The Role of Business Incubators and Accelerators in The Development of Firms

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C.I.F. G: 59069740 Universitat Ramon Llull Fundació Rgtre. Fund. Generalitat de Catalunya núm. 472 (28-02-90)

The Role of Business Incubators and Accelerators in The Development of Firms

Jorge Vinicio Murillo Rojas http://hdl.handle.net/10803/687433

Data de defensa: 13-12-2022

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C.I.F. G: 59069740 Universitat Ramon Llull Fundació Rgtre. Fund. Generalitat de Catalunya núm. 472 (28-02-90)

DOCTORAL THESIS

Title The Role of Business Incubators and Accelerators in The Development of Firms

Presented by Jorge Vinicio Murillo Rojas

Centre Esade Business School

Department Department of Strategy and General Management

Directed by Dr. Jan Brinckmann

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Entrepreneurship changes the future.

Dr. Johan Wiklund, Professor

To God,

To my parents Jorge E. Murillo-Rojas and María-Isabel Rojas-Aguilar,

To my siblings Leonardo, Alberto, Marlene, and Francisco.

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Acknowledgements

Earning a Ph.D. required a great deal of perseverance and hard work, but the most important ingredients were the support and encouragement I got from so many people along this journey. Words cannot fully express my gratitude for the amazing support I received from them. First, I want to thank foremost my parents Jorge and María-Isabel for their unconditional love and support of all my endeavors. Los amo con todo mi corazón y toda mi alma! I would also like to thank my beloved siblings Leonardo, Alberto, Marlene, and Francisco, as well as the rest of my family for being always there for me. I will be there for you all my life.

I am extremely grateful for Pere, Raquel, and Emilio, my “guardian angels” in Barcelona.

Thank you for welcoming me into your homes, for your genuine affection, and for all the care you have shown me over the years. Also, I would like to thank my Ph.D. family at Esade: María, Eirini, Khaled, Nathania, Rocío, Aya, and Harris. As I told you when we first met, I would not have made it without your support. Also, many thanks to my fellow Ph.D.

students at Esade from the senior cohorts, my own cohort, and the junior cohorts. What an amazing opportunity to learn from you all! I will never forget your friendship and support, especially during difficult times after Covid-19.

I would also like to thank my friends in Costa Rica and all over the World. Your continuous interest in me, your encouraging words, and our conversations have been crucial to my advancement. Special thanks to my colleague and friend, Andrey Elizondo, who is a doctorate student at The University of Edinburgh. Your close friendship, even being faraway, has made this experience considerably more enjoyable.

My thesis would not have been possible without the outstanding guidance and help of my Ph.D. supervisor, Dr. Jan Brinckmann. I consider it a tremendous privilege to have studied under your supervision, Jan. During difficult times, you were tremendously supportive, and you always maintained a truly optimistic attitude, which continues to inspire me. I would also like to thank my mentor Dr. Marc van Essen for sharing his knowledge and experience performing and publishing articles based on meta-analysis. I appreciate your hospitality

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throughout my visiting period at The University of South Carolina. Thanks also to Dr. Jaume Villanueva for being an insightful mentor and supervisor during the MRes program at Esade.

I would like to express my gratitude to my professors at Esade for providing me rigorous training and invaluable advice and feedback during the Ph.D. program. I especially thank to Dr. Vicenta Sierra for being always friendly, supportive, and generous. I really appreciate your outstanding work as director of the Ph.D. program. Also, thanks to Ms. Pilar Gallego, Miss. Silvia Espin, and Mr. Josep Alias for your administrative support during my time at Esade.

This thesis would be twice as lengthy if I attempted to acknowledge every person who has helped me on this journey. However, I do not want to conclude this section without acknowledging the help I got from my professors, colleagues, and friends back home and in the United States during the Ph.D. application process. I hold every letter, proofreading, piece of advice, and word in my heart with gratitude.

Finally, I would like to acknowledge the generous financial support of Esade Business School, Incae Business School, and The Esade Entrepreneurship Institute, as well as the information provided by the Entrepreneurship Database Program at Emory University, which was supported by the Global Accelerator Learning Initiative.

My sincere appreciation to everybody! Pura vida!

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Abstract

Business incubators and accelerators have become popular players in the modern entrepreneurial ecosystem. However, they are a group of heterogeneous organizations whose effectiveness in enhancing the performance of new firms by providing resources has not been demonstrated. The current literature on this subject is fragmented and based on contradictory empirical findings. Moreover, these support organizations have yet to be exhaustively examined for how they influence a new venture’s success. This doctoral thesis synthesizes the extant quantitative literature on the effects of incubators and accelerators on firms’ performance, showing the divergent effects of these support organizations on different prominent firms’ performance dimensions. Furthermore, this thesis accounts for incubators and accelerators heterogeneity to shed further light on significant factors that impact the extent to which new firms acquire resources and improve their performance through incubators and accelerators. Lastly, this thesis hypothesizes and tests an additional function that accelerators play in the early phases of a venture’s resource acquisition, in addition to providing resources directly to the firm. Overall, this doctoral thesis aims to leverage a resource-based perspective and its interrelations with resource dependency and signaling theories to understand how incubators and accelerators impact the performance of a venture.

The major arguments are anchored on both theory and empirical evidence, resulting in a strategy well-suited to make substantial contributions to the study of incubators and accelerators.

