Utilizing one dimension of the value
proposition of Accelerators -‐ Knowledge
resource acquisition of Startups at
Accelerator Programs
An exploratory study
A Master thesis by Bekir Gündelik (10481745) Amsterdam Business School
Supervisor: Prof. Dr. J. Strikwerda August 31st, 2015
A thesis submitted for the Degree of Master of Science in Executive Management Studies, Strategy Track, at the University of Amsterdam, The Netherlands.
Preface and Acknowledgements
This thesis marks the end of my studies at the Amsterdam Business School. When I chose the subject of this thesis, I had recently lost my job and was out on self-‐discovery, trying to find out what the next step in my professional career should be. One of my conclusions was to work within a smaller company, an entrepreneurial environment and lots of responsibility on these wide shoulders. While exploring new phenomena within the setting of entrepreneurships I came along the website of an accelerator program in The Netherlands. After examining theory on acceleration, I ended with this subject.
During the writing process of this thesis, I went back and forth between having the feeling of paying the prize for not taking my responsibilities in my youth and having made awkward decisions at crucial moments. For sure, I will never regret the choice to start this part-‐time master and, of course, am pleased to put a checkmark next to it.
Starting and finishing my studies would not have been possible without the support of various, personally and professionally important, people. First of all, I would like to thank my supervisor, Prof. Dr. J. Strikwerda, who was there when I was not, and intensively advised on how to get to a better product. I would also like to thank the rest of the teaching and supportive staff at the Amsterdam Business School. Furthermore, I would like to thank the interviewees who did take time to sit for an hour and the insights they gave me.
Last but not least, I would like to thank my wife Burcu, who supported me in every decision I took, even when she totally disagreed.
Abstract
Accelerator programs are the new phenomenon of the last decade. They help startups define and build their initial products, identify promising customer segments and secure resources. In meanwhile two international accelerator programs have settled in Amsterdam. Nonetheless, the growing role they take in accelerating startups, the extant literature on knowledge acquisition at accelerator programs is extremely limited. The aim of this study is to contribute to the literature by exploring and analyzing the type of knowledge resources that startups are able to acquire during accelerator programs. The results indicate that startups are able to acquire knowledge on execution, financial and legal knowledge with regards to investments and fundraising, business knowledge and other knowledge like teamwork. Moreover, the largest knowledge resource the startups have been able to acquire, internalize and use is knowledge on execution; how to exploit a product with technological base with the use of the Lean Startup Methodology. Startups were of the opinion that the Lean Startup Methodology really helped them to accelerate and without the program they would not have been able to achieve this progress. Considering the knowledge acquisition through the mentors and the network of the accelerator program, accelerator programs show large similarities with the networked incubator.
Keywords: Incubation, Accelerators, Knowledge Acquisition, Entrepreneurship, Startup,
Statement of Originality
I declare that the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it and take full responsibility for the content of this document. The faculty of Economics and Business of the University of Amsterdam is solely responsible for the supervision of completion of this work, not for the contents.
