BUSINESS ANGEL DECISION MAKING: AN EXPLORATION OF THE INFLUENCE OF ‘FIT’

Wageningen University - Department of Social Sciences

Management Studies

MSc Thesis

BUSINESS ANGEL DECISION MAKING: AN EXPLORATION OF THE INFLUENCE OF ‘FIT’

Cover page image courtesy of The Startup Garage (https://thestartupgarage.com/equity-capital-angel- investors-2/)

May, 2015

Student Suzanne Kroeze Registration number 900501479020 MSc program Master Management, Economics and Consumer Studies Specialisation Management, Innovation and Life Sciences Supervisor Stefano Pascucci Examiner/2nd supervisor Valentina Materia Thesis code MST-80436

PREFACE AND ACKNOWLEDGEMENTS This thesis is written as a final assignment for the master Management, Economics and Consumer Studies, at Wageningen University.

Taking care of your own wage, and not just receiving a pay check from your side-job, but actually taking care of your own wage: I never thought it would give so much satisfaction. When I started a small business with three of my roommates in my bachelor, I got up close and personal with the new venture creation process, and experienced the importance of opportunity- and venture team strength in practice. Small-scale as we were, we were able to skip a crucial new venture creation process step that so many ventures are forced to take: obtaining external financing.

Now, long after we earned back our scraped together investment and closed down, an entrepreneurial itch remains. So, I was happy to find a thesis project that ‘scratched the itch’ and allowed me to immerse myself in literature and discussion on new venture creation and the entrepreneur. I am grateful, therefore, to my supervisor Stefano Pascucci, who introduced me to the topic and kept me from wandering in every possible direction (and producing a not-so-relevant piece of book-like proportions in the process).

Valentina Materia, my second supervisor, was hands-on involved in the empirical part of this research. I am grateful not only for her guidance in the field of econometrics, but also for her empathy and understanding of how econometric modelling can, sometimes, drive you crazy.

Further, I would like to thank my friends, my family and Psychotel for their support in the shape of elaborate coffee breaks, fully-catered meals in the park and constructive criticism. This thesis would not have been possible without their support in- and outside of thesis working hours.

i

ABSTRACT In the new venture creation process, obtaining funding is a crucial step. However, many potential ventures are denied existence because they are unable to acquire external financing. This study aims to provide insight in the influence of opportunity-investor fit and entrepreneur-investor fit on the likelihood of obtaining angel funding, and the subsequent probability of achieving a deal similar to the deal initially requested. Footage of the BBC television programme ‘Dragons’ Den’ provided 1791 business angel-entrepreneur interactions which are analysed econometrically using the two part hurdle model (TPM). The influence of entrepreneur- and opportunity related factors are modelled for the decision of investing or not, and the similarity of the final deal compared to the initial request.

Related to the opportunity-investor fit, we find that angel involvement in the opportunity’s sector and the degree of innovation of the opportunity positively influence the probability of a positive funding decision. The entrepreneur-angel fit related factors ‘experience entrepreneur’ and ‘same gender entrepreneur-angel’ have a significant positive effect on the probability of a positive funding decision; a good pitch quality also increases the chance of obtaining a deal similar to what was initially requested. Compared to male entrepreneurs, female entrepreneurs are found to be more successful in negotiating a deal similar to what they initially requested.

Keywords: entrepreneurship, business angel, investment, factor analysis, two-part model

ii

TABLE OF CONTENTS Preface and acknowledgements ...... i Abstract ...... ii Tables and figures...... iv 1. Introduction ...... 1 1.1 Angel financing: the broader context ...... 2 1.2 Problem analysis ...... 6 1.3 Relevance of the research ...... 6 2. Conceptual research design ...... 8 2.1 Research objective ...... 8 2.2 Research questions...... 8 2.3 Research framework ...... 8 3. Literature study ...... 10 3.1 The concept of ‘fit’ ...... 10 3.2 The opportunity ...... 10 3.3 The entrepreneur ...... 13 3.4 Conclusions literature study: conceptual framework ...... 16 4. Methodology ...... 18 4.1 The data ...... 18 4.2 The model ...... 25 5. Results ...... 28 6. Discussion ...... 31 7. Conclusions ...... 33 7.1 Limitations ...... 35 7.2 Further research ...... 36 8. References ...... 38 Annex A: List of variables in original Dragons’ Den database ...... A Annex B: Descriptive statistics ...... D B1: Correlation table ...... D B2: Summary statistics applied variables ...... E B3: First step probit output table for reduced- and complete model ...... F

iii

TABLES AND FIGURES Table 1: Financial capital sources linked to venture development stage. Black is considered applicable to that stage; grey is less applicable (Gregory et al., 2005, p. 384) ...... 4 Table 2: Comparison of angel financing and crowdfunding ...... 5 Table 3: Categories determining opportunity strength (Maxwell et al., 2011) ...... 10 Table 4: Innovation typology (Henderson & Clark, 1990, p. 12) ...... 11 Table 5: Overview desirable entrepreneurial attributes ...... 13 Table 6: Overview of dependent variables...... 20 Table 7: Categories and corresponding frequencies variabel 'C4 innovation type' ...... 21 Table 8: Recoded 'C4 innovation type' into RADICAL ...... 21 Table 9: Frequency table INVOLVE and EXPERIENCE ...... 22 Table 10: The entrepreneur's gender, captured in variable GEN_ENT ...... 22 Table 11: Gender interaction entrepreneur-angel (GEN_ENT*GEN_BA) ...... 22 Table 12: Labels gender interactions Entrepreneur-angel ...... 23 Table 13: Introduction of SMLR_GENDER variable in entrepreneur-angel interactions ...... 23 Table 14: Frequency table PITCHQ ...... 23 Table 15: Control variables ...... 24 Table 16: Specific explanatory variable combinations used to compare means ...... 27 Table 17: Output Two-Part Model, including marginal effects of first step probit estimates ...... 28 Table 18: Predicted values for constructed observations ...... 30

Figure 1: Evolution of the entrepreneur. Adapted from the annual report of the Global Entrepreneurship Monitor (Bosma & Amoros, 2014) ...... 1 Figure 2: The progressive stages in angel financing (Paul et al., 2007) ...... 2 Figure 3: The emergence of crowdfunding, as illustrated by an increasing number of Google Search queries (Google) ...... 3 Figure 4: Research framework ...... 9 Figure 5: Conceptual framework ...... 17 Figure 6: Updated conceptual framework. A green arrow indicates a positive relation between the factor and the outcome...... 34

iv

1. INTRODUCTION This research explores the relationship between an investor and an investee. The investee in this case is an entrepreneur; the investor is a particular type of informal investor, the business angel (BA). This research is structured according to Verschuren & Doorewaard’s ‘Designing a research project’ (2010).

Entrepreneurship and organization- or new venture creation is increasingly looked upon as part of the solution to unemployment and a stimulant of economic growth (Valdez & Richardson, 2013). Moreover, new ventures act as a stage for innovation: through the creation of a new venture, the opportunities for successful commercialisation of an innovation are increased (Curley et al., 2013). The creation of new ventures is thus considered a benefit, if not necessity, to further societal development.

Consequently, many scholars have considered the process of creating a new venture a process worth exploring. Research has delved into the success- and risk factors in creating a venture (Cooper et al., 1994; Feeney et al., 1999; Gelderen et al., 2005; Miloud et al., 2012; Shepherd et al., 2000), the psyche of the entrepreneur and his team (Amit & Muller, 1995; William B Gartner, 1988; C. R. Mitteness et al., 2010) and entrepreneurial- and new venture creation process models (Bhave, 1994; Carter et al., 1996; Cha & Bae, 2010; William B. Gartner, 1985; Webster, 1976).

Out of the abundant entrepreneurial process models, Gelderen et al. (2005) distil four generally accepted phases in new venture creation: first, the intention to start a new venture has to be developed. Next, an opportunity is recognized. To be able to exploit the opportunity, the third phase is about procuring resources. Provided that the entrepreneur successfully progresses through all three aforementioned phases, the final phase encompasses exchanging with the market.

Simultaneously, the entrepreneur himself passes several stages to ultimately become the owner- manager of an established business. This research will focus on the nascent entrepreneur, and consequently on phases 2 and 3 of the new venture creation process. The entrepreneurial evolution paired with the progress through the venture creation phases is portrayed in Figure 1 below.

Owner-manager Owner-manager Potential Nascent of a new of established entrepreneur entrepreneur business (up to business (> 3.5 3.5 years old) years old)

•Entrepreneurial •Opportunity •Exchanging with the •Exchanging with the intention recognition market market •Exploration of •Resource procurement opportunities, knowledge and skills

FIGURE 1: EVOLUTION OF THE ENTREPRENEUR. ADAPTED FROM THE ANNUAL REPORT OF THE GLOBAL ENTREPRENEURSHIP MONITOR (BOSMA & AMOROS, 2014)

The road to owning a successful business is rocky, though. In the United Kingdom, on average 46 % of nascent entrepreneurs fail to successfully move from nascent entrepreneur to new business owner- manager (Bergmann & Stephan, 2013; Bosma & Amoros, 2014). This high mortality rate is in great part due to, as scholars argue, unavailability of financial capital (Grünhagen, 2008; Shane, 2003). The

1 new venture funding process and investor decision making are therefore crucial in successfully creating a new venture.

This study aims to analyse how entrepreneurs set up relationships with investors in order to raise funds for their new ventures. More specifically, we study the investment-based relation between a particular type of informal investor, the business angel, and the entrepreneur in the screening phase of the investment process. This stage is characterised by the interaction occurring between the business angel and the entrepreneur (see Figure 2 below).

Origination Screening Due-diligence

•Before interaction •The interaction between •Verification of claims between entrepreneur entrepreneur and investor, made by entrepreneur and investor; reference of reaching a deal entrepreneur to investor

FIGURE 2: THE PROGRESSIVE STAGES IN ANGEL FINANCING (PAUL ET AL., 2007)

1.1 ANGEL FINANCING: THE BROADER CONTEXT As an entrepreneur, several options exist for obtaining the financial resources required to move from a lingering idea to an actual start-up. The type and amount of financing that the entrepreneur can attract, depends on both personal and firm-specific factors. In general, a financial-sourcing pecking order or preference exists: the nascent entrepreneur will first use internally generated cash flows to fund the new venture, and will only finance any remaining funding requirements with external financing sources. This phenomenon can be explained by a presumed asymmetric distribution of information between the nascent entrepreneur and the investing party (Grünhagen, 2008). The Dutch nascent entrepreneur can serve as an illustration of this pecking order as only 20% sought financing methods other than self-funding in 2009. Of the entrepreneurs that did seek external financing, 56% was obtained from the bank through loans and the remaining 44% was acquired through family and friends (Valk & Smit, 2010). Aside from or complementing self-funding or loans from family, friends or the bank, the entrepreneur has several options to procure financial capital. These are discussed shortly below.

Business angels - Aside from self-funding and calling on family and friends to fulfil financial capital requirements, the nascent entrepreneur can try to obtain financing through informal risk capital, or so called “business angels”. Business angels are entrepreneurs themselves, who wish to re-invest their acquired capital in new ventures (Bessant & Tidd, 2011). Other than providing financial capital, a business angel can provide the nascent entrepreneur with experience, expertise and access to a large professional network (Peirone, 2007).

Venture capital - Although venture capitalists (VCs) bring in similar side-benefits, the difference is that venture capitalists do not pay the investments done out of their own pockets. Instead, they collect capital from other sources and combine it in a fund, which they will then use to provide a high-potential start-up with financial capital. Venture capitalists therefore have ‘deeper pockets’ than the regular business angel.

2

Crowdfunding - Changes in the economic climate, such as the economic crisis and the housing crises, have provided space for, and have made the emergence of new financial resource methods necessary (Cunningham, 2012). Such a relatively new method is crowdfunding: the efforts of the entrepreneur to fund his/her venture by drawing relatively small contributions from a relatively large number of people, making use of the internet (Mollick, 2014). The increased popularity of crowdfunding as a new venture financing method is depicted in Figure 3 below.

Government schemes – When the private sector does not provide new ventures with sufficient capital, the government can decide to step in. The assumption is made that in doing so, the government stimulates those new ventures that will either yield high social- or private returns. The availability and type of financing offered by the government varies per country. A distinction can be made, however, in government schemes focussing on funding entrepreneurial firms directly, or schemes that focus on encouraging or subsidising other investors, such as venture capitalists (Lerner, 2002).

FIGURE 3: THE EMERGENCE OF CROWDFUNDING, AS ILLUSTRATED BY AN INCREASING NUMBER OF GOOGLE SEARCH QUERIES (GOOGLE)

Of course, a fundamental step in obtaining funding from any of the investment sources mentioned above, is actually getting in touch with the investor. An intermediary institution that can connect the nascent entrepreneur with venture capitalists and business angels as well as prepare them for meetings with all kinds of investors, is the business incubator (National Business Incubation Association, 2015). The mission of a business incubator is to help ventures at their most vulnerable time, and figures show that they are successful at it. A client venture is considered successful when it has overcome resource gaps and has developed sustainable business structures; an unsuccessful client venture discontinues operations while still an incubator tenant (Hackett & Dilts, 2004). On average, the successful/unsuccessful ratio, or ‘tenant graduation rate’, is 0.87, as reported by the National Business Incubation Association (National Business Incubation Association, 2015).

Incubators are often sponsored by academic institutions, economic development organizations or governmental entities. An example of a local incubator can be found in the Wageningen UR sponsored business incubator StartHub (see www.starthubwageningen.nl for more information).

3

LINKING SOURCES AND CATEGORIES Although an abundance of capital sources exists, not all sources prove to be attainable for the nascent entrepreneur. Despite many categories being negotiable to the nascent entrepreneur, some are not. ‘Moment of investing’ is one such category, as the nascent entrepreneur cannot start without receiving funding already in the seed stage of the venture. Table 1 below provides an overview of the financial capital sources that are deemed appropriate for the different stages that a venture goes through: the seed stage, early stage, expansion, late stage and exit stage (not included in Table 1).

