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    A social media text analytics framework for double-loop learning for citizen-centric public services: A case study of a local government Facebook use


    Reddick, Christopher G. and Chatfield, Akemi Takeoka and Ojo, Adegboyega (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. pp. 110-125. ISSN 0740-624X

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    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.

    Item Type: Article
    Keywords: Double-loop learning; Text analytics; Citizen engagement; Local government; Social media; Facebook;
    Academic Unit: Faculty of Social Sciences > Research Institutes > Innovation Value Institute, IVI
    Faculty of Social Sciences > School of Business
    Item ID: 15779
    Identification Number: https://doi.org/10.1016/j.giq.2016.11.001
    Depositing User: Adegboyega Ojo
    Date Deposited: 05 Apr 2022 14:34
    Journal or Publication Title: Government Information Quarterly
    Publisher: Elsevier
    Refereed: Yes
    URI:

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