Vanderstraeten 2013 ph d - studies on the strategy and performance of business incubators

STUDIES ON THE STRATEGY AND

PERFORMANCE OF BUSINESS INCUBATORS

Studies on the Strategy and Performance of Business Incubators

Johanna Vanderstraeten, 2013

ISBN: 978-9089-940-78-0

Printed by: Universitas Antwerp

University of Antwerp

Faculty of Applied Economics

Department of Management

Belgium

With the financial support from

Provinciale Ontwikkelingsmaatschappij (POM) Antwerp – Research project 2009

Intercollegiate Center for Management Science (I.C.M./C.I.M.) – Doctoral Fellowship

Johanna Vanderstraeten, October 2009 until September 2011

Antwerp Management School (AMS) – Applied Management Research Grant 2011

Vereniging van Educatieve en Wetenschappelijke Auteurs (VEWA) – Academic Fellowship

Fund 2012

Faculty of Applied Economics

STUDIES ON

THE STRATEGY AND PERFORMANCE

OF BUSINESS INCUBATORS

Johanna Vanderstraeten

Proefschrift voorgedragen tot het behalen van de graad van

doctor in de Toegepaste Economische Wetenschappen

11 september 2013

Supervisors:

Prof. dr. Paul Matthyssens

Prof. dr. Arjen van Witteloostuijn

DOCTORAL JURY

Prof. dr. Paul Matthyssens (supervisor)

University of Antwerp and Antwerp Management School, Belgium

Prof. dr. Arjen van Witteloostuijn (supervisor)

Tilburg University, the Netherlands and University of Antwerp, Belgium

Prof. dr. Koen Vandenbempt (chair)


Prof. dr. Tales Andreassi

Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo (FGV-

EAESP), São Paulo, Brazil

Prof. dr. Marcus Dejardin

University of Namur, Belgium

Prof. dr. Eddy Laveren


Prof. dr. Roy Thurik

Erasmus University Rotterdam and Free University Amsterdam, the Netherlands

i

TABLE OF CONTENTS

Acknowledgements .................................................................................................................... vii Introduction ................................................................................................................................. 1

Overarching research goals and data gathering ................................................................................ 3 Theoretical anchors ............................................................................................................................ 5 Appendix A: Fact sheet Brazilian incubators ................................................................................... 10 Appendix B: Fact sheet European incubators (Belgium-Flanders; the Netherlands, United Kingdom, Ireland) ............................................................................................................................. 15 Appendix C: Fact sheet Brazilian and European sample incubators ............................................... 16 Bibliography ...................................................................................................................................... 17

Chapter 1: Service-based differentiation strategies for business incubators: Exploring external and internal alignment .................................................................................................................... 21

Abstract ............................................................................................................................................. 21 Highlights .......................................................................................................................................... 21 Keywords .......................................................................................................................................... 21 1.1. Introduction ......................................................................................................................... 23 1.2. Theoretical background ...................................................................................................... 25

1.2.1. Strategic positioning theory ........................................................................................... 25 1.2.2. Strategic fit: internal and external alignment ............................................................... 27

1.3. Methodology ....................................................................................................................... 29 1.3.1. Population ....................................................................................................................... 29 1.3.2. Research design, data gathering process, and study quality ........................................ 31

1.3.2.1. In-depth interviews ................................................................................................ 32 1.3.2.2. Focus groups ........................................................................................................... 33

1.4. Empirical results .................................................................................................................. 34 1.4.1. Customer value creation leading to incubator differentiation ..................................... 34 1.4.2. The identification of necessary competence configurations ........................................ 41

1.5. Discussion ............................................................................................................................ 45 1.5.1. Customer value creation leading to incubator differentiation ..................................... 45 1.5.2. Necessary competence configurations .......................................................................... 47

1.6. Conclusion ........................................................................................................................... 49 1.6.1. Contribution to the literature ........................................................................................ 50 1.6.2. Implications for practice and policy ............................................................................... 51 1.6.3. Limitations and directions for future research .............................................................. 52

Appendix A: Characteristics of sample incubators .......................................................................... 54 Appendix B: Characteristics of sample incubator tenants .............................................................. 55 Bibliography ...................................................................................................................................... 56

Chapter 2: Toward a balanced framework for business incubator evaluation and improvement . 63

Abstract ............................................................................................................................................. 63 Highlights .......................................................................................................................................... 63 Keywords .......................................................................................................................................... 63 2.1. Introduction ......................................................................................................................... 65 2.2. Theoretical background ...................................................................................................... 67

2.2.1. Individual incubator evaluation measures .................................................................... 67 2.2.2. Integrated incubator evaluation systems ...................................................................... 69 2.2.3. The balanced scorecard and strategy map as incubator evaluation tools ................... 72

ii

2.3. Methodology ....................................................................................................................... 73 2.3.1. Population ....................................................................................................................... 74 2.3.2. Research design, data gathering, and study quality ...................................................... 75

2.4. Empirical results .................................................................................................................. 77 2.4.1. Financial sustainability ................................................................................................... 77 2.4.2. How to attain financial sustainability: long-term strategic goals and alignment ........ 79

2.4.2.1. Structurally stable and diverse tenant portfolio ................................................... 79 2.4.2.2. Value creation effectiveness.................................................................................. 80 2.4.2.3. Efficient functioning ............................................................................................... 82 2.4.2.4. Entrepreneurship and business development ...................................................... 82

2.4.3. How to measure internal and external alignment ........................................................ 84 2.4.3.1. External alignment ................................................................................................. 84 2.4.3.2. Internal alignment .................................................................................................. 85

2.5. Discussion ............................................................................................................................ 88 2.6. Conclusion ........................................................................................................................... 91

2.6.1. Contribution to the literature ........................................................................................ 91 2.6.2. Implications for practice and policy ............................................................................... 92 2.6.3. Limitations and directions for future research .............................................................. 93

Bibliography ...................................................................................................................................... 94 Chapter 3: Incubator strategy, institutional context, and incubator performance: A moderated mediation analysis of Brazilian incubators ............................................................................... 101

Abstract ........................................................................................................................................... 101 Highlights ........................................................................................................................................ 101 Keywords ........................................................................................................................................ 101 3.1. Introduction ....................................................................................................................... 103 3.2. Theoretical background and hypotheses ......................................................................... 105

3.2.1. Strategic positioning and service logic ......................................................................... 106 3.2.2. Institutional environment ............................................................................................ 109

3.3. Methodology ..................................................................................................................... 112 3.3.1. Target population ......................................................................................................... 112 3.3.2. Data gathering and sample representativeness .......................................................... 112 3.3.3. Questionnaire ............................................................................................................... 114

3.3.3.1. Incubator performance ........................................................................................ 115 3.3.3.2. Entrepreneurial institutional context .................................................................. 116 3.3.3.3. Service customization and focus strategy ........................................................... 117 3.3.3.4. Control variables .................................................................................................. 118

3.3.4. Brazilian context ........................................................................................................... 120 3.3.5. Regression analysis ....................................................................................................... 120

3.4. Empirical results ................................................................................................................ 122 3.5. Discussion and conclusion ................................................................................................ 132

3.5.1. Implications for practice and policy ............................................................................. 134 3.5.2. Limitations and directions for future research ............................................................ 135

Appendix A: Sample representativeness incubator sample ......................................................... 137 Appendix B: Sample representativeness entrepreneurship expert sample................................. 140 Appendix C: Factor analyses .......................................................................................................... 141 Appendix D: Measurement scales ................................................................................................. 143 Appendix E: Plots assumption checks Model 8 ............................................................................. 144 Appendix F: Robustness checks – model without outliers ........................................................... 146 Appendix G: Non-significant interaction plot ................................................................................ 147 Bibliography .................................................................................................................................... 148

iii

Chapter 4: Service co-creation intensity in European business incubators: The impact of the incubator’s human capital and institutional entrepreneurial environment ................................ 157

Abstract ........................................................................................................................................... 157 Highlights ........................................................................................................................................ 157 Keywords ........................................................................................................................................ 157 4.1. Introduction ....................................................................................................................... 159 4.2. Theoretical background and hypotheses ......................................................................... 162

4.2.1. Human capital and service co-creation intensity ........................................................ 162 4.2.2. Institutional entrepreneurial environment and service co-creation intensity ........... 163 4.2.3. Human capital, institutional entrepreneurial environment and service co-creation intensity ...................................................................................................................................... 165

4.3. Methodology ..................................................................................................................... 167 4.3.1. Target population ......................................................................................................... 167 4.3.2. Data gathering and sample description ....................................................................... 168 4.3.3. Questionnaire ............................................................................................................... 169

4.3.3.1. Human capital and service co-creation ............................................................... 170 4.3.3.2. Entrepreneurial institutional context .................................................................. 171 4.3.3.3. Control variables .................................................................................................. 171

4.3.4. European context .......................................................................................................... 172 4.3.5. Regression analysis ....................................................................................................... 174

4.4. Empirical results ................................................................................................................ 176 4.5. Discussion and conclusion ................................................................................................ 181

4.5.1. Implications for practice and policy ............................................................................. 183 4.5.2. Limitations and directions for future research ............................................................ 184

Appendix A: Sample representativeness and sample descriptives .............................................. 186 Appendix B: Measurement scales .................................................................................................. 188 Appendix C: Factor analyses .......................................................................................................... 189 Appendix D: Graphs assumption checks ........................................................................................ 191 Appendix E: Robustness check – model without outliers ............................................................. 193 Appendix F: Non-significant interaction effects ............................................................................ 194 Bibliography .................................................................................................................................... 195

Conclusion ................................................................................................................................ 201

Overall contribution and link between the different chapters .................................................... 201 Implications for practice and policy ............................................................................................... 208 Overall limitations and directions for future research.................................................................. 210 Appendix A: Regulative institutional context as moderator ........................................................ 213

Samenvatting (Nederlands)....................................................................................................... 217

Lacunes in bestaand onderzoek, bijdrage van het proefschrift en link tussen de verschillende hoofdstukken .................................................................................................................................. 217

iv

LIST OF TABLES Chapter 1 Table 1-1: Tenant expectations of service offerings (no sector/technology focus) ............................. 38 Table 1-2: Tenant expectations regarding service offerings (sector/technology focus) ...................... 39 Table 1-3: Competence configuration: generalists ............................................................................... 43 Table 1-4: Competence configuration: specialists ................................................................................ 44 Table 1-5: Characteristics of sample incubatorsa .................................................................................. 54 Table 1-6: Characteristics of sample incubator tenants ....................................................................... 55 Chapter 2 Table 2-1: Output prerequisites for integrated evaluation systems ..................................................... 70 Chapter 3 Table 3-1: Means, standard deviations, maximum, minimum and bivariate correlations ................. 121 Table 3-2: Hierarchical linear regression and path model coefficients............................................... 127 Table 3-3: Hierarchical linear regression for simple mediation, and path model coefficients ........... 130 Table 3-4: Bootstrap quantiles for the conditional indirect effect of the moderated mediation ...... 131 Table 3-5: Sample representativeness: description of incubator samples ......................................... 137 Table 3-6: Sample representativeness: number of tenants and year of operation ............................ 138 Table 3-7: Sample representativeness: description entrepreneurship expert sample ...................... 140 Table 3-8: Component matrix principal component analysis: incubator performance ...................... 141 Table 3-9: Rotated component matrix (VARIMAX rotation) principal component analysis: institutional context ................................................................................................................................................ 141 Table 3-10: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy ............................................................................................................................................................. 142 Table 3-11: Measurement scales for independent variables .............................................................. 143 Table 3-12: Robustness check: hierarchical linear regression for full model without outliers ........... 146 Chapter 4 Table 4-1: Means, standard deviations, maximum, minimum and bivariate correlations ................. 175 Table 4-2: Hierarchical linear regression ............................................................................................. 179 Table 4-3: Sample representativeness: description incubator sample ............................................... 186 Table 4-4: Sample descriptives: number of tenants ........................................................................... 186 Table 4-5: Sample descriptives: year of operations, average occupancy rate and inside space ........ 187 Table 4-6: Measurement scales for variables ..................................................................................... 188 Table 4-7: Rotated component matrix (VARIMAX rotation) principal component analysis: institutional context ................................................................................................................................................ 189 Table 4-8: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy 189 Table 4-9: Rotated component matrix (VARIMAX rotation) principal component analysis: human capital .................................................................................................................................................. 190 Table 4-10: Robustness checks: hierarchical linear regression for full model without outliers ......... 193

v

LIST OF FIGURES Introduction Figure I-1: Overview data gathering ........................................................................................................ 5 Figure I-2: Integrative overview of the doctoral thesis ........................................................................... 9 Figure I-3: Map of Brazilian states ......................................................................................................... 10 Figure I-4: Number of inhabitants/incubator in the North region of Brazil .......................................... 11 Figure I-5: Number of inhabitants/incubator in the Northeast region of Brazil ................................... 11 Figure I-6: Number of inhabitants/incubator in the Central-West region of Brazil .............................. 12 Figure I-7: Number of inhabitants/incubator in the Southeast region of Brazil ................................... 12 Figure I-8: Number of inhabitants/incubator in the South region of Brazil .......................................... 13 Figure I-9: Number of inhabitants/incubator in Brazil (all states) ........................................................ 14 Figure I-10: Number of inhabitants/incubator in Belgium (Flanders), the Netherlands, United Kingdom and Ireland (contact database) .............................................................................................. 15 Figure I-11: Year incubator started its operations (mean, max, min) ................................................... 16 Figure I-12: Occupancy rate incubators (mean, max, min) a ................................................................. 16 Chapter 1 Figure 1-1: Service-based differentiation strategies ............................................................................. 40 Chapter 2 Figure 2-1: SMEDI: strategy map for nonprofit economic development incubators ............................ 83 Figure 2-2: BSEDI and targets: balanced scorecard for nonprofit economic development incubators 87 Chapter 3 Figure 3-1: Conceptual second stage moderation model ................................................................... 111 Figure 3-2: The conceptual models in Figure 3-1 represented in the form of a path model ............. 123 Figure 3-3: Johnson-Neyman region of significance for the conditional effect of service customization strategy given regulative dimension ................................................................................................... 128 Figure 3-4: Interaction service customization strategy and regulative dimension ............................. 128 Figure 3-5: Johnson-Neyman region of significance for the conditional effect of service customization strategy given cognitive dimension..................................................................................................... 129 Figure 3-6: Interaction service customization strategy and cognitive dimension .............................. 129 Figure 3-7: Sample representativeness: number of tenants .............................................................. 138 Figure 3-8: Sample representativeness: year of operations ............................................................... 138 Figure 3-9: Sample representativeness: number of incubator per statea ........................................... 139 Figure 3-10: Homoscedasticity and linearity ....................................................................................... 144 Figure 3-11: Normality ........................................................................................................................ 145 Figure 3-12: Interaction service customization strategy and normative dimension .......................... 147 Chapter 4 Figure 4-1: Conceptual moderation model ......................................................................................... 167 Figure 4-2: Johnson-Neyman region of significance for the conditional effect of regulative dimension given human capital ............................................................................................................................ 180 Figure 4-3: Interaction regulative dimension and human capital ....................................................... 180 Figure 4-4: Homoscedasticity and linearity ......................................................................................... 191 Figure 4-5: Normally distributed errors: normal P-P plot ................................................................... 191 Figure 4-6: Normally distributed errors: histogram ............................................................................ 192 Figure 4-7: Interaction human capital and cognitive dimension ........................................................ 194 Figure 4-8: Interaction human capital and normative dimension ...................................................... 194

vi

Conclusion Figure C-1: Research questions, main contributions and link between the qualitative and quantitative research ............................................................................................................................................... 206 Figure C-2: Johnson-Neyman region of significance for the conditional effect of human capital given regulative dimension ........................................................................................................................... 213 Figure C-3: Interaction human capital and regulative dimension ....................................................... 213

vii

Acknowledgements

The creation and completion of this doctoral thesis would not have been possible

without the help, suggestions and willingness to cooperate of a number of people and

organizations.

There are a number of people from university life that deserve a special “thank you”. I

would like to thank my two supervisors, Paul Matthyssens and Arjen van Witteloostuijn. Paul

made me enthusiastic about academic research; he showed me the path toward the

business incubator topic. His many research ideas and creative insights often helped me at

moments when I doubted about the direction this doctoral thesis should take. His reading of

the different chapters substantially improved their structure.

I would also like to thank Arjen, for his never-ending belief in me as a doctoral

researcher. Arjen supervises his Ph.D. students as a true teacher and lives under the motto

“stupid questions do not exist”. I want to thank him for always making time for me.

Whenever needed, we had weekly meetings, which were always very efficient and effective.

I also highly value his experience in model development and testing.

Of course, I also want to thank the members of my doctoral jury. Thanks to the insightful

comments of Koen Vandenbempt, Marcus Dejardin, Eddy Laveren, Roy Thurik and Tales

Andreassi, I was able to further improve the different chapters.

There are also a number of people who were involved in my day-to-day life as a doctoral

student at the department of Management. First of all, I would like to thank Kim and Eline S.,

my two roommates. Kim showed me the way in the “doctoral student world” and made me

feel at home in our department. Eline S., my current roommate, patiently listened to my

talks about the ups and downs of doing a Ph.D. She is one of the most attentive persons I

know; I will never forget that she gave me a little flower to encourage me during the last

weeks before handing in the doctoral thesis.

Of course, not only my roommates gave an extra dimension to my life as a Ph.D.

researcher. In particular, I would like to thank all members from the department of

Management for the nice moments we spent together. I hope many more will follow!

Wouter, thank you for our talks about our research and university life. Tine, Nathalie, Sandy,

viii

Eline V.P., Alain, Bruno, Anja and many others, thank you for all the nice lunches we spent

together. I really enjoyed getting a breath of fresh air with you. Bruno, Sofie R. and Dendi,

you deserve a special “thank you” for your help with statistical problems. I also really

enjoyed all moments with the ACED members, such as the ACED workshop. Without Anne, I

would probably never have started at the University of Antwerp. She told me about a job

opening at the department. Also a special “thank you” to Tanya and Caroline, for their well-

organized administrative and operational support.

Next to the University of Antwerp, that supported me throughout the whole doctoral

research route, the following organizations and affiliated people deserve an explicit “thank

you” for – among others – their financial support. In 2009, the Provinciale

Ontwikkelingsmaatschappij (POM) Antwerp financially supported part of our research about

nine business incubators in the province of Antwerp, Belgium. Jade Verrept and Luc Broos

brought us into contact with incubator managers and experts. Without their support, the

qualitative study that forms the basis for Chapters 1 and 2 would not have been possible. I

also want to thank Filip for his help during the data gathering process and all tenant,

incubator manager and expert interviewees that participated in our research.

The Intercollegiate Center for Management Science (I.C.M./C.I.M.) granted me a doctoral

fellowship from October 2009 until September 2011. This allowed me to go to Brazil for a

research stay at Fundação Getulio Vargas. Françoise Charlez and Dirk Symoens helped me

with all operational and administrative aspects during this research stay.

From January 2010 until August 2011, Fundação Getulio Vargas – Escola de

Administração de Empresas de São Paulo (FGV-EAESP) and specifically the Entrepreneurship

and Small Business Center (Centro de Empreendedorismo e Novos Negócios - CENN) provided

me support during my research stay in Brazil. Tales, Laura, Claudia, Marcelo, René, Daniel,

Chris, Yara, Raffaella, Eliane, Malu, Stela and many others made me feel welcome when I

arrived in Brazil. Claudia and Laura, thank you for all our nice lunches, I hope we will stay in

touch!

Next to Fundação Getulio Vargas, there are a number of other organizations that

facilitated the data gathering process in Brazil. For example, SEBRAE (Serviço Brasileiro de

Apoio às Micro e Pequenas Empresas) and specifically Renato Fonseca and Maria de Lourdes

da Silva brought me into contact with Brazilian incubators and other entrepreneurship

ix

organizations. ANPROTEC (Associação Nacional de Entidades Promotoras de

Empreendimentos Inovadores) organized a workshop and conference that brought me into

contact with many incubators and experts. This was crucial for the start of the Brazilian data

gathering process. I also want to thank all Brazilian incubator managers, employees and

experts that filled out the questionnaire. I know that you probably get many requests for

filling out questionnaires, so thank you for your time. Your input forms the basis of Chapter

3. Also the Vereniging van Educatieve en Wetenschappelijke Auteurs (VEWA) deserves a

specific mention. In 2012, this organization provided an Academic Fellowship Fund that

enabled me to gather additional Brazilian data after my research stay in Brazil. Thank you for

your belief in our research project!

For the data gathering process in Europe, I want to thank The Antwerp Management

School (AMS). In 2011, they gave us an Applied Management Research Grant that has been

used to search for European incubator contact details and finance the sending out of

questionnaires in Europe. Eline S., thank you for your help during the data gathering process!

I also want to thank all participants that filled out this questionnaire. Your input forms the

basis of Chapter 4.

Finally, the doctoral thesis would never have been finished without the support from the

following people that saw me struggling at home: my family and friends. First, I would like

to thank my parents. Mama en papa, you have always supported me. I can call you day and

night, which is something that even a 30+-year old “child” appreciates very much. Mama, I

really liked our lunches when I was working from home and needed a break. Papa, you are

the person that keeps our family together. I want to thank you for always checking whether I

needed something. Mama and papa, many children will say this about their parents, but you

really are the best!

I would also like to thank my lovely sister Pia, and her fantastic family. Pia, I am so happy

that we get along so well, and that we spend so many time together. I really love your

family: you have a fantastic husband (thank you Tom, for being who you are!) and as you

know, I really adore your children. Tijs and Oscar, you are still too young to realize it, but

whenever needed, you were the ones who could make me forget the papers that needed to

be written and analyses that needed to be done.

x

Thank you to my family-in-law for their support and interest in my job. Joris, I really

enjoyed our long talks when Maarten was in Brazil and we were both “home alone” in “de

Berkenlaan”.

I would also like to thank my friends for just being who they are. I want to thank them for

giving me the opportunity to pick up our friendship after stressful periods, and I hope that

we can spend many more moments together. There is one particular person who has an

important role in all this, and that is Tine. Tine, thank you for organizing so many activities,

for our talks, and your little presents to wish me good luck.

Last but certainly not least, I would like to thank the most important person in my life:

my husband Maarten. Maarten, you were the one who had to deal with me during the most

stressful moments. I want to thank you for all the time we spent together, for our long walks

and biking trips, for your advice and for your help with the lay-out of this doctoral thesis. But

most of all, I just want to thank you for who you are.

Antwerp, Belgium, September 2013

1

Introduction

Entrepreneurial activities are seen as the engine of economic growth (Audretsch et al.,

2007; van Stel et al., 2005) and employment changes (Baptista et al., 2008). Also indirect

effects of new business formation for the economy have been widely documented, such as

increased innovative activities (Giarratana, 2004), technological development (Licht and

Nerlinger, 1998) or increased competitiveness (Fristch and Mueller, 2004).1 Despite the

stimulating effects of new businesses for the economy, liabilities of newness and smallness

(Freeman et al., 1983; Stinchcombe, 1965) provoke high start-up failure rates. For example,

start-ups often lack legitimacy, do not have the necessary connections, and have fewer

resources or access to knowledge than their established counterparts. Such externalities

lead to market failure (Audretsch et al., 2007). Figures of thirty to forty per cent of start-ups

not surviving their first year of existence (OECD, 2002; Shepherd et al., 2000) stimulate

government to correct for market failures. For example, information asymmetries between

start-ups and financers (Audretsch et al., 2007) and increased hesitance to invest in high-

tech projects during economic recession (Sauner-Leroy, 2004) often lead to government

intervention.

One type of government intervention is the nurturing of start-ups2 in business

incubators3 (Peña, 2004). Although little is known about the actual incubation process

1 Researchers such as Fritsch and Mueller (2004), van Stel and Storey (2004) and Baptista et al. (2008) argue that time lags explain the often ambiguous research results about the impact of start-up formation on employment or economic growth. Moreover, Anokhin and Wincent (2012) nuance the widely accepted belief that start-up rates positively relate to innovation. Their research shows that this relationship is only positive in developed countries, and that it becomes negative in countries in early development stages. Also van Stel et al. (2005) provide evidence that a country’s economic development stage influences the impact of entrepreneurial activities on economic growth. 2 In the incubator literature, start-up companies located in an incubator are called “tenants” or “incubatees”. These terms will be used interchangeably in this doctoral thesis. 3 Some researchers use the term “incubator” for an entrepreneurial environment (Phan et al., 2005). Within such an entrepreneurial environment, various actors collaborate to stimulate (innovative) entrepreneurial activities. For example, the triple-helix model (Etzkowitz and Leydesdorff, 2000) analyzes how collaboration among research institutions, industrial organizations and government can foster regional development. Although such a “system view” can provide interesting insights into the interaction processes among several actors and the way they jointly stimulate regional development, this is not the topic of this doctoral thesis. Instead, this thesis follows Bergek and Norrman (2008) and reserves “the concept of incubator for organizations [and not systems] dedicated to the support

2

(Hackett and Dilts, 2004a), an incubator’s model components are selection, infrastructure,

business support, mediation and graduation (Bergek and Norrman, 2008). Selection refers to

the criteria employed to decide whether companies are allowed to enter the incubator or

not. Infrastructure consists of office space, administrative services and logistic facilities.

Tenants can rent an office and make use of meeting rooms or parking spaces. A common

secretary helps them with administrative support such as answering telephone calls.

Business support implies that the incubator offers business coaching and/or training. This

often occurs through mediation; which means that the incubator brings its tenants into

contact with external organizations. For example, offering support during the search for

finance can help small companies that suffer from information asymmetries with potential

financers. Mediation also implies that the incubator stimulates interaction among its

incubatees. Thus, mediation addresses potential network externalities through the

stimulation of information or knowledge spillovers and increased potential for collaborations

(Audretsch et al., 2007). The final incubator model component is graduation. It relates to the

incubator’s exit policy and stipulates the criteria for tenants to leave the incubator.

Through service offerings, incubators allow tenants to benefit from economies of scale

for office space and shared resources (Bruneel et al., 2012). Moreover, accessible business

support and networking opportunities accelerate the tenant’s learning curve and help

companies to overcome resource constraints (Bruneel et al., 2012). Tenants can also gain

credibility through the incubator’s networking contacts and image (Ferguson and Olofsson,

2004; Studdard, 2006). Although results are not consistent4, studies find that start-up

companies located in a business incubator have higher survival (e.g.; Sherman, 1999;

Ferguson and Olofsson, 2004) and growth rates (e.g.; Löfsten and Lindelöf, 2001, 2002) than

their counterparts not located in a business incubator.

Therefore, it is not surprising that the number of incubators grew exponentially

throughout the world.5 For example, the National Business Incubation Association reports an

of emerging ventures” (p. 21). Thus, it’s level of analysis is the incubator, and not the entrepreneurial system as a whole. 4 The reason for inconsistent findings might be that research often ignores environmental conditions. Amezcua et al. (2013) argue that resource munificence, provided by organizations such as business incubators, does not automatically predict start-up survival. A fit between environmental conditions such as an area’s founding density turned out to be a prerequisite for increased survival rates. 5 Important to note, is that during economic recession market failures might be more present (cfr. the hesitance of financers to invest in projects during economic recession (Sauner-Leroy, 2004)). As a

3

approximate of 1400 incubators in 2006. This is a doubling of the number of North American

incubators in only one decade (Knopp, 2007). Also in emerging countries such as Brazil, the

amount of incubators grew substantively, from only two incubators in 1988 to 377 in 2006

(Anprotec, 2006). In Europe, figures from the United Kingdom Business Incubation

organization and the European Commission also report a doubling of UK incubators in a

decade (European Commission, 2002; UKBI, 2011).

This exponential growth in the number of incubators contrasts sharply with the state-of-

the-art of research on this topic. Academics started to be interested in the incubator

phenomenon in the mid-1980s. At that time, studies were mainly descriptive (e.g., Campbell

et al., 1985). Although, since then, scholars also examined relationships among incubator

service offerings (Schwartz and Hornych, 2008), strategic positioning (Grimaldi and Grandi,

2005) or external influences (Sofouli and Vonortas, 2007), research is still in its infancy. It is

argued that it “just begun to scratch the surface of [this] phenomenon” (Hackett and Dilts,

2004b, p. 55). A better understanding of the incubator-incubation phenomenon is badly

needed (Phan et al., 2005).

Overarching research goals and data gathering

To respond to the quest for additional research on incubator organizations, this doctoral

thesis opts for a wide lens. It examines (but not always simultaneously) the relationships

between four main building blocks: an incubator’s strategic positioning, its internal

characteristics, its environment and its performance. Two overarching research goals are

envisioned: (1) mapping the conditions of fit between these building blocks, and (2) getting a

deeper understanding of some of the mechanisms influencing these relationships. For this,

we rely both on qualitative and quantitative research. Chapters 1 and 2 start off with

qualitative research in nine incubators in the province of Antwerp, Belgium. Qualitative

research is typically used for “how” and “why” questions (Eisenhardt and Graebner, 2007;

Yin, 1990) and is useful in complex research domains where little is known about the

consequence, the nurturing of start-ups in incubators might be more important than ever to adjust for such market failures. However, evidence shows that during recession, government is more reluctant to invest in business incubators (Almeida, 2005). For example, in Brazil, “a continuing economic crisis led to the demise of the NITs (Nuclei of Technological Innovation) and the science park program was also abandoned” (Etzkowitz et al., 2005, p. 415). Thus, economic recession can (temporally) decrease the number of incubators.

4

phenomenon. In total, we conducted 18 in-depth interviews with incubator managers, 30 in-

depth interviews with tenants, 3 focus groups with incubator managers and experts, and an

overarching discussion meeting with incubator managers and experts.

Based on the insights gathered from this qualitative research and an additional literature

review, we developed a questionnaire for Brazilian incubator managers, incubator

employees and entrepreneurship experts. Through these questionnaires, we assessed

aspects from all four building blocks, such as the incubator’s human capital, competitive

scope, institutional entrepreneurial environment, and tenant survival and growth. This

questionnaire was qualitatively pre-tested in Brazil and forms the basis for Chapter 3. In

total, we gathered 187 incubator manager, 113 incubator employee and 184

entrepreneurship expert responses in Brazil (see appendices A and C for a description of

some Brazilian business incubators characteristics; location, year of operations and

occupancy rate).

Based on insights from the Brazilian research, we slightly adapted the Brazilian

questionnaire. This version formed the basis for a European questionnaire. Again, we

qualitatively pre-tested the survey and sent it out to incubator managers, incubator

employees and entrepreneurship experts. We received a total of 140, 18 and 30 responses,

respectively.6 The data gathered in Belgium (Flanders), the Netherlands, the United Kingdom

and Europe forms the basis for Chapter 4 (see appendices B and C for a description of some

characteristics of the European business incubators in our study; location, year of operations

and occupancy rate). To avoid cultural biases, all questionnaire translations occurred

through the collaborative and iterative translation method (Douglas and Craig, 2007). Figure

I-1 visualizes the different data gathering stages.

6 Due to the very low number of responses for incubator employees and entrepreneurship experts, we only analyzed the incubator manager data.

5

Figure I-1: Overview data gathering

Theoretical anchors

This doctoral thesis is anchored into six research domains.7 We briefly summarize their

underlying ideas. First, we investigate the importance of an organization’s task and

institutional environment. Chapters 1 and 2 focus on the incubator’s task environment and

take a strategic constituencies stance (Connolly et al., 1980; Tsui, 1990). Here, we follow

research arguing that an incubator’s dominant stakeholders are its tenants (Jungman et al.,

2004), taking into account tenant service and incubator functioning expectations. In

contrast, Chapters 3 and 4 turn toward the incubator’s institutional entrepreneurial

environment. In these chapters, we adopt the institutional theory perspective (Scott, 2001,

2005), arguing that an incubator’s regulative, cognitive and normative environment

influence its functioning (Meyer and Rowan, 1977; Zucker, 1987).

Second, we employ strategic positioning theory in Chapters 1, 3 and 4. An organization’s

strategic position is determined by its competitive scope and competitive advantage (Porter,

1980, 1996; Mintzberg, 1988). These dimensions determine where (scope) and how

(advantage) the organization differentiates itself from its competitors. We examine whether

incubators opt for a focused or a diversified scope (Plosila and Allen, 1985; Sherman, 1999;

7 Without doubt, other theoretical perspectives can provide additional insights. For example, the theory of planned behavior could form the basis for a study on an incubator managers’ strategic intentions and decisions (Ajzen and Fishbein, 1980; Fishbein and Ajzen, 1975) and population ecology theory could form the foundations to explain why some incubators in the incubation market survive and others not (Hannan and Freeman, 1977).

6

von Zedtwitz, 2003), and investigate which is the more favorable strategic option. In these

chapters, we also go deeper into the incubator’s competitive advantage. First, we examine

which service offerings can lead to differentiation. Then, we turn towards the actual service

offering process and investigate the added value of a service customization strategy and

focus on determinants of the level of service co-creation.

Third, Chapters 1, 2 and 4 adopt the resource-based view. This theory argues that access

to valuable, rare, inimitable, and non-substitutable resources leads to a competitive

advantage (Barney, 1991; Barney et al., 2001), taking a resource perspective to develop

strategic options (Wernerfelt, 1984). In Chapters 1 and 2, we simultaneously incorporate

several internal characteristics, such as the incubator’s selection process, its graduation

process, and its organizational culture. In these chapters, our qualitative research method

allows us to take on a holistic view. In Chapter 4, we turn towards an in-depth understanding

of the impact of an incubator’s human capital on the level of service co-creation.

Fourth, we recognize multiple viewpoints of organizational performance. In Chapter 2,

we examine an incubator’s performance and functioning through four organizational

viewpoints (Daft, 2009): the goal (Bluedorn, 1980; Price, 1982), stakeholder (Connolly et al.,

1980; Tsui, 1990), system resource (Seashore and Yuchtman, 1967) and internal process

approach (Lewin and Minton, 1986; Nadler and Tushman, 1980). In this chapter, we adapt

the balanced scorecard and strategy map (Kaplan and Norton, 2000, 2005) and present an

integrated evaluation model in which we focus on internal functioning evaluation. Incubator

performance and functioning are also examined in Chapters 3 and 4. In Chapter 3, we take

on a narrower viewpoint and follow incubator researchers that argue that an incubator’s

most important outcome measure is tenant survival and growth (Aerts et al., 2007; Lalkaka,

1996). Hence, we focus on the goal approach, examining how the interplay between an

incubator’s strategy and environmental context can impact its tenant survival and growth. In

Chapter 4, we turn towards the internal process perspective and focus on the determinants

of an internal incubation strategy that proved to be effective: service co-creation (Rice,

2002).

Fifth, we employ service-dominant logic in all four chapters. The traditional goods-

dominant logic assumes that a supplier produces a product that a customer buys afterwards.

There is no interaction between customer and producer during the development process.

7

According to the service-dominant logic, the customer is actively involved “with their

supplier in every aspect, from product design to product consumption” (Payne et al., 2008,

p. 379). As such, the customer is not the target of value, but the co-creator of value (Vargo

and Lusch, 2004). Through co-creation, organizations are pushed to continuous service

improvements (Xie et al., 2008). Customers get more realistic expectations of what is

possible. This can result in higher appreciations of the end result (Hoyer et al., 2010). We

argue that also in incubators, tenants can be involved in the service development and

transformation process. Incubator managers that engage in continual, proactive service co-

creation closely follow-up individual tenant needs (Rice, 2002). In Chapters 1 and 2, the

importance of tenant involvement during service development is stressed to assure tenant

value creation. In Chapter 3, we examine the determinants of an incubator’s service

customization strategy and its impact on incubator performance. Customization involves

both resource preparation and transaction activities (Jacob, 2006). Resource preparation

means that an organization’s internal resources such as personnel, equipment or

infrastructure offer potential for customization if they are organized and planned. Once this

planning and organizing took place, the organization can move on to the second sub-

process: the transaction activities. Here, the customer can provide the necessary

information so that the organization’s pool of internal resources can be transformed into

customized services (Jacob, 2006). This interaction involves service co-creation. In Chapter 4,

we take a closer look onto the transaction activities and examine some of the determinants

of the level of incubator-tenant service co-creation. More specifically, we investigate its

relationship with the incubator’s human capital and the institutional entrepreneurial

context.

Sixth and finally, all four chapters follow fit theory. In this theory, it is argued that an

organization’s optimal functioning and performance are influenced by fit among external,

internal, and strategic variables (Naman and Slevin, 1993; Yakamawa et al., 2011). There are

three fit perspectives (Heijltjes and van Witteloostuijn, 2003): formulation, implementation

and integration. Formulation refers to the match between the organization’s strategy and its

environment (e.g., Ceci and Masini, 2011). Chapter 3 follows this perspective and examines

the interplay of an incubator’s strategy and institutional context, two aspects which only

sporadically have been combined in incubator research (e.g., Amezcua, 2013; Sofouli and

8

Vonortas, 2007). The implementation perspective examines the relationship between the

organization’s internal characteristics and its strategy (e.g., Newbert et al., 2007). The

relationship between an incubator’s strategy and its internal characteristics such as service

offerings has been examined extensively in incubator research (e.g., Bruneel et al., 2012;

Schwartz and Hornych, 2008) but there is a lack of understanding of the impact of internal

characteristics on internal incubation strategies (Hackett and Dilts, 2008). Therefore, this

doctoral thesis examines this relationship in Chapter 4. Moreover, we also go one step

further and apply the integration perspective in Chapters 1, 2 and 4. Here, we combine

elements from the incubator’s strategy (e.g., competitive scope, competitive advantage),

internal characteristics (e.g., organizational culture, selection process, human capital) and

environment (e.g., tenant expectations8, entrepreneurial context).

Figure I-2 visualizes an integrative overview of the overarching research goals, building

blocks, research methods, and theoretical anchors of this doctoral thesis. The subsequent

four chapters elaborate upon the relevant theoretical anchors and provide the results of our

empirical research. In a conclusion section, we summarize the research questions and most

important contributions of the different chapters, highlight the overall contribution to

science, practice and policy, explain our studies’ limitations and suggest future research

avenues.

8 Important to note, is that the customer (that is, incubator tenant) viewpoint gradually shifts throughout the doctoral thesis. In Chapters 1 and 2, tenant expectations are analyzed from an external perspective. Here, we recognize that an incubator has a large amount of strategic constituencies (e.g., government, university, tenants, etc.), but follow researchers such as Jungman et al. (2004) who argue that the external viewpoint of incubator tenants dominates. Thus, we focus on the tenant as an external actor and take on a traditional dyadic customer view. In Chapters 3 and 4, we go deeper into service customization and co-creation. Here, the tenant is the co-creator of value. Incubator-tenant interaction is central and the tenant is no longer treated as an external stakeholder, but integrated in the incubator’s strategy and functioning.

9

Figure I-2: Integrative overview of the doctoral thesis

10

Appendices

Appendix A: Fact sheet Brazilian incubators9

Figure I-3: Map of Brazilian states

9 The information visualized in this Appendix is based on the incubator contact database we developed (see Chapter 3).

11

Figure I-4: Number of inhabitants/incubator in the North region of Brazil

Figure I-5: Number of inhabitants/incubator in the Northeast region of Brazil

12

Figure I-6: Number of inhabitants/incubator in the Central-West region of Brazil

Figure I-7: Number of inhabitants/incubator in the Southeast region of Brazil

13

Figure I-8: Number of inhabitants/incubator in the South region of Brazil

14

Figure I-9: Number of inhabitants/incubator in Brazil (all states)

15

Appendix B: Fact sheet European incubators (Belgium-Flanders; the Netherlands, United Kingdom, Ireland)10

Figure I-10: Number of inhabitants/incubator in Belgium (Flanders), the Netherlands, United Kingdom and Ireland (contact database)

10 The information visualized in this Appendix is based on the incubator contact database we developed (see Chapter 4).

16

Appendix C: Fact sheet Brazilian and European sample incubators11

Figure I-11: Year incubator started its operations (mean, max, min)

Figure I-12: Occupancy rate incubators (mean, max, min) a

a For occupancy rate, there are 10 categories; 1=0-10%, 2=11-20%; 3=21-30%; 4=31-40%; 5=41-50%; 6=51-60%; 7=61-70%; 8=71-80%; 9=81-90%; 10=91-100%.

11 The information visualized in this Appendix is based on the incubator samples (see Chapters 3 and 4).

17

Bibliography

Aerts, K., Matthyssens, P., Vandenbempt, K., 2007. Critical role and screening practices of European business incubators. Technovation, 27, 5, 254-267.

Ajzen, I., Fishbein, M., 1980. Understanding attitudes and predicting social behavior. Prentice-Hall, Englewood Cliffs, NJ.

Almeida, M., 2005. The evolution of the incubator movement in Brazil. Internationl Journal of Technology and Globalisation, 1, 2, 258-277.

Amezcua, A., Grimes, M., Bradley, S., Wiklund, J., 2013 (forthcoming). Organizational sponsorship and founding environments: a contingency view on the survival of business incubated firms, 1994-2007. The Academy of Management Journal, forthcoming.

Anokhin, S., Wincent, J., 2012. Start-up rates and innovation: a cross-country examination. Journal of International Business Studies, 43, 1, 41-60.

Anprotec, 2006. Panorama de incubadoras de empresas e parques tecnológicos 2006, Anprotec, Brazil.

Audretsch, D.B., Grilo, I., Thurik, A.R., 2007. Explaining entrepreneurship and the role of policy: a framework. In: Audretsch, D.B., Grilo, I., Thurik, A.R., (Eds.), Handbook of research on entrepreneurship policy. Edward Elgar Publisinh, Northampton, MA, USA, p. 1-17.

Baptista, R., Escária, V., Madruga, P., 2008. Entrepreneurship, regional development and job creation: the case of Portugal. Small Business Economics, 30, 1, 49-58.

Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management, 17, 1, 99-120.

Barney, J., Wright, M., Ketchen, D.J., 2001. The resource-based view of the firm: Ten years after 1991. Journal of Management, 27, 625-641.

Bergek, A., Norrman, C., 2008. Incubator best practice: a framework. Technovation, 28, 20-28.

Bluedorn, A.C., 1980. Cutting the Gordian knot: A critique of the effectiveness tradition in organizational research. Sociology and Social Research, 64, 477–496.

Bruneel, J., Ratinho, T., Clarysse, B., Groen, A., 2012. The evolution of business incubators: comparing demand and supply of business incubation services across different incubator generations. Technovation, 31, 110-121.

Campbell, C., Kendrick, R.C., Samuelson, D.S., 1985. Stalking the latent entrepreneur: business incubators and economic development. Economic Development Review, Summer, 43-48.

Ceci, F., Masini, A., 2011. Balancing specialized and generic capabilities in the provision of integrated solutions. Industrial and Corporate Change, 20, 1, 91-131.

Connolly, T., Conlon, E.J., Deutsch, S.J., 1980. Organizational effectiveness: a multiple-constituency approach. Academy of Management Review, 5, 211–217.

Daft, R.L., 2009. Organization Theory and Design. South-Western College Pub, Mason, OH. Douglas, S.P., Craig, C.S., 2007. Collaborative and iterative translation: an alternative

approach to back translation. Journal of International Marketing, 15, 30-43. Eisenhardt, K.M., Graebner, M.E., 2007. Theory building from cases: opportunities and

challenges. Academy of Management Journal, 50, 1, 25-32. Etzkowitz, H., Leydesdorff, L., 2000. The dynamics of innovation: from national systems and

"mode 2" to a triple helix of university-industry-government relations. Research Policy, 29, 2, 109-123.

18

Etzkowitz, H., Carvalho de Mello, J.M., Almeida, M., 2005. Towards “meta-innovation” in Brazil: the evolution of the incubator and the emergence of a triple helix. Research Policy, 34, 411-424.

European Commission, 2002. Benchmarking of Business Incubators. Centre for Strategy and Evaluation Services, Brussels, Belgium.

Ferguson, R., Olofsson, C., 2004. Science parks and the development of NTBFs: location, survival and growth. Journal of Technology Transfer, 29, 5–17.

Fishbein, M., Ajzen, I., 1975. Belief, attitude, intention, and behavior: an introduction to theory and research. Reading, Addison-Wesley, MA.

Freeman, J., Carroll, G.R., Hannan M.T., 1983. The liability of newness: age dependence in organizational death rates. American Sociological Review, 48, 5, 692-710.

Fritsch, M., Mueller, P., 2004. The effects of new business formation on regional development over time. Regional Studies, 38, 961-975.

Giarratana, M.S., 2004. The birth of a new industry: entry by start-ups and the drivers of firm growth - the case of encryption software. Research Policy, 33, 5, 787-806.

Grimaldi, R., Grandi, A., 2005. Business incubators and new venture creation: an assessment of incubating models. Technovation, 25, 111-121.

Hackett, S.M., Dilts, D.M., 2004a. A real options-driven theory of business incubation. Journal of Technology Transfer, 29, 41-54.

Hackett, S.M., Dilts, D.M., 2004b. A systematic review of business incubation research. The Journal of Technology Transfer, 29, 1, 55-82.

Hackett, S.M., Dilts, D.M., 2008. Inside the black box of business incubation: study B – scale assessment, model refinement, and incubation outcomes. The Journal of Technology Transfer, 33, 439-471.

Hannan, M.T., Freeman, J., 1977. The population ecology of organizations. American Journal of Sociology, 82, 5, 929-964.

Heijltjes, M., van Witteloostuijn, A., 2003. Configurations of market environments, competitive strategies, manufacturing technologies and human resource management policies. Scandinavian Journal of Management, 19, 31-62.

Hoyer, W.D., Chandy, R., Dorotic, M., Krafft, M., Singh, S.S., 2010. Consumer co-creation in new product development. Journal of Service Research, 13, 3, 283-296.

Jacob, F., 2006. Preparing industrial suppliers for customer integration. Industrial Marketing Management, 35, 45-56.

Jungman, H., Okkonen, J., Rasila, T., Seppä, M., 2004. Use of performance measurement in V2C activity. Benchmarking: An International Journal, 11, 2, 175-189.

Kaplan, R.S., Norton, D.P., 2000. Having trouble with your strategy? Then map it. Harvard Business Review, September-October, 167-176.

Kaplan, R.S., Norton, D.P., 2005. The balanced scorecard: measures that drive performance. Harvard Business Review, July-August, 172-180.

Knopp, L., 2007. 2006 State of the Business Incubation Industry. Athens, Ohio, NBIA Publications.

Lalkaka, R., 1996. Technology business incubators: critical determinants of success. Annals of the New York Academy Sciences, 798, 270-290.

Lewin, A.Y., Minton, J.W., 1986. Determining organizational effectiveness: another look, and an agenda for research. Management Science, 32, 514–538.

Licht, G., Nerlinger, E., 1998. New technology-based firms in Germany: a survey of the recent evidence. Research Policy, 26, 9, 1005-1022.

19

Löfsten, H., Lindelöf, P., 2001. Science parks in Sweden: industrial renewal and development. R&D Management, 31, 3, 309-322.

Löfsten, H., Lindelöf, P., 2002. Science Parks and the growth of new technology-based firms: academic-industry links, innovation and markets. Research Policy, 31, 859-876.

Meyer, J.W., Rowan, B., 1977. Institutional organizations: formal structure as myth and ceremony. American Journal of Sociology, 80, 340-63.

Mintzberg, H.T., 1988. Generic strategies: toward a comprehensive framework. Advances in Strategic Management, 5, 1-67.

Nadler, D.A., Tushman, M.L., 1980. A congruence model for organizational assessment. In Lawler E.E., Nadler, D.A., Cammann C. (Eds.), Organizational Assessment, Wiley, New York.

Naman, J.L., Slevin, D.P., 1993. Entrepreneurship and the concept of fit: a model and empirical tests. Strategic Management Journal, 14, 2, 137-153.

Newbert, S.L., Kirchhoff, B.A., Walsh, S.T., 2007. Defining the relationship among founding resources, strategies, and performance in technology-intensive new ventures: evidence from the Semiconductor Silicon Industry. Journal of Small Business Management, 45, 4, 438-466.

OECD, 2002. OECD Small and medium enterprise outlook. OECD Publications service, Paris, France.

Payne, A.F., Storbacka, K., Frow, P., 2008. Managing the co-creation of value. Journal of the Academy of Marketing Science, 36, 1, 83-96.

Peña, I., 2004. Business incubation centers and new firm growth in the Basque country. Small Business Economics, 22, 3/4, 223-236.

Phan, P.H., Siegel, D.S., Wright, M., 2005. Science parks and incubators: observations, synthesis and future research. Journal of Business Venturing, 20, 2, 165-182.

Plosila, W.H., Allen, D.N., 1985. Small business incubators and public policy: implications for state and local development strategies. Policy Studies Journal, 13, 4, 729-734.

Porter, M.E., 1980. Competitive Strategy. The Free Press, New York. Porter, M.E., 1996. What is strategy? Harvard Business Review, November-December, 61-78. Price, J.L., 1982. The study of organizational effectiveness. Sociological Quarterly, 13, 3–15. Rice, M.P., 2002. Co-production of business assistance in business incubators: an exploratory

study. Journal of Business Venturing, 17, 2, 163-187. Sauner-Leroy, J.B., 2004. Managers and productive investment decisions: the impact of

uncertainty and risk aversion. Journal of Small Business Management, 42, 1, 1-18. Schwartz, M., Hornych, C., 2008. Specialization as strategy for business incubators: An

assessment of the Central German Multimedia Center. Technovation, 28, 436-449. Scott, R., 2001. Institutions and Organizations, 2nd edition, Sage, Thousand Oaks, CA. Scott, R., 2005. Institutional theory: contributing to a theoretical research program. In:

Smith, K.G., Hitt, M.A. (Eds.), Great Minds in Management: The Process of Theory Development, University Press, Oxford, p. 460-484.

Seashore, S.E., Yuchtman, E., 1967. Factorial analysis of organizational performance. Administrative Science Quarterly, 12, 377–395.

Shepherd, D., Douglas, E., Shanley, M., 2000. New venture survival: ignorance, external shocks and risk reduction strategies. Journal of Business Venturing, 15, 393-410.

Sherman, H., 1999. Assessing the intervention effectiveness of business incubation programs on new business start-ups. Journal of Developmental Entrepreneurship, 4, 2, 117-133.

20

Sofouli, E., Vonortas, N.S., 2007. S&T parks and business incubators in middle-sized countries: the case of Greece. Journal of Technology Transfer, 32, 525-544.

Stinchcombe, A.L., 1965. Social structure and organizations. In March, J.G. (Ed.), Handbook of Organizations, Rand McNally, Chicago, p. 153-193.

Studdard, N.L., 2006. The effectiveness of entrepreneurial firm’s knowledge acquisition from a business incubator. International Entrepreneurship and Management Journal, 2, 211–225.

Tsui, A.S., 1990. A multiple constituency model of effectiveness: empirical examination at the human resource subunit level. Administrative Science Quarterly, 35, 458–483.

UKBI, 2011. Business Incubation. Retrieved on April 4, 2011 from http://www.ukbi.co.uk/about-ukbi/business-incubation.aspx.

van Stel, A., Carree, M., Thurik R., 2005. The effect of entrepreneurial activity on national economic growth. Small Business Economics, 24, 3, 311-321.

van Stel, A., Storey, D., 2004. The link between firm births and job creation: is there an Upas tree effect? Regional Studies. 38, 893-909.

Vargo, S.L., Lusch, R.F., 2004. Evolving to a new dominants logic for marketing. Journal of Marketing, 68, 1-17.

von Zedtwitz, M., 2003. Classification and management of incubators: aligning strategic objectives and competitive scope for new business facilitation. International Journal of Entrepreneurship and Innovation Management, 3, 1/2, 176-196.

Wernerfelt, B., 1984. A resource-based view of the firm. Strategic Management Journal, 5, 171-180.

Xie, C., Bagozzi, R.P., Troye, S.V., 2008. Trying to prosume: toward a theory of consumers as cocreators of value. Journal of the Academy of Marketing Science, 36, 1, 109-122.

Yamakawa, Y., Yang, H.B., Lin, Z.A., 2011. Exploration versus exploitation in alliance portfolio: performance implications of organizational, strategic and environmental fit. Research Policy, 40, 2, 287-296.

Yin, R.K., 1990. Case study research: design and methods. Applied Social Research Methods Series, Vol. 5, Beverly Hills, Sage Publications, California, US.

Zucker, L.G., 1987. Institutional theories of organization. Annual Review of Sociology, 13, 443-64

21

Chapter 1: Service-based differentiation strategies for business incubators: Exploring external and internal alignment 12 13 14

Abstract

Strategic positioning and fit theories may inform the service-based differentiation strategies

that incubators use to secure external and internal alignment. External alignment relates to

tenant service expectations and perceptions; internal alignment involves a competence

configuration for each strategy alternative. By implementing the proposed framework, an

incubator can achieve service differentiation and ultimately enhanced customer (tenant)

value. Qualitative research among nonprofit economic development incubators reveals two

service-based differentiation positions: specialists and generalists. Whereas extant research

advocates only a specialist stance, the present analysis confirms that service-based

differentiation can result from a generalist stance. This study offers the first typology of

service-based differentiation strategies for incubators that aligns strategy with external and

internal variables.