The seed- and early stage are most relevant when considering new venture financing: they are located at the beginning of the venture life cycle, and are marked by the progression of opportunity identification to actually starting a business. In the early stage, revenue will start coming in, which can serve as a means to generate more financing opportunities for later stages.

Considering financial sourcing, the problematic part therefore lies with these early stages before the venture starts realizing profits. Most relevant for the nascent entrepreneur is the seed stage, as capital obtained during this stage is essential for start-up and will possibly still be employed in the early stage. Here, the entrepreneur has limited options to obtain financial capital: he can self-fund, ask his family and friends, consult a business angel, or try his luck at crowdfunding (see Table 1 below).

TABLE 1: FINANCIAL CAPITAL SOURCES LINKED TO VENTURE DEVELOPMENT STAGE. BLACK IS CONSIDERED APPLICABLE TO THAT STAGE; GREY IS LESS APPLICABLE (GREGORY ET AL., 2005, P. 384)

Seed Early stage Expansion Late stage Self-funding Family and friends Business Angels Crowdfunding Venture Capital Government schemes Banks Venture capital may be available, but venture capitalists generally are not very fond of the high-risk and low amounts of cash the initial stages represent; the business angel is found to invest 16 times as often in seed ventures than venture capitalists do (Sohl, 2008). The nascent entrepreneur is thus left with two sources: angel financing or crowdfunding. While both angel financing and crowdfunding have their advantages, angel financing is ultimately deemed more suitable for funding a new venture in the seed- or early stage. Angels generally invest more than the entrepreneur would be able to gather through crowdfunding. The fact that the entrepreneur has to give up part of control over his company when engaging in angel equity financing is compensated by the extensive list of additional attributes brought to the table by the angel investor. Crowdfunding is considered a commendable alternative for less capital-demanding start-ups, also considering its high yield or success rate of 88 % (see Table 2 on the next page for quick comparison between the two).

4

TABLE 2: COMPARISON OF ANGEL FINANCING AND CROWDFUNDING

Angel financing Crowdfunding Moment of Seed/early Seed/early/expansion investing Legal position Equity Debt Amount invested Typically between USD 100 000-500 000 Typically no more than USD (Volkmann et al., 2010) 10 000 (Kickstarter, 2015) Frequency Occasional Occasional Investing motivation Intrinsic Extrinsic Intrinsic Fun To attain high capital Identification with the growth project's subject and goals To support young For tax purposes Being part of a community companies with similar priorities To play a role in the For recognition in Contribute to a socially entrepreneurial process the community and desirable project society To exert influence on an For dividends Satisfaction from watching investment (Brettel, 2003) the project succeed To support socially Being involved in a desirable products or pioneering technology or services innovation To help friends or family Attracting funders for own members project Expanding one’s network (Tomczak & Brem, 2013) Additional Yes (Brettel, 2003); Yes (Mollick, 2014; Panteia & attributes Ondernemerschap.nl, 2014); Coaching Strategy Demonstrating consumer demand Network and contacts Management know- Campaign works as how marketing tool Financial know-how Personnel development Marketing know-how Industry know-how Yield rate 10-15% (Sohl, 2013) 88% (Kickstarter, 2015)

5

1.2 PROBLEM ANALYSIS Having selected the business angel as the type of investor to pursue as a nascent entrepreneur, the real challenge starts: actually obtaining funding. While the previous sections have emphasized the unavailability of capital for start-ups, it is fair to believe that the blame cannot solely be placed at the supply side of the deal. C. M. Mason and Harrison (2002) argue, for instance, that availability of capital is not always the limiting factor. As an illustration, they put forward a statement by David Grahame, the Managing Director of a Scottish business angel network saying that ‘there are companies out there saying that they cannot find money, and our angels are saying that they can only place 10% of what they want to invest’ (Nicholson, 2000). The problem in financing start-ups is therefore both a supply- and demand issue.

The problem with funding start-ups, we argue, therefore occurs during the matching of the supplier of capital with the recipient of capital. The desired outcome of the matching activity is the establishment of an investment-based relationship between entrepreneur and angel. Looking from the supplier’s perspective, investment proposals are more often rejected than not for a variety of reasons that can roughly be attributed to a poor opportunity-investor fit, or a poor entrepreneur- investor fit (Collewaert, 2012; C. Mason & Stark, 2004; C. Mitteness et al., 2012).

ENTREPRENEUR AND ANGEL: PRINCIPAL AND AGENT In the angel-entrepreneur relationship, both parties need each other: the angel brings capital, networks and expertise, while the entrepreneur offers a novel competitive opportunity and, regularly, the human capital to practically realize the opportunity (Strätling et al., 2012). In practice, this means that ownership and control are separated: the principal (i.e. business angel) delegates work and responsibility to the agent (i.e. entrepreneur) (Van Osnabrugge, 2000).

This delegation of work and responsibility inevitably leads to information asymmetries: certain pieces of material information are available to one party only. This information asymmetry is only of consequence when the agent decides to use this ‘information advantage’ to his advantage only. Following this asymmetry, two potential agency problems can occur: moral hazard and adverse selection. The first occurs when the agent does not put in the effort that was agreed upon; adverse selection occurs when the agent misrepresents his abilities.

It is in the principal’s interest, therefore, to assess the level of risk of encountering these agency problems when getting involved with an entrepreneur. To further complicate matters, the angel only has the duration of the entrepreneur’s pitch and the subsequent negotiation to assess if entering into a relationship with the entrepreneur is wise or not. Lahti (2011) argues that contrary to venture capitalists, business angels are more concerned about risks related to entrepreneurs and management than market risks when making investment decisions. The entrepreneur’s characteristics, or the ‘fit’ of the entrepreneur with the business angel, is therefore crucial in deciding whether to invest or not.

1.3 RELEVANCE OF THE RESEARCH As previously mentioned, new venture creation is an important tool to further societal development. It offers a platform for innovative ideas, creates jobs and stimulates economic growth overall (Valdez & Richardson, 2013). The high business proposal rejection rate prohibits new ventures from coming

6 into existence, denying innovations the right to exist and the loss of potential employment opportunities. This research aims to look into the investment process, and, more specifically, the decision criteria applied by angels during the screening phase of the investment process. Ultimately, insight in the investment process and the investor-investee relationship is one step in the direction of increasing the efficiency of new venture financing.

7

2. CONCEPTUAL RESEARCH DESIGN Having introduced the topics of interest, the research design can be determined. The research design elaborates on what, why and how much will be studied (Verschuren et al., 2010). This will be explored by first introducing the objective of this research, followed by the research questions and the research framework to conclude.

2.1 RESEARCH OBJECTIVE This research aims to explore the investment-based relation between business angels and entrepreneurs to better understand the heterogeneity of angel investing. As business angels are found to grant decision criteria different weight depending on what stage of the investment process they are in, this research is limited to the screening stage (Maxwell et al., 2011). By focusing on both the opportunity-investor fit and the entrepreneur-investor fit, a broader scope than the one-sided approach focusing on either the supply- or demand side of investing is applied. In short, the above leads to the following research objective:

To explore the influence of the opportunity-investor fit and the entrepreneur-investor fit during the screening stage on the BA-entrepreneur investment process by performing econometric analyses on secondary data

The research objective is theory oriented in nature, and aims to develop theories or hypotheses through literature research, that can be tested by applying econometric models to qualitative secondary data.

2.2 RESEARCH QUESTIONS As a means to reaching the research objective, the following questions have been formulated:

Main: How do opportunity-investor fit and entrepreneur-investor fit influence the screening stage of the business angel-entrepreneur investment process?

1. What factors determine the opportunity-investor fit? 2. What factors determine the entrepreneur-investor fit? 3. How do these factors influence the likelihood of obtaining funding? 4. How do these factors influence the likelihood of obtaining the envisioned deal?

2.3 RESEARCH FRAMEWORK In order to properly answer the research questions, several steps have to be taken, which are indicated in the research framework in Figure 4 on the next page. The first cluster of activities, the research background, has provided the necessary insight in the new venture creation process and has placed the focus of the research on new venture financing, and angel financing in particular. This section was already elaborated on in the introduction. Next, the concept of fit is introduced, after which literature research provides insight in the constituents of the opportunity and the entrepreneur respectively (research questions 1 and 2). Several hypotheses are formed on the influence of the different factors on the likelihood of obtaining funding, and the consequent similarity of the obtained deal versus the envisioned deal, which are all depicted graphically in the conceptual framework. This conceptual framework is then used as the basis of the empirical part of

8 the research, in which econometric analyses are performed to provide answers on research questions 4 and 5.

FIGURE 4: RESEARCH FRAMEWORK

9

3. LITERATURE STUDY In the previous section, several important themes were introduced. In this section, further elaboration will be given on the concept of ‘fit’ and what we understand to encompass ‘the opportunity’ and ‘the entrepreneur’ in the respective opportunity-investor fit, and entrepreneur- investor fit. As the investor is considered a given in this research (i.e. the angel investor selects ventures with desirable attributes, not the other way around), we will not focus on the individual investor.

3.1 THE CONCEPT OF ‘FIT’ We have seen that many different investor types exist. The concept of investor fit, however, is only applied by business angels (C. Mason & Stark, 2004). When comparing venture capitalists and business angels, for instance, business angels invest their own money and can therefore install personal investment criteria. These personal investment criteria often come forth out of a mix of intrinsic- and extrinsic motivations (Brettel, 2003). Remaining in the VC-BA comparison, we can argue that VCs have to be extrinsically motivated (i.e. create substantial returns on their investments), because they have to answer to a principal of their own. Acting autonomously, BAs can allow themselves to be driven more by intrinsic motivations (i.e. enjoying the investment activity in itself, rather than requiring substantial returns on investment).

This mix of intrinsic- and extrinsic motivations is reflected in the concepts opportunity-investor fit, and the entrepreneur-investor fit. Together, the two fits determine whether or not a deal matches with the angel’s investment goals, and, ultimately, if the deal receives funding.

3.2 THE OPPORTUNITY As a nascent entrepreneur, in order to successfully progress to the next step in the evolution of the entrepreneur, a sound opportunity has to be recognized. While in economics an opportunity is generally defined as an unexploited activity, here we employ the definition as proposed by Casson and Wadeson (2007, pp. 285-286): ‘an opportunity is best conceived as a potentially profitable but hitherto unexploited project’. The investor’s job is to recognize the profitable from the less profitable opportunities from an often limited flow of information. Maxwell et al. (2011) argue that business angels weigh eight categories when deciding on the strength of the opportunity, these are elaborated on in Table 3 below.

TABLE 3: CATEGORIES DETERMINING OPPORTUNITY STRENGTH (MAXWELL ET AL., 2011)

Category Keywords Explanation Adoption Product interest; benefits; represents the attractiveness of the innovation opportunity for potential customers in the target segment Product status Status; technology risk; deals with technology- and financial risk in development risk getting the product to market Protectability Role of intellectual property; discusses whether the opportunity can be barriers shielded from potential competitors Financial model Cash flow; profitability; realistic discusses the profitability of the venture forecast Route to market Operations; market entry; describes potential supply chain issues

10

distribution partners and market dynamics Customer Market validation; customer describes if potential customers have engagement engagement been consulted in the development Market potential Size; growth; competitiveness the size, growth and competitiveness of the target market The profitability of an opportunity can be pursued through either the extension or adaption of an existing product (or service), the application of existing products in new market segments, by adding value to an existing product, or by the development of a new product altogether (Bessant & Tidd, 2011). These different profitability strategies roughly correspond to different levels of innovativeness of the opportunity, and accordingly, the new venture.

Innovation, defined by the dictionary simply as ‘change’, can have many different and more elaborate meanings depending on the different disciplinary perspectives dealing with the term. Baregheh et al. (2009, p. 1334) define innovation as ‘the multi‐stage process whereby organizations transform ideas into new/improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace’. This definition of innovation leads to the introduction of different types of innovation, as captured in Table 4 below.

TABLE 4: INNOVATION TYPOLOGY (HENDERSON & CLARK, 1990, P. 12)

Core concepts Reinforced Overturned

Incremental Modular Unchanged innovation innovation

Architectural Radical

Changed

core concepts and and concepts core components Linkages between between Linkages innovation innovation The four types of innovation can be distinguished through a combination of observed change in the product’s components and/or its architecture, where the typologies contrast with their diagonal counterpart. Modular innovation, for instance, requires product components to have changed (e.g. the substitution of ferrite needle heads with tin-metal needle heads in hard-disks) while the product’s architecture remains the same. In architectural innovation, however, it is the linkage between components that evolves, not the components themselves (e.g. the reduction of hard-disk size through better knowledge on linking components). If we want to place the four innovation typologies on a scale depicting the degree of innovation, incremental- and radical innovation would be at opposite ends, and modular and architectural innovation would be placed somewhere in between.

Each opportunity is thus innovative to a certain degree, with incremental innovation and radical innovation at each opposite of the innovation spectrum. McDermott and O'Connor (2002, p. 424) distinguish radical innovation from incremental innovation as follows: ‘while incremental innovations are typically extensions to current product offerings or logical and relatively minor extensions to existing processes, radical product innovations involve the development or application of significantly new technologies or ideas into markets that are either non-existent or require dramatic behaviour changes to existing markets’. Thus, while incremental innovation is considered improving what we are already doing, radical innovation concerns doing something completely different.

