Analysis of keyword networks in MIS research and implications for predicting knowledge evolution

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New concepts and ideas build on older ones. This path dependence in knowledge evolution has promoted research to identify important knowledge elements, research trends, and opportunities by analyzing publication data. In our study, keyword networks formed from published academic articles were analyzed to examine how keywords are associated with each other and to identify important keywords and their change over time. Based on MIS publication data from 1999 to 2008, our analysis provided several notable findings. First, while the MIS field has changed rapidly, resulting in many new keywords, the connectivity among them is highly clustered. Second, the keyword networks show clear power-law distribution, which implies that the more popular a keyword, the more likely it is selected by new researchers and used in follow-on studies. In addition, a strong hierarchical structure is identified in the network. Third, the network-based perspective reveals interdisciplinary keywords which are different from popular ones and have the potential to lead research in the MIS field. (C) 2011 Elsevier B.V. All rights reserved.
Publisher
ELSEVIER SCIENCE BV
Issue Date
2011-12
Language
English
Article Type
Article
Keywords

SCALE-FREE NETWORKS; TECHNOLOGY; TRENDS

Citation

INFORMATION & MANAGEMENT, v.48, pp.371 - 381

ISSN
0378-7206
DOI
10.1016/j.im.2011.09.004
URI
http://hdl.handle.net/10203/214869
Appears in Collection
RIMS Journal Papers
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