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Defesa da tese de doutorado de Johanna Vanderstraeten, ocorrida na Universidade de Antuérpia, na Bélgica. Teses sobre o tema incubadoras.

Defesa da tese de doutorado de Johanna Vanderstraeten, ocorrida na Universidade de Antuérpia, na Bélgica. Teses sobre o tema incubadoras.

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

  • 1. STUDIES ON THE STRATEGY AND PERFORMANCE OF BUSINESS INCUBATORS
  • 2. 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
  • 3. 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
  • 4. 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) University of Antwerp and Antwerp Management School, Belgium Prof. dr. Tales Andreassi Fundação Getulio Vargas, Escola de Administração de Empresas de São Paulo (FGVEAESP), São Paulo, Brazil Prof. dr. Marcus Dejardin University of Namur, Belgium Prof. dr. Eddy Laveren University of Antwerp and Antwerp Management School, Belgium Prof. dr. Roy Thurik Erasmus University Rotterdam and Free University Amsterdam, the Netherlands
  • 5. 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 i
  • 6. 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 ii
  • 7. 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 iii
  • 8. 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 iv
  • 9. 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 v
  • 10. 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 vi
  • 11. 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, vii
  • 12. 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 wellorganized 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 viii
  • 13. 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. ix
  • 14. 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 x
  • 15. xi
  • 16. xii
  • 17. 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 hightech 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 1
  • 18. (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 2
  • 19. 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-ofthe-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. 3
  • 20. phenomenon. In total, we conducted 18 in-depth interviews with incubator managers, 30 indepth 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. 4
  • 21. 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). 5
  • 22. 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 goodsdominant 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. 6
  • 23. 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 cocreation 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 subprocess: 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 7
  • 24. 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. 8
  • 25. Figure I-2: Integrative overview of the doctoral thesis 9
  • 26. 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). 10
  • 27. 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 11
  • 28. 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 12
  • 29. Figure I-8: Number of inhabitants/incubator in the South region of Brazil 13
  • 30. Figure I-9: Number of inhabitants/incubator in Brazil (all states) 14
  • 31. 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). 15
  • 32. 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). 16
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  • 37. 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, Servicebased 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. 21
  • 38. 22
  • 39. 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. 23
  • 40. 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 resourcebased 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 24
  • 41. 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. 25
  • 42. 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 26
  • 43. 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., 27
  • 44. 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). 28
  • 45. 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 29
  • 46. 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 30
  • 47. 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. 31
  • 48. 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 semistructured 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 32
  • 49. 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 indepth 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 33
  • 50. 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 34
  • 51. 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 35
  • 52. 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. 36
  • 53. 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. 37
  • 54. Table 1-1: Tenant expectations of service offerings (no sector/technology focus) Incubator failure preventing services Interview example Administrative services A common secretary (e.g., reception, telephone, postal delivery, welcoming visitors) Logistic services Basic equipment (e.g., flexible office space, Internet connection, telephone line, photocopier) Business support services In-depth business support services focusing on operational business activities Network of (high-quality) partners for offering these services Networking Access to (high-quality) partners such as venture capitalists, bookkeepers, and lawyers Access to other tenants 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” 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” 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” 38