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List of Tables

Table 1. Bivariate Results: Effect of I&A on Different Outcomes and Different Types of

Programs 41

Table 2. Meta-regression Analysis: Moderating Impact of Different Outcomes and

Different Program Types 42

Table 3. Effect Sizes by Program Type and Performance Outcome 43

Table 4. Summary of Hypothesis and Results 43

Table 5. Bivariate Results: Moderating Impact of Mechanisms, Ownership, Industry

Specificity, and Firm Age 83

Table 6. Meta-regression Analysis: Moderating Impact of Mechanisms, Ownership,

Industry Specificity, and Firm Age 85

Table 7. Summary of Hypothesis and Results 86

Table 8. Effect Sizes by Moderating Subgroups and Performance Outcome 92 Table 9. Effects on Different Performance Dimensions by Type of Support 93 I Am Not Like Everyone Else! The Moderation Influence of Accelerators on Startup’s

Table 10. Probit Estimates in Heckman’s Two-stage Models 125 Table 11. Marginal Effect Analysis: Acquisition of External Equity Probability by

Participation in Accelerators and Signal 127

Table 12. Contrast Analysis: Comparison of External Equity Likelihood by Participation

in Accelerator and Signal 130

Table 13. Summary of Hypotheses and Results 130

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List of Figures

Figure 1. Model of Studied Relationships: I&A Participation Impact on a New Firm

Performance 29

Figure 2. Model of Studied Relationships: Moderating Effect of Mechanisms, Ownership,

Industry Specificity, and Firm Age 71

I Am Not Like Everyone Else! The Moderation Influence of Accelerators on Startup’s

Figure 3. Model of Studied Relationships: Moderating Impact of Accelerators on The

Signaling Effect on Equity Investment 111

Figure 4. Accelerator Influence on The Change of Marginal Effects by Signals 128 Figure 5. Comparison of External Equity Likelihood by Participation in Accelerator and

Signal 129

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Chapter 1 General Introduction

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Governments, universities, and organizations have promoted entrepreneurship as a critical factor for innovation, employment, and economic growth (Baumol, 2010; Bergek &

Norrman, 2008; Schumpeter, 1934). In the context of new venture creation, a firm’s success is contingent on the resources it can acquire (i.e., money, equipment, human resources, and technology) (Villanueva et al., 2012). Therefore, acquiring external resources is a crucial aspect of entrepreneurship (Aldrich & Martinez, 2005). However, due to scale and novelty disadvantages (Aldrich & Auster, 1986; Freeman et al., 1983; Stinchcombe, 1965), emerging firms face difficulties securing the resources necessary for survival and growth.

Consequently, several sorts of entrepreneurial assistance programs (i.e., incubators, accelerators, technology parks, and science parks) have emerged to give emerging firms below-market-cost access to resources (Hanlon & Saunders, 2007; Hausberg & Korreck, 2020). Science parks or technology parks are often meant to support mature firms.

Alternatively, business incubators and accelerators (I&A) focus on ventures in their early phases of development (Bergek & Norrman, 2008).

Globally, I&A have become two of the most prominent forms of supporting organizations (Peña, 2004; Venâncio & Jorge, 2021). Allen and Rahman (1985) define business incubators as “centers that help young companies to grow in their early stages by providing them with rental space, shared office, and assistance through business consulting services.” In contrast, business accelerators are “fixed-term, cohort-based programs providing education, monitoring, and mentoring to startup teams and connecting them with experienced entrepreneurs, venture capitalists, angel investors and corporate executives and preparing them for public pitch events in which graduates pitch to potential investors” (Cohen, 2013a;

Cohen & Hochberg, 2014; Hochberg, 2016).

Although I&A are similar organizations, they have distinguishing characteristics. For example, commonly, accelerators often provide seed funding and assistance to selected startups in exchange for equity (Cohen et al., 2019; Gonzalez-Uribe & Leatherbee, 2018).

In contrast, incubators frequently offer real estate and support services subsidized through government funding. Additionally, incubators are typically not-for-profit support organizations (Cohen, 2013b; Mason & Brown, 2014). Moreover, while incubators commonly aim to foster the early stages of firm creation, accelerators aim to select the most promising new firms and foster their rapid development. Therefore, the selected new firms in accelerators are supported as a cohort for a limited period.

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1.1. Problem Definition

In the last two decades, the number of I&A has increased significantly worldwide (Hochberg, 2016; Mungila-Hillemane et al., 2019; van Weele et al., 2020) and I&A-related research has been gaining momentum (Albort-Morant & Ribeiro-Soriano, 2016; Hausberg

& Korreck, 2020). However, the literature on this subject is fragmented, heterogeneous, and contradictory. Consequently, empirical investigation of the I&A phenomena has not produced robust and conclusive evidence that they impact the performance of supported firms (Albort-Morant & Ribeiro-Soriano, 2016; Bergman & McMullen, 2022; Phan et al., 2005).

Additionally, researchers have emphasized the influence of I&A on the performance of new firms (Hausberg & Korreck, 2020), but little is known about the underlying processes or mechanisms that affect certain performance dimensions of these firms (Bergman &

McMullen, 2022). Moreover, the contextual conditions that facilitate (or impede) firms’

resource acquisition in the context of I&A are not fully understood (Priem & Butler, 2001).

Thus, not only is it uncertain if I&A benefit new ventures, but it is also unknown how and under what conditions I&A can be beneficial.

Finally, while the literature on the impact of resource acquisition in the context of incubators is varied, research on the role and efficacy of acceleration programs is scarce (Hochberg, 2016), creating an opportunity and need for further research that reveals inherent mechanisms. Calls for additional quantitative studies are popular in accelerator research, particularly for large–scale studies on a broader population of entrepreneurs and from diverse geographical regions (Crișan et al., 2021). Additionally, empirical research has mostly focused on the direct relationship between resource and service provision and firm performance (Bergman & McMullen, 2022; Hausberg & Korreck, 2020). However, few studies address the simultaneous benefits I&A may have for resource acquisition processes, such as their impact on the efficacy of signaling processes to acquire external resources.

To properly assess the role of I&A as instruments for resource acquisition in the early phases of a firm, this thesis examines three intriguing aspects of this developing phenomenon:

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1. Is there evidence that participation in I&A yields positive benefits for new firms? If so, how are they benefited by I&A?

2. Under what contextual conditions and configurations do I&A benefit new firms?

3. How does participation in acceleration programs impact simultaneous resource acquisition processes in the early stages of a firm?

