A social media text analytics framework for double-loop learning for citizen-centric public services: A case study of a local government Facebook use
RIS ID
111382
Abstract
This paper develops a framework for facilitating organizational learning through social media text analytics to enhance citizen-centric public service quality. Theoretically, the framework integrates double-loop learning theory with extant models of e-participation in government. Empirically, the framework is applied to a case study of citizen-to-government online interactions on a local government's department Facebook page. Our findings indicate that the missed double-loop learning opportunity resulted from two factors. First, Facebook government-posts were primarily used to advocate the government agenda by educating citizens to change their recycling behaviors without efforts to learn citizens' needs/questions. Second, this single-loop learning orientation sustained the single-loop learning nature of Facebook citizens' posts, precluding their direct and meaningful participation in the city's recycling governance. New insights generated from the case study suggest the framework's usefulness in showing more promising directions for government's double-loop learning through social media platforms to enhance public service quality.
Publication Details
Reddick, C., Chatfield, A. & Ojo, A. (2017). A social media text analytics framework for double-loop learning for citizen-centric public services: A case study of a local government Facebook use. Government Information Quarterly, 34 (1), 110-125.