11

In literature, an opportunity representing a higher degree of innovation is predominantly regarded to have a lower funding potential. Arrow (1962) states that a free enterprise economy is simply expected to underinvest in invention and research, because of the risks such an investment holds and because the returns can only partly be appropriated by the investor. The product of invention and research, they argue, is knowledge for instance, from which it is difficult to reap returns. Carpenter and Peterson (2002) join Arrow’s argument by stating that the probability of success in R&D projects is low, which in turn makes the return on investment uncertain. This, they state, would deter investors. The following hypothesis considering the investment potential of more or less innovative opportunities is therefore introduced:

Hypothesis 1: An opportunity involving a more radical innovation is less likely to receive funding than an opportunity involving less radical types of innovation

Aside from the degree of innovativeness, the opportunity can also be categorized by the sector it will be developed and marketed in. In their 2002 article, Mason and Harrison state that a business angel typically is reluctant to invest in industries and markets unfamiliar to them. McMullen and Shepherd (2006) surpass this statement by claiming that an angel having no domain-specific knowledge, will not know when to act on an opportunity. In other words, an opportunity born in a sector that is unfamiliar to the business angel is less likely to receive funding. Thus:

Hypothesis 2: An opportunity born in a sector familiar to the business angel is more likely to receive funding than an opportunity born in a sector unfamiliar to the business angel

12

3.3 THE ENTREPRENEUR Numerous researchers have invested themselves in obtaining a definition for entrepreneurship, or the entrepreneur, his qualities and what distinguishes the entrepreneurial mind-set from a non- entrepreneurial mind. Although this has returned a list of personal characteristics that are deemed important for an entrepreneur, no single psychological model for entrepreneurs can be drawn up. Table 5 below provides an insight in what scholars have found to be desirable attributes in an entrepreneur, or an entrepreneurial team. Although the list is quite elaborate, some attributes are mentioned several times: experience, track record and integrity/trust. As experience is instrumental to having a track record, we will disregard track record and focus on the attribute experience only.

TABLE 5: OVERVIEW DESIRABLE ENTREPRENEURIAL ATTRIBUTES

Desirable attribute Author Leadership potential Landström (1998) Track record Industry expertise Track record Feeney et al. (1999) Realism Integrity and openness Enthusiasm Van Osnabrugge (2000) in Sudek (2006) Trustworthiness Expertise Track record Experience Mason and Harrison (2002) Commitment Integrity Experience C. Mason and Stark (2004) Background Track record Commitment Skills Similar education Franke et al. (2006) Similar prior professional experience Experience Sudek (2006) Track record Commitment Integrity Experience C. Mitteness et al. (2012) Track record Integrity Coachability Passion In their 1985 article, MacMillan et al. research the criteria applied by venture capitalists when they evaluate venture proposals. They conclude that it is indeed the quality of the entrepreneur that determines the venture capitalist’s decision whether or not to invest. This entrepreneurial quality is, they argue, determined by both the entrepreneur’s personality and the entrepreneur’s experience. Entrepreneurial experience is described according to 5 criteria: familiarity with the market targeted by the venture, past demonstrated leadership ability, track record relevant to venture, referral of the entrepreneur to the VC by a trustworthy source and familiarity with the entrepreneur’s reputation.

13

Kotha and George (2012) expand these criteria by adding prior start-up experience: serial entrepreneurs, entrepreneurs that have prior start-up experience, raise more resources than entrepreneurs without this experience: the novice entrepreneurs. They even add that serial entrepreneurs possessing human capital in the industry of their start-up are able to retain more equity in the negotiation of the deal. Zhang (2011) further nuances the statements made by Kotha and George (2012) by claiming that serial entrepreneurs with venture-backed founding experience raise more capital in the first round than serial entrepreneurs without connections to VC investors. All of the above leads to the following hypotheses:

Hypothesis 3a: Serial entrepreneurs are more likely to receive funding than novice entrepreneurs

Hypothesis 3b: Serial entrepreneurs are more likely to negotiate the deal they had envisioned than novice entrepreneurs

Table 5 also suggests that business angels prefer entrepreneurs similar to them in terms of education and professional experience (Franke et al., 2006). Of course, similarity or resemblance between entrepreneurs and angels can occur in many different areas. As the goal of the entrepreneur-investor interaction is to establish a relationship, we can draw from findings in psychological literature. In 1967, Byrne found a linear relationship between personality similarities and attraction between psychology students; Fawcett and Markson (2010) looked at friendship preferences in children and adolescents and found that similarities in physical appearance and attitudes positively influenced interpersonal liking. Similarities in values, interests and background were found to positively influence interpersonal attraction in a middle-class and middle-aged adult population (Johnson, 1989). Clement and Krueger (2002) found that individuals more often use social projection to fill in the unknowns of another person when they are similar in terms of race, religion and nationality. As people project their own attitudes, beliefs and values on the other person (e.g. make them similar to themselves in their minds), social projection can be considered beneficial to the interpersonal relationship (Collisson & Howell, 2014).

Especially in the entrepreneur-investor relationship, looking at the influence of similar gender on the relationship established is interesting. In the US, for instance, the number of women-owned firms increased with 50% between 1997 and 2011 (American Express Open, 2011). Women now own almost 30% of all US businesses and 1 in 5 firms with a revenue exceeding 1 million US dollars is woman-owned (Center for Women in Business, 2014). So, women are increasingly becoming active as entrepreneurs, and are successful in doing so. Although women entrepreneurs are increasing in number, over 95% of business angels is male (Parker & Mason, 2007). As difference in gender is an immediately observable dissimilarity, we suggest the following hypothesis:

Hypothesis 4a: Same-sex interactions are more likely to lead to a positive funding outcome than interactions between an entrepreneur and angel of different sex

Today, European female employees still receive on average 16% less wage per hour compared to their male colleagues (European Commission, 2014). This can be due to the fact that women more often hold down part-time jobs than men, which indirectly influences the gender pay gap (EUROSTAT, 2014). Part-time workers generally have less elaborate pension rights and unemployment benefits, and limited opportunities for progressing their careers or becoming involved in training opportunities, all of which influence the wage (European Commission, 2014).

14

However, evidence from negotiation- and organizational literature also suggests that women routinely obtain poorer negotiation outcomes than men do (Kulik & Olekalns, 2012; Stuhlmacher & Walters, 1999). Scholars even argue that the traits that make up a good negotiator (assertiveness, rational, decisive, intelligent and constructive) are masculine traits in essence (Kray et al., 2001; Raiffa, 1982). Thus, the following hypothesis is established:

Hypothesis 4b: Compared to male entrepreneurs, female entrepreneurs are less likely to negotiate the deal they had envisioned than male entrepreneurs

Finally, in any successful partnership, trust is considered essential, as partners depend on one another to satisfy mutual goals (Whipple & Frankel, 2000). Trustworthiness of an entrepreneur is therefore an important attribute influencing the funding decision of the angel. But, how do we define trust, and relatedly, how do we measure trust in the entrepreneur-investor interaction?

Here, we employ Mayer et al.’s definition of trust: “the willingness of a party to be vulnerable to the actions of another party based on the expectation that the other will perform a particular action important to the trustor, irrespective of the ability to monitor or control that other party” (Mayer et al., 1995, p. 712; C. R. Mitteness et al., 2010). So, trust is especially important in collaborations where monitoring is difficult; an example of such a collaboration is the establishment of an investment- based relation between the angel and the entrepreneur. Trust in the other party is based on beliefs or assumptions, which are shaped through information exchange (Thomas et al., 2009). The quality of the information provided therefore determines the amount of trust placed in the individual, and ultimately whether an investment-based relationship is established between angel and entrepreneur. Thus:

Hypothesis 5: The quality of the entrepreneur’s pitch influences the angel’s funding decision; a higher quality pitch is more likely to obtain funding than a lower quality pitch

15

3.4 CONCLUSIONS LITERATURE STUDY: CONCEPTUAL FRAMEWORK The extensive literature research of the previous section aimed to provide an answer to research questions 1 and 2, ‘what factors determine the opportunity-investor fit?’ and ‘what factors determine the entrepreneur-investor fit?’. In order to answer these questions, we looked into what factors constitute the opportunity and the entrepreneur respectively. We found that the degree of innovation of the opportunity, and the involvement of the business angel in the opportunity’s sector to be factors determining the opportunity-investor fit. Regarding the entrepreneur, we identified the entrepreneur’s experience, the entrepreneur’s gender and trust to be factors of interest in the entrepreneur-investor fit.

Through establishing hypotheses on the relation between the found factors and the funding decision and the similarity of the final deal compared to the initial deal, this section also laid the foundation for answering research questions 3 and 4 (RQ3: How do these factors influence the likelihood of obtaining funding?; RQ4: How do these factors influence the likelihood of obtaining the envisioned deal?).

Seven hypotheses have been established:

- H1: An opportunity involving a more radical innovation is less likely to receive funding than an opportunity involving less radical types of innovation - H2: An opportunity born in a sector familiar to the business angel is more likely to receive funding than an opportunity born in a sector unfamiliar to the business angel. - H3a: Serial entrepreneurs are more likely to receive funding than novice entrepreneurs - H3b: Serial entrepreneurs are more likely to negotiate the deal they had envisioned than novice entrepreneurs - H4a: Same-sex interactions are more likely to lead to a positive funding outcome than interactions between an entrepreneur and angel of different sex - H4b: Compared to male entrepreneurs, female entrepreneurs are less likely to negotiate the deal they had envisioned than male entrepreneurs - H5: The quality of the entrepreneur’s pitch influences the angel’s funding decision; a higher quality pitch is more likely to obtain funding than a lower quality pitch

The hypothesized relationships are summarized in the conceptual framework in Figure 5 on the next page, where a green arrow represents an expected positive relation between the factor and the outcome (e.g. familiarity with the opportunity’s sector increases the likelihood of obtaining funding); a red arrow represents an expected negative relation between the factor and the outcome (e.g. a higher degree of innovation decreases the likelihood of obtaining funding).

16

FIGURE 5: CONCEPTUAL FRAMEWORK

17

4. METHODOLOGY The previous chapter has put forward several hypotheses and a conceptual framework based on an extensive literature research. To test the established hypotheses in reality, the conceptual framework will be put in practice by performing an econometric analysis of a secondary dataset, making use of the statistical software package STATA. This chapter elaborates on the data used and consequently, the methodology applied in constructing STATA-compatible variables, and the subsequent analyses performed.

4.1 THE DATA As we have previously stated, obtaining funding is a crucial part of the new venture creation process (Grünhagen, 2008; Shane, 2003). We have seen that nascent entrepreneurs are most likely to obtain seed-stage funding from a distinct group of informal investors: the business angel. For nascent entrepreneurs, knowledge on how to increase their chances of obtaining angel funding is a valuable good. However, actually studying the angel-entrepreneur relationship is difficult: angels prefer their anonymity, so the investment process takes place behind closed doors and there are no publicly accessible registers with angel names and corresponding investment activities. This wish for anonymity is understandable, as public display of an angel’s wealth may lead them to be flooded by unwanted investment- and sponsoring requests (Brettel, 2003). Previous studies into the angel investing process often deal with these limitations, resulting in a restricted amount of observations. Brettel (2003) interviewed 48 business angels, Landström (1993) describes the informal risk capital market in Sweden on the basis of information from 52 angels and Reid (1999) uses data on 20 paired investor-investee cases to investigate the applicability of the principal-agent framework.

This research overcomes sample size problems by making use of footage of investment pitches of entrepreneurs towards business angels. In the BBC programme “The Dragons’ Den”, entrepreneurs have the opportunity to pitch their business idea to a group of five business angels (see Box 1 below for an overview of the Dragons’ Den rules). BOX 1: RULES OF THE DRAGONS’ DEN (BBC, 2015A)

Rule 1: The pitch Rule 5: Multi-Dragon investments Entrepreneurs start the meeting by stating their Each Dragon works as an individual investor and name, the name of their business, the amount of they can invest as little or as much as they want. A money they are pitching for and the percentage of full investment therefore involves between one and equity they are willing to give away. The maximum five Dragons duration of the pitch is three minutes, after which Rule 6: Refusing investment the Dragons can stop the entrepreneur An entrepreneur can refuse investment from a Rule 2: The Q&A Dragon if they think they are an unsuitable investor Entrepreneurs do not have to answer all questions or if the deal on the table is not right for them asked, but this decision can of course influence the Rule 7: The deal outcome The deal agreed on the day is an unwritten Rule 3: Opting out agreement depending on due diligence checks and The entrepreneur’s time is over when all five relies on the integrity of the Dragon and the Dragons have declared themselves ‘out’. Once a entrepreneur. The deal is solely between the Dragon Dragon is out, he/she cannot re-enter negotiations and the entrepreneur. If after additional meetings Rule 4: Investments no agreement is reached, neither party is legally The entrepreneur must secure at least the total obliged to complete the deal amount they asked for at the beginning of the pitch. Rule 8: The advocate If a Dragon offers less than that amount, the It is permitted for entrepreneurs to have an entrepreneur can try to make up the total by advocate on standby in the Den, to help them securing an additional investment from another answer some of the Dragons’ questions. This Dragon. The entrepreneur can negotiate more advocate must be someone who is directly involved18 money than was initially requested in the business. This advocate must be pre- approved.

The entrepreneur either pitches alone, or with a business partner. After a three minute pitch, one or more of the five angels can decide to provide the necessary funding in exchange for equity in the entrepreneurs business to be. Rule of the game is, that the entrepreneur has to secure at least the amount they asked for at the beginning of the pitch; if a dragon offers less than the full amount, the entrepreneur must convince other dragons to invest to make up the total amount. It is possible to negotiate more money than envisioned, but his often goes hand in hand with giving up a greater deal of equity (BBC, 2015a). Based on a Japanese concept, the show has been broadcast worldwide.

Observations from 11 seasons of the British Dragons’ Den are available, during which 359 entrepreneurs pitched their business idea. As each of the five angels decides to invest independently from the other angels, the dataset consists of 1791 unique interactions between entrepreneur and angel. In order to be featured on the show, an entrepreneur has to fill out an online application form, after which the programme directors decide to include the entrepreneur or not based on the strength of the idea, the robustness of the business plan and the projected turnover (BBC, 2015b) . It is understandable that using reality TV in academic research raises scepticism. The following points will elaborate on why the artificial setting of pitching in a TV-show can indeed approach real-life situations: 1. Other researchers have made use of television footage as well In his 1993 research, Gertner studied the individual risk-taking behaviour of contestants during the television game show ‘Card Sharks’. Similar studies in measuring game show contestant risk attitudes uses data from the Dutch television game show Lingo (Beetsma & Schotman, 2001), the Italian game show ‘Affari Tuoi’ (Bombardini & Trebbi, 2012) and the Dutch version of ‘Deal or No Deal’ (Post et al., 2008). Bucci and Tenorio used footage of the television game show ‘The Family Feud’ to assess the impact of member characteristics on group decisions and performance in their 2010 article, while Metrick (1995) used footage from the game show ‘Jeopardy!’ to analyse contestant behaviour. Aside from noting potential bias in the candidate selection process, the mentioned studies do not indicate other validity concerns in using televised footage for academic research purposes. 2. On-air decision making is comparable to real-life decision making Maxwell et al. (2011) suggests that using television footage in academic research does not have to hamper validity. When studying footage from entrepreneur’s business pitches on the BBC’s ‘Dragons’ Den’ for instance, we study people under situations of real consequences. Would the situation have taken place in real-life, then the angel- and entrepreneur’s choice options would not have been different: to invest or not to invest, and what amount of equity to hand over. The study of Post et al. (2008) confirmed that participant’s TV behaviour was similar to behaviour in real-life, by replicating the ‘Deal or No Deal’ risky decision making process in a subsequent experiment (Maxwell et al., 2011). 3. Practical Although some may question the validity of television footage in academic research, it is undisputed that the use of TV materials extends sample size greatly. The current Dragons’ Den database, for example, contains observations on over 300 entrepreneur-investor interactions.

