# What determines initial coin offering success: a cross-country study

Muhammad Farooq Ahmad, Oskar Kowalewski, Pawel Pisany

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Muhammad Farooq Ahmad, Oskar Kowalewski, Pawel Pisany. What determines initial coin offering success: a cross-country study. Economics of Innovation and New Technology, 2023, 32 (5), pp.622-645. 10.1080/10438599.2021.1982712. hal-04273865

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# What determines initial coin offering success: a cross-country study

Muhammad Farooq Ahmad, Oskar Kowalewski & Paweł Pisany

**To cite this article:** Muhammad Farooq Ahmad, Oskar Kowalewski & Paweł Pisany (2023) What determines initial coin offering success: a cross-country study, Economics of Innovation and New Technology, 32:5, 622-645, DOI: 10.1080/10438599.2021.1982712

**To link to this article:** https://doi.org/10.1080/10438599.2021.1982712

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Published online: 01 Oct 2021.

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# What determines initial coin offering success: a cross-country study

Muhammad Farooq Ahmad ID a, Oskar Kowalewski ID b,c,d,e and Paweł Pisany ID e

aSKEMA Business School – Université Côte d’Azur, Lille, France; bIESEG School of Management, UMR 9221 - LEM - Lille Économie Management Lille, France; cUniv. Lille, UMR 9221 - LEM - Lille Économie Management Lille, France; dCNRS, UMR 9221 - LEM - Lille Économie Management Lille, France; eInstitute of Economics, Polish Academy of Sciences, Warsaw, Poland

## ABSTRACT

This study investigates the determinants of the presence and success of initial coin offering (ICO) campaigns using data on 503 ICOs from 60 countries launched between 2015 and 2018. The analysis is based on an individual project, considering a country-wide perspective. The results show that while insider retention and resource-related signals, such as the number of team members and advisors, contribute positively to the ICO's funding success and post-ICO activity, presale offers and bonuses contributed negatively. Additionally, the results highlight that a country's financial system development and ICO-related legal friendliness boost the market. The study also documents that a country's culture strongly determines the ICO issue but less so its long-term performance.

## ARTICLE HISTORY

Received 13 January 2021
Accepted 14 September 2021

## KEYWORDS

Initial coin offering;
corporate finance;
innovation;
entrepreneurship

## JEL CODES

G10; L26; M13; O30

## 1. Introduction

Entrepreneurs require funding to develop new products or services. This funding can be substantial for highly innovative ventures owing to high research and development costs. Previously, several ventures such as Apple, Skype, and Facebook used angel investors or venture capitalists to finance their early growth stages. However, today, the development of social media and blockchain technology has led to new ways of raising capital.

An initial coin offering (ICO) is a new method of raising capital for entrepreneurial projects wherein funds are raised by selling digital coins or tokens.¹ Coins or tokens are usually listed on a specialized exchange soon after an ICO is launched, creating a secondary market. ICOs have become the prevalent source of financing for startups that use blockchain technology. While, the crypto market is growing in terms of the number of digital coins, their market capitalization, and volume traded on the market. There are 4,431 cryptocurrencies traded, and their market capitalization was close to 257 billion USD as of July 2020.² However, the market is dominated by Bitcoin that represents around 65% of the value and trade on the market.

In our research, we use the definition common in the literature (Fisch 2019; Huang, Meoli, and Vismara, 2020; Block et al. 2021) that says that an ICO is an event where a venture sells tokens to
a crowd using infrastructure based on distributed ledger technologies (DLTs), such as blockchain technology. Blockchain technology, a potentially revolutionary innovation, is, as Bellavitis, Fisch, and Wiklund (2021) state, essential for issuing and selling tokens and is at the very core of token-based business models. Tokens are units of value and can provide utility (utility tokens) or resemble securities (security/investments tokens). The digital tokens acquired in ICO confer certain rights, such as the right to use the platform service that is being developed or ownership right. Nonetheless, the payment varies and depends on the nature of the ICO's structure and the participants' activities (Financial Stability Board 2018). The coins or tokens can be exchanged for other crypto-assets or even potentially fiat currencies in the secondary market. ICOs are a mix of crowdfunding and blockchain. Yet, however quite similar ICO is to Initial Public Offering (IPO), uniqueness of the former lies in basing the entire process on crypto-assets and avoiding the usual regulations and restrictions on IPOs (Allen et al. 2020).

Moreover, there is no single commonly accepted taxonomy of the new-type assets, though one can assume that the term 'crypto-asset' has the widest scope. The European Banking Authority (2019) defines crypto-assets very broadly as a type of private assets that depends primarily on cryptography and distributed ledger technology. The basic categories of crypto-assets include payment, exchange, currency tokens, investment tokens, and utility tokens.

In the process of ICO, the company issues tokens to finance a business venture, usually at an early development stage. Investors can engage in the project and receive tokens. Tokens may have features of financial instruments or incorporate some utility rights, in particular, the right to use the product or service when finally launched on the market. The venture is presented to the public often along with a document called a *white paper*. Moreover, the company organizes a marketing campaign to raise interest in the planned business venture and prepares the supporting blockchain-based infrastructure. Ethereum has been so far the platform for the vast majority of the ICOs (over 84% in 2018) (Pozzi 2019). Tokens may be traded directly or on the crypto-assets exchanges.

The innovative technology involved has made ICO a flexible and quite convenient funding mechanism for start-up companies. The global scale of ICO is economically significant toward the end of our sample period. The total amount raised worldwide in 2018 was almost $11.4 billion, while in 2017, it amounted to $10 billion (Pozzi 2019). However, the dynamic growth of ICO has significantly declined since 2019. One explanation for the situation is the declining trust in the market by institutional investors, who could not differentiate between the different quality of ICOs. The potentially spectacular rise of digital currencies may however once again switch the trend as the interest in digital currencies, both private and public, is gaining momentum.

ICO is a new phenomenon in the financial system and remains a largely under-explored area of research. Understanding the determinants of ICO presence and success would be of immense interest to the broad audience. In this paper, we strive to shed light on the factors that foster or hamper the ICOs market in the period of its flourishing development. To do this, we conduct research following two empirical approaches. First, we focus on the individual ICO project perspective, such as the elements of the ICO strategy chosen by the issuer company (ICO beneficiary). Second, we aim to establish the country-level factors that shape widely understood conditions for ICO as a new fundraising method in the economy.

We contribute to the literature in two ways. First, we add to the emerging literature on ICOs presenting a joint analysis of country-level and individual ICO-level determinants, which seems to be a reliable model of reality. Our study allows us to verify the thesis that the ICO campaign success factors are well described by signaling theory (Spence 1973). Our results complement those of Fisch (2019), who, based on the signaling theory, investigated the role of signaling ventures' technological capabilities in ICOs. These results supported the prior findings of Adhami, Giudici, and Martinazzi (2018) who documented that the probability of an ICO's success is higher if the code source is available, when a token presale is organized, and when tokens allow contributors to access a specific service. In our study, we complement the existing findings and document that signals need to be
chosen carefully with a deep understanding of specific ICO market features. We believe that this research is essential from a practical business perspective and as an empirical contribution to the signaling theory.

Second, we extend existing studies by associating culture dimensions by Hofstede (1980) and ICO activity, which—to the best of our knowledge—has not been so far empirically verified on individual ICO data and discussed. Culture, understood as a collective programming of the minds (Hofstede 2011), influences people's expectations, ways of thinking, and consequently, decisions, including business decisions. The empirical evidence supporting the view that culture has a paramount impact on financial and investment decisions is rapidly growing (Guiso, Sapienza, and Zingales 2006). Recent research shows that national culture influences stock markets (Guiso, Sapienza, and Zingales 2008), including IPO activity and underpricing (Chourou, Saadi, and Zhu 2018; Gupta, Veliyath, and Rejie 2018). Culture seems also to co-determine the investors' risk appetite and trading frequency (Beracha, Fedenia, and Skiba 2014; Lee, Pantzalis, and Park 2019), and investment styles (Tan, Cheong, and Zurbruegg 2019), or propensity to exhibit herding behavior (Chang and Lin 2015). We complement this literature by showing that national culture influences the fundraising campaigns' presence and successes of ICOs and showing that it has a lower impact on ICOs post-performance. In our opinion, the results show that culture determines mainly the decision to invest in ICO and consequently determines the success of ICOs.

The rest of the paper is organized as follows. Section 2 briefly reviews the two main components of empirical research in finance that this paper refers to. Section 3 describes our data and methodological approach. The empirical results are presented and discussed in Section 4, and Section 5 concludes the paper.

## 2. Literature review

The ICO is a new phenomenon that enables startups to raise significant capital while reducing the costs of compliance and intermediaries. Consequently, the literature that focuses directly on ICOs, although currently under development, remains limited. Brochado and Troilo (2021) have presented an interesting and up-to-date review of ICO literature. As ICOs resemble IPOs, this study will also focus on the literature related to public offerings and crowdfunding. Ofir and Sadeh (2020) have presented an excellent survey of empirical studies on ICO success factors, comparing their results with literature on IPOs and crowdfunding.

However, the ICO process differs from that of an IPO in the way entrepreneurs raise funds and the associated risk borne by investors. Entities conducting ICOs have unproven business models and are most often in the pre-product stage. Additionally, the ICO market is unregulated and has virtually no substantial information on new ventures. Consequently, we expect common factors, such as signaling, to explain the success of an ICO. Nevertheless, some elements differentiate IPOs and ICOs—in particular, cultural and social attitudes. The literature review outlines what we believe to be the most important determinants of ICO success and market development. Results from an empirical test of these determinants are presented in the following sections of the study. The market, however, continues to evolve. Consequently, the factors affecting ICOs and their importance have been found to change over time.

Information asymmetry may play a role in the ICO market, which, according to Ofir and Sadeh (2020), are linked to three sources. These are the absence of disclosure obligations, investors' lack of fundamental knowledge, and the early stage of the ICO projects, often even in the pre-product development stage.

Owing to the low transparency in ICO procedures, investors may try to assess investment attractiveness by tracing signals sent by a company (Ahlers et al. 2015). Signaling theory helps reduce information asymmetry between two parties (Spence 1973, 2002). Here, one party—the sender (for example, entrepreneur-issuer)—chooses whether and how to communicate (signal) the information to the other party (for example, potential investor). This theory is helpful when describing
the behaviors of agents in many strands of management studies, including strategy, entrepreneurial finance, entrepreneurship, and human resources. Generally, signals may refer to the intent of a party or the quality of a particular project (Connelly et al. 2011), which is more important from the perspective of this study. According to literature cited by Connelly et al. (2011), signals are expected to be expensive as they create a chance for high-quality entrepreneurs (ICO beneficiaries in our case) to distinguish themselves from low-quality competitors. However, as the aim of a company launching an ICO should be to maximize the difference between expected remuneration (financing obtained) and signaling cost, signals sent by thin-capital startups can be assumed to not be resource-consuming. This study verifies whether an ICO is similar to other markets analyzed in literature through the prism of signaling theory or the alternative mechanism.