1.2. Theoretical Perspectives

The resource-based view (RBV) (Amit & Schoemaker, 1993; Barney, 1991) has spawned a substantial body of work in the field of entrepreneurship. The RBV emphasizes that a firm’s success relies on its internal and external resources. Consequently, securing external resources is a crucial task in new venture creation (Aldrich & Martinez, 2005). To study the effects of I&A on the performance of new firms, this thesis draws on RBV literature.

Researchers have predominantly investigated and assessed I&A phenomena through the RBV lens (Bergman & McMullen, 2022; Hausberg & Korreck, 2020; Mian et al., 2016), predicting that the provision of resources by I&A will have a favorable impact on the success of firms. However, the seemingly conflicting positive and negative empirical findings about this impact must be evaluated further (Eveleens et al., 2017).

To contribute to solving this impasse, this thesis also leverages the resource dependency theory (RDT) (Pfeffer & Salancik, 2003), which suggests that firms are vulnerable to power imbalances due their dependence on external resource providers. Firms may use distinct strategies to address external dependencies, but these strategies are rarely foolproof;

consequently, firms frequently develop new patterns of dependence. For instance, I&A’s support new firms with resources to isolate them from external resource dependencies, but new firms might create new dependencies on the I&A, and thus, they may suffer detrimental consequences on their performance.

Lastly, this thesis uses the signaling theory (Spence, 1973) to address the third research question. Signaling theory is a useful theoretical instrument to explain how observable signals indicate unobservable features and reduce the information asymmetry between two parties (i.e., the information a venture’s founder knows regarding the venture’s quality vs the information a potential investor has). Drawing on the interplay of signaling theory and the literature on venture resource acquisition, this study highlights different practices

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through which accelerators intervene in a particular resource acquisition process in the early stages of the firm.

The empirical research on I&A, driven by these theories has not yet been comprehensive enough. Existing research identifies numerous resources firms can obtain from I&A.

However, literature drawn from the abovementioned theories reveals conflicting empirical results. Generally, literature on this topic lacks more comprehensive analyses of the mechanisms driving particular observable outcomes and the contextual circumstances that moderate them, leaving room for further theoretical contributions.

1.3. Thesis Structure

This thesis is organized into six chapters: a general introduction, a body of three chapters composed of a compendium of three related essays to address the research questions, a chapter of general conclusions that analyzes the academic and managerial implications derived from the findings, and a final chapter of general references. The essay in Chapter 2 received an R&R from the Journal of Business Venturing, while the other chapters have not yet been submitted for publication. Each chapter from Chapter 2 to Chapter 4 addresses one of the interconnected research questions: First, do I&A affect the performance of the participating firms, and if so, how? Second, under what conditions and configurations do I&A benefit new ventures? Third, how does participation in acceleration programs affect other simultaneous processes associated with resource acquisition of new firms?

Chapters 2 and Chapter 3 employ meta-analytic procedures to synthesize the fragmented empirical findings on the I&A phenomena to uncover core relationships and contextual dependencies. Chapter 2 focuses mainly on understanding the efficacy of I&A in assisting early-stage firms in acquiring resources and, thus, improving their performance. Chapter 3 analyzes the contextual circumstances that impact the effectiveness of I&A support in benefiting new firms’ performance. Chapter 4 leveraged the most extensive available dataset on accelerator programs to evaluate the moderating effect of accelerators on new firms’

signaling processes for acquiring external investment. Together, these three chapters give a deeper understanding of the efficacy of the I&A phenomena as a tool to support resource acquisition in a firm’s early stages. Moreover, they offer a systematic assessment of key circumstances that moderate the efficacy of I&A programs. Lastly, they offer a robust analysis of the influence these support programs have on resource acquisition processes in

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the early phases of a firm. In the following paragraphs, it is presented a briefly explanation of each chapter’s content in the body of this thesis.

Chapter 2 combines prior empirical literature on I&A to explore whether and how participation in these programs improves new firms’ performance. I&A aim to provide valuable resources to new firms and strengthen their resource base directly or through their extended network (Peters et al., 2004; van Weele et al., 2017). However, studies investigating the influence of I&A programs on supported new ventures have yielded mixed and contradictory findings. Strikingly, until now, no researcher has created a systematic quantitative synthesis of the various empirical findings on the performance effects of participation in I&A programs. Consequently, research lacks systematic evidence on whether and how I&A impact new firms’ performance. Drawing on resource-based arguments (Amit & Schoemaker, 1993; Barney, 1991) and related literature, this chapter anticipates I&A’s provision of resources to have a positive effect on the supported new firms’ performance. Furthermore, to enhance the understanding of which performance dimensions are affected and how, this chapter theorizes the differential effects I&A have on distinct performance dimensions.

Previous empirical literature was synthesized to test the hypotheses in Chapter 2 using meta- analytic techniques, which are relevant when empirical literature remains inconclusive (Brinckmann et al., 2010; Hedges & Olkin, 1985; Rosenbusch et al., 2013). Overall, this study finds participating in business I&A programs to have a positive influence on firm performance. However, the results suggest this influence varies depending on the performance dimensions and type of program examined. The study concludes that I&A effectively facilitate resource acquisition in the early stages of the firm and can improve its overall performance; however, the effect differs for accelerators and incubators and is not uniform across all performance dimensions. Therefore, Chapter 3 explores contextual circumstances and configurations that moderate the effectiveness of I&A in providing resources and services to new ventures.

Chapter 3 investigates how distinct characteristics of I&A and firms influence the impact of I&A support on the performance of participating firms. For instance, I&A characteristics such as the ownership or the industry focus can impact their design features, and thus, the type of resources they provide (Cohen et al., 2019; Dutt et al., 2016). Little is known about

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the different sorts of I&A and how these distinctions and the resources these organizations provide affect supported ventures’ performance dimensions, such as innovation, survival, growth, profits, and employment. Drawing on the RBV, this research scrutinizes performance differences resulting from the diverging characteristics of I&A organizations.