19

VARIABLES In the previous analysis of the twelve seasons of the Dragons’ Den, numerous variables have been constructed. These variables are categorized under identification variables, food related, invention/idea, entrepreneur, pitch, investment proposal and result. An overview of the variables constructed in previous research with a short description can be found in Annex A: List of variables in original Dragons’ Den database1. A table with descriptive statistics pertaining to all variables used in this research can be found in Annex B: Descriptive statistics.

In this research, not all previously constructed variables are relevant, and some variables have to be adapted to make them applicable here. First, dependent variables will be discussed. The outcome of the investment process can be defined by the questions if an investment is done, and if so, how much the eventual deal resembles the entrepreneur’s initial offer. The dependent variable used here has to tell us these two things. The first outcome of the model, funding obtained, can take on binary values only: either funding is obtained (1), or funding is not obtained (0) (see Figure 5: Conceptual framework on page 17). The decision to invest can be read from variable G9 Final business value2: if G9 > 0, an investment is made, if G9 = 0 no investment is made.

The second outcome, describing the resemblance of the final deal to the initial offer, is only relevant when funding has actually been obtained. Assuming that funding has been obtained, a variable has to be constructed that allows us to compare between the outcome of the different funding proposals. Taking the absolute figure of funding obtained (represented by ‘G8 Third offer (£)’) would not be an accurate way to describe the differences in funding obtained, as some (technical) proposals require more funding to enable start up than others. Here, we will look at how G9 Final business value deviates from F15 Initial business value. We are, in fact, constructing a variable that indicates the degree of similarity of the final business value established by the business angel, compared to the initial business value sought by the entrepreneur. Initial business value is defined as the amount of money (£) requested by the entrepreneur divided by the equity offered; final business value is defined as the angel’s third offer (£) divided by the final amount of equity offered. This leads to the construction of the proportional variable FINAL_INITIAL (see Table 6 below).

TABLE 6: OVERVIEW OF DEPENDENT VARIABLES

Dependent Variable(s) used Variable explained variable Funding obtained  G9 Final  Fractional variable [0, 1] business value  Funding is obtained when G9 Final business value > 0 Resemblance  F15 Initial  FINAL_INITIAL= G9 Final business value/ F15 Initial initial offer business value business value  G9 Final  Fraction indicating to what extent the final offer business value resembles the entrepreneur’s initial offer [0, 1]. High similarity/success for FINAL_INITIAL values approaching 1

1 The variables from previous research are consequently indicated with their alphabetical code. Newly constructed variables do not have this code 2 As explained in Annex A, G9 Final business value is obtained from dividing G8 Third offer (£) by G8 Third offer (%) following methods of Jaffe and Randolph Westerfield (2005) and Drury (2008). In other words, the amount of money offered by the angel is divided by the percentage of equity he requires in return. When the angel does not invest, G8 Third offer = 0, and as a result G9 Final business value will equal 0 as well. 20

EXPLANATORY VARIABLES The outcome of the dependent variables is hypothesized to be predicted by two opportunity-related factors (degree of innovation and familiarity sector), and three entrepreneur-related factors (experience entrepreneur, gender entrepreneur and pitch quality).

The degree of innovation is represented by variable ‘C4 Innovation type’. This is a categorical variable, representing the categories incremental innovation, modular innovation, architectural innovation and radical innovation, as introduced by Henderson and Clark (1990) (see Table 7 below).

TABLE 7: CATEGORIES AND CORRESPONDING FREQUENCIES VARIABEL 'C4 INNOVATION TYPE'

Frequency Percentage Cumulative Incremental 628 35.06 35.06 Modular 290 16.19 51.25 Architectural 669 37.35 88.61 Radical 204 11.39 100 Here, we are especially interested in the difference in the likelihood of obtaining funding between radical innovative opportunities and other opportunities. Radical innovative proposals are not that prevalent in the dataset, though: only 11% of proposals concerns a radical innovative opportunity. A division is therefore made between the less innovative proposals (i.e. incremental and architectural innovation types) and more innovative proposals (i.e. modular and radical innovation types)3. The new dummy variable RADICAL is constructed, where 0 represents proposals dealing with incremental- and architectural innovation typologies, and 1 represents proposals dealing with modular and radical innovation typologies (see Table 8 below).

TABLE 8: RECODED 'C4 INNOVATION TYPE' INTO RADICAL

Dummy Frequency Percentage Cumulative value Not radical 0 1118 69.35 69.35 Radical 1 494 30.65 100 The familiarity of the business angel with the idea’s sector, and the experience of the entrepreneur are both already represented in the original database (based on work of Ucbasaran et al. (2008) and Zott and Huy (2007)) . Both are dummy variables, where 0 means that the business angel is not familiar with the sector and that he is a novice entrepreneur respectively, and 1 means that he is familiar with the sector and that he is a serial entrepreneur respectively (see Table 9 on the next page).

3 Here, modular innovation is deemed more innovative than architectural innovation, as architectural innovation involves arranging existing components in new ways, while modular innovation introduces new technologies in specific components. In architectural innovation, the components have at least been tested previously; modular innovation introduces developments new to the angel (Magnusson et al., 2003) 21

TABLE 9: FREQUENCY TABLE INVOLVE AND EXPERIENCE

Dummy value Frequency Percentage Cumulative Not involved in sector 0 1685 94.08 94.08 Involved in sector 1 106 5.92 100 Novice entrepreneur 0 313 17.48 17.48 Serial entrepreneur 1 1478 82.52 100 Next, the entrepreneur’s gender is of interest. This variable originally represents the percentage of females in the entrepreneurial team. Here, we are interested in the effect of same-sex interactions on funding decision, and the effect of the interaction between the entrepreneur’s gender and the angel’s gender and its effect on the similarity of the final business value compared to the initial business value. The variable ‘D2 Gender’ takes on values of 0 (male entrepreneur or exclusively male entrepreneurial team), 0.5 (same amount of men and women in entrepreneurial team) or 1 (female entrepreneur or exclusively female entrepreneurial team). This variable is therefore recoded into the categorical variable GEN_ENT, containing the categories ‘male’, ‘mixed’ and ‘female’ (see Table 10 below). Similarly to real life, where 61.6 % of the Dutch entrepreneurs is male, in our sample 62.42 % is represented by male entrepreneurs.

The values 1, 2 and 3 are assigned to the respective GEN_ENT categories. Next, the angel’s gender has to be accounted for. The new variable GEN_BA is constructed and the values 4 and 5 are assigned to the male- and female angels respectively. Normally, the values 0 and 1 would be assigned to a variable like GEN_BA determining either male (=0) or female (=1), but as we are interested in the interaction between the gender of the entrepreneur, and the gender of the business angel, we have to omit using zeroes (as the whole equation would end up being zero), and recurrent values (when using 1,2,3 for the entrepreneur’s gender and 1,2 for the angel’s gender for instance, we can obtain a value of ‘2’ in two different ways). Assigning each gender category with a unique number allows us to create an interaction variable that represents every possible gender interaction between entrepreneur and angel (see Table 11 below).

TABLE 10: THE ENTREPRENEUR'S GENDER, CAPTURED IN VARIABLE GEN_ENT

Frequency Percentage Cumulative Male (1) 1123 62.42 62.42 Mixed (2) 218 12.12 74.54 Female (3) 458 25.46 100

TABLE 11: GENDER INTERACTION ENTREPRENEUR-ANGEL (GEN_ENT*GEN_BA)

Gender business angel Male(=4) Female (=5) Male (=1) 4 5

Mixed (=2) 8 10 entrepreneur Gender Gender Female (=3) 12 15 These numeric values are then recoded into specific terms indicating the gender-based type of interaction (see Table 12 on the next page for the code and the corresponding frequencies), allowing us to create eponymous dummy variables.

22

TABLE 12: LABELS GENDER INTERACTIONS ENTREPRENEUR-ANGEL

Gender business angel

Male Female Male M_M M_F Frequency 828 295

Mixed MIX_M MIX_F Frequency 153 65

Gender entrepreneur Gender Female F_M F_F Frequency 333 125 A last gender related variable, is SMLR_GENDER. This variable indicates whether the entrepreneur and angel are of different sex (SMLR_GENDER=0), or of similar sex (SMLR_GENDER=1). In practice, this means we assign values of 0 for the M_F and F_M entrepreneur-angel interactions, and a value of 1 to the MIX_M, MIX_F, M_M and F_F entrepreneur-angel interactions (see Table 13 on the next page).

TABLE 13: INTRODUCTION OF SMLR_GENDER VARIABLE IN ENTREPRENEUR-ANGEL INTERACTIONS

Gender business angel Male Female Male M_M M_F

Frequency 828 295 SMLR_GENDER 1 0

Mixed MIX_M MIX_F Frequency 153 65 SMLR_GENDER 1 1

Gender entrepreneur Gender Female F_M F_F Frequency 333 125 SMLR_GENDER 0 1

Finally, the quality of the pitch is used as a proxy for trust placed in the entrepreneur. In the original dataset, the variable E5 Pitch quality is based on Clark (2008) and complemented by work of Chen et al. (2009). Pitch quality is found to be determined by the determinants clarity, structure, information provided, and confidence exerted. These determinants are evaluated on a three-point scale, where 1 is poor, and 3 is good. To obtain an overall score for pitch quality, the scores of the four determinants are combined to form the new variable PITCHQ. PITCHQ takes on values between 4 and 12, allowing us to recode the scores between 4 and 6 as ‘poor’, between 7 and 9 as ‘average’ and scores between 10 and 12 as ‘good’ (see Table 14 below).

TABLE 14: FREQUENCY TABLE PITCHQ

Frequency Percentage Cumulative Poor 250 14 14 Average 504 28.22 42.22 Good 1032 57.78 100

23

CONTROL VARIABLES In their article, Maxwell et al. (2011) define 8 critical factors determining the strength of the opportunity, as summarized in Table 3 on page 10. Following these critical factors, several control variables were distilled from the original list of variables. The dummy variable IP represents Maxwell’s protectability category and MARKET_SIZE incorporates the market potential category (Kotler et al., 2009). The fractional variable FRAC_TOTAL shows how the magnitude of the entrepreneur’s request compares to the seasonal average amount invested per proposal (see Table 15 below).

TABLE 15: CONTROL VARIABLES

Values Frequencies Description IP 0 898 Dummy variable indicating whether intellectual 1 893 property related issues occur (=1) or not (=0) FRAC_TOTAL 0-37.6 - Fraction representing amount initially requested (£) divided by the average amount invested per season per proposal MARKET_SIZE Regional 35 Dummy variables indicating the scale of the National 1003 potential market size for the Continental 64 invention/innovation Global 510

24

4.2 THE MODEL In the previous sections, we have seen that we are dealing with two steps in the angel investment decision process: first, the angel has to decide whether to invest in the opportunity or not, and if so, how much he will deviate from the equity offered by the entrepreneur. The appropriate model, therefore, has to make use of two steps. The decision of what model to use depends on the shape of our dependent variable.

In the first step, we model a ‘yes’ or ‘no’ outcome: does the angel invest, or not? This yes/no decision can be recoded into binary values, assigning a value of 1 to the decision to invest, and a 0 to the decision to not invest. We will estimate this binary outcome making use of the probit model. In the previous section on the dependent variables, we have constructed two dependent variables: one for the first step, and one for the second step. Inherent to the probit model, though, is a strict ‘decision- making’ mechanism: if X>0, the outcome is 1 and an investment is made (see Equation 2 below). All other values lead the probit to register a 0 outcome/no investment made (Long, 1997). Bearing this characteristic in mind, we can logically reduce our two dependent variables to one, as if G9 Final business value=0, then FINAL_INITIAL=04. In both cases, the first step probit model will register the outcome as 0/no investment made.

As normal regression analysis is applicable for the prediction of a continuous dependent variable from a number of independent variables, we need a link function that can transform our dichotomous Y into a continuous Y’. The probit link function makes use of a latent variable Y* to bridge the gap between the dichotomous Y and the continuous Y’:

EQUATION 1: PROBIT LINK FUNCTION

푌∗ = 푋훽 + 휀

In linear regression, the Y* is observed directly (i.e. a unit increase in x increases Y* by β), but in the latent model we only observe whether the individual did (y=1) or did not (y=0) invest:

EQUATION 2: PROBIT MEASUREMENT EQUATION LINKING LATENT VARIABLE TO OBSERVED VARIABLE Y

∗ 0 푖푓 푦푖 ≤ 0 푌푖 = { ∗ 1 푖푓 푦푖 > 0

Here, the probability of an individual making an investment choice is modelled. Therefore, the interpretation of the output coefficients in probit is different from the interpretation of a linear regression output. In the probit model, the coefficients determine the increase of the z-score of the probability that Y=1. Raising the value of an independent variable therefore has a constant effect on Y’, but not on the original Y. By taking the marginal effects of the estimated coefficients after the probit regression, we can find the effect of a unit change of a particular variable on the probability that Y=1, given that all other variables are constant. The marginal effects are then to be interpreted differently for continuous- and dummy variables; an infinitesimal change of a continuous variable

4 Recall that FINAL_INITIAL=G9 Final business value/F15 Initial business value. If G9 Final business value = 0, as would be the case when the angel does not make an investment offer, the whole equation will become 0 25 changes the probability that Y=1 by X%, while a change in a dummy variable from zero to one changes the probability that Y=1 by X percentage points5.

