THE INDUSTRY’S INFLUENCE ON
LENDING-BASED CROWDFUNDING SUCCESS
MASTER THESIS
FINAL VERSION
Program: MSc Business Administration Track: Entrepreneurship & Innovation Student name: Luc Onink
Student number: 11924764
Supervisor: Dr. G.T. (Tsvi) Vinig Second examiner: Dr. B. (Balazs) Szatmari Due date: June 22, 2018
Statement of originality
This document is written by Student Luc Onink 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 completion of the work, not for the contents.
TABLE OF CONTENT
1. INTRODUCTION 5
2. LITERATURE REVIEW 8
2.1THE PHENOMENON OF CROWDFUNDING 8
2.2TYPES OF CROWDFUNDING 11
2.3CROWDFUNDING SUCCESS AND ITS DETERMINANTS 14
2.4THE DUTCH CROWDFUNDING MARKET 17
2.5COLLIN CROWDFUND 18
3. CONCEPTUAL FRAMEWORK 20
3.1INDUSTRY –TIME TO CROWDFUNDING SUCCESS 21
3.2INDUSTRY –FUNDING AMOUNT 22
3.3INDUSTRY –RISK 22
3.4 MULTIPLE MEDIATION 23
4. METHODOLOGY 28
4.1SAMPLE &DATA COLLECTION 28
4.2INDEPENDENT VARIABLE:INDUSTRY 29
4.3DEPENDENT VARIABLE:TIME TO LENDING-BASED CROWDFUNDING SUCCESS 30
4.4MEDIATING VARIABLES 31 4.5CONTROL VARIABLES 32 4.6METHODS 32 5. RESULTS 34 5.1DESCRIPTIVE STATISTICS 34 5.2HYPOTHESES TESTING 37 5.2.1NON-PARAMETRIC TEST 37 5.2.2MULTIVARIATE ANALYSIS 38 6. DISCUSSION 45 6.1FINDINGS 45 6.2CONTRIBUTIONS 50 6.3LIMITATIONS 51 6.4FUTURE RESEARCH 52 7. CONCLUSION 54 8. REFERENCES 55
Abstract
Over the past years crowdfunding has gained popularity as a financing method. The crowdfunding industry is now moving from its nascent phase to an established industry. Despite the development of the crowdfunding industry, there is still a lack of research that leaves the crowdfunding phenomenon unexplored. Specifically, the relationship between the industry of a fundraising company and the (time to) crowdfunding success is not studied yet. This research is designed to examine whether some industries have quicker crowdfunding success than others. This is important, since organizations may find difficulties with launching and growing processes when there is a lack of financial resources. Real-life data of a market leading lending-based crowdfunding platform in the Netherlands was used. Quantitative (database method, N = 470) research was conducted with some focus on the catering industry, that is highly represented at crowdfunding platforms. Multiple regression analysis is used to analyze the direct effect and the role of three mediating variables regarding the indirect effects. Findings reveal that the industry of a fundraising company has a significant effect on the time to crowdfunding success. This study discovered that this effect is not caused by hard investment criteria such as the risk and the return. Identified was that crowdfunding campaigns with lower funding goals quicker reach their targeted funding goals and that companies belonging to the catering industry have significantly lower targeted funding goals. Furthermore, several limitations are identified. Further research has to examine those findings.
Keywords: Crowdfunding, Lending-based crowdfunding, Loan-based crowdfunding,
1. Introduction
Financial resources are still one of the most essential factors for organizational welfare. Without sufficient financial resources, any organization may find difficulties with among others launching and growing processes (Allison, Davis, Short, & Webb, 2015). The variety of fundraising sources increased over the last decades, strengthened by technological development and liberalization of economic systems. Due to these developments, crowdfunding had the chance to become a sustainable alternative to traditional ways of fundraising (Agrawal, Catalini, & Goldfarb, 2014).
Crowdfunding is a relatively new, collective way of raising capital for social, cultural or for-profit initiatives. It is derived from the concept of crowdsourcing, which is defined as “a way to harness the creative solutions of a distributed network of individuals“ (Gerber, Hui, & Kuo, 2012). Crowdfunding is one of the service models developed within the ‘fintech’ industry, that tries to deliver value to customers in an alternative way by using technology (Maier, 2016). With the use of the internet and intermediate crowdfunding platforms (CFPs), a large group of investors (the “crowd”) is asked to give financial support, often in exchange for a future product, equity or monetary reward (Belleflamme, Lambert, & Schwienbacher, 2014).
With the growth that the crowdfunding industry underwent, the literature about the topic also grew. In both the popular press and the scientific literature, crowdfunding has been extensively discussed (Belleflamme, Omrani, & Peitz, 2015; McKenny, Allison, Ketchen, Short, & Ireland, 2017). However, crowdfunding is still a relatively new phenomenon in the business world, with a lot of unexamined dimensions and opportunities for further research (McKenny et al., 2017; Short, Ketchen, McKenny, Allison, & Ireland, 2017). This approves the relevance and significance of the chosen topic for this Master Thesis research. By linking theoretical concepts and real-world figures, this research will contribute to the further development of crowdfunding knowledge. This will be done by focusing on lending-based
crowdfunding within the Dutch crowdfunding industry. There are several reasons why specifically this focus area is relevant and deserves more serious attention:
1. Most of the past research in crowdfunding focused on reward-based crowdfunding. 2. Most of the existing literature is based on Kickstarter projects, and thus placed within an American context (which is not presumably applicable to the Dutch context).
3. Most of the past research focused on the financing processes of start-up companies, while existing companies also use crowdfunding as method of financing.
The need for research is also acknowledged by Wardrop, Zhang, Ray, and Gray (2015), who confirm that apart from some studies in the United Kingdom “no objective, independent and reliable research exists to scientifically benchmark and regularly track the development of key alternative finance markets in respective European countries” (Wardrop et al., 2015).
This Master Thesis focuses on success determinants of crowdfunding. Many papers are written about this topic. But, as mentioned before, most of this took place within the above mentioned setting. According to McKenny et al. (2017), we are increasingly understanding crowdfunding investors and the preferences they have. But “we need to understand the higher-level antecedents, consequences, and contexts of crowdfunding” (McKenny et al., 2017, p. 298). A question that remains unanswered hitherto is whether the industry of a fundraising company influences the crowdfunding success (McKenny et al., 2017). The main research goal of this Master Thesis is to identify whether this correlation exists and if this possible relation is direct or indirect.
In order to reach the before mentioned research goal, the following research question is designed:
Is the type of industry influencing the time to funding completion of lending-based crowdfunding campaigns and how is this relation mediated by the campaign characteristics?
More specifically, this research will provide insight to the possible correlation between industry and time to lending-based crowdfunding success. This will be done by applying quantitative methods and using secondary data obtained from Collin Crowdfund, the market leader of lending-based crowdfunding in the Netherlands. The focus of this article lies on small and medium sized enterprises, since these play an important role in economic growth and job creation (Coleman, 2000; Golić, 2014).