Signature ... Bekir Gündelik
Contents
Preface and Acknowledgements 2
Abstract 3 Part I 6 1. Introduction 6 1.1 Research Question 9 1.2 Thesis Structure 10
Part II – Literature Review 11 2. Entrepreneurship 11
3. The Resource Based View 13 3.1 Entrepreneurs and Knowledge Resources 13 3.2 Absorptive Capacity of the Firm 15
4. Business Incubation 15 4.1 Business incubation 17 4.2 Networked Business Incubators 19 4.3 The New Business Incubators 21 4.3.1 Accelerators 22 4.3.2 The Accelerator Program 23 4.3.3 The Educational Program; Lean Startup Methodology 24
Part III – Research 28 5. Methodology 28 5.1 Research Context 29 5.1.1 Background 29 5.1.2 A Multiple Case Study 29 5.2 Interviews 30 5.3 Analytical Strategy 31 5.4 Data Processing 32
Part IV – Results 33 6. Results 33 6.1 Knowledge Resources 33 6.1.1 Execution 35 6.1.2 Technological Knowledge 36 6.1.3 Legal Knowledge 37 6.1.4 Business Knowledge 38 6.1.5 Financial Knowledge 38 6.1.6 Other Knowledge 40 6.2 Other Findings 40 6.2.1 Reasons to join an Accelerator Program 41 6.2.1.1 Access to Network(s) 42 6.2.1.2 Investment 43 6.2.2 Startup progress during Accelerator Program 46 6.2.3 Control; Decision-‐Making 46 6.2.4 Control; Accelerator Program Intensity 48 6.2.5 A Practical View on Accelerator Programs 50
7. Discussion 52 7.1 Findings on Knowledge Resource Acquisition 52 7.1.1 The Lean Startup Methodology 52 7.1.2 Financial Knowledge 53 7.2 Other findings on Accelerator Programs 54
8. Conclusion 56 8.1 Management recommendations 56 8.2 Limitations and future research 57
9. References 60
Part V – Appendices 65 Appendix A – Interview Protocol 65 Appendix B – Startup Classifications 67
Figures
Figure 1: Value Matrix (Hughes et al., 2007) 21 Figure 2: Build-‐Measure-‐Learn loop illustrated 26
PART I
1. Introduction
“American Idol” has proved to be a major success in identifying and establishing entertainment stars. A similar “Idol” formula is emerging among entrepreneurs and venture capitalists who are radically transforming the way high-‐tech entrepreneurs are identified, their businesses are launched, and their growth accelerated. This new approach to finding and nurturing high-‐tech entrepreneurial enterprises seems to be catching on like wildfire, not only in the United States but in Europe and other parts of the world. (Fishback et al., 2007)
The first definition of “the entrepreneur’, defined by Richard Cantillon in 1755, was a term to describe “someone who exercises business judgment in the face of uncertainty” (Bull & Willard, 1993). The last 50 years have brought us a new insight on entrepreneurship. According to the current understanding, entrepreneurship is a disruptive activity. It challenges the status quo by disrupting accepted ways of doing things. It is a change process that introduces something new or different in response to needs in the market. Schumpeter (1943) was the first one to link innovation with entrepreneurial activity. In Schumpeter’s vision of capitalism, innovative entry by entrepreneurs was the disruptive force that sustained economic growth, even as it destroyed the value of established companies that enjoyed some degree of monopoly power derived from existing technological and organizational standards.
With the entrance of the Internet and smartphones to our lives, the way we can and
conduct business has evolved dramatically. Some examples of nowadays billion dollar companies are Uber and Airbnb, companies who changed the way we experience and book services. These new business models are all enabled with IT platforms and
widespread availability of the Internet through PC’s and smartphones. The possibility to innovate and scale products and services at very high speeds has made small startups able to grow into giants in very short times. Uber for example, has grown to a company with a valuation 40 billion dollars, in five years from scratch. These are figures Schumpeter would not have been able to dream of.
Still, startups are founded in the face of uncertainty. The possibility of failure in the first five years is about 50% (EC, 2009). The key ingredient for a successful startup is early, high quality mentorship (Bluestein & Barrett, 2010) Startups who join Business Incubators (further referred to as BI) have a significant longer breath and thus a higher potential of becoming successful (NBIA; Swamidass, 2014). This is due to the guidance and mentorship given by the BI and its network on different aspects of the firm. After the Internet boom, the structures of the classic BI have changed into accelerators. Accelerator programs (further referred to as AP) include intensive education, workshops, networking and high-‐level mentorship. This environment is established to provide startups with an opportunity to learn from key experts and mentors in their field. Mentors work with startup founders throughout the duration of the program, dispense advice, and provide valuable feedback based on personal experience as business owners and entrepreneurs. Accelerators select mentors based on their level of expertise, experience, profitability and desire to help new entrepreneurs succeed (Radojevich-‐Kelley & Hoffman, 2012). These AP’s are much more intensive programs than the classic BI’s which can take up to several years. One of the larger Dutch AP website states “This program requires complete focus and availability. The AP can’t be combined with a day job, university degree, or any other distraction. Even your personal life has to go on hold temporarily”.
