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    Please use this identifier to cite or link to this item: https://nccur.lib.nccu.edu.tw/handle/140.119/135157


    Title: An integrated data analytics process to optimize data governance of non-profit organization
    Authors: 李博逸
    Li, Bo-Yi
    Chen-ShuWang
    Shiang-LinLin
    Tung-HsiangChou
    Contributors: 資管博三
    Keywords: Call center optimization;Customer relationship management;Data governance;Big data;Non-profit organization
    Date: 2019-12
    Issue Date: 2021-05-26 10:44:05 (UTC+8)
    Abstract: Many nations have successfully implemented their own e-Government systems in response to changes in many subjective and objective environments. However, different government agencies now hold a tremendous quantity of client data and have encountered the challenge of how to effectively manage, analyze, and apply that data in order to improve service quality and customer satisfaction levels. In Taiwan, the National Health Insurance Administration (NHIA) is a state-run national health insurance agency and a nonprofit organization (NPO) responsible for managing health insurance affairs and improving healthcare quality for all people in Taiwan. In this study, we apply an integration of the Knowledge Discovery in Databases (KDD) process to analyze the call center data of the NHIA. We take the major processes of processing, selection, data mining, and, evaluation as our foundation for two types of data mining analyses: data sorting and data association. Furthermore, we generalize the analysis results and consult professionals in NHIA for their professional opinion about those results and existing health insurance policies. We also present, interpret, and draw conclusions from these results via data visualization. This visualized analysis can help NHIA decision makers quickly understand and reflect the public`s needs and discover deeper client requirements to achieve the goal of upgraded public service quality and performance.
    Relation: Computers in Human Behavior,101, 495-505
    Data Type: article
    DOI link: https://doi.org/10.1016/j.chb.2018.10.015
    DOI: 10.1016/j.chb.2018.10.015
    Appears in Collections:[Department of MIS] Periodical Articles

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