or active industrial associations. This is mostly due to the great variety of the HT companies, which operate into several different industrial sub-sectors. The full potential of the HT associations already present in Pisa is not fully understood or exploited. Another important limitation on the HT community is the local dimension of the market, which local companies are selling to. For example, 35% of Pisa HT companies acknowledge the province or the region as their primary market, while only 11.3% are mostly active in EU or world markets (Table 9). The data that have been collected as well as the in-depth interviews, which have been conducted throughout 2002, have led to the identification of four main types of companies. Established innovators These are large firms, which spend a significant amount of their resources in R&D. They also have developed effective commercial strategies, and seek to
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or do, operate in large and even global markets. Collaborations with public R&D institutions and with the national HT industrial community are considered very important. However, these firms argue they have to face severe obstacles that obstruct their effective interaction with the local entrepreneurial community, and the local public research system. Technology integrators These firms specialize in the adaptation and integration of technologies — that are already present on the market — for local or national customers. These firms usually do not invest significantly in human resources or in R&D projects, and do not collaborate intensively with the public research system. Obviously, for these firms the interaction with other companies (both suppliers and potential customers) is critical for the definition of the market and for the organization of the business process. Learning by doing and technical consultancy with clients represent the most strategic activities. Technology labs These firms invest heavily in R&D, but they have not fully developed a coherent market strategy or at least they were not able to identify their markets. Most of the clients are local, even if the interaction with the local business and research communities is not fully exploited. However, this category has experienced a significant turnover growth in recent years. Also, these firms are increasing the number of people employed, and most of all, they absorb a growing number of graduates from the local universities. Emerging innovators and research spin-offs These firms are still mostly focused on R&D activities, but they operate in the national market. Emerging innovators frequently interacts with the local HT community, whereas the spin-offs cite significant difficulties in finding appropriate partners. These initiatives are quite likely the most interesting, and they deserve particular help to allow them to overcome the difficulties of the start-up phase. Apart from the first category, the three other types fall within the definition of “technological artisans”, because of their small size, regional markets and one-to-one relationship with customers and clients. The core business for some of these firms is technical consultancy. In the IT sector in particular, companies assist their clients in the adoption of new technologies or software. For these and other technological artisans, one of the main deficiencies is the lack of adequate marketing skills. Technological competence is not always coupled with a capacity to identify the most appropriate markets or clients. Although, the level of internationalization of Pisa HT firms is not high, these companies nonetheless need to face international competition on the local and national market. Only a few companies in the survey stated that their local market is beyond the threat of international competitors. These are companies, which adapt foreign technologies for the specific needs of local adopters. Their knowledge of clients’ needs offer them some protection from an immediate threat of the producers of the technologies, even if this particular niche of market does not offer them the possibility to secure and expand their technological leadership, nor is it a stable or lasting market. Even if Pisa HT sector offers examples of impressive innovative and R&D activities, which are successfully exported abroad by internationally oriented companies (such as Baxter, Abiogen Pharma, Netikos, Siemens, Ids), overall the firms’ small size, the exclusively local (and not particularly sophisticated) market, and the lack of intense networking, represent major limitations.
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Another major problem for the technological development of the area is an endemic one among peripheral Italian HT communities. Growth is sometimes limited by the lack of specialized capital, in particular venture capital (VC). It is not surprising that the survey indicated that most of the local companies in Pisa have been set up with personal funds belonging to the entrepreneur themselves. The lack of a specialized VC community has many negative consequences. Besides the most obvious ones, the lack of a pre-screening on behalf of a specialized financial expertise increases the mortality of firms, and even when personal funds are available, the lack of resources for an expansion lead to choices that might be too conservative and not competitive. Firms without the appropriate support of outside capital might never get out of the start-up phase. Also, the active presence of a VC leads to an important networking effect among assisted firms, which can only be beneficial for the local competitiveness. The already mentioned redefinition of the proprietary structure might offer a way out of this dilemma. The Public Research System and its Importance for Human Resources and Entrepreneurship Even if the public research system has not been able to offer a lasting technology transfer mechanism, the survey shows the role of the university system as a factory of entrepreneurs. The largest majority of HT entrepreneurs graduated from the local universities, but the most striking data is that 66.7% of them were not born in Pisa, and moved to the city during their university studies. Only 25.6% moved from other cities in Tuscany, while 41% moved to Pisa from other regions. The undergraduate programs of the university are therefore able to attract and, to some extent, retain HT entrepreneurs within the area. The significant presence in the area of public research activities leads to a relatively large local market for qualified labor, which clearly distinguishes Pisa from other peripheral parts in Italy. This is among the most important elements that local entrepreneurs have indicated as reasons for locating in Pisa. Potentially, public labs represent important research partners for the largest companies. Marconi, Ericsson and Telecom, Austriamicrosystem International in the microelectronics industry, Komo Machine Inc. in mechanical engineering and Ital Tbs in the biomedical sector have set up research facilities in the city, to exploit links with a big (but provincial) university.
Conclusion: HTSF in Peripheral Areas This study shows how the case of Pisa might be useful to our understanding of the limits and opportunities that a HT-led growth strategy might encounter in a peripheral region. The complexity of the research question called for both a quantitative and an in-depth qualitative empirical study of the economic and technological potentials of the area. The steady growth trends of the HT sector has led to increasing political attention from local administrators, who envisaged a “HT Mecca” and provide a balance to the crisis facing traditional sectors (furniture and transportation in particular). The development of the HT sector in the area seems an important opportunity not to be missed since it is highly compatible with the local research and knowledge intensity, qualified human resources and entrepreneurship. However
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the lack of innovative financial instruments, networking activities and technology transfer initiative seem to represent “black clouds” in the HT “sky” over the city. Three main recommendations for policy makers in mid-sized cities and researchers involved in similar studies emerge from this study. First, it is crucial to fully comprehend the extent of local resources endogenously available. In particular, the triangulation of different approaches should define what is not yet fully exploited, and which resources need further development. In the case of Pisa, a worldclass university system and research community needs to be coupled with adequate linkings with the industry. Second, the Pisa case shows the importance of institutionalized partnerships and lobbying groups in contributing to the definition of and, if necessary, redirection of HT policies, according to the particular needs of the industrial community. Similarly, the lack of leadership, both in the private sector and within the public research community is likely to cause shortermism in technology and industrial market strategies. Third, a main challenge for the area is to replicate the intense networking, typical of the more traditional Italian industrial districts, within the HT sector. Agglomeration of firms in the same sector does not automatically lead to the benefits of clustering. Local firms need to increase their reciprocal knowledge, and the right incentives for this should be put in place. The facilitation of personal contacts among entrepreneurs, technological partnerships and labor mobility are all likely to facilitate the local transfer of tacit knowledge.
References Arora, A., & Gambardella, A. (1990). Complementarity and external linkages: The strategies of the large firms in biotechnology. The Journal of Industrial Economics, 38(4), 361–379. Autio, E., & Yli-Renko, H. (1998). New, technology-based firms in small open economies — an analysis based on the Finnish experience. Research Policy, 26, 973–987. Bade, F. B., & Nerlinger, E. A. (2000). The spatial distribution of new technology-based firms: Empirical results for West-Germany. Papers in Regional Science, 79, 155–176. Carlino, G. A., Chatterjee, S., & Hunt, R. (2001). Knowledge spillovers and the new economy of cities. Federal Reserve Bank of Philadelphia Working Papers, 1–14. Chiesa, V., & Piccaluga, A. Exploitation and diffusion of public research: The general framework and the case of academic spin-off companies. Economic Innovation and New Technologies, 8, 197–223. Cooke, P. (2002). Knowledge economies. Clusters, learning and cooperative advantage. London, New York: Routledge. Counts, P. M., & Di Minin, A. (2002). Research and innovation in mid-size cities. Local technologybased development experiences in Savannah (GA-USA) and Pisa (Italy). Paper presented at Regional Studies Association Conference, Pisa, Italy. Delapierre, M., Madeuf, B., & Savoy, A. (1998). NTBFs-the French case. Research Policy, 26, 989–1003. Ferrucci, L., & Porcheddu, D. (2002). Start up and evolution of an ICT district: The Cagliari case. Software in emerging regions, Pisa, Italy: Scuola Superiore Sant’Anna. Heydebreck, P., Klofsten, M., & Maier, J. C. (2000). Innovation support for new technology-based firms: the Swedish Teknopol approach. R&D Management, 30(12), 89–100. Jaffe, A. (1998). Patents, patent citations and the dynamics of technological change. NBER, 2002.
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Jones, S. (1992). The biotechnologists. London: The Macmillan Press. Lawton Smith, H. (2000). Innovation systems and “local difficulties”: The Oxfordshire experience. In: Z. J. Acs (Ed.), Regional innovation, knowledge and global change. London, New York: Pinter. Lazzeroni, M. (2004). Geografia della conoscenza e dell’innovazione tecnologica. Milano: FrancoAngeli. Lee, C., Lee, K., & Pennings, J. M. (2001). Internal capabilities, external networks, and performance: A study on technology-based ventures. Strategic Management Journal, 22, 615–640. Lipparini, A., & Lomi, A. (1999). Interorganizational relations in the modern biomedical industry: A case study in local economic development. In: A. Grandori (Ed.), Interfirm networks: Organization and industrial competitiveness. London: Routledge. Mowery, D. C. (1995). The boundaries of the US Firm in R&D. Coordination and Information. Chicago, NBER: Lamoreaux and Raff. Piore, M., & Sabel, C. (1984). The second industrial divide. New York: Basic Books. Porter, M. (1998). Clusters and the new economics of competition. Harvard Business Review, Nov/Dec, 77–90. Sabel, C. (1993). Studied trust: Building new forms of cooperation in a volatile community. In: R. Swedberg (Ed.), Explorations in economic sociology. New York: Russell Sage. Saxenian, A. (1994). Regional advantage. Cambridge, Massachusetts, London, England: Harvard University Press. Schillaci, C., Di Gesù, C., & Di Guardo, C. (2000). Microelettronica e Imprenditorialità: Verso lo sviluppo di un polo Hi-Tech nell’area Catanese. Economia e Management, 1, 75–90. Shan, W. (1990). An empirical analysis of organizational strategies by entrepreneurial high-technology firms. Strategic Management Journal, 11, 129–139. Storey, D. J., & Tether, B. S. (1998). New technology-based firms in the European Union: An introduction. Research Policy, 26, 933–946. Yli-Renko, H., Autio, E., & Sapienza, H. J. (2001). Social capital, knowledge acquisition and knowledge exploitation in young technology-based firms. Strategic Management Journal, 22, 587–613.
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Chapter 13
Alliance Business Models for University Start-Up Technology Ventures: A Resource-Based Perspective Tim Minshall
Introduction Knowledge moves from the academic environment to industrial application through many routes including recruitment of graduates, public availability of research outputs, consulting activities by academics, production and publication of research derived tools and techniques, licensing of intellectual property, and the spin-out of ventures based upon research outputs. Much attention has been focused in recent years around the last of these knowledge transfer routes — the formation of new ventures based around university-derived intellectual property and ways in which this process can be improved (e.g. Siegel et al., 2003; Di Gregorio & Shane, 2003). The Greater Cambridge sub-region in the United Kingdom has attracted much attention for its perceived success at supporting this form of technology transfer. A subset of the community of technology-related ventures that are now based around the city of Cambridge — some 3,500 companies, many leaders in their industry sector, employing around 50,000 people making a contribution to the national economy of around UK£7.6 bn1 — are either direct or indirect university spin-outs.2 Many of these university-related entrepreneurial ventures have moved from concept to successful revenue generation by addressing opportunities in international markets. Analysis of the business models of a subset drawn from these ventures shows that the majority are based around some form of alliance or alliances, and have been so from the very early days of the evolution of the venture. This, in itself, is unsurprising as research shows that alliances 1
Gross value added (GVA) figures. Data from PACEC (2003). That is either ventures in which the university retains an equity shareholding in return for allowing the commercial exploitation of its intellectual property, or ventures formed by recent graduates, academics and former employees of the university. 2
New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 203
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are often necessary to support innovative activities (e.g. Osborn & Baughn, 1990; Forrest, 1991; Granstrand et al., 1992; Duysters, Kok, & Vaandrager, 1999; Alvarez & Barney, 2001) and the formation of new technology ventures represents one example of an innovative activity. However, there is also a strong body of research showing that firms find strategic alliances frequently to be more resource intensive, less efficient and more difficult to manage than anticipated, with alliances based around new technologies being particularly problematic (e.g. Bidault & Cummings, 1994; Minshall, 1999; Duysters et al., 1999). Research also shows that in the case of alliances between new entrepreneurial firms and larger, mature firms, it is often the larger firm that is able to appropriate most of the value from the relationship, and that the general performance of start-ups in such partnerships can be adversely affected (Alvarez & Barney, 2001). This presented an interesting area for analysis and led to the preliminary research reported in this paper that seeks to address the question: Given the challenges already faced in exploiting a technology emerging from university research through the formation of a new venture, how were these new ventures able to counterbalance the high risk of entering into strategic alliances with larger, more mature organisations and enhance the development of their business? The remainder of this paper is structured around four sections. Following a review of literature relating to the themes of this research, concepts are drawn from the resource-based perspective to provide a conceptual framework for analysing the empirical evidence. The evidence is then presented in the form of four case studies that are then discussed in the final section along with suggestions for further work.
Strategic Alliances The term ‘strategic alliance’ is used to specify a range of inter-organisational relationships ‘in which the parties … maintain autonomy but are bilaterally dependent to a non-trivial degree’ (Williamson, 1991, p. 271). They can be either equity, contractual or informal in structure. Equity alliances may include equity joint ventures, minority equity cross-shareholdings and acquisitions. Contractual alliances encompass agreements where there is no equity stake between partners. In terms of the strengths of the ties between the two companies, a continuum of alliance types is shown in Figure 1. A number of theoretical perspectives can be taken when considering the differing roles that alliances can play for a firm, and these have been summarised by de Rond (2003) and are given in Table 1.
Mergers & Acquisitions Strong links
Equity agreement Joint Ownership
Informal agreement Contractual agreement
Weak links
Figure 1: Types of strategic alliances (adapted from Lorange & Roos, 1992).
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Table 1: Key research perspectives on strategic alliances. Theory/view
Key concepts
Economics-based theories Market power theory A strategy of cooperation that might enable alliance partners to achieve a stronger position together rather than ‘going it alone’. Transaction cost theory Posits that a strategy of cooperation can be a cost-reducing methodology, but that there are costs inherent in any alliance. Resource-based view Sustainable-competitive advantage results from trading of imperfectly imitable and mobile resources through strategic cooperation. Agency theory Concerned with the ability of ‘principals’ to monitor and control ‘agents’. Agents seek to exploit or access resources. Game theory ‘Players’ are interconnected and interdependent. Optimal outcome is gained through cooperation not competition. Real options theory Derived from finance modelling: spread risk, portfolio of low-risk options, incremental ‘wagers’. Organisation- and sociology-based theories Resource dependence Posits that firms are dependent on theory resources that reside outside themselves. Organisational behaviour is demand-centric, focusing on its environment to provide resources necessary for survival. Relational contract Exchange is not discreet; theory relationships need ongoing interactions. Trust is critical for smooth exchanges.
