A Recognition Primed Decision Model for Public Consumption of Government Data

Abstract

The U.S. Census Bureau (Census) collects and maintains data on the nation’s population, neighborhoods, and economy, and shares that information with the public digitally. To successfully provide value to the public, data platforms and the data itself must be constructed in a way that individuals can easily use and understand. However, like many data providers, Census introduces technical terminology that can complicate the user’s experience. This paper describes how such terms affect individuals’ ability to consume data, by applying a recognition primed decision making method. The data used come from 36 in-depth interviews and 1 focus group with data consumers. An empirical model of the recognition-primed decision process uncovered in the data is presented, also elucidating key variations in decision paths according to factors like technical training or experience. These findings illustrate that failing to present user-centered data can limit the ability of consumers to benefit from public information.