1 Introduction
Over the past decade, initial coin offerings (ICOs) have gained significant momentum as a crowdfunding market. They attract attention from investors, speculators, media, and entrepreneurs. The rapid growth of ICOs has posed challenges for practitioners, scholars, and regulators (Giudici et al. 2020; Sharma et al., 2020).
ICOs are a new method for early-stage firms to raise capital using blockchain technology. However, this market faces significant challenges in promoting investor trust and confidence. Trust and confidence are crucial for the future success of ICOs. Third-party affiliation, quality, and credibility certification can play a critical role in restoring trust in the market and ICO issuers. Based on the certification hypothesis (Megginson and Weiss 1991) and information cascades, this study offers novel insights into the role of VC specialization in blockchain technology and assesses its impact on ICO crowdfunding campaign success.
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Early-stage firms face constraints in raising debt capital (Coleman et al. 2016). The entrepreneurial finance literature addresses the role of founders' capital (e.g., Wasserman 2008), angel investors (e.g., Lerner et al. 2018), and venture capitalists (e.g., Davila et al. 2003; Keuschnigg et al., 2004; Cavallo et al. 2019) in financing early growth stages (hereafter briefly mentioned as early-stage). Recently, internet-based technologies and blockchain applications have promoted a new wave of digital and decentralized finance (DeFi). DeFi promises to expand capital-raising alternatives for early-stage firms (Block et al. 2021). ICOs and crypto-assets have emerged in this new financial wave.
ICOs allow entrepreneurs to raise funds in exchange for tokens, which are units of value intended to provide utility or act as securities. These tokens can be traded on secondary markets (Fisch 2019; Benedetti and Kostovetsky 2021) or used to obtain products and, in some cases, profits (Adhami et al. 2018; Sharma et al., 2020; Ahmad et al. 2021). Most tokens issued are not usable at the time of the ICO. Instead, they promise future rewards (Fisch 2019; Fisch and Momtaz 2020; Bellavitis et al. 2021).
Amid early regulatory efforts, ICOs are considered the latest player in the risk capital market. This is due to the high volatility and speculation of issued tokens (Harrison and Mason 2019). The market has grown due to reduced capital costs, open-source product development on P2P platforms, and the creation of secondary markets. Blockchain technology's disintermediated nature eliminates investment and geographic barriers that firms face in traditional financial markets. This allows new ventures to cut financial intermediaries and raise money at lower costs without giving equity in exchange for funding (Adhami et al. 2018; Chen and Bellavitis 2020; Fisch et al. 2021).
The innovative technology of ICOs has made them a flexible and convenient funding mechanism. They facilitate innovation and new business models (Chen and Bellavitis 2020; Ahmad et al. 2021). Increased public interest in crypto-assets has enabled entrepreneurs to access investors globally, promoting financial investment democratization. From the supply perspective, ICOs offer investors an alternative strategy to diversify their portfolios. They provide numerous projects worldwide and anytime exit options (Adhami and Guegan 2020).
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DeFi's promise for financial investment democratization, ease of execution, and low transaction costs has attracted financially constrained entrepreneurs to the ICO market. However, despite offering innovative and faster ways to raise capital, policymakers (e.g., European Commission 2018) and researchers (e.g., Bellavitis et al. 2021) warn of opportunistic behavior and potential fraud due to the lack of regulation and low DeFi literacy in the ICO and crypto-assets market. This high-risk investment environment increases risks for investors, especially non-professional and small investors lacking experience in evaluating investment opportunities (Courtney et al. 2017; Howell et al. 2020). For constrained firms, these perceived risks, coupled with strong price volatility, may undermine the success of early-stage projects.
Third-party professional investor affiliation, such as venture capitalists, may reduce investor and market uncertainties. Equity finance research shows that firms receiving prior VC investments have greater success in further funding rounds (e.g., Ahlstrom and Bruton 2006; Schwienbacher 2007). VC ex-ante investments signal firm quality and future returns to uninformed investors with limited screening ability. VC-backed firms also benefit from VCs' networks and experience, gaining access to external resources and competencies otherwise out of reach (Colombo et al. 2006; Hsu 2006; Lindsey 2008).
In the ICO context, empirical evidence shows that VC-backed ventures have a higher ability to raise funds (Hackober and Bock 2021; Belitski and Boreiko 2022; Alshater et al. 2023). The advantage of VC-affiliated new ventures arises from VCs' screening, monitoring, and advisory roles. However, this effect is not universally agreed upon (e.g., Sharma et al., 2020). Moreover, the impact of VC knowledge of DeFi-related topics on ICO success remains underexplored.
This study expands the debate on third-party affiliation by examining the certification role of VCs' specialization in the ICO market. Inspired by crowdfunding (e.g., Kleinert et al. 2020) and IPO (e.g., Megginson and Weiss 1991; Lee and Wahal 2004) literature on the certification hypothesis, we assess whether VC affiliation and VC expertise in decentralized technologies influence ICO success. This topic is particularly relevant for market development as research suggests specialized blockchain knowledge is decisive in this industry (e.g., Hackober and Bock 2021) more than professional experience or other non-expertise human quality signals (e.g., Campino et al. 2021). The mixed evidence on VCs' roles and the scant literature on ICOs enhance the enthusiasm for our research.
The ICO market is highly volatile and complex. In this study, we argue VCs' specialization in blockchain enables a better understanding of DeFi mechanisms and crypto-asset opportunities. Thus, we argue that, in the absence of a centralized financial mechanism, affiliation with a VC specialized in blockchain technologies can provide credible third-party certification. This certification validates the quality signals emitted by the ICO issuer and legitimizes the market and the ICO itself.
Based on data from 191 ICOs, our results confirm that early-stage firms affiliated with third-party VCs have higher ICO success, especially amid higher market and investor uncertainty. This association is driven by uncertainty and VC specialization (the novelty). For more opaque and riskier firms, backing by a single generalist VC does not confer superior ICO success compared to non-VC-backed firms. To positively impact ICO success, opaque firms should affiliate with multiple generalist VCs, triggering a certification channel bolstered by complementary information. We find that the certification effect of specialized VC affiliation is more powerful than that of generalist VCs. ICO success is higher for firms affiliated with VCs specializing in DeFi, particularly blockchain-based technologies, especially if they are opaque and risky. Specialist VC affiliation leads investors to buy more tokens, with the effect amplified by incremental affiliations with other specialized VCs.
Our results offer three main contributions. First, for theory-building, we show that the certification effect of third-party affiliation depends on VCs' blockchain knowledge and expertise, aligning with Hackober and Bock's (2021) arguments on the role of specialized knowledge. The specialization effect is particularly notable amid higher uncertainties faced by seed stage investors. This suggests that investors, typically retail and informal, recognize the challenges posed by their limited DeFi knowledge and seek entities offering blockchain and crypto-asset mastery. Second, for entrepreneurs, our evidence suggests early-stage firms should affiliate with specialized VCs to maximize investor engagement and project financing. Finally, for researchers and practitioners, this study contributes to the open debate on blockchain-based decentralization opportunities, especially the potential growth of ICOs as a financial alternative for early-stage firms to raise substantial capital. Given the limited information available, accurately assessing ICO success likelihood is crucial for researchers (Xu et al. 2021).
