Initial Coin Offerings
- Understanding Why Small Investors Fund an
Initial Coin Offering, and Which Signals Influence
Their Decisionmaking
-Author: Paulien Kodde
Student Number: 10899618
Thesis Supervisor: Jeroen Kraaijenbrink PhD
University of Amsterdam | Amsterdam Business School
Master Thesis | Executive Programme of Management Studies - Strategy Track
Version 1.0 | 18-08-2018
Statement of Originality
This document is written by Student Paulina Johanna Kodde 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.
Contents
Abstract ... 6
1. Introduction ... 7
2. Literature Review ... 10
2.1 Initial Coin Offerings and Other Early Stage Financing Mechanisms ... 10
2.1.1 Small Initial Coin Offering Investors ... 11
2.1.2 Initial Coin Offerings and Adjacent Forms of Funding ... 11
2.2. Decision-making Theory ... 13
2.2.1 Investor Decision-making Process ... 14
2.2.2 Rational and Irrational Decision-making ... 15
2.3 Why Do Small Investors Fund Initial Coin Offerings? ... 19
2.3.1 Intrinsic Motivation ... 20
2.3.2 Extrinsic Motivation... 22
2.4 By What Signals are Small Investors' Decision-making Influenced?... 24
2.4.1 Human Capital ... 26
2.4.2 Partnerships and Alliances with Prominent Third Parties ... 27
2.4.3 Early Funding from Private Networks ... 28
2.4.4 Whitepaper (Business Plan) ... 29
2.4.5 Online Social Capital ... 30
2.4.6 Limited Availability ... 32 2.5 Propositions ... 33 3. Methodology ... 35 3.1 Research Design ... 35 3.2 Data Collection ... 35 3.2.1 Sampling ... 35 3.2.2 Semi-Structured Interviews ... 37 3.3 Data Analysis ... 38 4. Results ... 40
4.1 Small Initial Coin Offering Investors ... 40
4.1.1. Demographics and General Investment Experience ... 40
4.2 Why Do Small Investors Fund an Initial Coin Offering? ... 41
4.2.1 Intrinsic Motivations ... 41
4.2.2 Extrinsic Motivations ... 43
4.3 Which Signals Influence Small Investors' Decision-making? ... 48
4.3.1 Human Capital ... 48
4.3.2 Advisors and Partners ... 50
4.3.3 Early Funding ... 51
4.3.4 The Whitepaper ... 53
4.3.5 Online Social Capital ... 56
4.3.6 Scarcity ... 60
5. Discussion ... 64
5.1 Implications for the Research Question ... 64
5.2 Practical Implications and Theoretical Contribution ... 70
5.3 Limitations of the Research ... 72
5.4 Recommendations for Future Research ... 73
6. Conclusions ... 74
7. References ... 75
Appendices ... 85
Appendix 1: Interview Guide ... 85
Appendix 2: Consent form ... 86
List of Tables
Table 1: Key features of ICO's and adjacent forms of funding 12
Table 2: Demographics participants of the research 36
Table 3a: Motivation(s) for investing in ICOs 42
Table 3b: Motivation(s) for investing in ICOs 43
Table 4: Motivation(s) for investing: literature versus findings of this study 46
Table 5a: Signals influencing decision making 48
Table 5b: Signals influencing decision making 50
Table 5c: Signals influencing decision making 51
Table 5d: Signals influencing decision making 53
Table 5e: Signals influencing decision making 56
Table 5f: Signals influencing decision making 57
Table 6: Signals influencing decision making: literature versus findings of this study 61
Table 7: Findings of small ICO investors' motivations 64
Table 8: Findings of signals that influence small investors' decision making 66
List of Figures
Figure 1: History of ICO funding (nr) 10
Figure 2: History of ICO funding ($mn) 10
Figure 3: General investor decision making process 15
Figure 4: A taxonomy of human motivation 20
Figure 5: Signaling Timeline 25
Figure 6: Motivations for investing 33
Abstract
Initial coin offerings (ICOs) are becoming an increasingly important method for (blockchain) start-ups to attract funding, but research into this new phenomenon is in an embryonic state, and of the few existing studies nearly no research focuses on contributors. The aim of this qualitative research is therefore to gain insights into small ICO investors' decision-making process. The results indicate that small investors fund ICOs because they: 1) like the project; 2) are curious to learn about the funding concept; 3) expect a reward on the investment; 4) want to be part of "the next big thing", and/or 5) want to contribute to the blockchain community while getting a financial reward. The results indicate that small investors can have several motivations for investing in ICOs, which can simultaneously be both pro-social as well as self-interested, and that intrinsic motivations are not lowered when investors are offered rewards and compensation. The signals found in this research study that can positively influence small investors' decision regarding in which ICO to invest are: 1) the skills, knowledge, and experience of the entrepreneurs; 2) the composition of the team; 3) partnerships with prominent parties an ICO has; 4) well-known advisors an ICO has; 5) early funding an ICO gets from well-known reputable investors; 6) the perceived quality of the ICO's whitepaper (in terms of the explanation of the project, roadmap, lay-out, and token economics); 7) hype; 8) active social media presence of the team; 9) reviews and ratings; 10) Telegram and WhatsApp community views; and/or 11) scarcity of the initial coins. The signals that can have a negative influence are: 1) the bonus and lock-up time of the tokens early investors receive; and 2) Telegram and WhatsApp community views. The combination of rather rational signals and the use of heuristics indicates that the central route and the peripheral route can be used simultaneously by small ICO investors, which suggests that rational as well as irrational behavior is present. The findings have important practical implications, as these can give insights to entrepreneurs on how to improve their funding success, to policy-makers on how to design regulations and programs, and to more traditional financing mechanisms on the threat ICOs might pose. Finally, this study contributes to decision-making research by building on motivational theory, signaling theory, and biases and heuristics research, as it extends several findings from equity crowdfunding, venture capital, and business angel research to the ICO context, and gives insights into new motivations and signals, thereby also contributing to the very limited state of knowledge in the field of ICOs.
1. Introduction
With the decentralization movement on the rise, blockchain start-ups have embraced an innovative fundraising method called "initial coin offerings" (ICOs) (Conley, 2017; Yadav, 2017). Even though ICO projects are characterized by strong information asymmetry and ambiguity (Adhami et al., 2017), and numerous scams have been reported (Elendner et al., 2016), ICOs are becoming increasingly important in capital formation (Kaal & Dell'Erba, 2017; Robinson, 2017). In 2017 alone over
$5,5 billion was raised for 344 projects, and by June 2018 more than $8 billion had been raised for 333 projects (Coindesk, 2018). However, very little is known about this increasingly important new
phenomenon, which is not yet regulated in most countries (Adhami et al, 2017).