The specific industry this thesis will focus on to represent the strength of the (possible) relationship between industry and time to lending-based crowdfunding success is the catering industry. The catering industry represents almost 23 percent of all the cases within the database and will therefore give the most reliable view. Moreover, the strong presence of the catering industry is no exception at the used platform for this thesis. The catering industry is highly represented at other platforms as well (Chunlei & Liyun, 2016).
This Master Thesis consists of six sections. The second section is aimed to provide a theoretical overview of the previous literature on crowdfunding and its success determinants. The third section describes the conceptual framework of this research and proposes hypotheses for the analysis. Section 4 amplifies the research methodology and focuses on description of the data sample. The fifth section shows the results of the analysis and discusses the main findings. In the last section, relevant conclusions are explained, limitations of this research are provided, and ideas for further research are proposed.
2. Literature review
The paragraph is aimed to review and analyze the main theoretical aspects of crowdfunding developed in previous research. Insights in the different types of crowdfunding will be given as well as insights in success determinants and the Dutch crowdfunding industry.
2.1 The phenomenon of crowdfunding
Crowdfunding is a relatively new, collective way of raising capital for social, cultural or for-profit initiatives. Crowdfunding is mostly defined as: “an open call, essentially through the Internet, for the provision of financial resources either in form of donation or in exchange for some form of reward and/or voting rights in order to support initiatives for specific purposes” (Schwienbacher & Larralde, 2010, p. 4). However, according to Zheng, Li, Wu, & Xu (2014), this definition does not cover investment-based models such a lending-based and equity-based crowdfunding. Therefore, Mollick (2014) gives another definition based on an entrepreneurial context: “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” (Mollick, 2014, p. 2). Crowdfunding is often seen as a corollary of crowdsourcing, where a certain task is outsourced to a large group of people in the form of an open call (Howe, 2006). The difference between the two phenomena is that crowdfunding collects capital, where crowdsourcing looks for i.e. labor resources or creative solutions (Harms, 2007; Gerber et al., 2012).
Initially, crowdfunding primarily developed within the creative industries. For example, by musicians and other artists who tried to fund their artistic projects (Agrawal et al., 2014). Nowadays, crowdfunding is used as a way to find venture capital in a wide range of industries. This development was mainly caused by the financial crisis, which made it hard for banks and other established parties to lend money to firms (Beck & Demirgüç-Kunt, 2006; Bruton,
Khavul, Siegel, & Wright, 2015; Lee, Sameen, & Cowling, 2015). Now crowdfunding is moving from its nascent phase to an established industry (Maier, 2016). Crowdfunding campaigns vary greatly in both goal and magnitude. From creative projects that are still funded by the “crowd”, to entrepreneurs using crowdfunding as an alternative way of finding venture capital (Mollick, 2014; Schwienbacher & Larralde, 2010).
Four types of crowdfunding can be distinguished: lending-based, equity-based, reward-based, and donation-based crowdfunding (Moritz & Block, 2016; Crowdfundinghub, 2016). Every type of crowdfunding has its own characteristics and features, and every type of crowdfunding has associated crowdfunding platforms (CFPs). CFPs are specialized online intermediaries, that connect entrepreneurs (fundraisers) and funders (Lehner, Grabmann, & Ennsgraber, 2015). A common feature on all CFPs is that fundraisers are looking for capital, where funders – except on donation-based platforms – are looking for a certain reward (Belleflamme et al., 2015). The rewards that funders receive for their financial contribution are very diverse. Depending on the type of crowdfunding, rewards can be either financial or non-financial (Kshetri, 2015). Therefore, scholars also see crowdfunding as a form of “prosocial lending” in which lenders “evaluate prospective borrowers on both traditional lending criteria and prosocial, charitable criteria” (Allison et al., 2015, p. 54).
Just like all other funding methods, crowdfunding has both advantages and disadvantages. One important advantage of crowdfunding is the fact that raising capital through crowdfunding is a mode of validation of the company’s business idea as it gives a projection of the target market (Manchanda & Muralidharan, 2014). But, despite the numbers of benefits, the cons of crowdfunding cannot be ignored. The pros and cons of crowdfunding are summarized in Table 1below.
Table 1 – Advantages and disadvantages of crowdfunding (Evsyukhin, 2016)
2.2 Types of crowdfunding
As mentioned before, four types of crowdfunding can be distinguished: lending-based, equity-based, reward-equity-based, and donation-based crowdfunding (Moritz & Block, 2016; Crowdfundinghub, 2016). In this paragraph, all four different types of crowdfunding will be discussed in order to get a better understanding of the crowdfunding phenomenon.
Donation-based crowdfunding is mostly used for charity initiatives (Marelli & Ordanini, 2016). Social projects try to attract donations, without giving the donors any tangible benefits in return for their donation (Ramos & González, 2016).
In case of reward-based crowdfunding, funders receive a pre-determined reward for their contribution. The most popular form of reward-based crowdfunding is “pre-selling” or “pre-ordering” (Hemer, 2011; Mollick, 2014). The financial contribution is meant to help produce something (a film, an album, some new technological product, a new service concept, etc.) and in return the entrepreneur will deliver an early version of the product or service (Hemer, 2011). Lehner (2013) indicates that sometimes there is no clear boundary between donation-based and reward-based campaigns. Sometimes, popular crowdfunding platforms like
Kickstarter and Indigogo give the entrepreneur an option to set customized reward models
As shown in Figure 1, funders ask different things in return for their support (Kleverlaan, 2017). Not-for-profit initiatives are more focused on social and material rewards, while equity- and lending-based crowdfunding are more focused on financial rewards. Equity-based and lending-Equity-based crowdfunding are often merged under the overarching term investment-based crowdfunding (Belleflamme et al., 2015). Investment-based crowdfunding platforms can be considered alternative financial investment instruments (Belleflamme et al., 2015). When using equity-based crowdfunding, entrepreneurs offer their funders ‘’equity stakes or similar consideration in return for their funding’’ (Mollick, 2014, p. 3). The development of equity-based crowdfunding went relatively slow, since most countries around the world have regulatory issues with selling financial security to the “crowd” (Ahlers, Cumming, Günther, & Schweizer, 2015).
The other type of investment-based crowdfunding is lending-based crowdfunding. This is also the type of crowdfunding this Master Thesis focuses on. Lending-based crowdfunding is also known as loan-based crowdfunding, crowdlending or C2B crowdfunding (Belleflamme et al., 2015; Maier, 2016). On lending-based CFPs, entrepreneurs borrow money from a large group of funders in exchange for a “a certain interest rate on successful projects if the project pays out” (Belleflamme et al., 2015, p. 5; Mollick, 2014). Only a tiny minority of the number of successful crowdfunding campaigns is lending-based, but this small number of campaigns creates most of the volume (Belleflamme et al., 2015). This fact shows that the campaign sizes of lending-based crowdfunding are much higher than other types of crowdfunding (Belleflamme et al., 2015).
