Comparing all-author and first-author co-citation analyses of information science

Abstract

Although it is generally understood that different citation counting methods can produce quite different author rankings, and although “optimal” author co-citation counting methods have been identified theoretically, studies that compare author co-citation counting methods in author co-citation analysis (ACA) studies are still rare. The present study applies strict all-author-based ACA to the Information Science (IS) field, in that all authors of all cited references in a classic IS dataset are counted, and in that even the diagonal values of the co-citation matrix are computed in their theoretically optimal form. Using Scopus instead of SSCI as the data source, we find that results from a theoretically optimal all-author ACA appear to be excellent in practice, too, although in a field like IS where co-authorship levels are relatively low, its advantages over classic first-author ACA appear considerably smaller than in the more highly collaborative ones targeted before. Nevertheless, we do find some differences between the two approaches, in that first-author ACA appears to favor theorists who presumably tend to work alone, while all-author ACA appears to paint a somewhat more recent picture of the field, and to pick out some collaborative author clusters.