Analysis of direct and indirect network effects in ethereum

Abstract

Research Purpose: This research set out to investigate different network effects in the Ethereum ecosystem. The aim was to understand what kind of network effects Ethereum might be subject to and what factors cause them. Research Methodology: The research was conducted through a case study methodology, utilizing both qualitative and quantitative data. The selected sample consisted of Ethereum blockchain and its native cryptocurrency Ether, users transacting on the blockchain, and decentralized projects with their ERC-20 tokens building on the blockchain, as observed from July 2015 until the end of 2021. Quantitative data consisted of different numeric datasets collected as publicly available secondary data from a reputable industry online source. Qualitative data consisted of research, journals and articles from reliable academic and emerging technology covering sources. Data analysis was applied to quantitative data and textual analysis to qualitative data. Research Results: The case study showed that Ethereum is subject to direct network effects from users transacting on its blockchain and indirect network effects from decentralized projects building on it. Ethereum has reached critical mass, but it has not been subject to continuous winner-takes-all effect. Ethereum has positive feedback loops and decentralized applications act as complementary products reinforcing the network effects. The switching costs are low, but stickiness caused by the requirement of owning Ether creates lock-in for users. Practical Implications: The research contributed to the industry by showing how ERC-20 tokens and Dapps affect the formation of direct and indirect network effects. A notable contribution was also made by analyzing the role of Ethereum developers in the creation of positive feedback loops. Research Originality: This research took a holistic approach of applying network effects theory to Ethereum, expanding knowledge of the existing literature. The research also presented new information about network effects formation for example by closing a research gap about positive feedback loops in Ethereum. Limitations and Further Research: The research successfully identifies factors leading to the creation of network effects, but it does not measure the strength of the phenomenon. This leaves room for further research, especially regarding indirect network effects.