Different types of crowdfunding are also associated with different levels of risk and uncertainty. The level of uncertainty increases with an increase of the funding goal, information asymmetry between fundraiser and funder, and the complexity of a crowdfunding campaign (Agrawal et al., 2014). As shown in Figure 2, investors in investment-based crowdfunding face higher levels of risk and uncertainty than people who invest in not-for-profit campaigns.
Figure 2 – Complexity and Uncertainty of Crowdfunding Types (Massolution, 2012).
Level of uncertainty C om pl ex it y Donation-based Reward-based Lending-based Equity-based
2.3 Crowdfunding success and its determinants
In the previous paragraph the four types of crowdfunding are described, with some associated features and processes. It is good to know that not every crowdfunding campaign reaches its funding goal. In this paragraph the focus is on the success determinants of crowdfunding, which make campaigns reach their funding goal and play a central role in this research.
Crowdfunding success is an often researched topic. However, the research conducted so far has mainly relied on data from reward-based crowdfunding platforms, often within an American context (Cordova, Dolci, & Gianfrate, 2015). Kickstarter is the platform that provides most of this data. Kickstarter is a reward-based platform that works on an all-or-nothing basis. If a fundraiser does not raise the funding goal before the set deadline, all money is returned to the funders. Therefore, crowdfunding success is often defined as goal attainment, which is – according to Calic & Mosakowski (2016) – a “dichotomous variable that indicates whether a project reached or exceeded its funding goal (1 = goal attained; 0 = goal not attained)” (Calic & Mosakowski, 2016, p. 746). The Dutch platform that provides the data for this research – Collin Crowdfund – also works on an all-or-nothing basis. Sometimes (only for exceptional cases) a minimum is set, which is a certain percentage of the funding goal.
Looking at the statistics of Collin Crowdfund, one statistic catches the eye: Collin
Crowdfund has a success rate of 99.5 percent (Collin Crowdfund, 2018a)1. This success rate measures the campaigns that reached the funding goal. Since there are almost no differences in successfulness on this platform, it is far more interesting to look at the differences that do exist between these campaigns. For example, there are some significant differences in the time that the funding required. Sometimes, a campaign only needs a few hours to reach the funding goal, while other campaigns need (more than) a week to reach the funding goal, regardless of the funding amount. According to Allison et al. (2015), the time to funding depends on lender
preference, just like the attainment of the funding goal. Allison et al. (2015) conducted a research where they used ‘time to funding’ as depending variable. It operationalizes “the attractiveness of the loan to the pool of investors by measuring how long it takes for the loan to be funded” (Allison et al., 2015, p. 63). The comparable dependent variable ‘time to lending-based crowdfunding success’ will be used as the definition of crowdfunding success in this research. The quicker organizations reach their funding goal, the more successful they are considered to be. The time of gathering resources is an important type of entrepreneurial performance, because an organization cannot launch or grow without these resources (Allison et al., 2015).
The determinants that influence the crowdfunding success are studied several times (Yuan, Lau, & Xu, 2016). Those studies chose different angles of approach by looking at among others cultural factors, geographical factors, pitch characteristics, and the entrepreneurs’ crowdfunding experience (Burtch, Ghose, & Wattal, 2013b; Mollick, 2014; Dorfleitner, Priberny, Schuster, Stoiber, Weber, de Castro, & Kammler, 2016; Yuan et al., 2016). According to McKenny et al. (2017), we are increasingly understanding crowdfunding investors and the preferences they have. But “we need to understand the higher-level antecedents, consequences, and contexts of crowdfunding” (McKenny et al., 2017, p. 298). Therefore, they suggested that a possible link between the industry of a fundraising company and crowdfunding success should be researched. This will be done by investigating whether there is a direct link between industry and time to funding, but also looking at whether there is an indirect link between these variables through the mediation of the already known success determinants. Once again, these determinants are often studied based on reward-based crowdfunding data (Cordova et al., 2015). Because those determinants are based on reward-based crowdfunding data, they are not all applicable to lending-based crowdfunding. Therefore, in this article there is chosen for
success determinants that are (based on the data) applicable to lending-based crowdfunding and more specifically to Collin Crowdfund.
The first success determinant applicable to Collin Crowdfund is return. Funders of lending-based crowdfunding are looking for personal benefits (Belleflamme et al., 2015). Lending-based crowdfunding is also called investment-based crowdfunding. Therefore, funders of lending-based crowdfunding projects can therefore also be called investors (Ordanini, Miceli, Pizzetti, & Parasuraman, 2011). Lending-based crowdfunding funders are extrinsically motivated, which means that they base their investment decisions predominantly on an evaluation of ‘hard criteria’, such as the interest rate and repayment period (Maier, 2016; Ryan & Van Wingerden, 2011). They are purely looking for economic value (Cordova et al., 2015). Return is also a predictor of the financing speed. Financing speed increases when the return is higher (Maier, 2016).
The interest rate is often based on the second success determinant. This is the risk associated with the project. Among existing literature the term ‘project quality’ is often used interchangeably (Mollick, 2014; Calic & Mosakowski, 2016). Project quality is a good predictor of a “guaranteed tangible output of the project” (Cordova et al., 2015, p. 117). Even in more altruism driven markets, quality projects appear to be more attractive to funders (Burtch, Ghose & Wattal, 2013a). When the crowdfunding phenomenon was very novel, lending-based platforms completely bypassed traditional banks by not screening the projects intensively. The CFPs let the funders decide whether a specific campaign should be successfully funded (Belleflamme et al., 2015). Nowadays, risk assessments are often made and credit grades are assigned to the campaign to decrease information asymmetry between fundraiser and funder (Courtney, Dutta, & Li, 2017). Besides that risk is a good predictor of crowdfunding success, it is also a good predictor of financing speed. The lower the risk, the higher the financing speed (Maier, 2016).
The risk of a campaign may partly depend on the targeted funding amount, which is the last success determinant that is applicable to Collin Crowdfund (Griffin, 2012; Yuan et al., 2016). Fundraising companies are mostly looking for small to modest amounts (Macht & Weatherston, 2014), but crowdfunding projects nowadays vary greatly in magnitude. In lending-based crowdfunding, the funding goal of a campaign is mostly high compared to other types of crowdfunding (Belleflamme et al., 2015). This might be a negative fact for lending-based crowdfunding campaigns, since existing literature shows that the likeliness of crowdfunding success decreases when the targeted funding amount increases (Cordova et al., 2015). This also has to do with “herding” behavior, investors want to “contribute to projects that already have a lot of support from other community members” (Kuppuswamy & Bayus, 2015, p. 3). Since the progress is mostly displayed in already funded percentages, this is more often the case for small loan amounts. However, the existing research on targeted funding amounts is often done based on reward-based crowdfunding data. Therefore, it is not sure whether this effect also plays a role within lending-based crowdfunding.
