IDEAS home Printed from https://ideas.repec.org/a/sae/iimkoz/v14y2025i1p61-74.html
   My bibliography  Save this article

Experience from E-Government Services: A Topic Model Approach

Author

Listed:
  • Satya Bhusan Dash
  • Avinash Jain

Abstract

Governments globally are striving to improve citizens’ service delivery by adopting digital technologies, such as online portals and call centres. Although digitization provides an opportunity to improve citizens’ satisfaction, to design citizen centric e-government services, agencies need to proactively understand citizens’ experiences. This study explored the Google Play reviews of UMANG (an aggregated e-government mobile application of the Government of India). We aggregated 4,921 reviews provided from March 2020 to April 2021. We first theoretically (using the S-O-R framework) and empirically (link between sentiment polarity and user rating) examined the validity of user reviews to extract insights into citizens’ experiences. Subsequently, we extracted eight topics related to citizens’ experiences by using latent Dirichlet allocation, an unsupervised machine learning algorithm. The following topics were identified—perceived usefulness, ease of use, product feature experience, delivery turnaround time, technological experience, login experience, customer care experience, and payment experience. We validated identified topics by determining the inter-rater agreement between LDA and human rater output. Finally, we calculated the relative importance of the identified topics and topic-wise sentiment polarity. The findings of this study can help in designing citizen centric e-government services and prioritizing the right dimension of citizens’ experience.

Suggested Citation

  • Satya Bhusan Dash & Avinash Jain, 2025. "Experience from E-Government Services: A Topic Model Approach," IIM Kozhikode Society & Management Review, , vol. 14(1), pages 61-74, January.
    • Handle: RePEc:sae:iimkoz:v:14:y:2025:i:1:p:61-74
    DOI: 10.1177/22779752221126571
    as

    Download full text from publisher

    File URL: https://journals.sagepub.com/doi/10.1177/22779752221126571
    Download Restriction: no
    File URL: https://libkey.io/10.1177/22779752221126571?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:sae:iimkoz:v:14:y:2025:i:1:p:61-74. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: SAGE Publications (email available below). General contact details of provider: .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.