Manifestation of research teams in journal literature: A growth model of papers, authors, collaboration, coauthorship, weak ties, and Lotka's law

• Published in: Journal of the American Society for Information Science and Technology Vol. 58; no. 12; pp. 1764 - 1782
• Main Authors: Morris, Steven A., Goldstein, Michel L.
• Format: Journal Article
• Language: English
• Published: Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.10.2007; Wiley; Wiley Periodicals Inc
• Subjects: Ablation; Author productivity; Bibliometrics. Scientometrics; Bibliometrics. Scientometrics. Evaluation; Co authorship; Collaboration; Collection; Cooperation; Cooperative learning; Distance Education; Distance learning; Educational Technology; Exact sciences and technology; Growth models; Information and communication sciences; Information management; Information science. Documentation; Law; Library and information science. General aspects; Lotka's Law; Mathematical Formulas; Nontraditional Education; Productivity; Researchers; Sciences and techniques of general use; Self directed work teams; Studies; Success; Teams; Lotka law; Coauthorship analysis; Mathematical model; Periodical; Example; Author productivity
• ISSN: 1532-2882; 2330-1635; 1532-2890; 2330-1643
• DOI: 10.1002/asi.20661

This article introduces a team‐based model of researchers in a specialty and investigates the manifestation of such teams in a specialty's literature. The proposed qualitative behavioral model, with its mathematical expression as a growth model, is significant because it simultaneously describes the two phenomena of collaboration and author productivity (Lotka's law) in a specialty. The model is nested: A team process models the creation of research teams and the success‐breeds‐success process of their production of articles, while at a lower level the productivity of authors within teams is also modeled as a success‐breeds‐success process. Interteam collaboration (weak ties) is modeled as random events. This simple growth model is shown to faithfully mimic six network metrics of bipartite article‐author networks. The model is demonstrated on three example article collections from specialties that have a wide range of degree of collaboration: (a) a distance education collection with low collaboration degree, (b) a complex networks collection with typical collaboration degree, and (c) an atrial ablation collection with heavy collaboration degree.