English

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

Human-Computer Interaction 2026-04-06 v2

Abstract

Data donation, an emerging user-centric data collection method for public sector research, faces a gap between participant willingness and actual donation. This suggests a design absence in practice: while promoted as "donor-centered" with technical and regulational advances, a design perspective on how data choices are presented and intervene on individual behaviors remain underexplored. In this paper, we focus on pre-donation data exploration, a key stage for adequately and meaningful informed participation. Through a real-world data donation study (N=24), we evaluated three data exploration interventions (self-focused, social comparison, collective-only). Findings show choice framing impacts donation participation. The "social comparison" design (87.5%) outperformed the "self-focused view" (62.5%) while a "collective-only" frame (37.5%) backfired, causing "perspective confusion" and privacy concerns. This study demonstrates how strategic data framing addresses data donation as a behavioral challenge, revealing design's critical yet underexplored role in data donation for participatory public sector innovation.

Keywords

Cite

@article{arxiv.2603.24995,
  title  = {Framing Data Choices: How Pre-Donation Exploration Designs Influence Data Donation Behavior and Decision-Making},
  author = {Zeya Chen and Zach Pino and Ruth Schmidt},
  journal= {arXiv preprint arXiv:2603.24995},
  year   = {2026}
}

Comments

This work has been accepted for inclusion in DRS Biennial Conference Series, DRS2026: Edinburgh, 8-12 June, Edinburgh, UK

R2 v1 2026-07-01T11:38:25.816Z