Numerical Correlation Between Impact Factor And Web Ranking Of Electronic Scientific Journals Using Regression Analysis
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Numerical Correlation between Impact Factor and Web Ranking of Electronic Scientific Journals Using Regression Analysis
Author: Giorgos Kouropoulos
language: en
Publisher: National Taiwan University
Release Date: 2017-12-15
The present study attempts to examine the numerical correlation between web ranking of electronic scientific journals and impact factor of these journals using the method of regression analysis. Regression analysis allows the option of investigating and predicting the numerical relationship between website ranking of scientific journals on the World Wide Web and the value of impact factor of the journals. A sample of 57 publishers with 6,272 scientific journals and 50 standalone scientific journals was analyzed during research procedure. In this study, two different indicators about websites classification on World Wide Web were examined separately for 57 publishers and 50 standalone journals, Alexa rank and Statscrop rank. The electronic databases through the internet constitute the main information resources of this study about the impact factors. The general conclusion that arises is that the impact factor of electronic scientific journals illustrates a very strong positive correlation with classification of websites on the World Wide Web. Furthermore, it is concluded that the change of web ranking as a function of impact factor is governed by a Gaussian function or rational function with lower Pearson coefficient and presents non-linearly correlation. Even if there is very strong correlation between impact factor and web rank for electronic journals, the prediction of impact factor from web rank is not possible and presents many divergences.
Innovation and Knowledge Communities
Author: Upham, Phin
language: en
Publisher: Edward Elgar Publishing
Release Date: 2022-02-18
Breakthroughs in science and technology increasingly happen outside of firms in informal interorganizational communities of innovators. The effort of a group on a specific topic across firms, expertise, and geography can function as an emergent organizational form, capable of great productivity. Using data from computer science, basic research, and management strategy to identify and study these intense clusters of innovators, or “knowledge communities,” this book illuminates the new organizational logics that govern such collective success.
Progress in Artificial Intelligence
This book constitutes the refereed proceedings of the 16th Portuguese Conference on Artificial Intelligence, EPIA 2013, held in Angra do Heroísmo, Azores, Portugal, in September 2013. The 45 revised full papers presented were carefully reviewed and selected from a total of 157 submissions. The papers are organized in the following topical sections: ambient intelligence and affective environments; artificial intelligence in transportation systems; artificial life and evolutionary algorithms; computational methods in bioinformatics and systems biology; general artificial intelligence; intelligent robotics; knowledge discovery and business intelligence; multi-agent systems: theory and applications; social simulation and modeling; and text mining and applications.