This study employs a quantitative meta-analysis methodology to test the predictions. Unlike Chapter 2, which analyzes the relationship between participation in I&A and its effect on firms’ performance, Chapter 3 is a meta-analysis of primary studies that measure the effect of any I&A service or resource (e.g., mentoring, office space, financing) or set of I&A services or resources on a firm’s performance. Although it is a subtle difference, it allows increasing the sample size of primary studies, resulting in a more diverse sample of firms and I&A. Additionally, the primary studies used in Chapter 3 facilitate a more detailed analysis of the types of support (i.e., resources and services) offered by the I&A.

Findings in Chapter 3 suggest that the effect of I&A support on a firm’s performance is contingent on the sponsorship mechanism (i.e., bridging or buffering). Moreover, ownership of the I&A influences the supported firms’ performance, possibly as a result of its effect on the program’s goals, designs, and hence the type of resource given to firms. In this chapter, the synthesis and analysis of prior empirical literature improve the contextual understanding of the I&A support and firm performance relationship and contribute to reconciling previous contradictory findings on how and under what circumstances I&A support has a more substantial effect on firm performance. Additionally, this study compares which type of resources and which I&A characteristics most affect the different performance dimensions.

The findings provide key insights to identify mechanisms and conditions in which I&A favors particular outcomes.

Chapter 4 deepens the inquiry into a specific resource acquisition process in the context of acceleration programs. The studies in Chapter 2 and Chapter 3 reveal a lack of quantitative empirical studies on business accelerators (Casanova et al., 2017; Hochberg, 2016; Pauwels et al., 2016). Chapter 4 analyzes the accelerators’ influence on the effectiveness of entrepreneurs’ signals for acquiring equity investment. This study draws on signaling theory and literature on financial resource acquisition and explores the moderating role of accelerators on the effectiveness of signals for attracting equity investment. While signals can enhance the decision-maker’s capacity to make informed decisions by reducing information asymmetry (Spence, 1973), not all signals effectively solve this issue. Scholars

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have identified third-party affiliations with other organizations as a factor that enhances signaling effects on resource acquisition (Plummer et al., 2016). However, these studies focus on the signal-endorsing role of these organizations and have not explored other relevant practices such as training, connectedness, and exposure that may also boost signals’

observability and credibility and, thus, their effectiveness. Furthermore, these studies only analyzed the interaction of a few signals with developing organizations such as accelerators.

Chapter 4 reveals that accelerators’ support exceeds direct support through resources and services. This study utilizes the most comprehensive dataset on acceleration programs, including almost 23,000 new ventures from 175 countries that applied to 408 acceleration programs between 2013 and 2019. Employing the two-stage Heckman method (Heckman, 1979) to overcome selection bias constraints, this study uses a quantitative methodology based on probit regression, marginal effects, and contrast analysis. The results provide empirical evidence to improve understanding of the moderating role of accelerators in signal effectiveness, which extends the theory on the conditions under which signals influence investment in early-stage firms. Furthermore, the proposed moderating practices highlight how accelerators influence the observability and credibility of signals in financial resource acquisition processes.

Overall, this thesis offers several contributions for the I&A literature. Using meta-analysis techniques, this work combines the extant quantitative literature on the performance effects of I&A programs and provides evidence regarding how resource acquisition through these programs impacts positively new firm success. Additionally, this thesis unpacks the diverging effects of I&A in distinct prominent firms’ performance dimensions, which can help to reveal specific mechanisms of how these programs impact different venture’s performance indicators. Moreover, building on the RBV and the RDT, this thesis shows that the positive effect of resource acquisition through I&A is contingent on the configuration of the support organization, the sponsorship mechanism employed, and the ownership of the I&A. As a result, this research work reconciles prior contradictory findings by showing some conditions affecting the I&A support effect on a firm’s success. Lastly, building on previous literature on entrepreneurial signaling processes to acquire resources, this thesis suggests that besides delivering resources directly to the venture, business accelerators play an additional role in venture’s resource acquisition. These programs can moderate the effect of entrepreneurs’ signals for acquiring equity investment. In addition to the endorsement

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practice, this study proposes that accelerators moderate the observability and credibility of signals to attract external resource investment via three additional practices: training, connectedness, and exposure.

The three essays in the following chapters constitute the body of this doctoral thesis. Each chapter provides information on the research gap, research question, methods employed, findings, and conclusions. A final chapter in this thesis synthesizes the overall general conclusions, implications, and limitations. References are included at the end of each chapter.

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Chapter 2 Hatching and Fledging? A Meta-Analysis of the Performance Effects of Business

Incubators and Accelerators

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2.1. Introduction

Incubators and accelerators (I&A) have become popular instruments that aim to stimulate innovation, employment generation, and economic progress through the creation and growth of new firms (Bergek & Norrman, 2008; Lukeš et al., 2019). I&A aim to provide valuable resources such as office space, equipment, business services, technical and management knowledge, advice, and networking possibilities (Cohen, 2013a; Grimaldi & Grandi, 2005;

Hackett & Dilts, 2004) to resource-scarce new firms facing high risks of failure (Keupp &

Gassmann, 2013; Stinchcombe, 1965). Because of the expected benefits of I&A, vast amounts of resources have been invested in offering such programs by governments, universities, research institutions, municipal agencies, and other interested parties (Bergek

& Norrman, 2008). Meanwhile, founders also have high expectations about the I&A’s effect on their firm’s performance (i.e., venture survival, growth, and profits, among others) (Lukeš et al., 2019). As a result, the number of I&A has increased substantially worldwide and, with them, the supported firms (Amezcua et al., 2013; Hughes et al., 2007; Peña, 2004). However, questions remain whether and how I&A affect the performance of the participating firms and how the diverging contexts and configurations shape their effectiveness.

