User-Centric Design of Web3 Applications for Mass Adoption

Authors

  • Lukas Hansen Associate Professor, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden Author
  • Pierre Popescu Associate Professor, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland Author
  • Daniel Novak Senior Lecturer, Institute of Intelligent Systems, Nordic Technical University, Stockholm, Sweden Author

Keywords:

Web3 user experience, account abstraction, embedded wallets, gasless transactions, intent-based UX, progressive onboarding, blockchain usability, mass adoption

Abstract

Web3 has a user experience problem, and it is the primary reason that blockchain adoption remains confined to a technically sophisticated minority. A first-time user trying to use a DeFi protocol must install a browser extension, generate a seed phrase they are terrified of losing, fund a wallet with a gas token they do not understand, approve a token spend, confirm a transaction, wait 12 seconds, and then check a block explorer to verify it worked. Every step is a potential dropout point. We present the Web3 User Experience Assessment Framework (W3UXAF), a mixed-methods study combining quantitative usability testing with 120 participants (60 crypto-experienced, 60 crypto-naive) and qualitative analysis of five UX design patterns -- account abstraction, embedded wallets, gasless transactions, progressive onboarding, and intent-based interactions. Our UX Quality Score (UXQS) measures task completion rate, time-to-first-action, error rate, cognitive load (NASA-TLX), and user satisfaction (SUS). Key findings: account abstraction with embedded wallets increases crypto-naive task completion from 34.2% to 87.6% and reduces time-to-first-action from 8.4 minutes to 42 seconds. Intent-based interactions -- where users express what they want rather than specifying how to achieve it -- achieve the highest UXQS (0.912) by eliminating the need for users to understand blockchain mechanics at all.

Author Biographies

  • Lukas Hansen, Associate Professor, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden

    Associate Professor, Department of Machine Learning, Nordic Technical University, Stockholm, Sweden

  • Pierre Popescu, Associate Professor, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland

    Associate Professor, Department of Machine Learning, Swiss Institute of Machine Intelligence, Zurich, Switzerland

  • Daniel Novak, Senior Lecturer, Institute of Intelligent Systems, Nordic Technical University, Stockholm, Sweden

    Senior Lecturer, Institute of Intelligent Systems, Nordic Technical University, Stockholm, Sweden

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Published

2025-09-28

How to Cite

User-Centric Design of Web3 Applications for Mass Adoption. (2025). Blockchain, Web3 & Digital Trust Journal P-ISSN 3117-597X and E-ISSN 3117-5988, 2(3), 19-26. https://galaxiauniverse.com/index.php/BWDTJ/article/view/455