New Technology-Based Firms in the New Millennium 9781785600326, 9781785600333

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NEW TECHNOLOGY-BASED FIRMS IN THE NEW MILLENNIUM VOLUME XI

New Technology-Based Firms in the New Millennium Volume X (2013) Edited by Ray Oakey, Aard Groen, Gary Cook and Peter van der Sijde New Technology-Based Firms in the New Millennium Volume IX (2012) Edited by Aard Groen, Ray Oakey, Peter van der Sijde and Gary Cook New Technology-Based Firms in the New Millennium Volume VIII (2010) Edited by Ray Oakey, Aard Groen, Gary Cook and Peter van der Sijde New Technology-Based Firms in the New Millennium Volume VII: The Production and Distribution of Knowledge (2009) Edited by Ray Oakey, Aard Groen, Gary Cook and Peter van der Sijde New Technology-Based Firms in the New Millennium Volume VI (2008) Edited by Aard Groen, Ray Oakey, Peter van der Sijde and Gary Cook New Technology-Based Firms in the New Millennium Volume V (2006) Edited by Aard Groen, Ray Oakey, Peter van der Sijde and Saleema Kauser New Technology-Based Firms in the New Millennium Volume IV (2005) Edited by Wim During, Ray Oakey and Saleema Kauser 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 IN THE NEW MILLENNIUM VOLUME XI

EDITED BY

AARD GROEN University of Twente, Enschede, The Netherlands

GARY COOK University of Liverpool Management School, Liverpool, UK

PETER VAN DER SIJDE VU University Amsterdam, Amsterdam, The Netherlands

United Kingdom
North America
Japan India
Malaysia
China

Emerald Group Publishing Limited Howard House, Wagon Lane, Bingley BD16 1WA, UK First edition 2015 Copyright r 2015 Emerald Group Publishing Limited Reprints and permissions service Contact: [email protected] No part of this book may be reproduced, stored in a retrieval system, transmitted in any form or by any means electronic, mechanical, photocopying, recording or otherwise without either the prior written permission of the publisher or a licence permitting restricted copying issued in the UK by The Copyright Licensing Agency and in the USA by The Copyright Clearance Center. Any opinions expressed in the chapters are those of the authors. Whilst Emerald makes every effort to ensure the quality and accuracy of its content, Emerald makes no representation implied or otherwise, as to the chapters’ suitability and application and disclaims any warranties, express or implied, to their use. British Library Cataloguing in Publication Data A catalogue record for this book is available from the British Library ISBN: 978-1-78560-033-3 ISSN: 1876-0228 (Series)

ISOQAR certified Management System, awarded to Emerald for adherence to Environmental standard ISO 14001:2004. Certificate Number 1985 ISO 14001

Contents

vii

List of Contributors

1.

Introduction Gary Cook

1

PART I INTERNATIONALISATION 2.

Managerial Mindset and the Born Global Firm Paula Danskin Englis and Ingrid Wakkee

11

3.

Acquisition of Knowledge in Networking for Internationalisation Valerie A. Bell and Sarah Y. Cooper

29

PART II START-UP AND COMMERCIALISATION 4.

Barriers to Biomedical Engineering Commercialisation Charlotte Norrman, Christina O¨berg and Peter Hult

5.

Bringing Technology Projects to Market: Balancing of Efficiency and Collaboration Mozhdeh Taheri and Marina van Geenhuizen

6.

High-Tech Entrepreneurial ‘Soft Starters’ in a University-Based Business Incubator: Space for Entrepreneurial Capital Formation and Emerging Business Models Fumi Kitagawa and Susan Robertson

57

73

97

vi

Contents PART III CLUSTERS AND ENTREPRENEURSHIP

7.

8.

The Dynamics of Industrial Clustering in the German Enterprise Software Sector Joachim Viehoever

117

Cluster Initiatives within the European Context: Stimulating Policies for Regional Development Dreams Inessa Laur

147

PART IV HIGHER EDUCATION AND ENTREPRENEURSHIP 9.

10.

Assessing the Effect of Different Dimensions of Top Management Team Diversity on the Growth of University-Based Spin-Off Firms Francesca Visintin and Daniel Pittino

173

University
Business Co-operation in Indonesian Higher Education for Innovation Firmansyah David and Peter van der Sijde

187

PART V STRATEGY AND GROWTH 11.

12.

13.

14.

The Impact of the Financial Crisis on the Financing and Growth of Technology-Based Small Firms: Some Survey Evidence from the United Kingdom Robert Baldock, David North and Farid Ullah Network Openness and Learning Ambidexterity of New Technology-Based Firms at Incubators Danny Soetanto From Communicative Practices to Communication Strategies: A Model of Entrepreneurs’ Communication Strategies in the Start-Up Process Pia Ulvenblad Social Media Espionage — A Strategic Grid Joni Salminen and William Y. Degbey

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227

247

261

List of Contributors

Robert Baldock

Centre for Enterprise and Economic Development Research, Middlesex University Business School, Middlesex University, London, UK

Valerie A. Bell

University of Edinburgh Business School, University of Edinburgh, Edinburgh, UK

Gary Cook

University of Liverpool Management School, Liverpool, UK

Sarah Y. Cooper

University of Edinburgh Business School, University of Edinburgh, Edinburgh, UK

Firmansyah David

Department of Organization Sciences, Faculty of Social Science, VU University, Amsterdam, The Netherlands

William Y. Degbey

Turku School of Economics, University of Turku, Turku, Finland

Paula Danskin Englis

Campbell School of Business, Berry College, Floyd County, GA, USA

Peter Hult

Department of Biomedical Engineering, Linko¨ping University, Linko¨ping, Sweden

Fumi Kitagawa

Manchester Business School, University of Manchester, Manchester, UK

Inessa Laur

Department of Management and Engineering, Project, Innovation and Entrepreneurship, HELIX VINN Excellence Centre, Linko¨ping University, Linko¨ping, Sweden

Charlotte Norrman

Department of Management and Engineering, Linko¨ping University, Linko¨ping, Sweden

viii

List of Contributors

David North

Centre for Enterprise and Economic Development Research, Middlesex University Business School, Middlesex University, London, UK

Christina O¨berg

School of Business, O¨rebro University, O¨rebro, Sweden

Daniel Pittino

Department of Economics and Statistics, University of Udine, Italy

Susan Robertson

Graduate School of Education, University of Bristol, Bristol, UK

Joni Salminen

Turku School of Economics, University of Turku, Turku, Finland

Danny Soetanto

Lancaster University Management School, Lancaster University, Lancaster, UK

Mozhdeh Taheri

Faculty of Technology Policy and Management, Delft University of Technology, Delft, The Netherlands

Farid Ullah

Aberdeen Business School, Robert Gordon University, Aberdeen, UK

Pia Ulvenblad

Centre for Entrepreneurship, Innovation and Learning, School of Business and Engineering, Halmstad University, Halmstad, Sweden

Peter van der Sijde

Department of Organization Sciences, Faculty of Social Science, VU University, Amsterdam, The Netherlands

Marina van Geenhuizen

Faculty of Technology Policy and Management, Delft University of Technology, Delft, The Netherlands

Joachim Viehoever

Manchester Business School, University of Manchester, Manchester, UK

Francesca Visintin

Department of Economics and Statistics, University of Udine, Italy

Ingrid Wakkee

Department of Organization Science, Faculty of Social Science, VU University, Amsterdam, The Netherlands

Chapter 1

Introduction Gary Cook At the time of writing there is still much hand-wringing over the sclerotic state of the EU economy. Another year of stagnation is forecast, on top of several which have ensued from the onset of the financial crisis on 2008. Policy debate is dominated by two big issues. The first is how best to manage the combination of very low growth with burgeoning public sector debt in many EU member countries. The second concerns the future of the single currency, with the Euro at a nine-year low and renewed speculation about the sustainability of membership by Greece in particular. It is understandable given the major impact the Financial and Euro crises have had on economic performance and, indeed, confidence in Europe. Living standards have stagnated in many countries and there seems to be little relief in prospect. The urgency for an effective policy response is keenly felt. Sight, however, should not be lost of the fact that, in the long run, rising living standards depend on continual innovation. Whilst sound macroeconomic policy can contribute to innovation and entrepreneurship by providing a stable economic environment, it cannot, in itself, do much to promote and foster high technology entrepreneurship. Indeed, EU and OECD policy continues to assert the prime importance of innovation for long-run prosperity. The weakness of the policy framework in the EU and in many of its constituent countries has been an abiding theme of the High Technology Small Firms (HTSF) Conference since its inception in 1993. That weakness persists and it is as important as ever that it be addressed if economic dynamism is to be achieved in the EU. More broadly, such entrepreneurship can contribute in every country to rising prosperity and living standards. The fact that rapid growth in economic development in many emerging economies has lifted hundreds of millions of people out of the most abject poverty is testament to the importance of promoting such dynamism. In the debate about the financial crisis and how best to respond to it, there has been renewed interest in Austrian business cycle theory. At its most naı¨ ve, the argument of the proponents of Austrian business cycle theory has it that the excesses of poor investment in a credit boom will be swept away by a recessionary period. The dead wood will be cleared, leaving fertile ground for more efficient and innovative

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Gary Cook

businesses to spring up. The name of Schumpeter is often invoked and the idea that recessionary periods can be conducive to periods of ‘creative destruction’ advanced. Evidence from the deep recessions of the early 1980s and 1990s in the United Kingdom do not lend much credence to this view. Recessions have proved to be much less discriminating in which businesses they kill off. It is not exclusively the laggards, the non-innovators and the inefficient that perish. There has been particular concern in the financial crisis regarding the deterioration in credit conditions facing small and medium-sized enterprises. The availability of credit to such firms has diminished and, even where it is available, the terms on which it is offered have worsened. This compounds the long-standing problem which high-technology ventures face in raising finance to support the commercialisation of innovations. The recession has made matters worse, rather than providing support via some mysterious economic process. What the evidence does show, however, is that firms which maintain their innovation effort in recession do tend to emerge relatively strongly from recession. There are clearly several ways in which this stylised fact might be interpreted. Spending on innovation may be a wise investment, a good example of effective counter-cyclical investment. It is not obvious that this will be true for all firms regardless of their circumstances. Another reading is that some firms have superior capabilities and innovative routines and these firms are characterised by both consistent R&D activity and by their economic success and resilience. Two different policy recommendations would follow. In the first case a ‘one size fits all’ recommendation to invest in innovation in recession. In the second a more detailed understanding would be required of how firms acquire such innovative capabilities and how they profit from them via commercialisation. Much of the answer may lie in factors internal to the firm, part would relate to the institutional and economic environment in which the firm is embedded. Both are amenable to policy action, but it is less obvious that there will be a simple ‘one size fits all’ solution. Despite the avowed importance of technology policy in policy making circles and at transnational level, such as the EU and the OECD, it is open to the criticism that it remains too simplistic, too narrow and insufficiently based on detailed evidence of innovation processes at the firm level. One area where policy has been very active is in placing greater responsibility on universities to diffuse knowledge to business, both by cooperative research and through the spin-off of enterprises from university research programmes. This has been advanced with a good deal of exhortation and use of financial sticks and carrots. Our understanding of what promotes effective outcomes in such endeavours is increasing, but there is much still to learn. Other mainstays of policy to encourage technological entrepreneurship include science parks, technology incubators and cluster development policies. Whilst such policies are widely adopted, again our knowledge of how, exactly, they might support development at the firm level is incomplete. This is important given the widespread policy goal of promoting the formation and growth of high technology start-ups as a key arm of technology policy. Governments and their agencies typically emphasise administratively convenient policy tools such as tax breaks, soft loans and subsidies, despite the considerable evidence that these tools provide very limited ‘additionality’ relative to the expenditure. Firms are either paid to do what they would have done

Introduction 3 anyway, or firms are rewarded for being good at playing the system of applying for support. Another general weakness of such support programmes is that they tend, in practice, to be very narrowly focussed on a small set of favoured industries, such as life sciences and renewable energy. Inevitably, they also err in the direction of governments trying to ‘pick winners’, an endeavour in which they have displayed little evident talent. Given that innovation is a widely dispersed process, this narrow focus on a few chosen firms does not seem optimal. Technology-based firms are not just creators but also users of new technology. Policy debate has embraced the idea that diffusion of technology is at least as important as the creation of new technology. Despite this, much policy effort is still directed towards start-up and the creation of new technology. Again, whilst policy debate has espoused the idea that institutional arrangements matter greatly in the creation and diffusion of technologies, in practical terms policy measures such as loans, grants, tax breaks and patents are predicated on the need to correct market failures. It is not that this perspective is wrong, rather it is only one aspect of what may impede or promote innovation in practice. Cluster development toolkits have arisen, which purport to provide guidance on building local ‘innovative capacity’ by strengthening the institutional environment (typically for business networking), yet these remain very much in a ‘one size fits all’ mode. Better understanding is needed of how to identify what is most needed in particular local and regional environments and how best to go about such capacity building in practice. The HTSF conference has been dedicated to improving our understanding of the processes which support innovation among high-technology small firms, both new and existing. The chapters in this volume shed further light on the nature of such firms, the nature of the innovation processes in which they are involved and critically assess how policy may best be designed to support such innovation. The need for better policy in this area is as great as ever.

The Chapters Englis and Wakkee consider the question of how the mind-set of the entrepreneur shape decisions about internationalization. They argue that the attitude of entrepreneurs towards international expansion is influenced by the amount of experience the entrepreneur has. The more internationally minded entrepreneurs are, the better able they are to recognise business opportunities overseas. Moreover, greater experience helps in the formulation and execution of successful internationalisation strategies. They explore what is necessary to successfully exploit overseas opportunities. They provide case study evidence of both successful and frustrated overseas expansion to bring empirical evidence to bear on their theoretical arguments. No matter how much an entrepreneur may wish to internationalise, such strategies will be hard to bring to fruition without the right resources and capabilities. Bell and Cooper provide an in-depth analysis of the use of networks to assist internationalisation by firms in the Canadian Nutritional Health Products industry.

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Gary Cook

The analysis is based on nine case studies. Three types of firm are investigated: two regulatory service consultancies; two integrated ingredient supply and contract manufacturing companies; and, five manufacturing firms selling under their own brand. Networks were established with a wide range of actors, including other firms, export intermediaries, government bodies, trade associations and consultants. They find that firms use networks for three principal reasons. First, to find out relevant information about overseas markets. Second, to acquire tacit knowledge about how to compete in particular overseas markets. This is linked to the acquisition of resources and capabilities which are needed to succeed in overseas markets. Third, to gain practical assistance, such as introductions to potential customers. The evidence from the case studies provides support for several schools of thought in the literature, particularly the network perspective, the Uppsala stages model and the Resource-Based View. In an extension to the existing literature Bell and Cooper provide evidence on the importance of weak ties to help develop the competitive advantages required and to overcome the liability of outsidership. The chapter by Norrman, O¨berg and Hult considers the barriers which limit the commercialisation of biomedical engineering innovations from the academic sector. They identify three types of barrier. The first relate to the biomedical engineering field itself. The sector is dominated by a few large corporations who are generally reluctant to introduce new innovations. The many micro enterprises in the sector mainly seek to develop and sell intellectual property, rather than exploiting the fruits of their research themselves. The heavy regulation of the sector also inhibits commercialisation. This relates to the second barrier, which is dealing with public procurement rules. In addition, customers are typically more interested in holistic solutions, rather than innovations which relate to only part of a particular health technology. The third barrier is that academic researchers do not, in the main, want to be entrepreneurs and the incentives within universities do not encourage them in that direction either. They go on to demonstrate that particular types of commercialisation path suffer in differentiated ways from each of these barriers. The lack of entrepreneurial ambition bears most heavily on spinning out a start-up venture. Problems protecting intellectual property render licencing unattractive. A shortage of suitable buyers makes it difficult to sell ideas outright. Taheri and van Geenhuizen examine factors which influence university-based commercialisation projects. This is a neglected topic and one which is increasingly important as universities come under increased pressure to diffuse their innovations into the wider economy. The study is based on 42 university-driven technology projects and Data Envelopment Analysis and Rough Set Analysis are used to identify projects on the efficiency frontier and which factors appear to contribute most to efficiency. Long-term collaboration between university scientists and industrial partners emerges as a very influential factor. Efficiency in the use of resources and the commercial sense of the scientists also matters. Projects between partners who had collaborated between 5 and 10 years were most likely to come to market quickly. Kitagawa and Robertson consider the processes through which university incubators can aid the growth and development of high-technology start-ups. As they note, the current evidence on whether incubators play a significant role is somewhat

Introduction 5 contested. The chapter focuses on two questions. First, how does the incubator aid in the process of network formation and access to resources. Second, how do startups develop different business models. Evidence is based on an online survey of firms located at a particular university incubator, semi-structured interviews and some more detailed case studies of particular firms. Both university spin-outs and spin-ins are included in the study. The chapter finds that the incubator does play an important role in developing both business networks and interaction with scientists at the university. These things, the authors conclude, do aid more rapid incubation and growth. The incubator also provides a fertile environment within which firms can learn how to develop their business models and hence find ways to effectively access and combine resources. The association with the university itself emerges as a valuable resource, giving credibility to the start-up. Somewhat contrary to the conventional wisdom, the authors found that start-ups relied mainly on internal funds and bank loans rather that venture capital or business angel investment. Viehoever examines the relationship between SMEs and large hub firms in the German enterprise software industry, which is dominated by SAP, which holds 40% of the market. He examines rich data based on over 200 structured interviews. He compares the attitudes and practices of firms located in the SAP dominated cluster with two groups of firms: those in other agglomeration; firms which are not located in an agglomeration. Many significant differences emerge. Competition emerges as being more intense in SAP-dominated environments. This makes sense as they contain particularly high concentrations of firms. Fitting with the idea that competition and cooperation are held in dynamic tension in clusters, firms in agglomerated environments emerge as being more disposed to collaborate than those which are not. Firms in the SAP agglomeration are also more involved in networks. Viehoever demonstrates that networking with former colleagues is particularly important, particularly in efforts to win customers. Laur investigate what factors will support successful cluster initiatives, with a focus on the promotion of regional development. The chapter draws on a very extensive literature review and in-depth interviews carried out with 140 cluster initiatives in Europe. On the basis of her analysis she puts forward four key recommendations. First, that public funding should be balanced between supporting established cluster initiatives and investing in new ones. Second, that initiatives should draw on the idea of the Triple Helix and draw in industry, government and universities. Third, cluster initiatives should encourage a variety of activities, including intermediary services, which are sometimes neglected. Fourth, regional authorities should promote key success factors, based on a holistic view of what it takes to make a cluster initiative successful. These success factors can serve a role both in the management and assessment of cluster initiatives. This fourth, she argues, is of the highest priority and an area where many current cluster initiatives fall short. Visintin and Pittino examine the relationship between the diversity of the top management team (TMT) and rate of employment growth of academic spin-offs in Italy. They develop a multi-dimensional conceptualisation of diversity as a basis for the analysis via linear regression. They find that diversity of educational specialisation impacts negatively on employment growth, which may reflect problems of

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Gary Cook

cohesion and communication. Having members of the TMT with business experience seems to have a positive influence, however the result is not significant. Contrary to expectation, they find that having a TMT biased heavily towards academic or non-academic members impacts negatively on growth, whereas having a more balanced TMT has a positive influence. High disparity in status among the academic members of the TMT has a negative effect on growth. Concentration of ownership in the hands of a small number of people has a significant and positive impact on growth. David and van der Sijde probe the factors which may inhibit successful government policy in promoting university-business collaboration, using a case study of policy in Indonesia. The chapter traces three waves of policy, culminating in the most recent version of the so-called Technology and Science programme. They note that despite improvements in the design of policy, it is still falling some way short of achieving its ambitious objectives, particularly in terms of the number of universities and academics engaged. There is also a gap which exists between the academic and business worlds, which has not, as yet, been successfully bridged. This is one of the three main inhibitors identified in their chapter: business people and academics have a different world view and different objectives. Academics remain somewhat suspicious of the profit motive, whilst business people perceive academics as being too much in the ‘ivory tower’ and as not being able to respond in the rapid timescale required by commercial imperatives. They also note that each side has some problems communicating with and understanding the other because of differences in language: they may use the same words but with differences in meaning. Finally, they identify that bureaucracy associated with gaining funding under the scheme has been formidable and a deterrent. A slimming of this bureaucracy in 2012 did lead to an increased uptake of the scheme among academics. Baldock, North and Ullah examine the financing of high technology small firms during the recession, specifically over the period 2007
2010, spanning the trough of the financial crisis. They surveyed 100 small firms in the bioscience, life science and IT sectors. Two thirds of these firms sought to grow over this period and half did achieve growth. Firms were able to prosper through a combination of new product development, investment in marketing and trimming costs. Demand for finance generally remained strong over the period, particularly to finance R&D and working capital. 53% of firms were at last partially successful in raising external finance and took the finance up. In some cases firms were offered finance but did not take it up due to onerous terms and conditions, particularly high interest rates, high fees and requirements for personal guarantees by directors. The survey evidence indicated that financing conditions did worsen during the financial crisis, both in terms of the quantity of finance offered and terms and conditions. Most loans came from banks, with increased overdrafts being the most common form. Around one third of the sample sought risk finance to support early stage R&D, typically from business angels and venture capitalists. Only half of applicants for such finance were offered loans and not all loans were taken up. The authors conclude that the funding gap for HTSF has worsened during the financial crisis and that government ought to act to address this.

Introduction 7 Soetanto considers the question of how technology-based firms learn through networks. He argues that the need to be ambidextrous in balancing learning which allows them to exploit their existing competencies and learning which allows them to develop new competencies. He argues that where firms are learning in an exploratory way to develop mew capabilities, they are better to have a high degree of network openness, which is to be loosely coupled with a wide range of other actors, including universities and research institutes. This increases the chance of learning something novel. By contrast, where firms are looking to exploit existing competencies, they are better to forge deeper links with a smaller range of partners, which is to have a low level of network openness. Soetanto tests his hypotheses using a sample of 62 technology-based firms based at the Daresbury Science and Innovation Centre (a long-established science park in the North of England). These firms completed a questionnaire survey. His empirical results bear out that this is how firms do behave in practice, with a higher index of openness where they are primarily seeking to learn new capabilities. In both cases he establishes that there is an inverse-U relationship between network openness and firm growth performance. Excessively low or high openness is sub-optimal. Ulvenblad analyses the importance of communication strategy at the start-up phase of an entrepreneurial venture, when the entrepreneur is seeking resources in order to commercialise their idea. This is particularly important in respect of financial resources. Ulvenblad conceives communication strategy to be part of strategic entrepreneurship. She identifies three ideal types of strategy: content-centred strategy, which focuses on the substance of the business model and the entrepreneur’s own credentials; behaviour-centred strategy, which is centred on gaining the trust and empathy of the business partner, thereby building a functioning business relationship; and, adaptive-centred strategy, which focuses on adaptation to the encounter as it unfolds, by perceiving what needs to be communicated and how. Thus it is essential for the entrepreneur to be able to make a good impression and establish credibility and legitimacy, in order to be able to attract the resources necessary to successfully launch the business. Salminen and Degbey develop a concept of social espionage. This refers to the systematic observation by firms of data available in social media on their competitors and their customers. There is nothing illegal in such activity, as this is information in the public or semi-public (e.g. where information is only disclosed to ‘friends’ in Facebook) domains. In this respect social media present opportunities for firms to gain useful competitive information, but it also creates threats. Firms which, for sound commercial reasons, engage in social media can also be ‘spied on’ by their rivals in turn. Espionage can provide useful information of the strengths and weaknesses of competitors and also information about dissatisfied customers, who may be persuadable to switch their allegiance. Salminen and Degbey develop a strategic grid based on whether or not firms engage themselves with social media and whether they engage in social espionage. They provide a series of recommendations to managers on how best to use social media in the light of espionage. Small firms are held to have particular advantages over large firms, being able to forge close relationships with their customers and having greater flexibility to respond to insights gained from espionage.

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PART I INTERNATIONALISATION

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Chapter 2

Managerial Mindset and the Born Global Firm Paula Danskin Englis and Ingrid Wakkee

Abstract Using a series of case studies, we show that global mindset is at the heart of global growth and opportunity for entrepreneurial ventures. We review how having an entrepreneurial mindset and international experience influence the rapidity of internationalization by discussing the entrepreneurial process and how the global mindset of founders of born global firms influences their choices in the competitive landscape. This chapter closes with a discussion of a continuum — globalization frustrated (focusing on firms with entrepreneurs that have global mindsets but cannot internationalize) to globalization mandated (focusing on firms that are forced to be global).

Introduction In 2002, a regional hospital in the Netherlands was closed for cleaning due to a large number of cases of infection (MRSA, ESBL, TBC, etc.). The hospital administrators appealed to the local technology community in Enschede to develop new methods to handle the increase of infections in hospital settings. A local scientist had an idea to “clean the air” where many infectious agents are carried. Thus the Virobuster® technology was created. This technology uses no chemicals, ionization or ozone, and leaves no residues (P. D. Englis, personal interview with Herbert Silderhuis, founder, Virobuster, Enschede, The Netherlands, 2008). In addition the technology has no negative effect on humans or animals. But how was this idea to grow into a full-fledged firm when the founder had few resources and needed external validation of the product? Virobuster immediately

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Paula Danskin Englis and Ingrid Wakkee

recognized the importance of a scientific basis when offering a medical technology to the market and realized that hospitals worldwide experience problems with airborne infection. Virobuster decided to partner with research labs around the world to test the technological process of killing micro-organisms. First, the Virobuster® technology was tested by Microsearch (UK) and then Biotec GmbH (Germany) and both had successful results. By 2005 the firm had partnerships with international scientific institutes (universities) and laboratories all over the world testing the technology. Virobuster followed the plan shown in Table 1. When the product was fully tested and could move into production, the firm was faced with problems of limited resources and a lack of experience in scientific equipment manufacturing. To solve these problems and move forward, Virobuster turned to an international firm, JK Holding, to begin worldwide mass-production in Windhagen (Germany) and in Frankfurt, Kentucky (USA). Currently, Virobuster® is in use in three continents and is trying to increase sales penetration globally (P. D. Englis, personal interview with Herbert Silderhuis, founder Virobuster, Enschede, The Netherlands, 2008; www.virobuster.com). Firms like Virobuster are born global. These firms are ventures that pursue opportunities across national borders by combining resources and selling outputs around the world from inception (e.g. Ghannad & Andersson, 2010; Madsen & Servais, 1997; Moen, Sørheim, & Erikson, 2008). At the heart of this global growth and opportunity pursuit is the mindset of the entrepreneur. Utilizing insights from previously published case studies of born global firms this study focuses on the mindset of the born global entrepreneur. First we review how an entrepreneurial mindset and international experience influence rapidity of internationalization. Then we examine the entrepreneurial process and discuss how the global mindset of founders of born global firms influences their choices and how they see the competitive landscape. Finally we close with a continuum — globalization frustrated focusing on entrepreneurs that have global mindsets but nevertheless cannot internationalize and globalization mandated focusing on entrepreneurs that are forced to be global. In doing so, we contribute to the ongoing debate on born global firms from an entrepreneurial perspective.

Table 1: Virobuster’s scientific basis for global expansion. Date

Activity

2002
2005: Accreditation

• Research and development • Laboratory tests at international accreditation institutes

2005
2007: Validation

• Multiple validations in hospitals and laboratories worldwide

>2007: Publication

• Support of scientific research at international institutes • Scientific publications

Managerial Mindset and the Born Global Firm 13

Internationalization, Entrepreneurial Mindset and the Creation of the Born Global Firm Traditionally, the literature of international management has focused on large firms. These firms typically have internationalized through a process of evolutionary development in terms of product offerings (e.g. products, know-how, services and systems) and depth of operational mode (Chandler, 1986; Johanson & Vahlne, 1990; Vernon, 1966). The internationalization process is most typically described in the Uppsala model also known as the stages model of internationalization (Johanson & Vahlne, 1977, 1990; Johanson & Wiedersheim-Paul, 1975). As can be seen in Figure 1, the stages of internationalization are (1) domestic activity only with no export activities, (2) export activity through independent representatives, (3) establishment of overseas sales subsidiary and (4) foreign production and processing. The main premise behind the stages theory of internationalization (Johanson & Vahlne, 1977) is that this process of international expansion is gradual. The stages model proposes that firms internationalize incrementally by targeting foreign markets that are both physically proximate and culturally similar (Johanson & Vahlne, 1977, 1990). It is in these markets that firms develop routines, procedures, systems and structures (Johanson & Vahlne, 1977). After these administrative structures are developed, ventures will gradually begin to expand into overseas markets and accumulate experiential knowledge (Eriksson, Johanson, Majkgard, & Sharma, 1997; Thai & Chong, 2008). Then, after gaining experiential knowledge, ventures move into new markets that are of increasing “psychic distance” (Anderson, 1993; Johanson & Vahlne, 1977). Under this model, entrepreneurs will not increase resources or commitment to the internationalization process until they feel comfortable about their knowledge. Thus, the internationalization process proceeds at a slow pace because acquiring this type of experiential knowledge and comfort level takes an extended period of time. As ample evidence shows, this incremental model of internationalization is no longer the only model that exists. Rather particularly in knowledge-intensive

Extent of Internationalization

4

5

3 2 1

Time

Figure 1: Stages model of incremental internationalization. Source: Adapted from Harveston (2000).

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sectors of industry there are many ventures that engage in a more rapid form of internationalization, often spanning the entire globe within a few years from inception. Such firms have been called by many names including international new ventures, global start-ups and born global firms. For born global firms the start-up process and the globalization process are highly integrated and cannot be seen in isolation. Rather than building on the established resources and skills that the firm accumulates over time, founders of born global start-ups seem to rely both on their personal resources and skills to achieve such remarkable results. Empirical research has shown for instance that by developing and activating a network across national borders rapidly born global firms are able to overcome their own lack of resources and knowledge of local markets (Coviello & Munro, 1995; Freeman, Edwards, & Schroder, 2006; Gabrielsson & Gabrielsson, 2011; Mele´n & Nordman, 2009; Moen et al., 2008; Wakkee, 2004). While the number of born global firms has increased in the past three decades, they are still a minority among entrepreneurial ventures. What are the reasons for this difference in the internationalization process? Clearly, market-based factors such as the size of the potential domestic demand or (unsolicited) demands from overseas customers play a role in this process (Roberts & Senturia, 1996). Yet, personal characteristics and attitudes of the founding entrepreneur are shown to be crucial in explaining the difference between traditional internationalizers and born global firms (McDougall, Shane, & Oviatt, 1994; Moen, 2002; Weerawardena, Mort, Liesch, & Knight, 2007). Indeed March and Simon (1958) already argued that due to a large behavioral component strategic choices reflect the idiosyncrasies of decisionmakers. To each administrative situation, each decision-maker brings his or her own set of “givens” which reflect his/her values and principles. Research has supported this assertion and shown that the characteristics of the entrepreneur contribute to the decision to internationalize (Bilkey, 1978; Reid, 1984). Indeed, more attention and greater examination of the entrepreneur and managerial characteristics have been called for in international entrepreneurship research (McDougall et al., 1994).

Managerial Mindset and the Born Global Firm In terms of entrepreneurial influences, the attitudes and mindsets of the founding team play an important role in determining the extent to which a firm engages in international activities. The entrepreneurial mindset can be defined as the attitude of the founding entrepreneur towards internationalization. Conceptually, this line of reasoning has its foundations in the work of Perlmutter (1969). He posited that, “the more one penetrates into the living reality of an international firm, the more one finds it is necessary to give serious weight to the way executives think about doing business around the world” (p. 11). Perlmutter (1969) described three types of mindsets. Ethnocentric mindsets reflect more of a home country orientation with a tendency to view the world from the perspective that the home country is best while polycentric mindsets reflect a host country orientation with the view that the host country is the

Managerial Mindset and the Born Global Firm 15 best. The final way of thinking reflects a geocentric mindset where the view is worldbased. No single country market is best and the world is viewed as a whole. The idea of an entrepreneurial mindset affecting internationalization of small and medium size enterprises (SMEs) is supported by many researchers (i.e. Bartlett & Ghoshal, 1989; Ghannad & Andersson, 2012; Harveston, 2000). Bartlett and Ghoshal (1989) argue that managers’ cognitive perspectives affect the international strategic capabilities of the firm. Managerial experience abroad has also been shown to affect the internationalization process. This concept is defined as the amount of experience that an entrepreneur has accumulated in an international context. Roth (1995) posits that mere exposure to the international arena is not sufficient for development of a deep understanding. Rather, entrepreneurs are more likely to develop a deeper understanding when they have been posted in other countries or are required to spend considerable time overseas compared to domestic entrepreneurs who are merely responsible for overseas functions. Consequently, entrepreneurs with international work experience or who have attended schools in other countries would tend to be more familiar with the foreign market conditions and opportunities than entrepreneurs without such experience. Contrary to the idea that global mindset leads to better performance, in a study of 126 MNEs Bouquet (2005) however shows a concave relationship between global mindset of top management and firm performance. So, Bouquet (2005) concludes that excessive as well as insufficient amounts of managerial attention to global strategic issues can have a negative effect on firm performance. At the end of the last century a flurry of research activity investigated the effect of international experience on the process of internationalization of multinational corporations (MNCs). For instance Sambharya (1996) surveyed US MNCs to extend and test Hambrick and Mason’s (1984) upper echelon theory to international business. He found that top management teams (TMTs) with higher proportion of managers with foreign experience were significantly associated with the firm’s international involvement. In a similar vein, Fischer and Reuber (1996) studied the determinants of internationalization and found that top management team experience is associated with earlier firm internationalization. Also, Almeida and Bloodgood (1996) found that managers’ international experience was associated with extent of internationalization of new ventures. Bloodgood, Sapienza, and Almeida (1997) studied the determinants of internationalization from a resourcebased perspective. Their results showed that managerial international experience was associated with the extent of internationalization. More simply put, the mindset of the entrepreneur and the top management team affects the willingness to expand the firm’s activities into international markets. More recently, Nummela, Saarenketo, and Puumalainen (2004) found that market characteristics — the level of globalization and turbulence of the market in which a company operates — and international work experience are positively related to global mindset within the company. International education however was not related to having a global mindset. Finally, Nummela et al. reported a positive relationship between global mindset and financial indicators of the firm’s international performance, whereas global mindset is not related to managers’ subjective evaluations of international

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performance. Shifting the focus towards the start-ups and entrepreneurs, in this century there have been a number of studies addressing experience that tended to focus on whether or not entrepreneurs had lived or worked abroad. Generally, the results of these studies showed that start-ups and SMEs in general are more likely to export when entrepreneurs have lived or worked abroad (Andersson & Wictor, 2003; Dichtl, Koeglmayr, & Mueller, 1990; Madsen & Servais, 1997). According to Tung and Miller (1990) the international experience of top management is essential to the formulation and implementation of international strategies. Empirical studies have tended to support this argument. Dichtl et al. (1990) studied small- and medium-sized firms from Finland, Japan, South Africa, South Korea and West Germany to investigate determinants of export success. In their investigation, they used amount of time spent abroad in the form of foreign assignments or education or vacations as a proxy to international orientation. Their results showed that start-ups with founders who had more international experience were more successful exporters. Recently, Freeman and Cavusgil (2007) studied attitudinal orientations of Australian born global firms. They found that the founders’ managerial mindset in smaller born global firms led to commitment to accelerated internationalization. These results are consistent with Stuart and Abetti’s (1990) findings that the entrepreneur’s international experience and the managerial level were the most significant factors affecting early performance of new ventures. In addition, Andersson and Evangelista (2006) examined founders of Australian and Swedish born global firms to study common characteristics and behaviors that affected rapidity of internationalization. They found that individual-level characteristics enhance the understanding of internationalization. They concluded that entrepreneurs can use their international ambitions, experience, networks and visions as competencies in global expansion.

The Entrepreneurial Process and the Born Global Entrepreneur If we want to understand the nature of born global firms understanding how these firms come into existence is essential. As already suggested by Madsen and Servais (1997) this would involve looking beyond the actual inception of the firm to its origins. Within the domain of entrepreneurship research several models have been presented to describe the process by which companies emerge (e.g. Gartner, 1989; Van Der Veen & Wakkee, 2006). In this study we adopt the model presented by Van Der Veen and Wakkee (2006) as it has three major advantages. First it reflects the notion that ventures do not arise as complete and finished entities but rather that they come into existence by an evolutionary process that is characterized by many feedback loops as well as fast-forwards. Second the model incorporates the role of the initiating entrepreneur. In new and small firms it is almost impossible to differentiate between the actions of the firm and those of the entrepreneur. Yet many other models of organizational emergence neglect this notion. Finally, the model does justice to the importance of the network environment in which all firms are embedded

Managerial Mindset and the Born Global Firm 17 Entrepreneur

idea

Opportunity recognition

Preparation for exploitation

Opportunity exploitation

Value creation

Network

Figure 2: The entrepreneurial process. Source: Adapted from Van Der Veen and Wakkee (2006). and recognizes the changing role and composition throughout the entrepreneurial process. Figure 2 shows this process.

Opportunity Recognition Based on a review of many previous studies, Puhakka (2002) defined an opportunity as: ‘A new means-ends relationship between goods, services, raw materials, and organizing methods coming into existence as a long-term profit potential based on a recognized market position, in which a venture is competitive beyond the short run and through which a venture can offer products and services that are attractive, durable, and timely and add value to buyers and/or end users’ (pp. 27
29). Various authors argue that during the opportunity recognition process, entrepreneurs first discover an initial idea and then develop this initial idea into a viable business opportunity by (mentally) matching attainable resources and perceived market needs (e.g. Ardichvili, Cardozo, & Ray, 2003; Baron & Ensley, 2006; Corbett, 2005; De Koning, 2003; Ozgen & Baron, 2007). Born global founders do not only scan the world in order to find ideas to develop into business opportunities whether these could be exploited locally or globally, their global mindset also enables access to a diverse network consisting of internationally operating individuals and organizations. The network in which the entrepreneur is embedded plays an important role in this process. Particularly the network position affects the process of opportunity recognition in three ways (Aldrich & Zimmer, 1986; Birley, 1986; Elfring & Hulsink, 2003): (1) availability of information, (2) timing and (3) referrals. First, it forms a source of information for entrepreneurs about current activities and developments in the environment and market. Depending on the position in the network, each entrepreneur has a different set of information available to him. Secondly, position in the network determines when information will reach the entrepreneur and thus which opportunities can be identified and evaluated. Finally, referrals imply that a venture’s interests are mentioned and represented in a positive light, at the right

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time, and in the right place, thus enabling the venture to build legitimacy for its activities. Central positions or positions that allow entrepreneurs to bridge structural holes are often equated with access to more, better and earlier information (Burt, 1992). This is a source of advantage and may exert influence on the internationalization of firms and the ability of entrepreneurs to see opportunities. The network may also produce unexpected random information to entrepreneurs. Entrepreneurs may observe the benefits of this knowledge and may integrate it in their own structures and behaviors. Throughout the entrepreneurial process, entrepreneurs build up their networks and interact with different members of their network to accommodate the different needs. The opportunity recognition process is global when the original idea is discovered and/or developed into a business opportunity in interaction with international actors; when the opportunity can only be exploited successfully when combining globally dispersed resources and/or through the establishment of partnerships around the world; and/or creates sufficient value to sustain the venture over longer periods of time when outputs are sold around the world. An example of a global opportunity recognition process that was facilitated by the global mindset of a highly experienced entrepreneur concerns the case of the Seawater Farms in Eritrea that was first presented by Muhawi (2003). The opportunity underlying Seawater Farms was discovered when American entrepreneur John Sperling enjoyed a holiday in Eritrea. Seeing the desert reaching the coastline in the recently independent country he recognized that recent modifications of salt-loving Salicornia made in a US laboratory could offer a solution to the shortage of fresh water hindering agriculture in the region. He wondered, “What if salt water were able to irrigate farmland? How would that change the ironclad equation whereby fresh water and energy are the prerequisites for life and prosperity?” By combining American technology and local knowledge he set up a chain of agricultural activities that both yielded good crops and that purified the water making it fresher every step of the way. Today, Seawater Farms sells shrimps, textiles and other agricultural products in the Middle East, Asia and Russia for the benefit of the local population who do not only find employment at the Seawater Farms but who also obtain education, access to healthcare and who now enjoy a much better infrastructure in the area as a result from the economic activities of their employer (Muhawi, 2003).

Preparation for Exploitation (Resource Building) and the Born Global Manager During the preparation, the business opportunity is translated in a concrete business concept leading to exchange with the market. The business concept incorporates all ingredients that are necessary to enable this exchange. One of the most important steps in this process is the development of the necessary resource base and of knowledge-related capabilities. Knowledge-related capabilities refer to capabilities, which are knowledge intensive, tacit and dynamic in nature. They are produced

Managerial Mindset and the Born Global Firm 19 through internal (and external) learning processes and they determine how the initial idea is eventually transformed into the offering that will be introduced in the market as such it determines the direction of the opportunity that the firm’s entrepreneurs’ see and can take advantage of. This is a challenge for all firms but can be particularly difficult for born global firms. For born global firms there are several kinds of knowledge-based resources. • Codified knowledge: Intellectual property (IP) and intellectual property rights (IPR). Depending on the origin of the knowledge — more often the ownership of the knowledge lies with the institute the entrepreneur originates from (university or research laboratory), meaning that a huge share of the intellectual property is owned by this institute (“codified knowledge”) and the rights (IPR) to use it must be obtained. • Knowledge embedded in research facilities: Many global start-ups are spin-offs from universities and can get access to its research facilities (at or below market price during a certain period or in return for equity). These facilities are most commonly found in the institute from which the entrepreneur used to work. • Knowledge embedded in human resources (“tacit knowledge”) may be obtained wherever it is available; meaning it could be internationally obtained. Entrepreneurs leading born global firms seems to have an exceptionally keen eye with respect to where the most valuable knowledge can be found and perhaps more importantly so how to organize their venture in such a way that they can benefit most from the globally dispersed knowledge. As described in one of the earliest case studies on born global firms, Logitech SA was truly international from the very start. Dreaming of setting up a company with a global reach, the founders decided to set up headquarters both in Switzerland and the United States. The choice for a bipolar headquarter was motivated by the entrepreneurs awareness of where relevant resources could best be obtained: in the United States it would be easier to attract highly skilled employees and recent technology while in their home region they found moral support as well as management and market knowledge. Within a few months operations were expanded to Taiwan and Ireland. The rest of the world soon followed. By 1989 when the case study was first published the company sold 65% of its produce in the United States, 28% in Europe and 7% in the Far East. Besides sales, international activities included engineering and manufacturing in three continents. For instance, R&D activities were moved from Switzerland to California quickly to take benefit of huge presence of specialized and well-trained human resources and enormous R&D activity in Silicon Valley. By the end of 2003 Logitech employed over 4800 people and had offices in over 25 countries. Company shares are sold on the stock markets in Switzerland and on the American NASDAQ (Jolly, Alahuhta, & Jeannet, 1992). Besides creating a knowledge and resource-base, gaining legitimacy is another very important element of the preparation stage. New ventures need to convey the message that their actions are desirable, proper or appropriate within some socially constructed system of norms, values, beliefs and definitions (Suchman, 1995). Even

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though every new venture has to deal with this issue, gaining legitimacy poses an additional challenge to born global start-ups as they simultaneously operate in multiple social systems. Consequently by adhering to the values and norms of one country a firm might actually violate the norms of another. Entrepreneurs with a global mindset have shown various creative ways to deal with the conflicting demands of different national systems/market including the active use of networking, cultural intelligence (Story & Barbuto, 2011) and the use of external endorsements (Andersson, 2011).

Opportunity Exploitation and the Born Global Entrepreneur After a venture has created marketable products or services, the exchange processes between the venture and its customers begin to take place. In the opportunity exploitation phase, the market exchange increases to a higher level. In order to engage global customers and to obtain their feedback, having a working prototype of the product or service or a working beta version of software is essential at this stage. For instance, Virobuster made their products available for scientific testing at laboratories worldwide when they had a prototype. Englis, Englis, Groen, and van der Sijde (2008) noted that the earlier in the entrepreneurial process the venture can engage the “voice of the consumer,” the more likely they are to bring a viable product to the market. But, earlier in the process it is much more difficult and firms will be forced to use qualitative techniques such as watching how global consumers behave in the product or service environment. When the firm engages the market to a higher level, the firm will establish a more secure position in the market. While in this phase of the process, entrepreneurs continuously update the opportunity by adding new or improved features that the market indicates are needed in the products and services and focuses on improving the internal operations of the venture to become more efficient and effective. The result of this work is the creation of economic and social value in terms of meeting consumer needs, increasing innovation and knowledge and improving financial performance (Davidsson, 2004; Englis et al., 2008; Zahra & Dess, 2001). Global mindset is important throughout the entrepreneurial process from production to distribution and fulfillment. For example, after gaining scientific legitimacy, Virobuster had to decide how to produce their Steritube. The global mindset was evident in their choice of a German firm that used their US facilities to produce the Steritube for the global market. Other examples of global mindset throughout the value chain include distribution and fulfillment (e.g. online purchases delivered to the home via FedEx, UPS, etc.), and communication (i.e. e-mail systems, instant messenger (IM), Facebook, Swirl). Wakkee (2004) using the case of Sound Inc is another example of global mindset affecting opportunity recognition. When being confronted with considerable skepticisms regarding the feasibility and desirability of his recently discovered technology for measuring (directionality and intensity) of sounds based on particle velocity, the founder and inventor of Sound Inc did not hesitate to share his ideas, blueprints and prototypes with a variety of scientists and potential lead users

Managerial Mindset and the Born Global Firm 21 around the work. By ensuring their (moral) support, feedback on both the quality of the current applications and suggestions for future expansions of the product range and endorsements Sound Inc rapidly built the credibility he needed to obtain funding for further R&D and patenting while simultaneously attracting his first real international customers. Within five years from making the initial discovery the company was selling its range of sensors in over 30 countries either through its global distribution network or via direct exports. Above we have presented a discussion of how born global firms come into existence by exploring how entrepreneurs engage in interactions with international counterparts during opportunity recognition, preparation and exploitation stages. In each of these stages having a global mindset was shown to facilitate the successful transition towards founding a born global firm. Table 2 presents an overview of the role of the global mindset in the entrepreneurial process.

Table 2: Summary of role of global mindset in entrepreneurial process. Stage of the Entrepreneurial Process Opportunity recognition

Preparation for exploitation

Opportunity exploitation

Role of Global Mindset • Global mindset reveals network “gap” in product/ service offerings • Global mindset leads to increased alertness to opportunities • Global mindset looks for global product/service focus in niche markets • Global mindset focuses on the needs, wants and resources of customers across the world as the starting point of the business planning process • Global mindset involved in “co-creation” of products/ services with collaboration across national boundaries with key groups (i.e. upstream/downstream suppliers, technology partners, consumers) • Global mindset infused throughout design, production and consumption of the product or service — looking for ways to create relationships wherever in the world competencies lie • Global mindset understands need to create better and more customized products to meet localized needs which leads to including the voice of the consumer • Global mindset integrated throughout firm value chain to facilitate product creation (e.g. Virobuster), pricing (e.g. priceline.com), distribution and fulfillment (e.g. online purchases delivered to the home via FedEx, UPS, etc.) and communication (i.e. e-mail systems, instant messenger (IM), Facebook, Swirl).

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Globalization Frustrated Having a global mindset is not enough to ensure expansion of a start-up beyond its national borders. Consider the case of paperbackswap.com. The founder, Bobby Swarthout, developed the idea for his venture while he was a college student at Berry College. As a student on a limited budget he had become tired of paying high prices for textbooks (P. D. Englis, personal interview with Bobby Swarthout, founder paperbackswap.com, Berry College, GA, 2006). So he developed and launched an online textbook swapping service. Along with a small group of students, he managed to assemble a group of 12 colleges and universities across the United States to participate in textbook swapping. However, after a few months, very few students had used the site. By listening to the potential customers who chose not to participate, Bobby found out that there were too many easy substitutes for the swapping service (e.g. bookstore returns, half.com, efollet, etc.). These alternatives either offered greater convenience or cash in return for used books (especially appealing to students who did not pay for their books themselves), or other appealing features. However, Mr. Swarthout believed in his concept and moved his business idea into different consumer/product space: that of paperback books. Along with a few lead users attracted to his original idea, he refined the original idea, gathered resources (an angel who invested in the business) and added technological capabilities to improve innovation. One year later he launched paperbackswap.com (P. D. Englis, personal interview with Bobby Swarthout, founder paperbackswap.com, Berry College, GA, 2006). At the core of the business is a crucial technological approach that has served to greatly simplify the users’ task in swapping books. For users to swap books easily over wide distances they must use the mail system. In order to simplify this process, the founder of paperbackswap.com developed the technology needed to develop a database of “international standard book numbers (ISBNs),” integrate this into a second database that contains the dimensions and weight of the books and tie both to an output system that produces a wrapper using standard paper in the user’s printer. The user folds along the dotted lines and tapes the wrapper around the book. The process whereby the wrapper is printed provides a seamless connection between return and delivery addresses as well as printing the correct postage on the pieces of paper that provide the book wrapper. While the products being swapped are “old school,” the software technology behind paperbackswap.com is sophisticated, knowledge intensive and has several patents pending (P. D. Englis, personal interview with Bobby Swarthout, founder paperbackswap.com, Berry College, GA, 2006). As a high-tech knowledge-intensive business, paperbackswap.com has grown to be a leader in the industry and continues to grow at a rapid rate — this online community has been doubling in size every six months. As the US business was starting to expand, Bobby realized that people all over the world read paperback books. And the same opportunity that he found in America could be captured across the world. He immediately bought domain names in Australia and Canada (www.paperbackswap.au and www.paperbackswap.ca)

Managerial Mindset and the Born Global Firm 23 thinking that the main language was English. Then he started researching the markets — particularly the postal system. What he found were fragmented markets with huge barriers to entry and wide ranging cost differences. In the United States using media mail the cost to mail a paperback book is less than $3 wherever you ship it across the States. However in Australia the cost to mail a paperback book ranged from $10
25 Australian. Why would someone swap a book and pay a huge shipping fee when they could buy the book for less? In Canada, the situation was even more complex. Each province and territory has its own structure of fees for mailing books depending on the weight and this changes depending on whether the mailing is within or between provinces or territories. He decided that unless there were changes in these country postal systems, the barriers were just too big to overcome. So instead of expanding the same business across national borders, the firm decided to leverage its innovation business model into other related services (Swapacd.com and Swapadvd.com). The challenges of globalization may have a differential impact on firms depending on the industry.

Globalization Mandated Even though born global start-ups are founded in almost all countries around the world it is clear that domestic market size forms an important explanation for why some firms start global as is the sophistication of the domestic market’s technology. Many born global firms operate in home markets that will not support the product/service that they provide either due to technological standards (i.e. lack of sophisticated and standardized mobile phone infrastructure, Neomedia, USA) or core technologies of the firm are global in nature (i.e. Virobuster, NL). A good example of such a case is Lionix in the Netherlands (Wakkee, 2004). Lionix was founded in 2000 as a spin-off from the University of Twente in the Netherlands under the name Lion Photonix. After merging in April 2002 with another high-tech firm the name was changed into Lionix. Lionix develops and produces innovative products based on Microsystems technology and MEMS. The core technologies are integrated optics and micro fluids. Lionix’s founders who had previously worked at CERN in Switzerland, realized that due to the high level of specialization and small domestic market, the company could only survive if it would operate internationally from the start. Today, Lionix’s customers operate in telecom, industrial process control, life sciences and space markets and includes OEM’s, multinationals, as well as research institutions from around the world. By the end of 2003 the company was involved in several strategic alliances in production and R&D with both Dutch and foreign partners from Denmark, Norway and the United Kingdom. Furthermore, the firm has sales representatives in Israel, the United Kingdom and the United States. Finally, the company staff includes several foreign employees, which has resulted in a multinational culture.

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Summary and Conclusion In this chapter, we focused on the mindset of the born global firm. First we reviewed how entrepreneurial mindset and international experience influence rapidity of internationalization. Then we reviewed the entrepreneurial process and discussed how the global mindset of founders of born global firms influences their choices and how they see the competitive landscape. Finally we closed with a continuum — globalization frustrated focusing on firms with entrepreneurs that have global mindsets but cannot internationalize and globalization mandated focusing on firms that are forced to be global. Through a series of case studies, we have shown that global mindset is at the heart of this global growth and opportunity.

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Managerial Mindset and the Born Global Firm 27 Thai, M. T. T., & Chong, L. C. (2008). Born-global: The case of four Vietnamese SMEs. Journal of International Entrepreneurship, 6, 72
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Chapter 3

Acquisition of Knowledge in Networking for Internationalisation Valerie A. Bell and Sarah Y. Cooper

Abstract Business networks are of critical importance to firms and essential to the internationalisation of born-global and international new venture firms. Networking literature focuses on what are, generally, co-operative relationships and their effects between actors, activities and resources and illustrate the importance of quality and change in the networking process. Utilising Fletcher and Harris’ (2012) framework for understanding knowledge acquisition processes in internationalisation, this study investigates the importance of direct and indirect roles played by third parties in the networking for internationalisation processes of three different firm types within the knowledgebased natural health products (NHPs) (pharmaceutical) sector in Canada. The research presented here examines nine case studies of Canadian NHP firms and reveals that they utilised all network-related internationalisation processes simultaneously to internationalise including Johanson and Mattsson’s (1988, 1994) network theory, Johanson and Vahlne’s (2003) updated the Uppsala Model and the resource-based perspective on network theory (Ruzzier et al., 2006). They networked with and extensively utilised third parties, including government bodies, trade associations, government advisors, consultants and other domestic networks with international ties, in Canada and internationally to gain technical, market and internationalisation knowledge, and direct and indirect experiential knowledge which contributed to the internationalisation process confirming the study by Fletcher and Harris (2012). In a departure from the literature, this study found that weak ties (Granovetter, 1973) developed with third parties who were new to the networks allowed the NHP firms to develop competitive advantages necessary for them to overcome the liability

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Valerie A. Bell and Sarah Y. Cooper of outsidership in entering new international markets. The type of technical, market and internationalisation knowledge gained, its content and the direct and indirect sources of knowledge from third parties were all shown to contribute to the internationalisation process.

Introduction Business networks have been shown to be of critical importance to firms (Forsgren & Johanson, 1992) and essential to the internationalisation (Johanson & Mattsson, 1988; Johanson & Vahlne, 2009; Welch & Luostarinen, 1988) of born-global and international new venture firms (Chetty & Blankenburg Holm, 2000; Chetty & Campbell-Hunt, 2004; Coviello & McAuley, 1999; Loane, McNaughton, & Bell, 2004; Sharma & Blomstermo, 2003). Business network theory states that networks are sets of connected business exchange relationships between firms doing business with one another (Anderson, Ha˚kansson, & Johanson, 1994; Cook & Emerson, 1984) where ‘the network manifests itself in the flow of goods and knowledge between the actors in the network’ (Forsgren, 2004, p. 33). ‘Insidership’ in networks is necessary for internationalisation (Johanson & Vahlne, 2009) but the liability of ‘outsidership’ or the firm’s inability to form relationships in networks can prevent internationalisation, so distinctions between entry and expansion may be less relevant for internationalisation than ‘insidership’ and ‘outsidership’ in networks (Johanson & Vahlne, 2009). Knowledge-based or knowledge-intensive firms have been shown to internationalise rapidly while other firms internationalised more slowly (Crick & Spence, 2005), following gradualist models, unless they created and utilised strong international networks (Johanson & Vahlne, 2009; Oviatt & McDougall, 2005; Rialp, Rialp, & Knight, 2005). Internationalisation literature focuses on the management process of how firms acquire foreign knowledge but not on the process of how firms learn (Forsgren, 2002) or the challenges SME managers face in determining which knowledge is relevant or not relevant for that process and how exactly to resource it (Sapienza, De Clercq, & Sandberg, 2005; Zahra, 2004). A lack of research exists on the internationalisation practices of SMEs in particular economic sectors (Zahra, 2004). The Canadian government identified knowledge-based bio-industries, and health and life sciences as rapidly growing priority sectors, important for economic competitiveness (DFAIT, 2011) as a result of the export contribution, including that by SMEs. Within this category, natural health products (NHPs), that is the dietary supplement industry, is regulated in Canada as non-prescription drugs. Internationally, this market is valued at $68 billion US (Anonymous, 2007) while in Canada it is valued at $4.5 billion. The Canadian firms employ over 25,000 people in 10,800 businesses (75% Canadian and 25% foreign owned), 95% of which are privately held entrepreneurial firms, and the largest being for economic contribution, a medium-sized enterprise with 350 employees (CHFA, 2011). The NHP industry was, therefore selected as the research

Acquisition of Knowledge in Networking for Internationalisation 31 subject given a lack of prior research on the sector and anecdotal evidence that these firms were heavily engaged in international markets. The research presented here examines nine case studies of Canadian NHP SMEs and revealed that third parties, including government bodies, trade associations, government advisors, consultants and other domestic networks with international ties, played important roles in the knowledge acquisition process both directly and indirectly or serendipitously. It was found that Canadian NHP firms networked extensively using all network-related internationalisation processes simultaneously to internationalise, including Johanson and Mattsson’s (1988, 1994) network theory, Johanson and Vahlne’s (2003) updated Uppsala Model and the resource-based perspective on network theory (Ruzzier, Hisrich, & Antoncic, 2006). As part of this process, they utilised third parties extensively, including government bodies, trade associations, government advisors, consultants and other domestic networks with international ties, in Canada and internationally to gain technical, market and internationalisation knowledge, and direct and indirect experiential knowledge which contributed to their internationalisation processes confirming the study by Fletcher and Harris (2012). In a departure from the literature, this study found that weak ties involving domestic third parties who were not part of these firms’ networks and had other valuable international ties which they shared with the NHP firms allowed the NHP SMEs to develop competitive advantages necessary for them to overcome the liability of outsidership (Johanson & Vahlne, 2011) in entering new international markets. The type of technical, market and internationalisation knowledge gained, its content and the direct and indirect sources of knowledge from third parties were all shown to contribute to the internationalisation process. The chapter is structured as follows: in the next section we review the literature on networking and learning for internationalisation related to this research study, then outline the data collection methods and finally, provide a discussion of the findings and the implications.

