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Digitization and urban governance: The city as a reflection of its data infrastructure

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journal contribution
posted on 2023-02-27, 14:24 authored by Ali Bayat, Peter KawalekPeter Kawalek
This article introduces the ‘House Model’, an integrated framework consisting of four data governance modes, based on the urban and smart city vision, context, and big data technologies. The model stems from engaged scholarship, synthesizing and extending the academic debates and evidence from existing smart city initiatives. It provides a means for comparing cities in terms of their digitization efforts, helps the planning of more effective urban data infrastructures and guides future empirical research in this area. The article contributes to the literature examining the issue of big data and its governance in local government and smart cities. Points for practitioners: Data is a vital part of smart city initiatives. Where the data comes from, who owns it and how it is used are all important questions. Data governance is therefore important and has consequences for the overall governance of the city. The House Model presented in this article provides a means for organizing data governance. It relates questions of data governance to the history and vision of smart city initiatives, and provides a typology organizing these initiatives.

Funding

National Infrastructure Commission, Ministry of Housing, Communities & Local Government, London, UK

History

School

  • Business and Economics

Department

  • Business

Published in

International Review of Administrative Sciences

Volume

89

Issue

1

Pages

21 - 38

Publisher

SAGE Publications

Version

  • VoR (Version of Record)

Rights holder

© The Authors

Publisher statement

This is an Open Access Article. It is published by SAGE Publications under the Creative Commons Attribution-NonCommercial 4.0 International Licence (CC BY-NC 4.0). Full details of this licence are available at: https://creativecommons.org/licenses/by-nc/4.0/

Publication date

2021-08-05

Copyright date

2021

ISSN

0020-8523

eISSN

1461-7226

Language

  • en

Depositor

Prof Peter Kawalek. Deposit date: 8 September 2021

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