Article

Modified collaborative coefficient: A new measure for quantifying the degree of research collaboration. Scientometrics, 84(2), 365-371

Scientometrics (Impact Factor: 2.18). 08/2010; 84(2):365-371. DOI: 10.1007/s11192-009-0100-4
Source: DBLP

ABSTRACT

Collaborative coefficient (CC) is a measure of collaboration in research, that reflects both the mean number of authors per
paper as well as the proportion of multi-authored papers. Although it lies between the values 0 and 1, and is 0 for a collection
of purely single-authored papers, it is not 1 for the case where all papers are maximally authored, i.e., every publication
in the collection has all authors in the collection as co-authors. We propose a simple modification of CC, which we call modified
collaboration coefficient (or MCC, for short), which improves its performance in this respect.

  • Source
    • "Research collaboration is a complex phenomenon with several aims. These include increasing publishing productivity and the number of citations, publishing in journals with high impact factors, minimizing risks and maximizing opportunities for single researchers and expanding the base of knowledge and producing economic value.12345The quality, form, process, costs, and motivations of collaboration have been studied and reported over the last J S C I R E S, Kuopio, Finland "
    [Show abstract] [Hide abstract]
    ABSTRACT: The purpose of this study was to test the usefulness of a slightly modified Shannon’s diversity index (H) as a numerical measure of intragroup research collaboration diversity based on coauthorship. Altogether, 527 peer‑reviewed scientific papers by two university departments were used as the study material. Nonrandom rationalized sampling was executed to enable the confirmation of the authors’ affiliations. The smallest unit of collaboration, i.e., a pair of authors, was created by matching every author with each of the coauthors from the same department he or she collaborated with. H was calculated at the department level and compared with the previously published, coauthorship based measures of research collaboration: The collaborative index (CI), degree of collaboration (DC) and collaboration diversity index (CDI). Obviously, H expressed a different aspect of research collaboration than the existing indexes. Compared to CI, DC, and CDI, H revealed novel aspects of collaboration when the abundance of collaboration increased and the distribution of collaborative relations between coauthors moved closer to the uniform distribution at the same time. H can provide additional information about collaborative relationships between researchers based on coauthorship, and it should be considered as a partial indicator of research collaboration.
    Full-text · Article · Dec 2015
  • Source
    • "One is the average number of authors per paper (COPP) at each stage. It's a widely used indicator of collaboration, which is in fact known as Collaboration Degree in previous literature (Savanur and Srikanth 2009). COPP is calculated stage by stage. "
    [Show abstract] [Hide abstract]
    ABSTRACT: Collaboration is believed to be influential on researchers’ productivity. However, the impact of collaboration relies on factors such as disciplines, collaboration patterns, and collaborators’ characters. In addition, at different career stages, such as the growth or the establishment career stages of scientists, collaboration is different in scale and scope, and its effect on productivity varies. In this paper, we study the relationships between collaboration and productivity in four disciplines, Organic Chemistry, Virology, Mathematics and Computer Science. Our study found that the productivity is correlated with collaboration in general, but the correlation could be positive or negative on the basis of which aspect of collaboration to measure, i.e., the scale or scope of the collaboration. The correlation becomes stronger as individual scientists progress through various stages of their career. Furthermore, experimental disciplines, such as Organic Chemistry and Virology, have shown stronger correlation coefficients than theoretical ones such as Mathematics and Computer Science.
    Full-text · Article · May 2014 · Scientometrics
  • Source
    • "For this purpose, co-authorship analysis has been particularly recognised by some studies (e.g. Glanzel, 2001; Savanur and Srikanth, 2009) as being the most common tool in investigating the co-authorship relations and the quantitative patterns in scienti¯c collaboration. "
    [Show abstract] [Hide abstract]
    ABSTRACT: This document critically reviews the papers that investigated the impact of funding on scientific output and on scientific collaboration. For the output, the focus is on the number of articles as a measure of the scientific productivity and the number of citations that a paper received as an indicator of the quality. Various methodological approaches have been adopted (e.g. bibliometrics (a set of methods to analyse the scientific literature quantitatively), statistical analysis) for this purpose. Reviewing the literature revealed that although the general assumption of the positive effect of funding on scientific development is completely (or partially) acknowledged in some studies, one can also find some contradictory results. In addition, we note that analysing the impact of funding on scientific output has attracted more attention of the researchers while investigating the impact of funding on collaboration has been only recently taken into consideration. The paper concludes by comparing the major results and methodologies of the reviewed studies while highlighting the research gaps. Read More: http://www.worldscientific.com/doi/abs/10.1142/S0219649213500378
    Full-text · Article · Dec 2013 · Journal of Information & Knowledge Management
Show more