Frontline Workers and Civic Tech: Bridging the Responsiveness Gap in Digital Client Encounters

Abstract

As governments increasingly digitalize client encounters, there are growing concerns that standardized platforms may reduce bureaucratic responsiveness, particularly for historically underserved communities. We examine whether frontline workers help close that gap through their use of civic-tech platforms. Analyzing 8037 service requests from Newark's Open311 system (2019–2022) using machine learning classification and spatial analysis, we find that frontline workers submitted nearly 25% of all requests on this platform. Worker submissions concentrated in low-income areas, while resident requests clustered in more affluent neighborhoods. Interviews reveal that human-centered design features encourage frontline worker reporting, while their community commitment and organizational priorities explain proactive engagement in underserved areas. We conceptualize this pattern of engagement as digitally afforded discretion, where frontline workers leverage platform features to address community needs before formal complaints arise. Our findings suggest that human-centered digital platforms can complement frontline discretion, offering novel insight into how technology and human judgment interact to shape equity and responsiveness.