Literature Review Learning and Knowledge Sources in Networks Organisational learning for internationalisation is an important process that should not be overlooked by knowledge-based firms if they are to internationalise both rapidly and successfully. Learning theory (Autio, Sapienza, & Almeida, 2000; Zahra & George, 2002) suggests that a firm learns best ‘when new knowledge is related to prior knowledge and when it devotes significant intensity of effort in processing new external knowledge’ (Sapienza et al., 2005, p. 6). The firm’s entrepreneurial orientation helps to establish rules and norms for expending effort towards knowledge development and renewal (Sapienza et al., 2005). Its ability to acquire and integrate domestic and foreign knowledge is critical to a multi-country firm’s development and performance (Zahra, Ireland, & Hitt, 2000). Sapienza et al. (2005, p. 6) found that the ‘earlier firms entry into foreign markets, the greater the

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international and domestic learning effort’ and suggesting that early internationalisation may create a company-wide learning culture where international operations involve the whole organisation rather than just units dedicated to international activities. Knowledge and the capabilities both to create and use or manage it can be a firm’s most important source of competitive advantage (Nonaka, Toyama, & Nagata, 2000). Companies learn either through their own experiences by experimenting and interpreting previous results or through the experiences of others by transferring knowledge indirectly through knowledge embedded in their products and services or directly from the firm (Ha˚kansson, Havila, & Pedersen, 1999, p. 443). This means that managers must make choices about who to learn from and what form of learning should take place. Learning occurs simultaneously at both senior management and other levels within a firm and can include such things as searching for new alternatives or experiential learning such as ways in which to increase the effectiveness of current operations (Forsgren, 2002). Some knowledge-based firms may develop a deliberate strategy of forming relationships with knowledge producers or lead customers and suppliers that may provide information or opportunities to generate competitive advantage (Ha˚kansson et al., 1999). In networks, learning is affected by two factors: (a) the characteristics of the parties in the relationship, their commitment to that relationship and their competencies in teaching and learning and (b) the type of relationship between the parties that has nothing to do with learning, for example age, products, duration and content (Ha˚kansson et al., 1999). The greater the number of interfaces for learning, that is products, people with different backgrounds and competencies, production facilities, etc. the greater the learning that occurs at both the individual and the firm levels (Ha˚kansson et al., 1999). Fletcher and Harris (2012) examined the relationship between objective (explicit and codified, e.g. published documents and training) and experiential (tacit or implicit) learning which cannot easily be acquired, taught or transferred, and internal and external sources based on the work of Huber (1991) and their implications for internationalisation based on the work of Casillas, Moreno, Acedo, Gallego, and Ramos (2009), Fernhaber, McDougall-Covin, and Shepherd (2009) and Prashantham and Young (2011). They then developed the model shown in Table 1. Firms acquire knowledge directly through experience (Huber, 1991) resulting from either intentional or more often from unintentional actions, the latter of which arises from learning gained from previous outcomes that can then be applied to Table 1: New knowledge acquisition sources. Internal Source of Knowledge Experiential knowledge Objective knowledge

i. Direct experience iv. Internal information

Source: Fletcher and Harris (2012, p. 34).

External Source of Knowledge ii. Indirect experience iii. External search

Acquisition of Knowledge in Networking for Internationalisation 33 present decisions. This provides opportunities for firms to acquire, integrate and use the knowledge they gain about foreign markets and operations (Johanson & Vahlne, 1977, 1990). Internal experiential knowledge that can be sourced from internal managers, staff and systems may sometimes be lost in a firm’s organisational memory, that is the firm ‘does not know what they know’ (Huber, 1991, p. 100). This information needs to be codified and recorded in information systems so that it can be retrieved and distributed (Huber, 1991; Prashantham & Young, 2011) through effective formal and informal communication linkages within intra-firm and interpersonal networks to improve learning so that internationalisation is not hindered (Karlsen, Silseth, Benito, & Welch, 2003). Firms are also able to acquire, adapt and integrate experiential knowledge from networks (Petersen, Pedersen, & Lyles, 2008) in foreign markets (Blomstermo, Eriksson, Lindstrand, & Sharma, 2004). Learning in Internationalisation Internationalisation is a learning-intensive process where a firm’s ability to acquire and integrate domestic and foreign knowledge facilitates internationalisation (Johanson & Vahlne, 2003, 2009) and is critical to a multi-country firm’s development and performance (Forsgren, 2002; Prashantham & Young, 2011; Zahra et al., 2000). Business networks are of critical importance to firms (Forsgren & Johanson, 1992) and essential to the internationalisation (Johanson & Vahlne, 2009) of born-global and international new venture firms (Loane et al., 2004; Sharma & Blomstermo, 2003). Network literature on internationalisation has focused on how firms acquire resources and gain knowledge about foreign markets (Gilmore, Carson, & Rocks, 2006); how personal relationships influence internationalisation (Ellis, 2000); the effects of firm relations; the influence of international experience; and the acquisition of three types of knowledge in networks (Hohenthal, Johanson, & Johanson, 2014). ‘Insidership’ in networks has also been shown to be necessary for internationalisation but the liability of ‘outsidership’ or a firm’s inability to form network relationships can also prevent it, so distinctions between market entry and expansion in internationalisation may be less relevant than ‘insidership’ and ‘outsidership’ in foreign market entry (Johanson & Vahlne, 2009, 2011). Resource-poor entrepreneurial firms seeking new opportunities must develop network relationships if they are to find valuable intangible resources and generate competitive advantage (Chetty & Blankenburg Holm, 2000). Competitive advantages of individual firms, and their ability to internationalise, may be dependent on whether they are ‘insiders’ or ‘outsiders’ in networks that can provide accumulated experience, resources and knowledge needed for internationalisation (Johanson & Vahlne, 2009). Strong and weak ties (Granovetter, 1973) also contribute to the competitive advantage of firms since the characters of network relationships arise as a consequence of the interaction strategies of the parties (Cunningham & Homse, 1986) and can be conditioned by relationships with third parties. While information sharing only requires weak tie relationships (Granovetter, 1973), finding, recognising and sharing entrepreneurial opportunities require much stronger ties (Mainela, 2007; Welch & Luostarinen, 1993). Information must also be

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present before this can occur although its presence is a good indication of how usefully connected firms are within the market (Yli-Renko, Autio, & Tontti, 2002). By assembling many weak ties that focus on learning exchange from the beginning, entrepreneurs can aid their early internationalisation process (Kontinen & Ojala, 2011; Sharma & Blomstermo, 2003). Existing relationships can sometimes represent the most valuable assets for internationalising firms (Harris & Wheeler, 2005) by acting as introducers to valuable internationally embedded partners both reactively and proactively (Freeman, Hutchings, Lazaris, & Zyngier, 2010). Successful born-global and international new venture firms use a variety of ways to find, develop and maintain relationships (Freeman & Cavusgil, 2007) including seeking out new partners in new countries or working with partners they already know (Kontinen & Ojala, 2011) and they continue to learn how to form relationships over time (Johanson & Vahlne, 2003). Given resource constraints, they must, however, carefully select where to invest their time and efforts and whether to be proactive or reactive in that search (Johanson & Mattsson, 1988; Kontinen & Ojala, 2011). Knowledge-based firms seeking commercialisation (Freeman & Cavusgil, 2007) or those needing first-mover advantages to survive (Bell, McNaughton, Young, & Crick, 2003) may proactively seek out new weak tie relationships but developing stronger relationships with other firms requires time, commitment and can be costly. While networks provide many positive benefits for small firms many born-global companies start-up without having previous networks (Rasmussen, Madsen, & Evangelista, 2001) and for those that do, in some instances, they can have negative effects such as increasing rigidity (Mort & Weerawardena, 2006); constraining the scope and nature of opportunities presented (Coviello & Munro, 1995); or limiting the extent and level of information exchanged (Kenny & Fahy, 2011). According to Huber (1991) indirect or second-hand experience is knowledge which has not been directly learned but rather gained through observation in networks, licensing, strategic alliances or via corporate intelligence that can be used to overcome the liability of foreignness (Schewns & Kabst, 2009) or stimulate rapid and early internationalisation (Forsgren, 2002). Specialist groups such as export intermediaries (Peng & Ilinitch, 1998) and other commercial or government sources (Leonidou & Adams-Florou, 1999) can act as external sources of knowledge. Fletcher and Harris (2012) built on Huber’s (1991) organisational theory of learning within larger firms to determine how different types of knowledge were sourced by SMEs. They found smaller firms may not have either the relevant experience or useful networks and are, therefore, forced to rely on recruitment, government advisors and consultants to acquire experience indirectly. Recruitment was a source of both market and technological knowledge while government advisors and consultants were sources of internationalisation knowledge (Fletcher & Harris, 2012). Firms may also mimic the activities of others who have successfully entered particular foreign markets as is often the case with high-tech firms who follow the actions of one lead firm or a group of others in entering markets (Forsgren, 2002). Firms may also graft knowledge by hiring people with experience or by acquiring business units with essential knowledge. This avoids the slow process of learning from one’s

Acquisition of Knowledge in Networking for Internationalisation 35 own experience and allows the firm to internationalise more rapidly by focusing its efforts on integrating personnel and the knowledge into the business (Barkema & Vermeulen, 1998). External sources of knowledge have been shown to be particularly important for innovation and exploratory learning (Huber, 1991). Firms may search externally for objective knowledge in publications or other objective sources of information or they may scan their external environment for new information (Chetty & Blankenburg Holm, 2000; Forsgren, 2002; Huber, 1991) in an effort to problem-solve or enhance strategic effectiveness (Chandler & Lyon, 2009). A wide variety of marketing studies from sources such as chambers of commerce, banks, trade associations, consultancies/research agencies, trade publications, government outlets (Leonidou & Katsikeas, 1996) have been used but their usefulness has been questioned (Leonidou & Adams-Florou, 1999). Firms may also conduct their own market research or take education and trainings to secure information from others (Leonidou & Katsikeas, 1996). Unfortunately we know little about how managers actually find, develop and utilise network relationships or what value they offer in the process (Chetty & Blankenburg Holm, 2000; Loane & Bell, 2006; Sigfusson & Harris, 2012). We do know, however, that the roles these relationships play in international entrepreneurship can be split into three types: (a) market knowledge and information, (b) knowhow in countries new to the firm and (c) the provision of assistance (Sigfusson & Harris, 2012). Market Knowledge and Information It is important that firms seek out information about the new environments in the markets they wish to enter and become more closely connected to it in the form of intangible commitments rather than simply collecting and analysing information (Forsgren, 2002). Firms must possess and leverage information-based intangible resources, including institutional knowledge such as knowledge of laws and regulations (Eriksson, Johanson, Majkgard, & Sharma, 1997); knowledge of local conditions and opportunities (Chetty & Blankenburg Holm, 2000); business knowledge of resources, capabilities and market behaviour of suppliers, competitors and customers (Blomstermo et al., 2004); and local relationships which provide ‘home court’ advantages to local firms (Dunning, 2001) in order to be successful. Entrepreneurs acquire foreign market knowledge innovatively and proactively by pursuing international opportunities rather than passively accumulating experience (Zhou, 2007). Market knowledge is tacit, and country- and market-specific, albeit not firm-specific, so the main source of this knowledge is the firm’s own operations (Johanson & Vahlne, 1990). Experiential knowledge plays an important role in market selection, the method of entry and the firm’s speed to launch in those markets (Casillas et al., 2009). Market knowledge may allow firms to expand into new markets and to enjoy successful growth in those markets but it also requires sustained interaction between the providers and the recipients of the knowledge (Fletcher & Harris, 2012). They must actively integrate this knowledge quickly along with additional knowledge provided by individuals, firms and networks if internationalisation is to be rapid (Casillas et al., 2009; Sapienza, Autio, George, & Zahra, 2006). The

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speed at which knowledge is accumulated and learning occurs is dependent on how individuals, firms and networks share their knowledge with one another (Prashantham & Young, 2011). Foreign direct investment decisions and implementations are made when the firm knows more about the market and that information then helps to lower the perceived risk of making such investments (Johanson & Vahlne, 1977). Know-How in Countries New to the Firm Internationalisation knowledge is firmspecific knowledge that must be internally integrated with the firm’s other resources to be useful (Johanson & Vahlne, 2009; Prashantham & Young, 2011). Firms accumulate, systemise and abstract internationalisation knowledge and in doing so, develop skills and abilities, and aid strategic market entry decisions by allowing them to search for information, identify and evaluate markets, potential partners and opportunities, and manage customs and foreign exchange processes (Prashantham & Young, 2011). With the huge diversity of institutional environments globally, entrepreneurs must also have the ability and understanding to learn about and address the challenges of operating in multiple institutional environments (Drori, Honig, & Wright, 2009). Learning in initial markets aids firm market development as well as their entry into other markets (Johanson & Vahlne, 2003) by introducing new network relationships and increasing their ability to co-ordinate and manage these relationships and routines (Blomstermo et al., 2004) and to build new networks (Loane & Bell, 2006). Know-how in countries new to the firm requires deeper, tacit-based or experiential knowledge. It involves learning how to accomplish things such as locating and evaluating partners, distributors and suppliers (Chetty & Campbell-Hunt, 2004; Johanson & Vahlne, 2003) and requires interactions between the source of the knowledge and the recipient that enable the formation of strong trust between the participants over time. Internationalisation occurs through a series of incremental decisions where firms increase their involvement beginning first in psychically and physically close markets and moving gradually into more distant markets as they accumulate knowledge and experience (Johanson & Wiedersheim-Paul, 1975). Founding entrepreneurs and top managers use prior knowledge, experience and abilities to support new firms’ entry into international markets (Oviatt & McDougall, 1995). New knowledge added during the start-up experiential phase augments this process (Huber, 1991) and can allow internationalising firms to do so successfully (Fernhaber et al., 2009). Inherited and accumulated knowledge that becomes outdated has been shown to impair performance (Fernhaber & Li, 2010). Experiential knowledge necessitates the participants to be more embedded and have deeper, more extensive in-country experience (Sigfusson & Harris, 2012). Firms may gain access to the knowledge of other firms through business relationships; by watching other firms and acting in similar ways; and by acquiring other organisations or hiring people with the necessary knowledge; and therefore do not necessarily have to have the same experiential learning (Levitt & March, 1988). In the context of a learning curve, they gain greater competence and effectiveness in their knowledge and skills over time but since learning activities are linked to current

Acquisition of Knowledge in Networking for Internationalisation 37 activities in markets, firms prefer to stick to one market and learn more about it before trying alternatives (Forsgren, 2002). Knowledge-intensive firms may use new technological knowledge that is specific to each firm rather than country-specific, to develop and adapt products for new and existing markets (Autio et al., 2000); to help them to overcome disadvantages of newness and size (Oviatt & McDougall, 1995), and recognise and exploit opportunities (Autio et al., 2000; Zahra et al., 2000). While networks provide both technical and market information, network partnerships have ‘neither sufficient knowledge of the firm’s capabilities and resources, nor the time or interaction with the firm to provide internationalisation knowledge … but that government bodies and specialist consultants can do this and that internationalisation knowledge may be acquired vicariously (through these groups) by working closely with the firm rather than from network relationships’ (Fletcher & Harris, 2012, p. 632). Indirect experiential technical and market knowledge can also be grafted through recruitment but typically not internationalisation knowledge, given that people with internationalisation knowledge are rare.

Provision of Assistance The third role network relationships play in international entrepreneurship is that of the provision of assistance which includes, for example introductions to potential customers or into supply chains (Welch & Luostarinen, 1993) that would otherwise remain disconnected (Oviatt & McDougall, 2005). These actions require greater commitment to action and, therefore, stronger relationships with network partners (Freeman & Cavusgil, 2007). These ‘introducers’ (Johanson & Mattson, 1988) can be located in the entrepreneurs’ own countries (with access to foreign markets); can include a foreign market presence; or have an important reputation that can signal a relationship to others and which may be critical to market entry (Sharma & Blomstermo, 2003). They may provide advice and support in the internationalisation process (Harris & Wheeler, 2005). When the provision of assistance includes such things as negotiating, persuading, influencing, or providing marketing capabilities these activities require higher levels of trust and motivation and, therefore, stronger relationships as well as in-country linkages to people, firms and institutions which may make the provision of assistance more effective (Sigfusson & Harris, 2012). It is important that strong ties are formed with parties that are deeply embedded in networks of relationships (cognitively, culturally, structurally or politically) in one or more of the countries entrepreneurs wish to enter (Sigfusson & Harris, 2012). Some of these relationships may be of greater value than others depending on the situation and needs of the entrepreneurial firm involved (Yli-Renko et al., 2002). Fletcher and Harris (2012) built on Huber’s (1991) organisational theory of learning within larger firms to determine how different types of knowledge were sourced by SMEs. They found smaller firms which may not have either relevant experience or useful networks, rely on recruitment, government advisors and consultants to acquire experience indirectly. Recruitment was a source of both market and technological knowledge while government advisors and consultants were sources of internationalisation knowledge (Fletcher & Harris, 2012).

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Firms may search externally for objective knowledge in publications or other objective sources of information or they may scan their external environment for new information (Chetty & Blankenburg Holm, 2000; Forsgren, 2002; Huber, 1991) in an effort to problem-solve or enhance strategic effectiveness (Chandler & Lyon, 2009). They may also conduct their own market research or take education and training to secure information from others (Leonidou & Katsikeas, 1996). In summary, firms require a variety of different types of internationalisation knowledge to develop and enter foreign markets, successfully sustain their operations there and for the overall performance of the firm. Each of these, depending on the type of knowledge involved, may be acquired either directly or indirectly through different sources including networks, third parties and market research amongst others.

Research Focus and Method The overall objective of this research was to gain a deeper understanding of the internationalisation networking and learning activities of Canadian SMEs in the NHP sector and to explore and explain variations in the patterns and pace of these firms’ activities compared with those of other SMEs in the literature. Since extremely limited management research had been conducted on this sector this first exploratory research study incorporated a wide range of potential variables for consideration. The findings reported here were derived from evidence collected inductively from nine semi-structured interviews conducted with Canadian NHP SMEs and used to develop cases. The multiple case study method used allows for the in-depth investigation and understanding of real life complex phenomena and processes that occur when SMEs internationalise, allows and extends this experience to learning from previous research, and provides a vehicle through which several qualitative methods or sources of evidence can be combined (Yin, 1992). Firms were drawn from the supplier membership directory of the Canadian Health Food Association (CHFA), the largest trade association representing the interests of this sector in Canada. Firms were then selected based on their ability to meet the Canadian definition for SMEs, were based in one of two large NHP industry clusters in Toronto or Vancouver’s greater metropolitan areas, and in each case, had already internationalised, that is were involved in international operations. A minimum export ratio/sales turnover was not specified in an effort to obtain a range of firms exhibiting varying degrees of internationalisation. The firms selected were all independent, and not subsidiaries of larger domestic or international companies, in an effort to avoid the effects of potential resource and cultural influences on decision-making. Participating firms were then subdivided into three firm sub-types and individual firms assigned letters in an effort to maintain confidentiality. Respondents included two regulatory service consultancies (RSCs), that is firms A and B, which work exclusively within the industry; two

Acquisition of Knowledge in Networking for Internationalisation 39 combined ingredient supplier and contract manufacturer (ISCM) firms, that is firms C and D; and five manufacturing firms with their own brands (MFB), that is firms E, F, G, H and I. A firm founder, or member of each firm’s senior management team, who was known to one of the researchers,1 was interviewed for 45
90 minutes either in person in Toronto, or using Skype internet telephone service if located in Vancouver. All interviews were transcribed verbatim and the resulting text reviewed by the participants to ensure accuracy. The approved texts were then coded to identify key themes, and identify similarities and differences that existed among firms, firm subtypes and the industry. A Delphi panel consisting of three industry and academic experts was utilised to review the findings and their interpretation, and agreed with the conclusions of the study. In addition to a review of SME and internationalisation literature, a small number of private, CHFA-initiated economic studies and leading trade publications were used to gather data and develop context and background on the industry’s Canadian and international business environment.

Findings and Discussion The nine NHP firms (Table 2) ranged in size from 8 to 350 employees, with founding dates varying from 1922 to 2003. All internationalised prior to the introduction of the new Natural Health Products Regulations (NHPR) in 2004 (Health Canada, 2011) in Canada and, therefore, operated under both the old and new Canadian regulatory models for NHPs (Health Canada, 2011). Background to International Activity Knowledge is an important component of organisational success in health sciences and drug-based industries and in internationalisation. In this study, entrepreneurs established eight of the nine firms based on prior experience in the NHP or drug industry, NHP-specific or on their academically related knowledge. The remaining firm (H) was established by a father who had extensive unrelated export experience and a personal health condition mitigated by the firm’s NHP products and his son, who had a strong academic background. All nine firms internationalised rapidly. Only one firm (G) required more than two years evolving first from retail bakery operations to manufacturing NHPs and then 20 years later in response to unexpected events, began to export and rapidly increased exports markets thereafter as noted in Table 2. Five ‘small’ NHP firms (A, B, C, G and H) serviced between 2 and 40 or more markets; service-based

1. The lead author is a former president of the CHFA, who has represented the association’s interests both domestically and internationally and is therefore well-known within the industry.

Owner’s Background

Year Founded

Year Internationalised

Regulator Service Consultants (RSC) A Strong academic science background; 2003 2003 instrumental in development of NHPR B Involved in herbal industry from 2003 2003 childhood; instrumental in development of NHPR and industry association Combination Ingredient Suppliers and Contract Manufacturing (ISCM) Firms C Previous owner of two NHP-related firms 1998 1998 D Previously worked in NHP industry 1990 1990 Manufacturers with their Own Brands Firms (MFB) Ec Pharmacist; now operated by journalist 1922 USA Shortly after 1922 son 1965 Can (exact date unknown) Fd An accountant in the NHP industry 1982 1982 Ge Retail baker using natural ingredients 1967 2000 H Son has an MBA and father unrelated 2003 2003 export experience and health condition mitigated by NHP I Father herbologist; son established 1980 1982 company to sell father’s products a

Number of Countries Internationalised

Number of Employees

More than 20

15

More than 20

10

More than 40a More than 50b

50 140

More than 40

350

More than 20 More than 20 2

160 125 8

More than 10

27

Includes countries where ingredients are sourced (import from) and where contract manufacturing products are exported and sold. Ibid. c Began as an American pharmaceutical firm that moved to Canada after internationalising there first and became Canadian firm in 1965. d Began as direct mail business but evolved into distribution and manufacturing firm within a year; firm sold to larger Canadian entrepreneurial firm that distributes NHPs and organic products in 2004. e Firm evolved to manufacturing and left retail bakery business in late 1980s. b

Valerie A. Bell and Sarah Y. Cooper

Firm

40

Table 2: Firm backgrounds.

Acquisition of Knowledge in Networking for Internationalisation 41 firms (A, B and C) serviced 20
40 or more markets and the two smallest MFB firms (H and I), exported to 2 and 10 markets respectively. The four ‘medium’ sized MFB firms (D, E, F and G) serviced 20
50 or more markets.

Learning during Internationalisation Regulatory Service Consultancies RSCs internationalised within their first year of operation (Table 2), first to the United States, and then gradually to over 20 other markets as illustrated in Table 2. Psychic distance played an important role in their choice to follow Canadian customers to the US market but not afterwards. As the RSC firms learned more about their customers, they gained important market, technical and regulatory knowledge by watching and listening to both their existing clients and new clients that they secured in each of the countries entered; listening to the advice of other regulatory firms they networked with both at home in Canada and in each of the new countries entered; and learning from government officials in each country how to create and submit regulatory applications for NHP. A desire for new business growth motivated the RSCs to investigate additional new markets where they marketed their academic backgrounds or experience, Canadian NHPR knowledge, absorptive capacity for the regulatory environment and range of services to foreign firms in specific and non-specific markets through international trade shows. Firm A focused on entering European Union countries because its owner, a European immigrant, already understood languages, business practices and the regulatory systems in the region; then went to the United Kingdom and Australia, where it already had customers; and finally to China as part of a CHFA-led trade mission. Firm B focused on increasing its US business, then targeted Mexico because its owner holidayed there and spoke Spanish, and later participated in CHFA-led trade missions to Mexico, the United States and China. In each case, the owners continually built on prior networks and knowledge, and new experiences to enter these markets. The RSCs’ ability to invest in FDI in international markets was fundamentally blocked by their lack of technical knowledge and absorptive capacity related to a wide diversity of drug regulatory models being ‘synonymous with national sovereignty’ (Vogel, 1998, p. 1). Their lack of competitive advantage in those markets led to their creation of business networks, formation of strategic alliances and development of mutual referral systems with competitive RSCs. Both groups benefited from additional new business opportunities, larger foreign markets, increased growth and reduced risk. They overcame the need for and lack of power, profile, knowledge and entry obstacles in markets which were otherwise closed to them, thus confirming Cooper’s (2001) work. Simultaneously, but independent of these events, MFBs from the United States, EU, Eastern Europe, the Middle East, Asia, India and Central and South America began seeking out RSCs’ expertise to either support their new Canadian market entry or to bring products already in Canada into compliance with the new NHPR. It is likely that the knowledge obtained from early markets assisted RSCs to develop

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more appropriate international marketing strategies for new markets by illustrating industry leadership, and attracting foreign firms to their services. Ingredient Supplier and Contract Manufacturer Firms ISMC firms internationalised from inception. They were motivated differently and, as a result, were able, eventually, to export to 40
50 different markets as noted in Table 2. These firms, given their dual business roles as ingredient suppliers and contract manufacturers, first used networks to locate ingredient products and resources globally that were required to meet customer demand. As they learned more about international suppliers and technical standards they sought better quality, lower-priced and unique products to increase their competitive advantage and as their absorptive capacity for those products and standards increased, so did demand for their products. Using networks on the contract manufacturing side of their businesses, they learned how to service US-based customers immediately, and later other markets with them. Other customers sought out their growing industry expertise, to create unique formulations; their technical knowledge and related absorptive capacity for product development, innovation, production and marketing; and as their knowledge and experience grew, their competitive advantages increased significantly. As their experiential knowledge in international markets also grew and given that many of their customers had also already internationalised on their own, ISCMs provided products to customers for those additional markets. In the process, they learned about cultural issues related to products, foreign customs, currencies, regulatory requirements, insurance, shipping and how to get paid in foreign markets. Motivated internally by increasing reputations for quality products and services, ISCMs marketed products and services at domestic and international trade shows where they found increased opportunities they continued to enlarge their networks as they opened additional markets. Firm D took part in a government-sponsored trade mission to China where it secured new suppliers and firm C, unexpectedly, received ownership rights to a customer’s branded products in reciprocation for non-payment, of goods. Firm C found that having branded products meant that it competed with its customers’ products, so it later sold the brands to reduce that competition and because it was unwilling to provide the additional investment it found was required for branded products marketing. ISCM firms increased their commitment and deepened export relationships by developing business networks, strategic alliances and contractual relationships with their trusted American counterparts in an effort to increase the sale of competitively priced goods to customers there. Firm C also established strategic alliances with Mexican firm owners who they had met through social networks in Canada prior to start-up and planned to make additional foreign direct investments into existing contract partners in both markets as trust and familiarity increased. Firm D made an FDI later to purchase its US strategic alliance partner. The North American Free Trade Agreement’s access to large markets was important in these decisions, so through government liaisons, they had learned how to leverage the agreement to benefit each of their firms in different ways.

Acquisition of Knowledge in Networking for Internationalisation 43 Networking in each instance was an important precursor to their progress. In accordance with Dunning’s OLI model (1997), they gained international exclusives on high quality or specialised ingredients in individual markets (i.e. location or country-specific advantages); delivered competitive pricing to US customers by learning to ally themselves with US competitors (i.e. internalisation advantages); and manufactured specialty goods where they did not have existing capabilities (i.e. ownership-specific advantages). Manufacturing Firms with their Own Brands (MFB) MFB firms internationalised by simultaneously entering the United States while also developing their domestic market. Each firm quickly learned that the American market was completely different from the Canadian market, ran into financial and knowledge obstacles, withdrew from that market and subsequently re-entered it on a regional basis. Firm E began exporting to Canada soon after its establishment under American ownership, and was, therefore, already internationalised when it became a Canadian firm in 1965. The remaining firms (G and H) internationalised first through opportunities that arose serendipitously from domestic third parties that were not part of their business or trade networks. Owner’s travel that, unexpectedly, located international markets for the company, the domestic acquisition of brands already present in foreign markets, domestic and international network relationships that fuelled new business opportunities, and participation in international trade missions and trade shows also provided internationalisation opportunities. Canada’s multicultural environment created significant external export motivation for all three NHP firm types (RSC, ICSM and MFB) by identifying numerous new business opportunities and export markets through immigrants’ diasporas, networks or family connections in their home countries. These individuals and groups sought out MFB firms, in particular, with successful Canadian brands and products with high regulatory standards for quality and safety, and then carried them to foreign markets, distributors and agents. After using distributors, brokers or agents and familiar practices to enter export markets initially, all MFBs formed specialised internal ‘international’ departments to support market expansion, developed new criteria based on their direct experiential knowledge to evaluate markets and to qualify new distributors and agents, and, later, used strategic alliances, FDI and international trade consultants to increase penetration of their existing export markets and locate new markets. The remaining firm continues to react to export opportunities as they arise. Firm E, the largest SME in the Canadian industry, indicated that it formed a strategic alliance with a larger, global pharmaceutical firm to allow it to learn new systems and gain international experience by extending its product reach and profile, and gaining insights into unfamiliar markets while also generating new business opportunities, confirming Cooper’s (2001) work. Four MFB firms (E, F, G and I) internationalised extensively to markets in Asia, the Middle East, India, and to a lesser extent South and Central America, Europe, Australia and Africa. The three largest firms (E, F and G) export to more than 20

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countries and most continents. Firms G, H and I encountered two domestic nontariff regulatory obstacles that partially, albeit perhaps temporarily, restricted their export markets to 20, 2 and 10 markets, respectively. In summary, all three types of NHP SMEs gradually and incrementally increased their resource commitments and investments in international markets; gained both direct and indirect experiential knowledge, consistent with Johanson and Vahlne’s (1977, 1990) findings. Firms gradually and incrementally deepened resource commitment and investment in foreign markets and that internationalisation became a function of market knowledge, commitment decisions, current activities and the difficulty of identifying and transferring funds to an alternative use consistent with Johanson and Vahlne’s (1977) findings. In this instance the establishment, use and increasing penetration of networks were keys to the firms’ overall process and rapid internationalisation.

Networking Networking has been shown to be important to three different internationalisation models and theories, each of which appeared, uniquely, to apply to the NHP participants in this study. All NHP SMEs were first members of CHFA, the largest Canadian domestic trade association and business network of its kind representing the NHP industry. Through their attendance at CHFA’s meetings, educational programmes and trade shows, member firms in this study extended their networks to include ever increasing numbers of international members and visitors attending their trade shows in particular, and as a direct result, found numerous new business opportunities and customers both domestically and internationally. Other CHFA members whom they had known previously and new international customers acted as introducers to foreign market customers, important government officials and distribution and marketing channels. They would either not have met those individuals or it would have taken much longer than it actually did to locate these groups on their own. They, next, networked and attended international trade shows and conferences in the United States because those were the largest internationally. Later they participated in the same at trade shows in other export markets where they developed new networks, and penetrated and extended existing ones. As their international experience grew they learned to manage and maintain existing networks and to establish new networks. Despite the wide variety of SME export promotion programmes available from the Canadian government only three MFBs (F, H and I) utilised them. One ISCM (C) and two MFBs (E and F) indicated that they had participated in Department of Foreign Affairs and International Trade (DFAIT) led trade missions that allowed them to join networks in China or Taiwan by introducing them to new customers, distributors or agents. All three firms, however, utilised government market research information and in-country Canadian embassy or consulate staff to help arrange meetings with foreign buyers, services provided by the Trade Commissioner Service

Acquisition of Knowledge in Networking for Internationalisation 45 of the DFAIT. This allowed these firms to gain important indirect knowledge and experience about those markets which aided their internationalisation processes. Other firms did not use these services because the process was too complicated, it failed to provide a value versus time benefit, or because DFAIT had provided funding to the CHFA for industry-specific trade missions to six countries including the United States, Mexico, China, Taiwan, Japan and Thailand which they saw as more valuable than generic trade missions. These industry-specific trade missions provided important country-specific market orientations, introductions to senior industry association staff and members, important distributors, marketing and distribution channels that would have otherwise taken lengthy time periods to achieve on their own. Each firm developed extensive and indirect market knowledge immediately and was able to shorten significantly the cycle of establishing network relationships in the countries where these trade missions were taken. Whereas previously they may have encountered structural holes in their networks to these countries, the trade missions generated weak ties that they could then begin to strengthen as they took advantage of those initial relationships to locate others or to use them to introduce them to other firms that could allow them to become more embedded in each country. Firms that participated in trade missions (A, B, D, F, G and H) or whose executives or owners held leadership roles within domestic or international trade associations acted as multipliers to, for example other association members, distribution and marketing channels, and government officials in new markets and to the networks in those countries. This allowed the firms to overcome quickly the liability of foreignness by rapidly becoming insiders in local networks. Later, all firms began connecting international networks between countries (Johanson & Mattsson, 1988). For example, six firms including RSCs (A and B), ISCM (D) and MFBs (F, G and H) took part in CHFA-led missions to Mexico, Hong Kong, China and the United States where each was able to expand extensively their business networks to include executive and non-executive members of the local dietary supplement and herbal medicines trade associations as well as local suppliers, distributors and retailers. Several of the trade associations developed Memorandums of Understanding and Co-operation which allowed firms to gain rapidly market, technical and regulatory knowledge and know-how and to quickly become more trustworthy as well as embedding firms more rapidly in new markets. Johanson and Vahlne’s updated Uppsala Model (2003, 2009) includes a network perspective, supported by this study’s findings, that if relationships between firms are networks, then internationalisation occurs because other firms in national and international networks are also internationalising. Firm background data presented in Table 2 indicate that the earliest internationalisation of Canadian NHP firms occurred between 1965 and 1982, but that the majority of firms internationalised together, immediately prior to and following implementation of the NHPR and, using the method just described, developed, penetrated and interconnected networks to internationalise. These activities were supported by industry association actions to assist them in doing so.

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The rate of internationalisation increased by building relationships in new markets and connecting to existing networks in other countries, thereby building trust and commitment prior to increasing investment in them, rather than through any specific strategic or firm-level advantages (Johanson & Vahlne, 2003). The data indicate that it was only after NHP firms connected networks and increased commitment to internationalisation that they entered into strategic alliances, contractual arrangements and FDI. This supports Granovetter’s (1973) findings on strong and weak ties and how firms progress from weak ties to stronger ties over time and with proactive action on their part. Networking became more crucial when firms were active in several countries (Forsgren, 2002). It was evident that all NHP firms internationalised to between 2 and 50 plus markets, and the most active networkers were the most internationalised and committed firms. This illustrates that these firms overcame the liability of outsidership when the firms became insiders in domestic and international networks, and that they also likely learned to manage network ties to make both the relationships and internationalisation successful, thereby confirming the study by Johanson and Vahlne (2009). Ruzzier et al.’s (2006) resource-based perspective on network theory indicates that resource ownership, absorptive capacity and intangible knowledge-based information, are key drivers of performance and competitive advantage and affect the rate of internationalisation. Findings of this research confirmed that all firms used networks as resources. They also confirmed that Johanson and Vahlne’s (2009) business network theory that exchange within networks allows firms to acquire privileged knowledge about their relationship partners, their resources, needs, capabilities, strategies and other relationships that are necessary for successful internationalisation. For example, RSCs used networks as resources in three different ways: professional knowledge networks aided the acquisition of knowledge about international markets that improved service and built reputation; membership of domestic and international trade association networks helped with the identification and development of new client relationships as well as potential partners; and customer networks recommended RSCs to new domestic and international customers. ISCMs and MFBs used domestic and international industry-related supply and trade association networks, and their strategic alliance partners, as resources, to locate new ingredients, suppliers, regulatory and market research knowledge, and identify new business opportunities and partners. ISCMs identified the importance of business-related social media networks and indicated that they intend to use these tools more in the future. Canada’s multicultural environment created the most important and extensive domestic and international networks that identified numerous business opportunities, generated additional export markets and developed extensive customer networks in those countries to build brand awareness. These individuals and groups sought out MFBs, with successful Canadian brands, and products with high regulatory standards for quality and safety in particular, and then carried them to foreign markets, distributors and agents where they were adapted to fit the local market

Acquisition of Knowledge in Networking for Internationalisation 47 requirements and customer needs using direct and indirect international, technical and regulatory experiences. MFB firms also learned to use networks to gain important marketing endorsements for their products (firm H); to locate competitive information and new markets (F); and to identify and seek resolutions for challenging international trade issues (D). Data and participant comments confirm that small firms used certain types of relationships with larger firms to overcome shortcomings (Cooper, 2001). Relationship maintenance was an important issue for small firms with limited resources (Cooper, 2001). All SMEs studied used domestic and international trade shows and conferences to develop and maintain networking contacts. Regular person-to-person contact and proximity to the individual customer location, previously found to be important to relationship maintenance, especially initially, was not as important to NHP firms that used e-mail, social media and annual trade shows to maintain these relationships. Larger MFB firms also used travel and hired external international trade consultants to search out specific regional opportunities, and they acquired additional internationalisation knowledge and know-how from these individuals and used them to secure important local network introductions. Networking efforts of the NHP SMEs in this study were extensive and involved all network theories and models of internationalisation. NHP SMEs did not identify any of their networks as being purely social in nature. RSC and ISCM firms thought their networks to be a combination of social and business aspects where they enhanced the overall customer experience by getting to know these individuals and understanding their expectations. The only confirmation in this study that social relationships are extremely important to both entrepreneurs and their businesses (Davidsson & Honig, 2003) came from ISCM firm D where a former property tenant entered into a friendship with the firm owner that later extended to include his family and friends. This relationship deepened and later generated a number of significant business deals in Mexico.

Internationalisation Obstacles The literature identifies four main categories of obstacles to SME internationalisation, internal
domestic, internal
foreign, external
domestic and external
foreign (Leonidou, 2004), each of which can be exacerbated by a lack of knowledge and resources (Johanson & Vahlne, 1977). All NHP SMEs encountered internal
foreign and external
foreign obstacles related to psychic distance commonly associated with the internationalisation of SMEs (Johanson & Vahlne, 1977), particularly in China. ISCMs and MFBs encountered external
foreign obstacles in partner selection, distribution systems, financing, human resources and mature markets with strong brands. Strong competitors were an issue for all groups. Additional obstacles specifically discouraged MFBs, in contrast to RSCs and ISCMs, from entering the US market, and encouraged them to seek alternative export markets.

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This study confirmed that as firms learned more about foreign markets and increased their exposure to requests to extend their operations they were able to overcome obstacles such as lack of resources and knowledge (Johanson & Wiedersheim-Paul, 1975). The two smallest firms (H and I) which were unable to overcome a specific regulatory obstacle that reduced their export capabilities, confirmed findings that firms that did not overcome obstacles were less likely to grow (Johanson & Wiedersheim-Paul, 1975); and that younger firms (A, B and H) appear to be more sensitive to financial and regulatory issues (Leonidou, 2004). SMEs faced different types and severity of problems (Bilkey & Tesar, 1977); and obstacles present at all export stages and those which also differed significantly between stages (Bilkey & Tesar, 1977) were also confirmed for the first time in this industry study.

Conclusions The study confirmed many traditional findings, challenged or extended others and made several unique contributions. It also generated research findings for the first time on the previously unstudied NHP industry, and on the knowledge acquisition for internationalisation processes of knowledge-based service, combination and manufacturing SMEs in Canada. While the conclusions may appear to be limited by the small sample size, the nine firms interviewed represent all of the firms which had internationalised at the time in this sector in Canada, and are therefore, representative of both individual firm types and the Canadian NHP industry. The research revealed that Canadian NHP firms extensively networked, simultaneously and proactively, utilising all three network-related processes to internationalise, including Johanson and Mattsson’s (1988) network theory, Johanson and Vahlne’s (2003, 2009) updated Uppsala Model and the resource-based perspective on network theory (Ruzzier et al., 2006). Exchanges within these networks allowed these firms to acquire privileged knowledge about their relationship partners, their resources, needs, capabilities, strategies and other relationships that aided them to become successful internationalisers confirming (Johanson & Vahlne, 2009). While the study confirmed both Peng and Ilinitch (1998) and Fletcher and Harris (2012) that export intermediaries and other commercial or government sources (Leonidou & Adams-Florou, 1999) can act as external sources of knowledge, in a significant departure from the literature, the NHP firms extensively utilised trade and third-party networks in Canada, uniquely, to not only gain market and internationalisation knowledge but to: (a) accelerate the time required to accumulate knowledge and experiences and access and deepen market penetration; (b) overcome psychic distance, risk and constrained resource obstacles; (c) affect foreign market selection and (d) leapfrog internationalisation stages. Given the success of industry-specific trade missions in creating a wealth of initial opportunities to establish networks prior to market entry, other governments may wish to consider devoting resources to similar industry-specific networking programmes in future as the Canadian government did in this instance.

Acquisition of Knowledge in Networking for Internationalisation 49 Government and other international trade organisations in other countries, such as Australia which have already signed mutual recognition agreements for the Canadian NHP regulatory model, may also gain new insights from this research related to their policy development and export promotion efforts in their own countries. Given that this was the first study, outside practice-based research on regulatory model comparisons that included the NHPR, research on this industry might usefully be extended to include additional areas including further investigation of this unique networking model. Research is also needed on the NHP industry in other international markets, and on the implications of this and future studies on the regulatory framework for dietary supplement products in other nations and mutual recognition of those systems between countries. Combined, all of these further research efforts would help to provide a multidimensional and complementary base of academic literature about this rapidly growing industry, and its business needs.

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PART II START-UP AND COMMERCIALISATION

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Chapter 4

Barriers to Biomedical Engineering Commercialisation Charlotte Norrman, Christina O¨berg and Peter Hult

Abstract The healthcare sector faces severe problems due to increasing costs, decreasing workforce and an increasing share of elderly people. Innovation is proposed as the main cure. However, there are several barriers that prevent new ideas from becoming innovations. In this chapter we focus on the biomedical engineering sector and the barriers to commercialisation that are present for applied research projects within this sector. We describe and categorise the barriers and discuss their implications and how they could be overcome. This study has a longitudinal approach and is based on data collected annually through half-structured interviews for approximately 40 research and development projects at four universities, two hospitals and one municipality healthcare centre, across eastern and central Sweden. Our results found a broad range of barriers to commercialisation, which have been categorised as follows: (1) Barriers coupled to the healthcare sector per se, for example security regulations, procedures for governmental procurement and the industry structure. (2) Barriers related to the market structure, for example public procurement matters and the fact that hospitals commonly look for holistic solutions rather than pieces and gadgets that solve isolated parts of problems. (3) Barriers related to entrepreneurship attitudes among researchers. The findings contribute to research on the ability to create innovation in a highly prioritised sector.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Introduction Within the EU, the debate about how to foster entrepreneurship and innovation to achieve competitiveness, jobs and wealth, is currently intensive. In Sweden, where the economy is declining at a pace similar to several other European countries, there is an intense discussion on these topics. Parallel to this, the societal age structure is shifting, which puts increased pressure on the healthcare sector of the future, as longer life is correlated to increased risk of health problems. The number of elderly people will increase to incorporate about 25% of the population. Furthermore, elderly people tend to suffer from multiple and chronic conditions. We have also, and especially in the Western countries, witnessed dramatically increased costs within the healthcare sector (Christensen, Bohmer, & Kenagy, 2000). Although people are healthier and richer (McCleary, Rivers, & Schneller, 2006; Regeringskansliet, 2010), the aging society calls for novel solutions in the healthcare sector, since the work-active part of the population is becoming relatively smaller. Innovation is regarded as tool to make healthcare more efficient, but it is a far from straightforward way, and surrounded by barriers that prevent new ideas from becoming innovations. This is the case for each step of the innovation process, but not the least so for commercialisation, that is taking the idea into a phase that allows its diffusion. The biomedical engineering sector describes parties involved in the development of medical equipment. Such equipment can include everything from advanced technological solutions to easy solutions that will help elderly people in their daily life. The aspiring situation in healthcare has shifted focus to the home treatment of patients. The healthcare sector needs innovative and more effective solutions, which in turn imply opportunities for new business within the biomedical engineering sector (cf. Christensen et al., 2000; Huges, 2006; Regeringskansliet, 2010; Weinberger & Weeks, 2004). Rather than being hospitalised, not only the recovery but also the diagnosis and analyses of risk factors should increasingly take place in the individuals’ homes, to steer costs away from treatment and care. The ICTrevolution has opened opportunities for such new treatments, new devices and new solutions for communication (cf. McCleary et al., 2006; Sanandaji, 2012). Elderly people can live in their ordinary homes longer if they can benefit from local home health service, customised homes, ICT solutions and different types of aid devices (Regeringskansliet, 2010). It has also enabled people to access information and empower them as healthcare customers. This empowerment can be described as a paradigm shift to fit the growing demand, while controlling costs. An increased focus on customisation and adaption to individual needs can also be observed in the recent development (McCleary et al., 2006; Regeringskansliet, 2010; Sanandaji, 2012). It seems there are plenty of opportunities, especially within the area of personalised medicine, home-based care and the care given by primary care centres, for entrepreneurs to pursue (Sanandaji, 2012; Weinberger & Weeks, 2004). It is argued that the status of research within this field ought to be increased and that more funding has to be dedicated to this area (Puusepp & Malmquist, 2011). At the current stage, many initiatives may be taken to develop new ideas in the biomedical engineering sector, but few have turned into actual solutions. While society continues to support the development of new ideas, not the least in terms of

Barriers to Biomedical Engineering Commercialisation 59 research-related output, there seems to be many barriers to commercialise these ideas. The purpose of this chapter is to describe and discuss barriers to commercialisation of applied research projects within the biomedical engineering sector. We describe and categorise the barriers and relate them to different ways to commercialise innovation. The following research questions are addressed: • What barriers are present to commercialisation of biomedical engineering ideas? • How could the barriers to commercialisation of applied research projects within the biomedical engineering sector best be overcome? • What way to commercialise should entrepreneurs choose? The findings contribute to research on entrepreneurship and innovation in general and to such issues in the biomedical engineering sector in particular. Due to the work with the innovation framework for entrepreneurship and innovation for 2014
2020 the debate about how to foster entrepreneurship and innovation, to reach competitiveness, jobs and wealth has intensified. The large corporations which have worked as locomotives for the national growth are either moving abroad or struggling under the competition from emerging economies (Frankelius & Norrman, 2013). To keep the position several initiatives to breathe life into the slumbering innovation and entrepreneurial spirit have been made. Among such initiatives, the EU structural fund programmes could be mentioned. Such a programme, with the aim to foster innovation and entrepreneurship in the biomedical engineering sector in Sweden constitutes the empirical base for this chapter. Sweden has for decades been recognised for its large and export-oriented biomedical engineering devices sector (Guve, 2007; Puusepp & Malmquist, 2011). However, most of the revenues in this sector are generated from aged innovations that are between 30 and 50 years old. The findings contribute to research on the ability to create innovation in a highly prioritised sector and indicate that barriers occur and interact among different areas. Compared to other innovations, market and industry structure barriers are very prevalent, while these also have negative impact on the entrepreneurship-orientation of researchers. The rest of the chapter is structured as follows. The theoretical basis for the chapter consists of different ways to commercialise innovation. This is so as to capture the alternatives for the research-based initiatives to actually reach their commercialisation stage. Following thereafter the method is described. Results from the programme in the biomedical engineering sector in Sweden are presented. Barriers are iterated from these results and categorised. In the analysis section, these different types of barriers are discussed and related to previous research. The chapter ends with a concluding discussion that relates the different barriers to different ways of commercialising innovation.

Theoretical Framing: Ways to Commercialise Innovation According to Frankelius and Norrman (2013), there are several ways that can be used to commercialise an idea. The most common ways to commercialise research-based

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Table 1: Ways to commercialise innovation. Way to Commercialise Start-up

Licencing

Joint venture

Divesture

Free share

Description In this case the idea is commercialised within a venture started by the idea-owner(s). This strategy is often supported by, for example university-based incubators. In cases where the idea-owner(s) hold one or more patents on the idea, the solution could be licenced to, for example a company, who in turn takes the idea to the market, through production or sales of the product or by integrating the technology within its contemporary product or product portfolio. The idea could be commercialised through a joint venture started by the idea-owner(s) and an industrial partner. The joint venture could be in case of a jointly owned start-up or through contracting the ownership of the actual idea/technology and revenue distribution between the parties involved. The technology (with or without patents) could be packaged, for example in a firm or in case of a specified contract, and sold to an external industrial actor. Sharing the idea for free is another word for technology transfer without any economic compensation to the researcher(s). This way is rather common; especially in the case of research projects that include industrial partners. In such cases the research commonly is financed by, for example governmental research grants.

ideas are described in the table above (Table 1) including a non-action alternative, that is to cancel the idea and abandon its commercialisation.

Method The empirical part of this chapter is based on data from an EU-financed biomedical engineering initiative, NovaMedTech1 (NMT). This initiative has provided support

1. NMT is a network organisation that aims to support the development of new products and services within the area of biomedical engineering. The project started in 2008 and until 2011 the focus was innovation (this period is studied within this chapter). In the middle of 2011 NMT entered a new programme period and focus was shifted towards entrepreneurship. The programme ran until 2014. The network of NMT has since the start been organised mainly with partners from the healthcare organisations, the healthcare sector, the academy and the innovations support organisations in the region. The programme has developed a structure that covers the steps from the identification of new ideas and tools for support to market entrances. The programme was financed by the ERDF and regional funds in Sweden (approximately h3.1M vs. h4.2M during its first period).

Barriers to Biomedical Engineering Commercialisation 61 (financial and otherwise) to R&D projects focused on facilitation of innovation within the area of biomedical engineering. The projects were supported during the years 2009
2011, in the phase of developing ideas, and since 2011 for the commercialisation of them. The authors have worked as project managers, operational staff and researchers to follow the initiative and its development. This has included participation in meetings, decisions on how to develop the programme and decide its funding strategies, and interviews with owners of funded ideas. Methodologically, this chapter is hence based on qualitative, and to some extent quantitative, analysis of data generated from mainly three sources: survey data, documentations and participation in programme activities. We have surveyed the projects through semi-structured interviews on three occasions during the years 2009
2012. In 2009 about 20 projects were investigated through a questionnaire. In 2010 the number of projects had increased and 33 projects were investigated through semi-structured interviews. In 2011 the number of projects was 37 and all were surveyed through in-depth interviews, either person-to-person or over the phone. In 2012, as the programme reached its second phase, the number of sponsored projects were reduced and about 20 projects were investigated through personal or telephone interviews. The projects were during the years supported with sums ranging from about 1000 euros to 100,000 euros per year. Most projects have received financing annually, but not all. During the years the portfolio of projects has undergone rather small changes (but for the reduction of projects in the second phase of the programme), hence the majority of the projects has been the same over time. This has given opportunity for longitudinal studies. We have not used exactly the same questionnaires for all interviews; however most of the issues relevant for this study have been reoccurring. Professionally the vast majority of the respondents (and projects) are researchbased. Either the projects derive from research within academic technology institutions with focus on biomedical engineering, or from physicians at the larger hospitals within the NMT region (The region is east middle Sweden, i.e. the counties of O¨stergo¨tland, O¨rebro, Va¨stmanland and So¨rmland.) A few of the projects derive from nurses and other employees in municipal healthcare organisations such as retirement homes. The questions have been of two main types; firstly, we have asked what type of resources the project owners need to be able to commercialise their ideas and what main barriers to commercialisation they perceive. Secondly, we have asked questions that give us an opportunity to judge the projects based on their business maturity, that is questions about market, competitors, level of product development, level of business idea/model development and level of organisation development. Besides the above-mentioned interviews, documents, such as applications for project funding from the NMT programme, protocols and project presentations have been studied. We have also participated in board meetings and attended activities such as innovation fairs (annually arranged by the NMT programme) and project

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workshops, and have had discussions with the business coaches that support the R&D projects. In the analysis procedure, data from interviews, notes from board meetings and related, were coded so as to conclude what barriers were mentioned. This included the iteration of barriers, and the subsequent categorisation and re-categorisation of them (Glaser & Strauss, 1967). Initially, notes from interviews, reports, and notes from board meetings were labelled so as to capture their content. In a second round of coding (Pratt, 2009), instances describing problems or obstacles were compared so as to create initial categories from them. The iteration followed until the number of categories was reduced to a level that meant that their representation was exclusive to other categories. This part of the analysis was performed in a process moving between empirical findings and theory (Dubois & Gadde, 2002) so as to establish rigour, supporting arguments from previous research and ensure the chapter’s contribution towards previous research findings.

Results The definition of biomedical engineering within the programme has been wide, which implies that the projects consist of a range of projects, from those researchbased and of high complexity, such as fluorescence spectroscopy measurements for treatment of brain tumours and an electronic nose for detection of sepsis, to product projects of low complexity, such as a chair pad that prevents disabled people from falling out of their chair, and a specially designed bottom that makes it easier for disabled to fasten blinds in the bedroom window. Besides these there are also some projects that could be labelled as infrastructure projects, that is environments or facilities, such as test-beds where other R&D projects could test their ideas, such as a research department, where new devices are tested, and a huge database that could be used for validation of software solutions.

On Needs and Barriers From our questions regarding the respondents’ apprehension of needs and barriers for commercialisation, the main results can be described as follows: With regard to needs it is obvious that a commercial partner, that is an established firm with working market channels, is seen as an important resource. Some of the projects already have their industrial partnerships, and of those lacking such partnerships, most parties want to find one that can help commercialise their product. Co-operation with other research colleagues is another important factor. According to the interviews most projects express positive attitudes towards cooperation in general. However, in practice, and for their specific case, they are more reluctant and express fears that co-operation will lead to their ideas being stolen or that their cases are so narrow that they are forced to work alone, since there are no

Barriers to Biomedical Engineering Commercialisation 63 one else in their area. Other things needed are finance, test and verification facilities, help to make prototypes and business support, especially coupled to marketing issues. Regarding barriers, we can conclude that most of the barriers reported are correlated to the above displayed needs. Lack of finance is one of the most frequently reported — product development, tests to verify the technology, prototyping, patents, CE-marking and marketing is costly. Lack of time to work with the project was another frequently mentioned barrier — partly coupled to lack of financial resources. Opportunity to make tests in order to verify the technical solutions was also mentioned by several respondents as a barrier — it is not easy to get access to make tests in hospitals and healthcare institutions. Barriers coupled to marketing were identified as the lack of marketing and business knowledge and lack of competence to handle the public procurement rules. There are also barriers coupled to attitudes. As an example the attitude of the universities as employers, that by some projects were reported as averse towards entrepreneurship, in as much as the employer does not support the entrepreneurial strivings of their employees; ‘there is no encouragement (from the employer) to commercialise’. There are also barriers in the form of attitudes among the projects. Most of the respondents with research background put forth that entrepreneurship is not a core choice. Some expressed it as: ‘I don’t want to become an entrepreneur, I am a researcher’. Furthermore, the answers indicate that the respondents perceive that they lack enough strong driving force to manage to take the product all the way to the market themselves.

On Business Maturity We have investigated the business maturity of the projects and from this the following main results emerged; the majority of the projects are well aware of what part of their technologies should be commercialised. They have also relatively clear ideas of what should be included in their offering; however packaging of the offer is one aspect where support is needed. About one-third of the projects have solutions that have been or can be patented, another third claims that their ideas are unique, although not patentable, while the last third claims that their ideas are not spread or widely used and not patentable. Ownership, restricted by a signed agreement, is present in about one-third of the projects. For the rest, oral agreements are used and for some there are no ownership agreements present at all. Regarding business maturity, the vast majority of the projects are relatively far from a market launch, most of them are in prototyping and technology verification stages. About half of the projects have done some work to verify their market and investigate their competitors. However, according to the interviews most of these investigations are shallow. Competitor analysis is, especially among the research-based projects, commonly based on what is presented on academic conferences, that is what other research groups are doing — not of what is done in the industry. Knowledge about customers and customer benefits also seem to be rather limited for most of the

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projects. Competences in case of management, marketing and sales are, by most projects, put forth as something that they are lacking. Technology competence, on the other hand, is an area where almost all of the projects perceive that they are strong.

Analysis As indicated by the empirical data, entrepreneurship in the biomedical engineering sector is complex. The barriers can be categorised accordingly: (1) Barriers coupled to the biomedical engineering sector per se, such as security regulations, and the fact that the industry consists of large international corporations, few middle-sized firms and a large amount of emerging technology-based micro firms. (2) Barriers related to the market (customer) structure, such as regulations, public procurement matters and the fact that hospitals commonly look for holistic solutions rather than pieces and gadgets that solve isolated parts of problems. (3) Barriers related to entrepreneurship among researchers. It seems that many researchers are reluctant to become entrepreneurs, therefore starting own businesses commonly is problematic. These categories of barriers are discussed below and related to previous research. Barriers Coupled to the Biomedical Engineering Sector The biomedical engineering sector is in need of innovative solutions, however, as, for instance, Christensen et al. (2000) show, this is not easy. The sector acts as a barrier due to its characteristics. The sector is shaped like a chalice, that is a bowl on a thin pillar with a wide foot. The bowl consists of a few large incumbent corporations (Guve, 2007). These incumbent actors are reluctant to introduce new and radical innovations (cf. Christensen et al., 2000), as these are seen as threats against their own business models and product portfolios (cf. Porter, 2008). Incumbent firms are also often reluctant to be the commercial partner for the projects. The pillar symbolises the lack of SME firms and the base is the micro firms with academic origins. Among the latter, a large portion operates on consultancy basis or are started in order to hold intellectual property rights. Hence, they commonly have no growth aspirations (Ejemo & Kander, 2006). The lack of aspiration to grow both links to the attitude among the firms (see the third barrier), and to how it is difficult for the small firms to break ground in a sector dominated by the large companies. SME firms are often assumed to be more open minded to new solutions, but their number is rather small. Hence the industrial structure of the biomedical engineering sector implies that there is a lack of firms that are interested in becoming the industrial partners needed by the researchbased projects. Stimulation of firms with growth aspirations that could take a future industry leadership therefore seems important and is also provided by governmental

Barriers to Biomedical Engineering Commercialisation 65 actions such as support policies. To be able to co-operate with the large incumbent firms, the small new technology-based firms need patents. These are costly to obtain, especially if larger geographical coverage including EU- and US-patent is needed. Another barrier coupled to the biomedical engineering sector is the rules and regulations for patient security that surrounds the healthcare industry. CElabelling, for example is a prerequisite for market entry. Taken together it could be concluded that the barriers for new entrance within the biomedical engineering sector are rather large, at least for small and new firms with limited assets. In addition, intellectual property rights are important, but difficult to achieve, due to the rules of the healthcare sector that puts a focus on clinical testing and related. Among the studied projects, only a minority of them have patented or patentable solutions. This is another obstacle. Without a patent, the commercialisation strategy of licensing or sales of patent is disabled.