Our second step then looks at how similar the final offer is compared to the initial offer. In this second step, we have to deal with several difficulties inherent to either the character of the dependent variable, or the nature of the angel decision process. As this second step involuntarily requires us to make a selection from our sample (the question ‘how similar is final offer versus initial offer’ is only relevant if the outcome of the first step is ‘yes’, or 1), we may be dealing with a selection bias6. The fact that we require a probit model for the first step of the two-step model combined with this selection bias immediately invites us to use the commonly applied Heckman model; this model inserts the inverse Mills ratio calculated from the first part of the model as a correction factor into the second part (Bushway et al., 2007). Although the Heckman model corrects for the potential bias that sample selection may create, the proportional nature of our second-step dependent variable does not allow for the subsequent OLS regression that is used in the Heckman model7.

It may therefore be wise to look at another model that does accept a proportional second step dependent variable. Papke and Wooldridge (1996) suggest to use a generalized linear model (GLM), where the standard setting for the family is altered from Gaussian to Binomial and a logit link function is employed. We use a logit link function as Y is limited to the [0,1] interval, and residuals are unlikely to be normally distributed. Here, we assume that the errors are logistically distributed. The binomial family is used to accommodate for our bounded dependent variable. The GLM systematic part assumes that

EQUATION 3: LINEAR PREDICTOR GLM

휂 = 푥훽 where η is the linear predictor: its value predicts the outcome of the dependent variable. The expected value µ is linked to the linear predictor through the link function g, which in the case of normal errors leads to the ordinary linear regression model. In our case, however, our link function is as follows:

EQUATION 4: LINK FUNCTION GLM µ g = ln ( ) = 휂 = 푥훽 1 − µ

5 The difference between the two is that the term percentage is relative, while percentage points are absolute. This can be illustrated by making use of the example of the increasing number of female entrepreneurs. In 2007, 32.4% of all Dutch entrepreneurs was female. An increase in the number of Dutch female entrepreneurs of 6 percentage points indicates that the number of female entrepreneurs has increased with 6% of the total number of entrepreneurs (making 38.4% of all entrepreneurs female); an increase of 6% means that the number of female entrepreneurs has increased with 6% of the current number of female entrepreneurs (making 32.6% of all entrepreneurs female). 6 Sample selection bias is defined here as the systematic difference between the collective of individuals that do not participate and those who do (Cuddeback et al., 2004). In our case, the systematic difference between the individuals that participate in the second step and those who do not, is a negative investment decision of the angel. 7 In the case of a fractional dependent variable, Y is bounded between 0 and 1 and the assumption of linearity required for OLS regression is violated (Choi, 2013). 26

This customized GLM can then be embedded in the two step model (TPM), to allow us to have a different set of explanatory variables determining the outcome of the first step (if an investment is made) and the second step (how similar is the final offer versus the initial offer).

OPERATIONALIZATION Now that we have decided what variables to focus on, we can divide them over the two steps making use of the conceptual framework as introduced in Figure 5.

Our regression equations for the first and second step respectively are as follows:

EQUATION 5: FIRST STEP PROBIT EQUATION

푦[0,1] = 훽0 + 푆푀퐿푅_퐺퐸푁퐷퐸푅 훽1 + 푅퐴퐷퐼퐶퐴퐿훽2 + 푃푂푂푅훽3 + 퐺푂푂퐷훽4 + 퐹푅퐴퐶_푇푂푇퐴퐿훽5

+ 퐸푋푃퐸푅퐼퐸푁퐶퐸훽6 + 퐼푁푉푂퐿푉퐸훽7 + 퐼푃훽8 + 푁퐴푇퐼푂푁퐴퐿훽9 + 퐶푂푁푇퐼푁퐸푁푇퐴퐿훽10 + 퐺퐿푂퐵퐴퐿훽11

EQUATION 6: SECOND STEP GLM EQUATION

휂(0,1) = 훽0 + 푀_퐹 훽1 + 푀퐼푋_푀훽2 + 푀퐼푋_퐹훽3 + 퐹_푀훽4 + 퐹_퐹훽5 + 퐸푋푃퐸푅퐼퐸푁퐶퐸훽6 + 푃푂푂푅훽7

+ 퐺푂푂퐷훽8

For the first step probit, marginal effects will be computed so that conclusions can be drawn on the impact of changing the value of one of the explanatory variables on the probability that y=1 (investment made). In running the second step GLM, the explanatory variable coefficients can already tell us something about the impact of that particular variable on the similarity of the final offer to the initial offer. However, we are especially interested in the effect of for instance gender on the similarity of the final offer to the initial offer. By constructing new observations based on hypothesized values for the regressors, we use the actual observations to model the predicted mean proportion for a number of combinations of the variables that we are interested in (see Table 16 below)8.

TABLE 16: SPECIFIC EXPLANATORY VARIABLE COMBINATIONS USED TO COMPARE MEANS

Constructed Gender Gender Experience Pitch observation entrepreneur angel entrepreneur quality 1792 Male Male No Poor 1793 Male Male Yes Good 1794 Male Female No Poor 1795 Male Female Yes Good 1796 Female Male No Poor 1797 Female Male Yes Good 1798 Female Female No Poor 1799 Female Female Yes Good

8 This method is touched upon in Baum (2006, p. 107); information on how to apply the STATA PREDICT command that enables the construction of observations can be found in the STATA manual (STATA corporation, 2015). 27

5. RESULTS In the following section, the series of STATA outputs produced are combined to provide insight in the investment decisions made, starting from the first probit step. Robust standard errors (displayed in brackets) are used to deal with heteroskedasticity, and the marginal effects of the first step probit analysis are provided for easy coefficient interpretation. Table 17 below provides the results from running a reduced- and complete two-part model. Descriptive statistics, such as a correlation matrix and a separate output table for the first step probit analysis can be found in Annex B: Descriptive statistics.

TABLE 17: OUTPUT TWO-PART MODEL, INCLUDING MARGINAL EFFECTS OF FIRST STEP PROBIT ESTIMATES

Estimates Estimates Variables model model 1 2 Similar gender entrepreneur-angel (SMLR_GENDER) 0.0309 ** 0.0309 ** (0.0136) (0.0136) Innovation type (RADICAL) 0.0333 ** 0.0333 ** (0.0161) (0.0161) Pitch quality = poor (POOR) -0.0576 *** -0.0576 *** (0.0197) (0.0197) Pitch quality = good (GOOD) 0.0786 *** 0.0786 *** (0.0158) (0.0158) Experience entrepreneur (EXPERIENCE) 0.0423 *** 0.0423 *** (0.0162) (0.0162) Business angel is involved in sector (INVOLVE) 0.1788 *** 0.1788 *** (0.0441) (0.0441) Issues with intellectual property recorded (IP) -0.0224 -0.0224 (0.0143) (0.0143) Proportion of money requested compared to -0.0227 *** -0.0227 *** average amount given out per proposal per season (0.0072) (0.0072) (FRAC_TOTAL) Potential market size = national (NATIONAL) 0.0822 0.0822 (0.0534) (0.0534) Potential market size = continental (CONTINENTAL) 0.1711 0.1711 (0.1351) (0.1351) Potential market size = global (GLOBAL) 0.1360 0.1360 (0.0869) (0.0869) GLM Experience entrepreneur (EXPERIENCE) 0.0685 0.0920 (0.2400) (0.2362) Pitch quality = good (GOOD) 0.3420 ** 0.3307 ** (0.1560) (0.1630) Pitch quality = poor (POOR) 0.5894 0.6252 (0.5130) (0.4980) Gender entrepreneur = female (FEMALE_ENT) 0.3286 ** (0.1442) Male-female entrepreneur-angel interaction (M_F) 0.3401 * (0.2040) Mixed-male entrepreneur-angel interaction 0.2607 (MIX_M) (0.2269)

28

Mixed-female entrepreneur-angel interaction 0.4639 (MIX_F) (0.4342) Female-male entrepreneur-angel interaction (F_M) 0.3996 ** (0.1896) Female-female entrepreneur-angel interaction (F_F) 0.4876 ** (0.2115) Constant -0.4030 * -0.5233 ** (0.2494) (0.2529) Observations 1st step 1612 1612 McFadden’s Pseudo R2 0.1070 0.1070 Wald chi2 108.96 108.96 Prob > chi2 0.0000 0.0000 Observations 2nd step 215 215 Deviance9 0.2650 0.2652 ***, **, *: statistically significant at the 1%, 5% and 10% confidence level, respectively

From the output, we read that an entrepreneur-angel dyad in which both parties are of the same sex increases the probability of a positive funding decision. In other words, the probability of obtaining funding is higher for male-male, female-female, mixed-male and mixed-female interactions combined than for the combination of male-female and female-male interactions. Next, we see that the dummy variable representing more innovative investment opportunities influences our funding probability positively. This is a surprising result, as previous research has repeatedly indicated that investors tend to shy away from a more radically innovative opportunity because of the associated risks and the irretrievability of inputs (see for instance, Arrow (1962, p. 619)).

The quality of the pitch is an important factor contributing to the likelihood of obtaining funding: when comparing a poor and a good pitch with a pitch that is considered average, we see a relatively large negative coefficient for a poor pitch, and a comparably large positive coefficient for a pitch considered to be good. Next, we observe that a serial entrepreneur is indeed more probable of being acknowledged a positive investment decision, as is delineated by the positive sign of the coefficient.

The involvement of the business angel in the sector in which the opportunity is born, is of importance in the decision of granting the entrepreneur funding or not. The relatively large positive coefficient that is significant at the 0.01 level makes angel involvement in the opportunities’ sector the variable that influences the probability of obtaining funding the most in this model.

Next, we see negative relationships between both the occurrence of issues with intellectual property (IP) and the likelihood of obtaining funding, and a bigger amount of money requested (FRAC_TOTAL) and the likelihood of obtaining funding. The relationship between IP and the investment decision is not significant, however. Finally, when looking at market size, we see that a change in potential market size from regional to national positively influences the probability of obtaining funding.

Subsequently, we will have a look at the second step of the process, and we will look at the influence of gender interactions, pitch quality and experience on the deviation from the initial business value (see section indicated with ‘GLM’ in Table 17 on the previous page).

9 The model’s goodness of fit is determined by the deviance that has been corrected for the sample size; this number measures how close the predicted values from the fitted model match the actual values from the raw data. A lower deviance indicates a better fit of the model. 29

First, we see a significant positive effect of a good pitch on the similarity of the final offer versus the initial offer. A good pitch therefore not only influences the decision to fund or not, but also contributes to achieving the deal envisioned. Next, we see that a female entrepreneur is more likely to negotiate the deal she envisioned than her colleague male entrepreneurs. We further look into this by replacing FEMALE_ENT of the first model with the gender interaction terms in the second model. In model 2, we see that female-male and female-female entrepreneur-angel interactions do indeed influence the probability of obtaining a deal similar to that envisioned when compared to a male-male interaction. Also, we see that this positive influence is strongest for female entrepreneurs pitching to female angels.

Finally, experience of the entrepreneur has not been found to have a significant effect on the similarity of the final deal versus the initial deal.

For a more detailed look at how the explanatory variables of the second part of the model interact and influence the outcome, we refer back to the hypothetical observations we constructed earlier. Our model used the 1791 actual observations to predict the dependent variable in these eight constructed observations. In Table 18 below, the different combinations of our second step explanatory variables and their corresponding predicted means are captured.

First, we see that a male, novice entrepreneur that pitches poorly to a male angel is expected to deviate most from their proposed deal when compared to the other scenarios. A female novice entrepreneur with a pitch similar in quality to that of her male colleague for instance, is observed to deviate less from her initial proposal (outcomes 0.4788 and 0.5178 respectively).

Next, having assigned half of our hypothetical entrepreneurs the female gender, we see that this does not negatively impact the predicted mean outcome. A serial female entrepreneur adequately pitching to a female angel, for instance, is observed to be the closest to her initial deal compared to her hypothetical colleagues. A novice female entrepreneur pitching poorly to a female angel is observed to be much closer to her initial deal than a male entrepreneur with similar characteristics pitching to a female angel (0.5178 versus 0.3595)

TABLE 18: PREDICTED VALUES FOR CONSTRUCTED OBSERVATIONS

Constructed Gender Gender Experience Pitch Predicted observation entrepreneur angel entrepreneur quality mean 1792 Male Male No Poor 0.3244 1793 Male Male Yes Good 0.4612 1794 Male Female No Poor 0.3595 1795 Male Female Yes Good 0.5001 1796 Female Male No Poor 0.4788 1797 Female Male Yes Good 0.6209 1798 Female Female No Poor 0.5178 1799 Female Female Yes Good 0.6568

30

6. DISCUSSION In this chapter, the results presented in the previous section are interpreted so as to establish whether the hypotheses formulated in chapter 3 are supported or not.

First, we considered the impact of the degree of innovation of the opportunity on the likelihood of obtaining funding. Based on literature discussing predominantly venture capital investment cases, we came to the following hypothesis: (H1) An opportunity involving a more radical innovation is less likely to receive funding than an opportunity involving less radical types of innovation. Based on the results of the econometrical estimation of the coefficients using the first step of the two-part model, we do not find support for H1. The positive significant marginal effects in Table 17 make clear that a switch from an incremental or architectural innovation to a modular or radical innovation increases the probability of obtaining funding with 0.03 percentage points, given that all other variables are held constant. This outcome can be explained, though, by the fact that we based our hypothesis on literature on venture capitalists.Nofsinger and Wang (2011) clearly make a distinction between formal- and informal investor decisions. Formal investors can indeed be deterred by the lack of return appropriation and risks associated with more radical innovation, but informal investors are said to prefer investing in new products, which are inseparably tied to innovation.

Next, we considered the sector in which the opportunity has been born. In the second hypothesis, we put forward the following: (H2) An opportunity born in a sector familiar to the business angel is more likely to receive funding than an opportunity born in a sector unfamiliar to the business angel. This hypothesis is supported by the results of the first step probit analysis: we see that the probability of obtaining funding increases with 0.18 percentage points when the angel is involved in the opportunity’s sector.