Signaling theory suggests that founder involvement and/or the presence of well-respected professional investors are positively associated with the success of a given fundraising campaign on new business ventures. This is because these factors send a positive message about the company to the public (Ahlstrom and Bruton 2006; Jääskeläinen, Maula, and Seppä 2006; Schwienbacher 2007). Based on signaling theory, ICO issuers need to send a strong signal to mitigate information asymmetry concerning project quality and founder credibility to increase the chances of success. Ahlers et al. (2015) analyzed the determinants of success by comparing donation, reward-based, and lending crowdfunding exercises. They argued that assessing entrepreneurial signaling to small investors is the most relevant method for studying equity crowdfunding.

Vismara (2016) found that the founder's equity retention may signal their beliefs in the project and contribute to fundraising success in the crowdfunding market. Mollick (2014) showed that management team composition might be a part of a company's signaling strategy and foster the fundraising campaign. Courtney, Dutta, and Li (2017) documented that media usage in promoting fundraising campaigns and prior experiences increases the likelihood of crowdfunding success. Accordingly, Ayarci and Birkan (2020) show that high-quality white paper, website news, social media activity, founder, and team characteristics are among the most important project-level drivers enhancing interest in ICO campaigns.

An ICO is a close substitute for an IPO or a fundraising exercise, including attributes of all funding mechanisms. Fisch (2019), however, indicates that some of the underlying mechanisms in ICO funding are presented in entrepreneurial finance research, while others are unique to the ICO process. An ICO has several attributes that make it popular, such as fractional ownership, ease of transferability, and potential liquidity (Amsden and Schweizer 2018). However, whether an investor can cash out depends on the ICO's success and the tradability of the coin. Additionally, investors' proper due diligence is theoretically of utmost importance because of the lack of regulations and adequate screening mechanisms. Thus, sending the right signal to investors may determine the success of the ICO. Fisch (2019) explored the role of signaling ventures' technological capabilities in ICOs. They showed that technical white papers and high-quality source codes increase the amount raised in ICOs, while patents were not associated with increased amounts of funding raised by an ICO. Similarly, Feng et al. (2019) documented that providing technical details in white papers could effectively signal an ICO project's quality.

Relations between human capital signals and project success have been researched extensively in the area of equity crowdfunding. McKenny et al. (2017) offer a comprehensive literature survey on the topic. Additionally, Piva and Rossi-Lamastra (2018) have conducted one of the broadest empirical studies on human resources-related signals in crowdfunding.

Amsden and Schweizer (2018) showed that CEO characteristics and team size are important signals of venture quality that correlate positively with ICO success. De Jong, Roosenboom, and van der Kolk (2018) reported that ICOs are more successful when disclosing more information to investors. Additionally, refraining from offering bonus schemes, shorter planned token sale durations, and larger project teams increase the likelihood of successful fundraising. Giudici and Adhami (2019) examined the relationship between governance issues and ICO success. They documented that the project team and advisory committee size were positively and significantly
correlated with ICO success. A recent study by Fisch, Meoli, and Vismara (2020) also details the relations between social diversity (for example, gender and ethnicity) in team composition and the ICO's popularity and campaign successes.

Another research strand refers to an experimental study on the role of management team appearance in building confidence in the success of an ICO fundraising campaign. Huang, Vismara, and Wei (2021) documented that ICO investors make their decisions without the advice of financial analysts and professional intermediaries. Thus, they conclude that emotional factors, such as the physical appearance of managers (available on the website of the ICO), can contribute to building confidence among investors.

Research on investors' motives in the ICO market is a parallel and complementary research strand that is critically important. Fisch et al. (2021) present a survey-based study on individual investors' motives while investing in ICO projects. They show that investors in the ICO market are driven primarily by technological factors (for example, enthusiasm for new technology), followed by financial and ideological factors (for example, preference for being serviced without intermediaries with guaranteed anonymity). Fahlenbrach and Frattaroli (2021) showed that investors contribute small amounts, and many sell their tokens before the underlying product is developed. Nevertheless, they find positive returns, on average, nine months after the launch of the ICO, which is mostly driven by the value of the Ethereum cryptocurrency.

Implementing the project owner's efficient signaling activities is crucial for overcoming the significant information asymmetry present in the ICO procedure. Fahlenbrach and Frattaroli (2021) underline that ICO investors lack the protection traditionally offered to investors in early-stage financing. However, there are also vital environmental and country-level determinants of fundraising success. There is a scarcity of literature investigating the features of a favorable environment for the ICO business. Huang, Meoli, and Vismara (2020) aimed to explore the force behind the emergence of ICOs across countries. They found that developed financial systems, public equity markets, advanced digital technologies, flourishing crowdfunding sector presence, and ICO-friendly regulations can influence the launch of ICOs. Similarly, Zhao et al. (2020) documented that the regulations protecting shareholders are positively associated with the funds raised at the country-level during ICOs.

Howell, Niessner, and Yermack (2020) and Momtaz (2020) argued that the absence of regulation for the ICO markets increases investment risk due to malignant behavior, as tokens do not lead to any proper legal entitlement and because of considerable potential for fraud. Moreover, Momtaz (2020) documented that the absence of institutions verifying signals about venture quality ex-ante or punishing biased signals ex-post creates a moral hazard in signaling. Thus, poor-quality ventures can expropriate funds from retail investors by exaggerating the true quality of a business project. Moreover, notably, studies have also investigated the determinants of an ICO's initial returns; for example, Hsieh and Oppermann (2021) show that not holding a presale, a well-written whitepaper, and the creation of an independent are positively associated with an ICO's early return.

Recent research shows that culture affects the development of stock markets and influences the attitudes of market participants. Gupta, Veliyath, and Rejie (2018) emphasized that cultures can either dissuade or encourage IPO activities. They found that countries with high power distance, high collectivism, and long-term orientation have higher levels of IPO activity. The scale of the under-pricing phenomenon that usually accompanies IPOs also depends on national cultural values. Chourou, Saadi, and Zhu (2018) showed that IPOs in countries with high uncertainty avoidance tend to experience a lower level of underpricing. Conversely, IPOs in countries with higher collectivism, masculinity, and power distance demonstrate a higher level of underpricing. In this study, we expect that culture may determine the development and success of the ICO market. The culture traits determining the IPO and ICO will likely differ as the risks related to both markets differ substantially. To the best of our knowledge, confirmed by the current literature review by Brochado and Troilo (2021), ICOs have not been the subject of empirical research in the context of national culture dimensions thus far.
### 3. Data and research strategy

Hartmann et al. (2018) identified 28 different websites that provide proper ICO evaluations. Out of these 28 websites, the five most popular websites are icobench.com, icorating.com, icoalert.com, ico-tracker.net, and icodrops.com. We decided to use ICOBench as it provides the most extensive and accurate collection of ICO data along with ICO expert ratings. When data were missing on ICOBench, they were manually complemented with data from other websites, in particular ICO trackers, the ICO official whitepaper or website, and with the data available on etherscan.io, which tracks Ethereum wallets, tokens, and transactions.

Our initial sample consisted of 2,568 ICOs listed on ICOBench,³ made public from August 2015 to September 2018. We retrieved details about the ICO either from ICOBench or the company's website. However, detailed information on an ICO is available only for a limited number of projects. As a result, the final sample consisted of 503 ICOs from 60 countries worldwide. The sample represents 20% of the original sample, and in our opinion, is representative of the overall sample.

However, the distribution of the ICOs across the countries differs over the sample period. In our sample, we observe the highest number of ICOs in the USA, Russia, Singapore, and the UK. Those countries also dominated the number of soft cap and hard cap reached. Notwithstanding this, the largest number of ICOs do not correspond with the funds raised from investors. The first three countries with raised funds are Singapore, the USA, and the UK, respectively. The following countries however differ between the number of ICOs and the fundraised. Moreover, several countries recorded only one ICO and reached 0 soft cap, for example Austria, Brazil, Finland, Greece, and Vietnam. The existing ICOs' geographic dispersion shows, in our opinion, that country-level factors may be important for the ICO market's development, which justifies our empirical analysis.

#### 3.1. ICO-level variables

In our study, we included capped and uncapped ICOs. However, the capped ICOs represent the vast majority (De Jong, Roosenboom, and van der Kolk 2018). In uncapped ICOs, the token supply is unlimited, and/or its price is priorly unknown. Conversely, in the capped ICOs, the company sells a limited number of tokens at a given price. The company may set two funding goals in a capped ICO: a soft cap and a hard cap. Similar to classic crowdfunding, the ICO team might choose to set up a soft cap, which is a minimum amount to be raised. If this amount is not reached, the ICO does not take place, and the company returns funds to participants. The team of a beneficiary company usually sets a hard cap, which is the maximum amount that can be raised in the ICO. An ICO takes place only if the funds raised exceed the given soft cap, even if the hard cap has not been reached. In some ICOs, the hard cap is provided. Nonetheless, such a process may not be well received by potential investors (Bachmann et al. 2019). Hence, an ICO without a hard cap is rare in the ICO market.

In our study, we employed two dependent variables to reflect funding success. The first variable is a binary variable *Softcap* that takes the value 1 if the ICO reaches its minimum threshold, otherwise 0. In some of the ICOs, there is no soft cap. Consequently, our variable takes the value 0. Similarly, the second binary variable *Hardcap* equals 1 if the maximum amount had been raised, otherwise 0. In cases where no soft cap or hard cap was raised, the binary variables equal 0, respectively. The data on hard cap and soft cap for the projects in our sample were retrieved from ICOBench.