This study is organized as follows: Sect. 2 describes the theoretical framework and establishes research hypotheses. Section 3 describes data, variables, and methodology. Section 4 presents empirical results. Section 5 extends analysis to alternative measures of VC affiliation. Section 6 discusses main findings and implications. Section 7 concludes.
2 Theoretical framework
2.1 Initial coin offerings
The advent of digital and decentralized finance (DeFi) has introduced ICOs and crypto-assets, creating a new dynamic in entrepreneurial finance and challenging traditional economic efficiency perspectives (Fisch et al. 2021; Campino et al. 2021). ICOs provide a low-threshold, fast financing method for small firms, enabling platform development, media attention, and marketing among potential customers (Sharma et al., 2020), which helps early-stage firms gauge product or service success. Digital finance can create more efficient markets by democratizing entrepreneurship and creating new fundraising and stakeholder engagement methods, bypassing traditional intermediaries like banks (Alshater et al. 2023), reducing funding costs, and increasing market flexibility.
ICOs resemble peer-to-peer (P2P) crowdfunding by providing early investment opportunities in primary markets from a crowd of investors (Chen 2019), not necessarily formal and professional, via an open internet call. Tokens also resemble traditional securities, as some offer dividends and financial benefits (Fisch 2019). This similarity leads researchers to compare ICOs to initial public offerings (IPOs) (e.g., Adhami et al. 2018; Amsden and Schweizer 2018). For investors, ICOs and IPOs enable private firms to offer shares publicly for the first time and provide secondary market liquidity (Chen 2019). Issuers in both markets must choose target proceeds, issuance fraction, distribution method, lockups, and exchange listing (Howell et al. 2020; Sharma et al., 2020; Belitski and Boreiko 2022). However, ICOs rely on digital and decentralized markets, unlike traditional capital markets, and operate on crypto-assets, avoiding ordinary regulatory frameworks and disclosure obligations associated with IPOs (Ahmad et al. 2021; Alshater et al. 2023; Aslan et al. 2023; Belitski and Boreiko 2022). Lower regulation and transaction costs have led early-stage firms to prefer ICOs over IPOs (Adhami et al. 2018; Howell et al. 2020).
Both IPO and ICO literature highlight the importance of transparency for successful fundraising (Loughran and Ritter 2002; Howell et al. 2020). However, early-stage ICO firms often struggle with transparency due to lack of data and noisy information about their products, technologies, and market relationships (Vohora et al. 2004; Amsden and Schweizer 2018). This creates high uncertainty and ambiguity among stakeholders, making it difficult to gain investor confidence (Zott and Huy 2007).
2.2 Information asymmetries and the certification effect
Like crowdfunding (Ahlers et al. 2015), the ICO market is complex, unregulated, and operates in a high-noise context. New ventures may struggle to capture investors' attention and trust, particularly in early stages when their fate is uncertain. Unlike traditional entrepreneurial environments subject to regulations, crowdsourcing platforms make investors more vulnerable to potential exploitation (Gama et al. 2023).
Evidence shows many firms mislead investors through exit scams (Fisch 2019; Giudici and Adhami 2019; Howell et al. 2020). Opportunistic behavior and frauds are exacerbated in ICOs due to low legal enforcement (Davydiuk et al. 2023), absence of disclosure obligations, low screening ability (Giudici et al. 2020), investors' lack of fundamental knowledge, especially on crypto-assets and blockchain technology, and the early stage of ICO projects (Ahmad et al. 2021).
Reducing uncertainty is crucial to rebuild investor trust and willingness to invest, ensuring ICO success and sustainable growth. Entrepreneurs face challenges in drawing attention to substantive information amid competing information. The campaign phase often involves publishing a whitepaper to inform potential investors about the ICO campaign (Masiak et al. 2020). The whitepaper, like a business plan, details the issuer's project and discloses the token for the required monetary investment (Adhami et al. 2018). Voluntary disclosure through whitepapers mitigates market transparency issues, reducing ICO failure rates (e.g., Howell et al. 2020). However, communicating a whitepaper can be challenging for founders, especially in start-up and seed phases. Without performance measures, founders provide information on their capabilities and business quality as alternative indicators of future performance to guide investment decisions (Chitsazan et al. 2022). Typically, whitepapers focus on financing aspects, core business, key milestones, and team members (Florysiak and Schandlbauer 2022; Xu et al. 2021) but may also provide in-depth technology analysis (Masiak et al. 2020). Some whitepapers are brief, while others are extensive.
The information in whitepapers can be either too limited or excessively detailed, complicating accurate communication. Early-stage firms may use quality signals that potentially diminish each other's effects (Drover et al. 2017). Consequently, signals about the issuer and project's quality from whitepapers may be difficult to interpret in ICO campaigns.
The venture capital certification hypothesis (Megginson and Weiss 1991) offers an approach to reduce information asymmetries and noisy signals, attracting new investors with verifiable credentials. Certification involves third-party verification of project quality, acting as a reliable signal to investors that the ICO is trustworthy, reducing fraud likelihood, and enhancing investor confidence. In ICOs, certification can be achieved through third-party endorsements, financial statement audits, and industry-specific certifications.
Momtaz (2021) suggests external certification can be achieved through reputable auditor due diligence or VC backing, overcoming biases from voluntary signals. We propose that VC affiliation's value in ICO markets depends on specialization and the firm's development stage.
VC backing represents certification (Megginson and Weiss 1991), signaling venture quality to other market participants during the ICO (Hackober and Bock 2021). This signal indicates a lucrative investment opportunity, enhancing ICO attractiveness to potential investors. VC backing not only provides capital but also endorses the venture's potential.
The literature explores various ICO success drivers (see Table A1 in supplementary material), including bounty programs and presale discounts (Sharma et al., 2020), design choices (Howell et al. 2020), social media marketing (Courtney et al. 2017; Howell et al. 2020; Sharma et al., 2020; Ayarci and Birkan 2020), and founders' equity retention and business experience (Vismara 2018; Campino et al. 2021; Chitsazan et al. 2022). We explore the role of information cascades and the certification effect on ICO success, focusing on issuers' affiliation with third-party specialized actors.
3 Research hypotheses
Previous research highlights information cascades among investors in crowdfunding (e.g., Vismara 2018) and IPOs (e.g., Aggarwal et al. 2002; Amihud et al. 2003). Information cascades occur when external information from prior participants supersedes one's individual private signal (Welch 1992). Among individual investors, cascades lead late investors to base their decisions on early investors' behavior (Masiak et al. 2020). In crowdfunding markets, uninformed investors actively promote cascades, positively affecting market functioning (Parker 2014).