2.4 The Dutch crowdfunding market
This paragraph endeavors to explain the context in which this research is done, the Dutch crowdfunding industry. Together with the general crowdfunding phenomenon, the Dutch crowdfunding market grew fast over the last years. In order to give an indication; in 2013, a volume of 32 million euros was funded by the crowd. 87.5 Percent of this total volume was raised by enterprises, with an average campaign of 75,000 euros (Crowdfundingcijfers, 2014). In 2017, a volume of 223 million euro was funded by the crowd. Compared to 2013, this is a growth of 597 percent. 193.7 Million of the volume of 2017 was raised by enterprises. An average campaign for enterprises raised 102,000 euros (Crowdfundingcijfers, 2018). Like in the rest of the world, in the Netherlands, lending-based crowdfunding also creates the highest volume. 76 Percent of all the successfully campaigns were lending-based
(Crowdfundingcijfers, 2018). In 2016, the Netherlands were market leader of the European mainland regarding lending-based crowdfunding within a business context (Bijster, 2016).
Since 2013, a lot of crowdfunding platforms (CFPs) have emerged in the Netherlands. At this moment, around 100 CFPs are actively working in the Netherlands. Most of these CFPs focus on a specific branch, are locally active, and/or focus on a specific type of crowdfunding (Investeerders, 2018a). In the Netherlands, the equity-based and loan-based CFPs are supervised by the Authority of Financial Markets (AFM) and the Dutch National Bank (DNB) (Authoriteit Financiële Markten, 2018). The AFM is responsible for regulation around crowdfunding. Examples of rules they set up are the fact that CFPs have to give insight in default rates, that project information has to be available 48 hours before funders can invest in the campaign, and that funders have a maximum amount they can invest per platform (Authoriteit Financiële Markten, 2018). These regulations help the crowdfunding industry rapidly professionalize. CFPs are improving their risk assessments, are creating more transparency, and make sure that the pitch looks professionally (Investeerders, 2018b). The Dutch crowdfunding market is still growing, although other forms of investing, such as private equity and venture capital, are still more popular (Investeerders, 2018b). In lending-based crowdfunding, a few parties are leading the market. The market leader is Collin Crowdfund, whose data will be used during this research.
2.5 Collin Crowdfund
This research will be based on secondary data obtained from Collin Crowdfund (CCF). Therefore, this paragraph will give some more information about this platform. CCF was founded in June 2014. CCF connects fundraisers and investors for lending-based crowdfunding for established small and medium sized enterprises. The minimum campaign at CCF is €50,000, the maximum campaign is €2,500,000. Looking at monthly volume, CCF is market leader in
lending-based crowdfunding in the Netherlands. At this moment2, 474 campaigns are successfully funded at CCF with a total volume of more than 100 million euros. The biggest successfully funded campaign at CCF is €1,750,000. The average entrepreneur at CCF asks for about €200,000 for an interest rate of 7.56 percent. CCF has a success rate of 99.5 percent (Collin Crowdfund, 2018a).
As described in the existing literature, crowdfunding platforms now often make risk assessments to decrease information asymmetry between fundraiser and funder (Courtney et al., 2017). Collin Crowdfund does this too by screening the performance ability (Dun & Bradstreet score) and profitability, solvency and liquidity (Collin Credit Score) of a fundraising company (Collin Crowdfund, 2018b). Due to these risk assessments, almost two-third of all the applications is rejected.
It is important to mention that CCF works with a group of registered investors. Once registered, every investor will receive push messages when a campaign is going live on the website. In this way, every investor is informed about a new investment opportunity.
3. Conceptual Framework
This section of the Master Thesis is dedicated to formulate the main research question and propose hypotheses based on detailed analysis of the existing academic and managerial literature. The main research goal is to identify whether there is a correlation between industry of the fundraising company and the time to lending-based crowdfunding success. The following
research question is designed to contribute to the knowledge regarding crowdfunding and to
help filling up the identified gap in the existing crowdfunding literature:
Is the type of industry influencing the time to funding completion of lending-based crowdfunding campaigns and how is this relation mediated by the campaign characteristics?
More specifically, this research will analyze and reveal whether there is a relation between industry of the fundraising company and the time to lending-based crowdfunding success, as proposed by McKenny et al. (2017). Next to a direct relation between those two variables, this research will also investigate the possible mediating relations of the success determinants as suggested in existing literature. In order to answer the main research question, the following hypotheses will be tested and discussed in the next sections of this Master Thesis. For the regression analysis, the focus will be on the catering industry. Therefore, one-tailed hypotheses are drafted from the viewpoint of the catering industry as well.
3.1 Industry – Time to crowdfunding success
As proposed by McKenny et al. (2017), the main goal of this Master Thesis is to examine whether there exists a relationship between the industry of a fundraising company and the time it takes to reach the funding goal. In order to examine the (possible) link between the industry of a fundraising company and the time to lending-based crowdfunding success, several hypotheses are drafted. Since the independent variable “Industry” is a nominal variable, the effect size cannot be measured (Field, 2013). Therefore, the hypotheses are two-tailed and do not give a direction. The following hypothesis is drafted:
H1A: Time to lending-based crowdfunding success is not associated with the industry of a fundraising company.
When talking about the direct relationship between industry and the time to lending-based crowdfunding success, the actual thing we talk about is lender preference or investor trust (Allison et al., 2015). No study has been done on the differences in financing opportunities between industries. What we do know, is that the catering industry in the Netherlands is normally quite a hard industry to find financial resources for (MKB Servicedesk, 2017). Therefore, expected is that companies belonging to the catering industry find more difficulties in finding financial resources. The following hypothesis is drafted.
H1B: Belonging to the catering industry negatively influences the time to lending-based crowdfunding success.
3.2 Industry – Funding amount
In the existing academic literature is stated that the targeted funding amount effects crowdfunding success (Cordova et al., 2015; Yuan et al., 2016). Fundraising companies are mostly looking for small to modest amounts (Macht & Weatherston, 2014). But there are some differences regarding the dependence on external financing exist across industries (Rajan & Zingales, 1996). Therefore, assumed is that the funding amount can significantly differ between industries. Businesses in the catering industry almost always belong to the small businesses. According to the Central Bureau for Statistics (2018a), businesses within the catering industry have relatively small investments to make compared to companies from other industries. Those investments may sometimes even be payable with the entrepreneur’s own capital. Therefore, the following hypothesis drafted:
H2: Belonging to the catering industry negatively influences the funding amount.
3.3 Industry – Risk
In the reviewed literature is stated that the risk that a crowdfunding campaign entails effects crowdfunding success (Burtch et al., 2013a; Cordova et al., 2015). Therefore, this paper tends to examine whether there is a relationship between the industry of a fundraising company and the risk associated with a campaign.