According to Radojevich-‐Kelley & Hoffman (2012), the greatest obstacles that new ventures face are not understanding their target market, not having a strong marketing expert working for the business, difficulty reaching their customers, and lacking overall experience in their proposed business. Nevertheless, AP’s are as successful as the absorptive capacity of the start-‐up founders and employees because of the short duration of these programs. It is therefore important to identify the resources startups are able to acquire during AP’s.
1.1 Research Question
The research question of this thesis is as follows:
Which knowledge resources are startups able to acquire through accelerator programs, and which not?
The aim of this thesis is to understand and give more insights in the effectiveness of AP’s. As Cohen & Hochberg (2014) state, AP’s represent a relatively new model of assistance for entrepreneurs. It is therefore important to identify the knowledge resources acquired by startups through an AP. To understand what these intangible resources are, the resource-‐based view will be used to provide a general perspective on the matter, with a further emphasis on the knowledge-‐based view.
A large percentage of the existing research on BI’s focuses on the incubation process from a BI perspective and/or focus on the success of entrepreneurs in terms of long-‐ term survival. As Hackett & Dilts (2004) state, much attention has been devoted to the description of BI (accelerator) facilities, less attention has been focused on the
incubatees (startups), the innovation they seek to diffuse, and the BI (AP) outcomes that have been achieved. With identifying the key knowledge resources for startups, valuable insight will be given to startups if and how AP’s can help them achieve a next level in the entrepreneurial process (Baron & Shane, 2003).
1.2 Thesis Structure
An introduction to the thesis and the research question are covered in Part I. In order to understand which knowledge resources startups are able to acquire, it is important to determine what is already known about accelerators and their programs. Research on BI’s will be used, as empirical research on accelerators is limited. This is covered in Part II with the inclusion of the theoretical review on entrepreneurship in chapter 1 and resources in chapter 2. Chapter 3 covers the theoretical introduction on BI’s and the rise of accelerators. In Part III I will set out the research method and will give a description of the case study. In Part IV I will discuss my results and link them back to the theory. Furthermore, findings outside the scope of the research question will also be presented in Part IV, followed by a discussion and conclusion of this study.
PART II – Literature Review
2. Entrepreneurship
The last two decades have witnessed a wealth of studies analyzing the determinants of entrepreneurship. The entrepreneur is the single most important player in a modern economy (Lazear, 2002). Linking entrepreneurship to economic growth is certainly not new. In his 1934 classic, Theorie der wirtschaftlichen Entwicklungen (Theory of Economic Development), Schumpeter proposed that entrepreneurs starting new businesses provided the engine for economic growth. Also in his 1942 classic, Capitalism, Socialism and Democracy, Schumpeter still argued that large corporations tend to resist change, forcing entrepreneurs to start new firms in order to pursue innovative activity.
The function of entrepreneurs is to reform or revolutionize the pattern of production by exploiting an invention, or more generally, an untried technological possibility for producing a new commodity or producing an old one in a new way... To undertake such new things is difficult and constitutes a distinct economic function, first because they lie outside of the routine tasks which everybody understand, and secondly, because the environment resists in many ways.
Eckhardt and Shane define entrepreneurial opportunities as “... situations in which new goods, services, raw materials, markets and organizing methods can be introduced through the formation of new means, ends, or means-‐ends relationships” (2003). Launching a new business has always been a kind of a gamble, whether it’s a tech startup, a small business in retail, or an initiative within a large corporation and have
accompanied high rates of failure. The rate of failure among startup companies may range from 30% to 95%. A study by Shikhar Ghosh, serial entrepreneur and senior lecturer at Harvard University, reports, “Of all companies, about 60% of startups survive to age three and roughly 35% survive to age 10 . . .” (Blank, 2013).
The European Commission sees entrepreneurship as the backbone of the European economy with their role in innovation and R&D, next to their contribution to wealth and economic growth. Issues of entrepreneurial activities are the considerably high failure rates; around 50% of all startups fail within the first 5 years (EC, 2009). General failure reasons could be summed as lack of management skill, poor management strategy, lack of capitalization, lack of vision, poor product design, incompetent key employees, poor product timing and poor external market conditions (Zacharakis, Meyer & DeCastro, 1999).