Our next hypothesis moves the discussion towards the entrepreneur. Here, we hypothesize that (3a) Serial entrepreneurs are more likely to receive funding than novice entrepreneurs, and, consequently, (3b) Serial entrepreneurs are more likely to negotiate the deal they had envisioned than novice entrepreneurs. While the outcome of the probit analysis supports hypothesis 3a, we do not find significant proof that experienced entrepreneurs are more likely to negotiate the deal they initially envisioned.

For the entrepreneur’s gender, we put forward that (H4a) Same-sex interactions are more likely to lead to a positive funding outcome than interactions between an entrepreneur and angel of different sex and (H4b) Compared to male entrepreneurs, female entrepreneurs are less likely to negotiate the deal they had envisioned than male entrepreneurs. As hypothesis 4a was based on findings from literature that investors (and humans in general) prefer similarity, we constructed a binary variable that indicates whether or not the angel and entrepreneur are of the same gender. Here, we see that entrepreneurs pitching to angels of the same gender are more likely to obtain funding.

When looking at the deal obtained, we can draw conclusions in favour of the female entrepreneur. The significant positive coefficient for the F_M dummy variable indicating a female entrepreneur- male angel interaction, indicates that a F_M interaction is more likely of obtaining a deal similar to their initial offer compared to a male entrepreneur-male angel interaction. This is confirmed by our hypothetical entrepreneurs that are represented by constructed observations, as the predicted mean for female entrepreneurs is systematically higher than that of their male entrepreneur colleagues.

31

We therefore do not find support for hypothesis 4b. An explanation for this result can lie in the fact that in general, women are found to be less confident in negotiations than men (Kulik & Olekalns, 2012). This would influence the outcome of the negotiation negatively, but here we look at how close the final offer resembles the initial request. Compared to men, women are found to consistently allocate themselves less resources when asked to determine their own compensation (Stuhlmacher & Walters, 1999). This notion could imply that women entrepreneurs already request less money in their pitch than male entrepreneurs, allowing the final deal to be closer to their initial request.

Finally, we looked into the influence of the quality of the pitch on the likelihood of obtaining funding. We hypothesized as follows: (H5) The quality of the entrepreneur’s pitch influences the angel’s funding decision; a higher quality pitch is more likely to obtain funding than a lower quality pitch. Indeed, we observe that a qualitatively good pitch positively and quite substantially influences the probability of obtaining funding: hypothesis 5 is supported by the model. When pitch quality is entered in the second part of the model, we see that a good pitch positively influences the probability of obtaining a deal similar to the one envisioned. However, a poor pitch is found to have an even bigger positive impact on achieving a similar deal. This outcome therefore has to be treated with caution.

32

7. CONCLUSIONS This research aimed to provide insight in how the opportunity-investor fit, and the entrepreneur investor-fit influence the decision to invest, and the similarity of the final deal compared to the initial offer. To do so, the following sub-research questions have been introduced:

1. What factors determine the opportunity-investor fit? 2. What factors determine the entrepreneur-investor fit? 3. How do these factors influence the likelihood of obtaining funding? 4. How do these factors influence the likelihood of obtaining the envisioned deal?

We first examined what factors contribute to the strength of respectively the opportunity and the entrepreneur, hereby answering research questions 1 and 2. We found that the degree of innovation and the involvement of the angel in the opportunity’s sector are factors determining the opportunity-investor fit; the entrepreneur’s experience, his gender and trust (proxied by pitch quality) were found to influence entrepreneur-investor fit.

To examine the nature of the relation between the opportunity/entrepreneur-related factors, and the likelihood of obtaining funding and the likelihood of obtaining the envisioned deal, a set of seven hypotheses was established:

- H1: An opportunity involving a more radical innovation is less likely to receive funding than an opportunity involving less radical types of innovation - H2: An opportunity born in a sector familiar to the business angel is more likely to receive funding than an opportunity born in a sector unfamiliar to the business angel. - H3a: Serial entrepreneurs are more likely to receive funding than novice entrepreneurs - H3b: Serial entrepreneurs are more likely to negotiate the deal they had envisioned than novice entrepreneurs - H4a: Same-sex interactions are more likely to lead to a positive funding outcome than interactions between an entrepreneur and angel of different sex - H4b: Compared to male entrepreneurs, female entrepreneurs are less likely to negotiate the deal they had envisioned than male entrepreneurs - H5: The quality of the entrepreneur’s pitch influences the angel’s funding decision; a higher quality pitch is more likely to obtain funding than a lower quality pitch

We found support for H2, H3a, H4a and H5. Regarding H1 and H4b, we conclude that in this research, a more radical innovation positively influences the likelihood of obtaining a positive funding decision and female entrepreneurs are found to be more likely to negotiate the deal they had envisioned than male entrepreneurs. No significant relation between the entrepreneur’s experience and his likelihood of negotiating the deal he had envisioned was found. The answer to research questions 3 and 4 can be summarized graphically by updating the conceptual framework (see Figure 6 on the next page):

33

FIGURE 6: UPDATED CONCEPTUAL FRAMEWORK. A GREEN ARROW INDICATES A POSITIVE RELATION BETWEEN THE FACTOR AND THE OUTCOME

With all of the above in mind, we can answer the main research question: How do opportunity- investor fit and entrepreneur-investor fit influence the screening stage of the business angel- entrepreneur investment process? Based on the tested factors, we can conclude that the entrepreneur-investor fit is relevant in both steps of the funding process, as pitch quality and the entrepreneur’s gender influenced the outcome of both steps significantly. Regarding the opportunity-investor fit: we see that angel involvement in the opportunity’s sector and innovation degree significantly and positively influence the probability of obtaining funding. Both fits are therefore of interest in the angel-entrepreneur interaction, and the entrepreneur is wise to not only focus on the strength of his envisioned opportunity, but also on his own human capital.

34

7.1 LIMITATIONS During this research, several points of attention were found. The following section will explain issues arising from the inclusion or exclusion of variables, the model selection, the sample in itself, the robustness of the models used, and finally issues that may arise from using BBC footage.

1. Inclusion of variables

In Annex A: List of variables in original Dragons’ Den database, the list of variables as they were originally constructed can be found. In the Variables section of the Methodology chapter can be found that several of the original variables have not been included in this research, or have been transformed to fit this research better.

First, for some variables, it was merely the case that the variable contained what was considered too many missing observations. This was the case for several of the variables describing the financial aptitude of the business plan, such as variables F1, F3, F5 and F7. Here, the missing values attributed for 85%, 60%, 69% and 79% of all observations, respectively10. Had there been more observations for these variables, they would have been included in the model to increase its robustness.

Next, the disputable objectivity of the observer caused some variables to be omitted from inclusion in the model. This goes for variables E5 (‘trust’), F9 (‘soundness financials proposal’) and F11 (‘soundness business as a whole’). These variables had in common that the observer of the footage made an assumption on what the business angel thought of the trust established and the financial soundness of the business. These variables were inferred rather than observed, and are therefore omitted from the model.

The binary variable D5 ‘Experience’ indicates whether an entrepreneur displays market understanding, leadership and has proven his success in the past. The three separate evaluation criteria are evaluated on a three point scale, but no consistency was found in the appropriation of a 0 or 1 in the overarching variable D5 Experience. This variable is therefore also omitted from model inclusion.

2. Model selection

As explained in the Methodology chapter, the selected model is not flawless. While the popular Heckman model accounts for sample selection bias hazards, it only functions with a continuous dependent variable. In this research, we chose to represent the dependent variable as a success rate, by dividing the final business value by the initial business value. This returns a proportional dependent variable, bounded between 0 and 1, which is a problem in general because of the inclusion of zeroes as an actual value, and for the second step Heckman OLS regression.

To account for the proportional bounded dependent variable, the TPM is therefore chosen over the Heckman model. The TPM does allow for a proportional dependent variable, but does not correct for sample selection. This is something to take into account, because non-random sample selection is what we are doing here.

10 Aside from the variables discussing the financial aspects of the proposal, this also goes for C21 BA’s attitude towards industry and D11 Background 35

3. Sample issues

In the full sample, the total number of observations amounts to 1791. Only 215 of the 1791 unique pitches actually receive funding however; this means we are left with approximately 12% of the full dataset after the first step of the model. Although this is a big reduction, 215 observations is still considered substantial in the angel-entrepreneur dyad research: see The data in the Methodology chapter.

Also, we have to be careful in generalizing the findings from this research. Although we have established that 215 is a decent amount of pitch proposals to use in any research, we have to keep in mind that the composition of the group of business angels barely changes. We therefore have to bear in mind that the conclusions we draw here, are based on the behaviour and decision making processes of a very select group of business angels.

4. Robustness models

The dataset contains few continuous variables. As a result, our explanatory variables are mainly dummy- or categorical variables. This causes the model to be less nuanced than it had been if more continuous variables had been available. Also, in Table 17, we see that the constant term of the first model is not significant. Normally, this would lead to the removal of the intercept from the model, but removing the intercept would imply that the response function is zero when the predictors are all zero. This is not applicable in our case, thus the intercept is included in the model.

5. Using BBC footage

The BBC has selected Dragons’ Den candidates based on criteria that are unclear at the moment. Also, although the BBC states that the Dragons do not follow a script, we are unaware of potential implicit Dragons’ Den rules. Next to this, journalists found that several investment promises made on-air, were not cashed in after the programme (Burn-Callander, 2015). This can have several causes, varying from the angel not honouring the deal, the entrepreneur going on the show for marketing purposes only, or the fact that the entrepreneur receives a better deal after appearing on the show. At the moment, it is unclear which proposals did not receive their Dragon investment, and why. Angel- and entrepreneur ulterior motives for appearing on the show can downgrade the reliability of our findings.

7.2 FURTHER RESEARCH Further research is recommended in order to improve both the validity of the results and the usability of the data. Observations can always be added to the database, provided that the BBC is still recording new Dragons’ Den seasons. It is advisable to have multiple independent observers analyse the episode in future research, so as to increase the validity of the results. In order to relieve concerns regarding observer objectivity in this research, it would be advisable to have someone watch a random selection of episodes to see if their observations match those of the original observer.

Also, we currently have little information on a. the business case selection decisions employed by the BBC, b. the implicit rules of the Den, if any and c. ulterior motives of the entrepreneurs and/or angels for appearing on the show. Obtaining information these points will increase the research’s credibility.

36

Next, this research does not include one of the major variable categories already present in the database: the food-related variables. The database therefore is perfectly suited for future research regarding food-related business cases.

Ideally, a research of similar nature with a larger pool of business angels would allow us to generalize results to the angel population as a whole. This would maybe also allow the exploration of the influence of gender further. Based on the support for H4a: Same-sex interactions are more likely to lead to a positive funding outcome than interactions between an entrepreneur and angel of different sex, we would expect that female entrepreneurs would have to be less successful in obtaining funding if they would randomly select their investment partner. As over 95% of business angels is male, the chances of a female entrepreneur encountering a female angel are substantially lower than for a male entrepreneur meeting a male angel (Parker & Mason, 2007).

37

8. REFERENCES American Express Open. (2011). The American Express OPEN State of Women-Owned Businesses Report. Amit, R., & Muller, E. (1995). “PUSH” AND “PULL” ENTREPRENEURSHIP. [Journal of Small Business & Entrepreneurship]. Journal of Small Business & Entrepreneurship, 12(4), 64-80. doi: 10.1080/08276331.1995.10600505 Arrow, K. (1962). Economic welfare and the allocation of resources for invention The rate and direction of inventive activity: Economic and social factors (pp. 609-626): Princeton University Press. Baregheh, A., Rowley, J., & Sambrook, S. (2009). Towards a multidisciplinary definition of innovation. [Management Decision]. 47(8), 1323-1339. Baum, C. F. (2006). An introduction to modern econometrics using Stata: Stata press. BBC. (2015a). BBC Two - Dragons’ Den - About Dragons’ Den. Retrieved 16-05-2015, 2015, from http://www.bbc.co.uk/programmes/articles/4nhgBmsXHWDjRqC704jPLyD/about-dragons- den BBC. (2015b). BBC Two - Dragons' Den - Apply to enter the Den. Retrieved 27-05, 2015, from http://www.bbc.co.uk/programmes/b006vq92 Beetsma, R. M. W. J., & Schotman, P. C. (2001). Measuring Risk Attitudes in a Natural Experiment: Data from the Television Game Show Lingo. The Economic Journal, 111(474), 821-848. doi: 10.2307/798414 Bergmann, H., & Stephan, U. (2013). Moving on from nascent entrepreneurship: measuring cross- national differences in the transition to new business ownership. [Small Business Economics]. 41(4), 945-959. doi: 10.1007/s11187-012-9458-4 Bessant, J., & Tidd, J. (2011). Innovation and entrepreneurship. Hoboken, N.J [etc.]: Wiley [etc.]. Bhave, M. P. (1994). A process model of entrepreneurial venture creation. Journal of Business Venturing, 9(3), 223-242. doi: http://dx.doi.org/10.1016/0883-9026(94)90031-0 Bombardini, M., & Trebbi, F. (2012). RISK AVERSION AND EXPECTED UTILITY THEORY: AN EXPERIMENT WITH LARGE AND SMALL STAKES. Journal of the European Economic Association, 10(6), 1348-1399. doi: 10.1111/j.1542-4774.2012.01086.x Bosma, N., & Amoros, J. E. (2014). Global Entrepreneurship Monitor 2013 Global Report. Brettel, M. (2003). Business angels in Germany: A research note. [Venture Capital]. Venture Capital, 5(3), 251-268. doi: 10.1080/1369106032000122095 Burn-Callander, R. (2015, 21-02-2015). Half of Dragons' Den investments fall through after the show, The Telegraph. Retrieved from http://www.telegraph.co.uk/finance/businessclub/money/11425328/Half-of-Dragons-Den- investments-fall-through-after-the-show.html Bushway, S., Johnson, B. D., & Slocum, L. A. (2007). Is the magic still there? The use of the Heckman two-step correction for selection bias in criminology. Journal of Quantitative Criminology, 23(2), 151-178. Byrne, D., Griffitt, W., & Stefaniak, D. (1967). Attraction and similarity of personality characteristics. Journal of Personality and Social Psychology, 5(1), 82-90. doi: 10.1037/h0021198 Carter, N. M., Gartner, W. B., & Reynolds, P. D. (1996). Exploring start-up event sequences. Journal of Business Venturing, 11(3), 151-166. doi: http://dx.doi.org/10.1016/0883-9026(95)00129-8 Casson, M., & Wadeson, N. (2007). The discovery of opportunities: Extending the economic theory of the entrepreneur. Small Business Economics, 28(4), 285-300. doi: 10.1007/s11187-006-9037- 7 Center for Women in Business. (2014). Women-owned businesses. Carving a new American business landscape. In U. S. C. o. C. Foundation (Ed.). Cha, M.-S., & Bae, Z.-T. (2010). The entrepreneurial journey: From entrepreneurial intent to opportunity realization. The Journal of High Technology Management Research, 21(1), 31-42. doi: http://dx.doi.org/10.1016/j.hitech.2010.02.005

