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When text is not enough: Data-driven personas to explore crypto education affordances through learning platform analysis and survey insights

Lisa Straub et al.

Electronic Markets2026https://doi.org/10.1007/s12525-025-00863-yarticle
AJG 2ABDC A
Weight
0.50

What the paper says

Where established financial literacy initiatives fail to keep pace with rapidly evolving digital asset markets, new customer learning environments emerge. In the context of crypto assets and the blockchain ecosystem, platform providers, ranging from trading platforms to specialized educational portals, have taken on a central role in promoting knowledge-building among users. However, most providers lack educational expertise. In line with affordance theory, which focuses on possibilities for action emerging from the interplay between users and technology, it is not enough for platforms to provide merely learning opportunities. To promote broader crypto adoption, educational offerings should align with users’ needs. Therefore, this study employs a data-driven persona development approach and analyzes 20 crypto education platforms concerning their implemented learning components. In addition, we apply topic modeling to explore the educational content. These analyses aim to assess the current state of crypto education platforms and to identify the opportunities (affordances) embedded in the design. Based on the insights and an accompanying user survey, we examine which educational offerings are perceived as valuable by different user groups and derive four distinct types of crypto learners: Cautious Strategist, Critical Observer, Curious Gamified Explorer, and Hands-On Practical Experimenter. Our findings reveal a misalignment between current platform design and learner preferences. Across all types, users tend to favor dynamic formats over static, article-based content. Based on affordance theory, we highlight design implications for crypto learning and present data-supported personas to foster more differentiated and user-centered educational strategies.

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https://doi.org/https://doi.org/10.1007/s12525-025-00863-y

Or copy a formatted citation

@article{lisa2026,
  title        = {{When text is not enough: Data-driven personas to explore crypto education affordances through learning platform analysis and survey insights}},
  author       = {Lisa Straub et al.},
  journal      = {Electronic Markets},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1007/s12525-025-00863-y},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.