One of the determinants of entrepreneurial success is financial knowledge In their examination of the relationship between planning process sophistication and the financial performance of a select group of small firms in a growth industry, Keats & Montanari (1986) state the following: “In the growth stage of the firm's life cycle, one of the most difficult tasks the entrepreneur has to encounter is probably the initial planning for the firm. The opportunistic entrepreneur who incorporates sophisticated strategic management procedures as early as possible in a firm's history may be in the best position to survive the inevitable competitive challenge.“
3. The Resource Based View
3.1 Entrepreneurs and Knowledge Resources
Different factors are mentioned by researchers in an attempt to explore entrepreneurial success. Penrose (1959), in her book The Theory of the Growth of the Firm, advocated the resource based view which describes firms as bundles of resources, capabilities and competencies. If these resources are unique, they can become difficult to to emulate which leads to distinctive competitive advantages. For an entrepreneur, constructing an initial resource base is an exceptional challenge (Radojevich-‐Kelley & Hoffman, 2012). Because an emerging startup lacks administrative history, has no loyal customer base, cannot point to its reputation for performance and has no shared experience, its strategic resource decisions are based on judgments using only current information (McGrath, 1999). Resources are one, vital, determinant of entrepreneurial success. A challenge to young enterprises and entrepreneurial firms is to create or pick the best (most valuable) resources and build barriers to their mobility and inimitability (Barney, 1991). More specific, resources, which are Valuable, Rare, Inimitable and Non-‐ substitutable, are sources for startups in creating competitive advantage. This conception of resources can be traced back to the resource-‐based view (Barney, 1991; Barney & Peteraf, 1993). But what are resources? More formally, a firm’s resources at a given time could be defined as those tangible and intangible assets that are tied semi permanently to the firm (Wernerfelt, 1984).
The identification of resources has changed with time. Resources are typically defined as either assets or capabilities (Galbreath, 2005). Assets, which may be tangible or intangible, are owned and controlled by the firm. Capabilities are intangible bundles of
skills and accumulated knowledge exercised through organizational routines (Teece et al., 2007).
Tangible resources could be classified as financial resources and physical resources (Knight, 1996). Financial resources are of significant importance for startups in general and specifically for tenants following AP’s. Most of the accelerator programs have an event in the end of the program; a demo-‐day, where final products or product propositions are presented to angel investors and venture capitalists to raise investments. Intangible resources may be classified as ‘assets’ or ‘skills’ (Hall, 1992) and incline human capital, information capital and organizational capital. Assets are obviously things which one owns. Skills, or competencies, include the know-‐how of employees, and the collective aptitudes that add up to organizational culture (Hall, 1992). Consistent with Schultz (1961), human capital is tacit knowledge and skills embodied in people. The complexity of a resource may indicate the degree to which it can potentially be transformed, combined, or lead to a unique advantage (Brush et al., 2001).
But what is knowledge? In a broad context, the answer would be ‘that which is known’. Knowledge is modeled as an unambiguous, reducible and easily transferable construct, while knowing is associated with processing information. The philosopher Polanyi (1967) described tacit knowledge as knowing how to do something without thinking about it. It is local and is not to be found in books or on the Internet. It is something that people use face-‐to-‐face and hands-‐on methods to convey their ‘know-‐how’ to others (Smith, 2001).
3.2 Absorptive Capacity of the Firm
In the innovation literature it is claimed that, to a large extent, innovation derives from knowledge exchange and learning between firms. Knowledge exchange also plays an important part in the literature on networks. Networks, within and between firms, generate social capital, defined as follows: “The set of resources, tangible or virtual, that accrue to an organization through social structure, facilitating the attainment of goals” (Nooteboom, 2000). Where most firms internally develop much of the knowledge used in innovation, few firms possess all the inputs required for successful and continuous technological development. Organizations often turn to external sources to fulfill their knowledge requirements. In fact, suppliers, buyers, universities, consultants, government agencies and competitors all serve as sources of vital knowledge (Almeida, Dokko & Rosenkopf, 2003). An important element of social capital is knowledge exchanged, shared and created between firms with different capabilities and absorptive capacities (Cohen & Levinthal, 1990).
