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The relationship between business angels’

backgrounds and amounts invested in start-up

projects

Thesis seminar Business Studies

Academic year 2014-2015

15 July 2015

Author: Alex Schilder 10181423

Supervisor: Joeri Sol

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Statement of Originality

This document is writer by Alex Schilder, who declares to take full responsibility for the contents of this document.

I declare that the text and the work presented in this document is original and that no sources other than those mentioned in the text and its references have been used in creating it.

The Faculty of Economics and Business is responsible solely for the supervision of complet ion of the work, not for the contents.

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3 Abstract

There is little academic literature known on the background of the backers. This might be very important, since the crowdfunding campaign could be made much more specific if there was a target group of backers to aim for. Segmentation of backers and business angels could increase the chance of successful and sufficient funding for a specific project or business. This in turn might increase the success of the project or business in the long run. Since backers in equity crowdfunding are comparable to business angels, and there is no data available on the background of backers due to privacy policies, this study analyzes the background of business angels to see whether there is a relationship between this background and the amount invested. Regression analysis showed that there is only a positive significant effect of the quality of education on the frequency of investments made by business angels. Future research is needed to gain more insight in the relationship between the background of business angels and their investment behavior.

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Table of Contents

Abstract………3

Table of contents………4

1. Introduction………..5

2. Literature Review………...7

2.1 Crowdfunding………7

2.1.1 Goals of Founders & Crowdsourcing………...7

2.1.2 Goals of Funders & Crowdfunding Models………..8

2.1.3 Business Angels………...9

2.1.4 AngelList……….………..10

2.1.5 Segmentation……….11

3. Research Design & Data Description……….12

3.1 Methodology………..14

3.2 Conceptual Limitations ………..15

4. Data Analysis & Results ……….16

5. Discussion ………21

5.1. Key Findings………21

5.2. Limitations………..22

5.3. Recommendations for Future Research……….22

5.4. Practical Contributions………23

6. Conclusion………2 4

7. References………25

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1. Introduction

Crowdfunding is a new method of financing for all kinds of new ventures, allowing founders of for-profit, non-profit and social projects to raise funding from a large audience, which is called the crowd (Mollick, 2014). Crowdfunding originated from founders of ventures facing difficulties in attracting external finance in their startup stage (Belleflamme, Lambert, & Schwienbacher, 2014). As an answer to these problems, creative founders developed a new way of funding by tapping the crowd instead of specialized investors (Belleflame et al., 2014).

To acquire financing, various paths can be chosen. Most of the times, in the early stages of a start-up, funding is provided by friends, family, and the founder himself (Morrissette, 2007). When these funds are insufficient, the venture faces a funding gap (Collins & Pierrakis, 2013). Business Angels often fill the gap between founders, family, and friends on one side, and institutional venture capitalists on the other side, as a financing source (Ramadani, 2009)1. However, Hemer, Schneider, Dornbusch and Frey (2011) see crowdfunding as a way to reduce the funding gap in the early stages of new ventures. So, in the literature the definition of what a business angel does and what the function of crowdfunding is, does not seem to differ a lot. Taking into account that a business angel provides more than money, since they are hand-on investors and contribute their skills, expertise, knowledge and contacts in the business in which they invest (Ramadani, 2009).

This thesis explores the relationship between the background of a business angel and the amount invested in start-ups. This is done by collecting public data from the AngelList platform and analyzing this with a regression analysis. This results in a partial support for only one hypo thesis, since the frequency of investments is apparently positively influenced by the rank of the university attended by a business angel. This is a relevant contribution to the academic literature on the background of informal investors and the investment pattern of informal investors.

Among crowdfunded projects, failures happen by large amounts and successes by small amounts (Mollick, 2014). According to Mollick (2014), an important factor for someone to support a crowdfunding project, which is called a “backer”, is the quality of the project. Another important factor that the theory suggests should influence crowdfunding success is network size of the founders of the project (Stam and Elfring, 2008). So far, the academic literature has mostly focused on the characteristics of crowdfunding projects and the founders of a project, there is far less literature on the background of the backers. This might be very important, since the crowdfunding campaign could be made much more specific if there was a target group of backers to aim for.

1 Please note that often may be misleading here, as roughly 90% of entrepreneurs fail to obtai n funding from

business angels (see references in Maxwell and Levesque). Yet, at the same time often seem accurate given they are in the market for seed capital compared to other sources of funding such as government grants, and recently, crowdfunding.

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6 Moreover, if the campaign would have a more specific target, this part of the population of backers would be more easily penetrated, by which the success of the campaign might increase. Just like a market for consumers of products and services is segmented, the market of backers or business angels can be segmented as well.

If we could segment the population of backers for a crowdfunding campaign, on what criteria could this segmentation take place? Ramadani (2009) used gender, age, education, occupation and wealth as demographics to describe the profile of a business angel. Like most online communities, information on demographic and personal characteristics are not collected (Kuppuswamy, & Bayus, 2014). Since the definition of the function of crowdfunding and the goal of a business angel are nearly identical according to the literature, an analysis based on business angels is very likely to deliver results which are applicable to backers as well. Although it must be taken i nto account that business angels invest a significant higher amount compared to backers, and that motivations to invest may differ significantly as well, perhaps only equity-based crowdfunding is comparable to business angels.