Early institutional investors, such as VCs, may enhance early-stage firms' prestige by providing third-party endorsements of firm quality to uninformed external investors. This affiliation helps interpret noisy information conveyed by signals (Courtney et al. 2017), influencing further investors' decisions. Chemmanur et al. (2006) state that prior VC investment is the strongest signal of future growth rate, as greater backing leads retail investors to be more optimistic about the firm.
The literature supports VCs' role beyond traditional financial intermediation due to two factors: selection effect and treatment effect. VCs invest in firms with higher growth potential, linking them to companies with higher future performance (Fisch 2019). Prestigious institutional investors value their reputation and carefully consider investments, creating a market perception of firm quality (Ahlstrom and Bruton 2006; Schwienbacher 2007; Ahmad et al. 2021). VCs also perform value-adding services, such as professional coaching and providing network access (Fisch 2019). Networks and business linkages serve as critical channels for firms to access potential suppliers, customers, and financial resources (Courtney et al. 2017). These characteristics give VCs certification and monitoring power, benefiting potential external investors by enhancing venture legitimacy and reputation.
VCs' signaling and certification roles in helping entrepreneurs raise capital have been extensively studied. In IPO settings, VCs increase company value and mitigate underpricing events (e.g., Megginson and Weiss 1991; Baker and Gompers 2003). This evidence aligns with entrepreneurial finance literature (e.g., Ahlstrom and Bruton 2006; Schwienbacher 2007). In crowdfunding, affiliation with reputable third-party microfinance institutions increases campaign success (e.g., Gama et al. 2023). VCs also play an increasing intermediary role in new blockchain-based financial opportunities (Fisch and Momtaz 2020).
In the ICO context, VC backing is associated with higher amounts raised, higher ranking, and a higher likelihood of reaching the hardcap (Belitski and Boreiko 2022). These effects are stronger for younger firms (Fisch 2019; Fisch et al., 2020; Bertoni et al. 2011; Busenitz et al. 2005). However, Sharma et al. (2020) found VC backing positively relates to token sale price and returns but negatively to ICO success. Given the mixed evidence on VCs' roles in digital finance, we start with the following hypothesis:
Hypothesis 1 (H1)
VC-backed firms have higher ICO success than non-backed firms.
We posit that the certification effect depends on market and investor uncertainty. Investor uncertainty and information asymmetry occur when investors lack confidence in predicting future market developments and investment performance (Fisch et al. 2022a, b). These factors may lead to reluctance in investing in ICOs. Market uncertainty relates to demand uncertainty and exogenous factors like customer preferences and competition (Fisch et al. 2022a, b). Many early-stage ICO firms lack a prototype or product to demonstrate (Fahlenbrach and Frattaroli, 2019), making it difficult for investors to assess market reactions. Adverse selection problems may arise in such cases. VCs' role in certifying very early-stage projects is crucial, leveraging their reputation to assure firm quality. Thus, we hypothesize:
Hypothesis 2 (H2)
The advantage of VC backing is higher for ICO issuers without a product/service to demonstrate.
The ICO market's volatility and complexity pose challenges for market actors to understand blockchain-based opportunities, crypto-assets, and technological risks (Hackober et al., 2021; Fisch 2019). Negative investor sentiment can undermine early-stage firms' ability to attract capital (Alshater et al. 2023). The literature shows that high team profile (Roosenboom, 2020; Alshater et al. 2023), experience (Gompers et al. 2009; Gu et al. 2018), and third-party expert evaluation (Xu et al. 2021) play significant roles in ICO success. VC expertise in complex ICO technologies is crucial. Partnerships with reputable players enhance firm legitimacy and credibility, significantly impacting success prospects (Chang 2004). However, in a new sector where the product has not yet been tested, inexperienced VCs may increase the likelihood of ICO failure (Sharma et al., 2020). Thus, we argue that third-party endorsements influence investors only if the endorsing parties are field experts. One can argue that third-party endorsements can influence potential investors, but only if those parties are experts; such expertise is crucial to accurately assess quality in uncertain circumstances. Otherwise, ICO investors can wrongly screen the better ICOs or even lead early-stage firms to failure due to flawed advice. Vanacker and Forbes (2016) show that campaigns supported by VCs with industry-specific experience have a greater impact on financial resource providers than media exposure does. When potential investors recognize that a VC possesses significant expertise in a particular industry, they are more likely to positively perceive the VC’s reputation, as the accumulation of such knowledge is considered valuable information about the VC’s ability to choose and guide a company effectively. Therefore, we posit that the effects of VC specialization in blockchain technologies may also influence the effect of third-party affiliation under the certification hypothesis. Formally, we hypothesize the following:
Hypothesis 3 (H3)
Firms backed by specialized VCs have higher ICO success than counterparts.
Hypothesis 4 (H4)
The effect of specialized VC backing on ICO success is higher for issuers without a product/service to demonstrate.
4 Data, variables, and method
4.1 Data
We use primary and secondary market data, publicly available, collected by Fahlenbrach and Frattaroli (2019) on 306 completed ICOs between March 2016 and March 2018. Due to data scarcity, only records of ICOs with funding exceeding $1 million were retained, sharing similarities in industry composition, state of incorporation, KYC1 policies, and employee numbers. Data on Ethereum's market prices were collected from coinmarketcap.com. Data on Financial Development Index were collected from the International Monetary Fund database. Due to missing values, our final sample includes 191 ICOs.
4.2 Variables
Table 1 contains detailed definitions and sources of the dependent and independent variables.
Table 1
Variables definition
Variables | Type | Definition | Data Source |
|---|---|---|---|
Dependent variable | |||
Hardcap_raised | Percentage | Fraction of the maximum amount the company manages to raise during its ICO | Fahlenbrach and Frattaroli (2021) |
Independent variables | |||
Main covariates | |||
VC | Binary | Binary variable: = 1 if the issuers are backed by a venture capitalist, = 0 otherwise | Fahlenbrach and Frattaroli (2021) |
VC_G | Binary | Binary variable: = 1 if the issuers are backed by venture capitalists (only) generalist in blockchain support, = 0 otherwise | Fahlenbrach and Frattaroli (2021) |
VC_S | Binary | Binary variable: = 1 if the issuers are backed by at least one venture capitalist’s specialist in blockchain support, = 0 otherwise | Fahlenbrach and Frattaroli (2021) |
Product | Binary | Binary variable: = 1 if the product/service for which funding is being raised or an early “alpha” or “beta” version of it has been developed; = 0 otherwise (i.e., seed stage) | Fahlenbrach and Frattaroli (2021) |
Controls | |||
Campaigns | |||
Roadmap | Binary | Binary variable: = 1 if there is a whitepaper containing a roadmap with dates and milestones for the development and commercialization of the product; = 0 otherwise | Fahlenbrach and Frattaroli (2021) |
Whitepaper | Discrete | Number of pages in the whitepaper document | Fahlenbrach and Frattaroli (2021) |
I nvestors ID | Binary | Binary variable: = 1 if the ICO’s promoter required participants to identify themselves by submitting personal documents such as a passport copy, utility bills, etc.; = 0 otherwise | Fahlenbrach and Frattaroli (2021) |
Maturity | Discrete | Number of days between ICO start date and planned end date | Fahlenbrach and Frattaroli (2021) |
Issuer | |||
Age | Discrete | Years since the founding team started working on the project for which the ICO is being conducted, rounded to the nearest integer (where unavailable, the date of incorporation from the commercial register is used) | Fahlenbrach and Frattaroli (2021) |
Market | |||
ETH yield | Continuous | 90-day yield of Ethereum | Coin market |
Country | |||
Financial Development | Index (Ranging from 0 = low to 1 = high) | Ranking of countries on the depth, access and efficiency of the financial institutions and financial markets | IFM |
4.2.1 Dependent variable
Following Davydiuk et al. (2023) and Roosenboom et al. (2020), we examine the percentage of the hardcap raised as a measure of ICO success, including funds raised during crowdsale and presale stages. Hardcap_Raised is the ratio (in %) between the amount raised and the cap target amount (i.e., the hardcap). We use the logarithm form of this variable to reduce skewness.
