McLoughlin, Shane, Puvvala, Abhinay, Maccani, Giovanni and Donnellan, Brian (2019) A Framework for understanding & classifying Urban Data Business Models. Hawaii International Conference on System Sciences (HICSS). ISSN 978-0-9981331-2-6
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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.
Item Type: | Article |
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Keywords: | Framework; understanding; classifying; Urban Data Business Models; |
Academic Unit: | Faculty of Social Sciences > School of Business |
Item ID: | 10520 |
Identification Number: | hdl.handle.net/10125/59765 |
Depositing User: | Prof. Brian Donnellan |
Date Deposited: | 20 Feb 2019 15:08 |
Journal or Publication Title: | Hawaii International Conference on System Sciences (HICSS) |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
Refereed: | Yes |
Related URLs: | |
URI: | https://mural.maynoothuniversity.ie/id/eprint/10520 |
Use Licence: | This item is available under a Creative Commons Attribution Non Commercial Share Alike Licence (CC BY-NC-SA). Details of this licence are available here |
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