38

Chen, X.-P., Yao, X., & Kotha, S. (2009). Entrepreneur passion and preparedness in business plan presentations: a persuasion analysis of venture capitalists' funding decisions. Academy of Management Journal, 52(1), 199-214. Choi, Y. M. (2013). Estimation of Fractional Dependent Variables Observed on [0, 1) With an Application to Firm Capital Structure. International Journal of Digital Content Technology & its Applications, 7(13). Clark, C. (2008). The impact of entrepreneurs' oral ‘pitch’ presentation skills on business angels' initial screening investment decisions. Venture Capital, 10(3), 257-279. doi: 10.1080/13691060802151945 Clement, R. W., & Krueger, J. (2002). Social Categorization Moderates Social Projection. Journal of Experimental Social Psychology, 38(3), 219-231. doi: http://dx.doi.org/10.1006/jesp.2001.1503 Collewaert, V. (2012). Angel Investors' and Entrepreneurs' Intentions to Exit Their Ventures: A Conflict Perspective. Entrepreneurship Theory and Practice, 36(4), 753-779. doi: 10.1111/j.1540-6520.2011.00456.x Collisson, B., & Howell, J. L. (2014). The Liking-Similarity Effect: Perceptions of Similarity as a Function of Liking. The Journal of Social Psychology, 154(5), 384-400. doi: 10.1080/00224545.2014.914882 Cooper, A. C., Gimeno-Gascon, F. J., & Woo, C. Y. (1994). Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing, 9(5), 371-395. doi: http://dx.doi.org/10.1016/0883-9026(94)90013-2 Cuddeback, G., Wilson, E., Orme, J. G., & Combs-Orme, T. (2004). Detecting and statistically correcting sample selection bias. Journal of Social Service Research, 30(3), 19-33. Cunningham, W. (2012). Startup Financing Environment The Jobs Act (pp. 21-30): Apress. Curley, M., Formica, P., Curley, M., & Formica, P. (2013). Introduction The Experimental Nature of New Venture Creation (pp. 1-9): Springer International Publishing. Drury, C. (2008). Management and cost accounting. European Commission. (2014). Tackling the gender pay gap in the European Union (pp. 2). Luxembourg: Publications Office of the European Union. EUROSTAT. (2014). Gender distribution in selected labour categories (Barchart). Retrieved 22-05- 2015 http://ec.europa.eu/eurostat/statistics- explained/index.php/File:Gender_distribution_in_selected_labour_categories,_age_15- 74,_EU-28,_2014.png Fawcett, C. A., & Markson, L. (2010). Similarity predicts liking in 3-year-old children. Journal of Experimental Child Psychology, 105(4), 345-358. doi: http://dx.doi.org/10.1016/j.jecp.2009.12.002 Feeney, L., Haines, G. H., & Riding, A. L. (1999). Private investors' investment criteria: Insights from qualitative data. Venture Capital, 1(2), 121-145. doi: 10.1080/136910699295938 Franke, N., Gruber, M., Harhoff, D., & Henkel, J. (2006). What you are is what you like-similarity biases in venture capitalists' evaluations of start-up teams. Journal of Business Venturing, 21(6), 802-826. Gartner, W. B. (1985). A Conceptual Framework for Describing the Phenomenon of New Venture Creation. The Academy of Management Review, 10(4), 696-706. doi: 10.2307/258039 Gartner, W. B. (1988). Who is an entrepreneur? is the wrong question. American journal of small business, 12(4), 11-32. Gelderen, M., Thurik, R., & Bosma, N. (2005). Success and Risk Factors in the Pre-Startup Phase. [Small Business Economics]. 24(4), 365-380. doi: 10.1007/s11187-004-6994-6 Gertner, R. (1993). Game Shows and Economic Behavior: Risk-Taking on "Card Sharks". The Quarterly Journal of Economics, 108(2), 507-521. doi: 10.2307/2118342 Google. (2015). Google Trends - Interesse in Google Zoeken: crowdfunding. Wereldwijd, jan 2007-jan 2015. Retrieved 21-01, 2015

39

Gregory, B. T., Rutherford, M. W., Oswald, S., & Gardiner, L. (2005). An empirical investigation of the growth cycle theory of small firm financing. Journal of Small Business Management, 43(4), 382-392. Grünhagen, M. (2008). The evolution of entrepreneurs' fund-raising intentions: A multiple case study of financing processes in new ventures. Hackett, S., & Dilts, D. (2004). A Systematic Review of Business Incubation Research. The Journal of Technology Transfer, 29(1), 55-82. doi: 10.1023/B:JOTT.0000011181.11952.0f Henderson, R. M., & Clark, K. B. (1990). Architectural Innovation: The Reconfiguration of Existing Product Technologies and the Failure of Established Firms. Administrative Science Quarterly, 35(1), 9-30. doi: 10.2307/2393549 Jaffe, J., & Randolph Westerfield, R. (2005). Corporate finance: Tata McGraw-Hill Education. Johnson, M. A. (1989). Variables Associated with Friendship in an Adult Population. The Journal of Social Psychology, 129(3), 379-390. doi: 10.1080/00224545.1989.9712054 Kickstarter. (2015). Kickstarter Stats. Retrieved 22-01, 2015, from https://www.kickstarter.com/help/stats?ref=footer Kotha, R., & George, G. (2012). Friends, family, or fools: Entrepreneur experience and its implications for equity distribution and resource mobilization. Journal of Business Venturing, 27(5), 525- 543. doi: http://dx.doi.org/10.1016/j.jbusvent.2012.02.001 Kotler, P., Broere, F., & Roozenboom, T. (2009). Principes van marketing. Amsterdam: Pearson Education. Kray, L. J., Thompson, L., & Galinsky, A. (2001). Battle of the sexes: Gender stereotype confirmation and reactance in negotiations. Journal of Personality and Social Psychology, 80(6), 942-958. doi: 10.1037/0022-3514.80.6.942 Kulik, C. T., & Olekalns, M. (2012). Negotiating the Gender Divide: Lessons From the Negotiation and Organizational Behavior Literatures. Journal of Management, 38(4), 1387-1415. doi: 10.1177/0149206311431307 Lahti, T. (2011). Categorization of angel investments: An explorative analysis of risk reduction strategies in Finland. Venture Capital, 13(1), 49-74. doi: 10.1080/13691066.2010.543322 Landström, H. (1993). Informal risk capital in Sweden and some international comparisons. Journal of Business Venturing, 8(6), 525-540. doi: http://dx.doi.org/10.1016/0883-9026(93)90037-6 Landström, H. (1998). Informal investors as entrepreneurs: Decision-making criteria used by informal investors in their assessment of new investment proposals. Technovation, 18(5), 321-333. doi: http://dx.doi.org/10.1016/S0166-4972(98)00001-7 Lerner, J. (2002). WHEN BUREAUCRATS MEET ENTREPRENEURS: THE DESIGN OF EFFECTIVE `PUBLIC VENTURE CAPITAL' PROGRAMMES. The Economic Journal, 112(477), F73-F84. doi: 10.1111/1468-0297.00684 Long, J. S. (1997). Regression models for categorical and limited dependent variables. Thousand Oaks, Calif., [etc.]: Sage. Macmillan, I. C., Siegel, R., & Narasimha, P. N. S. (1985). Criteria used by venture capitalists to evaluate new venture proposals. Journal of Business Venturing, 1(1), 119-128. doi: http://dx.doi.org/10.1016/0883-9026(85)90011-4 Magnusson, T., Lindström, G., & Berggren, C. (2003). Architectural or modular innovation? Managing discontinuous product development in response to challenging environmental performance targets. International Journal of Innovation Management, 7(01), 1-26. Mason, C., & Stark, M. (2004). What do Investors Look for in a Business Plan?: A Comparison of the Investment Criteria of Bankers, Venture Capitalists and Business Angels. International Small Business Journal, 22(3), 227-248. doi: 10.1177/0266242604042377 Mason, C. M., & Harrison, R. T. (2002). Barriers to investment in the informal venture capital sector. Entrepreneurship and Regional Development, 14(3), 271-287. doi: 10.1080/08985620210142011

40

Maxwell, A. L., Jeffrey, S. A., & Lévesque, M. (2011). Business angel early stage decision making. Journal of Business Venturing, 26(2), 212-225. doi: http://dx.doi.org/10.1016/j.jbusvent.2009.09.002 Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of Management Review, 20(3), 709-734. McMullen, J. S., & Shepherd, D. A. (2006). Entrepreneurial action and the role of uncertainty in the theory of the entrepreneur. Academy of Management Review, 31(1), 132-152. Metrick, A. (1995). A Natural Experiment in "Jeopardy!". The American Economic Review, 85(1), 240- 253. doi: 10.2307/2118006 Miloud, T., Aspelund, A., & Cabrol, M. (2012). Startup valuation by venture capitalists: an empirical study. Venture Capital, 14(2-3), 151-174. doi: 10.1080/13691066.2012.667907 Mitteness, C., Baucus, M., & Sudek, R. (2012). Horse vs. Jockey? How stage of funding process and industry experience affect the evaluations of angel investors. Venture Capital, 14(4), 241- 267. doi: 10.1080/13691066.2012.689474 Mitteness, C. R., Sudek, R., & Baucus, M. S. (2010). Entrepreneurs as authentic transformational leaders: critical behaviors for gaining angel capital. Frontiers of Entrepreneurship Research, 30(5), 3. Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study. Journal of Business Venturing, 29(1), 1-16. National Business Incubation Association. (2015). About NBIA. Retrieved 26-05, 2015, from http://www.nbia.org/resource_library/what_is/index.php Nicholson, M. (2000). Scotland survey: angels venture further afield. Financial Times, 6. Nofsinger, J. R., & Wang, W. (2011). Determinants of start-up firm external financing worldwide. Journal of Banking & Finance, 35(9), 2282-2294. doi: http://dx.doi.org/10.1016/j.jbankfin.2011.01.024 Panteia, & Ondernemerschap.nl. (2014). Bedrijfsfinanciering, zo kan het ook (pp. 25). Zoetermeer. Papke, L. E., & Wooldridge, J. M. (1996). Econometric Methods for Fractional Response Variables With an Application to 401 (K) Plan Participation Rates. Journal of Applied Econometrics, 11(6), 619-632. doi: 10.2307/2285155 Parker, S., & Mason, C. (2007). Informal Sources of Venture Finance The Life Cycle of Entrepreneurial Ventures (Vol. 3, pp. 259-299): Springer US. Paul, S., Whittam, G., & Wyper, J. (2007). Towards a model of the business angel investment process. [Venture Capital]. Venture Capital, 9(2), 107-125. doi: 10.1080/13691060601185425 Peirone, D. (2007). Knowledge and venture funding: complementarities and financial contracts. Industrial and Corporate Change, 16(5), 851-873. doi: 10.1093/icc/dtm029 Post, T., van den Assem, M. J., Baltussen, G., & Thaler, R. H. (2008). Deal or No Deal? Decision Making under Risk in a Large-Payoff Game Show. American Economic Review, 98(1), 38-71. doi: doi: 10.1257/aer.98.1.38 Raiffa, H. (1982). The art and science of negotiation: Harvard University Press. Reid, G. C. (1999). The application of principal-agent methods to investor-investee relations in the UK venture capital industry. Venture Capital, 1(4), 285-302. doi: 10.1080/136910699295820 Shane, S. (2003). A general theory of entrepreneurship : the individual-opportunity nexus. Cheltenham [etc.]: Edward Elgar. Shepherd, D. A., Douglas, E. J., & Shanley, M. (2000). New venture survival: Ignorance, external shocks, and risk reduction strategies. Journal of Business Venturing, 15(5), 393-410. Sohl, J. (2008). The angel investor market in 2008: A down year in investment dollars but not in deals. Center for Venture Research, 26. Sohl, J. (2013). The Angel Investor Market in 2012: A Moderating Recovery Continues. Center for Venture Research Report, University of New Hampshire. STATA corporation. (2015). Title Syntax Menu for predict - STATA. Retrieved 20-05, 2015, from http://www.google.nl/url?sa=t&rct=j&q=&esrc=s&source=web&cd=3&ved=0CDMQFjAC&url =http%3A%2F%2Fwww.stata.com%2Fmanuals13%2Frpredict.pdf&ei=dp5cVfXpDcXfUefOgIA

41

G&usg=AFQjCNF18S_8SM1cTff4DuBwTqLy7A56vA&sig2=dNnh9P5xoTjOjbb8pN9VDQ&bvm= bv.93756505,d.d24 Strätling, R., Wijbenga, F. H., & Dietz, G. (2012). The impact of contracts on trust in entrepreneur– venture capitalist relationships. International Small Business Journal, 30(8), 811-831. doi: 10.1177/0266242610388822 Stuhlmacher, A. F., & Walters, A. E. (1999). Gender differences in negotiation outcome: a meta‐ analysis. Personnel Psychology, 52(3), 653-677. Sudek, R. (2006). Angel investment criteria. Journal of Small Business Strategy Vol, 17(2), 2007. Thomas, G. F., Zolin, R., & Hartman, J. L. (2009). The Central Role of Communication in Developing Trust and Its Effect On Employee Involvement. Journal of Business Communication, 46(3), 287-310. doi: 10.1177/0021943609333522 Tomczak, A., & Brem, A. (2013). A conceptualized investment model of crowdfunding. [Venture Capital]. Venture Capital, 15(4), 335-359. doi: 10.1080/13691066.2013.847614 Ucbasaran, D., Westhead, P., & Wright, M. (2008). Opportunity identification and pursuit: does an entrepreneur’s human capital matter? Small Business Economics, 30(2), 153-173. Valdez, M. E., & Richardson, J. (2013). Institutional Determinants of Macro-Level Entrepreneurship. Entrepreneurship Theory and Practice, 37(5), 1149-1175. doi: 10.1111/etap.12000 Valk, P. v. d., & Smit, L. (2010). Financieringsmonitor MKB Starters. In E. Panteia (Ed.). Van Osnabrugge, M. (2000). A comparison of business angel and venture capitalist investment procedures: An agency theory-based analysis. [Venture Capital]. Venture Capital, 2(2), 91- 109. doi: 10.1080/136910600295729 Verschuren, P., Doorewaard, H., Poper, R., & Mellion, M. J. (2010). Designing a research project. The Hague: Eleven International Publishing. Volkmann, C., Tokarski, K., & Grünhagen, M. (2010). Start-up and Growth Financing Entrepreneurship in a European Perspective (pp. 283-345): Gabler. Webster, F. A. (1976). A model for new venture initiation: A discourse on rapacity and the independent entrepreneur. Academy of Management. The Academy of Management Review (pre-1986), 1(000001), 26. Whipple, J. M., & Frankel, R. (2000). Strategic Alliance Success Factors. Journal of Supply Chain Management, 36(2), 21-28. doi: 10.1111/j.1745-493X.2000.tb00248.x Zhang, J. (2011). The advantage of experienced start-up founders in venture capital acquisition: evidence from serial entrepreneurs. Small Business Economics, 36(2), 187-208. Zott, C., & Huy, Q. N. (2007). How entrepreneurs use symbolic management to acquire resources. Administrative Science Quarterly, 52(1), 70-105.