Highlights

> This chapter examines service-based differentiation alternatives for business incubators

> Tenant service expectations are aligned with strategic service-based positions

> A competence configuration is developed for each service-based differentiation alternative

Keywords

Business incubator; Strategic positioning; Incubator strategy; Strategic fit; Internal

alignment; External alignment

12 This chapter is an adapted version from: Vanderstraeten, J. and Matthyssens, P., 2012, Service-based differentiation strategies for business incubators: Exploring external and internal alignment, Technovation, 32, 656-670. 13 An earlier version of this chapter has been published as a Dutch research paper: Vanderstraeten J. , Matthyssens P., Ledent-De Smet F., 2010, Strategische positioneringsopties voor business incubators, Working paper 2010:1, Antwerp Management School, Antwerp, Belgium [Dutch paper]. 14 An earlier version of this chapter has been presented at the XX Brazilian National Seminar on Science Parks and Business Incubators and the XVIII Anprotec Workshop, Campo Grande, Brazil, September, 20-24, 2010.

23

1.1. Introduction

In the business incubation industry, various challenges constantly force incubators to

adopt unique differentiation tactics. First, incubators offer office space, a pool of shared

support services, professional business support or advice, and internal/external network

provision to start-up firms (Bergek and Norrman, 2008; Hackett and Dilts, 2004). However,

they are not the only organizations active in the incubation industry, and many players offer

overlapping services (Becker and Gassman, 2006; von Zedtwitz, 2003). In general, five main

actors participate in the incubation market: business incubators, logistic infrastructure

providers, nonprofit advice organizations, for-profit advice organizations, and finance

providers (Becker and Gassmann, 2006; von Zedtwitz, 2003). Logistic infrastructure providers

also have working spaces, event spaces, workshop rooms, or creativity rooms (e.g., The Hub,

2011) and advice organizations such as chambers of commerce organize seminars,

information sessions, and financing programs for start-up firms. Thus, “business incubator”

has become an umbrella term that refers to various initiatives designed to support start-ups

(Aernoudt, 2004).

Second, the number of incubators also has grown (Bruneel et al., 2012). The National

Business Incubation Association estimates that between 1998 and 2006, the number of

North American incubators almost doubled to approximately 1400 (Knopp, 2007), and

developing and emerging countries showed similar growth. For example, Anprotec (2006)

estimates that in 2006, Brazil featured 377 incubators, compared with two in 1988. The

United Kingdom Business Incubation organization (UKBI, 2011) also reports about 300

incubators there, compared with 10 years ago, when there were only 144 (European

Commission, 2002).

These changes in the incubation market have prompted scholars to devote more

attention to how incubators can strategically position themselves (e.g., Chan and Lau, 2005;

Grimaldi and Grandi, 2005; Schwartz and Hornych, 2008), especially as start-up

entrepreneurs seek complementary assets (Wright et al., 2008) and technological, cognitive,

or vision proximity (Cantù, 2010) within a geographical region (Pe’er et al., 2008), which

likely hosts several start-up support organizations.15 Butz and Goodstein (1996) argue that

organizations can attain differentiation and a competitive advantage by creating customer

15 Of course, in geographical regions with few start-up support organisations, competitiveness is much lower or even nonexistent.

24

value, which demands in-depth knowledge about customer expectations. This approach

reflects Priem’s (2007, p. 220) encouragement to “learn much about successful strategy

through a long-ignored consumer lens on value creation”. In the incubator context, Bruneel

et al. (2012) confirm that incubator value propositions must reflect an in-depth

understanding of tenant viewpoints. Therefore, we aim to investigate incubator

differentiation possibilities, through the function of customer value creation, by answering:

how can business incubators, located in the same region, differentiate themselves in the

incubation market through customer value creation?

Beyond the general need for differentiation, strategic fit scholars stress the importance

of external and internal alignment (Venkatraman, 1989; Venkatraman and Prescott, 1990) as

a means to attain performance benefits (Naman and Slevin, 1993; Yamakawa et al., 2011).

Organizations that undertake a strategic change or repositioning need profound knowledge

of both external market and internal organizational factors to ensure strategic fit. Insights

into customer needs, organizational capabilities (Brax and Jonsson, 2009), value

chain/network partners (Cova and Salle, 2008), and the link between organizational strategy

and resources (Newbert et al., 2007) are all critical, which leads us to investigate as well:

how can incubators ensure external and internal alignment for any differentiation

alternative?

These research questions are rooted in strategy literature, where strategy scholars

advocate the importance of competitive positions (e.g., Gavetti and Rivkin, 2007; Porter,

1980, 1996, 1998; Wen and Chen, 2011). In particular, academics in the positioning school

(e.g., Alipour et al., 2010; Ng et al., 2005; Yamin et al., 1999) assert that strategy formulation

requires examining the organization’s competitive scope and advantage (Mintzberg, 1988)

to determine where (competitive scope) and how (competitive advantage) the organization

can compete (Juga et al., 2008). Li and Tsai (2009) explain that research on sustained

competitive advantages mainly draws on two theories. Industrial organization theory

focuses on strategic market positions and has an external viewpoint, whereas the resource-

based view (RBV) starts with an organization’s internal resources and capabilities and argues

that access to valuable, rare, inimitable, and non-substitutable resources leads to a

competitive advantage (Barney, 1991; Chiu et al., 2008; Newbert, 2008). Newbert et al.

(2008) also link the RBV to a dynamic capabilities approach and show that both the

25

possession and exploitation of resources through an optimal use of capabilities are

necessary to attain competitive advantages. Although strategy scholars thus argue that an

organization should try to find a position that gives it a unique competitive advantage

(Kalafatis et al., 2000), increased competition and imitation by competitors almost invariably

erode differentiation bases and hamper organizational success (Hitt et al., 2007).

Organizations must continually find new ways to differentiate themselves (e.g., Cho et al.,

1996; Smith and Sharif, 2007).

To address the research questions, we conducted an in-depth qualitative study with an

embedded research design (Yin, 1990), in which we account for tenant viewpoints, expert

and incubator manager opinions on tenant value creation options (Bruneel et al., 2012; Butz

and Goodstein, 1996; Huber et al., 2001), and implementation issues, such as the necessary

skills, resources, processes, and systems (Bharadwaj et al., 1993; Matthyssens et al., 2009).

Accordingly, in the next section, we examine strategic positioning and fit theories to

establish our theoretical background. We then explain the methodology for our empirical

research before we present and explain our results, particularly in relation to extant

literature. Finally, we discuss our main contributions and results, research limitations, and

some possible avenues for further research.

1.2. Theoretical background

1.2.1. Strategic positioning theory

According to positioning scholars (e.g., Ibrahim and Gill, 2005; Lawton, 1999; Wen and

Chen, 2011), an organization’s competitive scope and competitive advantage determine its

competitive position. To recognize competitive advantage possibilities in a specific industry,

the firm must have insights into the critical success factors that prevail in that industry (e.g.,

Barbiroli and Focacci, 2003; Sharma, 2003), an analysis that must refer to the strategic group

level (Aaker, 2008). Because strategic groups arise from the same generic strategy (Johnson

et al., 2011), the competitive scope dimension then provides the basis for such an analysis.

Varadarajan (1985) suggests subdividing critical success factors into “failure preventers” and

“success producers”: the organization must attain some threshold level of failure preventers,

but substantial resources devoted to this area cannot lead to above-average performance.

26

Instead, greater effort, compared with competitors, devoted to success producers might

enable the firm to outperform its rivals and thus attain a competitive advantage.

Furthermore, a competitive advantage might accrue through customer value creation

(Cooper, 2001; Woodruff, 1997). However, the customer value construct is very complex

(Jaworski and Kohli, 1993; Khalifa, 2004; Naumann, 1995), which makes it difficult to

measure accurately how customers determine the value of a particular product or service

(Smith and Colgate, 2007). In this sense, it is pivotal for an organization to develop a deep

understanding of what customers seek (O’Cass and Ngo, 2011) and thus the resources it

needs to address customer expectations (Srivastava et al., 2001). Prior research has

integrated the intuitive viewpoints (Bowman and Ambrosini, 2007) of various industry

players, including executives, external experts, and customers, to determine customer value

creation possibilities and the potential success that can be attained through such options

(e.g., Ibrahim and Gill, 2005; Lawton, 1999; Matthyssens et al., 2009).

Research into incubators suggests that sector choices and fields of related technologies

define an incubator’s competitive scope (Aernoudt, 2004; Grimaldi and Grandi, 2005;

Haapasalo and Ekholm, 2004; Plosila and Allen, 1985; Sherman, 1999; von Zedtwitz, 2003;

von Zedtwitz and Grimaldi, 2006). The consensus seems to indicate that incubators opt for

either a focused or a diversified scope (Plosila and Allen, 1985; Sherman and Chappell,

1998). Focused incubators only allow entry to companies active in a specific sector or

technology field; diversified incubators include tenants from a wide variety of areas. Yet

other literature shows that incubators can differentiate themselves and attain competitive

advantages in various ways, such as from their unique location (Hu et al., 2005) or their

provision of a manager who devotes extensive time to tenants (Rice, 2002). These customer

value creation and incubator differentiation possibilities reflect the added value of the

incubator’s service offering, “such as shared rental space, shared office services, business

assistance [and] inside and outside networking,” (Mian, 1994, p. 523).

Despite some agreement that a service offering creates customer value, the perspectives

on which types of services do so remain somewhat one-sided: incubators create value by

offering industry- or technology-specific services (e.g., Bruneel et al., 2012; Schwartz and

Hornych, 2008), including not just sector or technology knowledge but also infrastructure

and network connections. Schwartz and Hornych (2008) note that the Mitteldeutsches

27

Multimediazentrum Halle in Germany provides a wide variety of media-related services,

featuring specialized infrastructure services, such as television, film, and audio studios with

state-of-the-art equipment, as well as sector-specific business knowledge. Thus the services,

applicable only to companies in one sector or technology field, create customer value that

cannot be easily replicated by other players.

In contrast, real-world examples show that many incubators do not choose sector- or

technology-specific services. The UK Rotherham Investment and Development Office (RiDO)

business centers focus their competitive advantage and customer value creation efforts on

the delivery of operational support services to US-based companies willing to invest in the

United Kingdom (RiDO, 2011) and the Antwerp Business Center (2011) in Belgium

differentiates itself by providing in-depth marketing research for companies active in a wide

variety of sectors. That is, though the availability of sector- or technology-related services

adds value for tenants, they are not the only route to customer value.

1.2.2. Strategic fit: internal and external alignment

Generic strategies often work to link internal and/or external organizational variables to

“ideal” strategies. This effort implies the assumption that fit among external, internal, and

strategic variables leads to better performance (Naman and Slevin, 1993; Yamakawa et al.,

2011). Heijltjes and van Wittelloostuijn (2003) distinguish three fit perspectives: formulation,

implementation, and integration. A formulation perspective refers to the organization’s

industry structure, such that strategy efficacy depends on the match between the

organization’s strategy and its industry structure (e.g., Ceci and Masini, 2011). This viewpoint

relates closely to the industrial organization view, which connects external factors to an

organization’s strategy (Li and Tsai, 2009). The implementation perspective instead centers

on the relationship between an organization’s internal aspects and its strategy (e.g.,

Newbert et al., 2007; Xu et al., 2006), which reflects the RBV and the strategic importance of

internal resources, capabilities, and competences (Barney, 1991; Li and Tsai, 2009; Newbert,

2008) as the roots of systematic competitive advantages (Banerjee, 2003; Prahalad and

Hamel, 1990). Finally, the integration perspective combines the former two approaches to

focus on the relationship among an organization’s strategy, structure, and environment (e.g.,

28

Beer et al., 2005). This latter perspective has not, to the best of our knowledge, been applied

previously in incubator literature.

In contrast, the formulation perspective considers the relationship between an

incubator’s stakeholders16 and its strategy. For example, Sofouli and Vonortas (2007) relate

Greece’s external policy context to the (strategic) objectives of its science parks and

incubators. Although incubators have a wide variety of stakeholders (McAdam and Keogh,

2006; Mian, 1997), the viewpoint of their tenants tends to be prioritized (Jungman et al.,

2004), such as when Abduh et al. (2007) examine tenant service satisfaction and McAdam

and Marlow (2007) consider the (dis)advantages of services offered to tenants. Bruneel et al.

(2012) also propose that an incubator’s value proposition (and strategy) should be evaluated

by its tenants. We accordingly consider tenant service expectations as a measure of external

fit.

With regard to internal fit from the implementation perspective, von Zedtwitz (2003)

lists good incubator management practices and links each of them to an incubator strategy;

Allen and McCluskey (1990) argue that the incubator’s value position determines its

resource offering. Clarysse et al. (2005) adopt the RBV and link research institution

incubation strategies to resource implications, whereas von Zedtwitz and Grimaldi (2006)

use it to investigate the relationship between incubator strategies and service

characteristics. Yet despite these efforts to link strategy to internal aspects, Hackett and Dilts

(2008) conclude that an incubator’s internal functioning remains a black box, without any

comprehensive, systematic connection between internal aspects and strategy. Their

extensive literature review allows them to propose three dimensions of an incubator’s

internal functions: selection, resource munificence, and monitoring and business assistance.

During the selection process, the business incubator accounts for the start-up’s market,

financial, and team characteristics (Aerts et al., 2007). Resource munificence refers to

internal networking and incubator resource utilization, including whether tenants use the

services provided. Finally, monitoring and business assistance involves strategic 16 The formulation perspective thus looks at the industry structure as a whole and the organization’s strategy alignment. However, incubator studies mainly focus on one aspect from the incubator’s external environment: stakeholder expectations. Although studying the industry structure as a whole in relation to the incubator’s strategy would provide valuable insights, this is not the topic of this doctoral thesis. For the external viewpoint, we take on a narrower stance and focus on the incubator’s main stakeholder: its tenants (Jungman et al., 2004). By doing so, we employ a traditional dyadic customer view (cfr. introduction of this doctoral thesis).

29

management and the incubator’s monitoring comprehensiveness and quality. We use all

three dimensions to examine an incubator’s internal functioning in relation to its strategy,

including all relevant processes, systems, assets, knowledge, capabilities, and cultures

(Matthyssens et al., 2009). Miles and Snow (2003) and Kaplan and Norton (2008) have

established the importance of adequate processes, systems, and organizational factors;

Barney and Clark (2007) do the same for assets, knowledge, and capabilities.

1.3. Methodology

Our empirical study draws on extensive qualitative research, which is often required

when the research domain is broad and complex and the context is important (Dul and Hak,

2008; Yin, 1990). Eisenhardt and Graebner (2007) also find it particularly useful in new

research areas or situations in which researchers know little about the phenomenon. Thus, it

is typically used to address “how” and “why” questions (Eisenhardt and Graebner, 2007; Yin,

1990); both our research questions (see the Introduction section) represent “how”

questions—that is, how incubators differentiate themselves while also ensuring internal and

external alignment with their differentiation strategy. Thus, qualitative research is highly

appropriate for addressing the complex topics we consider (Bryman and Bell, 2007;

Eisenhardt, 1989; Yin, 1990).

Between June 2009 and February 2010, we conducted 9 in-depth interviews with

incubator managers, 30 in-depth interviews with tenants, 3 focus groups with incubator

managers and experts, and then a final presentation and discussion meeting with incubator

managers and experts. We briefly discuss this population, before we explain our research

design and data collection process.

1.3.1. Population

Specifying the population under investigation “is crucial, because the population defines

the set of entities from which the research sample is to be drawn” (Eisenhardt, 1989, p.

537). This helps to lower extraneous variation and increase external validity. For this study,

we assert that customer value creation demands a good understanding of the needs and

expectations of customers (O’Cass and Ngo, 2011), namely, of the tenants who consider

incubator value propositions (Bruneel et al., 2012). Therefore, we must investigate incubator

30

tenant service expectations. In turn, we compare these customer viewpoints with manager

and expert experiences, to gain insights into differentiation possibilities (see also Ibrahim

and Gill, 2005; Lawton, 1999). Moreover, the incubator managers and experts offer

information about the internal functioning of an incubator, such as its processes, systems,

resources, and capabilities, in reference to tenant expectations on implementation issues

(e.g., Mian, 1996; Rice, 2002).

Across the varied objectives for incubators (e.g., economic development, research

commercialization, integration of social classes; Aernoudt, 2004; von Zedtwitz, 2003), we

chose to focus on nonprofit economic development incubators, because worldwide, most

incubators match this profile (Bruneel et al., 2012; Knopp, 2007). This focus also aligns our

study with previous research (e.g., Grimaldi and Grandi, 2005; von Zedtwitz, 2003) and

acknowledges that the profit decision is one of the most important strategic choices an

incubator makes, so combining insights from both for-profit and nonprofit incubators might

bias the data. We choose economic development incubators specifically, in line with Ratinho

and Henriques’ (2010) assertion that business incubators are mostly linked to economic

development.

Most economic development incubators also focus on local development, which

represents our second selection criterion. We sought areas with a relatively large number of

incubators in a relatively small space; ultimately, we targeted Belgium, a small country

(30.530 km2) located in the economic and political center of Europe. Across its three

economically and culturally distinct regions—Flanders, Wallonia, and Brussels—Belgium

hosted 68 incubators at the time of our study: 9 in Brussels, 13 in Wallonia, and 56 in

Flanders. Flanders in turn comprises five provinces: Antwerp (16 incubators), East-Flanders

(11), West-Flanders (9), Limburg (10), and Flemish-Brabant (10). Because Antwerp has the

most incubators, we chose this province for our empirical research.

In Antwerp, nine of the sixteen incubators were nonprofit economic development

incubators that worked closely together with the Development Authority of the province. By

working with business incubators, this local government organization aims to stimulate

economic development in the region (POM Antwerp, 2012). Economic development is an

important strategic objective for all nine incubators, so we included them all in the empirical

analysis. These nine incubators are located within a 60 km radius; distances less than 80 km

31

are considered geographically close (De Silva and McComb, 2012). Thus, the province of

Antwerp offers an appropriate research location.

The sample characteristics in Appendix 1 further reveal that five of the nine incubators

can be categorized as mixed-use, such that that they did not focus on a particular sector or

technology field, whereas four adopted a focus strategy. Specifically, Incubator D focused on

the building sector and included sustainable building start-ups; E featured companies active

in the creative sector (e.g., architects, designers); G focused on companies in the energy and

environmental technology field; and I attracted high-tech companies with a focus on life

sciences and information and communication technologies.

To avoid the potential for bias that would occur if we incorporated the viewpoints of

only one type of tenant (Mian, 1996), our tenant sample represents the total tenant

population. Although incubators are designed to stimulate small start-up companies (Bergek

and Norrman, 2008) and generally support companies for three to five years (European

Commission, 2002), tenants in our target population represent a wide variety of ages (1–35

years), sizes (1–45 full-time employees), and incubation periods (1–18 years). Furthermore,

the combination of mixed-use and focused incubators led to a sample of tenants engaged in

a wide variety of activities (e.g., air transport, communications, catering). This diversity (see

Appendix 2) enabled us to gather rich data while avoiding a bias associated with the opinions

of only one type of tenants.

1.3.2. Research design, data gathering process, and study quality

Mathison (1988, p. 13) explains that “good research practice obligates the researcher to

triangulate, that is, to use multiple methods, data sources and researchers, to enhance the

validity of research findings,” which in turn improves reliability and validity (Bryman and Bell,

2007; Eisenhardt and Graebner, 2007; Ghauri, 2004; Pratt, 2009). We pursued these benefits

in three main ways. First, to achieve data triangulation, we gathered primary data from

multiple respondent groups (incubator managers, tenant managers, and experts) and

secondary data from various sources, such as websites (of incubators, tenants, incubator

partners, and coordinating organizations), internal incubator documents, internal

documents from the Development Authority, and incubator and tenant brochures. Second,

we attained method triangulation by employing different qualitative research methods.

32

Specifically, we executed in-depth interviews, focus groups, and presentations with open

discussions while also analyzing secondary data. Third, to ensure researcher triangulation

and minimize researcher bias (Bøllingtoft, 2007), the team of three researchers engaged in

regular team meetings (Eisenhardt, 1989). We divided the research tasks as follows: two

researchers conducted the data collection and analysis, and a third played a reviewing,

consulting, and guidance role. The third researcher also took the lead in the focus groups.

To confirm our interpretations, we used member checks (Danneels, 2002), such that we

confirmed our intermediary interpretations at several moments throughout the data

collection and analysis processes (Hirschman, 1986; Lincoln and Guba, 1985). The incubator

managers and external incubator experts who participated in the focus groups, in-depth

interviews, and final presentation served as our research auditors. In member checks, we

asked the interviewees for approval of our summaries of the in-depth interviews. We also

organized focus groups and formal presentation moments to present and discuss the

(intermediary) results.

1.3.2.1. In-depth interviews

For the in-depth interviews with incubator managers, two members of the research

team participated: The first interviewer conducted the interview, and the second took field

notes (Eisenhardt, 1989) and confirmed that all questions had been asked. The semi-

structured interview protocol focused on the incubator’s strategy, its service offering, its

internal organization, and external influences. We recorded and transcribed all interviews,

then sent the summary to the interviewees, who could make comments. If necessary, we

clarified any uncertainties through telephone or e-mail conversations.

The tenant interviews were similarly organized, except that for most of these interviews,

only one interviewer was present. To ensure consistency, the interviewer who conducted

the interviews with the incubator managers also conducted the tenant interviews. These

interviews focused on the incubator’s strategy, service offering, internal organization, and

external influences. We also pursued a deeper understanding of the service offering by

asking tenants which services they found valuable, rare, inimitable, and not easily

substitutable (Barney, 1991)—that is, which services led to customer value creation. By

33

checking the tenants’ viewpoints against those of the incubator managers and experts, we

discerned the incubator’s competitive advantage and differentiation possibilities.

1.3.2.2. Focus groups

We conducted three focus groups with incubator managers and experts. Combining

focus groups and individual interviews can be valuable, because this combination offers both

depth (interviews) and breadth (focus groups); according to Morgan (1996), if the in-depth

interviews take place first, the focus group can serve as a check on the conclusions of the

interview analysis. If in-depth interviews occur after focus groups, the specific opinions and

experiences that emerged from the focus group can be examined in-depth (Morgan, 1996).

To attain both advantages, we conducted the focus groups both before and after the in-

depth interviews.

Overall, nine incubator managers and five external experts participated in the focus

groups. The incubator managers also had been interviewed, so we could corroborate and

deepen the observations. Because our study sample consists of nonprofit economic

development incubators, we also invited experts from nonprofit government agencies to

participate in the focus groups.

We took great care in addressing sampling, moderator involvement, and group size

issues. First, with regard to sampling issues, we undertook a careful segmentation of focus

groups to create relatively homogenous groups (Krueger, 1988; Morgan, 1988). The first

focus group included incubator managers, the second external experts, and the third

combined their viewpoints. Thus in the first focus group, we discussed the incubator context

and factors that might influence its strategy. During the second focus group, we presented

the intermediary results and investigated the external context more closely. Finally, the last

focus group featured a discussion of the intermediary results and a more in-depth

consideration of internal aspects and the external context.

Second, moderator involvement refers to (1) asking questions and (2) managing group

dynamics. Despite a lack of consensus about the number of questions a moderator should

ask, Morgan (1996) stresses that the research goal determines how structured a focus group

should be and how many questions the moderator should ask. With our clear research goal

and pursuit of knowledge about service-based differentiation strategies and their necessary

34

organizational elements, we followed a specific outline during the focus groups. Thus we

discouraged participants from diverging into topics with less relevance for this research. To

manage group dynamics, the moderator must find some way to ensure everyone has an

equal opportunity to participate, such that the opinions of both quiet and dominant

participants enter the analysis. Therefore, the moderator carefully posed questions to

prompt all participants to participate in the discussion. For example, from time to time, he

asked each participant to provide his or her individual opinion about the discussion topics.

Third, Morgan (1996) states that both smaller and larger groups have advantages: In

smaller groups, each participant can discuss his or her opinions and experiences, but larger

groups bring together a wider variety of viewpoints. Thus, we kept the first two focus groups

rather small, with five and six participants, to enable in-depth discussions, then in the third

focus group, we included eleven participants. This larger group matched the goal of this

focus group, namely, to combine the viewpoints of both incubator managers and external

experts.

1.4. Empirical results

In presenting the findings from our empirical study, we start with tenant expectations

about the incubator’s competitive scope and service offering, which we compare with the

incubator manager and expert experiences and differentiation possibilities. Then we present

how each service-based strategic position can be implemented in practice, including the

necessary processes, systems, assets, knowledge, capabilities, and culture.

1.4.1. Customer value creation leading to incubator differentiation

We discern two opposite opinions regarding the preferred incubator scope. Some

tenants seek complementary activities and opt for an incubator with diverse tenants and no

specific sector or technology focus. Company F2 was “pro collaboration.… I would find it

fantastic if I knew what this or that company can do…. I would really like to have an IT

company in the incubator. If you have a problem, they are nearby. Now, I have to shop

around to find an appropriate solution and that annoys me”. Company F3 offered another

reason to prefer diversity: “we are all little companies. Let us be diversified. I would

definitely not choose a business incubator that focuses on a specific sector”. This same

35

interviewee worried that “there are people who take their publicity issues to other

companies [outside the incubator] because they don’t even know there is somebody in the

incubator that creates publicity. I think it is important to stimulate cross-selling and keep

business between the four walls [of the incubator]”.

But not all tenants opted for this diversified focus. The group that assigned greater

importance to a focused scope tended to be active in fields with relatively few other players,

so collaboration with them was critical. They hoped to be able to use the incubator’s image

and network to enter or grow in that sector or technology field; Company G1 specifically

stressed the importance of image and credibility: “if an incubator focused on companies

employing the same technology we use, I would choose that incubator. That is much easier

to identify yourself”. In the audiovisual sector, Company H1 recognized that “if another

company making documentaries was located here, it would be very valuable,” an opinion

based on this interviewee’s broader industry experience: “I just became chairman of a

coordinating organization of companies in the documentary sector and I see that we are

definitely in need of such contacts”.

Thus we find a clear dichotomy in the type of incubator scope tenants want, and the

incubator managers and experts confirm this finding. Both diversified and focused

incubators can create customer value through their service offerings, but which services

create customer value depends on the incubator’s competitive scope. Tenants opt for

incubators with a diversified scope when they seek collaboration in their operational

business activities or need partners with complementary competencies. The tenants in this

group obtain access to on-site, in-depth services, such as secretarial functions (e.g., taking

minutes, filing documents), business advice, and personal network connections that they

would otherwise have difficulty finding. These services help start-ups considerably during

their development process. Incubator managers and experts agree that such services create

substantial customer value and might provide a basis for incubator differentiation. Thus,

these services represent success producers for incubators with a diversified scope (see Table

1-1). In contrast, basic secretarial functions such as organizing the post or logistic equipment

such as a meeting room, and non-personal lists of operational business advice partners are

services that tenants expect to receive from all diversified incubators. Tenants do not

consider them rare; instead, they appear part of the basic service offering by each diversified

36

incubator. Tenants think they could easily switch to another diversified incubator to receive

similar support. According to the incubator managers and experts, a decade ago these basic

services might have led to differentiation, but today, all diversified incubators offer them.

Thus these services represent failure preventers for diversified incubators (see Table 1-1).

Some tenants prefer to be co-located with other companies active in the same sector or

that employ related technologies, because this focused scope supports core business

network opportunities. They want to use the incubator’s sector/technology image and

credibility as a foundation for their own development, often through core business-related

partnerships. They still expect basic services from their incubators, in that their expectations

include sector- or technology-specific infrastructure and a contact list of organizations active

in their core business that might be interested in forming partnerships with small firms. The

tenants we interviewed perceived that other specialists could offer these services too, such

that they could easily find another focused incubator with a similar offering. Incubator

managers and experts concur that a lack of such basic infrastructure and networking services

would result in incubator failure. However, focused incubators also can create high levels of

customer value by offering on-site in-depth business support related to the tenants’ core

business, which tenants consider difficult and expensive to find elsewhere. They also value

the availability of personal network connections with other organizations active in their core

business, because it can be difficult for a new, unknown company to establish good contacts

with a well-known organization. Personal introductions are often necessary. Similarly, the

incubator managers and experts explicitly mentioned on-site core business and personal

network contacts as a basis for incubator differentiation. The overview of tenant

expectations of service offerings by focused incubators in Table 1-2 consists of both

incubator failure preventing and success producing services.

These results suggest two service-based differentiation alternatives: generalist and

specialist stances.17 The Development Authority has a saying in the incubator’s positioning.

With Figure 1-1, we classify target clients of a generalist incubator as start-up companies

that are active in a wide variety of sectors or technologies. Thus, the generalist incubator’s

17 Very large incubators might opt for portfolio management and for example develop several specialist incubators, each focusing on their own sector. Because our empirical study only involved small incubators (most have an inside space smaller than 1.000 m2, see Appendix A), portfolio management was not possible.

37

strategic intent is to offer on-site, in-depth operational business activity services and

personal contacts to start-up firms active in a wide variety of sectors or technologies. If they

provide these services, they can differentiate themselves from other generalists. For

example, one of the generalists in our sample employs an incubator manager who previously

worked for a large, nonprofit advice organization and thus can suggest personal network

connections related to tenants’ operational business activities. Other generalists do not offer

incubator managers with similar personal networks, so the focal incubator can achieve

differentiation.

In contrast, the target clients of specialist incubators are start-up companies that are

active in a particular sector or technology field, so their strategic intent should be to offer

on-site, in-depth sector- or technology-specific services and personal contacts to start-up

firms active in a specific sector or field of related technologies. One of the specialist

incubators in our sample maintained an on-site library with information about the sector.

Thus tenants had easy access to on-site, in-depth sector knowledge, which they could not

attain from other specialists. This gave this incubator a competitive advantage.

38

Table 1-1: Tenant expectations of service offerings (no sector/technology focus)

Administrative services Logistic services Business support services Networking

Incubator failure preventing services

A common secretary (e.g., reception, telephone, postal delivery, welcoming visitors)

Basic equipment (e.g., flexible office space, Internet connection, telephone line, photocopier)

In-depth business support services focusing on operational business activities

Network of (high-quality) partners for offering these services

Access to (high-quality) partners such as venture capitalists, bookkeepers, and lawyers

Access to other tenants

Interview example

Company A4: “When you are not there, they can take a message. It is also interesting that they manage your post, that the post does not get lost when you are not in the office. If we for example are expecting large postal packages, the postal office can deliver it at the reception”

Company A2: “They [the incubator personnel] were very flexible. They adapted the incubator’s infrastructure for us”

Company A4: “You can also make photocopies in the incubator. We have a photocopier, but if we have a large amount, we use the incubator’s photocopier, which is a very good machine. That is interesting”

Company B2: “I am an occupational therapist, so I know a lot about human resources, but I can imagine that other people do not know how to handle human resources. For example, recruiting the first employee. We already had our accountant, but if a start-up company needs this kind of advice, then I expect that the incubator can give a list of the accountants in the neighborhood, with a little more information than just the accountant’s telephone number. They [the incubator] should be able to tell which accountants have experience with which kind of company, and who could do a good job” Company F2: “If the incubator would suggest an IT partner, it has to be a good one.… They should use some kind of quality label”

Incubator success producing services

In-depth secretarial services (e.g., taking minutes, filing documents, organizing agendas, organizing business trips)

No differentiation On-site operational business knowledge (e.g., bookkeeping)

Personal network connections related to the company’s support activities

Interview example

Company C4: “It would be very interesting if there would be more administrative support.… Instead of hiring somebody, we would be interested in outsourcing a number of administrative tasks to them [the incubator]”

N/A Company C2: “If the business incubator would offer general services such as human resource management, then we would definitely use it. Today, we have to search for this kind of knowledge elsewhere.… It would be very interesting if this kind of services would be offered by the incubator itself, if the services would be ‘physically’ close. A little bit like in a big company, where you can go and talk with the accountancy or HR department”

Company A1: “He [the incubator manager] knows many people with knowledge about support activities such as a lawyer. That is very interesting”

39

Table 1-2: Tenant expectations regarding service offerings (sector/technology focus)

Administrative services Logistic services Business support services Networking

Incubator failure preventing services

A common secretary Basic equipment (e.g. office, conference room)

Sector- or technology-specific infrastructure (e.g., incubators focusing on companies active in the information and communication technology sector, with state-of-the-art and reliable infrastructure such as a server room)

In-depth business support services focusing on a company’s operational activities

Sector- or technology-specific, in-depth business support services

Network of partners for offering these services

Access to possible partners active in the company’s operational activities

Access to possible partners in the same sector or field (both inside [other tenants] and outside the incubator)

Interview example

See Table 1-1. Company A4: “If you are a hardware company, then you need more than the basic equipment. You need for example an antistatic floor. An incubator focusing on a particular sector should offer sector-specific infrastructure”

Company G2: “We are located in this incubator because we work closely together with a large knowledge organization active in our field of related technologies. Being physically close to this knowledge organization is very important”

Company D1: “I work together with all my competitors.… If somebody calls me and I have to deliver a large amount of products in a short time, I work together with my competitors. To be honest, I expected the incubator to have more tenants active in our sector”

Incubator success producing services

No differentiation No differentiation On-site sector- or technology-specific in-depth business support services, such as knowledge centers

Personal network connections related to the company’s core business (e.g., funding organizations focusing on the company’s core business)

Interview example

N/A N/A Company G1: “If there would be an information intelligence center in our sector available which would be shared with many partners, I would definitely make use of it”

Company E2: “Good networking contacts are invaluable. For example, it would be very interesting if the incubator manager could say that he/she has already done business with an organization in my sector. That way, he/she could introduce me to that organization”

40

Figure 1-1: Service-based differentiation strategies

SPECIALIST

GENERALIST

Competitive advantage

Co

mp

etit

ive

sec

tor

sco

pe

- Support business activities - Personal contacts

- Core business activities - Personal contacts

Div

ersi

fied

Fo

cuse

d

Strategic intent

Offer on-site operational business activity services and personal contacts to start-up firms active in a

wide variety of sectors or fields of related

technologies

Offer on-site sector- or technology-specific services and personal contacts to start-up firms active in a specific sector or field of related technologies

Service offering differentiation

- On-site, in-depth sector- or technology-related services

- Personal contacts in a specific sector or field

- In-depth secretarial services

- On-site, in-depth operational business

support services - Personal contacts related

to operational business activities

41

1.4.2. The identification of necessary competence configurations

To attain these strategic service-based positions, the incubators must achieve alignment

between their internal organization and functions. The first internal incubation element,

tenant selection, entails gearing a selection process to the incubator’s strategic intent. Both

specialist and generalist incubator managers assert that they must select companies that

appear likely to develop into successful businesses; the incubator experts confirm this

aspect is part of the mission statement of economic development incubators. Therefore,

incubator personnel need to develop experience with start-up firms, such as by attending

business plan competitions, entrepreneurship seminars, and workshops. The difference

between generalist and specialist incubators arises only because specialists focus on one

sector or technology field and analyze whether the potential tenant is active in a relevant

area and able to survive and grow in that particular market. Generalists focus more on

financial, personal, or team aspects.

Because specialists and generalists likely target companies with different service

expectations, they also should select companies on the basis of those expectations.

Incubator managers and experts stress that specialists only select companies searching for

co-location with companies active in the same sector or field. Their pool of potential tenants

prioritizes sector- or technology-related business support above operational business

support. Generalists’ selection process instead focuses on companies that value co-location

with a wide diversity of sector or technology offerings, because they seek operational, not

core business, support.

With regard to the second internal incubation element, resource munificence, we note

internal networking and resource utilization among tenants, which generally requires

regular contacts among incubator personnel and tenants. For example, the incubators might

host introduction days for new tenants or regular meetings in which incubator personnel

and tenants can discuss the services offered and used. These interactions also require

specific competences and capabilities. For example, the incubator personnel needs to be

friendly, with good interaction skills and trustworthiness. Tenants stressed that without

trust, they would be afraid that their core business activities might be exposed to others if

they shared their needs and problems with the incubator manager/staff. Incubator

managers also stressed the importance of a “willingness-to-interact” attitude; it is difficult

to organize activities if (some) tenants are not willing to engage. To encourage an open

42

culture, the selection process often incorporated this attitude as a selection criterion.

Finally, to offer viable interaction possibilities with external organizations, the specialist

managers stressed the need for strong, active network partners in the relevant sector;

generalist managers underlined the importance of close partners who could offer

operational advice.

The third internal incubation element, monitoring and business assistance, refers to

strategic management and the incubator’s monitoring comprehensiveness and quality.

Strategic management requires strategy-related knowledge and a strategic planning

committee to follow up in a (long-term) development process. To provide comprehensive,

high quality support, the incubator needs a control system that can verify whether the

services offered are of sufficient quality and satisfy tenant needs. Regarding the latter

criterion, the specialist managers highlight their focus on sector- or technology-related

expectations, whereas generalist managers focus on operational business support needs.

Comprehensive services often require a range of standard and adaptable (customized)

service packages. For example, one generalist incubator manager explained that it provided

Internet access to all tenants and then allowed tenants to choose a package of additional

services, such as accounting or human resource support. The incubator managers and

experts suggested that generalists encourage their employees to update their knowledge

constantly, to ensure they can offer comprehensive operational business support.

Employees of specialist incubators instead must remain up to date in their sector- or

technology-related knowledge. Finally, involving (potential) tenants and experts in quality

controls and checking for comprehensiveness provides valuable insights into tenants’

service expectations, the offerings available in other incubators, and tenant satisfaction. In

Tables 1-3 and 1-4, we depict the competence configurations for generalist and specialist

incubators, respectively.

43

Table 1-3: Competence configuration: generalists

Processes and systems Assets, knowledge, and

capabilities

Culture

Process to ensure tenants come into contact with one another, incubator management, and third-party service providers (e.g., accountants). Examples:

Introduction day for new tenants

Regular meetings of tenants and incubator management to confirm that tenants are aware of and use the services offered

Selection process/system to identify companies that potentially can develop into successful, growing businesses. Mainly take personal, team, and financial characteristics into account

Selection process/system to identify companies searching for co-location and interaction with companies from diverse sectors/technologies and operational incubator support services

Entrepreneurship experience

Strategic planning committee, which provides strategic advice to the incubator and its tenants

Quality control and improvement processes

Process to ensure comprehensive operational business activity services

Open culture in which tenants and the incubator personnel have regular meetings about the services offered, tenant needs, and possible synergies between tenants and third-party network partners

Encourage strategic planning, for both the incubator and tenants

Involve (potential) tenants and operational business advice experts in quality improvement processes

Continuous search for innovative and state-of-the-art operational business support services

Encourage incubator employees to update their knowledge on operational business activities, such as human resources and firm infrastructure

Strategy-related knowledge, both for the incubator and its tenants

Quality controller

Standard and adaptable service packages

On-site, up-to-date, innovative operational business support services

Encourage incubator employees to attend business plan competitions, entrepreneurship seminars, and workshops. Goal: incubator employees gather sufficient entrepreneurial experience

Selection process

Resource munificence

Monitoring and business assistance

Interpersonal skills: introduce and connect tenants, talk with tenants to gain insights into the services they need

Empathic capability and friendliness

Strong network partners (operational business advice)

44

Table 1-4: Competence configuration: specialists

Processes and systems Assets, knowledge, and

capabilities

Culture

Interpersonal skills: introduce and connect tenants, talk with tenants to gain insights into services they need

Empathic capability and friendliness

Capability to develop and maintain a strong basis for trust

Strong network partners in the incubator’s sector or field

Process to ensure tenants come into contact with one another, incubator management, and third-party service providers active in the incubator’s sector or field. Examples:

Introduction day for new tenants

Regular meetings between tenants and incubator management to ensure tenants are aware of and use the services offered

Selection process/system to identify companies that potentially can develop into successful growing businesses. Mainly take into account personal, team, financial, and market characteristics, with a focus on sector- or technology-related characteristics

Selection process/system to identify companies searching for co-location and interaction with others active in the same sector or employing related technologies. Companies should search for sector-/technology-specific business support

Sector/technology experience, especially knowledge about customer needs, products/services offered by companies active in this sector, and financing needs, which reveal whether the start-up has potential to differentiate itself on its market

Strategic planning committee, which provides strategic advice to the incubator and its tenants

Quality control and improvement processes

Process to ensure comprehensive sector- or technology-specific service offerings

Open culture in which tenants and the incubator personnel have regular meetings about services offered, tenant needs, and possible synergies between tenants and third-party network partners

Encourage strategic planning, for both the incubator and tenants

Involve (potential) tenants and experts in quality improvement processes

Continuous search for innovative and state-of-the-art sector- or technology-specific services

Encourage incubator employees to update their knowledge of sector activities, such as operations and logistics, and latest technological changes

Strategy-related knowledge pertaining to the incubator’s sector or field, both for the incubator and its tenants

Quality controller

Standard and adaptable service packages

On-site, up-to-date, and innovative sector- or technology-specific services

Encourage incubator employees to attend sector- or technology-related events, such as seminars and workshops, to gather sufficient sector- or technology-related and entrepreneurial experience

Selection process

Resource munificence

Monitoring and business assistance

45

1.5. Discussion

Our empirical results relate to extant literature in several respects. We begin with tenant

service expectations and incubator differentiation options, then discuss the necessary

organizational components for each strategy alternative.

1.5.1. Customer value creation leading to incubator differentiation

Existing incubator classifications identify diversified and focused scopes (Plosila and

Allen, 1985; Sherman, 1999; von Zedtwitz, 2003; von Zedtwitz and Grimaldi, 2006). We

confirm this dichotomy. We also reaffirm the notion that tenants in a focused incubator aim

to use that incubator’s image or credibility to enter and grow in their sector or field

(Ferguson and Olofsson, 2004; Studdard, 2006). However, our empirical analysis contradicts

the common view that network cooperation is more effective in focused incubators. That is,

prior literature argues that to help tenants locate the right contacts in a complex network,

the incubator must organize its network connections (Rice, 2002), which seemingly should

be easier for focused incubators (Bruneel et al., 2012), such that stimulating cooperation

opportunities and synergies may be more effective in an incubator with a focused scope

(Haapasalo and Ekholm, 2004). But our analysis shows that networking can equally be

effective within diversified incubators. Schwartz and Hornych (2010) also have recently

provided evidence that networking is effective in both diversified and focused incubators.

Although further research is needed, our findings suggest that the benefits of networking

effectiveness achieved in focused incubators might arrive through diversified incubators.

Another contrast with previous research relates to the definition of the incubator’s

scope. Prior research suggests that “the professional preferences or competences of

incubator managers” determine the incubator’s scope (von Zedtwitz, 2003, p. 181), because

senior incubator managers offer key personal contacts, networks, and experiences that he

or she can leverage through the incubator (Hannon and Chaplin, 2003). We find instead that

the Development Authority had an important influence on the incubator’s focus. It was the

driving force for a re-focus by one of the incubators in our study, which reflected a shift in

company needs, not the incubator manager’s knowledge base. The Development Authority

even asked this incubator manager to gain more in-depth knowledge in a sector in which

the manager previously had no familiarity.

46

Although not a direct contrast, we also consider it surprising that few prior studies

acknowledge how an incubator’s scope influences its service offering. Many studies fail to

specify the incubator’s scope (e.g., Abduh et al., 2007; Tötterman and Sten, 2005) or simply

mention technology incubators, without specifying whether they focus on one or several

fields (e.g., Mian, 1996). Schwartz and Hornych (2008) instead delineate the advantages and

disadvantages of incubators with a sector or technology focus, and our data partly confirm

their findings, in that focused incubators can offer focused infrastructure, knowledge, and

know-how. However, our data also suggest that in focused incubators, companies seek core

business networking possibilities with other tenants, whereas Schwartz and Hornych (2008)

conclude that a negative climate impedes internal networking in focused incubators.

Further research should address this conflict; we posit that the difference might reflect the

particular context of Schwartz and Hornych’s (2008) case study, in which incubator

managers attached substantial value to the opinions of established, better known tenants

when making strategic decisions. By excluding smaller, less established firms, the incubator

might have created a negative working climate and poor internal cooperation. We did not

encounter any comparable situations in the focused incubators studied.

Few existing studies make explicit distinctions between success producing and failure

preventing services either. Our evaluation of tenant, manager, and expert viewpoints on

customer value creation options adds some nuance by including the distinction between

incubator failure preventing and success producing services. For example, prior studies offer

evidence that basic office and administrative services, such as a flexible office space or

postal delivery, save costs and time for tenants (Abduh et al., 2007). Furthermore, on-site,

in-depth services, such as marketing advice from specialists (Hackett and Dilts, 2008;

Heydebreck et al., 2000), university faculty expertise (Lalkaka, 2003), advice on financing

their technology development (Heydebreck et al., 2000; Macdonald and Joseph, 2001), and

access to personal contacts (Aaboen, 2009; Abduh et al., 2007) are important services for

incubator tenants. However, in all these cases, it remains unclear which services actually

create high levels of customer value and which not. Because this subdivision gets largely

overlooked, even very recent studies argue that “business incubators do not differ greatly in

terms of what they offer to tenants” (Bruneel et al., 2012, p. 115). Our results contradict this

assertion. Although service groups (e.g., administrative, logistic, business support,

networking) may be comparable across incubators, the interpretations of these service

47

offerings differ notably. For example, some incubators offer on-site business support, while

others opt for external coaches. Despite acknowledging such differences (e.g., Bruneel et al.,

2012; Bergek and Norrman, 2008), previous scholars have not considered the possibility that

they provide a basis for incubator differentiation.

We uncovered one study that makes an explicit subdivision in the level of added value;

our analysis contradicts its results. That is, Mian (1996) examines whether a service adds

major, minor/moderate, or no value to tenants, and 20–50% of the respondents to that

study report that administrative services (e.g., mail sorting, word processing) and basic

logistic services (e.g., conference room) add major value. In contrast, we found that such

basic administrative and logistic services do not create high customer value. Further

research should investigate these differences, but we posit that a decade ago, such services

created high value, whereas today, they are simply part of the basic service offering of an

incubator and thus have transformed into failure preventers for the incubator. In the

modern business world, business support and networking services appear relatively more

important than administrative and logistic services (Bergek and Norrman, 2008).

Our more nuanced analysis of tenant service expectations and customer value creation

options also reveals that, in contrast with the conventional wisdom (e.g., Bruneel et al.,

2012; Schwartz and Hornych, 2008), focused incubators are not the only route to success.

Rather, many tenants prefer generalist incubators, and customer value creation is possible

through both specialists and generalists. Companies opt to locate in the type of incubator

that creates the most value for them. These results align with findings that show companies

choose among locations within their region (Pe’er et al., 2008) and select the location that

best fits their needs and expectations (Cantù, 2010; Wright et al., 2008).

1.5.2. Necessary competence configurations

Several elements of the competence configurations apply to both specialist and

generalist incubators (e.g., empathic capability, service quality control). However, because

their competitive scopes and client service expectations differ so considerably, their internal

structural and organizational aspects demand different adaptations. A generalist incubator

should offer services that reflect the value chain, whereas a specialist should focus on

primary activities (Porter and Millar, 1985).

48

With regard to the selection internal alignment aspect, we confirm that incubators

should select weak but promising firms (Hackett and Dilts, 2008). Yet we also note a clear

distinction between the selection criteria for generalists and those for specialists.

Generalists focus relatively more on personal, team, and finance-related selection criteria;

specialists devote more attention to market-related criteria. Existing research does not

seem to make this distinction and instead suggests that a balanced selection process, using

all these criteria, is preferable (Aerts et al., 2007; Lumpkin and Ireland, 1988; Merrifield,

1987), and does not include a recognition that specialists might need different selection

criteria than generalists.

In addition, we discover a selection criterion that has been largely overlooked by prior

studies, namely, whether companies have a “willingness-to-interact” attitude. Although

previous research suggests that value creation occurs only when tenant use networking

opportunities extensively (Hughes et al., 2007), it has not established the willingness-to-

interact attitude as a selection criterion. Instead, most personal selection criteria focus on

the management team’s age or gender; technical, financial, and marketing skills;

aggressiveness/persistence; or references (Aerts et al., 2007; Lumpkin and Ireland, 1988).

The failure to recognize the importance of openness to interactions seems strange, because

studies show that frequently acting on and using incubator services, such as networking and

business support, effectively characterize innovative, internationally focused, growth-

oriented firms (Hytti and Mäki, 2007). Furthermore, the consensus opinion is that

incubators strive for such company development processes (Hackett and Dilts, 2008).

For the resource munificence internal dimension (Hackett and Dilts, 2008), we find that

regular contact among tenants, incubator personnel, and external experts is indispensable

for stimulating cooperation and resource usage. Existing literature confirms that incubator

managers exert significant effects on contact frequency, and furthermore that their efforts

to establish a good working climate and trust can influence cooperation frequency (Tamásy,

2002). Trust is necessary because tenants fear that their ideas and business secrets might be

stolen by other tenants (McAdam and Marlow, 2007) or experts/consultants (Chan and Lau,

2005). Our study reinforces these concerns, which stresses the importance of an open

culture in which tenants are not afraid to interact with other tenants, incubator personnel,

or external networking partners. In this situation, efficient, effective technology transfer and

innovation processes (Gibson and Naquin, 2011) and interactions that support optimal

49

technology usage (Berg and Einspruch, 2009) can take place. Again, a willingness-to-interact

attitude offers a strong foundation for such an open culture.

We also find discussions of monitoring and business assistance in prior literature,

especially pertaining to the role and importance of good strategic management practices

(Autio and Klöfsten, 1998; Westhead and Batstone, 1998). For example, incubator managers

and experts might use Walsh and Linton’s (2011) “Strategy-Technology Firm Fit Audit” tool

to examine whether a potential tenant’s managerial capabilities and technical competencies

fit their “potential product, strategic business unit, acquisition or innovation efforts” (p.

213). Thus they can avoid potential failures by adopting in-depth audit and strategy

planning.

Finally, our study is in line with previous research that stresses the importance of quality

control and improvement (Hackett and Dilts, 2008) and customized support offerings that

reflect tenants’ changing development stages (Bruneel et al., 2012; Chan and Lau, 2005).

Such approaches help increase tenant satisfaction with counseling and business assistance

effectiveness, an important aspect of business support (Bergek and Norrman, 2008) that

many tenants consider currently ineffective (Abduh et al., 2007). For example, for

technology-intensive companies, coaching related to application efforts for R&D investment

programs, such as the Taiwanese Technology Development Program (Lu and Hung, 2011),

can entail a form of customized monitoring. Although an in-depth study of changing tenant

needs and incubator service offerings falls beyond the scope of our study, our results

indicate the need for such studies. To the best of our knowledge, Chan and Lau’s (2005)

study is the only one that explicitly links tenant development stages to changing incubator

service offerings.

1.6. Conclusion

In presenting the conclusion of this study, we start with offering an answer on the

chapter’s research questions and summary of its most important contributions to extant

literature. Then we present practical implications for incubator managers, potential clients

and incubator sponsors. Finally, we discuss the study’s most important limitations and

future research possibilities.

50

1.6.1. Contribution to the literature

We investigate two research questions: how can business incubators, located in the

same region, differentiate themselves in the incubation market through customer value

creation? And how can incubators ensure their external and internal alignment for each

differentiation alternative? Our empirical analysis supports a nuanced evaluation of

customer value creation options through service offerings, which reveals that some services

lead to incubator differentiation, whereas others only prevent incubator failure. This

distinction depends on the amount of customer value created by the incubator service

offerings, rooted in tenant service expectations. Though prior research recognizes the

importance of a customer viewpoint on service offerings (Abduh et al., 2007) and incubator

strategies (Bruneel et al., 2012), it has not differentiated sufficiently between high and low

value-creating services (Mian, 1996). We also confirm the findings with opinions from

incubator managers and experts related to the differentiation possibilities for incubators.

Contrary to the common belief that only focused incubators create high customer value

(Bruneel et al., 2012; Schwartz and Hornych, 2008), we empirically find two service-based

differentiation options for incubators. With a generalist stance, the incubator attracts

tenants from a wide variety of sectors and technologies, and it can attain differentiation by

offering on-site, in-depth operational business support, in-depth administrative services,

and personal network contacts. With a specialist stance, the incubator instead appeals to

tenants from a specific sector or field and should offer on-site, in-depth sector- or

technology-specific services and personal contacts to attain differentiation.