At the start of this study, in early 2018, only eight research articles on the ICO phenomenon had been published and most of these were descriptive rather than empirical (Kaal & Dell'Erba, 2017; Conley, 2017; Enyi & Le, 2017; Venegas, 2017; Yadav, 2017; Adhami et al., 2017; Robinson, 2017). As of July 2018, new studies continue to be published (e.g., Adhami et al., 2018, Howell et al., 2018), but most of the research remains focused on the (legal) issues and risks and the successfulness of ICOs, and hardly any research has focused on investors' side of the story (excluding a few interviews conducted by Yadav, 2017). However, it is the contributors who determine whether a funding method is successful or not (Adhami et al., 2017; Bretschneider et al., 2014), which makes their view of the utmost importance. Additionally, with such a large number of contributors funding these projects (Coindesk, 2018), so many risks involved (e.g., Kaal & Dell'Erba, 2017; Elendner et al., 2016), and many warnings, even by regulators, that the sudden rise of this phenomenon might constitute a financial bubble (Edwards, 2017; AFM, 2018), it is important to find out why consumers are so eager to fund these ICO projects (Adhami et al. 2017). This research contributes to extending the very embryonic state of ICO research (Yadav, 2017; Adhami, 2017), and also offers important insights into small ICO investors' decision-making for entrepreneurs wanting to increase their ICO funding success, but also to regulators on the rationality of this behavior to be able to estimate the risks and help design regulations and programs.1
As several studies have claimed that ICOs have certain similarities to equity crowdfunding (Adhami et al., 2017; Kaal & Dell'Erba, 2017; Yadav, 2017), existing research into equity crowdfunding and neighboring and more developed forms of funding and venture capital and business angel research (Drover et al., 2017) might be applicable to ICOs. Research on these subjects have revealed that investors make decisions by following a decision-making process, by which these most promising ventures are selected (e.g., Haines et al., 2003; Van Osnabrugge, as cited in Scheder & Arbøll, 2014; Tyebee & Bruno, 1984; Paul et al., 2007). It is assumed that in this decision-making process, people
1 Shortly after this study was conducted, the importance of gaining insights was also confirmed by a survey the Dutch independent market conduct authority (AFM) recently conducted to gain insights into how small investors deal with crypto and ICO investments, in order to measure the risks involved (AFM, 2018a).
either fully process the information that is offered, or use heuristics to process the information (Petty & Cacioppo, 1986; Kahneman & Tversky, 1973).
In general the determinant of the actual step that leads an investor to seek investment opportunities can be defined as the investor's motives being activated into actual motivation (Bretschneider et al., 2014). Prior work suggests that in order to understand why consumers invest in start-ups, it is important to understand what motivates their behavior (Bretschneider et al., 2014). Self-determination theory, developed by Ryan and Deci (2000), is one of the most used motivational concepts, and draws a distinction between intrinsic motivations, such as joy and curiosity, and extrinsic motivations, such as rewards or recognition (Ryan & Deci, 1993, from Ryan & Deci, 2000). Prior findings on equity crowdfunding and business angels suggest both motivational factors can play a role in deciding to fund start-ups (Bretschneider & Leimeister, 2017; Shane, 2009), while the findings on venture capital suggest only extrinsic reward motivations influence decisions (e.g., Sudek, 2006). Other than
speculations about people's motivations to invest in ICOs, such as Conley (2017) stating that crypto tokens are new and "cool" and people might be willing to invest on basis of that, and Adhami et al. (2017) stating that ICO contributors are probably also driven by intrinsic motivations, there has been no research to back any of these assumptions.
Activated motivation(s) are followed by the screening and evaluating of start-ups (e.g., Paul et al., 2007; Fried & Hisrich, 1994; Scheder & Arbøll, 2014). An important theory that has proven helpful in finding out what kind of information leads investors to invest in neighboring forms of funding is Spence's (1973) signaling theory (e.g., Ahlstrom & Bruton, 2006; Cosh et al., 2009; Robb & Robinson, 2014; Ahlers et al. 2015). Existing research into these neighboring forms of funding indicates several signals; for example, the skills and experiences of the team, partnerships, and the business plan are found to be important (e.g., MacMillan et al., 1985; Ahlers et al., 2015;
Bretschneider, 2017). Conducting research into equity crowdfunding, Mollick (2013) proved that entrepreneurial quality is assessed in a similar way by both venture capitalists (VCs) and
crowdfunders, suggesting that crowdfunders have a similar ability to professional investors to distinguish quality projects from less promising ones. However, some findings suggest that
crowdfunders can exhibit rather irrational behavior, such as herding (e.g., Bretschneider & Leimeister, 2017; Agrawal et al., 2011; Banerjee, 1992). As mentioned, the existing research on ICO evaluation by investors is very limited (Yadav, 2017; Adhami et al., 2017). Furthermore, these studies do not analyze the rationality of behavior, and include contrasting arguments, such as whether whitepapers (business plans) are important.
How ICOs, an emerging source of financing, complement or challenge the existing knowledge about neighboring forms of funding is yet unknown. Taking the mentioned research gaps into account, the aim of this research is therefore to gain insights into small investors' decision-making. The following research question has been formulated:
"Why do small investors fund an ICO, and which signals influence their decision-making?"
As existing research is very incomplete, qualitative research was conducted to get an understanding of the meanings humans attach to an event (Saunders & Lewis, 2012). To answer the research question, data were collected from multiple case studies by conducting 16 semi-structured interviews. As the population is unknown, due to the anonymity of investing in ICOs, a purposive sampling technique was used, where, on the basis of the author's judgment, cases were selected that were expected to best answer the research question (Braun & Clarke, 2013).
By building on existing decision-making theory (Petty & Cacioppo, 1986; Cialdini, 2007; Kahneman & Tversky, 1973), motivational theory (Ryan & Deci, 2000), and signaling theory (Spence, 1973), this study contributes to the existing research by drawing parallels with findings on neighboring forms of funding to ICOs (e.g., Cholakova & Clarysse, 2014; Bretschneider & Leimeister, 2017; Ahlers et al., 2015), and offering insights into new findings, thereby extending the very embryonic state of
knowledge in the field of ICOs (Yadav, 2017; Adhami, 2017).
This paper is structured as follows. The next chapter offers a comprehensive review of the current literature on the main concepts, from which propositions are derived. The methodology, including the research design, data collection, and data analysis method are explained in Chapter 3. In Chapter 4 the results extracted from the collected interviews are described. Chapter 5 discusses the implications of these results, along with the main contributions of the research, its limitations, and recommendations for future research. Finally, the conclusion summarizes the answer to the research question.
2. Literature Review
This chapter reviews the existing theory regarding the concepts of ICOs and neighboring funding forms. First, a short explanation of ICOs and their investors is provided, as well as the choices of neighboring forms of funding which constitute an umbrella research field. The second section includes a general description of investors' making processes, and the use of heuristics in decision-making. The third section focuses on deriving propositions from the literature on why small ICO investors possibly invest in ICOs. Finally, propositions are derived based on the existing literature concerning what influences small investors to fund projects.
2.1 Initial Coin Offerings and Other Early Stage Financing Mechanisms
Initial coin offerings is a powerful new and evolving phenomenon for blockchain entities to raise funds from investors across the globe (Koetsier, 2017; Jackson, 2017; Robinson, 2017). Initial coin offerings are defined by Adhami et al. (2017) as: "open calls for funding promoted by project initiators, where crypto currencies are provided in exchange for tokens that can be sold on the secondary market or used in the future to gain products or services." That ICOs are a new and booming funding mechanism can be seen from the figures below.
Figure 1: History of ICO funding (nr), graph Figure 2: History of ICO funding ($mn), graph made on the basis of data from Coindesk (2018) made on the basis of data from Coindesk (2018)
An ICO typically requires the disclosure of a whitepaper, in which information is disclosed about the IT protocols, the adopted blockchain, the token supply, pricing and distribution, details about the project that is to be developed, including a team description (Adhami et al., 2017). At a given time the fundraising opens, and pledgers will be able to (pre-)buy tokens with, usually, Bitcoins and Ethers (Adhami et al., 2017).
