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A Framework for understanding & classifying Urban Data Business Models

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Title:A Framework for understanding & classifying Urban Data Business Models
Authors:Mcloughlin, Shane
Puvvala, Abhinay
Maccani, Giovanni
Donnellan, Brian
Keywords:Smart and Connected Cities and Communities
Digital Government
Smart cities, business models, urban data, Internet of Things, digital transformation
Date Issued:08 Jan 2019
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.
Pages/Duration:10 pages
URI:http://hdl.handle.net/10125/59765
ISBN:978-0-9981331-2-6
DOI:10.24251/HICSS.2019.398
Rights:Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
Appears in Collections: Smart and Connected Cities and Communities


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