The knowledge spillover theory of entrepreneurship identifies new knowledge as a source of entrepreneurial opportunities, and suggests that entrepreneurs play an important role in commercializing new knowledge developed in large incumbent firms or research institutions (Qian & Acs, 2013). Accelerators could be acknowledged as these large incumbent firms. It is clear that knowledge sharing takes place between accelerators and startups. The absorptive capacity construct is used to explain organizational phenomena that span multiple levels of analysis. Absorptive capacity is a firm’s receptivity to technological change or the ability of a firm to use outside knowledge (Zahra & George, 2002). To generalize, we could state that absorptive capacity is the firms’ ability to absorb and manage knowledge from external knowledge
resources and therefore, the absorptive capacity of the startup plays an important role in the effectiveness of such a program. Nevertheless, there are differences in the long-‐ term survival and success between startups who have graduated from the same AP. These differences can be explained through internal and external dynamics. The internal dynamics of startups are especially of importance in absorbing external knowledge and exploit these within the firm. Educational levels of the founders have been found to be positively linked to levels of entrepreneurship, growth and the internal development practices associated with high absorptive capacity. Although graduates are more growth-‐oriented than average and owners with no qualifications more growth averse, it are the owners with technical and vocational qualifications that appear to be the most growth-‐oriented (Gray, 2006).
4. Business Incubation
4.1 Business Incubation
It has been argued that technological startups typically fail due to the lack of managerial skills and/or access to high-‐risk capital (Smilor & Gill, 1986). For a startup, running a business can often turn out to be tough and difficult during the first critical years.
In ancient times, in order to have a visionary dream, people would go to a Roman or Greek temple, and lay themselves down on fresh hide from newly sacrificed animals. This practice was was called incubatio. One of the most advanced reasons for practicing the incubation was to obtain a vision on how to overcome one or another disease, which explains why the incubatio preferably took place in the temple of Aescupalius, the God of medicine. Aesculapius is well known for his incarnation into the form of an animal, namely a snake. It is his picture that we still find on a lot of medicines today. When comparing humans with entrepreneurial activities and diseases with failures in the startup phase, we better understand why we are talking of business incubation or incubation.
It is generally accepted that the first business incubator (BI) was established as the Batavia Industrial Center in 1959 at Batavia, New York (Lewis, 2001). A local real estate developer acquired an 850.000 feet square building left vacant after a large corporation exited the area (Adkins, 2001). Unable to find a company capable of leasing the entire facility, the developer opted to lease subdivided partitions of the building to a variety of companies, some of them requested business advice and/or assistance with raising capital (Adkins, 2001). The first BI was born. In the 1960s and 1970s BI’s diffused slowly, and were typically government-‐sponsored responses to the need for urban
revitalization. In the 1980s and 1990s the rate of BI diffusion increased significantly when [...] the U.S. legal system increasingly recognized the importance of innovation and intellectual property rights protection (Hackett & Dilts, 2004). The U.S.-‐based National Business Incubation Association estimates that there are about 7.000 BI’s worldwide. As of October 2006, there were more than 1.400 BI’s in North America, up from only 12 in 1980.
There is a distinction between non-‐profit BI’s and for profit BI’s. The primary mission of a university based BI, a common example for a non-‐profit BI, is to increase economic development in the region by assisting entrepreneurial firms during their growth and development phase (Studdard, 2006). Non-‐profit BI’s focus on providing support to startups by furthering local development and other community social purposes (Gassmann & Becker, 2006). On the other hand, for-‐profit BI’s merely concentrate on financial returns for the entrepreneur, themselves (and their investors).