Industry Funding amount
amount
Industry
Risk
Collin Credit Score Dun & Bradstreet
A lot of business choose crowdfunding as a financing method after trying to get access to traditional financing parties, such as banks (Macht & Weatherston, 2014). The main reason they did not get access to capital at the traditional parties, is that traditional parties are not risk takers. They are not interested in small, young businesses with a lack of track record, credit, and trading history (Mitter & Kraus, 2011). Instead of ‘risk’ the term ‘project quality’ is often used interchangeably (Mollick, 2014; Calic & Mosakowski, 2016). Project quality is a good predictor of a “guaranteed tangible output of the project” (Cordova et al., 2015, p. 117). Because businesses in the catering industry are highly represented in the dataset and relatively the most bankruptcies in the Netherlands took place within the catering industry (Central Bureau for Statistics, 2018b), there is assumed that the risk for this industry is higher compared to other industries. Therefore, the following hypothesis is drafted.
H3: Belonging to the catering industry positively influences the risk associated with a campaign.
3.4 Multiple mediation
Based on existing literature, assumed is that there is multiple mediation in this model. Multiple mediation means that mediating variables are influenced by and/or have an influence other mediating variables. The existing literature contains several examples that describe relationships between the mediating variables funding amount, risk, and return.
According to Griffin (2012), a small funding amount is often seen as an indication of a low risk. Investors should see smaller loans as “easier to serve and thus less likely to default” (Maier, 2016, p. 147). So, argued is that the funding amount positively influences the risk a campaign entails. The higher the funding amount, the higher the risk. The following hypothesis is drafted:
H4: The targeted funding amount of a campaign positively influences the risk that a campaign entails.
Mollick (2014) reported: “failures happen by large amounts, successes by small amounts” (Cordova et al., 2015, p. 118). Kuppuswamy & Bayus (2015) found that crowdfunding investors show “herding” behavior, especially at the beginning and end stages of the funding. Investors want to “contribute to projects that already have a lot of support from other community members” (Kuppuswamy & Bayus, 2015, p. 3). Since the progress is mostly displayed in already funded percentages, this is more often the case for small loan amounts that seem to be highly supported when only a small amount is funded. Assumed is that large funding amounts can be made more attractive to investors by increasing the return. Therefore, the following hypothesis is drafted:
H5: The targeted funding amount of a campaign positively influences the return.
Funding amount
+
Risk
Collin Credit Score Dun & Bradstreet
Funding amount
amount
+
Crowdfunding projects nowadays vary greatly in magnitude. In lending-based crowdfunding, the funding goal of a campaign is mostly high compared to other types of crowdfunding (Belleflamme et al., 2015). This might be a negative fact for lending-based crowdfunding campaigns, since existing literature shows that the likeliness of success decreases when the targeted funding amount increases (Cordova et al., 2015). One reason for this is above mentioned “herding” behavior (Kuppuswamy & Bayus, 2015). The smaller the funding goal, the earlier investors are convinced by a high percentage of the funding amount that is already funded. Besides, through logical reasoning can be assumed that a higher funding amount needs more investors and thus more time. Therefore, the next hypothesis suggests the following:
H6: The targeted funding amount of a campaign positively influences the time to lending-based crowdfunding success.
According to existing literature, there is a positive relationship between the risk of a crowdfunding campaign and the associated return (Courtney et al., 2017). In contrast to the early stages of the crowdfunding industry, where the crowd was supposed to decide whether a campaign should be completely funded or not, crowdfunding platforms now make risk assessments and assign credit grades to decrease information asymmetry between fundraiser
Time to lending-based crowdfunding success
Funding amount
+
+
Return Risk
Collin Credit Score Dun & Bradstreet
and funder. The relationship between the risk and the expected return is often linear (Fama & MacBeth, 1973). Expected is that the mediator risk will positively influence the mediator return. Therefore, the following hypothesis is drafted.
H7: The risk associated with a campaign positively influences the associated return.
According to the literature, even in more altruism driven markets, quality projects with low risk appear to be more attractive to funders (Burtch et al, 2013a). The higher the risk, the less a tangible output of the project is guaranteed and the longer it will take to accomplish crowdfunding success (Cordova et al., 2015; Maier, 2016). Therefore, the following hypothesis is drafted:
H8: The risk associated with a campaign positively influences the time to lending-based crowdfunding success.
In the academic literature reviewed in the previous section is stated that the promised return of a campaign influences the attractiveness to investors. The higher the return, the more attractive the campaign (and the faster the funding goal is reached) (Maier, 2016; Ryan & Van Wingerden, 2011). Since the relationship between the risk and the expected return seems to be linear very often, investors are willing to search for the exceptional campaign that offers a better
Time to lending-based crowdfunding success
Return
-
Time to lending-based crowdfunding success
+
Collin Credit ScoreRisk
risk-return rate (Fama & MacBeth, 1973; Maier, 2016). This rate only gets better when the return increases (or the risk decreases). Therefore, the next hypothesis suggests the following:
H9: The return negatively influences the time to lending-based crowdfunding success.
To summarize all proposed hypothesis, it is worth mentioning that H1 analyses the (possible) direct relation between the industry of a fundraising company and the time needed for reaching lending-based crowdfunding success. Hypotheses 2, 3, and 4 intend to reveal the correlation between the catering industry and the mediating variables funding amount, risk, and return. Hypotheses 5 till 9 intend to reveal the relationships between the mediators and the relationships between the mediators and the dependent variable time to crowdfunding success.
Figure 3. Conceptual model
+
Industry Time to lending-based crowdfunding success
Funding amount
amount Return
+
+
Collin Credit Score
Risk
Dun & Bradstreet
+
-
+
4. Methodology
In this section, the methods and data that are used for conducting the further analysis and answering the main research question will be explained. First, this section provides a short description of the research type. Second, the variables used in the analysis will be further explained. Finally, the methods chosen for the analysis will be explained.
The research is deductive in nature, which means that predictions are derived from existing theories (Everaert & Van Peet, 2006). The results of this research are aimed at testing the predictions and, if necessary, supplementing them (Bleijenbergh, 2013). This will be done by accepting or declining the proposed hypotheses. The selection of this research approach is caused by a lack of evidence on this thesis’s topic in the existing academic literature. As explained in the research agenda of McKenny et al. (2017), there is a research gap regarding this topic. Therefore, there is no definite answer to the main research question of this thesis that can be found in the existing crowdfunding literature. This research will give an answer to the main question after detailed analysis of the crowdfunding campaigns.
4.1 Sample & Data collection
The data for this research is provided by Collin Crowdfund. The database consists of a book of loans concerning all successfully funded campaigns till March 12, 2018. The raw data included 474 cases. After filtering out the incomplete samples the data included 470 cases with a total worth of €103,178,500. The database included basic information of each project (project number, loan amount, risk grades, issuance date, interest rate, industry). Some information, such as time to funding, was not included in the database. This data was obtained from Collin Crowdfund’s website. According to Saunders & Lewis (2012), using secondary data may be a disadvantage for research conducting, since it can be inaccurate. However, the collected data matches all the goals of this research.