Beyond this distinction, we determine the necessary organizational aspects for each

strategy alternative and uncover two main differences in the internal organizations of

specialists and generalists: the selection process and the service offerings. Specialists

evaluate market-related features of their potential tenants, whereas generalists focus more

on personal, team, and financial characteristics. This distinction has not appeared in

previous research, which is surprising, considering that the incubator’s internal functioning

and value proposition is rooted in its selection process (Bruneel et al., 2012). Furthermore,

each strategy type has its own resource focus: whereas generalists offer operational

business support, specialists focus on primary business support, and their critical internal

resources and competences differ accordingly. If the incubator can align these

competencies with its strategic position, it enjoys differentiation possibilities (Newbert et

51

al., 2007; Prahalad and Hamel, 1990). In this respect, Ray et al. (2004) argue that it is not

having access to a large amount of resources but having access to critical resources that

results in a competitive advantage.

In addition to these differences between specialists and generalists, our empirical study

offers two key results that hold for both incubator types but have largely been overlooked

by or contradicted in existing research. First, we contest the common claim that networking

and cooperation efforts are more effective in focused than in diversified incubators

(Haapasalo and Ekholm, 2004; Rice, 2002). We find that many companies search for

diversified incubators, because they prioritize operational support, a finding that matches

recent work by Schwartz and Hornych (2010). Second, we identify a willingness-to-interact

attitude as pivotal for optimal support offerings. Existing research on incubator selection

criteria has overlooked this selection criterion (Aerts et al., 2007; Lumpkin and Ireland,

1988; Merrifield, 1987), even as it recognizes that business and networking support can lead

to optimal incubation outcomes only if they are used extensively by tenants (Hughes et al.,

2007; Hytti and Mäki, 2007).

1.6.2. Implications for practice and policy

Drawing on these results, incubator managers can more effectively choose an

appropriate service-based differentiation strategy. They should assess the competitive

scope and competitive advantage they hope to achieve by analyzing company expectations

and the competitive positions of other incubators in their region. In turn, they can reach a

well-supported determination of the most appropriate strategic position. Both generalist

and specialist stances can result in differentiation, but the incubator’s internal organization

must be adapted to its stance for success to result. Incubator sponsors, such as universities

and government organizations, also might offer support by undertaking a market and

internal feasibility study before forming the incubator (Zablocki, 2007). This study could

define the service expectations of potential clients (Zablocki, 2007), which would reveal the

necessary internal organizational aspects. Incubator sponsors also might develop a subsidy

or sponsorship scheme that provides incentives for implementing appropriate processes,

systems, assets, knowledge, capabilities, and culture that lead to internal fit (Matthyssens et

al., 2009). Finally, our study results might help potential tenants choose their incubator

more effectively. Depending on the support services they need, different locations might be

52

advisable. Entrepreneurs with a technical background, for example, often lack marketing

and financing knowledge (Heydebreck et al., 2000), so they might benefit most from a

generalist incubator offering in-depth, customized operational business knowledge. In

contrast, companies in a sector that features few other players might prefer a specialist

incubator that offers them a strong image and the credibility to attract core business-

related partners.

1.6.3. Limitations and directions for future research

There are four main limitations related to our research design and three main questions

surged during our empirical investigation. Each suggests a potential research extension.

First, the complexity of the topic pushed us to undertake a qualitative study, which means

its results cannot be generalized. We thus call for additional research that pursues a

widespread, quantitative analysis and provides insight into the general applicability of our

results. Second, we focused on nonprofit economic development incubators, though various

other types of incubators exist (e.g., Aernoudt, 2004; von Zedtwitz, 2003). Researchers

therefore might conduct a similar study among for-profit incubators or those focused on

social or basic research (Aernoudt, 2004). Third, further research should move beyond

service-based differentiation tactics, to include different bases for the competitive scope,

such as the type of entrepreneurs or the incubator’s geographical segment (von Zedtwitz,

2003). For example, interaction and cooperation require some similarity in tenants’

knowledge bases (Mowery et al., 1998), so a particular type of entrepreneur might be

beneficial, such as university faculty and students (von Zedtwitz, 2003). An incubator’s

geographical segment also might influence its differentiation possibilities, because “network

access is a crucial element of successful incubation” (von Zedtwitz, 2003, p. 181), and

institutional factors in the geographic segment might influence the organization’s

functioning as well (Lalkaka, 2003). Fourth, although we examined economic development

incubators and acknowledged that the Development Authority has a saying in the

incubator’s strategic position, we did not include an in-depth analysis of public authority

strategies, or their impact on the incubator’s strategies and differentiation options. Fifth,

current knowledge about incubators’ selection processes is insufficient. In addition to the

willingness-to-interact attitude criterion, other selection criteria may have been overlooked

by extant research. Because an incubator’s selection process defines how it functions

53

(Bruneel et al., 2012), we consider further research on this aspect of great importance.

Sixth, the incubator’s development process also influences its functioning (Bruneel et al.,

2012), yet this internal aspect has not been examined. Finally, we show that tenant service

needs might change, depending on tenant characteristics such as age, development stage,

or sector. Previous research implies the importance of such service offering changes

(Bruneel et al., 2012; Hackett and Dilts, 2008), but more studies should explicitly investigate

how these changing service offerings relate to tenant development stages (Chan and Lau,

2005).

54

Appendices

Appendix A: Characteristics of sample incubators

Table 1-5: Characteristics of sample incubatorsa

Incubator Interviewee job

Year of founding

Size, office space (m2)

Average occupancy rate (07–09)

Sector or technology focus

A Manager 1986 2.267 m2 57% None

B Manager 1986 649 m2 84% None

C Manager 1985 1.069 m2 91% None

Db Manager 1st: 2006 2nd: 2009

1st: 350 m2

2nd: 450 m2 1st: 21% 2nd: 0% (2009)

Building sector, sustainable building

E Manager 2000 618 m2 97% Creative sector

F Manager 2000 550 m2 70% None

G Manager 2009 720 m2 17% (2009) Energy and environmental technology

H Manager 1995 1.045 m2 95% None

I Manager 1993 1.160 m2 85% High-tech, life sciences and information & communication technology

a The average year of founding, inside space and occupancy rate from our incubator sample in Chapter 1 are 1996, 890 m2 and 62%, respectively. For comparison: the average year of founding of the Belgian incubators in our Chapter 4 sample is 1994; the average inside space 1.001-2.000 m2, and the average occupancy rate 61-70%. b This incubator has two buildings in the same location.

55

Appendix B: Characteristics of sample incubator tenants

Table 1-6: Characteristics of sample incubator tenants

Inc. Tenant Interviewee job

Year of founding

N° of FTE

Year joined incubator

Activity

A

A1 CEO 2008 2 2008 Gifts for private businesses

A2 CEO 2001 45 2009 Catering services

A3 General Manager

1982 (BE: ‘92)

9,2 1992 Multimedia, gaming, and computer accessories for retail distribution

A4 CEO 1999 1 2006 Wireless systems and appliances

A5 CEO 1995 4 2007 Computerizing processes

B B1 Coordinator 1984a 2 2004 Promotion and awareness stimulation of

sustainable energy use and environmental protection

B2 Managing partner

1992 40 1993 Information and communication technology service provision

B3 Manager 2009 6 2009 Integrated services for health care

C

C1 CEO 1998 3 1998 Air transport

C2 Sales Manager

2000 (BE: ‘08)

2 2008 Market research in foodservices market

C3 General Manager

1998 7 2007 Chemical: water and paper treatment

C4 CEO 2006 1 2006 Consultancy: crisis management

C5 CEO 2006 18 2008 Information and communication technology: optimizing business processes

D D1 CEO 2009 4 2008 Sales and installation of solar panels and applications

D2 CEO 2007 1 2007 Study center: engineering

D3 Regional Director

1811 (BE: ‘75)

27 2008 Elevators

E E1 CEO 2001 4 2009 Interior designer

E2 CEO 2008 2 2008 Communication expert

E3 Adviser 1998 (Antwerp: ’07)

2 2007 Advice organization for artists

F F1 CEO 1998 14 2001 Specialized dry-cleaning cars

F2 CEO 2007 1 2008 Consultancy: events

F3 CEO 1999 1 2005 Design and publicity

G G1 CEO 2008 32 2009 Research and development: pharmaceuticals

G2 CEO 2009 4 2009 Study center: subterranean energy storage and heat pumps

H H1 CEO 1991 4 2000 Audiovisual communication and documentaries

H2 Employee 1999 (BE: ‘03)

2 2005 Hardware development

H3 CEO 1999 2 2000 Communication expert

H4 CEO 2008 2 2008 Publishing company

I I1 Employee 1977 1,5 1993 Training, advice, and knowledge center creative thinking

I2 CEO 2006 2 2009 Software development a First as part of a large organization, then a separate company.

56

Bibliography

Aaboen, L., 2009. Explaining incubators using firm analogy. Technovation, 29, 657-670. Aaker, D., 2008. Strategic Market Management, John Wiley & Sons, New York. Abduh, M., D’Souza, C., Quazi, A., Burley, H.T., 2007. Building futures or stealing secrets?

Entrepreneurial cooperation and conflict within business incubators. Managing Service Quality, 17 (1), 74-91.

Aernoudt, R., 2004. Incubators: tool for entrepreneurship? Small Business Economics, 23, 127-135.

Aerts, K., Matthyssens, P., Vandenbempt, K., 2007. Critical role and screening practices of European business incubators. Technovation, 27 (5), 254-267.

Alipour, H., Davabi, K., Mehrabi, Z., Moshtaghi, M., 2010. The role of knowledge management in the achievement of competitive advantage: a case study of Iran Alborze Insurance Company in Western Mazandaran. African Journal of Business Management, 4 (7), 1346-1350.

Allen, D.N., McCluskey, R., 1990. Structure, policy, services, and performance in the business incubator industry. Entrepreneurship: Theory and Practice, Winter, 61-77.

Anprotec, 2006. Panorama de incubadoras de empresas e parques tecnológicos 2006. Anprotec, Brazil.

Antwerp Business Center, 2011. Welcome to ABC. Available from: < http://www.abc.be/en/ > (accessed 25.04.11).

Autio, E., Klöfsten, M., 1998. A comparative study of two European business incubators. Journal of Small Business Management, 30-43.

Banerjee, P., 2003. Resource dependence and core competence: insights from Indian software firms. Technovation, 23, 251-263.

Barbiroli G., Focacci, A., 2003. Product diversification in the vehicles industry: a techno-economic analysis. Technovation, 23 (6), 461-513.

Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management, 17 (1), 99-120.

Barney, J.B., Clark, D.N., 2007. Resource-Based Theory: Creating and Sustaining Competitive Advantage, London: Oxford University Press.

Becker, B., Gassmann, O., 2006. Corporate incubators: industrial R&D and what universities can learn from them. Journal of Technology Transfer, 31, 469-483.

Beer, M., Voelpel, S.V., Leibold, M., Tekie, E.B., 2005. Strategic management as organizational learning: developing fit and alignment through a disciplined process. Long Range Planning, 38 (5), 445-465.

Berg, D., Einspruch, N.G., 2009. Research note: intellectual property in the services sector: innovation and technology management implications. Technovation, 29, 387-393.


Bharadwaj, S.G., Varadarajan, P.R., Fahy, J., 1993. Sustainable competitive advantage in service industries: a conceptual model and research propositions. Journal of Marketing, 57 (4), 83-99.

Bøllingtoft, A., 2007. A critical realist approach to quality in observation studies, in: Neergaard, H. and Ulhøi, J.P. (Eds.), Handbook of Qualitative Research Methods in Entrepreneurship, Edward Elgar, Cheltenham, UK, 406-433.

Bowman, C., Ambrosini, V., 2007. Identifying valuable resources. European Management Journal, 25 (4), 320-329.

57

Brax, S., Jonsson, K., 2009. Developing integrated solution offerings for remote diagnostics. International Journal of Operations & Production Management, 29 (5), 539-560.


Bryman, A., Bell, E., 2007. Business Research Methods. Oxford, UK: Oxford University Press. Butz, H.E., Goodstein, L.D., 1996. Measuring customer value: gaining the strategic

advantage. Organizational Dynamics, 24 (3), 63-77 Cantù, C., 2010. Exploring the role of spatial relationships to transform knowledge in a

business idea: beyond a geographic proximity. Industrial Marketing Management, 39 (6), 887-897.

Ceci, F., Masini, A., 2011. Balancing specialized and generic capabilities in the provision of integrated solutions. Industrial and Corporate Change, 20 (1), 91-131.

Chan, K.F., Lau, T., 2005. Assessing technology incubator programs in the science park: the good, the bad and the ugly. Technovation, 25, 1215-1228.

Chiu, Y.-C., Lai, H.-C., Lee, T.-Y., Liaw, Y.-C., 2008. Technological diversification, complementary assets and performance. Technovation, 75, 875-892.

Cho, H.D., Lee J.K., Ro K.K., 1996. Environment and technology strategy of firms in government R&D programmes in Korea. Technovation, 16 (10), 553-560.

Clarysse, B., Wright, M. Lockett, A., Van de Velde, E., Vohora, A., 2005. Spinning out new ventures: a typology of incubation strategies from European research institutions. Journal of Business Venturing, 20, 183-216.

Cooper, R.G., 2001. Winning at New Products, 3d ed., New York: Perseus. Cova, B., Salle, R., 2008. Marketing solutions in accordance with the S-D logic: co-creating

value with customer network actors. Industrial Marketing Management, 37 (3), 270 – 277.

Danneels, E., 2002. The dynamics of product innovation and firm competences. Strategic Management Journal, 23 (12), 1095-1121.

De Silva, D.G., McComb, R., 2012. Research universities and regional high-tech firm start-up and exit. Economic Inquiry, 50 (1), 112-130.

Dul, J., Hak, T., 2008. Case Study Methodology in Business Research. Oxford, UK: Elsevier. Eisenhardt, K.M., 1989. Building theories from case study research. Academy of

Management Review, 14 (4), 532-550. Eisenhardt, K.M., Graebner, M.E., 2007. Theory building from cases: opportunities and

challenges. Academy of Management Journal, 50 (1), 25-32. European Commission, 2002. Benchmarking of business incubators. Centre for Strategy and

Evaluation Services, Brussels. Ferguson, R., Olofsson, C., 2004. Science parks and the development of NTBFs – location,

survival and growth. Journal of Technology Transfer, 29, 5-17. Gavetti, G., Rivkin, J.W., 2007. On the origin of strategy: action and cognition over time.

Organization Science, 18 (3), 420-439. Ghauri, P., 2004. Designing and conducting case studies in international business research.

In R. Marschan-Piekkari and C. Welch (Ed.). Handbook of Qualitative Research Methods for International Business (109-124). Northampton, MA: Edward Elgar.

Gibson, D.V., Naquin, H., 2011. Investing in innovation to enable global competitiveness: the case of Portugal. Technological Forecasting and Social Change, 78, 1299-1309.

58


Haapasalo, H., Ekholm, T., 2004. A profile of European incubators: a framework for commercializing innovations. International Journal of Entrepreneurship and Innovation Management, 4 (2/3), 248-270.

Hackett, S.M., Dilts, D.M., 2004. A systematic review of business incubation research. The Journal of Technology Transfer, 29 (1), 55-82.

Hackett, S.M., Dilts, D.M., 2008. Inside the black box of business incubation: Study B – scale assessment, model refinement, and incubation outcomes. The Journal of Technology Transfer, 33, 439-471.

Hannon, P.D., Chaplin, P., 2003. Are incubators good for business? Understanding incubation practice: the challenges for policy. Environment and Planning C: Government and Policy, 21, 861-881.

Heijltjes, M., van Witteloostuijn, A., 2003. Configurations of market environments, competitive strategies, manufacturing technologies and human resource management policies. Scandinavian Journal of Management, 19, 31-62.

Heydebreck, P., Klöfsten, M., Maier, J.C., 2000. Innovation support for new technology-based firms: the Swedish Teknopol approach. R&D Management, 30 (1), 89-100.

Hirschman, E.C., 1986. Humanistic inquiry in marketing research: philosophy, method, and criteria. Journal of Marketing Research, 23, 237-249.

Hitt, M.A., Ireland, D.R., Hoskisson, R.E., 2007. Strategic management: competitiveness and globalization (concepts and cases), 7th edition, Mason, OH: Thomson Higher Education.

Hu, T.-S., Lin, C.-Y., Chang, S.-L., 2005. Technology-based regional development strategies and the emergence of technological communities: a case study of HSIP, Taiwan. Technovation, 25, 367-380.

Huber, F., Herrmann, A., Morgan, R.E., 2001. Gaining competitive advantage through customer value oriented management. Journal of Consumer Marketing, 18 (1), 41-53.

Hughes, M., Ireland, R.D., Morgan, R.E., 2007. Stimulating dynamic value: social capital and business incubation as a pathway to competitive success. Long Range Planning, 40, 154-177.

Hytti, U., Mäki, K., 2007. Which firms benefit most from the incubators?. International Journal of Entrepreneurship and Innovation Management, 7 (6), 506-523.

Ibrahim, E.E., Gill, J., 2005. A positioning strategy for a tourist destination, based on analysis of customers’ perceptions and satisfactions. Marketing Intelligence and Planning, 23 (2), 172-188.

Jaworski, B.J., Kohli, A.K., 1993. Market orientation: antecedents and consequences. Journal of Marketing, 57, 53-70.

Johnson, A.J., Johnson, H.C., Devadoss, S., Foltz, J., 2011. Strategic group analysis of US food businesses using the two-step clustering method. International Food and Agribusiness Management Review, 14 (2), 83-102.

Juga, J., Pekkarinen, S., Kilpala, H., 2008. Strategic positioning of logistic service providers. International Journal of Logistics: Research and Applications, 11 (6), 443-455.

Jungman, H., Okkonen, J., Rasila, T., Seppä, M., 2004. Use of performance measurement in V2C activity. Benchmarking: An International Journal, 11 (2), 175-189.

Kalafatis, S.P., Tsogas, M.H., Blanson, C., 2000. Positioning strategies in business markets. Journal of Business and Industrial Marketing, 15 (6), 416-437.

59

Kaplan, R.S., Norton, D.P., 2008. Mastering the management system, Harvard Business Review, (January), 63-77.

Khalifa, A.S., 2004. Customer value: a review of recent literature and an integrative configuration. Management Decision, 42 (5), 645-666.

Knopp, L., 2007. 2006 State of the Business Incubation Industry. NBIA Publications, Athens, Ohio.

Krueger, R.A., 1988. Focus Groups. Sage, Newbury Park. Lalkaka, R., 2003. Business incubators in developing countries: characteristics and

performance. International Journal of Entrepreneurship and Innovation Management, 3 (1/2), 31-55.

Lawton, T.C., 1999. The limits of price leadership: needs-based positioning strategy and the long-term competitiveness of Europe’s low fare airlines. Long Range Planning, 32 (6), 573-586.

Li, S.-T., Tsai, M.-H., 2009. A dynamic taxonomy for managing knowledge assets. Technovation, 29, 284-298.

Lincoln Y.S., Guba, E.G., 1985. Naturalistic Inquiry. Beverly Hills, CA, Sage. Lu, W.-M., Hung, S.-W., 2011. Exploring the operating efficiency of technology development

programs by an intellectual capital perspective–a case study of Taiwan. Technovation, 31, 374-383.

Lumpkin, J.R., Ireland, R.D., 1988. Screening practices of new business incubators: the evaluation of critical success factors. American Journal of Small Business, 12, 59-81.

Macdonald, S., Joseph, R., 2001, Technology transfer or incubation? Technology business incubators and science and technology parks in the Philippines. Science and Public Policy, 28 (5), 330-344.

Mathison, S., 1988. Why triangulate?, Educational Research, 17 (2), 13-17. Matthyssens, P., Vandenbempt, K., Weyns, S., 2009. Transitioning and co-evolving to

upgrade value offerings: a competence-based marketing view. Industrial Marketing Management, 38, 504-512.

McAdam, M., Keogh, W., 2006. Incubating enterprise and knowledge: a stakeholder approach. International Journal of Knowledge Management Studies, 1 (1/2), 103-120.

McAdam, M., Marlow, S., 2007. Building futures or stealing secrets? Entrepreneurial cooperation and conflict within business incubators. International Small Business Journal, 25 (4), 361-382.

Merrifield, D.B., 1987. New business incubators. Journal of Business Venturing, 2, 277-284. Mian, S.A., 1994. US university sponsored technology incubators: an overview of

management, policies and performance. Technovation, 14 (8), 515-528. Mian, S.A., 1996. Assessing value-added contributions of university technology business

incubators to tenant forms. Research Policy, 25, 325-335. Mian, S.A., 1997. Assessing and managing the university technology business incubator: an

integrative framework. Journal of Business Venturing, 12, 251-285. Miles, R.E., Snow, C.C., 2003. Organizational Strategy, Structure, and Process. Stanford:

Stanford University Press. Mintzberg, H.T., 1988. Generic strategies: toward a comprehensive framework. Advances in

Strategic Management, 5, 1-67. Morgan, D.L., 1988. Focus Groups as Qualitative Research. Sage, Newbury Park. Morgan, D.L., 1996. Focus groups. Annual Review Sociology, 22, 129-152.

60

Mowery, D.C., Oxley, J.E., Silverman, B.S., 1998. Technological overlap and interfirm cooperation: implications for the resource-based view of the firm. Research Policy, 27, 507-523.

Naman, J.L., Slevin, D.P., 1993. Entrepreneurship and the concept of fit – a model and empirical tests. Strategic Management Journal, 14 (2), 137-153.

Naumann, E., 1995. Creating Customer Value. The path to sustainable competitive advantage. Cincinnati, OH: Thomson Executive Press.

Newbert, S.L., 2008. Value, rareness, competitive advantage, and performance: a conceptual-level empirical investigation of the resource-based view of the firm. Strategic Management Journal, 29, 745-768.

Newbert, S.L., Gopalakrishnan, S., Kirchhoff, B.A., 2008. Looking beyond resources: exploring the importance of entrepreneurship to firm-level competitive advantage in technologically intensive industries. Technovation, 28, 6-19.

Newbert, S.L., Kirchhoff, B.A., Walsh, S.T., 2007. Defining the relationship among founding resources, strategies, and performance in technology-intensive new ventures: evidence from the Semiconductor Silicon Industry. Journal of Small Business Management, 45 (4), 438-466.

Ng, P.T.A., Lu, D., Li, C.K., Chan, H.Y.H., 2005. Strategic lessons of value migration in IT industry. Technovation, 25 (1), 45-51.

O’Cass, A., Ngo, L.V., 2011. Examining the firm’s value creation process: a managerial perspective of the firm’s value offering strategy and performance. British Journal of Management, 22, 646-671.

Pe’er, A., Vertinsky, I., King, A., 2008. Who enters, where and why? The influence of capabilities and initial resource endowments on the location choices of de novo enterprises. Strategic organization, 6 (2), 119-149.

Plosila, W.H., Allen, D.N., 1985. Small business incubators and public policy: implications for state and local development strategies. Policy Studies Journal, 13 (4), 729-734.

POM Antwerp, 2012. POM Antwerp: flexible & dynamic government organization. Available from: < http://investinantwerp.be/pom-antwerp/ > (accessed 04.04.12).

Porter, M.E., 1980. Competitive Strategy. The Free Press, New York. Porter, M.E., 1996. What is strategy? Harvard Business Review, 61-78. Porter, M.E., 1998. Competitive Advantage: Creating and Sustaining Superior Performance.

The Free Press, New York. Porter, M.E., Millar, V.E., 1985. How information gives you competitive advantage. Harvard

Business Review, 149-174. Prahalad, C.K., Hamel, G., 1990. The core competence of the corporation. Harvard Business

Review, 79-91. Pratt, M.G., 2009. From the editors: for the lack of a boilerplate: tips on writing up (and

reviewing) qualitative research. Academy of Management Journal, 52 (5), 856-862. Priem, R.L., 2007. A consumer perspective on value creation. Academy of Management

Review, 32 (1), 219-235. Ratinho, T., Henriques, E., 2010. The role of science parks and business incubators in

converging countries: evidence from Portugal. Technovation, 30, 278-290. Ray, G., Barney, J.B., Muhanna, W.A., 2004. Capabilities, business processes, and

competitive advantage: Choosing the dependent variable in empirical tests of the resource-based view, Strategic Management Journal, 25 (1), 23-37.

61

Rice, M.P., 2002. Co-production of business assistance in business incubators: an exploratory study. Journal of Business Venturing, 17 (2), 163-187.

RiDO, 2011. Soft Landing Zone. Available from: < http://www.ridobusinesscentres.co.uk/soft-landing-zone > (accessed 11.04.11).

Schwartz, M., Hornych, C., 2008. Specialization as strategy for business incubators: an assessment of the Central German Multimedia Center. Technovation, 28, 436-449.

Schwartz, M., Hornych, C., 2010. Cooperation patterns of incubator firms and the impact of incubator specialization: Empirical evidence from Germany. Technovation, 30, 485-495.

Sharma, B., 2003. R&D strategy and Australian manufacturing industry: an empirical investigation of emphasis and effectiveness, Technovation, 23 (12), 929-937

Sherman, H., 1999. Assessing the intervention effectiveness of business incubation programs on new business start-ups. Journal of Developmental Entrepreneurship, 4 (2), 117-133.

Sherman, H., Chappell, D.S., 1998. Methodological challenges in evaluating business incubator outcomes. Economic Development Quarterly, 12 (4), 313-321.

Smith, J.B., Colgate, M., 2007. Customer value creation: a practical framework. Journal of Marketing Theory and Practice, 15 (1), 7-23.

Smith R., Sharif N., 2007. Understanding and acquiring technology assets for global competition, Technovation, 27 (11), 643-649.


Srivastava, R.K., Fahey, L., Christensen, K., 2001. The resource-based view and marketing: the role of market-based assets in gaining competitive advantage. Journal of Management, 6, 777-802.

Studdard, N.L., 2006. The effectiveness of entrepreneurial firm’s knowledge acquisition from a business incubator. International Entrepreneurship and Management Journal, 2, 211-225.

Tamásy, C., 2002. Are there too many innovation centres in Germany? In: Schätzl, L., Diez, J.R. (Eds.), Technological Change and Regional Development in Europe. Physica, Heidelberg, 112–131.

The Hub, 2011. Spaces for innovation and meeting rooms for hire. Available from: < http://brussels.the-hub.net/public/spaces.html > (accessed 03.03.11).

Tötterman, H., Sten, J., 2005. Start-ups: Business incubation and social capital. International Small Business Journal, 23 (5), 487-511.

UKBI, 2011. Business incubation. Available from: < http://www.ukbi.co.uk/about-ukbi/business-incubation.aspx > (accessed 04.04.11).

Varadarajan, P.R., 1985. A two-factor classification of competitive strategy variables. Strategic Management Journal, 6, 357-75.

Venkatraman, N., 1989. The concept of fit in strategy research: towards verbal and statistical correspondence. Academy of Management Review, 14, 423-444.

Venkatraman, N., Prescott, J.E., 1990. Environment-strategy coalignment: an empirical test of its performance implications. Strategic Management Journal, 11 (1), 1-23.

Von Zedtwitz, M., 2003. Classification and management of incubators: aligning strategic objectives and competitive scope for new business facilitation. International Journal of Entrepreneurship and Innovation Management, 3 (1/2), 176-196.

Von Zedtwitz, M., Grimaldi, 2006. Are service profiles incubator-specific? Results from an empirical investigation in Italy. Journal of Technology Transfer, 31, 459-468.

62

Walsh, S.T., Linton, J.D., 2011. The strategy-technology firm fit audit: a guide to opportunity assessment and selection. Technological Forecasting and Social Change, 78, 199-216.

Wen, C.-H., Chen, W.-Y., 2011. Using multiple correspondence cluster analysis to map the competitive position of airlines. Journal of Air Transport Management, 17 (5), 302-304.

Westhead, P., Batstone, S., 1998. Independent technology-based firms: the perceived benefits of a science park location. Urban Studies, 35, 2197-2219.

Woodruff, R.B., 1997. Customer value: the next source for competitive advantage. Journal of the Academy of Marketing Science, 25 (2), 139-153.

Wright, M., Liu, X., Buck, T., Filatotchev, I., 2008. Returnee entrepreneurs, science park location choice and performance: an analysis of high-technology SMEs in China. Entrepreneurship: Theory and Practice, 131-155.

Xu, S., Cavusgil, S.T., White, J.C., 2006. The impact of strategic fit among strategy, structure, and processes on multinational corporation performance: a multimethod assessment. Journal of International Marketing, 14, 1-31.

Yamakawa, Y., Yang, H.B., Lin, Z.A., 2011. Exploration versus exploitation in alliance portfolio: performance implications of organizational, strategic and environmental fit. Research Policy, 40 (2), 287-296.

Yamin, S., Gunasekaran, A., Mavondo, F.T., 1999. Relationship between generic strategies, competitive advantage and organizational performance: an empirical analysis. Technovation, 19 (8), 507-518.

Yin, R.K., 1990. Case study research: design and methods. Applied Social Research Methods Series, vol. 5, Beverly Hills, California, USA: Sage Publications.

Zablocki, E.M., 2007. Formation of a business incubator. In: Krattiger, A., Mahoney, R.T., Nelsen, L., et al., (Eds.), Intellectual property management in health care and agricultural innovation: a handbook of best practices. MIHR, Oxford, UK and PIPRA, Davis, USA, 1305-1314.

63

Chapter 2: Toward a balanced framework for business incubator evaluation and improvement 18 19 20 21

Abstract

Nonprofit organizations, such as economic development incubators, may adapt

management tools that originally were developed for the private sector in their efforts to

improve their internal functioning. Qualitative research among nonprofit economic

development incubators reveals that adapted versions of the strategy map and balanced

scorecard tools represent adequate evaluation frameworks for tackling shortcomings in

current incubator evaluation literature. Extant literature predominantly advocates individual

measures; this analysis confirms that an integrated system enables incubators to improve

their functioning, and that governing bodies and other external funders can perform

strategic and operational evaluations of the economic development incubators in which

they invest.

Highlights

> We present current shortcomings from incubator evaluation literature

> The strategy map and balanced scorecard are adequate tools to improve internal

incubator functioning

> We adapt the strategy map and balanced scorecard for nonprofit economic development

incubators

Keywords

Business incubator; Internal functioning; Evaluation; Balanced scorecard; Strategy map;

Nonprofit; Economic development

18 This chapter is co-authored with Paul Matthyssens and Arjen van Witteloostuijn. 19 This chapter is currently in the second round of an international peer-reviewed journal. 20 An earlier version of the theoretical part of this chapter has been presented at the ICSB (International Council for Small Business) conference, Cincinnati, US, June 23-27, 2010. At this conference, it received the “Journal of Small Business Management” Best Theoretical Paper Award. 21 An earlier version of this chapter has been published as a research paper: Vanderstraeten, J., Matthyssens, P., van Witteloostuijn, A., 2012, Measuring the performance of business incubators, Research paper 2012-012, University of Antwerp, Antwerp, Belgium.

65

2.1. Introduction

By stimulating the development of start-ups, business incubators hope to compensate

for the liabilities of smallness and newness typically attributed to new ventures (Freeman et

al., 1983; Stinchcombe, 1965). A start-up’s lack of build-up credibility, few networking

partners or inexperience often lead to network externalities and information asymmetries

between start-ups and incumbents (Phan et al., 2005). Through the offering of support

services such as business coaching, networking opportunities (Bergek and Norrman, 2008)

and the use of the incubator’s image (Ferguson and Olofsson, 2004), incubators try to

dempen the consequences of such market failure.

Historically, incubators have been stimulated by governmental agencies (Plosila and

Allen, 1985). In particular economic development incubators often receive substantial public

funding (Bergek and Norrman, 2008; Grimaldi and Grandi, 2005). In this way, policy makers

hope to foster (local) economic development (Grimaldi and Grandi, 2005; Hannon and

Chaplin, 2003; Ratinho and Henriques, 2010; Thierstein and Wilhelm, 2001). The incubator’s

role in job creation, employment growth (Fonseca et al., 2001) and the development of

innovative products and services (European Commission, 2000; Schwartz and Hornych,

2010) is expected to improve a region’s “capacity to act and innovate” (Beauregard, 1994, p.

271).

In return for the support offered, policy makers expect optimal functioning and

continuous improvement (Bigliardi et al., 2006; McMullan et al., 2001; Schwartz and

Göthner, 2009a). Although the incubation process is pivotal for incubator functioning

(Bergek and Norrman, 2008), internal processes are often ignored during evaluation

exercises (e.g., Schwartz and Göthner, 2009a). Most incubator evaluation methods focus on

tenant growth and survival (Aerts et al., 2007; Hackett and Dilts, 2008). This contradicts

performance measurement literature stating that a focus on outcome measures limits

organizations in their ability to find ways to improve their functioning (Johnston et al., 2002;

Neely, 2005; Neely et al., 2000). “Balanced” evaluation frameworks are suggested, like those

developed for the private sector (Moxham, 2009). Through such tools, outcome

accountability (McLaughlin, 2004) together with organizational improvements (Boyne, 2003;

Slack and Lewis, 2008) might be attained.

The balanced scorecard and strategy map are two tools that evaluate organizational

functioning by including a balanced set of configurational characteristics and measures. A

66

balanced scorecard offers an easily comprehensible and accessible presentation of

evaluation measures (Kaplan and Norton, 2005). It “supplements traditional financial

measures with criteria that measure performance from three additional perspectives –

those of customers, internal business processes, and learning and growth” (Kaplan and

Norton, 1996, p. 75). Strategy maps depict how an organization’s financial goals drive its

strategic objectives (Kaplan and Norton, 2000). It uses a “visual framework … that embeds

the different items on an organization’s balanced scorecard into a cause-and-effect chain,

connecting desired outcomes with the drivers of those results” (Kaplan and Norton, 2000, p.

169-70). Following strategic fit scholars (Venkatraman, 1989; Venkatraman and Prescott,

1990), the strategy map assures that an organization’s strategic objectives are aligned with

external and internal elements. For external alignment, Kaplan and Norton (2000) rely on

customer expectations.22 Addressing client expectations is also at the heart of an

incubator’s strategic value creation possibilities and internal functioning (Bruneel et al.,

2012). For internal alignment, Kaplan and Norton (2000) incorporate an analysis of internal

business processes and innovation and learning mechanisms. Elaborating upon these

processes and mechanisms addresses the need for internal functioning evaluation in

incubator literature (Bergek and Norrman, 2008; Phan et al., 2005).

Although the added value of the balanced scorecard and strategy map is widely

recognized (Neely et al., 2000), they have not yet been adapted to the incubator context. To

address this, we aim to investigate the possibility of adapting the balanced scorecard and

strategy map, by answering the following question: How can the balanced scorecard and

strategy map be adapted into adequate evaluation tools for nonprofit economic

development incubators? This research question is rooted in performance measurement

literature, where scholars stress the importance of combining individual evaluation

measures and integrated systems (Neely, 2005). Specifically, the use of integrated, balanced

evaluation systems, such as the strategy map and the balanced scorecard (Kaplan and

Norton, 2000), is advocated. This chapter also draws on studies about the development of

evaluation tools. In particular Tangen’s (2004) system output prerequisites are at the basis

of the adaptation process, like the importance of comprehensive lists of individual measures

and easily understandable system visualizations.

22 Thus, in this chapter, we employ a traditional dyadic view and analyze incubator tenants from an external perspective.

67

To empirically address our research question, we conduct a qualitative study. We

account for incubator manager, expert and customer (that is, incubator tenant) opinions

(Kaplan and Norton, 2005) on integrated incubator evaluation systems and their related

individual measures (Neely, 2005). Because most economic development incubators focus

on local or regional development (e.g., Ratinho and Henriques, 2010), we selected economic

development incubators active in a specific region in the economic heart of Europe: the

province of Antwerp in Belgium.

In the next section, we examine incubator evaluation literature to establish its state-of-

the-art. Here, we also explain how the balanced scorecard and strategy map may address

current shortcomings in this literature. We then discuss the methodology of our empirical

research. Subsequently, we present how the strategy map and balanced scorecard can be

adapted to the context of nonprofit economic development incubators, and discuss why

some of the suggested tools and mechanisms are not employed by the incubators in our

study. Finally, we provide the study’s main contributions, results and research limitations,

and offer some possible avenues for future research.

2.2. Theoretical background

2.2.1. Individual incubator evaluation measures

For structuring purposes, we classify individual incubator evaluation measures into four

approaches (Daft, 2009): the goal, stakeholder, system resource and internal business

process. Most researchers use tenant survival and growth as indicators of incubator goal

performance (Aerts et al., 2007; European Commission, 2002; Hackett and Dilts, 2008;

Lalkaka, 1996). Yet no univocal method exists to measure tenant “growth”. For example, as

is the case in most company growth studies (Shepherd and Wiklund, 2009), incubator

academics tend to employ growth measures interchangeably, including sales growth,

profitability growth, or growth in the number of employees (Colombo and Delmastro, 2002;

Löfsten and Lindelöf, 2002; Westhead and Storey, 1994). Nor is the definition of “success”

clear (Schwartz, 2009). Some researchers (Avnimelech et al., 2007, p. 1183) advocate the

use of various success degrees and claim that high tenant success refers to “start-ups which

had initial public offerings (IPOs) or were targets of significant acquisition”, whereas

moderate success features “start-ups that did not have IPOs and were not targets of

significant acquisition, but are still active”.

68

Although most researchers equate tenant success with incubator success, this link may

be more nuanced (Hackett and Dilts, 2008). Specifically, tenants that terminate their

activities with minimal losses are incubator successes, because even though incubators’

raison d’être is to support company development and growth, their aim is not to artificially

keep struggling companies alive (Bergek and Norrman, 2008). If a company is not fit to

survive, the incubator is expected to provide guidance to keep losses to a minimum during

company closure (Hackett and Dilts, 2008). In parallel fashion, tenants that have suffered

large losses at their termination represent incubator failures. Hackett and Dilts (2008)

employ similar reasoning in relation to tenant survival: Only those tenants that are

profitable or on a path toward profitability are incubator successes, and those surviving but

not profitable are incubator failures (Hackett and Dilts, 2008). Finally, although researchers

recognize that tenants undergo a development process through the incubator (Chan and

Lau, 2005), no research explicitly links incubator evaluation to tenant development

milestones.

Incubator scholars drawing on a stakeholder approach acknowledge that an incubator is

part of a wider entrepreneurial ecosystem (Etzkowitz, 2002; Hsu et al., 2003) and that there

are various constituencies involved in incubator evaluation. However, there is no consensus

about which stakeholders should be taken into account when assessing an incubator’s

functioning. In general, two viewpoints emerge: Some researchers incorporate a wide

stakeholder community (McAdam and Keogh, 2006), arguing that, for example, citizen

opinions are pivotal (Mian, 1997), whereas others opt for a limited stakeholder set and

advocate incorporating only the viewpoint of the most important stakeholders (Haapasalo

and Ekholm, 2004; Ratinho and Henriques, 2010; Sherman, 1999). Although most

researchers argue that this should involve the tenant (Abduh et al., 2007; Bruneel et al.,

2012; Chan and Lau, 2005; Jungman et al., 2004), some focus on incubator funders, such as

venture capitalists (Jungman et al., 2004), the government (Haapasalo and Ekholm, 2004;

Rice, 2002; Sherman, 1999), or universities (Patton and Marlow, 2011).

Many researchers adopt a system resource approach, looking at the incubator’s office

space, its shared logistic and administrative services, its business support, and networking

offerings (Bergek and Norrman, 2008; Bøllingtoft and Ulhøi, 2005; Costa-David et al., 2002;

Mian, 1997; Studdard, 2006; Tamásy, 2007). They might count the number of services

offered (Chan and Lau, 2005; Smilor, 1987) or dive deeper into the type and quality of

69

incubator resources and services offered. For example, incubators able to recruit managers

with broad networks (Bøllingtoft and Ulhøi, 2005; Studdard, 2006) or considerable

entrepreneurship experience (Costa-David et al., 2002; Mian, 1997) likely achieve better

evaluations. The type and quality of the incubator’s connection to a university (Tamásy,

2007), its professional service network (Lalkaka, 1996), its financial means and public

support (Lalkaka, 1996; Mian, 1994, 1997; Zablocki, 2007), and its image/prestige (Mian,

1997) also have been recognized as important incubator resources.

Finally, some researchers address an incubator’s internal business processes. For

example, Lalkaka (1996, p. 270) suggests that optimal incubator functioning depends on

“the careful planning and implementation of the incubation process”. That is, it is not the

incubator facility or the number of services offered but the incubation process itself that

defines incubator success (Adkins, 2001). Costa-David et al. (2002, p. 8) explain that “the

adoption of a business-like approach to running incubators and monitoring clients” is a

prerequisite of incubator success. Unfortunately, incubator researchers have not yet fully

unraveled these incubation processes (Campbell et al., 1985; Smilor, 1987), which remain

something of a “black box” (Hackett and Dilts, 2008). Efforts stress the importance of

selection, monitoring and business assistance, resource munificence (Hackett and Dilts,

2008) and networking mediation processes (Bergek and Norrman, 2008), but the link

between these internal processes and incubator performance continues to be largely

unknown.

2.2.2. Integrated incubator evaluation systems

Most incubator researchers tend to use one or a couple of individual measures to

examine incubator functioning. However, we also found four more extensive incubator

evaluation systems published in academic peer-reviewed journals. Löfsten and Lindelöf

(2001) provide an incubation process framework; O’Neal (2005) maps anticipated incubator

success elements; Mian (1997) provides a conceptual model for assessing and managing

incubators; and Voisey et al. (2006) relate incubator functioning to outcome measures.

Although all four studies develop conceptual frameworks, two of them (Mian, 1997; O’Neal,

2005) also apply these frameworks to case studies. Interestingly, none of the frameworks

focus on nonprofit economic development incubators. Mian (1997) and O’Neal (2005) study

university-based technology incubators, Löfsten and Lindelöf (2001) technology incubators

70

without any specific link to the university, and Voisey et al. (2006) business incubators in

general. This is surprising, given that nonprofit economic development incubators account

for the main share of incubators (Knopp, 2007). In what follows, we evaluate these four

evaluation systems using Tangen’s (2004) system output prerequisites (see Table 2-1 for a

summary of these prerequisites).

Table 2-1: Output prerequisites for integrated evaluation systems

Output prerequisite Explanation (Tangen, 2004)

Support strategic objectives

The system supports the organization’s strategic objectives and is flexible enough to allow for strategic changes

Have an appropriate balance

The system has an appropriate balance and incorporates

Short- and long-term results

Different types of performance (for example, cost, quality, delivery, flexibility and dependability)

Various perspectives (such as the customer, the shareholder, and the competitor)

Various organizational levels (for example, global and local)

Guard against sub-optimization

The system guards against the “productivity paradox” (Skinner 1986)a. Avoiding sub-optimization can be done by establishing a clear link between the company’s top (strategy) and bottom (what can employees do to reach these strategic goals)

Have a limited number of measures

The system does not constitute of too many measures because this could result in data ignorance and/or information overload

Be easily accessible The system provides information “at the right time, to the right person” (p. 728). The necessary information is easily obtainable, it is presented in an accessible way, and it is easily understandable

Consist of measures that have comprehensible specifications

The system measures’ purpose is clearly defined. It is clear who will use and act upon the measure. This implies that appropriate targets and timeframes for target reaching are developed

a Skinner’s (1986) “productivity paradox” refers to the fact that poor performance measures might have a negative impact on employee behavior.

First, with regard to whether the evaluation systems support the incubator’s strategic

objectives, we find that O’Neal (2005) does not provide an explicit link to strategy. He

argues that an incubator’s goal is to reduce “infant mortality among new ventures” (O’Neal,

2005, p. 11) and cites three result areas (companies, products, and people), yet it is unclear

which objective(s) the incubator aims to achieve in these areas. The other studies

emphasize the importance of strategic objectives and refer explicitly to management goals

71

(Löfsten and Lindelöf, 2001), meeting targets (Voisey et al., 2006), or the need for the

business incubator to act on the university’s expectations when establishing its goals,

objectives, or strategy (Mian, 1997).

Second, the existing frameworks address an appropriate balance by looking at various

areas of expected results, stakeholders, and organizational levels. Löfsten and Lindelöf

(2001) suggest including the tenant, incubator, and community levels, whereas Mian (1997)

includes these three levels and adds the university as well. Voisey et al. (2006) examine the

incubator and tenant level, and O’Neal (2005) refers to tenants, people, and products. The

areas of expected results suggested by the measurement frameworks also are diverse.

Löfsten and Lindelöf (2001) recognize the importance of tenant and incubator growth and

profitability, along with community-related impacts. O’Neal (2005) notes job creation,

economic impacts, employee characteristics, financial measures, and the intellectual capital

of the incubator’s tenants. However, no studies provide a possible timeframe for capturing

differences among short-, medium-, and long-term results.

Third, the risk of sub-optimization can be mitigated by accounting for the impact of the

evaluation systems on employees, yet none of the assessment frameworks explicitly explain

which activities the incubator employees can undertake or how they are expected to act.

From a balanced measurement perspective, ignoring (the impact on) incubator employee

behavior is surprising, because the incubator employees provide services to and come in

daily contact with tenants.

Fourth, our analysis reveals that three of the four frameworks reviewed here offer

substantial lists of evaluation measures, comparable in their length. Mian (1997), Voisey et

al. (2006), and O’Neal (2005) suggest 23, 19, and 17 measures, respectively, and some of the

measures have subdivisions. For example, Mian (1997) subdivides tenant and graduate

employment into number and type of employment. The fourth measurement framework of

Löfsten and Lindelöf (2001) favors the use of a limited number of general evaluation areas

(that means, tenant survival and growth, program growth and sustainability, and

community-related impacts), but does not specify which measures to use to evaluate them.

Fifth, regarding the assessment frameworks’ accessibility, we find that some of the

measures are difficult to obtain. Mian (1997), for example, refers to measuring the

incubator’s impact on the university environment. However, the vast number of interested

parties in a university ecosystem (for example, students, professors, and technology transfer

72

centers) means that determining this impact entails a time-intensive, complex data

collection process. No studies provide suggestions for how to present the information

obtained. Moreover, great differences exist in framework complexity. Those by O’Neal

(2005) and Voisey et al. (2006) are straightforward and easy to understand, but the two

other frameworks are more complex.

Sixth, regarding the need for clearly defined actions, our analysis indicates that none of

the frameworks emphasize the importance of pre-identifying progress and “markers”,

interpretation, or follow-up of the results. Specific targets and timeframes are lacking in all

four studies, and none of the frameworks provide information about the frequency at which

to collect data.

2.2.3. The balanced scorecard and strategy map as incubator evaluation tools

The state-of-the-art of incubator evaluation literature reveals two overarching

shortcomings. First, most researchers only employ one or a couple of individual measures,

instead of using integrated evaluation systems. The individual measures mainly focus on the

goal (e.g., tenant growth) or system resource (e.g., the availability of secretary or

networking partners) approach. Although difficulties in gaining access to the necessary

information often forces researchers to employ such individual measures, it limits an

incubator’s internal functioning evaluation and hampers the incubator in gaining insights

into the necessary areas for improvement (Kaplan and Norton, 2000; Moxham, 2010).

Second, the incubator evaluation systems that we did find in academic literature do not

comply to all of Tangen’s (2004) system output prerequisites. As a consequence, these

systems might be neither easily comprehensible nor accessible. Thus, an integrated

evaluation tool that focuses on internal functioning and that complies to all six output

prerequisites (Tangen, 2004) is badly needed.

To address these shortcomings, we propose to adapt the balanced scorecard (Kaplan

and Norton, 2005) and strategy map (Kaplan and Norton, 2000) tools to the incubator

context. These instruments emphasize the translation of long-term strategic goals into

short-term objectives, measures, and targets to enhance the efficient and effective

functioning of an organization (Tangen, 2004). In so doing, they take into account the

incubator’s strategic goals and translate them into the necessary processes and systems.

This allows us to address the lack of attention given to internal business processes in

73

existing incubator evaluation methods (Hackett and Dilts, 2008). In a nonprofit context such

as those of economic development incubators, success cannot be measured by financial

results alone (Kaplan, 2001). The strategy map and balanced scorecard also comply to

Tangen’s (2004) system output prerequisites. They underscore not only the organization’s

financial results, but also the importance of its customers, internal business processes, and

innovation and learning processes (Kaplan and Norton, 2005). Moreover, these evaluation

tools stress the importance of setting clear goals, educating employees, and communicating

goals to those who are implementing them. For example, involving all employees in the

development of a strategy map and balanced scorecard (Kaplan and Norton, 2000, 2005)

can help resolve possible problems for the practical execution of these evaluation tools,

such as sub-optimization (Tangen, 2004). In addition, depicting the four performance areas

means that the measures and targets are easily obtainable and understandable (Kaplan and

Norton, 2000, 2005). Ideally, the organization periodically draws on these evaluation reports

to examine its performance and tackle potential problem areas (Kaplan and Norton, 2008).

This also addresses the development of actions and related target achievement (Tangen,

2004).

2.3. Methodology

To adapt the strategy map and balanced scorecard to the context of nonprofit economic

development incubators, insights into the incubator’s strategic objectives, customers (that

is, tenants) expectations, financial perspectives, internal processes, and environmental

influences23 are needed (Gumbus and Lussier, 2006; Kaplan and Norton, 2000, 2005). In

such a broad, complex context, qualitative research is appropriate (Bryman and Bell, 2007;

Dul and Hak, 2008; Eisenhardt, 1989; Yin, 1990). With qualitative research, we aim to show

that the strategy map and balanced scorecard, originally developed for the private sector,

can be adapted to nonprofit economic development incubators (Siggelkow, 2007).

Following Kaplan and Norton’s (2005) suggestion, we employed mixed qualitative

methods, and executed in-depth interviews and workshops. Between June 2009 and

December 2011, we first constructed an intermediary balanced scorecard and a strategy

23 In this paper, environmental influences mainly relate to the expectations of the incubator’s funding organization. Because our study population is economic development incubators, this is predominantly the government.

74

map based on nine in-depth interviews with incubator managers, three focus groups with

incubator managers and experts, and 30 in-depth interviews with tenants. We then

presented the intermediary tools during a discussion and presentation meeting to incubator

managers and experts. Finally, we evaluated and finalized the tools during nine additional

in-depth interviews with the incubator managers from the initial interviews.

2.3.1. Population

To increase external validity and lower extraneous variation, a clear description of the

target population is necessary (Eisenhardt, 1989). We selected the incubators24 for our

qualitative study based on three criteria. First, because most incubators are nonprofit

organizations aimed at enhancing local economic development (Bruneel et al., 2012; Knopp,

2007; Ratinho and Henriques, 2010), we only selected nonprofit incubators that seek to

pursue local economic development. In Belgium, our study context, each province assigns a

development authority to stimulate local economic development (POM Antwerp, 2012). At

the time of our study, Antwerp, a province located in northern Belgium, hosted the greatest

number of nonprofit local development incubators (that is, nine out of sixteen incubators).

We thus chose this province for our qualitative research.

Second, most economic development incubators receive some kind of government

support (Bergek and Norrman, 2008; Grimaldi and Grandi, 2005). Therefore, we selected

incubators that were linked with the provincial development authority. For example, they

received original funding money, support during incubator marketing campaigns or expert

advice at reduced fees.

Third and finally, there seems to be consensus in incubator literature that incubators can

opt for either a focused or a diversified sector portfolio (Plosila and Allen, 1985; Schwartz

and Hornych, 2008; Sherman and Chappell, 1998). To incorporate the opinions of managers

and tenants from both incubator types, we included five incubators that allow companies

from a wide variety of sectors (that is, generalists), and four incubators with a sector focus

(that is, specialists).

24 For an overview of the characteristics of the sample incubators, see Appendix A in Chapter 1.

75

We employed a rather straightforward criterion to define our tenant sample.25 Because

incorporating the opinions of only one type of tenants can lead to biased results (Mian,

1996), we represented the total tenant population in our sample, in terms of the wide

variety of sizes (1–45 full-time employees), ages (1–35 years), and incubation periods (1–18

years). Furthermore, because our incubator sample consists of both generalists and

specialists, the tenants are engaged in a wide variety of activities (for example, air transport,

communications, and catering).

2.3.2. Research design, data gathering, and study quality

To improve this study’s reliability and validity (Bryman and Bell, 2007; Eisenhardt and

Graebner, 2007), we achieved researcher, method, and data source triangulation (Mathison,

1988). To minimize researcher bias (Bøllingtoft, 2007), we worked as a team of four

researchers and held regular team meetings (Eisenhardt, 1989). Two researchers took

charge of the data gathering and analysis process; two other researchers served consulting

roles. Moreover, we achieved method and data source triangulation by combining primary

data from in-depth interviews, focus groups, and open discussion presentations with

secondary data from websites, internal documents, and brochures.