According to the National Business Incubator Association, historically, BI’s report that 87% of their graduates remain in business at least three years after completing the program. Nevertheless, no commonly accepted definition of business incubation exists, largely because of the fragmented research base underlying it. Some researchers characterize business incubation as a model that seeks to link skills, technology, capital and know-‐how to leverage entrepreneurial talent and accelerate a firm’s development (Smilor & Gill, 1986). This definition takes a deterministic view that a firm’s mere presence in an BI ensures its success, and implies that incubating firms need only interact to cultivate value and increase performance (Hughes, Duane Ireland & Morgan, 2007). From an RBV perspective, BI’s are a systematic approach to controlling
resources and reducing costs during the early stages of a venture’s development (Hackett & Dills, 2004).
4.2 Networked Business Incubators
To date, studies have focused almost exclusively on the BI as a creator of value. More recent definitions of business incubation, however, attempt to formalize the role of interaction. According to these definitions, the incubation of a firm improves when that young firm is located in a networked BI, so that incubation gets defined as the process that enables fledgling businesses to create value by embedding them in a network system that provides extensive powerful business connections (Hughes, Duane Ireland & Morgan, 2007). The main objectives of networking are access to resources through peers and acquisition of knowledge through mentors. Through the network, startups are able to acquire the tacit knowledge or the know-‐how of the network around the incubator. The extent to which a firm pursues these objectives, and thereby develops interactive relations, determines its degree of social capital and governs the likely degree and nature of value subsequently created (Grant & Baden-‐Fuller, 2004).
Hansen et al. (2000) have employed network theory to argue that primary value-‐added feature of networked BI’s is the set of institutionalized processes that carefully structure and transfer knowledge throughout the BI network in order to create conditions that facilitate the development of startups and the commercialization of their innovations. They find that the degree of entrepreneurial intensity, economies of scale and scope, and network design are important factors for incubation success. The importance of the network design factor is supported by research that concludes that network relationship
building is the most important ant value-‐added component of the incubation process (Lichtenstein, 1992).
Along with the availability of the network to reach out to, BI’s create a positive ‘clustering’ effect in that firms with shared entrepreneurial ambitions, at similar stages of growth and in broadly related sectors, are in close proximity (McAdam & Marlow, 2007). Clustering has the potential to create a positive synergy as it attracts the attention of those with interest, knowledge and expertise in the specific type of firms located in proximity to each other. It also encourages and facilitates effective networking and knowledge ‘spillovers’ between the organizations (McAdam & Marlow, 2007).
According to Lichtenstein (1992), the most important contribution of BI’s to entrepreneurship lies in the opportunities they provide for entrepreneurs to interact and develop relations with other entrepreneurs, the BI manager and other individuals associated with the BI.
Furthermore, Hughes et al. (2007) have proposed a two-‐by-‐two classificatory matrix of value creation potential for four different incubation outcomes, which they label as the value matrix. They found that when the magnitude of networking behavior of startups differs, it raises or limits social capital and thus changes the extent to which value is created. Extensive interaction is wide-‐ranging, comprehensive interactive behavior in which the firm is highly active in pursuing, developing and managing relationships based on the specific networking activity. Narrow interaction is focused interactive behavior in which the firm is reluctant to pursue, develop and manage relationships, which results in limited collaboration in terms of the specific networking activity.
Different combinations of the two behaviors result in different classifications of incubation outcomes. The four classes of incubation outcomes, each with its own value creation potential, are: enclosed, specialized, community and dynamic. Enclosed incubation as an outcome holds the least potential for value creation, whereas dynamic incubation offers the greatest (Hughes et al., 2007).
Figure 1: Value Matrix (Hughes et al., 2007)
4.3 The New Business Incubators
With the rise of the Internet in the 1990s and the accompanying opportunities that arose, venture capitalists showed a growing interest in young and promising technology startups. In a four-‐year span 350 for-‐profit BI’s have setup worldwide. The better ones, generally backed or owned by venture capitalists, typically shared common characteristics as nurturing entrepreneurship by allowing startup founders to retain ownership, facilitate economies of scale from top-‐tier service providers and access to a
network of companies (Hansen, Chesbrough, Nohria & Sull, 2000). With the market correction in early 2000, many independent BI’s shrank, or disappeared along with venture capital funds (Gassmann & Becker, 2004). This left angel investors, individuals who provide capital for a business startup in exchange for debt or ownership equity, alone in carrying the risk of equity invested in startups. Angel investors are successful entrepreneurs who mentor and guide nascent ventures with the intention of reducing high failure rates (Radojevich-‐Kelley & Hoffman, 2012).
