Position paper : benchmarking the performance of global and emerging knowledge cities
Yigitcanlar, Tan (2014) Position paper : benchmarking the performance of global and emerging knowledge cities. Expert Systems with Applications, 41(10), pp. 4680-4690.
Knowledge-based development has become a new urban policy approach for the competitive cities of the global knowledge economy era. For those cities seeking a knowledge-based development, benchmarking is an essential prerequisite for informed and strategic vision and policy making to achieve a prosperous development. Nevertheless, benchmarked knowledge-based development performance analysis of global and emerging knowledge cities is an understudied area. This paper aims to contribute to the field by introducing the methodology of a novel performance assessment model—that is the Knowledge-Based Urban Development Assessment Model—and providing lessons from the application of the model in an international knowledge city performance analysis study. The assessment model puts renowned global and emerging knowledge cities—that are Birmingham, Boston, Brisbane, Helsinki, Istanbul, Manchester, Melbourne, San Francisco, Sydney, Toronto, and Vancouver—under the knowledge-based development microscope. The results of the analysis provide internationally benchmarked snapshot of the degree of achievements in various knowledge-based urban development performance areas of the investigated knowledge cities, and reveals insightful lessons on scrutinizing the global perspectives on knowledge-based development of cities.
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|Item Type:||Journal Article|
|Keywords:||City benchmarking, City performance, Knowledge-based development, Knowledge-based urban development, Knowledge city|
|Subjects:||Australian and New Zealand Standard Research Classification > BUILT ENVIRONMENT AND DESIGN (120000) > URBAN AND REGIONAL PLANNING (120500) > Urban Analysis and Development (120507)|
|Divisions:||Current > Schools > School of Civil Engineering & Built Environment
Current > QUT Faculties and Divisions > Science & Engineering Faculty
|Copyright Owner:||Copyright 2014 Elsevier|
|Copyright Statement:||This is the author’s version of a work that was accepted for publication in Expert Systems with Applications. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in Expert Systems with Applications, [VOL 41, ISSUE 10, (2014)] DOI: 10.1016/j.eswa.2014.02.006|
|Deposited On:||17 Feb 2014 01:32|
|Last Modified:||05 Nov 2016 08:16|
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