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ToolsGupta, Anushri, Panagiotopoulos, Panos and Bowen, Frances (2020) An orchestration approach to smart city data ecosystems. Technological Forecasting and Social Change, 153. ISSN 0040-1625
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Abstract
Research on smart cities has illustrated the use of data analytics, open data, smart sensors and other data-intensive applications that have significant potential to transform urban environments. As the complexity and intensity of these projects has increased, there is a need to understand smart city data ecosystems as an integrated view of data applications by the various city entities that operate within an institutional environment. This paper examines how authorities involved in such ecosystems coordinate data initiatives from an orchestration perspective. A case study of London's city data initiatives highlights the challenges faced in complex city data environments and the importance of an integrated view. Three elements of orchestration in smart city data ecosystems – namely openness, diffusion and shared vision– are identified as the main enablers of city data initiatives within London's local government authorities. The study contributes to our theoretical understanding of orchestration within data ecosystems, as well as the social and technological impacts of city data.
| Item Type: | Article |
|---|---|
| Uncontrolled Keywords: | data ecosystems,local government,london city data,orchestration,smart cities,business and international management,applied psychology,management of technology and innovation,sdg 11 - sustainable cities and communities ,/dk/atira/pure/subjectarea/asjc/1400/1403 |
| Faculty \ School: | Faculty of Social Sciences > Norwich Business School |
| UEA Research Groups: | Faculty of Social Sciences > Research Centres > Centre for Competition Policy Faculty of Social Sciences > Research Groups > Responsible Business Regulation Group |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 01 Feb 2020 04:25 |
| Last Modified: | 21 Apr 2023 00:22 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/73947 |
| DOI: | 10.1016/j.techfore.2020.119929 |
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