Lately, it appears that younger people from a variety of backgrounds and with promising careers have participated in informal investments (Abernethy and Heidtman, 1999). However, others found that many business angels have previous entrepreneurial experience and accumulated their wealth through entrepreneurial activities instead of high income occupations (Mason and Harrison, 2002). So, apparently this relation between participating in informal investments and occupation is not very clear in the literature. In addition, the relation between occupation and the amount of capital invested and number of investments is still unknown. This could be valuable since different approaches might be used to attract entrepreneurs or usual employees to invest in a proposal or business. So, the main research question will be: What is the relation between occupation of a

business angel and the invested amount?

According to research, business angels are typically people with an academic degree or professional qualifications (Ramadani, 2009). About 75% of business angels have a university degree and about 20% enrolled in university but never finished their studies (Ramadani, 2009). The amount invested in informal investments might be influenced by the amount of education of a business angel. Since there appears to be a lack of literature on this subject, the sub-question addressed is:

What is the relation between education of a business angel and the invested amount?

In examining these two questions, first an overview of crowdfunding, crowdsourcing, business angels, and segmentation theories will be given. Next, the methodology and data will be described. Susequently, the results of the analysis will be examined. Finally, a conclusion will be formed and suggestions for further research will be offered.

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2. Literature Review

2.1. Crowdfunding

The concept of a crowd generating funding for a specific project exists for a long time. For example, Mozart and Beethoven financed their concerts and publications of new music manuscripts through subscription from interested parties (Hemer, 2011). In addition, the statue of Liberty in New York has been financed through small donations among many people (Hemer, 2011). More recently, founders have started to rely on the internet for financial support from the public, which we call the crowd (Schwienbacher, & Larralde, 2010). It is hard to give an exact definition of crowdfunding, since there are many projects that can be seen as crowdfunding. Nonetheless, Mollick (2014) tries to give a more specific definition: “crowdfunding refers to the efforts by entrepreneurial individuals and groups – cultural, social, and for-profit – to fund their ventures by drawing on relatively small contributions from a relatively large number of individuals using the internet, without standard financial

intermediaries.” There are two aspects not addressed in this definition: the goal of the crowdfunding project and the goal of the investors, which are of great importance, but also the aspects that are subjected to the most variation (Mollick, 2014).

2.1.1. Goals of Founders & Crowdsourcing

Many crowdfunding projects have a goal to raise a small amount of funding to initiate a particular one-time project (Mollick, 2014). On the other hand, crowdfunding recently appears to function frequently as a viable source for entrepreneurial seed capital (Schwienbacher, & Larralde, 2010). Moreover, crowdfunding offers a potential set of resources that go beyond capital, which can provide benefits for the founders of a project (Mollick, 2014). This is where crowdsourcing comes into play:

The concept of crowdfunding is rooted in the broader concept of crowdsourcing, which refers to using the crowd to obtain ideas, feedback, and solutions to develop corporate activities. In the case of crowdfunding, the objective is to collect money for investment, generally by using online social networks. In other words, instead of raising money from a small group of sophisticated investors, crowdfunding helps firms obtain money from large audiences, the crowd, in which each individual provides a very small amount. Such investment can take the form of equity purchase, loan, donation, or pre-ordering of the product (Belleflamme et al.,

2014, p. 586).

In addition, crowdfunding might also be used to demonstrate demand for a product, which can enlarge the chance of funding from traditional sources (Mollick, 2014). This is where it is more obvious that crowdfunding originates from the more general concept of crowdsourcing. A solid

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8 definition of crowdsourcing, according to Kleemann, Voß, and Rieder (2008):

Crowdsourcing, takes place when a profit oriented firm outsources specific tasks essential for the making or sale of its product to the general public (the crowd), in the form of an open call over the internet, with the intention of animating individuals to make a contribution to the firm’s production process for free or for significantly less than the contribution is worth to the firm (Kleemann et al., 2008, p. 6).

Kleemann et al. (2008) add that “crowdsourcing has been made possible on a large scale by the emergence of Web 2.0, a shorthand term for new internet applications that make two-way communication easier to manage.” The Web 2.0 has three core properties according to Lee,

DeWester, and Park (2008): collaboration, openness, and participation. They state that collaboration allows information to be used in new ways, openness increases social interaction over the web, and participation enables and requires new business models (Lee et al., 2008). Openness here is

particularly an important factor, since crowdsourcing represents the openness of Web 2.0, customers can participate in innovation and designing new products (Lee et al., 2008). Besides, participation has increased through the internet, which is an important factor in the growth of crowdfunding last years (Gerber, Hui, & Kuo, 2012).

2.1.2. Goals of Funders & Crowdfunding Models

Crowdfunding differs from other methods of start-up funding in that the relationship between funders and founders varies by context and nature of the funding effort (Belleflamme et al., 2014). According to Mollick (2014), there are four different models of crowdfunding:

1. The patronage model, in which funders function as philanthropist who donate money for the good cause, with no direct return for their donations.

2. The lending model, in which funds are offered as a loan, where some rate of return on capital invested is expected from the funders’ perspective. This might be combined with a social perspective as well, like microfinance, where the lender is more interested in the social good promoted by the venture than any return on capital.

3. The reward-based model, in which funders receive a reward for supporting a project. This is the most prevalent model nowadays. The reward can range from being credited in a movie, to meeting the creators of a project. Alternatively, funders are treated as early-customers, allowing them to access a product at an earlier date, better price or with some special benefit.