4.2 Independent variable: Industry
This paragraph is aimed to introduce and explain the variables that are used in this research. The variables are summarized in Table 2 but are also further explained in this paragraph. This research investigates whether there is a correlation between the industry of a fundraising company and the time it takes accomplish crowdfunding success. Since the data is provided by
Collin Crowdfund, the industrial categories they used are used in this research as well. Thirteen
industries are distinguished: 1 = Business services, 2 = Construction/Real estate, 3 = Health/Healthcare, 4 = Retail, 5 = Culture, sports, and recreation, 6 = ICT and Media, 7 = Wholesale, 8 = Agri- and horticulture, 9 = Industry/Automotive, 10 = Logistics/Shipping, 11 = Catering industry, 12 = Financial institutions, and 13 = Energy, water, and environment. The variable industry is a nominal variable, which means that the number of the item merely represents the name (Field, 2013). The numbers have no meaning other than denoting the type of industry. With nominal variables, it is possible to examine whether there is a correlation or not. But it is impossible to examine the strength of this correlation. Therefore, dummy variables are created. Creating dummy variables is a way of recoding categorical variables into series of dichotomous variables (Field, 2013).
For the regression analysis, the focus will be on the catering industry. This is the dummy variable that will probably give the most reliable results and interesting insights, since it represents almost 23 percent of all cases in this dataset. This is not only the case at the platform used for this thesis, the catering industry seems to be highly represented at other platforms as well (Chunlei & Liyun, 2016). After dummy coding, this variable had two scores: 0 = All other industries, 1 = catering industry.
4.3 Dependent variable: Time to lending-based crowdfunding success
As mentioned in before in chapter 2, Collin Crowdfund has a success rate of 99.5 percent (Collin Crowdfund, 2018a). Because of this high success rate, defining crowdfunding success as “reaching the funding goal”, as done by most scholars (Calic & Mosakowski, 2016), would not have a big change of giving interesting insights. Insights in lender preference can also be given by looking at the time to reach the funding goal. Allison et al. (2015) conducted a research where they used ‘time to funding’ as depending variable. It operationalizes “the attractiveness of the loan to the pool of investors by measuring how long it takes for the loan to be funded” (Allison et al., 2015, p. 63). This same definition of “success” was very applicable to Collin
Crowdfund, since there are big differences in the time campaigns need to get funded. Some
campaigns only need a few minutes to reach the funding goal, where other campaigns need weeks, regardless of the funding amount.
The variable “time to crowdfunding success” is a ratio variable that is measured in hours. The quickest campaigns reached the funding goal within 1 hour, the slowest campaigns in 720 hours (30 days). The maximum time to funding is 30 days, so campaigns that needed more time did not reach the funding goal. The money that was already invested is then returned to the investors.
The dependent variable is also categorized, since categorical variables are needed for the Chi-Square analysis. The four categories are (funding within) 1 = 1 hour, 2 = 1 day, 3 = 1 week, and 4 = 1 month.
4.4 Mediating variables
Funding amount
According to Cordova et al. (2015), the funding amount is one of the success determinants of a crowdfunding campaign. The mediating variable funding amount is a ratio variable that indicates the targeted funding amount of a crowdfunding campaign. The smallest funding amount findable in the dataset is €50,000. The biggest funding amount findable in the dataset is €1,750,000.
Risk
The risk associated with a crowdfunding campaign is also a success determinant of crowdfunding success. Collin Crowdfund analyses the risk associated with a campaign using two risk assessments: the Dun & Bradstreet score and the self-developed Collin Credit Score. The Dun & Bradstreet score is a country specific method that measures the performance ability of a company. The Collin Credit Score is an assessment of the profitability, solvency, and liquidity of a company (Collin Crowdfund, 2018b). A higher score means a lower risk, this applies to both risk assessments. The scores cannot be conglomerated into one item because the Cronbach’s alpha score is .379 and therefore too low (George & Mallery, 2003; Tavakol & Dennik, 2011). The items will be treated separately.
Return
Since funders of lending-based crowdfunding are basically investors, the return of a campaign is an important success determinant. Lending-based crowdfunding funders are extrinsically motivated, which means that they base their investment decisions predominantly on an evaluation of ‘hard criteria’, such as the interest rate (Maier, 2016; Ryan & Van Wingerden, 2011). The interest is used to represent the return of an investment. The lowest interest
percentage findable in the dataset is 5 percent, the highest interest rate findable in the dataset is 9 percent.
4.5 Control variables
Gender of the entrepreneur
Gender is an often discussed topic in all kinds of research. Because different kinds of gender discrimination are proven in the established investment literature (Barasinska & Schäfer, 2014), controlling for gender will exclude the possibility that an effect will exist due to gender discrimination. Sometimes, more entrepreneurs are involved in one project. Therefore, it is also possible that both males and females are involved in one campaign. The covariate “gender” has three categories: 1 = Male, 2 = Both, and 3 = Female.
Size of investor group
Only registered funders can invest in the projects of Collin Crowdfund. When you are registered, you will receive a push message or email when a new campaign is going live. Logically, the group of registered investors grew over the years. Logical reasoning brought me to the conclusion that the chance that a project will accomplish quick funding is bigger when the potential group of investors is bigger. Therefore, the size of the registered investor group is included as a control variable, so that an effect cannot be credited to this variable.
4.6 Methods
This analysis will be done by using different types of tests. First of all, the non-parametric Chi-Square will be used to examine whether there exists a relation between the nominal independent variable ‘Industry’ and the time in which a campaign reaches the funding goal.
The multiple regression analysis that focuses on the catering industry, will be done by using the process tool written by Andrew F. Hayes. During this analysis, both direct and indirect effects will be measured.
5. Results
The following section provides the main results and findings of the conducted analysis that tested all proposed hypotheses. First, the descriptive statistics of the sample are presented. Thereafter, all hypotheses are tested and the results are presented.
5.1 Descriptive statistics
Before testing the proposed hypotheses, the data should be prepared. This is done by computing the descriptive statistics and frequencies for all the variables using SPSS, which are exemplified in Table 3. This table shows the mean, standard deviation, skewness and kurtosis of each variable. In order not to violate the assumption of a normal distribution, the skewness should not be higher than 3 or lower than -3 (Kendall, Stuart, & Ord, 1968). In the academic literature, there is a lot discussion about the acceptable limits of kurtosis. According to Rampersad, Quester & Troshani (2010), kurtosis can be problematic if the scores are higher than 7 or lower than -7. Table 3 shows that the variables ‘Loan amount’ and ‘Timehour’ are not normally distributed, since both the skewness and kurtosis are higher than the above mentioned ranges. However, according to Fidell & Tabachnick (2003), normality will not have implications on analyses when a reasonably large sample (e.g. 300 or larger) is used. According to the Central
Limit Theorem, the sample size is even sufficiently large when N > 30. Since transforming the
data has obviously implications for the interpretation of the results, it may be even worse to apply the wrong transformation than analyzing untransformed scores (Field, 2013). To overcome this, and due to the fact that the research consists of a sufficient large sample, we could have ignored the violation of the normality assumption for the variables ‘Loan amount’ and ‘Timehour’. However, normal distribution is a key assumption for the multivariate analysis that is used. Therefore, the normality assumption is not ignored and the variables are transformed.