Literature on the liability of smallness highlights that new firms have an incomplete resource and capability base and thus are vulnerable to environmental changes (Aldrich & Auster, 1986; Freeman et al., 1983). Further, they face a liability of newness as they face a lower perceived legitimacy by their potential customers and are likely operating inefficiently due to the lack of experience of the founders (Stinchcombe, 1965). Hence, as these firms acquire resources to complement their limited resource base, they face adverse conditions resulting in a greater risk of failure for these new and small firms vis-à-vis their more established, larger counterparts. Moreover, from a resource-based perspective (Amit & Schoemaker, 1993; Barney, 1991), business performance relies on internal and external resource dimensions that form the basis of a company’s competitive advantage. While many resources are located within the firm, scholars have indicated that crucial resources are also frequently provided externally (Dyer & Singh, 1998; Lavie, 2006). I&A aim to address the liabilities of newness and smallness of new firms by providing new ventures with ample resources (Albert & Gaynor, 2000; Amezcua et al., 2020; McAdam & McAdam, 2008); fostering the development of their new, innovative offerings and in consequence; benefiting the development of the participating new firms.

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A substantial number of scholars aimed at empirically assessing the performance effects of I&A (e.g., Becker & Gassmann, 2006; Chen, 2009; Lasrado et al., 2016; McAdam &

McAdam, 2008). Drawing on resource-based arguments, some scholars have identified positive performance effects of I&A considering both: tangible resources provided, such as funding, office space, or equipment (Hallen et al., 2020; McAdam & McAdam, 2008; van Rijnsoever et al., 2017) and more intangible resources, such as technical or managerial knowledge (Bøllingtoft & Ulhøi, 2005; C. E. Cooper et al., 2012; Rice, 2002; Rothaermel &

Thursby, 2005). Others show how startups overcome the liability of newness and acquire legitimacy by associating themselves with a reputable organization such as I&A (Lasrado et al., 2016; Rao et al., 2008; Scillitoe & Chakrabarti, 2005; van Weele et al., 2020).

Nevertheless, another group of scholars has challenged whether firms’ participation in I&A improves their performance (Amezcua et al., 2013; Dvouletý et al., 2018; Lerner & Haber, 2001; van Weele et al., 2017). For example, Lerner and Haber (2001) pointed out that ventures that did not receive support from government incubators performed better than those that participated in such programs. In the analysis of 606 incubated firms in Italy, Lukeš et al. (2019) found a significant negative effect of incubator tenancy on sales revenues and a non-significant effect of incubation on job creation. Moreover, Yu (2020) observed that accelerated new firms fail earlier and more frequently than non-accelerated firms in a sample of 1,800 new firms. Furthermore, e.g., Schwartz (2013) found no evidence that incubated new firms had a greater chance of survival than non-incubated firms over ten years. Instead, some supported new firms even had a reduced likelihood of survival.

In sum, extant findings on the performance effects of I&A remain fragmented and contradictory (Albort-Morant & Ribeiro-Soriano, 2016; Bergman & McMullen, 2021; Phan et al., 2005). Some researchers highlight positive effects of I&A participation on firm performance (Niammuad et al., 2013; Schwartz, 2013; van Rijnsoever et al., 2017), other investigators have found only marginal effects (Colombo & Delmastro, 2002), while others document adverse performance effects for the supported firms (Dvouletý et al., 2018; Lerner

& Haber, 2001). Strikingly, until now, no systematic quantitative synthesis of the various empirical findings assessing the performance effects of the participation in I&A programs exists. Consequently, research lacks systematic evidence about whether and how I&A impact new firms’ performance.

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We identify two issues in the literature that may have contributed to these inconclusive findings. First, heterogeneity in previous studies of incubated and accelerated firms’

performance might be explained by the wide range of performance measures utilized and discrepancies in how researchers operationalize and assess these outcomes (Bergek &

Norrman, 2008; Bergman & McMullen, 2021; Eveleens et al., 2017; Mungila-Hillemane et al., 2019). Combining measures such as innovation, survival, profitability, and revenue and employment growth likely obscures the specific mechanisms and outcomes that participation in I&A programs implies. Outcomes will also likely vary, given the heterogeneity of performance conceptions and measurements. Hence, a synthesis of findings needs to systematically aggregate and differentiate between diverging performance measures to foster our understanding of how participation in these programs can affect the development of new firms.

Second, while incubators and accelerators are programs designed to provide core resources to fledgling ventures, they vary in their fundamental design (Cohen, 2013b; Pauwels et al., 2016). Incubators are typically focusing on supporting recently formed firms in physical locations from the early days over an extended period - commonly years. In contrast, accelerators focus on supporting ventures that have demonstrated initial promise and are then selected to be supported over a briefer period, commonly two or three months.

Accelerators usually rely less on the physical collocation of the participating ventures but rather allow for founders’ cohort interaction through joint program activities. Given their diverging focus and configuration, it appears important to scrutinize how the actual configuration of the support program affects outcomes along the different performance dimensions. Such an analysis enables a better understanding of when and how the provision of resources can affect the development of new firms.

Our research uses meta-analysis to investigate the relationship between firms’ participation in I&A and their performance. Meta-analyses have become increasingly popular in entrepreneurship and management research because they aggregate extant fragmented empirical findings. They provide an overview and a more robust assessment of the overall effect strengths and uncover novel characteristics that affect the intensity of core relationships, for instance, by analyzing content and method characteristics of the underlying studies (Miller & Cardinal, 1994). In doing so, meta-analysis can provide insights that go beyond the findings an individual underlying study can provide (Rosenbusch et al., 2013).

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In this meta-analysis, we contribute to extant research on the performance effects of I&A in different ways. First, we synthesize the extensive quantitative studies on performance effects of I&A. This helps to advance research as we can depict the main areas of investigation and research areas that need further empirical exploration. Second, we provide a robust initial assessment of the participation in I&A on different prominent performance outcomes. We specifically scrutinize the diverging effects that I&A cause for the participating firms.

