Qi, J., Mazumdar, S. orcid.org/0000-0002-0748-7638 and Vasconcelos, A. (2023) Datafication in smart cities: understanding how the public experience urban environments. In: Proceedings of the 22nd European Conference on Research Methodology for Business and Management Studies. European Conference on Research Methodology for Business and Management Studies, 08 Sep 2023, Lisbon, Portugal. Academic Conferences International Ltd , pp. 266-269. ISBN 9781914587719
Abstract
Datafication has become a prominent feature of smart cities, where sensors, monitoring devices, and AI are being integrated with city infrastructures and facilities, resulting in rapidly changing urban areas informed by data-driven decision-making processes. Although there is a vast amount of data being generated about urban environments and citizens, research on understanding citizens’ social experience in smart cities has been limited. This study proposes a three-stage research design that provides datafication solutions to understand citizens’ experience of urban environment in a synergistic manner. We employ a mixed methods approach drawing upon multiple data collected by the researcher, from the citizens, and sourced across smart cities open data platforms. It is designed to undertake a place-based and citizen-centric approach to understand the lived social experiences of citizens in urban environments. This work will contribute to our current understanding in developing socially sustainable smart cities, providing methodological insights for future research on how datafication process can be leveraged to improve quality of urban life.
Metadata
Item Type: | Proceedings Paper |
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Authors/Creators: |
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Copyright, Publisher and Additional Information: | © 2023 European Conference on Research Methodology for Business and Management Studies. This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. (https://creativecommons.org/licenses/by-nc-nd/4.0/) |
Keywords: | online survey; spatial statistical modelling; moderation and mediation analysis; text mining; open data |
Dates: |
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Institution: | The University of Sheffield |
Academic Units: | The University of Sheffield > Faculty of Social Sciences (Sheffield) > Information School (Sheffield) |
Depositing User: | Symplectic Sheffield |
Date Deposited: | 08 Jan 2024 16:05 |
Last Modified: | 08 Jan 2024 16:06 |
Status: | Published |
Publisher: | Academic Conferences International Ltd |
Refereed: | Yes |
Identification Number: | 10.34190/ecrm.22.1.1576 |
Open Archives Initiative ID (OAI ID): | oai:eprints.whiterose.ac.uk:207349 |