4. The equity-based model, in which funders are investors and treated as shareholders. This model is subjected to high levels of regulation (Heminway and Hoffman, 2010)

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9 There is one thing that the four different models have in common, according to Mollick (2014): “Ultimately, all forms of crowdfunding are based on similar principles, in that funders are investing funds in a project, and thus are expecting a successful outcome”. In this study, only the equity-based model of crowdfunding will be relevant.

2.2. Business Angels

According to Ramadani (2009), the term ‘business angels’ comes from Broadway:

At the end of the 19th century, rich investors began providing funds for directors to finance production of new musicals an plays. Besides financial benefits, their motivations came from their love for the theater and the opportunity to meet and socialize with famous actors, writers and producers. These investors secured high-risk capital and were motivated by something larger than money. Even today, writers, actors, producers, and musicians often depend on the altruism of other to promote their projects and careers (Ramadani, 2009).

Eventually, these business angels became an important source of funding for high risk, ambitious projects. For instance, Alexander Graham Bell received funding from business angels to found Bell Telephone in 1874; in 1903Henry Ford got funding from five business angels who invested $40.000, and more recently, in 1977, a business angel invested $91.000 in Apple Computers, which became one of the biggest brands in the world (Ramadani, 2009).

Morrissette (2007) defines business angels, or ‘angel investors’ as “wealthy individuals, typically fellow entrepreneurs, willing to invest in the very early stages of a venture’s development. Business angels typically fill the gap between funding from the founder, family and friends on one side, and venture capitalists or other financial institutions on the other side (Ramadani, 2009). Angel investors are wealthy individuals who, contrary to venture capitalists, invest their own money in start-ups. The investments made by business angels are mostly private and not publicized, so statistical data on these investments are usually hard to collect (Morrissette, 2007).

According to Ramadani (2009), research on business angels generally leads to similar characteristics. Most business angels are male, which might be due to the fact that less women have established successful enterprises, but the ‘glass ceiling’2 might play a role here as well. The age of business angels ranges generally from 40 to 65 years. At this age, the business angels apparently gained enough expertise and financials to make investments as business angel. A business angel has

2The ‘glass ceiling’ is the “unseen, yet unbreakable barrier that keeps minorities and women from rising to the

upper rungs of the corporate ladder, regardless of their qualifications or achievements.” (Federal Glass Ceiling Commission, 1995b. Solid Investment: Making Full Use of the Nation's Human Capital. U.S. Department of Labor.)

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10 generally a university diploma, although masters and doctorates are rare. Some of them have

enrolled in universities but never finished their studies. According to Alterovitz and Zonderman (2002), 25% have worked in finance as financial directors or accountants, 20% have worked in the machines and equipment sector, and the remaining 55% comes from areas such as medicine,

production, construction, biotechnology etc. However, others found that many business angels have previous entrepreneurial experience and accumulated their wealth through entrepreneurial activities instead of high income occupations (Mason and Harrison, 2002). Van Osnabrugge and Robinson (2000) cite multiple studies which reveal that in general the investment ranges from $50,000 to $150,000, with a median of $75,000. Most business angels have a portfolio consisting of three investments, and make an investment every 18-24 months, although these data vary slightly

(Morrissette, 2007). Business angels prefer to invest in local enterprises, within a 50-100 mile radius (Ramadani, 2009). In short, “angel investors are affluent individuals who invest their own personal funds in entrepreneurial concerns within their geographical area, and often within their prior areas of professional expertise of interest” (Nanda and Kind, 2013).

2.3. AngelList

AngelList started off as an idea of Naval Ravikant and Babak Nivi. They started Venture Hacks in 2007, which became an online blog covering topics that were useful for entrepreneurs. After a while, more and more entrepreneurs started asking for referrals to investors. This was inspiration for Ravikant and Nivi to make a list of investors (Nanda and Kind, 2013). This list contained data on where these potential investors were based, their interests, and the composition of their portfolio (Nanda and Kind, 2013). In addition, Nivi told:

“We made an online form, emailed it to our investors friends, and asked them to fill out their

name, location, number of investments they were going to make during the year, the typical dollar amount of their investments, how they could add value, and generally, what kinds of companies they were interested in investing in. We took their answers and cut and pasted them into a bid blog post that, within a few months, grew to be over 100 investors. That giant blog post was the first version of AngelList. From there, it grew a bit at a time.” (Nanda and

Kind, 2013).

Nowadays, AngelList has grown to a platform where approximately 500.000 start-ups are listed, searching for investors or business angels. According to Ravikant: “The breakdown is something like 70% software information technology, 20% to 25% enterprise, 5% to 10% hardware, 5% consumer goods, some healthcare, and then all kinds of miscellaneous” (Nanda and Kind, 2013). On behalf of the investors, approximately 40% are venture capitalists and 60% angels (Nanda and Kind, 2013).

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11 2.4. Segmentation

The underlying logic for market segmentation is well established. “Market segmentation helps businesses deal with this heterogeneity by balancing the variability in customers’ needs with the limits of available resources” (p. 394), according to Dibb (1998). Segmentation lies at the heart of successful marketing strategies. Until competitors copy your segmentation strategy, you have a competitive advantage, even if you offer your segment a standard product or service. If the product or service specially made or adapted for a specific segment, then your competitive advantage is enlarged (McBurnie and Clutterbuck, 1988). Dibb adds that “the result of segmentation can be a better understanding of customers’ needs and wants, allowing greater responsiveness in terms of the product on offer” (Dibb, 1998, p. 394). In addition:

The enhanced appreciation of the competitive situation also allows the business to better understand the appropriate segments to target and the nature of competitive advantage to seek. Furthermore, a segmentation approach can add clarity to the process of marketing planning, by highlighting the marketing programme requirements of particular customer groups (Dibb, 1998, p. 394).

