Ledger

Investigating Similarities Across Decentralized Finance (DeFi) Services

2024-12-13T15:46:01-05:00

We explore the adoption of graph representation learning (GRL) algorithms to investigate similarities across services offered by Decentralized Finance (DeFi) protocols. Following existing literature, we use Ethereum transaction data to identify the DeFi building blocks. These are sets of protocol-specific smart contracts that, similarly to “financial LEGO bricks”, are utilized in combination within single transactions and encapsulate the logic to conduct specific financial services such as swapping or lending cryptoassets. We propose a method to categorize these blocks into clusters based on their smart contract attributes and the graph structure of their smart contract calls. We employ GRL to create embedding vectors from building blocks and agglomerative models for clustering them. To evaluate whether they are effectively grouped in clusters of similar functionalities, we associate them with eight financial functionality categories and use this information as the target label. We find that in the best-case scenario purity reaches .888. We use additional information to associate the building blocks with protocol-specific target labels, obtaining comparable purity (.864) but higher V-Measure (.571) and discuss plausible explanations for this difference. In summary, this method helps categorize existing financial products offered by DeFi protocols, and can effectively automatize the detection of similar DeFi services, especially within protocols.

A Framework for the Profitable Integration of Distributed Ledger Technologies in Enterprise Networks

2025-03-18T12:26:33-04:00

Distributed ledger technologies (DLT) have been piloted in enterprises to improve transparency and trust relationships among multiple partners but have not managed to mature as planned. Even though most implementation projects demonstrate various opportunities for the involved partners, enterprises experience difficulties in assessing the technology’s impact on profitability in order to make valid investment decisions and improve productivity. This paper employs the dynamic capabilities theory to explore how enterprises can adapt and leverage DLT for improved profitability. It presents an applicable profitability assessment model and a collection of quantitative profitability factors of DLT in enterprise networks. The framework and its associated models are developed through an inductive Grounded Theory-based approach, composed of literature reviews and qualitative empirical studies from 40 participating mixed-industry blockchain, tangle, and hashgraph experts. To retrieve profitability factors in a structured manner, the framework features an integration model covering necessary assessment steps; a taxonomy and heat map characterizing the maturity and assessment situation of the DLT; as well as an assessment model to identify and monetize profitability factors.

Development of the Blockchain Technology Literacy Test (BTLT): A Scoping Review of Current Literature

2024-12-13T15:51:30-05:00

This study addresses the substantial gap in the academic literature on blockchain technology literacy by proposing a Blockchain Technology Literacy Test (BTLT). Through a comprehensive literature review of eight databases, only nine publications with limited focus on blockchain technology literacy were identified. The inconsistency in definitions and predominant emphasis on cryptocurrency literacy further complicate the development of standardized assessments. Based on the results of the literature review and the development of the BTLT, with a focus on terms and technological aspects, an updated list of cryptocurrency literacy questions was proposed as the Cryptocurrency Literacy Test (CLT) to avoid duplications and redundancy in the assessment of relevant blockchain technology and cryptocurrency-related knowledge. The BTLT and CLT aim to distinguish between blockchain technology and cryptocurrency literacy, thereby ensuring comprehensive and accurate assessments. The findings emphasize the need for clear definitions and frameworks within the blockchain ecosystem and call for expanded research to include emerging applications, such as DeFi, DeSci, DAOs, and Web3. This study provides a foundation for future educational efforts and literacy assessments in blockchain technology and cryptocurrencies.