Barriers Related to the Market (Customer) Structure The market for biomedical engineering solution is the healthcare sector, a sector dominated by rules and regulations. The healthcare ‘market’ is restricted by strict rules for patient security (Christensen et al., 2000). Publicly owned healthcare organisations, such as hospitals also have to follow the rules for public procurement. Additionally, the healthcare actors are to a large degree publicly owned, which implies that they have to meet with the rules of governmental procurement. For new and small firms that aim to become suppliers of new solutions, these rules act as barriers to entry to the market, as the bureaucracy that surrounds them is complicated to handle. Weinberger and Weeks (2004) argue that the healthcare sector provides great opportunities for new and small firms with niche products. However, such firms commonly have limited assets to put on research (cf. Norrman, 2008). It is a wellknown fact that research institutions, such as universities, have played an important role as providers of knowledge and technology to the industry (Bercovitz & Feldman, 2007), and SMEs are therefore dependent on establishing co-operation with academic research, in order to benefit from academic breakthroughs. Therefore support in case of how to negotiate and make agreements seem to be an important task for the actors within the innovation support system. The match between research projects on one side and customer/user needs on the other side is an important aspect related to marketing. In order to be successful in commercialising new ideas these must address true market needs and solve true problems. Based on the analysis of the business maturity of the projects, there seems to be an apprehension discrepancy regarding what solutions and devices are needed and there are some indications of myopia among the researchers regarding the commercial potential of their ideas. Therefore, when it comes to commercialisation of university-based research, it is to a large extent about creation of entrepreneurial awareness among researchers (Atherton, 2007; Lundstro¨m & Stevenson, 2005), facilitation of technology transfer between research and industry (Goldfarb & Henrekson, 2003) and access to, or co-operation with, hospitals and other

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healthcare actors in order to permit ‘market/customer’ verification, clinical verification and tests (Guve, 2007; Puusepp & Malmquist, 2011). Stimulation of cooperation between university research and industry is also put forward as a solution for Sweden to become more innovative by the department of trade and industry (Frankelius & Norrman, 2013). Another aspect related to the market structure is that healthcare actors often look for holistic solutions rather than individual products or even ICT software. The solutions that researchers come up with often solve a minor part of a larger problem, but do not fit with contemporary procedure. Related to this is how new products, in order to succeed, need parallel development of a suitable business model (Chesbrough & Swartz, 2007). This is a challenge for both established firms that come up with new products and for technology-based ventures. These latter, especially those research-based that are occupied with advanced technology, are recognised as ‘technology focused’ (cf. Jones-Evans, 1997; Oakey, 2003; Westhead & Storey, 1997). Issues like business modelling and marketing are therefore often a real challenge for these firms (cf. Frankelius & Norrman, 2013; Mason & Harrison, 2001).

Barriers Related to Entrepreneurship among Researchers The last group of barriers is related to the unwillingness to actually become entrepreneurs among those owning the projects. Just because (a group of) researchers have come up with an innovative idea, this does not necessary imply that they are able to become the entrepreneurs that can take the idea to market. The result is that there is lack of correlation between R&D investments and patent activity on one hand and commercialisation on the other. According to Ejemo and Kander (2006) this is linked to academic research. Academics seem to be rather reluctant about becoming entrepreneurs, which Goldfarb and Henrekson (2003) explain as being due to a lack of incentives for researchers to become entrepreneurs. Goldfarb and Henrekson (2003, p. 655) claim that ‘researchers risk being penalized for attempting commercialising their ideas’. Teirlinck and Spithoven (2012, p. 680) argue that academic researchers, in general, ‘that university researchers are more preoccupied with basic research aimed at publication, with teaching and with administration, leaving relatively less time to engage in cooperation with industry’. One explanation for this could be that publication in highly ranked journals is a stronger merit in the academic career than, for example having filed a patent or started a research spin-off firm. The projects examined in this study clearly indicate such a problem. Few of the project owners looked forward to becoming entrepreneurs. The barrier of lack of entrepreneurial commitment is also recognised by authors such as Klofsten and Norrman (2010) and Vohora, Wright, and Lockett (2004). Atherton (2007) and Lundstro¨m and Stevenson (2005) suggest that before firms can be entrepreneurial, awareness has to be created among those that own the ideas. All types of commercialisation imply that entrepreneurship or ‘businessman ship’, at least to some

Barriers to Biomedical Engineering Commercialisation 67 extent, is needed. Either the idea-owner wants to run the race him/herself or do so through another actor. A complicating factor that cannot be neglected is the fact that innovation takes time (Drucker, 1985), and hence the new idea/venture must be given time and space to develop (Schumpeter, 1934). Time is, in this context, commonly synonymous with money, and therefore can be interpreted as the need for sufficient funding. Space could be interpreted as admission to engage in commercialisation from the department where the researcher works. In the process of transforming research into commercial products several resources and abilities are needed. The entrepreneurship literature shows that resources such as finances, business skills, legitimacy and credibility are commonly missing within research groups and therefore must be added (Barney, 1991; Birley & Norburn, 1985; Klofsten, 1992; Norrman, 2008; Oakey, 2003; Penrose, 1959; Stinchcombe, 1965; Zimmerman & Zeitz, 2002). In summary, we identified three categories of barrier: (1) barriers coupled to the biomedical engineering sector per se; (2) barriers related to the market (customer) structure; and (3) barriers related to entrepreneurship among researchers. These barriers together impact the ability of applied research projects within the biomedical engineering sector to be commercialised. They also impact the way of commercialising. The theory section described such different ways as: start-up ventures, licencing, joint ventures, divesture to external party or the free share of the idea to an established actor. In the concluding discussion, these ways of commercialising will be matched with the barriers iterated from our study.

Conclusions This chapter describes and discusses commercialisation of applied research projects within the biomedical engineering sector. It describes and categorises the barriers and relate them to different ways to commercialise innovation. The chapter points to the complexity of the biomedical engineering sector, and that several barriers for commercialisation exist. The introduction raised three research questions that are elaborated on below. What Barriers Are Present for Commercialisation of Biomedical Engineering Ideas? Three categories of barrier were identified and related to previous research: (1) barriers coupled to the biomedical engineering sector per se; (2) barriers related to the market (customer) structure; and (3) barriers related to entrepreneurship among researchers. For the commercialisation of new ideas in the biomedical engineering sector, each such barrier needs to be addressed, however, they would be complicating to various degrees for individual projects. This relates for instance to the source of the idea, whether it comes from a university of practicing nurses, for instance, the complexity of new technology and the ‘newness’ of

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the idea; that is how well it fits with current structures, and the individual attitude of the project owner. How Could the Barriers of the Commercialisation of Applied Research Projects Within the Biomedical Engineering Sector Best be Overcome? The analysis section provides some ideas on how to overcome, at least partially, the barriers identified. The barriers in the biomedical engineering sector need to be decreased through promoting interaction between the large established actors and the researchers. This in general suggests further exchanges between universities and firms in the industry. Initiatives such as spinning in and out ideas from and to the universities, and seeing the universities as grounds for further development, not just entirely new ideas, are suggested. Another important aspect is to increase the interaction between the healthcare sector actors and those parties contributing new ideas in the biomedical engineering sector. This would allow for an improved match between customer needs and research output. Additionally, it would presumably decrease barriers of ‘not invented here’ attitudes. In addition, it is important to actively promote entrepreneurship by universities and create incentives for such attempts in the academic career profile. Incubators and entrepreneurship promotion activities are other important ways to make researchers aware of what is required to launch a new venture and to strengthen their competencies within business development. To overcome present barriers and decrease risks for future ones, are processes that will take several years and are complicated, not the least when they have to do with attitudes and regulations that will still be required. However, also small measures may at least partly reduce the impact of present barriers. What Way to Commercialise Should They Choose? The theory section described the most common ways that can be used to commercialise an idea. This section discusses these ways and what barriers follow for each of them. Below we summarise these aspects. Start-Up Researchers are reluctant about becoming entrepreneurs; therefore starting their own business becomes problematic. According to our study, the alternative of commercialising the idea through their own venture appears rather unattractive to a majority of researchers. The most preventing barrier is probably the barrier in mind (the attitude to entrepreneurship). Researchers are technology focused, which implies that entrepreneurship skills are something that they lack. Incentives for entrepreneurship endeavours are low in the academic career. These problems are well known and many governmental efforts have been put upon support, such as incubators, support organisations, however, attitudes seem to need time to change.

Barriers to Biomedical Engineering Commercialisation 69 Licencing Licensing requires a patent, which in a large proportion of cases investigated seems complicated to obtain, not least due to the fact that researchers tend to show their results at conferences before patenting, and therefore risk making their findings public before patenting. It also relates to what kind of ideas is developed. Since only a third of the projects have or can obtain a patent, this way can only be used by a few. The main barrier in this case is hence the lack of a patent, related to the biomedical engineering sector. One way to minimise this barrier in the future is to educate researchers regarding intellectual property rights issues. Joint Ventures Research
industry co-operation is probably the path where the chance for commercialisation is best, however, there is a risk that this path leaves the researcher without profit. Despite this, our results indicate that joint venture with an established firm would be the most attractive way. The main reasons for this are that this way is seen as solution to overcome several of the barriers listed, especially those barriers coupled to packaging of the offer and all issues related to the market and entrepreneurship. The established firms have the business knowledge that is required. They are able to construct commercial prototypes and products and have the know-how to deal with regulations, competitors, customers and end users. Commonly they also have an established customer base. However, this way is not straightforward and among the main barriers related to this way, the following could be listed: Firstly, as described by Christensen et al. (2000) new innovative solutions are regarded as threats by incumbent firms, and therefore these firms are reluctant to engage. Following the theories of Porter (2008) on the other hand, incumbents occupied with incremental improvements create opportunities for other actors to find niches where radical innovations can be developed. The prerequisite for this is though, that the researchers are in the forefront and put their efforts on product ideas that have the potential either to replace the contemporary solutions, or can fit new markets, where cheap or lean solutions are demanded. Secondly, as we showed in the introduction, the base of suitable SME firms is small, which implies that partners may be hard to find. Thirdly, to attract an industry partnership the idea needs to have a commercial potential, that is it has to meet with a clear market need and solve a problem for a large enough number of customers to become profitable. To conclude, although this path is seen as attractive among the researchers, it is surrounded with barriers both connected to the industrial structure of the biomedical engineering sector and to barriers related to marketing issues. Divesture to External Party This path has large similarities with the one above, however, in the above case the researchers become part of the team and can continue to improve the technology. In a divesture case the technology is packaged and sold. This requires package and illumination of the benefits that the idea can create to the firms that acquire the idea and to customers and end users. This, in turn requires entrepreneurship and marketing skills. Furthermore it requires negotiation

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skills and often patented solutions. The barriers here link to the biomedical engineering sector in terms of finding a suitable acquirer of the idea. Free Share of the Idea to an Established Actor This path is best described as technology transfer and leaves no profit to the researcher. It is the one that is surrounded by the lowest barriers and the least efforts. Through publication the knowledge is spread and free for all to find and use, however, the researcher is left without the profit, and for the continuation of the idea, the input from the researcher might be needed, as is the suitable party that takes to drive the idea to commercialisation. Such a party would need to struggle with barriers related to the sector and to the market.

Contribution The purpose of this study was to describe and discuss barriers to commercialisation of applied research projects within the biomedical engineering sector. Our study shows that for biomedical engineering R&D projects aimed at the healthcare sector there are three main types of barriers that complicates commercialisations. We have also shown that there are no straightforward or sovereign path to commercialisation, but rather that irrespective of what path is chosen, the idea-owner(s) will meet with these barriers in various degrees. The entrepreneurship barrier is probably the one that is most important to deal with on short sight. Without basic understanding of customer needs and benefits, offer package and competitors, commercialisation is hard to reach.

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Chapter 5

Bringing Technology Projects to Market: Balancing of Efficiency and Collaboration Mozhdeh Taheri and Marina van Geenhuizen

Abstract Commercialization of research projects at the university, in particular, its efficiency and performance, have attracted little attention in the empirical literature to date. This despite the fact that commercialization of university knowledge is increasingly seen as a third task of universities and understanding of what enhances and what blocks the processes involved, is virtually lacking, particularly on the project level. The purpose of this chapter is therefore to identify factors that influence the performance of university-driven knowledge projects, including efficiency, in the context of commercialization of knowledge at universities. In this context, the study employs Data Envelop Analysis combined with Rough-Set Analysis on a sample of 42 projects in the Netherlands. The major factors influencing overall performance in commercialization turn out to be years of collaboration with large firms and efficiency in use of resources in the projects, but the affinity of the project managers at university with the market also plays a role. The best overall results in commercialization (introduction to market in a relatively short time) are gained with a longer period of collaboration with large firms (5
10 years) and a medium level of efficiency. There are also some contradictory trends. The chapter concludes with implications of the results, as well as some future research paths.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Introduction Developments in the 1990s and 2000s, both in the United States and Europe, including measures that regulate intellectual property rights (Mowery, Nelson, Sampat, & Ziedonis, 2004), have implied a more direct involvement of universities in the business world (Etzkowitz, 2008; Geuna & Muscio, 2009; van Looy et al., 2011). Universities are not only seen as creators of new knowledge but also as being involved in contract-research commissioned by the business sector, in collaborative research projects with business partners, in the creation of spin-off firms, etc. (Huggins & Johnston, 2009; Shane, 2004; van Geenhuizen & Nijkamp, 2006). In Europe, this new role of universities started to grow in the early 1980s (Charles & Howells, 1992) and has now fully entered the research policy of modern universities (e.g. Hussler, Picard, & Tang, 2010; Rasmussen & Borch, 2010), and knowledge commercialization today is officially accepted as a task of universities. The issue nowadays is not to establish knowledge interaction or transfer, but to improve the performance and low efficiency of the existing interaction, in a time in which the pressure from the knowledge economy and society gets stronger. A limited efficiency holds true for university spin-off firms in Europe as most of them stay (very) small (Mustar, Wright, & Clarysse, 2008; van Geenhuizen & Soetanto, 2009), for technology transfer offices suffering from a lack of capabilities (Geuna & Muscio, 2009), and for direct university
business links that are less productive due to different cultures and attitudes (Bruneel, D’Este, & Salter, 2010). Knowledge commercialization, as conceived in this chapter, refers to interaction between the university and businesses and the larger society and is the ‘process of creation of value from knowledge, by adapting it and/or making it available for economic and/or societal use, and transform it into competing products, services, processes and new economic activity’ (Innovation Platform, 2009, p. 8). Knowledge commercialization includes chains of processes (partly cycles) that start with first thoughts by researchers about market introduction, eventually together with a firm, and about steps to be taken to reach this through various channels (Bekkers & Bodas Freitas, 2008). Among the many channels used in knowledge commercialization, the collaboration between universities and firms through research projects has attracted little attention in the literature, and the level of success in such collaborations has largely remained unknown (Caloghirou, Ioannides, & Vonortas, 2003; D’Este & Patel, 2007; Gilsing, Bekkers, Bodas Freitas, & van der Steen, 2011). We mention as an exception, the study by Bekkers and Bodas Freitas (2011) on university
industry collaborative projects in the Netherlands, in which they explore factors, namely, organizational structures, that affect the performance of university
industry collaborations. Most studies that touch upon the subject suggest that there is a lack of knowledge on how research projects develop in reaching the market and on the factors determining the outcomes and overall efficiency (Nu´n˜ez-Sa´nchez, Barge-Gil, & Modrego-Rico, 2012; Perkmann & Walsh, 2007).

Bringing Technology Projects to Market 75 While commercialization of university knowledge is receiving more policy attention today, we find no literature investigating the efficiency in commercialization of research projects taking an input and output approach to the subject. Using an input
output approach is common practice in policy areas today, such as just in higher education (Jones, 2006), but this includes a broader defined efficiency of universities (Thursby & Kemp, 2002) and academic research projects (Cherchye & van den Abeele, 2005; Lee, 2011). Overall, as virtually nothing is known about the background of the performance and efficiency of research projects at university in terms of commercialization of the results, the aim of the study presented in this chapter is to explore the underlying factors to projects’ performance with regard to reaching the market. We limit ourselves to technology projects and exclude research projects, for example in sociology, psychology, history, accountancy, etc. Accordingly, the following questions are addressed: To what extent and at which level of efficiency are technology-based projects at universities reaching the market and what is the role of efficiency among other factors in this development? A database will be used which is derived from in-depth interviews with managers of technology-based projects at the university. The Netherlands serves as an example for a larger group of European Union countries that are facing the so-called ‘knowledge paradox’ of a high R&D input and a low innovation output (or growth), including Norway, Sweden and Austria and parts of United Kingdom (Audretsch & Keilbach, 2007; Bitard, Edquist, Hommen, & Rickne, 2008; ProInno Europe, 2012). The structure of the chapter is as follows. Model building, including the choice of factors in the efficiency analysis and the analysis of overall performance in commercialization is discussed in the section ‘Knowledge Commercialization Processes’. Section ‘Methodology, Data and Measurement’ deals with methodology and measurement issues. This is followed by a descriptive analysis of the sampled projects in ‘Descriptive Analysis and Model Exploration’ section. In the section ‘Efficiency Levels’, the results of the efficiency analysis are discussed and in the section ‘What Influences the Overall Performance in Commercialization?’, the results of the overall performance model are examined. The chapter closes with implications and future research paths.

Knowledge Commercialization Processes Resource-Based View on Research Projects’ Efficiency Taking a resource-based perspective (Barney & Clark, 2007), research teams at the university can be conceived as organizational units that own or access scarce resources through different channels. Though competition is not a primary driver, research teams enhance their competitiveness and strength partly through in-house resources, namely, education and experience of the team (leader), or through their networks, namely, by applying for research grants, collaboration with large firms and collaboration with other research teams at university. Accordingly, learning as

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a core function of research teams works through two main processes: (1) learning within teams, such as learning by doing and (2) learning from external sources and inter-organizational learning. Connecting with external sources, especially large firms, is regarded as very important in facilitating learning in commercialization of new knowledge and getting access to additional funding resources (Bozeman, Fay, & Slade, 2013; D’Este & Perkmann, 2011; Lee & Bozeman, 2005; Perkmann & Walsh, 2007). To be able to learn from other organizations, research teams need to develop certain capabilities enabling them to recognize the new knowledge with regard to opportunities, acquire it and, next, assimilate it with existing knowledge in the team, called absorptive capacity (Lane & Lubatkin, 1998; Zahra & George, 2002). This holds specifically for bridging different cultures, academic versus business, while being involved in both the exploration of potentials of the technology invention and in exploitation of market opportunities, an ability named ambidexterity. Thus, different types of capabilities within a research team, namely, networking with firms and mainly the manager’s ambidexterity, may affect research teams’ learning possibilities and commercialization results (e.g. Datta, 2011; Simsek, 2009). Close to ambidexterity is the preference or emotional drive needed to bridge different worlds from the side of academia, which we label in this study as affinity. Different worlds include diverse time lines which in most firms are shorter than in university research, while firms need to adapt quickly to changing external circumstances, even ending collaboration when a new technology enters the firm following a merger/acquisition, or when reorganizations dictate the closure of the R&D department of the firm. Universities, in contrast, remain quite stable in their choices (Bruneel et al., 2010). In addition, researchers are keen to disclose information in journals as fast as possible, while firms often prefer to keep new knowledge secret or to appropriate the new knowledge (Bjerregaard, 2010; Westness & Gjelsvik, 2010). There are also differing capabilities of universities and firms in handling patent applications and licensing, and different strategies for maximizing benefits from patents. Thus, while collaboration with a firm produces many benefits, a lot of obstacles need to be overcome, pointing to importance of the duration of the collaboration. On the one hand, we may assume that due to the need for timeconsuming learning processes a long-lasting collaboration is favourable in commercialization, whereas on the other hand, we may assume that from an efficiency point of view firm collaboration with firms should not take too long, including the prevention of path-dependency and locked-in situations (Dahlander & Gann, 2010; Laursen & Salter, 2006). Following a production approach to efficiency in general (Farrell, 1957) we model efficiency of the commercialization process by taking projects as units (decision-making units, DMUs) that combine certain resources (named inputs), namely, labour, financial capital and knowledge, to produce certain outputs in bringing an invention to the market. From an output-oriented perspective (Farrell, 1957) efficiency is defined as the ratio of a unit’s observed output to the maximum output which could be achieved given its input levels. The inputs in the current study include the knowledge and expertise in research teams or the ones gained

Bringing Technology Projects to Market 77 through collaboration with large firms, and financing available for the projects. With regard to knowledge, one may think of accumulated or additional knowledge and experience in the team achieved on the basis of previously performed, related, projects and on parallel projects. Accumulated knowledge and experience of the research team is very important for the outcome of research projects. In studies measuring research performance, for example Lee (2011), full time equivalent staff in research is taken as an input factor. In the current efficiency model we include the accumulated knowledge within the research team, mainly through experience of the manager at his/her current chair and through predecessor projects. Research projects may start from ‘scratch’ but also from previous research in a predecessor project. The existence of predecessor and parallel projects is an indication of expertise within a research team and may increase the speed of knowledge commercialization because the current projects benefit from the past knowledge accumulation and learning processes, for example answers to more basic questions are already given. Also, the existence of parallel projects might increase the speed of commercialization, due to scale economies and synergy between adjacent contents. Thus, we expect embeddedness of a project in predecessor/parallel projects to end up in higher efficiency. As an additional factor, the influence of financial capital is conceptualized as the financial support available to execute the project, provided by the university herself, public research foundations, large firms, etc. In general, large support may speed up the process because a larger team can be established, in technology projects, including researchers and supporting staff in experiments and in maintenance of laboratory equipment (Christensen, 2003; Utterback, 1996). Larger financial resources also allow for travelling on a global scale to connect with the best knowledge. On the output side of commercialization processes, we conceptualize a development line of the projects in view of introduction to the market. This may vary between having reached market introduction, continuation and project ceased. As knowledge takes many shapes and channels, a ceased project means the end of the commercialization line that is observed, it does not mean that the project is totally useless.

Factors Influencing Overall Performance in Commercialization In terms of factors influencing overall performance, we insert — aside from the efficiency factors — various other factors into the performance model. These factors are mainly related to the nature of the technology project, the capability of the team and characteristics of the business environment. Market introduction of a new product or process depends among others on the product/process being radical or incremental, namely, radical inventions require structural changes in infrastructures, like the fuelling infrastructure in the case of electric cars, and in related institutions, reasons why they face more obstructions than inventions that are incremental and fit into existing structures (Geels, 2004). Thus, we include nature of invention/ innovation as an important factor in the performance model.

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Accumulated knowledge and expertise (academic and non-academic) of the research team may be another important factor affecting commercialization performance. Drawing on the notion of ‘entrepreneurial scientist’ (Etzkowitz, 2008; Zucker & Darby, 2001, 2009), if a scientist has a high profile in research as well as entrepreneurial capabilities to identify and shape the applications in the market — thus high ambidexterity — he/she is more likely to be able to perform better in commercialization results. We call this type of scientist a ‘star scientist’ and define it as a manager of a team displaying a high profile in winning prizes, filing patents, publications in peer reviewed journals and managing firms, and include this in the model accordingly. The type of learning and university may also play a role in speed and result of commercialization. A distinction can be made between science-based learning, including laws of nature and know-why like in life sciences and nanotechnology, and problem-based and engineering types of learning with new applications or combinations of existing knowledge (know-how), like in medical instrument development and the automotive industry (Asheim, Coenen, & Vang, 2007; Tidd, Bessant, & Pavitt, 2009). Problem-based learning gives a greater incentive for collaborative learning between the various (regional) partners involved and it may proceed faster, whereas science-based learning takes more time and is more often globally oriented. Firms and universities, may differ in this respect, namely, technical universities place a greater emphasis on applied, problem-based learning and engineering knowledge, and thus on collaborative learning with firms, compared to the beta-faculties of general universities. From a market point of view, it is expected to identify differences in commercialization when a mass market is foreseen like in fuel cell technology with application in replacing traditional batteries in many types of devices, compared to a limited market of a new technology, such as improving lithography machines with only a few customers in the world (Tidd et al., 2009). Also, markets may differ in amount of regulation, a heavy regulation, namely, in markets for new drugs and tissue engineering, makes market introduction a long-lasting process due to the necessary testing and approval procedures, and much longer than in markets facing lower level of regulations (Tidd et al., 2009; Utterback, 1996). Serious delay may arise if regulations are tightened and specific methods of testing banned. Thus, envisaged market size and market regulation (level) are included as factors in the model. And finally, we include the urban economic environment as a factor of influence. In agglomeration theory, concerning the ‘power’ of large metropolitan areas, an emphasis is put on important advantages of being located here (Audretsch & Feldman, 1996; Combes, Duranton, & Gobillon, 2011), namely, knowledge spillovers, the presence of pools of specialized workers, test markets (launching customers) and access to global traffic nodes. A high density and variety in information and a strong presence of high-level professionals may lead to relatively high levels of creativity and better possibilities to bring knowledge to the market compared to smaller city-regions (e.g. Sassen, 2005).

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Methodology, Data and Measurement Data Envelope Analysis (DEA) This study is a retrospective study of commercialization lines of technology projects. We draw on a sample of 42 of such projects, or DMUs, to measure the efficiency in terms of using multiple inputs (resources) and gaining outputs (commercialization results). DEA, as a non-parametric approach, uses linear programming to build a piece-wise linear frontier and can be applied when there are multiple outputs without a meaningful aggregation and when the number of DMUs is limited (Coelli, Rao, O’Donnell, & Battese, 2005; Ji & Lee, 2010; Thursby & Kemp, 2002). DEA uses the input and output data themselves to compute the production possibility frontier. The efficiency of each unit is measured as a ratio of weighted output to weighted input, where the weights are calculated to reflect the unit at its most efficient relative to all others in the dataset, including an estimation of the distance function (to this frontier) (Shephard, 1970). Accordingly, DEA produces efficiency scores for each technology project by first determining the set of projects which exhibit ‘best practice’ with regard to commercialization outcomes. Thus, for each project in our sample, DEA determines whether it lies on the frontier and if not, how ‘far’ from the frontier it lies. Units that lie on the frontier are termed efficient and those not on the frontier said to be inefficient (see Appendix). There are many models within DEA, they can be distinguished according to whether they are input or output oriented (Cooper, Seiford, & Tone, 2000). In an input orientation, outputs are assumed to be fixed and the possibility of proportional reduction in inputs is explored, whereas, in an output orientation approach, inputs are fixed while the possibility of a proportional expansion of outputs is explored. As the main reason of this study is to recommend on promoting the performance of technology projects in terms of market introduction and time needed, the output-oriented model is taken.

Rough-Set Analysis DEA lacks any explanatory power of the performance of research projects in terms of commercialization, it only provides us with ‘labels’ in terms of efficiency. We use the extended model concerning overall performance in commercialization (as discussed in the section ‘Factors Influencing Overall Performance in Commercialization’) and apply RSA drawing on the sample of 42 projects to identify the factors influencing this performance. The limited size of the sample and low level of measurement of some data, namely, categorical, including a somewhat fuzzy character, prevented us using regression analysis but applying a fuzzy based analysis, RSA (e.g. Pawlak, 1991; for details, see Polkowski, 2002; Polkowski & Skowron, 1998; for a new approach, see ´ 2010). In contrast to Kłopotek, Marciniak, Mykowiecka, Penczek, & Wierzchon,

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multiple regression analysis, in RSA no assumption is made about the distribution of the data and the factors can be categorized. Data from in-depth interviews with project managers on factors influencing the commercialization results are used to develop the information table, serving as a basis for a systematic analysis of the performance of technology projects in commercialization. The information table is a matrix in which rows are labelled by objects (in this study: projects) and columns are labelled by attributes (variables). Objects are arranged on the basis of their condition attributes (C) and decision attribute (D), these two types of attributes are analogous to the independent variables and the dependent variable like in regression analysis. The basic procedure in RSA works through attribute reduction, that is finding a smaller set of attributes with the same or close classificatory power as the original set of attributes. On the basis of a reduced information table, decision rules are derived from the decision attributes based on condition attributes values. A decision rule is presented in an ‘IF condition(s) THEN decision’ format. The strength of decision rules is reflected in a measure named coverage, indicating the share of all objects displaying the same combination of condition attributes as well as the same outcome on the decision attribute. Also, we use the frequency of attributes in the rules as an indicator of strength of attributes (see Tables 5 and 6). Sample The sample encompasses 42 university-driven technology projects executed at various technical and general universities in the Netherlands, on the basis of a grant mainly from Technology Foundation STW. The projects were selected to represent different commercialization outcomes, like having reached market introduction (application in society), failed or delayed, to represent older, established since the mid-1990s, and younger projects, established since the early 2000s, and to represent different city-regions in the Netherlands, that is in the core metropolitan area and other regions. The sample covered a limited number of technology segments, that is biotechnology (medical and industrial), medical technology (instruments/software), new materials (nanotechnology) and systems for sustainable energy and automotive technologies, typically the ones addressed as ‘grand challenges’ in the EU policy document Horizon 2020 (EC, 2011). Data were collected in 2010/2011 through indepth interviews with the project managers at university using semi-structured questionnaires, including characteristics of the invention, for example newness, risks, etc., various input variables, features of collaboration with industry and details of the commercialization line of the project, including various output variables. Measurement Using DEA first, we explore five input variables and use three of them in the classification of efficiency as presented in the section ‘Efficiency Levels’: financial support, duration of collaboration with large firms and accumulated knowledge

Bringing Technology Projects to Market 81 through parallel/predecessor projects (see Table 1). With regard to the size of financial support, it is difficult to get a complete picture. Therefore, we include financial support, as provided by Technology Foundation STW and eventually other (public) programmes, only in two broad categories, a limited support and extended financial resources, according to the experience of the project manager (see also Table 1). Duration of collaboration with a large firm is measured in years, whereas embeddedness in other projects (predecessor/parallel) is measured in three categories, one of the two exists, both exist, and no other projects exist (the last category means that the project under study starts from scratch and as a single project). The other two variables used in the exploration of efficiency, namely, amount of experience of the manager in holding the chair in the faculty and affinity with commercialization, are measured as number of years between starting the professorship and the end of the project or end of the observation period (2011), and as preference/emotional involvement in commercialization using three categories, respectively. We measure the commercialization output of technology projects in the efficiency analysis using various categories of outcomes: ceased, partially continued, continued in research, continued in pilot, market introduction. In addition to that, we also measure the satisfaction of the manager with the commercialization outcome to add a subjective dimension to the commercialization outcome, using a scale from 1 to 10. Filing a patent is not considered in this study as a commercialization output because it is not always relevant to market introduction while it may also take place early in the commercialization process or later on. It needs to be mentioned that start of the commercialization process (line) is measured in the interviews as ‘year in which for the first time the way to market introduction has been considered in a serious manner along various practical steps’, according to the project manager. This could be years after having started the STW project, for example after four years of PhD research, but also before having received STW support. In our next step, the ‘causal’ analysis using RSA, we explore aside from the efficiency, three variables connected with the research and research team, namely, nature of the invention, being a star scientist and type of university. Nature of the invention is measured in two categories, namely, radical and incremental. Being a star scientist is ‘captured’ as honoured by at least one large national prize and successful in receiving grants from the Dutch National Science Foundation (NWO), as well as grants from programmes of applied national/European research. The type of university is measured in three categories: general, technical and a combination through collaborative projects between different universities. We also explore a set of environmental influences concerning the market and the city of location. Envisaged market size is assessed by the project manager (respondent) and measured in three size categories, whereas the level of regulation in the market is measured using three categories of strength, for example with new drugs and bioimplants in the strongest regulated market. And finally, the type of city-region is measured in three categories, a core metropolitan area, a city-region outside the core and a combination of both due to collaboration between universities in different city-regions.

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Descriptive Analysis and Model Exploration The analyses reported in this chapter fit into a larger study in which development trends were identified in commercialization of technology projects drawing on a larger database (van Geenhuizen, 2013). On the basis of projects financed by Technology Foundation STW it was found that among older projects (take-off in 1995
1997) and younger projects (take-off in 2000
2002) more than a quarter (26 per cent) had failed in the sense that the project ceased without having reached the market. In addition, shares of 22 and 15 per cent, respectively, have been introduced to the market. The last development indicates a quite negative situation, but it needs to be realized that large shares of projects continue to be developed towards the market, eventually facing some years of stagnation (about 55 per cent). The size of the population of projects taken off in the above-indicated years was almost 370 and allows some statistical generalization for the defined population. With regard to the in-depth analysis of projects in terms of efficiency and commercialization performance in this chapter, it needs to be mentioned that the sample is selected in order to include substantial contrasts on factors thought to determine these outcomes, excluding a statistical generalization. In terms of economic sectors, the sampled projects are mainly concerned with the medical sector (59.5 per cent), sustainable energy (materials), including energy saving (24 per cent), and solid waste and waste water treatment (9.5 per cent) and the rest (7 per cent) has no direct link with these challenges. In total, 33 project leaders are involved, in 4 cases 2 leaders for 1 project and in several cases 1 leader for 2 or 3 projects. Among the project leaders we find 39 per cent so-called star scientists (13 out of 33) (Table 1). With regard to experience of the project manager there is a large variation, on average 12 years with a standard deviation of 10.0. Differences in affinity with the market can be summarized as follows. Most project managers do have a large affinity (50 per cent), with the share ‘very large affinity’ somewhat bigger (28.5 per cent) than the share of small affinity (21.5 per cent). Sixty-two per cent of the projects aim at incremental improvements to current technologies while 38 per cent of projects point to radical innovations and breakthroughs. Projects in the medical sector, namely, developing therapeutic drugs, diagnostics and tissue engineering (29 per cent) are in heavy regulated markets that may hinder commercialization processes, some projects that develop new surgery tools and imaging equipment are subject to medium level regulation (29 per cent) and for the rest of projects, there is low level of regulation (43 per cent). The envisaged market size of the innovation is well spread, with a large market for almost half of the projects (48 per cent) and a small market for more than a third of the projects (36 per cent). Collaboration with large firms or other market organizations takes on average five years, with some differentiation, witness a standard deviation of 5.2. The other major resource, financial support, is qualified ‘limited’ for 38 per cent of the projects, as opposed to 62 per cent qualified as ‘more financial resources’. Referring to ‘adjacent’ knowledge and accumulated experience as resources, the

Bringing Technology Projects to Market 83 Table 1: Descriptive statistics of project efficiency and overall performance factors. List of Variables

Measurement

Number of projects Star scientist

Two categories (dummy variable) Experience of project Years between manager (DPR) starting the professorship and end of project Affinity of project manager Three categories with the market Radicalness of invention/ Two categories Innovation Regulation level in market Three categories

Envisaged market size

Three categories

Duration of collaboration with large firms (DCF)

Years between starting the collaboration and project end Two categories

Financial support

Presence of parallel and predecessor projects

Three categories

Type of university

Three categories

City-region

Three categories

Outcome of DEA: Level of efficiency

Three categories

Remarks 42 Star scientist (39%) Non-star scientist (61%) Average: 12.1; SD: 10.0; min
max: 0
31

Very large (28.5%); large (50%); small (21.5%) Radical (38%); incremental (62%) Low (42.8%); medium level (28.6%); heavy regulation (28.6%) Large (47.6%); medium (16.7%); small (35.7%) Average: 5.1; SD: 5.2; min
max: 0
18

Limited financial resources (38%); more financial resources (62%) Absence (21.5%); presence of predecessor or parallel project (50%); presence of both (28.5%) Technical (54.7%); general (33.3%); combination due to combination of universities (12%) Core (52%); non-core (31%); combination due to collaboration between universities (17%) Low: 40.5%; medium: 26.2%; high: 33.3%

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presence of parallel and predecessor technology projects differs within the sample, with 50 per cent having one of the two projects present, 21.5 per cent both absent and 28.5 per cent both present. The sampled projects are mostly executed at technical universities (55 per cent), this is followed by general universities (33 per cent) and combinations of these universities based on collaboration (12 per cent). Fifty-two per cent of the sampled projects are at universities in the core metropolitan area of north Randstad (Amsterdam) and south Randstad (Delft) while 31 per cent is executed in the noncore area of south-east Netherlands (Eindhoven, Maastricht, as smaller cityregions); the remaining 17 per cent are based on collaborations between universities in both types of city-regions in the Netherlands. An introduction to the market has occurred for 11 of the sampled projects (26 per cent) as more than half continued in pilot and research activities sometimes without ever bringing the knowledge to the market (Table 2). In addition, we labelled the projects with regard to overall performance which also includes the time dimension. A project that is launched to the market, especially in a short time, is assigned the highest score. This type of scaling, including a check for robustness, produces the following performance as the dependent variable (decision attribute): Medium levels are observed for a share of 55 per cent of the sample, a relatively low level of performance for 19 per cent and a relatively high level of performance for 26 per cent. Project managers are relatively satisfied with the commercialization process, on average a 7.1 with a min
max of 4
9. Low scores are given if after some time of trial and error, no large firm turns out to be interested in bringing the project to market. The average time of commercialization processes, from the first ‘thinking Table 2: Outcomes of technology projects in terms of overall performance. List of Variables Commercialization line

Measurement In five categories

Overall performance In four categories incl. commercialization line and time neededa Satisfaction with the Ranked from 1 to 10 project (manager) Time to bring knowledge Years between first to market commercialization thoughts and market introduction a

Categories Ceased (16.5%); partially continued (5%); continued in research (43%); continued in pilot (9.5%); market introduction (26%) Low (19%); medium
low (38%); medium
high (17%); high (26%) Average: 7.07; SD: 1.41; min
max: 4
9 Average: 7.18; SD: 4.31; min
max: 1
15

We checked for robustness of the classification using various experiments.

Bringing Technology Projects to Market 85 on commercialization’ to launch to the market in these cases is 7.2 years with a standard deviation of 4.3 years, while it takes at least one year and at most 15 years to launch the new product/process to the market in our sample.

Efficiency Levels Using DEA, we start with a small number of inputs and outputs that are considered to be essential in evaluating the efficiency of research projects in terms of commercialization outcomes, while, progressively, more variables are added and their influence on model results are studied through a forward procedure (Cooper et al., 2000). The following steps are taken. Initially, DEA is applied to a dataset of three inputs and two outputs, namely, duration of collaboration with larger firms (input), financial support (input), existence of predecessor and parallel project (input) and commercialization outcome of the projects (output), while also paying attention to satisfaction of the manager (output), presented in M1, Table 3. Then at each successive step another relevant variable is added to the model and the additional efficient projects are identified. We maintain the rule that the minimum number of projects should be three times greater than the number of inputs plus outputs (Lee, 2011) and we limit the number of input and output variables to three and two to be able to interpret the results in a proper manner [42 > 3(3 + 2)]. Since this study is a first trial to apply DEA to measure the efficiency of technology projects in commercialization, the models were selected based on their commonality and applicability to the dataset. Applying different type of models (CRS & VRS) ensures that we may

Table 3: Summary of results of DEA analysis. Input and Output Variables Duration of collaboration with large firms Financial support Predecessor and/or parallel project Manager’s experience as a professor Manager’s affinity with commercialization Outcomes in terms of commercialization Outcomes in terms of manager’s satisfaction Average score Standard deviation Minimum value Number of efficient DMUs Correlation with M1 a

Included in the assessment.

M1

M2

a

a

a

a

a

a a a

a

a

a

a

0.57 0.24 0.2 8

0.78 0.24 0.37 16 0.71

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reach robust results in evaluating the projects efficiencies. More details and explanation are given in Appendix. A summary of the results is in Table 3. In the first assessment model (M1), we include three inputs and two output variables that could best describe the efficiency of commercialization projects. Applying the CRS model, eight projects are found to have a score of one, which means that they are the most efficient ones in the model. By including more input variables we reach the second assessment model (M2), indicating 16 projects at the highest level of efficiency. The correlation between the first model (M1) and the second model (M2) is high and significant (Spearman’s rank correlation = 0.71). This level of correlation makes us decide to take the results of M1 (efficiency variable) into account in the next step of the study, also because the number of efficient DMU is limited to eight, allowing for more variation in the sample compared to model M2. Next, a brief description will be given of the profile of most efficient projects, given the three input variables. Most efficient in terms of a quick market introduction is the following profile: a low level of investment, benefits from a predecessor or parallel project and no collaboration with a large firm. Note that efficiency analysis draws on the idea of a minimum input of resources. As a final step in this part on efficiency, we explore the relationship between efficiency level and overall commercialization performance of the technology projects (Table 4). Thirty-six per cent of the projects can be labelled with medium to high efficiency (>=0.5) and medium to high level of performance, while, 33 per cent of the projects has both low efficiency ( 20 yrs. Efficiency: 1, low (between 0.2 and 0.4); 2, medium (0.5); 3, high (between 0.6 and 1). MS (envisaged market size): 1, small; 2, medium; 3, large. C-Region (City-region): 1, non-core; 2, collaboration core and non-core regions; 3, core.

88

Mozhdeh Taheri and Marina van Geenhuizen The results of the best model can be summarized as follows:

• Among the condition attributes, duration of collaboration with large firms and research project efficiency have the highest frequency among the strongest rules (number 1, 2, 4 and 7), indicating an important influence of these attributes on overall commercialization performance. • Among the rules, three are best by number of projects covered and the strength of the rule (rule 1, 2 and 7). Rule 1 is the best given a strength of 75 per cent and coverage of six projects. The rule indicates a negative trend in which affinity of the project manager with commercialization is low and the (relative) length of collaboration with large firms is less than 0.5 leading to an overall performance at a low level (1). Rule 2, with a coverage of six projects and strength of 37.5 per cent indicates the trend that a longer collaboration with large firms (between 0.5 and 1.5) and a lower level of commercialization efficiency (less than 0.4) produce a lower medium level of overall performance. By contrast, rule 7, with a coverage of four projects and strength of 36.4 per cent, indicates the trend that a longer period of collaboration with large firms (between 0.5 and 1.5) together with a medium efficiency (50 per cent) produce the best results in terms of launching the product into the market in a relatively short period of time. There are also some contradictory trends, like in rule 6, as a combination of small affinity and absence of collaboration. Furthermore, we explore next best model by excluding the condition attribute ‘duration of collaboration with a large firm’ from the model and including the following four condition attributes: affinity of the project manager with commercialization, years of experience of the project manager as a professor, efficiency of the research projects and envisaged size of the customer market. The model reaches a quality of classification of the attributes/attributes in the core of 0.70/0.70, which is weaker than that of the previous model. Rules with a minimum coverage of two projects are presented in Table 6. The strongest rules can be summarized as follows: • Rule 1 is the strongest rule at a strength of 37.5 per cent, indicating the negative trend of a low level of affinity of the manager with commercialization and a small size of the customer market, resulting in the lowest level of performance in commercialization. This rule can be understood as follows, if the manager does not care about commercialization aimed at introduction of the invention to the market and the market is also expected to be small, the commercialization is at its lowest performance level. • Rule 2 shows the highest coverage of 5 out of 16 projects, and indicates the trend that more years of experience as a professor (between 10 and 20 years) and a low level of efficiency of projects result in a medium
low level of project performance. This rule confirms the idea that professors at higher levels of experience may find it difficult to move towards commercialization, due to a different routine for years (sticky routines).

Bringing Technology Projects to Market 89 Table 6: Best rules produced excluding collaboration with large firms. No.

1 2 3 4 5 6

Rule (Strongest in Bold)

Decision Attribute

Coverage (No. of Projects)

Strength (%)

VA = 1 & MS = 1 DPR = 3 & Efficiency = 1 MS = 3 & VA = 3 & Efficiency = 1 MS = 3 & DPR = 1 & Efficiency = 1 MS = 2 & VA = 2 MS = 3 & DPR = 4 & Efficiency = 2

1 2 2 2 3 4

3 5 3 2 2 2

37.5 31.25 18.75 12.5 28.6 18.2

Decision attribute (project performance): 1, worst; 2, medium
low; 3, medium
high; 4, best. Selected condition attributes: VA (commercialization affinity): 1, small; 2, large; 3, very large. DPR (duration of professorship): 1, 0 < tpro < =5 yrs; 2, 5 < tpro < =10 yrs; 3, 10 < tpro < =20 yrs; 4, tpro > 20 yrs. Efficiency: 1, low (between 0.2 and 0.4); 2, medium (0.5); 3, high (between 0.6 and 1). MS (market size): 1, small; 2, medium; 3, large.

• Rule 5 shows a moderate strength of 28.6 per cent, including two projects. It indicates a trend that relatively positive commercialization results can be gained with medium-sized markets and large affinity of the project manager with commercialization. Looking back to the RSA results, we have to recognize that particular trends are relatively strong, with small affinity, difficulty in connecting with firms, small efficiency, ‘sticky’ routines and an unattractive market as important obstacles. While some rules are contradictory to some previous strong rules, a situation that might indicate new trends in commercialization. For example, affinity of the project manager and collaboration with large firms may not matter anymore in situations where the market (customers) is already involved in the development of the invention at the start of the commercialization using models of co-creation. This may occur in so-called co-creation labs at university where researchers and firms collaborate in experiments at early stages.

Conclusion and Future Research Paths Given the European knowledge paradox, indicating a possible failure to ‘convert’ basic research into products and processes that can be used in the market, we explored commercialization of university-driven technology projects through efficiency and overall performance on the project level, in one of the first studies in its kind.

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Efficiency was measured based on diverse outputs concerning the commercialization line and satisfaction of the manager while taking the following inputs into account, namely, financial support, networking resources (years of collaboration with large firms) and team resources (presence of predecessor and parallel projects) into account. We applied DEA to find the frontier set of most efficient projects. Most efficient in terms of a quick market introduction is the following project profile: a low level of financial support, benefits from a predecessor or parallel project and no collaboration with a large firm. Note that efficiency analysis draws on the idea of minimizing input of resources. Next, we used the scores from DEA in an exploratory ‘causal’ analysis of overall project performance in terms of commercialization, alongside some other factors. Using RSA, the results achieved were stated in terms of rules indicating the following trends. Duration of collaboration with large firms and technology project efficiency tend to be important influences on the basis of their highest frequency in the strongest rules. In addition, a low affinity of the project manager with commercialization coupled with short collaboration with a large firm indicates low performance results. This trend clearly indicates persisting barriers. In contrast, a longer period of collaboration with large firms together with medium efficiency (50 per cent) produce the best results in terms of launching the product into the market and short length of commercialization period. By means of exploring a next best model, we found that if the manager does not care about commercialization (low affinity) and the market is also expected to be small, the commercialization tends to be at its lowest performance level. This situation confirms the idea that managers at higher levels of experience (longer periods) may find it difficult to move towards commercialization if market opportunities are not clear. Overall, we found a significant positive relationship between outcomes of efficiency of resources use and of overall commercialization performance. A positive relationship between project efficiency and project performance is clearly suggested by the analysis, meaning that project teams using more resources in producing outputs are less likely to bring technology projects to the market in a relatively short time. However, in some cases, just a medium level of efficiency works positively. The study also had some limitations. It drew on given data meaning that we were limited in selecting the input and output variables, especially since some of the variables namely, amount of financial support and size of research teams were not available. However, it should also be recognized that financing by a particular programme is often not something that stands on its own but may enjoy advantages from additional financing by the university or other programmes, which is difficult to measure exactly. In addition to this, future studies might use larger samples in applying stronger techniques to identify, for example non-linear relationships, namely, regression models. Again, it needs to be recognized that knowledge is ‘fluid’ causing fuzzy situations and fuzzy borderlines, situations with which RSA clearly complies. Moreover, some other influencing factors such as the structure of institutions in shaping university
industry relationships and accelerating commercialization projects (Perkmann & Walsh, 2007) could not be taken into

Bringing Technology Projects to Market 91 account in the current study, but these may be investigated in the future. And finally, there is a growing need and willingness to better use knowledge created at university, for example to develop solutions more quickly to persisting societal (sustainability) problems (e.g. Breznitz & Feldman, 2012; Goddard & Vallance, 2013; Trencher, Yarime, McCormick, Doll, & Kraines, 2014), like connected to climate change, traffic congestion, ageing population, health care, food and safety, etc., calling for an increased attention on factors that inhibit knowledge transfer and interaction on the project level.

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203. Zucker, L. G., & Darby, M. R. (2001). Capturing technological opportunity via Japan’s star scientists: Evidence from Japanese firms’ biotech patents and products. The Journal of Technology Transfer, 26, 37
58. Zucker, L. G., & Darby, M. R. (2009). Star scientists, innovation and regional and national immigration. In D. B. Audretsch, R. E. Litan, & R. J. Strom (Eds.), Entrepreneurship and openness: Theory and evidence (Vol. 2). Industrial Dynamics, Entrepreneurship and Innovation. Cheltenham, UK: Edward Elgar.

Bringing Technology Projects to Market 95

Appendix Consider a simple example of a single output and a single input for each of six research projects in Figure A1. The line linking A
D is the best practice frontier, among which no one dominates the others and each successively uses more input and produces more output. Research projects E and F are dominated by others, for example project C uses less input and produces more output compared to F. E and F lie below the efficiency frontier. The (input oriented) efficiency of project E in Figure A1 is OB/OE. DEA-CRS Output O

C B

E

DEA-VRS

D F

A

G

Input

Figure A1: DEA production frontier. Using CRS model, the efficiency of any project like E below the production frontier is calculated as follows and if E locates on AD line (the same point as B) the efficiency is equal to 1: DEA-CRS: OB/OE. The line connecting A
D is the frontier using VRS model. The efficiency of any project like E below the production frontier is calculated as follows and if E locates on A
D line (the same point as B) the efficiency is equal to 1: DEA-VRS: OB/OE.

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Chapter 6

High-Tech Entrepreneurial ‘Soft Starters’ in a University-Based Business Incubator: Space for Entrepreneurial Capital Formation and Emerging Business Models Fumi Kitagawa and Susan Robertson

Abstract This chapter examines the processes of entrepreneurial network and capital formation at a university-based incubator. Incubators could help overcome start-up firms to gain access to entrepreneurial networks and credibility with external stakeholders, by supporting the entrepreneurial processes including the acquisition of variety of capitals and resources. However, the actual evidence on the effectiveness of incubators as a policy tool for business support has been rather contested. This chapter makes a contribution to the entrepreneurship literature by addressing the underlying processes of incubation as a key factor critical to achieve accelerated firm growth at the university-based technology incubator. Drawing on interviews and survey of start-up firms at a university-based incubator, co-evolution of business models with capital mobilisation and re-combination of resources is illustrated. The chapter concludes by arguing that more detailed processes and trajectories of ‘soft starter’ business model would contribute to the understanding and development of policy support for entrepreneurial processes.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Introduction Incubators aim to help new start-up firms by supporting the entrepreneurial processes including the acquisition of business contacts, business skill sets and financial resources (Patton & Marlow, 2011). In order to pursue their business opportunities, entrepreneurs need to acquire various critical resources, particularly funding, at the very early stage of their start-ups. Whilst new start-up firms have limited access to entrepreneurial networks and lack credibility with external stakeholders, incubators could help overcome such liabilities. Studies show that nascent entrepreneurs often have limited experience of business and its context. Incubators are intended to link technology, resources and know-how to entrepreneurial talent for the purposes of accelerating the development of new companies, and thus speed the commercialisation of technology (Markman, Siegel, & Wright, 2008; Minshall & Wicksteed, 2005; Peters, Rice, & Sundararajan, 2004). This would condition their ability to successfully ‘engage with the entrepreneurial process of converting new ideas and opportunities into viable and sustainable ventures’ (Patton & Marlow, 2011, p. 913). The empirical study is based on a technology incubator located in a university. Existing work on academic entrepreneurship mostly focuses on the formation of university spin-offs (USOs) and their role in commercialising university research (e.g. Rothaermel, Agung, & Jiang, 2007). Incubators provide a hybrid space where early start-ups, both USOs and spin-in firms, develop social ties and build entrepreneurial resources and skills. This chapter, therefore is concerned to understand the underlying processes of incubation (Hannon & Chaplin, 2003), with particular attention paid to the institutional dynamics within the early stages of incubation, such as processes of firm network formation, combination of different resources at various phases of incubation and the effectiveness of the policy support to help connect different resources and relationships. This chapter focuses on a technology incubator within a university where nascent entrepreneurs learn to build new start-up firms through mobilising heterogeneous forms of capital that are then strategically deployed in inter-organisational firm behaviours. Specifically, the chapter builds on Chiles, Bluedorn, and Gupta’s (2007) theoretical challenge that the study of entrepreneurship must include the processes through which entrepreneurs act and interact, and the ‘broader organization, industrial and societal context in which they operate’ (2007, p. 478, emphasis added). The following questions are asked in this chapter: • In what ways, does a university incubator help high-tech start-up firms build capabilities through networks formation and a variety of resource mobilisation? • In what ways do start-up firms develop different business models in order to combine different resources and capitals? In order to investigate these research questions, the qualitative case study was conducted at a high-tech business incubator based at the University of Bristol in the United Kingdom. Based on the online survey results of the firms, and face-to-face

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 99 interviews with the Incubator Director and the founders of the firms located in the Incubator, we examine firms’ interactions, resource mobilisation and distinctive forms and processes of ‘capital’ formation. Whilst it is difficult for this study to provide a quantitative measure of how much the university incubator can aid spin-out companies on the business side, the qualitative case studies illustrate the ways in which the incubator supports interactions and networks through which firms combine different forms of resources and may develop forms of capital throughout their growth. The rest of this chapter is structured as follows. The chapter begins by focusing on the policy context of university-based technology incubators in the United Kingdom, identifying the gaps in the existing knowledge on processes of incubation for successful firm creation involving nascent entrepreneurs. The third section moves on to outline the theoretical framework for our study, conceptualising different forms of ‘networks’, ‘resources’ and different ‘capitals’ at work through entrepreneurial processes. In order to examine different contexts and trajectories of entrepreneurial business development, the chapter draws on the concepts such as a ‘soft business’ model (Connell & Probert, 2010) or ‘soft starters’ (Helm & Mauroner, 2011). In the second half of the chapter the research context and methodology are detailed, before presenting the research findings from the empirical study. In the concluding sections it is argued that university-based technology incubators should be viewed as a unique space that mobilises and makes available ‘knowledge heterogeneity’ where nascent entrepreneurs create, combine and mobilise different forms of networks, capital and resources.

Technology-Based Incubators and Policy Contexts in the United Kingdom The potential value of small firms characterised as ‘high-technology firms’ and/or ‘high-growth firms’ (see Anyadike-Danes, Bonner, Hart, & Mason, 2009) have been a policy focus in the United Kingdom for the past decade (e.g. DTI, 2002, 2003, 2004), particularly as larger manufacturing firms have tended to move abroad for cheaper and differently regulated locations and markets, such as China. High-tech entrepreneurs typically encounter the ‘liability of newness’ and ‘information asymmetry problem’ (Patton & Marlow, 2011; Zhang & Wong, 2008). In this context, the development of incubators has had support within government, because of the ‘acclaimed success’ (Hannon & Chaplin, 2003, p. 861), arguably positive impact of incubators upon job and wealth creation (e.g. Storey, 1994) on one hand, and the ‘apparent market failures’ (Patton, Warren, & Bream, 2009, p. 622) in the development of high-tech and/or high-growth firms, on the other. However, the nature of business support activities varies as well as their focused technology areas and financial and business models of their start-up support at each incubator (Becker & Gassmann, 2006; Cooper & Park, 2008). The actual evidence on the effectiveness of incubators as a policy tool for business support has been rather contested. In the United Kingdom, the development of

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university ‘technology-based incubators’ over the past decade seems to have been based on several, sometimes inconsistent policy assumptions related to the building of a competitive ‘knowledge-based’ economy. Firstly, the UK government, as well as many other governments in industrialised countries, has looked to universities’ research as an engine of innovation in the knowledge-based economy (see Lambert, 2003; Sainsbury, 2007; Wellings, 2008). With policy initiatives and public funding available for ‘knowledge transfer’ activity, there has been a growing pressure for universities to put more emphasis on transferring and commercialising knowledge from their research. The development of USOs has been seen as a favoured route by the government for commercialisation of knowledge from universities and a number of academic studies have been conducted on this subject (see Minshall & Wicksteed, 2005; Patton et al., 2009; Wright, Clarysse, Lockett, & Binks, 2006; Wright, Clarysse, Mustar, & Lockett, 2007). USOs are defined as ‘new ventures that are dependent upon licensing or assignment of [an] institution’s intellectual property for initiation’ (Lockett & Wright, 2005, pp. 1044
1045). In the United Kingdom, whilst rates of USO activity are reported to have increased markedly since the late 1990s (Hewitt-Dundas & Burns, 2014), recently, there has been a concern expressed among policy makers that USOs were being given ‘undue prominence’ in consideration of a broader research commercialisation performance of higher education institutions (Lambert, 2003; see also Minshall et al., 2008). Studies in different national contexts show that whilst USO formation rates have continued to grow in the past decade (e.g. Clarysse, Wright, Lockett, Van de Velde, & Vohora, 2005; Fini, Grimaldi, Santoni, & Sobrero, 2011; Mustar, Wright, & Clarysse, 2008) concerns are expressed about the ‘cost-benefit to universities from supporting these firms’ and the ‘high failure and low-growth rates’ of these businesses (Colombo & Piva, 2012; see also Hewitt-Dundas & Burns, 2014). The second policy assumption contends that the development of incubators has had supports within government because of the ‘apparent market failures’ (Patton et al., 2009, p. 622) in the development of high-tech and/or high-growth firms, and their positive impact upon job and wealth creation (Hannon & Chaplin, 2003). A recent study shows that the majority of UK technology incubators are supported in full, or in part, by government programmes offering a training ground for entrepreneurs and that many are focused on the commercialisation of science and/or technology-oriented applications (Patton et al., 2009; Warren, Patton, & Bream, 2009). Incubators have developed with diverse roles and purposes, which is characterised as the ‘dichotomies relating to the potential polarisation of incubator objectives’ (Hannon & Chaplin, 2003, p. 861). This ranges from ‘property development focus’ typically based at science parks with management of tenants, to ‘business development focus’ providing variety of innovation support, also contributing to the brand building of the local businesses. Incubators may have few or even no relationship with universities and university research; whereas university incubators can act as a ‘knowledge hub’ for the local/regional innovation system (Youtie & Shapira, 2008) by providing knowledge and skills from the universities. Thirdly, another policy assumption or a myth is that venture capital is the primary source of finance of high-tech entrepreneurial firms. A recent study (Brown,

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 101 Mason, & Mawson, 2014) argues that whilst high-growth entrepreneurial firms are perceived to be funded largely by sources of entrepreneurial finance such as venture capital or business angel funding, in reality, most high-growth firms, which are not technology-based, fund their businesses through bank loans or retained earnings. Furthermore, other studies show that even many of the technology-based start-up firms seem to follow what has been called a ‘soft business’ model (Connell & Probert, 2010) or a ‘soft starters’ (Helm & Mauroner, 2011) model, without relying on the venture finance, at least in their early phase of the business. Fourthly, the recent study point out that most of the current government support instruments are designed to create high-growth firms strongly by focusing on ‘transactional forms of support’ (Brown et al., 2014) in the form of R&D grants, standardised business development support services offered and public sector venture capital co-investment schemes, mostly aiming at technology-based firms, most of which don’t grow. For potential high-growth companies, many of which are nontechnology-based firms, more flexible, responsive and relational support, including ‘peer-to-peer support and specialised advice such as support for Management buyouts or acquisition of another company’ (Brown et al., 2014), is needed. With regards to the support for technology-based companies, it is believed that the best way for the government to support technology development in companies is ‘by funding multi-partner research collaborations between universities and private sector firms’ (Connell & Probert, 2010). The report for EEDA (Connell & Probert, 2010) argues that the success of high-tech companies rather relies on ‘solving customer problems and paid R&D contracts’. They suggest that a series of new policies are needed to ‘encourage more R&D contracts between small companies and lead customers’. The EEDA report also points out the significance of the firms that grow slowly through ‘regional-based consultancy and client contracts’. In light of these policy expectations and available evidence, combined with a review of wider literature, several gaps in our knowledge about university technology-based incubators are identified, which this chapter contributes to. Firstly, whilst existing studies have advanced general understanding of spin-out behaviour from universities, there are still a number of gaps in our knowledge around understanding the mechanisms and processes of incubators, for example inter-organisational network management between universities, other intermediate research institutes, innovation support organisations and the growth of high-tech venture firms, both USOs and spin-ins. Where located on these incubators, local ‘spin-in’ firms have access to university talents such as academics, graduates and students, and through them, would gain new networks and tacit knowledge. However, there is little understanding of such processes. University-based incubators can be important mechanisms for the local/regional development (Etzkowitz, 2002) by providing knowledge and skills from the universities. Incubators may be expected to play significant roles in territorial development through the spin-off process, as they support networking opportunities by bringing in financial resources (e.g. venture capital investors) from outside the area, by negotiating with the university and local government, in fostering innovation and business culture in the local area, and offering legal and daily business assistance

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(Peng, 2006). They also attract local people and local companies, and provide a space to develop a new business with interactions with research at the universities (see Druilhe & Garnsey, 2000; Patton & Marlow, 2011; Waters & Lawton Smith, 2002). Even if located adjunct to a university campus, incubators or firms located in the incubators may have few or even no relationships with universities and university research. However, when located in such incubators, arguably local entrepreneurial firms also will have access to university talent, such as academics, graduates and students, and through them, can arguably gain new ideas, networks and tacit knowledge. Furthermore, this leads to a question as to how universities might interact in their local communities to exchange knowledge and assist in advancing commercially viable firms based on ideas from the university. This is a key for the university incubator management to developing effective policy support for entrepreneurial processes of local and regional development. There is a dearth of research investigating how nascent entrepreneurs gain access to a variety of resources that they need, how they learn to combine these resources in new ways and thus turn these into new knowledge, new opportunities, new businesses and regional or national competitive advantage. Entrepreneurial firms acquire new resources and build new business models as new business opportunities arise. Companies tend to acquire new technologies and competences that align with their business models (Gassmann, 2006). A recent study shows that many technology-based entrepreneurial firms adopt a ‘soft business’ model (Connell & Probert, 2010) or a ‘soft starters’ (Helm & Mauroner, 2011) model, including ‘technical consulting firms’ and ‘research boutiques’. Other business models include ‘start-ups with product-oriented attitudes from day one’; and ‘soft starters’ or ‘transitional start-ups’ with specific intention to become product-oriented companies (Helm & Mauroner, 2011). In order to understand a variety of business trajectories and resource mobilisation processes, a further analytical framework is needed to aid empirical research. In particular, as discussed above, there seems to be a mismatch in the perceptions about the financial resources that entrepreneurial firms rely on and the kind of support mechanisms required.

