The understanding of scientific knowledge itself may promote further advances in science and research on the organization of knowledge may be an initiative to this effort. This stream of research, however, has been mainly driven by the analysis of citation networks. This study uses, as an alternative knowledge element, information on the keywords of papers published in business research and examines how they are associated with each other to constitute a body of scientific knowledge. The results show that, unlike most citation networks, keyword networks are not small-word networks but, rather, locally clustered scale-free networks with a hierarchic structure. These structural patterns are robust against the scope of scientific fields involved. In addition, this paper discusses the origins and implications of the identified structural characteristics of keyword networks.
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This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MEST) (No. 2009-0070359).
Appendix: Identifying independent keywords
Appendix: Identifying independent keywords
Stage 1: Identify independent keywords.
Uniform rule: For multiple keywords that are considered the same, transform them into a single form, preferably a simple and popular term. For example,
Agent, Agents → Agent.
Case study, Case study research → Case study.
IT, Information technology → IT (other examples are CEO, E-MAIL, R&D, M&A, E-business).
MCMC, Markov chain Monte Carlo, Markov chain Monte carlo (MCMC) → Markov chain Monte Carlo.
CRM, Customer relationship management, Consumer relationship management → Customer relationship management.
Technology management, Management of technology → Technology management.
Split rule: If a keyword consists of multiple independent keywords, split them. For example,
Efficiency and effectiveness → Efficiency, Effectiveness.
Discrete/Continuous Choice Model → Discrete choice model, Continuous choice model.
Stage 2: Double-check important keywords.
Identify important keywords that appear in many papers or have a high degree of betweenness centrality in the keyword network. Stage 1 errors for these keywords can have a relatively large impact on analysis results.
Rerun important keywords through the first stage to minimize errors with these words.
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Yi, S., Choi, J. The organization of scientific knowledge: the structural characteristics of keyword networks. Scientometrics 90, 1015–1026 (2012). https://doi.org/10.1007/s11192-011-0560-1
- Organization of knowledge
- Keyword network
- Small-world network
- Power-law distribution