Example authors Porter (1980)
Williamson (1985)
Barney (1991)
Das and Teng (2000)
Parkhe (1993)
Faulkner and de Rond (2000)
Pfeffer and Salancik (1978)
MacNeil (1980)
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Table 1: Continued. Theory/view Organizational learning theory
Social network theory
Key concepts Experience and sense of community are preconditions to learning. Organisations form alliances to acquire, disseminate and retain knowledge by these two methods of learning. Defined as ‘structured sets’ of autonomous players who cooperate both to adapt to the environment and to coordinate and safeguard exchanges.
Example authors Child and Faulkner (1998)
Nohria (1992)
Source: Adapted from Valli (2004).
Strategic Alliances and Conditions of Technological Uncertainty For firms operating within conditions of high uncertainty driven by emerging technologies alliances provide a means to tackle complex business opportunities that would otherwise be impossible, impractical or undesirable for them to manage alone. In some cases, the development of a new product segment has been dependent from very early in the development process upon having a number of firms knitted into alliances. Examples include the development of high-definition television (HDTV) (Cawson, 1994), the digital video disk (DVD) and GSM mobile telephony. Much of the research on the use of alliances within conditions of high uncertainty driven by emerging technologies has focused on the role that partnerships can play in enhancing a firm’s ability to innovate (e.g. Granstrand et al., 1992; Osborn & Baughn, 1990; Bidault & Cummings, 1994). This ability is based around the acquisition, development, management and exploitation of a particular category of resource, i.e. technology (Granstrand et al., 1992). By providing a mechanism to access a wider range of tangible and intangible resources than are available within one organisation (Leonard-Barton, 1995) as well as spreading risk, alliances would seem to be an effective way to enhance innovation. Writing from a knowledge management perspective, Leonard-Barton reinforces this view of the use of strategic alliances: … successfully absorbing technological knowledge from beyond the periphery of the firm is as important a managerial activity as integrating it across internal boundaries (Leonard-Barton, 1995). This view is echoed in Teece, Pisano, and Shuen (1992, 1997) in describing how extending the boundaries of the firm is becoming an essential requirement for firms seeking to remain competitive in uncertain competitive environments.
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… the boundaries of the firm have become fuzzy in recent decades. Strategic alliances — constellations of bilateral agreements among firms — are increasingly necessary to support innovative activities (Teece et al., 1992). The main justifications for entering alliances as a means to foster innovation have been summarised as follows (Bidault & Cummings, 1994, p. 33): ●
●
●
Alliances may appear as a faster and cheaper way to develop new products and processes; Co-operative R&D allows partners to reach a critical mass of human and financial resources needed to undertake large projects; Merging technological knowledge and skills from different companies can improve the innovation process.
However, these benefits may not be realised owing to the ‘fundamental tension’ (described by Bidault & Cummings, 1994) that can be seen to exist between the dynamics of innovation and the logic required for partnering to be a success. Innovation stems from creativity, uncertainty and risk-taking; it is ‘… complex, uncertain, somewhat disorderly, and subject to change of many sorts’ (Kline & Rosenberg, 1986, p. 275). Partnering, on the contrary, can be seen as needing to emphasise clarity and explicitness: The inherent nature of the two processes definitely seems incompatible: on the one hand, a co-operative agreement should be as specific as possible, while on the other, we can characterise innovation management, to a large extent, as an ambiguous and non-linear process (Bidault & Cummings, 1994, p. 41). Despite this incompatibility, strategic alliances are ‘… common in areas in which firms face daunting technological challenges’ (Osborn & Hagedoorn, 1997). Part of the reason for the use and success of such alliances in conditions of technological change rests with consideration of the link between alliance form and alliance purpose. Hagedoorn and Narula (1996) present the view that a variety of non-equity forms of alliance provide a more effective environment for the creation of new knowledge than equity forms. More flexible, evolution-oriented non-equity forms may promote negotiation and dayto-day co-operation more than equity forms. Non-equity alliances may also better promote reciprocal information exchange and the development of a common language than do joint ventures and partial equity alliances. Equity forms are more likely to stress issues of control. Specifically, there will be more emphasis on setting targets, measuring against these targets, and taking corrective actions (Osborn & Hagedoorn, 1997). The ability to form and manage non-equity strategic alliances can thus be considered as an essential strategic tool for firms seeking to compete in uncertain-competitive environment driven by emerging technologies. This ability can be considered as a distinctive organisational capability that the firm may develop which provides a mechanism for accessing and re-configuring resources in response to changing conditions. This organisational capability is one that can be developed and enhanced to allow a firm to extend the boundaries of its accessible resources.
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Strategic Alliances between Start-Up Ventures and Large Organisations Business models for technology exploitation can typically be grouped around four generic types from the standpoint of the technology originator: selling the technology, licensing the technology, exploiting the technology through some form of partnership, or exploitation through one new organisation in isolation. Clearly, in practice, business models for technology exploitation may combine elements of more than one of these, e.g. a new independent venture that works with strategic alliance partners to exploit market opportunities perhaps through licensing technology. Alliances between a start-up and a mature venture can deliver benefit to both parties (Alvarez & Barney, 2001). For the start-up, an alliance with a larger, better resourced organisation provides potential access to social legitimacy and direct improvement in the economic prospects of the firm, driven in part through linkages to organisational resources such as manufacturing, distribution, marketing and sales channels. For the larger firm, an alliance with a start-up can provide access to new technologies, new talent and hence an improvement to the larger firm’s innovative capability. From the start-up’s perspective, alliances can be considered as simply addressing an issue of resource scarcity (Park, Roger, & Gallagher, 2002). But, as described earlier, alliance formation and management is a process fraught with challenges, and these are in some ways amplified when small and large firms come together. Even in the ideal situation where both parties have a clearly articulated aim for entering into an alliance, can monitor performance and adjust the relationship as necessary, the success of the alliance is still reliant on the need to bring together two distinctly different organisational cultures and modes of operation (Table 2). Another key set of challenges facing the start-up firm in managing an alliance with a larger, mature firm are summarised by Alvarez and Barney (2001) as relating to the difference between the rate at which a large firm is able to learn about the details of a start-up core technology, and the rate at which the start-up firm is able to imitate the organisational resources of the large firm. The start-up firm often in a weak position once the alliance has been formed. Two ways in which a larger partner may abuse its position in relation to the
Table 2: Potentially conflicting organisational cultures (Senn, 1989). Mature firm Seniority-based compensation and promotion Conservative, risk-averse Cost- and control-driven Long-range planning and deliberate decision-making Analytical, cautious Autocratic
Start-up firm Performance-based compensation and promotion Innovative, risk-taking Service- and quality-driven Opportunistic, rapid decision making Intuitive, daring Participative
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start-up through under-investing in the partnership once the larger firm believes it has assimilated what it needs from the partnership are given by Alvarez and Barney (2001): ●
●
Resources can be shifted away from supporting the alliance to other activities within the larger firm; The larger firm can begin to place unreasonable demands on the start-up that can have direct impact upon the start-up’s performance.
Such issues highlight the major managerial challenges that face a start-up in forming a strategic alliance with a larger firm. Despite this, alliances are shown to be a common feature of the business models of new technology ventures. Before analysing the empirical evidence provided by the database of the University of Cambridge-derived technology ventures, an appropriate conceptual framework needs to be identified. For the purposes of this initial analysis, we examined the differing perspectives given earlier in Table 1 and selected a framework, that brings together a number of concepts from the resource-based view of the firm.
Resource-Based View of Alliances To view the use of strategic alliances between start-up ventures and mature organisations, a simple conceptual framework is used, drawn from the literature on the resource-based view of the firm (Figure 2 and Table 3). This framework provides a language for discussing
Prior experience & path dependencies
Will determine Competitive Environment Uncertain Resources
Dynamic
Opportunities Challenges
The company’s tangible and intangible firm-specific resources
Restrict/ enable
Strategy to ensure growth
Competences
Firm performance
The configuration of resources that form specific and distinctive skills areas
Implementation of strategy Capabilities The processes which allow the firm to reconfigure its resources
Can change
Figure 2: Simple conceptual framework.
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Table 3: Concepts from the resource-based view of the firm. Core resources
Complementary resources
Competences Capabilities
Competitive environment Path dependencies
The store of tangible and intangible firm specific resources that the firm has internalised. Such resources include in-house knowledge of technology, skilled personnel, brand names and contracts. The additional resources which may be required to be combined with certain core resources to form a competence. Such resources may include sales, distribution and complementary technologies or competitive-manufacturing facilities. The ways in which resources are bundled together to form specific and distinctive-skill areas. The routines or processes which allow the firm to reconfigure its resources. These include resource concentration, accumulation, complementing, conserving and recovery. The influence of competitive pressures in the firm’s external environment will influence how rapidly and in what way the firm must reconfigure resources. A firm’s previous investments and activities will, to a certain extent, restrict its behaviour. Attempts to reconfigure and adapt resources will naturally tend to close in on previous successful activities.
the use of strategic alliances in terms of resources (both tangible and intangible, core and complementary), competencies (the way in which resources are configured to add value) and organisational capabilities (the ways in which the firms have learned to re-configure their resources). In addition, the framework also allows consideration of how the competitive environment and the previous activities (or path dependencies) of the firm impact upon the firm’s strategic decisions. This framework is derived from the work of a number of authors, in particular Teece et al. (1992, 1997) and was developed in Minshall (1997) and applied in Minshall and Garnsey (1999).
Case Studies A short summary of four illustrative cases drawn from the population of the University of Cambridge-derived ventures are given below. Fuller versions of the case studies were developed for teaching purposes by the University of Cambridge Entrepreneurship Centre. The original cases were developed using a combination of primary (interviews with company founders and managers) and secondary (company produced information, analysts and commentator reports) over 9 months spanning from 1999 to 2000. The short overviews given provide an updated snapshot of the origins and current situation of these companies. At the end of this section, Table 4 presents an overview of examples of alliances formed by the case study companies.
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Table 4: Examples of alliances formed by case study companies. Company 1 Limited
Example partner Japanese consumer electronics manufacturer US sound engineering company UK contract manufacturer
ART-VPS
UK contract manufacturer US semiconductor manufacturer US software company
CPS
Mobile phone technology consultancy UK mobile network phone operator Korean electronics manufacturer UK semiconductor designer
Zeus technology
US semiconductor manufacturer US IT hardware manufacturer (*4) Japanese conglomerate
Resource, competence, capability accessed Market access (consumer electronics), manufacturing (DSP) Market access (cinema industry), R&D (cinema sound system technologies) Manufacturing (small-scale production) Manufacturing (RenderDrive) Manufacturing (ICs) Market access (content delivery to end user) R&D (mobile telecoms software) Market access (end user trials) Market access (handset production) R&D (chipset), market access (through to end user) Manufacturing (ICs) Market access (software sold with hardware) Market access (Japan)
Note: Not all of these alliances are still in operation. Source: Company websites, news articles, press releases and interviews.
Case 1: 1 Limited In 1995, 1 Ltd was founded by Dr Tony Hooley, a University of Cambridge physicist, with the aim of developing the world’s first digital loudspeaker. The strategy of the company has evolved to now cover two main areas: digital sound projector (DSP) technologies, and various applications of ceramic actuator (‘Helimorph’) technologies. The Helimorph technology was developed as a core technology for the DSP, but proved not to be needed for that particular application. Additional applications for the Helimorph technology were then sought, and this has resulted in 1 Ltd now providing technology and production expertise for autofocus camera modules for mobile phones. The company provides technology solutions for its partners based on bespoke developments, product supply and licensing of proprietary technologies. The company was funded in the early stages through a series of research and development grants from both the UK government and the European Commission, plus some equity investment from private investors.
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The company has now formed agreements with leading Japanese consumer electronics firms who now license the design of the DSP from 1 Ltd for home movie systems, and a US sound engineering company to study the application of 1 Ltd’s DSP technology in the commercial cinema market. Case 2: Advanced Rendering Technology (ART) ART was co-founded in 1995 by Dr Adrian Wrigley and Dr Daniel Hall of the University of Cambridge Computer Laboratory with venture capital investment to commercialise raytracing technologies to improve the performance of 3D-image rendering. The founders began with the intention of developing the world’s first dedicated ray-tracing chip and licensing this design to original equipment manufacturers (OEMs) to be added to graphics workstations and PCs. However, in doing this, ART would have had to rely on external parties to develop new products, which would demonstrate the capabilities of its technology. It would also have had to rely on high volume sales to generate sufficient revenue. Therefore, around 9 months after the company’s foundation, ART changed its focus to developing its own rendering appliances, the RenderDrive. ART (now ART-VPS) produces two core products: PURE (a PCI 3D-rendering board for graphics workstations), and a derivation of the original RenderDrive (a networked 3D rendering appliance providing high-quality rendering). Key market segments for ART-VPS include professional animation, architecture, engineering and construction, and industrial design and engineering. The company has formed a number of alliances with 3D content creation application companies, as well as having contractual alliances with hardware manufacturers. Case 3: Cambridge Positioning Systems (CPS) CPS was founded in 1995 by Dr Pete Duffett-Smith, a radio astronomer from the University’s Department of Physics to exploit novel location technology for mobile phones. The origins of the technology date back to 1982 when Dr Duffett-Smith developed a positioning system for remote antennae using radio station broadcasts from ground-base transmitters. After various development and testing processes, CPS developed its ‘Cursor’ technology based around enhanced observed-time difference (E-OTD) that allows location technology to be added to GSM-mobile phones (the world’s largest and fastest growing wireless technology with over 700 million customers worldwide). The company now has received substantial venture capital backing from international investors including Ericsson Holding International BV, Infineon Technologies, Intel Capital, Siemens’ Investments and 3i. CPS technology is now being rolled out thanks to partnerships that the company has formed with equipment, chipset and handset vendors. Case 4: Zeus Technology Zeus technology was founded in 1995 by Cambridge University Computer Science students Adam Twiss and Damian Reeves. Running their own website from college bedrooms, they decided to take a closer look at web server software that ran it and within
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weeks had developed a new web server architecture. By making their work available on the Internet and listening to feedback from people that downloaded it, the foundations of a successful software company were laid. Zeus technology grew through the measurable quality of its product — it dominated the Standard Performance Evaluation Corporation (SPEC) independent web server tests. By 1997, a survey showed that the Zeus Web Server was the third most deployed commercial web server solution within the UK, with just over 10 per cent market share. Hardware companies also liked Zeus because bundling high-performance Zeus software with their computers enhanced the image of the hardware in the eyes of key customers. As a result the technical teams of major US hardware companies began striking up working relationships with Zeus and negotiated to include Zeus software in their products. Zeus also saw the potential of the Japanese market and formed a partnership with a Japanese conglomerate to localise, distribute and support Zeus technology in Japan.
Overview of Case Study Alliances Examples of the diversity of alliances formed by these case study companies are presented in Table 4.