Entrepreneurship, Networks and Capital Theory Recently, a growing number of studies focus on the networks in which a firm is embedded, examining the network relations of spin-offs and their social capital formation (see Alvarez, Marin, & Fonfria, 2009; Anderson & Jack, 2002; Anderson, Park, & Jack, 2007; Soetanto & van Geenhuizen, 2015; Walter, Auer, & Ritter, 2006). Hughes, Morgan, Ireland, and Hughes (2011) show that incubator networks act as an ideal setting in which firms build relations with other incubating firms (‘localised social capital’) and with external firms affiliated with the incubator network (‘externalised social capital’). In order to understand the formation of networks and mechanisms through which entrepreneurial firms grow, the variety of

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 103 combined resources and perceived opportunities of entrepreneurs have been deemed to be of value, by drawing on the entrepreneurship literature and resource-based theory (Alvarez & Busenitz, 2001). Entrepreneurs discover resources and assets as they create new ways of ‘using assets to produce goods’ and services and respond to new market opportunities. However, there is little knowledge on ‘how entrepreneurs, through experience, develop entrepreneurial knowledge that enables them to create entrepreneurial opportunities to organise and manage new ventures’ (Karatas-Ozkan, 2011, p. 879). Alvarez and Busenitz introduce the concept of ‘entrepreneurial recognition’ — defined as both the recognition of opportunities and opportunity-seeking behaviour — as a resource. They also treat the ‘process of combining and organizing resources as a resource’ (p. 756). According to Alvarez and Busenitz (2001) entrepreneurial recognition and resource organisation are ‘heterogeneous’ because they are based on different information, personal backgrounds, heuristics; they are rooted in path-dependent processes that are difficult to emulate as they embody much tacit knowledge; and they are highly immobile, because they are typically linked to specific resources with which they co-specialise. Entrepreneurs’ task consists primarily of ‘choosing among combinations of specialised labour factors and heterogeneous capital assets’ (Foss, Foss, Klein, & Klein, 2007) whose ‘combinations … will be ever changing, will be dissolved and re-formed’ (Lachmann, 1977, p. 13). The theoretical conceptualisation of ‘capital’ in relation to entrepreneurship processes is still underdeveloped (see Erikson, 2002; Stringfellow & Shaw, 2009). In this light, Lachmann’s (1977) social constructivist ‘capital theory’ is particularly useful for the purpose of understanding the incubation processes of entrepreneurs in a formally organised, university-based technology incubator space. In Lachmann’s formulation, capital theory refers to ‘the capital structure’ or ‘intermediate goods’ which entrepreneurs deploy at particular times in order to produce consumer or final goods. According to Lachmann, these are the plans and other forms of goods, in particular the knowledge these goods embody. It is imperative to understand the processes and contexts of creating ‘knowledge heterogeneity’ by identifying rather different kinds of ‘capitals’ which are available and can be, and are, put to use in university incubators by nascent entrepreneurs in the creation of small firms. Applying a ‘knowledge-based view’ of the firm, Becker and Gassmann (2006, p. 1) identify different types of ‘knowledge that facilitates hatching and leveraging of technologies’ through the incubation process. Drawing on the work of Bourdieu (1986), and his development of different kinds of capitals (such as economic, social, cultural, symbolic and organisational) that can be deployed as part of the capital structure, the different ways in which the complexities of incubation itself can be dissected as part of the capital structure. Access to economic capital such as business angels and venture funding sources as well as subsidised infrastructure (such as rent, secretarial services, equipment), and targeted use of social capital in the form of trusted networks (Fuller-Love & O’Gorman, 2011) of expertise which operate within and outside of the university and across the boundaries of the university and the world of business are two

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distinct forms of capital to be made available to, and be mobilised by entrepreneurs. Furthermore, reputation and relationships within and outside of their technology and R&D communities seems to be critical for the high-tech start-up firms, which constitute the brand of the university-based incubators. In other words, being located in a university-based incubator often provides tenant companies with symbolic capital through ‘branding’ (Gassmann & Becker, 2006), with the name of the university as well as the scientific credibility (Marangos, Kitagawa, & Warren, 2010). Cultural capital is a resource which emerges as a result of learning from a resident expert and other nascent and more experienced entrepreneurs in the incubator, including a variety of institutional supports and contacts. Development of cultural capital and its utilisation in developing other forms of capital are closely linked to ‘relational and experiential learning’ (Karatas-Ozkan, 2011, p. 882) of nascent entrepreneurs. Finally, organisational capital derives from ongoing organised learning opportunities in the nascent firms arising from the activities within and beyond the incubator.

Research Context and Methodology Research Methodology This chapter focuses on the processes of entrepreneurial capital formation and networks and interaction between firms at a university-based incubator. The findings are drawn from a case study method (Yin, 2003) at one particular university-based incubator with sub-sets of firm case studies conducted between September 2009 and November 2010. The choice of case study method is justified as many of the interactive aspects of entrepreneurship and growth, such as networking and other institutional processes, can be better understood through qualitative analysis, especially by means of case studies. The research was designed in order to illustrate the particular context of the incubator, processes and the nature of incubation activities of particular firms, their characteristics and the ways in which inter-organisational relationships are developed and resources are mobilised. The case study combined with mixed qualitative research methods at the Incubator comprises the following: the primary online survey data with 24 start-up firms collected in collaboration with the Incubator Director; a further sub-set of case studies selected from the Incubator, based on face-to-face interviews. The interviews were supplemented by secondary data sources including annual reports and information available on the company websites. Initially, data were collected through conversations with a number of the university senior research commercialisation professionals in the Research and Enterprise Development unit, with the Incubator Director who acted as a ‘gate opener’ for our study, as well as a short period of observation at the Incubator site. The initial exploratory communication provided a detailed understanding of the Incubator at Bristol, and the personnel involved, and the relationship with the university. The

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 105 online survey was conducted in close collaboration with the Incubator Director between July and November 2010. In total, 24 member firms of the Incubator Centre responded to the online survey, which account for around 80% of the tenant firms at the time. The collection of data was facilitated by the Incubator Director, and all the data collected through the survey was anonymised. Out of 24 firms who participated in the online survey during the summer 2010, 3 are university spin-out firms (12.5%), and the rest of the respondents are ‘spin-in’ firms; that is, entrepreneurs with ideas who come from outside the university. The number of employees per firm varies between 1 and 54. The oldest firm was established in 1999, prior to the creation of the SETsquared incubator; the youngest firm was formed in 2010, a few months before the survey was conducted. A small number of face-to-face interviews with firms’ Directors and founders were conducted between September and November 2010 in order to illustrate the specific organisational contexts of individual start-up firms and their entrepreneurial processes. The interviews were supplemented by publicly available information including the firms’ website.

Contexts of the Incubator An important institutional background of the study needs to be noted — the university incubator is part of the partnership structure of the four universities, the ‘SETsquared Partnership’. The SETsquared is a partnership established in 2002 between four research intensive universities (Bath, Bristol, Southampton and Surrey) in a relatively wide area in South of England, encompassing the South West and South East regions. The SETsquared Partnership was funded following a process of competitive bidding including the Higher Education Innovation Fund (HEIF), a public initiative in England created to fund innovative collaborative projects between universities for enterprise activities. Since then, SETsquared has had four rounds of HEIF funding and is one of the longest HEIF funded initiatives. In 2011, University of Exeter joined the partnership as the fifth university. Launched in 2003, the Incubation Centre, the ‘SETsquared Acceleration Centre’ at the University of Bristol, is located adjacent to the university’s computer science department and Engineering Faculty, both of which are considered to be the two most enterprising parts of the university. As a high-tech start-up incubator, there are a number of ongoing links with these departments. For instance, SETsquared’s ‘Entrepreneur in Residence’ runs enterprise modules for the Computer Science degree so that students get exposed to many aspects of business creation, planning and acquiring the necessary skills to succeed in business. The Incubation Centre was awarded ‘Established Business Incubator of the Year 2008’ by the UK Business Incubation (UKBI) in recognition of its work with some of the early stage, high-technology, high-growth start-up businesses. As of November 2011, there were 36 firm members at the Incubation Centre, including start-up firms of various stages of development; there are also a growing number of ‘graduates’ from the Incubator. Between 80% and 90% of the firms incubated come

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from ideas from outside the university (spin-ins) whilst the rest (10
20%) come from inside the university (university spin-outs). This is a common feature across the four incubators as part of the SETsquared Partnership (Patton & Marlow, 2011; Robertson & Kitagawa, 2010). This ratio between university spin-outs and spin-in firms may reflect the nature of the incentive structures in place for start-up firms: the incubator is attractive to local entrepreneurs from outside the university because of several factors: the lower cost of rent for a start-up firm (begins lower then increases over time with the success of the start-up); they have shared access to administrative support; support through personal high-quality mentoring (a combination of incubator personnel/ business mentors); and access to wider industry and enterprise networks. Nevertheless, what makes the SETsquared Incubators unique is the potential benefits from being located close to the university, with access to university facilities and resources, including academic consultancy, students for projects and recruitment of graduates. The partnership of four universities also gives a powerful ‘brand value’ (Gassmann & Becker, 2006) to attract investors, with joint investor events held in London, and international scientific and financial linkages with San Diego, supported by the UK government (Marangos et al., 2010). In other words the brand value of the incubator in the university is a form of ‘organisational’ and ‘symbolic’ capital. These linkages and connections mediated by the incubator create ‘multiple-levels of spatiality’ of resources for the start-up firms (Marangos et al., 2010).

Empirical Observations and Analysis Network and Social-Economic Capital Creation The role of the Incubator Director as a ‘mediator of knowledge’ is the key in network formation (Robertson & Kitagawa, 2010). The unique brokerage or knowledge mediation role played by the Incubator’s Director in forming networks and interactions between firms is also noted in another study (Patton & Marlow, 2011). The Incubator Director has a wide range of business experiences, and has also ‘run and grown and exited technology businesses’, himself. He describes his approach as having the capacity to spot ‘talent’, to take that entrepreneurial talent and their idea to ‘pre-incubation and grow on’, and in doing so, building a ‘critical mass’ within the incubator to ensure that there is a buzz around the place through mentoring and a number of business support activities. He has also positioned the Incubator as a ‘shop window’ for the university. As he argues, the Incubator provides ‘the power of interaction between the companies’ (interview with the Director, 2009). However, the proactive network creation within the incubator with an aid of the Incubator Director might constrain other resource mobilisation opportunities. The Incubator creates and promotes networks externally within and beyond the local area. With regard to within the local area, the Incubation Centre founded the

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 107 local ‘Incubator Forum’ in 2006 to connect practitioners and stakeholders of business incubation in the city for the benefit of all early stage businesses. The membership includes other incubator facilities and support organisations in the city region area including other start-up support organisations, Business Link and the City Council. The university initiated Bristol Enterprise Network (BEN) in 2003, the network of high-tech, high-growth organisations in the Bristol city region. BEN is now independent from the university, extended the activities to Bristol and Bath areas and is associated with Science City Bristol, with support from the universities of Bristol, Bath, and West of England. The Incubation Centre has worked closely with BEN and their business members. In relation to beyond the local area, through the SETsquared Partnership the Incubation Centre in Bristol is part of a series of highly successful regional networks: Silicon South West, Low Carbon South West and open MIC (mobile innovation camp). The networks have grown rapidly in the region connecting entrepreneurs, industry and the universities. They have created sector-focused clusters of firms in the region and are open to firms at other partners. There are other regionally based, sector-focused networks, iNets South West, in microelectronics, aerospace and advanced engineering, biomedical, creative industries and environmental, supported by the European funding, which funds early start-ups.1 Firms located at the Incubation Centre have access to these local and regional networks, and some benefit financially from these networks. According to the survey, at least half of the firms located in the incubator seem to be providing consultancy services, and software and application based services and they do not rely on venture investment. Several firms have received grants and other public funding as part of their finance, combined with investment with friends and family; and individual investment. Only one firm identified venture capital as a major financial resource.

Cultural and Social Capital — Interactions of Firms within the Technology Incubator The survey results confirm that the Incubator creates and promotes linkages between the university and new high-tech companies with emerging technology, through potential research collaboration, alumni and student employment and more general collaborative interactions. The benefits of being at the Incubation Centre is summarised by the following comment from one of the firms (online survey, free comment): support, advice, services and facilities provided by SETsquared have put us on the right path. We’ve gone from a company making £20,000 a year to making £375,000 a year since we moved into SETSquared.

1. iNets South West (http://www.inets-sw.co.uk/, accessed on June 7, 2012).

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In terms of the interactions and networks among firms within the incubator, the key findings of the online survey are the following. Half of the respondents answered that they have had direct interactions with the University of Bristol over the last year, including the use of facilities, collaboration with academics, hosting student projects, acting as a consultant to the university research project. One of spin-out firms had two company board members from the university and have had a variety of relationship including sponsoring PhD projects, managing undergraduate projects and helping with the university’s enterprise workshops. The multiple relationships with the university also include business transactions — acting as a consultant, supplier and client. These interactions create the opportunity to acquire a range of knowledge and skills that enable them to talk to a wide range of potential communities that are valuable to advancing the growth of the firm. In terms of the interactions with other firms within the Incubation Centre, one of the questions asked: ‘How much do you interact with other companies at the Incubation Centre?’ as part of the online survey. The most common forms of interactions were through ‘workshops and networking events’; ‘social interactions physically around coffee area at the Incubator’ and ‘other social occasions’. Twenty per cent of the firms at the Incubation Centre indicated that they have commercial relationships with other firms at the Incubator. These relationships help build social capital and are thus available to the entrepreneur as a resource.

Human Capital and Heterogeneity of Resources In terms of human capital acquisition, the question was asked: ‘In terms of the staff that you have recruited, where have they come from (just before they worked for you)?’ Sub-sets of the question asked the geographical origins and sector origins of the employees. The University of Bristol provides human capital to the majority of the entrepreneurial firms within the incubator, in terms of both ‘fresh’ graduates and alumni graduates, both temporary and permanent jobs. The four spin-out firms from the university draw human capital directly from the university, including the founders, and also in one case, the company board members. Out of the 21 ‘spin-in’ firms, 7 firms answered that ‘the original founder team of the firms had members from University of Bristol alumni’. The survey results show that the graduates from the University of Bristol provide the pool of human capital for the incubating firms, and sometimes the contacts are made through student projects and PhD studentship. There is an apparent link between the social networks and interactions described in the section above and the flows of human capital between the university and the incubating firms. Geographically, whilst most of the firm employees come from Bristol and surrounding areas, there are cases of international recruitment also — one of the university spin-out firms answered that three staff were recruited from the United States. To supplement this picture, in terms of the ‘sources of management and directorship expertise’ in the SETsquared partnership (including incubators at Bristol, Bath,

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 109 Southampton and Surrey), a recent study shows strong links of the start-up firms with Oxford, Cambridge and London areas, implying the technological
financial linkages across the high-tech sector (Marangos et al., 2010). Entrepreneurial start-up firms draw human capital from a variety of industrial sectors, too. The survey responses demonstrate that one-third of the firms have employed people from different industrial sectors. This heterogeneity, or ‘variety’ of knowledge, competence and skill sets as well as the ‘relatedness’ (Boschma & Iammarino, 2009) of technological and industrial competences needs further investigation in order to understand the competitiveness, growth and sustainability of the business.

The Soft Business Model, Organisational Capital and Resource Combination Most of the firms at the Incubation Centre seem to follow one of the ‘soft starter’ business model identified by Helm and Mauroner (2011): • ‘service-oriented firms’ or ‘technical consulting firms and research boutiques’; • ‘start-ups with product-oriented attitudes from day one’; and • ‘soft starters’ or ‘transitional start-ups’ with the specific intention to become product-oriented companies. Whilst located at university incubators, these early start-up companies accumulate and mobilise their various resources as ‘capitals’ — including access to both ‘transactional forms of support’ and ‘relational support’ (Brown et al., 2014) such as mentoring by entrepreneurs, business advice; access to finance and business development — from entry to university enterprise competitions to contacts to wider venture communities; and access to space and credibility. The relationship between the firm and the Incubator Centre also changes over time. The Incubation Centre acts as catalyst to mobilise heterogeneous resources at different stages of the firm development, and there is a symbiotic relationship between the Incubation Centre, development of different forms of capital and mobilisation of heterogeneous knowledge and resources. In order to better understand the processes and contexts of ‘soft starters’, more information is required in terms of these firms’ development including consultancy and client contracts; as well as supports they received from public R&D grants and other local public funding mechanisms.

Conclusion: Towards Networks and Capitals Formation of Entrepreneurship Business Models University technology incubators are recognised as one of the ‘institutions that bridge the gap between the science base and industry’ (Frenz & Oughton, 2005), by facilitating links between universities and the potential users of knowledge.

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However, the processes and contexts of such institutions and relationships need further unpacking. This chapter focused on the role of ‘capitals’ in the formation of incubators. As illustrated earlier, university incubators attract local people and local companies, and provide a space to develop a new business with interactions with research at the universities. This has a significant effect not only on the extent and speed of incubation and growth, which has been rather understudied in the academic entrepreneurship literature, but also on the ways in which incubators help nascent entrepreneurs learn to acquire and combine resources in new ways. The analytical and empirical challenge remains in investigating the entrepreneurial processes and performance of firms over time. This chapter presented qualitative processes through which firms accumulate their capabilities by combining and mobilising different knowledge and resources. Being placed within the university with access to the brand image of the university, by using the incubator as a site for learning and connecting resources, those nascent entrepreneurs within the incubator are able to work at, and work out, those combinations and re-combinations of networks, capitals and knowledge that will be most valuable in developing themselves as entrepreneurs, and growing their businesses. Networks include both everyday social interactions and contacts to wider business, and social networks that are built for the purposes of selective engagement with and appropriation of new knowledge, contacts and opportunities. Network relations develop between the university and entrepreneurial firms, as well as between the firms, and the incubator connects external networks to the nascent entrepreneurs as they combine and develop various forms of capital. This chapter is based on a limited number of firms sampled. The findings are not generalisable and explanatory power of the case study is still limited. Further empirical evidence is required to investigate such accumulative and iterative processes. For example, Social Network Analysis can be conducted in order to better understand the structural relationships created by the incubator — comparing networks between USOs, the university academics and external partners on one hand, and between ‘spin-in’ firms and the university academics and external partners, on the other. The relationships with venture capitalists (Muller, Westhead, & Wright, 2012), the roles of Board of Directors and their positions in the social networks (see Hewitt-Dundas & Burns, 2014) seem to be worth exploring further in order to identify different forms of resources that are mobilised in terms of different types of ‘proximity’. The evolution of firms’ relational assets and heterogeneity of capital over time deserves further investigation in order to better understand how different forms of capital interact and influence each other, developing different types of entrepreneurial business models. Business models co-evolve with capital mobilisation and re-combination of resources — for example, changing from being ‘soft starter’ to ‘product-oriented firm’ with the accumulation of financial capital. For future studies, conceptually as well as empirically, the nature of different forms of capital and business model evolution needs further investigation. More detailed processes and contexts of trajectories of ‘soft starter’ model would contribute to the understanding of as well as development of policy support for

High-Tech Entrepreneurial ‘Soft Starters’ as Business Models 111 entrepreneurial processes. This will include further analysing ‘heterogeneity’, or ‘variety’ of knowledge, competences and skill sets as well as the ‘relatedness’ of technological and industrial competences and organisational forms in order to understand the competitiveness, growth and sustainability of the entrepreneurial businesses.

Acknowledgements The main empirical work was conducted at University of Bristol in 2009
2010 supported by the ESRC-funded LLAKES (Learning and Life Chances in Knowledge Economies and Societies) Research Centre — grant reference RES-594-28-0001. We acknowledge the kind support from the Incubator Director and the start-up firms, who participated in the study. Any remaining errors in the chapter are responsibility of the authors.

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PART III CLUSTERS AND ENTREPRENEURSHIP

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Chapter 7

The Dynamics of Industrial Clustering in the German Enterprise Software Sector Joachim Viehoever

Abstract Enterprise software is a predominant sector in the European software industry. Four of the five largest European software companies are found in this sector. Interestingly, two of these — among them SAP as one of the two global market leaders — are located within the same industrial agglomeration in South-Western Germany. This agglomeration, the SAP cluster, further consists of enterprise software SMEs forming a ‘satellite system’ centred around the large players, which fosters the formation of ‘mutualistic symbiotic’ relationships between large and small firms. At first sight, cluster formation in the context of the enterprise software industry might seem perplexing considering that traditional rationales of agglomeration economies seem obsolete in an environment where advances in communications technology would permit companies to locate in any location within a modern developed economy instead of concentrating in proximity to each other or to major players in the industry. This chapter explores possible explanations of this agglomeration phenomenon based on patterns of competition, collaboration and the formation of social capital between smaller firms and large anchor firms. The findings of a comparative analysis between the SAP cluster environment and two categories of controls (firms in other agglomerated environments and those unaffected by agglomeration effects within Germany) show that SAP cluster SMEs might simultaneously benefit from heightened intensity of competition and a more pronounced inclination towards collaboration. Moreover, the role of social capital derived from SAP as anchor firm clearly differentiates SAP cluster participants from firms located within other environments.

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Introduction In Europe, the largest 100 software companies generated combined revenues of 27.1 billion Euros in 2009 (Truffle Capital, 2010). Remarkable in this industry is the high extent of concentration: the Top Five companies’ share of industry revenues was 56%, the largest three players still had a 50% share of the entire market in terms of revenues, and SAP, the largest player, alone accounted for 40% of revenues within the industry. The picture moreover shows that business applications are a predominant sector in the European software industry. Sixty-seven per cent firms in this ranking of Europe’s 100 largest software firms form part of the enterprise software sector, generating revenues of 22.5 billion Euros. Four out of five of the largest European software companies are found in this sector. Interestingly, two of these — among them SAP as one of the two global market leaders — are also within the same industrial agglomeration in South-Western Germany. Notwithstanding this concentration found in the market, SMEs continue to play an important role. Increasing saturation in the corporate market segment leaves primarily the mid-market and small customer segments for expansion. These markets are characterised by intense dynamics of competition in which enterprise software SMEs traditionally have performed strongly (partly since larger players had largely neglected these segments in the past). This results in a particular competitive constellation in which larger players often need to follow acquisition-based expansion strategies (particularly in penetrating the mid-market segment) and, moreover, depend on the creativeness of smaller firms as a stimulus for innovation. Simultaneously, major innovative shifts still are driven by larger players who have sufficient bargaining power in a globalised B2B market. Thus, the industry is characterised by mutual interdependence between SMEs and large players. Consequently, large software vendors have sharply increased their efforts to maintain an ecosystem of collaborative arrangements primarily consisting of SMEs offering services, consultancy and add-on products which are complementary to their own product range. In the case of Germany’s software giant SAP this even finds an expression in the geographical agglomeration of enterprisesoftware-related SMEs centred around the headquarters of SAP in South-Western Germany. The significance of this agglomeration may be highlighted by the fact that 82 (or 8%) of the 1022 companies found in the German partner directory of SAP are located in the Heidelberg and Mannheim areas (i.e. in a radius of about 35 km around SAP’s headquarters) and, moreover, 161 (or 15.8%) are located within a radius of about 100 km SAP’s headquarters (SAP Partner Information Center, 2010). This phenomenon may occur to some degree counter-intuitively — namely despite the common disregard for location as a factor in this industry context. Explanatory rationales of clustering include traditional agglomeration economies approaches, particularly the existence of a labour pool facilitating access to skilled talent in this people-driven industry.

The Dynamics of Industrial Clustering 119 Frequently, research on clustering in software-industry-related contexts is based on observations of a combination of different sectors of the software industry, in other words, research takes an overall perspective of contemplating the software industry in its entirety. While this might be legitimate in many instances, this research specifically distinguishes between the enterprise software sector and other segments of the software industry based on the idiosyncratic mechanisms and constellations at work. In the light of business development and strategy, the enterprise software sector should be seen as a clearly distinct segment delimited from the many other types of software such as technical and medical applications; software embedded into technical products; software used for simulation, construction and design; game software; telecommunication and office applications. There are many reasons for this distinctiveness: Markets for many other software products are predominately consumer markets, and are therefore subject to a completely different kind of market dynamics. This applies to game software, telecommunications and office applications. In other cases, software may be viewed as components of other end products (such as embedded software) or be used in contexts not related to business. In the case of enterprise applications, however, the success hinges on managing the competitive B2B environment. The software product becomes an integral part of the customers’ business processes — as a result, interdependencies between software vendor and customer firms are significant. Enterprise applications software becomes interwoven in the customers’ operations affecting even its strategy and performance, almost like a part of the value chain. As a consequence of this interdependence, long-term relationships and issues related to reputation and legitimation become essential growth constraints for enterprise software SMEs. Competitiveness consequently is often predicated on socially based networking with peer firms (collaborative arrangements and competitors), customers and software vendors. Links through social networks to the software vendor are crucial in accumulating technological and marketing knowledge — especially in the light of the competitive constellations. Agglomeration could therefore be seen as an attempt to maximise serendipity though the accumulation of industry-related ‘social capital’. This study highlights differences observed in various constellations in terms of agglomerated versus nonagglomerated settings in the enterprise software industry contemplating the environment of SAP in the context of the German market as an illustrative example.

Literature Among the dimensions used in the analytical description of clusters are the cluster-inherent dichotomy between competition and collaboration, horizontal and vertical relationships, and network-related externalities radiating perhaps even globally. Marshall’s (1890) seminal explanation of clustering based on labour market pooling — resulting in ‘a more efficient firm-worker matching mechanism’ (Wheeler, 2007), input sharing and technological spillovers combines both vertical

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and horizontal aspects, as does Porter’s (1998, pp. 77
78) definition: ‘clusters are geographic concentrations of interconnected companies, specialised suppliers, service providers, firms in related industries, and associated institutions … in particular fields that compete but also cooperate’. The horizontal perspective is predicated on the notion of specific constellations of competition and collaboration prevalent in clustered environments. The mechanisms through which increased levels of competition may benefit cluster participants are assumed to be predicated on efficiency forces arising from an increased pressure of substitution (Dei Ottati, 1994) — that is companies losing their competitive advantage if incremental standards set by competitors are not adopted, coupled with improved transparency and optimal observability (Dei Ottati, 1994; Maskell, 2001). The cluster-specific constellation of horizontal competition of smaller firms allows ‘parallel experimentation and testing’ (Maskell, 2001) to take place in the most efficient way. In this sense, from an overall economic system’s perspective, clusters perform in a superior way compared to the hypothetical construct of an allencompassing solitary company collapsing the regional-level industry’s production into one entity (Maskell, 2001). According to Chellappa, Sambamurthy, and Saraf (2010) the ‘interaction of multimarket contact and market overlap’ entails superior firm performance in the context of the enterprise software industry: ‘Enterprise systems software vendors can significantly benefit from participation in crowded markets when they have higher multimarket contact with their rivals’. Intensified competition in clustered environments might be conducive to these multimarket contacts. In this context, a further mechanism at work in environments characterised by higher competitive density might be mutual forbearance leading to ‘tacit collusion’ (Chellappa et al., 2010; Feinberg, 1985). While the literature largely converges on the existence of competition as an essential but not sufficient prerequisite of a cluster (Hill & Brennan, 2000; Porter, 1998), the nature of collaboration is a more equivocal topic. Collaboration within clusters is often seen as related to the higher potential for complementarities (Maskell, 2001; Porter & Stern, 2001) within environments characterised by diversity and high extent of specialisation. According to Porter and Stern (2001), ‘complementary relationships involved in innovating are more easily achieved among participants that are nearby’. Collaborative arrangements based on complementarities may be crucial for SMEs within clusters given their limited resources (Mohannak, 2007). But doubts have been raised with respect to the efficacy and prevalence of collaboration particularly on the non-complementary horizontal level. Few advantages may be gained from collaborative externalities in terms of technological innovation strategies and R&D activities. Therefore, ‘the advantages provided by clustering, involving external collaboration with local firms within the cluster, will be less advantageous than is commonly asserted by advocates of cluster development’ (Oakey, 2007, p. 237). In a similar vein, Maskell (2001) suggests that locational economies exist that ‘are independent of the internal degree of interaction’: ‘While suppliers and customers simply need to interact with each other in order to do business, competitors don’t. … firms in the horizontal dimension of the cluster … may interact regularly. … On the other hand, they might just as well hate each other

The Dynamics of Industrial Clustering 121 intensely, never exchanging anything useful’ (Maskell, 2001). Consequently, the majority of collaborative linkages may be on the vertical rather than the horizontal dimension (Maskell, 2001; Porter, 2000). One aspect of collaborative behaviour, however, may comprise elements of both the vertical and the horizontal dimensions and may be idiosyncratic to the enterprise software industry: that of collaboration between SMEs and major software vendors which, in turn, might find its expression in the typology of the hub-and-spoke cluster (Gray, 2006; Press, 2008). Hub-and-spoke clusters might be determined by the history of large industry leaders at their centre and perhaps even more so by the history of related downstream industries. One of Krugman’s (1991) models is predicated on fixed costs and transportation costs as determinants of centres of gravity during industry formation. One might deem these factors irrelevant in the context of the enterprise software industry. However, history and demand proximity matter. As Campbell-Kelly, Danilevsky, Garcia-Swartz, and Pederson (2010) have shown for the US software industry, clearly a significant concentration in relatively small number of major metropolitan areas occurred during the early stages of industry growth. They adopt a Krugmanstyle explanatory model for the software industry context and attribute the observed clustering phenomenon to advantages arising in terms of fixed cost minimisation from concentration of software development activities leading to proportionally lower hardware costs (single large mainframes in one location being disproportionately more cost-efficient as compared to several smaller mainframes in several locations). Moreover, regional demand linkages were a further factor in local concentration as a consequence of ‘transportation costs’ stemming from sales and service intensity of early software products, in other words the fact that marketing and support staffs’ visits to customers were an essential part of business (CampbellKelly et al., 2010). This still applies in the context of the enterprise software sector — which relies to a high extent on customisation of software packages involving presales activities, consultancy and other support services. In the same vein, Klimenko’s (2005) argumentation that agglomeration-generating mechanisms in young and highly innovative high-growth industries differ from those relevant in mature manufacturing contexts and can be attributed to ‘high labour intensity and non-tradability of upstream products and services’ — although perhaps less applicable to other more standardised and consumer-orientated software sectors — also applies to the enterprise software sector as a consequence of the need for customisation and service-orientation. In this sense, Klimenko’s (2005) notion of a ‘lack of stable interface standards between vertically linked segments of the high-growth industry’ implying that supplier products are customised to customer’s specification could be relevant in promoting agglomeration economies. This potential divide between different sectors of the software industry in its entirety is also expressed by Campbell-Kelly et al. (2010, p. 238): ‘In addition, sectors of the software industry where proximity to customers matters more (the corporate software products sector) may exhibit clustering patterns that are different from those where it does not matter at all (the personal computer software product sector)’.

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Methodology This research focuses on constellations found with respect to agglomeration specifically in the context of the enterprise software industry. Analysis centres around the evaluation of differences and similarities found between firms located in clustered environments and those unaffected by clustering — moreover, concentrating on SMEs up to 350 employees. Being the largest market for enterprise software in Europe, the German market was selected as a representative environment for this research. As a specific case of a cluster in the enterprise software industry, the agglomeration of SMEs around SAP as one of two global market leaders in the enterprise software sector was selected. Inclusion into the SAP cluster was defined as location within a radius of 100 km centred around SAP’s headquarters in Walldorf (Baden), Germany. The cluster area is roughly equivalent to the RhineNeckar region — a major conurbation in South-Western Germany — plus the region surrounding the city of Karlsruhe to the South and the area towards the south of Darmstadt. A survey-based research design relying on data collection using structured, questionnaire-based interviewing was selected as basis for quantitative analysis using various statistical techniques. The ideas behind this research are predicated on a definite delimitation of the enterprise software sector from other sectors of the software industry. Unfortunately, this delimitation is not reflected in most commonly used industry classifications in such a clear-cut way. Thus, the relevant NACE codes (cf. Table A.4) do not permit differentiation between different sectors of the software industry. The usefulness of commonly used company databases even as a selection tool for the population under investigation is compromised not only by this lack of detail in classifications systematics but also by the fact that most SMEs in this sector are not publicly listed and information on small privately owned companies frequently is not available on these databases. To cope with these shortcomings, multiple sources were used to compile a dataset which as closely as possible represents the complete population of the enterprise software sector in Germany: (1) the commonly used Hoppenstedt (2010) company database (selection was based on the use of industry-specific keyword in addition to NACE codes), (2) the partner directories of SAP (SAP Partner Information Center, 2010), Oracle (Oracle Partner Network, 2010), Microsoft Dynamics (Microsoft Dynamics, 2010) and IBM Cognos (IBM Cognos, 2010), (3) the ‘ERP finder’ and ‘WLW’ databases (ERP finder, 2010; WLW, 2010) — Internet-based commercially orientated marketing databases aimed at the German market containing firms offering enterprise-software-related products and services (including consultancy). The resulting database of companies was filtered again with respect to industry affiliation, size and residency. The result of this exhaustive selection process was a

The Dynamics of Industrial Clustering 123 database specifically designed for this research containing information with respect to addresses, locations, industry segment and size category on 2644 companies (after filtering the original selection of 5314 companies). This remaining set of companies (as quasi-representation of the entire population) was selected for the subsequent survey and contacted in its entirety first by mail (a feedback sheet was included) and e-mail. Subsequently, follow-up telephone calls were made in the case of nonresponse to the initial mail-outs. Data was collected by interviewing owners, management directors or other senior management involved in strategic decision making of the companies using a predefined questionnaire. Interviews were conducted personally by the researcher on the companies’ site in cases where respondents were located within the SAP cluster. In other cases, the interview was conducted via telephone. In a minority of cases (approximately 15%) the questionnaire was returned by respondent in a written format after ascertaining that the question were fully understood. This measure was accepted given the dilemma of being faced with a relatively small population characterised by highly limited accessibility and the need to achieve the highest possible number of cases for statistical purposes. The resulting data comprises 206 cases with over 100 variables each — representing 7.8% of the entire population.

Empirical Analysis and Interpretation In this section, I turn to an empirical analysis comparing the behaviour and characteristic constellations found in the environment of the agglomeration centred around SAP vis-a`-vis other environments. Since a representative sample reflecting the population in its entirety was available as a consequence of the comprehensive research design selected, comparative scenarios could exploit three different sets of firms: first of all, the ‘quasi-experimental’ set as representation of SAP cluster participants; secondly, controls consisting of firms that are not in agglomerated geographical environments; and, thirdly, a control group of firms from other industrial agglomerations.1 This permits bivariate analysis comparing measurements across the three different groups (i.e. focusing on differences of the SAP environment vis-a`-vis either of the control sets). Much of the data consists of categorical ordinal-scale (frequently Likert-type scale) or binary rather than genuinely continuous variables. For these ordinal-scale variables normality was tested by using the non-parametric Kolmogorov
Smirnov test statistic. As the results of the Kolmogorov
Smirnov test show, none of the variables meets the requirements set for the normality assumption. The identical procedure was followed for the remaining small number of quasi-continuous variables. Again, the

1. The following major conurbations or metropolitan areas containing high concentrations of enterprisesoftware-related firms were interpreted as further agglomerations in this industry context: Rhine-Main (Frankfurt), Stuttgart, Munich, the Cologne-Bonn Metropolitan Region and Hamburg.

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Kolmogorov
Smirnov test disproves the normality assumption. As a consequence of the failure to adhere to the prerequisite of normality the use of the t-test statistic for the purpose of comparison would be inappropriate. Instead, the following empirical analysis is predicated on the evaluation of the non-parametric Mann
Whitney U test statistic for all non-binary variables. Conversely, in the case of binary variables Fisher’s Exact test is used for identifying potential deviations between experimental group and controls — the null hypothesis with respect to all variables examined naturally being that there are no differences between the experimental group of SAP cluster participants and either one of the control groups (applying to both Fisher’s exact test and Mann
Whitney’s U). A further point merits mention. As this research is concerned with effects and mechanisms that are structured by firms’ locations, it is necessary to be stringent with respect to the representation of location and geographical structure. In this respect, obviously sampling results in proportional deviations regarding the frequency of firms in the sample vis-a`-vis the frequency in the population if categorisation according to geographical structure were introduced, in other words over- or under-sampling in certain geographical areas. In order to compensate for sampling deviance and to achieve results reflecting structural conditions present in the population, a post-stratification approach is established. This is possible, as population proportions are known as a result of establishing a study-specific industry-wide company database. The strata implemented as a representation of geography-related structural conditions are orientated along the lines of the federal state system of Germany. Finer strata below the state level were used for major conurbations, metropolitan areas, large cities (above 500,000 inhabitants) and agglomerations. The resulting corrective effects through this post-stratification approach are introduced into the statistics by adding a weighting variable and using weighting accordingly throughout all statistical analyses. The weighting factor for each stratum is calculated by dividing the population-specific fraction of firms in the stratum by the sample-specific fraction: F(weightingStratum) = population frequency (in relation to stratum)/total size (number of firms) in population)/(sample frequency in stratum/ total size of sample). Test statistics were calculated using statistical software by SPSS (2007). The use of weighting entails a multiplication of cases which, in turn, can result in a situation in which ‘tests of significance are inflated’ (SPSS 16.0 Command Syntax Reference, 2007, p. 1980). To compensate for this undesired effect the following evaluation considers all test statistics both in their weighted and unweighted form (cf. the appendix for unweighted statistics). In addition, statistical significance is used in a more restrictive way and statistics calculated based on weighting the sample are only interpreted as significant if they exceed the 1% level of significance. The following analysis proceeds starting from aspects of competition and collaboration concluding with social capital.

Competition The evaluation of responses in relation to aspects characterising the competitive environment of companies (Table 1) substantiates a higher perceived level of

Table 1: Means and significance values of Mann
Whitney U statistics for measurements of competition. Variable

Description

Means

Value Range

Mann
Whitney Statistics (Significance)

SAP Unclustered Other Total SAP Cluster SAP Cluster Cluster Clusters versus versus Other Unclustered Clusters 1 = ‘high intensity’ to 4 = ‘not noticeable’ cmc_pattern_competitors 1 = ‘never’, 2 = ‘sporadically’, 3 = ‘on a regular basis’ knowl_attri_cust_context Risk of knowledge attrition due to Likert-type: co-operation with other third1 = ‘insignificant’ to party contractors in the context 5 = ‘highly significant’ of customer projects knowl_attri_empl Risk of knowledge attrition linked Likert-type: to staff turnover within region 1 = ‘insignificant’ to 5 = ‘highly significant’ niche_contacts_competition Influence of contacts to Likert-type: competitors in terms of 1 = ‘insignificant’ to knowledge required for niching 5 = ‘highly significant’ strategy strat_transp_competitors_general Extent to which competitors’ Likert-type: 1 = ‘not business strategies are transparent’ to transparent in general (≤100 km) 5 = ‘highly transparent’ Likert-type: 1 = ‘not strat_transp_competitors_region Extent to which competitors’ business strategies are transparent’ to transparent within region (≤100 5 = ‘highly km) transparent’

cllife_dens_comp

Rating of regional intensity of competition Communication pattern with competitors

*Significant at the 5% level of significance. **Significant at the 1% level of significance.

SAP Cluster versus Others

2.292

2.987

2.192

2.610

0**

0.366

0.000

2.106

1.864

2.092

1.977

0.002**

0.828

0.037**

2.449

2.567

2.726

2.589

0.226

0.018*

0.072

3.034

2.897

2.921

2.931

0.284

0.430

0.292

1.822

2.000

2.195

2.022

0.261

0.009**

0.056

2.738

2.896

2.661

2.791

0.265

0.577

0.623

2.778

2.941

0.959

2.910

0.234

0.093

0.126

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competition found in clustered environments in general, as demonstrated by the ratings (comp_int) of 2.292 (on a scale from 1 to 4 in which 1 denotes intense perception of regional competition compared to 4 for insignificant levels of competitions) for the SAP cluster and 2.192 for the clustered control group versus 2.987 for the unclustered control group. These differences in the perception of competition are statistically significant at the 1% level. At the same time, the communication pattern with competitors was described as more systematic and habitual by SAP cluster participants in comparison to the unclustered controls which tend to be sporadic in their exchange with competitors — primarily occurring in the context of industry trade fairs, if at all (cmc_pattern_competitors). This aspect is more pronounced in the SAP cluster compared to other agglomerations. The underlying rationales for these findings remain somewhat opaque: There is no evidence for a beneficial observational effect in terms of higher transparency of competitors’ strategic orientation found in clustered environments, as well as no indication for a differential level of perceived risk of knowledge attrition — for instance as a consequence of workforce turnover or ‘forced collaboration’ with competitors in the context of customer project work — between clustered vis-a`-vis unclustered environments (strat_transp_ competitors_ general/strat_transp_competitors_region/knowl_attri_cust_context/ knowl_attri_empl). Conversely, the findings point towards several differences between the SAP cluster and other agglomerations: The latter on average have a tendency to perceive a higher risk of knowledge attrition through contacts with competitors in customer environments (knowl_attri_cust_context), although this might not be explained through geographically based differences. One might conjecture that this may be a reaction to a sometimes even higher extent of specialisation in combination with the existence of large customers possessing high bargaining power which may accentuate the importance and simultaneously frangibility of intellectual-capital-based competitive advantages. This aspect of competitive intensity aligns furthermore to the significantly elevated importance attributed to competitors as a source or reference point for knowledge concerning niches (niche_contacts_competition) — that is information related to discovery and sustainability of niches which for SMEs in the given industry context is vital. Both factors point to a higher intensity as well as higher significance of competition found in other cluster environments compared to the SAP cluster. Interestingly, while there evidently is no difference between the SAP cluster and the unclustered control group, other cluster environments seem to be characterised by a higher transparency of competitors’ financing strategies.

Collaboration Two aspects are characteristic for collaborative arrangements in the context of the enterprise software industry. The first aspect is that smaller firms, and particularly start-ups, frequently are dependent to some degree on collaboration with other SMEs possessing complementary or analogous competencies as a consequence of

The Dynamics of Industrial Clustering 127 scarcity of their resources (Mohannak, 2007). SMEs in this context are characterised by a high degree of focus and specialisation. Collaborative arrangements are essential for them in this context in terms of leveraging their own competencies. Efficient collaborative arrangements allow firms to tap into information sources for highly specific knowledge in areas adjacent to the firm’s own focus of competencies while committing their sparse resources to focus on their own chosen competencies. Thus, they become a source and differentiator of competitive advantage. Moreover, not only is complementary knowledge a motive behind collaborative arrangements, but even collaboration with firms possessing analogous competencies is an issue. As a consequence of the prevalence of large customers in possession of superior bargaining power a chain of hierarchical relationships involving larger competitors can exist towards the final customer. SMEs frequently have to overcome thresholds in terms of minimum firm size. Alliances or even consortia-like collaborative arrangements may be formed in response. A further issue is flexibility in an environment of simultaneous customer involvements. The second aspect characterising collaboration in this industry context is the fact that frequently SMEs are not independent software manufacturers but a high extent of interdependency between SMEs and major software manufacturers can be found. Commonly, SMEs pursue niching strategies based on extensions, enhancements of and services offerings based on the product ranges of larger manufacturers. Therefore, collaboration with related large software vendors is an essential element in SMEs’ strategic alignment. Reciprocally, large software vendors have recognised the need to maintain a large network of related SMEs in order to achieve localisation and customisation. Tailoring to individual customers’ needs is commonly accomplished by the related ecosystem surrounding large vendors of standardised software packages. The latter have established ‘partnership programmes’ in an effort to nurture their ecosystems in recognition of the fact that their own success is dependent on the efficaciousness of these networks with related SMEs. Even SMEs that develop their own ‘independent’ software products frequently join these partnership programmes — often to raise their own perceived levels of visibility, legitimacy and reputation vis-a`-vis potential customers (e.g. ‘Microsoft certified products’, ‘SAP certified software interface’). The findings (Table 2) show that while SAP cluster firms on average had a slightly higher overall number of collaborative arrangements (collab_no_firms) than controls (particularly when compared to other agglomerations — p-value = 0.068) these differences do not quite reach statistically significant levels. What turns out to be statistically significant, however, is the clearly higher regional fraction (both in absolute figures and percentage terms as fraction of the overall number) of collaborative arrangements found in clustered (both SAP-related and others) vis-a`-vis unclustered conditions: If we analyse the spatial distances between firms and their ‘partnering’ companies with which collaborative arrangements had been formed (collab_no_proximity_perc), we find that the share of ‘partnering’ companies located within a 100 km radius of a firm’s principal location rather than at distant locations was clearly higher in agglomerated regions (39% for firms in the SAP and 40% for firms in other agglomerations) compared to unclustered regions (31%). In other

Table 2: Means and measures of significance for variables related to collaboration. Collaboration (Categorical and Continuous Variables) Variable

Description

Means

Mann
Whitney Statistics (Significance)

SAP Unclustered Other Total Cluster Agglomerations

adv_prox_competitorsa

Rating of proximity advantages related to 1.476 interaction with competitors niche_contacts_competitiona Influence of contacts to competitors in terms of 1.822 knowledge required for niching strategy transp_fin_competitorsb Transparency of competitors’ financing 1.857 strategies collab_no_firms Number of firms with which collaborative 8.352 arrangements exist (excluding major software vendors) collab_no_proximity_perc Percentage of collaborating firms within spatial 39.162 proximity (≤100 km)

SAP Cluster versus All Others

1.834

1.672

0.076

0.006**

0.019**

2.000

2.195

2.022

0.261

0.009**

0.056

2.047

2.304

2.096

0.112

0**

0.009

7.088

5.464

6.859

0.091

0.099

0.068

31.072

40.201

35.220

0.005**

0.740

0.081

Description

Extent to which complementary arrangements are sought with complementary firms rather than competitors collab_pref_proximity Preference for collaborative arrangements with firms in spatial proximity collab_prox_common_knowl Expected common stock of knowledge as reason for preferring regionally based collaborations collab_prox_conformity_culture Expected cultural conformity as reason for preferring regionally based collaborations collab_prox_fac_col_empl Expected facilitation of collaboration on employee level as reason for preferring regionally based collaborations collab_complementarity

SAP Cluster versus Other Agglomerations

1.657

Collaboration (Binary Variables) Variable

SAP Cluster versus Unclustered

Means

Fisher’s Exact Test Exact Significance

SAP Unclustered Other Total Cluster Agglomerations

SAP SAP Cluster SAP Cluster versus Other Cluster versus Agglomerations versus All Others Unclustered

0.12

0.21

0.29

0.22

0.014*

0**

0.001**

0.44

0.27

0.29

0.31

0.001**

0.009**

0.001**

0.04

0.16

0.27

0.16

0.05*

0.001**

0.006**

0.33

0.17

0.48

0.31

0.025*

0.083

0.867

0.82

0.71

0.64

0.72

0.156

0.051

0.069

Expected facilitation of face-to-face contacts on board level as reason for preferring collaborations within regional confines collab_prox_knowl_exch Expected facilitation of knowledge exchange though network formation on employee level as reason for preferring regionally based collaborations collab_prox_transparency Higher transparency of geographically proximate firms as reason for preferring regionally based collaborations collab_prox_trust_levels Higher levels of trust as reason for preferring regionally based collaborations collab_prox_conformity_practice Expected conformity of business practices and conduct as reason for preferring regionally based collaborations connection_large_sw Products/services to a large extent based on products of large software vendor partner_microsoft Microsoft partner partner_sage SAGE partner partner_sap SAP partner collab_prox_knowl_exch Expected facilitation of knowledge exchange reason for preferring regional collaborations collab_prox_fac_col_mgmt

Value range is Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’. Value range is Likert-type: 1 = ‘not transparent’ to 5 = ‘highly transparent’. *Significant at the 5% level of significance. **Significant at the 1% level of significance. a

b

0.72

0.65

0.7

0.68

0.449

0.833

0.597

0.58

0.52

0.3

0.47

0.592

0.006**

0.099

0.16

0.38

0.54

0.37

0.011*

0**

0**

0.2

0.31

0.7

0.39

0.228

0**

0.001**

0.12

0.24

0.48

0.28

0.118

0**

0.003**

0.74

0.57

0.65

0.63

0**

0.25 0.02 0.34 0.58

0.37 0.11 0.22 0.52

0.29 0.06 0.28 0.3

0.32 0.07 0.26 0.47

0.006** 0** 0.004** 0.592

0.72

0.001**

0.412 0.117 0.257 0.006**

0.026* 0.003** 0.017* 0.099

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words, the network of collaborative arrangements seems to be more spatially concentrated in the case of firms in clustered areas and more dispersed in the case of unclustered firms. Some respondents rejected the idea of any underlying systematic pattern saying that this occurrence was accidental rather than premeditated. Nonetheless, also a more pronounced preference for collaborating with local rather than distant firms found in SAP cluster firms (44% of them saying they would prefer local partner firms in contrast to 27% of unclustered controls) shows that differences exist with regards to the nature of collaboration, and paints a picture in which benefits apparently are sought in spatially proximate collaborative arrangements: 44% of SAP cluster participants in the sample stated they would prefer collaborative arrangements (collab_pref_proximity) with spatially proximate firms (i.e. in a radius of 100 km from their own location), while merely 27% in the unclustered control group and 29% in other agglomerations viewed proximity as a factor. As principal reasons for this preference, SAP cluster firms cited facilitation of personal contacts on an operative level (collaborating of employees and on-site management meetings (collab_prox_fac_col_empl/collab_prox_fac_col_mgmt)). Moreover, facilitation of knowledge exchange and cultural conformity play an important role for them (collab_prox_conformity_culture/collab_prox_knowl_exch) — in contrast to unclustered controls for which cultural conformity was of lower importance (significant only at the 5% significance level). Other than this and a higher value placed on transparency and visibility (collab_prox_transparency), no significant differences in possible reasons for preferring local partner firms are evident between SAP cluster participants and the unclustered control group. Conversely, a number of differences are evident vis-a`-vis controls from other agglomerations: this group places less emphasis on the possibility of facilitated knowledge exchange (collab_prox_ knowl_exch), but rather expects an already higher level of existing common knowledge stock (collab_prox_common_knowl). They also stress expected conformity of proximate partner firms in terms of culture and business practices (collab_prox_conformity_culture/collab_prox_conformity_practice), and differ sharply from the SAP environment in their level of trust vis-a`-vis spatially proximate partner firms (collab_prox_trust_levels). In contrast, operational factors are of lower importance to them. This paints a picture of SAP cluster participants emphasising benefits from networking, information sharing (even expected benefits from operational exchange align with this notion) and knowledge exchange — all of which are collaborative aspects, while firms in other agglomerations accentuate expectations of pre-existing knowledge overlaps, conformity and opportunities for monitoring (enhanced visibility and transparency) — emphasising aspects related to competitive control. Differential findings relating to aspects of competition further substantiate this picture of competitor-orientation: Firms from other agglomerations perceive the proximity of competitors as more beneficial (adv_prox_competitors), they profit more from exchange with competitors in relation to niche knowledge (niche_contacts_competition), and from the transparency of their competitors’ financing strategies (transp_fin_competitors). Moreover, collaborative arrangements were more likely to include competitors in the case of SAP cluster firms whereas controls from both

The Dynamics of Industrial Clustering 131 unclustered environments and other agglomerations were more anxious to base collaboration on complementarities and avoid co-operation with competitors (collab_complementarity). In other words, SAP cluster participants seem to adopt a perspective emphasising collaboration whereas firms in other agglomerations focus on aspects of competition. At the same time, the high valuation placed on common regionally based aspects — commonality and conformity in knowledge, culture and business practices; facilitation of observation through proximity; and, most of all, significantly differing expectations of trust based on co-location — suggests a business culture characterised by some degree of introspectiveness in non-SAP cluster environments. The population within the SAP cluster consists of a higher fraction of SAP partner firms — 34% in the sample versus 22% in the non-cluster control group (partner_sap); the difference is statistically significant at the 1% level of significance. This corroborates the expectation of the existence of an SAP ecosystem that might even be partially predicated on spatial proximity. Conversely, the opposite constellation is found in the case of partner firms of Microsoft (partner_ microsoft): Here the sample fraction in unclustered areas is 37% versus 25% in the sample of SAP cluster participants (again the difference is significant at the 1% level of significance). This signifies furthermore a shift in the structure of the target markets: the SAP ecosystem is geared towards corporate clients while the larger portion of Microsoft-related firms in unclustered areas are targeting primarily SMEs as customers (the so-called ‘Mittelstand’ segment in the German market). In the same vein, the significant difference (partner_sage) in partnerships with SAGE (a major software vendor focusing on the SME market), that is a clearly lower proportion found in the SAP cluster versus unclustered controls also points towards this constellation of asymmetrical concentrations of firms with respect to different target market segments. This also means that the higher portion of SAP ecosystem-affiliated firms gravitate more towards SAP in terms of socially based business network formation — in other words, accumulation of social capital — with SAP in contrast to Microsoft-affiliated firms in unclustered geographical areas. The difference in spatial concentrations with respect to species of firms becomes even more salient when examining the overall proportion of firms which are in some way connected to large software vendors in terms of their line of products or services. This includes ‘partner programmes’ with other larger software vendors, but does not represent the sum of all these programmes, as there is a certain fraction of companies which do not join partnership programmes (especially in the case of SAP) as a consequence of restrictions and costs involved. The findings show a clearly elevated proportion of ‘affiliated’ firms for the SAP cluster environment versus unclustered controls (connection_large_sw). So far, indicators suggest higher levels of competition paired with a perhaps more homogeneous and collaboration-oriented population found in the SAP cluster. The inclination to co-operate with regional firm with overlapping focus seems to differentiate the SAP cluster from other regions. Moreover, the nature

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Joachim Viehoever

of collaboration with SAP itself is idiosyncratic to the SAP ecosystem. The following section evaluates to what degree social capital effects are affecting this constellation.