4.2.2 Independent variables
4.2.2.1 Main covariates
To test Hypothesis H1, we use the variable VC, a binary variable that takes the value 1 if the issuer is backed by a venture capitalist, and 0 otherwise. To test Hypothesis H2, we rely on the binary variable Product, which takes the value 1 if the firm has a product/service in an early “alpha” or “beta” version, and 0 if the firm does not have a product/service to demonstrate. To test Hypothesis H3, we use a set of two binary variables: VC_G, which takes the value 1 if the firm is backed only by a VC not specialized in blockchain, and 0 if the firm is not backed by a VC at all; and VC_S, which takes the value 1 if the firm is backed by at least one VC specialized in blockchain-based technologies and businesses, and 0 if the firm is not backed by a VC at all. To test Hypothesis H4, we rely on the interaction terms VC_S x Product and VC_G x Product.
4.2.2.2 Control variables
We divide our independent variables into three main control groups: campaign characteristics, issuer characteristics, and market and country characteristics.
Regarding the ICO campaign characteristics, we control for the following: (i) the existence of a project Roadmap, a binary variable that takes the value 1 if there is a whitepaper containing a roadmap with dates and milestones for the development and commercialization of the product (e.g., Ahmad et al. 2021); (ii) the number of pages in the whitepaper document (e.g., Davydiuk et al. 2023; Amsden et al., 2018); (iii) the investor ID, which is a binary variable indicating if the ICO’s promoter required participants to identify themselves by submitting personal documents such as a passport copy or utility bills (e.g., Davydiuk et al. 2023; Fisch and Momtaz 2020; Burns and Moro 2018; Aslan et al. 2023); and (iv) the ICO Maturity, measured as the number of days between the ICO start date and planned end date (e.g., Roosenboom et al. 2020; Xu et al. 2021).
We also control for the issuers’ Age, defined as the number of years since the founding team started working on the project for which the ICO is being conducted, rounded to the nearest integer. If this information is not feasible, we use the date of incorporation from the commercial register (e.g., Fisch et al. 2022a, b). Market and country characteristics are captured by the ETH yield, which studies the 90-day return of Ethereum close prices (e.g., Ahmad et al. 2021), and the Financial Development, which ranks the country in which the ICO was launched in terms of the depth, access, and efficiency of the financial institutions and financial markets on a scale of 0 to 1 (e.g., Huang et al. 2020; Ahmad et al. 2021).
4.3 Descriptive statistics
Table 2 reports the descriptive statistics. Table A2 (in the supplementary material) displays the correlation matrix of the different covariates used. The correlation coefficients reported do not indicate that multicollinearity is a problem.
Table 2
Descriptive statistics
Variables | Measure | # Obs | Mean | Std. dev | Min | Max |
|---|---|---|---|---|---|---|
Dependent variable | ||||||
Hardcap_raised | % | 191 | 71.162 | 39.046 | 2 | 181 |
Independent variables | ||||||
VC | Binary (1 = VC backed; 0 = otherwise) | 191 | 0.251 | 0.435 | 0 | 1 |
VC_G | Binary (1 = VC backed by a specialist; 0 = otherwise) | 191 | 0.068 | 0.253 | 0 | 1 |
VC_S | Binary (1 = VC backed by a generalist only; 0 = otherwise) | 191 | 0.183 | 0.388 | 0 | 1 |
Product | Binary (1 = yes; 0 = no) | 191 | 0.534 | 0.500 | 0 | 1 |
Controls | ||||||
Campaigns | ||||||
Roadmap | Binary (1 = yes; 0 = no) | 191 | 0.812 | 0.392 | 0 | 1 |
Whitepaper | Discrete (#pages) | 191 | 30.529 | 17.064 | 0 | 89 |
Investors ID | Binary (1 = yes; 0 = no) | 191 | 0.550 | 0.499 | 0 | 1 |
Maturity | Discrete (#days) | 191 | 31.859 | 22.342 | 1 | 148 |
Issuer | ||||||
Age | Discrete (#years) | 191 | 1.644 | 1.892 | 0 | 9 |
Market & Country | ||||||
ETH yield | Continuous (g) | 191 | 2.003 | 2.677 | -0.436 | 12.351 |
Financial Development | Index (ranging 0(low)-1(high)) | 191 | 0.707 | 0.220 | 0.101 | 0.967 |
On average, the ICOs in our dataset raise 71% of the amount defined for the hard cap (standard deviation = 39 percentage points). We use the logarithm of this variable as the key measure of ICO success in our multivariate analysis to reduce the skewness of the sample due to the high standard deviation relative to the variable’s average. Regarding the independent variables, 25.1% of the ICOs are backed by a venture capitalist, with 6.8% backed by generalist VCs and 18.3% backed by at least one specialist VC in blockchain-based solutions. Only 53.4% of early-stage firms have a product or prototype developed when the ICO is launched. Additionally, 81% of ICO issuers provide a roadmap with dates and milestones for the development and commercialization of the product/service.
Significant discrepancies are found in the whitepaper length. While some whitepapers are non-existent or not reported, others contain up to 89 pages. The mean length of the whitepapers is 31 pages. We also observe that 55% of the ICOs have implemented KYC procedures. On average, ICOs are open for 32 days from launch to the planned end date. Regarding the issuer, it takes an average of 1.6 years to issue tokens in an ICO from the moment they start working on the project. Finally, examining market and country characteristics, we find that, on average, Ethereum has a positive 90-day return, with the price at the ICO launch being twice as high as the price 90 days prior. Most of the ICOs in our dataset are well-developed in terms of the breadth, accessibility, and effectiveness of financial institutions and markets, as indicated by the average Financial Development score (0.7, on a scale from 0 to 1).