Discussion and Conclusions The discussion below focuses on the alliances from the standpoint of the start-up firm. The alliances used by the case study companies have provided a route to access a wide range of resources, competences and capabilities. The reasons for forming such alliances encompass simple access to trusted contract manufacturing of high-technology products (e.g. ART-VPS and the US IC manufacturer), access to new geographic markets (e.g. Zeus technology with the Japanese conglomerate to enter Japan), access to new market sectors (e.g. 1 Ltd with the Japanese consumer electronics manufacturer to enter high-end consumer markets), and access infrastructure technologies that underpin the start-up’s products and services (e.g. CPS with the mobile phone technology consultancy). For each start-up company, a small number of alliances were entered into at an early stage of the start-up’s evolution, but the majority of the alliances were entered into following the receipt of investment from venture capitalists or private investors. Almost all of these alliances were contractual, with very few informal or equity alliances (only one example was found of the latter). Given the challenges described in the earlier literature review of forming and managing alliances between start-ups and larger firms, two key questions emerge from these cases that move beyond just describing the ‘what’ to the ‘how’: ● ●
How did these firms find their alliance partners? How did/do these firms manage their alliance partners?
At one level the first question seeks to know whether partners were found via serendipity or through some structured process linked to a clear strategic intent. For example, did the
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companies have the organisational capability to go through a process of identify needs, search, filter, select, form, manage and monitor, based on an assessment of the evolving need for core and complementary resources and competences linked to their strategy? If so, how had they developed this process? For a number of case study companies, partners found in the early days of the company’s evolution were not necessarily accessed through a structured process — it was often through a chance connection or suggestion made by a customer, investor or other stakeholder. Without experience of partner management, how would the company have internalised the organisational capability to allow them, in a structured manner, to access and manage resource and competences external to their own organisation? Three possible answers to this can be gleaned from the case studies: ●
●
●
Investors — Investors can play a very key role in the process of alliance formation and management. High-quality venture capital brings much more than just capital to startups — it is also the quality of their industry contacts. This is largely self-motivated, as if the fund is to see a satisfactory return on its investment over the typically tight venture capital investment period, then it needs to ensure that its connections are leveraged for the benefit of all its portfolio companies; Non-executive directors — The use of experienced and well-connected non-executive directors — a common feature of technology start-ups seeking to sell into corporate markets — provides a route to partially internalising the organisational capability to find, form and manage alliances; Recruitment activities — Recruitment activities to bring experienced professional management to the start-up as it grows can help to ensure that the company develops its own alliance-building capability. However, in the early days of the company’s evolution the options to do this may be limited.
An additional perspective put forward by one interviewee within one of the case studies is that of the experience that academics bring to start-up environment. Many academics are quite comfortable with the management of networks of groups of people beyond the core organisation. With so much research now taking place on a collaborative basis between groups in different universities, academics who become entrepreneurs find themselves able to bring this organisational capability and collaborative mindset to their commercial activities. In conclusion it would seem that these companies may have been able effectively to access and integrate resources and competences from beyond their own organisational boundaries — and to overcome some of the challenges posed at the start of this paper — initially through the use of ‘outsourced capability’, i.e. the use of the skills, experience and connections of investors and non-executive directors with substantial industry experience. Over time, through recruitment of professional management to these firms, this capability to manage partnership becomes more internalised. The success of the early partnerships in particular may therefore linked to path dependencies relating to the origins of the start-up and its desired route to accessing resources to fund its commercial exploitation activities. There is also an element of experiential learning within these alliances from the startups perspective. Through experience gained by managing alliances at the very early stages of the start-ups evolution, the case study companies were able to enter into future alliances
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with a much clearer view of what to expect, and where resource and effort needs to be applied. This is a well researched area within the field of general strategic alliances, as summed up by Osborn and Hagedoorn (1997): … Perhaps the net benefit from participation is not so much what a firm learns from a specific alliance or network as it is the increase in the firm’s learning capacity. Based on these initial findings, it would seem that further work would be useful in the following areas. One clear signal fed back through the research into the case study companies and broader population of similar companies was that a structured approach to support the selection and management of potential alliance partners would be most useful, particularly for technology ventures at the very earliest stages of development, i.e. before they are able to access the experience and connections of their potential investors, non-executive directors and professional management. It is also interesting to note the view put forward by some of the entrepreneurs themselves that they would have welcomed access to know-how from past experience to help raise awareness of the importance and challenges of forming and managing alliances for technology spin-outs. This pilot research has now led to two new research projects: ●
●
Alliance-based business models for university spin-outs: This project is looking at the links between the alliances formed by university spin-out companies and their ability to attract investment; Alliances between technology start-ups and established firms: This project is looking at the development of tools and techniques to support the use of alliances between small and large firms.
Alliances remain a commonly used component of business models for new firms seeking to exploit technologies. This research has attempted to bring together existing research and the findings of some pilot research to help us better understand the opportunities and challenges presented by such alliances.
References Alvarez, S. A., & Barney, J. B. (2001). How entrepreneurial firms can benefit from alliances with large partners. Academy of Management Executive, 15(1), 139–148. Barney, J. B. (1991). Firm resources and sustainable competitive advantage. Journal of Management, 17(1), 99–120. Bidault, F., & Cummings, T. (1994). Innovating through alliances: Experiences and limitations. R&D Management, 24(1), 33–45. Cawson, A. (1994). Innovation and consumer electronics. In: M. Dodgson & R. Rothwell (Eds), The Handbook of Industrial Innovation. Edward Elgar. UK: Aldershot. Child, J., & Faulkner, D. O. (1998). Strategies of cooperation: Managing alliances, networks and joint ventures. Oxford: Oxford University Press. Das, T. K., & Teng, B. S. (2000). Instabilities of strategic alliances: An internal tensions perspective. Organization Science, 11(1), 77–101.
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de Rond, M. (2003). Strategic alliances as social facts: Business, biotechnology and intellectual history. Cambridge: Cambridge University Press. Di Gregorio, D., & Shane, S. (2003). Why do some universities generate more start-ups than others? Research Policy, 32(2), 209–227. Duysters, G., Kok, G., & Vaandrager, M. (1999). Crafting successful strategic technology partnerships. R&D Management, 29(4), 343–351. Faulkner, D. O., & de Rond, M. (Eds). (2000). Perspectives on cooperative strategy. Cooperative strategy: Economic, business and organisational issues. Oxford: Oxford University Press. Forrest, J. E. (1991). Models of the process of technological innovation. Technology Analysis and Strategic Management, 3(4), 439–453. Granstrand, O., Bohlin, E., Oskarsson, C., & Sjöberg, N. (1992). External technology acquisition in large multi-technology corporations. R&D Management, 22(2), 111–133. Hagedoorn, J., & Narula, R. (1996). Choosing organizational modes of strategic technology partnering: International sectoral differences. Journal of International Business Studies, 27, 265–284. Kline, S. J., & Rosenberg, N. (1986). An overview of innovation. In: R. Landau & N. Rosenberg (Eds), The positive sum strategy. USA, Washington DC: National Academy Press. Leonard-Barton, D. (1995). Wellsprings of knowledge: Building and sustaining sources of innovation. USA, Boston Mass: Harvard Business School Press. Lorange, P., & Roos, J. (1992). Strategic alliances. Oxford: Blackwell. MacNeil, I. R. (1980). The new social contract: An enquiry into modern contractual relations. New Haven, CT: Yale University Press. Minshall, T. H. W. (1997). Japanese innovation strategy and the acquisition of UK IT firms, Ph.D. thesis, Engineering Department, University of Cambridge. UK: Cambridge. Minshall, T. H. W. (1999). A resource-based view of alliances: The case of the handheld computer industry. International Journal of Innovation Management, 3(2), 159–183. Minshall, T. H. W., & Garnsey, E. W. (1999). Building production competence and enhancing organisational capabilities through acquisition: The case of Mitsubishi Electric. International Journal of Technology Management, 17(3), 312–333. Nohria, N. (1992). Is a network perspective a useful way of studying organizations? In: N. Nohria & R. G. Eccles (Eds), Networks and organizations: Structure, form and action. Boston MA: Harvard Business School Press. Osborn, R. N., & Baughn, C. C. (1990). Forms of interorganizational governance for multinational alliances. Academy of Management Journal, 33(3), 503–519. Osborn, R. N., & Hagedoorn, J. (1997). The institutionalization and evolutionary dynamics of interorganizational alliances and networks. Academy of Management Journal (April 1997), 40(2), 261–278. PACEC. (2003). The Cambridge Phenomenon — Fulfilling the Potential. PACEC — Economic Development Consultants report for the Greater Cambridge Partnership, UK. Park, S. H. C., Roger, R., & Gallagher, S. (2002). Firm resources as moderators of the relationship between market growth and strategic alliances in semiconductor start-ups. Academy of Management Journal, 45(3), 527–545. Parkhe, A. (1993). Strategic alliance structuring: A game theoretic and transaction cost examination of interfirm cooperation. Academy of Management Journal, 36(4), 794–829. Pfeffer, J., & Salancik, G. (1978). The external control of organisations. New York: Harper and Row. Porter, M. (1980). Competitive strategy. New York: The Free Press. Senn, L. (1989). Culture. In: R. S. Bibler (Ed.), The Arthur Young management guide to mergers and acquisitions (pp. 229–243). New York: Wiley. Siegel, D. S., Waldman, D., & Link, A. (2003). Assessing the impact of organizational practices on the relative productivity of university technology transfer offices: An exploratory study. Research Policy, 32(1), 27–48.
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Teece, D. J., Pisano, G., & Shuen, A. (1992). Dynamic capabilities and strategic management. University of California at Berkeley working paper. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533. Valli, R. (2004). Alliance-based business models: Links between partner solutions and funding opportunities — A comparison of university spin-outs exiting Cambridge & Stanford universities. Ph.D. first year report, University of Cambridge Centre for Technology Management. Williamson, O. E. (1985). The economic institutions of capitalism: Firms, contracts and relational contracting. New York: The Free Press. Williamson, O. E. (1991). Comparative economic organisation: The analysis of discrete structural alternatives. Administrative Science Quarterly, 36, 269–296.
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Chapter 14
Needs of Start-Up Technology-Driven Enterprises: Starting Point for Governmental Policy Aard Groen, Ineke Jenniskens and Peter van der Sijde
Introduction At the Lisbon Council of 2000 the EU countries committed themselves to bring about economic, social and environmental renewal in the EU. The 10-year strategy set out in 2000 aimed at making the EU the world’s most dynamic and competitive economy. On EU and national level, new directions for innovation and enterprise policy are being proposed and already partly implemented. According to the Dutch government, the Dutch knowledgedriven economy should be one of the leading countries in Europe in this respect. However, reality shows that within the Netherlands a ‘European paradox’ exists where little use is made by business and industry of outstanding, high quality of (publicly financed) science, research and knowledge. Of which little use is made by business and industry. Start-up technology-driven enterprises (in this contribution also called techno starters) play an important role in solving this paradox. These enterprises are positioned between science on the one hand and the business community on the other. They focus on newly developed technologies (new technology-based firms, Oakey, 1995) or on new applications of existing technologies (application-oriented technology-based firms). Both types of enterprise are technology-driven, highly innovative and in this sense we typify them as Schumpeterian enterprises. Schumpeter (1934) defined entrepreneurship as putting together a new combination of already existing materials, which led to something novel and innovative. Technology was seen by Schumpeter as one of the key driving forces for entrepreneurship. An overall supportive climate will help to accommodate the start-up and development of technology-driven enterprises. One of the starting points for governmental policy in this area is a better understanding of the needs and challenges of techno starters. In this contribution, based on a report for the Dutch Ministry of Economic Affairs, we report on these needs in order to identify possible areas for policy making. New Technology-Based Firms in the New Millennium, Volume IV Copyright © 2005 by Elsevier Ltd. All rights of reproduction in any form reserved ISBN: 0-08-044619-1 219
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Methods of Research In this research, we have basically used two different methods to gain more insight into the needs of technology-driven start-ups. In the first place, we have scanned the international literature on techno starters. Second, we have explored the experiences of Dutch techno starters’ through interviews with 14 entrepreneurs. The interviewees were selected from three regions in the Netherlands: Delft, Twente and Groningen. The selection for these three regions was based on the presence of universities active in the field of techno starters. Twente and Delft both have a technical university, whereas Groningen University is especially strong in the field of life sciences.
Theoretical Background: Dimensions of the Entrepreneurial Process Entrepreneurship can be seen as the process in which actors interact in such a way that opportunities are recognised, preparatory steps are taken in order to exploit the recognised opportunity, which subsequently led to the creation of value (Shane & Venkataraman, 2000, 2001; Singh, 2001). Van der Veen and Wakkee (2004) used this process approach to structure their review of more than 100 articles. In the first stage of the entrepreneurial process, opportunity recognition, the discovery and evaluation of opportunities are the key elements. Initial ideas are developed into fully fledged business opportunities. The second step is to match necessary resources and perceived market needs in order to enable market success. This preparation will lead to the translation of the business opportunity into a concrete business concept. When the concrete offering is absorbed by the market in the third stage, the opportunity exploitation results in value creation. The entrepreneurial process, as depicted in these three stages, is not a linear process. Changing circumstances may alter decisions made at an earlier stage. Any course of action is influenced by the environment and is sensitive to various influences. This model of the entrepreneurial process is opportunity-based, and the entrepreneur is the driving force throughout the process. Yet, the entrepreneur is not an independent actor. Rather, we regard the entrepreneur as being embedded in a social context in which he or she needs to interact with other actors to exchange information and resources in order to exploit opportunities and create value. Recognising that the entrepreneurial process includes multiple-actors and multiple levels of aggregation, where actors interact and construct new technologies into new business, we use a multidimensional framework inspired by the work of Parsons on social systems theory (e.g. Parsons, 1951, 1977; Groen, 1994; Groen et al., 2002). A basic axiom is that entrepreneurs act purposefully in interactions with other actors (see also Granovetter, 1985, 1992). Originally, a social system was defined by Parsons as follows: … a social system consists in a plurality of individual actors interacting with each other in a situation which has at least a physical or environmental aspect, actors who are motivated in terms of a tendency to the “optimization of gratification” and whose relation to their situations, including each other, is defined and mediated in terms of culturally structured and shared symbols (Parsons, 1964, pp. 5–6).
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Four mechanisms are embedded in this definition: (1) interaction between actors; (2) striving for goal attainment; (3) optimisation of processes; and (4) maintaining patterns of culturally structured and shared symbols. Each of these mechanisms produces its own type of processes, with its own specific type of required capital. Each mechanism can be related to a specific dimension. Striving for goal attainment (mechanism 2) is associated with the scope dimension, and deals with strategic goals strived for and strategic capital needed. Optimisation of processes (mechanism 3) refers to the efficient organisation of entrepreneurial processes and is, in that sense, related to the scale dimension, with money as the basic resource. Skills and values, related to pattern maintenance and institutionalisation of shared symbols (mechanism 4) are embodied in cultural and human capital, and they can be found in organisations, values, knowledge, skills, experience, and technology. Finally, interactions between actors (mechanism 1) are related to the social network dimension. A central assumption in this theoretical framework is that on each of the four dimensions, enterprises will need sufficient ‘capital’ to be sustainable over time. This also implies that starting enterprises need to cover these four dimensions in order to establish a viable enterprise. Although techno starters have several challenges in common with ‘regular’ starting entrepreneurs, we will see that they face some particular challenges, all directly or indirectly related to the sophisticated technological character of their business. Figure 1
Cultural capital
Opportunity recognition
Strategic capital
Economic capital
Opportunity exploration
Opportunity exploitation
Social capital
Figure 1: Four dimensions in the entrepreneurial process (adapted from Van der Veen & Wakkee, 2004).