Networking and Social Capital In order to evaluate the nature, quality and efficacy of socially based networking respondents were asked to rate different types of networks in a number of contexts such as access and accumulation of technological and market knowledge, innovation and customer acquisition (Table 3). Some of the networks and social capital are accumulated over long periods. Accordingly, shared experiences at educational establishments might play a role (location_university_owner_local): In 71% of the cases at least one of the owners had graduated from a regional (≤100 km) university or completed vocational training regionally if the company was located within the SAP cluster. This compares to a 53% fraction in the control group comprising unclustered companies, and a prevalence of 62% found in other agglomerations. This difference between clustered and unclustered environments is statistically significant at the 5% level of significance. This might reflect the higher prevalence of universities and other educational establishments in agglomerations. At the same time, however, this prevalence may also provide a nurturing component for new start-ups and may attract entrepreneurs to stay within agglomerations whereas graduates in unclustered regions might be more likely to migrate to other areas. Consequently, the academic/educational environment in agglomerations may act as a facilitator for the accumulation of social capital which is used in the context of business development later on. The efficacy of direct networks formed during educational periods in the lives of entrepreneurs is contradicted, however, by measurements of their impact: the findings show significantly higher rating of the importance of networks with former peers (e.g. co-students) in the context of customer acquisition (cuacq_costudent) and as knowledge sources for technological change (tech_dev_costudent) by firms in the unclustered control group versus the clustered industry segments (although only the 5% level of significance if weighting is used, and not significant if cases are not weighted). An analogous tendency can be observed in the comparison of the SAP cluster and the unclustered control group with respect to networking contacts from former fellow students as a source for customer acquisition. In general, however, the rating of the importance of locally based networks shows the opposite propensity: SAP cluster firms rated the efficacy of networks in geographical proximity significantly higher when compared to the unclustered control group with respect to a number of aspects such as (1) contacts leading to customer acquisition (p-value = 0.000 (regnet_custacq)), (2) monitoring of evolving market trends (p-value = 0.000 (regnet_market_trend)) as well as (3) reputation building, that is to what extent local network contacts were instrumental in their reputation building strategies (p-value = 0.01 (regnet_reputation_building)). Ratings of these aspects were on comparable levels for controls from other agglomerations.

Table 3: Means and significance measures of differences with respect to measurements of networking. Networking (Categorical Variables) Variable

cuacq_costudenta cuacq_employeesa cuacq_former_colleaguesa cuacq_former_customersa

cuacq_local_institutionsa

niche_contacts_competitiona

regnet_custacqa regnet_market_trenda regnet_reputation_buildinga tech_dev_costudenta

Role of contacts to former costudents in customer acquisition Role of employees’ networks in customer acquisition Role of contacts to former colleagues in customer acquisition Role of contacts to former customers (from previous employment) in customer acquisition Role of national/international institutions in customer acquisition Role of regional organisations (chamber of commerce etc.) in customer acquisition Influence of contacts to competitors in terms of knowledge required for niching strategy Influence of regional network on customer acquisition Role of regional network on knowledge about market trends Influence of regional network on reputation building Role of contacts to former costudents in accumulation of technological expertise

Mann
Whitney Statistics (Significance)

SAP Unclustered Other Total Cluster Agglomerations

SAP SAP Cluster Cluster versus Other versus Agglomerations Unclustered

SAP Cluster versus All Others

1.705

1.944

1.866

1.875

0.027*

0.105

0.03**

2.699

2.762

3.067

2.835

0.445

0.007**

0.106

2.565

2.238

2.796

2.455

0.006**

0.068

0.240

2.912

2.918

3.214

2.997

0.962

0.056

0.424

2.075

2.100

2.249

2.137

0.670

0.232

0.436

2.054

1.910

2.048

1.997

0.356

0.967

0.540

1.822

2.000

2.195

2.022

0.261

0.009**

0.056

3.238

2.775

3.258

3.006

0**

0.920

0.019**

2.850

2.307

2.809

2.555

0**

0.735

0.006

3.129

2.809

3.341

3.025

0.01**

0.079

0.313

1.605

1.840

1.426

1.677

0.016*

0.047*

0.359

The Dynamics of Industrial Clustering 133

cuacq_larger_organisationsa

Description

Means

134

Table 3: (Continued ) Networking (Categorical Variables) Description

Mann
Whitney Statistics (Significance)

SAP Unclustered Other Total Cluster Agglomerations

tech_dev_former_colleaguesa Role of contacts to former colleagues in accumulation of technological expertise tech_dev_local_institutionsa Role of regional organisations (chamber of commerce etc.) in accumulation of technological expertise transp_fin_competitorsa Transparency of competitors’ financing strategies tech_dev_virtual_networksa Role of virtual networks in accumulation of technological expertise

SAP SAP Cluster Cluster versus Other versus Agglomerations Unclustered

2.137

2.119

2.493

2.227

0.721

0.017*

0.464

1.939

1.863

1.856

1.876

0.811

0.454

0.637

1.857

2.047

2.304

2.096

0.112

0**

0.009

2.136

2.424

2.330

2.340

0.005**

0.090

0.009

Networking (Binary Variables)

Means

Fisher’s Exact Test Exact Significance

SAP Unclustered Other Total SAP Cluster Cluster Agglomerations versus Unclustered location_university_owner_localb At least one of the owners went to local educational establishment

0.71

Value range is Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’. Binary variable: 0 = does not apply, 1 = applies. *Significant at the 5% level of significance. **Significant at the 1% level of significance. a

b

SAP Cluster versus All Others

0.53

0.62

0.6

0**

SAP Cluster versus Other Agglomerations

SAP Cluster versus All Others

0.097

0.002**

Joachim Viehoever

Variable

Means

The Dynamics of Industrial Clustering 135 This seems to suggest that while some other indicators demonstrate the national or even international orientation of clustered firms in respect of market, competition and collaboration, cluster participants may simultaneously have a higher awareness of their local social capital and perhaps are able to exploit dense regional networks in a more efficient way. Reconsidering the opposite trend (comparing the SAP cluster with unclustered settings) of ratings indicating the impact of networks formed early on in educational settings and regional networks in general, this constellation may be an expression of two effects: frequently, not only first-order contacts (such as long-term relationships with fellow students) in networks are most efficacious, but a certain degree of far-reaching contact chains are more essential. This view basically represents a ‘small-worlds’ perspective on networks (Cowan & Jonard, 2004). Secondly, networks relating to former educational periods are not as powerful in general, as indicated by the fact that this factor is rated lowest of all observed factors related to social capital and networking. The higher impact of regional networks perceived by clustered firms is, however, based on several other long-term network components: In particular, firms in the SAP cluster tend to benefit to a significantly higher extent from networking with former colleagues in terms of customer acquisition than the non-cluster control group (for other agglomerations this aspect is more pronounced). Interestingly, this differential effect based on networking with former colleagues applies only in the context of customer acquisition (cuacq_former_colleagues) not technological knowledge (tech_dev_former_colleagues). This may be conjectured to be an indication for the relative ubiquity of technological knowledge vis-a`-vis the relative constriction of marketing knowledge — a constellation which would act as a gravitational mechanism in agglomerations. This is further corroborated by the clearly higher impact attributed to collaboration by SAP cluster participants versus unclustered controls. Here again the discrepancy between accentuated marketing- and pre-sales-related effects (slightly above the 1% significance level if weighting is used, and slightly above the 5% significance level without weighting) and parity with respect to technological innovation is noteworthy. Conversely, SAP cluster participants rely to a clearly higher extent on collaboration for technological innovation when compared to firms in other agglomerations (tech_dev_collaborations). This behaviour in combination with the significantly higher importance attributed to contacts with former colleagues could point towards the importance of social capital with larger software vendors for firms in the SAP cluster (i.e. including most likely cases contacts to SAP which, in turn, aligns to the notion of a hub-and-spoke cluster). This would fit a scenario in which SAP cluster participants are more homogeneous when compared to SMEs in other agglomerations and more orientated in terms of both marketing and technology towards the large software vendor on which products and services are centred. Innovation is determined by the large software vendor — SMEs depend on social capital with one or several large software vendors for monitoring of future technological developments as well as marketing opportunities and ‘structural holes’ to be exploited in niching strategies, but also are dependent on mutual collaboration in order to achieve critical size and flexibility (particularly, in their need to have an understanding of and stretch their capabilities to a range of components of

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enterprise software systems as offered by the large software vendor). Conversely, in other agglomerations SMEs might be more heterogeneous, and while they have a need for observation of collaborators and competitors, they would profit less from collaboration with other SMEs and have a higher reluctance towards co-operation in order to protect their own intellectual property. Symptomatically, the findings suggest that observation of competitors and collaborators is a more significant source of knowledge pertaining to niching strategies for firms in other agglomerations compared to SAP cluster participants. A further difference between the two groups is the higher importance of employee’s networks (cuacq_employees) and networking with former customers (cuacq_former_customers) rather than with collaborators in acquiring new customers for firms in non-SAP agglomerations. This would conform to the picture of a more diverse and individualistic environment found in these other agglomerations. No differences between SAP cluster participants and any of the control groups are evident with respect to the value of networks with organisations or institutions (such as industry associations or chamber-of-commerce-like organisations (cuacq_ larger_organisations/cuacq_local_institutions/tech_dev_larger_organisations/tech_dev_ local_institutions)). Interestingly, a higher importance was attributed to ‘virtual networking’ by both unclustered controls and firms in other agglomerations in comparison to SAP cluster participants (both at the 1% significance level). This again illustrates the different dynamics of networking — other groups relying to some degree on longer-distance network contacts whereas SAP cluster firms may show a higher degree of cluster-internal collaboration and networking.

Conclusions When analysing factors leading to industrial agglomerations found in the enterprise software industry, it becomes self-evident that causalities change over time and history therefore matters. From an economic geography perspective it is obvious that the industry did not start under ‘economic greenfield’ conditions. At the same time, it would be far-fetched to maintain that the entrepreneur establishing a start-up in this industry is always pre-occupied with selecting a specific location as the result of a premeditated decision process. Contrariwise, co-location may have its roots in the history of predecessor and downstream related industries as well as the history of individual entrepreneurs which again reflects regional density of industries and educational establishments, but also illustrates the significance of spin-off behaviour, network formation and accumulation of social capital. This chapter takes advantage of the availability of a broad spectrum of observations that can facilitate a comparative analysis between the SAP cluster and both other agglomerated environments and those unaffected by agglomeration effects. The SAP environment is characterised by heightened intensity of competition compared to non-agglomerated contexts. At the same time, findings suggest that it is more cluster-level collaboration-oriented in contrast to other agglomerations in

The Dynamics of Industrial Clustering 137 which firms show less inclination towards collaborating unless stringently based upon complementarities. Not unexpectedly, a higher fraction of firms in the SAP cluster are affiliated or related to SAP in terms of their product strategies. This enhances opportunities to benefit from network-related externalities in relation to the larger software vendor, but simultaneously increases the need to accumulate social capital as a source for technological as well as, more importantly, marketing-related knowledge. The results corroborate this, as they point to an elevated significance of networking with the software vendor in relation to niching strategies, a substantial impact of networks with former colleagues with respect to customer acquisition, and a generally higher significance attributed to the local network seen in SAP cluster participants vis-a`-vis non-agglomerated environments. In this sense, the findings suggest that marketing-related knowledge might be in a way geographically constrained as it is linked to social capital. Naturally, constellations of causalities change throughout the life cycles of the industry and agglomerations. Nonetheless, the current examination highlights a number of differences between apparently distinct types of clustered and unclustered environments which seem to result in ‘gravitational forces’ keeping an equilibrium in existence which supports the current industrial agglomerations found in this industry context.

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Appendix Table A.1: Means and significances for variables related to competition. Variable

Description

cllife_dens_comp

Rating of regional intensity of competition

cmc_pattern_competitors

Communication pattern with competitors

knowl_attri_cust_context

Risk of knowledge attrition due to cooperation with other third-party contractors in the context of customer projects Risk of knowledge attrition linked to staff turnover within region

knowl_attri_empl

Means

Mann
Whitney Statistics (Exact Significance 2-Tailed)

SAP Unclustered Other Total Cluster Clusters

SAP SAP SAP Cluster Cluster Cluster versus versus versus Unclustered Other Others Clusters

Value Range

1 = ‘high intensity’ to 4 = ‘not noticeable’ 1 = ‘never’, 2 = ‘sporadically’, 3 = ‘on a regular basis’ Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’

2.217

3.018

2.114

2.478

0*

0.643

0.01*

1.984

1.895

2.114

1.981

0.519

0.450

0.997

2.506

2.579

2.744

2.581

0.565

0.215

0.322

Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’

3.096

2.933

3.051

3.025

0.483

0.895

0.567

Table A.1: (Continued ) Variable

Description

Influence of contacts to competitors in terms of knowledge required for niching strategy strat_transp_competitors_general Extent to which competitors’ business strategies are transparent in general (≤100 km) niche_contacts_competition

strat_transp_competitors_region

Extent to which competitors’ business strategies are transparent within region (≤100 km)

*Significant at the 1% level of significance.

Means

Mann
Whitney Statistics (Exact Significance 2-Tailed)

SAP Unclustered Other Total Cluster Clusters

SAP SAP SAP Cluster Cluster Cluster versus versus versus Unclustered Other Others Clusters

Value Range

Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’ Likert-type: 1 = ‘not transparent’ to 5 = ‘highly transparent’ Likert-type: 1 = ‘not transparent’ to 5 = ‘highly transparent’

1.889

2.108

2.208

2.026

0.372

0.194

0.198

2.652

2.860

2.742

2.740

0.373

0.667

0.397

2.710

3.000

2.968

2.859

0.192

0.249

0.133

Table A.2: Means and significances for variables referring to collaborative aspects. Collaboration (Categorical and Continuous Variables) Variable

Description

adv_prox_competitorsa

niche_contacts_competitiona

transp_fin_competitorsb collab_no_firms

collab_no_proximity_perc

Rating of proximity advantages related to interaction with competitors Influence of contacts to competitors in terms of knowledge required for niching strategy Transparency of competitors’ financing strategies Number of firms with which collaborative arrangements exist (excluding major software vendors) Percentage of collaborating firms within spatial proximity (≤100 km)

Means SAP Unclustered Other Total Cluster Agglomerations

collab_complementarity

Description

Extent to which complementary arrangements are sought with complementary firms rather than competitors

SAP Cluster versus Unclustered

SAP Cluster versus Other Agglomerations

SAP Cluster versus All Others

1.486

1.610

1.909

1.614

0.441

0.067

0.158

1.889

2.108

2.208

2.026

0.372

0.194

0.198

1.957

2.089

2.385

2.103

0.533

0.039*

0.157

8.025

7.701

5.675

7.421

0.383

0.217

0.231

39.270

29.969

38.678

35.498

0.031*

0.920

0.107

Collaboration (Categorical and Continuous Variables) Variable

Mann
Whitney Statistics (Significance)

Means

Fisher’s Exact Test Exact Significance

SAP Unclustered Other Total Cluster Agglomerations

SAP SAP Cluster SAP Cluster versus Other Cluster Agglomerations versus versus All Unclustered Others

0.12

0.19

0.29

0.18

0.258

0.035*

0.082

Table A.2: (Continued ) Collaboration (Categorical and Continuous Variables) Variable

collab_pref_proximity

collab_prox_common_knowl

collab_prox_conformity_culture

collab_prox_fac_col_empl

collab_prox_fac_col_mgmt

collab_prox_knowl_exch

collab_prox_transparency

Description

Preference for collaborative arrangements with firms in spatial proximity Expected common stock of knowledge as reason for preferring regionally based collaborations Expected cultural conformity as reason for preferring regionally based collaborations Expected facilitation of collaboration on employee level as reason for preferring regionally based collaborations Expected facilitation of face-to-face contacts on board level as reason for preferring collaborations within regional confines Expected facilitation of knowledge exchange though network formation on employee level as reason for preferring regionally based collaborations Higher transparency of geographically proximate firms as reason for preferring regionally based collaborations

Means

Fisher’s Exact Test Exact Significance

SAP Unclustered Other Total Cluster Agglomerations

SAP SAP Cluster SAP Cluster versus Other Cluster Agglomerations versus versus All Unclustered Others

0.45

0.29

0.35

0.37

0.092

0.399

0.097

0.03

0.19

0.25

0.12

0.114

0.063

0.048*

0.35

0.19

0.53

0.35

0.23

0.359

1

0.77

0.75

0.67

0.74

1

0.699

0.767

0.77

0.69

0.67

0.72

0.726

0.699

0.561

0.63

0.56

0.25

0.53

0.754

0.04*

0.188

0.2

0.44

0.5

0.33

0.167

0.069

0.05*

Higher levels of trust as reason for preferring regionally based collaborations collab_prox_conformity_practice Expected conformity of business practices and conduct as reason for preferring regionally based collaborations connection_large_sw Products/services to a large extent based on products of large software vendor partner_microsoft Microsoft partner partner_sage SAGE partner partner_sap SAP partner collab_prox_knowl_exch Expected facilitation of knowledge exchange reason for preferring regional collaborations

collab_prox_trust_levels

Value range is Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’. Value range is Likert-type: 1 = ‘not transparent’ to 5 = ‘highly transparent’. *Significant at the 5% level of significance. a

b

0.23

0.38

0.67

0.36

0.328

0.013*

0.055

0.1

0.25

0.42

0.21

0.216

0.031*

0.053

0.74

0.57

0.65

0.66

0.032*

0.312

0.052

0.26 0.02 0.36 0.63

0.38 0.09 0.22 0.56

0.3 0.05 0.28 0.25

0.32 0.05 0.29 0.53

0.131 0.091 0.057 0.754

0.677 0.604 0.429 0.04*

0.222 0.205 0.084 0.188

Table A.3: Means and significances for variables related to aspects of networking. Variable

cuacq_costudenta cuacq_employeesa cuacq_former_colleaguesa cuacq_former_customersa cuacq_larger_organisationsa cuacq_local_institutionsa niche_contacts_competitiona regnet_custacqa regnet_market_trenda regnet_reputation_buildinga tech_dev_costudent

Means

Mann
Whitney Statistics (Significance)

SAP Unclustered Other Total Cluster Agglomerations

SAP SAP Cluster SAP Cluster versus Other Cluster Agglomerations versus All versus Others Unclustered

Description

a

Role of contacts to former costudents in customer acquisition Role of employees’ networks in customer acquisition Role of contacts to former colleagues in customer acquisition Role of contacts to former customers (from previous employment) in customer acquisition Role of national/international institutions in customer acquisition Role of regional organisations (chamber of commerce etc.) in customer acquisition Influence of contacts to competitors in terms of knowledge required for niching strategy Influence of regional network on customer acquisition Role of regional network on knowledge about market trends Influence of regional network on reputation building Role of contacts to former costudents in accumulation of technological expertise

1.805

1.922

2.053

1.898

0.476

0.194

0.269

2.738

2.737

3.053

2.799

0.894

0.194

0.492

2.630

2.289

2.806

2.528

0.110

0.479

0.393

3.074

2.776

3.194

2.979

0.150

0.794

0.341

2.111

2.158

2.289

2.164

0.634

0.506

0.515

2.160

1.961

2.184

2.087

0.503

0.821

0.699

1.889

2.108

2.208

2.026

0.372

0.194

0.198

3.262

2.870

3.341

3.129

0.062

0.779

0.225

2.928

2.385

2.900

2.711

0.015*

0.854

0.064

3.096

2.922

3.475

3.105

0.300

0.118

0.975

1.580

0.760

1.526

1.639

0.312

0.735

0.553

tech_dev_former_colleaguesa Role of contacts to former colleagues in accumulation of technological expertise tech_dev_local_institutionsa Role of regional organisations (chamber of commerce etc.) in accumulation of technological expertise transp_fin_competitorsa Transparency of competitors’ financing strategies tech_dev_virtual_networksa Role of virtual networks in accumulation of technological expertise

2.225

1.987

2.486

2.182

0.258

0.327

0.702

1.988

1.904

1.947

1.948

0.909

0.831

0.850

1.957

2.089

2.385

2.103

0.533

0.039*

0.157

2.111

2.514

2.421

2.326

0.026*

0.177

0.025*

Means

Fisher’s Exact Test Exact Significance

SAP Unclustered Other Total SAP Cluster Cluster Agglomerations versus Unclustered location_university_owner_localb At least one of the owners went to local educational establishment Value range is Likert-type: 1 = ‘insignificant’ to 5 = ‘highly significant’. Binary variable: 0 = does not apply, 1 = applies. *Significant at the 5% level of significance. a

b

0.7

0.56

0.6

0.63

0.097

SAP Cluster versus Other Agglomerations

SAP Cluster versus All Others

0.325

0.103

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Joachim Viehoever

Table A.4: NACE codes relevant to the selection of companies in the enterprise software sector. The following NACE code were used a. 62.01.9 Other software development b. 62.02.0 Computer consultancy activities c. 62.09.0 Other information technology and computer service activities d. 70.22.0 Business and other management consultancy activities

Chapter 8

Cluster Initiatives within the European Context: Stimulating Policies for Regional Development Dreams Inessa Laur

Abstract This chapter aims to enrich knowledge about cluster initiatives acting as intermediaries primarily between members in a cluster or in regional context. This is a practically oriented manuscript written to contribute to refinement of existing policies by proposing recommendations based on recent empirical studies regarding funding, actors’ and activities’ content, as well as cluster initiatives’ assessment. It is proposed that public support should be balanced, targeting new as well as established, well-functioning cluster initiatives. Furthermore, regional authorities should encourage multifaceted collaboration (e.g., Triple Helix), stimulate variation in activities to maximize the benefit of cluster initiatives as well as define and communicate success factors that make it possible to evaluate cluster initiatives from a holistic perspective. These recommendations are primary aimed for regional authorities and reflect a bottom-up perspective where both logic of initiatives’ actions and their development are captured. Yet, even national authorities can make use of the recommendations in this chapter to improve governance of cluster initiatives and to determine further directions of regional policies.

Introduction This chapter discusses clustering, a crucial component in stimulating regional development, and focuses on the roles of cluster initiatives and public policy in

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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cluster development. Clusters are regional agglomerations of related firms and other institutions with colocation advantages and a suitable mix of collaboration and competition (Martin & Sunley, 2003; Porter, 1998). These agglomerations can provide conditions for business start-ups, productivity, and innovation (Feldman, 1999; Gordon & McCann, 2000; Porter, 2000; Pouder & St. John, 1996; Simmie, 2004). Cluster members have access to special resources, competencies, and a developed infrastructure; as well as network channels and education and R&D (Forsman & Solitander, 2004; Lagendijk & Cornford, 2000; Malmberg & Maskell, 2002). Documented advantages of clustering have convinced public authorities on various levels of the value of supporting formation and development of such agglomerations. Driven by such mission the authorities generate cluster promoting policies and strategies as well as contributing resources and facilities. Despite public support mechanisms, many clusters fail, and regional development dreams remain unachieved (Feldman & Francis, 2004; Laur, Klofsten, & Bienkowska, 2012; Rosenfeld, 1996; So¨lvell, 2009). One cause of cluster failure is the unwillingness of cluster member firms to dedicate resources to seeking networking partners, especially when potential benefits are unclear. Thus, governments continually seek new ways of facilitating networks among cluster actors and boosting economic performance and prosperity (Singh, 2003). Research and practice have recently shown that supportive or intermediary organizations in the cluster environment greatly assist the expansion of new clusters and enhance innovativeness in mature clusters (So¨lvell, Lindqvist, & Ketels, 2003). Such organizations fulfill networking, circulating, and facilitating gaps within the cluster space (Burt, 2002) and have come to be viewed as a vital prerequisite in the development of clusters and regions (cf. Andersson, Serger, Soervik, & Hansson, 2004; Arthurs, Cassidy, Davis, & Wolfe, 2009; Bergek, 2014; Laur et al., 2012; Porter & Emmons, 2003; So¨lvell et al., 2003). One example of an intermediary organization taking mediating position between other cluster actors and delivering middle-hands services (Hospers & Beugelsdijk, 2002; Howells, 2006), is the cluster initiative. So¨lvell and Williams (2013) describe the triple mission of cluster initiatives as value-adding in the areas of regional development, business growth, and cluster identity. Initiatives assist in the formation of broad networks and access to these; assist in resource acquisition; circulate new knowledge, information, and technologies; and facilitate joint projects (Stewart & Hyysalo, 2008). Regional and national authorities in Europe and the United States often initiate and actively support these intermediary organizations (Fromhold-Eisebith & Eisebith, 2005). In Ingstrup’s view (2010), the critical contribution of cluster initiatives to regional growth is largely unrecognized, and no framework currently focuses on this aspect of cluster development. Existing cluster initiative policies continue to resemble those for clusters and comprise programs designed to stimulate emerging cluster initiatives, streamline linkages between member actors, and increase the added-value of their activities (Boekholt & Thuriaux, 1998; Frykfors & Klofsten, 2011). These touch central aspects of the start-up process, and its actors and activities, enabling cluster initiatives to function and grow, but these policies are known for being

Cluster Initiatives within the European Context 149 broad, fragmented, highly institutionalized, and unable to adapt to market change (Cooke, Boekholt, & Todtling, 2000; Diez, 2001; Feser & Luger, 2003; Kiese & Wrobel, 2011; Mills, Reynolds, & Reamer, 2008; Sternberg, Kiese, & Stockinger, 2010; Wolfe & Gertler, 2004). Advances in technology and know-how require reforms in actor needs and cluster initiative work approaches and make policy instruments ineffective for stimulating the development of cluster initiatives (Andersson et al., 2004; Castells, 2011; North, 1998; So¨lvell & Williams, 2013). This chapter discusses how time-limited public funding policies are unaligned with the performance and development phase of cluster initiatives (Aziz & Norhashim, 2008; Brown, 2000; Lindqvist, So¨lvell, & Ketels, 2013; Royer et al., 2009), how rarely general and institutionalized policy programs facilitate Triple Helix collaborations and fulfill the triple mission of initiatives (i.e., enhance their own, the public, and the business good) (Ranga & Etzkowitz, 2013; So¨lvell & Williams, 2013), and how last-generation management and assessment tools restrict actor willingness to become involved in initiatives (Asheim, Coenen, & Vang, 2007; Klofsten, Bienkowska, Laur, & So¨lvell, 2013). One consequence of such program limitations is the recent decrease in number of functioning initiatives (Laur & Fayolle, 2013). Thus scholars have called for next-generation policy programs designed to promote cluster initiatives that improve cluster management and performance (Andersson et al., 2004; Arago´n, Aranguren, Iturrioz, & Wilson, 2014; Mills et al., 2008). Other researchers suggest that policies should be rooted in the cluster initiatives themselves rather than government offices, that is, a bottom-up rather than a top-down process (Frykfors & Klofsten, 2011). Bottom-up policies tend to be more responsive to diverse territorial needs and involve interactive processes between policymakers and other actors (Crescenzi & Rodrı´ guez-Pose, 2011; Lindberg, 2011). This chapter uses a bottom-up perspective to address limitations in current cluster initiative policies and generate recommendations for refinements of existing instruments at the regional level: funding, member constitution, activity content, and assessment. The recommendations suggest ways of nurturing the emergence and growth of cluster initiatives and achieving greater cluster and regional performance. The bottom-up perspective applied is especially efficient at exploiting variations in knowledge and expertise across sectors.

Regional and Cluster Policies Raines (2002) states that clustering could practically improve regional development (cf. Arthurs et al., 2009; Cooke et al., 2000; Feser & Luger, 2003; Isaksen, 2001). Driven by dreams of regional growth, national and regional authorities have generated numerous cluster policies in various areas, including economic, scientific, technological, industrial, and small- and medium-sized enterprises (SMEs). But as Hospers and Beugelsdijk (2002) point out, only joint implementation of these policies will achieve cluster and regional goals. The number and complexity of cluster

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programs and policies make this task challenging, due to varying situational perspectives. As an example, regional programs concentrate on providing technical support, training, leadership development, and collaboration initiation among Triple Helix actors (Hanusch & Pyka, 2007; Jacobs & de Man, 1996; Perry, 2007). National policies focus on provision of information, knowledge, and finance, spanning of political boundaries, and national coverage. Thus, a microeconomic perspective concerns regional authorities while a macroeconomic concerns national. If joint implementation of these policy programs fails, the resulting imbalance would allow some programs to dominate while marginalizing others and risk a bandwagon effect (i.e., unique situation needs would remain unaddressed in favor of general solution implementation), along with ineffective regional reorientation (Gustafsson & Autio, 2011; Jacobs & De Man, 1996; To¨dtling & Trippl, 2005). To avoid such potentially negative effects, scholars generally advise regional authorities to formulate cluster policies in line with general national objectives. Negative effects occur for several reasons. One is that regional authorities tend to concentrate on a particular region and cluster (their own), which they know best, in contrast to national policies, which consider all constituent regions and strive for non-replication of programs (Cooke et al., 2000; Feser & Luger, 2003; Hospers & Beugelsdijk, 2002; Isaksen, 2001). Another reason for negative effects is that national policies tend to lag behind regional policies (e.g., reducing unemployment and raising per capita incomes) than do established regions willing to develop further (Andersson, 2008; Schienstock & Ha¨ma¨la¨inen, 2001; To¨dtling & Trippl, 2005). Such national programs are known for their extreme top-down perspective, tunnel vision, and strict implementation deadlines (Diez, 2001; Mills et al., 2008; Perry, 2007; Wolfe & Gertler, 2004; Ylinenpa¨a¨, Tham, Johansson, & Klofsten, 2014). This suggestion is a cornerstone of this chapter, thus primary suggestions for policy refinements will concern regional level authorities. Knowledge of a region’s specifics and proximity to local initiatives make these bodies ideal for designing a bottom-up perspective in refined policies. Knowledge, trade and industry, investments, and entrepreneurship are the pillars on which the primary focus of regions rests while learning, open innovation, network economy, complex problem solving, and governance articulate this focus (Cooke et al., 2000; Dobbins et al., 2007; Frykfors & Klofsten, 2011; Huggins, 2000; To¨dtling & Trippl, 2005). Examples of policies generated to realize this focus include support for knowledge circulation, marketing and international promotion, and forecasting. The specific focus is broker policies that strive through special agents — intermediaries — to build dialogs, to develop competencies, and to initiate international collaborations (Klerkx & Leeuwis, 2008; Lagendijk & Cornford, 2000; Rosenfeld, 2002). Brokering is considered especially important for cluster attractiveness and growth, along with policies that support initiation of Triple Helix interactions, technological development, and knowledge transfer. In the cluster context, the primary vehicles for brokering or intermediary assistance are cluster initiatives.

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Cluster Initiatives and Policies A cluster is a geographical space in which one or several cluster initiatives operate. Cluster is concept that was popularized by Michael Porter in the 1990s and has since become a mantra among researchers and practitioners (Porter, 1998). Cluster initiatives, one of many types of organizations that have emerged, are the promoters of clusters (So¨lvell et al., 2003). Despite numerous studies and attempts at definition, the two concepts are often used interchangeably, with subsequent misperceptions (Martin & Sunley, 2003). While clusters entail a geographical aspect of spatial proximity, cluster initiatives have no spatial boundaries; they function within clusters to foster innovation and competitiveness (Andersson et al., 2004; Ketels, Lindqvist, & So¨lvell, 2006; Porter, 2000; So¨lvell, 2009), and they operate outside of clusters to initiate and develop trustworthy networks between public and private actors, and academia (Laur et al., 2012; Leydesdorff & Zawdie, 2010; Turner, Monnard, & Leete, 2013). Ketels and Memedovic (2008, p. 384) define cluster initiatives as: Collaborative actions by groups of companies, research and educational institutions, government agencies and others, to improve the competitiveness of a specific cluster [… for example] by raising the awareness of companies within a cluster and creating more effective platforms for interaction [… or providing] a platform for a better dialogue between the private and the public sector when making decisions about how to improve the cluster-specific business environment. In the guise of intermediaries, cluster initiatives enable linkages between actors by organizing brokering, facilitation, and promotion activities (Fromhold-Eisebith & Eisebith, 2005; Intarakumnerd, 2005; Laur, Bienkowska, & Klofsten, 2014) that can be used to create platforms for interaction and initiate dialogs among cluster members and broader audiences (cf. Granovetter, 1973; Gretzinger & Royer, 2014; Jack, Drakopoulou Dodd, & Anderson, 2008; Ruuska & Teigland, 2009; Uzzi, 1996). Some scholars classify cluster initiatives as innovation intermediaries due to their focus on initiation of partnerships and use of innovative solutions (Roberts & Benneworth, 2001; Shou & Intarakumnerd, 2013). One driving force behind cluster initiatives is the ambition of the entrepreneurs; thus, entrepreneurial drive is an important factor in securing the survival and growth of these organizations (cf. Beck & Demirgu¨c¸-Kunt, 2004; Busenitz & Barney, 1997; Cardozo & Ang, 1993; Carland, Hoy, Boulton, & Carland, 1984; Gartner, 1990; Macdonald, 1965). Such individuals often occupy leadership positions. The other driving force at work in initiatives resides with the part- or fulltime employees who work for no or low compensation (Klofsten, 2010). When competencies and experience are lacking, initiatives engage external consultants or technicians for specific tasks. Mature initiatives in particular consult external experts regularly; such have been found to play an important role for their survival and

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prosperity (Audretsch, 1991; Klofsten et al., 2013; Laur & Fayolle, 2013; Phillips & Kirchhoff, 1989). Previously, the entrepreneurial forces that launched cluster initiatives were individual efforts of independent enthusiasts in the private sector. New research, however, indicates those public authorities, or a mix of public and private entities now initiate and finance a larger share of cluster initiatives (Laur & Fayolle, 2013). Triple Helix actors often provide additional funding of cluster initiatives, but in unequal shares. The combination of actors and the shares they receive often vary throughout the life-cycle phases of an initiative; for instance, in the maturation phase, many cluster initiatives are largely self-financed and may have established financial sources, which involve a greater breadth of members than in previous phases. They may also have found their market niche (Enright, 2003; Laur, Klofsten, Bienkowska, Wincent, & Ylinenpa¨a¨, 2013).

Cluster Initiative Policies Cluster initiative policies have come to play an important role in cluster policy programs because of their direct impact on cluster attractiveness and competitiveness (Feser & Luger, 2003; Wolfe & Gertler, 2004). Policy dedicated to initiatives began to develop rapidly in the last decade. When initiatives first emerged, policy focused on research program support and technology assistance services with the aim of aligning local and regional interests to those of businesses in a particular sector and providing guidelines for intermediary assistance for exploiting global opportunities (Arthurs et al., 2009). Over time, policy has evolved. Creation of competitive research and technology bases, support of leadership and knowledge-based strategies, collaborative action toward acquiring new funds, stimulation of fresh investments and new ventures, and job creation are now ways of achieving the aims of cluster initiatives. These comprise the cluster initiative’s mission on a policy level; they define a cluster’s function in society, and in regional and cluster development. To increase the impact of policy interventions, scholars now tend to recommend guiding policies that are either proactive and self-directed, or explicit. Some scholars state that establishing common goals for initiatives, rather than creating special policy instruments, could suffice (Martin & Sunley, 2003). The pragmatic mind-set of these organizations, and their small size and capability to adapt, would be conducive for positive outcomes while complex policy programs might block their flexibility and create high formalization. Other scholars wish to improve existing cluster initiative policies (Andersson et al., 2004; Mills et al., 2008) by improving and supporting both hard (e.g., physical infrastructure and funding) and soft instruments (e.g., awareness raising, networking support, consultancy, and training) (Edler & Georghiou, 2007; Rasmussen, 2008). Policies should also repair policies with a weak focus on cluster initiatives and their alignment with up-to-date market needs (Boekholt & Thuriaux, 1998; Castells, 2011; Cooke et al., 2000; Feser & Luger, 2003; North, 1998). This chapter recommends guiding policies, rather than explicit

Cluster Initiatives within the European Context 153 directives, for cluster initiatives due to their relative newness and the pitfalls inherent in anything without a track record. Thus, the policy gaps that other studies discuss below are analyzed to generate more reliable, up-to-date recommendations for improving the current situation. Three types of collaborations are often captured in policy programs: Triple Helix (i.e., between industry, research and government agencies), R&D (i.e., between different companies and research institutions), and intercompany cooperation (horizontal or vertical) (Cluster Management Guide, 2006). But according to Diez (2001), these types of collaborations mainly describe how interactions between firms develop, and they fail to discuss their initiation, exchange mechanisms, and reciprocity. Such policies, which inspire and facilitate the organization of brokering and networking activities, are considered tools for retaining members and attracting new participants to cluster initiatives. In practice, however, it seems that focus tends to rest on the block of activities that furthers the aims of the dominant actor while activities dedicated to the other Triple Helix actors receive less attention. The capability of initiatives to serve each Triple Helix actor is thus limited by lack of simultaneous support from the state, business, and academia (cf. Etzkowitz & Ranga, 2011; So¨lvell & Williams, 2013). Entrepreneurial activity in cluster initiatives and support of management excellence are also considered drivers of regional growth and prosperity in current policy programs, although measurement of managerial excellence and overall cluster initiatives performance remains a challenge (Autio, Kanninen, & Gustafsson, 2008; Brulin, Sjo¨berg, & Svensson, 2009; Curran, 2000; Klofsten, 1992; Lindqvist et al., 2013). Some attempts to refine cluster initiative policy programs have been made in the United States and Sweden (Mills et al., 2008; So¨lvell & Williams, 2013). In particular, it was proposed that the US government should create an information center responsible for registering and tracking cluster initiatives as well as establishing a grand program that would manage each cluster initiative in the nation. Sweden focused on stimulating innovative activities among all Triple Helix actors through academia and research centers, with fairly satisfactory outcomes, but there are lessons to be learned.

Methodology This chapter, the final in a doctoral dissertation project, aims to interrelate and systematize previous findings. It relies on theoretical and empirical evidence collected over the last four years (2010
2014): more than 500 scientific articles and reports in the fields of management, networking, and organization were reviewed and analyzed, and nearly 100 operation manuals and reports studied. Approximately 140 European cluster initiatives participated in this longitudinal project, and more than 200 hours of conversations were recorded (around 1.5 hours per respondent by interview and other survey methods). Several initiatives were closely observed and

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their operative details documented. All material was synthesized and summarized to provide policy recommendations for fostering improvements in cluster initiative programs. A key respondent approach that selected primary cluster reference persons (i.e., leaders, directors) was considered most appropriate due to their crucial importance to the initiatives (Heckathorn, 1997). Interviews in person or on the phone were the methods of choice so that an interactive approach could be used (Svensson, Ellstro¨m, & Brulin, 2007). Interview and survey questions can be summarized into (a) general information about the initiatives, (b) stakeholders and their role in the initiatives, (c) intermediary activities performed by the initiatives, and (d) development of the initiatives over time. Scholars and practitioners in the field pilot tested the interview and survey questions, which were then modified (Field, 2009; Hair, Black, Babin, & Anderson, 2009). For a more detailed description of the methods and results of previous studies, see Table 1. The next section presents the central results of these papers on which the thesis of this work is based. These crucial results help explain, in the words of the interviewees, (i) why the initiatives are not functioning as well as they could and (ii) how policy input could affect improvements. The results discussed below serve as a base on which recommendations with a bottom-up perspective, that is, concerning operational aspects of cluster initiatives, could be formulated for policymakers.

Results and Recommendations Current research shows that public authorities initiate a large share of cluster initiatives (Laur & Fayolle, 2013) and tend to support them financially through their start-up and establishment phases. Table 2 shows how financing changes as initiatives mature. At the start, public sources finance nearly half of the initiatives. Over time, fewer of the growing initiatives receive public financing compared with those in the start-up or early establishment phases. Funding opportunities in the public sector decrease by nearly half upon maturation of the initiative. The state finances only one-fifth of mature, growing initiatives in the sample. Private sector financing appears to be steady throughout the life cycle of the cluster initiative. Businesses are the sole support of 6% of the start-up as well as growing initiatives. A mix of private and public sources finances the major share of growing initiatives. Several of the interviewees for this chapter in Sweden, Belgium, and the Netherlands corroborated the trend in lower public financing over time that Table 2 shows. They discussed how the reduction in primary financial support challenged operations in their initiatives. Funding tends to be limited to a time period of three to five years and rarely takes account of the performance or potential of the supported initiatives (Perry, 2007). Policy targeted active support of start-ups and the early development of cluster initiatives (Aziz & Norhashim, 2008; Brown, 2000). Mature, well-functioning initiatives were overlooked, seemingly viewed as stagnant and unable to create new jobs (Boekholt & Thuriaux,

Table 1: Studies underlying the policy recommendations. Paper

Aim

Method

Main Results

Implications

An interactive case study Two types of intermediary Initiatives must sense and adapt to the constantly changing approach studied four activities were found: needs of stakeholders. Swedish cluster anchored targeted and Relevant assessment tools are initiatives through inexperimental. Involved needed. depth interviews and actors are key players, examination of and target and support operational manuals. groups representing different Triple Helix actors. A quantitative approach Cluster initiatives are small Authorities should encourage Laur and To enrich knowledge of formation of cluster initiatives used a survey organizations comprising Fayolle cluster initiatives as in mature sectors and key individuals and a few questionnaire in a (2013) intermediaries concerning involvement of entrepreneurial employees who, with their general sample comprising personnel. Evaluation tools support from financiers, characteristics, actors initiatives from 8 and benchmarking are needed organize operations and involved, and activities European countries; 136 (53%) initiative ensure direction to deliver for long-term financing and organized. development of the initiative. leaders responded. a mix of general and aimspecific activities for each Triple Helix member. Long-term financing is A quantitative approach Two factors influence Laur et al. To determine how cluster attraction of new target important for well-functioning used a survey (2013) initiatives mediate within members to a cluster cluster initiatives, regardless of questionnaire in a a Triple Helix context initiative: age of the their age. sample comprising concerning actor initiative and presence of initiatives from 8 involvement and the influential key players European countries; dependency patterns and support groups. 136 (53%) initiative between maturity and leaders responded. member enrollment. Laur et al. To investigate activities (2012) organized by cluster initiatives and to create a typology of the actors involved.

.

Cluster Initiatives within the European Context 155

Paper

Aim To map central qualitative success factors at the cluster initiative level.

Laur et al. To identify a set of (2014) common intermediary activities carried out by cluster initiatives as well as stakeholders that influence change in the activities.

Method An extensive literature review and five case studies of Swedish cluster initiatives used semi-structured, indepth telephone interviews with cluster initiative leaders and managers. A quantitative approach used a survey questionnaire in a sample comprising initiatives from 8 European countries; 136 (53%) initiative leaders responded.

Main Results

Implications

To reliably assess a cluster, Five qualitative success factors in cluster initiative cluster initiatives should add quantitative performance assessment and indicators to the qualitative management were found: success factors that they idea, driving forces and currently use. commitment, activities, critical mass, and organization. Purpose and goals should be Cluster initiatives clearly defined, as well as the organize three types expected Triple Helix mission. of intermediary Proactivity and attraction of activities, to merited competencies should enhance: be reinforced. ▪ Identity and attractiveness ▪ Business growth ▪ Innovativeness and regional development. Leaders, employees, external experts, and sources of financing are the change facilitators of the activities.

Inessa Laur

Klofsten et al. (2013)

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Table 1: (Continued )

Cluster Initiatives within the European Context 157 Table 2: Changes in financing sources of cluster initiatives over time. Financing

At the Start

2014

Public only Private only Self-financing only Mix of public and private Mix of public and self-financing Mix of private and self-financing Mix of public, private, and self-financing

44 (37%) 7 (5.9%) 6 (4.9%) 56 (47.1%) 4 (3.4%) 1 (0.8%) 1 (0.8%)

24 (20%) 7 (5.8%) 4 (3.3%) 72 (60.0%) 4 (3.3%) 4 (3.3%) 5 (4.2%)

Source: Adopted in part from Laur and Fayolle (2013).

1998; Diez, 2001; Feser & Luger, 2003). Similar policy attitudes occurred with cluster initiatives that were self-financed or had alternative means of support (Cassidy, Davis, Arthurs, & Wolfe, 2005). Such attitudes potentially endanger the long-term survival of mature, well-functioning clusters and can be considered a policy weakness. On the contrary, policymakers should accord these clusters special attention because of their proven track record: their potential to attract new members, organize diverse activities, and adapt to market changes is higher than in a newly started entity. The first policy recommendation of this chapter addresses this concern. Policy recommendation 1: Public support should be balanced, targeting new as well as established, well-functioning cluster initiatives. Another weakness of cluster initiative policies is the misconception that selffinancing and alternative means of support are ineffective in new job creation (Cassidy et al., 2005). This leads to the idea that public actors are best suited to creating well-functioning initiatives which contribute to regional development and that other actors, such as private or academic, will be less effective. FromholdEisebith and Eisebith (2005) support these views, stating that current policy programs which are publicly created and financed, explicitly promote top-down initiatives, in comparison with programs started and financed by groups of firms and which employ bottom-up initiatives; public actors view these as implicit and less attractive for achieving established regional goals (Benneworth, Danson, Raines, & Whittam, 2003; Formica, 2003). As an outcome, bottom-up initiatives have difficulties embedding in a multi-actor process (Klerkx & Leeuwis, 2008; Smits & Kuhlmann, 2004), and they derive little benefit from Triple Helix collaboration policies. In this line of thought, Lindqvist et al. (2013) points out that policy programs primarily address interaction and collaboration matters by describing how interactions occur and not their mechanisms (Diez, 2001; Morgan & Nauwelaers, 1999; Rosenfeld, 1996). This exposes the unspecified content of policies and their weak support of Triple Helix collaborations in clusters (Andersson et al., 2004; Etzkowitz, 2003; Irawati, 2006).

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Empirical data find that the public sector is the dominant actor initiating and financing initiatives (Laur et al., 2012). Many initiatives around Europe have remarked this, but Finland is a particularly clear example. Finnish policy programs promote public financing of initiatives and tend to consider self-financing opportunities (e.g., membership fees) as credible sources of support. The over-dominance of authorities can promote “organizational thinness” (i.e., an underdeveloped actor structure) and, as a consequence, “lock-in effects” within the initiatives (To¨dtling & Trippl, 2005) which limit Triple Helix collaborations of initiatives. The top-down perspectives of policy programs, “organizational thinness,” and “lock-in effects” may also reduce the focus on intermediary activities in cluster initiatives, or even lead to their loss (To¨dtling & Trippl, 2005). When government is the only key actor and recruitment of other actors is weakly supported, intermediary activities tend to focus on the public good. In such situations, cluster initiatives experience difficulties in understanding the nature and value of intermediary activities, especially activities that are only partially funded or lack funding. The triple mission of initiatives (i.e., to work for their own good, the public good, and the good of targeted businesses) is thus hardly fulfilled by organizing their intermediary activities in these three directions (Etzkowitz & Ranga, 2011; So¨lvell & Williams, 2013), and these initiatives, unattractive for any members other than the state, face a subsequent loss of identity (Klerkx & Leeuwis, 2008; Meyer, 2003). These two weaknesses — inadequate Triple Helix policy programs and the resource dependency of cluster initiatives — limit Triple Helix collaborations and intermediary activities that would benefit all Triple Helix actors, especially those in initiatives with private or a mixture of public and private funding. Policy recommendation 2: Regional authorities should encourage multifaceted collaboration (e.g., Triple Helix). Policy recommendation 3: Regional authorities should stimulate variation in activities to maximize the benefit of cluster initiatives. The final weakness of cluster initiative policies that this policy chapter will discuss concerns limitations in management and assessment tools (Arthurs et al., 2009; Aziz & Norhashim, 2008; Cassidy et al., 2005; Klofsten et al., 2013). The lack of a single, suitable, recognized tool for assessing the potential of an initiative is one reason behind the inward orientation of governments, contributions of actors, and single-track thinking concerning intermediary activities (e.g., recommendations 2 and 3). Actors are also hesitant to provide long-term funding, or any other benefits, to initiatives when returns on their investments are uncertain (e.g., recommendation 1). Designing indicators to justify resource spending and measure actual cluster initiative benefits appears to be difficult (Autio et al., 2008; Brulin et al., 2009; Klerkx & Leeuwis, 2008; Lindqvist et al., 2013). Asheim, Cooke, and Martin (2008) state that underdeveloped tools for management, coordination, and assessment of cluster initiatives hinder the

Cluster Initiatives within the European Context 159 refinement of current policies. So, to address these weaknesses of cluster initiative policies, a more effective assessment tool is needed. Policy recommendation 4: Regional authorities should define and communicate success factors that make it possible to evaluate cluster initiatives from a holistic perspective.

Discussion The theoretical and empirical data discussed in this chapter underpin the four recommendations for refining cluster initiative policy to improve cluster and regional development. Although the recommendations are intended for regional authorities, authorities at all levels could use the recommendations as guidelines for maximizing the effectiveness of policy interventions. The belief that mature cluster initiatives have stagnated and no longer deserve support gave rise to recommendation 1 (Diez, 2001; Feser & Luger, 2003). Contrary to this belief, some scholars point out that, compared with start-ups, mature initiatives should be more attractive with visions and goals that are more clearly established, larger portfolios of members, more diversified intermediary activities, and more stable operations (Brown, 2000; Enright, 2003). Mature initiatives tend to intensify the number of intermediary activities organized for their members over time, which then attracts a greater number and breadth of members (Laur et al., 2013). Long-term initiatives are prerequisites for strengthening the entrepreneurial culture and innovative climate of a region (Ylinenpa¨a¨ et al., 2014). This requires a shift in focus on the part of financing authorities: from one of almost exclusive support of new initiatives to one of making mature and established initiatives a priority. The intention of recommendation 1 is not that financing of initiative start-ups should be completely stripped, but that investments in this phase should be reduced in order to pump substantial amounts of funding into later phases. Self-proven initiatives that have developed an internal capability to continue on a chosen path should be the recipients of such support. Among numerous established initiatives are unsuccessful examples that struggle with adapting to market change and attracting new members. Analyzing the performance and future potential of such initiatives remains a challenge due to the great limitations of assessment tools (Asheim et al., 2007). Choosing to finance a start-up may be less troublesome for public authorities since it has no history, and the only aspect that can be evaluated is the potential of the idea. Constant changes in the activities of a mature initiative in order to fulfill diversified needs may signal that the initiative is struggling to find its niche. Pumping new money into such organizations would not yield favorable outcomes due to the initiative’s loss of identity, reduction in innovativeness, and inability to retransform (Klerkx & Leeuwis, 2008; Stewart & Hyysalo, 2008; Van Lente, Hekkert, Smits, & Van Waveren, 2003). In

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contrast, activities that are in line with the strategic goals of the initiative evidence good functionality; capital infusions would most likely be worthwhile. New financing could be used for improvement, where existing members and staff act as catalysts to escalate the tempo of innovativeness and development. Other assessment parameters include critical mass of members, driving forces, and commitment (see recommendation 4). Substantial amounts of public funds have been spent on the development of numerous cluster initiative start-ups (Boekholt & Thuriaux, 1998; Cassidy et al., 2005), some of which failed after a period of operation, causing large, uncompensated expenditures of public funds. Failure was often linked with an inability to adapt to change, to acquire new sources of financing, to establish a clear vision and goals, and to attract members and staff (Ketels et al., 2006). Clearly, public funds could be spent more effectively. So choosing to finance start-ups, as several scholars propose (Cluster management guide, 2006; So¨lvell et al., 2003), appears less desirable than choosing to finance well-functioning, proven initiatives, as this policy chapter proposes. This proposal rests on a bottom-up perspective (logic of cluster initiative actions) and the knowledge that the risk of failure in later phases, as entrepreneurship theory states, is lower than at the start (cf. Audretsch, 1991; Phillips & Kirchhoff, 1989). Moreover, the transformation phase follows the mature phase and accelerates the functioning of the initiative, preparing the groundwork to acquire an international focus and spin-off mature businesses (Aziz & Norhashim, 2008; Brown, 2000; Royer et al., 2009). In contrast, the startup stage is known to be shaky and unpredictable as organizations struggle to build their identity. Business literature discourages entrepreneurial activity in small businesses due to financial shortages in early stages of development (Beck & Demirgu¨c¸-Kunt, 2004). Although this could also be a potential problem for cluster initiatives, small firm research shows that this issue is not as acute as feared because the entrepreneur tends to acquire needed resources from personal sources, for example, savings and family members. As a result, the number of small businesses is increasing, not decreasing (van de Vrande, de Jong, Vanhaverbeke, & de Rochemont, 2009). Many cluster initiatives also arise as entrepreneurial initiatives of public and private employees, or other individuals, who start initiatives because of personal interest and enthusiasm (Laur et al., 2012). These traits, as with small businesses, would reduce dependency on public investment during launch; a launch does not need to be the beginning of sponsorship. This discussion strives to point out that entrepreneurs should exercise problemsolving skills in order to meet challenges. Successful training in this area inspires and encourages new trials. Massive investments at the start deadens the survival instinct that drives entrepreneurs to seek opportunities and solve problems, discouraging entrepreneurial drive and turning them into managers and public servants (Busenitz & Barney, 1997; Carland et al., 1984; Gartner, 1990; Macdonald, 1965). This issue is not considered a threat to recommendation 1 but rather a platform for entrepreneurs in cluster initiatives to experience entrepreneurship and train their capabilities.

Cluster Initiatives within the European Context 161 Recommendation 2 seeks to address the resource dependency of cluster initiatives and the loose specification of Triple Helix collaboration programs, which are stumbling blocks in creating and maintaining networks of Triple Helix actors. The recommendation suggests a model for actors based on examples of several successful cluster initiatives; the model depicts the exchanges and power distribution between the various members. The model developed by Laur et al. (2012) illustrates the composition of actors, such as key players and support and target groups, and their hierarchical positions in relation to the cluster initiative. The top position of the key player, often a public authority, highlights its dominance. However, dyadic negotiations (i.e., exchanges) with cluster initiatives restrict the dominance of the key player, which the bidirectional arrows in the model depict. Exchange is the central element, which if enabled by policy instruments, could assist in redistributing the power of key players to the initiatives. The proposed redistribution would confer on initiatives, for example, the right to veto decisions of key players concerning strategic aspects, to resist prescribed sets of activities and resource use, and to propose their own. Currently, relations between key players and cluster initiatives are not as liberal as proposed. Furthermore, in the model in Laur et al. (2012), support and target groups are also power holders; by communicating their views through the initiatives to other members, they reinforce a reasonable balance and beneficial exchange among all involved. Effective exchange and balance of power are considered necessary attributes for sustaining relationships (Diez, 2001; Morgan & Nauwelaers, 1999; Rosenfeld, 1996). Contributions from support and target groups during the development of cluster initiatives may influence public authorities to reconsider the capabilities of initiatives and emphasize the need to refine Triple Helix policy instruments. In addition, involving such groups might reduce the risk that resource dependency on public financing will transform initiatives into “hidden messengers” of government policy, leading to the loss of credibility and legitimacy (Klerkx & Leeuwis, 2008). Reinforcing multi-actor collaborations is one of the most widely discussed and effective strategies for initiatives as a whole and for the individual members, with potential benefits in the areas of techno-economic renewal and market internalization (Meyer, 2003; Ranga & Etzkowitz, 2013). To sustain cluster initiatives, proper soft instruments should always accompany funding like, for example, awareness and networking (cf. Edler & Georghiou, 2007; Rasmussen, 2008). The dyadic arrows in the model illustrate the give-and-take reciprocity between members and the initiative; for example, provision of resources to members carries the unwritten expectation of monetary, reputational, and knowledge returns to the initiative (Klerkx & Leeuwis, 2008; Meyer, 2003). The model, however, lacks such reciprocity in member-to-member relations, which reinforces the intermediary nature of initiatives and emphasizes their value in addressing the demands of members (Hospers & Beugelsdijk, 2002; Howells, 2006). From a networking viewpoint, lack of direct links between members might be considered a drawback of the model. Such links, however, would eliminate the need of an intermediary and leave other member needs and demands unfulfilled. Lack of an intermediary has proven to

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reduce cluster efficiency due to lack of time and professional skills of firms and other institutions to create their own networks while concerned with the daily operations of their businesses (Forsman & Solitander, 2004). When policymakers enable the proper function of Triple Helix collaborations, platforms develop where cluster initiatives can fulfill their triple mission of organizing intermediary activities for the public, for private, and for their own good (Etzkowitz & Ranga, 2011; Lindqvist et al., 2013). Recommendation 3 proposes activities modeled on the So¨lvell and Williams (2013) investigation of over 100 cluster initiatives in Europe and on the analytical work of several other well-known scholars. So¨lvell and Williams (2013) depict three blocks of intermediary activities that cluster initiatives perform: identity and attractiveness, innovations and R&D, and business development focus. The activities in these blocks help initiatives fulfill their Triple Helix mission (i.e., their own, and public and private good) (Ranga & Etzkowitz, 2013). Such a broad portfolio of intermediary activities tends to send a message of good health and a promising future for these organizations (Aziz & Norhashim, 2008; Stewart & Hyysalo, 2008; Turner et al., 2013). On the downside, the broad portfolio of activities, which extends beyond network formation and the articulation of demands, competes with traditional intermediary service providers such as R&D, knowledge-intensive business services, chambers of commerce, and industry associations (Klerkx & Leeuwis, 2008; Van Lente et al., 2003). This can be an important drawback of recommendation 3, especially when enrollment in these activities is insufficient, thus reducing funding for popular activities like networking and identity focus. The risk of attracting an insufficient number of participants to cluster initiative activities is high; due to the higher profile other professional intermediaries offer to similar activities. To prepare, cluster initiatives should always first investigate the market situation and changes in demand before planning special intermediary activities. This is particularly important if such events require massive budget expenditure (Laur et al., 2014; Martin & Sunley, 2003). The outcome of recommendation 3 depends much on the adaptation mechanisms of the intermediary activities. Cluster initiative leaders and personnel are the main forces driving such adaptation, while intermittent contributors to adaptation include the external experts hired for special tasks, such as carrying out market research, monitoring changes in actor demands, and coordinating services that members request (Klofsten et al., 2013; Laur & Fayolle, 2013). Reliable monitoring of market needs and adaptation of intermediary activities rests on the education and experience of the initiative staff (Laur et al., 2014). For special tasks, Triple Helix partners may be the best source of competent personnel, one more reason for authorities to refine existing programs involving multi-actor collaborations. Policies that support training can also assist in the development of available competence (Cooke et al., 2000; Edler & Georghiou, 2007; Jacobs & de Man, 1996; Perry, 2007; To¨dtling & Trippl, 2005). But there is always the risk that well-trained individuals leave to work for other members. In such cases, cluster initiatives should have contingency plans for replacing missing competence with, for example, external experts. The initiatives should also have sources of support for the new candidate (see recommendation 1)

Cluster Initiatives within the European Context 163 and be an attractive employer, by organizing activities that enhance the attractiveness and identity of the cluster initiatives in the region and internationally (So¨lvell & Williams, 2013). Recommendation 4 focuses on the need to identify management and assessment tools. Many scholars find lack of suitable tools to be a limitation of current policy instruments and an underlying cause of, for example, short-term financing, unspecified mechanisms of Triple Helix collaborations, and unfocused intermediary activities (Arthurs et al., 2009; Aziz & Norhashim, 2008; Cassidy et al., 2005; Klofsten et al., 2013). Recommendation 4 is based on the business platform framework, which has proven to be a reliable assessment tool for firms. The recommendation translates and adapts the platform to cluster initiatives (cf. Klofsten, 1992), defining five success factors that are crucial to the operation and survival of initiatives: idea, driving forces and commitment, activities, critical mass, and organization. Moreover, bottom-up perspective identifies relevant factors that allow targeted management and assessment of cluster initiatives. Using these factors, policy programs can address individual needs and phases of development. In contrast to existing quantitative models, the Laur et al. model does not rely on quantitative measures and can be applied to continual analyses, learning processes, and cause-and-effect connections (Brulin et al., 2009). The holistic tools in the recommendation aid cluster initiative leaders in maintaining strategic direction. But, recommendation 4 should not be used alone without quantitative supplements (Curran, 2000). Other indicators that measure soft issues like network formation and institutional linkages should also be added to create a commonly recognized and widely fitting tool for cluster initiative management and assessment (Autio et al., 2008). Some scholars advise policymakers to promote benchmarking for management and assessment of initiatives’ work (Cooke et al., 2000; To¨dtling & Trippl, 2005). Learning from examples of other successful initiatives and clear guidelines for management strategy may prolong the life cycle of cluster initiatives. These recommendations may also serve as a tool for self-evaluation on an organizational level and be an instrument for making decisions on long-term cluster initiative financing. But, the practical use of this type of instrument has not eliminated the need for other, more advanced assessment tools that provide a more profound basis for strategic decision-making.

