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Data Science or Process Science? How to Promote the Next Digital Transformation in the Public Sector

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Electronic Government (EGOV 2021)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12850))

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Abstract

As citizen orientation and public value creation are more in the focus, how do we set priorities for the upcoming digital transformation in the public sector? Distinguishing data science and process science as paradigms that promote different directions for the transformation, this research seeks to improve the transparency of how IT-related decisions are directing projects and resources and thus promoting directions of public value production and delivery. Digital government research along this line may help constituents, IT experts and other stakeholders to engage in the needed discourse about the (not) wanted future of government performance and related technology usage.

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Author information

Authors and Affiliations

  1. German International University, Am Borsigturm 162, 13507, Berlin, Germany

    Ralf Klischewski

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  1. Ralf Klischewski
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Correspondence to Ralf Klischewski .

Editor information

Editors and Affiliations

  1. University of Washington, Seattle, WA, USA

    Hans Jochen Scholl

  2. Center for Technology in Government, University at Albany, State University of New York, Albany, NY, USA

    J. Ramon Gil-Garcia

  3. Delft University of Technology, Delft, The Netherlands

    Marijn Janssen

  4. University of Macedonia, Thessaloniki, Greece

    Evangelos Kalampokis

  5. Linköping University, Linköping, Sweden

    Ida Lindgren

  6. Department of Accounting and Finance, University of Granada, Granada, Spain

    Manuel Pedro Rodríguez Bolívar

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Klischewski, R. (2021). Data Science or Process Science? How to Promote the Next Digital Transformation in the Public Sector. In: Scholl, H.J., Gil-Garcia, J.R., Janssen, M., Kalampokis, E., Lindgren, I., Rodríguez Bolívar, M.P. (eds) Electronic Government. EGOV 2021. Lecture Notes in Computer Science(), vol 12850. Springer, Cham. https://doi.org/10.1007/978-3-030-84789-0_1

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  • DOI: https://doi.org/10.1007/978-3-030-84789-0_1

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-84788-3

  • Online ISBN: 978-3-030-84789-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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