Moreover, we contrast performance dimensions that are particularly influenced by the availability of resources, such as innovative activity (Audretsch, 1995), with other dimensions typically assessed after I&A processes (Bergman & McMullen, 2021; Eveleens et al., 2017). Such an analysis can help uncover specific mechanisms and areas in which the support is substantiated and other areas not benefiting from the participation. Hence, our analysis also allows identifying opportunities for further support of I&A. Third, we contrast the diverging effects of incubators vis-à-vis accelerators on the different performance dimensions. Such an analysis depicts how the common configurations of incubators and accelerators shape the performance outcomes and, thus, how other outcomes may be achieved by redesigning or complementing existing support programs. This analysis also uncovers opportunities to enhance the support of new firms in incubators vis-à-vis accelerators. Our resulting research framework is presented in Figure 1.

Figure 1. Model of Studied Relationships: I&A Participation Impact on a New Firm Performance

I&A participation

New Firm performance

- Analysis 1: Diverging Performance Outcomes: Innovation vs other Performance Outcomes - Analysis 2: Innovation performance deep-dive:

Innovation inputs vs innovation outputs

Incubators vs. Accelerators - Analysis 3: Exploration of their diverging performance effects

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2.2. Theoretical Background and Hypotheses

2.2.1. Participation in Incubators and Accelerators and Firm Performance

Scholars have a tradition to highlight that a firm’s resource base and the management of their resources affect firms’ success (Barney, 1991; A. D. Chandler, 1977; Nelson & Winter, 1982; Penrose, 1959; Schumpeter, 1934; Teece et al., 1997). This argument is especially salient for new firms facing the liability of smallness (Aldrich & Auster, 1986; Freeman et al., 1983) and the liability of newness (Freeman et al., 1983; Stinchcombe, 1965). When a new venture enters a market, it suffers from a competitive disadvantage as a result of a lack of customer loyalty (Porter, 1980), a lack of legitimacy as a viable provider of a product or service, and a lack of legitimacy to obtain the necessary capital and other resources to establish themselves and grow (Aldrich, 1999). Moreover, smallness and newness reduce the chances of survival and growth of new firms when the entrepreneur lacks adequate managerial skills and experience (Fairlie & Robb, 2008; Shepherd et al., 2000) to acquire and leverage the resources. Thus, new firms may be unable to compete effectively against these more established firms and face challenges acquiring or controlling needed resources, leading to high new firm failure rates and other negative performance outcomes (Nason et al., 2019).

To mitigate the risks of newness and smallness of the fledging firms and their adverse consequences, I&A were created to provide valuable resources to the new firms and strengthen their resource base directly or through their extended network (Peters et al., 2004;

van Weele et al., 2017). I&A provide valuable resources such as physical space or financial support (Peters et al., 2004; van Weele et al., 2017). Those programs also aim to improve the management of the new firms’ resources by providing information and knowledge, as well as training and mentorship for the founders, assisting in recruiting additional human capital and providing external managerial support (Assenova, 2020; Blank, 2021; Woolley

& MacGregor, 2021). Thus, some research suggests that the provided resources and support positively affect the success of new firms (Castrogiovanni, 1991; G. N. Chandler & Hanks, 1994; Starbuck, 1976). Literature enumerates several explanations for this positive effect of I&A on new firm performance.

First, early-stage firms pass through resource acquisition processes that allow them to obtain financial resources, space, technical and administrative support, and other types of resources

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that improve their performance (Rawhouser et al., 2020; Yli-Renko et al., 2001). I&A help new firms assemble and complement their initial resource-based and develop basic operating routines, providing the conditions for the firm to preserve its capital and diminish the resources needed to start and maintain its activities (Amezcua et al., 2013). Therefore, I&A fundamentally aim to provide a rich environment in which new firms acquire the resources they need, especially in areas with significant resource gaps (Breivik-Meyer et al., 2020;

Rice, 2002).

Second, I&A support the new firms to develop relationships with other actors, which in turn can facilitate the further provision of resources for the new firm (Granovetter, 1985). For example, in I&A programs spatial proximity allows tenants to create network relationships to obtain insights, learn, and acquire other resources based on personal and business relationships. In other words, I&A facilitate the new firm’s connections with internal actors and foster an internal network, which increases the new firm’s possibilities to acquire resources controlled by others (Breivik-Meyer et al., 2020). Also, I&A provide the new firm with a variety of connections with investors, mentors, and other external actors (Amezcua et al., 2013; Assenova, 2021; Cohen et al., 2019) who support them with financial resources, knowledge, and other types of resources. Hence, internal and external networks, developed by participating in I&A programs, might facilitate and gain access to resources that new ventures do not control in the early stages (Adler & Kwon, 2002; Groen et al., 2008). Given the established social ties and the frequent mutual support orientation, these resources can be frequently accessed below market costs (Brinckmann et al., 2019; Davidsson & Honig, 2003; Newbert et al., 2013).

Lastly, I&A not only help the new venture to deal with resources deficiency directly and through the establishment of network ties but also enhance legitimacy of the new firm (Lasrado et al., 2016; Rao et al., 2008; Scillitoe & Chakrabarti, 2005; van Weele et al., 2020).

Legitimacy is a critical asset that enables the new venture to secure the necessary capital and other resources to establish themselves and grow (Aldrich, 1999).

Taken together, I&A’s resources and services to new firms aim to counteract their liability of smallness and newness. I&A foster the resource acquisition, which in consequence benefits new firm development with regards to pursuing innovation, survival, growth, profitability, and employment, which are the most frequent performance outcomes

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highlighted by entrepreneurship scholars (Brinckmann et al., 2010; Eveleens et al., 2017;

Honig & Karlsson, 2004).