2.1.1 Small Initial Coin Offering Investors
Literally anyone can participate in an ICO, as long as one buys some base currency, which is usually Bitcoin or Ether (Dolce, n.d.). Because of its wide accessibility, ICOs allow start-ups to fundraise and bypass banking and non-banking entities, such as venture capitalists (Kaal & Dell'Erba, 2017). Traditionally only a select group of investors are allowed by venture capital funds to invest in highly innovative projects. These projects therefore generally remain unknown to small investors (Guarda, 2017). By contrast, ICOs allow small investors from all over the world to invest in their highly innovative ventures, and because of the low barrier to entry and the borderless nature of online token sales, an exceptional number of investments from small investors is unlocked (Kaal & Dell'Erba, 2017; Kostinuk, 2016). Initial coin offerings are thereby increasing the diversity and heterogeneity of the start-up funding landscape (Kaal & Dell'Erba, 2017). Besides that small ICO investors are represented worldwide (Kaal & Dell'Erba, 2017), little is known about their demographics and behavior as the investments are private. Because of the inclusion of small investors in funding highly innovative ventures, and the limited knowledge about their demographics and behavior, the focus of this research is on these small investors. This research makes a start to profile small investors in ICOs by examining their motivations for investing and the signals that influence the investment decision.
2.1.2 Initial Coin Offerings and Adjacent Forms of Funding
As there is a lack of literature on ICOs, existing research into adjacent forms could help to investigate the reasons why investors back an ICO and how they evaluate these opportunities. Table 1 gives an overview of the key features of ICOs and adjacent forms of funding (Robinson, 2017; Wilson & Testoni, 2014; Lukkarinen et al., 2016; Dolce, n.d.). In the literature ICOs have mainly been compared to initial public offerings (IPOs) (Adhami et al., 2017; Kaal & Dell'Erba, 2017) and equity
crowdfunding (Adhami et al., 2017; Chen, 2017; Kaal & Dell'Erba, 2017; Yadav, 2017). Both these forms of funding will therefore be examined in the following sections.
Initial Public Offerings
Initial coin offerings have often been compared to IPOs (Adhami et al., 2017; Kaal & Dell'Erba, 2017). Both have been used to attract funds for developing companies, they can attract huge amounts of money, and thus have the potential to make the founders wealthy in an instant (Griffith, 2017). The similarities end there though, as IPOs are subject to comprehensive regulatory oversight, and are known to have healthy track records and credibility (Robinson, 2017; Dolce, n.d.). Furthermore, stocks acquired through an IPO represent an ownership stake in the future earnings of the company, which is not the case with ICOs. Besides that, the IPO process is a lengthy process, and it is hard for the small retail investor to gain access to these shares (Dolce, n.d.). In the case of IPOs, investors are typically institutional investors, such as investment banks, mutual funds and endowments (Dolce, n.d.). Considering the research focus of this study on gaining insight into small ICO investors' decision-making, it can be concluded that the average IPO investor cannot be compared to small ICO investors.
Table 1: Key features of ICOs and adjacent forms of funding. Retrieved and modified from Wilson and Testoni, 2014; Lukkarinen et al., 2016; Dolce, n.d.
Features Equity crowdfunding IPO Business Angel Venture Capitalist ICO
Typical funder background
Various, many have no professional investment experience
Mostly institutional investors, such as investment banks, mutual funds and endowments
Former entrepreneurs Finance, consulting industry
Various, many have no professional investment experience
Source of funds Investing own money Mostly investing other
people's money
Investing own money Investing other people's money
Investing own money Funding
instruments
Shares Shares Shares Shares Crypto coins for usage
of the service and/or trading
Deal flow Through web platform Investment banks Through social
and/or angel networks
Through social networks and active outreach
Online, through the website of the ICO
Due diligence Very limited; conducted
by individual, if at all Conducted by outside firms such as investment banks (high level of regulatory oversight) Conducted by individuals based on their own experience
Conducted by VC firm or outside firm Very limited; conducted by individual, if at all Geographic proximity of funders Investments made online; funders often distant from ventures
Nationally (or internationally with partners) Mostly local investments Nationally (or internationally with partners) Investments made online; funders are distant from ventures Post-funding role
of funders
Most remain passive Most remain passive Active (hands-on) Active (strategic) Passive Return on
investment
Financial return important (but not the only reason for investing)
Financial return important (but not the only reason for investing)
Financial return critical
Equity Crowdfunding
Initial coin offerings have far more similarities with early-stage equity financing mechanisms, because they also have to deal with much stronger information asymmetry and high uncertainty (Adhami et al., 2017). Of the different equity financing sources, ICOs share the most features with equity
crowdfunding (Adhami et al., 2017; Kaal & Dell'Erba, 2017; Yadav, 2017). These features include low protection of contributors, limited set of information available, and no relevant track record for the proponents. There are however major differences. First of all, equity crowdfunding portals collect their funding via a platform (e.g., Kickstarter) and through traditional payment channels, while ICOs rely on cryptocurrency blockchains with no need to rely on a platform (Adhami et al., 2017). Furthermore, ICOs often include an element of a speculative purpose developed on platforms and cryptocurrencies, which is not the case with equity crowdfunding (Kaal & Dell'Erba, 2017). Lastly, there is no relevant regulation yet which applies to ICOs (Enyi & Le, 2017), while equity crowdfunding is subject to higher levels of regulation (Heminway & Hoffman, 2010).
In conclusion, on the basis of the abovementioned differences, which are summarized in Table 1, it can be concluded that ICOs are a new independent form of funding, which differs significantly from other early-stage capital funding mechanisms. However, there are also similarities. As this paper focuses on small ICO investors, it makes sense to draw on research into a funding form with similar types of investors, namely equity crowdfunding. Since this research field is also relatively young (Moritz & Block, 2016), the areas of equity crowdfunding together with the more mature neighboring areas of venture capital and business angels, to which equity crowdfunding has often been compared (e.g., Mollick, 2012; Drover et al., 2017), will be considered as an umbrella research field for this research study. This complies with the recommendation of Drover et al. (2017) to use existing
knowledge about venture financing research and other forms such as angel financing when examining new forms of funding, as the existing knowledge might help to gain insights.
2.2. Decision-making Theory
In order to understand why small investors would fund an ICO and which signals influence their decisions, it is important to understand how decisions are made and what influences the decision-making process. Many scientists, including psychologists and economists, have produced theories and performed experiments to gain insights into individual decision-making (Edwards, 1954). The basic neoclassical assumption was that decision-making was a process in which humans act fully rationally, as they are aware of all possibilities, have all information that is available, and make decisions based on maximizing utility while using minimum effort (Simon, as cited in Karimi, 2013). Simon (as cited in Karimi, 2013) was one of the first to criticize this dominant economic assumption, as his theory states that the limitations of a human's mental computational power, the accessibility of information, the complexity of circumstances, and limited time reduce the rationality of a decision to a state of bounded rationality in the decision-making process (March, 1978; Buchanan & O’Connell, 2006).