  • 55. Table 1-2: Tenant expectations regarding service offerings (sector/technology focus) Incubator failure preventing services Administrative services A common secretary Logistic services 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) Business support services In-depth business support services focusing on a company’s operational activities Sector- or technology-specific, in-depth business support services Networking 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) Network of partners for offering these services 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 sectorspecific 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 indepth 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” 39
  • 56. Focused SPECIALIST Diversified Competitive sector scope Figure 1-1: Service-based differentiation strategies GENERALIST - Core business activities - Personal contacts - Support business activities - Personal contacts Competitive advantage 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 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 Strategic intent - 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 Service offering differentiation 40
  • 57. 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 41
  • 58. 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. 42
  • 59. Table 1-3: Competence configuration: generalists Processes and systems Assets, knowledge, and capabilities Culture Selection process Selection process/system to identify companies that potentially can develop into successful, growing businesses. Mainly take personal, team, and financial characteristics into account Entrepreneurship experience Selection process/system to identify companies searching for co-location and interaction with companies from diverse sectors/technologies and operational incubator support services Encourage incubator employees to attend business plan competitions, entrepreneurship seminars, and workshops. Goal: incubator employees gather sufficient entrepreneurial experience Resource munificence Process to ensure tenants come into contact with one another, incubator management, and thirdparty 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 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) Open culture in which tenants and the incubator personnel have regular meetings about the services offered, tenant needs, and possible synergies between tenants and thirdparty network partners Monitoring and business assistance 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 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 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-theart operational business support services Encourage incubator employees to update their knowledge on operational business activities, such as human resources and firm infrastructure 43
  • 60. Table 1-4: Competence configuration: specialists Processes and systems Assets, knowledge, and capabilities Culture Selection process 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 technologyrelated characteristics 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 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/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-/technologyspecific business support Resource munificence 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 Interpersonal skills: introduce and connect tenants, talk with tenants to gain insights into services they need Empathic capability and friendliness 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 Capability to develop and maintain a strong basis for trust Strong network partners in the incubator’s sector or field Monitoring and business assistance 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 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 technologyspecific services 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 44
  • 61. 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. 45
  • 62. 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 46
  • 63. 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). 47
  • 64. 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-tointeract 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, growthoriented 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 48
  • 65. 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. 49
  • 66. 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 50
  • 67. 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 51
  • 68. 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 businessrelated 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 52
  • 69. (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). 53
  • 70. Appendices Appendix A: Characteristics of sample incubators Table 1-5: Characteristics of sample incubatorsa Incubator Interviewee job Year of founding Size, office space (m2) Sector or technology focus 2.267 m2 Average occupancy rate (07–09) 57% A Manager 1986 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) E Manager 2000 618 m2 97% Building sector, sustainable building Creative sector F Manager 2000 550 m2 70% None G Manager 2009 720 m2 17% (2009) H Manager 1995 1.045 m2 95% Energy and environmental technology None I Manager 1993 1.160 m2 85% None 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. 54
