Please use this identifier to cite or link to this item: http://hdl.handle.net/10125/41494

Big Data and Evidence-Driven Decision-Making: Analyzing the Practices of Large and Mid-Sized U.S. Cities

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Title: Big Data and Evidence-Driven Decision-Making: Analyzing the Practices of Large and Mid-Sized U.S. Cities
Authors: Ho, Alfred
Keywords: Big Data
institutional theories
institutional logics
isomorphism.
Issue Date: 04 Jan 2017
Abstract: With the growing ease of collecting, transmitting, storing, processing, and analyzing massive amounts of data, Big Data has caught the attention of local officials in recent years. Based on a multi-layered institutional theories and an extensive analysis of the 30 largest cities and 35 selected mid-sized cities in the U.S, this study examines how U.S. cities are using mobile phone apps, sensors, data analytics, and open data portals to pursue Big Data opportunities, and what institutional factors influence their choices. The results show three distinct clusters of data practices among the selected 65 cities. Socio-demographics, cultural institutions, professional networks, and an internal data-driven culture as indicated by the use of performance budgeting are significantly associated with more extensive Big Data initiatives. The paper concludes by discussing the implications for Big Data practices and the theoretical development of e-government research.
Pages/Duration: 10 pages
URI/DOI: http://hdl.handle.net/10125/41494
ISBN: 978-0-9981331-0-2
DOI: 10.24251/HICSS.2017.338
Rights: Attribution-NonCommercial-NoDerivatives 4.0 International
Appears in Collections:Smart Cities, Smart Government, and Smart Governance Minitrack



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