The comparison of various similarity measurement approaches on interdisciplinary indicators

Ying Huang, Wolfgang Glänzel, Bart Thijs, Alan L Porter and Lin Zhang

No 670612, Working Papers of Department of Management, Strategy and Innovation, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven

Abstract: How to measure the interdisciplinary is a crucial topic in Interdisciplinary research (IDR), and the integrated indicators (e.g., Rao-Stirling) that combine three distinct components (variety, balance and disparity) has become one of the most promising attempts. Among the three components, variety and balance play relatively straightforward roles in diversity assessment but to what extent the (dis)similarity measuring approaches may affect the interdisciplinarity indicators is seldom discussed in the literature. In this paper, we compare various similarity measurement approaches from (1) different subject classification systems, (2) different normalization of (dis)similarity measure, (3) different (dis)similarity matrices of subjects, (4) different time windows; and (5) different levels of aggregations, using the academic publications labeled “Article” in eight selected journals published during the period 2009–2018 were selected as the sample dataset. Our results corroborate the following findings: First, a finer classification system with more subject categories increases the possibility that one cites sources from different subject categories. Second, different normalization approaches may lead to obviously different interdisciplinarity results, and such a finding is supported by the relatively low correlations between the interdisciplinarities calculated by Salton’s Cosine and Ochiai’s Cosine. Third, on the basis of Salton's cosine normalization, the interdisciplinary values obtained by different settings are highly correlated, especially in terms of different citation similarity matrices (cited, citing and cross-citation) and, in general, with different time windows. Fourth, results based on an aggregated dataset tend to overly expand the 'interdisciplinarity' degree of a journal, especially when the focused journal is actually 'multidisciplinary'.

Keywords: Interdisciplinary research (IDR); Similarity; Rao-Stirling; True Diversity (search for similar items in EconPapers)
Pages: 24
Date: 2021-02-15
New Economics Papers: this item is included in nep-hme
Note: paper number MSI_2102
References: Add references at CitEc
Citations:

Published in FEB Research Report MSI_2102, pages 1-24

Downloads: (external link)
https://lirias.kuleuven.be/retrieve/610308 Published version (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ete:msiper:670612

Access Statistics for this paper

More papers in Working Papers of Department of Management, Strategy and Innovation, Leuven from KU Leuven, Faculty of Economics and Business (FEB), Department of Management, Strategy and Innovation, Leuven
Bibliographic data for series maintained by library EBIB ().