4.3.1 Accelerators
Accelerators are (groups of) experienced and successful entrepreneurs who provide services, office space, guidance, mentorship, networking, management services, knowledge and expertise to help startups succeed in the early stages of a venture (Radojevich-‐Kelley & Hoffman, 2012). The first accelerator (and still the most successful), Y Combinator, was founded by Paul Graham in 2005 in Massachusetts, and soon moved and established itself in Silicon Valley. In 2007, David Cohen and Brad Feld, two startup investors, set up TechStars in Colorado, hoping to transform its startup ecosystem through the accelerator model. Cohen and Feld stated that they started their company to help provide the assistance they could not find when they were starting ventures as entrepreneurs (Techstars, 2010). Today, estimates of the number of accelerators range from 400+ to over 2000, spanning six continents (Cohen & Hochberg, 2014).
Accelerator tenants, startups following a program at an accelerator, are selected from a pool of qualified candidates led by startup teams with stellar ideas. Like venture capitalists, accelerators fund equity along themes and in specific industries with which
they are familiar of knowledgeable (Fishback et al., 2007). Parallel to the rise of accelerators in the US, accelerators have emerged in Europe and in the Netherlands and are multiplying. The funding deficit for new ventures after the credit crunch in 2008 made accelerators also from a funding perspective fetching. More startups are applying to AP’s to help them launch and grow their ventures (Hackett & Dills, 2004). On the other hand, accelerators have become more selective in the programs they offer and in the tenant selection. More strict selection criteria and grip on reality are the new standards now advocated by experts (Bruneel, Ratinho, Clarysse & Groen, 2011). By applying to an AP that focuses on a specific industry (web & mobile, financial technology, digital health), the tenants make better use of the services provided by and are able to tab quicker into the specialized network of an accelerator.
According to Cohen and Hochberg (2014) accelerators differ significantly from previously known models such as BI’s, angel investors and co-‐working environments. Nevertheless accelerators are the historical antecedents of nascent firm assistance (Radojevich-‐Kelley & Hoffman, 2012).
4.3.2 The Accelerator Program
Accelerators assist with building the startup team, fine-‐tune the idea and mentor the business from idea to prototype through product development. According to Cohen and Hochberg (2014), accelerators differ from BI’s on four important dimensions. [1]. Duration; where BI’s assist entrepreneurs ranging from one to five years, an average AP usually takes three months. [2]. Cohorts; startups enter and exit the programs in groups, known as cohorts which fosters strong bonds and communal identity between startup founders. [3]. Incentives; by taking an equity stake in startups in exchange for
the program, accelerator directors’ incentives are often more closely aligned with the startups than those of professional BI managers. [4]. Educational Program; where BI’s in general offer fee-‐based professional services, such as accountants and lawyers, accelerators also offer seminars on topics as unit economics, search engine optimization, term sheet negotiation (for fundraising purposes) and last but not least; lean startup methodology (LSM) including topics as product, business and customer development.
4.3.3 The Educational Program; Lean Startup Methodology
The accelerator model provides significant amounts of education, mentoring and advice throughout the program. The education is intensive, boot camp-‐like training comparable to entrepreneurship classes at the collegiate level (Fishback et al., 2007). Most of the classes during the AP are based on the LSM, deriving from Lean Management. The origins of lean thinking lie in the Toyota Production Systems that was developed by Toyota in the 1970s due to “scarcity of resources and intense competition in the market for automobiles” (Hines, Holweg & Rich, 2004). These “lean” principles include a focus on the customer, continual improvement and quality through waste reduction, and tightly integrated upstream and downstream processes as part of a lean value chain (Morgan & Liker, 2006).