We enhanced the credibility of our interpretations through member checks, a research

method introduced by Lincoln and Guba (1985) and Hirschman (1986). These member

checks allow researchers to verify their intermediate interpretations throughout the data

collection and analysis process. First, all the interviews were tape recorded and transcribed;

we then sent summaries of the interviews to the participants and asked for their approval.

Participants could make any comments needed. Second, we organized focus groups and

formal presentations to discuss (intermediate) results, and invited comments from

participants.

We conducted two in-depth interview waves with incubator managers. In the first wave,

the semi-structured interview protocol focused on the incubator’s strategy, functioning, and

external influences. During the second wave, we discussed the first version of the adapted

balanced scorecard and strategy map, and asked incubator managers if they agreed with the

constructs and if the measures we proposed were feasible. The 30 tenant interviews took

25

For an overview of the characteristics of the sample incubator tenants, see Appendix B in Chapter 1.

76

place during the same period as the first wave of the incubator interviews. The semi-

structured interview protocol for tenants focused on the incubator’s strategy, service

offering, internal organization, and external influences. Thus, we checked tenant viewpoints

against those of incubator managers and experts.

We also conducted three focus groups sessions in the same period. Combining focus

groups and interviews offered the advantage of gathering both in-depth (interviews) and

more broad (focus groups) information. Moreover, alternating in-depth interviews with

focus groups has an advantage, in that the focus groups can be used to check interview

conclusions, as well as to gather input for interview topics (Morgan, 1996). Overall, nine

incubator managers and five external experts participated in the focus groups. The

incubator managers also participated in in-depth interviews, and the experts were members

of nonprofit government agencies. We created relatively homogenous focus groups

(Krueger, 1988; Morgan, 1988). The first included incubator managers and took place before

the individual in-depth interviews; during this focus group, we discussed factors that might

influence an incubator’s internal functioning and the context within which it operates. In the

second focus group, we included incubator experts, presented our intermediate results, and

encouraged a closer examination of the external context. Finally, the third focus group

combined the viewpoints from incubator managers and experts. Again, we presented our

intermediate results, followed by an in-depth discussion of internal aspects and the external

context.

From our first 9 incubator manager interviews, 3 focus groups, and 30 tenant interviews,

we made a first adapted version of the balanced scorecard and strategy map, which we

refer to as BSEDI (balanced scorecard for economic development incubators) and SMEDI

(strategy map for economic development incubators). We then presented these tools during

a discussion meeting with incubator managers and experts. Beyond this general

presentation, we discussed the SMEDI and BSEDI in-depth during a second wave of semi-

structured interviews with the nine incubator managers who participated in the first round,

focusing in this case on the practical applicability and usefulness of the intermediary SMEDI

and BSEDI. In line with our literature review, we used Tangen’s (2004) output prerequisites

to check their practical applicability, usefulness, and accessibility. That is, the interviewees

indicated whether they agreed with the presented long-term strategic objectives and

measurement balance. We also asked their opinion about our efforts to take the viewpoints

77

of incubator employees into account and the feasibility of the measures. Finally, we checked

whether the measurement tools were accessible and could result in action. From the input,

we adapted the tools to develop the final versions of our SMEDI and BSEDI.


To present the findings from our empirical study, we first report interviewee opinions

about an incubator’s financial situation, measurement methods and targets. Then, we

examine the suggested long-term strategic goals to attain healthy financial performance.

For each long-term strategic objective, customer expectations, internal processes, and

innovation and learning possibilities are discussed. These aspects lead to the different

constructs of the strategy map. Thereafter, we present how the customer, internal

processes, and innovation and learning perspectives can be measured. We also discuss the

suggested targets. This leads to a further development of the balanced scorecard. Although

the incubators in our study already apply parts of the balanced scorecard and strategy map,

none of them employ the tools as a whole. The reasons for this are explained throughout

the narrative of the empirical study.

2.4.1. Financial sustainability

Notwithstanding their nonprofit focus, interviewees stress that financial sustainability is

key for economic development incubators. To this end, all incubator managers rely on rent

from tenants. Some of them also receive income from paid services. The incubators in our

study work with a basic service package for tenants. On top of these services, some of them

also offer the possibility to buy additional services. Generalist incubators offer paid non-core

business support services, such as doing the bookkeeping or offering operational support

during the organization of events. Specialists provide core business support such as setting

up a business plan for potential investors. Our empirical study also reveals that some

tenants do not buy the incubator’s additional services, but search them elsewhere. The

reason for this is that, although the incubator’s services are often reasonable in price, they

are sometimes of inferior quality, or outdated. Incubator managers might detect such

quality problems during individual tenant or group meetings. Finally, incubator managers

warn against becoming too dependent upon income from such paid services. They argue

that because small start-ups are not in a financial position to yearly buy a large amount of

78

additional services, income from paid services can be very volatile. Therefore, they advocate

to mainly rely on income from rent, and suggest a minimum 85 per cent incubator

occupancy rate to attain sufficient rent income.

Besides income from rent and paid services, all interviewees stress cost cutting.

Although an in-depth yearly audit of the balance sheet and annual report does not take

place in our incubators because of time and money restrictions, interviewees do

acknowledge that such an analysis might increase the efficient use of resources. Currently,

the balance sheet and annual report mainly serve to check the incubator’s financial position,

and not to improve its efficient functioning. All incubator managers warn against situations

in which operating and overhead costs exceed income from rent and paid services. They

argue that in such cases, structural subsidies or sponsorships are necessary to attain break-

even. Some incubator managers strongly oppose such life-long support, because it does not

stimulate them to work as efficiently as possible. Although they do acknowledge that

subsidies and sponsorships might be required for sporadic, larger investments, they also

stress that cost cutting and high enough income from rent and paid services might help

them to (slowly) build their own reserves.

Finally, all incubators encounter frequent difficulties in attaining financial sustainability.

To address these issues, some of them execute commercial side-activities, such as call

centers or renting out business sites to larger companies. Such commercial activities can

generate additional profit flows that can be invested in the incubator and thus help it to

attain financial sustainability. However, not all incubators in need for additional income

execute such commercial activities. The reasons for this are a lack of space, manpower or

financial means for large investments. With regard to the latter, interviewees explain that

government does not provide support for the setting-up of commercial activities. Because

private sponsorship is rather limited, incubators often lack the financial means to start a

side-activity. Again, this shows that being too dependent upon subsidies (or sponsorship)

limits an incubator in its activities. The incubator managers and experts also warn for

becoming financially too dependent upon a commercial activity. They argue that an

incubator’s main activity remains incubation and stimulating economic development. One of

the incubator managers suggests a maximum side income of 10 per cent of the total

income. Because this commercial activity has little to do with the incubator’s main activities

(that is, nurturing start-ups), we do not consider its implications for the four vertical building

79

blocks in the SMEDI (see Figure 2-1). For the same reason, we do not discuss this aspect

further in the context of internal and external alignment.

2.4.2. How to attain financial sustainability: long-term strategic goals and alignment

To attain the four financial pillars – rent from tenants, paid services, cost cutting, and

subsidies/sponsorships – an incubator must pursue long-term strategic goals. We

summarize the strategic goals suggested, and discuss the external and internal alignment

prerequisites for each.

2.4.2.1. Structurally stable and diverse tenant portfolio

All interviewees stress that to ensure income from rent, a structurally stable and diverse

tenant portfolio is pivotal. Incubators whose companies are all in the same incubation stage

face the risk that these companies will reach their final incubation stage around the same

time. In this case, the incubator might lose a relatively large number of tenants at the same

period, prompting a drastic subsequent drop in rent income. Moreover, tenant interviewees

indicate that each time a new tenant enters the incubator, resource and information sharing

thrive. This gradually decreases when there are no new entrants. Thus, both incubator and

tenant interviewees prefer a mix of young starters and companies in later development

stages. To achieve this goal, they suggest the development of a professional selection

process. Although the incubators in our study already employ some “objective” financial and

market criteria, interviewees emphasize the need to address an entrepreneur’s personal

and team characteristics as well. When asked to explain which selection criteria they would

like to add to the selection process, they suggest to add the willingness to cooperate. They

argue that it is a strong foundation for a good working climate in the incubator because

cooperation leads to cross-fertilization. Tenants state that it is in particular this cross-

fertilization that makes them choose for location in an incubator rather than another type of

location.

Moreover, because tenant expectations differ and incubators have different service

offerings (for example, generalists offer more basic services, and specialists offer services

adapted to a particular sector; see above), tenant and incubator interviewees indicate the

need for a “fit” between the incubator’s service offerings and the company’s service

expectations. Some tenant interviewees point out that their core business does not match

80

with the incubator’s sector focus. Consequently, they cannot use the incubator’s core

business services, such as a laboratory or contacts with sector organizations. This shows that

some companies would fit better in another incubator than the one they originally

contacted. A structured incubator network, in which multiple incubators work together to

attain optimal service fit for each company, can help in this effort. Although some incubator

interviewees give examples of companies changing location to attain a better fit, others

state that their difficulties in attaining financial sustainability often force them to select

companies without perfect match.

Although the selection process is pivotal for a structurally stable and diverse tenant

portfolio, the graduation process also has a prominent role. Most tenants stress that they

are eager to learn new things. This demands periodic changes in the tenant portfolio to

allow them to broaden their networks and find new possibilities for knowledge transfer

constantly. Although all incubators and experts agree that tenants need thus leave the

incubator in timely fashion, the appropriate graduation criteria for an incubator are

somewhat unclear. Some incubator managers employ a time-bound criterion, such as three

to five years. Others merely look at the tenant’s service needs and urge tenants to leave as

soon as they are less in need of the services offered or start to grow too large. Although

employed in practice, our interviewees warn against pure time-related criteria, because

they can obligate the tenant company to leave at a moment when it is not yet “ready” for

the market. Neither do they believe that a focus on pure space-bound criteria is advisable,

because it would imply that small, non-growing companies never have to leave. They agree

that service need criteria are most adequate: Companies no longer in need of incubator

services are ready to leave the incubator. Because many incubator services require a

willingness-to-interact attitude (for example, internal incubator networks require idea and

information exchanges), changes in this attitude often result from service need changes. In

turn, companies not willing to cooperate are urged to leave the incubator.

2.4.2.2. Value creation effectiveness

Another long-term strategic objective is value creation effectiveness. The tenant

interviewees in our study stress that value creation is indispensable for convincing tenants

to pay for incubator services. Although service proximity is a clear advantage of incubators

that cannot be replicated easily by other start-up service providers such as advice

81

organizations, tenant interviewees indicate that high service quality, proactiveness, and in-

depth service offerings are equally important. Unfortunately, the interviewees also explain

that it are exactly these service characteristics that their incubators often struggle to

provide.

Proactiveness, according to the tenants, requires the incubator to offer services adapted

to their specific development stage. That is, they expect the incubator management team to

follow up actively with its tenants. This is possible if incubator employees possess

entrepreneurship-related knowledge pertaining to company needs in varying development

stages, and regular contact occurs between the incubator management team and its

tenants. However, incubator managers indicate that stressful day-to-day preoccupations

often prevent them from closely following upon their tenants. In addition, generalist

incubators face challenges in their efforts to offer sector-related, in-depth services to all

tenants. To resolve this, our interviewees suggest close networking schemes with external

experts, which would mean that the incubator employees do not need to possess in-depth

knowledge about a wide variety of sectors. Instead, they can refer tenants to the

appropriate experts. However, the tenants emphasize that, currently, some incubators only

offer a list of possible experts. They find this insufficient; personal incubator contacts with

external experts are indispensable to “open doors”.

Our analysis further reveals that interviewees highly value a knowledge management

system to store relevant service-related knowledge, such as sector information or ways to

find company funding. Such a system turns out to be a perfect example of in-depth service

offering. Currently, none of the incubators have such a system in place, again because of

resource constraints. Finally, one of the incubator managers suggests that offering high-

quality services can be assured through a total quality management (TQM) or ISO system,

which is designed to follow up on and improve organizational quality. Another incubator

manager, who already had implemented an ISO system, confirms this impression by noting

that reflecting on and structuring the incubator’s procedures allowed it to work more

efficiently while also offering higher quality and continuous service innovation and

improvement.

82

2.4.2.3. Efficient functioning

A third long-term strategic objective is efficient functioning. Incubator experts explain

that in times of scarcity, subsidy and sponsorship organizations have constrained budgets,

and incubators often receive less financial resources. As a consequence, the incubator

interviewees explain that they feel great pressure to ensure optimal, efficient resource uses.

As explained above, continuous cost cutting is pivotal and the incubator constantly must

find ways to increase its efficient functioning. Although not often used in practice because of

time and resource constraints, the incubator interviewees stress that the pressure to work

more efficiently demands a permanent search for explicit formats and standardized

procedures, as well as a focus on potential synergies with external experts and incubators to

offer services that are needed only sporadically. Although this point may seem obvious, it is

difficult to realize in an organization with a reactive mindset (that means serving each

customer in response to a simple request) and limited staff (that means no time for

reflection on existing processes). Some interviewees suggested, though, that their

implementation of an ISO system allows them to work more efficiently.

2.4.2.4. Entrepreneurship and business development

Finally, the interviewees indicated that subsidy and sponsorship organizations

increasingly apply a commercial logic, expecting something in return for their funding.

Namely, that the incubator pursues the funding organization’s goals. With our focus on

economic development incubators, it is not surprising that interviewees indicate that these

goals focus on the facilitation and stimulation of entrepreneurship and successful business

development in their local area. The tenants we interviewed argue that business

development and entrepreneurship stimulation require an organized platform that allows

them to broaden their networks beyond the incubator borders. Such a platform can be

created easily by organizing entrepreneurship-related activities, such as business plan

competitions, seminars, workshops, or (international) conferences. Our research also

reveals that the economic development incubators tend to relocate their tenants in the local

region after they graduate, to stimulate the local entrepreneurial spirit and economic

development. For example, one of the incubator managers offers active relocation support

and works closely with local real estate agents to find nearby offices.

83

Figure 2-1 visualizes the above-discussed aspects from the strategy map for nonprofit

economic development incubators (SMEDI).

Figure 2-1: SMEDI: strategy map for nonprofit economic development incubators

Internal alignment

Cu

sto

me

r

pe

rsp

ect

ive

Inte

rnal

Bu

sin

ess

Pro

cess

es

pe

rsp

ect

ive

Inn

ova

tio

n a

nd

Le

arn

ing

pe

rsp

ect

ive

Long-term strategic

goals

Stable and diverse tenant portfolio

Efficient functioning

Entrepreneurship and business development

Value creation effectiveness

External alignment Offer proactive,

qualitative and in-depth services adapted to the company’s development phase

Proactive tenant follow-up system

External networking system with experts

Develop a knowledge management system

Resource sharing process with other incubators or external partners

Continuously seek for new ways to improve efficiency

Create a platform for the establishment & development of start-ups and small businesses

Organize entrepreneurship-related activities

Continuously seek for new and innovative ways to establish and develop start-ups and small businesses

Resource and information sharing for tenants

Optimize the selection process

Optimize the graduation process

Continuously improve the selection and graduation process

Subsidies and sponsorship Cost cutting Paid services Rent

Financial sustainability

Fin

anci

al

pe

rsp

ect

ive

Commercial side-activity

Continuous service innovation

Set up a process to assure an efficient and lean organization

Set up a process to assure high quality

Process to refer possible tenants within the incubator’s network

Efficiently offer a wide variety of services

Offer relocation support to graduating companies

84

2.4.3. How to measure internal and external alignment

In what follows, we suggest measures and targets to check for internal and external

alignment. Combining these results with those suggested for the financial perspective leads

into the BSEDI (Balanced Scorecard for nonprofit Economic Development Incubators)

summarized in Figure 2-2.

2.4.3.1. External alignment

For external alignment, we followed Kaplan and Norton (2000) and incorporated the

customer perspective. Interviewees indicate that tenant satisfaction is key. Periodic (for

example, semi-annual) tenant meetings, to discuss tenant needs and complaints, are

deemed appropriate. Interestingly, tenant interviewees complain that sometimes decisions

resulting from these meetings are not clearly communicated. Therefore, they suggest to

take minutes of the meetings and explicitly follow-up and communicate on issues raised. The

meetings can be organized in groups or individually. Incubator interviewees indicate that

the former offer the advantage of uncovering complaints or suggestions from a large

number of tenants in one meeting; the latter have the advantage of more detailed

discussions of individual needs.

Our analysis also revealed that beyond measuring tenant satisfaction, other assessments

are necessary. Because tenant satisfaction is rather subjective, interviewees suggest to also

employ objective measures. They indicate that knowledge transfer and networking

stimulation can be measured with objective criteria, such as the number of organized

contact moments between tenants (for example, presentation seminars, formal discussion

groups, receptions, and team-building activities). Tenants stress that such contact moments

are most appropriate when held on a monthly basis. One incubator manager advocate that

the architectural design of the incubator site also provides a measure of knowledge transfer

and networking. A well thought-out design facilitates “accidental” contacts among tenants

as much as possible, such as in pleasant gathering rooms. Measuring such accidental

contacts quantitatively is not easy, though, so the incubator manager suggests comparing

the incubator’s communal surface against its total individual office space. The managers

prefers offices that are small, relative to the available communal spaces and advocates that

at the least, the total communal surface should be as large as the total individual office

space.

85

Finally, interviewees explain that networking and knowledge transfer can be stimulated

through the creation of an entrepreneurship and business development platform, which not

only enhances tenant development but also can incorporate other entrepreneurs and

companies in the region. To measure the creation of such a platform, they suggest a listing

of the number and type of events, as well as the number of participants and their affiliations.

Counting the number of events shows how active the incubator is in creating a platform,

whereas the number of participants and their affiliations indicate whether the incubator can

reach both tenants and entrepreneurs outside the incubator. Finally, making a list of the

topics that these events cover reveals whether the incubator is reaching entrepreneurs

without a venture and those already running a business but seeking business development

after their initial founding.

2.4.3.2. Internal alignment

If we turn our attention to the internal business processes perspective, most of our

interviewees suggest employing tenant satisfaction about, for example, the selection

process, the incubator’s knowledge about company development phases, the accessibility of

external experts, and service quality. As explained above, such assessments are possible

through systematically organized individual or group meetings. Again, follow-up and

communication on the issues raised appears very important for the tenant interviewees.

Furthermore, the interviewees suggest a direct evaluation of incubation processes, such

as close examinations of the selection criteria. Specifically, they argue for a balanced

selection process, with attention focused on not only the company’s market and financial

characteristics but also its personal and team characteristics, such as a willingness to

cooperate. In close relation, they emphasize the need for clearly defined graduation criteria.

Because these interviewees value service-bound graduation criteria, they argue that a

company should leave the incubator once its business reaches a predefined development

phase.

The selection and graduation processes provide the basis for good incubator

functioning, yet our analysis shows that the incubation process itself cannot be ignored. For

example, external networking turn out to provide a basis for incubator service offerings,

such as business support and the development of an entrepreneurship and business

development platform. To measure it, interviewees suggest to count networking events and

86

the number of external experts affiliated with the incubator. In the latter case, a tenant

interviewee suggests to map the areas of expertise of external experts because it would

reveal the breadth of the incubator’s expert network. Moreover, the tenant argue that this

mapping and counting exercise could help the incubator provide tenants with a structured

overview of its network.

Our analysis of the strategy map reveals that resource sharing is pivotal for incubators.

To measure this, interviewees suggest to count the number of fellow incubators with which

a focal incubator has close connections. For example, a classification might divide incubator

partners into privileged partners and member organizations. For the former, resource

sharing and referrals of possible tenants occur frequently; for the latter, contacts can be

more sporadic. The incubator managers we interviewed indicate that incubators willing to

work as efficiently as possible need to have at least one incubator with which they work

closely together for resource sharing, to increase their scale advantages. Finally, to sustain

high quality, they suggest systems such as TQM or ISO; the evaluation markers associated

with these systems can be tracked by the incubator.

Our analysis of the innovation and learning perspective shows that there are two main

sources to increase innovation and learning within the incubator. The first is an analysis of

tenant satisfaction and tenant expectations. These form an important starting point for the

necessary services innovations. Our interviewees suggest that this can be examined directly

by asking tenants their opinions about the incubator’s need for innovativeness. Again, this

can occur through the semi-annual individual or group meetings and follow-up we noted

previously.

The second source is related to the incubator’s in-house expertise and openness to

learning. Interviewees indicate that incubator employees need to constantly develop their

incubation and business knowledge. Some incubator managers give examples from

conferences or workshops that they attended, and that helped them to learn new

incubation practices. Therefore, it is suggested to count the number of networking and

information events in which incubator employees participate. Although, in practice, many

incubator managers in our study do have neither the time nor the resources to participate in

these events, interviewees do suggest that a two-yearly participation seems a minimum.

87

Figure 2-2: BSEDI and targets: balanced scorecard for nonprofit economic development incubators

Financial situation Measures Thresholds - Income from rent and a package of basic services - Occupancy rate

- At least 70 % of income - At least 85 %

- Income from additional, paid services - Maximum 30 % of income - Income from sponsorship/subsidies - Only for large investments - Income from a commercial side-activity - Maximum 10 % of income - Costs analyses - Operating & overhead costs should not

exceed income from rent & paid services - Search for cost cutting possibilities

- Yearly audit from the balance sheet and annual report

- At least break-even, or preferably (small) profit margins that can be used for future investments

Customers Measures Thresholds - Tenant satisfaction: individual or group meetings

- Half-yearly

- Follow up all questions/solicitudes raised during individual or group meetings

- All concerns should be followed up and decisions should be communicated to tenants

- Number of organized meetings/contact moments between and among tenants and external entrepreneurs/companies

- Monthly

- Architectural infrastructure of the incubator: compare the incubator’s communal surface with the total individual office space

- Communal space should be at least as large as the total individual office space

- Topics of the networking events such as seminars and workshops organized for tenants and external companies/entrepreneurs

- Half of the topics should focus on business initiation, the other half on business development

- Number of participants and their affiliations in networking events such as seminars and workshops

- Half of the participants should be external companies/entrepreneurs

Internal Business Processes Measures Thresholds - Tenant satisfaction: individual or group meetings

- Half-yearly



- Balanced selection process: team, financial and market characteristics

- All three characteristics should be taken into account

- Clearly defined graduation criteria - Pre-defined development phase has been reached

- Map the areas of expertise of the external experts

- Wide variety of areas of expertise

- Number of external experts affiliated to the incubator

- At least one expert in each area

- Number of privileged and member incubators

- At least one privileged incubator partner

- Number of organized meetings/contact moments between and among tenants and external entrepreneurs/companies

- Monthly

- Does the incubator have a quality system in place?

- Evaluation markers associated with this system

Innovation and Learning Measures Thresholds - Tenant satisfaction and tenant expectations: individual or group meetings

- Half-yearly



- Number of events such as conferences and workshops incubator employees participate in

- Twice a year for each incubator employee

88

2.5. Discussion

Our empirical results confirm that evaluation frameworks, such as Kaplan and Norton’s

(2000, 2005) strategy map and balanced framework, originally developed for the private

sector, can be translated to a nonprofit context (Kaplan, 2001; Moxham, 2009). In particular,

we have adapted the balanced scorecard and strategy map approaches to nonprofit

economic development business incubators, which suffer constant challenges in determining

appropriate internal functioning evaluation systems (Sherman, 1999) that are in line with

Tangen’s (2004) output prerequisites. Accordingly, this setting is ideal for demonstrating that

existing evaluation systems can be useful for not only multinationals (Kaplan and Norton,

2001) and small ventures (Gumbus and Lussier, 2006; Spivey et al., 2007), but also for

nonprofit organizations.

We also find that the current economic climate exerts negative impacts on the funding of

nonprofit organizations in general (Moxham, 2010) and on the funding of business

incubators in particular.26 Financial sustainability has always been a main preoccupation of

incubators (von Zedtwitz, 2003); small incubators often seem unable to reach a financially

sound situation due to their capacity restrictions (Zablocki, 2007). The pressures on

nonprofit organizations to find efficiencies thus affect business incubators too (Brainard and

Siplon, 2004; Privett and Erhun, 2011), and some societies question whether these support

organizations offer sufficient returns (Bergek and Norrman, 2008). Our analysis suggests that

incubators should avoid depending too heavily on subsidy or sponsorship organizations, and

should consider a commercial side-activity that provides some additional income while

minimizing their costs for society (Bøllingtoft, 2012).

A deeper assessment of incubator evaluation measures reaffirms the importance of both

tenants (Bruneel et al., 2012; Chan and Lau, 2005; Jungman et al., 2004) and funding

organizations (Haapasalo and Ekholm, 2004; Patton and Marlow, 2011; Rice, 2002; Sherman,

1999) as incubator stakeholders that should not be ignored in measures of incubator

evaluation. In turn, we reject the common practice of using one stakeholder perspective (for

example, Abduh et al., 2007) following Schwartz and Göthner (2009b, p. 9) who advocate

that “the employment of sole indicators is insufficient to capture the performance of

business incubators”. We advocate integrated measurement frameworks that combine

26 This is often problematic because in particular in periods of economic recession, additional government support might be needed to adjust for market difficulties.

89

various measures instead.27 Moreover, by investigating various business incubation

processes, we follow general performance measurement research, such as Simons’ (2000, p.

59) argument that “performance measurement and control information can be understood

only by reference to some model of underlying organizational processes”. This also

addresses the lack of individual measures focusing on internal business processes.

The measures and targets we suggest for the balanced scorecard extend current

incubator measurement literature, in which only one contribution offers specific thresholds:

Lalkaka (2000) offers a list of evaluation measures and targets for some measures. Thus, this

study offers the results of a first attempt to suggest measures and targets for nonprofit

economic development incubators.

When discussing our results in relation to extant incubator functioning literature, we find

that even small incubators can attain scale advantages by working together with external

actors (Hansen et al., 2000). We however also find that most research attends only to tenant

advantages, such as networking possibilities with consultancy firms or government agencies

(Mian, 1994; Schwartz and Hornych, 2010; Spithoven and Knockaert, 2011). Our results

reintroduce the incubator level, and show that an incubator can improve its efficient and

effective functioning by working together with service experts and with other incubators to

gain economies of scale at logistic and administrative levels.

As another extension of existing incubator functioning literature, we reaffirm that a

selection process must be balanced (Aerts et al., 2007; Lumpkin and Ireland, 1988;

Merrifield, 1987), but also highlight the lack of prior attention granted to personal attitudes,

such as the entrepreneur’s attitude toward cooperation and interaction with other tenants

and the incubator team.28 Social network theory similarly states that “the value in a network

is only realized through the owner-manager’s positive use of the resources contacted

within” (Ostgaard and Birley, 1994, p. 282). Findings from network (Falemo, 1989; Hormiga

27 However, as stated earlier, difficulties in access to the necessary data often obliges academic researchers to employ only one or a few measures. This is also the case for Chapter 3 of this doctoral thesis. 28 An incubator’s selection process can provoke selection bias and thus bias an incubator’s outcome performance. By employing strict selection criteria, incubators might only select tenants that would also survive without incubator support. However, our analysis shows that, for example, service fit and a willingness to interact attitude are important prerequisites for optimal incubator functioning, and that it is useless to allow companies that only want to be incubator tenants because of cheap office space. It is thus suggested that an incubator selects “weak but promising firms” (Hackett and Dilts, 2008) that fit with the selection criteria discussed above.

90

et al., 2011; Ostgaard and Birley, 1996) and incubator (Bøllingtoft, 2012; McAdam and

Marlow, 2007) research indicate that interaction and cooperation can channel resources and

enhance company development. Our extension also emphasizes the need for a good “fit”

between company expectations and incubator offerings. Service literature also notes the

criticality of fit—in that case, between service offerings to achieve good quality perceptions

and customer satisfaction (Grigorescu, 2008).

In this context, we introduce another key incubation process (Bergek and Norrman,

2008: Patton et al., 2009): the incubator’s graduation process, which surprisingly has been

largely overlooked in studies on incubation functioning (Hackett and Dilts, 2008). The most

widely used graduation criterion relates to the time the company stays in the incubator

(European Commission, 2002). Our more fine-grained analysis shows that this time-bound

criterion is too simplistic. We advocate service-bound criteria instead, and thereby advance

the notion of changing service needs based on a company’s development stage (Chan and

Lau, 2005), including the moment the company can survive on its own and should thus leave

the incubator (Hackett and Dilts, 2008). Forcing companies to leave the incubator when they

are not in need of the services offered allows other weak but promising firms to enter

(Hackett and Dilts, 2008), which creates new learning (Wu et al., 2007), interaction (Cooper

et al., 2012; Ozel, 2012; Scillitoe and Chakrabarti, 2010), and knowledge diffusion and

transfer (Ozel, 2012; Salvador, 2011) opportunities, and thus accelerates company

development and growth (Soetanto and Jack, 2011; Sohal et al., 2002).

Finally, we also find that an incubator’s functioning and value creation possibilities

largely depend upon service quality (Priest, 1999), proactiveness (Chan and Lau, 2005), and

the offering of in-depth services (Mole et al., 2011). Extant incubator literature suggests that

these characteristics can be attained through incubator service co-creation (Rice, 2002), in

accordance with more general co-creation studies, in which scholars argue that co-creation

offers customers the possibility to provide ideas for the development of new, or the

improvement of existing, products and services (Ernst et al., 2010). Through co-creation,

customers become empowered to participate in product or service value creation (Hoyer et

al., 2010), which leads to improved quality (Bendapudi and Leone, 2003), proactiveness

(Hoyer et al., 2010), and products or services better adapted to customer needs (Ernst et al.,

2010).

91

2.6. Conclusion

In presenting our conclusions, we start by summarizing the key contributions to

literature before we present this study’s practical implications for incubator managers,

tenants, and funding organizations. Finally, we discuss some limitations and research

directions.

2.6.1. Contribution to the literature

In addressing evaluation methods of a specific nonprofit organization – namely,

nonprofit economic development business incubators – we add to a research domain that

has received substantial attention (for example, Amezcua, 2010) but that remains far

removed from consensus (Phan et al., 2005). Balanced frameworks developed for the private

sector can be translated to a nonprofit context (Moxham, 2009) to address the shortcomings

in extant evaluation literature, as we show by transforming Kaplan and Norton’s (2000,

2005) strategy map and balanced scorecard to the incubator context, and developing our

SMEDI and BSEDI. By gathering the viewpoints of incubator managers, tenants, and external

experts, we have incorporated both internal and external perspectives, as advocated by

general performance measurement literature (Andrews et al., 2011).

As a result, the SMEDI and BSEDI are balanced tools that integrate various incubator

evaluation perspectives and link the incubator’s long-term strategic goals to its medium-

term objectives and short-term pursuits. An annual evaluation of the SMEDI and BSEDI

assures feedback loops. Measures such as the number of times incubator employees

participate in events incorporate incubator employee behavior. Because we conferred again

with the incubator managers to discuss the practical usefulness of a working version of the

SMEDI and BSEDI, we emerged with an easily obtainable, accessible, and comprehensible

incubator evaluation toolkit with clear markers. To the best of our knowledge, this toolkit is

the first to meet Tangen’s (2004) prerequisites for evaluation systems in the incubator

domain.

In addition to measurement tools for a specific nonprofit context, our empirical analysis

offers three key results for incubator functioning literature. First, we contest the lack of

attention granted to an incubator’s graduation process (Hackett and Dilts, 2008) and argue

that this process has important impacts on the incubator’s functioning (Patton et al., 2009).

92

Contrary to the conventional wisdom (European Commission, 2002), service-bound

graduation criteria work better than time-bound criteria, and our analysis reveals the added

value of devoting extra attention to members’ changing service needs. Second, a company’s

willingness to cooperate and interact turned out to play a pivotal role in the incubation

process. We extend current literature that advocates the use of personal and team-related

selection criteria, such as their age, gender, or skills (Aerts et al., 2007), and recommend

instead the use of personal attitudes. An entrepreneur’s attitudes toward cooperation have

impacts not just on internal and external incubator networking (Bøllingtoft, 2012; McAdam

and Marlow, 2007) but also on the company’s own development (Ostgaard and Birley,

1994). Third, we reintroduce the importance of service co-creation, a service development

strategy that can enhance service quality (Bendapudi and Leone, 2003), proactiveness

(Hoyer et al., 2010), and satisfaction (Ernst et al., 2010) but that thus far has received

marginal attention in incubator literature (Rice, 2002).


Incubator managers and funding organizations can draw on these results to measure the

effectiveness and efficiency of their nonprofit economic development incubators (Schwartz

and Göthner, 2009a; Sherman, 1999), benchmark such organizations against other

incubators, and make more informed resource allocation decisions (Tornatzky et al., 2002).

Moreover, the SMEDI and BSEDI help incubator managers and funding organizations target

internal incubator processes that need improvement (Hackett and Dilts, 2008).

Because “training and assistance programs for practicing entrepreneurs are expensive

both in money for sponsors and in time for participants” (McMullan et al., 2011, p. 37) and

the effectiveness of incubators has been questioned (Schwartz, 2012), the development of

relevant evaluation tools is well justified. Functional improvements and performance

enhancements require transparent evaluation tools (Boyne, 2003; Giannakis, 2007; Slack and

Lewis, 2008), so adequate evaluation tools should offer clearer insights into the effective and

efficient functioning of these organizations.

Finally, tenant opinions take a prominent place in the SMEDI and BSEDI, extending

current measurement methods that often integrate only objective measures, such as tenant

survival (Aerts et al., 2007) or occupancy rate (European Commission, 2002). By valuing

93

tenant opinions, the SMEDI and BSEDI might guide potential tenants to choose appropriate

locations (Bigliardi et al., 2006).


This study features limitations and possible future research avenues, related to the

nonprofit sector, the incubator domain, and public policy. First, we detail different

constructs, measures, and targets for nonprofit economic development incubators, yet

much work remains to develop in-depth understanding of these tools in other nonprofit

organizations. Future researchers might provide assessments of adequate constructs for

other nonprofit organizations, such as hospitals or voluntary organizations (Kaplan, 2001).

Second, the qualitative methodology we employed provides insights into the applicability

and feasibility of the SMEDI and BSEDI in the incubators that participated, but for the many

and varied incubator types (von Zedtwitz, 2003), the SMEDI and BSEDI need to be adapted to

their specific situations. For example, basic research incubators aim to commercialize

university research (Aernoudt, 2004), so the university must be a primary stakeholder in

related basic research incubator models. Third, because the SMEDI and BSEDI mainly focus

on incubator functioning, they offer only limited attention to the incubator’s role in the

wider, regional, entrepreneurial ecosystem. Further research might expand our evaluation

frameworks, perhaps using the vast research on regional innovation systems (Cooke, 2005),

such as the triple-helix model (Etzkowitz and Leydesdorff, 2000) or Brännback et al.’s (2008)

bottom-up double helix framework. In such frameworks, an in-depth analysis of the funding

organization’s strategy can provide insights into the incubator’s degrees of freedom during

strategy formulation. Fourth, from a public policy perspective, our research does not denote

the added value of specific, government-related incentives for incubators. Longitudinal

SMEDI and BSEDI case studies might help governing bodies follow up on the support

provided and performance resulting from such public incentives. For example, governing

bodies might zoom in on the entrepreneurship and business development pillar and thereby

further unravel the performance indicators that result from public subsidies.

94

Bibliography

Abduh, M., D’Souza, C., Quazi, A., Burley H.T.,2007. Building futures or stealing secrets? Entrepreneurial cooperation and conflict within business incubators. Managing Service Quality, 17, 1, 74-91.

Adkins, D., 2001. A Report for the Japan Association of New Business Incubation Organizations (JANBO): Summary of the U.S. Incubator Industry. Athens, OH: National Business Incubation Association.

Aernoudt, R., 2004. Incubators: Tool for Entrepreneurship? Small Business Economics, 23, 127-135.

Aerts, K., Matthyssens, P., Vandenbempt, K., 2007. Critical Role and Screening Practices of European Business Incubators. Technovation, 27, 5, 254-267.

Amezcua, A.S., 2010. Boon or Boondoggle? Business incubation as entrepreneurship policy. PhD thesis, Syracuse University.

Andrews, R., Boyne, G., Walker, R., 2011. The Impact of Management on Administrative and Survey Measures of Organizational Performance. Public Management Review, 132, 227-255.

Avnimelech, G., Schwartz, D., Bar-El, R., 2007. Entrepreneurial High-tech Cluster Development: Israel’s Experience with Venture Capital and Technological Incubators. European Planning Studies, 15, 9, 1181-1198.

Beauregard, R.A., 1994. Constituting economic development. In R.D. Bingham, Mier R. (Eds.), Theories of local economic development (p. 267-283). Thousand Oaks, CA: Sage.

Bendapudi, N., Leone, R.P., 2003. Psychological Implications of Customer Participation in Co-Production. Journal of Marketing, 67, 1, 14-28.

Bergek, A., Norrman, C., 2008. Incubator Best Practice: a Framework. Technovation, 28, 20-28.

Bigliardi, B., Dormio, A.I., Nosella, A., Petroni, G., 2006. Assessing science parks’ performances: directions from selected Italian case studies. Technovation, 26, 20-28.

Bøllingtoft, A., 2007. A critical realist approach to quality in observation studies. In H. Neergaard, Ulhøi J.P. (Eds.), Handbook of Qualitative Research Methods in Entrepreneurship (p. 406-433). Cheltenham, UK: Edward Elgar.

Bøllingtoft, A., 2012. The Bottom-up Business Incubator: Leverage to Networking and Cooperation Practices in a Self-generated, Entrepreneurial-enabled Environment. Technovation, 32, 304-315.

Bøllingtoft, A., Ulhøi, J.P., 2005. The Networked Business Incubator - Leveraging Entrepreneurial Agency? Journal of Business Venturing, 20, 2, 265-290.

Boyne, G.A., 2003. Sources of Public Service Improvement: A critical review and research agenda. Journal of Public Administration Research and Theory. 13, 3, 367-394.

Brainard, L.A., Siplon, P.D., 2004. Toward Nonprofit Organization Reform in the Voluntary Spirit: Lessons from the Internet. Nonprofit and Voluntary Sector Quarterly, 33, 3, 435-457.

Brännback, M., Carsrud, A., Krueger, N., Elfving, J., 2008. Challenging the triple helix model of regional innovation systems: A venture-centric model. International Journal of Technoentrepreneurship, 1, 3, 257-277.

Bruneel, J., Ratinho, T., Clarysse, B., Groen, A., 2012. The Evolution of Business Incubators: Comparing Demand and Supply of Business Incubation Services across Different Incubator Generations. Technovation, 31, 110–121.

95

Bryman, A., Bell, E., 2007. Business Research Methods. Oxford, UK: Oxford University Press. Campbell, C., Kendrick, R.C., Samuelson, D.S., 1985. Stalking the Latent Entrepreneur:

Business Incubators and Economic Development. Economic Development Review, Summer, 43-48.

Chan, K.F., Lau, T., 2005. Assessing Technology Incubator Programs in the Science Park: The Good, the Bad and the Ugly. Technovation, 25, 10, 1215-1228.

Colombo, M., Delmastro, M., 2002. How effective are technology incubators? Evidence from Italy. Research Policy, 31, 7, 1103-1122.

Cooke, P., 2005. Regional asymmetric knowledge capabilities and open innovation Exploring ‘globalisation 2’: a new model of industry organization. Research Policy, 34, 1128–1149.

Cooper, C.E., Hamel, S.A., Connaughton, S.L., 2012. Motivations and Obstacles to Networking in a University Business Incubator. Journal of Technology Transfer, 37, 4, 433-453.

Costa-David, J., Malan, J., Lalkaka, R., 2002. Improving Business Incubator Performance Through Benchmarking and Evaluation: Lessons Learned from Europe. Paper presented at the 16th International Conference on Business Incubation, Toronto, Canada, April.

Daft, R.L., 2009. Organization Theory and Design. Mason, OH: South-Western College Pub. Dul, J., Hak, T., 2008. Case Study Methodology in Business Research. Oxford, UK: Elsevier. Eisenhardt, K.M., 1989. Building Theories from Case Study Research. Academy of

Management Review, 14, 4, 532–550. Eisenhardt, K.M., Graebner, M.E., 2007. Theory Building from Cases: Opportunities and

Challenges. Academy of Management Journal, 50, 1, 25–32. Ernst, H., Hoyer W.D., Krafft, M., Soll, J.-H., 2010. Consumer Idea Generation. Working

paper, WHU, Vallendar. Etzkowitz, H., 2002. Incubation of incubators: innovation as a triple helix of university-

industry-government networks. Science and Public Policy, 29, 2, 115-128. Etzkowitz, H., Leydesdorff, L., 2000. The dynamics of innovation: from national systems and

‘Mode 2’ to a triple helix of university-industry-government relation. Research Policy, 29, 109–123.

European Commission, 2000. Mededeling van de Commissie aan de Raad en het Europees Parlement: Innovatie in een Kenniseconomie. Brussels: European Commission.

European Commission, 2002. Benchmarking of Business Incubators. Brussels: Centre for Strategy and Evaluation Services.

Falemo, B., 1989. The Firm’s External Persons: Entrepreneurs or Network Actors? Entrepreneurship and Regional Development, 1, 167-177.

Ferguson, R., Olofsson, C., 2004. Science parks and the development of NTBFs – location, survival and growth. Journal of Technology Transfer, 29, 5-17.

Fonseca, R., Lopez-Garcia, P., Pissarides, C.A., 2001. Entrepreneurship, Start-Up Costs and Employment. European Economic Review, 45, 4/6, 692-705.


Giannakis, M., 2007. Performance Measurement of Supplier Relationships. Supply Chain Management: An International Journal, 12, 6, 400-411.

Grigorescu, A., 2008. Quality and Customer Satisfaction in Public Services. Amfiteatru Economic, 130-135.

Grimaldi, R., Grandi, A., 2005. Business incubators and new venture creation: an assessment of incubating models. Technovation, 25, 2, 111-121.

96

Gumbus, A., Lussier, R.N., 2006. Entrepreneurs use a balanced scorecard to translate strategy into performance measures. Journal of Small Business Management, 44, 3, 407-425.

Haapasalo, H., Ekholm, T., 2004. A profile of European incubators: a Framework for Commercializing Innovations. International Journal of Entrepreneurship and Innovation management, 4, 2/3, 248-270.

Hackett, S.M. Dilts, D.M., 2008. Inside the Black Box of Business Incubation: Study B – Scale Assessment, Model Refinement, and Incubation Outcomes. The Journal of Technology Transfer, 33, 439-471.

Hannon, P.D., Chaplin, P., 2003. Are incubators good for business? Understanding incubation practice – the challenges for policy. Environment and Planning C: Government and Policy, 21, 861-881.

Hansen, M.T., Chesbrough, H.W., Nohria, N. Sull, D.N., 2000. Networked Incubators: Hothouses of the New Economy. Harvard Business Review, 78, 5, 74-84.

Hirschman, E.C., 1986. Humanistic inquiry in marketing research: philosophy, method, and criteria. Journal of Marketing Research, 23, August, 237-249.

Hormiga, E., Batista-Canino, R.M., Sanchez-Medina, A., 2011. The Impact of Relational Capital on the Success of New Business Start-Ups. Journal of Small Business Management, 49, 4, 617-638.

Hoyer, W.D., Chandy, R., Dorotic, M., Krafft, M., Singh, S.S., 2010. Consumer Co-creation in New Product Development. Journal of Service Research, 13, 3, 283-296.

Hsu, P.-H., Shyu, J.Z., Yu, H.-C., You, C.-C., Lo, T.-H., 2003. Exploring the interaction between incubators and industrial clusters: The case of the ITRI incubator in Taiwan. RandD Management, 33, 1, 79-90.

Johnston, R., Brignall, S., Fitzgerald, L., 2002. ‘Good Enough’ Performance Measurement: a Trade-Off between Activity and Action. Journal of the Operational Research Society, 53, 256-262.

Jungman, H., Okkonen, J., Rasila, T., Seppä, M., 2004. Use of Performance Measurement in V2C Activity. Benchmarking: An International Journal, 11, 2, 175-189.

Kaplan, R.S., 2001. Strategic Performance Measurement and Management in Nonprofit Organizations. Nonprofit Management and Leadership, 11, 3, 353-370.

Kaplan, R.S., Norton, D.P., 1996. Linking the balanced scorecard to strategy. California Management Review, 39, 1, 53-79.


Kaplan, R.S., Norton, D.P., 2001. On balance. CFO, February, 73-78. Kaplan, R.S., Norton, D.P., 2005. The balanced scorecard: Measures that drive performance.

Harvard Business Review, July-August, 172-180. Kaplan, R.S., Norton, D.P., 2008. Mastering the management system. Harvard Business

Review, January, 63-77. Knopp, L., 2007. 2006 State of the Business Incubation Industry. Athens, OH: NBIA

Publications. Krueger, R.A., 1988. Focus Groups. Newbury Park: Sage. Lalkaka, R., 1996. Technology Business Incubators: Critical Determinants of Success. Annals

of the New York Academy Sciences, 798, 270-290.

97

Lalkaka, R., 2000. Assessing the Performance and Sustainability of Technology Business Incubators. Paper presented at the New Economy and Entrepreneurial Business Creation in Mediterranean Countries Conference, Trieste, Italy, December.

Lincoln, Y.S., Guba, E.G., 1985. Naturalistic Inquiry. Beverly Hills, CA: Sage. Löfsten, H., Lindelöf, P., 2001. Science Parks in Sweden – Industrial Renewal and

Development. RandD Management, 31, 3, 309-322. Löfsten, H., Lindelöf, P., 2002. Science Parks and the growth of new technology-based firms

- academic-industry links, innovation and markets. Research Policy, 31, 859-876. Lumpkin, J.R., Ireland, R.D., 1988. Screening Practices of New Business Incubators: The

Evaluation of Critical Success Factors. American Journal of Small Business, 12, 59–81. Mathison, S., 1988. Why triangulate? Educational Research, 17, 2, 13-17. McAdam, M., Keogh, W., 2006. Incubating Enterprise and Knowledge: a Stakeholder

Approach. International Journal of Knowledge Management Studies, 1, 1/2, 103-120. McAdam, M., Marlow, S., 2007. Building Futures or Stealing Secrets? Entrepreneurial

Cooperation and Conflict within Business Incubators. International Small Business Journal, 25, 4, 361–382.

McLaughlin, K., 2004. Towards a “modernized” voluntary and community sector? Public Management Review. 6, 4, 555-562.

McMullan, E., Chrisman, J.J., Vesper, K., 2001. Some Problems in Using Subjective Measures of Effectiveness to Evaluate Entrepreneurial Assistance Programs. Entrepreneurship: Theory and Practice, Fall, 37-54.

Merrifield, D.B., 1987. New Business Incubators. Journal of Business Venturing, 2, 277–284. Mian, S.A., 1994. US university sponsored technology incubators: an overview of

management, policies and performance. Technovation, 14, 8, 515-528. Mian, S.A., 1996. Assessing Value-added Contributions of University Technology Business

Incubators to Tenant Forms. Research Policy, 25, 325–335. Mian, S.A., 1997. Assessing and Managing the University Technology Business Incubator: an

Integrative Framework. Journal of Business Venturing, 12, 251-285. Mole, K.F., Hart, M., Roper, S., Saal, D.S., 2011. Broader or Deeper? Exploring the most

Effective Intervention Profile for Public Small Business Support. Environment and Planning A, 43, 1, 87-105.

Morgan, D.L., 1988. Focus Groups as Qualitative Research. Newbury Park: Sage. Morgan, D.L., 1996. Focus Groups. Annual Review Sociology, 22, 129–152. Moxham, C., 2009. Performance Measurement: Examining the Applicability of the Existing

Body of Knowledge to Nonprofit Organizations. International Journal of Operations and Production Management, 29, 7, 740-763.

Moxham, C., 2010. Help or hindrance? Examining the Role of Performance Measurement in UK Nonprofit Organizations. Public Performance and Management Review, 33, 3, 342-354.

Neely, A., 2005. The Evolution of Performance Measurement Research – Developments in the Last Decade and a Research Agenda for the Next. International Journal of Operations and Production Management, 25, 12, 1264-1277.

Neely, A., Mills, J., Platts, K., Richards, H., Gregory, M., Bourne, M., Kennerly, M., 2000. Performance Measurement System Design: Developing and Testing a Process-Based Approach. International Journal of Operations and Production Management, 20, 10, 1119-1145.

98

O’Neal, T., 2005. Evolving a Successful University-Based Incubator: Lessons Learned from the UCF Technology Incubator. Engineering Management Journal, 17, 3, 11-25.

Ostgaard, T.A., Birley, S., 1994. Personal Networks and Firm Competitive Strategy – A Strategic or Coincidental Match? Journal of Business Venturing, 9, 281-305.

Ostgaard, T.A., Birley, S., 1996. New Venture Growth and Personal Networks. Journal of Business Research, 36, 37-50.

Ozel, B., 2012. Collaboration Structure and Knowledge Diffusion in Turkish Management Academia. Scientometrics, 93, 1, 183-206.

Patton, D., Marlow, S., 2011. University technology business incubators: helping new entrepreneurial firms to learn to grow. Environment and Planning C: Government and Policy, 29, 911-926.

Patton, D., Warren, L., Bream, D., 2009. Elements that Underpin High-tech Business Incubation Processes. Journal of Technology Transfer, 34, 621-636.

Phan, P.H., Siegel, D.S., Wright, M., 2005. Science Parks and Incubators: Observations, Synthesis and Future Research. Journal of Business Venturing, 20, 2, 165-182.

Plosila, W.H., Allen, D.N., 1985. Small business incubators and public policy: implications for state and local development strategies. Policy Studies Journal, 13, 4, 729-734.

POM Antwerp, 2012. POM Antwerp: Flexible and Dynamic Government Organization. Available from: /http://investinantwerp.be/pom-antwerp/S, (accessed 04.04.12).

Priest, S.J., 1999. Business Link services to Small and Medium-sized Enterprises: Targeting, Innovation, and Charging. Environment and Planning C: Government and Policy, 17, 2, 177-193.

Privett, N., Erhun, F., 2011. Efficient Funding: Auditing in the Nonprofit Sector. MandSOM – Manufacturing and Service Operations Management, 13, 4, 471-488.

Ratinho, T., Henriques, E., 2010. The role of science parks and business incubators in converging countries: evidence from Portugal. Technovation, 30, 278-290.

Rice, M.P., 2002. Co-Production of Business Assistance in Business Incubators: an Exploratory Study. Journal of Business Venturing, 17, 2, 163-187.

Salvador, E., 2011. Are science parks and incubators good "brand names'' for spin-offs? The case study of Turin. Journal of Technology Transfer, 36, 2, 203-232.

Schwartz, M., 2009. Beyond incubation: an analysis of firm survival and exit dynamics in the post-graduation period. Journal of Technology Transfer, 34, 403-421.

Schwartz, M., 2012. A Control Group Study of Incubator’s Impact to Promote Firm Survival. Journal of Technology Transfer. Online available: DOI 10.1007/s10961-012-9254-y.

Schwartz, M., Göthner, M., 2009a. A Multidimensional Evaluation of the Effectiveness of Business Incubators: an Application of the PROMETHEE Outranking Method. Environment and Planning C: Government and Policy, 27, 1072-1087.

Schwartz, M., Göthner, M., 2009b. A Novel Approach to Incubator Evaluations: The PROMETHEE outranking procedures. IWH-Discussion Papers, January, no1.

Schwartz, M., Hornych, C., 2008. Specialization as strategy for business incubators: An assessment of the Central German Multimedia Center. Technovation, 28, 436-449.


Scillitoe, J.L., Chakrabarti, A.K., 2010. The Role of Incubator Interactions in Assisting New Ventures. Technovation, 30, 3, 155-167.

99

Shepherd, D., Wiklund, J., 2009. Are we Comparing Apples with Apples or Apples with Oranges? Appropriateness of Knowledge Accumulation Across Growth Studies. Entrepreneurship: Theory and Practice, January, 105-123.

Sherman, H., 1999. Assessing the Intervention Effectiveness of Business Incubation Programs on New Business Start-Ups. Journal of Developmental Entrepreneurship, 4, 2, 117-133.

Sherman, H., Chappell, D.S., 1998. Methodological challenges in evaluating business incubator outcomes. Economic Development Quarterly, 12, 4, 313-321.

Siggelkow, N., 2007. Persuasion with case studies. Academy of Management Journal, 50, 1, 20-24.

Simons, R., 2000. Performance Measurement and Control Systems for Implementing Strategy: Text and Cases. Upper Saddle River, NJ: Prentice Hall.

Skinner, W., 1986. The Productivity Paradox. Harvard Business Review. July-August, 55-59. Slack, N., Lewis, M., 2008. Operations Strategy. Harlow, UK: Pearson Education. Smilor, R.W., 1987. Commercializing Technology through New Business Incubators. Research

Management, 30, 5, 36-41. Soetanto, D.P., Jack, S.L., 2011. Business Incubators and the Networks of Technology-based

Firms. Journal of Technology Transfer, Online available, DOI:10.1007/s10961-011-9237-4. Sohal, A.S., Morrison, M., Pratt, P., 2002. Creating a Regional Learning Environment for

Accelerating Company Development and Growth. Total Quality Management, 13, 2, 183-194.

