Toward Understanding the Use of Centralized Exchanges for Decentralized Cryptocurrency
Abstract
Cryptocurrency has been extensively studied as a decentralized financial technology built on blockchain. However, there is a lack of understanding of user experience with cryptocurrency exchanges, the main means for novice users to interact with cryptocurrency. We conduct a qualitative study to provide a panoramic view of user experience and security perception of exchanges. All 15 Chinese participants mainly use centralized exchanges (CEX) instead of decentralized exchanges (DEX) to trade decentralized cryptocurrency, which is paradoxical. A closer examination reveals that CEXes provide better usability and charge lower transaction fee than DEXes. Country-specific security perceptions are observed. Though DEXes provide better anonymity and privacy protection, and are free of governmental regulation, these are not necessary features for many participants. Based on the findings, we propose design implications to make cryptocurrency trading more decentralized.
Keywords: Blockchain, Cryptocurrency, User Experience, Security
DOI: 10.54941/ahfe1001455
Cite this paper
More from this volume
- Won’t you see my neighbor? User predictions, mental models, and similarity-based explanations of AI classifiers
- Using Artificial Intelligence to Improve Human Performance: A Predictive Management Strategy
- Robust AI for Accident Diagnosis of Nuclear Power Plants Using Meta-Learning
- Detection of inappropriate images on smartphones based on computer vision techniques
- Econometric Modeling for the Management and Decomposition of Financial Risk
- Artificial vision system to detect the mood of an Alzheimer's patient
- Analysis of citizen's sentiment towards Philippine administration's intervention against COVID-19
- The Effect of Varying Levels of Automation during Initial Triage of Intrusion Detection
- Generating a Multimodal Dataset Using a Feature Extraction Toolkit for Wearable and Machine Learning: A pilot study
- Hepatitis predictive analysis model through deep learning using neural networks based on patient history
- An analysis model for Machine Learning using Support Vector Machine for the prediction of Diabetic Retinopathy
- Supradyadic Trust in Artificial Intelligence


AHFE Open Access