  • 71. 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 1982 (BE: ‘92) 1999 9,2 1992 A4 General Manager CEO 1 2006 Multimedia, gaming, and computer accessories for retail distribution Wireless systems and appliances A5 CEO 1995 4 2007 Computerizing processes B1 Coordinator 1984 2 2004 B2 1992 40 1993 B3 Managing partner Manager 2009 6 2009 Promotion and awareness stimulation of sustainable energy use and environmental protection Information and communication technology service provision Integrated services for health care C1 CEO 1998 3 1998 Air transport C2 2000 (BE: ‘08) 1998 2 2008 Market research in foodservices market 7 2007 Chemical: water and paper treatment C4 Sales Manager General Manager CEO 2006 1 2006 Consultancy: crisis management C5 CEO 2006 18 2008 D1 CEO 2009 4 2008 D2 CEO 2007 1 2007 Information and communication technology: optimizing business processes Sales and installation of solar panels and applications Study center: engineering D3 1811 (BE: ‘75) 2001 27 2008 Elevators E1 Regional Director CEO 4 2009 Interior designer E2 CEO 2008 2 2008 Communication expert E3 Adviser 2 2007 Advice organization for artists F1 F2 F3 G1 CEO CEO CEO CEO 1998 (Antwerp: ’07) 1998 2007 1999 2008 14 1 1 32 2001 2008 2005 2009 G2 CEO 2009 4 2009 H1 CEO 1991 4 2000 H2 Employee 2 2005 H3 H4 I1 CEO CEO Employee 1999 (BE: ‘03) 1999 2008 1977 Specialized dry-cleaning cars Consultancy: events Design and publicity Research and development: pharmaceuticals Study center: subterranean energy storage and heat pumps Audiovisual communication and documentaries Hardware development 2 2 1,5 2000 2008 1993 B C C3 D E F G H I a a I2 CEO 2006 2 2009 First as part of a large organization, then a separate company. Communication expert Publishing company Training, advice, and knowledge center creative thinking Software development 55
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  • 79. 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. 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. 19 63
  • 80. 64
  • 81. 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 65
  • 82. 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. 66
  • 83. 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-ofthe-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”. 67
  • 84. 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 68
  • 85. 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 69
  • 86. 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 suboptimization 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 The system measures’ purpose is clearly defined. It is clear who will use that have and act upon the measure. This implies that appropriate targets and comprehensible timeframes for target reaching are developed specifications 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 70
  • 87. (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 71
  • 88. 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 72
  • 89. 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. 73
  • 90. 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. 74
  • 91. 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. 75
  • 92. place during the same period as the first wave of the incubator interviews. The semistructured 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 semistructured 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 76
  • 93. 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. 2.4. Empirical results 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 77
  • 94. 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 breakeven. 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 78
  • 95. 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 crossfertilization 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 79
  • 96. 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 80
  • 97. organizations, tenant interviewees indicate that high service quality, proactiveness, and indepth 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 highquality 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. 81
  • 98. 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. 82
  • 99. 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 Financial perspective Commercial side-activity Long-term strategic goals Customer perspective External alignment Innovation and Learning perspective Internal Business Processes perspective Internal alignment Financial sustainability Subsidies and sponsorship Rent Paid services Cost cutting Stable and diverse tenant portfolio Value creation effectiveness Efficient functioning Resource and information sharing for tenants Offer proactive, qualitative and in-depth services adapted to the company’s development phase Efficiently offer a wide variety of services Create a platform for the establishment & development of start-ups and small businesses Optimize the selection process Proactive tenant follow-up system Resource sharing process with other incubators or external partners Organize entrepreneurshiprelated activities Optimize the graduation process Process to refer possible tenants within the incubator’s network External networking system with experts Develop a knowledge management system Set up a process to assure an efficient and lean organization Entrepreneurship and business development Offer relocation support to graduating companies Set up a process to assure high quality Continuously improve the selection and graduation process Continuous service innovation Continuously seek for new ways to improve efficiency Continuously seek for new and innovative ways to establish and develop start-ups and small businesses 83
  • 100. 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. 84
  • 101. 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 85
  • 102. 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. 86