Within this sample, 470 cases are analyzed. Those cases have an average loan amount of almost €220,000 (M=219528.72, SD=209836.533, [50,000, 1750000]). The average result for the Collin Credit Score risk assessment is 54.3 (M=54.3, SD=17.06, [21, 100]. This is higher than the Dun & Bradstreet score, that measures other factors and shows slightly more differences between the campaigns (M=36.33, SD=24.37, [5, 100]. In return for the associated risk, an average interest rate of 7.57 percent is offered (M=7.57, SD=0.59, [5, 9]. Investors react on this risk-return rate with an average funding time of 47.57 hours (M=46.57, SD=131.31, [1, 720]. The average fundraising company is being led by at least one man (M=1.37, SD=0.65, [1, 3]. The campaigns in this dataset had access to an average group of 7093 registered investors (M=7092.61, SD=3740.62, [9, 13301].
5.2 Hypotheses testing
This section provides the results from the statistic tests that will lead to the acceptance or rejection of the hypotheses, in order to answer the main research question.
5.2.1 Non-parametric test
A Chi-Square analysis is conducted for all the relevant variables within this research to see whether there is a correlation between the dependent and the independent variable. The Chi-Square test is commonly used to test relationships between categorical variables (Field, 2013). For this test the categorial dependent variable time category (Time cat) was used, since the Chi-Square test does not work for numerical variables.
The formula for the Chi-Square analysis is:
The Chi-Square formula compares the observed data to the expected if no relationship existed between the variables.
The assumptions of a Chi-Square test with a table larger than 2x2 are that no more than 20% of the expected counts should be less than 5 and all individual expected counts should be 1 or greater (Field, 2013). These assumptions are violated. In this case, 54.2% of all cells have an expected count less than 5 and the minimum expected count is 0.45. Therefore, we looked at the Likelihood ratio statistic, which is based on “comparing observed frequencies with those predicted by the model” (Field, 2013, p. 724).
The Likelihood ratio rejects H1A with a 95% confidence level (LR (33, n=470) = 51.597, p<0.05). This means that the observed frequencies differ significantly from the expected frequencies. This means that – according to the Likelihood ratio – time to lending-based crowdfunding is correlated with the industry of a fundraising company.
5.2.2 Multivariate analysis
Multivariate analysis can be used to simultaneously analyze data with more than two variables. Multivariate analysis has the ability to detect complex relationships between multiple variables at the same time (Field, 2013).
To be able to run a multivariate analysis, the data has to meet several assumptions. First of all, the independent variables should not be too highly correlated with each other. The second assumption is the fact that the variables should be normally distributed. Since this assumption was violated, the variables that were not normally distributed (‘Loan amount’ and ‘Timehour’) were transformed with a log10 transformation. Third, there should be a linear relationship between the dependent and the independent variable. This is automatically the case when using dummy variables, which is shown in Attachment 1. Lastly, homoscedasticity has to be checked. Which means that the variance of error terms are corresponding across the values of independent variables. The scatterplot in Attachment 2 shows that this is the case.
The multivariate analysis is done by using the process tool written by Andrew F. Hayes. According to Hayes (2012) process is “a versatile computational tool for observed variable mediation, moderation, and conditional process modeling” (Hayes, 2012, p. 1). During the analysis both direct and indirect relationships between the independent variable catering industry and the dependent variable time to crowdfunding success are examined. According to Field (2013), relationships are significant when p<0.05 or when the confidence intervals
Model 6 in the process tool is used. As shown in Figure 4, the independent variable used in the model is ‘DumInd11’, which is the dichotomous (dummy) variable catering industry. The score “1” means that a fundraising company belongs to the catering industry, whereas a “0” means that a fundraising company belongs to another industry. The dependent variable is ‘Logtime’, which is the time in hours after the log10 transformation. The mediating variables in the model are ‘Logloan’ (which is the loan amount after the log10 transformation), the Collin Credit Score and the Dun & Bradstreet score (a separate analysis is done for both of those risk determinants), and the interest rate. During the analysis, the gender of the entrepreneur and the size of the investors group functioned as control variables. The results of the process analysis are presented in Table 6 and 7.
Figure 4: Statistical model
DumInd11 Logtime Logloan Interest CCSRisk Risk DBRisk c1’ a3 a5 a4 a2 b2 b3 b1 a1
Direct relationship
According to the results, there is no significant direct relationship between whether a fundraising company belongs to the catering industry and the time to crowdfunding success. The analysis is done twice; with both the Collin Credit Score and the Dun & Bradstreet score used as risk assessment. In both analyses, the dummy variable catering industry and the time to lending-based crowdfunding success are not significantly correlated with each other (c1’ = -.0080, p>0.10). H1B that states that the catering industry negatively influences the time to crowdfunding success, is therefore rejected.
Mediating effects
Companies raising money by crowdfunding are mostly looking for small to modest amounts (Macht & Weatherson, 2014). However, compared to companies from other industries, catering industry companies have relatively small investments to make (Central Bureau for Statistics, 2018a). Therefore, it was expected that belonging to the catering industry would negatively influence the targeted funding amount. This is confirmed by the results of the regression analysis (a1 = -.0925, p<.05). H2 is therefore accepted.
For the second mediating effect we examine the mediating effect of the catering industry on the risk associated with a campaign. It can be concluded that there exists no significant effect of catering industry on the risk scores of a campaign. This applies to both the Collin Credit Score (a2.1 = -.0892, p>.10) and the Dun & Bradstreet score (a2.2 = 4.4628, p>.10). Therefore,
H3 is rejected.
There is also no significant mediating effect found on the targeted funding amount on the risk associated with a campaign. This also applies to both the Collin Credit Score (a3.1 = -1.4968, p>.10) and the Dun & Bradstreet score (a3.2 = -5.9250, p>.10). Expected was that an
increase of the targeted funding amount would lead to higher risk (Griffin, 2012; Maier, 2016). According to the results of this analysis, this effect is not significant. Therefore, H4 is rejected. Mollick (2014) reported: “failures happen by large amounts, successes by small amounts” (Cordova et al., 2015, p. 118). The likeliness of success decreases when the funding amount increases. Large amounts often seem to be less attractive to investors. To solve this lack of attractiveness, there was expected that an increase of the funding amount would lead to a higher return. This is not the case as this relationship is not significant (a4 = .0727, p>.10), leading to the rejection H5.
Because investors show “herding” behavior and it is logic to assume that a higher funding amounts needs more investors and thus more time, expected was that the increase of the targeted funding amount would lead to an increase of the time needed to reach this funding goal.The regression analysis shows that this effect is significant (b = 1.1830, p<.001), thereby supporting H6. The targeted funding amount of a campaign positively influences the time to lending-based crowdfunding success.
The higher the risk, the higher the return. This is a logical expectation, which is also confirmed by the existing literature (Fama & MacBeth, 1973; Courtney et al., 2017). This expectation is confirmed by the results of this multiple regression analysis, for both the Collin Credit Score (a5.1 = -.0207, p<.001) and the Dun & Bradstreet score (a5.2 = -.0070, p<.001). As a high score means a low risk, this significant negative relationship shows that a higher score for the risk assessments decrease the interest rate. Despite the fact that the effect size is very small, H7 is accepted.