Conclusions The aim of this chapter was, using a bottom-up perspective, to suggest refinements in funding, member constitution, activity content, and management of cluster initiatives for policy instruments at the regional level. To address these, four recommendations were proposed: (1) increased funding of established, well-functioning initiatives, (2) stronger Triple Helix collaborations that secure exchange and reciprocity principles in relationships, (3) a wider variety of intermediary activities to fulfill

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the Triple Helix mission, and (4) better definition and communication of the cluster initiative success factors model to improve management and assessment. Although each recommendation is important, the crucial one at this time is recommendation 4. Lack of suitable cluster initiative assessment tools has been a challenging task for policymakers in recent decades and the reason for numerous policy limitations. Recommendation 4, which the Klofsten et al. (2013) model in this chapter supports, addresses the problem in part; for the model to become a reliable source of assessment, supplemental tools are necessary. The bottom-up perspective of the proposed recommendations shows how policymakers could enhance the emergence and growth of cluster initiatives, thus improving cluster and regional performance. The recommendations lay the foundation for improvements in how entrepreneurs exercise their skills and train entrepreneurial drive, strengthen the transformation process and refine a new identity, increase development tempo and internationalization, facilitate exchange between members, and attract competent personnel. Potential negative consequences should be actively counteracted since no recommendation fits every situation perfectly (Andersson et al., 2004; Etzkowitz, 2003). Examples of these include reduction in entrepreneurial activity driving formation of cluster initiatives, indirect network linkages and restricted exchange between members, competition with traditional intermediary service providers, and insufficient numbers of participants in activities. This chapter has discussed techniques for reducing potential adverse consequences; some, however, remain matters that cluster initiatives must solve as the issues emerge in practice, and which must be left to the guidance of common market principles. Tighter intervention by policymakers at this level might restrict actors’ freedom and disturb market self-regulation (cf. Martin & Sunley, 2003).

Acknowledgments The author wants to thank professors Magnus Klofsten, Anna Bergek, and Every Vedung who provided great support in terms of suggestion of relevant literature sources, brainstorming discussions during the idea generation process and valuable comments on the chapter content.

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PART IV HIGHER EDUCATION AND ENTREPRENEURSHIP

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Chapter 9

Assessing the Effect of Different Dimensions of Top Management Team Diversity on the Growth of University-Based Spin-Off Firms Francesca Visintin and Daniel Pittino

Abstract In this chapter we aim at examining the influence of early top management teams (TMTs) on the growth performance of university-based spin-off firms, presenting an empirical research on spin-off companies in Italy. The chapter proceeds along the following lines. First we describe the context of analysis, briefly reviewing the literature on TMT and performance. In the second section we outline the hypotheses of our research. The third section describes the sample and the method for the empirical analysis. The fourth section presents and discusses the results. In the last section we highlight the main implications and limitations of our results and suggest further lines of research.

Introduction In the past few decades, universities have progressively included, among their traditional activities, a new one, namely technology transfer, consisting of a series of processes and instruments aimed at transferring knowledge, scientific discoveries and ideas produced within universities to the external world, often through the means of the market. Through licencing agreements, publications and mobility of researchers universities have been encouraging processes of innovation by supporting the transformation of new knowledge into products, services and industrial processes.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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There is, however, a peculiar instrument through which universities can become principal actors in the innovation process and this is the spin-off firm. Through this instrument, professors, researchers and students get involved into the business process of taking an innovative idea or technology produced within the university labs or research centres to the market. Indeed, in recent years, due also to the affirmation in the public debate of the idea of the ‘entrepreneurial university’ (e.g. Etzkowitz, 2003), the most successful spin-off firms have been increasingly recognised as important drivers of regional and national competitiveness in the global landscape, for their leading role in fostering technology transfer and innovation processes (Breznitz & Anderson, 2006; Clarysse, Wright, Lockett, van de Velde, & Vohora, 2005; Di Gregorio & Shane, 2003; O’Shea, Allen, Chevalier, & Roche, 2005; Shane, 2004; Slater & Mohr, 2006). Several empirical investigations show that with few exceptions, the majority of public-research spin-offs are and stay very small enterprises (see, e.g. Mustar, Clarysse, & Wright, 2007). Analysing and understanding this phenomenon, to identify the obstacles that might limit the growth of this type of high-tech start-ups, appears therefore of crucial importance, particularly in light of the present crisis. The existing contributions dealing with the performance of university spin-offs tend to concentrate either on the characteristics of the university systems and their spinoff policies, on the effectiveness of technology transfer offices or on the availability of funding from university, industry and venture capitalists (e.g. Chang, Phil, & Chen, 2009). Only very few, mostly qualitative, studies open the black box of the new ventures by considering the characteristics of the founding teams (degree of heterogeneity in particular), type of social, scientific and business networks and development stages. However, these contributions appear to overlook the peculiarities of the top management teams (TMTs) of university spin-off firms and arising mostly by the confluence of business and academic roles (see Clarysse & Moray, 2004; Ensley & Hmieleski, 2005). In this chapter we aim at filling this gap by analysing the relationship between early TMTs and performance of spin-off companies by employing an enriched version of the traditional demographic approach dominating the entrepreneurship literature. Indeed, we try to account on the one hand for a number of very peculiar mechanisms that shape the functioning of the academic world and arise mostly from differences in academic status and hierarchical position and on the other, on the effects of an injection of ‘non-academic organisms’ in an otherwise homogeneous system anchored to a set of strong and shared values and organisational culture. In our view, the spin-off, in terms of decisional processes, positions and roles, should be looked at as an extension of a research lab or university department, with an addition of an economic/business dimension that complicates certain dynamics instead of substituting them. The structure of the chapter is as follows. First we review the literature on TMT and performance; in the second section, we develop the hypotheses; in the third section we include the sample and the empirical method; in the fourth section we present and discuss the results, and finally, in the last section, we highlight the current limitations of the research and outline possible future developments.

Assessing the Effect of Top Management Team Diversity 175

Top Management Teams and Business Performance Following a resource-based and upper-echelons perspectives, it has been put forward that the quality of the entrepreneurial-managerial group is among the most important determinants of the growth and success of start-up (Eisenhardt & Schoonhoven, 1990; Hambrick & Mason, 1984; Heirman & Clarysse, 2004; Mustar et al., 2007). In small companies, particularly, the TMT is more likely to be influential and to directly impact on the company performance, since the communication channels are not mediated by bureaucratic structures and levels (Finkelstein & Hambrick, 1996); moreover, in start-up companies direct supervision, power and authority is easier. In recent years, a significant number of contributions on the performance of high-tech new ventures has concentrated on the TMTs (among others, Amason, Shrader, & Tompson, 2006; Ensley & Hmieleski, 2005; Ensley, Pearson, & Amason, 2002; Ensley, Pearson, & Pearce, 2003; Schjoedt & Kraus, 2009), by analysing the level of team’s heterogeneity in terms of age, background, education, functional experience (Beckman, Burton, & O’Reilly, 2007; Ensley & Amason, 1999) and values; leadership and cognitive structures (Ensley & Pearce, 2001; Page West, 2007); degree of conflict and extent of cohesion (Ensley & Pearson, 2005; Ensley et al., 2002). As for the diversity/heterogeneity approach, notwithstanding the numerous contributions on the topic, the results have not been satisfactory and different empirical analyses have produced over time contrasting evidence. Indeed, on the one hand Ensley and Hmieleski (2005) show that heterogeneity of the start-up’s TMT in terms of education, functional expertise, industry experience and business skills positively influences the net cash flow and the growth in sales. Further, Beckman et al. (2007) present evidence that TMTs presenting diverse functional backgrounds are able to reach entrepreneurial achievements more quickly than homogeneous teams. On the contrary, Simons, Pelled, and Smith (1999) found a negative relationship between the extent of diversity in the TMT (in terms of functional background and age) and level of performance (profitability and sales) whereas West and Schwenk (1996) suggest terminating this field of research due to the inconclusiveness of the findings. In recent years, studies adopting the heterogeneity perspective have started to extend so as to include university-based spin-offs. Ensley and Hmieleski (2005) show that the TMTs of academic spin-offs from three US universities present a higher level of homogeneity (industry experience, education, functional background) than a sample of independent new ventures and claim that the founders of university spin-offs tend to select the TMT members from their colleagues within the universities, and ‘reproduce’ the executives composition of other university-based firms. Similar results are found also by Heirman and Clarysse (2004). A very interesting work, for the aims of this chapter, is the one by Bonardo, Paleari, and Vismara (2011). The authors show that the presence of academics in the TMT after the IPO negatively influences the levels of operating and market performances achieved by university-based spin-offs. This is indeed the

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only contribution, as far as we know, that makes an attempt to explain the performance of university-based ventures, by considering the peculiar characteristics of academics. To explain the inconclusiveness of the results, some authors suggest adopting a contingency approach and considering that the relationship between team diversity and performance might be context-specific and connected to the level of complexity of the context (Carpenter, 2002). In particular, Amason et al. (2006) show that TMT heterogeneity relates negatively to performance in dynamic environments, where frequency of interactions and quality of communication are more important than information seeking and decision comprehensiveness. This result also finds support in a more articulated analysis performed by Ensley and Hmieleski (2005) who interact TMT heterogeneity, environmental dynamism and leadership style. Another explanation might be found in the use of an inaccurate definition of diversity and in particular from the use of a unidimensional definition, that is the horizontal dimension of variety. According to Harrison and Klein (2007, p. 1200), diversity can originate also from other sources, such as the level of separation, that is some sort of polarisation that arises from differences in position or opinion among the members and/or the level of disparity, that is ‘differences in concentration of valued social assets or resources such as pay and status among unit members — vertical differences that, at their extreme, privilege a few over many’. Such dimensions and their combination can have different effects on the functioning of the team and, as a consequence, on the performance of the company. Diversity originating from separation could have both negative and positive effects on performance as teams whose members are characterised by a high degree of separation might experience high levels of conflict and withdrawal (see, e.g. Tsui, Ashford, Clair, & Xin, 1995) but lower problems in terms of organisational silence and group think effects. The concept of disparity draws mostly from the sociological theories of stratification (Grusky, 1994). As recalled by Harrison and Klein (2007), the few studies considering disparity in organisations focus on three sources, namely pay dispersion (Bloom, 1999), status and power, and social capital and predict on the one hand an increase in competition and resentful deviance among team members (e.g. Bloom, 1999; Gomez-Mejia, 1994; Homans, 1961; Pfeffer & Langton, 1993) and on the other higher conformity, silence, suppression of creativity and withdrawal (Hollander, 1958; Pfeffer, 1998; Pfeffer & Davis-Blake, 1992). As will be shown in the next section, we find the extended definition of diversity particularly useful in the study of the relationship between TMTs and performance in academic spin-offs as it allows incorporation of a number of peculiarities characterising university business ventures that are not covered by the simple concept of variety. We are here referring in particular to the impact of the presence of both academics and non-academics in the managing team and to the existence of status differences extending from the university world to affect the dynamics within the spin-off.

Assessing the Effect of Top Management Team Diversity 177

Variety, Separation and Disparity of Top Management Teams in Academic Spin-Offs: Hypothesis Building As explained above, the empirical studies reviewed suggest that TMT diversity may have an impact on spin-off performance; the mentioned researches focus mainly on demographic variety. We aim at providing further evidence on the diversity
performance relationship, exploring also additional dimensions of diversity that are particularly observable in the context of academic start-ups. We focus on the new venture employment growth as the relevant indicator of spin-off performance, since job creation is one of the main goals related to the promotion of academic entrepreneurship. As for the first dimension of diversity, variety, we focus on the sources most commonly analysed in the literature, namely, age, education, entrepreneurial experiences within the family and function. In line with the contingency approach, we expect a negative relationship between the degree of variety in the TMT and spin-off performance, as spin-off companies operate mainly in the high-technology highly dynamic sectors and, in these contexts variety has been found to be negatively connected to innovativeness and performance (Hmieleski & Ensley, 2007). Hypothesis 1. TMT variety is negatively related to spin-off growth. It has been highlighted that in the context of university-based spin-offs one source of variety might have a positive impact on performance, namely the degree of complementarity between specific profiles. In particular, teams where technological competences and business-related competences are mixed may have an advantage compared to TMTs lacking members with business-commercial specialisation. This is because academic founders, due to their educational specialisation, often lack the market orientation necessary for the commercial development of their ideas (e.g. Druilhe & Garnsey, 2004; Vohora, Wright, & Lockett, 2004); therefore an integration of the team with commercial and business-related profiles can enhance the likelihood of team success. Effects of team complementarity have been assessed, for example by Mu¨ller (2006) who finds that spin-off firms in technology-oriented services with engineers and business administrators among their founders experience higher employment growth. Thus, it makes sense to test the following: Hypothesis 2. The presence in the TMT of members with a business-related specialisation is positively related to spin-off growth. As for the second dimension of diversity, separation, we focus on the affiliation of TMT members, and, particularly, on the distinction between academic and non-academic members. Surrogate entrepreneurs, that is individuals without an academic background and with commercial experience, who may be included in the TMT to develop the start-up (Franklin, Wright, & Lockett, 2001; Lockett,

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Wright, & Franklin, 2003), can have a significant role in influencing the performance of the company during the critical stages of development (Mustar et al., 2007; Vohora et al., 2004) and, more in particular, when the academics maintain their positions. Non-academic members may also operate as mentors to develop the entrepreneurial skills of the start-up team (Clarysse & Moray, 2004). Diversity in affiliation may however generate a separation/faultline effect. The extensive literature on university
industry relationships has highlighted the important differences in institutional norms, procedures, goals and ‘moral’ codes shaping the two worlds. As suggested by Van Dierdonck, Debackere, and Engelen (1990), the academic community operates according to the Mertonian norms of science. The scientific ethos is often viewed as a major obstacle to close interactions with industry. One can speak of a real cultural barrier between the academic and the industrial world. We can assume that non-academic members of a TMT, particularly those with a professional experience in the business world exhibit different cognitive maps and goals from scientists operating in the academic world, particularly if with the same affiliation (e.g. Vohora et al., 2004). Such differences, would create in the management team some sort of polarisation (separation) resulting in cognitive conflict which is recognised to have both positive and negative effects on performance. On the positive side, some level of cognitive conflict is found to improve the process of creating a shared cognition (Floyd & Wooldridge, 1992; Wooldridge & Floyd, 1989) as it improves the clarity of the group’s strategic mental map (see Klimoski & Mohammed, 1994 as quoted by Ensley & Pearce, 2001) and to reduce the risk of pathologies of group think. On the negative side, as explained above, separation might result in demotivation, withdrawal effects and poor decisional processes. In particular, it seems reasonable to argue that in high-tech sectors, characterised by high levels of complexity and dynamism, requiring quick and effective decision processes, some degree of separation can have a negative impact on performance as it creates some sort of noise that will not effectively and constructively support the creation of a shared cognition generating at the same time obstacles, delays and possibly resulting in the withdrawal of those whose goals, values and opinions are not considered. With a growing number of outsiders in the TMT, the dominant ‘academic’ cognition weakens and it is counterbalanced by a more business-oriented cognition. This might both result in a stalemate and in a loss of competitiveness or in the creation of an enriched shared cognition, resulting from the collaboration between the two poles (Ensley & Pearce, 2001). Thus, we formulate the following: Hypothesis 3a. Low and high degrees of separation between academics and nonacademics in the TMT have a positive effect on spin-off growth, whereas medium levels of separation have negative effects. Hypothesis 3b. The degree of separation between academics and non-academics in the TMT is negatively correlated with performance.

Assessing the Effect of Top Management Team Diversity 179 So far our research hypotheses focused on a ‘horizontal view of diversity’ (Sessa & Jackson, 1995). In addition to horizontal diversity, academic spin-offs are a favourable context to test the effect of vertical team diversity, that is disparity. Disparity, along with its three main sources (pay, status, social capital) appears to be rather useful in the analysis of diversity among the members of top teams university-based start-ups. Academics with a different hierarchical position can differ enormously in terms of pay (in Italy, e.g. a full professor’s salary can be three times that of a researcher); status and power (deriving both from hierarchical position and/or academic prestige); and size of social capital (in a research team usually the leader plays the institutional role of boundary spanning and over the years builds a network of interpersonal ties that play a fundamental role in publishing in ranked journals or in obtaining grants). We argue that the negative effects of such disparity extend over the boundaries of the research group to affect the dynamics of the spin-off, both in terms of decision-making processes and in terms of creativity and innovation and might exert even more pressure than other forms of diversity such as variety. According to previous research, group disparity fosters conformity, silence, suppression of creativity, lack of participation (e.g. Pfeffer, 1998) which hinders the team’s innovative performance (e.g. De Dreu & West, 2001). We could assume therefore, that academic disparity within teams tends to limit participation and discussion, thereby hampering team performance and the consequent development of the new venture. Group disparity, however, may also be related to centralised decision making and directive behaviour by members who ‘outrank’ the rest of the group. In particular phases of the decision-making process these features may have a positive impact on performance, for example when there is the need to rapidly choose among various alternatives of action with respect to strategic issues, and/or when the actor that holds the centralised decision-making power is in the ‘right position’ in terms of incentives and commitment. Moreover, it has been shown that centralised decision making has a positive impact on performance in the case of high-technology sectors where technologies change rapidly and the competitive environment is particularly dynamic. Hypothesis 4a. Academic disparity is positively associated to spin-off performance. Hypothesis 4b. Academic disparity is negatively associated to spin-off performance. The academic disparity, however, is not the only source of disparity within a spin-off. Disparity might indeed arise also from the distribution of equity stakes, and, as a consequence, of control. Indeed, the distribution of equity stakes in the spin-off can be interpreted as an indicator of dynamics occurring within the research team. The combination of the two sources of disparity gives rise to a number of possibilities. In the first case, a high academic disparity might be mirrored in the distribution of the ownership stakes. In this case, in the process of spin-off formation the team must have considered that the characteristics (competencies, skills, social capital,

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leadership, etc.) that brought the leader to assume the leadership position in the university environment will be of crucial importance also in the business world. The same situation might occur when the weak academic members of the research group have no other choice as a consequence of the power that the leader has over their university career or when they join the spin-off because they think that this can have a positive connotation in their CVs. In either case, the company will be likely be conducted with an academic logic rather than a business one and the spin-off replicates the same dynamics of the research group. In a second case of academic disparity, one or few persons other than the academic leader, have the control over the spin-off. This case might occur when the academic hierarchy is perceived not to reflect an equal distribution of competencies or when one or more researchers decide to commit their efforts to the spin-off rather than to an academic career (because they perceive that the university does not satisfy their goals in terms of pay, power, self-realisation and so on). This case suggests that in the process of spin-off formation the team has recognised that the dynamics that should govern a company are different that those that shape the functioning of a research team. A third case is characterised by a high level of academic disparity but a low level of shares disparity. Also this case, as the previous one, reflects some sort of acknowledgement of the need to adopt a different approach to the management of a business. In this second case, all the members of the TMT signal their will to commit to the success of the company. The final result however might be a second best as, in a contingency view, the distribution of control and power among various people might slow the decisional process and result in an impasse in a highly competitive environment. In a fourth case, which falls into the second in terms of results, a low academic disparity is accompanied by a high level of shares disparity. In a final case, a low academic disparity is mirrored by a low shares disparity. Also in this case, as in the third, it appears that an egalitarian rather than a business logic dominates. In this case, everybody is willing to take up the same level of responsibility and control in the business either because they feel that everybody is equally endowed with competences and entrepreneurial spirit or because the spin-off is some sort of a pet-project and the academic career maintains the priority. We can therefore suggest the following. Hypothesis 5. Disparity in the equity shares is positively related to spin-off growth when it signals a deliberate choice by the top management team.

Sample and Method The research has been carried out on a sample of 103 Italian academic spin-off firms taken from the population of 745 Italian spin-offs officially listed in 2009 in the directory of the NETVAL network, the Italian Network of Universities engaged in

Assessing the Effect of Top Management Team Diversity 181 technology transfer activities. To properly assess the effect on venture growth we have chosen spin-off established before the year 2006. Data were collected through a phone interview based on a structured questionnaire aimed at collecting data on the profile of the spin-off and features of their TMT. Interviews were carried out in the period December 2009
December 2010 by two junior researchers with a master degree in business administration and previous experience in research on academic entrepreneurship. Analysis of the data was aimed at estimating the variables that affect the employment growth of the university-based spin-offs. We used multiple regression models. The dependent variable in our analysis is the average yearly employment growth. The following variables have been used as independent measures. We used the measures of various dimensions of diversity suggested by Harrison and Klein (2007): • Diversity in educational specialisation: measured through a Blau index referred to categorical classification of academic specialisations of TMT members. • Diversity in previous experiences: measured through a Blau index referred to categorical classification of previous experience of TMT members. • Members with a business-commercial specialisation: dichotomous variable assuming value 1 if at least one member of the TMT has an educational specialisation in business field. • Separation between academic and non-academic members: presence of nonacademics (0
0.2; 0.80
100: low; 0.40
0.60: high; remaining: medium). • Disparity in academic status: dichotomous variable assuming value 1 if there exists an ‘academic hierarchy’ among members within the team. • Disparity in ownership stakes: coefficient of variation in equity stakes held by TMT members. We also employed a number of control variables, namely, the ownership stakes held by the university, the sector of activity, the business model, the size of the TMT, the age of the new venture.

Results Results of the regression analysis are presented in Table 1. We reported three of the estimated models. Model 1 includes the control variables and the measures of horizontal diversity; in model 2 we added the variables accounting for team disparity in status and equity position. Model 3 includes measures of separation between academic and non-academic members, with the quadratic specification of the variable in order to capture the curvilinear effect postulated in the Hypothesis 3. Results of the estimation provide support at the 0.10 level to Hypothesis 1 with respect to the disparity in educational specialisation. High diversity in TMT educational specialisation negatively impacts on the employment growth of the spin-off. Diversity in previous experience seems not to influence team performance.

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Table 1: OLS regression of the dependent variable average employment growth. Dependent Variable

Constant Business model (consultancy) Share held by the university TMT size Age of the spin-off Diversity in education specialisation Diversity in previous experiences Members with business specialisation Separation academics/non-academics (Separation academics/non-academics)2 Disparity in academic status Disparity in members’ equity stakes Adj. R2 Model F Significance N

Employment Growth Model 1 β

Model 2 β

Model 3 β

0.654 −0.26** 0.17 −0.208 0.183 −0.152* 0.013 0.01

0.145 −0.354*** −0.253** −0.045 0.100 −0.171* −0.056 0.10

0.113 1.729 0.077 94

−0.143* 0.326*** 0.269 3.950 0.000 94

0.000 −0.559*** −0.241** 0.047 0.096 −0.181* −0.063 0.143 −2.731*** 2.673*** −0.109 0.379*** 0.384 3.644 0.000 94

***Coefficient is significant at the 0.01 level; **coefficient is significant at the 0.05 level; *coefficient is significant at the 0.1 level.

Hypothesis 2 is not supported; the presence of members with business specialisation has a positive effect, but the coefficient is never significant. Hypothesis 3 does not find support, although variables involved exhibit significance in their coefficients. Signs of the coefficients are in fact opposite to the expected: low and high levels of separation between academics and non-academics lead to better performance compared to medium levels of separation. Hypothesis 4 finds support only in model 2 at 0.10 level, indicating that disparity in status may influence TMT functioning and spin-off results. Finally, Hypothesis 5 is strongly supported by the analysis, suggesting that concentration in ownership stakes in the hands of one or few TMT members may have a positive effect on the spin-off growth potential.

Discussion and Conclusion This research aims at providing additional insights to the topic of TMTs of university-based spin-off firms, thereby contributing also to the literature on early TMTs and entrepreneurial teams in high-technology ventures. This study also aims at contributing to the general research on TMT, through the observation of

Assessing the Effect of Top Management Team Diversity 183 variables that have received little attention so far by the TMT researchers and are particularly evident in university spin-off firms (e.g. status and power disparity). Our main goal in particular is to assess the impact of different dimensions of TMT diversity on the employment growth of the university-based new ventures. Empirical analysis confirms that TMT diversity is important in explaining firm performance. We assess the impact of horizontal diversity through the observation of educational specialisation and previous experience; results suggest that, according to earlier studies (e.g. Amason et al., 2006), in highly novel contexts demographically homogeneous teams perform better than diverse teams, since they have an advantage in quality of communication and goal commitment. Complementarity between scientific and business skills does not seem to have an impact on spin-off growth. Group disparity related to academic status has a weakly significant negative impact on performance, whereas disparity in ownership distribution seems to have an important effect. This result may be due to the fact that ownership concentration realises a clear and unambiguous division of tasks, responsibilities and incentives between entrepreneurial and managerial component of the TMT. Finally, analysis on the separation between academic and nonacademic members suggests that better performing teams are those that are homogeneous in their composition in terms of academic affiliation or have an equal distribution between categories of members. The present research is at its early stages, therefore, several limitations need to be addressed and various future developments are possible. In addition to team composition, team dynamics in terms of leadership, cohesion and conflict need to be examined; dimensions and consequences of disparity need to be specified more accurately, separation between academic and non-academic members needs to be considered in interaction with horizontal diversity dimensions and also with disparity measures, since analysis reveals that these measures are not independent. More control variables have to be employed to improve validity and reliability of the analysis.

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Shane, S. (2004). Academic entrepreneurship: University spinoffs and wealth creation. Cheltenham, UK: Edward Elgar. Simons, T., Pelled, L. H., & Smith, K. A. (1999). Making use of difference: Diversity, debate, and decision comprehensiveness in top management teams. Academy of Management Journal, 42, 662
673. Slater, S. F., & Mohr, J. J. (2006). Successful development and commercialization of technological innovation: Insights based on strategy type. Journal of Product Innovation Management, 23, 26
33. Tsui, A. S., Ashford, S., Clair, L., & Xin, K. R. (1995). Dealing with discrepant expectations: Response strategies and managerial effectiveness. Academy of Management Journal, 38, 1515
1543. Van Dierdonck, R., Debackere, K., & Engelen, B. (1990). University-industry relationships: How does the Belgian academic community feel about it? Research Policy, 19, 551
566. Vohora, A., Wright, M., & Lockett, A. (2004). Critical junctures in the development of university high-tech spinout companies. Research Policy, 33, 147
175. West, C. T., Jr., & Schwenk, C. R. (1996). Top management team strategic consensus, demographic homogeneity and firm performance. Strategic Management Journal, 17, 571
576. Wooldridge, B., & Floyd, S. W. (1989). Strategic process effects on consensus. Strategic Management Journal, 10, 295
303.

Chapter 10

University
Business Co-operation in Indonesian Higher Education for Innovation Firmansyah David and Peter van der Sijde

Abstract This chapter explores emerging concerns and issues of University and Business Co-operation (UBC) at Indonesian universities. Over decades, the Indonesian government has been implemented policies and strategies to stimulate collaboration between universities and business by offering them a variety of funding schemes. It has been aimed to foster innovation and to reach the government ambition, to make Indonesia as a country in the innovation-driven economy by 2020. Our study was based on a desk evaluation and the secondary data. We collected and examined documents of the governmental policies, universities’ strategies, relevant UBC articles, etc. in order to get an overview of UBC in Indonesia. Our findings suggests that the participation rate of universities and academics in UBC, especially with those funded by the government, remains low. The government expected more participation by offering more funds; however, it was not successfully achieved. We conclude that to increase the participation of universities and academics in UBC, they need to resolve the different institutional logics with their business counterparts.

Introduction Universities worldwide are now moving more and more to the centre of the innovation-ecosystem (Etzkowitz & Klofsten, 2005; Lundvall, 1992). Aligned with this repertoire, policy-makers have been stimulating universities to cooperate with business in an effort to develop a knowledge-based economy (Mowery & Sampat, 2004). Universities have been encouraged to get involved with business in the form

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of generation, use, application, and exploitation of knowledge and to contribute their capabilities beyond the academic environment (C´ulum, Roncˇevic´, & Ledic´, 2013). The interaction and co-operation between university and business can determine the pace of innovation, especially when the most important actor promotes it, namely, the government which has a coordination, regulation and funding function (Callon, 1998; Etzkowitz & Leydesdorff, 2000). The innovation-ecosystem would ideally result in common (economic) advantages when the actors of innovation such as industries, universities and government can work together (Mars, Bronstein, & Lusch, 2012). Many countries are shaping their innovation-ecosystem in the form of the National Innovation System (NIS) by placing firms and private institutions as the main actors to foster innovation (Freeman, 1987; Lundvall, 1992; Metcalfe, 1995; Nelson, 1993). Further, the Organization for Economic Co-operation and Development (OECD) highlights that universities are the important players in improving a country’s innovation performance (OECD, 1997). The policy-makers, universities and business may be settled in the innovation-ecosystems when these actors can incorporate their roles and are able to work together (Etzkowitz & Leydesdorff, 2000; Lundvall, 1992). In an ideal situation, the government provides funding, universities play a role as the source of knowledge and do technology transfer, and business gets benefits from the collaboration. A university is an organization that traditionally carries out its mission in research and teaching. In the innovation-ecosystem, universities should expand their mission beyond the boundaries of research and teaching (C´ulum et al., 2013; Sam & van der Sijde, 2014). The ‘third mission’ of a university is considerably as a ‘tool’ to enhance the contribution of universities in regional development (Chatterton & Goddard, 2000). Etzkowitz and Leydesdorff (2000) argue that the ‘third mission’ of universities may be varied depending on the universities’ goals and visions. In this chapter, we assume the University and Business Co-operation (UBC) as the shape of the ‘third mission’ as other studies used the terms ‘technology transfer’, ‘knowledge transfer’, ‘community service’, ‘university and business interaction’ (Groen & van der Sijde, 2002), and Rothaermel, Agung, and Jiang (2007) coined it as the ‘academic entrepreneurship’ to describe UBC. Further, Davey, Baaken, Muros, and Meerman (2011) formulated UBC as the mechanism of collaboration between university and business in: Research and Development (R&D); personnel mobility (academics, students and business professionals); commercialization of R&D results; curriculum development and delivery; long-life learning; and governance. The ‘third mission’ is rather a new task for some universities, while others might have performed the ‘mission’ for decades. Marshall (1920), in his book the Principle of Economics, introduced a theory of ‘knowledge spillovers’ between academics and society in a region. The theory suggests that there is an exchange of knowledge among individuals between organizations or more as well as from one employee to another. Etzkowitz and Klofsten (2005) argued that universities can manage the ‘knowledge spillovers’ via a strategic collaboration. The idea of a strategic collaboration among universities, government and business was also proposed by Bush (1945) in his book Science: The Endless Frontier by arguing that government should be in place to respond to the emerging sciences and technologies after the World

University
Business Co-operation in Indonesian Higher Education 189 War II. However, universities have to find their ‘place’ in the Triple Helix constellation. Etzkowitz and Leydesdorff (1995) argue the Triple Helix partnership has remained as a framework to run the NIS in the late 20th century. Nonetheless, the UBC is still a problematic issue (Barnes, Pashby, & Gibbons, 2002; Etzkowitz & Klofsten, 2005; Geisler & Rubenstein, 1989; Lind, Styhre, & Aaboen, 2013). Etzkowitz, Webster, Gebhardt, and Terra (2000) argue that many universities and business and the government in the Triple Helix relationship are kept operating within its ‘institutional sphere’. The sphere states that the different beliefs, values, norms and practices between universities and business may cause problems and be rooted in differences of cultures, objectives and strategy (Cyert & Goodman, 1997). These differences have led to difficulties in creating and preserving a strategic alliance between universities and business (Elmuti, Abebe, & Nicolosi, 2005). In this chapter, we discuss how the government stimulates universities to have collaborations with business which have not been successful. Further, we illustrate that in the innovation-ecosystem, universities and academics are being exposed with different ‘institutional logics’ with their business counterparts, which might hinder the relationship between them.

The Context of the Indonesian Innovation Performances and Universities An NIS can be used as a framework for countries to enhance their economic growth and to survive in the global competition. It is emphasized that the ‘flows of technology and information among people, enterprises and institutions are key to the innovative process’ (OECD, 1997, p. 7) by which it also offers an integral framework for policy-makers to identify points which can foster innovation and to improve a country’s competitiveness. Via the NIS, the policy-makers are supposed able to ‘see’ and to overcome ‘mismatch’ among the actors and to resolve it. The government of Indonesia adopted this framework in an effort to improve the Indonesian innovation performances by having ambition to be a country in the innovationdriven economy by 2020 (KIN, 2012). The stage where Indonesia should compete with new and/or unique products, services, models, and processes and make sure the companies can produce new and different goods through new technologies and sophisticated production processes (Schwab & Sala-i-Martin, 2012). Presently, Indonesia still remains as the country in the stage of the Efficiency-Driven Economy according to the Global Competitiveness Index (GCI) published by the World Economic Forum (WEF). The index put Indonesia in the 50th position of the most competitive country out of 144 nationalities in 2012 (see Table 1). This stage shows that Indonesia has been successful in competing based on efficient production processes and increased product quality (Schwab & Sala-i-Martin, 2012). Experiences from countries that have managed to be in the innovation-driven economy are examples for the Indonesian government to refer to or to learn from. In the Asian context, Singapore and Republic of Korea are two countries that

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Table 1: The Indonesia competitiveness position based on the global competitiveness report of the world economic forum. Country

Singapore Korea (Rep.) China Indonesia India

Stage of Developments

Overall Rank

Higher Education and Training

Innovation

Innovation-driven Innovation-driven Efficiency-driven Efficiency-driven Factor-driven

2 19 29 50 59

2 17 62 73 86

8 16 33 39 41

Source: Schwab and Sala-i-Martin (2012).

successfully managed to become innovation-driven economies (see Table 1). By spending at least 3% of its GDP on Research and Development, Singapore has succeeded in ‘bringing’ its universities into the arena of the innovation-ecosystems (KIN, 2012). The BIOPOLIS, an example of an innovation cluster, has been managed by the National University of Singapore, Singapore Polytechnics, National University Hospital and Business School which mainly focused in the Research and Development of Biosciences (Biopolis, 2013). Republic of Korea spent over 3% of its GDP in 2011 and 5% in 2012 (KIN, 2012) and its government also succeeded in ‘stimulating’ 232 universities, 40 corporate research centres and 20 research institutes that formed an innovation cluster (Daedeok, 2009). The government of Indonesia has set a plan to increase the R&D budget from 0.07% in 2007 up to 1% of the country’s GDP in 2014 (KIN, 2012). Increasing the R&D budget may be a promising step towards improving product and research capacities; however, it does not necessarily serve as the way to foster innovation. OECD Reviews of Innovation Policy underline that UBC has a strong foothold in the assessments of the NIS (see OECD, 2006, 2007, 2008, 2009, 2013). The innovation-ecosystem is demanding a well-established co-operation between universities and business as well as a stimulation for universities to narrow the gap with companies and firms. Thus, the innovation-ecosystem not only requires governmental stimulation but also demands some initiative from universities. It implies in the NIS that a (entrepreneurial) university is not just interested in interaction with its social and economic environment but also in adapting itself to the changes (Clark, 2004; Etzkowitz, 2004; O’Shea, Allen, Morse, O’Gorman, & Roche, 2007). We highlight the contradictory achievements of Indonesia between the ranking of the Higher Education and Innovation. The Higher education is ranked in 73rd position, which lags far behind Indonesia’s performance in innovation, which is ranked 39th. This indicates that universities in Indonesia are still in the developing phases even though they have been guided by the government for decades in terms of educational structure, curriculum, lecturers’ regulation, laws, etc. (DGHE, 2003). Universities are diverse in forms of public and private institutions, types of

University
Business Co-operation in Indonesian Higher Education 191 universities and size. Innovation performance shows that Indonesia has a sufficient number of resources to foster innovation such as skills, know-how and working condition (Schwab & Sala-i-Martin, 2012). However, the role of universities to enhance innovation is actually not a new paradigm. In 1975, the Directorate General of Higher Education (DGHE) regulated the strategies and operationalization of universities by enacting the Higher Education Long-Term Strategies (HELTS). This strategic framework emphasizes the universities’ contribution to societal and economic development (DGHE, 2003). In the last 10 years, the DGHE has offered grants to universities in forms of research on ‘applied-knowledge clinic’ and ‘business incubator’, to support universities on preparing their contribution to business within an innovation-ecosystem. And these grants also aimed in promoting academics and professionals to have a place to meet, negotiate and deal. To conduct this, each university in Indonesia has the office of Research and Community Service (Lembaga Penelitian dan Pengabdian Masyarakat) or LPPM to manage all funds and activities related to research and community service. LPPM is the central office at universities to manage the knowledge and technology transfer similar to a Technology Transfer Office or TTO (van der Heide, van der Sijde, & Terlouw, 2008). In general, academics consider LPPM as a ‘one stop shop’ when they want to be involved in UBC, whether the funding comes from their own university or from external sources. The most important role of LPPM is to bridge the interests of academics and business professionals to work together.

Evaluations of the UBC Programmes in Indonesia The main attention on the government policy is to improve the interaction between university and business. The government stimulates universities to have relationship with business by using the ‘third mission’ schema or by the university’s Community Service programme (DGHE, 2003). A variety of funding schemes has been offered over times through the Community Service programme existing in order to narrow the distance between academics and professionals and to reach the government goal, to ‘bring’ university into the arena of the innovation-ecosystem. The UBC done through the Community Service activities will open opportunities to transfer technology resulted from a collaboration in R&D and be used for the development or commercialization of technologies (Elmuti et al., 2005). Every university obliges its academics to be involved in a Community Service programme at least one time in a semester (DGHE, 2003) both as a voluntary involvement and or by fundingbased schema. After conducting this, academics will get ‘credits’ that will be accumulated as the ‘career credits’ together with research and teaching (DGHE, 2013). Academics will use these ‘credits’ as the preference to determine their academic career/track at the university. To evaluate the UBC programmes of universities over decades, we collected documents of Community Service and Research Collaboration through the websites of DGHE, evaluation from Community Service

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reviews; and other sources and we grouped the numbers of universities and business into three distinct periods.

In the Period before 2009 The very first structured UBC programme that funded by the government was called Penerapan Ilmu Pengetahuan dan Teknologi or the Implementation of Science and Technology (IST). It was a common form of UBC programme to all universities using the Community Service schema implemented before 1992. Universities formulated the programme in forms of knowledge dissemination, training, counselling, and seminars and discussed the latest technologies or manufacturing methods with individuals, groups or entrepreneurs. Nevertheless, the programme did not entirely succeed due to its limitations based on individual initiative and action. The programme did not have a significant impact to the society due to its short duration and a small amount of funds Soewandhi (2012). In 1994, the government introduced the Vucer as a new UBC programme to replace the IST. The programme required academics to be in a group, instead of individual basis, when they apply the government grant and do the Community Service activities. The programme has a goal to foster collaboration between academics and Micro, Small, and Medium Enterprises (MSME) and large group business such as farmers, fishermen and entrepreneurs. The Vucer had been a successful UBC programme when compared to the IST during the period until a massive economic crisis hit Indonesia in 1997. After the economic crisis, in 1999, the government continued the Vucer for several years and formed the multi-years Vucer on a three-year basis. The DGHE only considered grants for those academics who have research proposals in the research of the production of export products. After some years, the programme was confronted by a variety of problems. Purwadaria (cited in Susilo, 2010) argued that 53% the problems are in the lack of commitment and trust between academics and business professionals; 27% was a result of unmatched technologies; 10% due to from the business internal problems and another 10% from products which did not fit with market demand. Kurniadi (2009) evaluates that the multi-year Vucer is limited to several university types and regions. Table 2 describes the distribution of universities and academics in the UBC multi-year Vucer programmes in the period of 1997
2008. UBC funding is on a competitive basis. We highlight that only, at least, 2% of the total number of universities were involved in this UBC programme. Furthermore, the total number of academics was only 0.5% of the total academics population in Indonesia. The public universities are rather in the domination to ‘win’ the grants than the private ones, at 89% versus 11%. The disparities in research capacities, infrastructures and the qualification of academics are considered as the root of the differences. Although the multi-year Vucer UBC programme was not fully considered as the successful UBC, at least, there was a positive impact. Around 62% of SMEs had developed their business performances and more than a half of business partners were successfully developed.

Table 2: Evaluation of multi-year Vucer UBC programme in 1997
2008.

Number of proposals Number of academics Number of universities Type of universities Region of universities

Granted Proposals

±300 persons 50 89% Public, 11% private Java region 63%, outside Java 37% Crafting 40%, food and agro-business 48%, metal and electronics 12% Develop 62% Undeveloped 33% Bankrupt 5% Local 24%, inter-province 42%, export 29%, bankrupt 5%

MSMEs condition during the programme

Source: Kurniadi (2009).

MSMEs

67

Types of MSMEs partners

Marketing region of MSMEs Status of the programme

Universities/Academics

Completed (3 year) 23%, ongoing 49%, transfer to another programme 5%, fail 23%

University
Business Co-operation in Indonesian Higher Education 193

Categories

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The Period of 2009
2012 and afterwards In 2008, the government launched a new UBC programme to replace Vucer. Susilo (2010) argued that the background why the government changed the form of Vucer based on the following reasons: • Both academics and professionals are often confused on the goals of the programme. For example, the IST and Vucer have a similar goal and item. • The multi-years Vucer failed to achieve its goal to improve the productivity and profits of business that produce export products. • Universities are not able to comply with business targets, in particular, to get profits in a very short period. • Academics were not fully interested in the programme. • Only universities from the mainland of Indonesia, for example Java, have succeeded to ‘win’ the competition. Therefore, in 2010, the government introduced the UBC programme named Technology and Science (TS) in a way to overcome problems noted above. Five themes of the TS programmes included the TS for society, entrepreneurship, export products, innovation and technologies for region. Each of the TS programmes has kept a similar goal to the previous ones a goal which is to foster academics of doing technology transfer in their region. The TS programmes are more structured and cleared in the timeframe, and more funds are offered than its predecessor. As a result, the TS has given an impact that increases the participation of academics and universities in UBC. By compiling data from the DGHE we come up with Figures 1 and 2. These figures show the percentage of universities and academics in UBC during the period of 2010
2013. 16

14.1

14 12 10

8.1 6.5

8 4.2

6

3.9

4 2

1.4

1.1

0.6

2010

2011

0 Academics

2012

2013

Universities

Figure 1: The percentage of universities and academics that engaged in (UBC) TS programmes 2010
2013. Source: DGHE documents of Community Service Programme 2010
2013.

University
Business Co-operation in Indonesian Higher Education 195 80

69 61.9

70 57.6 60 50

55

42.3

45 38

40

31

30 20

Public Universities

10

Private Universities

0 2010

2011

2012

2013

Figure 2: The percentage of public and private universities in TS programmes 2010
2013. Source: DGHE documents of Community Service Programme 2010
2013. A big leap in participation by academics occurred in 2013. During this period, the DGHE simplified the bureaucracy associated with grant application by the support of information technology. Universities could easily track the status of the TS application online. Figure 2 shows that the public universities are in domination in the academics involvement. The result is that, in 2013, private universities have increased their quality of academics, research capacities, infrastructures and the relationship with business.

Discussion An innovation-ecosystem remains a complex framework to be implemented because it involves actors with different institutional practices and beliefs operated in their own ‘institutional spheres’ (Etzkowitz, 2002; Etzkowitz et al., 2000). Our study shows that government, with its funding policies, has been ‘pushing’ strict agendas of UBC. Universities in Indonesia are depending on the government policies and strategies. Van den Kroonenberg (1989) argues that these circumstances will influence UBC. He proposed an (entrepreneurial) university should be independent from the government and business. He noted that when a university depends too much on the government, it will result in paralysis because of bureaucracy; when a university depends too much on business, it will result in slavery. The present government agenda is to engage at least 30% of the academics in the UBC programmes (Soewandhi, 2012). Increasing fundings and cutting down bureaucracy have indeed influenced the attraction of a greater number of universities and academics to participate in UBC programmes, as shown in Figures 1 and 2. However, bureaucracy cannot be the only reason for academics not participating in UBC as Davey et al. (2011) established this in the European context. There must be other reasons why UBC programmes in Indonesia

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have failed to start up. The Directorate of Research and Community Service of Indonesia (2012) states that academics and professionals found it difficult to set up a co-operation because they have a different thought on the programmes’ goal and nature. KIN (2012) suggests that many UBC programmes in Indonesia have failed because the programmes give fewer benefits to business. They also argue an effective sectoral coordination among government, universities and business is a necessary matter in order to reduce the different focuses of the actors. Other ‘factors’ maybe at play and we suggest that approaching this from an ‘institutional logics’ perspective will shed (more) light onto this situation. Thornton and Ocasio define the institutional logics as the ‘socially constructed, historical patterns of cultural symbols and material practices, including assumptions, values, beliefs, by which individuals and organizations provide meaning to their daily activity, organize time and space, and reproduce their lives and experiences’ (Thornton & Ocasio, 1999, p. 804). Accordingly, Merton (1973) argued that universities are founded based on the norms and values of science, and academics are trained to abide them. The world of business is populated with professionals who practice the logic of the market, are profit-oriented, and work in exogenous times (Cyert & Goodman, 1997; Elmuti et al., 2005; Lind et al., 2013). Because they ‘live’ in different organizations, they have different logics. Sauermann and Stephan (2013) state that universities and business are respectively ‘operated’ in different spheres, in the sphere of the ‘academic logic’ and ‘commercial logic’. The two worlds have their own norms and values which might be opposing to each other. The ‘academic logic’ emphasizes the search of fundamental knowledge and shapes the independences in research activities, peer recognition and the openness of the research results. The ‘commercial logic’ expects a different story. The business expects an applied research, limited disclosure or close dissemination of research results and private allocation of financial returns of research results (Sauermann & Stephan, 2013). Academics could perceive this as a benefit or as a threat to their academic career when they are involved in UBC. Further, academics are exposed to the logic of the university. Public and private universities might have different views on the ‘value’ of the UBC programmes. The evaluations of the UBC programmes described in the section ‘Evaluations of the UBC Programmes in Indonesia’ can be interpreted as being the result of differences in institutional logics. The UBC programmes originate as undifferentiated programmes and expanded through the years, but the evaluation is not taking into account the differences between universities and business. To increase participation of universities and academics in UBC programmes, they need to look at their own policies and strategies as well as find ways to overcome the differences in logics. We deduce from our study that there are three major issues that need to be addressed.

Differences in ‘Language’ and ‘Communication’ Academics and professionals have a difference in ‘languages’. Amalia, Pawennei, Anggara, Tanaya, and Nugroho (2011) argue that ‘language’ problems are the main

University
Business Co-operation in Indonesian Higher Education 197 obstacles in the research implementation in Indonesia. Further, they argue that the ‘language’ and ‘communication’ problem are not only between academics and professionals but also among academics, professionals and government officers. The difference in the ‘language’ is an inhibitor of a collaboration in the starting up of UBC. Many projects were failed to carry out because of the misunderstanding between academics and professionals. Academics often used words such as ‘model’, ‘variable’, ‘ideas’, while professionals give these words a different meaning (Cyert & Goodman, 1997). This has happened in many UBC programmes in Indonesia with the result that academics and professionals did not have a common understanding regarding the goals and benefits of the programmes. Moeliodiharjo, Soemardi, Brodjonegoro, and Hatakenaka (2012) argue that the lack of understanding and trust between academics and professionals can hinder the UBC programmes in Indonesia.

Differences in the Nature of Work and Culture Universities and business have a different work organization and outcomes. Universities have a ‘logic’ to produce and to spread knowledge. Academics work in a well-defined timeframe and they have an ultimate goal to contribute in new knowledge, concepts, models and empirical findings (Cyert & Goodman, 1997; Lind et al., 2013). Professionals expect (high) profits in the short period and they used to dealing with market that can change dramatically (Elmuti et al., 2005). Universities in Indonesia required academics to do a Community Service programme as a part of getting the ‘career credit’. However, this activity is sometimes regarded as taken for granted. Academics choose to do this activity in a short period, for example giving a seminar or short courses, rather than being engaged in a long term or in a big scope of UBC. DRCS (2012) found that many small enterprises were not interested in UBC because they cannot see that UBC will benefit them in a short period. Professionals argue that academics are too idealistic and, whilst being experts, they cannot contribute useful help and fast solutions, whereas academics consider professionals as money-oriented people, very practical minded and lacking idealism (Moeliodiharjo et al., 2012).

Bureaucracy Before 2012, there were many administrative rules and procedures that academics must follow to apply the UBC grants. This would make a complex interaction among university, business and government. Moeliodiharjo et al. (2012) argued that the rigidity of the government bureaucracy has a strong impact on the academics’ mindset. In other words, academics are having a thought that to apply the grant is a difficult task. In 2012 (see Figure 2), with less bureaucracy, many universities were successful to get the grants since the DGHE cut bureaucracy, which resulted in less time being required and greater transparency and clarity.

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Concluding Remarks and Future Studies The chapter has presented the UBC situation in Indonesian higher education which is designed to foster innovation in the way to reach the Indonesian ambition to be an innovation-driven country by 2020. One of the focal points of economic policy is to incorporate and involve universities in this endeavour by stimulating the cooperation between universities and the business world. To achieve this goal, the Indonesian government has prepared UBC programmes to bring universities in many forms and shapes. One set as a critical point is to engage at least 30% of the total academics in UBC. Nevertheless, until 2013, the total academics who engaged in UBC are remained low, only about 4%. This number has not reached the critical point and even it is still too far. We argue that this is a result from the conflicting logics of two different institutions, universities and business. Besides bureaucracy, universities and business have differences in languages and the nature of works and products. Bringing universities to the innovation-ecosystem, as the government’s goal, would mean a merging of logics. Future studies should focus on how university strategies impact the involvement of its academics in UBC and how academics cope with two different logics. Identifying barriers and obstacles would probably give a new perspective to academics to engage in UBC and how they manage the multiple logics in the complexities of UBC environment.

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Metcalfe, S. (1995). The economic foundations of technology policy: Equilibrium and evolutionary perspectives. In P. Stoneman (Ed.), Handbook of the economics of innovation and technological change. Oxford: Blackwell. Moeliodiharjo, B. Y., Soemardi, B. W., Brodjonegoro, S. S., & Hatakenaka, S. (2012). University, industry, and government partnership: Its present and future challenges in Indonesia. Procedia Social Behavioral Sciences, 52, 307
316. Mowery, D., & Sampat, B. (2004). The Bayh-Dole Act of 1980 and university-industry technology transfer: A model for other OECD government? Journal of Technology Transfer, 30, 115
127. Nelson, R. (1993). National innovation system: A comparative analysis. New York, NY: Oxford University Press. OECD. (1997). National innovation systems. Paris: OECD. OECD. (2006). OECD reviews on innovation policy: Switzerland. Paris: OECD Publishing. OECD. (2007). OECD reviews on innovation policy: South Africa. Paris: OECD Publishing. OECD. (2008). OECD reviews of innovation policy: Hungary. Paris: OECD Publishing. OECD. (2009). OECD reviews of innovation policy: Korea. Paris: OECD Publishing. OECD. (2013). OECD reviews of innovation policy: Sweden 2012. Paris: OECD Publishing. O’Shea, R. P., Allen, T. J., Morse, K. P., O’Gorman, C., & Roche, F. (2007). Delineating the anatomy of an entrepreneurial university: The MIT experience. R&D Management, 37, 1
16. Rothaermel, F. T., Agung, S. D., & Jiang, L. (2007). University entrepreneurship: A taxonomy of the literature. Industrial and Corporate Change, 16, 691
791. Sam, C., & van der Sijde, P. (2014). Understanding the concept of the entrepreneurial university from the perspective of higher education models. Higher Education, 68(6), 891
908. Sauermann, H., & Stephan, P. (2013). Conflicting logics? A multidimensional view of industrial and academic science. Organization Science, 24, 889
909. Schwab, K., & Sala-i-Martin, X. (2012). The global competitiveness report 2012
2013. Geneva: World Economic Forum. Soewandhi, S. N. (2012). Gagasan forum layanan ipteks bagi masyarakat (FLIPMAS). Majalah Aplikasi Ipteks Ngayah, 3, 2
8. Susilo, J. (2010). Hibah pengabdian pada masyarakat. Universitas Islam, Indonesia. Thornton, P. H., & Ocasio, W. (1999). Institutional logics and the historical contingency of power in organizations: Executive succession in the higher education publishing industry. American Journal of Sociology, 104, 801
844. Van den Kroonenberg, H. (1989). Getting a quicker payoff from R&D. Long Range Planning, 22, 51
58. van der Heide, S., van der Sijde, P. C., & Terlouw, C. (2008). The institutional organisation of knowledge transfer and its implications. Higher Education Management and Policy, 20(3), 1
13.

PART V STRATEGY AND GROWTH

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Chapter 11

The Impact of the Financial Crisis on the Financing and Growth of Technology-Based Small Firms: Some Survey Evidence from the United Kingdom Robert Baldock, David North and Farid Ullah

Abstract This chapter presents research to assess the impact of the recent financial crisis on technology-based small firms (TBSFs) in the United Kingdom based on findings from an extended telephone survey with the owner-managers of 49 young and 51 more mature TBSFs, undertaken in 2010. Even before the onset of the global financial crisis in 2007, it was generally acknowledged that TBSFs faced greater obstacles in accessing finance than conventional SMEs. This is because banks have difficulty assessing the viability of new technologybased business ventures due to information asymmetries, whilst risk capital providers may have difficulty providing appropriate or sufficient funds on terms acceptable to entrepreneurs. Given the recent difficulties that SMEs, in general, have faced in obtaining external finance, we would expect TBSFs to have been particularly adversely affected by the financial crisis. Our evidence showed that TBSFs exhibited a strong demand for external finance between 2007 and 2010, related to their growth ambitions and achievements. They sought finance mainly from banks but also with younger TBSFs seeking business angel finance and more mature TBSFs seeking venture capital finance. However, our evidence indicates that both debt and equity finance became harder to access for TBSFs, particularly for early-stage and more R&D-intensive firms. Where funding was offered, it was often on unacceptable terms with regards to the levels of collateral or equity required. The chapter provides evidence of a growing funding gap and concludes that the ability of TBSFs to

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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contribute to economic recovery is hampered by ongoing problems in obtaining external finance.

Introduction: Context and Aims It is often argued that a dynamic technology-based small firm (TBSF) sector1 is pivotal to enhancing entrepreneurship and innovation, leading to economic growth and the creation of new jobs (Siegel, Westhead, & Wright, 2003). At the same time it is commonly thought that these TBSFs face greater obstacles than conventional SMEs and so deserve government support in overcoming them (Bank of England, 1996; Oakey, 2003). This is primarily because of market failures that prevent TBSFs from gaining access to key inputs, notably in relation to external finance. Even before the onset of the financial crisis that hit most of the western world in late 2007, research evidence was indicating that the growth and development of TBSFs was being hindered by a shortage of external finance, particularly the availability of relatively small amounts of equity finance (Pierrakis & Mason, 2008). Given the difficulties that SMEs in general have been facing in obtaining external finance in recent years (Cowling, Liu, & Ledger, 2012; Fraser, 2009, 2012; IFF Research Ltd, 2010; Irwin & Scott, 2010), it seems reasonable to expect that TBSFs have been particularly adversely affected by the financial crisis. Although the recession may be over and recovery of the UK economy underway, there continue to be concerns about the lack of external funding to SMEs in general and TBSFs in particular (BIS, 2010, 2013). Given this context, it is clearly important to understand the impact of the financial crisis on TBSFs. This chapter therefore reports on evidence from research funded under the Institute for Small Business and Entrepreneurship’s Research and Knowledge Exchange (RAKE) initiative. The research aimed to assess the impact that the financial crisis had on a sample of TBSFs. It examined TBSF external finance requirements, both debt and equity finance, between 2007 and 2010 and the extent to which this was met from different sources. Different characteristics of TBSFs were explored, including trading age — younger and more established; broad sector — bio/life-science and electronics/IT; and enterprise origins, with a focus on comparing spin outs and non-spin outs. A key underlying question was the extent to which the ability of both young and more established TBSFs to respond to the economic recovery was being affected by ongoing problems in obtaining the external finance needed for growth.

1. TBSFs are defined broadly here as independently owned and managed enterprises with less than 250 employees whose products and services embody innovative and advanced technologies developed by the application of scientific and technological expertise and fit within the high-tech sectors defined by Bullock and Millner (2003).