Additionally, we followed the development of the ICO and examine whether the project was successful. In our study, we employed three measures of ex-post success. Our first measure was a proxy for the survival of the project. We measured it using a binary variable *Online* that takes the value of 1 if the project website is still online in March 2019, otherwise it receives a value of 0. The information about whether a project is still alive was checked using Google search tools. Our second measure was strongly related to the first measure and the activity of the project on Twitter. The binary variable *Twitter* equals 1 if the company's official Twitter account is running and the ICO has tweeted at least
once since 1st January 2019. The data is extracted from Twitter and recovered on March 12, 2019. Lastly, we measured ICO tradability by employing the variable *Coinmarket* that takes the value 1 if the token sold in ICO is listed on Coinmarketcap as of March 2019, and 0 otherwise. Coinmarketcap is an extensive and transparent tracking website that has strict guidelines for listing a new token. A new token to be tracked by Coinmarket must be publicly and actively traded on at least two quality exchanges recognized by the website.

Our study draws on signaling theory (Spence 1973), which argues that high-quality ventures can attract higher amounts of funding by sending signals to potential investors. The signals allow investors to reduce information asymmetry, which especially serves ICOs as they are typically early stage ventures (Fisch 2019). Moreover, when the quality and level of information about an ICO is very low, there is a considerable risk of fraud (Shifflett and Jones, 2018). Moreover, in our opinion, ICOs are similar to the IPOs and consequently, after conducting a relevant corporate finance literature review, we expect that country-level factors that stimulate the development of the capital market should also encourage the development of the ICO market.

With this in mind, we controlled for several characteristics of the ICO process that may determine its success and post-performance (De Jong, Roosenboom, and van der Kolk 2018; Fisch 2019). In the analysis, we controlled for the number of *Advisors* in the ICO disclosed prior. Additionally, we controlled for the size of the project using the variable *Team*, which reflected the number of team members disclosed before the ICO. Moreover, we controlled for the number of tokens kept by the team or insiders using the variable *Insider*, which represented the number of tokens of the total supply of the ICO token kept by insiders. We may assume that a higher number of advisors, size of the team, and tokens kept by the insiders are positively related to ICO success and its post-performance.

In the ICO process, the entrepreneurs can offer investors security tokens or utility tokens. Utility tokens can be exchanged for a product or service now or in the future. They can also be exchanged for voting rights depending on how a specified community or platform acts. A utility token does not bear any right to profits for investors. However, holders may exchange it for a product or service at a given time. Conversely, security tokens are designed to bear financial rights. The tokens allow the holder to receive a share of the profits of a company and can additionally bear voting rights regarding the operations of the project. Consequently, they resemble traditional securities and entitle their holder to shares of ownership, dividends, or other financial benefits but remain unregulated. During an ICO, investors can buy these tokens at a predefined price directly from the ICO-conducting venture.

We used dummy variables *U-Token* and *S-Token* if the proposed token type is a utility or security in the ICO, or not, respectively. The currency token (*C-Token*) is captured in the regressions as constant. This type of token is, in the assumptions of issuers and users, similar to traditional currency, but it can only be used in a specific environment. Subsequently, it does not bear any rights, whether financial or non-financial and is solely considered as a medium of exchange.

The variable *Presale* is a dummy variable equal to 1 if a presale was organized and equal to 0 if not. The variable *Bonus* is a binary variable that takes the value of 1 if a bonus was offered to early investors, otherwise 0 applies.

Some ventures divulge their code online, so the source code is observable, whereas it is usually published on the platform GitHub. Fisch (2019) documents the importance of signaling the venture's technological capabilities for an ICO's success, including the importance of assessing GitHub. We follow Fisch (2019) and employ a dummy variable *GitHub* that takes the value of 1 if any source of code was available on the GitHub repository before the launch of the ICO and 0 if not. The data were extracted from GitHub with the help of a Google Chrome extension that displays the creation date of the repository (Ivarayut 2017). The *roadmap* is a dummy variable that is equal to 1 if the roadmap details future milestones for at least two years and 0 if it does not, or if there is no roadmap shared by the ICO team.
<figure><img src="image_15.png" /></figure>

We provided a proxy for the performance of the cryptocurrency market using the market return on Ethereum (ETH) relative to the US dollar as most of the ICOs in our sample are launched on the ETH blockchain. We used the market return of the ETH over for two months (ETH) and expected that it would be strongly associated with ICO success.

The final variables are related to investment. The dummy variable *Investment* is equal to 1 if there is a minimum investment required to be able to invest in the ICO and 0 if otherwise. The variable *Crypto* presents the number of cryptocurrencies accepted as a means of payment to participate in the ICO. Lastly, the variable *Fiat* is a dummy variable that is equal to 1 if the ICO accepts fiat currencies (Dollar, Euro, Yuan, and other government-issued currencies) as a means of payment to participate in the ICO and 0 if not.

In Panel A of Table 1 we present basic descriptive statistics for the variables used in our study for the ICO-level regressions. The detailed information regarding the explanatory variables used in the research is presented in the Appendix in Table A1. We checked the pairwise correlation of all the variables, but did not report them for brevity.⁴ We accounted for the correlations between the variables in the process of selecting variables (*From-General-To-Specific*) to minimize the multicollinearity problem in the following estimations.

## 3.2. Country-level variables

In the analysis of the country-level determinants, we retrieved data from ICOBench and used three dependent variables, which measure different outcomes of the process. The first dependent variable presents the number of ICOs in a given country. The second and third variables reflect the number of soft cap and hard cap reaches in a given country, respectively. We use the first dependent variable as a proxy that provides information on the overall activity of ICOs in a given country. We hope that the

<table><caption>**Table 1.** Descriptive statistics for ICO and country characteristics.</caption><thead><tr><th>Variables</th><th>N</th><th>Mean</th><th>Std. dev.</th><th>Min</th><th>Max</th></tr></thead><tbody><tr><td colspan="6"><em>Panel A: ICO-level variables</em></td></tr><tr><td>Softcap</td><td>503</td><td>0.903</td><td>0.297</td><td>0</td><td>1</td></tr><tr><td>Hardcap</td><td>503</td><td>0.354</td><td>0.479</td><td>0</td><td>1</td></tr><tr><td>Online</td><td>503</td><td>0.763</td><td>0.425</td><td>0</td><td>1</td></tr><tr><td>Coinmarket</td><td>503</td><td>0.505</td><td>0.500</td><td>0</td><td>1</td></tr><tr><td>Twitter</td><td>503</td><td>0.577</td><td>0.495</td><td>0</td><td>1</td></tr><tr><td>Advisors (number)</td><td>503</td><td>2.636</td><td>3.587</td><td>0</td><td>19</td></tr><tr><td>Team (number)</td><td>503</td><td>8.334</td><td>6.466</td><td>1</td><td>50</td></tr><tr><td>Insider (in %)</td><td>503</td><td>0.214</td><td>0.264</td><td>0</td><td>0.994</td></tr><tr><td>U-Token</td><td>503</td><td>0.718</td><td>0.451</td><td>0</td><td>1</td></tr><tr><td>S-Token</td><td>503</td><td>0.0855</td><td>0.280</td><td>0</td><td>1</td></tr><tr><td>C-Token</td><td>503</td><td>0.197</td><td>0.398</td><td>0</td><td>1</td></tr><tr><td>Presale</td><td>503</td><td>0.274</td><td>0.447</td><td>0</td><td>1</td></tr><tr><td>Bonus</td><td>503</td><td>0.439</td><td>0.497</td><td>0</td><td>1</td></tr><tr><td>GitHub</td><td>503</td><td>0.491</td><td>0.500</td><td>0</td><td>1</td></tr><tr><td>RoadMap</td><td>503</td><td>0.328</td><td>0.470</td><td>0</td><td>1</td></tr><tr><td>ETH</td><td>503</td><td>0.670</td><td>1.370</td><td>0.690</td><td>6.919</td></tr><tr><td>Investment</td><td>503</td><td>0.199</td><td>0.399</td><td>0</td><td>1</td></tr><tr><td>Crypto</td><td>503</td><td>1.817</td><td>2.202</td><td>1</td><td>40</td></tr><tr><td>Fiat</td><td>503</td><td>0.078</td><td>0.268</td><td>0</td><td>1</td></tr><tr><td colspan="6"><em>Panel B: Country-level variables</em></td></tr><tr><td>Fin. Dev.</td><td>446</td><td>0.682</td><td>0.216</td><td>0.069</td><td>0.946</td></tr><tr><td>Common law</td><td>481</td><td>0.559</td><td>0.497</td><td>0</td><td>1</td></tr><tr><td>Legal friendly</td><td>503</td><td>0.414</td><td>0.493</td><td>0</td><td>1</td></tr><tr><td>EPU</td><td>481</td><td>156.579</td><td>9.747</td><td>121.297</td><td>213.445</td></tr><tr><td>Uncertainty avoidance (UAI)</td><td>446</td><td>54.491</td><td>25.511</td><td>8</td><td>100</td></tr><tr><td>Individualism (IND)</td><td>446</td><td>59.906</td><td>27.192</td><td>11</td><td>91</td></tr><tr><td>Power distance (PDI)</td><td>446</td><td>53.854</td><td>21.558</td><td>11</td><td>100</td></tr><tr><td>Masculinity (MAS)</td><td>446</td><td>51.659</td><td>16.404</td><td>5</td><td>95</td></tr><tr><td>Long-/short-term orientation (LTI)</td><td>448</td><td>55.436</td><td>22.675</td><td>12.846</td><td>100</td></tr></tbody></table>
results will shed some light on the ICOs' geographic dispersion, which we can observe in our sample. The success of an ICO can, however, vary. While the second and third dependent variables are proxying for the ICOs' successes, which differ across projects, their definition is the same as used for the analysis of ICOs' determinants.

Similarly, as before, we used the same three measures of the post-performance of the ICOs, namely *Online*, *Twitter*, and *Coinmarket*, respectively. Jain and Kini (1994) document, however, that there is a significant decline in operating performance subsequent to the IPO. Fisch and Momtaz (2020) find a positive association between backing by institutional investors and post-ICO performance. They find that institutional investors fund ventures with higher observable quality at the time of the ICO (selection) and higher post-ICO performance (treatment) is related to higher ICO performance. It means that selection and consequently the quality of signals play an important role in the ICO performance. We do not expect, however, that the determinants of ICO success are related to its post-performance, especially the country-level variables.

To include a wide country-level perspective to the research we employed financial system-related, legal system-related, national culture, and policy uncertainty proxies. We controlled for financial system development using the IMF composite indicators, the Financial Development Index (Fin. Dev.). The index is built to aggregate information on three dimensions, that is, depth, access, and efficiency of financial markets and financial institutions. However, ICO is a funding tool for young and daring entrepreneurs, thus quite similar to private equity funding. It is also a decentralized and strongly tech-based process. It would be essential to verify whether countries with advanced financial systems are also favorable markets for ICOs.