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(adapted from Van der Veen & Wakkee, 2004) illustrates the combination of the entrepreneurial process model with the four dimensions originating from the social systems theory. Applying the multidimensional framework of entrepreneurial processes on starting entrepreneurs, these entrepreneurs face the following challenges: 1. The strategic dimension: the entrepreneur has to set a goal for the enterprise. This concerns the business idea, the product and/or service, and the positioning of the enterprise in the market; 2. The economic (or financial) dimension: the enterprise probably will not be profitable from the start. Some ‘bridging’ money might be necessary. Starting enterprises often lack sufficient capital and/or a track record to attract investments; 3. The skills and values dimension (also called organisational dimension): the entrepreneur has to develop patterns of organisational behaviour and structures in order to create value in an efficient and effective way; 4. The network dimension: starting entrepreneurs need to develop a network of suppliers, clients, knowledge providers (like universities), governmental bodies, the Chamber of Commerce, trade organisations, support agencies, etc. Figure 2 illustrates these four categories of different needs. The results of the literature scan and the interviews are grouped around these four dimensions. Before presenting them, we explore the ‘technology-driven entrepreneurship’ phenomenon in some more depth.
• Facilities • Personnel • Arrangements & systems • Knowledge
Skills: Patterns of Organisation
• Contacts with: • clients • suppliers • Experts • Support agents • Etc.
Scope: From idea to Strategy
Entrepreneur and Enterprise
Social network contacts
• Ideas • Assessing opportunity • Strategic processes • Business plan
Scale: Economic and Financial Means
• Own capital • Bridging development period • Financing investments: • Venture Capital • Loans
Figure 2: The needs of a starting entrepreneur.
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Technology-Driven Entrepreneurship Schumpeter’s definition of entrepreneurship (1934) is one of the most well known and most often used definitions within entrepreneurship research. His ‘Theory on Economic Development’ (1934) is regarded as the classic statement on entrepreneurship, not only by economists but also by other social scientists. As mentioned earlier in our contribution, in Schumpeter’s view entrepreneurship is about new combinations of products, processes, organisations and markets. These new combinations imply innovations, which may be incremental or radical. Creating a new combination of existing products, processes, organisations or markets is called an incremental innovation where no major changes or adaptations are necessary to use the new combination in existing contexts. Radical innovations, on the other hand, will lead to, in Schumpeter’s words, ‘structural re-adjustments’: existing socioeconomic structures dissolve and are replaced by new actors with new technologies which create significantly more value than the replaced technologies. Two ideal types of technology-driven enterprises can be distinguished based on this degree of technology-intensity: (1) front-end technology-based firms (or new technologybased firms); and (2) application-oriented technology-based firms. Differences in technologyintensity have several implications for the needs of starting entrepreneurs. Table 1 categorises these differences, grouped in our four dimensions. Finally, of relevance for technology-driven enterprises are the different paths of technology development that can be distinguished. This is illustrated in Figure 3. Starting point (1)
Table 1: Comparing two types of technology-driven start-ups on four dimensions. Front-end technology-based firms (or New technology-based firms)
Application-oriented technology-based firms
Strategy
Inherently uncertain because of the technology dynamics
Relatively certain on basis of thorough market analysis. Uncertainty with regard to market dynamics (e.g. competitors)
Financing
Huge need for financial capital, often for longer periods of time. Risks are high, return may be high
Smaller need for financing, because of shorter time to market and less uncertainty. More often informal investment possible in (pre) seed stage
Networks
In first instance, emphasis on academic networks. Later on, more focus on business and industry
Emphasis on production and market networks (lead users)
Organisation
Shared facilities, lab scale, patents versus confidentiality
Own facilities or network organisations for large-scale production and marketing. Patent owners
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3
A patchwork of regimes
2 Novel “configurations that work”
1
[1] Novelty, shaped by existing regime [2] Evolves, is takenup, may modify regime [3] Landscape is transformed
Figure 3: Paths of technology development (Source: Rip & Groen, 2002). represents a new technology with possibilities to create value. Normally there are, or will soon arise, competing technologies. When inventions are recognised as useful technologies (at point 2), a so-called regime evolves around this new technology, including standardisation. In this developmental phase, different regimes may compete with one another. After a certain period of time, one regime will dominate and will become part of the so-called sociotechnical landscape (Rip & Groen, 2002; Tushman Anderson & O’Reilly, 1997). Having discussed briefly some of the origins of technology-driven start-ups, it is now time to turn to the results of our literature scan and interview sessions. As said, they are grouped around the above four dimensions of entrepreneurial networking. We will first describe the needs and challenges techno starters face when they develop their idea into a business strategy.
Needs of Techno Starters Regarding the Strategy Dimension Results from the Literature Scan Strategy formation, the process of deciding what value for whom will be created in which way, is a difficult but important part in the entrepreneurial process. As we described earlier in this contribution, the entrepreneurial process can be divided into three major stages: (1) opportunity recognition; (2) preparation for opportunity exploitation; and (3) value creation. In each of these stages embryonic entrepreneurs may experience serious difficulties.
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Opportunity Recognition The business idea for a new technology-driven enterprise is often based on the assumed value of technological know-how and expertise of the entrepreneur. However, assessing the value of new technologies (involving radical innovations) appears to be extremely difficult (Rip & Groen, 2002). The process of converting global possibilities of new technologies (like nanotechnology or biomedical technology) into an enterprise with very specific and limited goals, is very risky and highly uncertain. New technologies provide great entrepreneurial opportunities, but the outcomes are equally highly uncertain (Drucker in Timmons, 1999). Therefore, one of the most important problems techno starters encounter is that they are not able to estimate the commercial opportunities of technology (Oakey, 1995). This problem is also partially caused by an asymmetry of information since most techno starters are not willing (and often not able) to share information with others (such as investors), especially when the latter is better equipped to develop the business opportunity. Techno starters are believed to be not very capable of dealing with these kinds of strategic sensitivities. Preparation for Exploitation Having isolated a business idea, this idea must be developed into a full-fledged business opportunity. Several authors (e.g. Barney, 1991; Hamel & Prahalad, 1994; Timmons, 1999) enumerate the following characteristics of a good business opportunity: ● ● ● ● ● ●
The opportunity is able to meet a demand; Customers can be reached within a reasonable period of time; The opportunity generates relatively much added value; Competition is small or manageable; The business opportunity is difficult to copy; Intellectual property can be safely guarded.
In general, it appears to be difficult to assess these characteristics for technological innovations. Moreover, relatively un-experienced techno starters lack the knowledge and experience to assess the potential value of their business idea (Anderson & Tushman, 1997). More generally, the strategic orientation and marketing skills of techno starters are mediocre (e.g. Watson, Hogarth-Scott, & Nicholas, 1998; Rice, 2002). As a consequence, many changes in business ideas and concepts can be witnessed in the conceptualisation stage (Karnebeek, 2001). Exploitation and Value Creation In most instances, a solid business plan forms the basis for exploiting the business opportunity. Necessary resources need to be combined to enable exchange with the market. Exploitation is necessary to generate cash flow. The techno starter, often exclusively oriented towards technological potential of his idea, has limited marketing skills and financial capacities. Technological knowledge is not necessarily needed on the market (Maxwell & Westerfield, 2002). The lack of marketing and financial skills also becomes evident in the
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business plan. This plan is often used to convince providers of capital and not seen as an instrument for reflection and purposive action (Tidd, Bessant, & Pavitt, 2001). Results from the Interviews Surprisingly, the 14 techno starters we interviewed did not explicitly mention strategy formation as a bottleneck: only two of them (14%) explicitly stated that strategy determination and goal orientation had been a problem at the start of their company. Finding and defining your market, and developing a marketing strategy were more often mentioned, but were not seen as problems but as challenges. It is the essence of entrepreneurship to find your way in the market. From these findings we concluded that the degree of articulation of strategic marketing problems is insufficiently developed among techno starters. Summary The needs and problems described above can be summarised as follows. In the first place our analysis shows that it is a long and difficult process to turn a (new) technology into a commercial success. As a result, a techno starter has difficulties in assessing the market potential of this new technology. Secondly, we conclude that techno starters are driven mainly by the technology, and not by the market. So, technology-driven start-ups are challenged by four major needs to develop a general idea into a sound business strategy: 1. Techno starters often experience difficulties in assessing the business opportunities of new technologies; 2. The larger the gap between the new technology (technological innovation) and the customer market, the more difficult it is to determine the success rate of a new enterprise, and consequently the more difficult it is to get a chance to really start an enterprise; 3. Techno starters often lack adequate marketing skills and a sound strategic orientation; 4. Techno starters insufficiently acknowledge and articulate their strategic and market problems.
Needs of Techno Starters Regarding the Financial Dimension Results from the Literature Scan Securing financial capital to start and develop an enterprise appears to be one of the major challenges techno starters encounter. Many studies focus on this issue. As is often the case with ‘regular’ start-ups, techno starters also face financial difficulties in setting up their enterprise (e.g. Van Tilburg & Hogendoorn, 1997; Watson et al., 1998; Bygrave et al., 2001). Laboratory and production facilities are expensive, time to market is long, and it is difficult to assess the future return on investment (ROI). Use of regular investment selection criteria (based on ROI and cost recovery) often lead to negative investment decisions by banks. Besides, a starting entrepreneur lacks a track record, necessary for banks to estimate financial risks.
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Since the return on investment of techno starters is rather long term an investment decision by third parties usually turns out negative. Further, the risk analysis that financial institutions normally apply for ‘normal investments’ do not apply in the case of techno starters, since it is almost impossible to make a cost-effective investment. As a result starting techno entrepreneurs find it hard to find investors; moreover, the world of venture capital and business angels in the Netherlands is not yet developed. This problem is aggravated by underestimation (on the side of starting entrepreneurs) of time and energy needed to obtain financial capital (Timmons, 1999). Technology-driven enterprises are often faced with a financial gap between prototype development and full-scale production and sales. The time needed to develop successful product — market combinations may take years; Kohli, Lehman, and Pae (1999) observed a period of 12–17 years in large companies, which according to Clarysse, Heirman, and Degroof (2001) also applies to starting companies. Cash flow generated in this developmental period will generally not cover costs (Tidd et al., 2001). As a solution to this cashflow problem techno starters switch (partly) to consultancy instead of investing time and money in production (e.g. Clarysse et al., 2001; EU, 2000; Karnebeek, 2001). As indicated above, providing securities to financiers is often problematic. Technologydriven start-ups have little tangible assets. They represent a high intangible value, such as their knowledge (including patents), networks and market strategy. Assessing (quantifying) these intangible assets is extremely difficult. Results from the Interviews The problem most often mentioned by the techno starters we interviewed was to find seed and venture capital. About half of the interviewees (6 of 14, 43%) mentioned they had encountered financial problems during the start-up phase. But not only at the start of a technology-driven enterprise is this a major problem, but in the later stages of development techno starters still need money and have difficulties in securing capital in order to survive. Companies focussing on ‘front end’ R&D-driven technologies particularly face these problems. Sometimes, financial problems are a consequence of technical problems: the time delay in the process from business opportunity to exploitation must be financed in some way or another. The interviews also revealed that techno starters underestimate the time and effort needed to secure capital (including grants and subsidies). They prefer to spend their time and energy on the technological development of their business, not on funding matters. Summary The interviews with techno starters revealed that many of them have difficulties in securing enough capital for their enterprise. The findings of the literature study confirmed these experiences, but it also explained the reasons for this phenomenon. A low return on investment, prolonged time periods to recover costs and investments, and difficulties in assessing intangible assets, are among the explanations provided. We identified the following five major financial needs of technology-driven start-ups.
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1. As a consequence of the (financial) risks associated with technology-driven start-ups (due to lack of track record of entrepreneur, extended time-to-market, unclear ROI, intangible assets such as knowledge and patents), these start-ups have great difficulties in securing financial seed and venture capital. For front-end technology-based firms these problems are even more severe than for application oriented technology-based firms; 2. Techno starters underestimate the time and energy necessary to secure venture capital and/or subsidies; 3. In general, commercial banks do not offer tailor-made products to technology-driven start-ups; 4. The provision of securities is difficult, since the major assets of technology-driven startups are tacit, intangible and embodied in knowledge and technology; 5. Informal investors are more likely to bridge the gap between proof of principle and prototype. However, there are relatively few informal investors or business angels in the Netherlands.
Needs of Techno Starters Regarding the Organisational Dimension Results from the Literature Scan Obviously, the entrepreneur plays a vital role in the entrepreneurial process. Much research into the fundamental characteristics of an entrepreneur has been carried out. Table 2 (Timmons, 1999) summarises some of the characteristics most often studied. Some characteristics of entrepreneurs have negative connotations like a tendency for solo-performance, difficulties in delegating tasks, and avoidance of insecurity (McGrath, MacMillan, & Scheinberg, 1992). Successful high-tech companies are more often founded by teams than by individual entrepreneurs (Feeser & Willard, 1990). Each team member brings his or her expertise, knowledge and working experience into the enterprise. But often, the founder(s) and employees primarily reflect the technical skills necessary and not the necessary entrepreneurial skills with regard to marketing, business administration and organisation, financial management and human resource management. Table 2: Characteristics of entrepreneurs. Entrepreneur characteristics Risk-bearing Source of formal authority Innovation; initiative Need for achievement Drive Communication ability; technical knowledge Networking with resource providers Recognising and seizing opportunities
Author Mill Weber Schumpeter McClelland Pickle Pickle Aldrich and Zimmer Timmons et al.
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Results from the Interviews With regard to the organisational dimension of entrepreneurship, the 14 techno starters interviewed mentioned various problems, ranging from business and financial management (five times mentioned), finding adequate staff (three times mentioned), technical problems (related to the newness of the technology; four times mentioned), and problems with the social security system or tax authorities (three times mentioned). Summary Entrepreneurial knowledge and skills are essential for the successful development of an enterprise. A right balance of entrepreneurial characteristics is necessary, since a lack and or excess of specific characteristics may hinder enterprise development. Summarising, we postulate the two major problems related to the organisation of a new technology-driven enterprise. 1. Techno starters are too much focused on the technological side of their enterprise; 2. Techno starters have limited knowledge and skills related to business administration and management.
Needs of Techno Starters Related to the Network Dimension Results from the Literature Scan In the previous sections, we identified various needs related to the strategy, financial and organisational dimension. Given these needs, the support network of the entrepreneur is an important supplement to his/her own resources (Deakins & Wittham, 2000; Groen & Nooteboom, 1998; Groen, de Weerd-Nederhof, van Drongelen-Kerssens, Badoux, & Olthuis, 2002). One of the most important steps in the enterprise development process is the selection of network actors. The networks of techno starters are mainly technologically oriented. The entrepreneurs have few social (and network) skills and do not see the value of maintaining their network. Deschamps and Nayak (1995) stress that attention has to be paid to all stakeholders within the entrepreneur’s network. Often, network contacts are restricted to the largest customer, the venture capitalist and shareholders, while suppliers and smaller customers are neglected. Results from the Interviews Half of the techno starters interviewed (7 of 14) mention problems with finding and developing relevant network contacts. These problems mainly occur in relation to potential clients and suppliers. How do I find potential clients, how do I approach them, and how do I sell my product or service, are challenging questions for techno starters. The interviews revealed, that for most techno starters, this is a process of trial and error and learning on the job. The interviewees indicated that it took a few years of struggling to find their way in setting up a good and reliable network. Sometimes, they received support from their business mentor or coach in establishing a relevant network.
