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The MAKCi index: using logistic regression modelling for predicting most admired knowledge cities

The MAKCi index: using logistic regression modelling for predicting most admired knowledge cities This study applied logistic regression modelling for the development of a quantitative index for most admired knowledge cities. Drawing on the MAKCi framework and the theoretical model of the generic capitals system, a MAKCi index was defined as the probability a city has of being selected as the most admired knowledge city. The resulting logistic regression model was satisfactorily tested for validity, and it was utilised for evaluating and ranking cities. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Knowledge-Based Development Inderscience Publishers

The MAKCi index: using logistic regression modelling for predicting most admired knowledge cities

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Publisher
Inderscience Publishers
Copyright
Copyright © Inderscience Enterprises Ltd. All rights reserved
ISSN
2040-4468
eISSN
2040-4476
DOI
10.1504/IJKBD.2012.045571
Publisher site
See Article on Publisher Site

Abstract

This study applied logistic regression modelling for the development of a quantitative index for most admired knowledge cities. Drawing on the MAKCi framework and the theoretical model of the generic capitals system, a MAKCi index was defined as the probability a city has of being selected as the most admired knowledge city. The resulting logistic regression model was satisfactorily tested for validity, and it was utilised for evaluating and ranking cities.

Journal

International Journal of Knowledge-Based DevelopmentInderscience Publishers

Published: Jan 1, 2012

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