Legal issues shape the environment for ICOs. The regulatory environment is identified in a recent market review by Bellavitis, Fisch, and Wiklund (2021) as a key supply driver for ICOs. First, as ICO is a relatively new financial phenomenon, it may face various challenges related to legal uncertainty and lack of a solid dedicated regulatory scheme that may hamper its development. Thus, we added to our research binary variable *Legal friendly* that takes a value 0 if ICO is banned or ignored in a particular jurisdiction (the whole ICO activity is unavailable or legal uncertainty arises, which creates a significant obstacle), while the value 1 applies in the case of proper regulations by applicable laws and/or guidelines explaining the legal status and obligations according to the token type (regulatory friendliness that we interpret not as lack of obligations but thoughtful and carefully designed legal schemes). Moreover, we did not set the value of 1 if the regulator's activity is limited only to issuing warnings, or if some ICO market practices had been assessed as suspicious. These kinds of actions are obviously interpreted in our framework as manifestations of an unfriendly attitude. The variable *Legal friendly* is based on our expert judgment made on the data presented in the comparative reports by PricewaterhouseCoopers (2018) and EY (2017).

Second, we take into consideration legal system origin by employing binary variables denoting particular system types. We follow La Porta et al. (1998, 1999) and identify the legal origin of each country's company or commercial law, based on common law and civil law origins. La Porta et al. (1998) argued that stock markets tend to be underdeveloped in civil law countries compared to common law countries. Moreover, common law is positively related to the anti-director rights and anti-self-dealing index, which in turn was shown to be positively associated with the country-level raised funding of ICOs (Zhao et al. 2020). Thus, we expect to observe a positive correlation between ICO and common law legal origin.

Third, we investigated potential relations between the ICO market and the level of uncertainty in the economy using the uncertainty measures taken from the comprehensive research by Baker, Bloom, and Davis (2015). We assume that uncertainty may be a vital factor in shaping the attitudes of buyers and issuers in the ICO process. Further, we believe that rising uncertainty deteriorates ICO funding campaigns. On the one hand, a high global uncertainty level may discourage people from financial markets, including the ICO. On the other hand, it may only discourage them from traditional financial equity and debt markets. ICOs may be perceived as an alternative class of assets. Thus, they
may become more popular in times of rising general uncertainty when trust toward traditional financial intermediation fails.

More importantly, we further extend our analysis by employing proxies of national culture dimensions developed by Hofstede (2011), who identified four major cultural dimensions to investigate cultural values—uncertainty avoidance (UAI), individualism (IND), power distance (PDI), and masculinity (MAS). We supplemented this list with an additional dimension for the long-/short-term orientation (LTI) developed by Minkov and Hofstede (2011). Hofstede's data on the first four cultural dimensions cover 48 countries, while data for the long-term, based on the World Value Surveys, encompasses 53 countries.

Despite some criticisms (Osland and Bird 2000), there is a growing agreement in the literature that culture, its dimensions, and its components like social trust significantly affect a vast array of financial phenomena. They influence investor decisions (Karolyi 2016), constraints (El Ghoul and Zeng 2016; Boubakri and Saffar 2016), company financial and social performance (Frijns et al. 2016; Cai et al. 2016), and outcomes of mergers and acquisitions (M&A) processes (Lim et al. 2016). Gupta, Veliyath, and Rejie (2018) confirmed that there is also a link between culture and the IPO market. They established that, in particular, PDI and long-term orientation are strongly related to IPO success. The aspect of the cultural dimension of ICO has not been studied so far. Consequently, to the best of our knowledge, this study is the first to analyze culture's impact on ICO presence and success.

Including all the above mentioned independent variables, in particular, the institutional ones, allows us to discuss the nature of ICOs as a new funding tool for entrepreneurs. In Panel B of Table 1 we present basic descriptive statistics for the variables used in our study. All the details of indexes, most of which are quite complex, used in the study are summarized in Appendix Table A1.

### 3.3. Methods

Separate databases were developed, and different methodological approaches were used for the two components of the study. First, we focused on the factors related to the strategy of individual ICOs. Second, we followed De Jong, Roosenboom, and van der Kolk (2018) and employed a logit model to investigate the determinants of an ICO's success and the post-ICO performance of business ventures. In the estimation tables, we report odds ratios. Thus, a value of less than one indicates a negative relationship between the independent variable and our measure of ICO success. However, an odds ratio greater than one indicates a positive relationship between the given independent variable and ICO funding or post-ICO performance success of the given business project. Explanatory variables that refer to the ICO or project features known at the beginning of the campaign were included in our model to avoid any casual misinterpretations.

Negative binomial regression models with robust standard errors were employed, similar to Huang, Meoli, and Vismara (2020), as the number of ICOs in a given country is the dependent variable in the following estimations.⁵ The negative binomial model is a generalized linear model in which the dependent variable is a count of the number and follows a negative binomial distribution. This means that the values of the independent variables are non-negative integers (Zwilling 2013). Negative binomial regression is a generalization of Poisson regression, which allows for the loosening of the assumption that the variance is always equal to the mean. The regression results—the coefficients—are estimated using the maximum likelihood method.⁶

We chose the negative binomial model as our method to analyze the country-level ICO drivers because our dependent variables are observed counts that approximate the negative binomial distribution. Our dependent variables, retrieved from the ICOBench—the number of ICOs and soft cap and hard cap reaches in a given country (See: 3.2)—can take only positive integer values (0,1,2, and so on) due to their economic sense. Negative binomial models have thus far been used in similar research contexts. For example, Haddad and Hornuf (2019) used it to study the determinants of FinTech startups and Dushnitsky et al. (2016) on crowdfunding platform formation. In the case of our second research direction, to ensure interpretation consistency, we include the regression's
<figure><img src="image_16.png" /></figure>

cultural dimensions, institutional features, and market advancements. These are previously shaped and well-embedded in the business environment of a given country and can interact with and foster the relatively new phenomenon of ICOs.

Lastly, we expanded the model and further investigated the country-level independent variables, controlling for the individual characteristics of the ICO. We did this by applying a hierarchical/multi-level logistic regression (Goldstein 2011), which allowed us to distinguish the effects that take place at the country-level from those that take place at the individual ICO-level, both to understand the role of country- versus ICO-level determinants. In our data, the set of ICOs within countries form the base-level observations, while countries provide the higher-level observations.

The hierarchical approach separates the variance attributable to ICO and country-level variables. Thus, homogeneity is not a prerequisite. Multilevel models consider the sample size within a country by applying weights, and accurately include cross-level interactions between the ICOs- and country-level variables (Li et al. 2013). Moreover, multilevel logit models can correctly group ICO-level effects across countries while also examining country-level relationships.

# 4. Results

In this section, we first present the results for the determinants of ICO success and the company's post-ICO performance using individual fundraising data. Second, we present the results explaining the ICO activity using country-level data. Lastly, we present the results for the post-ICO performance using both individual fundraising data as well controlling for country-level data.

## 4.1. ICO perspective

In Table 2 we present the estimation results: the odds ratios of the logit model of ICO success. In Table 2 the dependent variable is *Softcap*, while in Table 3 the dependent variable is *Hardcap*. The results indicate that the number of team members and the number of advisors show at a very basic level the composition and power of the team involved in the project. The model confirms that the higher those numbers, the higher the probability of ICO success. Revealing the number of the crew may build positive publicity at the fundraising stage and then it simply refers to the company resources that allow for better market performance. In line with this, insider retention contributes positively to the odds ratio of reaching *Hardcap* during the ICO, which is also consistent with the findings of Vismara (2016). In our opinion, it shows that insider retention is a strong signal for investors.

Surprisingly, we found that bonus and presale programs have a negative impact on the probability of success of the ICO campaign (odds ratios less than 1). In our opinion, it could indicate that overly active/aggressive promotion may discourage people from investing in tokens as some doubts may arise. It seems quite justified, especially by taking into consideration the potentially low level of trust toward ICOs and tokens business among publicity, as it is still considered as a new, unregulated segment of the financial market.

As expected, we found that positive tendencies on the ETH market are also positively associated with the ICO successes, because ETH price changes may be treated as a proxy for the sentiment toward the whole market of crypto-assets.

The role of other examined variables for reaching *Softcaps* and *Hardcaps* in the ICO process by companies is limited. In particular, the model does not show the significance of minimum investment requirements and presenting a long-term roadmap for future milestones.

Moreover, we controlled in our estimations for an industry represented by a particular company that is looking for financing through issuing tokens.⁷ We found that companies offering wide business services have weaker perspectives on the ICO market. The case is similar for entertainment firms, but only when reaching *Hardcap* in the ICO process is treated as a dependent variable.
<figure><img src="image_17.png" /><figcaption>**Table 2.** The ICO success (softcap) – ICO-level determinants.</figcaption></figure>

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Advisors</td><td>1.163***<br>(0.057)</td><td>1.143***<br>(0.058)</td><td></td><td>1.110**<br>(0.052)</td><td>1.103**<br>(0.054)</td></tr><tr><td>Team</td><td></td><td>1.073**<br>(0.034)</td><td></td><td>1.059*<br>(0.032)</td><td>1.048*<br>(0.029)</td></tr><tr><td>Insider</td><td>1.040<br>(0.647)</td><td></td><td></td><td>0.979<br>(0.548)</td><td>0.734<br>(0.387)</td></tr><tr><td>U_Token</td><td>1.338<br>(0.481)</td><td>1.470<br>(0.533)</td><td>1.442<br>(0.495)</td><td>1.641<br>(0.605)</td><td>1.799*<br>(0.638)</td></tr><tr><td>S_Token</td><td>1.267<br>(0.765)</td><td>1.220<br>(0.715)</td><td>0.928<br>(0.554)</td><td>1.312<br>(0.793)</td><td>1.078<br>(0.636)</td></tr><tr><td>Presale</td><td></td><td>0.357***<br>(0.118)</td><td>0.499**<br>(0.153)</td><td></td><td></td></tr><tr><td>Bonus</td><td>0.244***<br>(0.087)</td><td></td><td></td><td></td><td></td></tr><tr><td>GitHub</td><td>0.607<br>(0.203)</td><td></td><td>0.790<br>(0.253)</td><td></td><td></td></tr><tr><td>RoadMap</td><td></td><td>0.732<br>(0.232)</td><td>1.004<br>(0.319)</td><td>0.684<br>(0.216)</td><td></td></tr><tr><td>ETH</td><td>1.244<br>(0.188)</td><td>1.343**<br>(0.194)</td><td>1.253<br>(0.183)</td><td>1.454**<br>(0.216)</td><td></td></tr><tr><td>Investment</td><td>0.965<br>(0.358)</td><td>0.846<br>(0.308)</td><td>1.049<br>(0.385)</td><td></td><td></td></tr><tr><td>Crypto</td><td></td><td></td><td>0.926**<br>(0.034)</td><td></td><td></td></tr><tr><td>Fiat</td><td>0.723<br>(0.426)</td><td></td><td>1.007<br>(0.602)</td><td>0.835<br>(0.471)</td><td></td></tr><tr><td>Industry FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td></tr><tr><td>Pseudo R2</td><td>0.097</td><td>0.080</td><td>0.065</td><td>0.051</td><td>0.054</td></tr><tr><td>χ²</td><td>36.610</td><td>24.540</td><td>20.565</td><td>17.276</td><td>14.894</td></tr></tbody></table>

This table presents the odds ratios of the logit model of ICO success on the set individual ICO-level variables. In columns (1)-(5) the dependent variable is the binary variable softcap. All regressors are defined in Table A1. All specifications include constants but not reported for brevity. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.