EIsenhardt (1989) argue that BI’s can help incubated firms avoid the process of trial and error and ascend more quickly the learning curve. As a result, these firms should be able to make better and faster decision, resulting in higher firm performance. Nevertheless, the notion of trial and error avoidance is not applicable to the successful innovative startups in the 21st century. Jim Manzi is a former consultant at McKinsey & Company, entrepreneur and currently a private investor in various technology startups. In his
book ‘Uncontrolled: The surprising pay-‐off of trial-‐and-‐error for business, politics and society’, he states that it is no surprise that the most successful companies in information technology -‐Google, Amazon and eBay-‐ are relentless experimenters. Google alone ran about 12.000 randomized experiments in 2009, with about 10% leading to business changes (Manzi, 2012). His view is in line with the philosophy of the LSM.
Traditionally, you write a business plan, pitch it to the investors and/or to the bank, introduce a product, assemble a team and start selling as hard as you can. And somewhere in this series of events, the probability you suffer a hindrance is high. Lean startups, in contrast, begin by searching for a business model. They test, revise, and discard hypotheses, continually gathering customer feedback and rapidly iterating on and reengineering their products (Blank, 2013).
Blank (2013) quotes Mike Tyson about his opponents’ prefight strategies “Everybody has a plan until they get punched in the mouth” about business plans, arguing it to be a static document, written in isolation and including a five-‐year forecast for financial figures. Blank is one of the architects of the LSM and lists the three key principles of the lean method as:
1. Entrepreneurs accept that all they have is a series of untested hypotheses. Instead of writing a business plan, founders summarize their hypotheses in a framework called a business model canvas; a diagram of how a company creates value for itself and its customers.
2. Lean startups use a "get out of the building" approach called customer development to test their hypotheses. They ask potential users and purchasers for feedback on all elements of the business model, including product features, pricing, distribution
channels, and customer acquisition strategies. The emphasis is on speed: New ventures rapidly assemble minimum viable products and elicit customer feedback. With this input to revise their assumptions, they start the cycle over again, testing redesigned offerings and making further small adjustments (iterations) or more substantive ones (pivots) to ideas that aren't working.
3. Lean startups practice agile development, which originated in the software industry. Agile development works hand-‐in-‐hand with customer development. Instead of typical year-‐long product development cycles, agile development eliminates waste (time and resources) by developing the product iteratively and incrementally. It's the process by which startups create the minimum viable products they test. In short, this is called the Build-‐Measure-‐Learn loop as illustrated below.
Figure 2: Build-‐Measure-‐Learn loop illustrated
From what the startup has learned during the measure phase, the startup should either pivot their idea of preserve their idea. A pivot is a big change in direction with the message to investigate new ideas with new leap of faith assumptions, preserve is the
confirmation of being on the right path, where one should continue testing more assumptions and building towards executing the current vision.
The open culture of the LSM is in contradiction with the dot-‐com boom, where everyone tried to avoid competitors exploiting your market opportunity. With the rise of the lean startup, the focus on ‘what next’ and ‘how’ to put ideas and customer feedback into execution has become far more important than the idea itself.
Part III – Research
5. Methodology
In the first chapters I have described what is already known about about the type of knowledge resources startups are able to acquire at BI’s. The theoretical review has shown that there is little in-‐depth empirical work identifying, analyzing and interpreting the type of knowledge resources acquired at AP’s. This study aims to contribute to the literature by carrying out an exploratory case study on the actual type of knowledge resources startups are able to acquire at AP’s. As the phenomenon cannot be separated from its context and the perspectives of the interviewees are relevant for the analysis, the subject is appropriate for a multiple case study (Eisenhardt, 1989).
This study contains a descriptive exploratory approach to contemporary events (Yin, 2009). The interviews helped me to understand which knowledge resources startups are able to acquire. The multiple cases provided me with the possibility to signal possible differences between different AP’s. Additionally, the multiple cases provided me further insights to understand the process of acceleration. With a descriptive orientation, creating a roadmap for building theories from case study research is aimed (Eisenhardt, 1989) and thus, this study will attempt to provide in-‐depth insight in the topic for further research. A statistical study could have answered a study on which knowledge resources startups are able to acquire, nevertheless this would not have given the insight of interviewees on the topic.