Spithoven, A., Knockaert, M., 2011. The Role of Business Centres in Firms’ Networking Capabilities and Performance. Science and Public Policy, 38, 7, 569-580.

Spivey, W.A., Munson, J.M., King, A., 2007. Implementing the balanced scorecard to achieve strategic management objectives: The case of the small engineering consultancy. Proceedings from the PICMET conference, Portland International Center for Management of Engineering and Technology, Vol. 1-6, 119-124.


Studdard, N.L., 2006. The Effectiveness of Entrepreneurial Firm’s Knowledge Acquisition from a Business Incubator. International Entrepreneurship Management Journal, 2, 211-225.

Tamásy, C., 2007. Rethinking Technology-Oriented Business Incubators: Developing a Robust Policy Instrument for Entrepreneurship, Innovation, and Regional Development. Growth and Change, 38, 3, 460-473.

Tangen, S., 2004. Performance Measurement: From Philosophy to Practice. International Journal of productivity and Performance Management, 53, 8, 726-737.

Thierstein, A., Wilhelm, B., 2001. Incubator, technology, and innovation centres in Switzerland: features and policy implications. Entrepreneurship and Regional Development, 13, 315-331.

Tornatzky, L., Sherman, H. Adkins, D., 2002. A National Benchmarking Analysis of Technology Business Incubator Performance and Practices. US: National Business Incubation Association.

Venkatraman, N., 1989. The concept of fit in strategy research: towards verbal and statistical correspondence. Academy of Management Review, 14, 423-444.

Venkatraman, N., Prescott, J.E., 1990. Environment-strategy coalignment: an empirical test of its performance implications. Strategic Management Journal, 11, 1, 1-23.

100

Voisey, P., Gornall, L., Jones, P., Thomas, B., 2006. The Measurement of Success in a Business Incubation Project. Journal of Small Business and Enterprise Development, 13, 3, 454-468.

Von Zedtwitz, M., 2003. Classification and Management of Incubators: Aligning Strategic Objectives and Competitive Scope for New Business Facilitation. International Journal of Entrepreneurship and Innovation Management, 3, 1/2, 176-196.

Westhead, P., Storey, D.J., 1994. An Assessment of Firms Located On and Off Science Parks in the United Kingdom. London: HMSO.

Wu, W.-L., Hsu, B.-F., Yeh, R.-S., 2007. Fostering the Determinants of Knowledge Transfer: a Team-Level Analysis. Journal of Information Science, 33, 3, 326-339.

Yin, R.K., 1990. Case study research: design and methods. Applied Social Research Methods Series, Vol.5, Beverly Hills, California: Sage Publications.

Zablocki, E.M., 2007. Formation of a business incubator. In A. Krattiger, R.T. Mahoney, L. Nelsen, et al. (Eds.), Intellectual property management in health care and agricultural innovation: a handbook of best practices (p. 1305-1314). Oxford, UK: MIHR, and Davis, US: PIPRA

101

Chapter 3: Incubator strategy, institutional context, and incubator performance: A moderated mediation analysis of Brazilian

incubators 29 30

Abstract

A conceptual model is developed and tested to understand how an incubator’s focus and

service customization strategy influence its performance. It is also examined in which

entrepreneurial context these relationships exist. The sample consists of 180 incubators

located in a high-growth emerging country, Brazil, and allows the exploration of institutional

variations across Brazilian states. The analysis reveals that a service customization strategy

directly influences the performance of Brazilian incubators, and that it also serves as a

mediator in the relationship between an incubator’s focus strategy and its performance. The

results show that non-entrepreneurial cognitive and regulative institutional contexts impede

these relationships.

Highlights

> The black box of the functioning and impact of an incubator’s strategy is opened

> A service customization strategy directly influences incubator performance

> Customization mediates the relationship between focus strategy and performance

> A non-entrepreneurial context impedes the positive impact of an incubator’s strategy

Keywords

Business incubator; Service customization strategy; Focus strategy; Institutional

environment; Incubator performance

29 This chapter is co-authored with Arjen van Witteloostuijn, Paul Matthyssens and Tales Andreassi. 30 Earlier versions of this chapter have been presented at the following conferences: (1) Second Annual ICSB (International Council for Small Business) Global Entrepreneurship Conference, Washington, DC, US, October 6-8, 2011, (2) Third International ECFED (Entrepreneurship, Culture, Finance and Economic Development) Workshop, Namur, Belgium, June 14-15, 2012, (3) ACED (Antwerp Centre of Evolutionary Demography) workshop, Antwerp, Belgium, September 26-27, 2012, and (4) AEI (Académie de l'Entrepreneuriat et de l'Innovation) Conference organized by EDHEC business school, Lille, France, April 11, 2013.

103

3.1. Introduction

Business incubators are organizations offering office space, administrative services,

logistic facilities, business advice, and networking opportunities to young ventures

(Aernoudt, 2004; Bergek and Norrman, 2008). They often receive substantial public funding

from government organizations (Bergek and Norrman, 2008; Bruneel et al., 2012; Grimaldi

and Grandi, 2005). Incubator government support can take various forms, such as the

development of the technical infrastructure, initial funding (Lalkaka, 2003) or steering

strategy (refocusing) efforts (Carayannis and von Zedtwitz, 2005).

Through incubator support, government agencies aim to catalyze and accelerate

economic development (Ratinho and Henriques, 2010). Incubators stimulate the

development of innovative products and services (Schwartz and Hornych, 2010). Moreover,

increased start-up survival and growth rates enhance job creation and employment growth

(Ferguson and Olofsson, 2004; Fonseca et al., 2001; Löfsten and Lindelöf, 2001, 2002;

Schwartz and Göthner, 2009; Sherman, 1999).

In return for the support offered, policy actors expect outstanding incubator

performance. To better understand how and when to support incubators, they aim to gain

insights into the factors influencing incubator performance variations (Amezcua, 2010;

Bergek and Norrman, 2008; Mian, 1997). However, contrasting research results (Amezcua,

2010; Phan et al., 2005) and methodological, theoretical, and empirical limitations (Yu and

Nijkamp, 2009) foster growing criticism about incubator performance studies.

Traditional explanations tend to emphasize the influence of the amount and type of the

services offered (Colombo and Delmastro, 2002; Fang et al., 2010; Löfsten and Lindelöf,

2001, 2002; Mian, 1996; Rothaermel and Thursby, 2005a, 2005b; Sherman, 1999). Also

environmental conditions such as university linkages (Mian, 1994) have been examined. The

overarching argument is that in particular networking and business coaching explain

observed differences in tenant survival and growth rates (Allen and McCluskey, 1990; Bergek

and Norrman, 2008; Hansen et al., 2000).

The field is moving from merely listing services (Allen and Rahman, 1985) and

recognizing the influence of stakeholder opinions and linkages (Aaboen, 2009; McAdam and

Keogh, 2006; Mian, 1997; Rothaermel and Thursby, 2005a; Sofouli and Vonortas, 2007) to

understanding the mechanisms behind these service offerings.

104

Studies show that an incubator’s service strategy highly impacts its performance. For

instance, researchers advocate that a focused strategy is most effective (Chan and Lau, 2005;

Grimaldi and Grandi, 2005; Schwartz and Hornych, 2008). Moreover, although studies about

the impact of how services are developed and offered are scarce, they do show that, for

example, service co-creation positively influences business assistance (Rice, 2002).

The impact of the environmental context (Hackett and Dilts, 2004; Ratinho and

Henriques, 2010; Sofouli and Vonortas, 2007) has also been illustrated, suggesting that

incubators can help tenants to better understand and interpret institutional environmental

characteristics such as regulations, values, entrepreneurship-related cognition or norms

(Bergek and Norrman, 2008).

These results suggest that research about incubators is ready to shift from describing the

relationship between an incubator’s service strategy, its environmental context and its

performance to understanding how and when the mechanisms behind these relationships

work. The “how” relationship can be tested through mediation, and the “when” relationship

with moderation effects.

In order to be able to mature further (Hayes, 2012), the number of quantitative studies

in the field must increase (Amezcua et al., 2013). Our study introduces one of the first

quantitative studies in which an incubator’s strategy, its environmental context and its

performance are concurrently examined. In so doing, we capture variation that catalyzes the

incubator’s effectiveness. As widely accepted in the incubator literature, in this study

incubator performance is measured through tenant survival and growth (Aerts et al., 2007;

Hackett and Dilts, 2008; Lalkaka, 1996).

We develop and test a conceptual model that covers an incubator’s focus strategy

(Schwartz and Hornych, 2008; Skaggs and Huffman, 2003), its service customization strategy

(Rice, 2002; Skaggs and Huffman, 2003), and the regulative, normative, and cognitive

dimensions of the environmental context (Bergek and Norrman, 2008; Busenitz et al., 2000;

Kostova, 1997; Scott, 2001). We get a deeper understanding of how and in which context the

incubator’s focus and service customization strategy influence its performance. We draw

from strategic positioning, service logic and institutional theory to justify our research

model.

105

The model is tested using a sample of 180 incubators in Brazil. We estimate the direct

and interaction effects of the incubator’s strategy and explore institutional variations across

Brazilian states. The study allows us to answer our research questions – how and when does

an incubator’s strategy influence its performance – with a deeper level of understanding

than previous studies not considering these mediation or moderation effects (e.g., Löfsten

and Lindelöf, 2002). As a results, our study generates a higher level of generalizability than

earlier, mainly qualitative work in which an incubator’s service strategy, external context and

performance outcomes are combined (e.g., Bruneel et al., 2012).

The chapter proceeds as follows. In section 2, we draw from literature on strategic

positioning, service logic and the institutional environment to detail the conceptual model

and hypotheses. Section 3 describes the research setting, data, variables, and methods. We

present the results of our analyses in section 4. Section 5 discusses our findings, and

provides implications for both theory and practice.

3.2. Theoretical background and hypotheses

As stated, we build on strategic positioning, service logic and institutional theory to

develop our conceptual model and hypotheses. The baseline argument of strategic

positioning is that there are two dimensions that constitute an organization’s competitive

position (Porter, 1991). The first dimension examines how the organization can attain a

competitive advantage by differentiating itself from its competitors. Here, the service logic

literature introduces the argument that if the customer is actively involved in the service

development, processing and delivery process (Grönroos, 2011), service co-creation and

customization can be attained (Jacob, 2006). As such, the organization is able to fulfill

individual customer demands (Jacob, 2006) and can attain a superior competitive advantage.

The second is its competitive scope and signals whether the organization follows a focused

or a diversified strategy. It indicates the breadth of the organization’s activities (Porter,

1998). It is argued that both a focused (e.g., Singh et al., 2007) and a diversified (e.g., Nath et

al., 2010) strategy can result in high performances.

To examine the influence of the context in which the incubator operates, we turn toward

institutional theory. The key idea of institutionalism is that institutional systems exert power

on organizations. Organizations that adopt the prevailing models of their environment are

106

granted legitimacy by stakeholders and external parties (Scott, 2005). The idea of

institutional arrangements has been examined extensively in entrepreneurship literature

(Stenholm et al., 2013), and the influence of Scott’s (2001) institutional regulative, cognitive

and normative pillar on entrepreneurial activities is widely acknowledged (Busenitz et al.,

2000). Evidence has been found that the institutional entrepreneurial environment explains

differences in starting and growing a business (Baumol and Strom, 2007; Levie and Autio,

2008; Peng and Heath, 1996).

The regulative dimension refers to rules and laws. Legal legitimacy can be acquired by

following the dominant regulations active in the organization’s institutional environment.

The cognitive pillar is the constitutive schema of shared knowledge. Organizations complying

to the mimetic forces coming from shared knowledge execute actions that are

comprehensible and recognizable for their stakeholders. As such, they can acquire cognitive

legitimacy. Finally, the normative pillar consists of normative mechanisms such as social

obligations and expectations. Organizations can gain public endorsement by complying to

“appropriate” behavior (Scott, 2001, 2005).

3.2.1. Strategic positioning and service logic

For hypotheses development, we first explore the first dimension of strategic

positioning: attaining a competitive advantage through service customization. Customization

requires two sub-processes: resource preparation and transaction activities (Jacob, 2006).

The first includes the organization’s internal aspects such as personnel, equipment or

infrastructure. These resources are potential for customization when they are organized and

planned (Jacob, 2006). The second sub-process involves transaction activities. Here, the

customer provides external information such as individual needs and expectations. He

becomes the interactor (Alam and Perry, 2002; Karpen et al., 2012) and his input results in

the provision of customized goods and services (Jacob, 2006). Contrary to the goods-

dominant logic where the customer is the target of value, in the service-dominant logic he

becomes the co-producer of value (Vargo and Lusch, 2004).

In business-to-business service firms, customers are actively involved in the service

development and offering process (Ordanini and Pasini, 2008). They provide inputs for the

whole value generation process, which does not only comprise value-in-use, but also value

107

generation during the service development, processing and delivering process (Aarikka-

Stenroos and Jaakola, 2012; Grönroos, 2011). Reciprocal interaction between clients and

service deliverers can lead to (small) changes in the service development process and actual

service offering (Aarikka-Stenroos and Jaakkola, 2012; Bitner et al., 1990). Interaction is key

for increasing service quality and organizational performance (Fornell et al., 1996;

Ghobadian et al., 1994; Lewis and Entwistle, 1990; Menor and Roth, 2008; Ramani and

Kumar, 2008), and fundamental for customer value creation (Aarikka-Stenroos and Jaakkola,

2012; Karpen et al., 2012; Wang et al., 2010). Through value co-creation, the organization

can attain a superior competitive advantage (Ghobadian et al., 1994) and differentiation

(Ramani and Kumar, 2008).

Specifically for business incubators, service customization through tenant-incubator

interactions is expected to result in successful incubation (Scillitoe and Chakrabarti, 2010).

Because most incubators have a relatively small amount of tenants, they can engage in

extensive, personal interactions (McAdam and McAdam, 2008). Anticipating individual

tenant needs through service co-creation is expected to lead to improved business

assistance offerings (Rice, 2002) such as counseling (Bergek and Norrman, 2008) and

networking (Hansen et al., 2000). Moreover, in-depth knowledge on individual tenant needs

results in increased tenant satisfaction (Abduh et al., 2007) and success (Peña, 2004).

Therefore, we hypothesize:

Hypothesis 1: An incubator’s service customization strategy positively relates to incubator

performance.

The second dimension of strategic positioning is the competitive scope dimension.

Research on incubators suggests that compared to diversified incubators, focused incubators

are more effective (Haapasalo and Ekholm, 2004). This is rooted in two arguments, the first

related to the incubator’s network organization, and the second to the type of services

offered. First, a transparent and well organized network (Cooper et al., 2012) allows tenants

to easily find the connections they need (Rice, 2002). As such, tenants can quickly locate the

necessary resources through network opportunities and synergies (Cooper et al., 2012). The

consensus is that, compared to diversified incubators, focused incubators are better able to

organize such easily accessible linkages (Bruneel et al., 2012; Phillimore, 1999). Focused

108

incubators can thus more effectively stimulate cooperation (Haapasalo and Ekholm, 2004),

which positively influences company survival chances and growth rates (Witt, 2004).

The second argument states that focused incubators are better able to offer a set of

services tailored to the tenant’s core business. Young tenants can differentiate themselves

from their competitors by targeting niches not of value to the existing competition (Bamford

et al., 2009; Phillips-McDougall et al., 1994). Incubators able to strengthen their tenants

through support offerings in these specialized areas are able to stimulate tenant

performances. For example, incubators focusing on a specific industry can not only offer

network connections valuable for that industry, but also tailored infrastructure. An example

is the Mitteldeutsches Multimediazentrum Halle in Germany (Schwartz and Hornych, 2008).

This incubator does not only offer specialized film and audio studios, but also industry-

specific network connections and business knowledge, leading to increased tenant growth

and survival. As a result, we hypothesize:

Hypothesis 2: An incubator’s focus strategy positively relates to incubator performance.

To examine the relationship between an incubator’s focus strategy, its service

customization strategy and its performance, we turn to strategy literature. It has extensively

been argued that focused strategies allow for higher customization and customer service

levels than diversified once (Dess and Davis, 1984; Phillips-McDougall et al., 1994). The

argument goes that a narrower focus (irrespective whether it is, for example, an industry or

a customer segment focus) makes it easier for an organization to understand customer

expectations. This is mainly rooted in the idea that a focused strategy allows the company to

allocate its resources to only one customer segment or industry niche. As such, it can

provide higher service levels (Phillips-McDougall et al., 1994).

This reasoning also applies to incubators, where a focused strategy implies that the

incubator, for example, only allows tenants active in one specific industry niche (Schwartz

and Hornych, 2008) or where the tenants are required to be affiliated to a specific

organization, such as a university (Mian, 1994). Incubators following such a focused strategy

are able to allocate their resources to better understand their “narrow” tenant segment.

This makes it easier for them to attain high customization levels. Therefore, we hypothesize:

109

Hypothesis 3: An incubator’s focus strategy positively relates to its service customization

strategy.

Given that we also expect a positive relationship between service customization and

incubator performance (see Hypothesis 1), we also expect that:

Hypothesis 4: An incubator’s service customization strategy mediates the relationship

between its focus strategy and its performance. Specifically, we predict that there will be

a positive indirect effect of an incubator’s focus strategy on its performance, through its

service customization strategy.

3.2.2. Institutional environment

The ease of gaining entrepreneurial legitimacy depends heavily upon institutional

acceptance of starting and growing a business (Bruton and Alhstrom, 2003). This explains

why factors such as culture, regulations or social norms influence entrepreneurial success

(Baumol and Strom, 2007). As stated, institutional arguments are subdivided into three

dimensions; a regulative, cognitive and normative dimension.

The regulative entrepreneurial institutional dimension relates to the government’s role

in new business support, risk associated in starting and growing a business, and resource

acquisition facilitation for small and new businesses (Busenitz et al., 2000). The baseline

argument is that the more supportive, easy-to-understand and transparent rules and

regulations for young ventures are, the lower the risk associated with starting and growing a

firm is (Baumol and Strom, 2007). This also makes it easier to have access to the necessary

resources (Busenitz et al., 2000). Indeed, it has extensively been argued that unstable and

inconsistent regulatory frameworks increase uncertainty (Aidis, 2005), and that

untrustworthiness of regulations impedes entrepreneurial activities (Aidis et al., 2008).

The cognitive entrepreneurial dimension focuses on knowledge dispersion about

founding and growing a business (Busenitz and Lau, 1996). In some countries or regions,

entrepreneurship-related knowledge is widely spread (Spencer and Gómez, 2004). In others,

inhabitants have difficulties with starting and growing a business because they do not know

where to find the necessary information (Bosma and Levie, 2010). In environments where

entrepreneurship-related knowledge is dispersed, entrepreneurs can easily find the

110

necessary information to, for example, execute a market analysis or find seed capital (Estrin

et al., 2006). Moreover, variances of entrepreneurial cognitive patterns are expected to lead

to differences in opportunity recognition (Baron, 2007; Bosma and Levie, 2010), perceived

entrepreneurial ability (Krueger et al., 2000) and growth orientation preferences (Bowen and

De Clerq, 2008). In environments with high entrepreneurial cognition, it is easier to gain

legitimacy (Scott, 2001) because entrepreneurial activity is expected to be higher (Stenholm

et al., 2013). Many people recognize the skills and knowledge associated with starting and

growing a business (Busenitz et al., 2000).

Finally, the entrepreneurial normative dimension incorporates the level of admiration for

entrepreneurial activities, creativity and innovativeness (Busenitz et al., 2000). This

dimension is closely related to Hofstede’s (1980) cultural dimensions. Evidence shows that

Hofstede’s uncertainty-avoidance and collectivism dimensions influence entrepreneurial

activities (e.g., Mueller and Thomas, 2001; Tiessen, 1997). Entrepreneurial orientation

appears to be higher in individualistic and low uncertainty avoidance cultures (Mueller and

Thomas, 2001), and entrepreneurship is perceived negatively in uncertainty avoidance

cultures (Bowen and De Clerq, 2008). Social norms, values and beliefs impact whether

potential entrepreneurs believe they are capable to set up a venture (Mueller and Thomas,

2001). Although culture is deeply rooted in communities (Hofstede, 1980), education might

help to boost a potential entrepreneur’s confidence (Mueller and Thomas, 2001). This might

result in a more favorable perception of entrepreneurial activities (Verheul et al., 2002).

Non-entrepreneurial environments are characterized by inhabitants with little

knowledge about starting and growing a business and resistance toward entrepreneurial

activities, or a regulative environment with inconsistent rules. In such environments,

entrepreneurial organizational activities might be impeded because entrepreneurs do not

know where to find the necessary resources, face non transparent regulations and have

difficulties in gaining credibility (Baker et al., 2005; De Clercq et al., 2010). As a result,

incubators face increasing demand variability from their tenants. If demand variability is

high, service customization is most appropriate (Skaggs and Youndt, 2004). However, we

expect that because non-entrepreneurially minded institutional contexts can have very

strong negative effects on activities in the entrepreneurship domain, that in extreme

111

contexts, the positive effect of service customization might negatively interact with the

entrepreneurial environment and might thus decrease. Therefore, we hypothesize that:

Hypothesis 5: The marginal effect of an incubator’s service customization strategy on its

performance is positive at all values of the institutional context but is negatively

moderated by very low values of the perceived entrepreneurial context. In extremely non-

entrepreneurially minded environments, the marginal effect of an incubator’s service

customization strategy on its performance decreases.

Finally, we hypothesized that an incubator’s focus strategy is positively associated with

its service customization strategy (Hypothesis 3). Combining Hypothesis 3 with our reasoning

about an incubator’s institutional environment results in the following:

Hypothesis 6: The institutional environment will moderate the positive and indirect effect

of an incubator’s focus strategy on its performance, through its service customization

strategy. Specifically, the indirect effect is negatively moderated at very low values of the

institutional context. In such environments, the indirect effect decreases in magnitude.

Figure 3-1 visualizes our hypotheses.

Figure 3-1: Conceptual second stage moderation model

Note: The second-stage conditional indirect effect of focus strategy on incubator performance through service customization strategy (Hypothesis 6) is not visualized. Also, because the indirect

effect of focus strategy is a function of institutional context, there is no single indirect effect of focus strategy on incubator performance through service customization strategy that meaningfully can be described or interpreted in this Figure. However, because we expect a single indirect effect of focus strategy on incubator performance through service customization, we also perform a separate basic

mediated analysis to test Hypothesis 4.

112

3.3. Methodology

3.3.1. Target population

We sent out a questionnaire to Brazilian incubators to test our hypotheses. Because

there does not exist a publicly available list of incubators operating in Brazil, we first

developed our own incubator database. This was done by combining an incubator list

provided by Sebrae (the Brazilian Service of Support for Micro and Small Enterprises) with

information available on the World Wide Web. We searched for incubators in the publicly

available Anprotec site (the National Business Incubator and Science Park Association),

references in popular media, reports and other documents.

To make sure that we included all Brazilian incubators, we systematically searched for

incubators in each Brazilian state. For each incubator encountered, we searched contact

details from the incubator manager and the secretariat. This resulted in a contact database

of 332 incubators. We left 68 incubators out because they were not active anymore or were

still engaged in a start-up process. Our final contact database consisted of 264 up-and-

running incubators.

The incubator managers were our main target for questionnaire completion. However,

to avoid problems with common-method variance (CMV) (Brannick et al., 2010; Chang et al.,

2010), we also asked two other respondent groups to independently fill out parts of the

incubator manager questionnaire. We asked a second incubator employee to fill out tenant

performance questions and contacted entrepreneurship experts to give their opinions about

the institutional environment. This allowed us to compare incubator performance and

institutional context data provided by the incubator managers with data given by incubator

employees and entrepreneurship experts.

3.3.2. Data gathering and sample representativeness

For the incubator manager and employee samples, we applied the following data

gathering procedure to increase the response rate. First, we stressed that our research was

supported by a university (Fox et al., 1988). We mentioned one of the leading Brazilian

business schools in all communication. Second, we used a pre-notification strategy (Fox et

al., 1988). One of the researchers participated in the annual business incubator and science

park conference organized by Anprotec to introduce the research. Third, we e-mailed a

113

(personalized) cover letter to all incubator managers from our contact list, asking for their

participation. Participants could access the questionnaire online. Fourth, we executed

follow-up telephone calls to the incubator managers, as suggested by Chiu and Brennan

(1990) and Dillman (1972). When necessary, the electronic questionnaire was sent again.

When incubator managers agreed to participate, we explained that, for methodological

reasons, we also wanted to ask some questions to one of the incubator’s employees. We

explained that this could not be the manager her or himself, because s/he was already

answering the first questionnaire. We asked the incubator managers to indicate somebody

with knowledge of tenant performance. This employee also received follow-up telephone

calls.

In total, 187 incubator managers and 113 incubator employees returned the

questionnaire, which results in a response rate of respectively 70.8 and 42.8 per cent.

Compared to other quantitative studies on incubators, these response rates are very high.

For example, Aerts et al. (2007) attained a response rate of 27.7 per cent in their study on

European incubators. Our high response rate can be explained by our pre-notifications,

university sponsorship and follow-up telephone calls strategy.

Our missing data analysis revealed that seven cases missed 60 per cent or more of the

variables in the incubator manager database. These cases missed data on all dependent

variables. Hair et al. (2006) state that deleting cases with missing data on the dependent

variables avoids artificial increase in relationships with independent variables. After deleting

these cases, our final incubator manager database included 180 cases. The remaining

missing data was MCAR (Missing Completely At Random), which means that the missing data

pattern is random (p value = .252 > .05; thus the Null Hypothesis that missing data are MCAR

could not be rejected). In the incubator employee database, one case missed 91 per cent of

the data. This case was deleted, resulting in a final incubator employee sample of 112

cases.31

We examined the representativeness of our incubator sample in terms of number of

incubators per state, year of operation and number of tenants. Until 2006, Anprotec

31 For the incubator manager sample, Little’s MCAR test has been applied for the following variables: focus strategy, service customization strategy, incubator performance, regulative dimension, cognitive dimension and normative dimension. Because the incubator employee sample only contains the incubator performance variable, a Little’s MCAR test is not useful for this database.

114

conducted a yearly panorama of Brazilian incubator characteristics (Anprotec, 2005, 2006).

We compared our sample with Anprotec’s results. Paired samples t tests indicate that there

are no significant differences (α = .01) between the distributions of the number of incubators

per state, the number of tenants and the year of operation (see Appendix A for an overview

of our sample and Anprotec’s database).

For the entrepreneurship expert sample, we used the snowball sampling technique (also

applied by, for example, Farrington et al. (2011) and Venter et al. (2005) in SME family

business research). Entrepreneurship experts were participants of the Brazilian satellite of

the Roundtable Entrepreneurship Education conference, entrepreneurship experts from

Sebrae as well as experts from the Entrepreneurship and New Ventures Centre from one of

the leading business schools in Brazil. We asked all experts who received the questionnaire

to forward the cover letter and on-line questionnaire to their own entrepreneurship expert

contacts. This resulted in a total of 184 returned questionnaires.

There are no cases with missing data. As explained by Reynolds et al. (2005),

unavailability of entrepreneurial expert lists makes it impossible to check for sample

representativeness. Our data represents the opinions of entrepreneurship experts from a

substantial range of backgrounds and knowledge, with respondents with various levels of

entrepreneurship experience, educational backgrounds and jobs (see Appendix B). This is in

line with GEM’s National Expert Survey (Reynolds et al., 2005), in which a similar method

was employed to make sure that experts have knowledge on a variety of institutional

context aspects.

3.3.3. Questionnaire

The measures used for this research are part of a larger questionnaire in which – among

others – the incubator’s focus strategy, its service customization strategy, its tenant

performances and its institutional context were examined.32 The questionnaire instrument

was first established in English, after which we translated it in Brazilian Portuguese.

Capturing the same meaning in each language is a complex process (Douglas and Craig,

32 Without doubt, also other variables might impact incubator performance measured in tenant survival and growth. For example, an incubator’s network connections (Hansen et al., 2000) or type of funding (Mian, 1994) might influence its functioning and outcome effectiveness. Also tenant characteristics such as sector, size or age can have an impact on tenant survival and growth. However, in this chapter, we focus on aspects from an incubator’s strategy and environment.

115

2007). To test the accuracy of the translation, back translation has been widely used (Brislin,

1970). Although back translation results in a correct literal translation, it does not reveal

problems regarding different meanings in another context, such as those resulting from

cultural biases (Douglas and Craig, 2007). To address this, we followed Douglas and Craig’s

(2007) collaborative and iterative translation method, applied by researchers such as Danis

et al. (2010) and Ha et al. (2010).

The collaborative approach entails “that different points of view are represented”, while

iteration ensures “that the “best” translation evolves” (p. 40). As suggested by this approach,

we assured category, functional and construct equivalence by qualitatively pretesting the

questionnaire. Category equivalence means that categories “have similar status and perform

similar functions in each context under examination” (p. 36). For example, we checked

whether an incubator manager has similar job descriptions in Brazil. Functional equivalence

refers to the interpretation of behavior (Douglas and Craig, 2007). For example, we checked

whether offering network support is a desirable incubator service. Construct equivalence is

a matter of conceptualization. Literal translations may have different connotations or reflect

varying intensity levels in different contexts. Overall, both incubator managers and

entrepreneurship/incubator experts were asked to fill out the questionnaire, while

commenting on “their understanding of the meaning of questions, the ease of

comprehension, clarity, and so forth” (p. 38).

3.3.3.1. Incubator performance

Tenant survival and growth (Allen and McCluskey, 1990; Ferguson and Olofsson, 2004;

Schwartz and Göthner, 2009; Sherman, 1999) are frequently cited as the most important

effectiveness indicators for incubators. This is not surprising, given the fact that survival and

growth measures are often used to evaluate venture success (Brush and Vanderwerf, 1992;

Cooper et al., 1994; Hmieleski and Baron, 2008; Westhead and Storey, 1995). Moreover,

evaluating growth measures such as employment effects allows us to examine long-term

incubator impacts instead of short-term outcomes such as occupancy rate (Costa-David et

al., 2002).

We used three performance measures, integrated into one scale. We first asked the

incubator manager to indicate how many tenants left the incubator in the last three years.

116

For these graduates, we asked them to compare the moment the company entered the

incubator with the moment it left the incubator. We asked them how many tenants were

surviving (that is, active) at the moment they left the incubator, how many had grown in

number of employees and how many had grown in terms of sales revenue. This information

allowed us to make a composite measure, combining the percentage of active graduates, the

percentage of tenants that had grown in terms of number of employees and the percentage

of tenants which had grown in terms of sales revenue.

As stated, both the incubator employee and the incubator manager filled out these

tenant performance questions. Exploratory factor analyses show that the performance

measures in the two samples both load onto one factor. Reliability tests reveal that the

Cronbach alphas are similar: .938 for the incubator employee database and .933 for the

incubator manager database (see Appendix C). This makes us confident that we did not have

any problems regarding CMV for the performance measures. A paired samples t test (α =

.01) confirms that there are no significant differences between the distribution of the

performance measures provided by the incubator employee and the incubator manager. For

further analysis, we use the performance data from the incubator manager database.

3.3.3.2. Entrepreneurial institutional context

An entrepreneurial institutional profile is argued to consist of a regulative, cognitive and

normative institutional dimension (Busenitz et al., 2000; Kostova, 1997; Kostova and Roth,

2002). The Global Entrepreneurship Monitor (GEM) team developed internationally

applicable questions to measure this (Reynolds et al., 2005). For example, the perceived

regulative institutional context was measured by asking respondents whether support for

new and growing firms is high on the policy agenda and whether taxes and government

regulations for these firms are predictable and consistent. For the cognitive institutional

context, respondents were asked whether many people have experience in starting a new

business, and whether they know how to organize the resources required for a new

business. Questions about the desirability of being an entrepreneur and the level of respect

and status entrepreneurs receive are used to measure the perceived normative institutional

context (see Appendix D for separate items).

117

We employed a five-point Likert scale from strongly disagree to strongly agree to

measure the items. The GEM team employed the questions we used in a variety of

countries, including Brazil. Factor analyses executed by the GEM team and other researchers

applying the GEM questions (e.g., De Clerq et al., 2010) provide evidence of internal

consistency and reliability. Our factor analyses confirm face validity with the items loading

onto three factors; i.e., a regulative, cognitive and normative dimension.

Again, to avoid CMV, both the incubator managers and entrepreneurship experts filled

out the institutional context questions. Exploratory factor analyses show that the same

factors are retained, with a small nuance for the regulative dimension. More specifically,

factor analysis of data provided by entrepreneurship experts suggests to leave out the last

item of the regulative dimension, resulting in a Cronbach alpha of .645. Data provided by

incubator managers is slightly less fine-tuned, with all four items loading onto one factor and

a Cronbach alpha of .713. For the cognitive and normative dimensions, the same items are

retained with Cronbach alphas of .915 and .730 respectively for the incubator manager

database, and .891 and .697 for the entrepreneurship expert database (see Appendix C).

Paired samples t tests (α = .01) confirm that there are no significant differences between the

distributions of the regulative, cognitive and normative state means provided by the

incubator managers and the entrepreneurship experts. For further analysis, we use the

incubator manager database.

3.3.3.3. Service customization and focus strategy

The strategic service positioning variables service customization and focus strategy are

measured using scales developed and applied by Skaggs and Huffman (2003) and Skaggs and

Youndt (2004). We employed a seven-point Likert scale ranging from “I strongly disagree” to

“I strongly agree”. Based on in-depth interviews with incubator managers and experts, the

items have been adapted to the business incubator context. For example, we asked the

incubator manager whether they follow standard incubation procedures and whether they

focus on a specific industry niche (see Appendix D for separate items). Two factors emerged:

a five-item service customization scale (Cronbach alpha = .693) and a three-item focus

strategy scale (Cronbach alpha = .774) (see Appendix C). These items also conceptually load

118

onto the two factors that emerged, confirming face validity. This is in line with previous

research, with Cronbach alphas reaching .70 (Skaggs and Huffman, 2003).

Our qualitative pre-test suggested that Brazilian incubators follow a service

customization strategy, but combined with some elements typically attributed to service

standardization. More specifically, incubator managers and experts indicated that (a)

although standard incubation procedures are followed and similar service types are offered

to all tenants, (b) the incubator consults each tenant about his/her needs, requires a great

deal of information from the companies for service development and changes the way the

services are offered. Therefore, we expect that, in Brazil, incubators blend a customization

strategy with some standardization aspects, the result being a hybrid strategy that mainly

but not exclusively focuses on customization. This is also in line with service logic literature,

where it is argued that service customization requires procedures and planning, typically

attributed to standardization, as well as customer interaction (Jacob, 2006). Indeed, factor

analysis confirms that all five items load onto one factor, and that reverse coding is not

appropriate.

3.3.3.4. Control variables

Because we measure the incubator’s strategy, its performance and its institutional

environment, we include control variables at the incubator and the state level.33 For the

incubator level, we add the incubator’s age, size and occupancy rate. Previous studies have

indicated that organizational size and age influence performance (Aldrich and Auster, 1986;

Stinchcombe, 1965). Likewise, incubator literature suggests that incubator size and age

influence tenant survival rate and growth measures (Aerts et al., 2007; Allen and McCluskey,

1990; Schwartz, 2008).

Following existing research on incubators, incubator size is measured by examining the

incubator’s surface, subdivided into seven categories; 1=1-1000 m2; 2=2001-2000 m2;

3=2001-4000 m2; 4=4001-6000 m2; 5=6001-8000 m2; 6=8001-10,000 m2; and 7= >10,000 m2.

For an indication of the incubator’s age, we employ the year that the incubator started its

operations (Schwartz, 2008). Because occupancy rate informs us about the incubator’s

possibility to generate income (Costa-David et al., 2002) and its consecutive resources for

33 Our data did not allow us to include control variables at the tenant level.

119

strategy implementation, we measure its occupancy rate through ten categories; 1=0-10%,

2=11-20%; 3=21-30%; 4=31-40%; 5=41-50%; 6=51-60%; 7=61-70%; 8=71-80%; 9=81-90%;

and 10=91-100%.

Control variables at the incubator’s state level are Gdp, the percentage of high-growth

companies and education quality. Although the usefulness of Gdp to indicate social welfare

and human progress has been questioned (van den Bergh, 2009), it is widely employed in

entrepreneurship studies taking into account institutional influences (Pinillos and Reyes,

2011) and entrepreneurial activities (Peterson, 2008; Valliere and Peterson, 2009). Examples

are studies about differences in entrepreneurship rates (Peterson, 2008; Pinillos and Reyes,

2011) and entrepreneurial types (Valliere and Peterson, 2009).

The percentage of high-growth companies indicates the type of entrepreneurship in the

relevant state. State-level institutional aspects such as property rights and contracting

influence high-growth aspirations (Troilo, 2011). Moreover, high-growth business impacts

knowledge spillovers and economic growth (Sternberg and Wennekers, 2005).

Finally, education is expected to influence entrepreneurial activities (Verheul et al.,

2002), influencing access to resources and capabilities needed for venture creation

(Chandler and Jansen, 1992). To measure Brazilian education levels, we combine the IDEB

quality rank developed by the federal government (MEC, 2013) with the weighted

percentage of students reaching the relevant educational level, but dropping out afterwards.

More specifically, we calculate the following:34

Education quality = a*IDEB1 + b*IDEB2 + c*IDEB3 ,

with a = % of students graduating from the initial years of primary school, and dropping out

afterwards; b = % of students graduating from the final years of primary school, and

dropping out afterwards; c = % of students graduating from high school; IDEB1 = Index of

Basic Education Development for the initial years of primary school; IDEB2 = Index of Basic

Education Development for the final years of primary school; and IDEB3 = Index of Basic

Education Development for high school.

34 We also added an illiteracy measure to our analysis, but because of a very high correlation with the Education quality measure (-.736), we left this control variable out.

120

3.3.4. Brazilian context

Brazil is a Latin American country of approximately 8,500,000 km2. It has a population of

approximately 200,000,000 inhabitants, which makes it the fifth largest country of the

world. This federal republic consists of 26 states and 1 federal district. Expected variance for

our model across Brazilian states is visualized in Table 3-1. Here, we see that for example

Gdp varies widely across Brazilian states, and that the cognitive and regulative dimensions of

the institutional context have greater variance than the normative dimension.

3.3.5. Regression analysis

Data were analyzed using the hierarchical multiple regression technique available in SPSS

and Hayes’s (2012) PROCESS macro for moderated mediation modeling (Cole et al., 2008).

This technique is appropriate to gain insights into the (relative) importance of and

relationship amongst independent variables in their prediction of the dependent variable

(Hair et al., 2006). Maintaining power at .80 and obtaining generalizability of results requires

a ratio of observations to independent variables of at least 5:1, with preferable a ratio of

15:1 (Hair et al., 2006). Given the fact that we work with a maximum of eleven variables (two

independent variables, six control variables and three moderator variables; see below), the

required number of observations is minimally 55 and preferably 165 incubator responses.

The number of cases in our incubator manager database is 180, with 149 cases providing

valid information (listwise) on all variables used in our model.

Because regression analysis can be sensitive for outliers (Stevens, 1984), we execute

univariate and multivariate detection of possible outliers (Hair et al., 2006). For multivariate

detection, we use Cook’s distance and interpret the residuals (Field, 2009). Applying the

standardized residuals rule shows that there are no standardized residuals with an absolute

value greater than 1.96, which indicates that the model is a good representation of the

actual data. Because all Cook’s distances are < 1, there is no individual case that influences

the model as a whole. For univariate detection, we apply standardized scores (z scores).

Following Tabachnick and Fidell (2007), we employ an upper limit of 3.29 (p < .001).

Univariate detection suggests outliers in “Service customization strategy”, “High-Growth

companies”, “Year of operation”, and “Size”.

121

Table 3-1: Means, standard deviations, maximum, minimum and bivariate correlations

M SD Min Max 1 2 3 4 5 6 7 8 9 10 11

1. Tenant survival and growth

5.51 3.47 0 10

2. Focus strategy

3.68 1.75 1 7 -.051

3. Service customization strategy

5.59 1.02 1.6 7 .163* .318***

4. Regulative dimension

2.87 .97 1 5 .014 .272*** .150*

5. Cognitive dimension

2.34 .99 1 5 .141* .163* .193** .256***

6. Normative dimension

3.69 .78 1.25 5 -.025 -.114 -.070 .182* .354***

7. Year of operations

2001.71 5.20 1984 2011 -.325*** .137* -.011 -.006 -.081 .070

8. Size (m

2)

1.61 1.09 1 7 .117 -.189** .104 -.146* -.011 -.138* -.245***

9. Occupancy rate

7.33 2.78 1 10 .285*** -.028 .140* .071 .133 .050 -.180* .136*

10. Gdp (Brazilian Real)

375966 389186 4883 996717 .148* -.020 .116 -.018 -.013 -.079 -.077 .137* .351***

11. High-growth growth companies

1.61 .30 1.28 2.98 -.057 -.003 .033 -.137* -.147* -.148* .101 .140* -.070 -.118

12. Education quality (IDEB)

3.56 .49 2.30 4.01 .199** .041 .091 .102 .160* .061 .000 .032 .270*** .519*** -.529***

Variables are not mean centered. Tenant survival and growth is a percentage (composite scale; mean) that consists of the percentage of tenants that survived, percentage of tenants that had grown in number of employees and percentage of tenants that had grown in sales revenue at the moment they left the incubator. Focus strategy and service customization strategy are measured on a 7-point Likert scale (“I strongly disagree” to “I strongly agree”). Regulative, cognitive and normative dimensions are measured on a 5-point Likert scale (“I strongly disagree” to “I strongly agree”). For size, there are 7 categories; 1=1-1000 m2; 2=1001-2000 m2; 3=2001-4000 m2; 4=4001-6000 m2; 5=6001-8000 m2; 6=8001-10,000 m2; 7= >10,000 m2. For occupancy rate, there are 10 categories; 1=0-10%, 2=11-20%; 3=21-30%; 4=31-40%; 5=41-50%; 6=51-60%; 7=61-70%; 8=71-80%; 9=81-90%; 10=91-100%. Gdp is in Brazilian real with approximate exchange rates of 1 BRL = 0.4 EURO and 1 BRL = 0.6 USD. High-growth companies is a percentage. Education quality is a composite of weighted ranks (IDEB) of the education quality of primary and secondary education, taking into account the percentage of population that obtained a specific education level. The IDEB rank ranges from 0 (lowest quality level) to 10 (highest quality level). * < .05; ** p < .01; *** p < .001. Two-tailed significance.

122

An in-depth examination shows that the outliers detected for “Size” and “High-Growth

companies” are interesting cases for robustness analyses. The outliers detected for “Size”

are the only incubators with a surface of 8,000 m2 or more, which is expected to influence an

incubator’s financial independence (Zablocki, 2007). For the variable “High-Growth

companies”, it is apparent that Amazonas is the state that has an outlier on this variable,

with a relatively high percentage of 2.98% high-growth companies. This state receives

relatively high government incentives for company development, to make sure that

economic activity is maintained in the rain forest. Therefore, we will also perform robustness

checks without the cases located in the state Amazonas.

To reduce the risk of multicollinearity, only independent variables with a bivariate

correlation of maximum .7 are included (Tabachnick and Fidell, 2007). The correlation matrix

of the independent variables can be found in Table 3-1. To locate possible multicollinearity

problems, we report the variance inflation factors in the results section. Homoskedasticity

and linearity have been checked by plotting the standardized predicted values against the

standardized residuals. There is no sign of a “tooter” shape, which makes us confident that

there are no heteroskedasticity problems. Also, there is no sign of a nonlinear relationship.

Finally, the Histogram and the Normal P-P plot of the standardized residuals show that the

errors are normally distributed (see Appendix E for all graphs and detailed information about

the assumption checks). For the ease of interpretation, all independent variables are mean

centered (Cohen et al., 2003).35


We tested our study’s hypotheses in three interlinked steps. First, we examined a simple

moderation model to examine Hypotheses 1, 2, 3 and 5. Second, we turned to a simple

mediation model to test Hypothesis 4. Finally, we integrated the proposed moderator in the

simple mediation model and evaluated Hypothesis 6 through a moderated mediation model.

Figure 3-2 represents the conceptual model of Figure 3-1 in the form of a path model.

Important to note is that the path coefficients b1 and c’1 visualized here should be

interpreted separately for the simple mediation (Model 10) and moderation models (Models

35 As a limitation of this research, it might be possible that error terms are dependent within groups, such as incubators located in the same state or receiving financial support from the same funding organization.

123

5, 6 and 7) because the simple mediation model does not take into account the moderator

variables. In addition, because of sample differences in there a slight difference between the

path coefficient a1 from Model 9 and the path coefficient a1 in Model 10a.

Table 3-2 provides the results of the hierarchical linear regression analyses for the simple

moderation and moderated mediation models. Results are represented in Model 1 (control

variables), Models 2, 3, 4 and 9 (control variables and separate direct effects), Models 5, 6

and 7 (control variables, and separate direct and interaction terms) and Model 8 (control

variables and integration of all direct and interaction effects). We performed a regression

analysis for the full model (Model 8) without outliers of the variable Size and the variable

High-growth companies, respectively. Results are the same, which confirms robustness (see

Appendix F).

Table 3-3 provides the results of the hierarchical linear regression analyses for the simple

mediation model, represented in Model 10. Table 3-4 provides the bootstrap quantiles for

the conditional indirect effect of the moderated mediation model.

Figure 3-2: The conceptual models in Figure 3-1 represented in the form of a path model

124

Tests of simple moderation. As expected, Model 1 shows that the control variables Year

of operations, Occupancy rate and Education quality significantly affect incubator

performance (p < .05). The younger incubators are, the lower their performance. The higher

the incubator’s occupancy rate and the incubator’s state education quality, the better the

incubator’s performance. Incubator size, Gdp and the percentage of high-growth companies

in the state where the incubator is located do not significantly influence incubator

performance.

Hypotheses 1, 2 and 3 are unconditional direct effects on Incubator performance

(Hypothesis 1 and 2) and Service customization strategy (Hypothesis 3), respectively. The

hypotheses are tested in Models 2-4 and Model 9 in Table 3-2. Model 2 (B = .497; p < .05),

Model 3 (B = .460; p < .1) and Model 4 (B = .490; p < .05) demonstrate that service

customization positively relates to incubator performance. Thus, we find support for

Hypothesis 1. Models 2-4 indicate a non-significant negative relationship (B = -.1; p > .1). We

find no support for Hypothesis 2. The coefficient of focus strategy in Model 9 is positive and

highly significant (B = .194; p < .001), supporting Hypothesis 3.

Models 5 and 6 show significant negative interaction effects for the regulative and

cognitive dimension (B = -.458; p < .05 and B = -.586; p < .05, respectively). The negative

interaction effect of the normative dimension is non-significant (B = -.048; p > .1). Thus, we

find support for the regulative and cognitive dimension of Hypothesis 5, but not for the

normative dimension (see Appendix G for a visualization of the interaction of the normative

dimension).

To further examine this moderation effect, we formally probed the interactions by using

the Johnson-Neyman technique (Hayes, 2012). This technique derives the values within the

range of the moderator in which the association between service customization strategy and

incubator performance is significant. Figures 3-3 and 3-5 plot the effect of service

customization given the regulative and cognitive dimension, respectively. The y-axes show

the moderator variables, and the right x-axes represent the percentages of observations for

each moderator value (also visualized by the frequency diagram). The dotted lines represent

the 90% bootstrap confidence intervals. The conditional effect of service customization is

significant when both confidence interval lines lie above or below zero (Berry et al., 2012).

125

The plot for the regulative dimension shows that the marginal effect of service

customization is significant until the regulative dimension reaches -.188 (or 2.686 without

mean centering). This is true for 42.5% of the observations. For regulative dimension values

higher than -.188, the 90% confidence intervals are never both above or below zero. Thus,

although the effect sign of service customization on incubator performance given the

regulative dimension turns around a value of 1.13 (or 4 without mean centering), this effect

is non-significant. The interaction plot is visualized in Figure 3-4, where only the low

regulative dimension line is significant.

The plot for the cognitive dimension shows that the marginal effect of service

customization is significant until the cognitive dimension reaches -.406 (or 1.936 without

mean centering). This is true for 29.4% of the observations. For cognitive dimension values

higher than -.406, the 90% confidence intervals are never both above or below zero. Thus,

although the effect sign also turns, and this around a value of .46 (or 2.803 without mean

centering), this effect is non-significant. The interaction plot is visualized in Figure 3-636,

where only the low cognitive dimension line is significant.

Test of simple mediation. Table 3-3 presents the results for Hypothesis 4. The results for

the three direct effects are as follows. A significant positive direct effect of focus strategy on

service customization strategy (B = .187; p < .001)37, a significant positive direct effect of

service customization strategy on incubator performance (B = .556; p < .05) and a non-

significant negative direct effect of focus strategy on incubator performance (B = -.154; p >

.1). Model 10c depicts the total effect of focus strategy on incubator performance, without

taking into account the service customization strategy mediator. This effect is non-significant

(B = -.049; p > .1). Finally, we find evidence of a significant positive indirect effect of focus

strategy on incubator performance (B = .104; p < .1) , through service customization

36 Because the number of observations where the effect of service customization given regulative dimension becomes negative is low, and the interaction plot of Figure 3-4 only visualizes “low” versus “high” values of the regulative dimension, the interaction plot does not visualize this turning. This contrasts the effect of service customization given cognitive dimension, where the number of observations where the effect becomes negative is higher and this turning is also visualized in Figure 3-6. 37 The coefficient for focus strategy in Model 10a slightly differs from the coefficient in Model 9, although the same variables have been used. The reason for this is the different sample size: Model 9 uses a sample size of 149 and Model 10a a sample size of 166.

126

strategy, with both bootstrapped 90% confidence intervals around the indirect effect above

zero (.022; .213). Thus, Hypothesis 4 receives support: there is a positive relationship of

focus strategy on incubator performance if the incubator follows a service customization

strategy.

Test of moderated mediation. To evaluate whether the simple mediation effect

described above is moderated by the institutional context variables, we examined the

conditional indirect effect of focus strategy on incubator performance, through service

customization strategy, at various institutional context levels. For this, we employ bootstrap

quantiles, with values of the moderator at the 10th, 25th, 50th, 75th and 90th percentile, and

90% bootstrap confidence intervals.38 The results in Table 3-4 show that for Model 5* and

Model 6*, the positive indirect effect decreases with increased moderator values. For high

values of the moderator effect, it even becomes negative. However, because bootstrap

intervals straddle zero for moderator values starting from -.124 (or 2.75 without mean

centering) and -.343 (or 2 without mean centering), respectively, this negative sign is non-

significant. Thus, Hypothesis 6 is supported for the regulative and the cognitive dimensions.

Table 3-4 also indicates that because 90% bootstrap confidence intervals straddle zero for all

normative values, there is no conditional indirect effect for the normative dimension.

38 Table 3-4 needs to be interpreted as follows (for this explantion, we refer to Model 5*). The first column of Model 5* shows moderator values for the regulative dimension. The second column provides the coefficients of the indirect effect. The third and fourth columns give the confidence intervals (lower and upper limit confidence intervals, respectively). The reader can see that with increasing moderator values (column 1), the coefficient of the indirect effect decreases (column 2). Moreover, only the first two moderator values have confidence intervals that do not straddle zero (see columns 3 and 4). Thus, only the first two moderator values have significant coefficients.