  • 103. Figure 2-2: BSEDI and targets: balanced scorecard for nonprofit economic development incubators Financial situation Measures - Income from rent and a package of basic services - Occupancy rate - Income from additional, paid services - Income from sponsorship/subsidies - Income from a commercial side-activity - Costs analyses - Yearly audit from the balance sheet and annual report Internal Business Processes Measures - Tenant satisfaction: individual or group meetings - Follow up all questions/solicitudes raised during individual or group meetings - Balanced selection process: team, financial and market characteristics - Clearly defined graduation criteria - Map the areas of expertise of the external experts - Number of external experts affiliated to the incubator - Number of privileged and member incubators - Number of organized meetings/contact moments between and among tenants and external entrepreneurs/companies - Does the incubator have a quality system in place? Thresholds - At least 70 % of income - At least 85 % - Maximum 30 % of income - Only for large investments - Maximum 10 % of income - Operating & overhead costs should not exceed income from rent & paid services - Search for cost cutting possibilities - At least break-even, or preferably (small) profit margins that can be used for future investments Thresholds - Half-yearly - All concerns should be followed up and decisions should be communicated to tenants - All three characteristics should be taken into account - Pre-defined development phase has been reached - Wide variety of areas of expertise - At least one expert in each area - At least one privileged incubator partner - Monthly Customers Measures - Tenant satisfaction: individual or group meetings - Follow up all questions/solicitudes raised during individual or group meetings - Number of organized meetings/contact moments between and among tenants and external entrepreneurs/companies - Architectural infrastructure of the incubator: compare the incubator’s communal surface with the total individual office space - Topics of the networking events such as seminars and workshops organized for tenants and external companies/entrepreneurs - Number of participants and their affiliations in networking events such as seminars and workshops Innovation and Learning Measures - Tenant satisfaction and tenant expectations: individual or group meetings - Follow up all questions/solicitudes raised during individual or group meetings - Number of events such as conferences and workshops incubator employees participate in Thresholds - Half-yearly - All concerns should be followed up and decisions should be communicated to tenants - Monthly - Communal space should be at least as large as the total individual office space - Half of the topics should focus on business initiation, the other half on business development - Half of the participants should be external companies/entrepreneurs Thresholds - Half-yearly - All concerns should be followed up and decisions should be communicated to tenants - Twice a year for each incubator employee - Evaluation markers associated with this system 87
  • 104. 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. 88
  • 105. 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. 89
  • 106. 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). 90
  • 107. 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 mediumterm 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). 91
  • 108. 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). 2.6.2. Implications for practice and policy 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 92
  • 109. tenant opinions, the SMEDI and BSEDI might guide potential tenants to choose appropriate locations (Bigliardi et al., 2006). 2.6.3. Limitations and directions for future research 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. 93
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  • 117. 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. 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. 30 101
  • 118. 102
  • 119. 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. 103
  • 120. 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. 104
  • 121. 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 105
  • 122. 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 goodsdominant 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 106
  • 123. generation during the service development, processing and delivering process (AarikkaStenroos 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 107
  • 124. 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 industryspecific 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: 108
  • 125. 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 109
  • 126. 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 110
  • 127. 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 nonentrepreneurially 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. 111
  • 128. 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-andrunning 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 112
  • 129. (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. 113
  • 130. 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. 114
  • 131. 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. 115
  • 132. 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). 116
  • 133. 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 117
  • 134. 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. 118
  • 135. 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 IDEB 3 = 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. 119
  • 136. 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”. 120
  • 137. Table 3-1: Means, standard deviations, maximum, minimum and bivariate correlations 1. Tenant survival and growth 2. Focus strategy 3. Service customization strategy 4. Regulative dimension 5. Cognitive dimension 6. Normative dimension 7. Year of operations 8. Size 2 (m ) 9. Occupancy rate 10. Gdp (Brazilian Real) 11. High-growth growth companies 12. Education quality (IDEB) M 5.51 SD 3.47 Min 0 Max 10 1 2 3 4 5 6 7 8 9 10 11 3.68 1.75 1 7 -.051 5.59 1.02 1.6 7 .163* .318*** 2.87 .97 1 5 .014 .272*** .150* 2.34 .99 1 5 .141* .163* .193** .256*** 3.69 .78 1.25 5 -.025 -.114 -.070 .182* .354*** 2001.71 5.20 1984 2011 -.325*** .137* -.011 -.006 -.081 .070 1.61 1.09 1 7 .117 -.189** .104 -.146* -.011 -.138* -.245*** 7.33 2.78 1 10 .285*** -.028 .140* .071 .133 .050 -.180* .136* 375966 389186 4883 996717 .148* -.020 .116 -.018 -.013 -.079 -.077 .137* .351*** 1.61 .30 1.28 2.98 -.057 -.003 .033 -.137* -.147* -.148* .101 .140* -.070 -.118 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 5point 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=8190%; 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. 121
