A Framework for understanding & classifying Urban Data Business Models

dc.contributor.author Mcloughlin, Shane
dc.contributor.author Puvvala, Abhinay
dc.contributor.author Maccani, Giovanni
dc.contributor.author Donnellan, Brian
dc.date.accessioned 2019-01-03T00:14:07Z
dc.date.available 2019-01-03T00:14:07Z
dc.date.issued 2019-01-08
dc.description.abstract Governments’ objective to transition to ‘Smart Cities’ heralds new possibilities for urban data business models to address pressing city challenges and digital transformation imperatives. Urban data business models are not well understood due to such factors as the maturity of the market and limited available research within this domain. Understanding the barriers and challenges in urban data business model development as well as the types of opportunities in the ecosystem is essential for incumbents and new entrants. Therefore, the aim of this paper is to develop a framework for understanding and classifying Urban Data Business Models (UDBM). This paper uses an embedded case study method to derive the framework by analyzing 40 publicly funded and supported business model experiments that address pressing city challenges under one initiative. This research contributes to the scholarly discourse on business model innovation in the context of smart cities.
dc.format.extent 10 pages
dc.identifier.doi 10.24251/HICSS.2019.398
dc.identifier.isbn 978-0-9981331-2-6
dc.identifier.uri http://hdl.handle.net/10125/59765
dc.language.iso eng
dc.relation.ispartof Proceedings of the 52nd Hawaii International Conference on System Sciences
dc.rights Attribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subject Smart and Connected Cities and Communities
dc.subject Digital Government
dc.subject Smart cities, business models, urban data, Internet of Things, digital transformation
dc.title A Framework for understanding & classifying Urban Data Business Models
dc.type Conference Paper
dc.type.dcmi Text
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