## 4.2. Country-level perspective

In Table 4 we present the results (coefficients) of the negative binomial regression models focused on potential environmental, country-level determinants of ICO presence and successes in providing companies with financing. In the regression, we employ the total number of ICOs as a dependent variable.

Our results show positive and statistically significant coefficients for countries' financial development and activity of ICOs in almost all the specifications. Financial development is strongly related to economic development, technological development, and the attractiveness of a particular country for investors in the private equity asset classes. We repeated our estimations using the soft cap and hard cap as dependent variables and we found that the results were unchanged, yet we did not report them for brevity. Therefore, in our opinion, the results indicate that ICO activity is strongly related to country's economic and financial development, which is not surprising. Thus, ICOs should not be treated today as part of an alternative financial system, but rather as a supplementary solution for the markets, where the traditional financial products are available. Moreover, we claim that ICO, while a decentralized financing form, has something in common with venture capital, as both forms are focused on innovative and risky businesses. As a result, it is not surprising that markets that are attractive for venture capital are also attractive for token issuances.

Next, we controlled for legal origin and legal friendliness. In all the specification, the coefficient for common law is positive, yet only significant in the last regression. Hence, we find only weak evidence that the common law, in general, supports the ICO market versus civil law countries. Modigliani and Perotti (2000) argued that legal institutions determine the degree of financial development and the
<figure><img src="image_18.png" /><figcaption>**Table 3.** The ICO success (hardcap) – ICO-level determinants.</figcaption></figure>

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th></tr></thead><tbody><tr><td>Advisors</td><td></td><td>0.999<br>(0.029)</td><td></td><td>0.989<br>(0.029)</td><td>0.964<br>(0.028)</td></tr><tr><td>Team</td><td></td><td>1.039**<br>(0.017)</td><td></td><td>1.039**<br>(0.018)</td><td>1.029*<br>(0.016)</td></tr><tr><td>Insider</td><td>1.996*<br>(0.754)</td><td></td><td></td><td>1.920*<br>(0.731)</td><td>1.419<br>(0.530)</td></tr><tr><td>U_Token</td><td>1.092<br>(0.269)</td><td>1.146<br>(0.287)</td><td>1.098<br>(0.273)</td><td>1.149<br>(0.289)</td><td>1.296<br>(0.323)</td></tr><tr><td>S_Token</td><td>1.060<br>(0.443)</td><td>1.036<br>(0.434)</td><td>0.871<br>(0.366)</td><td>1.208<br>(0.515)</td><td>0.929<br>(0.386)</td></tr><tr><td>Presale</td><td></td><td>0.689<br>(0.164)</td><td>0.737<br>(0.173)</td><td></td><td></td></tr><tr><td>Bonus</td><td>0.488***<br>(0.102)</td><td></td><td></td><td>0.461***<br>(0.097)</td><td></td></tr><tr><td>GitHub</td><td>1.154<br>(0.232)</td><td></td><td>1.124<br>(0.228)</td><td></td><td></td></tr><tr><td>RoadMap</td><td></td><td>1.088<br>(0.231)</td><td>1.152<br>(0.243)</td><td>1.084<br>(0.233)</td><td></td></tr><tr><td>ETH</td><td>1.369***<br>(0.106)</td><td>1.398***<br>(0.105)</td><td>1.380***<br>(0.105)</td><td>1.383***<br>(0.106)</td><td></td></tr><tr><td>Investment</td><td>0.983<br>(0.251)</td><td>0.903<br>(0.228)</td><td>0.998<br>(0.252)</td><td></td><td></td></tr><tr><td>Crypto</td><td></td><td></td><td>0.836**<br>(0.071)</td><td></td><td></td></tr><tr><td>Fiat</td><td>1.332<br>(0.485)</td><td></td><td>1.637<br>(0.622)</td><td>1.204<br>(0.432)</td><td></td></tr><tr><td>Industry FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>Yes</td></tr><tr><td>Observations</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.065</td><td>0.052</td><td>0.067</td><td>0.074</td><td>0.023</td></tr><tr><td>X<sup>2</sup></td><td>36.234</td><td>30.527</td><td>37.497</td><td>42.406</td><td>14.707</td></tr></tbody></table>

This table presents the odds ratios of the logit model of ICO success on the set individual ICO-level variables. In columns (1)-(5) the dependent variable is the binary variable hardcap. All regressors are defined in Table A1. All specifications include constants but not reported for brevity. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.

<table><caption>**Table 4.** Country level determinants of number of ICOs.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>1.571**<br>(0.736)</td><td>1.143<br>(0.703)</td><td>2.074**<br>(0.940)</td><td>1.739**<br>(0.739)</td><td>2.886***<br>(0.975)</td><td>1.711**<br>(0.697)</td></tr><tr><td>Common law</td><td>0.537<br>(0.378)</td><td>0.360<br>(0.383)</td><td>0.480<br>(0.390)</td><td>0.491<br>(0.387)</td><td>0.460<br>(0.404)</td><td>1.085**<br>(0.429)</td></tr><tr><td>Legal friendly</td><td>0.970**<br>(0.424)</td><td>0.867**<br>(0.379)</td><td>0.884**<br>(0.409)</td><td>1.004**<br>(0.402)</td><td>0.833*<br>(0.452)</td><td>0.770*<br>(0.449)</td></tr><tr><td>EPU</td><td>-0.0136<br>(0.0116)</td><td>-0.0141<br>(0.0112)</td><td>-0.00963<br>(0.0105)</td><td>-0.0122<br>(0.0102)</td><td>-0.00570<br>(0.0100)</td><td>-0.00133<br>(0.00942)</td></tr><tr><td>UAI</td><td>0.00575<br>(0.0114)</td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td>0.00647<br>(0.00702)</td><td></td><td></td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td>-0.0136<br>(0.0104)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>0.0119<br>(0.0102)</td><td></td><td></td></tr><tr><td>INDG</td><td></td><td></td><td></td><td></td><td>-0.0201*<br>(0.0108)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>0.0247**<br>(0.0116)</td></tr><tr><td>Observations</td><td>44</td><td>44</td><td>44</td><td>44</td><td>45</td><td>46</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.0670</td><td>0.0678</td><td>0.0704</td><td>0.0743</td><td>0.0845</td><td>0.0918</td></tr></tbody></table>

This table presents coefficients of the negative binomial regression model of the number of ICOs in a country on a set of country-wide variables denoting diversified environmental factors and Hofstede's culture dimensions. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.
financial structure of a country. They argued that market-based systems flourish in environments
with strong institutions, while Ergungor (2004) presented evidence that countries with common
law financial systems are more likely to be market-oriented than civil law countries. In our
opinion, this evolution is a result of effective rule of law in common law countries, which improves
shareholder and creditor rights protection. Our results document that the legal origin only explains
the development of the ICOs market.

However, we found a strong positive and statistical relationship between legal friendliness and ICO activity. In our opinion, it shows that ICOs are more likely where proper regulations, applicable laws or guidelines explaining the legal status and obligations associated with token issuance are in place and are important for the development of the crypto-market. We are aware, however, that apart from regulations—both hard and soft law provisions—the supervisory approach (law in action) plays a significant role, especially for new markets like ICOs. Our models do not include this aspect directly and future studies should investigate it.

We also controlled the potential factors related to uncertainty on the markets measured by economic policy uncertainty indexes and found little evidence of associations between economic policy uncertainty and ICOs presence. In contrast, to our expectation, we did not find that economic policy uncertainty determines the activity of the ICO market. We used alternative variables proxying for economic policy uncertainty, which are not reported for brevity, yet none of them enters the specification significantly.

Lastly, we controlled further for culture-related factors that may determine the ICO activity in a
country. We found that the coefficient for indulgence index is negatively and statistically related
to the activity of ICOs in the country. However, in column (5) of Table 4, the coefficient is statistically
significant only at 10%. In contrast, in column (6), the coefficient for the LTI is positively and statisti-
cally significant at a 5% level. Our results are only partially in line with Gupta, Veliyath, and Rejie
(2018), who documented that countries with high PDI, high collectivism, and long-term orientation
were positively associated with higher levels of IPO activity. In columns (1)–(4), the other variables
proxying for a cultural dimension, such as UAI, IND, and MAS, were not statistically significant in
the regressions.

The ICO market is unregulated; in particular, there are no compulsory transparency standards or investor protection schemes. Our results, however, indicate that investors are more likely to invest in ICO projects that send strong signals about their quality. In line with this, we find that ICO activity is higher in countries with less indulgent cultures, that is countries with societies that consider indulgence and attention to duty as equally important. In those societies, the decision is more based on rational analysis and less on impulses.

These results complement our previous results showing that providing additional information
and sending some resource-related signals to investors contribute positively to the ICO's success.
However, as the ICO issuer presents only a white paper with selected information (not a prospectus
or even investment memorandum), the scope of information is rather narrow. Moreover, corporate
governance standards are much weaker than in the regulated capital market.

In less indulgent countries the societies are more likely to refrain from fun and focus on long-term goals. Indeed, we find that the LTI is positively associated with ICO activity and is highly significant. The LTI is associated with a forward-looking approach, perseverance (Zheng et al. 2012), will investment, and patience in waiting for results. It shows that ICO investors are long-term investors, who expect a future payoff. Moreover, the results may reflect the perspectives for dividend policy in ICOs. Lack of dividends or low dividends policy is more acceptable in long-term oriented societies. Thus, executives of innovative ventures in those countries may expect higher retained earnings and, as a consequence, wider scope of investment opportunities for companies (La Porta et al. 2000).