  • 138. 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 3.4. Empirical results 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 b 1 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. 122
  • 139. 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 123
  • 140. 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). 124
  • 141. 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 nonsignificant 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. 125
  • 142. 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 nonsignificant. 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. 126
  • 143. Table 3-2: Hierarchical linear regression and path model coefficients Outcome Constant CONTROL VARIABLES Incubator level Year operations 2 Size (m ) Occupancy rate State level Gdp (Brazilian Real) High-growth companies Education quality (IDEB) Model 1 IncP B 5.542*** (.248) Model 2 IncP B 5.603*** (.264) Model 3 IncP B 5.608*** (.263) Model 4 IncP B 5.602*** (.264) Model 5 IncP B 5.664*** (.264) Model 6 IncP B 5.734*** (.266) Model 7 IncP B 5.599*** (.265) Model 8 IncP B 5.784*** (.273) Model 9 SCS B .063 (.079) -.181*** (.052) .100 (.259) .229** (.097) -.211*** (.056) -.080 (.298) .231* (.107) -.207*** (.056) -.069 (.296) .221* (.107) -.209*** (.057) -.081 (.298) .232* (.108) -.213*** (.056) -.092 (.296) .236* (.107) -.197*** (.056) -.022 (.292) .236* (.106) -.209*** (.057) -.077 (.301) .232* (.108) -.201*** (.057) -.078 (.300) .243* (.107) -.001 (.019) .142+ (.082) .038 (.031) -5,613E-007 (.000) 1.302+ (.999) 1.733** (.706) -5,148E-007 (.000) 1.035 (1.068) 1.552* (.760) -4,136E-007 (.000) 1.101 (1.065) 1.476* (.763) -5,222E-007 (.000) 1.031 (1.070) 1.550* (.759) -4,798E-007 (.000) 1.162 (1.064) 1.635* (.756) -3,125E-007 (.000) .815 (1.058) 1.275* (.757) -5,346E-007 (.000) 1.024 (1.076) 1.548* (.762) -2.548E-007 (.000) .937 (1.080) 1.389* (.769) 7.213E-008 (.000) .170 (.280) .093 (.204) -.113 (.165) .497* (.289) -.074 (.288) -.136 (.162) .460+ (.290) -.128 (.163) .490* (.288) -.128 (.164) .499* (.297) -.069 (.365) -.105 (.170) .277 (.311) -.033 (.304) .298 (.305) -.162 (.397) -.048 (.366) 3.523*** .203 .146 -.359 (.310) -.531* (.289) .305 (.405) 3.083*** .244 .165 DIRECT EFFECTS Focus strategy Service customization strategy Regulative dimension Cognitive dimension c’1 b1 b2 -.118 (.164) .510* (.287) -.001 (.290) .208 (.278) Normative dimension c’1 b1 b2 -.108 (.160) .302 (.295) b2 b3 -.458* (.276) Service customization * Cognitive b3 -.586* (.262) Service customization * Normative F-statistic R2 Adjusted-R2 b1 .308 (.278) -.082 (.349) INTERACTION EFFECTS Service customization * Regulative c’1 b3 5.810*** .180 .149 3.942*** .203 .152 4.011*** .206 .155 3.941*** .203 .152 3.869*** .219 .162 4.213*** .234 .178 a1 .194*** (.043) 3.407** .151 .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. 127
  • 144. Figure 3-3: Johnson-Neyman region of significance for the conditional effect of service customization strategy given regulative dimension 42.5% -0.1882 Figure 3-4: Interaction service customization strategy and regulative dimension 128
  • 145. Figure 3-5: Johnson-Neyman region of significance for the conditional effect of service customization strategy given cognitive dimension 29.4% -0.4061 Figure 3-6: Interaction service customization strategy and cognitive dimension 129
  • 146. Table 3-3: Hierarchical linear regression for simple mediation, and path model coefficients Model 10a DIRECT effect SCS B .003 (.079) Model 10b DIRECT effect IncP B 5.540*** (.251) Model 10c TOTAL effect .001 (.017) .075 (.099) .039 (.031) -.181** (.064) .042 (.262) .207* (.110) -.180** (.064) .083 (.251) .228* (.105) High-growth companies .000 (.000) .289 (.290) .000 (.000) 1.149 (1.171) .000 (.000) 1.310 (1.188) Education quality (IDEB) .112 (.198) 1.678** (.682) 1.741** (.696) Outcome Constant CONTROL VARIABLES Incubator level Year operations Size (m2) Occupancy rate State level Gdp (Brazilian Real) DIRECT and TOTAL EFFECTS Focus strategy a1 .187*** (.044) Service customization strategy F-statistic R2 3.192** .347 c’1 -.154 (.152) b1 .556* (.253) 5.760*** .205 IncP B 5.542*** (.253) c -.049 (.145) 1 4.985*** .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 .022 .213 .055 (.059) + < .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. 130
  • 147. Table 3-4: Bootstrap quantiles for the conditional indirect effect of the moderated mediation Model 5* - Regulative dimension Reg. -1.374 B Model 6* - Cognitive dimension LL 90% CI UL 90% CI Cogn. B Model 7* - Normative dimension LL 90% CI UL 90% CI Norm. .221 .026 .0477 -1.343 .211 .052 .392 -1.186 (.113) (.087) -.874 .1762 .020 .386 -.743 .143 .022 .289 -.436 (.092) (.068) -.124 .110 -.008 .266 -.343 .097 -.011 .239 .064 (.069) (.063) .626 .043 -.071 .203 .658 -.016 -.177 .140 .564 (.068) (.078) 1.376 -.023 -.179 .168 1.658 -.130 -.391 .083 1.064 (.089) (.118) Sample size = 149. Values of the moderator at the 10th, 25th, 50th, 75th, and 90th percentile. B .108 (.128) .101 (.083) .096 (.070) .091 (.080) .087 (.107) LL 90% CI UL 90% CI -.136 .367 -.048 .284 -.021 .256 -.051 .267 -.110 .320 131
  • 148. 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 132
  • 149. 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 133
  • 150. 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. 3.5.1. Implications for practice and policy 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 134
