This study explores the determinants of initial coin offering (ICO) success, where success is defined as the amount of capital a project could raise. ICOs are a tool for startups in the blockchain ecosystem to raise early capital with relative ease. The market for ICOs has grown at a rapid pace since its start in 2013. We analyze a unique dataset of 278 projects that finished their ICOs by August 2017 to assess determinants of funding success that we derive from the crowdfunding and venture capital literature. Our results show that ICOs exhibit similarities to classical crowdfunding and venture capital markets. Specifically, we identify resemblances in determinants of funding success regarding human capital characteristics, business model quality, project elaboration, and social media activity.

This study reviews the current state of empirical literature on stablecoins. Based on a sample of 22 peer-reviewed articles, we analyze statistical approaches, data sources, variables, and metrics, as well as stablecoin types investigated and future research avenues. The analysis reveals three major clusters: (1) studies on the stability or volatility of different stablecoins, their designs, and safe-haven-properties, (2) the interrelations of stablecoins with other crypto assets and markets, specifically Bitcoin, and (3) the relationship of stablecoins with (non-crypto) macroeconomic factors. Based on our analysis, we note future research should explore diverse methodological approaches, data sources, different stablecoins, or more granular datasets and identify five topics we consider most significant and promising: (1) the use of stablecoins in emerging markets, (2) the effect of stablecoins on the stability of currencies, (3) analyses of stablecoin users, (4) adoption and use cases of stablecoins outside of crypto markets, and (5) algorithmic stablecoins.

With ownership estimates of up to 25%, Turkey is at the forefront of cryptocurrency adoption, rendering it an interesting example to study the proclaimed use cases of cryptocurrencies. Using exploratory factor analysis based on a sample of 715 Turkish cryptocurrency owners, we identified 3 different owner groups and their underlying motives. The first group (payment users) looks at cryptocurrency as an option for payments, thereby disregarding its speculative element, while the second group (crypto investors) can best be described as experienced investors holding cryptocurrency as part of their investment strategy. The third group (crypto traders) consists of risk-tolerant traders. Further analyses show that groups not only differentiate by demographics, income and education, but also by factors such as ideology, purchase intention and the use of domestic or foreign exchanges. The results contribute to the understanding of Turkish cryptocurrency owners, their intrinsic and extrinsic motivations and can be incorporated into the pending regulatory processes in the country. The findings suggest that cryptocurrencies have outgrown the use case of mere speculation in Turkey. Those in the group of Turkish payment users are identified as potential lead users whose current needs may represent common needs for crypto users in similar markets in the future. These findings motivate further research on the diffusion and usage patterns of cryptocurrency in emerging markets and innovation in general in the context of lead markets.

Understanding how the gambling industry generates revenue is of paramount importance. Questions about whether higher volumes of expenditure are concentrated among a small proportion of gamblers, and how this varies by problematic gambling status, underpin policy debate about consumer protection. Analyzing data from two timepoints (T0; T2) from a British longitudinal study of regular sports bettors, we explored both for total (gross) spend and gross spend on individual activities: (a) the concentration of self-reported spend on gambling among individuals; and (b) the extent to which spending was disproportionately generated by those with elevated Problem Gambling Severity Index (PGSI) scores. Results showed that gross gambling expenditure was unequal (GINI-coefficient >0.70 for most activities). At both timepoints, those with a PGSI score of 3+ had an elevated share of spending: at T2, 14.1% of PGSI 3+ gamblers accounted for 43.5% of gross gambling spend. There were differences by activity: lotteries displayed less reliance on those with a PGSI score of 3+ whereas this group contributed over 80% of gross spend on online casinos. Policy attention should focus on reframing the underlying economic model on which some gambling activities are predicated, creating more equal patterns of consumption and less reliance on those harmed.