Framing Data Choices: How Pre-Donation Exploration Design Influence Data Donation Behavior and Decision-Making

A new study from researchers Zeya Chen, Zach Pino, and Ruth Schmidt tackles a critical problem in participatory data collection: the gap between people's stated willingness to donate personal data for public research and their actual follow-through. Published for the DRS2026 conference, the research investigates how the design of the pre-donation interface—the moment users explore what data they are about to share—fundamentally shapes their decision. The team moved beyond technical and regulatory discussions to treat data donation as a behavioral design challenge.

Through a real-world study with 24 participants, the team evaluated three distinct 'data exploration' interface designs. The 'social comparison' design, which showed participants how their data compared to others', was the clear winner, achieving an 87.5% donation rate. The 'self-focused' view, showing only the individual's data, resulted in a 62.5% rate. Surprisingly, a 'collective-only' frame, which presented only aggregated community data without personal context, backfired dramatically, dropping the donation rate to 37.5% and inducing 'perspective confusion' and privacy concerns.

The findings provide concrete, evidence-based guidance for developers and civic tech organizations building data donation platforms. Simply asking for data isn't enough; the framing of the choice is a powerful intervention tool. The study proves that thoughtful Human-Computer Interaction (HCI) design can directly increase participation rates, turning theoretical support for data-driven public innovation into tangible contributions. This shifts the focus from merely building compliant systems to architecting persuasive, donor-centered experiences that align with human psychology.