  • 151. 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 nonentrepreneurial 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. 3.5.2. Limitations and directions for future research 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 135
  • 152. 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. 136
  • 153. Appendices Appendix A: Sample representativeness incubator sample Table 3-5: Sample representativeness: description of incubator samples State AC-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-Tocantins Total Anprotec (2005) N° of incubators 1 10 3 1 11 5 6 5 5 2 26 9 6 4 5 12 5 24 27 3 1 0 82 17 3 62 4 339 Own contact database (2010) N° of incubators 1 2 6 2 6 11 3 4 5 3 23 7 3 6 3 11 1 22 15 5 1 1 32 17 1 72 1 264 Incubator manager sample (2010) Incubator employee sample (2010) N° of responses 0 2 4 1 4 10 2 3 5 1 16 6 2 4 3 5 0 15 10 4 1 1 22 13 1 51 1 187 N° of responses 0 2 2 1 2 8 2 2 2 1 8 4 1 2 1 4 0 12 7 3 1 0 12 9 1 26 0 113 Response rate 0% 100% 66.7% 50% 66.7% 90.9% 66.7% 75% 100% 33.3% 69.6% 86.7% 66.7% 66.7% 100% 45.5% 0% 68.2% 66.7% 80% 100% 100% 68.8% 76.5% 100% 70.8% 100% 70.8% Response rate 0% 100% 33.3% 50% 33.3% 72.7% 66.7% 50% 40% 33.3% 34.8% 57.1% 33.3% 33.3% 33.3% 36.4% 0% 54.6% 46.7% 60% 100% 0% 37.5% 52.9% 100% 36.1% 0% 42.8% 137
  • 154. Table 3-6: Sample representativeness: number of tenants and year of operation N° of tenants 0-5 6-10 > 10 Year of operations --- - 1990 1991-1996 1997-2001 2002-2006 2007-2010 Anprotec (2005) 34% 38% 28% Anprotec (2006) 2.8% 7.8% 31.2% 58.2% NA Sample (2010) 28.9% 33.3% 37.8% Sample (2010) 3.9% 11.7% 24.4% 46.7% 13.3% Figure 3-7: Sample representativeness: number of tenants Figure 3-8: Sample representativeness: year of operations 138
  • 155. Figure 3-9: Sample representativeness: number of incubator per statea a AC-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. 139
  • 156. Appendix B: Sample representativeness entrepreneurship expert sample Table 3-7: Sample representativeness: description entrepreneurship expert sample Job Entrepreneur Investor, banker, financier Business support provider Educator, teacher, researcher Education 25.8% 1.6% 31.5% 41.1% High school Bachelor Master 2nd master Doctorate Post-doctorate Extension course(s) 0.5% 13.0% 30.8% 3.2% 13.0% 4.3% 34.1% Experience in entrepreneurship 0-6 months 7-12 months 13 months – 2 years 3-5 years 6-10 years 11-15 years 16-20 years 21-25 years 26-30 years 31-35 years 36-40 years > 40 years 4.1% 4.9% 7.4% 18.0% 23.8% 12.3% 19.7% 4.1% 3.3% 0.8% 0.8% 0.8% 140
  • 157. Appendix C: Factor analyses Table 3-8: Component matrix principal component analysis: incubator performance Item % of tenants surviving at the moment they left the incubator % of tenants that had grown in number of employees at the moment they left the incubator % of tenants that had grown in terms of sales revenue at the moment they left the incubator Incubator manager .946 .922 Incubator employee .953 .927 .951 .949 Table 3-9: Rotated component matrix (VARIMAX rotation) principal component analysis: institutional context Item I. 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 II. 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 III. 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 Incubator manager Experts .823 .849 .840 .814 .726 .594 .479 .756 .880 .897 .716 .831 .912 .859 .845 .890 .828 .599 .570 .780 .693 .745 .768 .838 .796 141
  • 158. Table 3-10: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy Item I. Service customization strategy 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 II. 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.) * Factor loadings of .45 are significant for sample sizes > 150 (Hair et al., 2006). Incubator manager .725 .451* .621 .741 .700 .786 .832 .837 142
  • 159. Appendix D: Measurement scales Table 3-11: Measurement scales for independent variables Variable Regulative dimension Cognitive dimension Normative dimension Service customization Focus strategy Items 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 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 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 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 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.) 143
  • 160. Appendix E: Plots assumption checks Model 8 Figure 3-10: Homoscedasticity and linearity 144
  • 161. Figure 3-11: Normality 145
  • 162. 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” Outcome Constant CONTROL VARIABLES Incubator level Year operations Size (m2) Occupancy rate State level Gdp (Brazilian Real) High growth companies Education quality (IDEB) DIRECT EFFECTS Focus strategy Service customization strategy Regulative dimension Cognitive dimension Normative dimension IncP B 5.775*** (.278) Model 8 – Without Outliers based on variable “High-growth companies” IncP B 5.745*** (.278) -.200*** (.057) -.151 (.383) .224* (.108) -.202*** (.057) -.264 (.335) .262** (.110) -1,445E-007 (.000) .555 (1.123) 1.343* (.771) -1,810E-007 (.000) .549 (1.514) 1.330* (.782) -.111 (.172) .296 (.312) -.006 (.305) .309 (.306) -.107 (.401) -.113 (.171) .290 (.312) -.070 (.306) .300 (.306) -.181 (.397) INTERACTION EFFECTS Service customization * -.343 -.320 Regulative (.311) (.311) Service customization * -.528* -.531* Cognitive (.290) (.288) Service customization * .349 .340 Normative (.407) (.405) F-statistic 3.008*** 3.102*** R2 .242 .250 2 Adjusted-R .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. 146
  • 163. Appendix G: Non-significant interaction plot Figure 3-12: Interaction service customization strategy and normative dimension 147
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  • 173. 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. 157