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Summary Techno starters are primarily interested in the potential of the technology or application they are working with. This technological orientation has several consequences, for their strategic orientation for example (they underestimate the business side of their enterprise, they have difficulties in focusing their business plan), but also for the network contacts they establish and use. In so far as they have networks, these networks appear to be primarily technologically oriented. From the literature and the interviews three areas emerge in which techno starters have needs. 1. The techno starter’s focus on technology is also evident in his/her network. These networks are often limited in size and mainly technology oriented; 2. Time spent on and priority attached to network building and maintenance is insufficient; 3. Techno starters have limited networking and social skills.
Overall Summary In the previous section, we described the results of our literature scan as well of the interviews we held with techno starters. The findings of both methods strongly reinforce each other. Figure 4 summarises the main findings for each of the four dimensions.
• Management skills • Entrepreneurial skills • Organise entrep. team • Practical mngt. support
Skills: Patterns of Organisation
• Contacts with stakeholders • Managing weak ties
Scope: From idea to Strategy
Entrepreneur and Enterprise
Social network contacts
• Recognise opportunities in market • Technology valuation • Market orientation
Scale: Economic and Financial Means
• Finding financiers • Provision of securities • Bridging developm. period
Figure 4: Main conclusions per dimension of the entrepreneurial process.
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As indicated in the introduction, this paper is based on a study for the Dutch Ministry of Economic Affairs to identify areas for policy making. Based on the results of our study we recommend four areas on which policy instruments might focus. Create Technology-Specific Techno Starters Networks (Network Dimension) Technology-specific techno starters networks are centred around specific technologies, and include enterprises as well as knowledge institutes. Also knowledge and skills related to entrepreneurship (business management) and technology dynamics are represented in these techno starters networks. In addition, the networks require housing and technological facilities (offices, laboratories, equipment). The network also includes financing in the form of a (publicly financed) pre-seed fund, meant for technology-driven companies in the pre-seed phase. Finally, the technology-specific techno starters networks should include all public and private organisations, which contribute to and support the development and growth of techno starters. These organisations should facilitate their own (regional, national and international) networks to the techno starters. In this way, technology-specific techno starters networks could form the basis for the survival and development of technology-driven start-ups. Life Cycle Financing Techno Starters (Economic Dimension) Just as in any other company, techno starters experience different stages in the development of their company. For each of these stages distinctive forms of financing can be employed. Given the specific characteristics and needs of techno starters (especially the front-end technology-based firms) we propose to establish a trajectory called ‘Life cycle financing for techno starters’ as follows: a. For the first (start-up) phase of a technology-driven company, during which a proof of concept is developed into a commercial prototype, a so-called ‘Proof of Concept Fund’ should be available. This fund would finance 80% of the development costs; the other 20% to be financed by the entrepreneur or by other private investors; b. A subsequent phase is the phase of commercialisation of the product. A ‘Start fund’ can provide at most 40% of the funding needed; the other 60% must come from private sources; c. When companies expand, and need capital to finance this growth, they should be able to apply for capital from a so-called ‘Growth fund’. Technology-driven companies, with insufficient tangible assets to attract formal and informal investors might especially benefit from this growth fund. Again, they should have to attract a majority of the capital needed from private sources; d. Finally, an ‘Innovation Fund’ should be created for (young and older) companies that want to innovate a new product or production process. Entrepreneurship Education for Engineering and Technological Experts (Organisation Dimension) Prospective techno starters need a sound management preparation for their career, parallel to their professional or engineering education. Special attention needs to be paid to knowledge
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and skills within such fields as business administration, marketing, financial management, juridical and legal matters and human resources management. Entrepreneurship courses should also be offered on demand to graduates, who want to start their own technology-driven enterprise. Techno Starters in Traditional Companies Techno starters can play an important role in introducing innovations within (more traditional) SMEs. This policy instrument could work in two ways. First, the techno starter would get facilities in a traditional company for his or her start-up (for instance office and lab space, secretarial support, coaching). Second, the best-established firm would benefit because the techno starter could play a role in the introduction and acceleration of innovations with the existing company by sharing his/her technological knowledge and expertise.
Conclusions Techno starters are often seen as among the key drivers of the knowledge economy. National and supranational governments recognise this but, in general, measures they take and instruments they develop address only one dimension of the actual needs. The support of techno starters should be more encompassing: all four dimensions of the techno starter process should be covered by (governmental) instruments. On the one hand, techno starters should be able to make their choice from the available instruments they need, while other techno starters should be made aware of how to best manage their enterprise, and its real needs. The instruments we suggest can fulfil both functions.
References Anderson, P. C., & Tushman, M. L. (1997). Managing through cycles of technological change. In: M. L. Tushman & P. C. Anderson (Eds), Managing strategic innovation and change. New York: Oxford University Press. Barney, J. (1991). Firm resources and sustained competitive advantage. Journal of Management, 17, 99–119. Bygrave, W. D. et al. (2001). Global entrepreneurship monitor. 2001 Executive report, Kansas City, MO: Kauffman Center for Entrepreneurial Leadership. Clarysse, B., Heirman, A., & Degroof, J.-J. (2001). Het fenomeen spin-off in België (The phenomenon spin-off in Belgium). IWT-studies, Brussel: IWT-Observatorium. Deakins, D., & Wittham, G. (2000). Business start-up: Theory, practice and policy. In: S. Carter & D. Jones Evans (Eds), Enterprise and small business: Principles, practices and policy. New York: Prentice-Hall. Deschamps, J. P., & Nayak, R. (1995). Product juggernauts: How companies mobilize to generate a stream of market winners. Boston: Harvard Business School Press. EU. (2000). Funding of new technology based firms by commercial banks in Europe, Brussels: EU, EUR 17025.
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Feeser, H. R., & Willard, G. E. (1990). Founding strategy and performance: A comparison of high and low growth tech firms. Strategic Management Journal, 11(2), 87–98. Granovetter, M. (1985). Economic action and social structure: The problem of embeddedness. American Journal of Sociology, 91(3) (November), 481–510. Granovetter, M. S. (1992). Problems of explanations in economic sociology. In: N. Nohria & R. Eccles (Eds), Handbook of economic sociology. (pp. 453–475). Boston: Princeton University Press. Groen, A. J. (1994). Milieu en MKB: Kennis en Kennissen, milieuinnovatie in de grafische industrie: modelmatig verklaard (Environment and SME; Environmental innovation in printing industry quantitatively explained). Groningen Ph.D. Thesis in Economics, Management & Organization, Groningen: Wolters-Noordhof. Groen, A. J., de Weerd-Nederhof, P. C., van Drongelen-Kerssens, I. C., Badoux, R. A. J., & Olthuis, G. P. H. (2002). Creating and justifying research and development value: Scope, scale, skill and social networking of R&D. Creativity and innovation Management, 11(1), 2–15. Groen, A. J., & Nooteboom, B. (1998). Environmental innovation: Knowledge and networks. Enschede: University of Twente, School of Management Studies Research Report 98R-004. Hamel, G., & Prahalad, C. K. (1994). Competing for the future. Boston: Harvard Business School Press. Karnebeek, A. J. (2001). Spin-off and the University of Twente. Sixteen years of experience in incubating start-ups. Enschede: Twente University Press. Kohli, R., Lehman, D. R., & Pae, J. (1999). Extent and impact of incubation time in new product diffusion. Journal of Product Innovation Management, 16, 134–144. McGrath, R. G., MacMillan, I. C., & Scheinberg, S. (1992). Elitists, risk takers, and rugged individualists? An explanatory analysis of cultural differences between entrepreneurs and non-entrepreneurs. Journal of Business Venturing, 7, 115–135. Maxwell, J. R., & Westerfield, D. L. (2002). Technological entrepreneurism characteristics related to the adoption of innovation technology. Advanced Management Journal, 67(1), 9–13, Winter. Oakey, R. (1995). High technology new firms. London: Paul Chapman. Parsons, T. (1951, 1964). The social system. New York: The Free Press. Parsons, T. (1977). Social systems and the evolution of action theory. New York: The Free Press. Rice, M. P. (2002). Co-production of business assistance in business incubators — An exploratory study. Journal of Business Venturing, 17, 163–187. Rip, A., & Groen, A. J. (2002). Many visible hands. In: R. Coombs, K. Green, V. Walsh & A. Richards (Eds), Demands, markets, users and innovation. Cheltenham: Edward Elgar. Shane, S., & Venkataraman, S. (2000). The promise of entrepreneurship as a field of research. The Academy of Management Review, 25(1), 217–226. Shane, S., & Venkataraman, S. (2001). Entrepreneurship as a field of research: A response to Zahra and Dess, Singh, and Erikson. The Academy of Management Review, 26(1), 13–16. Schumpeter, J. (1934). Theory of economic development. Cambridge, MA: Harvard University Press. Singh, R. P. (2001). A comment on developing the field of entrepreneurship through the study opportunity recognition and exploitation. The Academy of Management Review, 26(1), 10–12. Tidd, J., Bessant, J., & Pavitt, K. (2001). Managing innovation — integrating technological, market and organizational change (2nd ed.). New York: Wiley. Timmons, J. A. (1999). New venture creation: Entrepreneurship for the 21st century. Boston: Irwin/McGraw-Hill. Tushman, M. L., Anderson, P. C., & O’Reilly, C. (1997). Technology cycles, innovation streams, and ambidextrous organizations: Organization renewal through innovation streams and strategic change. In: M. L. Tushman & P. C. Anderson (Eds), Managing strategic innovation and change. New York: Oxford University Press.
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Van der Veen, M., & Wakkee, I. (2004). Understanding the entrepreneurial process. In: D. Watkins (Ed.), ARPENT, Annual Review of Progress in Entrepreneurship, 2002–2003 (Vol. 2). Brussels: EFMD. Van Tilburg, J. J., & Hogendoorn, P. (1997). Het succes van innovatief ondernemen. De spin-off van de ondernemende universiteit. The success of innovative entrepreneurship. The spin-off of the entrepreneurial university. Enschede: Twente University Press. Watson, K., Hogarth-Scott, S., & Wilson, N. (1998). Small business start-ups: Success factors and support implications. International Journal of Entrepreneurial Behaviour & Research, 4(3), 217–238.
Chapter 15
The Role of International Co-operation in University Technology Transfer — The Case of The Ural State Technical University Dina Akhmetova and Galina Lekhova
Introduction The modern approach to economic development is often based upon the knowledge-based economy or knowledge society (Jacob, Hellstrom, Adler, & Norrengen, 2000). Producing and utilising knowledge is increasingly becoming a crucial determinant of economic growth and competitiveness (Lundvall, 1995, 1999). Governments worldwide, search for ways to intensify the commercialisation of new technologies (Roberts & Malone, 1996). Increasingly, the effectiveness of technology transfer from academic organisations and university research laboratories has strategic implication for both nations and companies (Hong & Yunzhong, 2001). In the last two decades, there has been increasing interest in the linkage between academia and industry (Spenser, 2001; MacPherson, 1998; Jacob et al., 2000; Westhead, Batstone, & Martin, 2000; Oakey, Hare, & Balazs, 1996; Chiesa & Piccaluga, 2000; Jones-Evans, Steward, Balazs, & Todorov, 1998). Since the 1980s, universities in many western countries and Japan have experienced a transformation from traditional teaching, research and public service, to more active participation in their region’s economic development (Mian, 1997). Over the past 10 years Russia has been involved in dramatic changes of its social, political and economic systems. Before the 1990s, Russia was widely regarded as a science and technology powerhouse, able to hold its own in fields such as theoretical physics and nuclear technology, and a world leader in space technologies (Cervantes & Malkin, 2001). However, transferring technology from research institutions into industry is a major strategic issue for the future economic development of Russia. Published recently, the official report of the Russian Federation (RF) Government “Essentials of RF policy in area of development of science and technology until 2010 and further perspective” has admitted that the realisation of innovation policy, especially regarding innovation
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implementation, depends mainly on universities, and other technology transfer agents, extending the entrepreneurship supportive infrastructure. Russian universities are taking their first steps towards creating mechanisms for technology transfer. Because they lack knowledge and experience, they seek to share expertise with western colleagues whose experience is exceedingly valuable. This paper examines the experience of one of the leading Russian universities, the Ural State Technical University, in the area of building organisational infrastructure for technology commercialisation and transfer, and the role of international co-operation in this process.