If the arguments above would be converted into market segme ntation for backers and business angels, you might find that a better understanding of the investors’ needs and wants is possible, which allows a greater responsiveness in terms of the investment proposal offered. Thus,

segmentation of backers and business angels could increase the chance of successful and sufficient funding for a specific project or business. This in turn might increase the success of the project or business in the long run. Especially for crowdfunding projects it is important that the project goal is reached, which is an amount of funding set before the campaign. Many crowdfunding platforms follow an “all or nothing” model, so funders’ pledge money is only collected if the goal is reached (Mollick, 2014). This means that raising enough funding is of great importance for these projects, that is why segmentation theories for backers and business angels will be an useful contribution to the literature.

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3. Research Design & Data Description

New ventures require resources to succeed, and one of the most critical of these is financing (Gompers and Lerner, 2004). Recently, crowdfunding emerged as a new method for start-ups to acquire funding without interference of venture capital or other conventional ways of financing (Mollick, 2014). Crowdfunding facilitates its own unique way of fundraising through an increasing number of websites specialized in raising funding from the crowd (Mollick, 2014).

Though crowdfunding might currently be a valuable alternative source of funding for

entrepreneurs, the academic understanding on reaching investors most effectively is still lacking. This research might gain insight in the relation between the background of investors and the amount invested. Specifically, the occupation of an investor and the education followed will be taken into account to measure the relationship with the amount of capital invested in a project. This will be done by analyzing data from AngelList (see www.angel.co), which is a platform where founders of startups and business angels come together for advice and attraction of investments. This platform is suitable for this specific research, since there are 497,126 companies from all over the world listed and 15,368 investing business angels use the platform actively for both informal investments as syndicates, which looks like a hybrid between crowdfunding and business angels.

In this study, the relation between occupation of a business angel and the invested amount is investigated. In addition, the relation between education of a business angel and the invested amount is investigated. It might seem logical that education and occupation are related to one another, and that business angels with higher education might have a higher income than lower-educated business angels. As corollary of this, if business angels followed higher education, you might expect they will earn more, and invest more (often) in start-ups.

H1: Business Angels who followed higher education, will invest more.

Van der Sluis, Van Praag and Vijverberg (2005) did a meta-analysis on this subject. They found four conclusions which are relevant for this research. First of all, the choice to become an entrepreneur appears to have no relationship whatsoever with the education of this person. So, highly educated people do not choose more or less often to become an entrepreneur than others. Second, the success of entrepreneurs does appear to have a clear relationship with the education followed by the entrepreneur. This success is measured based on income, survival, growth and profit. In 67 percent of the cases, the relationship between education and entrepreneurial success appears to be positive. So, according to this study, it is true that entrepreneurs wi th higher education earn more, their ventures survive longer, they experience higher growth and eventually yield higher profits (Van der Sluis et al., 2008). Third, the relation between income and education followed for

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13 entrepreneurs and employees appears to be equally large. Fourth, all studies have studied whether there is a relationship between education followed and entrepreneurial success. These studies did not check whether there is an effect, so whether the number of years of education has a significant impact on this success, or whether there is a different unexplained variable which influences both this entrepreneurial success and level of education. Since it might be likely that intelligent and motivated people follow higher education and at the same time are better at entrepreneurial activities. These people might have performed better as entrepreneurs even when they did not follow higher education (Cardia and Van Praag, 2007)

A consistent finding from these studies, largely based on the American population – which suits the community of angel.co probably well, is that education matters for entrepreneurs. In addition, one year education added leads on average to a ten percent higher income for employees, while this is eighteen percent for entrepreneurs (Van der Sluis, Van Praag, & Van Witteloostuijn, 2006). This points out that education might be even more important for entrepreneurs than employees.

Based on the findings of Van der Sluis et al. (2006), you might expect that when a business angels has an entrepreneurial background, combined with high education, they will invest more than employees who followed high education. So, the second hypothesis is:

H2: The amount invested will rise faster with high education for entrepreneurs as compared to employees.

Starting a venture as entrepreneur is most of the times associated with an increase in risk. Entrepreneurs operate in uncertain environments, since approximately one -half of all start-ups files for bankruptcy within a year of launch (Cooper, Dunkelberg, & Woo, 1988). This could imply that entrepreneurs take more risks than non-entrepreneurs when investing as an angel investor in start-ups. According to Koudstaal, Sloof, and Van Praag (2014), “most of the entrepreneurs’ investment portfolios are totally undiversified.” So, the third hypothesis is:

H3: Business Angels with an entrepreneurial background make less investments than business angels with a non-entrepreneurial background.

Limitations

This thesis focuses on the independent variables education and occupation, and how these variables influence the dependent variable amount of investment. However, there might be more variables which influence the dependent variable, which are not measured in this research because these are

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14 unknown. In this study, the relationship between income and wealth is unknown, which might be valuable to form better conclusions about the relations shown in the data. In addition, the variables education and occupation might be interrelated with one another, although according to Van der Sluis et al. (2008) the education of a person does not influence the choice to become an

entrepreneur or an employee. 3.1. Methodology

This research is designed as a case study on the AngelList platform, since we explore the

phenomenon between the background of a business angel and their investment patterns (Saunders, Lewis, & Thornhill, 2012). The data will be collected using the website www.angel.co, where public data on the background of business angels is listed. Using the internet creates the ability to access a larger population (Wright, 2005).