4.4 Model
To test our research hypotheses H1 and H2, we employ an ordinary least squares (OLS) regression model as follows:
$$\text{ln}(\text{Hardcap}\_\text{raised})\text{OLS}={\beta }_{0i}+{\beta }_{1i}VC+{\beta }_{2i}\text{Product}+ {\beta }_{3i}(VC \text{x Product})+\sum_{k=1}^{7}{\gamma }_{ki}{Z}_{k}+ {\varepsilon }_{i}$$
(1)
Hypotheses H3 and H4 are tested by estimation the following OLS model:where Z is the vector of k control variables, and \(\varepsilon\) is the error term.
$$\text{ln}(\text{Hardcap}\_\text{raised})\text{OLS}={\beta }_{0i}+{\beta }_{1i}VC\_G {+ \beta }_{2i}VC\_S+{\beta }_{3i}\text{Product}+ {\beta }_{4i}\left(VC\_\text{GxProduct}\right)+ {\beta }_{5i}(VC\_\text{SxProduct})+\sum_{k=1}^{7}{\gamma }_{ki}{Z}_{k}+ {\varepsilon }_{i}$$
(2)
5 Main findings
In this section, we examine the estimations obtained from Eq. 1 (Table 3) and Eq. 2 (Table 4). Column I.1 displays the model with the VC dummy variable(s). In Column I.2, we add the dummy variable Product. Column II includes control variables. Column III displays the full model with the interaction effects between VC (and VC type) and Product. All p-values are based on robust standard errors (reported in parentheses).
Table 3
VC-backed issuer effect (binary)
Column I | Column II | Column III | ||
|---|---|---|---|---|
Main variables | [+ Controls] | [+ Cross-effects] | ||
Variables | I.1 | I.2 | II | III |
Independent variables | ||||
VC | 0.418*** | 0.384*** | 0.361*** | 0.646*** |
(0.108) | (0.108) | (0.112) | (0.189) | |
Product | 0.278** | 0.289** | 0.406*** | |
(0.125) | (0.120) | (0.154) | ||
Interactions | ||||
VC x Product | −0.465** | |||
(0.230) | ||||
Linear combinations | ||||
VC = 0 & Product = 1 | 0.406*** | |||
(0.154) | ||||
VC = 1 & Product = 0 | 0.646*** | |||
(0.189) | ||||
VC = 1 & Product = 1 | 0.587*** | |||
(0.152) | ||||
Controls | ||||
Campaigns | ||||
Roadmap | 0.380** | 0.366** | ||
(0.174) | (0.172) | |||
Whitepaper | −0.008** | −0.008** | ||
(0.004) | (0.004) | |||
Investors ID | 0.194 | 0.201 | ||
(0.133) | (0.133) | |||
Maturity | −0.002 | −0.002 | ||
(0.002) | (0.002) | |||
Issuer | ||||
Age | −0.034 | −0.048 | ||
(0.034) | (0.035) | |||
Market & Country | ||||
ETH yield | 0.041** | 0.044** | ||
(0.020) | (0.020) | |||
Financial Development | 0.547* | 0.565* | ||
(0.318) | (0.317) | |||
Intercept | 3.898*** | 3.758*** | 3.250*** | 3.211*** |
(0.079) | (0.109) | (0.313) | (0.316) | |
Observations | 191 | 191 | 191 | 191 |
R-squared | 0.043 | 0.068 | 0.162 | 0.174 |
Table 4
VC-type effect (binary)
Column I | Column II | Column III | |||
|---|---|---|---|---|---|
Main Variables | [+ Controls] | [+ Cross-effects] | |||
Variables | I.1 | I.2 | II | III.1 | III.2 |
Independent variables | |||||
VC_G | 0.249 | 0.195 | 0.171 | −0.024 | 0.160 |
(0.202) | (0.189) | (0.152) | (0.363) | (0.148) | |
VC_S | 0.480*** | 0.453*** | 0.443*** | 0.445*** | 0.822*** |
(0.107) | (0.110) | (0.124) | (0.124) | (0.171) | |
Product | 0.283** | 0.291** | 0.273** | 0.412*** | |
(0.126) | (0.119) | (0.126) | (0.143) | ||
Interactions | |||||
VC_G x Product | 0.287 | ||||
(0.394) | |||||
VC_S x Product | −0.633*** | ||||
(0.218) | |||||
Linear combinations | |||||
VC_G = 0 & Product = 1 | 0.273** | ||||
(0.126) | |||||
VC_G = 1 & Product = 0 | −0.024 | ||||
(0.363) | |||||
VC_G = 1 & Product = 1 | 0.536*** | ||||
(0.154) | |||||
VC_S = 0 & Product = 1 | 0.412*** | ||||
(0.143) | |||||
VC_S = 1 & Product = 0 | 0.822*** | ||||
(0.171) | |||||
VC_S = 1 & Product = 1 | 0.600*** | ||||
(0.169) | |||||
Controls | |||||
Campaigns | |||||
Roadmap | 0.394** | 0.403** | 0.392** | ||
(0.175) | (0.177) | (0.171) | |||
Whitepaper | −0.008** | −0.008** | −0.008** | ||
(0.004) | (0.004) | (0.004) | |||
Investors ID | 0.188 | 0.180 | 0.182 | ||
(0.132) | (0.133) | (0.132) | |||
Maturity | −0.002 | −0.002 | −0.002 | ||
(0.002) | (0.002) | (0.002) | |||
Issuer | |||||
Age | −0.036 | −0.033 | −0.048 | ||
(0.034) | (0.034) | (0.034) | |||
Market & Country | |||||
ETH yield | 0.037* | 0.037* | 0.040** | ||
(0.020) | (0.020) | (0.020) | |||
Financial Development | 0.559* | 0.547* | 0.555* | ||
(0.317) | (0.321) | (0.315) | |||
Intercept | 3.898*** | 3.755*** | 3.241*** | 3.247*** | 3.202*** |
(0.079) | (0.109) | (0.313) | (0.316) | (0.315) | |
Observations | 191 | 191 | 191 | 191 | 191 |
R−squared | 0.047 | 0.072 | 0.166 | 0.168 | 0.185 |
Table 3 reports a positive and statistically significant coefficient for the variable VC (Columns I.1–II, p < 0.01). This result reveals that early-stage firms backed by venture capitalists prior to the ICO have higher success than non-backed firms, as the former have a statistically higher ability to achieve their hard cap compared to the latter. This evidence supports Hypothesis H1. As anticipated, our estimates also report a positive and statistically significant coefficient for Product (Columns I.2–II, p < 0.05). Having a product/service developed at the time of the crowdsale increases the ability to raise more capital. The magnitude of the coefficients of our main independent variables suggests that, although both aspects contribute to ICO success, being backed by a VC has a greater influence than having a product or prototype to show prior to the ICO (\({\beta }_{VC}> {\beta }_{\text{Product}}).\)
Of particular relevance to our study is the effect of VC affiliation on the association between the Product and the ICO’s success. As many early-stage firms do not have a product or prototype to demonstrate and mitigate investor and market uncertainties (46.4% in our sample), it is critical to examine the role played by VCs for these firms. Column III reports a negative and statistically significant coefficient for the interaction term VC x Product (p < 0.05), while the coefficients of the constituent terms VC and Product remain positive and statistically significant (Column III, p < 0.01). The negative coefficient of the interaction term suggests that the positive effect of VC affiliation on ICO success is lower for firms that have a product to demonstrate. Linear combinations reported in Column III illustrate this main finding.