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  • 175. 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 cocreation (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 159
  • 176. 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 160
  • 177. (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 161
  • 178. 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 entrepreneurshiprelated 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 cocreation 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. 162
  • 179. 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 cocreation (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; 163
  • 180. 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 164
  • 181. 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 cocreation. 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 incubatortenant 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 165
  • 182. 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). 166
  • 183. 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. 167
  • 184. 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 emails, 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 168
  • 185. 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. 169
  • 186. 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 commonmethod 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 sevenpoint 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 cocreation, 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. 170
  • 187. 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). 171
  • 188. 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 172
  • 189. 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. 173
  • 190. 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. 174
  • 191. 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 2 8. Size (m ) 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. 175
  • 192. 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 4.4. Empirical results 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. 176
  • 193. 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 cocreation 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 cocreation 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 cocreation 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 nonsignificant 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. 177
  • 194. 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 nonsignificant. 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. 178
  • 195. Table 4-2: Hierarchical linear regression Model 1 CoCr B Model 2 CoCr B Model 3 CoCr B Model 4 CoCr B Model 5 CoCr B Model 6 CoCr B Model 7 CoCr B Model 8a CoCr B Model 8b CoCr B 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) .034+ (.024) .022 (.025) .033 (.026) .020 (.026) .023 (.025) .033 (.027) .020 (.027) .026 (.026) .027 (.026) Size (m ) 2 -.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) -.364** (.150) -.473** (.159) -.440** (.166) -.418** (.175) -.449** (.160) -.431** (.169) -.418** (.177) -.445** (.166) -.404** (.171) .361** (.144) .665*** (.200) .356** (.151) .384** (.153) .327* (.148) .598** (.209) .347* (.155) .384** (.155) .355** (.146) .622** (.219) .134 (.219) (p = .271) -.147 (.265) .314* (.154) .522* (.237) .178 (.228) (p = .219) -.171 (.270) 3.629*** .293 .212 .309 (.253) (p = .113) .001 (.176) -.086 (.201) 2.825** .308 .199 Outcome Constant CONTROL VARIABLES Incubator level Year operations Country level TEA index DIRECT EFFECTS Human capital Regulative dimension .381* (.203) Cognitive dimension .374* (.205) -.142 (.267) Normative dimension INTERACTION EFFECTS Regulative * Human capital -.141 (.270) .231 (.215) (p = .102) .047 (.148) Cognitive * Human capital Normative * Human capital F-statistic 2 R 2 Adjusted-R 1.775 .080 .035 4.586*** .284 .222 3.260** .220 .152 2.690* .189 .119 4.166*** .294 .223 2.833** .221 .143 .003 (.167) 2.325* .189 .107 + < .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. 179
  • 196. Figure 4-2: Johnson-Neyman region of significance for the conditional effect of regulative dimension given human capital 74.0% -0.499 Figure 4-3: Interaction regulative dimension and human capital 180
  • 197. 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 cocreation. 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 181
  • 198. 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 cocreation 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 cocreation 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 182
  • 199. 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. 4.5.1. Implications for practice and policy 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 183
  • 200. 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. 4.5.2. Limitations and directions for future research 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-tomouth, 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 cocreation are badly needed. 184
  • 201. 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 indepth. 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 cocreation 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. 185
  • 202. 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 0-5 21 6 11 28 (M = 12.6; SD = 32.8) 6-10 1 5 6 13 > 10 1 3 1 16 N° of companies incubated in 2012 0-5 15 2 4 13 (M = 19.8; SD = 23.6) 6-10 2 5 2 12 > 10 7 8 13 34 N° of companies post-incubated in 2012 0-5 17 13 9 29 (M = 11.3; SD = 30.9) 6-10 1 1 4 11 > 10 4 1 4 17 Total n° of companies that received 1-20 6 4 2 5 support since the incubator’s founding 21-40 2 2 3 10 (M = 8.3; SD = 7.4) 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. 186