Next, we applied a hierarchical logit model using the two levels in our sample data: ICO and country-level variables. We present the results in Tables 5 and 6 for the number of Softcap and Hardcap reached, respectively. In all the regressions, we controlled for the ICO characteristics and employing variables as in Column 5 of Tables 2 and 3, yet we did not report them for brevity.
<figure><img src="image_19.png" /><figcaption>**Table 5.** The ICO success (softcap) – both ICO-level and country-level determinants.</figcaption></figure>

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>8.050***<br>(4.546)</td><td>8.722***<br>(6.840)</td><td>10.255***<br>(9.161)</td><td>10.114***<br>(8.241)</td><td>5.761**<br>(4.604)</td><td>9.527***<br>(6.199)</td></tr><tr><td>Common law</td><td>0.738<br>(0.290)</td><td>0.368***<br>(0.139)</td><td>0.571<br>(0.241)</td><td>0.547<br>(0.219)</td><td>0.488<br>(0.241)</td><td>0.476**<br>(0.180)</td></tr><tr><td>Legal friendly</td><td>1.846*<br>(0.595)</td><td>1.630<br>(0.555)</td><td>2.148**<br>(0.700)</td><td>2.093**<br>(0.697)</td><td>2.287**<br>(0.762)</td><td>2.142**<br>(0.688)</td></tr><tr><td>EPU</td><td>1.012<br>(0.015)</td><td>1.014<br>(0.016)</td><td>1.015<br>(0.017)</td><td>1.014<br>(0.016)</td><td>1.013<br>(0.019)</td><td>1.011<br>(0.017)</td></tr><tr><td>UAI</td><td></td><td>0.981**<br>(0.009)</td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td></td><td>1.003<br>(0.006)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>0.994<br>(0.006)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td>1.018<br>(0.014)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>0.991<br>(0.007)</td></tr><tr><td>ICO controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>444</td><td>444</td><td>446</td><td>446</td><td>446</td></tr><tr><td>Countries</td><td>46</td><td>44</td><td>44</td><td>45</td><td>44</td><td>44</td></tr><tr><td>χ<sup>2</sup></td><td>72.476</td><td>86.385</td><td>55.986</td><td>48.471</td><td>47.622</td><td>41.512</td></tr></tbody></table>

This table presents the odds ratios of the hierarchical logit model. The dependent variable is the dummy variable softcap in all the specifications, which include ICO level variables as in Column 5 in Table 2 and constant but are not reported for brevity. All regressors are defined in Table A1. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.

Tables 5 and 6 present the results; we found that changing the estimation method and controlling for the ICO characteristics did not alter our results. As in Table 7, in almost all the specification, we found that the coefficient for financial development is positive and statistically significant at 1%

<table><caption>**Table 6.** The ICO success (hardcap) – both ICO-level and country-level determinants.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>3.831***<br>(1.628)</td><td>4.504***<br>(2.060)</td><td>5.889***<br>(3.072)</td><td>4.959***<br>(2.270)</td><td>2.239<br>(1.413)</td><td>4.202***<br>(1.751)</td></tr><tr><td>Common law</td><td>0.789<br>(0.163)</td><td>0.591*<br>(0.168)</td><td>0.726*<br>(0.139)</td><td>0.714*<br>(0.138)</td><td>0.693*<br>(0.131)</td><td>0.761<br>(0.145)</td></tr><tr><td>Legal friendly</td><td>1.506***<br>(0.222)</td><td>1.494***<br>(0.227)</td><td>1.576***<br>(0.244)</td><td>1.587***<br>(0.234)</td><td>1.594***<br>(0.235)</td><td>1.536***<br>(0.219)</td></tr><tr><td>EPU</td><td>1.007<br>(0.009)</td><td>1.004<br>(0.009)</td><td>1.004<br>(0.009)</td><td>1.004<br>(0.009)</td><td>1.001<br>(0.009)</td><td>1.004<br>(0.009)</td></tr><tr><td>UAI</td><td></td><td>0.993<br>(0.005)</td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td></td><td>0.997<br>(0.004)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>1.001<br>(0.005)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td>1.014*<br>(0.008)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>1.003<br>(0.005)</td></tr><tr><td>ICO controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>444</td><td>444</td><td>444</td><td>444</td><td>446</td></tr><tr><td>Countries</td><td>46</td><td>44</td><td>44</td><td>45</td><td>44</td><td>44</td></tr><tr><td>χ<sup>2</sup></td><td>37.880</td><td>91.891</td><td>116.104</td><td>85.069</td><td>86.310</td><td>57.727</td></tr></tbody></table>

This table presents the odds ratios of the hierarchical logit model. The dependent variable is the dummy variable hardcap in all the specifications, which include ICO level variables as in Column 5 in Table 2 and constant but are not reported for brevity. All regressors are defined in Table A1. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.
<figure><img src="image_20.png" /><figcaption>**Table 7.** The post-ICO performance – the ICO-level determinants.</figcaption></figure>

<table><thead><tr><th rowspan="2"></th><th colspan="4">Online</th><th colspan="4">Coinmarket</th><th colspan="4">Twitter</th></tr><tr><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th><th>(7)</th><th>(8)</th><th>(9)</th><th>(10)</th><th>(11)</th><th>(12)</th></tr></thead><tbody><tr><td>Advisors</td><td></td><td>1.140***<br>(0.049)</td><td>1.134***<br>(0.051)</td><td></td><td>1.112***<br>(0.033)</td><td>1.108***<br>(0.033)</td><td></td><td></td><td>1.129***<br>(0.041)</td><td></td><td>1.131***<br>(0.041)</td><td></td></tr><tr><td>Team</td><td></td><td>1.066***<br>(0.023)</td><td>1.065***<br>(0.023)</td><td></td><td>1.063***<br>(0.018)</td><td>1.065***<br>(0.018)</td><td></td><td></td><td>1.082***<br>(0.020)</td><td></td><td>1.088***<br>(0.021)</td><td></td></tr><tr><td>Insider</td><td>0.894<br>(0.389)</td><td></td><td>0.658<br>(0.294)</td><td>0.763<br>(0.283)</td><td></td><td>0.559<br>(0.211)</td><td>1.508<br>(0.560)</td><td></td><td></td><td></td><td>1.133<br>(0.437)</td><td></td></tr><tr><td>U-Token</td><td>0.904<br>(0.250)</td><td>0.909<br>(0.271)</td><td>0.922<br>(0.265)</td><td>0.900<br>(0.261)</td><td>0.982<br>(0.239)</td><td>1.030<br>(0.252)</td><td>1.012<br>(0.246)</td><td>1.017<br>(0.253)</td><td>0.782<br>(0.191)</td><td>0.847<br>(0.214)</td><td>0.822<br>(0.202)</td><td>0.799<br>(0.204)</td></tr><tr><td>S-Token</td><td>0.841<br>(0.351)</td><td>0.857<br>(0.384)</td><td>0.782<br>(0.338)</td><td>0.933<br>(0.421)</td><td>0.454*<br>(0.194)</td><td>0.466*<br>(0.200)</td><td>0.449*<br>(0.192)</td><td>0.499<br>(0.218)</td><td>0.530<br>(0.211)</td><td>0.583<br>(0.239)</td><td>0.498*<br>(0.199)</td><td>0.619<br>(0.264)</td></tr><tr><td>Presale</td><td></td><td>0.451***<br>(0.116)</td><td>0.611**<br>(0.150)</td><td></td><td>0.589**<br>(0.137)</td><td>0.753<br>(0.166)</td><td></td><td></td><td>0.743<br>(0.175)</td><td></td><td>1.032<br>(0.228)</td><td></td></tr><tr><td>Bonus</td><td>0.486***<br>(0.113)</td><td></td><td>0.425***<br>(0.099)</td><td>0.516***<br>(0.105)</td><td></td><td>0.402***<br>(0.084)</td><td>0.504***<br>(0.102)</td><td></td><td></td><td></td><td>0.417***<br>(0.090)</td><td></td></tr><tr><td>GitHub</td><td>1.300<br>(0.289)</td><td></td><td>1.350<br>(0.300)</td><td>1.715***<br>(0.340)</td><td></td><td>1.667**<br>(0.332)</td><td>1.221<br>(0.239)</td><td></td><td></td><td></td><td>1.202<br>(0.237)</td><td></td></tr><tr><td>RoadMap</td><td></td><td>0.818<br>(0.204)</td><td>1.062<br>(0.248)</td><td>0.846<br>(0.208)</td><td></td><td>1.025<br>(0.219)</td><td>1.284<br>(0.264)</td><td>1.033<br>(0.221)</td><td></td><td>0.933<br>(0.199)</td><td>1.217<br>(0.246)</td><td>0.972<br>(0.206)</td></tr><tr><td>ETH</td><td>0.900<br>(0.083)</td><td>0.976<br>(0.090)</td><td>0.917<br>(0.084)</td><td>0.946<br>(0.089)</td><td></td><td>1.060<br>(0.084)</td><td>1.158*<br>(0.090)</td><td>1.109<br>(0.087)</td><td>1.134<br>(0.091)</td><td>1.019<br>(0.078)</td><td>1.134<br>(0.088)</td><td>1.065<br>(0.080)</td></tr><tr><td>Investor</td><td>1.187<br>(0.333)</td><td>0.978<br>(0.285)</td><td>1.128<br>(0.320)</td><td></td><td>0.683<br>(0.169)</td><td>0.526**<br>(0.138)</td><td>0.599**<br>(0.149)</td><td></td><td>1.386<br>(0.331)</td><td>1.055<br>(0.262)</td><td>1.239<br>(0.301)</td><td></td></tr><tr><td>Crypto</td><td></td><td></td><td>0.941<br>(0.036)</td><td></td><td></td><td></td><td>0.953<br>(0.047)</td><td></td><td></td><td></td><td>0.961<br>(0.045)</td><td></td></tr><tr><td>Fiat</td><td>1.273<br>(0.537)</td><td></td><td>1.349<br>(0.579)</td><td>0.976<br>(0.414)</td><td></td><td>0.701<br>(0.240)</td><td>0.524*<br>(0.190)</td><td>1.401<br>(0.498)</td><td></td><td></td><td>1.439<br>(0.534)</td><td>1.065<br>(0.378)</td></tr><tr><td>Industry FE</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td><td>No</td><td>No</td><td>Yes</td><td>No</td></tr><tr><td>Observations</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td><td>503</td></tr><tr><td>Pseudo R<sup>2</sup></td><td>0.089</td><td>0.122</td><td>0.083</td><td>0.131</td><td>0.106</td><td>0.124</td><td>0.098</td><td>0.141</td><td>0.075</td><td>0.117</td><td>0.063</td><td>0.140</td></tr><tr><td>χ<sup>2</sup></td><td>43.43</td><td>57.42</td><td>45.13</td><td>57.85</td><td>67.58</td><td>72.13</td><td>62.61</td><td>76.30</td><td>44.62</td><td>63.80</td><td>41.26</td><td>67.08</td></tr></tbody></table>

This table presents the odds ratios of the logit model of ICO post-performance on the set individual ICO-level variables. The dependent variable is the binary variable Online in columns (1)-(4), in columns (5)-(8) it is the binary variable Coinmarket, and in columns (9)-(12) it is the binary variable Twitter. All regressors are defined in Table A1. All specifications include constants but not reported for brevity. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.
level. Hence, the results confirm that ICO and financial development complement each other. In line with this, we found that the coefficient for legal friendly is positive and statistically significant in most of the specifications. Moreover, the coefficient for common law is positive, yet once again significant only in selected specification. Overall, the results confirm that a country's financial development and institutional framework are important.

