An Algorithmic Blockchain Readiness Index ^†

This paper presents the design of a Blockchain Readiness Index (BRI) to be used as a tool for assisting nations to monitor the level of Blockchain maturity according to their suitability on hosting blockchain-based activities, and successfully adopting a blockchain regulatory framework. BRI is a composite index that combines a variety of indicators from a range of sources to a single score. The proposed methodology attempts to fill a knowledge gap by evaluating the relatively unexplored area of blockchain adoption per nation. The index presented in this paper aims to distinguish between the most promising and non-hostile countries, acting as the basis for professional work, decision making and operations of organizations, investors, academics, and other stakeholders within the blockchain space. As the index is updated regularly, all new developments on the fast-changing landscape of blockchain and cryptocurrencies are reflected. In addition, this research aims to go beyond the regulatory environment towards examining several other factors such as local engagement, expertise, investments, and the need for a decentralized provision of services. This paper presents an overview of the landscape of similar attempts on designing such indexes by reviewing and identifying potential gaps and opportunities for improving their methodological design that can lead to more accurate and relevant conclusions. In addition, with this paper we contribute a systematic methodology for building a BRI using techniques from the information retrieval domain to normalize the non-normalized values, and a cosine similarity measure to derive an index ranking consisting of various nations. More specifically the proposed BRI covers a wide range of blockchain readiness indicators which can be organized into the following “pillars”: (1) Government Regulation, (2) Research, (3) Technology, (4) Industry, and (5) User Engagement. An empirical evaluation reports preliminary but promising results of the algorithmic design methodology showing evidence that, the identified indicators are sufficient for developing our index when compared to judgements made by human experts.