Early-stage firms backed by a VC without a product (VC = 1 & Product = 0, Lincom = 0.646, p < 0.01) raise e^64.6 percentage points (ppts) more on average in terms of ICO hard cap than non-VC-backed firms without a product (i.e., the base outcome group). Firms backed by a VC with a product (VC = 1 & Product = 1, Lincom = 0.587, p < 0.01) raise e^58.7 ppts more than the base outcome. In practice, VC backing has a higher impact on the success of the ICO when the issuer lacks a finished product or a working prototype, thereby increasing information asymmetry. These results are consistent with Hypothesis H2.
Regarding our controls, the estimates report a positive and statistically significant coefficient for the variables Roadmap (p < 0.05), Financial Development (p < 0.1), and ETH yield (p < 0.1). This suggests that having a roadmap with dates and milestones for the development and commercialization of the product, the financial development of the country to which the early-stage firm belongs, and positive changes in the price of Ethereum (used as a proxy for the overall market for crypto-assets) all positively impact ICO success.
The results report a negative and statistically significant coefficient for Whitepaper (p < 0.05). Contrary to the findings of Samieifar and Baur (2021), we conclude that having a higher number of pages in the whitepaper document negatively impacts the amount raised. This may suggest that longer whitepapers provide noisy signals, possibly containing contradictory information, which discourages some investors from engaging in the ICO (Goldstein and Yang 2019; Courtney et al. 2017). The number of years the founding team had been working on the company before the launch, KYC requirements, and the length of the ICO have no appreciable effects in our model.
Table 4 displays our estimates regarding the role played by VC's historical association with businesses using blockchain technology on the success of the ICO (Eq. 2). The variable VC_S reports a positive and statistically significant coefficient (Columns I.1–II, p < 0.01), whereas the variable VC_G has no impact on the success of ICOs across the model (Columns I.1–II, p > 0.10). This evidence suggests that early-stage companies backed by specialized VCs prior to the ICO are better equipped to succeed in ICO campaigns than those backed by generalist VCs, who seem to have a similar ability to succeed as non-VC-backed firms. This result leads us to conclude that VC backing alone cannot forecast the success of an ICO, which warrants caution when interpreting our results and Hypothesis H1. In turn, the value of VC affiliation in mitigating uncertainties depends on their industry-specific specialization. This aligns with Hypothesis H3 and the theoretical arguments supporting the positive effect of specialized third-party affiliation and the certification hypothesis on investor engagement and ICO success.
The effect of product development on the hard cap amount raised is consistent with that reported in the previous model (Table 3). Our estimates show a positive and statistically significant coefficient for Product (Columns I.2–II, p < 0.05). Column III reports the effect of third-party affiliation with a specialized VC on the association between Product and ICO success. The coefficient of the interaction term VC_G x Product is not statistically significant (Column III.1, p > 0.10). This aligns with the evidence reported in Columns I–II.2, suggesting that association with a VC not specialized in blockchain-based technologies does not impact the ability of firms to fulfill the ICO’s target funding goal.
Linear combinations reported in Column III.1 provide a more detailed analysis of this effect. The success of ICOs issued by firms without a product is not statistically different between those backed by a generalist VC and those not backed at all (VC_G = 1 & Product = 0, Lincom = − 0.024, p > 0.10). However, linear combinations also show that among firms with a product, those backed by a generalist VC (VC_G = 1 & Product = 1, Lincom = 0.536, p < 0.01) raise a higher amount of funds (as a percentage of the hard cap) than those without VC affiliation (VC_G = 0 & Product = 1, Lincom = 0.273, p < 0.05).
In summary, this mixed evidence suggests that affiliation with a generalist VC does not mitigate uncertainties in contexts of higher information asymmetry. However, if early-stage firms possess a product/service to signal quality and marketability to investors, affiliation with a generalist VC plays a certification role that translates into higher ICO success.
The coefficient for the interaction term VC_S x Product is negative and statistically significant (Column III.2, p < 0.01), whereas the constituent terms of the interaction, i.e., VC specialist and Product, remain positive and statistically significant (Column III.2, p < 0.01). This suggests that the positive effect of VC specialization decreases when businesses have a product to show to potential investors. In other words, the role of a VC specialist on early-stage firms without a product (VC_S = 1 & Product = 0, Lincom = 0.822, p < 0.01) is more significant than on firms with a product (VC_S = 1 & Product = 1, Lincom = 0.6, p < 0.01). In line with Hypothesis H4, this indicates that the value of third-party affiliation with a specialist VC is crucial in mitigating the lower ICO attractiveness faced by issuers lacking a product/service or a functional prototype. The effects of the control variables remain consistent with those reported in Table 3.
6 Additional analysis
In this section, we extend our analysis of the relevance of VCs to ICO success by examining the number of VCs (both generalists and specialists) supporting ICO issuers. In other words, we replace VC binary variables with discrete variables measuring the number of VCs supporting the issuers (#VC). This additional analysis is relevant because the same firm may have a mix of generalist and specialist VCs, which was ignored in our previous estimates. The descriptive statistics of these variables are reported in Table A3 in the supplementary material. The results are presented in Tables 5 and 6.