127

Table 3-2: Hierarchical linear regression and path model coefficients

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8 Model 9

Outcome IncP IncP IncP IncP IncP IncP IncP IncP SCS

B B B B B B B B B

Constant

5.542*** (.248)

5.603*** (.264)

5.608*** (.263)

5.602*** (.264)

5.664*** (.264)

5.734*** (.266)

5.599*** (.265)

5.784*** (.273)

.063 (.079)

CONTROL VARIABLES Incubator level Year operations

-.181*** (.052)

-.211*** (.056)

-.207*** (.056)

-.209*** (.057)

-.213*** (.056)

-.197*** (.056)

-.209*** (.057)

-.201*** (.057)

-.001 (.019)

Size (m2)

.100 (.259)

-.080 (.298)

-.069 (.296)

-.081 (.298)

-.092 (.296)

-.022 (.292)

-.077 (.301)

-.078 (.300)

.142+ (.082)

Occupancy rate

.229** (.097)

.231* (.107)

.221* (.107)

.232* (.108)

.236* (.107)

.236* (.106)

.232* (.108)

.243* (.107)

.038

(.031) State level Gdp (Brazilian Real) -5,613E-007

(.000) -5,148E-007 (.000)

-4,136E-007 (.000)

-5,222E-007 (.000)

-4,798E-007 (.000)

-3,125E-007 (.000)

-5,346E-007 (.000)

-2.548E-007 (.000)

7.213E-008 (.000)

High-growth companies

1.302+ (.999)

1.035 (1.068)

1.101 (1.065)

1.031 (1.070)

1.162 (1.064)

.815 (1.058)

1.024 (1.076)

.937 (1.080)

.170 (.280)

Education quality (IDEB) 1.733** (.706)

1.552* (.760)

1.476* (.763)

1.550* (.759)

1.635* (.756)

1.275* (.757)

1.548* (.762)

1.389* (.769)

.093 (.204)

DIRECT EFFECTS Focus strategy -.113

(.165) -.136 (.162)

-.128 (.163)

c’1 -.118 (.164)

c’1 -.108 (.160)

c’1 -.128 (.164)

-.105 (.170)

a1 .194*** (.043)

Service customization strategy .497* (.289)

.460+ (.290)

.490* (.288)

b1 .510* (.287)

b1 .302 (.295)

b1 .499* (.297)

.277 (.311)

Regulative dimension -.074 (.288)

b2 -.001 (.290)

-.033 (.304)

Cognitive dimension .208 (.278)

b2 .308 (.278)

.298 (.305)

Normative dimension -.082 (.349)

b2 -.069 (.365)

-.162 (.397)

INTERACTION EFFECTS Service customization * Regulative b3 -.458*

(.276) -.359

(.310)

Service customization * Cognitive b3 -.586* (.262)

-.531* (.289)

Service customization * Normative b3 -.048 (.366)

.305 (.405)

F-statistic 5.810*** 3.942*** 4.011*** 3.941*** 3.869*** 4.213*** 3.523*** 3.083*** 3.407** R2 .180 .203 .206 .203 .219 .234 .203 .244 .151 Adjusted-R2 .149 .152 .155 .152 .162 .178 .146 .165 .109 + < .1; * < .05; ** p < .01; *** p < .001. IncP is Incubator performance, measured in tenant survival and growth. SCS is Service customization strategy. One-tailed significance. Standard errors in

parentheses. All VIF < or = to 2.143. All variables are mean-centered, except for the dependent variable Incubator performance. Listwise. Unstandardized coefficients. Sample size = 149.

128

Figure 3-3: Johnson-Neyman region of significance for the conditional effect of service customization strategy given regulative dimension

Figure 3-4: Interaction service customization strategy and regulative dimension

42.5% -0.1882

129

Figure 3-5: Johnson-Neyman region of significance for the conditional effect of service customization strategy given cognitive dimension

Figure 3-6: Interaction service customization strategy and cognitive dimension

29.4% -0.4061

130

Table 3-3: Hierarchical linear regression for simple mediation, and path model coefficients

Model 10a DIRECT effect

Model 10b DIRECT effect

Model 10c TOTAL effect

Outcome SCS IncP IncP

B B B

Constant .003 (.079)

5.540*** (.251)

5.542*** (.253)

CONTROL VARIABLES

Incubator level Year operations

.001 (.017)

-.181** (.064)

-.180** (.064)

Size (m2)

.075 (.099)

.042 (.262)

.083 (.251)

Occupancy rate

.039 (.031)

.207* (.110)

.228* (.105)

State level Gdp (Brazilian Real) .000

(.000) .000

(.000) .000

(.000) High-growth companies

.289 (.290)

1.149 (1.171)

1.310 (1.188)

Education quality (IDEB)

.112 (.198)

1.678** (.682)

1.741** (.696)

DIRECT and TOTAL EFFECTS

Focus strategy a1 .187*** (.044)

c’1 -.154 (.152)

c

1 -.049 (.145)

Service customization strategy

b1 .556* (.253)

F-statistic 3.192** 5.760*** 4.985*** R2 .347 .205 .180

INDIRECT effect of Focus strategy on Incubator performance through Service customization strategy

B LL 90% CI UL 90% CI p (Normal theory test)

a1b1 .104 (.059)

.022 .213 .055

+ < .1; * < .05; ** p < .01; *** p < .001. IncP is Incubator performance, measured in tenant survival and growth. SCS is Service customization strategy. One-tailed significance. Standard errors in parentheses. Listwise. Unstandardized coefficients. Sample size = 166.

131

Table 3-4: Bootstrap quantiles for the conditional indirect effect of the moderated mediation

Model 5* - Regulative dimension

Model 6* - Cognitive dimension

Model 7* - Normative dimension

Reg. B LL 90% CI UL 90% CI Cogn. B LL 90% CI UL 90% CI Norm. B LL 90% CI UL 90% CI

-1.374 .221 (.113)

.026 .0477 -1.343 .211 (.087)

.052 .392 -1.186 .108 (.128)

-.136 .367

-.874 .1762 (.092)

.020 .386 -.743 .143 (.068)

.022 .289 -.436 .101 (.083)

-.048 .284

-.124 .110 (.069)

-.008 .266 -.343 .097 (.063)

-.011 .239 .064 .096 (.070)

-.021 .256

.626 .043 (.068)

-.071 .203 .658 -.016 (.078)

-.177 .140 .564 .091 (.080)

-.051 .267

1.376 -.023 (.089)

-.179 .168 1.658 -.130 (.118)

-.391 .083 1.064 .087 (.107)

-.110 .320

Sample size = 149. Values of the moderator at the 10th, 25th, 50th, 75th, and 90th percentile.

132

3.5. Discussion and conclusion

Understanding the determinants of business incubator performance has been a key goal

of many incubator researchers (e.g., Mian, 1997; Udell, 1990; Voisey et al., 2006). The

present study adds to this body of knowledge and assesses the degree to which an

incubator’s service customization and focus strategies affect incubator performance. Also

the interaction effects of the perceived entrepreneurial context are examined. In line with

our hypotheses, we find support that following a service customization strategy positively

influences the performance of Brazilian incubators. In contrast with research on the

expected effectiveness of focused versus diversified incubators (e.g., Schwartz and Hornych,

2008), we find no evidence that following a focus strategy directly enhances incubator

performance. Instead, our results show that a mediating role of service customization is

required for a focus strategy influencing incubator performance. As expected, low values of

the entrepreneurial institutional context moderate the relationship between incubator

strategy and performance. More specifically, extremely non-entrepreneurial regulative and

cognitive contexts impede the positive effect of a service customization strategy. Likewise,

the positive indirect effect of a focus strategy on incubator performance through service

customization undergoes negative impacts from such regulative and cognitive

entrepreneurial environments.

Our results offer four contributions to the literature, implying a few practical and

theoretical implications. First, our research adds to the growing recognition that

“entrepreneurial behavior needs to be interpreted in the context in which it occurs” (Welter

and Smallbone, 2011, p. 107). Examining the influence of the institutional context is

particularly interesting in contexts with high levels of uncertainty, ambiguity and turbulence

(Welter and Smallbone, 2011). In a high-growth emerging country such as Brazil,

entrepreneurial knowledge is poorly dispersed (Reynolds et al., 2001) and entrepreneurs

starting and growing a business face extensive regulations (The World Bank, 2011).

Our results shed light on the influence of such an entrepreneurial environment on an

organization’s strategy. This answers Wright et al.’s (2005, p. 11) call that there is a need for

a deeper understanding as to “how institutional factors and the environmental dynamics in

emerging economies impact on strategic choices of managers in domestic firms”.

Institutional factors might have a different impact depending on the company’s

133

organizational form (Wright et al., 2005). To the best of our knowledge, ours is the first study

in which an incubator’s strategy and institutional context are examined.

More specifically, our research provides evidence that in Brazil’s entrepreneurial

institutional environment, a service customization strategy is more appropriate for

incubators. We also find that in extremely non-entrepreneurial contexts, this relationship

undergoes negative influences from the environment. In such institutional environments,

environmental uncertainty might become so high, that the positive effect of a service

customization strategy undergoes negative influences. Indeed, we find that the positive

effect of a service customization strategy on incubator performance decreases in

institutional Brazilian environments with inhabitants having extremely low entrepreneurial

knowledge and regulations not at all favoring new ventures. The fact that we did not find

significant results for the normative entrepreneurial dimension might be explained because

there is less variance across Brazilian states for this variable. The regression analysis might

simply not be able to capture the effect of this institutional dimension.

Second, our results add to a growing debate as to the effectiveness of a service focus

strategy for incubators. As said, our results show that in Brazil, a focused strategy does not

directly affect incubator performance. There might be two explanations for this. The first

argument says that both diversified and specialized incubators may be able to attain a

competitive advantage. Thus not only – as widely accepted – incubators adopting a focus

strategy are able to increase tenant survival and growth. Although additional research

focusing on the difference between specialized and diversified incubators has to confirm

this, this would imply that the positive effect of networking activities on venture

performance (Colombo et al., 2009; Gans and Stern, 2003) might hold true for both

diversified and specialized incubators. This would be in line with recent work from Schwartz

and Hornych (2010), that reveals that networking activities are effective both in specialized

and diversified incubators. The second argument is that there might not be a direct effect of

an incubator’s focus strategy on its performance. Instead, focused incubators can only

increase tenant survival and growth when they also follow a service customization strategy.

Our study’s results show evidence of such a relationship.

Our study’s third contribution is extending the prominent discussion regarding internal

versus external influences on organizational performance in the entrepreneurship context

(Romanelli, 1989). Short et al. (2009) show that industry level effects matter little for new

134

ventures. Our results extend this research in two ways. First, we shed light on the influence

of the institutional context, rather than industry effects. Second, we examine the impact of

external and internal effects on the performance of business incubators. To the best of our

knowledge, ours is the first study in this domain that focuses on new venture support

organizations. Our results suggest that organizations active in the entrepreneurship domain

can undergo negative influences from extremely non-entrepreneurial institutional

environments, but that this is not the case when the entrepreneurial environment becomes

more developed. Although further research is needed, this might suggest that the negative

influences from the environment are in particular present in extremely non-entrepreneurial

contexts. This finding nuances previous research that states that new ventures are always

relatively more sensitive to the environment (Singh et al., 1986; Stinchcombe, 1965).

Fourth and finally, we found that the influence of the institutional context becomes

insignificant when the entrepreneurial environment becomes entrepreneurial-minded.

Although this suggests that a service customization or focus strategy are not influenced by

more entrepreneurial-minded institutional contexts, it might also suggest that in such

contexts, institutional theory might not be the most appropriate theoretical lens. Although

future research will have to confirm this, an alternative might be that cluster theory

becomes relatively more important in such contexts. A cluster is as “a geographically

proximate group of interconnected companies and associated institutions in a particular

field, linked by commonalities and complementarities” (Porter, 2000, p. 16). Although some

skepticism exists (e.g., Kukalis, 2010), researchers expect that being located in a cluster leads

to superior firm performance (e.g., Krugman, 1991). Storper (1995), for example, states that

external interactions lead to technology development. In the literature on new ventures and

incubators, the importance of networking (Hansen et al., 2000; Scillitoe and Chakrabarti,

2010) and close proximity (McAdam and McAdam, 2008; Phillimore, 1999) is widely

acknowledged. Applying cluster theory might extend our research regarding the relationship

between an incubator’s strategy, its performance and the environment.


Our results suggest two implications for practice. First, our analysis on an incubator’s

focus and service customization strategy indicates that a stand-alone focus strategy does not

result in increased tenant survival and growth rates. Rather, increased performance can be

135

attained by simultaneously implementing a service customization strategy. This nuances

strategy development and implementation avenues for Brazilian incubators. More

specifically, it suggests that not only the incubator’s target audience is of importance, but

that also its service offering decisions influence its performance. Incubator managers and

incubator support organizations might use this information to simultaneously decide upon

the incubator’s scope and its service offering strategy.

Second, our results suggest that the entrepreneurial environment influences the

incubator’s degrees of freedom with regard to its strategic position. The positive effects of

service customization and focus strategies are negatively influenced by extremely non-

entrepreneurial contexts. Interestingly, we did not find these results for more developed

Brazilian contexts. Here, incubator managers and incubator support organizations might be

less restricted in their strategic positioning choices. In such contexts, the entrepreneurial

environment does not impede the positive effects of service customization or focus

strategies. This suggests to incubator managers in Brazil that service customization and focus

strategies are most adequate, and to policy to attribute sufficient attention to the

development of entrepreneurial-minded regulative and cognitive environments.


Besides the limitations and future research avenues already discussed above, there are

three additional limitations of the current study that deserve special attention. First, our

research only focuses on Brazil. The institutional context of emerging markets is not uniform

(Khanna and Palepu, 1997) and differs considerably from the context in developed

economies (Manolova et al., 2008). Moreover, the Brazilian incubator movement is a rather

atypical case in Latin America. Etzkowitz et al. (2005, p. 412) argue that “the Brazilian

incubator movement represents a new direction in Latin American science, technology and

industrial policy”. In particular the coordinating business incubation and science park

association Anprotec played an entrepreneurial role in “disseminating the idea, convincing

universities and institutions to participate in incubation, and persuading various

governmental and industrial institutions to support the incubators” (Etzkowitz et al., 2005, p.

418). Although our research allows us to better understand the Brazilian incubator

landscape, both Brazil’s specific incubator situation and its institutional context impede us

136

from transferring our results to other economies. Hence, further work in other countries is

badly needed.

Second, our study includes incubator and state level variables, but not information about

the incubator’s tenants. However, because we employ incubator survival and growth as

incubator outcome effectiveness, it is very likely that tenant characteristics such as tenant

size, age or sector influence our model. Although our data did not allow us to include such

variables, future researchers might do so.

Third and finally, although our quantitative approach allowed us to examine a large

amount of Brazilian incubators, it has the downside that it forces us to only include a limited

amount of variables. As explained in Chapter 2, an incubator’s performance and functioning

depends upon much more aspects than the variables included in this analysis. For example,

the incubator’s entrepreneurial orientation39 or the manager’s level of education40 might

influence incubator functioning and outcome effectiveness. The same reasoning applies to

the environment. For example, we did not include any information about the incubator’s

network partners, although it has been stated that networking has an important impact on

incubator functioning (Hansen et al., 2000). Therefore, we suggest future quantitative

researchers to further develop our model and include other internal and external variables,

and qualitative researchers to examine the functioning and outcome effectiveness of a few

(Brazilian) incubators through in-depth case studies.

39 Entrepreneurial orientation is a multidimensional construct that includes “a propensity to act autonomously, willingness to innovate and take risks, and a tendency to be aggressive toward competitors and proactive relative to marketplace opportunities” (Lumpkin and Dess, 1996, p. 137). Its dimensions may independently influence incubator performance, and be context-dependent (Lumpkin and Dess, 1996). 40 As a first attempt to address this shortcoming, we will examine the impact of the incubator’s human capital on service co-creation in Chapter 4.

137

Appendices

Appendix A: Sample representativeness incubator sample

Table 3-5: Sample representativeness: description of incubator samples

State Anprotec (2005)

Own contact database (2010)

Incubator manager sample (2010)

Incubator employee sample (2010)

N° of incubators

N° of incubators

N° of responses

Response rate

N° of responses

Response rate

AC-Acre 1 1 0 0% 0 0% AL-Alagoas 10 2 2 100% 2 100% AM-Amazonas 3 6 4 66.7% 2 33.3% AP-Amapá 1 2 1 50% 1 50% BA-Bahia 11 6 4 66.7% 2 33.3% CE-Ceará 5 11 10 90.9% 8 72.7% DF-Distrito Federal 6 3 2 66.7% 2 66.7% ES-Espírito Santo 5 4 3 75% 2 50% GO-Goiás 5 5 5 100% 2 40% MA-Maranhão 2 3 1 33.3% 1 33.3% MG-Minas Gerais 26 23 16 69.6% 8 34.8% MS-Mato Grosso do Sul 9 7 6 86.7% 4 57.1% MT-Mato Grosso 6 3 2 66.7% 1 33.3% PA-Pará 4 6 4 66.7% 2 33.3% PB-Paraíba 5 3 3 100% 1 33.3% PE-Pernambuco 12 11 5 45.5% 4 36.4% PI-Piauí 5 1 0 0% 0 0% PR-Paraná 24 22 15 68.2% 12 54.6% RJ-Rio de Janeiro 27 15 10 66.7% 7 46.7% RN-Rio Grande do Norte 3 5 4 80% 3 60% RO-Rondônia 1 1 1 100% 1 100% RR-Roraima 0 1 1 100% 0 0% RS-Rio Grande do Sul 82 32 22 68.8% 12 37.5% SC-Santa Catarina 17 17 13 76.5% 9 52.9% SE-Sergipe 3 1 1 100% 1 100% SP-São Paulo 62 72 51 70.8% 26 36.1% TO-Tocantins 4 1 1 100% 0 0% Total 339 264 187 70.8% 113 42.8%

138

Table 3-6: Sample representativeness: number of tenants and year of operation

N° of tenants Anprotec (2005)

Sample (2010)

0-5 34% 28.9% 6-10 38% 33.3% > 10 28% 37.8%

Year of operations

Anprotec (2006)

Sample (2010)

--- - 1990 2.8% 3.9% 1991-1996 7.8% 11.7% 1997-2001 31.2% 24.4% 2002-2006 58.2% 46.7% 2007-2010 NA 13.3%

Figure 3-7: Sample representativeness: number of tenants

Figure 3-8: Sample representativeness: year of operations

139

Figure 3-9: Sample representativeness: number of incubator per statea

aAC-Acre; AL-Alagoas; AM-Amazonas; AP-Amapá; BA-Bahia; CE-Ceará; DF-Distrito Federal; ES-Espírito Santo; GO-Goiás; MA-Maranhão; MG-Minas Gerais; MS-Mato Grosso do Sul; MT-Mato Grosso; PA-Pará; PB-Paraíba; PE-Pernambuco; PI-Piauí; PR-Paraná; RJ-Rio de Janeiro; RN-Rio Grande do Norte; RO-Rondônia; RR-Roraima; RS-Rio Grande do Sul; SC-Santa Catarina; SE-Sergipe; SP-São Paulo; TO-Tocantin.

140

Appendix B: Sample representativeness entrepreneurship expert sample

Table 3-7: Sample representativeness: description entrepreneurship expert sample

Job Education Experience in entrepreneurship

Entrepreneur 25.8% High school 0.5% 0-6 months 4.1% Investor, banker, financier 1.6% Bachelor 13.0% 7-12 months 4.9% Business support provider 31.5% Master 30.8% 13 months – 2 years 7.4% Educator, teacher, researcher 41.1% 2nd master 3.2% 3-5 years 18.0% Doctorate 13.0% 6-10 years 23.8% Post-doctorate 4.3% 11-15 years 12.3% Extension course(s) 34.1% 16-20 years 19.7% 21-25 years 4.1% 26-30 years 3.3% 31-35 years 0.8% 36-40 years 0.8% > 40 years 0.8%

141

Appendix C: Factor analyses

Table 3-8: Component matrix principal component analysis: incubator performance

Item Incubator manager

Incubator employee

% of tenants surviving at the moment they left the incubator .946 .953 % of tenants that had grown in number of employees at the moment they left the incubator

.922 .927

% of tenants that had grown in terms of sales revenue at the moment they left the incubator

.951 .949

Table 3-9: Rotated component matrix (VARIMAX rotation) principal component analysis:

institutional context


Experts

I. Regulative dimension

Government policies (e.g., public procurement) consistently favor new firms

.823 .849

The support for new and growing firms is a high priority for policy at the state level

.840 .814

New firms can get most of the required permits and licenses in about a week

.726 .594

Taxes and other government regulations are applied to new and growing firms in a predictable and consistent way

.479

II. Cognitive dimension

Many people have experience in starting a new business .756 .716 Many people can react quickly to good opportunities for a new business .880 .831 Many people have the ability to organize the resources required for a new

business .897 .912

Many people know how to start and manage a high-growth business .859 .890 Many people know how to start and manage a small business .845 .828

III. Normative dimension

Most people consider becoming an entrepreneur as a desirable career choice

.599 .570

Successful entrepreneurs have a high level of status and respect .780 .745 You will often see stories in the public media about successful

entrepreneurs .693 .768

Most people think of entrepreneurs as competent, resourceful individuals .838 .796

142

Table 3-10: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy


I. Service customization strategy

The incubator has standard incubation procedures .725 For each client company, the incubator changes the way each incubator service is

offered .451*

The services offered by the incubator are similar for each client company .621 The incubator consults the company about her needs, before developing a service .741 The incubator requires a great deal of information from each client company before

developing the services offered to that company .700

II. Focus strategy

The incubator focuses on a specific type of services .786 The incubator offers services that focus on a specific industry niche (e.g., IT,

biotechnology, creative sector, etc.) .832

The incubator offers a service that focuses on a specific type of entrepreneurs (e.g., engineers, academics, a specific social class, etc.)

.837

* Factor loadings of .45 are significant for sample sizes > 150 (Hair et al., 2006).

143

Appendix D: Measurement scales

Table 3-11: Measurement scales for independent variables

Variable Items

Regulative dimension Government policies (e.g., public procurement) consistently favor new firms

The support for new and growing firms is a high priority for policy at the state level

New firms can get most of the required permits and licenses in about a week

Taxes and other government regulations are applied to new and growing firms in a predictable and consistent way

Cognitive dimension Many people have experience in starting a new business Many people can react quickly to good opportunities for a new business Many people have the ability to organize the resources required for a

new business Many people know how to start and manage a high-growth business Many people know how to start and manage a small business Normative dimension Most people consider becoming an entrepreneur as a desirable career

choice Successful entrepreneurs have a high level of status and respect You will often see stories in the public media about successful

entrepreneurs Most people think of entrepreneurs as competent, resourceful

individuals Service customization The incubator has standard incubation procedures For each client company, the incubator changes the way each incubator

service is offered The services offered by the incubator are similar for each client

company The incubator consults the company about her needs, before

developing a service The incubator requires a great deal of information from each client

company before developing the services offered to that company Focus strategy The incubator focuses on a specific type of services The incubator offers services that focus on a specific industry niche

(e.g., IT, biotechnology, creative sector, etc.) The incubator offers a service that focuses on a specific type of

entrepreneurs (e.g., engineers, academics, a specific social class, etc.)

144

Appendix E: Plots assumption checks Model 8

Figure 3-10: Homoscedasticity and linearity

145

Figure 3-11: Normality

146

Appendix F: Robustness checks – model without outliers

Table 3-12: Robustness check: hierarchical linear regression for full model without outliers

Model 8 – Without Outliers based on variable “Size”

Model 8 – Without Outliers based on variable “High-growth companies”

Outcome IncP IncP

B B

Constant

5.775*** (.278)

5.745*** (.278)


-.200*** (.057)

-.202*** (.057)

Size (m2)

-.151 (.383)

-.264 (.335)

Occupancy rate

.224* (.108)

.262** (.110)

State level Gdp (Brazilian Real) -1,445E-007

(.000) -1,810E-007 (.000)

High growth companies

.555 (1.123)

.549 (1.514)

Education quality (IDEB) 1.343* (.771)

1.330* (.782)

DIRECT EFFECTS Focus strategy -.111

(.172) -.113 (.171)

Service customization strategy .296 (.312)

.290 (.312)

Regulative dimension -.006 (.305)

-.070 (.306)

Cognitive dimension .309 (.306)

.300 (.306)


-.181 (.397)

INTERACTION EFFECTS Service customization * Regulative

-.343 (.311)

-.320 (.311)

Service customization * Cognitive

-.528* (.290)

-.531* (.288)

Service customization * Normative

.349 (.407)

.340 (.405)

F-statistic 3.008*** 3.102*** R2 .242 .250 Adjusted-R2 .161 .170

+ < .1; * < .05; ** p < .01; *** p < .001. IncP is Incubator performance, measured in tenant survival and growth. One-tailed significance. Standard errors in parentheses. All VIF < or = to 2.096. Listwise. Unstandardized coefficients. Sample size = 149.

147

Appendix G: Non-significant interaction plot

Figure 3-12: Interaction service customization strategy and normative dimension

148

Bibliography

Aaboen, L., 2009. Explaining incubators using firm analogy. Technovation, 29, 657-670. Aarikka-Stenroos, L., Jaakkola, E., 2012. Value co-creation in knowledge intensive business

services: a dyadic perspective on the joint problem solving process. Industrial Marketing Management, 41, 15-26.

Abduh, M., D’Souza, C., Quazi, A., Burley, H.T., 2007. Building futures or stealing secrets? Entrepreneurial cooperation and conflict within business incubators. Managing Service Quality, 17 (1), 74-91.

Aernoudt, R., 2004. Incubators: tool for entrepreneurship? Small Business Economics, 23, 127-135.

Aerts, K., Matthyssens, P., Vandenbempt, K., 2007. Critical role and screening practices of European business incubators. Technovation, 27, 254-267.

Aidis, R., 2005. Institutional barriers to small- and medium-sized enterprise operations in transition countries. Small Business Economics, 25, 305-318.

Aidis, R., Estrin, S., Mickiewicz, T., 2008. Institutions and entrepreneurship development in RUSSIA: a comparative perspective. Journal of Business Venturing, 23 (6), 656–672.

Alam, I., Perry, C., 2002. A customer-oriented new service development process. Journal of Services Marketing, 16, 6, 515-534.

Aldrich, H., Auster, E.R., 1986. Even dwarfs started small: liabilities of age and size and their strategic implications, in: Staw, B.M., Cummings, L.L. (eds), Research in Organizational Behavior, Vol.8, JAI Press, Greenwich, CT, p. 165–198.

Allen, D.N., McCluskey, R., 1990. Structure, policy, services, and performance in the business incubator industry, Entrepreneurship: Theory and Practice, Winter, 61-77.

Allen, D.N., Rahman, S., 1985. Small business incubators: A positive environment for entrepreneurship. Journal of Small Business Management, 23 (3), 12-22.

Amezcua, A.S., 2010. Boon or Boondoggle? Business incubation as entrepreneurship policy. PhD thesis, Syracuse University.


Anprotec, 2005. Panorama de incubadoras de empresas e parques tecnológicos 2005. Anprotec, Brasília, Brazil.

Anprotec, 2006. Panorama de incubadoras de empresas e parques tecnológicos 2006. Anprotec, Brasília, Brazil.

Baker, T., Gedajlovic, E., Lubatkin, M., 2005. A framework for comparing entrepreneurship processes across nations. Journal of International Business Studies, 36, 492–504.

Bamford, C.E., Dean, T.J., McDougall P.P., 2009. Reconsidering the niche prescription for new ventures: A study of initial strategy and growth, in: Lumpkin, G.T., Katz, J.A. (eds), Entrepreneurial Strategic Content (Advances in Entrepreneurship, Firm Emergence and Growth), Vol.11, Emerald Group Publishing Limited, p. 9-39.

Baron, R.A., 2007. Behavioral and cognitive factors in entrepreneurship: entrepreneurs as the active element in new venture creation. Strategic Entrepreneurship Journal, 1 (1–2), 167–182.

Baumol, W.J., Strom, R.J., 2007. Entrepreneurship and economic growth. Strategic Entrepreneurship Journal, 1 (3-4), 233–237.


149

Berry, W.D., Golder, M., Milton, D., 2012. Improving tests of theories positing interaction. The Journal of Politics, 74, 3, 653-671.

Bitner, M.J., Booms B.H., Tetreault M.S., 1990. The service encounter: diagnosing favorable and unfavorable incidents. Journal of Marketing, 54 (1), 71-84.

Bosma, N., Levie, J., 2010. Global Entrepreneurship Monitor. 2009 Global Report. Babson College, Universidad del Desarrollo and Háskólinn Í Reykjavik at Reykjavik University.

Bowen, H.P., De Clercq, D., 2008. Institutional context and the allocation of entrepreneurial effort. Journal of International Business Studies, 39 (4), 747–767.

Brannick, M.T., Chan, D., Conway, J.M., lance, C.E., Spector, P.E., 2010. What is method variance and how can we cope with it? A panel discussion. Organizational Research Methods, 13, 407-420.

Brislin, R.W., 1970. Back translation for cross-cultural research. Journal of Cross-Cultural Psychology, 1, 185-216.


Brush, C.G., Vanderwerf, P.A., 1992. A comparison of methods and sources for obtaining estimates of new venture performance. Journal of Business Venturing, 7, 157-170.

Bruton, G.D., Alhstrom, D., 2003. An institutional view of China's venture capital industry: explaining the differences between China and the West. Journal of Business Venturing, 18 (2), 233–259.

Busenitz, L.W., Gómez, C., Spencer, J.W., 2000, Country institutional profiles: unlocking entrepreneurial phenomena, Academy of Management Journal, 43, 994-1003.

Busenitz, L.W., Lau, C.M., 1996. A cross-cultural cognitive model of new venture creation. Entrepreneurship: Theory and Practice, 20, 25-39.

Carayannis, E.G., von Zedtwitz, M., 2005. Architecting gloCal (global–local), real-virtual incubator networks (G-RVINs) as catalysts and accelerators of entrepreneurship in transitioning and developing economies: lessons learned and best practices from current development and business incubation practices. Technovation, 25, 95-110.


Chandler, G.N., Jansen, E., 1992. The founder’s self-assessed competence and venture performance. Journal of Business Venturing, 7 (3), 223-236.

Chang, S.J., van Witteloostuijn, A., Eden, L., 2010. From the editors: common method variance in international business research. Journal of International Business Studies, 41, 178-184.

Chiu I., Brennan, M., 1990. The effectiveness of some techniques for improving mail survey response rates: a meta-analysis. Marketing Bulletin, 1, 13-18.

Cohen, J., Cohen, P., West, S.G., Aiken, L.S., 2003. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd Ed.). Lawrence Erlbaum Associates, New Jersey.

Cole, M.S., Walter, F., Bruch, H., 2008. Affective mechanisms linking dysfunctional behavior to performance in work teams: a moderated mediation study. Journal of Applied Psychology, 93 (5), 945-958.

Colombo, M.G., Delmastro, M., 2002. How effective are technology incubators? Evidence from Italy. Research Policy, 31, 1103-1122.

150

Colombo, M.G., Grilli, L., Murtinu, S., Piscitello, L., Piva, E., 2009. Effects of international R&D alliances on performance of high-tech start-ups: a longitudinal analysis. Strategic Entrepreneurship Journal, 3, 346-368.

Cooper, A.C., Gimeno-Gascon, F.J., Woo, C.Y., 1994. Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing, 9, 371-395.

Cooper, C.E., Hamel, S.A., Connaughton, S.L., 2012. Motivations and obstacles to networking in a university business incubator. Journal of Technology Transfer, 37 (4), 433-453.

Costa-David, J., Malan, J., Lalkaka, R., 2002. Improving business incubator performance through benchmarking and evaluation: lessons learned from Europe. 16th International Conference on Business Incubation, National Business Incubation Association, April 28 – May 1, 2002, Toronto, Canada.

Danis, W.M., Chiaburu, D.S., Lyles, M.A., 2010. The impact of managerial networking intensity and market-based strategies on firm growth during institutional upheaval: A study of small and medium-sized enterprises in a transition economy. Journal of International Business Studies, 41, 287-307.

De Clerq, D., Danis, D., Dakhli, W.M., 2010. The moderating effect of institutional context on the relationship between associational activity and new business activity in emerging economies. International Business Review, 19, 85-101.

Dess, G.G., Davis, P.S., 1984. Porter's (1980) generic strategies as determinants of strategic group membership and organizational performance. The Academy of Management Journal, 27 (3), 467-488.

Dillman, D.A., 1972. Increasing mail questionnaire response in large samples of the general public. Public Opinion Quarterly, 36, 254-257.

Douglas, S.P., Craig, C.S., 2007. Collaborative and iterative translation: an alternative approach to back translation. Journal of International Marketing, 15, 30-43.

Estrin, S., Meyer, K.E., Bytchkova, M., 2006. Entrepreneurship in transition economies, in: Casson, M. (ed), The Oxford handbook of entrepreneurship, Oxford University Press, Oxford, p. 693–725.

Etzkowitz, H., Carvalho de Mello, J.M., Almeida, M., 2005. Towards “meta-innovation” in Brazil: the evolution of the incubator and the emergence of a triple helix. Research Policy, 34, 411-424.

Fang, S.H., Tsai, F.S., Lin, J.L., 2010. Leveraging tenant-incubator social capital for organizational learning and performance in incubation program. International Small Business Journal, 28, 90-113.

Farrington, S., Venter, E., Boshoff, C., 2011. The impact of intra-group processes on family business success. South African Journal of Economic and Management Sciences, 14, 8-23.

Ferguson, R., Olofsson, C., 2004. Science parks and the development of NTBFs – location, survival and growth. Journal of Technology Transfer, 29, 5-17.

Field, A., 2009. Discovering Statistics using SPSS, third ed. Sage, London, UK. Fonseca, R., Lopez-Garcia, P., Pissarides, C.A., 2001, Entrepreneurship, start-up costs and

employment, European Economic Review, 45, 692-705. Fornell, C., Johnson, M.D., Anderson, E.W., Cha, J., Bryant, B.E., 1996. The American

customer satisfaction index: nature, purpose and findings. Journal of Marketing, 60, 7-18.

Fox, R.J., Crask, M.R., Kim, J., 1988. Mail survey response rate: a meta-analysis of selected techniques for inducing response. Public Opinion Quarterly, 52, 467-491.

151

Gans, J.S., Stern, S., 2003. The product market and the market for ‘ideas’: commercialization strategies for technology entrepreneurs. Research Policy, 32, 333–350.

Ghobadian, A., Speller, S., Jones, M., 1994. Service quality: concepts and models. The International Journal of Quality & Reliability Management, 11 (9), 43-66.


Grönroos, C., 2011. A service perspective on business relationships: the value creation, interaction and marketing interface. Industrial Marketing Management, 40, 240-247.

Haapasalo, H., Ekholm, T., 2004. A profile of European incubators: a framework for commercializing innovations. International Journal of Entrepreneurship and Innovation Management, 4 (2/3), 248-270.

Ha, H.Y., Muthaly, S.K., Akamavi, R.K., 2010. Alternative explanations of online repurchasing behavioral intentions: A comparison study of Korean and UK young customers. European Journal of Marketing, 44, 874-904.

Hackett, S.M., Dilts, D.M., 2004. A systematic review of business incubation research. The Journal of Technology Transfer, 29, 55-82.

Hackett, S.M., Dilts, D.M., 2008. Inside the black box of business incubation: Study B – scale assessment, model refinement, and incubation outcomes. The Journal of Technology Transfer, 33, 439-471.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., 2006. Multivariate data analysis. Pearson Prentice Hall, New Jersey.

Hansen, M.T., Chesbrough, H.W., Nohria, N., Sull, D.N., 2000. Networked incubators: Hothouses of the new economy. Harvard Business Review, 78, 74-84.

Hayes, A.F., 2012. PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling, White paper. Available from: < http://www.afhayes.com/ >

Hmieleski, K.M., Baron, R.A., 2008. Regulatory focus and new venture performance: a study of entrepreneurial opportunity exploitation under conditions of risk versus uncertainty. Strategic Entrepreneurship Journal, 2,285-299.

Hofstede, G., 1980. Culture’s Consequences: International Differences in Work-related Values. Sage, Beverly Hills, CA.


Karpen, I.O., Bove, L.L., Lukas, B.A., 2012. Linking Service-Dominant Logic and Strategic Business Practice: A Conceptual Model of a Service-Dominant Orientation. Journal of Service Research, 15 (1), 21-38.

Khanna, T., Palepu, K., 1997. Why focused strategies may be wrong for emerging markets. Harvard Business Review, July-August, 41-51.

Kostova, T., 1997. Country institutional profiles: Concept and measurement. Academy of Management Best Paper Proceedings, 180-189.

Kostova, T., Roth, K., 2002. Adoption of an organizational practice by subsidiaries of multinational corporations: Institutional and relational effects. Academy of Management Journal, 45, 215–233.

Krueger, N.F., Reilly, M.D., Carsrud, A.L., 2000. Competing models of entrepreneurial intentions. Journal of Business Venturing, 15 (5–6), 411–432.

Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy, 99, 483-499.

152

Kukalis, S., 2010. Agglomeration economics and firm performance: the case of industry clusters. Journal of Management, 36, 453-481.

Lalkaka, R., 1996. Technology business incubators: critical determinants of success. Annals of the New York Academy Sciences, 798, 270-290.

Lalkaka, R., 2003. Business incubators in developing countries: characteristics and performance. International Journal of Entrepreneurship and Innovation Management, 3 (1/2), 31-55.

Levie, J., Autio, E., 2008. A theoretical grounding and test of the GEM model. Small Business Economics, 31 (3), 235–263.

Lewis, B.R., Entwistle, T.W., 1990. Managing the service encounter: a focus on the employee. International Journal of Service Industry Management, 1, 41-52.

Löfsten, H., Lindelöf, P., 2001. Science Parks in Sweden – Industrial Renewal and Development. R&D Management, 31, 309-322.

Löfsten, H., Lindelöf, P., 2002. Science parks and the growth of new technology-based firms – academic-industry links, innovation and markets. Research Policy, 31, 859-876.

Lumpkin, G.T., Dess, G.G., 1996. Clarifying the entrepreneurial orientation construct and linking it to performance. Academy of Management Review, 21 (1), 135-172.

Manolova, T.S., Eunni, R., Gyoshev, B.S., 2008. Institutional environments for entrepreneurship: Evidence from emerging economies in Eastern Europe. Entrepreneurship: Theory & Practice, 32, 203-218.

McAdam, M., Keogh, W., 2006. Incubating enterprise and knowledge: a stakeholder approach. International Journal of Knowledge Management Studies, 1 (1/2), 103-120.

McAdam, M., McAdam, R., 2008. High tech start-ups in University Science Park incubators: the relationship between the start-ups’s lifecycle progression and the use of the incubator’s resources. Technovation, 28, 277–290.

MEC (Ministério da Educação), 2013, Portal IDEB, Available from: < http://www.portalideb.com.br/estado/101-acre/ideb?etapa=EM&rede=privada > (accessed on 14.03.2013).

Menor, L.J., Roth, A.V., 2008. New service development competence and performance: an empirical investigation in retail banking. Production and Operations Management, 17 (3), 267-284.

Mian, S.A., 1994. US university sponsored technology incubators: an overview of management, policies and performance. Technovation, 14 (8), 515-528.

Mian, S.A., 1996. Assessing value-added contributions of university technology business incubators to tenant forms. Research Policy, 25, 325-335.

Mian, S.A., 1997. Assessing and Managing the University Technology Business Incubator: an Integrative Framework. Journal of Business Venturing, 12, 251-285.

Mueller, S.L., Thomas, A.S., 2001. Culture and entrepreneurial potential: a nine country study of locus of control and innovativeness. Journal of Business Venturing, 16 (1), 51–75.

Nath, P., Nachiappan, S., Ramanathan, R., 2010. The impact of marketing capability, operations capability and diversification strategy on performance: a resource-based view, Industrial Marketing Management, 39 (2), 317-329.

Ordanini, A., Pasini, P., 2008. Service co-production and value creation: the case for a service-oriented architecture (SOA). European Management Journal, 26, 289-297.

Peña, I., 2004. Business incubation centers and new firm growth in the Basque Country. Small Business Economics, 22 (3/4), 223.

http://apps.webofknowledge.com/full_record.do?product=WOS&search_mode=GeneralSearch&qid=7&SID=V25CgG77BJ@@L@lDIc1&page=1&doc=3



153

Peng, M.W., Heath, P.S., 1996. The growth of the firm in planned economies in transition: Institutions, organizations, and strategic choice. Academy of Management Review, 21, 492–528.

Peterson, R., 2008. Entrepreneurship and national economic growth: the European entrepreneurial deficit. European Journal of International Management Volume, 2 (4), 471-490.

Phan, P.H., Siegel, D.S., Wright, M., 2005. Science parks and incubators: observations, synthesis and future research. Journal of Business Venturing, 20 (2), 165-182.

Phillimore, J., 1999. Beyond the linear view of innovation in science park evaluation. An analysis of Western Australian Technology Park. Technovation, 19, 673-680.

Phillips-McDougall, P., Covin, J.G., Robinson, R.B., Herron, L., 1994. The effects of industry growth and strategic breadth on new venture performance and strategy content. Strategic Management Journal, 15 (7), 537–554.

Pinillos, M.-J., Reyes, L., 2011. Relationship between individualist-collectivist culture and entrepreneurial activity: evidence from Global Entrepreneurship Monitor data. Small Business Economics, 37 (1), 23-37.

Porter, M., 1991. Towards a dynamic theory of strategy. Strategic Management Journal, Winter Special Issue, 12, 95-117.

Porter, M.E., 1998. Competitive Advantage: Creating and Sustaining Superior Performance. The Free Press, New York.

Porter, M., 2000. Locations, clusters and company strategy, in: Clark, G., Feldman, M., Gertler, M. (eds), The Oxford Handbook of Economic Geography, Oxford University Press, Oxford, p. 253-74.

Ramani, G., Kumar, V., 2008. Interaction orientation and firm performance. Journal of Marketing, 72 (1), 27-45.

Ratinho, T., Henriques, E., 2010. The role of science parks and business incubators in converging countries: evidence from Portugal. Technovation, 30, 278-290.

Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., Lopez-Garcia, P., Chin, N., 2005. Global Entrepreneurship Monitor: data collection design and implementation 1998-2003. Small Business Economics, 24, 205-231.

Reynolds, P., Hay, M., Bygrave, W., Camp, S.M., Autio, E., 2001. Global entrepreneurship monitor: 2000 executive report. Babson College, Ernst&Young, Kauffman Center, London Business School.

Rice, M.P., 2002. Co-production of business assistance in business incubators: an exploratory study. Journal of Business Venturing, 17 (2), 163-187.

Romanelli, E., 1989. Environments and strategies or organization start-ups: effects on early survival. Administrative Science Quarterly, 34, 369-387.

Rothaermel, F.T., Thursby, M., 2005a. University-incubator firm knowledge flows: assessing their impact on incubator firm performance. Research Policy, 34, 305-320.

Rothaermel, F.T., Thursby, M., 2005b. Incubator firm failure or graduation?: The role of university linkages. Research Policy, 34, 1076-1090.

Schwartz, M., 2008. Incubator age and incubation time: determinants of firm survival after graduation? Working paper, Institut für Wirtschaftsforschung, Halle, Germany.

Schwartz, M., Göthner, M., 2009. A Multidimensional Evaluation of the Effectiveness of Business Incubators: an Application of the PROMETHEE Outranking Method. Environment and Planning C: Government and Policy, 27, 1072-1087.

154

Schwartz, M., Hornych, C., 2008. Specialization as strategy for business incubators: An assessment of the Central German Multimedia Center. Technovation, 28, 436-449.


Scillitoe, J.L., Chakrabarti, A.K., 2010. The role of incubator interactions in assisting new ventures. Technovation, 30, 155-167.

Scott, R., 2001. Institutions and Organizations, 2nd edition, Sage, Thousand Oaks, CA. Scott, R., 2005. Institutional theory: contributing to a theoretical research program. In:

Smith, K.G., Hitt, M.A. (eds.), Great Minds in Management: The Process of Theory Development, Oxford, University Press, p. 460-484.

Sherman, H., 1999. Assessing the intervention effectiveness of business incubation programs on new business start-ups. Journal of Developmental Entrepreneurship, 4, 117-133.

Short, J.C., McKelvie, A., Ketchen, D.J., Chandler, G.N., 2009. Firm and industry effects on firm performance: a generalization and extension for new ventures. Strategic Entrepreneurship Journal 3, 47-65.

Singh, M., Nejadmalayeri, A., Mathur, I., 2007. Performance impact of business group affiliation: an analysis of the diversification-performance link in a developing economy. Journal of Business Research, 60 (4), 339-347.

Singh, J.V., Tucker, D.J., House, R.J., 1986. Organizational legitimacy and the liability of newness. Administrative Science Quarterly, 31, 171-193.

Skaggs, B.C., Huffman, T.R., 2003. A customer interaction approach to strategy and production complexity alignment in service firms. Academy of Management Journal, 46, 775-786.

Skaggs, B.C., Youndt, M., 2004. Strategic positioning, human capital, and performance in service organizations: a customer interaction approach. Strategic Management Journal, 25, 85-99.


Spencer, J.W., Gómez, C., 2004. The relationship among national institutional structures, economic factors, and domestic entrepreneurial activity: A multicountry study. Journal of Business Research, 57, 1098-1107.

Sternberg, R., Wennekers, S., 2005. Determinants and Effects of New Business Creation Using Global Entrepreneurship Monitor Data. Small Business Economics, 24 (3), 193-203.

Stenholm, P., Acs, Z., Wuebker, R., 2013. Exploring country-level institutional arrangements on the rate and type of entrepreneurial activity. Journal of Business Venturing, 28, 176-193.

Stevens, J.P., 1984. Outliers and influential data points in regression analysis. Psychological Bulletin, 95, 334-344.

Stinchcombe, A.L., 1965. Social structures and organizations, in: March, J.G. (ed), Handbook of Organizations, Rand McNally, Chicago, p. 142–193.

Storper, M., 1995. Regional technology coalitions: An essential dimension of national technology policy. Research Policy, 24, 895-911.

Tabachnick, B.G., Fidell, L.S., 2007. Using multivariate statistics (5th edition). Pearson, Boston. The World Bank, 2011. Doing Business 2011 Brazil: making a difference for entrepreneurs.

The World Bank, Washington, DC. Tiessen, J.H., 1997. Individualism, collectivism, and entrepreneurship: a framework for

international comparative research. Journal of Business Venturing, 12, 367-384.

155

Troilo, M., 2011. Legal institutions and high-growth aspiration entrepreneurship. Economic Systems, 35 (2), 158-175.

Udell, G.G., 1990. Are business incubators really creating new jobs by creating new businesses and new products. Journal of Product Innovation Management, 7, 108-122.

Valliere, D., Peterson, R., 2009, Entrepreneurship and economic growth: evidence from emerging and developed countries, Entrepreneurship and Regional Development, 21 (5-6), 459-480.

Van den Bergh, J.C.J.M., 2009. The GDP Paradox. Journal of Economic Psychology, 30, 117-135.

Vargo, S.L., Lusch, R.F., 2004. Evolving to a new dominants logic for marketing. Journal of Marketing, 68, 1-17.

Venter, E., Boshoff, C., Maas, G., 2005. The influence of successor-related factors on the succession process in small and medium-sized family businesses. Family Business Review, 18, 283-303.

Verheul, I., Wennekers, S., Audretsch, D.B., Thurik, R., 2002. An eclectic theory of entrepreneurship: policies, institutions and culture. In: Audretsch, D.B., Thurik, R., Verheul, I., Wennekers, S. (Eds.), Entrepreneurship: Determinants and Policy in a European–U.S. Comparison. Kluwer Academic Publishers, Norwell, MA, p. 11–82.

Voisey, P., Gornall, L., Jones, P., Thomas, B., 2006. The Measurement of Success in a Business Incubation Project. Journal of Small Business and Enterprise Development, 13, 454-468.

Wang, G., Wang, J., Ma, X., Qiu, R.G., 2010. The effect of standardization and customization on service satisfaction. Journal of Service Science, 2, 1-23.

Welter, F., Smallbone, D., 2011. Institutional perspectives on entrepreneurial behavior in challenging environments. Journal of Small Business Management, 49, 107-125.

Westhead, P., Storey, D.J., 1995. Links between higher education institutions and high technology firms. Omega, 23, 345-360.

Witt, P., 2004, Entrepreneurs' networks and the success of start-ups. Entrepreneurship and Regional Development, 16 (5), 391-412.

Wright, M., Filatotchev, I., Hoskisson, R., Peng, M,W., 2005. Guest editors' introduction: Strategy research in emerging economies: Challenging conventional wisdom. Journal of Management Studies, 42, 1-33.

Yu, J., Nijkamp, P., 2009. Methodological challenges and institutional barriers in the use of experimental method for the evaluation of business incubators: Lessons from the US, EU and China. Paper presented at the Atlanta Conference on Science and Innovation Policy – ACSIP, Atlanta, art n° 5367841.

Zablocki, E.M., 2007. Formation of a business incubator, in: Krattiger, A., Mahoney, R.T., Nelsen, L., et al. (eds.), Intellectual property management in health care and agricultural innovation: a handbook of best practices, MIHR: Oxford, UK and PIPRA: Davis, USA, p. 1305-1314.

157

Chapter 4: Service co-creation intensity in European business incubators: The impact of the incubator’s human capital and

institutional entrepreneurial environment 41

Abstract

This chapter examines the determinants and mechanisms of the level of incubator-tenant

service co-creation (that is, its intensity). A conceptual model is developed to understand the

impact of internal and external elements on incubator functioning. We focus on the

incubator’s human capital and the entrepreneurial institutional environment, respectively.

The model is tested in four European countries: Belgium (Flanders), the Netherlands, the

United Kingdom and Ireland. The analysis reveals that, as expected, both the incubator’s

human capital and institutional context directly impact the level of service co-creation.

Moreover, the incubator’s human capital interacts with the formal institutional

entrepreneurial context, but not with cultural institutional elements.

Highlights

> Determinants and mechanisms of incubator-tenant service co-creation are explored

> An incubator’s human capital and the institutional entrepreneurial context directly

influence the level of co-creation intensity

> High levels of an incubator’s human capital interact with the positive effect of formal

entrepreneurially-minded institutions

> An incubator’s human capital does not interact with cultural institutional elements

Keywords

Business incubator; Service co-creation; Human capital; Formal institutions; Informal

institutions

41 This chapter is co-authored with Arjen van Witteloostuijn and Paul Matthyssens.

159

4.1. Introduction

Given liability of newness and smallness pressures for small start-ups (Freeman et al.,

1983; Stinchcombe, 1965), start-up support organizations such as business incubators are

exploring different modes to increase start-up survival and growth (e.g., Bruneel et al., 2012;

Grimaldi and Grandi, 2005). Through the offering of administrative services, logistic

infrastructure, business coaching and networking (Bergek and Norrman, 2008), incubators

aim to create a resource munificent environment. As such, they try to diminish resource

dependencies typically attributed to start-ups (Amezcua et al., 2013). Although nurturing

start-ups in a resource munificent environment leads to increased start-up survival and

growth (Löfsten and Lindelöf, 2001, 2002), at least two research questions remain

unanswered in studies about incubators.

First, although recent studies carefully state that incubator research is slowly maturing

(Bruneel et al., 2012), we contradict this by explaining that our knowledge on incubator

functioning is mainly rooted into service offerings (e.g., infrastructure, business support) and

incubator management procedures (e.g., selection criteria, graduation criteria). The actual

incubation process is largely a “black box” and we have little knowledge on the internal

determinants of internal incubation strategies (Hackett and Dilts, 2008, p. 439). Second,

research on external influences is mainly limited to a taken-for-granted belief that funding

programs for start-ups (Avnimelech et al., 2007) and incubators (Adegbite, 2001) have

positive impacts on start-up survival and development. Although differences in policy

initiatives have been highlighted in case-based studies (Clarysse et al., 2005), environmental

conditions have only recently been linked to incubation outcomes in nation-wide

quantitative studies. The first studies in this area focus on the tenant’s task environment,

such as the degree of market competitiveness (Amezcua et al., 2013) and do not question

whether and how a facilitating entrepreneurial institutional context contributes to optimal

incubator functioning.

To address our lack of knowledge on the internal and external determinants of

incubation strategies, the current study unravels some of the mechanisms influencing an

incubation strategy that has been proven to be effective: incubator-tenant service co-

creation (Rice, 2002). Through successful co-creation, organizations can attain two types of

competitive advantage: increased efficiency and improved effectiveness (Hoyer et al., 2010;

Payne et al., 2008). For example, co-creation reduces product failures (Cook, 2008; Ogawa

160

and Piller, 2006) and pushes organizations to continuous product/service improvements (Xie

et al., 2008). Moreover, it allows product/service features to be closely aligned to customer

needs, which results in high customer willingness-to-pay and positive word-to-mouth

(Franke et al., 2009). In addition, customers that are closely involved in the product/service

development process get more realistic expectations of what is feasible. This, in turn, can

result in higher appreciations of the end result (Hoyer et al., 2010; Joshi and Sharma, 2004).

Although most research in this area focuses on product development in a B2C context

(Sarker et al., 2012), also service offerings during B2B encounters involve high levels of client

interaction and co-creation (Skaggs and Huffman, 2003; Skaggs and Youndt, 2004). This is

the case for business incubators, where Rice (2002) found that incubator managers engaging

in continual, proactive co-creation closely follow-up individual tenant needs. In this chapter,

we aim to understand how and when incubator-tenant co-creation can thrive. For this, we

build upon two theories: the resource-based view and institutional theory, and thus take an

internal and external stance, respectively.