2.2.2. Overall Diverging Performance Effects

While there might be an overall positive outcome on new firm development based on the relationship highlighted above, inconsistencies in outcome measurements means the effects may significantly diverge, which help explain the previous contradictory findings (Ayatse et al., 2017; Bergek & Norrman, 2008; Bergman & McMullen, 2021; Eveleens et al., 2017;

Mungila-Hillemane et al., 2019).

Given that I&A predominantly focus on supporting firms in the early stages of firm development, a core focus is the actual development of the new products, services, and/or a new approach to offer these products and services (Allen & Mccluskey, 1991; Cohen et al., 2019; Lukeš et al., 2019; Mungila-Hillemane et al., 2019). Hence, an emphasis on achieving innovation-related outcomes appears salient even as this may come at the expense of forgoing profitability. The focus on innovation implies augmented costs, leading to negative cash flow and thus having to finance losses which are commonly labeled as new firm’s

“burn-rate.” Thus, the focus on innovation outcomes could come at the cost of increasing the new firm’s insolvency risk as more innovative offerings imply greater risks (Colombo &

Delmastro, 2002; Löfsten, 2014; Rosenbusch et al., 2011). Furthermore, as the new products and services are still under development and the product-market-fit is not proven, it might be premature to accelerate the growth of the firm at this stage. Thus, we identify an important trade-off between a focus on innovation and growth-related outcomes for the new firms.

Considering that new firms have scarce resources and carefully have to consider how to deploy their limited resource base, we expect that the participation in I&A will have a stronger effect on innovation outcomes than other dimensions such as survival, profitability, and growth. To draw attention to this important mechanic regarding the participation in I&A and resulting outcomes, we put forth:

H1: The positive relationship between incubation and acceleration participation and the innovation performance of the participant firms is stronger than between incubation and acceleration participation and the other performance measures.

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2.2.3. Innovation Performance Effect Differences

Given the expected importance of innovation outcomes for new firms as they participate in I&A, we can further distinguish different dimensions of innovation outcomes. Innovation activity can be modeled as a process (Parthasarthy & Hammond, 2002; Saren, 1984) where some factors are inputs to the innovation process (e.g., funds for R&D, number of researchers), and other factors can be considered outputs of the innovation process (e.g., number of patents, new products, or new services). Given that I&A researchers study both input measures (Colombo & Delmastro, 2002; Gorączkowska, 2017) and output measures (Breznitz & Zhang, 2019; Colombo & Delmastro, 2002; Rothaermel & Thursby, 2005;

Rydehell et al., 2019), it is important to discern further these innovation measures to uncover how specifically I&A might foster the innovation efforts of the new firms.

From a theoretical perspective, I&A provide critical inputs to firms’ innovation activities such as R&D, technical, financial, and other management resources (Colombo & Delmastro, 2002). However, although new firms may obtain these inputs for their innovation activities, a high degree of reciprocal integration (functional integration, tool integration, external integration) is required in the innovation process to transform technologies and other resources into innovation outputs (Parthasarthy & Hammond, 2002). This complex integration of resources in the innovation process is challenging to achieve due to the extensive coordination systems required, leading to a risky and uncertain achievement of innovation outcomes (Duran et al., 2016; Itami & Roehl, 1987; Tushman et al., 1997;

Williamson, 1985). For those reasons, we expect that while I&A programs can more directly and immediately affect the provision of innovation inputs, the effects on innovation outputs are less certain. For instance, I&A might be able to provide laboratory spaces, an environment to access and collaborate with researchers, or a network of investors to fund innovation activities. However, they may be less able to assure that these resources are used effectively to generate innovation outputs. For instance, by creating a rich innovation input environment, the I&A may reduce the perceived urgency of the founders to deliver innovation outputs.

Strikingly, however, initial empirical evidence found only marginal differences between innovation input and innovation outputs of incubated firms (Colombo & Delmastro, 2002), while other initial empirical evidence points to inconsistent results (Monck et al., 1988; Paul Westhead, 1997; P. Westhead & Storey, 1994).

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To draw more attention to this important performance effect distinction and develop a better understanding of the actual performance outcome achieved in the innovation domain, we hypothesize that:

H2: The positive relationship between incubation and acceleration participation and the innovation input is stronger than between incubation and acceleration participation and the innovation output.

2.2.4. Performance Effect Difference of Incubators Versus Accelerators

As depicted above, I&A serve as connections between startups and a complex environment of resource providers. However, I&A are facilitating organizations that differ in their configuration, which has important implications for when and how they provide the resources to new firms.

Incubators commonly offer real estate to the new firms in their early years of existence. This real estate is offered at attractive rates to support new firms, which have limited resources to afford more expensive office space. By collocating the new firms in one location, the supported firms can then network to share and exchange experiences and resources (Amezcua et al., 2013; Bøllingtoft & Ulhøi, 2005; Clayton et al., 2018). Further, the incubators frequently seek to provide other value-added services to the tenant companies, such as coaching and mentorship program (Bergek & Norrman, 2008; Dutt et al., 2016).

Moreover, a common characteristic of incubators is that the real-estate and support services are subsidized through government funding, and the incubators themselves are commonly non-for-profit support organizations (Cohen, 2013b; Mason & Brown, 2014). Due to their broader mission to foster local economic development, some researchers observed that participation in incubators is often not as competitive or selective (Cohen, 2013b; Mason &

Brown, 2014).

In contrast, accelerators commonly provide seed capital and support to selected startups in exchange for equity (Cohen et al., 2019; Gonzalez-Uribe & Leatherbee, 2018). The accelerators select new firms that have been in existence for some time and have shown some promising early development signals. The selected new firms are then supported as a cohort for a limited time duration of two or three months. The participating firms join in offline formats, often in one location or online, while the employees can commonly remain

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in their original location. The acceleration programs generally finalize with a demo-day in which the participating new firms are presenting to an investor’s audience in a competitive setting.