Table 5
Additional analysis: the effect of the number of VCs backing the issuer
Column I | Column II | Column III | ||
|---|---|---|---|---|
Main variables | [+ Controls] | [+ Cross-effects] | ||
Variables | I.1 | I.2 | II | III |
Independent variables | ||||
#VC | 0.109*** | 0.106*** | 0.361*** | 0.361*** |
(0.022) | (0.021) | (0.135) | (0.135) | |
Product | 0.301** | 0.313*** | 0.362*** | |
(0.124) | (0.119) | (0.136) | ||
Interactions | ||||
#VC x Product | −0.070 | |||
(0.051) | ||||
Linear combinations | ||||
#VC & Product = 0 | 0.115*** | |||
(0.033) | ||||
#VC & Product = 1 | 0.045 | |||
(0.039) | ||||
Controls | ||||
Campaigns | ||||
Roadmap | 0.326* | 0.318* | ||
(0.170) | (0.170) | |||
Whitepaper | −0.008** | −0.009** | ||
(0.004) | (0.004) | |||
Investors ID | 0.202 | 0.219 | ||
(0.132) | (0.135) | |||
Maturity | −0.002 | −0.002 | ||
(0.002) | (0.002) | |||
Issuer | ||||
Age | −−0.029 | −0.031 | ||
(0.032) | (0.032) | |||
Market & Country | ||||
ETH yield | 0.039* | 0.038* | ||
(0.020) | (0.020) | |||
Financial Development | 0.555* | 0.569* | ||
(0.318) | (0.320) | |||
Intercept | 3.929*** | 3.770*** | 3.304*** | 3.294*** |
(0.071) | (0.107) | (0.308) | (0.309) | |
Observations | 191 | 191 | 191 | 191 |
R-squared | 0.043 | 0.073 | 0.158 | 0.162 |
Table 6
Additional analysis: the effect of the number of VCs backing the issuer by type
Column I | Column II | Column III | |||
|---|---|---|---|---|---|
Main variables | [+ Controls] | [+ Cross-effects] | |||
Variables | I.1 | I.2 | II | III.1 | III.2 |
Independent variables | |||||
#VC_G | 0.076** | 0.075*** | 0.051 | 0.097** | 0.046 |
(0.031) | (0.028) | (0.033) | (0.048) | (0.034) | |
#VC_S | 0.149*** | 0.144*** | 0.130** | 0.121** | 0.174* |
(0.043) | (0.036) | (0.052) | (0.052) | (0.097) | |
Product | 0.300** | 0.307** | 0.337*** | 0.340** | |
(0.125) | (0.119) | (0.129) | (0.133) | ||
Interactions | |||||
#VC_G x Product | −0.087 | ||||
(0.069) | |||||
#VC_S x Product | −0.091 | ||||
(0.114) | |||||
Linear combinations | |||||
#VC_G & Product = 0 | 0.097** | ||||
(0.048) | |||||
#VC_G & Product = 1 | 0.010 | ||||
(0.050) | |||||
#VC_S & Product = 0 | 0.174* | ||||
(0.097) | |||||
#VC_S & Product = 1 | 0.083 | ||||
(0.069) | |||||
Controls | |||||
Campaigns | |||||
Roadmap | 0.339* | 0.335* | 0.332* | ||
(0.172) | (0.172) | (0.173) | |||
Whitepaper | −0.008** | −0.008** | −0.008** | ||
(0.004) | (0.004) | (0.004) | |||
Investors ID | 0.203 | 0.215 | 0.212 | ||
(0.132) | (0.134) | (0.134) | |||
Maturity | −0.002 | −0.003 | −0.002 | ||
(0.002) | (0.002) | (0.002) | |||
Issuer | |||||
Age | −−0.029 | −0.030 | −0.032 | ||
(0.032) | (0.032) | (0.032) | |||
Market & Country | |||||
ETH yield | 0.034 | 0.035 | 0.033 | ||
(0.022) | (0.022) | (0.022) | |||
Financial Development | 0.573* | 0.589* | 0.572* | ||
(0.319) | (0.321) | (0.320) | |||
Intercept | 3.925*** | 3.767*** | 3.288*** | 3.278*** | 3.289*** |
(0.072) | (0.107) | (0.309) | (0.310) | (0.311) | |
Observations | 191 | 191 | 191 | 191 | 191 |
R-squared | 0.045 | 0.075 | 0.160 | 0.163 | 0.162 |
Table 5 reports a positive and statistically significant coefficient for the variable #VC (Columns I.1–II, p < 0.01) and for the variable Product (Column I.2, p < 0.05). These results are consistent with those reported in Table 3. However, the interaction effect between #VC and Product on ICO success is statistically insignificant (Column III, p > 0.10), indicating that the number of VCs supporting the early-stage firm does not affect the amount of hard cap raised when a product/service or a prototype is available at the time of the ICO. Hence, the number of VCs backing the ICO issuer is not relevant in this certification channel among safer firms (Column III, #VC & Product = 1, Lincom = 0.045, p > 0.10). However, among firms without a product to show investors, an increase in the number of VCs backing the issuer increases the percentage of hard cap raised, as shown by linear combinations (#VC & Product = 0, Lincom = 0.115, p < 0.01). This suggests that for more opaque and risky firms, the certification effect of affiliation with VCs is as high as the number of affiliations.
Table 6 reports the estimates for the number of generalist and specialist VCs (#VC_G, #VC_S). The results reveal a non-statistically significant effect of the number of generalist VCs (#VC_G) when we include the variable Product in the model (Column II, p > 0.1) and a positive and statistically significant coefficient for #VC_S (Columns I.1–II, p < 0.01), consistent with the evidence reported in Table 4. However, when we include the interaction term #VC_G x Product, which is non-statistically significant (Column III.1, p > 0.10), the coefficient for the constitutive term #VC_G becomes positive and statistically significant (Column III.1, p < 0.05). As the linear combinations reveal, this evidence suggests that the number of generalist VCs is relevant for issuers without a product to show (Column III.1, #VC_G & Product = 0, Lincom = 0.097, p < 0.05) but not for those with a product (#VC_G & Product = 1, Lincom = 0.010, p > 0.10). This result does not align with the findings reported in Table 4, Column II.1. Similar effects are observed for specialist VCs. For generalist VCs, the results from Tables 4 and 6 suggest that, while being affiliated with a generalist VC is relevant for companies with a product—i.e., less opaque and less risky ones—the number of generalist VCs is not relevant for the success of ICOs issued by early-stage firms with a product/service (Table 6). Conversely, the success of ICOs issued by more opaque and risky firms depends on the number of generalist VCs associated with them (Table 6). Regarding the VC specialization effect, the results show that an increase in the number of specialized VCs affiliated with the ICO issuer produces an incremental positive effect on ICO success only for more opaque firms. We discuss these results in Sect. 6.
7 Discussion
The certification effect of affiliation with venture capitalists (VCs) has been extensively examined in initial public offerings (IPOs) and crowdfunding platforms. We extend this knowledge to the initial coin offering (ICO) context, which remains underexplored in the literature. Due to the novelty, complexity, lack of regulation, and greater information asymmetry in this market, early-stage firms’ affiliation with VCs, particularly those specialized in blockchain-based technologies, may play a crucial role in addressing these issues. We establish the importance of third-party certification in ICO performance and contrast the causal influence of VCs’ investment (and specialization) in campaigns with higher opacity to encourage further research into new financing instruments.
Overall, the results show that ex-ante VC funding significantly impacts the ability of early-stage firms to succeed in an ICO (see Table 3). To better understand the influence of third-party affiliation with VCs on ICOs, we expanded our research to entrepreneurial finance literature, highlighting how different signals operate in uncertain, risky, and unregulated environments. Specifically, we compare third-party certification with the presence vs. the absence of a product or prototype developed ex-ante the ICO as a measure of investor and market uncertainties related to the firm’s development stage. The presence of a product or service (ex-ante the ICO) signals the firm’s quality and lower uncertainties, as confirmed by our models. However, VCs are the most relevant information to investors, as evidenced by the consistently higher coefficients of the VC variable compared to the Product variable across our models. Among firms without a finished product or prototype, the impact of VC affiliation is amplified (Table 3). This evidence supports our Hypothesis H2, indicating that third-party affiliation is more critical for firms and investors facing greater information asymmetries and uncertainties (Chang 2004).