Since the research questions focus on what people do, the most logical way to find out is to observe this (Saunders et al., 2012). The data is systematically recorded, analyzed and eventually an

interpretation of the results will be made. The analysis in this research will be based on the market of angel investors. The results of this research are useful for entrepreneurs who are in need of financing for their start-up, since start-ups are in need of different resources, of which financing is the most important one. This research will generate more data for entrepreneurs to segment the market of business angels to create a specific target group and increase the chance to get funding. This might be useful to entrepreneurs searching for backers in equity-based crowdfunding as well. The data used in this study will be derived from the AngelList platform, using the website

www.angel.co, and will be collected manually3. The sample of business angels to study will consist of all investors listed on this website, who have a role as angel. There are 15368 cases that meet these requirements. Since we are looking at the relation between amount invested, education and occupation, the data blocks of amount invested and whether a business angel is a founder or

employee will be analyzed. Because the collection of data has to be done manually, this will take a lot of time. To save time and acquire data that is measurable, angels who do not give a specification of the amount invested per deal will not be collected in the sample. In addition, the sample will consist of 200 observations maximum. This way, we can collect a significant amount of data in a time efficient way.

Variables will be measured categorical. For the variable occupation the blocks of founder, employee and investor will be taken into account. Dummy variables will be created to select whether an angel is founder or employee. For the data on amount invested the actual amounts will be

3 Initially, the data would have been collected using a crawler. However, the website doesn’t allow data

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15 analyzed of the block “invests”, which can be for instance $10-100K per deal, $50-100K per deal, or typical amount unknown. These amounts will be measured by a minimum and maximum amount. The data on how many times a business angel has confirmed an investment will be collected by the block “confirmed investments”. For the data on education the data block “education” will be analyzed. Since not all universities are equal, a ranking will be made based on the ranking of

www.topuniversities.com where the world’s top 800 universities are ranked. When there is no education filed, these will be allocated to rank 800, since no educated business angel studied at a university which is ranked 701+.

3.2. Limitations

This study focuses on the business angels listed on AngelList, which are over fifteen thousand investors who function as a business angel. However, the population of angels extends far beyond the platform of AngelList. AngelList focuses mainly on American business angels, who might behave somewhat different from European or Asian business angels. To have a clear view of how different business angels from different regions behave, more research is requi red.

The angels listed on AngelList publish some public data on their personal page. However, not all business angels publish the amount of investments, the range of their investments, which kind of education they attended and more background information. It is not known why these angels do not publish these data, but this omission could possible influence this research in a negative way.

In this study, the data used is collected at one moment. Therefore, it is not possible to see how angels gain more experience through their investments and how they change their way of investing in start-ups. It might be that business angels invest more when they lack some experience, and when they gain more experience, they become somewhat more risk averse, which influe nces their investment behavior in the future. This however is not known and could be studied through a longitudinal study.

Another limitation of this research is the fact that the sample examined contains 121 observations from the community of AngelList. Since the collection of data is impossible to

automatically collect from this website due to the privacy policy of www.angel.co, we had to extract this data manually from the content of the website. It would have taken an enormous amount of time to extract all the data from this website, so we chose to collect a maximum amount of 200 observations.

Finally, a limitation is that there is no in-depth analysis to require more information on the background or motivations of specific business angels. For instance, in this study it is not possible to get to know why a business angel dropped out in high school, which may influence the results. It would take a lot of time to do this properly, which is not possible in the time planning of this study.

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16 Additional research is necessary on this aspect, combining quantitative and qualitative research to consider the aspects of why a business angel invests in a certain start-up on AngelList.

4. Data Analysis & Results

The data used has been collected from www.angel.co/people/investors. There, the subject “All Investors” is selected, and in “Advanced”, in the header “Roles”, Angel is selected, and finally in the header “Skill”, Angel Investing is selected. This leads to 263 results, of which not all business angels publish the amount invested and the confirmed investments. If one of these specifications is not indicated, the profile is neglected and omitted from the sample. In addition, w hen someone has the role of business angel and venture capitalist at the same time, the amount invested and confirmed investments are influenced due to the venture capitalism. So, these profiles are also omitted from the sample. In addition, when a business angel has experience both as employee and founder, this observation is omitted from the sample, as well as those who did not report any experience on their background, which were 6 observations in total. This way, only 121 observations meet the

requirements needed for the data.

To create the data set, first the name is filed, then the highest education attended, and the dummy variable whether a business angel attended higher education, then the rank of this university is filed, the dummy variable whether a business angel is a founder of a venture, and the dummy variable whether a business angel has been an employee or not. In addition, the amount of investments done is filed, the minimum amount and the maximum amount of the investments is filed.

It appears that 101 business angels have reported a background as founder, and 20 have reported a background as employee. Since there is one business angel in this sample that is not listed for any kind of background, this means that three business angels are listed as employee as well as founder, which means that these business angels most recently worked as employee or have had more employments than founder experiences.