  • 203. Table 4-5: Sample descriptives: year of operations, average occupancy rate and inside space N° of incubators* BE NL IE Year of operations … - 1985 3 1 1 1986-1990 12 2 0 1991-1995 3 0 3 1996-2000 2 0 2 2001-2005 2 8 8 2006-2010 3 7 5 > 2010 3 0 0 Average occupancy rate 0-50% 3 2 3 in the last 3 years 51-70% 5 2 3 71-80% 6 2 7 81-90% 5 7 2 91-100% 5 2 3 Size 1-1000 m2 5 4 4 2 2 (inside m ) 1001-2000 m 14 3 3 2001-4000 m2 6 4 8 4001-6000 m2 3 4 2 2 6001-8000 m 1 2 1 8001-10,000 m2 0 0 0 > 10,000 m2 0 1 1 * Full sample, before execution of missing data analysis. If number of incubators is not total number of incubators in the sample, then there was missing data. UK 1 3 4 9 19 21 2 4 10 13 15 10 24 7 12 3 6 4 3 the same as 187
  • 204. Appendix B: Measurement scales Table 4-6: Measurement scales for variables Variable Regulative dimension Cognitive dimension Normative dimension Service co-creation Focus strategy Human Capital Items 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 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 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 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 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.) 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 188
  • 205. Appendix C: Factor analyses Table 4-7: Rotated component matrix (VARIMAX rotation) principal component analysis: institutional context Item I. Regulative dimension – Cronbach’s alpha 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 II. Cognitive dimension – Cronbach’s alpha 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 III. Normative dimension – Cronbach’s alpha 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 Incubator manager .682 .818 .753 .622 .632 .890 .830 .789 .858 .789 .815 .672 .757 .795 .689 Table 4-8: Rotated component matrix (VARIMAX rotation) principal component analysis: strategy Item I. Incubator-tenant service co-creation – Cronbach’s alpha 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 II. Focus strategy – Cronbach’s alpha 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.) Incubator manager .910 .871 .950 .933 .628 .540 .852 .826 189
  • 206. Table 4-9: Rotated component matrix (VARIMAX rotation) principal component analysis: human capital Item Human capital – Cronbach’s alpha 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 Incubator manager .794 .786 .766 .883 .663 .611 190
  • 207. Appendix D: Graphs assumption checks Figure 4-4: Homoscedasticity and linearity Figure 4-5: Normally distributed errors: normal P-P plot 191
  • 208. Figure 4-6: Normally distributed errors: histogram 192
  • 209. Appendix E: Robustness check – model without outliers Table 4-10: Robustness checks: hierarchical linear regression for full model without outliers Outcome Constant CONTROL VARIABLES Incubator level Year operations Size (m2) Occupancy rate Focus strategy Country level TEA index DIRECT EFFECTS Human capital Regulative dimension Cognitive dimension Normative dimension Model 8a - without outliers based on occupancy rate CoCr B 3.257*** (.155) Model 8b - without outliers based on occupancy rate CoCr B 3.243*** (.157) .025 (.027) .014 (.104) .202* (.115) .087 (.106) .026 (.027) .010 (.105) .211* (.116) .067 (.109) -.435** (.177) -.399* (.182) .342* (.150) .627** (.231) .128 (.226) (p = .286) -.134 (.273) .298* (.159) .533* (.248) .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 2 Adjusted-R .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 cocreation (dependent variable), are mean-centered. 193
  • 210. Appendix F: Non-significant interaction effects Figure 4-7: Interaction human capital and cognitive dimension Figure 4-8: Interaction human capital and normative dimension 194
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  • 217. 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. 201
  • 218. 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 indepth 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 202
  • 219. 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 cocreation 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 203
  • 220. 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 indepth 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 204
  • 221. 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. 205
  • 222. Figure C-1: Research questions, main contributions and link between the qualitative and quantitative research 206
  • 223. Figure C-1 : Research questions, main contributions and link between the qualitative and quantitative research (continued) 207
  • 224. 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 208
  • 225. 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 cocreation 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. 209
  • 226. the setting up of a stimulating regulative framework appeared to positively influence cocreation. 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 subprocesses (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 210
  • 227. 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 (SaunerLeroy, 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 211
  • 228. 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. 212
  • 229. 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 73.2% -0.294 Figure C-3: Interaction human capital and regulative dimension 213
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  • 233. 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 technologiespecifieke 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 217
  • 234. 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 218
  • 235. 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 219
  • 236. 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 cocreatie 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 220
  • 237. 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. 221