For the internal viewpoint, we follow researchers that emphasize the effects of the

incubator’s internal characteristics, such as its service offering and infrastructure. Here, the

overarching argument goes back to the resource-based view, where it is advocated that an

organization can attain a competitive advantage through the acquisition of rare, valuable,

inimitable, and non-substitutable resources (Barney, 1991; Wernerfelt, 1984). Thanks to

access to these resources, the organization can differentiate itself from its competitors and

offer superior value to its customers. The vast majority of research on incubators argues that

it are in particular the incubator’s service bundles that lead to a competitive advantage. For

example, Bruneel et al. (2012) explain that an incubator’s value proposition can follow from

its infrastructure, business support or networking. These services can result in economies of

scale, acceleration of tenant learning curves and access to networks and legitimacy, and are

thus sources of tenant value creation.

Interestingly, there are only a few studies that break away from this commonly used

description of an incubator’s value proposition through its service bundles. In such studies, it

is argued that it is not just access to services that adds customer value. Instead, the

incubator’s human capital determines whether the incubator is able to optimally employ its

services during the incubation process or not. For example, incubator managers with

previous entrepreneurship experience (Hannon and Chaplin, 2003) or high education levels

161

(Zhang and Sonobe, 2011) have a significant effect on the incubator’s internal functioning.

These studies started to unravel the incubation process by showing that incubators are not

just a composition of services, but that the incubator’s human capital plays a key role in its

business processes and internal incubation strategies. We follow this research stream and

aim to answer the following research question: How does an incubator’s human capital

influence its internal incubation strategy “service co-creation”?

Besides the incubator’s human capital level, external factors are expected to be

influencers. Entrepreneurship scholars argue that external institutional partners can either

stimulate or obstruct entrepreneurial activities (Busenitz et al., 2000). A three-dimensional

profile – a regulatory, cognitive and normative dimension – can be used to examine how a

country’s institutional context affects its business activities (Kostova, 1997). The regulative

dimension refers to government policies, laws and regulations. For example, policy can give

priority to favoring new venture support, which would allow start-ups to gain access to

resources they would otherwise have difficulties with to obtain (e.g., McQuaid, 2002). The

cognitive dimension refers to shared knowledge about starting and growing a business, and

the normative dimension examines the inhabitants’ admiration for entrepreneurial activities

(Busenitz et al., 2000).

Interestingly, research on the impact of these institutional aspects on entrepreneurial

activities does not provide univocal results. Although the vast majority of studies suggests

that a facilitating environment stimulates and sustains entrepreneurial activities (Stenholm

et al., 2013; Xavier et al., 2012), others reveal that resource constraints might actually force

companies to be more creative and thus entrepreneurial (Bradley et al., 2011). Besides the

before-mentioned study results highlighting the importance of consistent governmental

subsidy programs for incubators (e.g., Adegbite, 2001), the impact of the institutional

context on internal incubator functioning remains largely unknown. Therefore, our second

research question is: How does the institutional entrepreneurship environment influence an

incubator’s internal incubation strategy “service co-creation”?

With our work, we envision one additional theoretical contribution, besides getting

insights into human capital and institutional context elements that might influence

incubator-tenant co-creation. More specifically, we contribute to the prominent discussion

regarding external versus internal characteristics influencing organizational functioning. In

the entrepreneurship domain, two viewpoints emerged (Short et al., 2009). On the one

162

hand, scholars argue that entrepreneurship-related activities are more strongly influenced

by external factors, because such activities face legitimacy problems, and have limited

resources. External shocks cannot be counterbalanced by internal resources, and are thus

relatively stronger. On the other hand, academics suggest that because entrepreneurship-

related organizations are more flexible, that they are less affected by external influences.

This reasoning follows the resource-based view and argues that typical entrepreneurial

factors such as the entrepreneur’s risk-taking behavior allow actors in the entrepreneurship

domain to quickly respond to environmental burdens (e.g., Ibeh, 2003). Although most

studies in this discussion focus on small ventures and not their support organizations (e.g.,

Short et al., 2009), the current study will examine the relative influence of internal (that is,

human capital) and external (that is, institutional context) aspects on the functioning

strategies of start-up support organizations.

The remainder of the chapter is organized as follows. In the subsequent section, we

explain the theoretical background for hypotheses development. Then, we discuss the

study’s empirical methods and results. Finally, a conclusion section highlights the study’s

main contributions for science, practice and policy, and suggests some future research

avenues for incubator researchers.

4.2. Theoretical background and hypotheses

4.2.1. Human capital and service co-creation intensity

Following Hoyer et al. (2010), we argue that the degree of service co-creation depends

upon its scope and intensity. Scope refers to the stage of the product/service development

process in which co-creation occurs, such as the ideation, service/product development or

commercialization stage (Hoyer et al., 2010). Intensity refers to the extent to which co-

creation occurs in one or several of the before-mentioned stages (Hoyer et al., 2010; Skaggs

and Huffman, 2003), such as the level of co-creation instructions given to the organization’s

customers during service transformation (Sichtmann et al., 2011). Because in particular

customer involvement during the idea generation and product/service development stages

can result in increased performances (Gruner and Homburg, 2000), and little is known about

the influencers of co-creation intensity during these stages (Sarker et al., 2012), our study

focuses on the determinants of the level of co-creation during the service transformation

stage.

163

There are numerous challenges involved in the co-creation process. More specifically,

dealing with multiple partners with varying ideas can lead to information overload (Sarker et

al., 2012) and subsequent unfounded data neglect. In addition, customers might propose

ideas which are not feasible (Magnusson et al., 2003), but nevertheless do expect that the

organization follows their suggestions. In particular uncertainty is expected to be a major

challenge during co-creation. The involvement of various customers can result in high

demand variability (Skaggs and Youndt, 2004) and information flows which are difficult to

control (Jones, 1987).

Organizations opting for high co-creation intensity try to foresee uncertainty by altering

aspects of the service development process. More specifically, they try to introduce high

human capital levels in order to reduce uncertainty typically attributed to intense co-

creation (Skaggs and Youndt, 2004). Human capital has proven to be influential during the

service production and delivering process (Pennings et al., 1998). It does not only positively

relate to high service quality (Becker, 1964), but can also help the organization to respond to

anticipated uncertainty (Becker, 1964; Snell and Dean, 1992) induced by customer

interactions (Bateson, 2002). Employees with high skills, knowledge and expertise are able to

filter out the information needed for high co-creation. They can give exact and precise

instructions to tenants, which allows them to avoid information overload and thus reduce

uncertainty. As a consequence, it is easier for incubators with high human capital levels to

attain the desired level of co-creation intensity. Therefore, we hypothesize:

Hypothesis 1: An incubator’s human capital positively relates to incubator-tenant service

co-creation intensity. Thus, the more developed the incubator’s human capital is, the

more service co-creation occurs.

4.2.2. Institutional entrepreneurial environment and service co-creation intensity

Next to organizational characteristics such as human capital, also external aspects such

as institutions influence organizational functioning. Although there is a notable increase in

studies employing the theoretical insights from institutional theory (Schildt et al., 2006),

relatively few studies actively test institutional elements in the entrepreneurship domain

(Bruton et al., 2010). This is remarkable, because it has extensively been argued that

institutional elements influence entrepreneurial activities and success (Busenitz et al., 2000;

164

Bruton et al., 2000). Also in the incubator domain, it has been suggested that institutional

factors might explain differences in incubator functioning (Phan et al., 2005).

Institutional theory draws on two research streams: an “economic/political branch” that

attributes its attention to rules, regulations and enforcement mechanisms, and a

“sociology/organizational theory branch” that posits that cultural frameworks bring about

values and norms that determine organizational and individual behavior (Bruton et al., 2010,

p. 429). Scott’s (2008) regulative dimension is formed by formal institutions (Assaad, 1993)

and relates to the economic/political branch. The cognitive and normative pillars are often

referred to as informal institutions (Assaad, 1993; Hillman and Aven, 2011) and draw on the

sociological/organizational research stream. According to Bruton et al. (2010), the vast

majority of entrepreneurship-related studies examined the impact of cultural, informal

institutional forces.

The overarching argument of institutions in the entrepreneurship domain is that an

entrepreneurially-minded environment easily “accepts” entrepreneurial activities and offers

a facilitating institutional setting (Bruton et al., 2010). Research shows that both an

underdeveloped institutional setting (e.g., Puffer et al., 2010) and an overly bureaucratic

environment (e.g., Ryglova, 2007) can hamper entrepreneurial activities. Therefore, policy

tries to find a balance between offering sufficient support and refraining from setting up

long bureaucratic procedures for start-ups. Such policy support mechanisms are typically

related to the regulative institutional dimension and examine a country’s level of resource

munificence provided by formal institutions. Countries with a well-developed regulative

institutional context put consistent and transparent support for new and growing firms high

on the agenda (De Clerq et al., 2010). Also informal institutions can contribute to facilitating

entrepreneurial activity. In countries with highly developed cognitive and normative

entrepreneurial contexts, entrepreneurial activities are deeply rooted in society (Xavier et

al., 2012). Organizations active in the entrepreneurship domain can relatively easily gain

legitimacy (Bruton et al., 2010).

The institutional entrepreneurial environment is expected to impact collaborative

activities like co-creation due to two mechanisms. First, individuals expecting negative

consequences from interactive activities might reduce their tendency to collaborate. For

example, the institutional framework might impose a decrease in freedom (e.g., Tartari and

Breschi, 2012) because of high bureaucracy involved in collaborative activities (e.g., Styhre

165

and Lind, 2010). Also a lack of perceived legitimacy might refrain actors from setting up

collaborations in non-entrepreneurially minded environments (e.g., Jansson, 2011).

Information sharing can be hampered, which would be problematic in an entrepreneurial

setting where collaborating with support organizations such as venture capitalists (Lee et al.,

2001) or business incubators (Hansen et al., 2000) positively influences entrepreneurial

success.

Second, environmental uncertainty can impact information sharing necessary for co-

creation. Here, in particular uncertainty created in the regulative and cognitive institutional

context impedes interaction. More specifically, intensive incubator-tenant co-creation

implies that incubators instruct their tenants about the information they need for service

transformation (Rice, 2002). Both an underdeveloped or overly bureaucratic environment

can hamper such instructions. In non-entrepreneurially minded regulative environments,

regulations for new businesses are neither consistent nor transparent, and those that do

exist are overly bureaucratic (De Clerq et al., 2010). Incubators experience difficulties in

having a clear view on the regulative framework, uncertainty increases, and co-creation will

be less intense. We expect similar mechanisms for the cognitive entrepreneurial context. A

well-developed cognitive context implies that many people know how to start a business

(Busenitz et al., 2000). In such environments, incubators experience fewer difficulties in

giving the right instructions, because tenants quickly grasp which input the incubator needs.

Thus, because of a decrease in environmental uncertainty, co-creation is stimulated.

Combining the above-mentioned arguments implies that we expect positive influences of an

entrepreneurially-minded environment on incubator-tenant co-creation intensity. Therefore,

we hypothesize:

Hypothesis 2: The institutional entrepreneurial context positively relates to incubator-

tenant service co-creation intensity. The more entrepreneurially-minded the

environment is, the more service co-creation occurs.

4.2.3. Human capital, institutional entrepreneurial environment and service co-creation

intensity

Hypotheses 1 and 2 suggest that internal incubator characteristics and external elements

influence incubator-tenant co-creation intensity independently. The internal characteristic

we examined is the incubator’s human capital, and the external influence the degree to

166

which the environment is entrepreneurially-minded. As stated above, the institutional

framework comprises both formal (that is, regulative) and informal (that is, cognitive and

normative) elements, and informal institutions are deeply rooted in culture. Although there

is no univocal definition of “culture” (Jahoda, 2012), researchers such as Cole and Parker

(2011) and Matsumoto (2009) agree that culture is inherent to a social group. It is created by

prior generations and determines how people will react on situations. Thus, it coordinates

social behavior (Matsumoto, 2009). Although culture consists of several layers (Hofstede,

1984), and some of these layers are visible and can thus be acted upon, it is predominantly a

recurring pattern of unobservable behavior (Brislin, 1990) that only gradually changes across

generations (Cole and Parker, 2011). Cultural elements are relatively fixed and difficult to be

influenced. Indeed, previous studies found that, for example, education levels do not

moderate the effects of culture (e.g., Chand et al., 2012). We can thus expect that, although

entrepreneurial dimensions rooted in culture can have direct effects on incubator

functioning, these effects will not be influenced by internal incubator factors such as human

capital. As a consequence, we hypothesize that:

Hypothesis 3a: The marginal effects of the cognitive and normative institutional

entrepreneurial contexts on incubator service co-creation intensity are not moderated by

the incubator’s human capital.

To the contrary, we expect different mechanisms when examining interactions between

the regulative institutional context and an incubator’s human capital. More specifically,

because these formal institutions are not rooted in culture42, people can more easily act

upon them. We argue that, although regulations are externally imposed by policy and can be

sources of environmental uncertainty (e.g., Engau and Hoffmann, 2011), people having

sufficient knowledge about the regulative framework can influence their effects. More

specifically, people with high skill levels, knowledge or experience can further stimulate the

42 In this chapter, we make abstraction of possible links between formal and informal institutions. We however do want to stress that there are likely interactions between the institutional dimensions. For example, van Waarden (2001) argues that formal institutions are an expression of cultural values, and gives the example of risk-averse societies that impose formal regulations to reduce uncertainty. By making abstraction of possible linkages, we follow Scott (2008), who argues that “rather than pursuing the development of a more integrated conception, I believe more progress will be made at this juncture [of institutional dimensions] by distinguishing among the several component elements [that is, the three institutional pillars] and identifying their different underlying assumptions, mechanisms, and indicators” (p. 51).

167

positive effects of an entrepreneurially-minded regulative institutional framework because

they can further lower the expected uncertainty attributed to rules, laws and enforcement

mechanisms. Indeed, researchers like Lee et al. (2001) indicate that human capital elements

such as knowledge and capabilities are powerful organizational characteristics that positively

interact with environmental policy aspects. Therefore, we hypothesize:

Hypothesis 3b: The marginal effect of the regulative institutional entrepreneurial context

on incubator-tenant service co-creation intensity is positive at all values of an

incubator’s human capital and increases in magnitude as the incubator’s human capital

becomes more developed.

Figure 4-1 visualizes the conceptual model and hypotheses.

Figure 4-1: Conceptual moderation model

4.3. Methodology

4.3.1. Target population

We sent out a questionnaire to incubators in Belgium (Flanders), the Netherlands, the

United Kingdom and Ireland. Because European incubator contact details are scattered

throughout the World Wide Web, we first developed our own incubator contact database.

For all four countries, we started with the publicly available CORDIS database, which

contains contact details from more than 800 European incubators.43 This database, however,

43 The Community Research and Development Information Science (Cordis) database is available through http://cordis.europa.eu/incubators/ and is an initiative from the European Commission.

168

was last updated in February 2007. For each contact, we checked whether the organization

was still active, and whether it concurrently offered office space, administrative support

services, business support and networking (Bergek and Norrman, 2008). Then, we searched

for additional contact details on the Internet. We used a large variety of sources, such as

references in popular media, reports and government sites.

For each incubator encountered, we listed its website, e-mail address and telephone

number. Whenever available, we listed the incubator manager’s personal coordinates. We

developed a new incubator contact database of 471 incubators: 49 in Belgium (Flanders), 50

in the Netherlands, 317 in the United Kingdom and 55 in Ireland. We left 97 incubators out

because they were still engaged in a start-up process or not active anymore: 3 in Belgium, 9

in the Netherlands, 75 in the United Kingdom and 10 in Ireland. The final contact database

consists of 374 up-and-running incubators: 46 in Belgium, 50 in the Netherlands, 242 in the

United Kingdom and 45 in Ireland.

4.3.2. Data gathering and sample description

To increase the response rate, we applied the following data gathering strategy. First, in

all communication, we stressed that that our research was supported by a university (Fox et

al., 1988). Second, each incubator manager received a (personalized) e-mail. In this e-mail,

we explained the purpose of the study, asked for their participation in an on-line

questionnaire and promised them the results. Third, the incubator managers received

follow-up telephone calls (Chiu and Brennan, 1990; Dillman, 1972). Again, we stressed the

importance of the research, explained how the research could add value to their strategy

formulation and promised them a report with the results. When asked for, we sent the

questionnaire link again.

In total, we received 140 responses: 29 in Belgium, 18 in the Netherlands, 70 in the

United Kingdom and 23 in Ireland. The overall response rate is 37.4%: a response rate of

63.0% in Belgium, 43.9% in the Netherlands, 28.9% in the United Kingdom and 51.1% in

Ireland (see Appendix A). These high response rates can be explained by our personalized e-

mails, university sponsorship and large number of follow-up telephone calls. There were only

three incubators that we did not have to contact again through follow-up telephone calls.

Our missing data analysis revealed that thirteen cases missed more than 70 per cent of

the variables. These cases missed data on the dependent variable: service co-creation. Hair

169

et al. (2006) explain that deleting cases with missing data on the dependent variable avoids

artificial increase in relationships with independent variables. Deleting these cases results in

a final database of 127 cases. The remaining missing data pattern was random (p = .236 >

.05). Listwise analyses on the final database results in a sample size of 89 cases: 18 in

Belgium, 14 in the Netherlands, 43 in the United Kingdom and 14 in Ireland.44

A detailed description of the incubators’ number of tenants, age, occupancy rate and

total inside space (see Appendix A) shows that the average incubator started its operations

in 2000. Since its foundation, it supported 121 to 140 companies. In the last three years, it

had an average occupancy rate of 61-70%. Its inside space is 1001 to 2000 m2. At the time of

the questionnaire, the average incubator pre-incubated, incubated and post-incubated a

total of 12, 19 and 11 companies, respectively. High standard deviations reveal that the

number of companies supported varies substantively from incubator to incubator (see

Appendix A).

4.3.3. Questionnaire

The questionnaire instrument was first established in English. For the Dutch translation,

we followed the collaborative and iterative translation method (Douglas and Craig, 2007).

This method avoids cultural biases by qualitatively pre-testing the questionnaire. The

researcher checks for category, functional and construct equivalence, and asks participants

whether all questions are easy to understand. Category equivalence refers to category

definitions, such as the difference between the service category “office space” and

“administrative services”. Construct and functional equivalence refer to conceptualization

and interpretation of behavior, respectively. For example, we checked the definitions of

training, education and experience. We checked whether – as indicated by the questionnaire

items for human capital – more experience, training or education is interpreted as having

higher levels of human capital.

We limited the likelihood of common-method variance (Podsakoff and Organ, 1986)

through a number of procedures. First, because common-method variance can be caused by

socially-desirable responses (Chang et al., 2010), we assured participants that responses are

44 The most recent incubator report containing detailed incubator information for the European incubator population dates from 2002 (European Commission, 2002). Because this report goes back for more than a decade and only contains information for Europe as a whole, we were unable to check for sample representativeness.

170

anonymous, confidential, and that there are no right or wrong answers. We also asked them

to fill out the questionnaire as honestly as possible. Second, Podsakoff et al. (2003) suggest

using different scale endpoints, because this reduces the likelihood of anchor effects (Chang

et al., 2010). Institutional context items are scaled on a five-point Likert scale, and human

capital, service co-creation and focus strategy items on a seven-point Likert scale. Third, the

fact that we employed a qualitative pre-test assures us that the different items are easily

comprehensible. Our collaborative and iterative translation method ensured that there were

no ambiguous or vague terms in the survey instrument. Fourth, an ex post remedy

advocated by Chang et al. (2010) is the use of more complex models, for example by adding

interaction effects. Our moderation analysis makes us confident that the model is not part of

the rater’s cognitive expectations. Fifth and finally, we conducted a Harman’s single factor

test to examine whether common-method variance is a major problem (Podsakoff et al.,

2003). We executed an exploratory factor analysis on all items of our model. Because seven

factors emerged from the unrotated component matrix and the first factor only accounted

for 21% of the covariance between the items, we can assume that there is no common-

method variance.

4.3.3.1. Human capital and service co-creation

To measure human capital and service co-creation we used scales developed and applied

by Skaggs and Youndt (2004) and Sichtmann et al. (2011), respectively. We employed seven-

point Likert scales from “strongly disagree” to “strongly agree”. Based on the results of

qualitative pre-tests, we adapted the questions to the incubator context. For human capital,

we asked respondents to indicate whether the incubator hires employees with a high level

of experience, education and training. In addition, we asked them whether incubator team

members spend many hours or a high amount of money on training. For service co-creation,

we examined whether and how tenants participate in the service transformation process. In

the service co-creation items, we focused on the instructions for service transformation that

the incubator gives to its tenants (see Appendix B for separate items). As expected, the five

human capital items loaded onto one factor, with a .794 Cronbach alpha. For service co-

creation, a three-item co-creation scale (Cronbach alpha = .910) emerged (see Appendix C

for factor loadings). Conceptually, the items load onto these factors, confirming face validity.

171

Moreover, the results are in line with previous research, where Cronbach alpha’s reached

.85 for human capital (Skaggs and Youndt, 2004) and co-creation (Sichtmann et al., 2011).

4.3.3.2. Entrepreneurial institutional context

To examine the perceived entrepreneurial institutional context, we follow researchers

such as Busenitz et al. (2000) who argue that the entrepreneurial environment consists of a

regulative, cognitive and normative dimension. We employed questions developed by the

Global Entrepreneurship Monitor (GEM) (Reynolds et al., 2005), measured on a five-point

Likert scale from “strongly disagree” to “strongly agree”. The questions have been applied in

a variety of countries, and proved to be internally consistent and reliable (e.g., De Clerq et

al., 2010).

As expected, our factor analysis resulted in three factors: a perceived regulative,

cognitive and normative institutional dimension. For example, for the regulative dimension,

we asked respondents whether taxes and other government regulations are applied to new

and growing firms in a predictable and consistent way. For the cognitive dimension,

respondents indicated, for example, whether many people have experience in starting a new

business. The normative dimension was assessed by asking – among others – whether

entrepreneurs are perceived as competent, resourceful individuals, and whether public

media often reports stories about successful entrepreneurs (see Appendix B for separate

items). In line with previous research (De Clerq et al., 2010), our Cronbach alpha’s are .682,

.890 and .672, respectively (see Appendix C for factor loadings).

4.3.3.3. Control variables

Because our conceptual model comprises information about the incubator’s co-creation

and its human capital, and the country’s entrepreneurial context, we include control

variables at the incubator and the country level. For the incubator level, we follow existing

studies that indicate that an incubator’s age and size can influence its functioning (Allen and

McCluskey, 1990; Hansen et al., 2000). In addition, we measure the incubator’s occupancy

rate because it gives an indication of its resources for strategy implementation (Costa-David

et al., 2002), and add focus strategy because research shows that a focus strategy impacts

incubator service offerings and functioning (Schwartz and Hornych, 2008).

172

Incubator size is measured through the incubator’s inside space, subdivided into eight

categories; 1=0 m2; 2=1-1000 m2; 3=1001-2000 m2; 4=2001-4000 m2; 5=4001-6000 m2;

6=6001-8000 m2; 7=8001-10,000 m2; and 8= >10,000 m2. For incubator age, we use the year

that the incubator started its operations (Schwartz, 2008). For occupancy rate, there are ten

categories; 1=0-10%, 2=11-20%; 3=21-30%; 4=31-40%; 5=41-50%; 6=51-60%; 7=61-70%;

8=71-80%; 9=81-90%; and 10=91-100%. For the focus strategy, we asked the respondents

whether the incubator focuses on a specific type of services, industry niche, or entrepreneur

type. We used three items existing research (Skaggs and Huffman, 2003) and adapted the

items to the incubator context. The three-item focus strategy factor has a Cronbach alpha of

.628 (see Appendix C for factor loadings).

For control variables at the country level, we employed Gdp/Capita (OECD, 2013), the

percentage of inhabitants that received a higher education diploma (OECD, 2013), and the

TEA index (GEM, 2013). In studies on entrepreneurial activities, Gdp/Capita is widely

employed (Peterson, 2008; Valliere and Peterson, 2009). Moreover, education is expected to

influence access to resources and capabilities, and hence entrepreneurial activities and

strategies (Chandler and Jansen, 1992; Verheul et al., 2002). The TEA refers to the Total

Early-stage Entrepreneurial Activity index and gives an indication of the level of

entrepreneurial activity in a country. It measures “the percentage of 18-64 population who

are either a nascent entrepreneur or owner-manager of a new business”. Nascent

entrepreneurs are “actively involved in setting up a business they will own or co-own; this

business has not paid salaries, wages, or any other payments to the owners for more than

three months”. An owner-manager of a new business owns and manages “a running

business that has paid salaries, wages, or any other payments to the owners for more than

three months, but not more than 42 months” (GEM, 2013).

4.3.4. European context

Belgium, the Netherlands, the United Kingdom and Ireland are four West-European

countries, covering an area of approximately 30,530 km2, 41,540 km2, 243,610 km2 and

70,270 km2, respectively. Belgium has approximately 11,000,000 inhabitants, the

Netherlands 16,500,000 inhabitants, the United Kingdom 62,000,000 inhabitants and Ireland

4,500,000 inhabitants. All four countries are located in the so-called innovation-driven

economies (Xavier et al., 2012). GEM adopted in its analysis the three phases of economic

173

development suggested by The World Economic Forum’s Global Competitiveness Report:

factor-driven, efficiency-driven and innovation-driven economies. The first mainly involves

agriculture and extraction businesses. Natural resources and (unskilled) labor dominate. In

efficiency-driven economies, industrialization, economies of scale and capital-intensive

organizations are the main drivers. In the innovation-driven phase, economies are

knowledge-intensive and the service sector is most dominant.

Xavier et al. (2012) employ this subdivision to locate differences in entrepreneurial

activities and environmental conditions. Interestingly, normative dimensions are relatively

low in innovation-driven economies. For example, although half of the respondents in

innovation-driven countries consider becoming an entrepreneur as a good career choice,

GEM reports an approximate percentage of 75 per cent of respondents with this opinion in

factor and efficiency-driven economies (Xavier et al., 2012).45 In addition, aspects from the

cognitive dimension are relatively low in innovation-driven countries: for example, about 35

per cent believe they are capable to start a business, whereas approximately 55 to 70 per

cent of respondents in factor and efficiency-driven economies have this opinion.46,47 Finally,

Xavier et al. (2012) report expert opinions on – among others – regulations for small

business. Here, we can see that, compared to all 54 countries where GEM executes its

research, experts in Ireland, the Netherlands and the United Kingdom do not indicate that

regulations for small businesses are extremely difficult. Only Belgian experts rated such

regulations as very negative.48

Table 4-1 visualizes expected variance for the variables in our conceptual model. Here,

we see that the normative dimension has a relatively lower standard deviation than the

regulative and cognitive dimensions. This indicates that variances across the four countries

45 For comparison, the normative dimension of our data is also rather high, with a 3.794 mean on a five-point Likert scale. 46 The reason for the low scores of innovation-driven economies on normative and cognitive entrepreneurial elements might be that compared to efficiency-driven countries, intrapreneurship rates are relatively higher. Intrapreneurship rates are measured with the Entrepreneurial Employee Activity (EEA) index, which refers to “employees that are currently actively involved in and had a leading role in idea development for a new activity or preparation and implementation of a new activity” (Bosma et al., 2012). 47 For comparison, the cognitive dimension of our data also scores lower than the normative one, with a 2.565 mean for the cognitive dimension and a 3.794 mean for the normative dimension. 48 For comparison, our regulative dimension also scores positive, with a mean of 2.923 on a five-point Likert scale.

174

are relatively low for the normative dimension. Observed differences are highest for the

cognitive dimension.

4.3.5. Regression analysis

We tested our hypotheses employing hierarchical multiple regression. With fourteen

variables (seven control variables, four direct effects and three interaction effects), a

minimum of 70 and preferably 210 observations is required to maintain power at .80 and

obtain generalizability of results (Hair et al., 2006). Our database contains 127 responses,

with 89 cases providing valid information (listwise) for our model.

Multicollinearity is assessed through bivariate correlations and the variance inflation

factor (VIF). Only independent variables with a bivariate correlation of maximum .7 are

included (Tabachnick and Fidell, 2007). All bivariate correlations are low, except for

education and Gdp/Capita, with a bivariate correlation of -.649. Indeed, our analysis of the

VIFs reveals that there are potential multicollinearity problems for the control variables

Gdp/Capita, inhabitants with higher education and the TEA index. For these variables, VIFs

are > 6; while for all other variables, VIFs are ≤ 2 (Field, 2009). To avoid potential

multicollinearity, we decided to leave out Gdp/Capita and education. We kept the TEA index

because its VIF is the lowest and bivariate correlations with the independent variables and

incubator control variables are below .4. Moreover, the TEA index gives a clear indication of

entrepreneurial activities in the four countries, whereas Gdp/Capita and education are

rather indirect influencers of entrepreneurial activities (Chandler and Jansen, 1992; Verheul

et al., 2002). Moreover, leaving out Gdp/Capita and education allows us to have fewer

variables in the regression model: twelve instead of fourteen variables (five control

variables, four direct effects and three interaction effects; see below). With twelve variables,

the required number of observations is minimally 60 and preferably 180 responses. See

Table 4-1 for the correlation table of the twelve variables in our model.

175

Table 4-1: Means, standard deviations, maximum, minimum and bivariate correlations

M SD Min Max 1 2 3 4 5 6 7 8 9

1. Service co-creation 3.225 1.548 1.00 7.00 2. Focus strategy 3.814 1.576 1.00 7.00 .089 3. Human capital 4.617 1.104 1.00 7.00 .275** .241** 4. Regulative dimension 2.923 .732 1.00 4.75 .293** .049 .049 5. Cognitive dimension 2.565 .796 1.00 4.80 .182+ .123 .113 .300** 6. Normative dimension 3.794 .611 2.00 5.00 -.070 .073 .023 .042 -.268** 7. Year operations 2000.53 8.054 1982 2013 .056 .242** .110 .018 -.188+ .124 8. Size (m2) 3.69 1.647 2 8 -.107 .024 .065 -.071 .184+ -.135 -.300*** 9. Occupancy rate 7.89 2.052 1 10 .053 .020 -.090 .073 -.128 -.010 -.408*** .266** 10. TEA 6.044 1.143 3.95 7.20 -.203* .078 .069 .032 -.089 .257* .393*** .125 .000

Variables are not mean centered. Service co-creation, focus strategy and human capital are measured on a 7-point Likert scale (“I strongly disagree” to “I strongly agree”). Regulative, cognitive and normative dimensions are measured on a 5-point Likert scale (“I strongly disagree” to “I strongly agree”). For size, there are 8 categories; 1=0 m2; 2=1-1000 m2; 3=1001-2000 m2; 4=2001-4000 m2; 5=4001-6000 m2; 6=6001-8000 m2; 7=8001-10,000 m2; 8= >10,000 m2. For occupancy rate, there are 10 categories; 1=0-10%, 2=11-20%; 3=21-30%; 4=31-40%; 5=41-50%; 6=51-60%; 7=61-70%; 8=71-80%; 9=81-90%; 10=91-100%. TEA is the Total Early-stage Entrepreneurial Activity. This is “the percentage of 18-64 population who are either a nascent entrepreneur or owner-manager of a new business”. Nascent entrepreneurs are “actively involved in setting up a business they will own or co-own; this business has not paid salaries, wages, or any other payments to the owners for more than three months”. An owner-manager of a new business owns and manages “a running business that has paid salaries, wages, or any other payments to the owners for more than three months, but not more than 42 months” (GEM, 2013). + < .1; * < .05; ** p < .01; *** p < .001. Two-tailed significance. Pairwise.

176

We executed univariate and multivariate outlier detection to find possible outliers

(Stevens, 1984). Univariate outlier detection through standardization of the variables

suggests four outliers in the variable “Occupancy rate”, with standardized scores higher than

the upper limit of 3.29 (p < .001) (Tabachnick and Fidell, 2007). The outlier cases have very

low occupancy rates (0-10%). Because cases were legitimate and data entry was correct,

case deletion is not legitimate. However, because occupancy rate can influence strategy

implementation (Costa-David et al., 2002), we will perform robustness checks without these

outlier cases. Multivariate outlier detection is assessed through Cook’s distance and

interpretation of the residuals (Field, 2009). Cook’s distance examines the overall influence

of one case on the model. Cook’s values are not greater than one, which indicates that there

is no single case that considerably influences the model (Cook and Weisberg, 1982). The

standardized residuals rule shows that there are only two cases for which residuals have

absolute values greater than 1.96. This represents 2.25% of the cases. Thus, the model is a

good representation of the actual data (Field, 2009).

To test for homoscedasticity and linearity, we plotted the standardized predicted values

against the standardized residuals. There is no sign of a nonlinear relationship and there is

no “tooter” shape, indicating homoscedasticity. We checked for normality through the

histogram and normal P-P plot of the standardized residuals. All errors are normally

distributed. Finally, all independent variables are mean centered. This makes it easier to

interpret the results (Cohen et al., 2003) (see Appendix D for all graphs for the assumption

checks).49


We tested the Hypotheses through a moderation model. Table 4-2 represents the results

in Model 1 (control variables), Models 2, 3, 4 and 8a (control variables and direct effects),

and Models 5, 6, 7 and 8b (control variables, direct and interaction effects). For robustness

checks, we performed a regression analysis on Model 8 without the outliers in “occupancy

rate”. Results are the same, indicating that they are robust (see Appendix E).

49 As also explained in Chapter 3, a limitation of this research is that we did not control for dependent errors within groups, such as incubators located in the same country or receiving financing support from the same funding organizations.

177

As expected, Models 1 to 8 (B = .2; p < .05) show that the control variable occupancy rate

significantly influences co-creation. The higher the incubator’s occupancy rate, the more co-

creation occurs. Also the TEA index significantly affects co-creation (B = -.4; p < .01). This

means that the more inhabitants are involved in entrepreneurial activities, the less co-

creation takes place. The incubator’s size and focus strategy do not affect its co-creation

activities, and the impact of the year of operations is only present in Model 1 (B = .034; p <

.1). Here, there is a very weak positive relationship between the incubator’s age and its co-

creation activities.

The unconditional direct effects represented by Hypotheses 1 (human capital) and 2

(institutional context) are represented in Models 2, 3, 4 (for each institutional effect

separately) and 8a (for all institutional effects together) (see Table 4-2). Model 2 (B = .361; p

< .01), Model 3 (B = .356; p < .01) and Model 4 (B = .384; p < .01) reveal that human capital

positively relates to co-creation. Thus, we find support for Hypothesis 1. Model 2 (B = .665; p

< .001) and Model 3 (B = .381; p < .05) test Hypothesis 2 for the regulative and cognitive

dimension, respectively, and provide positive and significant relationships between these

entrepreneurial context dimensions and co-creation. Thus, the regulative and cognitive

dimensions of Hypothesis 2 are supported. Model 4 (B = -.142; p > .1) shows a non-

significant negative relationship, not supporting Hypothesis 2 for the normative dimension.

Interestingly, Model 8a only provides a positive significant effect for the regulative

dimension (B = .622; p < .01).

Model 5 reveals a slightly significant positive human capital interaction effect for the

regulative entrepreneurial environment (B = .231; p = .102). These results suggest weak

support for Hypothesis 3b (see below for further examination of this interaction effect).

Model 6 (B = .047; p > .1) and Model 7 (B = .003; p > .1) indicate that there is no significant

interaction effect for the cognitive and normative dimension, respectively. Thus, we find

support for Hypothesis 3a. Our results are similar when all three institutional dimensions are

simultaneously added to the model (see Model 8b). To further examine the moderation

effects, we used the Johnson-Neyman technique (Hayes, 2012). Through bootstrapping, it

provides the values within the range of the moderator in which the association between the

institutional context dimension and co-creation is significant. The bootstrapping results

showed that there are no statistical significant interaction effects for the analyses of the

cognitive and normative dimensions. This confirms that we find support for Hypothesis 3a.

178

Figure 4-2 plots the effect of the regulative dimension given human capital.50 The y-axis

represents the moderator values (in this case, human capital), the right x-axis the percentage

of observations for these human capital values, and the left x-axis the marginal effect of the

regulative dimension given human capital. The marginal effect is visualized by the full line.

The dotted lines represent the 90% bootstrap confidence intervals. As indicated by Berry et

al. (2012), conditional effects are significant when both confidence interval lines lie below or

above zero. Figure 4-2 reveals that the marginal effect of the regulative dimension becomes

significant when human capital reaches -.499 (or 4.118 without mean centering)51 and that

ME(ICR|HC = HCmax) > 052. For values of human capital above -.499, the effect is not only

positive, but also statistically (i.e., the confidence intervals do not straddle at zero) and

substantively (i.e., the marginal effect line is not flat) significant. This is true for 74.0% of the

observations. For human capital values lower than -.499, the effect is statistically non-

significant. Thus, although the effect sign turns and ME(ICR|HC = HCmin) < 0, this effect is

non-significant. Following Berry et al. (2012), these results nuance our earlier results for

Hypothesis 3b. More specifically, we find that only high levels of human capital significantly

interact with the regulative dimension. The interaction plot is visualized in Figure 4-3, with a

significant high human capital line and a partly significant low human capital line. As stated,

there are no significant human capital moderator values for the cognitive and normative

dimensions. These non-significant interaction effects are visualized in Appendix F.

50 We did not make such plots for the cognitive and normative dimensions because there were no significant moderator values for these dimensions. Thus, Hayes’ (2012) PROCESS macro for moderation did not provide the necessary values for plotting. 51 Human capital is measured on a 7-point Likert scale, from “I strongly disagree” to “I strongly agree”. Thus, when the moderator value is above “neutral” (that is, above value 4 on the 7-point Likert scale), respondents answered that the incubator has high levels of human capital. Our analysis shows that in these cases, human capital has a significant stimulating effect on the regulative dimension. 52 HC = Human capital; ICR = regulative dimension; ICC = cognitive dimension; ICN = normative dimension.

179

Table 4-2: Hierarchical linear regression

Model 1 Model 2 Model 3 Model 4 Model 5 Model 6 Model 7 Model 8a Model 8b Outcome CoCr CoCr CoCr CoCr CoCr CoCr CoCr CoCr CoCr B B B B B B B B B Constant

3.258*** (.148)

3.277*** (.148)

3.280*** (.154)

3.279*** (.157)

3.267*** (.148)

3.276*** (.156)

3.279*** (.158)

3.275*** (.149)

3.263*** (.151)

CONTROL VARIABLES

Incubator level

Year operations

.034+ (.024)

.022 (.025)

.033 (.026)

.020 (.026)

.023 (.025)

.033 (.027)

.020 (.027)

.026 (.026)

.027 (.026)

Size (m2)

-.044 (.097)

.039 (.098)

-.031 (.103)

-.011 (.106)

.039 (.098)

-.031 (.104)

-.011 (.106)

.015 (.102)

.009 (.103)

Occupancy rate

.105 (.083)

.152* (.085)

.224** (.092)

.177* (.090)

.151* (.085)

.225** (.093)

.177* (.091)

.170* (.091)

.175* (.092)

Focus strategy .083 (.096)

.094 (.101)

.067 (.107)

.102 (.107)

.082 (.101)

.065 (.107)

.102 (.108)

.084 (.103)

.062 (.105)

Country level

TEA index -.364** (.150)

-.473** (.159)

-.440** (.166)

-.418** (.175)

-.449** (.160)

-.431** (.169)

-.418** (.177)

-.445** (.166)

-.404** (.171)

DIRECT EFFECTS

Human capital .361** (.144)

.356** (.151)

.384** (.153)

.327* (.148)

.347* (.155)

.384** (.155)

.355** (.146)

.314* (.154)

Regulative dimension .665*** (.200)

.598** (.209)

.622** (.219)

.522* (.237)

Cognitive dimension .381* (.203)

.374* (.205)

.134 (.219) (p = .271)

.178 (.228) (p = .219)


-.141 (.270)

-.147 (.265)

-.171 (.270)

INTERACTION EFFECTS

Regulative * Human capital .231 (.215) (p = .102)

.309 (.253) (p = .113)

Cognitive * Human capital .047 (.148)

.001 (.176)

Normative * Human capital .003 (.167)

-.086 (.201)

F-statistic 1.775 4.586*** 3.260** 2.690* 4.166*** 2.833** 2.325* 3.629*** 2.825**

R2 .080 .284 .220 .189 .294 .221 .189 .293 .308

Adjusted-R2 .035 .222 .152 .119 .223 .143 .107 .212 .199

+ < .1; * < .05; ** p < .01; *** p < .001. CoCr is Service co-creation. One-tailed significance. Standard errors in parentheses. All VIF < or = to 2.146.

Listwise. Unstandardized coefficients. Sample size = 89, except for Model 1 (control variables). Here, sample size = 108. All variables, except for Service co-creation (dependent variable), are mean-centered.

180

Figure 4-2: Johnson-Neyman region of significance for the conditional effect of regulative dimension given human capital

Figure 4-3: Interaction regulative dimension and human capital

74.0% -0.499

181

4.5. Discussion and conclusion

In literature on co-creation, the added value of co-creation activities has extensively

been highlighted (e.g., Prahalad and Ramaswamy, 2000, 2004). Through personalized

interactions with the most important stakeholders, an organization can optimally address

market needs and create a competitive advantage. Because an incubator’s main

stakeholders are its (potential) tenants (Jungman et al., 2004), it is not surprising that

authors such as Rice (2002) advocate intense incubator-tenant co-creation during

incubation. However, the determinants of internal incubation strategies such as co-creation

activities are largely unknown (Hackett and Dilts, 2008). The current study addresses this lack

of understanding and examines both internal organizational and external institutional

elements that impact co-creation. The results show that incubator human capital, formal and

informal institutions positively impact co-creation intensity. In addition, the study reveals

that when human capital levels are high, there is positive interaction with the regulative

institutional dimension, and that there are no interactions with informal institutional

dimensions.

Our study adds to existing research as follows. First, it highlights the importance of

internal incubator characteristics other than service offerings (Bruneel et al., 2012) or

organizational processes (Aerts et al., 2007). It suggests that internal aspects such as the

incubator’s human capital influence incubator functioning. Incubators attracting employees

with high levels of experience, education and training, and willing to spend resources on

additional training programs for these employees, are more likely to engage in intense co-

creation. Although our examination of human capital aspects is rather general and does not

provide insights into the type of training, education or experience that incubators can

envision, they do highlight that its importance cannot be neglected.

Our study’s second contribution is its insights into the usefulness and empirical

application of Scott’s (2008) institutional pillars to the functioning of start-up support

organizations. Following prominent researchers such as Bruton et al. (2010) and Busenitz et

al. (2000), we employed a three-dimensional institutional framework that comprises

regulative, cognitive and normative institutional elements. By simultaneously examining

these three dimensions, we address Bruton et al.’s (2010) call to not rely on only one

institutional theory perspective. In addition, we address their critique that most studies only

examine one country, and that researchers are often simply not able to capture sufficient

182

institutional variance. Also their concern that most researchers only use the theoretical

insights from institutional theory but do not empirically test them (Bruton et al., 2010) has

been addressed with our empirical study. Besides these more general contributions, there

are three study results that deserve special attention. Firstly, our results exemplify Scott’s

(2008) suggestion that one institutional dimension can dominate the others. In our case, the

regulative dimension dominates the relationship between the institutional context and co-

creation intensity. The standardized coefficient of the direct effect of the regulative

dimension is the highest of all three institutional elements (see Model 8a).

Secondly, another interesting result with regard to these direct effects is that the

coefficient of the cognitive dimensions becomes non-significant when all three dimensions

are simultaneously added to the model. Although further research is needed for an in-depth

understanding of the interaction processes between the institutional dimensions, this seems

to suggest that the cognitive effect is not only weaker than the regulative one, but that it

even disappears when all dimensions are at play. The reason for this might be that although

entrepreneurial firms have legitimacy problems (Stinchcombe, 1965), this is less the case for

established organizations such as start-up support organizations (Bruton et al., 2010). Such

organizations might be able to rely on past performances to get legitimized and gain

subsequent access to resources. This reasoning reinforces our earlier suggestion that in

relation with incubator-tenant co-creation, formal institutions are relatively more important

than culturally rooted, informal ones. Moreover, it reveals that, as expected, the cognitive

and normative dimension are both behavioral, culturally rooted dimensions.

Thirdly, the non-significant normative dimension deserves special attention. Although

there might be a purely statistical reason for this because standard deviations of the

normative dimension are relatively low, also more theoretical insights might explain this.

More specifically, it might be that the regulative and cognitive dimensions grasp the level of

resource munificence in the incubator’s environment, whereas the normative dimension just

measures whether entrepreneurs deserve high status levels. An incubator employing co-

creation instructs its tenants about the information it needs and expects its tenants to be

able to find the required information. As a consequence, it might be that in particular the

dimensions that impact environmental resource munificence dominate during co-creation.

Besides the theoretical contributions related to the separate direct effects of an

incubator’s human capital and the institutional environment, our study contributes to our

183

understanding of the relative importance of internal versus external variables (Romanelli,

1989) in the entrepreneurship domain (e.g., Short et al., 2009). More specifically, our

regression models suggests that the regulative dimension is always relatively stronger than

the human capital effect. Although our study focuses on start-up support organizations

where liability of newness and smallness (Freeman et al., 1983; Stinchcombe, 1965) are less

present, our study does reveal that external influences are relatively very important for

organizations active in the entrepreneurship domain and that start-up organizations do not

operate isolated from their environment (Amezcua et al., 2013). With these results, we

contradict Short et al. (2009), who suggested that in the entrepreneurship domain, internal

effects are the strongest. There might be three arguments to explain these contradicting

results. The first is that Short et al. (2009) focused on industry effects, whereas our focus is

on institutional effects. Although further research is needed to confirm this, it might thus be

the case that, in the entrepreneurship domain, institutional elements and in particular the

regulative context are relatively more important than industry effects. The second argument

is that we only focused on one very specific internal element: human capital. Short et al.

(2009) employ venture size (number of employees), the founder’s attendance at new

venture courses, and governmental new venture funding. It might be the case that the

relative importance of internal and external elements depends upon the internal elements

that have been taken into account. The third argument is that up till now, most researchers

(such as Short et al., 2009) examined small ventures and not their support organizations. It

might thus also be the case that although internal effects are relatively more important for

small ventures, that this is not the case for start-up support organizations. Again, additional

research concurrently examining industry and institutional effects is needed to explore this

further.


With this chapter, we provide practical contributions to incubator managers and policy

makers. Our results reveal that human capital is an internal aspect that deserves attention.

Incubators attracting employees with high levels of education, experience or training are

able to attain higher levels of co-creation than incubators not doing so. Besides prior levels

of human capital, our study assessed the level of attention attributed to training after

recruitment. Although our study did not make an explicit distinction between prior human

184

capital levels and subsequent training processes, our results do suggest that subsequent

training has positive effects on co-creation intensity. In addition, the added value of high

human capital levels is expressed in its stimulating impact on the effect of an

entrepreneurially-minded regulative context.

Moreover, we expect that attributing sufficient attention to high human capital would

help new incubators to attract sufficient start-ups. It has been proven that employees with

high education levels act as credentials for an organization without build-up legitimacy

(Bruton et al., 2010). Incubators located in an environment with high competition from other

start-up support organizations might be able to benefit from high human capital levels.

Ventures will rely on the level of experience, education and training of the incubator

employees when searching for support. Finally, our results suggest to policy organizations

willing to stimulate co-creation in incubators to attribute relatively more attention to formal

institutions than to culturally rooted informal aspects, such as media attention for

entrepreneurs. In particular the setting up of a stimulating regulative framework appeared

to positively influence co-creation.


Besides the limitations and future research avenues discussed above, there are a couple

of limitations leading to future research possibilities that deserve special attention. A first

limitation relates to our assumption that incubator-tenant co-creation always positively

impacts incubator functioning and performance, and that it is thus a desirable incubation

mechanism. By doing so, we ignore possible negative aspects from co-creation, such as a lack

of control over organizational processes and increased complexity because of client

interactions (Hoyer et al., 2010). Although we rely on an earlier incubator study (that is, Rice,

2002) to state that co-creation is an effective incubation mechanisms, our knowledge on the

impact of co-creation is still very limited. During our theoretical reasoning, we heavily relied

on more general co-creation literature to state that co-creation can lead to positive word-to-

mouth, more realistic expectations from tenants or a higher willingness-to-pay (Franke et al.,

2009; Hoyer et al., 2010). Although these results are expected to also be present in

incubators, Rice’s (2002) study is the only one that explicitly focuses on co-creation in the

incubation domain. Thus, incubator studies that more profoundly examine the effects of co-

creation are badly needed.

185

Second, although we focus on the relative importance and interplay of internal and

external influences, we disregard the possible impact of strategy on co-creation intensity.

Legitimacy-building strategies have been proven to impact co-creation activities (Skaggs and

Huffman, 2003), and they might also interact with the effects of the institutional context

(Ahlstrom et al., 2008). Therefore, we call for a contingency approach where not only

internal and external aspects are determinants, but also strategy is added. Moreover, adding

other external and internal variables might provide additional insights into the relative

importance of these variables.

Third, we do not profoundly examine the interplay between the three institutional

context dimensions. However, previous research suggests that informal elements such as

uncertainty avoidance might trigger formal aspects such as heavily regulated environments

(van Waarden, 2001). In addition, informal institutional elements can fill the voids of

underdeveloped formal institutional contexts (Puffer et al., 2010). Although our focus does

allow us to unravel the mechanisms of each institutional dimension (Scott, 2008), and our

results suggest that indeed, there is an interplay between for example cognitive and

normative institutional aspects in their impact on co-creation, we did not examine this in-

depth. Therefore, we call for further research investigating these relationships.

Fourth and finally, our study takes on an incubator stance and does not take into account

tenant viewpoints on co-creation. We do not examine how incubators can stimulate their

tenants to participate in co-creation, nor do we consider possible drivers or barriers for co-

creation participation. However, previous research reveals that client motivations can

considerably impact co-creation intensity (Hoyer et al., 2010), with financial benefits such as

increased value for their money or social benefits such as getting a higher status in the

incubator as possible motivators.53

53 In Chapter 3, we acknowledged that not adding tenant characteristics such as tenant size, age of sector is a limitation of our study. Of course, this weakness also applies to Chapter 4.

186

Appendices

Appendix A: Sample representativeness and sample descriptives

Table 4-3: Sample representativeness: description incubator sample

Contact database (2011) Incubator manager sample (2012)*

N° of incubators N° of incubators Response rate

Belgium (Flanders) 46 29 63.0% The Netherlands 41 18 43.9% Ireland 45 23 51.1% United Kingdom 242 70 28.9% Total 374 140 37.4%

* Full sample, before execution of missing data analysis.

Table 4-4: Sample descriptives: number of tenants

N° of incubators*

BE NL IE UK

N° of companies pre-incubated in 2012 (M = 12.6; SD = 32.8)

0-5 21 6 11 28 6-10 1 5 6 13 > 10 1 3 1 16

N° of companies incubated in 2012 (M = 19.8; SD = 23.6)

0-5 15 2 4 13 6-10 2 5 2 12 > 10 7 8 13 34

N° of companies post-incubated in 2012 (M = 11.3; SD = 30.9)

0-5 17 13 9 29 6-10 1 1 4 11 > 10 4 1 4 17

Total n° of companies that received support since the incubator’s founding (M = 8.3; SD = 7.4)

1-20 6 4 2 5 21-40 2 2 3 10 41-60 2 1 2 5 61-80 2 1 1 6 81-100 1 4 1 4 101-200 3 5 2 8 201-300 1 1 1 7 301-400 0 0 2 3 401-500 2 0 0 1 > 500 0 1 1 5

Note: Full sample, before execution of missing data analysis. If number of incubators is not the same as total number of incubators in the sample, then there was missing data. M = mean; SD = Standard deviation. For Total number of companies that received support since the incubator’s founding, there are 27 categories: 1 = 0 companies; 2 = 1-20 companies; 3 = 21-40 companies; 4 = 41-60 companies; 5 = 61-80 companies; 6 = 81-100 companies; 7 = 101-120 companies; 8 = 121-140 companies; … ; 27 = more than 500 companies.

187

Table 4-5: Sample descriptives: year of operations, average occupancy rate and inside space

N° of incubators*

BE NL IE UK

Year of operations … - 1985 3 1 1 1 1986-1990 12 2 0 3 1991-1995 3 0 3 4 1996-2000 2 0 2 9 2001-2005 2 8 8 19 2006-2010 3 7 5 21 > 2010 3 0 0 2

Average occupancy rate in the last 3 years

0-50% 3 2 3 4 51-70% 5 2 3 10 71-80% 6 2 7 13 81-90% 5 7 2 15 91-100% 5 2 3 10

Size (inside m2)

1-1000 m2 5 4 4 24 1001-2000 m2 14 3 3 7 2001-4000 m2 6 4 8 12 4001-6000 m2 3 4 2 3 6001-8000 m2 1 2 1 6 8001-10,000 m2 0 0 0 4 > 10,000 m2 0 1 1 3

* Full sample, before execution of missing data analysis. If number of incubators is not the same as total number of incubators in the sample, then there was missing data.