Quality projects with low risk are more attractive to funders (Burtch et al., 2013a). As a result, it was expected that the risk associated with a campaign would have an influence on the time to crowdfunding success. The higher the risk, the less a tangible output of the projects is guaranteed and the longer it will take to accomplish crowdfunding success (Cordova et al.,
2015; Maier, 2016). This expectation is not confirmed for both the Collin Credit Score (b2.1 = 0, p>.10) and the Dun & Bradstreet score (b2.2 = .0017, p>.10). No significant relationship was found and therefore, H8 is rejected.
A higher return implies a more attractive campaign and causes that the funding goal is reached quicker (Maier, 2016; Ryan & Van Wingerden, 2011). Therefore, it was expected that a higher return would lead to a shorter funding time. This is not confirmed by the results of this multiple regression analysis (b3 = .0148, p>.10), thereby rejecting H9.
Indirect relationship
Additional to the direct relationship, the process tool also analyzes possible indirect relationships between variables. Only one significant indirect effect has been found in the model. Indirect effect a1b1is significantly negative (indirect effect a1b1 = .109, SE = .04, CI: -.192 to -.035). This is the effect of the industry on the time to lending-based crowdfunding success through the targeted loan amount. Fundraising companies within the catering industry have lower targeted funding amounts (a1 = -.0925, p<0.05) and these lower funding amounts are further translated into shorter funding periods (b1 = 1.1830, p<.001).
Other significant relationships were found. Both of the risk assessments have an influence on the interest rate. The effect sizes of those relationships are very small for both the Collin Credit Score (a5.1 = -.0207, p<.001) and the Dun & Bradstreet score (a5.2 = -.0070, p<.001). Despite the fact that these scores are both significant, they do not cause a significant indirect effect between the industry of a fundraising company and the time to lending-based crowdfunding success.
Total effect
This model explains almost 7% of the variance of time to lending-based crowdfunding success, which is statistically significant (R2 = .067, p< .001). While no direct effect was found, one significant indirect was found. The total effect of the (catering) industry on the time to lending-based crowdfunding, however, is not significant (c1 = -.117, p>.10). This means that based on this model, companies within the catering industry do not find significantly quicker or slower funding than companies from other industries.
Table 6: Results process analysis
6. Discussion
The following section of this Master Thesis discusses the results and findings revealed during the analysis. First of all, the hypotheses are discussed. Second, based on existing literature and the conducted analysis the main research question is answered. Finally, limitations of this research are provided together with the main implications.
6.1 Findings
Following H1A, a relationship between the industry of a fundraising company and the time to lending-based crowdfunding was not expected. However, McKenny et al. (2017) addressed the research gap and they probably did this with the suspicion of a relationship between the industry of a fundraising company and crowdfunding success. This suspicion is confirmed by the results of the Chi-Square analysis. Industry has a significant effect on the time to lending-based crowdfunding success. Despite the fact that this already gives an interesting insight, it is far more interesting to know whether this relationship is positive or negative, what the effect size is and what industry characteristics induce this effect.
To examine the strength of the possible relationships between the variables, the catering industry is used as an indicator. The catering industry represents almost a quarter of all the projects in the database and is also highly represented at other platforms (Chunlei & Liyun, 2016). Therefore, it may give the most reliable insights. A direct relationship between companies that belong to the catering industry and the time to crowdfunding success is not discovered.
Looking at the mediating effects, some results of earlier articles are proved. The acceptance of H2 is an example of this. Fundraising companies belonging to the catering industry have significantly smaller targeted funding amounts than companies from other industries. This corresponds with the data about the investments made per industry in general
(Central Bureau for Statistics, 2018a). This is actually quite logic, since catering industry companies mostly belong to the small enterprises.
Contrary to H2, H3 is rejected. Although it was expected that belonging to the catering industry would increase risk associated with a campaign, the results did not show a significant relationship. The expectations were based on the fact that relatively the most bankruptcies in the Netherlands occur within the catering industry (Central Bureau for Statistics, 2018b). Furthermore, companies often choose crowdfunding as a second option after rejection of the traditional parties such as banks (Macht & Weatherson, 2014). The high representation of catering industry companies at crowdfunding platforms (Chunlei & Liyun, 2016) and the high number of bankruptcies could indicate a relatively high risk within this industry. This is negated by the results of the analysis. An explanation for this might be that all companies raising money through crowdfunding may have a comparable risk within a certain range, regardless of the industry they belong to. This could be caused by the fact that too risky campaigns are filtered out by the risk assessments and the campaigns with a very low risk often find traditional financing methods.
A positive relationship was expected between the targeted funding amount of a campaign and the risk that a campaign entails. A small funding amount is often seen as an indication of low risk, since small funding amounts are “easier to serve and thus less likely to default” (Griffin, 2012; Maier, 2016, p. 147). For both the Collin Credit Score and the Dun & Bradstreet score no significant relationship was found. This is quite surprising, especially for the Collin Credit Score, since an external loan has its influences on the solvency of a company. However, the solvency determines 25 percent of the Collin Credit Score, which does apparently not significantly make a difference.
Mollick (2014) reported: “failures happen by large amounts, successes by small amounts” (Cordova et al., 2015, p. 118). Campaigns with high funding amounts are less
attractive to investors. Also, the “herding” effect described by Kuppuswamy and Bayus (2015) is less strongly present with high funding amounts. To solve this ‘lack of attractiveness’, a positive relationship between the targeted funding amount and the promised return was expected. No significant relationship was found. An explanation for this may be that Collin
Crowdfund gathered such a number of registered investors over the years, that individual
investors seem to trust on the fact that every campaign, regardless of the funding amount, will reach the funding goal before the 30 days deadline. That would mean that the targeted funding amount does not way heavy in the consideration of the investors. Therefore, there is no need for increasing the interest rate.
As expected, the results of the analysis show that the funding amount has a positive relationship with the time to crowdfunding success. One reason for this is the “herding” behavior that investors show (Kuppuswamy & Bayus, 2015). The smaller the funding goal, the earlier investors are convinced to step in by a high percentage of the funding amount that is already funded. But, probably most of this relationship can be explained by logical reasoning. A higher funding amount (probably) needs more investors and thus more time.
Something that is often reported in existing literature is the fact that the return depends on the risk associated with a campaign (Fama & MacBeth, 1973; Courtney et al., 2017). Therefore, a positive relationship between the risk of a crowdfunding campaign and the associated return was expected. This is confirmed by the results of the analysis. The higher the Collin Credit Score and the Dun & Bradstreet score (the lower the risk), the lower the return. This relationship was expected, since the risk associated with a project is a good predictor of a “guaranteed tangible output of the project” (Cordova et al., 2015, p. 117). It seems to be all about the risk-return rate, which only becomes more attractive to investors when the risk decreases or the return increases (Maier, 2016).
