Best-in-class and Strategic Benchmarking of Scientific Subject Categories of Web of Science in 2010

TL;DR

This paper introduces a new statistical method using impact-factor histograms to compare scientific subject categories, enabling both best-in-class and strategic benchmarking within the Web of Science database.

Contribution

It presents a novel axiomatic approach to measure dissimilarity between subject categories based on impact factor distributions, applied to Web of Science categories in 2010.

Findings

Effective comparison of subject categories using impact-factor histograms.

Application to Web of Science categories demonstrates the method's utility.

Distinction between best-in-class and strategic benchmarking approaches.

Abstract

Here we show a novel technique for comparing subject categories, where the prestige of academic journals in each category is represented statistically by an impact-factor histogram. For each subject category we compute the probability of occurrence of scholarly journals with impact factor in different intervals. Here impact factor is measured with Thomson Reuters Impact Factor, Eigenfactor Score, and Immediacy Index. Assuming the probabilities associated with a pair of subject categories our objective is to measure the degree of dissimilarity between them. To do so, we use an axiomatic characterization for predicting dissimilarity between subject categories. The scientific subject categories of Web of Science in 2010 were used to test the proposed approach for benchmarking Cell Biology and Computer Science Information Systems with the rest as two case studies. The former is best-in-class benchmarking that involves studying the leading competitor category; the latter is strategic benchmarking that involves observing how other scientific subject categories compete.