Technology Transfer — Theoretical Issues University-industry technology transfer has been a continuing focus for attention of policymakers and academic policy analysts around the world. The importance of the issue rose since the competitive advantage of firms, regions and nations became heavily dependent on the ability to innovate. That is why there is pressure on universities, which highly are regarded to play a key role in new knowledge/technology generation, distribution and exploitation. Prior to discussing the models, mechanisms, stimuli and impediments for university technology transfer, let us identify the subject of this paper, namely “university technology transfer”, and distinguish it from “technology commercialisation”. In the literature, there exist different definitions of technology transfer depending on the context. For the purpose of this paper, we will adopt the definition of “technology transfer” as any process by which basic understanding, information, and innovations move from a university, an institute, or a government laboratory to individuals or firms in the private or quasi-private sector (Parker & Zilberman, 1993). According to this definition, there are different types and degrees of research results dissemination. Some of them are aimed purely at transferring information about the technology to either select audiences or a wider audience, generally without cost to the potential user; these comprise research publications and/or personal contacts (Hong & Yunzhong, 2001). Mejia (1998) adds graduating students as a valuable means of transfer of research results. Other forms are aimed at transferring the technology to selected companies or organisations with the specific goal of commercialisation. Most commonly mentioned forms, are licencing to establish companies or to new specially created firms (i.e. academic spin-offs) (Roberts & Malone, 1996). Other forms of commercial technology transfer are industry-sponsored contract research and technical consulting (Shane, 2002). This paper is concerned with the commercial types of technology transfer. The forms of technology transfer and effectiveness of technology transfer operations are dependant on multiple factors. Siegel et al. (2003b) have suggested internal input factors, environment/institutional factors and organisational factors as important. Although, we agree with these factors, our proposition would be to reorder them and put environment factors first. Organisational and internal factors reflect overall university policy, which is influenced by environment factors and mainly by governmental policy (Goldfarb, & Henrekson, 2003; Pang & Garvin, 2001). The proof of this statement comes from the United States,
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where a boost to commercial technology transfer has been given by the Bayh-Dole Act, allowing universities to patent the results of federally funded research and generate revenues by licencing their inventions (Colyvas et al., 2002). Since the mid-1990s the European governments also have shifted their policies towards facilitation technology diffusion (Corvers, 2000; OECD, 2001). Goldfarb and Henrekson (2003) contrast US and European public policies towards technology transfer and their effect on university commercial activities. They conclude that, in the case of the United States, policy is focused on creating economic (market) stimuli for universities to commercialise their research outcomes, and to experiment with different means for achieving the best results. In contrast, most European Union countries have adopted a model where the government directly creates mechanisms to facilitate commercialisation. The need for direct government intervention is advocated by Corvers (2000). However, Goldfarb and Henrekson (2003) claim that, in case of Sweden, despite significant advances in new knowledge creation in academia, the commercial results of its research output are unlikely to be as successful as in the US, partly due to the disincentive structure of public policy in Sweden. The authors also note that, in “… the American university system, whereby intellectual property is commonly awarded to universities, is more effective in facilitating the commercialisation than the Swedish system in which rights are awarded directly to the inventor” (Goldfarb & Henrekson, 2003, p. 640). The problem of the European policy is that although it creates opportunities for academic researchers to become involved in the commercialisation of their ideas, they often face strong hurdles barring their progress within their university. Hence, to be effective the public policy should balance individual and institutional interests and take into account the overall architecture of academic sector and its relations (past and present) with industry and government. Another environment factor influencing the effectiveness of the technology transfer process is the level of overall economic growth in the national economy, which determines the ability of companies to sponsor R&D at universities, and provide demand for the results of R&D activities (Siegel et al., 2003a). As a result of social and economic reforms in the Russian Federation, industrial enterprises that used to be major users of R&D have drastically decreased their innovation activities, and their “demand pull” for scientific services from the research institutes. On the other hand, due to the lack of experience and knowledge of methods of technology transfer, the existing science system failed to push research ideas into production. Additional factors in the Russian case are (a) increasing competition among universities based on commercial exploitation of research results; (b) social demand requiring the diffusion and exploitation of research results for regional development; (c) “scientification” of production — some research results are easily used by firms (Chiesa & Piccaluga, 2000). Alongside positive stimuli, there may be negative factors such as decrease in public funding for universities, which encourage universities to search for additional private sector research funding. This is true for Italian universities (Chiesa & Piccaluga, 2000), and is especially true for research institutions in Russia, where in some universities state funding covers only a third or half of the annual budget. Thus, the architecture of external environmental factors defines the forms and methods of technology transfer in a university. From an organisational point of view, the most
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common practice designed to promote university technology transfer is creating technology transfer offices (TTOs) to bringing university research into practice. Nowadays, nearly every major university in America and Europe has a TTO, which is created to market and manage intellectual property output. There are three basic models of TTO activities: legal (focuses predominantly oriented on protection of intellectual property rights (IPR) and sealing the licence agreements); administrative (combining licencing and processing sponsored research); and market-driven (developing new companies) (Mejia, 1998). Most recent studies (Colyvas et al., 2002; Colwell, 2002) when analysing the effectiveness of technology transfer operations, suggest that focus only on patenting might be important for enhancing university revenues, but this does not improve the implementation of the research results into practice. Scholars all over the world search for methods to improve the efficiency of technology transfer activities and maximise economic utilisation of science and technology through both IPR protection and “spin-off” firms. One recently developed framework for technology transfer is the Business Development Wheel (BDW) (Linton, Cesar, & Roming, 2001). The framework considers the technology transfer process, not as a linear sequence of steps, but as a wheel with seven spokes and a hub. The hub is an assessment and evaluation stage, which follows after each activity, represented as a spoke, and performed prior to selecting the next activity. In fact, the proposed BDW framework very much reminds of Cooper’s stage-gate model of new product development. We agree with these authors in a sense that the technology transfer process should start with developing an overall strategy of a research organisation. Although the role of an international dimension in facilitating university technology transfer was not broadly covered in the literature, we argue that such programmes of international co-operation may play a critical role, especially in cases where national or regional public policy cannot address the issues of university technology transfer properly. A book edited by Morgan and Nauwelaers (1999) sheds some light on the role of the EU Regional Innovation Strategies (RIS) programme in revitalisation of less-favourable regions. The focus of this initiative is on assisting regional policy-makers to develop regional policies that support growth of R&D sectors in the regions, strengthening co-operation with industry and foster entrepreneurship. Without undermining the importance of the RIS programme, we suggest that the effectiveness of measures for regional innovation policy heavily depends on the willingness and readiness of the region to implement these measures. In some cases, the intervention from international organisations is more effective on lower level of innovation decision-making — on the level of research organisations. We will try to prove this argument.
Situation with Innovations and Technology Transfer in Russia Russia inherited from the Soviet Union a strong tradition in the natural sciences and an impressive educational system devoted mainly to natural and technical science. What it did not inherit was systems, mechanisms and models for turning technical ideas into competitive products (Sirotkin, 1998). Wolff and Gibson (1997) cite a leading Russian economist Aganbegyan “[In Russia] we need to learn to see innovation as not simply the play of mind, or the greatness of human intelligence, but also as the potential source of economic
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prosperity. We need to look at new technology as a product. This is where the challenge of commercialisation arises. It is necessary to master the art, and to optimise the strategy for implementation of a research project”. As a starting point let us give a brief analysis of the current state of the Russian science and technology system. Table 1 indicates decrease of all indicators in the R&D sector. In recent years, the innovation activities of Russian industrial enterprise have been falling drastically. In 1992 16.3% of enterprises were engaged in development and implementation of innovation, but by 1998 the figure was only 6% of enterprises (OECD, 2001). Besides, the structure of innovation-related activities is archaic. Most of these activities were in fact related to the purchase of domestically or Commonwealth of Independent States (CIS)-produced machines and equipment (48.1%), while purchase of new technology accounted for only 2.4%. Actual R&D activities, such as the development of new products, services, production (transfer) technology and new production process accounted for only 18.3% of innovation-related activities of Russian enterprises (OECD, 2001). By the end of the 1990s the Russian science sector faced a number of problems such as of ageing research personnel, the depreciation of fixed assets, and so forth (Varshavskiyi, 2000). However, the main constraint was the decline in governmental funding of the science sector. Between 1992 and 1997 these expenditures decreased six-fold. At the beginning of the 1990s spending on science in Russia was equal to that of European countries. Then investment in R&D declined dramatically from just over 2% of GDP in 1990 to little more than 1% in 1999, compared with an OECD, 2001 average of 2.2% (Cervantes & Malkin, 2001). Another specific feature of Russian R&D system, inherited from the Soviet Union, is a low contribution from industry. Official figures show a relatively high share (about one-third) of R&D being performed by the business sector, but this is largely because Russia’s 2500 Table 1: Some indicators of development of Russian science in 1991–1997. Indicators R&D organisations R&D personnel
1991
1995 1996
1997
Number 4564 4555 4269 3968 4059 4122 % 100 99.8 93.5 86.9 88.9 90.3 Thousand 1677.8 1532.6 1315 1106.3 1061 990.7 persons % 100 91.3 78.4 65.9 63.2 59 Persons 227 213 186 162 160 150
4096 89.7 920.8
R&D personnel per 10,000 working population Average annual Thousand number of employees persons in sphere “Science” % Spending on science % from state budget in relation to total spending
1992
1993
1994
54.9 140
2769
2307
2237
1833
1688 1600 1464.9
100 7.43
83.3 4.47
80.8 4.58
66.2 2.83
61.0 3.25
Source: Russia’s Science in figures: 1997, Russia’s State Statistics Committee.
57.8 3.2
52.9 5.06
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research and technological institutes are included in these numbers. In fact, the weak supply– demand link of these institutes to business is a major flaw in Russia’s nascent innovation system (Cervantes & Malkin, 2001). Russia has a big pool of scientists, the per capita number of patents issued in Russia is nearly three-quarters of that of the USA. However, Russia’s global share of the high-tech market is around 0.3%, while USA’s is 130 times higher (Time International, 2001). Decrease in financing from government, and reduced industrial enterprise, as well as destruction of old bureaucratic administration system, have resulted in the following new trends in the development of Russian science (Kravchenko & Yusupova, 1999): ●
●
●
A greater orientation of research, on a diversity of customers. Among these, the share of the state is reducing while the share of private firms is growing. Foreign companies take the first place among customers of R&D institutes of Russian Academy of Science. They contribute about 40% of funding for R&D institutes (Kravchenko, 1999). For example, the budget of one of the leading R&D institutes in St. Petersburg consists 30% from exporting of its production (research and samples), 20% from governmental financing and 50% from domestic contracts with enterprises (Glukhikh, 1998). The director of the institute made a remarkable comment: “I told my directors: let’s work like there is no government but there is some economic activities. Search for your niche and do not count on governmental support” (Glukhikh, 1998); Development of international relations, a strengthening of the integration of Russian science with global scientific community. The negative consequence of such integration is a “brain drain”, although in some cases, the research topics are dictated by foreign investors. Foreign partners are interested, for example, in applied research in areas of nuclear physics, chemical engineering and instrument equipment; New forms of organisation of research, employment of competitive selection criteria for research projects, more interdisciplinary research.
Despite the widely held view that one of the reasons for poor technology transfer performance in Russia is the lack of entrepreneurial and commercial culture (Graham & Dezhiva, 2001), Russian scientists and engineers have been highly motivated to commercialise technologies and start-up new technology-based businesses — not only to help compensate the cut in traditional state funding of R&D, but also to be active players in the new Russian economy (Wolff, 1997). Thus, failure of the government to provide finance and explicit public science and technology policy has led to a greater degree of autonomy in R&D organisations, and openness to the world in searching for additional funding for R&D activities. The next section analyses an experience of a particular university in attempts to build on efficient mechanism of university technology transfer and the role of international organisations and programmes in assisting this process.
Technology Transfer at the Ural State Technical University The Ural State Technical University (USTU) is one of the oldest and largest higher educational establishments in the Urals region. It was founded in 1920 and in its first year had had
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660 students. Today USTU is a leading educational and Research Centre with 13 faculties employing over 2000 faculty and research staff and accommodating about 20,000 students. The University is famous for its contribution to innovation activities in the past, during the Soviet time, when it served the interests of regional industries. Nowadays, the University is committed to the tradition of innovation and is a pioneer in the area of technology transfer and commercialisation. While public policy only recently has acknowledged a necessity to fully exploit available scientific and technical potential to give a new dimension to economic growth, the USTU has accumulated nearly 10 years of such experience. The USTU was among the very first institutes to recognise the potential of managing innovations within the walls of the university. At present, the University has developed a procedure for Intellectual Property (IP) rights protection, which aims to create a civilised relationship between the university and authors. The University’s IP rights protection and commercialisation policy is based on “win-win” principles as follows: 1. All IP rights belong to the University for university-funded research; 2. IP rights for sponsored research should be defined in a contract for such research; 3. Income from commercialisation is distributed as follows: 20% goes to the University and 80% goes to author(s). The organisation dealing with issues of IP rights protection is the Centre of Intellectual Property (CIP), which is a successor of the Patent Department. The mechanism for technology commercialisation is presented in Exhibit 1. For now Stage 4 is an objective rather than a reality. The above procedure is familiar enough to the majority of readers of this paper. The University tries to adopt the best practice from international activities in the area of technology transfer. Although an understanding of the role of IPR is increasing in Russian society, industrial enterprises still underestimate the value of IPR for the future performance of a company. Thus, the dominating form of technology transfer at the moment at the USTU is sponsored contract R&D and technical consulting. From the beginning of the 1990s the University has actively supported spin-off initiatives by its research and faculty staff. The experience of the first years of reforms has led to an understanding of the necessity to build own infrastructure to support innovations and technology transfer. The senior Stage 1 Invention Disclosure Patentability Assessment Collecting Initial Information
Stage 2 Assessment of Commercial Potential Selection of Patenting Strategy (global or domestic)
Stage 3 Patenting
Stage 4 Commercialisation
Exhibit 1: Mechanism of commercialisation of intellectual property at the USTU.
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pro-vice-chancellor is in charge of innovation activities at the University. The CIP is a subordinate to him, as well as to the Centre of Independent Expertise and Certification. He supervises and monitors the activities of organisations created on the university’s initiative, such as the Centre for Innovative Business (CIB) and the technological park “Uralsky” (which was founded in 1996 but started operating only in the summer of 2002). These organisations have different objectives though together they serve the interests of technology transfer and commercialisation.
Outcomes of International Projects of the Centre for Innovative Business and Technology Transfer Division “Uraltransfer” The CIB is a not-for-profit organisation founded in 1996 within a framework of joint Ministry of Education programme “Support to small entrepreneurship in science and science services in higher school”. The mission of the CIB is “delivering support to technical entrepreneurs by means of education, management consulting, information supply, technology transfer; to ensuring personal development, progressing in creativity, initiative, formulating goals and objectives, and achieving results”. From the beginning, the CIB was aimed at meeting the long-term goals of its international partners such as USAID, The Know-How Foundation and the International Scientific and Technical Centre (ISTC). Table 2 summarises the history and outcomes of different projects fulfilled by CIB with its partnering organisations. A particular project, which has a direct link to the objectives of this paper, was a joint project with the British Council and Know-How Foundation entitled “Strategy for small and medium-sized business development in Sverdlovsk oblast”. One of the components of the project was “Technology transfer and innovation activities”. In the framework of this component in 1999 the CIB created a technology transfer division — “Uraltransfer” to commercialise new technologies. In the initial stages, the assistance from the British side was invaluable. The Know-How Foundation provided the future Technology Transfer Centre (TTC) team with training and consultancy on organisational issues of technology transfer and commercialisation, as well as with financial resources for office equipment and startup capital. The experts of the project contributed extensively in development of the concepts of the TTC. In 1998, the team of CIB visited several innovation sites in Manchester such as Campus Ventures, Manchester Science Park and others. The main goal of the visit was to gain first-hand experience of technology transfer and commercialisation. “Uraltransfer” has, adopted widely accepted mechanisms for technology commercialisation — a sequence of stages, depending on scale, market segment and required investments and sees its tasks as the following: 1. Revealing and assessing the commercial potential of the results of university research; 2. Marketing the IP and sponsored research of R&D institutions and enterprises in the field of new technology/products; 3. Searching for partners and investors; promoting new business contacts between technology developers and end-users;
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Table 2: International projects and their outcomes for the CIB.
Period
Project/ Partner
1995–1996 American Small Business Support Centre
Project Budget ($)
Activities
Outcomes
30,000 Training for CIB employees in USAID programmes; Financial support to set-up an office
It lays the foundation for dynamic development of the CIB according to international requirements to organisations of technical entrepreneurship support 1998–1999 Eurasia 20,000 Training and business 100 h of workshops Foundation consulting for technology- 400 h of consulting based firms on business sessions for companies in planning, marketing, cities and towns of the managing IP, quality region management, finance 1999– ISTC 40,000 Training for leaders of Regional Educational present R&D projects on Centre of ISTC has been technology set-up for organising commercialisation, regular workshops (about project management, 500 R&D team leaders and internet marketing, managers went through the development of training) communication skills Nine business plans have Support in technologies been developed Twelve commercialisation for R&D projects have promising projects (from received informational and idea to joint venture) marketing support 2001–2002 Eurasia 32,000 Developing and probing Thirteen technology-based Foundation an alternative to bank companies have received short loan scheme to bills of exchange (totally finance technology-based for $25,000) firms, namely a bill of exchange scheme
4. Licencing of university inventions; 5. Training and consulting for researchers and developers in areas of management, financial management, marketing, law and IP rights protection; 6. Organising exhibitions, conferences, workshops and presentations; 7. Providing up-to-date information on technological database in the region.