2.2.1 Investor Decision-making Process
Research indicates that investors often need to decide whether to fund an early-stage entrepreneurial venture on the basis of very limited information and under circumstances of high uncertainty where information asymmetries exist (e.g., Baum & Silverman, 2004; Ahlers et al., 2015; Dushnitsky, 2009; Mollick, 2013). In order to be able to select the most promising ventures instead of the less promising ones (Akerlof, 1970), it is important that investors mitigate these information issues (Ralcheva & Roosenboom, 2016). Venture capital, business angel and crowdfunding research has revealed that investors make decisions by following a decision-making process by which the most promising ventures are selected (e.g., Haines et al., 2003; Van Osnabrugge, as cited in Scheder & Arbøll, 2014; Tyebee & Bruno, 1984; Paul et al., 2007). However, this process is not a linear but rather an iterative process, including feedback loops, where the borders between stages are to a certain extent fluid (Paul et al., 2007; Scheder & Arbøll, 2014). In order of intensity and length, the venture capital decision-making process is the most extensive, followed by the business angel process, and finally (equity) crowdfunding (Paul et al., 2007; Scheder & Arbøll, 2014). Venture capitalists are known to have extensive resources and to go through a lengthy due diligence process (Paul et al., 2007; Fried & Hisrich, 1994). Furthermore, in the venture capital, but also in business angel decision-making
process, there is a great deal of direct contact between the investors and the entrepreneur. In the equity crowdfunding process, however, entrepreneurs make an open call for funding on a crowdfunding platform, and investors make their decisions based on the information provided on the platform (Belleflamme et al., 2014). This is similar to ICOs, where investors also need to base their decision-making on the information provided online (Yadav, 2017). However, the difference is that with ICOs there is no platform included.
In general the determinant of the actual step that leads an investor to seek investment opportunities can be defined as the investor's motives being activated into actual motivation (Bretschneider et al., 2014). Self-determination theory, developed by Ryan and Deci (2000), is one of the most frequently used theories to uncover people's motivations. Based on these motivations, screening and evaluations take place, which can consist of one or several steps, dependent on the funding mechanism under
consideration (e.g., Paul et al., 2007; Fried & Hisrich, 1994; Scheder & Arbøll, 2014). The process that ICO investors follow has not yet been described in the literature. However, it is known that small investors generally have very little resources and are not able to perform extensive research into potential investments (Ahlers et al., 2015). It is therefore assumed that screening and evaluation (from here: evaluation) will be one phase. This phase includes accessing and assessing the available
information regarding opportunities in order to determine a firm's potential (Paul et al., 2007; Scheder & Arbøll, 2014). Spence's (1973) signaling theory has proven helpful to establish what kind of information leads investors to invest in start-ups (e.g., Ahlstrom & Bruton, 2006; Cosh et al., 2009; Robb & Robinson, 2014; Ahlers et al., 2015). The evaluation phase then leads to the actual investment and further (e.g., Haines et al., 2003; Van Osnabrugge, as cited in Scheder & Arbøll, 2014; Tyebee &
Bruno, 1984; Paul et al., 2007). With such a large number of contributors funding ICO projects (Bussman, 2017), it is important to find out why people are so eager to fund these risky ICO projects (Adhami et al. 2017). Therefore, this research study is solely focused on the pre-investment stages of motivation and screening and evaluation, and the other stages will not be further elaborated on. In addition, the following phases are also much simpler with ICOs compared to, for example, more traditional investment methods such as business angels and venture capitalists investing in start-ups (Kaal & Dell'Erba, 2017). For example, small investors do not have to engage in contract negotiation when investing in ICOs, because the standards are set by the ICO (Kaal & Dell'Erba, 2017).
Furthermore, the investors have no influence or control over the venture (Kaal & Dell'Erba, 2017).
Figure 3: General investor decision-making process. Retrieved and modified from Paul et al., 2007; Fried and Hisrich, 1994; Haines et al., 2003; Von Osnabrugge and Robinson, as cited in Scheder and Arbøll, 2014.
2.2.2 Rational and Irrational Decision-making
As mentioned before, traditional making theory was based on the assumption that decision-making is a process in which humans act fully rationally (Simon, as cited in Karimi, 2013). Known as the utility model, people were regarded as fully aware of all possibilities, having all information that is available, and making decisions based on maximizing utility while using minimum effort (Simon, as cited in Karimi, 2013). Coming to such an optimal decision and correct judgment would then entail following complex algorithms where all relevant pieces of information should be acknowledged, processed and weighed (Payne et al., 1993). Such an algorithm not only requires great mental effort, but also requires people to have unlimited processing capacity. However, this seems to be impossible, as we live in the most rapidly moving, complex and extraordinarily complicated environment
(Cialdini, 2007). Many researchers have argued that it is often impossible for individuals to process information, as it is simply too much or too complex (Malhotra et al., Rubinstein, Iyengar et al., 2006, & Fasolo et al., as cited in Garcia, 2013). In addition, we are not able to recognize and analyze all aspects of events and situations and simply do not have the time and capacity for it. People must therefore operate within the constraints imposed by both their cognitive resources and the task environment—a concept known as "bounded rationality" (Simon, as cited in Shah & Oppenheimer, 2008). People may therefore employ methods that reduce effort and simplify decision-making, which are referred to as "heuristics" (Shah & Oppenheimer, 2008). The term "heuristic" originates from Newell and Simon (as cited in Shah & Oppenheimer, 2008), who used the word to describe simple processes that replace complex algorithms. Heuristics must therefore: "allow decision-makers to
process information in a less effortful manner than one would expect from an optimal decision rule".
Kahneman and Tversky (as cited in Shanteau, 1989) first demonstrated their famous heuristics and biases by challenging the idea that human actors behave fully rationally under uncertainty, by
examining how people manage risk and uncertainty (Kahneman & Tversky, 1979). Their view, which is called the prospect theory, can be described as follows: "In making predictions and judgments under uncertainty, people do not appear to follow the calculus of chance or the statistical theory of
prediction. Instead, they rely on a limited number of heuristics which sometimes yield reasonable judgments and sometimes lead to severe and systematic errors" (Kahneman & Tversky, 1973, p. 237). On the basis of their empirical studies, they argued that people rely on heuristic principles to simplify judgmental operations (Tversky & Kahneman, as cited in Kahnemen, 2011; Tversky & Kahneman, 1973):
Representativeness: the representativeness heuristic is used to assess the similarity of objects, and is often used to categorize a target object in a given class. When judging the
representativeness of a new event, people usually pay attention to the degree of similarity between the event and the standard, and to whether these features are salient (Kahneman & Tversky, 1972). Supporting evidence from empirical studies performed by Kahneman and Tversky demonstrates representativeness heuristics by, for example, people ignoring base rates and neglecting sample sizes (Kahneman & Tversky, 1973; Tversky & Kahneman, 1983).
Availability: this is a mental shortcut that is based on the belief that available information is the most relevant information, which relies on examples that come to a person's mind immediately when evaluating certain things (Garcia, 2013; Tversky & Kahneman, 1973). In an exemplary experiment by Tversky and Kahneman (1973), subjects heard the names of an equal number of males and females. In one case more famous men were mentioned, and in the other more famous women were mentioned. After reading out all names, the people were asked whether the list included more men or more women. In the case where more famous men were mentioned, a great majority of people answered that there had been more men, and vice versa for the famous women. Tversky and Kahneman (1973) argued that people's judgment was based on the availability heuristic, where the names of famous people were more easily recalled than the other names.
Anchoring and adjustment: a cognitive bias that is based on people's tendency to rely too much on an initial piece of information (the anchor) in their decision-making (Tversky & Kahneman, 1974). The anchor in fact minimizes the amount of information used and has been proven to determine people's final decision. Experimental evidence reveals that people "anchor" too much on the initial value (Barberis & Thaler, 2003; Tversky & Kahneman, 1974). Overall, experimental studies by Kahneman and Tversky have demonstrated biases in the evaluation of con- and disjunctive cases, insufficient review of probabilities (Tversky & Kahneman, 1974), and framing effects (Kahneman & Tversky, 1984).