42

ANNEX A: LIST OF VARIABLES IN ORIGINAL DRAGONS’ DEN DATABASE Variable name Short description A. Identification variables A1 Number Interaction number (ranging from 1-5) A2 Season Season in which the pitch took place A3 Episode Episode in which the pitch took place A4 URL URL linking to YouTube A5 Contestant Shows the order of pitches A6 Name The name of the entrepreneur(s) A7 Business angels Indicates who the business angels were A8 Start Time on the YouTube video that the pitch started A9 End Time on the YouTube video that the pitch ended A10 Duration Duration of the pitch on the YouTube video A11 Short description A short description of the entrepreneur’s innovation or idea B. Food related B1 Food related Whether the innovation is food related or not B2 Food product Whether a food product is involved in the innovation/idea B3 Food process/technology Whether a process itself is an important part of the innovation/ idea B4 Food service Whether a food service itself is an important part of the innovation/idea B5 Food marketing Whether a marketing strategy is included in the innovation/idea B6 Food packaging Whether a special packaging method is part of the innovation/idea B7 Sector Indicates the sector to which the food innovation/idea belongs B8 Area Indicates the scale of the target market B9 Consumer/B2B Indicates whether the innovation/idea will be marketed to consumers or B2B B10 Single product Whether the product is part of a range, or is a single product B11 Product innovation category Innovation category in which the food innovation/idea is placed B12 Food product innovation Whether the food product is considered innovative B13 Food process innovation Whether the food process is considered innovative B14 Food service innovation Whether the food service is considered innovative B15 Food marketing innovation Whether the food marketing is considered innovative B16 Food packaging innovation Whether the food packaging is considered innovative B17 Functional foods Whether a functional food or health claim is involved in the innovation/idea B18 Environmentally friendly If environmental sustainability of the product is mentioned by the entrepreneur B19 Fair trade If fair trade is mentioned by the entrepreneur C. Invention/idea C1 Product Whether the innovation/idea involves a product C2 Process Whether the innovation/idea involves a process C3 Service Whether the innovation/idea involves a service C4 Innovation type The degree of innovation (incremental-radical) C5 Sector The sector in which the innovation/idea will be marketed C6 Area Indicates the scale of the target market C7 Consumer/B2B Indicates whether the innovation/idea will be marketed to

A

Variable name Short description consumers or B2B C8 Single product Whether the product is part of a range, or is a single product C9 Revenue model Describes the way the value of the product is turned into profit C10 Revenue model explained Elaborates on variable C9 C11 Development stage Stage of development the innovation/idea is in C12 Life cycle Describes in what stage of the life cycle the innovation/idea is in C13 Internal route strategy Describes the internal route strategy applied by entrepreneur C14 External route strategy Describes the external route strategy applied by entrepreneur C15 Intellectual property issues Whether issues exist with intellectual property rights C16 Innovation insight source How the entrepreneur came to the innovation C17 Innovation insight explained Elaborates on variable C16 C18 Innovation process Whether the innovation comes from a demand-pull, technology push or coupled process C19 Exit strategy Whether issues occur related to the exit strategy C20 BA involved in industry Whether the business angel is involved in the innovation/idea’s industry C21 BA’s attitude towards industry Whether the BA has a positive attitude towards the industry D. Entrepreneur D1 Single/team Indicates number of entrepreneurs D2 Gender Indicates share of female entrepreneurs (%) D3 Age Indicates share of entrepreneurs below 40 years old (%) D4 Relation to innovation Indicates entrepreneur’s relation to the innovation D5 Experience Indicates to what degree the entrepreneur displays market understanding, leadership and proof of success D6 Involved in other job Whether the entrepreneur is involved in another job D7 Origin The ethnic origin of the entrepreneur D8 Other BA involved in business Whether other investors are involved in the business D9 Professional appearance Whether the entrepreneur is dressed formally D10 Professional experience as Whether the entrepreneur has experience in venturing (serial entrepreneur entrepreneur) D11 Background The educational background of the entrepreneur E. Pitch E1 Pitch quality Quality of the pitch determined by presentation clarity, structure, information provided, and confidence level portrayed E2 Product testing Whether a product test is involved in the initial presentation E3 BA participate Whether the entrepreneur invites the angel to test the product E4 Passion demonstrated Whether the entrepreneur demonstrates passion E5 Trust Whether trust is built or violated in the entrepreneur-angel interaction F. Investment proposal F1 Any debt/liabilities The value of the debt/assets of the entrepreneur F2 Any debt/liabilities explained Elaborates on variable F1 F3 Turnover/revenue/sales Current or expected sales and revenues total F4 Turnover/revenues explained Elaborates on variable F3 F5 Current profits/loss Value of financial result F6 Current profits/loss explained Elaborates on variable F5 F7 Expected profits/loss The profit/loss expected by the entrepreneur

B

Variable name Short description F8 Expected profits/loss explained Elaborates on variable F7 F9 Financial situation seen by BA Whether the feedback of the BA indicates that the financial situation of the pitched business is good F10 Financial situation seen by BA Elaborates on variable F9 explained F11 Financial situation seen by BA- Whether the feedback of the BA indicates that the financial total business situation of all of the entrepreneur’s businesses is good F12 Financial situation seen by BA- Elaboration on variable F11 total business explained F13 Initial % offered The share of company ownership offered by the entrepreneur F14 Initially requested money The initial amount of money requested by the entrepreneur F15 Initial business value Equals F14/F13 F16 Expected ROI Expected net profit divided by capital F17 Proposal mentioned Time in video when the proposal is started F18 Business angel role The role the angel will fulfil (active/passive) F19 Business angel role explained Elaborates on variable F18 G. Result G1 Business angel Indicates who were the angels in that particular episode G2 Order of action Indicates in which order the business angels stated their decision to invest or not G3 Investment made Whether an investment was made G4 Final % and money achieved Equals final money requested/final percentage offered G5 Negotiation Whether negotiation took place G6 First offer (%, £) Indicates the percentage of the business requested and the amount of money offered in the first round G7 Second offer(%, £) Indicates the percentage of the business requested and the amount of money offered in the second round G8 Third offer (%, £) Indicates the percentage of the business requested and the amount of money offered in the third round G9 Final business value Equals the money acquired/percentage company offered G10 BA’s conclusions negative Indicates what the angel’s negative conclusions were after the pitch G11 BA’s conclusion positive Indicates what the angel’s positive conclusions were after the pitch

C

ANNEX B: DESCRIPTIVE STATISTICS

B1: CORRELATION TABLE

FINAL_INITIAL SMLR_GENDER RADICAL POOR AVERAGE GOOD EXPERIENCE INVOLVE IP FRACT_TOTAL REGIONAL NATIONAL CONTINENTAL GLOBAL FEMALE_ENT M_M M_F MIX_M MIX_F F_M F_F FINAL_INITIAL 1 SMLR_GENDER 0.033 1 RADICAL 0.037 0.059 1 POOR -0.099 -0.028 -0.005 1 AVERAGE -0.112 0.004 0.052 -0.256 1 GOOD 0.172 0.017 -0.044 -0.480 -0.726 1 EXPERIENCE 0.075 0.027 0.098 -0.105 -0.133 0.196 1 INVOLVE 0.144 0.054 -0.038 0.008 -0.084 0.070 0.001 1 IP -0.049 -0.043 -0.189 0.045 0.050 -0.077 -0.093 0.020 1 FRAC_TOTAL -0.059 -0.044 -0.037 0.143 0.019 -0.119 -0.027 -0.034 0.084 1 REGIONAL -0.026 0.020 -0.008 -0.062 0.046 0.085 0.067 0.048 0.005 0.002 1 NATIONAL -0.038 -0.097 -0.163 0.135 -0.052 -0.049 -0.050 -0.012 0.293 -0.099 -0.193 1 CONTINENTAL 0.056 0.023 0.098 -0.084 0.015 0.046 0.005 -0.015 -0.099 -0.048 -0.031 -0.263 1 GLOBAL 0.023 0.085 0.131 -0.086 0.062 0.005 0.029 0.004 -0.265 0.123 -0.102 -0.871 -0.139 1 FEMALE_ENT -0.012 -0.466 -0.093 0.030 -0.052 0.026 -0.093 -0.070 0.113 -0.108 -0.037 0.183 -0.052 -0.157 1 M_M 0.008 0.685 0.076 -0.004 0.046 -0.039 0.071 0.046 -0.154 0.105 0.066 0.157 0.014 0.136 -0.540 1 M_F -0.003 -0.608 0.019 0.004 0.032 -0.032 0.049 -0.007 -0.039 0.034 0.002 -0.023 0.020 0.015 -0.258 -0.412 1 MIX_M 0.018 0.219 0.007 -0.039 -0.043 0.067 -0.039 0.033 0.098 -0.047 -0.045 0.011 0.009 -0.000 -0.173 -0.279 -0.134 1 MIX_F -0.012 0.141 -0.034 -0.012 0.000 0.008 0.008 -0.014 0.004 0.084 -0.027 -0.029 0.024 0.029 -0.028 -0.111 -0.179 -0.086 1 F_M -0.038 0.647 -0.091 0.031 -0.035 0.010 -0.080 -0.059 0.090 -0.086 -0.023 0.141 -0.048 -0.119 0.821 -0.443 -0.212 -0.142 -0.091 1 F_F 0.037 0.196 -0.021 0.005 -0.037 0.030 -0.037 -0.029 0.057 -0.054 -0.023 0.098 -0.016 0.088 0.462 -0.249 -0.119 -0.080 -0.051 -0.127 1

D

B2: SUMMARY STATISTICS APPLIED VARIABLES Variable Observations Mean Standard Minimum Maximum deviation FINAL_INITIAL 1791 0.0614 0.1847 0 1 INITIAL 1791 917678.7 1185265 125000 15000000 FINAL 215 323335.7 255469.1 62500 2000000 PITCHQ 1791 2.4375 0.7272 1 3 SMLR_GENDER 1791 0.6509 0.4768 0 1 INNO_CAT 1786 2.1848 1.0905 1 4 RADICAL 1786 0.3124 0.4636 0 1 EXPERIENCE 1791 0.8238 0.3811 0 1 INVOLVE 1791 0.0591 0.2360 0 1 IP 1627 0.5489 0.4978 0 1 FRAC_TOTAL 1791 1.9899 2.6415 0 37.5990 MARKET_SIZE 1612 2.6507 0.9508 1 4 FEMALE_ENT 1791 0.2546 0.4357 0 1 GEN_ENT 1791 1.6304 0.8617 4 5 GEN_INTER 1791 6.9661 3.7802 4 15

E

B3: FIRST STEP PROBIT OUTPUT TABLE FOR REDUCED- AND COMPLETE MODEL Marginal effects Marginal effects Variables reduced model full model 1 2 Explanatory variables Similar gender entrepreneur-angel (SMLR_GENDER) 0.0361 *** 0.0309 ** (0.0138) (0.0136) Innovation type radical (RADICAL) 0.0510 *** 0.0333 ** (0.0166) (0.0161) Pitch quality = poor (POOR) -0.0587 *** -0.0576 *** (0.0208) (0.0197) Pitch quality = good (GOOD) 0.0915 *** 0.0786 *** (0.0159) (0.0158) Experience entrepreneur (EXPERIENCE) 0.0526 *** 0.0423 *** (0.0163) (0.0162) Business angel is involved in sector (INVOLVE) 0.1808 *** 0.1788 *** (0.0453) (0.0441) Control variables Issues with intellectual property recorded (IP) -0.0224 (0.0143) Proportion of money requested compared to average -0.0227 *** amount given out per proposal per season (0.0072) (FRAC_TOTAL) Potential market size = national (NATIONAL) 0.0822 (0.0534) Potential market size = continental (CONTINENTAL) 0.1711 (0.1351) Potential market size = global (GLOBAL) 0.1360 (0.0869) Observations 1781 1612 McFadden’s Pseudo R2 0.0945 0.1093 Wald chi2 114.16 110.57 Prob > chi2 0.0000 0.0000 Correctly classified 88.18% 88.25% Multicollinearity condition number 10.6185 25.47 ***, **, *: statistically significant at the 1%, 5% and 10% confidence level, respectively

F