Financing and Growth of Technology-Based Small Firms 205

Funding Gaps There has been considerable research interest over the last 30 years in the financing of TBSFs, much of which has focused on the reasons for the persistence of a funding gap. For example, the work of Myers and Majluf (1984) and Sahlman (1990) suggested that financial markets are informationally opaque and that borrowers know more about the potential and nature of their businesses than do lenders. TBSFs have been shown to experience more acute asymmetric information issues than other SMEs in accessing bank funding (e.g. Bank of England, 1996, 2001; Cressy, 2002; Stiglitz & Weiss, 1981). Particularly at an early stage, information is limited and not always transparent and assets are often intangible and knowledge based (e.g. patents and human capital). Moreover, entrepreneurs may be reluctant to provide full information about the investment opportunity because of concerns that disclosure may make it easier for others to exploit, especially with technologybased firms (Shane & Cable, 2002). Banks typically aim to minimise risks when providing loans to firms, relying on information about the business that is comparatively robust as well as placing greater emphasis on collateral to provide security for their loans. They typically seek returns from investing in more established and relatively stable businesses which are not radically changing and which require minimal monitoring (Bank of England, 2001). With regards to venture capital (VC) finance, Berger and Udell (1998), Trester (1998) and Bruno and Tyebjee (1985) suggested that venture capitalists play an important role in screening, contracting with and monitoring small businesses which helps reduce information opacity as venture capitalists collect information about the business, potential markets, collateral and management teams of small businesses. VC finance becomes available in most cases after firms receive one or two rounds of business angel finance while bank finance often comes after firms’ assets become more tangible and able to be offered as collateral (Berger & Udell, 1998). From the entrepreneur’s perspective, there are some costs attached to venture finance as they invariably have to relinquish equity in return for the funds provided by the VCs and there are often delays while deals are negotiated which may affect the business adversely. Furthermore, rejection by VCs may affect the entrepreneur’s ability to seek alternative sources of finance. The issue of information asymmetry in TBSFs is connected to that of transaction costs. It has long been accepted that transaction costs do not rise pro rata with the size of the investment and may even fall in some circumstances as the investment becomes larger (HM Treasury/Small Business Service, 2003; Rowlands, 2009). This is due to increased professionalisation of management in larger businesses enabling them to provide better quality information more rapidly and to anticipate the needs of investors. This means that private sector VC funds tend to move up market in terms of investment size as it leads to more profitable investments relative to transaction costs, leaving an equity gap for smaller investments such as found in the seed and early-stage venture capital market. Thus, it could be argued that information asymmetry in the VC market arises not just because the information is unavailable

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but also because it is too expensive to collect relative to the potential benefit from the investment (Lockett, Murray, & Wright, 2002). Similarly, it could also be argued that in the case of small loans, banks typically rely on credit scoring criteria which are generally biased in favour of existing businesses with a track record and collateral as the cost of acquiring more information about new and early-stage businesses is prohibitive relative to potential income. Even before the onset of the financial crisis, available evidence indicated that TBSFs were more likely to experience difficulties obtaining external finance than SMEs as a whole. For example, analysis by the present authors of the UK Government’s 2007 Small Business Survey (SBS) data indicated that a significantly higher proportion of TBSFs seeking external finance experienced problems than was the case with other SMEs.2 This is in line with what other researchers have suggested (Mason & Harrison, 2004; Utterback, Meyer, Roberts, & Reitberger, 1988). In relation to bank finance this is partly a problem of ‘short termism’ with banks being unwilling to provide debt finance to cover the long lead times required for developing new products and partly because the terms and conditions on offer are not acceptable to the business (e.g. with respect to the level of security and personal guarantees that the bank requires). Thus, it has been argued by Oakey (2007) that there is a degree of latent demand for bank finance amongst TBSFs which is not helped by the ‘quasi-oligopolistic’ nature of bank lending in the United Kingdom.3 Although only a very small proportion of SMEs seek equity finance (with most large-scale SME surveys estimating this to be less than 2% of those firms seeking external finance4), this form of finance is vital for many innovative and growthorientated TBSFs. Equity finance is suitable for businesses that have high growth potential, but also a higher level of risk, lack physical assets to provide collateral on debt finance and may also either lack altogether or have uneven revenue streams that make servicing loan repayments difficult (Mason & Harrison, 2004). It has long been recognised by policy makers, practitioners and academics that there is an equity gap in the United Kingdom, particularly relating to the seed and early-stage venture capital market and generally considered at present to range from £½ million to £2 million (SQW Consulting, 2009), which means many potentially viable businesses struggle to raise the finance they need. For example, Oakey (2003, 2007) has suggested on the basis of available evidence that although around 5% of TBSFs do

2. The SBS is a survey of 9362 SMEs of which 156 were TBSFs. They were significantly more likely (at beyond the .001 level) to encounter problems obtaining finance (nearly two-thirds did not receive any finance from the first source approached compared to 16% of other SMEs). Ultimately, more than twofifths of TBSFs seeking finance did not obtain all the finance that they required, compared to just 16% of other SMEs. 3. In recent years, commercial banking in the United Kingdom has been dominated by four banks (Barclays, HSBC, Lloyds TSB and the RBS group), with these banks accounting for 76% of bank lending to SMEs in 2007 (Cosh, Hughes, Bullock, & Milner, 2008). Government action at the start of the financial crisis resulted in even greater concentration with the merger of Lloyds/TSB with HBOS (Bank of Scotland). 4. The UK Government’s Small Business Survey 2007, 2010 and 2012 (Cosh et al., 2008).

Financing and Growth of Technology-Based Small Firms 207 obtain external equity support, there is another 5% that are ‘probably fundable’ but are unlikely to receive funding because of supply-side (e.g. short-termism) or demand-side (e.g. fear of loss of control) problems.

Research Methodology According to government data sources (i.e. the 2008 Annual Business Inquiry (ABI)), TBSFs represent 6% of the UK business population, are predominately micro businesses (over 90% have less than 10 employees), and are dominated by IT businesses (80%), with bio and life sciences and specialist R&D businesses representing less than 5%. Our research did not intend to be representative of the whole TBSF sector, but aimed to survey sufficient numbers of digital electronics/IT and bio/life science TBSFs to provide reliable insights into their experiences of trying to access finance over the 2007
2010 period. To achieve this, the survey was weighted towards bio/life science businesses, with an emphasis on R&D-intensive activities rather than the IT consultancy services which swamp the TBSF sector numerically. This research was based on extended telephone surveys with owner-managers from two samples of TBSFs in the second half of 2010. The first sample comprised established TBSFs, defined as businesses established for at least five years (i.e. prior to 2005), drawn from the bioscience and electronics sectors and located mainly in four English regions (Greater London, East of England, South East, North West) and Scotland which together account for around two-thirds of Britain’s TBSFs (ABI, 2008). Initially the plan was to re-survey 50 out of 133 businesses that had participated in a previous on-line survey of TBSF financing carried out in 2003 (Ullah, 2005). However, this re-survey obtained a low response rate (22%) due mainly to TBSFs being untraceable (29%), sold or acquired (11%) and unable or unwilling to participate (38%). Interview requests were also sent to a sub-set of the previous nonrespondent firms, resulting in 51 completed interviews with established TBSFs. A second sample comprised younger TBSFs, established over the last five years (i.e. from 2005 onwards), random quota sampled from Dun & Bradstreet’s Global Reference Solutions UK database (3333 TBSFs distributed evenly across the above English regions and Scotland). A total of 245 firms were purposively contacted in order to ensure that sufficient numbers of bioscience, manufacturing and contract R&D firms were interviewed, resulting in 49 completed telephone interviews (20% response), the main reasons for failure to interview being unsuitable (8% in wrong sector, too large, overseas based or too old); untraceable (5%); refused (3%) and the remainder being unavailable within the timeframe (65%). It is important to recognise that the focus of this research was on surviving independent TBSFs, as it did not include trade sales or closures that occurred over the study period. An inevitable limitation of this survivorship bias in the sample is that we were unable to tell how many TBSFs went out of business (or never got off the ground) during the 2007
2010 period because of the difficulties of obtaining

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finance. In other words, our study looked at the experiences of those TBSFs which were still in existence in 2010 and continued to be independent businesses. Before focusing on the ability of the TBSFs to access different sources of external finance during the period of the financial crisis, the next section of the chapter profiles the characteristics of the surveyed businesses, including their growth orientation and performance over the period.

Characteristics and Growth Performance of Surveyed TBSFs Table 1 presents the range of activities undertaken by the surveyed TBSFs: more than one-third (35%) were in electronic and scientific instrument engineering; more than one-fifth (22%) undertook contract R&D; more than one-fifth (21%) provided IT software and services and almost one in seven (14%) were in pharmaceuticals and chemical engineering. The sector distribution of established and younger surveyed businesses was similar. In order to facilitate some comparison between different high-tech sectors, we assigned the TBSFs to one of two broad sectors: bioscience (including life science) activities (38 firms) and electronics (including IT activities) (62 firms). Most of the established TBSFs had been in existence for at least 10 years (the median year of establishment being 1997). It is notable that 11% took between one and three years and 7% took over three years to start trading. Bioscience firms were more likely to require a longer lead time to start trading than their electronics counterparts with 19% of bioscience firms taking more than a year to start trading compared with 11% of electronics firms.

Table 1: Sector distribution of surveyed TBSFs. All Firms

Established Firms

Younger Firms

No.

%

No.

%

No.

%

IT software IT services Electronic engineering Telecommunication Scientific instruments/engineering Chemical engineering Medical/pharmaceuticals Research and development Consultancy/business support

7 14 18 5 17 4 10 22 3

7.0 14.0 18.0 5.0 17.0 4.0 10.0 22.0 3.0

3 8 8 3 10 3 7 7 2

5.9 15.7 15.7 5.9 19.6 5.9 13.7 13.7 3.9

4 6 10 2 7 1 3 15 1

8.2 12.2 20.4 4.1 14.3 2.0 6.1 30.6 2.0

Total

100

100

51

100

49

100

Financing and Growth of Technology-Based Small Firms 209 Just over a third (37%) of all the firms originated as spin outs from other companies or universities. Interestingly, this was significantly (.01 level) higher amongst the younger firms (53%) than the established ones (22%) which may indicate that spin out companies, which tend to be the most innovative and high-technology firms, were more likely to have been sold onto or acquired by other companies once their products were traded than their non-spin out counterparts. A higher proportion of bioscience (45%) than electronics (32%) firms were spin outs. For just over two-thirds (71%) of the established TBSFs, their ownership had not changed during the seven years since the original survey and they were predominantly (88%) private limited companies.

Employment and Sales Turnover Performance Table 2 presents the actual growth performance of the interviewed TBSFs over the 2007
2010 period. In terms of the number of employees, half of the established firms experienced an increase in employment as did almost half (49%) of the young TBSFs. Overall, the mean (or median) employment size of the surveyed firms rose from 10.1 (four) employees in 2007 to 13.3 (5.5) employees in 2010, with part-time employment, representing 1 in 10 staff, rising at a similar rate to full-time employment. The established TBSFs increased their average (mean) employment by 26%,

Table 2: Growth characteristics of respondent firms. (a) Number of Employees Full-Time Part-Time

(b) Sales Turnover Total

Mean

Median

Mean Median All firms (n = 92) 2007 9.1 2010 12.2 2011 13.8

0.9 1.2 1.2

Established firms (n = 51) 2007 13.5 1.1 2010 16.8 1.4 2011 18.4 1.5 Younger firms (n = 41) 2007 3.7 0.8 2010 6.3 0.8 2011 8 0.8

10.1 13.3 15

4 5.5 7

2006
2007 £1,138,700 2009
2010 £1,625,200 2010
2011 £1,946,700

14.5 18.3 19.9

5 6 8

2006
2007 £1,840,300 £600,000 2009
2010 £2,490,000 £824,000 2010
2011 £2,931,800 £1,000,000

4.6 7.2 8.9

3 4 6

2006
2007 2009
2010 2010
2011

£292,600 £582,400 £758,800

£300,000 £500,000 £500,000

£106,000 £230,000 £400,000

Note: Employee data: established firms, n = 51; younger firms, n = 41 (8 TBSFs not established prior to 2007); sales turnover data: n = 75 (established, n = 41, younger, n = 34) where complete data for each year represented (78 had complete data for 2007 and 2010, but only 75 for 2007, 2010 and 2011).

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while the younger TBSFs increased by 56%. Thus, many of the surveyed TBSFs were able to increase their employment despite the economic recession. More than two-fifths (43%) also forecast a continued increase in their employment over the next year (i.e. 2010
2011), with young TBSFs being more likely to forecast employment growth (52% of them) than their established counterparts (34%). Nearly three-fifths (59% of n = 78) of TBSFs increased their sales turnover during the 2007
2010 period (this proportion remaining the same when deflated turnover figures were used5), whereas 28% experienced a decrease. Just over half (53%) of established TBSFs increased their sales turnover in this period, with one-third experiencing declining sales. Nearly two-thirds (66%) of younger TBSF increased their sales turnover, with only 23% experiencing a reduction. The mean sales turnover for all firms (where sales data was recorded) increased from £1.1 million in 2006
2007 to £1.6 million in 2009
2010 (i.e. an increase of 43%), with the established firms increasing from £1.8 million to £2.5 million (+35%) compared to £293k to £582k (+99%) in the case of the younger firms. Moreover, the forecasts for sales turnover in 2010
2011 reflected the more positive outlook with respect to the expected economic upturn with an overall forecast increase of 20% for all firms (based on the mean values); an increase of 30% for younger TBSFs compared to 18% for established firms. Comparing Bioscience and Electronics The bioscience firms tended to be larger than the electronics firms, having an average (mean) employment of 16 compared to 10.5 and a mean sales turnover of £1.7 million compared to £1.3 million in 2009
2010. They also exhibited faster growth, their mean sales turnover doubling between 2007
2010 compared to just a 6% increase for the electronics firms, resulting in mean employment increases of 6 and 1.5 employees, respectively. It is evident that the bioscience TBSFs include some high performing growth firms that appear to have been unaffected by the economic recession. Over half of them (58%) achieved sales turnover growth of more than 50%, compared to a quarter of the electronics firms, and twice the proportion of bioscience TBSFs increased their sales turnover by more than £500k in the period (32% compared to 17%). Comparing Spin Outs and Non-Spin Outs As already mentioned, spin outs were strongly represented amongst the young TBSFs. Compared to the young non-spin outs, they tended to be larger, having a mean of 7.7 employees in 2010 (compared to 5.7 for non-spin outs) and a mean sales turnover of £625k (compared to £505k). They also appear to have been the main

5. The 2010 sales turnover is deflated by 105.9 (=94.4%) to reflect UK GDP growth from base year of 2007 (Source: UK Office of National Statistics).

Financing and Growth of Technology-Based Small Firms 211 drivers of growth amongst the younger businesses, on average increasing employment by three employees (compared to two) and having stronger sales turnover growth forecasts (34% growth compared to 20%). This is what we might expect from our understanding of spin outs, where previous research (Mason & Kwok, 2010; Rowe, 2005; Wiklund & So¨derblom, 2006) has indicated that they are likely to have better management capability and resources, often taking on experienced interim managers and non-executive directors with knowledge of the VC/equity finance markets, being more willing to seek equity finance, better networked amongst the finance community and better equipped to obtain this type of finance than their non-spin out counterparts. It follows that they tended to be both more growth oriented and capable of achieving growth. Growth Orientation When asked what the aims of their business had been since 2007 (Table 3), twothirds reported that they had been seeking growth, with 15% aiming to survive and 18% not interested in pursuing growth, either because they were seeking size and performance stability (8%) or because the business was not ready to grow (10%) as it was still in the R&D phase prior to trading. There was little difference in the proportion of younger and established TBSFs seeking growth (69% and 65%, Table 3: Growth orientation and constraints of surveyed TBSFs. All TBSFs (n = 100)

Growth orientation Growth Survival No need Not ready Constraints None Finance Lack of demand Workforce Management time Technical barriers Trade regulations Premises Sales and marketing

Established TBSFs (n = 51)

Younger TBSFs (n = 49)

No.

%

No.

%

No.

%

67 15 8 10

67 15 8 10

33 10 6 2

64.7 19.6 11.8 3.9

34 5 2 8

69.4 10.2 4.1 16.3

14 24 34 8 6 6 3 3 5

14 24 34 8 6 6 3 3 5

8 11 19 8 3 3 1 0 0

15.7 21.6 37.3 15.7 5.9 5.9 2 0 0

6 13 15 0 3 3 2 3 5

12.2 26.5 30.6 0 6.1 6.1 4.1 6.1 10.2

Note: Three cases mentioned two main constraints.

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respectively), although spin outs were the most growth orientated (76% of them). Just over a fifth (21%) of bioscience firms responded that they were not ready to grow, which is indicative of the longer lead time to commercialisation for some young bioscience firms. Despite their growth orientation, most of these established TBSFs remained fairly small in employment terms (61% had less than 10 employees in 2010). However, high-tech firms specialising in R&D or consultancies may not grow beyond employing a certain number of employees (i.e. a core team of researchers, consultants and managers) as they may outsource key activities such as manufacturing and sales (e.g. to export agents) or they prefer to develop using a model of contractor consultants (e.g. IT software consultants) which facilitates flexible labour for business growth without commitment to employees. Furthermore, it should be noted that growth can mean different things to different entrepreneurs (Achtenhagen, Naldi, & Melin, 2010) and is not always associated with employment growth or increased sales turnover. Instead, responses, as captured in this survey, may relate to a wide variety of issues such as improved staff and management capacity, product developments and innovations and workplace efficiencies which can improve bottom-line profitability. Examples of such responses included: customise product development, rather than outright growth improve products and services to higher standards … within the existing capacity of business.

Effect of the Credit Crunch and Economic Downturn In answer to a question about what were the main factors constraining the growth of the firm since 2007 (Table 3), the most frequently mentioned constraint was falling demand and loss of trade (34%), followed by difficulties in accessing external finance (24%). Younger TBSFs were more likely to indicate financial constraints, with established TBSFs being more likely to refer to lack of demand. One in seven surveyed TBSFs experienced no constraints. Two-thirds of TBSFs had made significant changes and innovations to their business over the 2007
2010 period and for most of them these were in part a response to either the economic downturn (35 cases) or credit crunch (19 cases). Whilst the economic downturn equally affected established and younger TBSFs, the bioscience firms were less affected than their electronics counterparts (24% compared to 43%). On the other hand, the credit crunch was more likely to be identified as a factor effecting young TBSFs (28%), spin outs (27%) and bioscience firms (26%). Other actions that were taken included employing fewer people (including not replacing staff that left), reducing working hours, cutting costs generally and putting more effort into marketing. Interestingly, there were a few firms where the recession had presented new market opportunities, as for example in the case of a business

Financing and Growth of Technology-Based Small Firms 213 concerned with heat transfer and fluid flow technology that found an increasing demand for its services because customers wanted to make existing processes more efficient and cost effective rather than invest in new processes.

Access to Finance 2007
2010 The interviewed TBSF owner-managers were asked about how their businesses were financed over the 2007
2010 period and about any problems that they experienced in obtaining finance from various sources. As we might expect from their growth orientation and performance over the period, the surveyed TBSFs continued to have a relatively strong demand for finance, particularly to fund working capital and R&D. Table 4 shows that the majority of TBSFs (81%) financed their business wholly or partly from internal sources, using personal funding and ploughing back profits, with two-fifths (43%) solely dependent on internal sources. A higher proportion of younger TBSFs (10% compared to 2% of established TBSFs) used informal finance from family and friends, confirming previous research indicating that TBSFs rely on personal and informal finance in their early stages because they lack

Table 4: Funding for the business in the last 3 years.

All TBSFs Internal sources (e.g. ploughing back profits, personal funding) Informal external sources (e.g. family and friends) Formal external sources (e.g. banks, VC funds, public sector grants) Established TBSFs Internal sources (e.g. ploughing back profits, personal funding) Informal external sources (e.g. family and friends) Formal external sources (e.g. banks, VC funds, public sector grants) Younger TBSFs Internal sources (e.g. ploughing back profits, personal funding) Informal external sources (e.g. family and friends) Formal external sources (e.g. banks, VC funds, public sector grants) Note: Some businesses used more than one source.

No. of Firms

Percentage

81

81

6 53

6 53

44

86

1 26

2 52

37

75

5 27

10 55

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the collateral and track record required by banks, as well as often facing high R&D costs with lengthy lead times to commercialisation and repayment (Mason & Harrison, 2004). Just over half of the surveyed TBSFs used formal sources (i.e. banks, venture capital funds, business angels and public grants) over the period, often in combination with internal sources. There was little difference between the young and established TBSFs in their use of formal sources (55% and 52%, respectively), although the level of use of formal sources was highest amongst bioscience firms (66%) and spin outs (61%). Qualitative responses indicated that some TBSF owner-managers were ‘discouraged borrowers’ (Fraser, 2009; Kon & Storey, 2003) in that they did not make bank applications because they thought they would not be able to obtain funding on acceptable terms and conditions. There was also evidence of ‘equity aversion’ from owner-managers of some younger TBSFs who were wary of forfeiting a high share of equity during the early stage of their business when it was valued at considerably less than it could be at a later stage of development (Mason & Kwok, 2010). Table 5 indicates that the main reasons for seeking external finance between 2007 and 2010 were for working capital (44%), followed by R&D (30%). It is notable that the majority (41 of 66) of firms seeking formal external finance over the period tended to be active in doing so, seeking two or more types of formal external finance. Whilst this may indicate a degree of complementarity between alternative types and sources of finance, it may also suggest that TBSFs needed to approach multiple sources in order to raise the full amount of funding that they required. As shown in Table 6, debt finance from the banks was the most commonly sought external finance, with more than one-third of firms (36%) applying for an overdraft and one quarter applying for a term loan. In this respect TBSFs are not that different from SMEs as a whole, but they do differ in that a higher proportion were seeking equity/VC finance, with one in eight firms (12%) applying to VC funds and around one in seven approaching (14%) business angels. One quarter of TBSFs Table 5: Reason for seeking external finance. Reason

Working capital R&D Acquisition Staff development Asset purchase Other Total seeking

All TBSFs (n = 100)

Established TBSFs (n = 51)

Younger TBSFs (n = 49)

No.

%

No.

%

No.

%

44 30 5 5 6 13 70

44 30 5 5 6 13 70

16 13 2 2 3 3 30

31.4 25.5 3.9 3.9 5.9 5.9 60

28 17 3 3 3 10 40

57.1 34.7 6.1 6.1 6.1 20.4 80

Note: Table contains data from four cases which only sought informal external finance from family and friends.

Financing and Growth of Technology-Based Small Firms 215 Table 6: Key formal external funding sources approached 2007
2010 and outcomes. Source

Approached No. (% of na)

Success Rateb (Row %)

36 (36%) 16 (32%) 20 (40%)

81 81 80

Successful: 26; unsuccessful: 7; partial: 3 Successful: 13; unsuccessful: 3 Successful: 13; unsuccessful: 4; partial: 3

25 (25%)

58

8 (16%)

43

17 (34%)

65

Successful: 13 (9 taken up); partial: 1 unsuccessful: 10; pending: 1 Successful: 3 (3 taken up); unsuccessful: 4; pending: 1 Successful: 10 (6 taken up); partial: 1 unsuccessful: 6

Venture capital fund All TBSFs 12 (12%)

64

Bank overdraft All TBSFs Established Younger Bank loan All TBSFs Established Younger

Established 7 (14%) Younger 5 (10%) Business angels All TBSFs 14 (14%) Established 4 (8%) Younger 10 (20%) Public sector grant/award All TBSFs 25 (25%) Established 10 (20%) Younger 15 (30%)

Outcome

57 75

Successful: 7 (6 taken up); unsuccessful: 4; pending: 1 Successful: 4 (3 taken up); unsuccessful: 3 Successful: 3; unsuccessful: 1; pending: 1

46 33 50

Successful: 6; unsuccessful: 7 pending: 1 Successful: 1; unsuccessful: 2; pending: 1 Successful: 5 (3 taken up); unsuccessful: 5

96 90 100

Successful: 22; partial: 2; unsuccessful: 1 Successful: 8; partial: 1; unsuccessful: 1 Successful: 14; partial: 1

All TBSFs, n = 100; established TBSFs, n = 51; younger TBSFs, n = 49. Success rate calculated as at least partially successful in obtaining finance offer, excluding pending decisions. a

b

applied for public sector grants and awards and around one-sixth (16%) applied for a variety of other finance sources including bank asset finance, supplier finance, credit card and joint venture finance. Younger TBSFs were twice as likely as more established TBSFs to approach banks for loans and business angels for equity finance and more likely to apply for public sector grants/awards. However, the younger TBSFs were less likely to seek VC funding and invoice finance. Having given an overview of the demand from the surveyed TBSFs for finance from formal sources during the period of the financial crisis, we now turn to consider in more detail their experiences of different types of finance, starting with debt finance from banks.

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Debt Finance from Banks Half of the surveyed TBSFs approached banks to access debt finance during the 2007
2010 period, with a higher proportion of younger (60%) than established (40%) TBSFs doing so. This might not be quite what we would expect from what was said earlier about the difficulties young TBSFs face obtaining bank finance, but the young TBSFs seeking bank finance included a high proportion of ‘soft starts’ where founding entrepreneurs relied on revenue generated from contract R&D and consultancy to finance their own product development and commercialisation. Almost two-thirds (64%) of the firms seeking bank finance only considered their existing bank, with just 11 firms making enquiries to three or more banks, these being typically established firms seeking loan finance. This demonstrates a reluctance by owner-managers to shop around when seeking bank finance, preferring to stay with the bank with which they had an existing relationship (for new firms, often through personal banking experience) and assuming that if they were unsuccessful, they would be unlikely to fare any better with other banks. Whilst it has been generally thought that longer relationships improve the availability of finance and lending terms (e.g. lower collateral requirements and bank charges), recent research has shown an increase in bank switching during the financial crisis as a result of dissatisfaction with bank charges and terms (Fraser, 2009). Twenty-five businesses sought bank loans, ranging from £15k to £12 million (median of £125k), with younger TBSFs typically seeking less bank loan finance (median £100k) than established TBSFs (median £175k). Almost half (12) of these cases related to Small Firms Loan Guarantee (SFLG) or Enterprise Finance Guarantee (EFG) enquiries.6 This is a very high proportion referring to SFLG/ EFG, given that it is ‘… designed to operate at the margins of commercial lending decisions…’ affecting between 1 and 2% of all bank loans (BIS, 2009, p. 2), suggesting either that the respondents were more knowledgeable about the bank lending market than most SMEs or that they were seen as being more marginal in bank lending terms. More than a third (10 cases) of the 25 term loan applications were turned down by the bank.7 The reasons for application failure varied, relating to insufficient trading record for three new TBSFs and one more established TBSF which had reinvented itself, insufficient collateral in two new TBSF cases and changes resulting in more restrictive bank lending policy which impacted more on established TBSFs. For one established bioscience TBSF with growing sales turnover, this related to a package of finance involving a £25k overdraft, switching an existing SFLG loan of £100k to more advantageous terms under the EFG and £250k for equipment asset

6. The SFLG, first introduced in 1981, was the United Kingdom’s publicly funded debt guarantee scheme until January 2009 when it was replaced by the EFG in an attempt to stimulate additional bank lending to SMEs. 7 Analysis of bank debt rejection rates for SMEs by Fraser (2012) shows that term loan rejection rates reached 34% in 2012 compared to 5.4% from 2001 to 2004 and 14.1% from 2008 to 2009.

Financing and Growth of Technology-Based Small Firms 217 finance. They only approached their existing bank and the matter was referred to a central decision-making unit which after a three month delay rejected the whole package, indicating that transfer to EFG was not possible and that, despite the positive trading record of the business, further finance would not be forthcoming. The respondent owner-manager suggested that: ‘the bank does not understand bio-tech businesses and the timescales they operate on … there is no real relationship with the local bank manager’. Interestingly, making a successful loan application did not necessarily result in the TBSF taking up the offer from the bank. Of the 13 successful applications for term loans, only nine were taken up by the business. The successfully completed loan applications mainly related to young TBSFs seeking loans ranging from £15k to £250k, whilst the three established TBSF cases involved a £12 million finance package, £200k loan guaranteed by property and a £50k loan offer from several banks. In four younger TBSF cases the successful applicant declined the bank’s loan offer as the terms were considered unacceptable. In one further young TBSF case the bank turned down an initial application for a £200k loan and subsequent offers of a loan of up to £50k with directors’ guarantees, or a £20k overdraft were turned down by the business because the request for directors’ guarantees was considered unacceptable and the level of interest on the overdraft too high. Several respondents complained about the requirements for directors’ personal guarantees, with one mentioning that even under the EFG they were still required to put up 25% personal guarantees against the loan and this was unacceptable to them. There were also complaints about the high levels of interest and set-up fees which in one case was 9.7% fixed rate with a 1.5% set-up fee, whilst another mentioned paying 7% above base with a 0.5% set-up fee. One manager summed up why they rejected the bank’s offer as follows: ‘We had been in a similar position a few years ago, both parties knew that nothing had changed but, unlike in the past, the bank was prepared to offer but on totally unacceptable terms both with regard to interest rates and personal guarantees. There was clearly no point in pursuing matters further’. These findings are indicative of the sharp rise in UK bank lending costs and interest rates from 2008 and the more restricted commercial lending practices of the banks. Thirty-six businesses sought bank overdraft facilities, including six cases extending existing facilities. Overdraft requirements ranged from £1k to £250k with a median of £20k (median of £5k for younger businesses and £20k for established businesses). Bank overdraft applications were more likely to be successful than loan applications, with four-fifths of them being successful to some extent and nearly three quarters completely successful. Two established TBSFs successfully applied to banks for invoice discounting services to values of around £100k and £260k, respectively, set to 80% of order value at a cost of in excess of £5k per annum (dependent upon volume of use), although one subsequently failed to get the level of invoice financing increased by a further £100k. Overall, there were mixed feelings as to whether the banks understood the finance needs of TBSFs. This was strongly linked to concerns about the effectiveness of

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relationship management and continuity of communication with local bank contacts. Over one-third of bank finance seekers (18) stated that the banks did not understand their business. Established TBSF owner-managers cited lack of continuity with relationship managers and detached centralised decision-making. Some referred to not working to fast enough timescales to progress with proposed business development arrangements and in nine cases lending decisions took over two months. The impact on the 20 TBSFs which failed to secure the bank finance they required was that three were unaffected, managing to secure other types of finance, whilst the remainder proceeded more slowly, or on a smaller scale, with one not proceeding at all with their plans.

Equity and Venture Capital Finance Nearly one quarter (23%) of the surveyed TBSFs had tried to access equity and VC funding during the 2007
2010 period, considerably more than the less than 2% of SMEs seeking equity funding as found in recent SME finance surveys (e.g. Cosh et al., 2008). Most frequently this related to funding further R&D (11 cases) and working capital (10 cases), with other purposes including funding acquisitions and buyouts (two cases). As might be expected, bioscience firms (37%) and spin outs (29%) were most likely to seek equity/VC finance, particularly for R&D. Owner-managers indicated that they had sought equity/VC finance because they required risk capital which would not be provided by the banks, particularly where this required a significant amount of early-stage funding (i.e. early stage of new product development). Later stage equity finance was related more to working capital and finance for product proofing/ technical standards, sales, marketing and distribution. These firms were seeking funding ranging from £20k to £10 million (median £250k) and were willing to cede considerable equity share in their businesses, ranging from 10% to 66% (median 25%). Where early-stage equity investment was required, there was an understanding that higher levels of equity share would need to be ceded, as the value of the business would be considerably lower than at a later stage and this was a factor in some respondents only seeking equity at a later stage. Almost one in eight TBSFs (12%) had sought finance from venture capital funds, including combinations of public and private equity. Eight firms sought institutional VC funding, four firms sought public backed equity funding and three firms sought corporate equity funding (e.g. from large pharmaceutical companies, which is a growing trend in the bioscience sector). Overall, older TBSFs were more likely to be seeking institutional and corporate equity funds, whilst younger TBSFs were typically seeking government-backed equity funds. Almost one in seven TBSFs (14%), including 10 younger firms, sought equity finance from business angel investors and in six cases it was combined with searches for VC funds (e.g. combinations of the Scottish Business Angel Network and the Scottish Enterprise VC Fund). Only two TBSFs considered using hybrid debt/equity mezzanine type finance, but none took up this option.

Financing and Growth of Technology-Based Small Firms 219 Around half of those trying to access equity/venture capital finance were successful (11 out of the 23 TBSFs; 13 out of 26 applications, with one pending). In one case this related to successfully working with a Business Angel Network (BAN) to get three angel investors for a total of £300k for 40% equity. In two young Scottish TBSF cases this involved obtaining co-investment from the Scottish Enterprise VC Fund and Scottish Business Angel Network, raising £3 million for a 50% equity share in one case and £450k for a total equity share of 66% in the other. Another TBSF took six months to find and access £250k from an institutional VC fund for a 10% equity share. Three other successful VC cases involved established TBSFs receiving top-up, second-stage funding from existing VC funders — in one case this represented an 18-month extension of a £9 million European institutional VC funding facility, for up to a 20% equity share. Only four out of the six business angel offers were eventually taken up. One successful business angel investment included two investors providing a total of £135k for early-stage business working capital in return for 25% share in a deal which took almost four months to conclude. Two further offers from business angels were rejected because they wanted too much equity in the business. Owner-managers typically suggested that equity/VC investors understood their business. However, as one manager of a young TBSF indicated, investor understanding does not necessarily translate into application success: ‘VC investors know what they are looking at. Corporate investors know the sector well, but they are unwilling to enter at an early stage’. Furthermore, four respondents raised concerns that ‘equity investors have a short-term view, which is not always in the best interest of the business and they failed to see the potential that existed’ and ‘that they are just in it for the money’. For the majority of these businesses, an important attraction of equity/VC investment was the ability to work with an investor that had specialist industry/sector knowledge and could help develop the business, such as through a non-executive director/ board member role. The reasons attributed to failure to secure equity/VC funds varied. In one case a deal was struck with an institutional VC fund for £1.25 million at 25% equity share, but then the investor backed out as the market deteriorated during the recession. Another new TBSF rejected £100k of business angel funding after a search of 18 months because ‘although this funding would have been ideal for product development, the investor wanted more than the 25% share offered’. Five businesses were rejected because the finance was for early-stage product developments that were considered to be too risky and not ready for investment. One respondent seeking £500k for 25% equity complained that ‘We looked at US and UK VC funds and found that because we are a British company we cannot access US funds, whilst in the UK most institutional VC funds have left the market’. Finding equity/VC funding was typically a lengthy process and in some cases the search for funds had taken several years, with the shortest timescale recorded being three months. Younger TBSFs appeared to be less knowledgeable about equity funding than their more established counterparts (most of whom had managers with previous experience accessing this type of finance) and only five younger TBSFs had used external assistance to help find investors. Once a funder was found

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the process of application through to funding could take several months and could be delayed by extensive due diligence procedures and negotiations over terms and conditions (e.g. equity share). One respondent commented: ‘we have been seeking equity finance for more than three years and despite considering a wide range including public backed funds like the London Technology Fund and various institutional VC funds, there has been no interest and our best hope is that some “pharmas” are now considering earlier stage investments in order to keep the flow of new products coming through’. The cost of arranging and setting up equity/VC finance deals was expensive (typically ranging between 5 and 10% of the deal value) and where businesses were seeking to establish new equity/VC funding (as opposed to top-ups), it was noted that the cost of due diligence, legal fees and VC consultants could amount to tens of thousands of pounds (e.g. in one case £30k to gain £500k). Several respondents also mentioned that the effort required in order to find and secure funding was ‘very intensive and expensive in terms of management time’. Apart from the financial cost, one manager also stressed that ‘… the management time taken up during more than a two year period of fundraising was immense and probably halved R&D development in this time’.

Other Forms of Finance The most frequently mentioned alternative finance sourced between 2007 and 2010 was grant finance, which one quarter (Table 6) had approached with a high degree of success (88% were completely successful, with a further 8% receiving at least some of the grant funding that they applied for8). Grants ranged from small-scale marketing and export grants for up to £5k to sizeable UK SMART/Technology Strategy Board (TSB)9 and European FP7 awards for up to £675k. Younger TBSFs (30%) were particularly active in seeking grants, particularly in relation to innovation and product R&D. One-sixth (16%) of TBSFs actively sought other forms of finance, with a high degree of success. The most frequently mentioned alternative source was credit card finance, ranging between £5k and £14k, which was successfully used by five young TBSFs. Three businesses were successful in obtaining leasing and higher purchase arrangements for new equipment. Two businesses mentioned negotiating for joint venture finance, whilst in another case the joint venture agreement had fallen through at the onset of the recession when a manufacturing partner had withdrawn, resulting in the development of the business being put on hold.

8. Some grant funding, for example, Scottish Enterprise business development grants is retrospective and so not always able to be completely taken up. 9. In 2014, the Technology Strategy Board was renamed Innovate UK.

Financing and Growth of Technology-Based Small Firms 221

Future Financial Needs In order to obtain some estimate of the future demand for external finance, the interviewed TBSF owner-managers were asked about their anticipated finance needs over the next three years. Around half of them (52%) stated that they would be seeking formal external finance, with higher proportions of younger (57%), spin out (60%) and bioscience (60%) firms expressing this need. On average (median values), established firms would be seeking £1.3 million compared with £200k for younger firms. Interestingly, this time a slightly higher proportion of TBSFs expected to be seeking equity finance than debt finance (19% compared to 14%). This is in line with other recent evidence relating to SMEs in general (Cowling et al., 2012), suggesting that the difficulties in obtaining debt finance from the banks may be leading to more small businesses considering equity funding. Others would be seeking grants (12%) or joint venture/partner company finance (7%). More than one-fifth (21%) mentioned multiple types of finance, typically involving a combination of bank and equity or grant finance. As before, the main reasons for seeking formal external finance typically related to requiring working capital (27%) to facilitate business growth and sales and marketing development and R&D (16%) including for later stage product proofing and meeting technical regulations. However, more than two-thirds of those identifying a need for future external finance expressed doubts about their ability to obtain finance from these sources. Established TBSFs and spin outs were more likely to anticipate difficulties in finding suitable equity investors, citing the short term view of UK VC investors and the equity finance gap where the amount of funding required (i.e. typically £½ million to £1 million) would likely be too much for individual business angels, but too little to be of interest to VC funds, as well as an unwillingness to cede ownership at the level that business angels and VC funds would require. These findings are indicative of the trends towards smaller numbers of larger, later stage investments by UK VCs (NESTA, 2009; Pierrakis, 2010; Pierrakis & Mason, 2008) and the need for government-backed VC schemes to take a longer term view in order to finance innovations through to successful commercialisation (which can take more than five years for some bioscience firms). Some established TBSFs also mentioned the over cautious attitude and lack of sector knowledge of the banks in addition to the expense, terms and guarantees required for term loans. Younger TBSFs were more likely to suggest that their business was too risky, or had insufficient track record to obtain bank finance and also mentioned their lack of knowledge of equity finance, including government-backed early-stage VC funds, underlining the need for increased public information and transparency in the operation of government-backed VC schemes (National Audit Office [NAO], 2009). Several respondents also mentioned concerns over the future availability of suitable R&D grants from the Technology Strategy Board, given the switch from awarding grants against regional targets to adopting a national level competitive process.

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Conclusions This chapter has reported on the findings of a survey of 100 young and more established TBSFs, providing a number of observations about the impact of the recent financial crisis on TBSFs in the United Kingdom. First, it would seem that not only were the majority (two-thirds) of the TBSFs growth seeking over the 2007
2010 period, but also that around half of them achieved growth (as measured by employment and sales turnover). Thus, a significant proportion of them grew, despite the economic recession. That is not to say that they were immune from the economic downturn, as around a third of them faced demand constraints and more intense competition. However, they appear to have been able to respond through a combination of new product development, marketing strategies and efficiencies such as flexible working and subcontracting. The evidence here suggests that TBSFs have been making an important contribution to UK economic recovery by generating employment (both directly and through subcontracting) and innovative products for global markets. Furthermore, young spin outs and bioscience businesses (once they have overcome their long lead time to trading) appear to be important contributors to growth within the sector. Second, as a result of their growth orientation and achievements, the TBSFs continued to have a relatively strong demand for finance over the 2007
2010 period, particularly to fund working capital and R&D. Whilst this could be entirely funded from internal sources in many firms, two-thirds of TBSFs sought funding from at least one formal external source, with over half (53%) being at least partially successful and taking up the finance offered. This suggests that in a period when SME demand for formal external finance was declining, with increased incidence of ‘discouraged borrowers’ (Fraser, 2009), TBSFs were notably active in the SME finance market. Moreover, two-fifths of the TBSFs had applied for more than one type of external finance. Third, as expected, banks were the main source of formal external finance used, with most TBSFs applying for overdraft facilities or term loans, preferring to stick with their existing bank rather than applying to other banks. The majority applying for overdrafts were successful (81%), as were those applying for term loans although to a lesser extent (58%). An important finding is that several of the TBSFs that were offered bank loans rejected them because they found the conditions (e.g. level of personal guarantee and collateral) and costs (fees and interest rates) involved unacceptable. In this regard our findings are broadly consistent with those of a much larger survey of SMEs access to finance conducted in late 2008 which found that a higher proportion of firms reported that obtaining commercial loans had become more difficult than was the case with overdrafts and other forms of finance such as leasing and hire purchase finance (Cosh, Hughes, Bullock, & Milner, 2009). This was largely because SMEs complained that the costs of obtaining the finance had risen substantially in terms of arrangement fees, the level of collateral required and a doubling in above base interest rates. Thus, there is some indication here that the financing of some TBSFs was being constrained by the more stringent

Financing and Growth of Technology-Based Small Firms 223 requirements of banks following the credit crunch. Furthermore, the high incidence of TBSF bank loan applications involving the SFLG/EFG suggests that many of these applications were considered by bank lending officers to be on the margins of the bank lending market (BIS, 2009). Fourth, although many interviewed owner-managers remained reluctant to seek equity finance, largely because of concerns about the implications for retaining managerial control of their business, it is clear that access to equity and venture capital was important for a significant proportion of TBSFs and especially those with a strong commitment to R&D. This is indicative of the need for early-stage R&D risk finance in the TBSF sector, which is far greater than for the UK SME sector as a whole (Cosh et al., 2008). More than a third of the bioscience and 29% of spin out TBSFs sought this type of finance over the 2007
2010 period, with younger firms more likely to seek funding from business angels and public backed VC funds and older firms from corporate and institutional VC funds (Pierrakis & Mason, 2008). However, the findings also illustrate the difficulties involved in obtaining this type of finance. Only half of those applying received offers and not all of these were taken up, resulting in less than half of the cases successfully reaching a deal. Offers were normally rejected because business owner-managers were not prepared to relinquish the level of equity required by investors. Moreover, the process of searching for an appropriate source and then negotiating a deal was invariably lengthy and costly in terms of the due diligence and legal process and management time involved. This finding is consistent with other research showing that it has become more difficult to raise equity finance in recent years as VC funds and private investors have become more selective or have withheld completely from making new investments (Harrison, Don, Glancey Johnston, & Greig, 2010). Corporate and private VC funders have been moving out of the seed and early-stage market whilst other investors such as business angels have preferred to support their existing portfolios (or withhold making investments altogether) rather than make new investments. For example, data from the British Venture Capital Association [BVCA] (2013) shows that early-stage VC funding fell by 18% between 2009 and 2010 and subsequently stabilised at levels similar to 1998
1999. Fifth, early-stage seed finance is vital to the start-up and early development of TBSFs, with almost a third of younger TBSFs receiving ‘SMART’ awards and early-stage R&D and business development grants. In several cases these acted as a catalyst for bank and equity finance, for example, where Scottish Enterprise assistance facilitated combinations of grant and co-investment VC finance linked with business angel network finance. This approach highlights the important role that public assistance can have in facilitating potential high growth TBSFs, particularly at a time when debt and private equity are harder to obtain. Sixth, whilst TBSFs were very positive when they were surveyed in 2010 about their growth prospects in the next year, with 43% predicting employment growth and an overall 20% increase in mean sales turnover, around half required further external finance in order to facilitate that growth and it was the bioscience and spin outs with the most growth potential that were most in need of external finance.

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Interestingly, future finance was more likely to be sourced from equity/VC funds (19%) than banks (14%). This may indicate a growing acceptance by TBSFs of the need to obtain this type of risk capital, although it may also indicate growing dissatisfaction with bank finance. Importantly, two-thirds of those identifying a need for future external finance voiced concerns about access. Established TBSFs and spin outs cited the short-term views of UK VC investors, with younger TBSFs also indicating some unwillingness to cede equity share. Established TBSFs also mentioned the lack of sector knowledge and increasing caution of banks, whilst younger TBSFs were concerned about their lack of a trading record and banks’ aversion to risk. Younger TBSFs also mentioned the need for better promotion of public assisted equity/VC grant schemes. From the perspective of TBSF owner-managers, it would appear that, if anything, the funding gap has grown as a result of the responses of both the banks and private investors to the financial crisis, making it more difficult not only to access certain types of finance but also on terms that they find acceptable. Thus, the ability of existing TBSFs with growth potential to contribute to the United Kingdom’s economic recovery is clearly going to be conditional upon a greater willingness on the part of both equity and debt finance providers to address the longer term investment needs of such businesses or an increasing role for public backed schemes to fill the void (CEEDR, 2010, 2012; Oakey, 2007; Murray, 2007).

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Chapter 12

Network Openness and Learning Ambidexterity of New Technology-Based Firms at Incubators Danny Soetanto

Abstract For a new technology-based firm, the ability to learn is crucial to their growth process. However, firms constantly face the challenge of maintaining the ambidexterity of two different learning activities, namely learning by exploiting existing competencies and learning through exploring new ones. The purpose of this study is to examine how small technology-based firms at incubators perform both activities. Using the index of network openness, we argue that firms perform ambidexterity by maintaining a balance between a high and low level of network openness. Our first hypothesis was constructed as firms pursuing explorative learning will develop a high level of network openness while those pursuing exploitative learning will develop a low level of network openness. In the second hypothesis, we argue that firms need to balance network openness. Developing too low level of network openness will not add more benefits as the cost for maintaining relationship increases. Similarly, developing too high level of openness may potentially hinder firms’ progress as firms face distractions and difficulties in maintaining a wide variety of relationships. Using the empirical data from new technology-based firms located at the Daresbury SIC, we confirm the hypotheses. The result also found a trend of a curvilinear relationship between network openness and the firms’ performance which confirm the second hypothesis. The overall findings have illustrated how a network has a positive impact on helping small and new technologybased firms perform learning ambidexterity.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Introduction The network is becoming a ubiquitous phenomenon, especially for new technologybased firms (Hung, 2002; Ritter & Gemunden, 2003). Parallel to the increasing rise in network studies, researches on strategic networking have proliferated, with one strand focusing on the impact of network on firms’ learning capability (Gulati, 1999; Kim, Song, & Nerkar, 2011). Learning is crucial for every type of firm. For small firms, the ability of the entrepreneur or entrepreneurial team, to learn is crucial to the growth process (Deakins & Freel, 1998). However, firms learn through different ways and from different resources. In the learning process, firms are constantly faced with the challenge of keeping up with two different sides of learning activities, learning by exploiting existing competencies and learning through exploring new ones (Danneels, 2002; Filippini, Gu¨ttel, & Nosella, 2012; Vera & Crossan, 2004). Earlier studies have repeatedly regarded the trade-offs between these two activities as insurmountable, but more recent research has discovered the existence of ambidextrous organisations that are capable of simultaneous exploitation of existing competencies and exploration of new opportunities (Andriopoulos & Lewis, 2010). Building on the earlier work of Duncan (1976), Tushman and O’Reilly (1996) were the first to present a theory of organisational ambidexterity. Many studies suggest that superior performance is expected from the ambidextrous organisation (Kim & Atuahene-Gima, 2010; March, 1991). Hence, the literature has increasingly argued that successful firms generate competitive advantages through revolutionary and evolutionary changes (Tushman & O’Reilly, 1996) or adaptability and alignment (Gibson & Birkinshaw, 2004). In other words, firms need to simultaneously pursue exploratory and exploitative activities (Benner & Tushman, 2003). However, maintaining ambidexterity is a big challenge especially for small and new firms. Compared to large firms, small firms lack the kind of hierarchical administrative system that can help them manage ambidexterity. Due to their limited resources and priority in developing either products or market positioning, firms may choose to concentrate either on exploitation or on exploration activities. Whilst a lot of studies theoretically predict a positive effect of exploration and exploitation activities on firms’ performance, most of them have been conducted in large organisations. This study has seen the gap in the literature that ambidexterity in small firms has been rarely explored. Responding to this need, the purpose of this study is to examine how small technology-based firms perform both activities. We purposely focused on the knowledge network between small technology-based firms located at incubators and sources of learning, such as university, research institutes and other technology-based firms as these knowledge networks may be crucial for firms’ learning. In the empirical part, a network openness index was constructed as a proxy of ambidexterity. A high level of network openness means that firms simultaneously maintain a wide variety of relationship with several sources of knowledge while a low level of network openness means that firm concentrates on limited activities with a certain source of knowledge. The chapter is presented in the following way. In the next part, we discuss the ambidexterity and network of new technologybased firms. Following it is a discussion on the network model of ambidexterity in

Network Openness and Learning Ambidexterity 229 which hypotheses were constructed. Thereafter the research method is presented. The analysis of this study was provided from the interview with 62 firms located at the Daresbury Science and Innovation Center (SIC). Finally, we present our conclusions and recommendations.

Theoretical Background and Hypotheses In developing competitive advantages, learning plays an important role for firms that set out to increase their market share through learning and knowledge management strategies (Sadler-Smith, Spicer, & Chaston, 2001). Through learning, firms become more responsive to technological and socioeconomic changes. Learning is currently the focus of considerable academic and practitioner attention. Whilst an abundance of related literature has emerged, grounded in a broad range of interrelated disciplines, most studies still persist in favour of a resource-based view of knowledge. Here knowledge is treated as a tangible and configurable organisational resource (Jones & Macpherson, 2006; Thorpe, Holt, Macpherson, & Pittaway, 2005). Aligned with this thought, learning can be explained as a process of resource exchange. Social constructionists, however, argue that a significant learning process can occur through a continual interaction within a social relationship (EasterbySmith & Araujo, 1999). In other words, learning could not be described solely as a straightforward activity of knowledge transfer. In fact, learning is a dynamic process enforced through the engagement with other network partners (Beckman & Haunschild, 2002; Powell, Koput, & Smith-Doer, 1966). Especially for small firms, new skills and capabilities are often acquired through networks (Coviello & Munro, 1997). In the next section, we will discuss firms’ learning process and the types of network used by firms to perform their learning activities. Ambidextrous Learning and Network Openness For small firms, trade-offs between exploration and exploitation are certainly necessary because they compete for scarce resources. One the one hand, exploitation creates reliability in experience through refinement, routinisation, production and implementation of knowledge. Learning through exploration, on the other hand, creates varieties in experience through searches, discoveries, novelties, innovations and experimentations. Many studies (e.g. March, 1991; Tushman & O’Reilly, 1996) suggest that maintaining an appropriate balance between exploration and exploitation is critical for firms. Levinthal and March (1993) argue that the basic problem in every organisation is to engage in sufficient exploitation to ensure its current viability and, at the same time, to devote enough resources for exploration. The need for an appropriate balance between exploration and exploitation has been crystallised by Tushman and O’Reilly (1996) as they conceptualise the concept of the ambidextrous organisation. They use a metaphor to describe ambidextrous firms that have the capabilities to both compete in mature market, where cost, efficiency and

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incremental innovation are critical, and at the same time develop new products or services, where experimentation, speed, and flexibility are important. They conclude that an ambidextrous firm is likely to achieve a superior performance than firms emphasising on one learning activity at the expense of the other. Almost in a similar vein, a concept of dynamic capabilities employed by Eisenhardt and Martin (2000) and Ancona, Goodman, Lawrence, and Tushman (2001) suggests that firms need a combination of the two different strategic logics, namely the logic of exploration and the logic of exploitation. From the perspective of absorptive capacity, Katila and Ahuja (2002) confirm that both activities are closely related. Exploitation of existing capabilities is often needed to explore new capabilities, and exploration of new capabilities can enhance the firms’ existing knowledge base. As most of studies appraise the importance of balanced learning through exploration and exploitation, firms are expected to master this mechanism in order to survive and grow. However, balancing both exploitation and exploration activities through firms’ networks is a big challenge, especially for new technology-based firms. In understanding the learning ambidexterity in the context of new technologybased firms, we should look into the role of networks. In developing innovative and technology-based products, networks are regarded as essential. Knowledge is more easily transferred between close network partners than through market mechanisms, as learning is a socially embedded process (Shenkar & Li, 1999). The need of learning for small firms is derived from their vulnerable position (Ostgaard & Birley, 1996). Given the fact that new technology-based firms generally face resource constraints (Van Geenhuizen & Soetanto, 2009), it is likely that these firms rely on networks as their sources of learning (Fairtlough, 1994; Uzzi, 1996). As firms intend to bring new technology and innovation to market, practical knowledge such as understanding market conditions and how to manage a firm is important. Founders who mostly have a technical or engineering educational background are often found lacking in these kinds of knowledge. Networks become a place where collective social and practical learning takes place (Keeble, Lawson, Moore, & Wilkinson, 2010). It is through networks that firms learn new skills and access knowledge necessary for growth. Another reason for firms to learn from networks is driven by the presence of a tight competition. In this situation, resources are limited and profits are stressed (Klepper & Graddy, 1990). With many similar firms playing in the same market, it is difficult for firms to set themselves apart from the others. Firms need to develop competitive advantages by exploring and exploiting their networks (AaikkaStenroos & Sanberg, 2012). Network partners such as universities and government research institutes might offer opportunities and support (Baum & Oliver, 1991). Learning through networks allows firms to increase the speed of capability development and minimise uncertainties by acquiring and exploiting knowledge (Grant & Baden-Fuller, 2004; Lane & Lubatkin, 1998). As a result, firms generate competitive advantages necessary for their growth (March, 1999; Marengo, 1993). This strategy allows firms to share costs and risks, as well as develop or differentiate their products or service using knowledge from their partners.

Network Openness and Learning Ambidexterity 231 In developing networks for learning, firms develop at least two different strategies based on their learning (Kim et al., 2011; Low & Johnston, 2012). • Exploitative learning through a low level of network openness — A low level of network openness takes place when firms nurture a close and strong relationship with their network partners. Firms conduct a relatively limited networking activities but the intention is to exploit knowledge. Firms that employ low level of openness have a focus and strong connection with particular network contacts. In this first strategy, firms develop networks that facilitate the exploitation of valuable skills and knowledge essential for firms’ innovation activities. For instance, firms retain an intense collaboration with university in order to acquire knowledge, skills and other resources that are necessary to bring research from university to market. • Exploration learning through a high level of network openness — The second strategy is related to learning through exploration. A high level of openness describes how widely firms use their network contacts in learning process. In a high level of openness, firms develop a variety of networking activities and receive some benefits to firms due to the diversity in knowledge, skills and experiences. This wide interaction may bring firms to receive new knowledge or open a new potential application of their initial knowledge and skills.

Hypotheses Construction Learning ambidextrously means that firms need to maintain two learning activities simultaneously. For new technology-based firms, their limited resources may create a difficulty in managing both networks. Firms often face dilemmas and difficulties in making a priority especially when they need to perform exploration and exploitative learning with the same network partners. Building on the stream of work in network studies, we posit that the ambidexterity can be maintained by synchronising firms’ level of network openness. In the first hypothesis, we argue that different levels of network openness are employed for exploration and exploitation activities. As both activities require different network openness, we then argue in the second hypothesis that firms need to balance their network in order to receive benefits from both types of activities. New technology-based firms need to perform both exploration and exploitative learning (Kim et al., 2011). In exploration learning firms open their networks for potential collaborations and knowledge exchanges through which a high level of network openness is more likely to be created. According to Katila and Ahuja (2002), developing many networking activities offers certain advantages. First, the activities will increase knowledge repository as firms can have access to their partners’ knowledge. By accumulating knowledge from their network contacts, firms can generate multi-choice alternatives in solving problems (March, 1991). Secondly, having a wide range of partners will increase the firm’s capability in

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generating new products or services through the invention of new ideas. As there are limits to the number of ideas that can be created by firms, increasing access to wide partners adds new elements and endorses the creation of new ideas (Nelson & Winter, 1982). In contrast to exploration learning, firms need to develop a low level of network openness for exploitative learning. Firms have selected several close partners for long-term relationships. A continuous access to the same source of resources will facilitate the development of routines, reduce the likelihood of making errors and reduce the cost of searching. A strong relationship with their network contacts makes firms become familiar with knowledge, skills and capabilities of their contacts. Apparently, this relationship will endorse the transfer of knowledge, especially tacit knowledge (Laursen & Salter, 2006; Padula, 2008). Moreover, having repeated usage of the given knowledge can lead to a significantly deeper understanding of resources or knowledge and boost firms’ ability to identify valuable knowledge elements within them (Katila & Ahuja, 2002). To summarise, we conclude that the learning process of firms which is manifested in two activities, exploration and exploitation, can be understood as an interplay between a low and high level of network openness. Thus, firms will develop and maintain different network openness based on their learning objective. In the exploration, firms develop a high level of network openness while in the exploitation, firms develop a low level of network openness. Therefore, based on the above discussion, we constructed the following hypothesis: Hypothesis 1. A high level of network openness is likely to be developed for exploration while a low level of network openness is likely to be developed for exploitation. New technology-based firms often develop products based on innovation or technology from universities or government research institutions. The role of those institutions as a source of learning is of unarguable importance and new technologybased firms always need to either exploit the current technology or explore the development of new technology. As developing a relationship for exploration and exploitation need to be done simultaneously, the only solution of this ambidexterity is by balancing the network. In addition, the benefits received by firms in developing either exploration or exploitation may become limited beyond a certain point. When the limits of the trajectory are approached, benefits from exploitation and exploration decrease. In fact, there is a certain limitation on knowledge repository possessed by network partners. Further development based on the same knowledge elements becomes increasingly expensive and the solutions are excessively complicated, leading to the costs of depth eventually exceeding its benefits. As a result, firm face a difficulty in generating new ideas. Developing relationships continuously with the same source of knowledge for a long time may create a negative effect as this condition may create a lock-in effect and decrease the rate of solving problems (Dosi, Llrena, & Sylos Labini, 2006). Some conflicts may emerge as the relationship continuous with the same network partners. Firms which develop too high level of network openness may also create disadvantages. Firms may find difficulties in

Network Openness and Learning Ambidexterity 233 selecting knowledge necessary for growth. Moreover, the accumulation of knowledge may exceed the knowledge that is needed. We argue that this strategy will facilitate both the absorption of knowledge and development of new products or services which are relevant in both exploration and exploitation process. In addition, limited resources owned by firms have also constrained firms’ capability in pursuing both activities. Having a balanced network in exploration and exploitation will enhance firms’ competitive advantages as suggested by Winter (1984). He argues that by combining firm-specific accumulated understanding of certain knowledge elements (depth) with new solutions (breadth), firms are more likely to create new, unique combinations that can be commercialised. As we argue that exploratory and exploitation activities are manifested in networking activities, performing ambidexterity means that firms develop balanced network openness. Therefore, the following hypothesis is constructed. Hypothesis 2. In order to receive optimum benefits from exploration and exploitation activities, firms need to balance their network as developing very high or very low level of network openness might deteriorate the firms’ performance

Research Method We initiated this research based on the tenant survey (face-to-face interview) conducted on companies located at the Daresbury Science and Innovation Center (SIC) in 2008. The Daresbury SIC is part of UK government’s effort to encourage commercial exploitation of scientific research and knowledge exchange in the Northwest region. Currently, the Daresbury SIC hosts more than 80 innovative technology companies that typically come from the biomedical, digital/ICT, advanced engineering and energy and environmental sectors. After several rounds of phone and personal follow-ups, 62 questionnaires were returned, resulting in a response rate of 77.5%. In 2009, the Daresbury SIC replicated the study and found that the findings did not significantly change. Therefore, the 2008 dataset was used as it provided a bigger sample. In line with the ambidexterity hypothesis, we assume that firms in the sample simultaneously maintain two different types of learning, exploration and exploitation, with their source of knowledge. In this study, three main source of knowledge were covered, the Science and Technology Facilities Council (STFC), universities (Lancaster University, University of Liverpool and University of Manchester) and other firms at the incubator. The STFC is located within a close proximity while the universities are relatively distant to the Daresbury SIC. In defining the boundary between exploration and exploitation activities, we found that the distinction can be found in level of resources (including knowledge) that have been exchanged. In exploration learning, a low level of resources has been exchanged between firms and their source of knowledge. Firms maintain several activities but the intention is that this be on a short term basis, informal and rather temporary. In those activities, knowledge may not formally transferred or deeply exploited by firms. In contrast,

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firms perform exploitative learning by acquiring resources (including knowledge) owned by their network partners. In this activity, formal and long-term partnership with a high level of knowledge exchange. Table 1 shows the learning activities for each network partners. Table 1: Learning activities with network partners. Network Partners Exploration activities Buyer and seller relationship

Incubator firms

Description

Firms use services or products produced by other firms. Firms have not involved in exploiting capabilities or resources of other firms instead firms buy or sell the ‘end’ product. Reseller of product/service Firms sell other firms’ products and services to their customers. Use of equipment Firms use equipment in a contractual basis and do not involve a high level knowledge exchange. Informal networks through The STFC Through informal communication, firms communal facilities have the opportunity to learn by exploring resource, experience and knowledge owned by the STFC. Use of business services Business support offers an opportunity for firms to explore potential commercialisation of the STFC’ knowledge. Buyer and seller Firms use services or products produced relationship by the STFC. Buyer and seller University Firms form a business relationship with relationship university, such as selling products to university. Attending lecture/ Firms attend lectures/workshops in workshop at university specific topics or areas organised by university. Firms make use of university’s facilities. Use of facilities (including measuring, testing, diagnostic service) Exploitation activities Collaborative working Incubator In this activity, firms exploit other firms’ firms resources and skills by collaborating in projects. Merger and joint venture Resource acquisition through merger or joint venture.