Another main contribution in the area of heuristics is the elaboration likelihood model, developed by Petty and Cacioppo (1986). This framework argues that there are two primary ways in which
individuals process information (Petty & Cacioppo, 1984), which are conscious, rather rational, and unconscious, sometimes irrational, decision-making processes. The two key elements of this theory are ability and motivation to process, which determine the information processing route taken (Petty & Cacioppo, 1986). The model describes two routes for information processing, which are the central route and the peripheral route. When a person has the motivation as well as the ability to process a message, the central route is taken (Petty & Cacioppo, 1986). In this case, cognitive responses are much more relevant to the information, and therefore decisions are based on factors such as the quality of the argument. When a person has less of the message and/or the person does not have the (full) ability to process the information of the message, the peripheral route is taken (Petty & Cacioppo, 1986). In this route people do not examine the information as thoroughly, but instead rely on mental shortcuts (heuristics), such as rules of thumb, to process the information. In this case people are more likely to be persuaded by their affective judgment, which means they might be influenced by cues that are not relevant to the issue. Contrary to this research, recent findings suggest that these routes can actually overlap (Sparks et al., 2013). In the research study of Sparks et al. (2013), consumers’ attitudes and purchase intentions in terms of corporate social responsibility were influenced in ways that could be attributed to the central route as well as more peripheral cues. In conclusion, both routes can thus be combined and decisions can therefore (un)knowingly be rational and partially irrational on the basis of heuristics used.
In line with Kahneman and Tversky's (1974) prospect theory and Petty and Cacioppo's (1986) elaboration likelihood model, Cialdini (2007) identified several heuristics. These heuristics, based on persuasion, help explain the psychology of why people say yes. Cialdini (2007) identified the
following six heuristics that can be regarded as universal principles that guide human behavior: 1. Social proof can be defined as follows: “one of the means we use to determine what is correct,
is to find out what other people think is correct” (p. 116). One example of the use of social proof can be found in the reusing of towels in hotels, where signs include statements about the percentage of people in the hotel/room reusing towels in order to persuade the reader to do the same (Goldstein et al., as cited in Cialdini, 2007).
2. Liking can be defined as “the preference of people to say yes to the request of someone we know and like” (p. 167). Physical attractiveness, similarity, compliments, contact and cooperation (Cialdini, 2007) are all tools used by marketeers and salespeople to increase liking. In addition, Kahneman (2011) has proven the effect of the halo bias, in terms of which liking the subject has more impact on a person's decision than the actual message.
3. Authority can be defined as "the extreme willingness of people to go almost any lengths on the command of an authority” (Milgram, as cited in Cialdini, 2007). As examples, tools such as
titles and certain workwear are used to demonstrate a certain authority in order to convince people of a specific statement.
4. Scarcity is when “an opportunity seems more valuable to people when its availability is limited” (p. 238). Examples of companies using scarcity is offering products for a limited time, price or amount to persuade consumers to purchase. This theory, in which the prospect of losing an opportunity is used to persuade people, is consistent with the prospect theory (Kahneman & Tversky, 1975).
5. Consistency can be defined as “quite simply, our nearly obsessive desire to be (and to appear) consistent with what we have already done” (p. 57). Companies use the desire for consistency to get consumers to commit to something small and then to follow through on that
commitment (Cialdini, 2007).
6. Reciprocity is defined as “a rule that says we should try to repay, in kind, what another person has provided for us” (p. 17). Based on their feelings of indebtedness, reciprocity can lead consumers to accept offers that otherwise would surely have been refused. An example of reciprocity in restaurants is the waiter offering mints with the bill, thereby increasing
customers' tips tremendously.
These heuristics can have a strong influence on people's decision-making and can persuade people to make decisions that have a rather irrational basis.
In summary, one can conclude that the decision-making process consists of conscious as well as unconscious aspects, where people make decisions on the basis of mental shortcuts (heuristics) which can be very useful, but can also lead to rather irrational decision-making. However, in the
crowdfunding context, many researchers have focused on the rational behavior of crowdfunders (e.g., Ahlers et al., 2015; Moysidou, as cited in Bretschneider & Leimeister, 2017; Mollick, 2013). For example, Moysidou (as cited in Bretschneider & Leimeister, 2017) argues that crowdfunding investors are rationally behaving individuals. Mollick (2013) provided evidence that equity crowdfunding investors have a similar ability as professional investors (venture capital firms) rationally to distinguish quality projects from less promising projects. However, the use of heuristics, leading to emotional and irrational behavior, has been proven to exist amongst crowdfunders. For example, several researchers have found evidence that suggests (equity) crowdfunding investors exhibit rather irrational behavior through, for example, herding (e.g., Bretschneider & Leimeister, 2017; Agrawal et al., 2011; Banerjee, 1992), referring to behavior where “everyone is doing what everyone else is doing” (Banerjee, 1992; Agrawal et al., 2011). Furthermore, a study by Chen et al. (2016)
demonstrated the use of guilt appeals, and emotional message framing can improve the funding success of a crowdfunding campaign. Other than speculation about people's irrational behavior concerning investing in ICOs, such as Conley (2017) stating that crypto tokens are new and cool and people might be willing to invest solely on the basis of that, and many media reports suggesting the behavior of crypto investors is irrational (e.g., Edwards, 2017; The Guardian, 2018), there has been no
research in the ICO context confirming irrational behavior exists amongst ICO investors. The rational and more irrational aspects of decision-making that are expected to apply to small ICO investors will therefore be discussed in the following paragraphs, as propositions will be derived on the basis of the literature to answer why small investors fund ICOs, and which signals influence their decisions.
2.3 Why Do Small Investors Fund Initial Coin Offerings?
In order to answer why investors fund start-ups, it is important to understand what motivates their behavior, as this is the determinant of the actual step that leads an investor to seek an investment (Bretschneider et al., 2014). Backers may be driven by several different motivations. Insights into backers’ motivation to fund can inform the design of web-based systems (Bretschneider & Leimeister, 2017). Motivation psychology concerns research into the process of how an individual's motives become activated (Bretschneider et al., 2014). A motive in this case can be regarded as an individually developed and content-specific psychological disposition (Jost, as cited in Bretschneider et al., 2014). The interaction between a person's motives and situational factors (e.g., incentives) results in a current motivation that in turn results in a certain behavior (Bretschneider et al., 2014).
Self-determination theory developed by Ryan and Deci (2000) is one of the most frequently used motivational concepts. They argue that people do not only vary in their levels of motivation, but also in terms of different kinds of motivation (Ryan & Deci, 2000a). As Ryan and Deci (2000a) have stated, "the type of motivation concerns the underlying attitudes and goals that give rise to action— that is, it concerns the why of actions." Self-determination theory distinguishes different types of motivation on the basis of different reasons or goals that cause a certain action. A general distinction is made between intrinsic motivation and extrinsic motivation. Intrinsic motivation refers to doing of an activity for the inherent satisfaction it brings instead of for a separable consequence and can be activated by, for example, curiosity and joy (Ryan & Deci, 2000; Ryan & Deci, 2000a). Extrinsic motivation can be activated when the action leads to a separable outcome, such as compensation or recognition (Ryan & Deci, as cited in Ryan & Deci, 2000; Ryan & Deci, 2000a). Figure 4 illustrates a sub-theory of self-determination theory called organismic integration theory (OIT), which
demonstrates the different forms of extrinsic and intrinsic motivation, the contextual factors that promote or hinder internalization, and the integration of these behaviors (Deci & Ryan, as cited in Ryan & Deci, 2000a).