In Tables 5 and 6, where we employ as dependent variable soft cap and hard cap, respectively, all the coefficients for culture proximity are positive. We find, however, that only the coefficients for UAI and MAS are significantly related to soft cap and hard cap, respectively. In both specifications, the coefficient for national culture is at 5% level.

UAI reflects the extent to which societies are comfortable with ambiguity and uncertainty. The literature associates it in choosing financial solutions that minimize risk. For example, Chui and Kwok (2008) found that nations with a high uncertainty avoidance consume more life insurance, while Anderson et al. (2011) documented that institutional investors in countries with high uncertainty avoidance diversify less abroad. Our results suggest that in countries with a high uncertainty avoidance score, the ICO is more likely to fail or reach only the soft cap level, which can be attributed to risk avoidance.

In contrast, societies with high MAS are indicative of a society driven by the dominant values of competitiveness, achievement, and a drive to win. Anderson et al. (2011) found that individuals from masculine societies have a tendency to be overconfident and hold greater self-attribution biases, which have been shown to affect trading behavior (Barber and Odean 2000). Hence, Tan, Cheong, and Zurbruegg (2019) associate individuals from a cultural background with a higher masculinity score as people more likely to exhibit risk-taking behavior, which would explain its positive association with hard cap.

Overall, our results indicate that the national culture, in particular UAI and MAS, determine the ICO and its results. Indeed, our results supplement the findings of Chourou et al., who documented that IPOs in countries with high uncertainty avoidance scores tend to experience a lower level of underpricing, whereas IPOs in countries with higher masculinity tend to experience a higher level of underpricing, while Tan, Cheong, and Zurbruegg (2019) have shown that uncertainty avoidance and masculinity have an impact on an individual's trading behavior and risk taking. Finally, our results on culture show that ICO market interlinkages correspond well with the previously cited research by Huang, Vismara, and Wei (2021) on emotional factors supporting ICOs, such as investors' reactions to the appearance of the management staff of ICO beneficiary company. Moreover, our results complement to those presented by Fisch et al. (2021) that underlined the hierarchy of investors' motives on the ICO market: technological, financial, ideological ones.

### 4.3. Post-ICO performance

In Table 7 we present the results of the models focused on the post-ICO performance measures. In columns (1)–(4) the dependent variable is the binary variable showing website availability as a proxy of post-ICO activity. In columns (5)–(8) the dependent variable is the binary variable *Coinmarket*, while in columns (9)–(12) it is the binary variable *Twitter*.

The results show that a high number of staff members and advisors is positively related to the probability of successful ICO post-performance. The odd ratios for the two variables are significant in all the specifications at a 1% level. In line with previous results, we find that ICOs with presales and bonus schemes are negatively related to a successful ICO post-performance. The odd ratios are significant at least at a 5% level in most of the specifications. We also find some weak evidence that the disclosure of the codes in GitHub as well ETH market performance relative to US dollars increases the odds of the token being traded. It seems logical, yet the contribution of this variable to the probability of post success is smaller. The results document that what determines the post-performance of the ICO is the project and not the situation of the crypto-market.

Lastly, we applied a hierarchical logit model using the two levels in the sample data—ICO-characteristics and country-level variables. Tables 8–10 presents the results for where we employ as
<figure><img src="image_21.png" /><figcaption>**Table 8.** The post-ICO success – both ICO-level and country-level determinants.</figcaption></figure>

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>1.342<br>(0.630)</td><td>1.904<br>(1.126)</td><td>2.420<br>(1.585)</td><td>1.932<br>(1.163)</td><td>4.254*<br>(3.171)</td><td>2.327<br>(1.289)</td></tr><tr><td>Common law</td><td>1.198<br>(0.368)</td><td>0.765<br>(0.266)</td><td>1.070<br>(0.371)</td><td>1.050<br>(0.363)</td><td>1.117<br>(0.331)</td><td>1.085<br>(0.401)</td></tr><tr><td>Legal friendly</td><td>0.885<br>(0.218)</td><td>0.808<br>(0.195)</td><td>0.959<br>(0.223)</td><td>0.945<br>(0.248)</td><td>0.943<br>(0.198)</td><td>0.955<br>(0.205)</td></tr><tr><td>EPU</td><td>0.998<br>(0.011)</td><td>0.999<br>(0.011)</td><td>1.001<br>(0.011)</td><td>1.000<br>(0.011)</td><td>1.003<br>(0.011)</td><td>1.002<br>(0.011)</td></tr><tr><td>UAI</td><td></td><td>0.988**<br>(0.005)</td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td></td><td>0.999<br>(0.005)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>0.996<br>(0.005)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td>0.988<br>(0.010)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>0.999<br>(0.007)</td></tr><tr><td>ICO Controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>444</td><td>444</td><td>444</td><td>444</td><td>446</td></tr><tr><td>Countries</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>χ<sup>2</sup></td><td>142.431</td><td>165.911</td><td>193.542</td><td>162.537</td><td>177.177</td><td>306.231</td></tr></tbody></table>

This table presents the odds ratios of the logit model of post-ICO success on the set individual ICO-level variables and country-level characteristics including Hofstede's culture dimensions. The dependent variable is the dummy variable Online. All specifications include ICO control variables as in Column 3 of Table 5 and constants, but are not reported for brevity. All regressors are defined in Table A1. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.

<table><caption>**Table 9.** The post-ICO success – both ICO-level and country-level determinants.</caption><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>1.514<br>(0.816)</td><td>2.430<br>(1.404)</td><td>2.524<br>(1.472)</td><td>1.702<br>(1.185)</td><td>3.030<br>(2.454)</td><td>2.310<br>(1.202)</td></tr><tr><td>Common law</td><td>1.198<br>(0.333)</td><td>0.880<br>(0.292)</td><td>1.028<br>(0.273)</td><td>0.987<br>(0.271)</td><td>1.041<br>(0.257)</td><td>1.079<br>(0.411)</td></tr><tr><td>Legal friendly</td><td>0.726<br>(0.249)</td><td>0.789<br>(0.193)</td><td>0.841<br>(0.209)</td><td>0.734<br>(0.250)</td><td>0.836<br>(0.203)</td><td>0.809<br>(0.197)</td></tr><tr><td>EPU</td><td>0.996<br>(0.014)</td><td>1.000<br>(0.012)</td><td>1.000<br>(0.012)</td><td>0.997<br>(0.013)</td><td>1.001<br>(0.013)</td><td>0.999<br>(0.012)</td></tr><tr><td>UAI</td><td></td><td>0.994<br>(0.007)</td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td></td><td>1.001<br>(0.006)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>0.988<br>(0.008)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td>0.997<br>(0.010)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>1.000<br>(0.007)</td></tr><tr><td>ICO controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>444</td><td>444</td><td>444</td><td>444</td><td>446</td></tr><tr><td>Countries</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>χ<sup>2</sup></td><td>186.43</td><td>193.97</td><td>237.04</td><td>281.26</td><td>211.81</td><td>266.63</td></tr></tbody></table>

This table presents the odds ratios of the logit model of post-ICO success on the set individual ICO-level variables and some country-level characteristics including Hofstede's culture dimensions. The dependent variable is the dummy variable Coinmarket. All specifications include ICO variables as in Column 6 of Table 5 and constant but not reported for brevity. All regressors are defined in Table A1. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.
<figure><img src="image_22.png" /><figcaption>**Table 10.** The post-ICO success – both ICO-level and country-level determinants.</figcaption></figure>

<table><thead><tr><th></th><th>(1)</th><th>(2)</th><th>(3)</th><th>(4)</th><th>(5)</th><th>(6)</th></tr></thead><tbody><tr><td>Fin. Dev.</td><td>3.276**<br>(1.717)</td><td>3.139**<br>(1.602)</td><td>3.392*<br>(2.141)</td><td>2.568*<br>(1.353)</td><td>6.076***<br>(3.990)</td><td>4.565***<br>(2.433)</td></tr><tr><td>Common law</td><td>1.009<br>(0.252)</td><td>0.706<br>(0.247)</td><td>0.916<br>(0.244)</td><td>0.894<br>(0.237)</td><td>0.953<br>(0.239)</td><td>0.952<br>(0.321)</td></tr><tr><td>Legal friendly</td><td>0.846<br>(0.150)</td><td>0.778<br>(0.169)</td><td>0.873<br>(0.156)</td><td>0.853<br>(0.186)</td><td>0.859<br>(0.135)</td><td>0.888<br>(0.148)</td></tr><tr><td>EPU</td><td>0.995<br>(0.010)</td><td>0.993<br>(0.010)</td><td>0.994<br>(0.011)</td><td>0.993<br>(0.010)</td><td>0.996<br>(0.010)</td><td>0.997<br>(0.010)</td></tr><tr><td>UAI</td><td></td><td>0.990<br>(0.007)</td><td></td><td></td><td></td><td></td></tr><tr><td>IND</td><td></td><td></td><td>1.001<br>(0.006)</td><td></td><td></td><td></td></tr><tr><td>PDI</td><td></td><td></td><td></td><td>0.992<br>(0.006)</td><td></td><td></td></tr><tr><td>MAS</td><td></td><td></td><td></td><td></td><td>0.990<br>(0.008)</td><td></td></tr><tr><td>LTI</td><td></td><td></td><td></td><td></td><td></td><td>1.002<br>(0.007)</td></tr><tr><td>ICO controls</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td><td>Yes</td></tr><tr><td>Observations</td><td>466</td><td>444</td><td>444</td><td>444</td><td>444</td><td>446</td></tr><tr><td>Countries</td><td></td><td></td><td></td><td></td><td></td><td></td></tr><tr><td>χ²</td><td>141.71</td><td>119.55</td><td>170.71</td><td>186.57</td><td>142.75</td><td>173.34</td></tr></tbody></table>

This table presents the odds ratios of the logit model of post-ICO success on the set individual ICO-level variables and some country-level characteristics including Hofstede's culture dimensions. The dependent variable is dummy variable Twitter. All specifications include ICO variables as in Column 12 of Table 5 and constant but not reported for brevity. All regressors are defined in Table A1. Robust standard errors are presented in parentheses, and ***, **, and * denote statistical significance at 1%, 5% and 10%, respectively.

dependent variable the binary variable Online, Coinmarket, and Twitter, respectively. In all the specifications we found that the financial development is positively related to ICO post-performance, yet the relationship is only statistically significant when we employ Twitter as dependent variable. While the odd ratios for the variables common law and legal friendly are positive, yet statistically insignificant. Hence, the results show that the ICO post-performance is, in contrast to ICO success, not strongly related to the development of the financial system and its institutions.