188

Appendix B: Measurement scales

Table 4-6: Measurement scales for variables

Variable Items

Regulative dimension Government policies (e.g., public procurement) consistently favor new firms

The support for new and growing firms is a high priority for policy New firms can get most of the required permits and licenses in about a

week Taxes and other government regulations are applied to new and

growing firms in a predictable and consistent way Cognitive dimension Many people have experience in starting a new business Many people can react quickly to good opportunities for a new business Many people have the ability to organize the resources required for a

new business Many people know how to start and manage a high-growth business Many people know how to start and manage a small business Normative dimension Successful entrepreneurs have a high level of status and respect You will often see stories in the public media about successful

entrepreneurs Most people think of entrepreneurs as competent, resourceful

individuals Service co-creation We tell our client companies to participate in the service transformation

process We tell our client companies where and when they have to participate

in the service transformation process We tell our client companies which inputs and resources they have to

provide in the service transformation process Focus strategy The incubator focuses on a specific type of services (e.g., business

support, networking, etc.) The incubator offers services that focus on a specific industry niche

(e.g., IT, biotechnology, creative sector, etc.) The incubator offers a service that focuses on a specific type of

entrepreneurs (e.g., engineers, academics, a specific social class, etc.) Human Capital The incubator hires team members with a high level of experience The incubator hires team members with a high level of education The incubator hires team members with a high level of training Incubator team members spend many hours per year on training (both

paid and free training courses, seminars, etc.) Incubator team members spend a high amount of money on training

189

Appendix C: Factor analyses

Table 4-7: Rotated component matrix (VARIMAX rotation) principal component analysis: institutional context


I. Regulative dimension – Cronbach’s alpha .682

Government policies (e.g., public procurement) consistently favor new firms .818 The support for new and growing firms is a high priority for policy at the state level .753 New firms can get most of the required permits and licenses in about a week .622 Taxes and other government regulations are applied to new and growing firms in a

predictable and consistent way .632

II. Cognitive dimension – Cronbach’s alpha .890

Many people have experience in starting a new business .830 Many people can react quickly to good opportunities for a new business .789 Many people have the ability to organize the resources required for a new business .858 Many people know how to start and manage a high-growth business .789 Many people know how to start and manage a small business .815

III. Normative dimension – Cronbach’s alpha .672

Successful entrepreneurs have a high level of status and respect .757 You will often see stories in the public media about successful entrepreneurs .795 Most people think of entrepreneurs as competent, resourceful individuals .689

Table 4-8: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy


I. Incubator-tenant service co-creation – Cronbach’s alpha .910

We tell our client companies to participate in the service transformation process .871 We tell our client companies where and when they have to participate in the service transformation process

.950

We tell our client companies which inputs and resources they have to provide in the service transformation process

.933

II. Focus strategy – Cronbach’s alpha .628

The incubator focuses on a specific type of services (e.g., business support, networking, etc.)

.540

The incubator offers services that focus on a specific industry niche (e.g., IT, biotechnology, creative sector, etc.)

.852

The incubator offers a service that focuses on a specific type of entrepreneurs (e.g., engineers, academics, a specific social class, etc.)

.826

190

Table 4-9: Rotated component matrix (VARIMAX rotation) principal component analysis: human capital


Human capital – Cronbach’s alpha .794

The incubator hires team members with a high level of experience .786 The incubator hires team members with a high level of education .766 The incubator hires team members with a high level of training .883 Incubator team members spend many hours per year on training (both paid and free

training courses, seminars, etc.) .663

Incubator team members spend a high amount of money on training .611

191

Appendix D: Graphs assumption checks

Figure 4-4: Homoscedasticity and linearity

Figure 4-5: Normally distributed errors: normal P-P plot

192

Figure 4-6: Normally distributed errors: histogram

193

Appendix E: Robustness check – model without outliers

Table 4-10: Robustness checks: hierarchical linear regression for full model without outliers

Model 8a - without outliers based on occupancy rate

Model 8b - without outliers based on occupancy rate

Outcome CoCr CoCr

B B

Constant

3.257*** (.155)

3.243*** (.157)


.025 (.027)

.026 (.027)

Size (m2)

.014 (.104)

.010 (.105)

Occupancy rate

.202* (.115)

.211* (.116)

Focus strategy .087 (.106)

.067 (.109)

Country level TEA index -.435**

(.177) -.399* (.182)

DIRECT EFFECTS Human capital .342*

(.150) .298* (.159)

Regulative dimension .627** (.231)

.533* (.248)

Cognitive dimension .128 (.226) (p = .286)

.174 (.235) (p = .231)


-.155 (.279)

INTERACTION EFFECTS Human capital * Regulative .312 (.259)

(p = 116) Human capital * Cognitive .004

(.179) Human capital * Normative -.081

(.205) F-statistic 3.003** 2.358* R2 .262 .279 Adjusted-R2 .175 .161

+ < .1; * < .05; ** p < .01; *** p < .001. CoCr is Service co-creation. HC is Human capital. One-tailed significance. Standard errors in parentheses. VIF < or = 2.067. Listwise. Unstandardized coefficients. Sample size = 86. All variables, except for Service co-creation (dependent variable), are mean-centered.

194

Appendix F: Non-significant interaction effects

Figure 4-7: Interaction human capital and cognitive dimension

Figure 4-8: Interaction human capital and normative dimension

195

Bibliography

Adegbite, O., 2001. Business incubators and small enterprise development: the Nigerian experience. Small Business Economics, 17, 157-166.


Ahlstrom, D., Bruton, G.D., Yeh, K.S., 2008. Private firms in China: building legitimacy in an emerging economy. Journal of World Business, 43, 4, 385–399.

Allen, D.N., McCluskey, R., 1990. Structure, policy, services, and performance in the business incubator industry, Entrepreneurship: Theory and Practice, 61-77.


Assaad, R., 1993. Formal and informal institutions in the labor-market, with applications to the construction sector in Egypt. World Development, 21, 6, 925-939.

Avnimelech, G., Schwartz, D., Bar-El, R., 2007. Entrepreneurial high-tech cluster development: Israel's experience with venture capital and technological incubators. European Planning Studies, 15, 9, 1181-1198.

Barney, J., 1991. Firm resources and sustained competitive advantage. Journal of Management, 17, 1, 99-120.

Bateson, J., 2002. Are your customers good enough for your service business? Academy of Management Executive, 16, 4, 110–120.

Becker, G.S., 1964. Human Capital: A Theoretical and Empirical Analysis, with Special Reference to Education. Columbia University Press, New York.



Bosma, N., Stam, E., Wennekers, S., 2012. Entrepreneurial employee activity: a large scale international study. Discussion paper series 12-12, Utrect School of Economics, Tjalling C. Koopmans Research Institute.

Bradley, S.W., Wiklund, J., Shepherd, D.A., 2011. Swinging a double-edged sword: the effect of slack on entrepreneurial management and growth. Journal of Business Venturing, 26, 5, 537-554.

Brislin, R. W., 1990. Applied cross-cultural psychology? An introduction. In Brislin, R.W. (Ed.), Applied Cross-cultural psychology (p. 8–33). Sage, Newbury Park, CA.


Bruton, G.D., Ahlstrom, D., Li, H.-L., 2010. Institutional theory and entrepreneurship: where are we now and where do we need to move in the future? Entrepreneurship: Theory and Practice, 421-440.

Busenitz, L.W., Gómez, C., Spencer, J.W., 2000. Country institutional profiles: unlocking entrepreneurial phenomena. Academy of Management Journal, 43, 994-1003.

Chand, P., Cummings, L., Patel, C., 2012. The effect of accounting education and national culture on accounting judgments: a comparative study of Anglo-Celtic and Chinese culture. European Accounting Review, 21, 1, 153-182.

196

Chandler, G.N., Jansen, E., 1992. The founder’s self-assessed competence and venture performance. Journal of Business Venturing, 7, 3, 223-236.

Chang, S.J., van Witteloostuijn, A., Eden, L., 2010. From the editors: common method variance in international business research. Journal of International Business Studies, 41, 178-184.

Chiu I., Brennan, M., 1990. The effectiveness of some techniques for improving mail survey response rates: a meta-analysis. Marketing Bulletin, 1, 13-18.


Cohen, J., Cohen, P., West, S.G., Aiken, L.S., 2003. Applied Multiple Regression/Correlation Analysis for the Behavioral Sciences (3rd Ed.). Lawrence Erlbaum Associates, New Jersey.

Cole, M., Parker, M., 2011. Culture and cognition. In Keith, K.D. (Ed.), Cross-cultural psychology: contemporary themes and perspectives (p. 133–159). Wiley-Blackwell, Chichester, UK.

Cook, S. 2008. The contribution revolution. Harvard Business Review, 86, 60-69. Cook, R.D., Weisberg, S., 1982. Residuals and Influence in Regression. Chapman & Hall, New

York, US. Costa-David, J., Malan, J., Lalkaka, R., 2002. Improving business incubator performance

through benchmarking and evaluation: lessons learned from Europe. 16th International Conference on Business Incubation, National Business Incubation Association, April 28 – May 1, 2002, Toronto, Canada.

De Clerq, D., Danis, D., Dakhli, W.M., 2010. The moderating effect of institutional context on the relationship between associational activity and new business activity in emerging economies. International Business Review, 19, 85-101.

Dillman, D.A., 1972. Increasing mail questionnaire response in large samples of the general public. Public Opinion Quarterly, 36, 254-257.

Douglas, S.P., Craig, C.S., 2007. Collaborative and iterative translation: an alternative approach to back translation. Journal of International Marketing, 15, 30-43.

Engau, C., Hoffmann, V.H., 2011. Strategizing in an unpredictable climate: exploring corporate strategies to cope with regulatory uncertainty. Long Range Planning, 44, 1, 42-63.

European Commission, 2002. Benchmarking of business incubators. Centre for Strategy and Evaluation Services, Brussels.

Field, A., 2009. Discovering Statistics using SPSS, third ed. Sage, London, UK. Fox, R.J., Crask, M.R., Kim, J., 1988. Mail survey response rate: a meta-analysis of selected

techniques for inducing response. Public Opinion Quarterly, 52, 467-491. Franke, N., Keinz, P., Steger, C.J., 2009. Testing the value of customization: when do

customers really prefer products tailored to their preferences? Journal of Marketing, 73, 5, 103-121.


GEM, 2013, List of key indicators and definitions. Available from < http://www.gemconsortium.org/key-indicators > (accessed 23.04.2013).


197

Gruner, K.E., Homburg, C., 2000. Does customer interaction enhance new product success?’ Journal of Business Research, 49, 1, 1-14.

Hackett, S.M., Dilts, D.M., 2008. Inside the black box of business incubation: study B – scale assessment, model refinement, and incubation outcomes. The Journal of Technology Transfer, 33, 439-471.

Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., Tatham, R.L., 2006. Multivariate data analysis. Pearson Prentice Hall, New Jersey.


Hansen, M.T., Chesbrough, H.W., Nohria, N., Sull, D.N., 2000. Networked incubators: hothouses of the new economy. Harvard Business Review, 78, 5, 74-84.

Hayes, A. F., 2012. PROCESS: A versatile computational tool for observed variable mediation, moderation, and conditional process modeling, White paper. Available from < http://www.afhayes.com/ >

Hillmann, H., Aven, B.L., 2011. Fragmented networks and entrepreneurship in late imperial Russia. American Journal of Sociology, 117, 2, 484-538.

Hofstede, G., 1984. Culture’s Consequences. International Differences in Work-related Values. Sage Publications, Beverly Hills, CA.

Hoyer, W.D., Chandy, R., Dorotic, M., Krafft, M., Singh, S.S., 2010. Consumer co-creation in new product development. Journal of Service Research, 13, 3, 283-296.

Ibeh, K.I.N., 2003. Toward a contingency framework of export entrepreneurship: conceptualizations and empirical evidence. Small Business Economics, 20, 1, 49-68.

Jahoda, G., 2012. Critical reflections on some recent definitions of ''culture''. Culture & Psychology, 18, 3, 289-303.

Jansson, J., 2011. Emerging (internet) industry and agglomeration: internet entrepreneurs coping with uncertainty. Entrepreneurship and Regional Development, 23, 7-8, 499-521.

Jones, G.R., 1987. Organization-client transactions and organizational governance structures. Academy of Management Journal, 30, 197-219.

Joshi, A.W., Sharma, S., 2004. Customer knowledge development: antecedents and impact on new product performance. Journal of Marketing, 68, 3, 47-59.

Jungman, H., Okkonen, J., Rasila, T., Seppä, M., 2004. Use of performance measurement in V2C activity. Benchmarking: An International Journal, 11, 2, 175-189.

Kostova, T., 1997. Country institutional profiles: concept and measurement. Academy of Management Best Paper Proceedings, 180-189.

Lee, C., Lee, K., Pennings, J.M., 2001. Internal capabilities, external networks, and performance: a study on technology-based ventures. Strategic Management Journal, 22, 615-640.



Magnusson, P.R., Matthing, J., Kristensson, P., 2003. Managing user involvement in service innovation: experiments with innovating end users. Journal of Service Research, 6, 2, 111-124.

Matsumoto, D., 2009. Teaching about culture. In Gurung, R.A.R., Prieto, L.R. (Eds.), Getting culture: incorporating diversity across the curriculum. Stylus, New York, NY.

198

McQuaid, R.W., 2002. Entrepreneurship and ICT industries: support from regional and local policies. Regional Studies, 36, 8, 909-9019.

OECD, 2013, Country statistical profiles OECD. Available from < http://stats.oecd.org/ > (accessed 07.04.2013).

Ogawa, S., Piller F.T., 2006. Reducing the risks of new product development. Sloan Management Review, 47, 65-72.

Payne, A.F., Storbacka, K., Frow, P., 2008. Managing the co-creation of value. Journal of the Academy of Marketing Science, 36, 1, 83-96.

Pennings, J.M., Lee, K., van Witteloostuijn, A., 1998. Human capital, social capital, and firm dissolution. Academy of Management Journal, 41, 4, 425-440.

Peterson, R., 2008. Entrepreneurship and national economic growth: the European entrepreneurial deficit. European Journal of International Management Volume, 2, 4, 471-490.

Phan, P.H., Siegel, D.S., Wright, M., 2005. Science parks and incubators: observations, synthesis and future research. Journal of Business Venturing, 20, 2, 165-182.

Podsakoff, P.M., MacKenzie, S.B., Lee, J.-Y., Podsakoff, N.P., 2003. Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of Applied Psychology, 88, 5, 879-903.

Podsakoff, P.M., Organ, D.W., 1986. Self-reports in organizational research: problems and prospects. Journal of Management, 12, 4, 531–544.

Prahalad, C.K., Ramaswamy, V., 2000. Co-opting customer competence. Harvard Business Review, 78, 79-87.

Prahalad, C.K., Ramaswamy, V., 2004. Co-creation experiences: the next practice in value creation. Journal of Interactive Marketing, 18, 5-14.

Puffer, S.M., McCarthy, D.J., Boisot, M., 2010. Entrepreneurship in Russia and China: the impact of formal institutional voids. Entrepreneurship: Theory and Practice, 34, 3, 441-467.

Reynolds, P., Bosma, N., Autio, E., Hunt, S., De Bono, N., Servais, I., Lopez-Garcia, P., Chin, N., 2005. Global Entrepreneurship Monitor: data collection design and implementation 1998-2003. Small Business Economics, 24, 205-231.

Rice, M.P., 2002. Co-production of business assistance in business incubators: an exploratory study. Journal of Business Venturing, 17, 2, 163-187.

Romanelli, E., 1989. Environments and strategies or organization start-ups: effects on early survival. Administrative Science Quarterly, 34, 369-387.

Ryglova, K., 2007. Limiting factors in the field of business activities in rural tourism. Agricultural Economics-Zemedelska Ekonomika, 53, 9, 421-431.

Sarker, S., Sarker, S., Sahaym, A., Bjørn-Anderson, N., 2012. Exploring value co-creation in relationships between an ERP vendor and its partners: a revelatory case study. MIS Quarterly, 36, 1, 317-338.

Schildt, H.A., Zahra, S.A., Sillanpaa, A., 2006. Scholarly communities in entrepreneurship research: a co-citation analysis. Entrepreneurship: Theory and Practice, 30, 3, 399-415.

Schwartz, M., 2008. Incubator age and incubation time: determinants of firm survival after graduation? Working paper, Institut für Wirtschaftsforschung, Halle, Germany.


199

Short, J.C., McKelvie, A., Ketchen, D.J., Chandler, G.N., 2009. Firm and industry effects on firm performance: a generalization and extension for new ventures. Strategic Entrepreneurship Journal, 3, 47-65.

Scott, W.R., 2008. Institutions and Organizations: Ideas and Interests, third edition. Sage Publications, California, US.

Sichtmann, C., von Selasinsky, M., Diamantopoulos, A., 2011. Service quality and export performance of business-to-business service providers: the role of service employee- and customer-oriented quality control initiatives. Journal of International Marketing, 19, 1, 1-22.


Skaggs, B.C., Youndt, M., 2004. Strategic positioning, human capital, and performance in service organizations: a customer interaction approach. Strategic Management Journal, 25, 85-99.

Snell, S.A., Dean, J.W. Jr., 1992. Integrated manufacturing and human resources management: a human capital perspective. Academy of Management Journal, 35, 467-504.

Stenholm, P., Acs, Z., Wuebker, R., 2013. Exploring country-level institutional arrangements on the rate and type of entrepreneurial activity. Journal of Business Venturing, 28, 176-193.

Stevens, J.P., 1984. Outliers and influential data points in regression analysis. Psychological Bulletin, 95, 334-344.


Styhre, A., Lind, F., 2010. The softening bureaucracy: accommodating new research opportunities in the entrepreneurial university. Scandinavian Journal of Management, 26, 2, 107-120.

Tabachnick, B.G., Fidell, L.S., 2007. Using Multivariate Statistics (5th edition). Pearson, Boston.

Tartari, V., Breschi, S., 2012. Set them free: scientists’ evaluations of the benefits and costs of university–industry research collaboration. Industrial and Corporate Change, 21, 5, 1117–1147.

Valliere, D., Peterson, R., 2009, Entrepreneurship and economic growth: evidence from emerging and developed countries, Entrepreneurship and Regional Development, 21, 5-6, 459-480.

van Waarden, F., 2001. Institutions and innovation: the legal environment of innovating firms. Organization Studies, 22, 5, 765-795.

Verheul, I., Wennekers, S., Audretsch, D.B., Thurik, R., 2002. An eclectic theory of entrepreneurship: policies, institutions and culture. In: Audretsch, D.B., Thurik, R., Verheul, I., Wennekers, S. (Eds.), Entrepreneurship: determinants and policy in a European–U.S. comparison (p. 11–82). Kluwer Academic Publishers, Norwell, MA.

Wernerfelt, B., 1984. A resource-based view of the firm. Strategic Management Journal, 5, 2, 171-180.

Xavier, S.R., Kelley, D., Kew, J., Herrington, M., Vorderwülbecke, A., 2012, Global Entrepreneurship Monitor: 2012 Global Report. Global Entrepreneurship Research Association.

200

Xie, C., Bagozzi, R.P., Troye, S.V., 2008. Trying to prosume: toward a theory of consumers as cocreators of value. Journal of the Academy of Marketing Science, 36, 1, 109-122.

Zhang, H., Sonobe, T., 2011. Business incubators in Chine: an inquiry into the variables associated with incubate success. Economics: The Open-Access, Open-Assessment E-Journal, 5, 7, 1-26.

201

Conclusion

In In this doctoral thesis, we focus on a specific type of start-up support organizations:

business incubators. Through the offering of a wide variety of support services such as

business coaching and logistic facilities, the incubator accelerates tenant learning curves,

increases credibility and offers tenants the advantages of economies of scale. As such, the

incubator tries to increase start-up growth and survival rates (Ferguson and Olofsson, 2004;

Löfsten and Lindelöf, 2001, 2002; Schwartz and Göthner, 2009). Although research has

advanced since the first studies on these support organizations, there are a number of gaps

in the state-of-the-art of research on incubator/incubation strategies, internal organizational

aspects, external influences and performance evaluation methods. In what follows, we

highlight these gaps and explain how they have been tackled in this doctoral thesis. We

discuss the main contributions of this doctoral thesis for science, practice and policy and link

the different chapters to each other. Then, we identify its main limitations and suggest some

future research avenues.

Overall contribution and link between the different chapters

We detect five overarching research gaps that have been tackled in one or several

chapters of this doctoral thesis. First, in strategic positioning literature, differentiation

options for incubators are somewhat one-sided. Researchers predominantly suggest that

incubators can create tenant value by offering industry- or technology-specific services (e.g.,

Schwartz and Hornych, 2008). However, real-world examples show that many incubators do

not choose such services. This suggests that the availability of sector- or technology-specific

services might not be the only route to customer value creation. In Chapter 1, we examine

this, and find that there exist two service-based differentiation options for incubators:

specialists and generalists.54 In this first chapter, we follow the widespread viewpoint of

examining an incubator’s value proposition through its service bundles (e.g., Bruneel et al.,

2012) and examine which services can result in differentiation. The added value of Chapter 1

lies in the fact that we analyze incubator differentiation options at the strategic group level,

54 As explained in Chapter 1, larger incubators might opt for a combination of these positioning alternatives, and thus follow a portfolio strategy.

202

and that we explicitly recognize the difference between service-based qualifiers and

differentiators. Previous research did not make such a subdivision or turned out to be

outdated (e.g., Mian, 1996). Our results show that although in-depth, on-site services and

personal network connections are success producers for both differentiation alternatives,

generalists can attain differentiation through the offering of in-depth secretarial services and

operational business support. Specialists can offer on-site sector- or technology-specific in-

depth business support. In Chapter 3, we further examine the impact of a focus strategy and

add additional insights into the debate as to the outcome effectiveness of a focus strategy.

Current research assumes high effectiveness of a focus strategy (Chan and Lau, 2005;

Grimaldi and Gradi, 2005). Interestingly, we find that in Brazil (our study context), a focused

strategy does not directly affect incubator outcome effectiveness. Instead, our results are

more nuanced and show that focused incubators can only increase tenant survival and

growth when they also follow a service customization strategy.

A second research gap is our knowledge on the incubation process. The vast majority of

research on internal incubator functioning argues that it are in particular the incubator’s

service bundles that lead to a competitive advantage (e.g., Bruneel et al., 2012). There are

only a few studies that break away from this commonly used description, such as Hackett

and Dilts (2008) who suggest that besides business assistance, also an incubator’s selection

process and resource munificence define its internal functioning. We follow this viewpoint

and recognize that it are an incubator’s internal processes and assets/capabilities that

determine whether the incubator is able to optimally employ its services during the

incubation process or not. In Chapter 1, we take a first step in examining these internal

aspects and develop a competence configuration in which an incubator’s organizational

systems, assets, capabilities and culture are aligned to its strategy. We find that for example

regular contact among tenants, incubator personnel, and external experts is indispensable

for stimulating tenant cooperation and incubator resource usage. In Chapter 2, we further

develop an incubator’s internal aspects and incorporate them in our adaptation of the

balanced scorecard and strategy map for nonprofit economic development incubators. Here,

we stress that co-creation can enhance service quality, proactiveness and satisfaction.

In Chapters 3 and 4, we further examine this service co-creation concept from a strategic

positioning and internal incubation strategy perspective, respectively. Chapter 3 provides

evidence that, indeed, following a service customization strategy leads to higher

203

performances. Because previous researchers highlight the importance of human capital in

relationships with co-creation (Skaggs and Huffman, 2003), we add human capital to our co-

creation model in Chapter 4. We follow researchers that argue that incubator managers with

previous entrepreneurship experience (Hannon and Chaplin, 2003) or high education levels

(Zhang and Sonobe, 2011) have a significant effect on the incubator’s internal functioning

and find that human capital has a significant impact on service co-creation. This suggests to

incubator managers and policy makers to attract incubator employees with high levels of

education, experience and training, and devote sufficient attention to continuing training for

these incubator employees. To summarize, the results from our four chapters show that

besides service offerings, also other internal contingency factors such as an incubator’s

selection process, graduation criteria, service customization strategy or human capital level

impact incubator functioning and performance.

Third, research on external influences is mainly limited to a taken-for-granted belief that

funding programs for start-ups (Avnimelech et al., 2007) and incubators (Adegbite, 2001)

have positive impacts on start-up survival and development. Although differences in policy

initiatives have been highlighted in case-based studies (Clarysse et al., 2005), environmental

conditions have only recently been linked to incubation outcomes in nation-wide

quantitative studies. The first studies in this area focus on the tenant’s task environment,

such as the degree of market competitiveness (Amezcua et al., 2013), and do not question

whether and how an entrepreneurial institutional context affects incubator functioning. Our

research results show that, as expected, various external factors influence incubator

functioning. In Chapters 1 and 2, we examine the incubator’s task environment and analyze

tenant service expectations. Results show that tenant expectations have major impacts on

service-based differentiation options and evaluation systems. In Chapters 3 and 4, we focus

on the impact of the institutional entrepreneurial environment. In Chapter 3, we find that in

Brazil, an extremely underdeveloped regulative and cognitive entrepreneurial environment

negatively impact incubator functioning mechanisms. This suggests to policy makers to

attribute attention to the development of a transparent and consistent regulative

environment, as well as entrepreneurial education to increase knowledge on how to start

and grow a small business. Because of interaction symmetry (Berry et al., 2012), our results

from Chapter 4 show that the regulative institutional dimension positively interacts with

human capital when the regulative dimension is high (see Appendix A). This implies that in

204

highly entrepreneurially-minded regulative environments, the positive effect of increased

human capital is further stimulated. Thus, our results also suggest to European policy makers

to focus on a facilitating regulative entrepreneurial environment. For example, government

might assure that taxes and government regulations for new firms are easy-to-understand

and consistent. Also a low administrative burden seems important, such as quick and easy

access to the necessary licenses and permits. As such, government might be able to further

stimulate the positive effect of high incubator human capital on incubation strategies such as

service co-creation.

Fourth, in return for the support offered to incubators, policy makers expect optimal

functioning and continuous improvement (Bigliardi et al., 2006; McMullan et al., 2001;

Schwartz and Göthner, 2009). Although the incubation process is pivotal for incubator

functioning (Bergek and Norrman, 2008), internal processes are often ignored during

evaluation exercises (e.g., Schwartz and Göthner, 2009). Most incubator evaluation methods

focus on individual measures, such as tenant growth and survival (Aerts et al., 2007; Hackett

and Dilts, 2008) or the availability of networking partners (Hansen et al., 2000). Although

difficulties in gaining access to the necessary information often forces researchers to employ

such individual measures, it limits an incubator’s internal functioning evaluation (Kaplan and

Norton, 2000; Moxham, 2010). Unfortunately, in incubator evaluation literature, there exist

very few integrated evaluation systems. Those that we did find do not comply to all of

Tangen’s (2004) system output prerequisites, such as the importance of comprehensibility

and accessibility. To address these shortcomings in incubator evaluation literature, we adapt

the balanced scorecard and strategy map (Kaplan and Norton, 2000, 2005) to the context of

nonprofit economic development incubators in Chapter 2. As such, we provide an integrated

evaluation system that focuses on internal incubator aspects, such as efficient functioning

and value creation effectiveness. Because we do not examine incubator outcome

effectiveness (that is, tenant survival and growth) in this evaluation system, we opt for an in-

depth study about tenant survival and growth in Chapter 3. Here, we focus on strategic and

external determinants influencing incubator outcome effectiveness, and find that both

strategic (focus strategy, service customization strategy) and external (institutional context)

aspects influence tenant survival and growth.

Fifth and finally, this doctoral thesis addresses the lack of research on incubators

employing a mixed methodology. In Chapters 1 and 2, we start off with examining the link

205

between various aspects from the incubator’s environment, its internal structure, processes

and functioning, and its strategic choices. Such broad and complex topics call for a

qualitative research methodology (Dul and Hak, 2008; Yin, 1990). However, the disadvantage

of qualitative research is that it impedes generalization. Therefore, we also execute two

quantitative studies to zoom in on some of the internal, external and strategic variables that

are at the basis of incubator functioning and performance. Although qualitative research is

widespread in studies about incubators (e.g., Grimaldi and Grandi, 2005; Schwartz and

Hornych, 2008), quantitative data is scarce. With a few exceptions (e.g., Aerts et al., 2007;

Amezcua et al., 2013), the quantitative studies that have been executed often rely on small

samples conducted by incubator funding organizations (McAdam et al., 2006). Moreover,

most studies focus on western countries, such as Europe (Aerts et al., 2007) and the United

States (Amezcua et al., 2013). Few quantitative studies focus on non-western countries, like

our Brazilian incubator study in Chapter 3.

Figure C-1 summarizes the research questions and main contributions of each of the four

chapters of this doctoral thesis. We also highlight the limitations of the two first chapters

that have been tackled in the two final chapters.

206

Figure C-1: Research questions, main contributions and link between the qualitative and quantitative research

207

Figure C-1 : Research questions, main contributions and link between the qualitative and quantitative research (continued)

208

Implications for practice and policy

Besides the already above-mentioned implications for practice and policy, such as the

development of an entrepreneurially-minded regulative environment to stimulate the

positive effects of high incubator human capital, there are a number of additional

contributions to practice and policy.

First, the results from this doctoral thesis might help incubator managers to more

effectively choose an appropriate service-based differentiation strategy. Incubator sponsors,

such as government organizations, might offer support by undertaking a market and internal

feasibility study to guide the incubator in finding its optimal differentiation alternative

(Zablocki, 2007). They can analyze company expectations and the competitive positions of

other incubators in their region. As such, they can reach a well-supported determination of

the most appropriate strategic position. In Chapter 1, it has been stressed that both

generalist and specialist stances can result in differentiation. Moreover, the results from

Chapter 3 indicate that in Brazil, a stand-alone focus strategy does not result in increased

tenant survival and growth rates. Rather, increased performance can be attained by

simultaneously implementing a service customization strategy. This nuances strategy

development and implementation avenues for Brazilian incubators. More specifically, it

suggests that not only the incubator’s target audience is of importance, but that also its

service offering strategy influence its performance. Incubator managers and incubator

support organizations might use this information to simultaneously decide upon the

incubator’s scope and its service offering strategy.

Second, because “training and assistance programs for practicing entrepreneurs are

expensive both in money for sponsors and in time for participants” (McMullan et al., 2011,

p. 37) and the effectiveness and efficiency of incubators has been questioned (Schwartz,

2012), the development of relevant evaluation tools is well justified. Chapter 1 shows that

the incubator’s internal organization must be adapted to its strategic stance for success to

result. Such internal alignment does not only assure tenant value creation, but is also a

prerequisite for incubator value capturing, such as attaining financial sustainability. The

balanced scorecard and strategy map presented in Chapter 2 are a first attempt to offer

clearer insights into the effective and efficient functioning of business incubators, and make

an explicit link between long-term strategic goals and financial sustainability. Incubator

209

managers and funding organizations can draw on these results to evaluate their nonprofit

economic development incubators (Schwartz and Göthner, 2009; Sherman, 1999),

benchmark such organizations against other incubators, and make more informed resource

allocation decisions (Tornatzky et al., 2002). Moreover, the SMEDI and BSEDI help incubator

managers and funding organizations target internal incubator processes that need

improvement (Hackett and Dilts, 2008). Our results also reveal that human capital is an

internal aspect that deserves attention. Incubators attracting employees with high levels of

education, experience or training are able to attain higher levels of co-creation than

incubators not doing so. Besides prior levels of human capital, our study assessed the level

of attention attributed to training after recruitment. Although our study did not make an

explicit distinction between prior human capital levels and subsequent training processes,

our results do suggest that subsequent training has positive effects on co-creation intensity.

Third, our results show that even though internal incubation processes are of utmost

importance, the entrepreneurial environment influences the incubator’s degrees of freedom

with regard to its strategic position. In Brazil, the positive effects of service customization

and focus strategies are negatively influenced by extremely non-entrepreneurial contexts.

Interestingly, we did not find these results for more developed Brazilian contexts. Here,

incubator managers and incubator support organizations might be less restricted in their

strategic positioning choices. In such contexts, the entrepreneurial environment does not

impede the positive effects of service customization or focus strategies. As explained above,

this suggests to incubator managers in Brazil that service customization and focus strategies

are most adequate, and to policy to attribute sufficient attention to the development of

entrepreneurially-minded regulative and cognitive environments. Also in Europe, we find

that an entrepreneurially-minded regulative context impacts internal incubator elements.

More specifically, our results show that the added value of high human capital levels on

service co-creation is stimulated in a facilitating regulative entrepreneurial environment.55

Finally, our results from Chapter 4 suggest to policy organizations willing to stimulate co-

creation in incubators to attribute relatively more attention to formal institutions than to

culturally rooted informal aspects, such as media attention for entrepreneurs. In particular

55 See above for some examples of such a “facilitating” environment.

210

the setting up of a stimulating regulative framework appeared to positively influence co-

creation.

Fourth and finally, our study results might help potential tenants choose their incubator

more effectively. Depending on the support services they need, different locations might be

advisable. Entrepreneurs with a technical background, for example, often lack marketing and

financing knowledge (Heydebreck et al., 2000), so they might benefit most from a generalist

incubator offering in-depth, customized operational business knowledge. In contrast,

companies in a sector that features few other players might prefer a specialist incubator that

offers them a strong image and the credibility to attract core business-related partners.

Moreover, we expect that attributing sufficient attention to high human capital would help

new incubators to attract sufficient start-ups. It has been proven that employees with high

education levels act as credentials for an organization without build-up legitimacy (Bruton et

al., 2010). Ventures might rely on the level of experience, education and training of the

incubator employees when searching for support.

Overall limitations and directions for future research

Besides the limitations and future research avenues discussed in each of the four

chapters, there are four additional limitations that deserve special attention.

First, when combining the results from Chapters 3 and 4, we see that there is a positive

direct effect of focus strategy on service customization strategy, but that there is no effect

on incubator-tenant service co-creation. There might be two explanations for this. The first is

that service co-creation is a sub-process of a service customization strategy (Jacob, 2006).

The items used for co-creation focus on the amount of instructions the incubator gives to its

tenants, whereas service customization was measured by looking at the whole

customization process: both the resource preparation and information transaction sub-

processes (Jacob, 2006). It might be that a focus strategy positively relates to the whole

process, because it mainly influences the resource preparation process. This would explain

the non-significant effect of a focus strategy on service co-creation intensity in Chapter 4. A

second explanation is that Chapters 3 and 4 are conducted in very different environments.

Chapter 3 focuses on Brazilian incubators, Chapter 4 on European. In our analyses, we did

211

not examine how the institutional context interacts with focus strategy. Further research is

needed to unravel these relationships.

Another limitation is that we did not take on a cluster, network or system perspective,

nor did we solely focus on the functioning and performance of sector incubators. However,

research showed that organizations located in a geographically interconnected group of

organizations associated to a specific sector are able to attain superior performance

(Krugman, 1991; Porter, 2000). Although we do recognize the importance of close proximity

(McAdam and McAdam, 2008) and networking (Hansen et al., 2000) in our studies, and

reasoned about the effectiveness of incubator networks depending on the incubator’s

competitive scope, we did not take a closer look on the networking mechanisms among

sector incubators and their stakeholders. Further research examining network characteristics

in innovation systems with incubators might provide deeper insights into optimal incubator

networks.

Third, our cross-sectional quantitative study has the advantage that it allowed us to

gather data from a relatively large amount of incubators within the timeframe of this

doctoral thesis. However, it has the disadvantage that it cannot measure change and that

cause-and-effect chains cannot be uncovered with certainty. Moreover, it also does not

allow us to examine the reasons for incubator failure and correct for incubator survival bias.

Although we took great care in our conceptual models to avoid reversed causality as much

as possible, longitudinal research is needed to get decisive answers about cause-and-effect

chains. In addition, such longitudinal research might also provide insights into the reasons

for incubator failure.

Fourth and finally, in the beginning of this doctoral thesis, we explain that government

often employs incubators as intervention methods to correct for market failure. We explain

that externalities such as few networking partners may lead to market failure (Audretsch et

al., 2007). Information asymmetries between start-ups and financers (Audretsch et al., 2007)

and increased hesitance to invest in high-tech projects during economic recession (Sauner-

Leroy, 2004) often stimulate government to intervene. This doctoral thesis can however not

give any conclusive results about the impact of business incubators on economic

development or growth. Nor can we provide evidence as to whether the incubator can

adjust for market failures. For this, we suggest future researchers to adopt a matched pairs

212

sampling method. By searching companies located in and out an incubator that have similar

characteristics, the researcher might be able to investigate whether incubators really

stimulate start-up survival or growth, or that they just select companies that would also have

grown and survived without incubator support.

213

Appendices Appendix A: Regulative institutional context as moderator

Figure C-2: Johnson-Neyman region of significance for the conditional effect of human capital given regulative dimension

Figure C-3: Interaction human capital and regulative dimension

73.2% -0.294

214

Bibliography

Adegbite, O., 2001. Business incubators and small enterprise development: the Nigerian experience. Small Business Economics, 17, 157-166.



Audretsch, D.B., Grilo, I., Thurik, A.R., 2007. Explaining entrepreneurship and the role of policy: a framework. In: Audretsch, D.B., Grilo, I., Thurik, A.R., (Eds.), Handbook of research on entrepreneurship policy. Edward Elgar Publisinh, Northampton, MA, USA, p. 1-17.

Avnimelech, G., Schwartz, D., Bar-El, R., 2007. Entrepreneurial high-tech cluster development: Israel’s experience with venture capital and technological incubators. European Planning Studies, 15, 9, 1181-1198.



Bigliardi, B., Dormio, A.I., Nosella, A., Petroni, G., 2006. Assessing science parks’ performances: directions from selected Italian case studies. Technovation, 26, 20-28.


Bruton, G.D., Ahlstrom, D., Li, H.-L., 2010. Institutional theory and entrepreneurship: where are we now and where do we need to move in the future? Entrepreneurship: Theory and Practice, 421-440.



Dul, J., Hak, T., 2008. Case Study Methodology in Business Research. Elsevier, Oxford, UK. Ferguson, R., Olofsson, C., 2004. Science parks and the development of NTBFs – location,

survival and growth. Journal of Technology Transfer, 29, 5-17. Grimaldi, R., Grandi, A., 2005. Business incubators and new venture creation: an assessment

of incubating models. Technovation, 25, 111-121. Hackett, S.M., Dilts, D.M., 2008. Inside the black box of business incubation: study B – scale

assessment, model refinement, and incubation outcomes. The Journal of Technology Transfer, 33, 439-471.


Hansen, M.T., Chesbrough, H.W., Nohria, N., Sull, D.N., 2000. Networked incubators: hothouses of the new economy. Harvard Business Review, 78, 5, 74-84.

215

Heydebreck, P., Klöfsten, M., Maier, J.C., 2000. Innovation support for new technology-based firms: the Swedish Teknopol approach. R&D Management, 30 (1), 89-100.



Kaplan, R.S., Norton, D.P., 2005. The balanced scorecard: measures that drive performance. Harvard Business Review, July-August, 172-180.

Krugman, P., 1991. Increasing returns and economic geography. Journal of Political Economy, 99, 483-499.



McAdam, M., Gailbraith, B., McAdam, R., Humphreys, P., 2006. Business processes and network in university incubators: a review and research agenda. Technology Analysis & Strategic Management, 18, 5, 451-472.

McAdam, M., McAdam, R., 2008. High tech start-ups in university science park incubators: the relationship between the start-up’s lifecycle progression and the use of the incubator’s resources. Technovation, 28, 277–290.

McMullan, E., Chrisman, J.J., Vesper, K., 2001. Some problems in using subjective measures of effectiveness to evaluate entrepreneurial assistance programs. Entrepreneurship: Theory and Practice, Fall, 37-54.

Mian, S.A., 1996. Assessing value-added contributions of university technology business incubators to tenant forms. Research Policy, 25, 325-335.

Moxham, C., 2010. Help or hindrance? Examining the role of performance measurement in UK nonprofit organizations. Public Performance and Management Review, 33, 3, 342-354.

Porter, M., 2000. Locations, clusters and company strategy, in: Clark, G., Feldman, M., Gertler, M. (Eds.), The Oxford Handbook of Economic Geography, Oxford University Press, Oxford, p. 253-74.

Sauner-Leroy, J.B., 2004. Managers and productive investment decisions: the impact of uncertainty and risk aversion. Journal of Small Business Management, 42, 1, 1-18.

Schwartz, M., 2012. A control group study of incubator’s impact to promote firm survival. Journal of Technology Transfer. Online available: DOI 10.1007/s10961-012-9254-y.

Schwartz, M., Göthner, M., 2009. A multidimensional evaluation of the effectiveness of business incubators: an application of the PROMETHEE outranking method. Environment and Planning C: Government and Policy, 27, 1072-1087.


Sherman, H., 1999. Assessing the intervention effectiveness of business incubation programs on new business start-ups. Journal of Developmental Entrepreneurship, 4, 2, 117-133.


Tangen, S., 2004. Performance measurement: from philosophy to practice. International Journal of productivity and Performance Management, 53, 8, 726-737.

216

Tornatzky, L., Sherman, H. Adkins, D., 2002. A national benchmarking analysis of technology business incubator performance and practices. US: National Business Incubation Association.

Yin, R.K., 1990. Case study research: design and methods. Applied Social Research Methods Series, vol. 5, Beverly Hills, Sage Publications, California, USA.

Zablocki, E.M., 2007. Formation of a business incubator. In A. Krattiger, R.T. Mahoney, L. Nelsen, et al. (Eds.), Intellectual property management in health care and agricultural innovation: a handbook of best practices (p. 1305-1314). Oxford, UK: MIHR, and Davis, US: PIPRA

Zhang, H., Sonobe, T., 2011. Business incubators in Chine: an inquiry into the variables associated with incubate success. Economics: The Open-Access, Open-Assessment E-Journal, 5, 7, 1-26.

217

Samenvatting (Nederlands)

In dit proefschrift doen we onderzoek naar een organisatie die startende bedrijven

ondersteunt: business incubators. Door het aanbieden van een breed gamma van

ondersteunende diensten zoals business coaching en logistieke faciliteiten, versnelt de

incubator de leercurve van haar huurders. Bovendien laat ze haar huurders gebruik maken

van haar eigen naamsbekendheid en geloofwaardigheid, en biedt ze schaalvoordelen aan.

Op die manier tracht de incubator de groei en overleving van startende bedrijven te

stimuleren. Sinds het midden van de jaren 1980 wordt er onderzoek naar deze organisaties

gedaan. Echter, er zijn nog steeds een groot aantal onduidelijkheden over de werking en

prestaties van incubators. In wat volgt bespreken we de vier belangrijkste lacunes die we in

onderzoek over incubators detecteerden. We leggen uit hoe we elk van deze lacunes hebben

aangepakt in dit proefschrift en bespreken hoe de verschillende hoofdstukken aan elkaar

gelinkt zijn.

Lacunes in bestaand onderzoek, bijdrage van het proefschrift en link tussen de

verschillende hoofdstukken

De eerste lacune in bestaand onderzoek die we detecteerden, is dat strategische

positioneringsliteratuur een vrij eenzijdig standpunt inneemt wat betreft

differentiatiemogelijkheden voor incubators. De meeste onderzoekers geven aan dat

incubators meerwaarde voor hun huurders kunnen creëren door industrie- of technologie-

specifieke diensten aan te bieden. Echter, voorbeelden uit de praktijk geven aan dat er heel

wat goed werkende incubators zijn die dergelijke diensten niet aanbieden. Ons onderzoek in

hoofdstuk 1 toont aan dat er twee waarde-creërende differentiatiemogelijkheden zijn die

zich baseren op het aanbieden van diensten: specialisten en generalisten. In dit hoofdstuk

gebeurt de analyse op strategisch groepsniveau en maken we een expliciet onderscheid

tussen falingsvermijders en succesveroorzakers. Falingsvermijders zijn diensten die aanwezig

moeten zijn om ervoor te zorgen dat de incubator kan overleven. Indien de incubator

eveneens succesveroorzakers aanbiedt, kan ze zich onderscheiden van andere incubators.

Eerder onderzoek maakte deze onderverdeling niet of bleek verouderd te zijn. Onze

218

resultaten tonen aan dat diepgaande diensten die de incubator zelf aanbiedt (en niet via het

doorverwijzen naar externe partners) succesveroorzakers zijn. Ook de beschikbaarheid van

persoonlijke netwerkcontacten blijkt een succesveroorzaker te zijn. Daarnaast geeft ons

onderzoek aan dat generalisten differentiatie kunnen bereiken via het aanbieden van

diepgaande secretariaatsdiensten of operationele bedrijfsondersteuning. Specialisten

kunnen zich dan weer onderscheiden door sector-of technologie-specifieke

bedrijfsondersteuning aan te bieden.

In hoofdstuk 3 wordt de impact van een gespecialiseerde of focus strategie verder

onderzocht en biedt ons onderzoek bijkomende inzichten over de effectiviteit ervan. We

vinden dat in Brazilië (waar onze empirische studie werd uitgevoerd) een focus strategie

geen rechtstreekse impact heeft op de groei en overleving van de huurders van een

incubator. Onze resultaten geven aan dat een strategie die op maat gemaakte diensten

verzekert eveneens aanwezig moet zijn, om ervoor te zorgen dat een focus strategie in groei

en overleving van huurders resulteert.

Een tweede lacune in bestaand onderzoek is onze kennis over het incubatieproces. Het

overgrote deel van het onderzoek naar de interne werking van een incubator suggereert dat

het voornamelijk de diensten van een incubator zijn die belangrijk zijn voor de interne

werking van deze organisaties. Er zijn slechts een paar studies die hiervan afwijken, en die

suggereren dat naast diensten zoals bedrijfsondersteuning, ook interne processen zoals het

selectieproces of de aanwezige kennis/competenties in een incubator de interne werking

definiëren. We volgen dit standpunt en erkennen dat de interne processen, middelen, kennis

en kunde bepalen of een incubator in staat is om haar diensten tijdens het incubatieproces

optimaal te benutten. In hoofdstuk 1 erkennen we dit door een competentieconfiguratie te

ontwikkelen waarbij interne aspecten zoals de cultuur, systemen of kennis afgestemd

worden op de strategie van de incubator. We vinden bijvoorbeeld dat regelmatig contact

tussen huurders, incubator personeel en externe deskundigen onmisbaar is voor het

stimuleren van samenwerking tussen huurders en een optimaal gebruik van de middelen

van de incubator. In hoofdstuk 2 ontwikkelen we deze interne karakteristieken en systemen

verder, en integreren we ze in onze aanpassing van de balanced scorecard en strategy map

voor non-profit economische ontwikkeling incubators. In dit hoofdstuk benadrukken we het

belang van co-creatie om de dienstenkwaliteit en proactiviteit van een incubator te

219

verbeteren. Co-creatie betekent dat de huurder actief participeert in het

ontwikkelingsproces van de diensten die de incubator aanbiedt. Via co-creatie kan de

tevredenheid van huurders over de dienstverlening stijgen.

In hoofdstukken 3 en 4 werken we verder met dit concept van “co-creatie”. We bekijken

het vanuit een strategische positioneringsoptie enerzijds, en een interne incubatiestrategie

anderzijds. Hoofdstuk 3 geeft aan dat een strategie die op maat gemaakte diensten toelaat

tot hogere prestaties leidt. In hoofdstuk 4 onderzoeken we de impact van “menselijk

kapitaal” op co-creatie. In dit hoofdstuk sluiten we aan bij onderzoekers die aangeven dat de

ondernemerschapservaring en het opleidingsniveau van de werknemers van een incubator

een significant effect hebben op de interne werking van de incubator. We vinden inderdaad

dat hoog “menselijk kapitaal” (hoog opleidingsniveau en ervaring) een positieve invloed

heeft op co-creatie. Dit suggereert dat incubator managers en beleidsmakers die belang

hechten aan co-creatie best incubator medewerkers kunnen aantrekken met een hoog

opleidingsniveau en met relatief veel ervaring. Ook aandacht voor aanvullende opleidingen

na het aanwerven van de medewerkers wordt als zeer belangrijk ervaren. Onze resultaten

geven dus aan dat naast het dienstenaanbod, ook andere interne factoren zoals menselijk

kapitaal van belang zijn voor het optimaal functioneren van een incubator.

Een derde lacune die we detecteerden, is dat onderzoek naar externe factoren zich

voornamelijk beperkt tot het feit dat onderzoekers aangeven dat financieringsprogramma's

voor starters en incubators positieve gevolgen hebben voor start-up ontwikkeling en

overleving. De impact van omgevingsfactoren op de prestaties of werking van een incubator

werd slechts recentelijk voor het eerst onderzocht in een grootschalige kwantitatieve studie.

De eerste studies op dit gebied focussen zich op de concurrentiële omgeving van de huurder

en houden geen rekening met de impact van de bredere, institutionele context op de

werking van de incubator. Onze onderzoeksresultaten tonen aan dat externe factoren

inderdaad het functioneren van een incubator beïnvloeden. In hoofdstukken 1 en 2 gaat

onze aandacht voornamelijk naar de verwachtingen van de huurder. Hun verwachtingen

blijken een belangrijke impact te hebben op strategische differentiatie-opties en incubator

evaluatiesystemen.

In hoofdstukken 3 en 4 onderzoeken we de impact van de bredere, institutionele

omgeving op de werking van een incubator. In hoofdstuk 3 vinden we dat een

220

onderontwikkelde regulerende en cognitieve ondernemende omgeving een negatief effect

hebben op de werking van incubators. Deze resultaten suggereren aan beleidsmakers om

voldoende aandacht te geven aan de ontwikkeling van een transparante en consistente

regulerende omgeving, net als aan onderwijs dat studenten toelaat om kennis te maken met

ondernemerschap. Onze resultaten in hoofdstuk 4 laten eveneens zien dat een goed

ontwikkelde regulerende omgeving de positieve impact van “menselijk kapitaal” op co-

creatie verder kan stimuleren. Deze resultaten geven aan dat in de Europese landen/regio’s

die wij onderzocht hebben (namelijk; Vlaanderen, Nederland, het Verenigd Koninkrijk en

Ierland), beleidsmakers die aandacht schenken aan het ontwikkelen van een faciliterende

regulerende ondernemende omgeving er op die manier voor kunnen zorgen dat de werking

van een incubator verder gestimuleerd wordt. De overheid kan er bijvoorbeeld voor opteren

om belastingen en regelgevingen voor nieuwe bedrijven zeer eenvoudig en transparant te

maken. Ook zeer lage administratieve lasten blijken een positieve stimulans te zijn voor het

verder stimuleren van het positieve effect van “menselijk kapitaal” op co-creatie in

incubators.

Tenslotte blijkt dat in ruil voor de aangeboden steun aan incubators, beleidsmakers een

optimale werking en continue verbetering van incubators verwachten. Hoewel het

incubatieproces cruciaal is voor het optimaal functioneren van een incubator, worden

dergelijke interne processen vaak genegeerd tijdens het evalueren van incubators. De

meeste evaluatiemethoden focussen op individuele maatstaven, zoals de groei en overleving

van de huurders, of op de beschikbaarheid van netwerk partners. Dergelijke eenzijdige

evaluatiemethoden maken het moeilijk om interne processen te evalueren. We vonden

slechts vier geïntegreerde evaluatiesystemen in literatuur over incubators. Deze bleken niet

te voldoen aan een aantal basisprincipes voor het ontwikkelen van evaluatiesystemen, zoals

het belang van begrijpbaarheid of toegankelijk. Om een antwoord te kunnen bieden aan

deze tekortkomingen passen we in hoofdstuk 2 de balanced scorecard en strategy map aan

naar de context van non-profit economische ontwikkeling incubators. Op die manier bieden

we een geïntegreerd evaluatiesysteem aan dat zich richt op interne incubator aspecten.

Omdat we hierdoor in dit hoofdstuk slechts zeer beperkte aandacht schenken aan de groei

en overleving van huurder, onderzoeken we de impact van een aantal strategische (dat is,

een focus strategie en een strategie die het op maat aanbieden van diensten toelaat) en

221

omgevingsfactoren op de groei en overleving van huurders in hoofdstuk 3. Zoals hierboven

reeds aangegeven, heeft een focus strategie in combinatie met een strategie die het op

maat maken van diensten toelaat een positieve impact op de groei en overleving van

huurders. We zagen eveneens dat een onderontwikkelde ondernemerschapsomgeving een

negatieve invloed heeft op deze relaties.

Vanderstraeten 2013 ph d - studies on the strategy and performance of business incubators

Education

Transcript of Vanderstraeten 2013 ph d - studies on the strategy and performance of business incubators