Results

In table 1, descriptive statistics are shown. It appears that on average, a business angel in the sample has made just over 13 investments, of an average amount of $109,793.40 and a minimum and maximum amount of $3000 and $1,750,000 on average respectively. The average total amounts invested by a business angel in this sample is $1,776,942.10, with a minimum and maximum amount of $38,500 and $40,250,000 on average respectively. The mean rank of universities is 188,1983 , a test on skewness shows a statistic of 1,283 which implies that it is skewed to the right. This shows

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17 that, in general, the business angels who followed higher education, attended universities that were generally well ranked.

Table 1 Descriptive Statistics N M SD Min Max 1 Frequency_investments 121 13,46 11,864 3 68 2 University_rank 121 188,1983 250,54177 1,00 701 3 Average* 121 109,7934 233,22057 3,00 1750,00 4 Average_total** 121 1776,9421 5282,1854 38,50 40250,00

Note: *Average amounts invested in thousands

Note: **Average total amount in thousands

of dollars

The first hypotheses proposes that business angels who followed higher education, will invest more. This can both mean that the average amount is higher, or the frequency of investments is higher. This hypotheses is tested with a linear regression, where the predictor is Inverse_Rank, which is computed as (800-Rank), where Rank is the rank of the university, where 1 is high and 800 is low. This way, at Inverse_Rank 1 is low and 799 is high, so the relation becomes positive instead of negative. This is done as a robustness check. Table 2 shows the results, where will invest more is measured by the frequency of investments, which is model 1. It shows a significant positive effect of

Inverse_Rank on the frequency of investments (ß =.207, p <0.05).

Model 1 Table 2

The effect of 'Inverse_Rank' on frequency of investments

Variable B Std. Error Beta t Sig.

(Constant) 10,303 1,732 5,95 0

Inverse_Rank 0,007 0,003 0,207 2,307 0,023

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18 In model 2, the influence of higher education on the amount invested is measured, where the

dependent variable is Average, which is the average amount invested. Table 3 shows a non-significant negative effect of Inverse_Rank on the average amount invested (ß = -.022, ns.).

Model 2

Table 3

The effect of 'Inverse_Rank' on the average amount invested

Variable B Std. Error Beta t Sig.

(Constant) 116,457 34,783 3,348 0,001

Inverse_Rank -0,015 0,063 -0,022 -0,24 0,809

In model 3, the influence of higher education on the amount invested is measured, where the dependent variable is Average_total, which is computed by the product of Average and

Frequency_investments. Table 4 shows a non-significant positive effect of Inverse_Rank on the

average total amount invested (ß =.034, ns.).

Model 3

Table 4

The effect of 'Inverse_Rank' on the total average amount of investments

Variable B Std. Error Beta t Sig.

(Constant) 1543,947 787,522 1,961 0,052

Inverse_Rank 0,531 1,419 0,034 0,374 0,709

The expectation was that higher education would have a positive influence on the investments, whether in amount or frequency. It appears that higher education has a positive significant influence on the frequency of investments, a negative non-significant influence on the average amount of investments, and a positive non-significant influence on the total average amount of investments. This means that the higher the rank of universities attended by business angels, the more frequent these business angels make investments. So, the first hypothesis is only partially supported.

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19 The second hypothesis proposes that the amount invested will rise f aster with high education for entrepreneurs as compared to employees. This can be measured in two ways, either the average amount invested, or the average of maximum amounts as dependent variable. In model 4, the influence of Inverse_Rank and Inverse_Rank_Founder as predictor variables is shown, where

Inverse_Rank_Founder is computed by the product of Inverse_Rank and Founder, with the average

maximum amount as dependent variable. From table 5, it appears that there is a non-significant negative effect of the predictor Inverse_Rank on the average maximum amount of the investments (ß =-.166, ns.). In addition, it appears that there is a non-significant positive effect of the predictor

Inverse_Rank_Founder on the average maximum amount of investments, as compared to employees

(ß =.189, ns.).

Model 4

Table 5

The effect of 'Inverse_Rank' and 'Inverse_Rank_Founder' on the maximum amount of investments

Variable B Std. Error Beta t Sig.

(Constant) 177,605 56,858 3,124 0,002

Inverse_Rank -0,186 0,167 -0,166 -1,11 0,267

Inverse_Rank_Founder 0,206 0,162 0,189 1,272 0,206

In model 5, the influence of Inverse_Rank and Inverse_Rank_Founder as predictor variables is shown, with the average amount as dependent variable. Table 6 shows a non-significant negative effect of the predictor Inverse_Rank on the average amount of the investment (ß =-.171, ns.) and a non-significant positive effect of the predictor variable Inverse_Rank_Founder on the average amount of the investment (ß =.189, ns.).

Model 5 Table 6

The effect of 'Inverse_Rank' and 'Inverse_Rank_Founder' on the average amount of investments

Variable B Std. Error Beta t Sig.

(Constant) 116,305 34,695 3,352 0,001

Inverse_Rank -0,117 0,102 -0,171 -1,15 0,253

Inverse_Rank_Founder 0,125 0,099 0,189 1,268 0,207

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20 The expectation was that the amount invested will rise faster with high education for entrepreneurs as compared to employees. Unfortunately, none of the results appeared to be significant. This means there is not sufficient evidence to accept the hypothesis.