We also show that retail investors rely on VCs specialized in blockchain-based technologies to better assess the quality and risk of the ICO and its issuer, influencing their investment decisions. ICOs issued by firms affiliated with specialized VCs perform better than those issued by firms affiliated with generalist VCs or firms without any VC affiliation (see Table 4). This result underscores the importance of third-party certification in the ICO context, where traditional regulatory frameworks and financial literacy are often lacking.
Due to the ICO market's novelty and complexity, VCs with less industry experience sometimes struggle to identify the most promising companies, inadvertently leading investors to incorrect decisions (Bertoni et al. 2011). Being backed by a specialized VC reassures other potential resource providers and signals the issuer's quality, in contrast to a VC without blockchain technology experience. Regardless of industry expertise, our findings show that early-stage firms perform better with increased VC support (see Table 5). However, this result is nonlinear. The certification effects of affiliation with a specialized VC are particularly recognized when associated with at least one specialized VC (see Table 4, Column III.2). However, investors require complementary signals from multiple specialized VCs to certify the issuer's quality if the firm lacks a product or service (Table 6, Column III.2). Thus, while specialized VC affiliation signals credibility and quality among firms with low uncertainties, for more opaque and risky firms, the certification effect increases with additional specialized affiliations, translating into incremental quality signals.
Interestingly, we found mixed evidence regarding the cross-effects between affiliation with generalist VCs and the firm’s opacity. Our results suggest that affiliation with a generalist VC is relevant for less opaque issuers but not for more opaque issuers (Table 4). The number of generalist VCs backing an ICO issuer does not significantly impact success among less opaque early-stage firms (Table 6). However, the success of ICOs issued by more opaque and risky firms depends on the number of generalist VCs associated with them (Table 6). These results imply that the certification effect of generalist VCs among more opaque firms translates into ICO success only if multiple generalist VCs back the issuer. This makes sense theoretically, as the certification effect of generalist VCs is typically lower than that of specialized VCs. Further research should examine this result in more detail.
Our findings have practical implications. Our results suggest that VC certification impacts the hard cap raised more than presenting a product to potential investors. Thus, early-stage firms should prioritize attracting VC affiliations over developing a product or prototype by the time of the ICO. Additionally, VC specialization is a key driver of investor decision-making. Consistent with the certification hypothesis, third-party affiliation with specialized VCs significantly enhances ICO success for opaque early-stage firms. Finally, this study provides controversial evidence that may inspire future research. Contrary to expectations, we found that the number of VC specialists or generalists becomes insignificant when an early-stage firm has a product to present to potential investors.
8 Concluding remarks
The ICO market is experiencing considerable momentum. This rapid expansion presents challenges for practitioners, scholars, and regulators. ICOs offer a novel approach to raising capital for early-stage companies by leveraging blockchain technology, enabling entrepreneurs to secure low-cost funds from a crowd of investors, thus fostering innovation and new business models (Chen and Bellavitis 2020; Ahmad et al. 2021). For investors, ICOs offer an alternative strategy to diversify portfolios (Adhami and Guegan 2020). However, the market's decentralized nature and lack of regulation have raised concerns among policymakers, regulators, and academics regarding its opacity. The crisis of trust in these markets, partly due to fraud, complexity, and lack of regulation, may jeopardize early-stage firms' ability to succeed in their ICO campaigns.
This study examines the role of firm affiliation with third-party specialized VCs in overcoming the lack of regulation and transparency, thus restoring trust in the market and ICO issuers. Grounded in the certification hypothesis (Megginson and Weiss 1991) and information cascades, our research shows that VC funding before an ICO positively impacts early-stage firm success, consistent with the literature (Fisch and Momtaz 2020; Hackober and Bock 2021; Belitski and Boreiko 2022). We also found that the certification effect is amplified by greater information asymmetry between issuers and investors, particularly when firms lack a finished product or prototype.
Our findings suggest that VCs play a crucial endorsement role that certifies early-stage firm quality, especially when less information is available. Momtaz (2021) also found that VC certification mitigates biases in voluntary signals issued by ventures in the ICO market. Our study further suggests that external certification effectiveness in ICO markets is significantly influenced by VC specialization and firm developmental stage. Although investors often rely on VC affiliation to select ICO issuers, the newness of the ICO phenomenon affects retail investors and VCs, who may lack experience in choosing the most promising businesses (Bertoni et al. 2011). Thus, we explore the value of VC knowledge in predicting ICO success. We find that affiliation with a specialized VC is particularly relevant in contexts of higher investor and market uncertainties. Specialized VCs positively influence the proportion of the hard cap raised by early-stage firms by offering better support and guidance. Riskier firms may seek specialized VCs because the value of generalist VC contributions is limited. This aligns with Arthurs and Busenitz (2006).
In summary, among more opaque and riskier firms, those backed by generalist VCs do not have superior ICO success compared to non-VC-backed firms. For early-stage firms unable to signal their quality and marketability, the positive association between ICO success and VC backing is significant if the VCs are specialized in DeFI, specifically blockchain projects.
These findings contribute to theory by showing that third-party certification depends on knowledge and expertise in blockchain. For entrepreneurs, our evidence suggests affiliating with specialized VCs to maximize investor engagement and project financing. For practitioners, this research contributes to the ongoing debate about blockchain-based decentralization, highlighting the potential expansion of ICOs as a viable financial option for early-stage companies seeking substantial capital for innovation and growth.
This study is not without its limitations. First, the dataset used spans from March 2016 to March 2018, a period that captures significant early developments in the ICO market. The rapid evolution of blockchain technology and the regulatory environment since then may affect the applicability of our findings to the current market context, namely the value of external certification effect. Future research should consider using more recent datasets and control for different regulatory frameworks to validate and extend our findings, ensuring that the insights remain relevant as the market continues to evolve. Longitudinal studies could also provide valuable insights into how the impact of VC affiliations changes over time and under different market conditions.
Moreover, our focus on VC specialization in blockchain technology may not fully capture the nuances across different sectors and types of ICO projects. While we demonstrate the importance of specialized knowledge in blockchain, it remains to be seen whether similar patterns hold in other industries. Future research should explore the role of sector-specific expertise in different contexts to determine if the observed effects are consistent. These avenues for future research would offer valuable insights for both scholars and practitioners aiming to enhance the success of ICO campaigns.
Acknowledgements
The views expressed in this paper are those of the authors and do not necessarily represent the views of the institution with which they are affiliated. The authors acknowledge contributions from the anonymous referee.
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