Figure 4: A taxonomy of human motivation. Retrieved from Ryan and Deci, 2000a.
From existing research into venture capital, equity crowdfunding and business angels funding, it is known that both intrinsic and extrinsic motivational factors can play a role in deciding to fund a start-up (e.g., Bretschneider et al., 2014; Bretschneider & Leimeister, 2017; Shane, 2009). In the following sections these neighboring funding forms are therefore used as umbrella research fields for ICOs, to examine which of these motivations might apply to small ICO investors as well.
2.3.1 Intrinsic Motivation
Intrinsic motivation can be defined as an instance where a person is motivated by the fun or challenge of the act itself and not by the external rewards, reinforcements or pressure felt (Ryan & Deci, 2000a). Cognitive evaluation theory (CET) is a sub-theory of self-determination theory, presented by Ryan and Deci (as cited in Ryan & Deci, 2000a). Cognitive evaluation theory focuses on intrinsic motivation and examines which factors enhance or undermine it. Ryan and Deci (2000a) have argued that intrinsic motivation arises from satisfying three innate psychological needs, namely competence, autonomy, and relatedness. Cognitive evaluation theory has been used in the context of crowdfunding as a lens to explain investors' intrinsic motivations for backing a project, to analyze their behavior and the effect of narratives that can be used to promote backers' intrinsic motivations (Allison et al., 2015; Cholakova & Clarysse, 2015). Adhami et al. (2017) argue that ICO investors are probably not only driven by extrinsic motivations, but also by intrinsic motivations (such as liking and curiosity). Therefore, the following sections examine two possible intrinsic motivations that could be of importance in ICO motivations, in terms of CET.
Liking
Liking has been used as a construct in many research studies on human behavior (Bretschneider & Leimeister, 2017), as the feelings of liking are used in making decisions (Clark, 2010). Bretschneider
and Leimeister (2017) name as an early example of uncovering the construct when computers became more relevant and information systems research began to investigate users’ attitudes toward computers to enhance their understanding of user behavior and in order to find out how to shape future behavior (Al-Jabri & Al- Khaldi, 1997; Henderson et al., as cited in Bretschneider & Leimeister, 2017).
Liking has been found to be an important factor in business angels' motivation for investing in start-ups, as this often constitutes their first step to a potential investment (e.g., Brettel, 2003; Feeney et al., 1999; Mason & Stark, 2004). Benjamin and Margulis (as cited in Cholakova & Clarysse, 2014) even argue that angel investors are more motivated by these intrinsic factors than by return on investment. The crowdfunding literature states that the online information that is provided about the project via descriptions and short videos provides a solid basis for developing feelings of liking a project (e.g., Moysidou, as cited in Bretschneider & Leimeister, 2017; Ordanini et al., 2011). Bretschneider and Leimeister (2017) found evidence that liking is also an important motive in the context of equity-based crowdfunding. Initial coin offerings offer online information about their projects in the form of
whitepapers (including key terms, the investment approach, the investment strategy, signals, restrictions, processes, and returns), but also in videos about the service to be launched and on
Telegram channels. It is therefore expected that feelings of liking can be developed in the same way as with regard to equity crowdfunding. The first proposition is therefore as follows:
Proposition 1: Small investors fund ICOs because they like the project (based on the online information about the project).
Curiosity about Learning New Things
Curiosity is also mentioned by Ryan and Deci (1993, as cited in Bretschneider et al., 2014) as a probable intrinsic motivation. The general effect of curiosity motivation on the willingness to act has already been demonstrated as essential in a study by Füller (2006), which investigated consumer engagement in new virtual products. One of the reasons angel investors are known to invest in start-ups is because they are curious to learn something new (Shane, 2009, p. 25). Some angels are curious, because they want to challenge their existing ideas and learn about different/new views, and others are simply curious to learn about a new technology before it reaches the marketplace or before other people have heard of it (Shane, 2008, p. 55). Bretschneider et al. (2014) have argued that
crowdfunders may also invest in a crowdfunding project because they are curious to learn about this new investment alternative or simply because they want to escape boredom. Ordanini et al. (2011) have described this as the fundamental interest in how crowdfunding works being a reason for
participating in funding a project. As ICOs are a totally new funding mechanism, one also expects that the curiosity to learn about this funding mechanism can be a motivation in funding an ICO. The proposition is therefore as follows:
Proposition 2: Small investors fund ICOs because they are curious to learn about the funding concept.
2.3.2 Extrinsic Motivation
Extrinsic motivation can be activated when the action leads to a separable outcome, such as compensation or recognition (Ryan & Deci, 1993, as cited in Ryan & Deci 2000; Ryan & Deci, 2000a). Unlike some perspectives, which state that extrinsic motivation is invariantly
non-autonomous, self-determination theory argues that extrinsic motivation can vary greatly in the degree to which it is autonomous (Ryan & Deci, 2000a). As mentioned before, Figure 4 depicts the different forms of extrinsic motivation (Deci & Ryan, 1985), which will be discussed in the following sections.
2.3.2.1 External regulation
External regulation is the least autonomous form of extrinsic behavior and is central to the classic operant conditioning theory (Skinner, as cited in Ryan & Deci, 2000a) in terms of which a person is motivated to obtain rewards or avoid punishments. The extrinsic reward motivation further draws on the incentive theory in terms of which behavior is motivated by a desire for reinforcement and/or incentives (Bretschneider & Leimeister, 2017).
Rewards
The obvious explanation of why business angels invest in start-ups is obtaining profit on the invested capital (Feeney et al., 1999). As Shane (2008) has stated, angels invest in start-ups as they believe they will make more money investing in other people's businesses than via other investment alternatives. An angel investor is however typically motivated beyond return on investment (ROI) (Benjamin & Margulis, 2000; Van Osnabrugge & Robinson, as cited in Sudek, 2006). Venture capitalists are different in this respect, because their primary reason for existence is return on investment (Sudek, 2006). Venture capitalists' main reason for investing is the financial return, while angel investors fund start-ups because of other reasons as well, such as liking the project and helping others (Benjamin & Margulis, 2000; Van Osnabrugge & Robinson, as cited in Sudek, 2006). Bretschneider and Leimeester (2017) found that the main and obvious motivation for funding in all forms of incentive-based
crowdfunding are rewards, which are received in the form of a fixed periodic income and repayment of principal or some form of equity or equity-like arrangements (e.g., profit-sharing) from the venture (Ahlers et al., 2012). However, they have argued that other motivations can be present as well. Investors in ICOs may obtain future benefits in several ways, depending on how the coin is structured (Dolce, n.d.). In general the value of the crypto coin is directly linked to its alleged utility, and the more the coin is adopted, the higher the value will be (Dolce, n.d.). There are coins that create value by granting a stake in the company's future revenues, and coins that link their value to usage in the ecosystem that will be developed (Dolce, n.d.). As reward motivation is present in all neighboring funding mechanisms, it is expected this motivation will be present amongst investors in ICOs as well. The proposition is therefore as follows:
2.3.2.2 Introjected Regulation
Introjection refers to a type of action that is performed when a person feels pressured, to avoid feelings of guilt or anxiety or to achieve ego enhancements or pride (Ryan & Deci, 2000a). Ego involvement is known as a classic form of introjection (Nicholls, 1984; Ryan, as cited in Ryan & Deci, 2000a), and refers to a person acting in order to improve or uphold their feelings of self-worth and self-esteem (Ryan & Deci, 2000a).