The third hypotheses proposed that business angels with an entrepreneurial background would make less investments than business angels with a non-entrepreneurial background. This is tested in only one way, the effect of the predictor variables Inverse_Rank and Founder on the frequency of investments. In model 6, this effect is shown. Table 7 displays a significant effect of the predictor variable Inverse_Rank on the frequency of investments (ß =.211, p<.05) and a non-significant positive influence of the predictor variable Founder on the frequency of investments (ß =-.062, ns.).

Model 6 Table 7

The effect of 'Inverse_Rank' and 'Founder' on the frequency of investments

Variable B Std. Error Beta t Sig.

(Constant) 8,585 3,024 2,839 0,005

Inverse_Rank 0,007 0,003 0,211 2,341 0,021

Founder 1,988 2,864 0,062 0,694 0,489

The expectation was that that the frequency of investments would decrease when a business angel had an entrepreneurial background. Unfortunately, the predictor variable Founder appeared to be non-significant. This means there is not sufficient evidence to accept the hypothesis.

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21

5. Discussion

The main purpose of this study was to contribute to the literature on the background of business angels and informal investments, with literature linking to equity-based crowdfunding. More specific, we tested for a relationship between education and the investment pattern, and occupation and the investment pattern of business angels. In the following section, the key findings are discussed and linked to the existing theory where possible. In the end, the limitations of the research are explained and suggestions for future research are given.

5.1. Key Findings

In this thesis, the background of investors is taken into account to see whether there is a relationship with the amount invested. To check this, data on the education followed and occupation of business angels has been collected and analyzed. Since existing literature was lacking somewhat on this subject, first, I wanted to know whether more highly educated business angels would invest more. This comes forth from the idea that highly educated business angels would have a higher income, and more financing to possibly invest in start-ups. According to the analysis done, there is some evidence to state that this is the case. From the results of one analysis, it appeared that there was significant evidence for a positive relation between the rank of the university attended by a business angel and the frequency of investments. However, there was no significant evidence found for the rank of the university and the effect on the average amount invested. In addition, there was no significant evidence for the rank of the university and the effect on the total average amount of investments as well.

The second relationship examined in this thesis, is whether highly educated entrepreneurs would invest a higher amount compared to highly educated employees. Since Van der Sluis et al. (2006) state that education has an even higher positive impact on the income of entrepreneurs as compared to the income of employees, we expected that the entrepreneurs in the sample would invest a higher amount than employees. However, the results from this analysis appeared to be non -significant, so there is not enough evidence to state that this is the case.

The third relationship examined in this thesis, is about the investment portfolio of business angels. Since Koudstaal et al. (2014) state that the investment portfolio of most entrepreneurs are completely undiversified, we expected that business angels with an entrepreneurial background would make less frequent investments as compared to business angels with a non-entrepreneurial background. The results of the analysis showed that this relationship is non-significant, so there is not enough evidence to state that there is such an effect.

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22 Although most of the results from the analysis done were non-significant, we did found a significant relationship between the education of a business angel and the frequency of investments. This implies that the background of a business angel does i nfluence the investment pattern and composition of his or her portfolio.

5.2. Limitations

This study has many limitations, since the data used is from only one platform, AngelList, it might not give a good representation of the population of business angels and informal investors, since

AngelList focuses mainly on American business angels. In addition, this thesis focuses on the variables education and occupation and their influence of the investment pattern. However, many more variables could be influencing this relationship. For instance, education influences income, and income influences wealth. These two unknown variables could give a different perspective on the relationship between education, occupation and the investment pattern of business angels. On top of that, education and occupation could influence one another as well. Besides, this sample didn’t take the interests of business angels and their investment locations into consideration, which might create a different perspective on the results.

The data collected from the website lead to 121 observations, due to strict conditions necessary to include business angels in the sample and due to missing data, since not all business angels recorded the data needed for this research. Many results were not significant, which might be caused by the low number of observations. If more data was available, through a data dump for instance, the results might have been more significant and the conclusions on different hypothesis would be different as well. Moreover, if the data would be collected over time through a longitudinal study, the investment pattern of business angels would be more consistent as compared to collecting the data at one point in time. Since this is a quantitative research, it is not possible to interview business angels and check why they invest in specific start-ups and recognize patterns, which might be valuable.

5.3. Recommendations for Future Research

Following the limitations mentioned above, there are some good recommendations for future research on this subject. First of all, it would be good to focus on more than one platform for business angels, and make sure to collect data on business angels from different cultures and countries. Secondly, when there is more data on different variables which might possibly influence the relation between the background of a business angel and the investment pattern, this should be taken into account as well. Especially checking the influence of different variables on each other, whether there is a moderating or mediating effect between different variables. Third, it would be

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23 interesting to collect data over time through a longitudinal study and check whether the investment pattern of business angels alters through time and circumstances. Finally, it might be valuable to combine a quantitative research with qualitative research using interviews with business angels and recognizing patterns in why they invest in specific start-ups and possibly on how these business angels have been raised. All these recommendations combined could gain more insight in the causation on how a business angels acts, instead of correlation.

5.4. Practical Contributions

This research proves that, apparently, it is hard to create a segmentation approach for business angels. So, it is hardly impossible to specify the needs and wants of business angels and create greater responsiveness with regard to the investment proposal offered on a platform like AngelList. The only significant finding in this research is that highly educated business angels more frequently invest, but this is not very useful for founders of a start-up in search of financing. More research is needed to acquire better results and create segmentation theories for business angels which have impact on the success of finding enough funding to make sure the start-up survives.