The diverging characteristics of I&A can be expected to result in diverging performance effects for the new firms. Thus, we propose that the performance of firms participating in incubation programs is different from those participating in acceleration programs:

H3a: The type of program moderates the positive relationship between incubator and accelerator participation and the new firms’ performance, such that firms that participate in accelerators perform better than those that participate in incubators.

Furthermore, while incubators commonly aim to foster the early stages of firm development, accelerators aim to select the promising new firms and foster their rapid development. Given the focus on earlier stages of development, incubators can be expected to have a greater focus on innovation (Breivik-Meyer et al., 2020; Sedita et al., 2019) and especially on new firm survival (Schwartz, 2009, 2013) as non-surviving new firms would reduce the rental income of the incubators (Chan et al., 2020; Cohen, 2013a). In contrast, accelerators can likely more selectively pick the more promising new firms that have already achieved some early development progress or show other promising signals (Assenova, 2021; Cohen, 2013b). Thus, the aim of accelerators is rather on generating new firm growth. Since accelerators aim for a successful demo-day in which the participating firms can obtain funding, profitability is likely not a dominant goal. As accelerators take equity in contrast to incubators, their focus is to assure that the participating new firm achieves high valuations in subsequent investment rounds and exits. These arguments further underline that the focus of accelerators is likely on firm growth, which is a core predictor of firm valuations (Chan et al., 2020; Cohen et al., 2019). Drawing more attention to likely diverging performance effects of incubators vs. accelerators, we put forth the following exploratory hypotheses:

H3b: Incubators have a greater effect on innovation, survival, and profits than accelerators.

H3c: Incubators have a lesser effect on growth than accelerators.

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2.3. Method

Our research uses an evidence-based research approach drawing on meta-analysis techniques (Hedges & Olkin, 1985; Hunter & Schmidt, 2004; Lipsey & Wilson, 2001). Our meta-analysis follows salient literature on meta-analysis (Hedges & Olkin, 1985; Hunter &

Schmidt, 2004) and steps suggested by Lipsey and Wilson (2001), Schmidt et al. (2009), Huffcutt and Arthur (1995), Higgins et al. (2003), and Rosenbusch et al. (2013).

2.3.1. Study Identification and Sample

We conducted a meta-analytical study and used four complementary search strategies to identify the primary quantitative studies that analyze the I&A participation-performance.

First, we conducted a keyword search using electronic databases including Business Source Premier, Econlit, Proquest (including ABI/Inform), Web of Science – ISI, and Scopus. These databases allowed us to locate published and unpublished studies. We used following search terms: “incubat*” and “accelerator” in each paper’s title, abstract, and keywords. After eliminating the duplicated papers, we identified candidate studies by reviewing the title and abstract of each article obtained in the first step. The study needed to address the relationship between I&A participation and firm performance to be included in this meta-analysis.

Moreover, we checked that the empirical studies report statistics convertible into correlation- based effect sizes. If the study did not include the necessary statistics, we wrote to the authors and asked them to facilitate these statistics. We used Lipsey and Wilson’s effect size calculator (Lipsey & Wilson, 2001) to convert those statistics into correlations. Second, we searched all articles referenced in the retrieved studies and all articles citing them using Google Scholar and ISI Web of Knowledge. Third, we corresponded directly with I&A researchers, asking them for missing effect size information and additional studies.

Finally, in a fourth step, we removed the dependent studies that come from the same sample.

Following the procedure used in a previous meta-analysis (Rosenbusch et al., 2013) to detect repeated samples, we identified studies from the same authors. Then, we compared the sample descriptions and some relevant characteristics such as sample size and sample source.

We also compared sample characteristics of those that come from the same database. We aimed to avoid the prevalence of a specific effect size from the same sample in our meta- analysis. When two studies came from the same sample, we selected the study that used the

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largest number of firms (Hunter & Schmidt, 2004). At the end of the sampling stage, we end up with 170 distinct effect sizes from 37 articles.

2.3.2. Coding and Operationalization of Variables

After reading the articles, we developed a coding protocol (Lipsey & Wilson, 2001) to obtain data on dependent, independent, and moderator variables.

Firm Performance. The dependent variable in this meta-analysis is any indicator of the firm’s performance. The outcomes of I&A at the firm level are measured in various ways in the literature (Assenova, 2020; Ayatse et al., 2017; Hallen et al., 2020). We have coded five performance measure categories to understand better the relationship between I&A participation and venture performance: innovation performance measures, venture survival, venture growth, venture profits, and employment. Following innovation literature (Parthasarthy & Hammond, 2002) and previous meta-analysis on innovation measures (Rosenbusch et al., 2011), we classified innovation measures into input-related (e.g., investment in R&D, number of researchers) and output-related (e.g., number of patents, number of new products developed) measures.

I&A. As common in meta-analytical studies, our coding relied on the variables included in the primary studies. Since prior literature has not yet converged on a common operationalization of I&A, we coded whether the I&A of the primary study focused on (1) incubators (i.e., Rothaermel and Thursby (2005)); or (2) accelerators (i.e., Assenova (2020)).

Control Variables. We also employed multiple control variables related to the research design and the methodological approaches used by the primary studies such as published or an unpublished study, cross-sectional or longitudinal data, and whether I&A participation was coded as a dummy variable (e.g., Rawhouser et al., 2020) or if they used a continuous measure for participation time in the I&A program (e.g., Scillitoe & Chakrabarti, 2005).

2.3.3. Analyses

We used Hedges and Olkin’s meta-analysis (HOMA) to calculate the meta-analytic mean correlation between I&A participation and the firm performance and the corresponding confidence interval (Hedges & Olkin, 1985; Lipsey & Wilson, 2001). First, we coded the

The Role of Business Incubators and Accelerators in The Development of Firms

DOCTORAL THESIS

Acknowledgements

Abstract

Table of Contents

List of Tables

List of Figures

Chapter 1

General Introduction

Chapter 2

Hatching and Fledging? A Meta-Analysis of the Performance Effects of Business

Incubators and Accelerators