In line with this, we found only weak evidence that the culture characteristics determines post-performance of ICOs. In Table 8 the odd ratio for uncertainty avoidance is below 1 and statistically significant at 5% level. The results indicate that successful ICO post-performance is less likely in countries in risk aware societies. The results complement those of Fisch and Momtaz (2020), who argue that investors' superior screening (selection effect) and coaching abilities (treatment effect) enable them to report higher post-ICO performance.

## Summary

In recent years, ICOs have been a popular way of raising funds by entrepreneurs, who used the new opportunities given by the development of blockchain technology. We find, however, that the information asymmetry between the project team and the potential investors is high, which we attribute to the still-developing technology and the lack of regulations.

Our analysis shows that high-quality projects should try to mitigate this problem by signaling quality and disclosing more information to investors. We demonstrate that projects with larger teams and several advisors are more likely to be successful during ICOs and perform better in the long run. In line with this, we also find that large teams with insider retention are increasing their chances of success. In contrast, we find that implementing excessive use of promotional tools (presale programs or bonuses for early investors) during the fundraising process may be counterproductive in the case of the ICO market, as such activities probably produce a negative sign of
aggressive and, as a consequence, unreliable marketing. On the whole, we show that rather costly and resource-related signals are more effective for ICO promotion, which is in line with classical approach to signaling theory. Moreover, one can interpret the results of the study in a way that those factors contribute positively in the subsequent phases to post-ICO activities.

In terms of post-ICO performance, we confirm the fact that the hard cap was reached, number of staff members, as well as the number of advisors, are positively associated with all the post-ICO activity measures. Those results were expected as the abovementioned factors, at a very basic level, show the resources of particular company's issuers.

Moreover, we find that country's environment may determine the ICOs activity. We find that ICOs are more likely to take place and be successful in economically developed countries with good financial and information and communication technology structure. Interestingly, we find, however, that global economic policy uncertainty does not determine the activity of ICOs.

We document the role of legal friendliness, that is, the presence of hard and soft law referring to the legal nature of ICOs and tokenized assets. Furthermore, it seems that common law may support the development of ICO markets. The results are in line with the literature stating that legal origin and institutions strongly determine the development of the financial system and its structure.

Additionally, we investigate the role of the culture of a particular country for the ICO market. We use culture dimensions developed by Geert Hofstede (1980) similarly to many previous empirical studies focused on IPOs, for example, Gupta, Veliyath, and Rejie (2018). We contribute by linking culture-related factors with an innovative capital market segment, that is, ICO. We find that ICOs are more likely to develop in less indulgent societies that focus on their responsibilities. In our opinion, it confirms that ICO investors are monitoring the market and more likely to invest in projects with high-quality signals. We also find that the ICOs market is more active in long-term oriented societies. Moreover, masculinity fosters the campaign's success, while uncertainty avoidance may hamper fundraising. Thus, we see relationships between ICOs presence and successes, and national culture, while post-ICO performance is determined only by internal company capabilities.

## Notes

1. A crypto-asset (broadest category) may also be defined as a digital representation of the value or rights potentially transferred and stored electronically. This is done using a distributed ledger or similar technology (Proposal for a Regulation of the European Parliament and of the Council on Markets in Crypto-assets, and amending Directive (EU) 2019/1937; COM(2020) 593 final; 2020/0265 (COD)). Crypto-assets are sometimes equated with the category of cryptocurrencies. The most important are bitcoins (BTC) and ether (ETH). While terminological simplification may be convenient, the distinction between cryptocurrencies and the broader category of crypto-assets is justified due to the different functions of these tools. While cryptocurrencies are intended primarily for settlements, tokens issued during ICOs are essentially closer to shares.

2. https://www.coinlore.com/

3. https://icobench.com/

4. The results are available upon request.

5. We present in the study only the results for the number of ICOs for brevity; however, the results for soft cap and hard cap are available upon request.

6. Refer also to *Negative Binomial Regression* – description in Chapter 326 of NCSS statistical software: https://ncss-wpengine.netdna-ssl.com/wp-content/themes/ncss/pdf/Procedures/NCSS/Negative_Binomial Regression.pdf

7. The full results including industry dummies are available upon request.

8. The data has been extracted from with the help of a Google Chrome extension that displays the creation date of the repository https://github.com/lvarayutgithub-date-of-creation

## Acknowledgement

We thank Alina Gomez -Mejia, the editor and the three anonymous referees for helpful comments. We benefited from the thesis of the IESEG student Clément Jaffrain and the comments of the participants at the 2020 World Finance & Banking Symposium and the seminar of Institute of Economics, Polish Academy of Sciences.
<figure><img src="image_23.png" /></figure>

# Disclosure statement

No potential conflict of interest was reported by the author(s).

# Funding

This work was supported by the National Science Center, Poland, under grant OPUS 17 no. 2019/33/B/HS4/00369.

# ORCID

Muhammad Farooq Ahmad http://orcid.org/0000-0002-2891-7668

Oskar Kowalewski http://orcid.org/0000-0001-5520-3559

Pawel Pisany http://orcid.org/0000-0001-9665-8840

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# Appendix

<table><caption>Table A1. Variable definitions and data sources</caption><thead><tr><td>Variable</td><td>Description</td><td>Source</td></tr></thead><tbody><tr><td>Team</td><td>The number of team members that have been disclosed before the ICO.</td><td>ICObench</td></tr><tr><td>Advisors</td><td>The number of advisors shows the number of advisors that have been disclosed prior the ICO.</td><td></td></tr><tr><td>Token</td><td>The token proposed by an ICO can be of three types: Security Token, Utility Token, and Currency Token. It is measured by two different dummy variables (S) and (U) that takes the value of 1 if the token is of the variable type and 0 otherwise.</td><td></td></tr></tbody></table>

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<figure><img src="image_26.png" /></figure>

Continued.

<table><thead><tr><td>Variable</td><td>Description</td><td>Source</td></tr></thead><tbody><tr><td>Github</td><td>This variable is a dummy variable that takes the value of 1 if there was a GitHub repository prior to the launch of the ICO and 0 otherwise.</td><td>GitHub<sup>8</sup></td></tr><tr><td>Insider</td><td>Insider token retention is the amount of tokens kept by the team or insiders, expressed in percentage of the total supply of the ICO token.</td><td>ICObench</td></tr><tr><td>Presale</td><td>This variable is a dummy variable that is equal to 1, if a presale was organized and equal to 0 otherwise.</td><td></td></tr><tr><td>Bonus</td><td>This variable is a dummy variable that takes the value of 1 if a bonus was offered to early investors and 0 otherwise.</td><td></td></tr><tr><td>Roadmap</td><td>This variable is a dummy variable that equals 1 if the roadmap details future milestones for at least a period of two years and 0 otherwise or if there is no roadmap shared by the ICO team.</td><td></td></tr><tr><td>ETH</td><td>The performance of the ETH over two months before the starting date of the ICO.</td><td>Coinmarketcap</td></tr><tr><td>Invest</td><td>This is a dummy variable that equals 1 if there is a minimum investment required to be able to invest in the ICO and 0 otherwise.</td><td>ICObench<br>ICO's website</td></tr><tr><td>Crypto</td><td>This variable is the number of cryptocurrencies that are accepted as a mean of payment to participate in the ICO.</td><td></td></tr><tr><td>Fiat</td><td>This variable is a dummy variable that equals 1 if the ICO accepts fiat currencies (Dollar, Euro, Yuan, and other government-issued currencies) as a mean of payment to participate in the ICO and 0 otherwise.</td><td></td></tr><tr><td>Fin. Dev</td><td>Average value of the composite index presenting countries financial development that range from 0 to 1.</td><td>Svirydzenka 2016</td></tr><tr><td>Legal certainty</td><td>A binary variable that takes the value 1 if proper regulations by applicable laws and / or guidelines explaining the legal status and obligations according to the token type are present in the country, and 0 otherwise.</td><td>Pricewaterhouse Coopers<br>(2018)EY (2017).</td></tr><tr><td>Common law</td><td>A dummy variable that takes the value 1 if the countries legal origin is common law, and 0 otherwise</td><td>LLSV (1997, 1998)</td></tr><tr><td>EPU</td><td>A GDP-weighted average of national EPU indices for 20 countries.</td><td>Baker, Bloom, and Davis (2015)</td></tr><tr><td>UAI</td><td>This dimension deals with the need for well-defined rules for prescribed behavior.</td><td>Hofstede 2011</td></tr><tr><td>IND</td><td>In individualistic societies, individuals look after themselves and their immediate family only whereas in collectivistic cultures, individuals belong to groups that look after them in exchange for loyalty.</td><td></td></tr><tr><td>PDI</td><td>This dimension reflects the consequences of power inequality and authority relations in society. It influences hierarchy and dependence relationships in the family and organizational contexts.</td><td></td></tr><tr><td>MAS</td><td>Dominant values in masculine countries are achievement and success and in feminine countries are caring for others and quality of life.</td><td></td></tr><tr><td>LTI</td><td>A high long-term orientation value shows societies that take a more pragmatic approach where they encourage thrift and efforts in modern education as a way to prepare for the future.</td><td></td></tr></tbody></table>