Recognition
Recognition is defined as the acknowledgement of a person's position, accomplishments, or merit by a person or a group, and originates from a personal desire for fame and esteem (Maslow, 1943).
Recognition motivation has been found to play a role in equity crowdfunding, as a motivation for funding by project backers was found to be their expectation of positive reactions from other backers or the creators of the project (Bretschneider & Leimeister, 2017). It is suggested that project backers might be motivated by their expectation to receive recognition from the community as well as the society (Bretschneider, 2014). As Conley (2017) argues that crypto tokens are new and cool and people might be willing to invest on the basis of that, recognition might play a role in the motivations of ICO investors as well. The proposition is therefore as follows:
Proposition 4: Small investors fund ICOs because they expect to receive recognition from the community and society.
2.3.2.3 Identification
Motivation through identification occurs when a person has identified with the value and personal importance of a certain behavior (Ryan & Deci, 2000a). Ryan and Deci (2000a) cited as an example a boy who learns spelling lists because he perceives this to be an important step in order to learn how to write, which is of value in achieving his life goals.
Needs
It is argued that, when an investor views a project as relevant for them personally, the investor develops a personal need (Moysidou, as cited in Bretschneider & Leimeister, 2017; Ordanini et al., 2011). In a crowdfunding context, typically people with the highest levels of identification are the first ones to invest (Ordanini et al., 2011). Bretschneider and Leimeister (2017) found that a motivation of backers in equity crowdfunding to invest in a project was to increase the chances of the project launching successfully in order to fulfill their personal need. As the projects' underlying concepts did not yet exist in the market, backers tried to ensure the success of the project by backing it for the sole reason of the project outcome reflecting their personal need (Bretschneider & Leimeister, 2017). As ICO projects are also new and innovative, sometimes even groundbreaking concepts (Adhami et al., 2017; Yadav, 2017), the proposition is therefore as follows:
Proposition 5: Small investors fund ICOs because the outcome of the project fulfills a personal need.
2.3.2.4 Integrated Regulation
Integrated regulation is the most autonomous type of extrinsic motivation, and happens through self-examination and making new regulations fit into a person's existing values and needs (Ryan & Deci, 2000a). Integrated regulation motivation and intrinsic motivation share the fact that they both have an autonomous and unconflicted basis (Ryan & Deci, 2000a). However, integrated regulation also has the basis that the behavior is being performed with respect to a certain outcome that is separate from the behavior itself, even though it is volitional and valued by the self (Ryan & Deci, 2000a).
Altruism
Behaving altruistically is defined as "doing something for another at some cost to oneself," which is referred to as the opposite of selfishness (Ozinga, 1999, p. 133). Business angel research has revealed that a motivation amongst angel investors is to contribute to the community (Shane, 2008). Shane (2008) has argued that some angel investors see it as their mission to give something back to the community that has supported them in some way. This can also be a mission towards feeling the responsibility to encourage economic development through their investments (Shane, 2008). Bretschneider and Leimeister (2017) did not find proof for backers being altruistically motivated in equity crowdfunding. However, Gerber and Hui (2013) did find that, in general, some supporters of crowdfunding are motivated to contribute to a project if this is in line with their personal fundamental beliefs and values. Initial coin offerings can be regarded as an innovative cause for decentralizing power and trust in new innovations (Yadav, 2017; Robinson, 2018). It is therefore expected that some investors might be motivated to invest as they feel it is their mission to contribute to this greater cause. The proposition is therefore as follows:
Proposition 6: Investors in ICOs can be motivated by altruism.
2.4 By What Signals are Small Investors' Decision-making Influenced?
Information affects the decision-making processes used by individuals (Connelly et al., 2011). As mentioned before, information is no longer assumed to be perfect, but it is argued that "different people know different things" (Stiglitz, 2002, p. 469), which is when information asymmetries arise (Connelly et al., 2011). Research indicates that investors often need to decide whether to fund an early-stage entrepreneurial venture on the basis of very limited information and under circumstances of high uncertainty where these information asymmetries exist (e.g., Baum & Silverman, 2004; Ahlers et al., 2015; Dushnitsky, 2009; Mollick, 2013). In order to prevent the famous "lemon’s problem," where uncertainty and a lack of information make it nearly impossible to separate promising firms from less promising ones (Akerlof, 1970), it is important that investors mitigate these information issues (Ralcheva & Roosenboom, 2016). As Stuart et al. (1999, p. 317) have stated, “Because the quality of young companies often cannot be observed directly, evaluators must appraise the company based on observable attributes that are thought to covary with its underlying but unknown quality. Resource holders therefore assess value by estimating the conditional probability that a firm will
succeed, given a set of observable characteristics of the organization.” Potential investors therefore make use of the limited signals that are available to evaluate the quality of the venture and base their investment decisions upon these signals (Lerner, 2002; Ueda, 2004; Kortum & Lerner, as cited in Mollick, 2013).
Signaling theory (Spence, 1973) is concerned with understanding how parties resolve information asymmetries about this latent and unobservable quality (Connelly et al., 2011; Ralcheva &
Roosenboom, 2016). In terms of signaling theory, credible signals are used to communicate positive information about a venture, which in turn helps investors to separate good quality investment opportunities from lesser ones (Ralcheva & Roosenboom, 2016). Typically the sender must choose whether and how to communicate (via a signal) information (Connelly et al., 2011). The receiver must then choose how to interpret that signal, and finally send feedback to the signaler (Connelly et al., 2011). In terms of this research, the ICO is the signaler and the investor is the receiver. Figure 5 offers a representation of the signaling environment.
Figure 5: Signaling Timeline. Retrieved from Connelly et al., 2011.
In order for an observable action to be an efficacious signal, the action must have two specific characteristics, which are observability, referring to "the extent to which outsiders are able to notice the signal,", and signaling cost, which entails that a signal must be costly for the signaler, but should not outweigh the benefits (Connelly et al., 2011). Connelly (2011) named as an example "the costs associated with obtaining ISO9000 certification, for example, are high because the certification process is time consuming, and these costs make cheating, or false signaling, difficult."
Signaling theory has been proven helpful in finding out what signals are sent and how these are interpreted in investing environments and how these are valued by investors (e.g., Ahlstrom & Bruton, 2006; Cosh et al., 2009; Robb & Robinson, 2014; Ahlers et al. 2015). More specifically, in venture capital, business angel and equity crowdfunding research, signaling theory has been used to find out what kind of information (e.g., team characteristics) leads investors to invest in start-ups (Ahlstrom & Bruton, 2006; Cosh et al., 2009; Robb & Robinson, 2014; Ahlers et al. 2015). In the following sections these neighboring forms are therefore used as umbrella research fields for ICOs, to examine
which of these signals might apply to small ICO investors as well.
2.4.1 Human Capital
Human capital can be defined as “the skills, knowledge, and experience possessed by an individual or population, viewed in terms of their value or cost to an organization or country” (Oxford English Dictionary). Human capital is known to be positively related to entrepreneurial success (Doms et al., 2010; Unger et al., 2011). The quality aspects of human capital, such as the team's experience and educational background, can be seen as signals (Spence, 1973), as the acquiring process of these aspects requires considerable effort and investment which make them especially costly.