Network Openness and Learning Ambidexterity 235 Table 1: (Continued ) Network Partners

Description

New company formed

Another way of resource acquisition is by forming a new company. Staff recruitment The STFC Firms exploit the STFC’s knowledge through recruitment. Technical consultation and In this activity, the STFC provides a exploitation depth and specific consultation on technical domain. Spin-out firms Firms form a business alliance by establishing spin-out firms. Staff recruitment University Firms exploit university’s resources by hiring academic staff or students. Knowledge transfer This partnership benefits firms as through partnership including this funding firms can make use of student project and university’s knowledge and resources at placement a low cost. Spin-out firms and licensing Firms form a business alliance by establishing spin-out firms or buying licence.

In this study, the dependent variable was measured by the number of job growth and sales growth. In the analysis, we also used several control variables, size and level of innovativeness. The variable of size was measured as a number of full-time employees while level of innovativeness was measured as a dummy variable. Firms are categorised as innovative firms if they produce more than one new product or service. We did not make any distinction between products and services. However, we used Martin and Mitchell’s (1998) definition of a new innovation as a change in design characteristics. A product or service was labelled new if one or more of its design characteristics differed from existing products or services. In this case, an existing design introduced into a new market did not qualify as a new product. The independent variable for this study was a network openness index. A low value shows that firms have close and focus network, a network in which firms concentrate their learning activities on a relatively low number of contacts. A high value shows that firms maintain an open and wide network, a network in which firms develop learning activities with various network contacts. The formula for this index is shown below: Pn Oðexploration=exploitationÞ = 1 −

a=1



Xa =y n

2

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University

University Firm A

Firm B

STFC

STFC Other firms

Other firms

Figure 1: Illustration of network activities. where n is the number of contacts (max = three universities, government research institutes and other firms at incubator); x is the number of networking activities developed by incubator firms; y is the total of networking activities. In this study, three networking activities were found for each network partners, thus, y equals to 3. Example: For instance, firm A has maintained three types of network activities with university and one activity with the STFC. In contrast, firm B has maintained an activity with each network partner (Figure 1). Using the above formula, we calculated the network openness index for the exploration activities. Overall, the index of firm A is lower than the index of firm B meaning that firm A has connected and focused to less network partners compared to firm B.  3 3

Firm A: Oexploration = 1 −

þ

2

1 3

 Firm B: Oexploration = 1 −

1 3

þ

1 3

þ

! =2 = 1 − 2

1 3

!

8 1 = 9 9

=2 = 1 −

1 2 = 3 3

Results Firm Characteristics Table 2 shows the characteristics of the firms at the Daresbury SIC. The total number of the firms at Daresbury SIC at the time of observation (2008) was 62 firms. The majority of the firms were small with 3.96 fte average number of employees. Forty-two firms (67.7%) had less than five employees. In terms of target market, 24 firms (38.7%) have exported their products or services to countries outside the United Kingdom. The firms have delivered in total almost £15 million/year with an average annual sales of £240,000/year. However, the standard deviation was relatively high. This tells us that there was a big variation in terms of the firms’

Network Openness and Learning Ambidexterity 237 Table 2: Characteristics of firms (number of firms = 62). Frequency Size — number of employees (mean: 3.96; SD: 3.97) ≤5 fte (micro firm) 42 >5 fte (small and medium firm) 20

Percentage 67.7 32.3

Exported product Has no exported product 38 61.3 Has exported product 24 38.7 Annual sales — total: £14,935 million/year; mean: £240,000/year; SD: £423,071/year Jobs growth — mean: 1.04; SD: 1.69 ≤1 fte (weak growth) >1 fte (strong growth)

43 19

69.4 30.6

performance at the incubator. Growth in the number of employees was relatively moderate with an average of 1.04 ftes. Forty-three firms (69.4%) experienced a relatively weak growth of one or less fte per year and only 19 firms (30.6%) experienced a job growth of more than 1 fte per year. This relatively moderate growth pattern can be explained as the Daresbury SIC has accommodated a high percentage (30
40%) of the companies in the pre-revenue stage. The moderate job growth was caused by the firms’ strategy in focusing more on penetrating markets than on investing in employees.

Networking at Incubator This study focuses on the network of small technology-based firms located at incubators in either exploration or exploitation activities. In total, there are 18 different types of networking activities employed by the firms. Table 3 shows the types of exploration activities developed by the incubator firms with their partners. As displayed in Table 3, 53.2% of the firms were engaged in business partnership. These firms have either become a supplier, customer or reseller to the other firms at the incubator. 14.5% of the firms sold other firms’ products or services. Moreover, some of the firms were likely to engage in the use of equipment, comprising of only 12.9% of the firms. With regard to the network with the STFC, Table 3 shows that most of the relationships between the firms located in the incubator and the STFC were only manifested in informal networks and these occurred through the use of communal facilities and business services (79.0%). The next common relationship, the use of business service, was experienced by 32.3% of the incubator firms, followed by business relationship occupied by 17.7% of the firms. With regard to exploration activities with university, the majority of firms were engaged with the universities through business partnership, experiencing 35.5% of the incubator

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Table 3: Exploration activities. Network Partner Incubator firms The STFC

University

Type of Network Buyer and seller relationship Reseller of product/service Use of equipment Informal networks through communal facilities Use of business services Buyer and seller relationship Buyer and seller relationship Attending lecture/workshop at university Use of facilities (including measuring, testing, diagnostic service)

Frequency Percentage 33 9 8 49

53.2 14.5 12.9 79.0

20 11 22 9 6

32.3 17.7 35.5 14.5 9.7

firms developing this type of relationship. Moreover, in order to get access to knowledge, the firms made use of their relationships with the universities by attending lectures or workshops at the universities (14.5%) and using university facilities (9.7%). In exploitation activities, Table 4 shows that 56.5% of the firms developed a relationship with the other incubator firms to collaborate in projects. Exploitation happened where the firms access resources from the other firms through joint ventures (11.3%) and the formation of new companies (8.1%). In the relationship with the STFC, most of the firms exploited resources and knowledge from the STFC through consultation in which 29% of the firms employing this activity. Another exploitation activity was conducted through staff recruitment (3.2%) and establishment of spin-out firms (3.2%). Moreover, 29% of the firms developed knowledge transfer partnership with the universities. While 22.6% of the firms recruited their staff from university, 14.5% of the firms developed spin-offs or technology licence from the universities. In exploration, firms are assumed to develop networks by developing informal connections, exchanging resources or developing business contracts. As the STFC and other incubator firms were located in close proximity, the incubator firms were more likely to engage and develop different types of networking activities. The location of the universities, which was relatively far from the Daresbury SIC may have limited the possibility, however, based on the data we found quite a high number of networking activities developed between the incubator firms and the universities for exploitation activities. In fact, firms recruit staff and get involved in knowledge transfer partnership programme with the universities. The link to the universities may have been built due to the technology and innovative nature of the firms’ products and services. Table 5 shows the result of the statistical test on Hypothesis 1. The findings show that the firms developed a significantly higher level of network openness for the

Network Openness and Learning Ambidexterity 239 Table 4: Network exploitation stage. Network Partner Incubator firms The STFC

University

Type of Network Collaborative working/project collaboration Merger and joint venture New company formed Staff recruitment Technical consultation and exploitation Spin-out firms Staff recruitment Knowledge transfer partnership including student project and placement Spin-out firms and licensing

Frequency Percentage 35 7 5 2 18 2 14 18

56.5 11.3 8.1 3.2 29.0 3.2 22.6 29.0

9

14.5

Table 5: t-test result for network openness index on exploration and exploitation activities. Network Openness Index Exploration activities Exploitation activities t-test result

Mean: .369 SD: .464 Mean: .182 SD: .287 2.692**

**p < .01; min: 0; max: 1.

exploration than for the exploitation activities. The average level of network openness on the exploration stage was .369 (standard deviation .464) which was significantly higher than the average level of network openness on the exploitation stage, .182 (standard deviation .287). Thus, the findings confirmed Hypothesis 1 that firms develop a high level of network openness for exploration and a low level of network openness for exploitation. Table 6 reports the results of the regression analysis. As explained in the earlier section, this study examined the impact of network openness on exploration and exploitation activities using two dependent variables, job growth and sales growth. For each dependent variable, we follow similar steps. First, we introduced the network openness index for exploration to assess its effects on performance while the squared term of the variable was introduced in the second model. Next, we included the network openness index for exploitation and we added the squared terms of the variable. Lastly, all the variables were included in the model. With regard to job growth as the dependent variable, we first examined the coefficients of the variables in columns 1 and 3. The result shows a rather weak

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Table 6: Regression analysis. Dependent Variable: Job Growth

Size Level of innovativeness Oexploration Oexploitation Oexploration2 Oexploitation2 N F R2 Adj R2 †p < .10; *p < .05; **p < .01.

Dependent Variable: Sales Growth

1

2

3

4

5

6

7

8

9

10

.27** .34 .24†

.25** .03 .20*

.20* .04

.22* .04

.29** .19 .21*

.26** .19 .18*

.25** .19

.24* .19

.14†

.17†

.19* .03 .21* .12† −.19* −.14† 62 44.7** .54 .51

.02

.06

62 39.9** .48 .46

−.10 62 40.4** .49 .47

.24* .20 .21† .02 −.16† −.11 62 43.1** .57 .55

−.18* 62 40.2** .50 .48

62 42.3** .52 .50

62 40.1** .48 .46

−.12† 62 41.6** .50 .97

−.19* 62 41.9** .51 .49

62 42.8** .52 .50

Network Openness and Learning Ambidexterity 241 significance of network openness in exploration and exploitation activities. In columns 2 and 4, the squared term of network openness was negative and significant indicating that the relationship between network openness and job growth was not linear. In the next models, the dependent variable was sales growth. In columns 6 and 8 which show a linear effect of each network characteristic, we found that the coefficient of the network openness index for exploration was significant whereas the coefficient of the network openness index for exploitation was insignificant. Looking on the analysis of the squared terms of each network characteristic (models 7 and 9), again, we expected the coefficient of the squared terms of each network characteristic to be negative as our hypotheses predicted the presence of a ∩-shape relationship. The result shows that the squared term of network openness index for exploration activities was negative and significant. This finding was consistent with the previous result in model 2 claiming the presence of a curvilinear relationship instead of a linear relationship. In model 9, we found that the squared term of exploitation activities was insignificant. However, the negative sign on the coefficient of network openness indicates a trend of having a curvilinear relationship. Overall, the findings confirm the second hypothesis.

Discussion The study aims to examine how small technology-based firms located at incubators deal with the ambidexterity of explorative and exploitative learning. By integrating two streams of research, organisational learning and the network perspective, we argue that the exploration and exploitation activities can be examined through the networking activities between firms and their source of knowledge. Building on this assumption, we have constructed a network openness index to describe the intensity and concentration of networking activities performed by firms with their network partners. In a high level of network openness, firms develop a wide range of relationship with their network partners. With an intention to explore and learn through the variety of knowledge, the relationship with firms’ network partners are characterised as a temporary one in which firms invest a low level of resources. In contrast, a low level of network openness demands a high investment in firms’ resources. In this close and strong relationship, a depth of understanding has been built and the relationship acts as a conduit for knowledge exchange. Using the empirical data from the small technology-based firms located at the Daresbury SIC, we tested our hypotheses. Overall, we confirm that in learning activities, the firms develop different networking activities to accommodate the needs and objective of learning. Firms are likely to develop a high level of network openness for explorative learning while a low level of network openness is for exploitative learning. The findings may help the understanding of learning ambidexterity by showing the mechanism of network developed by firm to perform the activities. In this case, firms perform both activities simultaneously, but with a difference in the level of network openness meaning that resources spending on each activity are different.

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Moreover, we also argue that there is a limit on the trajectory of network openness. At a certain point, developing a further strong or weak relationship will not add more benefits. Using regression analysis, the result found a trend of curvilinear relationship between network openness and firms’ performance. Developing a very low level of network openness will increase the cost for maintaining relationships. Due to the lock-in effect, firms face limited access to new knowledge and skills. Similarly, developing a very high level of openness may potentially disturb firms’ progress as firms face distractions and difficulties in maintaining relationships. Firms may face uncertainty in their learning process as there is a wide variety of alternatives. Figure 2 shows the illustration of the relationship between network openness and firms’ performance. Overall, the finding may offer an alternative solution to the problem of ambidexterity. As the current understanding argues that firms need to perform both activities, the explanation offered by this study is based on the way in which firms maintain a relationship with their source of learning. Especially in the case of new technology-based firms, we found that firm need to balance their network and avoid in overspending resources. Through our study we have contributed to the discussion on the ambidexterity hypothesis in several ways. First, we have provided empirical evidence on how small technology-based firms manage the dilemma of exploration and exploitation from network perspective. This work is a response to the recent recommendations givens by some studies of Lubatkin, Simsek, Ling, and Veiga (2006) concerning the need for conducting more research in the context of small firms. In fact, the study has extended the understanding of network openness and its impact on firms’ performance. The nonlinear relationship observed in this study challenges the common concept in network studies which is still dominated by the linear effect between network characteristics and performance. This finding indicates a new understanding especially for small firms’ networking strategy. With limited resources, firms can maximise benefits from exploration and exploitative learning by maintaining a balance between exploration and exploitation activities.

High

Firms’ Performance

Exploitation Exploration

Low Low

High Network Openness

Figure 2: Network openness and firms’ performance.

Network Openness and Learning Ambidexterity 243 Despite these interesting results, we acknowledge that there are some limitations in our study. The first limitations are methodological in nature. In this study, we limited the number of network contacts in order to reduce a bias, however, in reality, a network is a complex phenomenon. Although defining the boundary of a network is difficult, some more depth and observation need to be conducted to understand the nature of relationship. This leads to the second suggestion to use longitudinal studies to explore how existing relationships lead to the creation of an impact on innovation performance. The last suggestion is to improve the validation of the study. We suggest increasing the sample size and using a control sample. As the focus of this study was on small technology-based firms located at incubators, the finding need to be generalised and validated into a wider context such as firms located outside incubators or firms from a non-technological background.

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Chapter 13

From Communicative Practices to Communication Strategies: A Model of Entrepreneurs’ Communication Strategies in the Start-Up Process Pia Ulvenblad

Abstract The aim of this chapter is to propose a model of entrepreneurs’ communication strategies in the start-up process by synthesizing previous empirical research. The focus on communication strategies in the start-up process is important for several reasons. We know that many businesses fail during the first year of existence and others are liquidated during the first three years of operation. We also know that new businesses face problems when entering the market. These problems are assumed to arise partly due to the liability of newness (LoN), that is lack of a track record and legitimacy. The model of communication strategies is built upon entrepreneurs’ communicative practices since strategy is seen as a social practice. The chapter also emphasizes communication strategies as being a part of the research field strategic entrepreneurship. The model focuses communicative behaviours in terms of the message and the conversation as well as the chosen strategy in terms of planned and emergent strategies. Three types of communication strategies emerge from the communication practices; (i) content-centred, (ii) behaviour-centred and (iii) adaptive-centred.

New Technology-Based Firms in the New Millennium, Volume XI Edited by A. Groen, G. Cook and P. van der Sijde Copyright r 2015 by Emerald Group Publishing Limited All rights of reproduction in any form reserved

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Introduction This chapter seeks to advance the understanding of entrepreneurs’ communication strategies in the start-up process by synthesizing previous empirical research and proposing a model of communication strategies. The model will also be validated in the chapter by using examples from new businesses. The focus on communication strategies in the start-up process is important for several reasons. We know that many businesses fail during the first year of existence (Timmons, 1994) and others are liquidated during the first three years of operation (Delmar & Shane, 2004). We also know that new businesses face problems when entering the market. These problems are assumed to arise partly due to the liability of newness (LoN), that is lack of a track record and legitimacy (Aldrich, 1999; Aldrich & Auster, 1986; Aldrich & Fiol, 1994; Shepherd, Douglas, & Shanely, 2000; Stinchcombe, 1965). Even though LoN has been questioned in recent research (Morse, Fowler, & Lawrence, 2007) because of the virtual possibilities of new technologies, there is still a risk that potential customers, financiers and suppliers may hesitate to engage in a new business (Choi, 2004). Due to the problems related to LoN, new businesses are assumed to face problems in securing resources from stakeholders (Stinchcombe, 1965). However, the findings in Zott and Huy (2007) indicate that entrepreneurs can actually increase the odds of acquiring resources needed, by explicitly focusing on how to present and communicate the product/service and themselves. In other words, a high level of communicative skills is important for the entrepreneur in acquiring resources (Baron, 2007; Baron & Markman, 2000, 2003; Baron & Tang, 2009) and for overcoming the liabilities of establishing a new business (Zott & Huy, 2007). In addition, it has been shown that it is important for the entrepreneur to have frequent and open communication (Shepherd & Zacharakis, 2001) and that the first impression and presentational factors are important for a potential financier (Clark, 2008; Svensson & Ulvenblad, 2000). Further, communication skills have been shown to influence investors’ decisions (Baron & Brush, 1999; Mason & Harrison, 2003). The entrepreneurs’ presentation of the business is important since it includes both information about the business and information about the entrepreneur’s ability to develop the business (Hoehn-Weiss, Brush, & Baron, 2004). Especially the entrepreneur’s ability is important for investor decisions (Baron & Brush, 1999; Clark, 2008; Mason & Harrison, 2003; Svensson & Ulvenblad, 2000; Ulvenblad & Ulvenblad, 2012). Further, in many cases the major resource the entrepreneur has in the start-up of the business is herself or himself (Brush, Greene, & Hart, 2001) which makes it especially important for the entrepreneur to think strategically about her/his communication (Hitt, Ireland, Camp, & Sexton, 2001). The focus on communicative practices and the focus on thinking strategically about the communicative interaction are therefore important for the entrepreneur in the start-up and further development of the business. Human and social resources are important in order to obtain other resources such as finance (Brush et al., 2001). Since the one resource we know for sure that the entrepreneur has from the start is herself or

From Communicative Practices to Communication Strategies 249 himself it will be especially important to emphasize this resource and what the entrepreneurs actually do, in entrepreneurship research and in the entrepreneurship strategy literature. From previous research we know that the focus often has been on businesses which are ongoing and more seldom on emergent phases (Davidsson & Honig, 2003). We also know that communication skills are important and that communicative practices are something that entrepreneurs have to take part in actively. However, there has been little research in this area (Baron, 2007; Tornikoski & Newbert, 2007; Zott & Huy, 2007). Since this is the fact there is also a lack of developed models for entrepreneurs’ communication strategies. This is valid for research about improvised behaviour and for understanding what the entrepreneur does to ‘act as if’ (Gartner, Bird, & Starr, 1992) the business is already an ongoing one. It is also valid for research about entrepreneurs’ communication, with a focus on both the message and the conversation. To fill a gap in previous research the aim of this chapter is to propose a model of entrepreneurs’ communication strategies. The model focuses communicative behaviours in terms of the message and the conversation as well as the chosen strategy in terms of planned and emergent strategies. Three types of communication strategies emerge from the communication practices: (i) content-centred, (ii) behaviourcentred and (iii) adaptive-centred. The chapter contributes to previous research in the following ways; first, by synthesizing previous empirical research about entrepreneurs’ communicative practices into a model of entrepreneurs’ communication strategies, second, by emphasizing communication strategies as being a part of the research field strategic entrepreneurship. By this the chapter also contributes to the building of a framework for research about entrepreneurs’ communication strategies. The chapter proceeds as follows. In the next section there will be an introduction to the research field of strategic entrepreneurship and strategy as a social practice as well as a brief overview of previous empirical research streams regarding entrepreneurs’ communication strategies. After this there will be a presentation of the model of entrepreneurs’ communication strategies based on entrepreneurs’ communicative practices with empirical examples validating the different types of communicative behaviour. Finally there will be discussion and implications for research and practice.

Literature Review Strategic entrepreneurship is a research field in emergence that has grown out of two disciplines — the entrepreneurship discipline and the strategic management discipline (Ireland, Hitt, Camp, & Sexton, 2001). The research field is regarded as ‘a domain without a clear research paradigm’ (Ireland, 2007, p. 9). The approach is rather broad since it involves micro-level studies such as those about cognitive

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factors (Adner & Levinthal, 2008; Baron, 2007; van de Ven, Sapienza, & Villanueva, 2007) as well as macro-level studies like those about economic growth (Agarwal, Audretsch, & Sarkar, 2007; Casson & Wadeson, 2007). Schendel and Hitt (2007) express in the introduction of the Strategic Entrepreneurship Journal that the intersection of entrepreneurship and strategic management involves what they label the four i’s: imagination, ideas, invention and innovation, with a focus on the creation of wealth. Further, they focus on ten themes that are held to be involved in strategic entrepreneurship. One of the themes, namely ‘behavioural characteristics of entrepreneurial activity’ is the theme that this chapter is related to. This microlevel focus involves entrepreneurial actions with a focus on developing opportunities. Since the activities of developing opportunities are of a social character there is need for the entrepreneur to think strategically about the interactions. The entrepreneur needs to ‘draw on alternative forms of communication’ (Aldrich & Fiol, 1994, p. 652) and engage in activities that create credibility (Starr & MacMillan, 1990). Further, Hitt et al. (2001) state that entrepreneurs need to act strategically regarding communication. Traditionally strategic research has been occupied with handling strategic plans for product development or market development. A strategy has been seen as a roadmap of actions to fulfil organizational goals (Zimmerer, Scarborough, & Wilson, 2008). Further, a strategy has been seen as a property of the organization (Mintzberg, Ahlstrand, & Lampel, 1998). However, there has been a shift in focus in strategic research from a strategy being a property of the organization (Whittington, 2006) to being something that people do — a practice (Johnson, Langley, Melin, & Whittington, 2007; Johnson, Melin, & Whittington, 2003; Whittington, 2006). Communicative practices are defined as the communicative behaviour of individuals that is essential in every organization (Orlikowski & Yates, 1994). Hence, the practice is what the entrepreneurs actually do in their communication. In previous literature and research on entrepreneurs’ communication, different labels have been used to categorize the research. Some examples are the concept of communication strategies (Shane, 2003; Wickham, 2006) as well as communication mechanisms (Shane, 2003), persuasion and influence techniques (Bird & Jelinek, 1988; Dees & Starr, 1992), the management of communication (Wickham, 2006), information management (Carter & Jones-Evans, 2006), impression management (Baron, 2007) and improvising behaviour (Tornikoski & Newbert, 2007). In general, the research copes with how entrepreneurs should act to attract potential stakeholders, obtain legitimacy and acquire further resources for their business. It has been shown in previous research that what the entrepreneur does in the emergent organization is essential for the business’s financial success and further development Previous research about entrepreneurs’ communicative behaviour in the start-up process has followed different research streams. On one hand there has been a focus on the WHAT question with focus on what the entrepreneurs do in terms of improvising behaviour (Tornikoski & Newbert, 2007; Zott & Huy, 2007), and on the delivered message as in story-telling (Holt & Macpherson, 2010;

From Communicative Practices to Communication Strategies 251 Lounsbury & Glynn, 2001; O’Connor, 2002). We know from this research that entrepreneurs take part in different types of improvised behaviour, such as preparing a business plan, starting marketing and promotional efforts and opening a bank account (Tornikoski & Newbert, 2007). These activities are of a relatively instrumental character, as also Katz and Gartner (1988) found in their list of properties of emergent businesses. We also know that entrepreneurs sometimes try to draw people’s attention to something that goes beyond their explicit activity, as when talking about an MBA degree and hoping that this will create credibility (Ulvenblad, 2009; Zott & Huy, 2007). On the other hand there has been a focus on the HOW question with research focused on the conversation and how different social skills are related to business performance (Baron, 2007; Baron & Brush, 1999; Baron & Markman, 2000, 2003; Baron & Tang, 2009; Hoehn-Weiss et al., 2004). Further research has included communicative skills and leadership skills together with social skills (Ulvenblad, 2008). These skills are related to relational activities and are not as easy to tick off from a list — they are continuously ongoing. From this research we know that several social skills (social perception, social adaptability, expressiveness) have an impact on the financial success of the business (Baron & Tang, 2009). Further, the study by Baron and Tang (2009) indicates that entrepreneurs’ success in obtaining information and essential resources serves as a mediating variable when it comes to the relation between social skills and business financial performance. Moreover, Billstro¨m, Ulvenblad, Winborg, and Lindholm Dahlstrand (2011) show that entrepreneurs that failed to succeed in convincing a stakeholder of their business idea, and by this obtaining essential resources, failed because of (i) the planning, (ii) the format of the presentation and (iii) the lack of other-orientation which shows the importance of planning both for the WHAT and the HOW questions in a communication.

From Communicative Practices … In order to build a model based on communicative practices previous research has been separated in two parts, in view of the categorization of human communication by Littlejohn and Foss (2008). First, research with focus on the message, which involves the content in the communication, and second, research with focus on the conversation, which involves what takes place between the individuals in a meeting and how the entrepreneur behaves in the interaction. In the following text there are examples of communicative practices from previous research (Tables 1 and 2). The communicative practices in respective part are in turn categorized; (i) self, (ii) others and (iii) structure (Table 1). By the centre on self is meant messages that are directly related to the entrepreneur herself or himself. In the category others the message in the communication is rather focused on referring to other people. Finally, the category structure involves activities of impression management in the message that do not refer to a particular individual or individuals, but to a technical aspect or a location aspect, for instance.

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Table 1: Centre of the message in the communicative practice. Centre Self Others

Structure

What? Tell personal stories, tell founding stories, highlight education or MBA degree Tell conventional stories that involves important individuals, refer to other people, refer to individuals in networks, refer to an advisory board, take investors along to suppliers Tell strategy stories that refer to a famous location, technical aspects, fast customer service, industry awards, telephone switchboard, stationary telephone subscription

In the studies there have been examples of communication related to all three categories. The entrepreneurs have told founding stories (self) to make impressions of their businesses in order to acquire resources (O’Connor, 2002). It has also been important for the entrepreneur to refer to other individuals (others). This could be to make an impression of knowing someone important, to refer to an advisory board in the business to make an impression of competence. It is incorporated in the label of impression management (Baron, 2007) in the part that highlights other important individuals being known to the entrepreneur. It could also be a situational story (O’Connor, 2002) that encompasses other people’s activities in the business where the founder plays a minor role. Activities of impression management or improvising behaviour that do not refer to a particular individual or individuals, but to a technical aspect or a location aspect, are included in the third category (structure). An example of this is when the entrepreneur wants the business to appear as if it is an established business and therefore, for example, has a stationary telephone subscription or chooses a location that hopefully signals something else than a newly started business (Ulvenblad, 2009). Other examples of this could be more instrumental activities, or the improvising behaviour like preparing a business plan or opening a bank account that Tornikoski and Newbert (2007) showed in their study of nascent entrepreneurs. Another example could be the symbolic management that Zott and Huy (2007) presented in their study as highlighting fast customer service. The conversation part of the entrepreneurs’ communicative practices involves what takes place between the individuals in a meeting and how the entrepreneur behaves in the interaction in relation to oneself and to others. In the category of self the focus is on the entrepreneur, while the category others involves taking more notice of the counterpart in the conversation (Table 2). In previous research we find examples of the centre on self by focusing on one’s own acting in asking questions and actively listen to gather information as well as expressing emotions clearly. The centre on others is to behave in other-oriented ways in the communication. It is to be helpful, directing attention to others or taking care about another individual’s interest (Ulvenblad, 2008). This is related to

From Communicative Practices to Communication Strategies 253 Table 2: Centre of the conversation in the communicative practice. Centre Self Others

How? Express emotions clearly, ask questions and actively listen in order to gather information Perceive others accurately, adapt one’s actions to current social contexts, act caring in the communication, act adaptive in the communication

the dimension of social skill that Baron (2007) labels social perception — the ability to perceive others accurately. Further, it could involve being adaptive in the communication. This is also related to what Baron (2007) writes about as social adaptability. Both in the communicative behaviour related to the message and the conversation, the entrepreneurs act in ways intended to draw attention to their business or themselves as businessmen or businesswomen. Since an entrepreneur’s presentation of the business includes both information about the business and information on the entrepreneur’s ability to further develop and manage the business, this is especially important (Hoehn-Weiss et al., 2004). The entrepreneurs are working with the presentations of themselves and their businesses. They are presenting their intentions so as to make a good first impression, create credibility and gain legitimacy, as well as creating contact with different individuals who later can contribute to the resources, financial as well as human, in their businesses. The different types of communicative practices altogether form a strategy of communication that either could have been planned and intended before the entrepreneurs took the step to take part in a communication activity, or has emerged during the process. The strategy may even have been shown afterwards to be a pattern (compare with Mintzberg et al., 1998). The strategy-choice models in theories about human communication involve trying to get people to do the things that you want them to (Littlejohn & Foss, 2008). Further, Burgoon (1998) argues that when you meet someone and begin to communicate you may have an idea of what will happen and where you will go.

… to Communication Strategies The model proposed for the entrepreneurs’ communication strategies (Table 3) is based on the entrepreneurs’ communicative practices in previous research and categorized according to the categorization of communication (Littlejohn & Foss, 2008) as well as planned and emergent strategies (Mintzberg et al., 1998). The empirical examples of respective communication strategy are based on data collections from both 2007 and 2011. Entrepreneurs in newly started businesses were interviewed about how they managed to get stakeholders interested in their

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Table 3: Entrepreneurs’ communication strategies. What? Message Planned strategy Emergent strategy

How? Conversation

Content-centred communication strategy

Behaviour-centred communication strategy

Adaptive-centred communication strategy

ideas and new-started businesses. The model should be seen as a typology in terms of Weber’s ideal typologies since the entrepreneurs act in various ways according to the specific situation. The presentation of examples is therefore illustrative of communicative strategic behaviour and not of the entrepreneurs themselves. A content-centred communication strategy focuses on the what question and involves communicative practices such as stories centred on self, others or structure (Table 1). Examples of these are when the focus is especially on the entrepreneurs, their characteristics, background and/or their roles in the start-up of the business (self) or communicative practices aimed at referring to someone important who helps the business, for example, to look even more experienced (others). One example of the latter is when an entrepreneur puts an advisory board on the homepage at an early stage to make an impression of looking both bigger and more experienced. The focus has also been on making an impression of the business to look as attractive as possible, not referring to a particular individual or individuals but to a technical aspect or a location aspect (structure). A behaviour-centred communication strategy focuses on the how question and involves communicative practices centred on self or others (Table 2) such as acting in ways that make the counterpart want to have more of the business relationship and hopefully take part as a resource provider. Examples of this are to ask questions and actively listen to gather information (self) or to behave caringly in the communication (other). An adaptive-centred communication strategy is here related to an emergent communication strategy. An example of this is to actively listen for the need of the counterpart and adapt to the situation what type of information that is communicated as well as adapt the communicative behaviour. A frequent way of planning for the content and the what question is to plan the amount of pictures and the content in a power-point presentation. Two entrepreneurs especially were thinking about the words they used. We were thinking about the vocabulary and used words such as growth and sustainability. However, there are also other explicit planned strategies among entrepreneurs. An example of a planned strategy in terms of both what and how is captured from a

From Communicative Practices to Communication Strategies 255 scenario in a newly started business. These two entrepreneurs planned to make a presentation of their business as a show. They got advice from an experienced theatre coach who helped them with the manuscript as well as the setting, the light, how to stand, how to talk, etc. They worked a lot with the details in order to make as good an impression as possible. We went in and made a public appearance
a distinguished show. The emergent strategy could be seen as more subtle but is also explicit for some entrepreneurs in specific situations. An example of an emergent strategy is the communicative behaviour to especially listen for the needs of the counterpart in a communication and adapt both the content and the own behaviour in the situation. During a conversation it is a matter of being aware of the needs of the other party, says one entrepreneur. I focus on what they want to hear and then they will get that. The model should be seen as a typology, as previously was mentioned, since the entrepreneurs act in various ways according to the specific situation. This means that adaptive behaviour could also be planned.

Discussion and Implications A model of entrepreneurs’ communication strategies has been proposed based on entrepreneurs’ communicative practices in previous research. Since the presentation of a business gives information not only about the business idea but also about the entrepreneur’s ability to further develop the business, it is important to have a communication strategy focused on both the what and how questions in the emergence of the business. This is important both regarding the message, which involves the content in the communication but also regarding the conversation, which involves how the entrepreneur behaves in the interaction. Since the activities of developing opportunities are of a social character there is a need for the entrepreneur to think strategically about the interactions. The entrepreneur needs to ‘draw on alternative forms of communication’ (Aldrich & Fiol, 1994, p. 652) and engage in activities that create credibility (Starr & MacMillan, 1990). Human and social resources are important in order to obtain other resources such as financial (Brush et al., 2001) and the one resource the entrepreneur has for sure in the start-up is herself or himself. This makes it even more important to think strategically about this valuable resource. Entrepreneurs’ communication strategies are also seen as being an important part of the research field strategic entrepreneurship. Hitt et al. (2001, p. 480) state that strategic entrepreneurship is ‘entrepreneurial action with a strategic orientation’. The strategic orientation is here for the entrepreneur to make a good

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impression, to get credibility, legitimacy and further acquire resources, financial and human, to be able to develop her/his growth intentions and growth orientations. The research field of strategic entrepreneurship is regarded as ‘a domain without a clear research paradigm’ (Ireland, 2007, p. 9). However, the approach is rather broad since it involves both micro-level studies such as studies focused at social skills and macro-level studies such as studies focused at economic growth. The micro-level focus in this chapter is related to one of the ten themes that are held to be involved in strategic entrepreneurship, namely ‘behavioural characteristics of entrepreneurial activity’ — entrepreneurs’ communications strategies. Implications for entrepreneurship practice is for the entrepreneur to be reflective regarding one’s own communicative behaviour, because it will have an impact on the people he/she has interactions with. Not least, this will be important when the entrepreneur meets someone for the first time to present the business idea, the vision and/or the growth intention to make a first impression. It is important not only to repeat and be ready to tell the ‘elevator speech’ but also to be aware of small movements regarding communication, when it comes to both the content of the message and one’s actual behaviour in the conversation. Since the presentation of the business is also a presentation of the entrepreneur and the entrepreneur’s ability to start and further develop the business, this is especially crucial. Implications for future research are to continue the building of a framework about entrepreneurs’ communicative practices and communication strategies. There is a need for more empirical studies as well as studies highlighting communication strategies as an important part of strategic entrepreneurship, and continuing to integrate entrepreneurship with strategy in the communication arena. It is also important within the strategic perspective to give a place to interpersonal communication strategies. We seldom see road maps for the smaller roads. Within strategy, the focus has often been on the market and product development. We also need maps for more subtle features like communicative practices in interaction with others.

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Chapter 14

Social Media Espionage — A Strategic Grid Joni Salminen and William Y. Degbey

Abstract The changing communication environment transforms social media as a source for obtaining competitive intelligence on rival firms through “social espionage.” This conceptual chapter discusses how the competitive information in the Web, along with the trend of corporate transparency, has created both opportunity and risk for firms in social media. Among the opportunities, we discuss (1) tactical marketing campaigns, (2) encouraging switching behavior, (3) identifying and targeting competitors’ weak points, and (4) learning from their success and failure. On the other hand, we discuss how engaging in social media results in a loss of total control in the dialogue between a firm and its customers and, ultimately, leaves a firm vulnerable to the same opportunistic tactics it may leverage in order to draw benefit from a competitor’s social media presence. Finally, we provide some recommendations aimed at reacting to social espionage in the form of a strategic grid.

Introduction In Wikipedia (2011), the collective online encyclopedia, social media is defined as any form of online media that enables communication among individuals and organizations. Social media enables a dialogue between firms and customers through newsfeeds and social media profiles containing comments, discussions and other types of interaction. The foundation of social media can be traced back to the Web 2.0 paradigm focusing on user-generated content with technology platforms (O’Reilly, 2005). Theoretically, the notion relates to the concept of customer

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participation, a logic emphasizing the co-creation of value and innovations by firms and companies together (Kietzmann, Hermkens, McCarthy, & Silvestre, 2011). Consequently, social networks can be seen as marketing channels in which the variables of the traditional marketing mix can be applied (Cvijikj & Michahelles, 2013). Not only do they enable outbound marketing communications, they also provide insight into consumer thinking, trends and current topics that can be leveraged in marketing planning as a part of the inbound marketing function. Furthermore, social media allows firms to harvest information on their competitors as a part of competitive intelligence activities (He, Zha, & Li, 2013). A lot of attention has been paid to positive effects of social media. However, the social media do not only include positive aspects for firms. As one practitioner notes, “the soft and fuzzy side of social media dominates the spotlight.” Some of the most vexing issues include showing a positive ROI (To¨llinen & Karjaluoto, 2011), competing against consumer-to-consumer interactions for attention in newsfeeds (Goh, Heng, & Lin, 2013; Libai et al., 2010), and reducing organic visibility guided by platform owners wishing to maximize their revenue by charging companies for advertising (Stier-Moses, L’Ecuyer, Maille´, & Tuffin, 2014). According to Heath and Singh (2012), firms have lost the control of their message and its dispersion. Consumers communicate their brand experiences and firms have no power to prevent these discussions taking place in social media. Linke and Zerfass (2012) argue that the potential of social media is not fully exploited due to lack of proper governance structures, rules and internal resources. In particular, although social media offer many possibilities such as engaging with customers and building stronger relationships, there is a “dark side,” resulting from firms’ increased exposure to competitors. Until recently the relationship between customer and firm has remained private to a great extent. This communication has been in the form of letters, e-mails, or phone calls that cannot be accessed by competitors with legitimate methods (Garrett & Meyers, 2005). The proliferation of social media, however, has exposed a central part of the customer relationship to rival firms — the conversations between firm and its followers are public on the Internet. As a consequence, this chapter will discuss the relevance of a shift from the concept of “industrial espionage” to “social espionage.” This shift has not been the focus of earlier studies. There is a need for a study that combines a strategic approach to social media with understanding of competitive intelligence. The purpose of this chapter is to provide firms with a useful framework for approaching the issue of public relationship information. The research question presented in this chapter is: How should companies react to public information relating to customer relationships? The research question is answered by conceptual synthesis of strategic thinking, social media, and competitive intelligence. The main contribution is a strategic grid presenting four alternative strategies along the dimensions of “Spy
Not spy” and “Participate
Not participate.” The structure of the chapter is as follows. First, relevant literature on competitive intelligence is discussed. Second, social espionage is presented as a concept. Third, strategic grid is presented as a framework for reacting to threats and opportunities associated with social espionage. Finally, the discussion section includes general

Social Media Espionage — A Strategic Grid 263 discussion, theoretical and managerial contribution, suggestions for future research, and the main limitations.

What is Competitive Intelligence? The focus of this section of the work is on competitive intelligence (CI) which is seen to offer a useful perspective in approaching the issue of transparent relationships. In the literature, business intelligence (BI) is typically seen as the umbrella for all other related intelligence including CI (Lo¨nnqvist & Pirttima¨ki, 2006). The concept of CI is mainly used when referring to BI activities in North American literature emphasizing external environment and external information sources (see Cottrill, 1998; Vibert, 2004). Contrary to the North American literature, the European literature considers BI as a broad umbrella concept for CI and other intelligence-related terms (Lo¨nnqvist & Pirttima¨ki, 2006). Myriad definitions and perspectives have been offered by different scholars, hence, the lack of a single, universally accepted conception of the term (see Pirttima¨ki, 2007). The use of the term BI is not new, as Tyson (1986) already identified and stressed the continuous monitoring of customers, competitors, suppliers, and other fields in the 1980s. According to Tyson (1986), BI is made up of a variety of information: customer intelligence, competitor intelligence, market intelligence, technological intelligence, product intelligence, and environmental intelligence. At a general level, BI is defined as a managerial concept or a tool that is used to manage and enrich business information and to produce up-to-date knowledge and intelligence for operative and strategic decision-making (Ghoshal & Kim, 1986; Gilad & Gilad, 1985). Pirttima¨ki (2007) synthesized the different point of views on the concept of BI into information type, information elements, human-source intelligence, process, measurement, and technology. It is important to emphasize that several perspectives have been offered in defining the term BI, but the core focus of data and information analysis has remained similar (see Casado, 2004; Lo¨nnqvist & Pirttima¨ki, 2006). Scholars like Combs and Moorehead (1992) and Gilad (1996) define CI as an alternate term for BI, whereas others such as Weiss (2003) and Mintzberg (1994) see CI as an integral part of BI. The description of CI by Miller (2005) includes competitor and market information as well as information pertaining to a company itself in relation to opportunities and weaknesses. The commonality between Miller’s (2005) description of CI and BI is the shared perspective of internal information. Other scholars include information relating to competitive situation, competitors, markets, and strategy (see McGonagle & Vella, 1996). Some scholars argue that strategic information, such as market and industry information, about competitors’ plans is much more valuable than other type of competitor information (Bernhardt, 1994) whereas Mintzberg (1994) uses the term CI interchangeably with competitor intelligence. However, competitor intelligence is generally considered as a subactivity, because CI stretches beyond competitor information.

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Bl Cl C int omp ell et ige ito nc r e

Nature of information

internal

external narrow

Scope of information gathering

broad

Figure 1: The relationship between intelligence concepts. Figure 1 shows the relationship among BI, CI, and competitor intelligence, and the other intelligence-related concepts, for example, product intelligence, market intelligence, and technological intelligence (Pirttima¨ki, 2007). Other intelligence-related concepts are also sub-activities of BI and may well be a sub-activity of competitive intelligence when using the definitions provided by Combs and Moorehead (1992) and Gilad (1996).

Social Espionage — The Concept Four major trends have created an opportunity for social espionage. The first one is the rise of social media. Already in 1994, Cronin et al., predicted that Internet would become a major strategic tool for what they called “advanced organizations.” This prediction has come true, although not all organizations are advanced in terms of embracing new opportunities. In a study by Lackman, Saban, and Lanasa (2000), over 90% of CI specialists considered technology as a crucial success factor of a firm’s CI function. However, Web technology has been subject to commoditization and therefore lost a major part of its differentiating features. On the other hand, technology commoditization has offered new tools for competitive intelligence;1 to a degree, where the focus is on correct filters to overcome information overflow issues (rather than developing

1. For example, there are several affordable, easy-to-use Web 2.0 solutions for tracking social media which challenge traditional proprietary systems provided by corporations such as Oracle.

Social Media Espionage — A Strategic Grid 265 sophisticated solutions), converting the data into actionable guidelines, operationalizing for tactical purposes, and internalizing for strategic long-term decisionmaking.2 Therefore, the value of social espionage should be obtainable in both short-term actions and long-term planning. Second, there is a trend of transparency in corporate actions through increased pressure from various stakeholders, such as environmentalists, shareholders, and employees (Bushman, Piotroski, & Smith, 2004); but also because some new leadership paradigms emphasize openness and dialogue with subordinates instead of a “command-and-control” culture (Pellegrini & Scandura, 2008). Quoting a practitioner (Rice, 2010): “Getting relevant insight into your competitors’ strategy used to be very difficult, especially if you wanted to keep it legal. Most of the strategy took place in board rooms, behind closed doors, and were documented in physically routed memorandums.” In contrast, companies embracing corporate transparency are now leaving around indices of their strategy similar to breadcrumbs, even publishing strategy changes on the Internet prior to execution. For example, Nokia’s CEO Stephen Elop released a memo for public distribution that predicted and explained the coming strategic shift of the company. Third, the CI function has traditionally focused on obtaining information on strategic capabilities, intellectual properties, product formulations, technological processes, business plans, and potential competitive threats (Fitzpatrick, 2003). However, less attention has been paid to acquiring information on the relationship between the competitor and its customers, although relationships have long been one of the dominant paradigms in the field of marketing (see e.g., Ravald & Gro¨nroos, 1996). Therefore, it makes sense to examine competitive intelligence as a function of customer relationship. In this type of environment, protecting trade secrets becomes replaced by protecting customer relationships.3 This trend has been reinforced by the economic shift from manufacturing industries to service industries (see e.g., Mills, 1986). Figure 2 depicts the trends promoting social espionage. Social espionage can be defined as an attempt to gain competitive advantage by acquisition and application of competitive intelligence through all publicly and semi-publicly available information in the social media. It must however be stressed here that social espionage does not signal stealing information because this information is publicly (or semi-publicly) available in the social media. However, particular characteristics are (1) rivals would prefer not to have all information available4 and (2) some information is semi-public, so that the firm needs to take some action to access it.5 These are efficient means when the competitor has no means to exclude

2. “… these public sources of information may not be aggregated or categorized in a manner consistent with client analytical needs, and the data may not be updated at regular and timely intervals” (Fitzpatrick, 2003). 3. We are talking about a conceptual shift in thinking; of course, trade secrets should be guarded also. 4. For example, customers complaining about a product failure or broken promise. 5. For example, following in Twitter or liking the competitor’s Facebook page to receive status updates, or befriending its employees in LinkedIn to get insight about recruiting and changes in organizational hierarchy.

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From manufacturing to service industries From privacy to corporate transparency

Social Espionage

From trade secrets to customer relationships

From closed dialogue to social media

Figure 2: Trends promoting shift to social espionage.

the firm, for example, by stopping, preventing, or removing the firm’s access to these social data streams — which is a typical case for the semi-public data sources.6 The challenge of semi-private networks is that, for example, Facebook conversations remain private unless an employee befriends a customer which is practically difficult when the number of customers is high and impossible if the firm does not know its customers by their name or social alias. It is commonly acknowledged that the value of information for an organization relates to the cycle of acquiring, analyzing, and acting upon it. Consequently, information on competitors on the Internet can be divided into controllable information released by the firm and uncontrollable information which is visible online whether the company wants or not.7 This information can be collected either directly by making search queries, creating alerts and filters and engaging with competitors and customer communities (e.g., following in Twitter, subscribing to a forum, liking the competitor’s Facebook page), or indirectly through various social media aggregation services. Social espionage emerges from the three meeting points of firms (suppliers, competitors, customers) as actors, the social media (technological platform) as the resource, and competitive intelligence as the activity linking the actors and resources together in a harmonious fashion (see Easton, 1998; Ha˚kansson & Ford, 2002; Ha˚kansson & Snehota, 1989). Accordingly, the broad conceptual framework of social espionage is derived from the aforementioned three roots, thus, competitive intelligence, firms (suppliers, competitors, customers), and social media. Figure 3 shows the framework with its constituents. Overall, social espionage indicates not stealing information but rather engaging in purely legal maneuvers to obtain information on competitors; thus, in accordance

6. A good heuristic rule for identifying this type of information is that it is not indexed by search engines — yet, it can be accessed if one knows where to look. 7. For example mentions in discussion forums, blogs, and other social media that customers use.

Social Media Espionage — A Strategic Grid 267

Firm’s customers, competitors & suppliers As actors

Competitive intelligence As an activity

Social media (technological platform) As a resource

SOCIAL ESPIONAGE

Figure 3: Social espionage framework. with the legality of competitive intelligence8 and in contrast to corporate espionage which is “obtaining information through stealing, without authorization, takes, carries or controls, or by fraud, artifice or deception.” Why we term the concept as “social espionage” and not intelligence is because it involves tapping in to semiprivate discussions between a competitor and its customers. Considering ethics, we follow the strategic view of Pech and Stamboulidis (2010, p. 37): “In business, the term ‘deception’ is often frowned on, but within a strategic context, strategies of deception can provide a legitimate and clever means for achieving competitive advantage.” This is not to promote unethical conduct but to recognize that in a strategic, competitive situation certain “constructive deception” as a means of defending and growing a firm’s businesses is sometimes needed, without malicious intent (Pech & Stamboulidis, 2010). It is generally acknowledged that firms engage in offensive and defensive marketing tactics (Erickson, 1993), and marketing is sometimes seen as a “warfare.” It is, then, only realistic for firms to prepare for opportunistic decision-making and react accordingly. The goal of social espionage, to gain competitive advantage, can be reached with interplay of different operational levels of the firm. The tactical level is concerned with actionable signals whereas strategic level focuses on detecting weak signals that are a part of a bigger change in the competitive landscape. The operational level is somewhere in between, focusing on improving operational efficiency and products. Revealed data on competitor’s actions opens many possibilities for opportunistic behavior, as presented in Table 1. For example, if a competitor suffers from problems, a possible reaction would be to simultaneously launch an opportunistic marketing campaign. Therefore, such

8. Competitive intelligence is “legal research efforts by business studying their competitor’s products, organizations and related matters” (Cronin et al., 1994).

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Table 1: Social espionage activities at three levels. Strategic Activity Analyze macro-competitive data Identify competitor’s weak points Focus on unfilled niche Drop prices Play benchmark Play war-game Tactical Activity Intercept messages Identify competitor’s weak points Launch rapid promotions Use direct selling Participate in industry discussion Identify unsatisfied customers Operational Activity Analyze customer’s communicative styles Analyze customer complaints

Rationale To detect patterns To detect deviations To improve own positioning To avoid direct competition To crush competition To improve own social media strategy To predict competitor’s next moves Rationale To understand competitor’s relationship to its customers To create offensive tactics To encourage switching behavior To encourage switching behavior To generate leads To generate leads Rationale To build customer profiles To encourage switching behavior To improve own products To avoid “easy” mistakes

process would aim to (1) detect competitor’s problems, (2) respond rapidly by offering alternatives, (3) win new customers. It is critical that the common pitfall of “increasing next quarter’s marketing budget” or similar delaying action is avoided — due to the lag of implementing this type of measure, the window of opportunity is easily lost as customers take adaptive behavior. In conservative decision-making there is a common bullwhip effect that hinders large corporations’ ability to leverage real-time information efficiently — the loop from awareness to action is lengthy. A possible solution involves removing the firm’s CI unit and instead empowering operational units to take direct action based on their proprietary judgment. This means that there is no CI unit to buffer the decisions but the CI function becomes everybody’s business within the organization — ideally, this would lead to efficient condition-driven decision-making.

Strategic Grid Because participating in social media brings both competitive advantages and risks, a firm may decide whether to participate at all. However, it may become a topic of

Social Media Espionage — A Strategic Grid 269 Table 2: Strategic grid of social espionage.

Spy Not spy

Engage

Not Engage

Full pot “Sucker’s payoff”

“Machiavellian payoff” Empty pot

discussion regardless of non-participation. Even though high engagement in social media involves positive effects on customer relationships, at the same time it leaves a firm more vulnerable to opportunistic marketing tactics by competitors. Table 2 represents some strategic approaches to this dilemma. The axes describe choices of whether or not to take part in social media communication or not (Engage
Not engage), and whether to monitor competitors or not (Spy
Not spy). In the first choice, a firm decides to both engage and monitor, resulting in full benefits and full risk. That is, the firm will achieve relational advantage toward its customers by being active in social media, and also social espionage advantages as discussed earlier. However, by engaging in social media the firm will place itself in a vulnerable position regarding opportunistic tactics, in other words it will have to take part in a game of social espionage against its competitors. The second alternative is to stay clear of customer engagement but engage in monitoring competitors. This is an opportunistic strategy; seeking benefits of intelligence while hiding own actions. The ideal result is the “Machiavellian payoff,” in which the firm applies advanced intelligence to drive opportunistic tactics against its unsuspecting competitors. The downside is that some benefits of social media will not be claimed; however, this can be compensated by direct communication especially if there is little negative information on the firm. Further, if the firm engages in social media but decides to ignore social espionage, it may end up in a victim’s position if there are other players who are playing, that is, reading its actions in the competitive space. Therefore, the firm gains relationship advantage toward customers but risks a “sucker’s payoff” in regards to competitive advantage as it is unaware of the competitive landscape, including the strategies and weak points of others. Finally, “Empty pot” describes a passive strategy in which the firm neither monitors competition nor engages in social media activity. This leads to a loss of competitive intelligence and the opportunity to nurture customer relationships. For a company operating in a stable competitive landscape, without customers who would value communication through social media this is the optimal solution. For a firm operating in the middle of dynamic competition, with a focus on customer relationships, this is the worst option. All of the aforementioned tactics can be applied in reverse to the firm engaging in social media; therefore, it is relevant to also study how firms should react to them. There are various responsive behaviors a firm can adopt to reduce the effects of social espionage. First, in selecting a responsive action to negative information, the firm should take priority to minimize damage. A firm can choose an offensive or a defensive strategy. Second, a firm should protect its strategic initiatives (past,

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Table 3: Responsive behaviors to social espionage. Responsive Action (Defensive) Assess the situation Limit information going out Limit internal access to information Increase communication Decrease communication (cloaking) Withdraw from social media Give false signals Correct the cause for complaints Offer compensation Create communication guidelines “Looking into the mirror” Responsive Action (Offensive) Drive public conversation to private Question the content Mimic

Rationale To understand communicative position To reduce risk To reduce risk To address customer concerns To avoid confrontation To avoid negative spotlight To misguide opportunistic competition To remove root cause To discourage switching behavior To guarantee appropriate communication To monitor own presence Rationale To prevent competitor’s access To reveal misinformation To counter-attack using competitor’s tactics

present, and future) from opportunistic competitors. Overall, a firm can resort to, for example, following responsive behaviors (Table 3). Clearly, there are two alternative approaches — either limiting the available information in the public domain or dealing with it. Notice, however, that actions that increase control are not effective against uncontrollable information. In these cases quick response marketing, including correcting mistakes and addressing concerns, may be more appropriate. As the social media reinforces the effect of wordof-mouth marketing through reach and speed (Leskovec, Adamic, & Huberman, 2007), it is a key concern for companies to be able to address negative messages before permanent damage is done to their image. Empirical cases demonstrate the risk of escalating brand damage in social media (Kietzmann et al., 2011; Klein & Dawar, 2004).

Discussion Theoretical Contributions The conceptual work in this study complements earlier research, in which it is acknowledged that social media bring forth both threats and opportunities. For example, Berthon, Pitt, Plangger, and Shapiro (2012, p. 261) note that “21st century managers need to consider the many opportunities and threats that Web 2.0, social media, and creative consumers present and the resulting respective shifts in loci of

Social Media Espionage — A Strategic Grid 271 activity, power, and value.” The strategic use of social media goes beyond the naı¨ ve hype, and requires an objective view, such as one given in this chapter. Earlier frameworks relating to management of social media have focused on various topics. To¨llinen and Karjaluoto (2011) outlined quantitative, qualitative, and financial metrics for measuring social media performance. Berthon et al. (2012) proposed five axioms for understanding social media. Our study is novel in proposing a connection between competitive intelligence and social media. Although the synergy between these two fields seems obvious, earlier studies have mostly overlooked it. Linke and Zerfass (2012) conducted a Delphi panel study according to which social media guidelines and structural aspects are likely to increase in the foreseeable future; they also concluded that common strategies are rarer than specific approaches by firms. Yet, at a general level, strategic approaches can be formulated, as shown in the strategic grid approach. It is applicable by companies of all size and type — the only requisite is a presence in social media which in itself is a strategic decision. Our study rationalizes that the advantage of small firms in applying the grid is their agility, not hindered by complex policy requirements and organizational hierarchy, and leading to faster response and a context-aware tone of voice. In turn, large organizations have resources to engage in multiple channels at the same time, while investing in customer service and systematic tracking of results.

Managerial Implications For managers, it is important to understand the potential and limitations of social media technology. For example, the use of monitoring and analysis tools should be known to understand how they can create business value. Along the lines of Berthon et al. (2012), we suggest training of employees and limiting bureaucracy as means of leveraging social media. An organization choosing a participatory strategy must stay constantly aware of the latest changes in social media in order to stay relevant. There are some benefits of being a small unit in regards to social media activities. First, the customer base is more likely to be less fragmented by geography, fewer in numbers, and may involve a personal relationship between business owner and customers, which is less common in large corporations. In addition, an agile organization may contribute to a faster decision-making which is required to leverage the tactical benefits of social espionage. Consequently, firms should consider adapting a bottom-up approach to management to achieve immediate tactical benefits, while keeping a backdoor open for long-term strategic benefits. The more the firm places emphasis on customer service, the more critical social espionage is. The disadvantages of large organizations to apply the strategic grid do not only relate to timeliness, but also cooperation between departments and organizational functions. This is a consequence of requiring effort from multiple organizational levels (i.e., strategic, tactical, and operational). Smaller units tend to have less hierarchy and more effective communication processes, due to, for example, physical

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proximity. A potential strategy for large firms, then, is to mimic small organizations through lean management structures. Limitations As a conceptual chapter, our study has limitations. Most importantly, we are unable to show how well firms applying different strategies perform. At this point, they remain theoretical. Further research can show the application of the strategic grid in business cases. Comparing the performance results and strengths and weaknesses of each strategy in empirical settings can shed new light on strategic management of social media efforts. Moreover, studies need to discover the best practices and potential barriers to enable pervasive social media efforts in terms of strategic, tactical, and operational levels, especially since the concept of social espionage highlights tactical readiness of business units.

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