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Nowadays, “Uraltransfer” is a dynamic fast-growing organisation. At an early stage (1999– 2000) the “Uraltransfer” team together with invited experts, carried out a technological audit of the universities and other R&D institutions and SMEs of the Region to (1) reveal promising technology-based products, and (2) assess their commercial potential and possible earnings from commercialisation. In the course of this technological audit, new applications for new technologies and products were revealed. The essential issue for licencing Russian technologies abroad is patenting the inventions in foreign countries. That is why one of the main tasks for the team is establishing and developing partnership with highly professional and reputable technology transfer companies worldwide. At present, there are defined policies to promote Russian technologies in foreign markets in different sectors and industries. The most promising of these are laser and optic technologies, ecological products and communication and information management systems. The number of inquiries both from domestic and foreign organisations is constantly increasing, from 2 in 1999 to 192 in 2002. Since CIB is accredited by ISTC, “Uraltransfer” has access to the database of ISTC project in the so-called “closed” or “nuclear” towns. For the last 3 years the CIB and its division “Uraltransfer” have been progressing significantly in the area of technology transfer and commercialisation. It has laid a foundation for further development in this area. It is worth mentioning that a major driver of this progress was the personal enthusiasm of the management team, a strong understanding from the University authority side that pursuing innovation was essentially the only way for (1) the survival of the University as a leading educational and research centre, and (2) future regional economic growth. Their activities commenced when there was no clear public policy towards knowledge commercialisation. Thus, this chosen way was exceedingly difficult in the early 1990s and only now is it bearing fruit. In April 2003 the US Civilian Research and Development Foundation (CRDF) announced the results of a competition among Russian universities for setting up TTO. According to the project, four TTO will be set up in Russia, and each will receive $150,000. CRDF will contribute two-third of the funding; the rest will come from the Russian Ministry of Education. Among the winners of this grant was the USTU, even though the scheme was open to the so-called classical universities only. However, the USTU already had an advanced infrastructure for promoting its technologies that was favourable for the final decision. No doubt new Russian-American initiative will give a new dimension to the existing Technology Transfer Division “Uraltransfer” and allow it to expand activities wider across the region and internationally. During the first stage of the project it is planned to update and complete the technology audit of research, and assess market need for and worthiness of technologies and look for commercialisation opportunities. Participation in the project is providing support for training of TTO staff, seminars on technology commercialisation and visits by Russian staff TTOs at US universities. The “Uraltransfer” team believes that the long-term outcome of the project should be a commercially sound TTO, the main shareholders of which would be the USTU, Ural State University, CIB, a bank, and an investment fund. Therefore, the new Ural Technology Transfer Office will lead to a quality breakthrough in technology transfer activities on the international level.
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Lessons Learnt from the International Projects All international projects were aimed mainly at institution building and developing mechanisms of technology transfer not existing in the University. As a result, new forms of interaction between science and industry were developed and the infrastructure for promoting innovation diffusion in the University was created. For the first time, the issues of IP rights protection and utilisation including distribution of the earning from their commercialisation were resolved at a professional level. Finally, and probably most important, these activities from the university side stimulated personal initiatives by the technical and research staff and faculties interested in establishing closer links with industry to promote their ideas and inventions. From a practical point of view the results from the international projects are that, in many cases, the enterprises which participated in the projects became clients of CIB and “Uraltransfer”. Thus, the CIB keeps its links with enterprises, and follows up and analyses the results of projects for the further development of these enterprises. In 50% of the cases the results are as predicted while, in the other 50% the real development exceeds estimates. Here we would like to elaborate on the lessons learnt from international projects and barriers to their accomplishment. First, even if a project proposal has been approved, an international funding body will make a few changes according to its priorities. However, these changes often do not take into account the level of market development in Russia and its suitability for such modification. The fulfilment of a “corrected” project requires extensive additional work to identify, for example, a new target audience, which may not exist. A new search may involve substantial cost, which was not anticipated in a project. As the CIB is progressing in its international activities this problem becomes less important. Nonetheless, international organisations operating in Russian markets should know better the territory, to realise regional differences of the country, and trust the initial information from applicants. The next issues are associated with objectives/costs for Russia/time relations. Accomplishment of any project involves meeting deadlines. However, if time is considerably limited, the successful execution of a project becomes almost impossible. Budgets for projects undertaken in Russia are often initially undervalued. The costs of a project are often inadequate to achieve its objectives. Thus, Russian counterpart is implied to accomplish an ambitious task with an unrealistic budget over an unrealistic time span. The lesson learnt from this experience should be to set less ambitious and more practical objectives, achievable over a realistic period of time. Another lesson concerns management discipline. Participation in international programmes must involve good organisation for meeting deadlines, terms and conditions and conforming to a project programme (schedule and activities). At the same time, this organisation should be flexible and adaptable to different requirements of different sponsors. The major lesson to be learnt is that international projects should deliver a deeper understanding of market research, training and consulting services for the technology-based business of the region. However, the results of this research are not reassuring since: 1. Very few professional consultants are able to meet clients’ needs; 2. There is low competition, where supply exceeds needs, small market segments are common, and there is an absence of price calculation methods;
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3. There is limited access to international market of information on organisation training and consulting services (i.e. advanced information).
Conclusion In this paper, we have tried to shed light on the process of university technology transfer in Russia and the role of international co-operation in this process. Our conclusions are that, at present, negative impacts such as cuts in state funding for R&D play a key role in enforcing university technology transfer. Low innovation activity in industrial enterprises shrinks the local market for technological innovations so that technology transfer initiatives often occur in the form of technology-based start-ups. However, this spin-off is a spontaneous, bottom-up process rather than a systematic, deliberately organised and managed function of a TTO. The example of the USTU showed that an individual effort could bring some results. Organisations such as the CIB can provide services and can realise a few technology transfers to the local market. However, the scope of the problem demands a different scale of activities. Despite certain progress there are many problems and impediments to technology transfer. These are: 1. A deficit of financial resources (both in universities and technology-based enterprises) for effective realisation of technology transfer activities at all stages from research to technology implementation; 2. A lack of knowledgeable and experienced professionals to work in the area of technology transfer and commercialisation; 3. A lack of strategic planning of the processes of technology transfer and commercialisation. 4. Weak support (from university side) for initiatives by research and technical staff on technology transfer and commercialisation; 5. Low motivation of research and technical staff towards pursuing technology transfer and commercialisation. It is obvious that universities alone are not able to accomplish these tasks; they are achievable only under conditions of support from government at all levels (local and federal) and higher degree of involvement from international organisations. Finally, in a situation where government has failed so far to create positive incentives for university technology transfer, international organisations and programmes must compensate for government involvement.
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Chapter 16
The Impact of Science Park and Incubator Location on the Finance Opportunities of Technology-Based Small Firms Farid Ullah and Peter Taylor
Introduction The role of technology-based small firms (TBSFs) as engines of economic growth, is well recognised and has attracted much public and private attention internationally, and there is a growing academic- and policy-related literature on the subject. It is believed that the invention and innovation provided by TBSFs, particularly in sectors such as computer software and hardware, biotechnology, pharmaceuticals, life sciences, communications and other aspects of high technology is vital for economic growth and development. The extraordinary success and performance of Silicon Valley and Route 128 technology-based start-up businesses in the US are seen by many as the sine-qua-non of future economic and business developments (Berger & Udell, 1998). Along with the recognition of the economic importance of TBSFs, there has been an increased interest by academics, policy-makers, public and private support agencies in tackling the financial problems of these firms. TBSFs are special Small and Medium-sized Enterprises (SMEs) and therefore have special problems. TBSFs go through certain stages of development in their life cycle as depicted in Kazanjian (1988), Clark and Guy (1998), Oakey (2003) and Ndonzuau, Pirnay, and Surlemont (2002). Depending on the nature of the technology and potential markets for TBSFs, finance could be a serious problem during early stages of development. It is argued that, due to their special characteristics, TBSFs may need special environments to grow and flourish and that these special conditions may be present in science parks and incubators. If this is the case, it is pertinent to ask what role, if any, does or should science parks and incubators play in overcoming the financing problems of TBSFs.
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Literature Review The separate literatures on TBSF finance and the role of location are large and diverse. Public interest in small firms’ finance is a long-standing phenomenon. Bolton (1971) and Wilson (1979) presented their reports on finance for small firms. The Bolton Committee (1971) reported that there was no institutional deficiency in the financial markets for small firms in the UK, and the Wilson Committee (1979) did not find any evidence that suggested a general shortage of finance for small firms in the UK. However, these public enquiries adopted a holistic approach towards the SME sector rather than focusing in detail on particular subgroups of the SME population. The first public report that addressed the problems of the TBSF sector was that of the Advisory Council on Science and Technology (ACOST) in 1990. This report focused on TBSFs finance and the financial constraints that these firms encounter at critical stages of their development. The report concluded that due to the higher risks associated with TBSFs and difficulties in assessing their technology and highinnovative nature, institutional investors (and banks in particular) were hesitant to provide financial assistance. The Bank of England (1996) has reported regularly on the subject of TBSF finance. Its first report on the financing of TBSFs was published in 1996 and this report suggested that TBSFs may face financial constraints at start-up and in their early stages of development, and that these constraints might be due to market imperfections. The report also argued that finance providers find it hard to assess the viability of TBSFs’ business ideas due to their scientific and/or high-technology nature of their products and as a result remain cautious, causing constraints on finance. The Bank of England (2001) concluded that financial constraints at early stages of development were a particular problem for those TBSFs that were distant from technology clusters. The House of Lords Select Committee (1997) also reflected these views and suggested that since at start-up TBSFs do not generally have initially viable products, it is extremely difficult to assess the size of potential markets, and it is difficult to evaluate the technological risks. TBSFs have to expand rapidly to overcome this problem, but are hampered by the non-availability of finance due to asymmetric information at the earlier stages. The situation of TBSFs may further deteriorate because of the reluctance of entrepreneurs to involve external equity finance due to a desire for independence. The Select Committee suggested that TBSFs should be encouraged to use incubators and science parks to improve mutual understanding and help overcome the associated risk and information asymmetries. The Confederation of British Industry report on Tech-Stars (CBI, 1997) also highlighted that TBSFs are financially constrained at early stages of development and suggested that the causes of problems in raising finance at early stages of development were related to perceptions of high risk, difficulties in understanding technology and heavy reliance on new product or process. They noted additionally that the detrimental role played by long gestation periods for products, lack of tangible assets and lack of entrepreneurial skills (rather than technological skills) exacerbates TBSFs funding problems. The Williams Report (1998) noted that it is crucial for TBSFs to obtain finance on the right terms, to develop products effectively, conduct market research and to fund all startup-related activities properly. According to Williams, equity finance is more appropriate than debt, assuming that TBSFs have no earnings at early stages of growth and hence have
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difficulty in repaying and servicing loans. This justifies the important potential role of the venture capital industry for TBSFs. Oakey (2003) also notes that funding is a key issue for TBSFs in the UK. He postulates that the problem is deep-rooted due to free market policies in the 1970s and 1980s. He suggests integrated support of the public and private sectors for TBSFs finance. He argues that, due to short-termism and associated risks, there exist a temporal- and a risk-gap for TBSFs. Oakey’s hypothesised temporal-gap suggests that the available public support diminishes at a point when TBSFs product is not yet ready for the market, and that there is a “fundinggap” in the development of high-technology products. The risk-gap notions suggest that as many as 50% TBSFs are clearly unfundable, 30% probably unfundable, 10% in the critical area of being probably fundable and as little as 10% receiving funding. By accepting as a working hypothesis, this means that even if the probably fundable all receive funding, only 20% of the TBSFs population are funded. One of the reasons most cited in the literature why TBSFs have financing problems is information opacity. Leland and Pyle (1977) suggest that financial markets are informationally opaque since borrowers know more about the potential and nature of their businesses than do lenders. Myers and Majluf (1984) suggest that the management of a firm knows more about the firm’s value than potential investors. Sahlman (1990) suggests that the environment in which venture capitalists invest is characterised by substantial uncertainty about the future returns on individual investments and a high degree of asymmetric information. Trester (1998) examines the problems of contracting by venture capitalists under conditions of asymmetric information and looks, in particular, at the examples of software and biotechnology. The development of these technologies involves tests that are only known to the entrepreneur or his/her staff. Later-stage developments are more transparent to outsiders. Muzyka, Birley, and Leleux (1996) suggest that new ventures are risky for all those involved. The more complex the technology and the larger the amounts of investment, the greater the risk of failure of the business. In such circumstances, entrepreneurs find it difficult to finance their businesses. Various policy interventions have been considered as means of reducing information asymmetries associated with TBSFs. Moore and Garnsey (1993) explore the effects of the SMART (small firms merit award for research and technology) scheme. They argue that small innovative firms are associated with uncertainty, and they explore the role of SMART scheme in offsetting this uncertainty. They conclude that the successful operation of SMART scheme justifies the government intervention since information asymmetries are reduced with the support of SMART. This appears to create added value to winner firms as non-winners under the SMART scheme face greater financial problems during the innovation life cycle. Similarly, Lerner (1999) examines the impact of the Small Business Innovation Research (SBIR) programme in the United States and argues that SBIR winners grew significantly faster than non-winners and were more likely to attract venture finance. Science parks, incubators and links to universities have been widely studied as means of improving the prospects and performance of TBSFs in all areas of their activity, including the raising of finance. Westhead and Batstone (1998) explored the perceived benefits of a science park location in the UK for independent technology-based firms. They suggest that sciencepark tenants value the contributions of their location because of the perceived image and the prestige of being linked to a higher educational institution or other research centre.
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Lindelof and Lofsten (2002) have investigated the potential added value that science parks might create for TBSFs in Sweden. They identify some differences between on-and-off park firms in respect of innovation, market issues, management and finance. However, obtaining finance was one of the key problems experienced by all TBSFs in this study and they found that personal funds were the major source of finance in the small firms in their sample. They examined performance of on-and-off park firms in three operating areas (i.e. employment growth, sales growth and profitability). Science-park firms were found to have a higher rate of job creation than off-park firms, but there was no direct relationship between science-park location and profitability. The science-park location appeared to enhance a firm’s links with universities. Science parks appeared to attract a different and more motivated group of entrepreneurs than off-park locations. For example, science-park firms showed higher levels of postgraduate education among management. Science-park firms also displayed a greater emphasis on R&D activity at early stages in their development. In contrast, Lindelof and Lofsten (2003) found less significant differences between science-park and off-park firms. Siegel, Westhead, and Wright (2003) suggest that the returns to a science-park location are insignificant. Westhead and Storey (1994) also assess the effectiveness of science parks as a location for high-technology or knowledge-based firms. They suggest that a science park provides benefits such as proximity to higher educational institutions (HEIs), high-quality accommodation, links between firms, exploitation of academics’ business ideas, management facilities and inter-trading between firms. Due to these benefits, firms located in science parks might be expected to perform better than firms located off-park. The overall conclusions of this detailed study show that science-park firms are higher-growth firms and that closure rates are lower for off-park firms. Science parks provide clustering for high-technology firms due to their proximity to academic institutions. However, this research was not able to demonstrate clearly that, for a high-technology firm, location in a science park provides clear benefits over alternative locations, leading to better overall business performance. Westhead and Storey (1995) explored the links between HEIs and TBSFs. They suggest that HEIs are an important resource network for TBSFs, and to investigate any added value of science parks, they compare firms on science parks and firms located off-park. By analysing on-park and off-park firms in 1986 and again in 1992, they found that firms in 1986 with a link with an HEI have survived and were still in operation in 1992. They also found that science-park firms increased their links with HEIs over a period, while the off-park firms tended to reduce the number of links with local HEIs. Quintas, Wield and Massey (1992) investigate the potential and actual role of science parks by linking academic research with industrial activity. Their findings suggest the absence of strong linkages in the UK and that the science-park model is problematic. Felsenstein (1994) examines the role of science parks and suggests that choice of location on science parks could be due to perceived status and prestige conferred rather than benefits in terms of technology transfer and information flow from academic institutions. Mian (1996) assesses the value-added contributions of university technology business incubators to their new technology-based tenant firms. He concludes that university resources (e.g. laboratories, equipment, student employees) add major values to the client firms. Colombo and Delmastro (2002) have examined the effectiveness of technology incubators in Italy. Their findings suggest that on-incubator firms have higher growth rates compared to off-incubator firms, and that it is easier for on-incubator firms to access public subsidies.