The quality of human capital is known to be a very important signal for experienced investors such as venture capitalist investors, business angels, and banks to base their funding decision on (Robb & Robinson, 2014; Zacharakis & Meyer, 2000). Signals that have been found to be effective (Spence, 1973) include the team/entrepreneur's experience and management skills, personality (MacMillan et al., 1985; Brettel, 2002), previous successes, and external references (Moritz & Block, 2016). Levie and Gimmon (2008) have found that higher educational degrees are also perceived as an effective signal for first-time "high technology" venture founders to raise capital. Venture capitalists seem to be particularly focused on the quality of the teams of start-ups in making investment decisions, as the background and past successes of the founders are seen as one of the best indicators of quality (Franke et al., 2008; Burton et al., 2002; Fried & Hisrisch, 1994; MacMillan et al., 1985). As Macmillan et al. (1985, p. 128) have argued on the basis of their study amongst 100 venture capitalists: "there is no question that irrespective of the horse (product), horse race (market), or odds (financial signals) it is the jockey (entrepreneur) who fundamentally determines whether the venture capitalist will place a bet at all."
Ahlers et al. (2015) examined the effectiveness of quality signals for persuading small investors to commit financial resources in the equity crowdfunding context. Human capital quality signals, measured as the percentage of board members with an MBA, were found to be of great importance in this research. According to Mollick (2013), even though crowd investors are usually not professional investors with similar know-how to venture capitalists (e.g., Kim & Viswanathan, 2014; Mollick, 2013; Schwienbacher & Larralde, 2012), the findings suggest crowdfundees have a similar ability to distinguish quality projects from less promising projects on the basis of human capital quality signals as professional investors such as venture capitalists (Mollick, 2013). It is therefore expected that ICO investors also use human capital quality signals in their evaluation of ICO investment opportunities. The proposition is therefore as follows:
Proposition 7: The skills, knowledge, and experience possessed by the ICO's entrepreneurs are signals that positively affect investors' decisions to fund an ICO.
Prior Initial Coin Offering Success
Venture capital firms believe that past performance is a good indicator for future performance, and therefore are more likely to fund projects that have had prior success (Franke et al., 2008). Previous crowdfunding successes of entrepreneurs are also regarded as a venture quality signal in crowdfunding contexts (Moritz & Block, 2016). Following the prospect theory derived from Tversky and Kahneman, (1973, 1974) investors focusing on prior ICO successes could become guilty of "sample size neglect," part of the representativeness heuristic. Sample size neglect occurs when people assume that a small sample is as representative as a large one (Barberis & Thaler, 2003), and is also known as the "law of small numbers" (Rabin, 2002, retrieved from Barberis & Thaler, 2003). Barberis and Thaler (2003) have cited examples, such as believing that "a financial analyst with four good stock picks is talented because four successes are not representative of a bad or mediocre analyst" and "a basketball player who has made three shots in a row is on a hot streak and will score again, even though there is no evidence of a hot hand in the data" (Gilovich et al., as cited in Barberis & Thaler, 2003). It could therefore be that small investors focus too much on entrepreneurs' previous ICO success, and neglect whether these successes are representative for future ICO projects. The proposition is therefore as follows:
Proposition 8: Previous ICO success of the ICO's entrepreneurs positively affects small investors' investment decision.
2.4.2 Partnerships and Alliances with Prominent Third Parties
Several researchers have found that partnerships and alliances with prominent third parties are important channels through which start-ups can access resources (e.g., Baum & Silverman, 2004; Hoang & Antoncic, 2003; Stuart et al., 1999; Baum et al., 2000), and valuable information (Ahlers et al., 2015). These can be established downstream, upstream or horizontally, or with single persons who have a certain special expertise (Streletzki & Schulte, 2013). Partnerships and alliances can be the basis for a venture’s subsequent success (Brüderl & Preisendörfer, 1998), and in general are found to enhance firm performance (e.g., Alvarez & Barney, 2001; Stuart & Sorenson, 2007; Zahra, Ireland & Hitt, 2000). During the early stages of a venture, partnerships and alliances are found to be particularly important (Ahlers et al., 2015). This crucial first phase is referred to as the "network founding
hypothesis" (Brüderl & Preisendörfer, 1998). As partnerships and alliances can enhance a venture’s legitimacy (Baum & Silverman, 2004), they can be regarded as a venture quality signal in terms of Spence's (1973) signaling theory (Hoang & Antoncic, 2003; Stuart et al., 1999).
Venture capitalists are known to look for endorsements from start-ups' partnerships and alliances with prominent third parties (Baum & Silverman, 2004). The reputational signals that are sent by other organizations being willing to use their reputation to back the start-up are regarded as an important signal of quality in the venture capitalists' evaluation of determining whether to fund the venture (e.g., Baum et al., 2000; Shane & Cable, 2002; Stuart et al., 1999). This is confirmed by Baum and
through assessments from knowledgeable third parties (Streletzki & Schulte, 2013). It could be that the principle of authority applies here, where people defer to experts, who can, in the seeming complexity of modern-day life, offer a valuable and efficient short-cut for decisions (Cialdini, 2007). However, the danger of this obedience to authority lies in what Cialdini (2007) has called its
mechanical character: "We don't have to think, therefore we don't." He has argued that mindless obedience to an expert authority, in this case the prominent third party, leads people to appropriate actions most of the time, but can cause inappropriate actions or bad decisions because people are reacting instead of thinking. In their study of quality signals to small investors in the equity crowdfunding context, Ahlers et al. (2015) however found no significant impact of alliances on funding success. This implies that alliances are not seen as an important signal of venture quality by equity crowdfunders.
There are several examples of ICOs that have formed partnerships and alliances with prominent third parties or have prominent advisors on their boards, which is assumed to have been an important factor in the ICO being successfully funded (Emsley, 2018). High-profile partnerships are seen as having a great impact on established crypto companies as well, for example, looking at the popularity of IOTA coins when they announced their partnership with Microsoft (Noble, 2018). Many ICOs are also known for forming partnerships with certain reputable advisors (Alois, 2018). However, these partnerships can sometimes be dubious; there are examples where ICOs faked prominent advisors or where prominent advisors have been paid tremendous amounts of money for just representing the ICO (Alois, 2018). Noble (2018) argues that publicized partnerships between prominent parties and ICOs are expected to become increasingly important for mainstream adoption. Therefore, even though Ahlers et al. (2015) might not have found a significant impact on funding success in the equity crowdfunding environment, the proposition is therefore as follows:
Proposition 9: Whether an ICO has partnerships with prominent third parties or well-known advisors connected to them positively influences small investors' decision-making.
2.4.3 Early Funding from Private Networks
Early funding from private networks can address adverse selection problems by certifying the quality of a venture's future prospects (e.g., Connelly et al, 2011; Stuart et al., 1999; Megginson & Weiss, 1991), which originates from a study by Booth and Smith (as cited in Ralcheva & Roosenboom, 2016).
A large part of the early funding raised in crowdfunding campaigns typically comes from private networks (Lukkarinen et al., 2016). Ralcheva and Roosenboom (2016) have examined the effect of early backing from private networks on funding success in the equity crowdfunding context. They argue that for the small investor, who is mostly targeted in these equity crowdfunding campaigns, evaluating investment opportunities is more difficult. Furthermore, evaluating the available projects to identify the promising projects that are worth investing in can be more costly to a small investor than