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24

6. Conclusion

The main goal of this thesis was to create new insights in the relationship between the background of a business angel and how this influences the investments made. The background could differ in terms of occupation and education. The research question was: What is the relation between

occupation of a business angel and the invested amount? And the sub-question was: What is the relation between education of a business angel and the invested amount? The results of this thesis

state that the quality of education of a business angel influences the frequency of investments made. No further relationships proved to be significant. Future research is valuable to gain more insight in the effect of the background of a business angel on the investment portfolio. When done properly, founders of start-ups in need of financing might have more success in finding business angels willing to invest in the start-up due to a more specified search on business angels.

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25

References

Abernethy, M., & Heidtman, D. S. (1999). Business Angels: How to be one, How to find one, How to use one. Allen & Unwin.

Alterovitz R, Zonderman, J. 2002. Financing your new or growing business: How to find and get capital for your venture. Entrepreneur Mentor Series. Entrepreneur Press: Irvine, CA.

Belleflamme, P., Lambert, T., & Schwienbacher, A. (2014). Crowdfunding: Tapping the right crowd. Journal of Business Venturing, 29(5), 585-609.

Cardia, F., & Van Praag, M. (2007). Onderwijs en ondernemerschap in Nederland.

Cooper, A. C., Woo, C. Y., & Dunkelberg, W. C. (1988). Entrepreneurs' perceived chances for success. Journal of business venturing, 3(2), 97-108.

Dibb, S. (1998). Market segmentation: strategies for success. Marketing Intelligence &

Planning, 16(7), 394-406.

Gerber, E. M., Hui, J. S., & Kuo, P. Y. (2012). Crowdfunding: Why people are motivated to post and fund projects on crowdfunding platforms. In: Proceedings of the International Workshop on

Design, Influence, and Social Technologies: Techniques, Impacts and Ethics.

Gompers, P. A., & Lerner, J. (2004). The venture capital cycle. MIT press.

Hemer, J. (2011). A snapshot on crowdfunding . Working papers firms and region. (No. R2/2011) Hemer, J., Schneider, U., Dornbusch, F., & Frey, S. unter Mitarbeit von Dütschke, E.; Bradke, C.

(2011): Crowdfunding und andere Formen informeller Mikrofinanzierung in der Projekt-und Innovationsfinanzierung.

Heminway, J. M., & Hoffman, S. R. (2010). Proceed at your peril: crowdfunding and the securities act of 1933. Tenn. L. Rev., 78, 879.

Kleemann, F., Voß, G. G., & Rieder, K. (2008). Un (der) paid innovators: The Commercial Utilization of Consumer Work through Crowdsourcing. Science, Technology & Innovation Studies, 4(1), 5-26.

Koudstaal, M., Sloof, R., & Van Praag, M. (2014). Risk, uncertainty and entrepreneurship: evidence from a lab-in-the-field experiment.

(26)

26 Kuppuswamy, V., & Bayus, B. L. (2014). Crowdfunding creative ideas: The dynamics of project backers

in Kickstarter. UNC Kenan-Flagler Research Paper, (2013-15).

Lee, S. H., DeWester, D., & Park, S. R. (2008). Web 2.0 and opportunities for small businesses. Service

Business, 2(4), 335-345.

Mason, C. M., & Harrison, R. T. (2002). Is it worth it? The rates of return from informal venture capital investments. Journal of Business Venturing, 17(3), 211-236.

McBurnie, T., and Clutterbuck, D. (1988). Give Your Company the Marketing Edge, Penguin Books, London.

Mollick, E. (2014). The dynamics of crowdfunding: An exploratory study.Journal of Business

Venturing, 29(1), 1-16.

Morrissette, S. G. (2007). A profile of angel investors. The Journal of Private Equity, 10(3), 52-66. Nanda, R., & Kind, L. (2013). AngelList.

Pierrakis, Y., & Collins, L. (2013). Banking on each other: peer-to-peer lending to business: evidence from funding circle.

Ramadani, V. (2009). Business angels: who they really are. Strategic Change, 18(7‐8), 249-258. Saunders, M. N., Saunders, M., Lewis, P., & Thornhill, A. (2012). Research methods for business

students (6th ed.). Pearson Education.

Schwienbacher, A., & Larralde, B. (2010). Crowdfunding of small entrepreneurial

ventures. Handbook of Entrepreneurial Finance, Oxford University Press, Forthcoming. Stam, W., & Elfring, T. (2008). Entrepreneurial orientation and new venture performance: The

moderating role of intra-and extra-industry social capital. Academy of Management

Journal, 51(1), 97-111.

Van der Sluis, J., Van Praag, M., & Vijverberg, W. (2005). Entrepreneurship selection and performance: A meta-analysis of the impact of education in developing economies. The

World Bank Economic Review, 19(2), 225-261.

Van der Sluis, J., Van Praag, M., & Vijverberg, W. (2008). Education and entrepreneurship selection and performance: A review of the empirical literature. Journal of economic surveys, 22(5), 795-841.

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27 Van der Sluis, J., Van Praag, M., & Van Witteloostuijn, A. (2007). Why are the returns to education

higher for entrepreneurs than for employees? IZA.

Wright, K. B. (2005). Researching Internet‐based populations: Advantages and disadvantages of online survey research, online questionnaire authoring software packages, and web survey services. Journal of Computer‐Mediated Communication, 10(3), 00-00.

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