Choice Architecture, Privacy Valuations, and Selection Bias in Consumer Data

How does choice architecture used during data collection influence the quality of collected data in terms of volume (how many people share) and representativeness (who shares data)? To answer this question, we run a large-scale choice experiment to elicit consumers’ valuation for their Facebook data while randomizing two common choice frames: default and price anchor. An opt-out default decreases valuations by 22% compared with opt-in, whereas a $0–$50 price anchor decreases valuations by 37% compared with a $50–$100 anchor. Moreover, some consumer segments are influenced by frames more while having lower average privacy valuations. As a result, conventional frame optimization practices that aim to maximize data volume can exacerbate bias and lower data quality. We demonstrate the magnitude of this volume-bias trade-off in our data and provide a framework to inform optimal choice architecture design.

History: Catherine Tucker served as the senior editor.

Funding: This research is funded by the Becker Friedman Institute at the University of Chicago and the Willard Graham Research Fund at Chicago Booth. It was approved by the Institutional Review Boards at the University of Chicago (IRB21-1376) and Boston University (IRB-6239X).

Supplemental Material: The online appendix and data files are available at https://doi.org/10.1287/mksc.2023.0373.