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Their findings support the concept of science park as an important element in TBSFs’ development. Ferguson and Olofsson (1998) examine the role of science parks in the support of TBSFs. They suggest that firms associate an image effect with their location. Their findings suggest that science parks must first meet the location needs of firms if they are to successfully support TBSFs. This review of the literature indicates that TSBFs are generally considered to face problems in raising finance and that these problems are associated with information asymmetry. These problems may be addressed in various ways, including through seeking locations (science parks, incubators and proximity to HEIs), which may be benevolent and favourable to general business performance and which may assist in reducing information asymmetry through a signalling effect. We shall now consider the results of our investigation on the impact of location on the circumstances of TBSFs in the UK.
Methodology The survey population is comprised of three groups of “on-park TBSFs”, “off-park UK TBSFs” and an additional group of TBSFs drawn from the North West of England. Lists of TBSFs were obtained from the United Kingdom Science Parks Association (UKSPA), the United Kingdom Business Incubation (UKBI) websites and in the North West of England. The North West list comprises firms linked to MerseyBio and the North West Development Agency (NWDA) database. The off-park firms were selected from the Forecasting Analysis and Modeling Environment (FAME) data source and technology cluster websites. We encountered some problems in the data collection process. In most cases, the contact details (e-mails and telephone numbers) of individual firms obtained from the UKSPA/UKBI websites were wrong and e-mails were bouncing. Data collection took place between 20th November 2002 and mid-March 2003. We received 133 (22.45%) usable responses, which were recorded in Statistical Package for Social Sciences (SPSS) for analysis. We also conducted five semi-structured interviews (not reported here) with science park and incubator start-up firms with the main purpose to discuss main issues and get more insight into TBSFs funding problems.
Main Characteristics of the Studied Firms Lofsten and Lindelof (2002) suggest that science parks attract a more motivated group of entrepreneurs than off-park locations. This implies that TBSFs are new firms established by young entrepreneurs with high qualifications. Tables 1 and 2 provide some evidence in favour of this notion from an age viewpoint. Only 13 (10%) sample firms were established before 1990 and the rest, i.e. 120 (90%) were established between 1990 and 2002. The median year of foundation is 1997.06, but if we exclude firms established in 1980s, median year of foundation becomes 1998.342. Table 2 shows that the average age of science park entrepreneurs is 37.30 years compared to nearly 40 years in off-park firms. Off-park entrepreneurs are approximately 3 years older than science park firms’ owners.
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Farid Ullah and Peter Taylor Table 1: Mean age and median year of business foundation.
Mean age (years) Median year Total
Science park
Off-park
All firms
17.47 1997.473 93
16.10 1996.1 40
17.06 1997.06 133
Table 2: Mean age of respondents in sub-group.
Founders’ mean age (years) Total
Science park
Off-park
All firms
37.30 76
39.91 34
37.96 133
For all respondents, the average age is nearly 38 years. It is in accordance with the previous studies, there is evidence to support the view that TBSFs’ founders/owners are younger, enthusiastic people.
Findings on the Finance Issues The literature reviewed in the second section (House of Lords, 1997; Tech Stars, 1997) suggest that due to the associated high risk, moral hazard and adverse selection problems, very high technology small firms might be facing financial constraints at the early stages of development. To test this theory, we asked the question, Did the firm initially experience problems in raising external funds? (Table 3). These responses represent those TBSFs that have actually been refused finance at the start. The refusal rate for science-park firms (42%) is higher than the off-park (31%) and North West (30%) firms. These might be reflecting the very high-technology firms whose products are difficult to understand by financiers (Tech Stars, 1997). One other reason for high percentage of refusal rate for science-park firms might be the academic background of entrepreneurs and lack of entrepreneurial and business skills. Such skills are highly valued by finance providers when making investment decisions. We also asked the survey firm executives to report the current stage of development of their enterprises. We found that an overwhelming majority (science-park firms 72.1%, off-park firms 73.1% and North West firms 65%) of survey firms were at the expansion stage. Only one science-park firm and one off-park firm reported that they are subject to an MBO (management buyout) or an MBI (management buyin). Only 17.4% of science-park, 8% off-park and 30% of North West firms were in the start-up stages. The rest of the firms were either in seed stage or in market research stages as shown in Figure 1. The Select Committee (1997), Tech Stars (1997), Bank of England (2001) and Trester (1998) suggest that TBSFs are financially constrained at the early stages of development.
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Table 3: Initial problems in fund-raising. Response
Science-park firms
Off-park firms
No. of firms
%
No. of firms
%
36 49 1 86
41.8 57.0 1.2 100.0
8 15 4 27
30.8 57.7 11.5 100.0
Yes No No answer Total
North West firms No. of firms
%
6 14
30.0 70.0
20
100.0
MBI/MBO Expansion stage Market research Start-up Seed stage Science-park 0
10
20
30
40 50 Percent respondents
Off-park
North West 60
70
80
Figure 1: Surveyed firm’s stages of development. To test this, we requested surveyed firms to report funding problems at seed, start-up, market research and expansion stages of development. Table 4 shows the respondents experiences from this study. On the whole, 28% science-park, 23% off-park and 10% North West firms reported funding difficulties at seed stage on a scale of Likert 1–5 (1 no problems and 5 severe problems) as shown in Table 4. At start-up, 48% science-park, 42.3% off-park and 30% North West firms reported funding problems. Similarly, at market research stage 21% science-park, 35% off-park and 10% North West firms reported finance problems. At the expansion stage, 44.1% science-park, 41.3% off-park and 30% North West firms encountered funding difficulties. The percentages at start-up and expansion stages indicate that all TBSFs in this study experienced severe funding problems as compared to other stages regardless of their locations. This means that majority of surveyed firms are looking for finance at this stage (after exhausting the personal capital if any) and literature suggests that venture finance is mostly concentrated in the later stages of development. On the whole, science-park firms in this study have comparatively more acute funding problems at seed, start-up and expansion stages, while at market research stage off-park firms have more financial difficulties than the other two groups. Williams’s (1998) report suggests that equity is more appropriate than debt for TBSFs
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Table 4: Funding difficulties at different stages of development (percent respondents). Scale
Seed-stage
Start-up
Market research
Expansion
On- Off- North On- Off- North On- Off- North On- Off- North park park West park park West park park West park park West 1 17.4 11.5 15.0 13.9 7.7 5.0 11.6 7.7 5.0 9.3 15.4 15.0 2 5.8 13.9 15.4 5.0 3.5 7.7 5.0 4.6 7.7 3 4.6 7.7 10.0 12.8 7.7 15.0 9.3 7.7 5.0 12.8 15.4 10.0 4 3.5 7.7 5.8 11.5 3.5 3.8 8.1 3.8 15.0 5 14.0 7.7 15.1 7.7 10.0 4.6 15.4 18.6 15.4 5.0 No 54.6 65.4 75.0 38.4 50.0 65.0 67.4 57.7 85.0 46.5 42.3 55.0 answer Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0
Table 5: The degree of difficulty in securing outside equity. Scale
1 2 3 4 5 No answer Total
Science-park firms
Off-park firms
North West firms
No. of firms
%
No. of firms
%
7 7 12 13 17 30 86
8.1 8.1 13.9 15.1 19.8 34.8 100.0
4 3 2 3 7 8 27
15.4 11.5 7.7 11.5 26.9 30.75 100.0
No. of firms 1 2 6 2 3 6 20
% 5.0 10.0 30.0 10.0 15.0 30.0 100.0
as they do not have earnings at the start to repay loans. The intensity of funding amounts needed may be positively related to high technology. Very high-tech firms may need large amounts at seed and start-up stages when personal savings and own funds prove inadequate. Based on the literature review, we constructed a statement and asked the surveyed firms to report their agreement/disagreement that: Securing outside equity investment in new hightechnology ventures is generally considered to be difficult. On a five-point scale (1 no difficulty to 5 severe difficulty), we received the following responses as shown in Table 5. An overwhelming majority 57% of science-park firms, 58% off-park and 65% of North West firms agreed with our statement with majority reporting the degree of difficulty to be within the range of 3–5 on the scale as shown in the table above. This may reflect the fact that the surveyed firms have either experienced or are experiencing current difficulties in obtaining the outside equity for their businesses. In order to know more about future finance problems, we asked a separate question in a different way and obtained the corresponding responses as shown in Figure 2.
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Don't know Revenue/growth Become worse in the future Depends on the state of the economy Recede as the firm grows Can't predict the behavior of financiers Always be a problem Science-park 0
5
10
15
Off-park
20 25 30 Percent respondents
North West 35
40
45
Figure 2: Past or current finance problems. Table 6: Technology as a finance constraint. Response
Yes No No answer Total
Science-park firms
Off-park firms
North West firms
No. of firms
%
No. of firms
%
No. of firms
%
25 48 13 86
29.1 55.8 15.1 100.0
4 17 6 27
15.4 65.4 19.2 100.0
4 15 1 20
20.0 75.0 5.0 100.0
This is a multiple response question and a firm may have ticked more than one statement. Most frequencies are around the four statements i.e. it will always be a problem, can’t predict the behaviour of financiers, recede as the firm grows and depends on the state of the economy. This also suggests that finance may be a problem for high-technology businesses at the early stages of their life cycle, but as they successfully develop through various stages, finance problems become less acute. A favourable macro-economic environment is also important for TBSFs’ positive growth. Upturns in the national and global economies are more desirable than the downturns in times of recession. We further asked the surveyed firms, which, so far, had not sought external finance (and did not intend to do so in the future), what could be the main reason (or reasons) for this? In response, 27% science-park, 19% off-park and 30% North West firms indicated that they wished to keep control of their businesses. These TBSFs may be relatively less technologybased than the others. Some reported other reasons for not accessing external finance. This suggests that financial constraints may be more of a demand side rather than the supply side. Founders/owners are hesitant to give up equity to external investors. But looking at the TBSFs’ finance problems, we asked a specific question on the nature of the firms’ technology: Was a lack of understanding by financiers of the unique nature of your business technology a hurdle in obtaining finance? The frequencies and percent responses in Table 6 indicate that technology is not a significant financial constraint in this study.
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The Impact of Location Mian (1996) suggests a major role of location in TBSFs development. On the basis of such studies, we asked: It is argued that location plays a key role in the development of a new business and may positively influence the investors decision to invest in small high-tech businesses. We asked the respondents to rate their answers on a five-point scale (1 strongly disagree and 5 strongly agree). The responses are shown in Table 7. Quite interestingly, 89% science- and 60% off-park firms reported agreement with our statement. Only 3.2% science-park firms reported strong disagreement with our statement and nearly 8% science- and 30% off-park firms did not answer this question. We also investigated the impact of location on the overall performance of the business? The responses are as follows. It is clear from Table 8 that firms believe strongly in the contributions of location and attach high added value to it (Mian, 1996; Westhead & Batstone, 1998; Ferguson & Olofsson, 1998). In order to be more precise, we requested the surveyed firms to report specifically on which aspects of the business activity the location has a positive impact. Therefore, we asked the respondents to report the impacts of location on the following aspects of their business activities: We received mixed results on the impacts of location for accessing finance (sciencepark 15.1%, off-park 42.3% and North West 25%). It is less significant in the case of science-park firms that finance providers invest in the high-technology businesses located in prestigious locations. The contributions of location are concentrated around the building security, networking, necessary facilities, interaction with tenants and R&D, etc. These results suggest that science parks and business incubators provide a clustering environment for TBSFs where they can share common values and resources. This was one of the key factors in the success of the Silicon Valley technology-based start-up firms in the USA. The Silicon Valley and Route 128 businesses benefited extensively from the Massachusetts Institute of Technology (M.I.T) and the Stanford University research and other facilities (Saxenian, 1994).
Table 7: Location as an influential factor in attracting finance. Scale
1 2 3 4 5 No answer Total
Science park
Off-park
N
%
N
%
3 7 21 32 22 7 93
3.2 7.5 22.6 34.4 23.7 7.6 100.0
4 6 13 5 12 40
10.0 15.0 32.5 12.5 30.0 100.0
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Table 8: Impact of location on the overall performance of business. Science park
Yes No No answer Total
Off-park
N
%
N
%
66 16 11 93
71.0 17.2 11.7 100.0
24 6 10 40
60.0 15.0 25.0 100.0
Conclusion This study provides some evidence on the TBSFs’ finance and the impact of location in the United Kingdom. The survey included 825 firms in total. The overall response rate was 133 (22.45%). The main conclusions of this paper are as follows: An overwhelming majority (90%) firms were established in the period 1990–2002 while only 10% were established in the period 1981–1989. The survey respondents were also young with a mean age of 38 years and in most cases founders/co-founders were still present. Science-park firms are more likely to have been refused by financiers compared to offpark and the North West firms. Nevertheless, North West firms report that securing external sources of funds is a major difficulty for them compared to other two groups. With regard to the stages of development, the majority of the studied firms reported to be at the expansion stage of development. However, science-park firms in this study have experienced comparatively more acute funding problems at seed, start-up and expansion stages, while at market research stage off-park firms report more funding difficulties than the other two groups. This may suggest that off-park firms are growing faster than the other two groups and may require more funds for development. Financial constraints vary with stages of development and firms believe that it recedes as firms grow successfully through the stages of development. Some firms do not want to seek external finance, mainly in order to keep the business simple and have control. The studied firms believe that business technology is not a constraint on seeking funds, which may have important implications for investors and policy-makers. With regard to the location, 89% science-park firms believe that it influences or attracts investors compared to 60% off-park firms. This suggests that science park firms strongly believe in the added value of location and that it plays a key role in their business development. However, this high perception of added value is far from reality, and investors seem more interested in firms located off-park where growth rates and business prospects may be higher. More interestingly, Figure 3 shows that off-park firms strongly believe that their location has a positive impact on various business aspects such as interaction, networking, access to facilities, R&D and access to finance. This research finding is remarkable in the sense that, although science park businesses are predominantly located in close proximity to academia,
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Access to academic advice Interaction with other tenants Access to necessary facilities Networking New information High level of building security Access to finance Science-park 0
10
20
Off-park
30 40 Percent respondents
North West 50
60
Figure 3: Impact of location on various aspects of business.
they still seem far from benefiting from the expertise and facilities available to them, which may be reflected in more acute funding problems and hence lower business performances.
Acknowledgements We are grateful for the comments of Dr Saeed Akbar on the first draft.
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