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The organization of scientific knowledge: the structural characteristics of keyword networks

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Abstract

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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References

  • Abrahamson, E. (1996). Management fashion. Academy of Management Review, 21(1), 254–285.

    Google Scholar 

  • Baldwin, C. Y., & Clark, K. B. (2000). Design rules: the power of modularity. MA: Cambridge.

    Google Scholar 

  • Barabasi, A. L., & Albert, R. (1999). Emergence of scaling in random networks. Science, 286(5439), 509–512.

    Article  MathSciNet  Google Scholar 

  • Barnett, G. A., Huh, C., Kim, Y., & Park, H. W. (2011). Citations among communication journals and other disciplines: a network analysis. Scientometrics, 88(2), 449–469.

    Article  Google Scholar 

  • Crane, D. (1972). Invisible colleges: diffusion of knowledge in scientific communities. Chicago and London: The University of Chicago Press.

    Google Scholar 

  • Derényi, I., Palla, G., & Vicsek, T. (2005). Clique percolation in random networks. Physical Review Letters, 94(16), 160202.

    Article  Google Scholar 

  • Ebel, H., Davidsen, J., & Bornholdt, S. (2002). Dynamics of social networks. Complexity, 8(2), 24–27. doi:10.1002/cplx.10066.

    Article  MathSciNet  Google Scholar 

  • Ethiraj, S. K., & Levinthal, D. (2004). Bounded rationality and the search for organizational architecture: An evolutionary perspective on the design of organizations and their evolvability. Administrative Science Quarterly, 49(3), 404–437.

    Google Scholar 

  • Fleming, L., & Sorenson, O. (2001). Technology as a complex adaptive system: evidence from patent data. Research Policy, 30(7), 1019–1039.

    Article  Google Scholar 

  • Granovetter, M. (1973). Strength of weak ties. American Journal of Sociology, 78(6), 1360–1380.

    Article  Google Scholar 

  • Hung, S. W., & Wang, A. P. (2010). Examining the small world phenomenon in the patent citation network: a case study of the radio frequency identification (RFID) network. Scientometrics, 82(1), 121–134.

    Article  Google Scholar 

  • Kuhn, T. (1962). The structure of scientific revolutions. Chicago: The University of Chicago Press.

    Google Scholar 

  • Lee, P.-C., Su, H.-N., & Chan, T.-Y. (2010). Assessment of ontology-based knowledge network formation by Vector-Space Model. Scientometrics, 85(3), 689–703.

    Article  Google Scholar 

  • Li, X., Chen, H., Huang, Z., & Roco, M. C. (2007). Patent citation network in nanotechnology (1976–2004). Journal of Nanoparticle Research, 9, 337–352.

    Article  Google Scholar 

  • McCloskey, D. N. (1998). The rhetoric of economics. Wisconsin: University of Wisconsin Press.

    Google Scholar 

  • McGrath, W. E. (1996). The unit of analysis (objects of study) in bibliometrics and scientometrics. Scientometrics, 35(2), 257–264.

    Article  Google Scholar 

  • Milgram, S. (1967). The small world problem. Psychology Today, 1, 61–67.

    Google Scholar 

  • Newman, M. E. J. (2005). Power laws, Pareto distributions and Zipf’s law. Contemporary Physics, 46(5), 323–351.

    Article  Google Scholar 

  • Palla, G., Barabasi, A.-L., & Vicsek, T. (2007). Quantifying social group evolution. Nature, 446(7136), 664–667.

    Article  Google Scholar 

  • Palla, G., Derenyi, I., Farkas, I., & Vicsek, T. (2005). Uncovering the overlapping community structure of complex networks in nature and society. Nature, 435(7043), 814–818.

    Article  Google Scholar 

  • Plotkin, H. C. (1997). Darwin machines and the nature of knowledge. MA: Harvard University Press.

    Google Scholar 

  • Price, D. J. D. (1965). Networks of scientific papers. Science, 149(3683), 510–515.

    Article  Google Scholar 

  • Ravasz, E., & Barabasi, A. L. (2003). Hierarchical organization in complex networks. Physical Review E, 67(2), 026112.

    Article  Google Scholar 

  • Ravasz, E., Somera, A. L., Mongru, D. A., Oltvai, Z. N., & Barabasi, A. L. (2002). Hierarchical organization of modularity in metabolic networks. Science, 297(5586), 1551–1555.

    Article  Google Scholar 

  • Sanchez, R., & Mahoney, J. T. (1996). Modularity, flexibility, and knowledge management in product and organization design. Strategic Management Journal, 17, 63–76.

    Google Scholar 

  • Simon, H. A. (1962). The architecture of complexity. Proceedings of the American Philosophical Society, 106(6), 467–482.

    Google Scholar 

  • Solé, R. V., Corominas-Murtra, B., Valverde, S., & Steels, L. (2010). Language networks: their structure, function, and evolution. Complexity, 15(6), 20–26.

    Article  Google Scholar 

  • Strogatz, S. H. (2001). Exploring complex networks. Nature, 410(6825), 268–276.

    Article  Google Scholar 

  • Su, H.-N., & Lee, P.-C. (2010). Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in technology foresight. Scientometrics, 85(1), 65–79.

    Article  Google Scholar 

  • Thompson, J. D. (1967). Organizations in action. New York: McGraw-Hill.

    Google Scholar 

  • Trajtenberg, M. (1990). A penny for your quotes: patent citations and the value of innovations. RAND Journal of Economics, 21(1), 172–187.

    Article  Google Scholar 

  • Upham, S., Rosenkopf, L., & Ungar, L. (2010). Positioning knowledge: schools of thought and new knowledge creation. Scientometrics, 83(2), 555–581.

    Article  Google Scholar 

  • Valverde, S., Sole, R. V., Bedau, M. A., & Packard, N. (2007). Topology and evolution of technology innovation networks. Physical Review E, 76, 56–118.

    Google Scholar 

  • Watts, D. J. (1999). Small worlds: the dynamics of networks between order and randomness. Princeton: Princeton University Press.

    Google Scholar 

  • Watts, D. J., & Strogatz, S. H. (1998). Collective dynamics of ‘small-world’ networks. Nature, 393(6684), 440–442.

    Article  Google Scholar 

  • Weick, K. E. (1976). Educational organizations as loosely coupled systems. Administrative Science Quarterly, 21(1), 1–19.

    Article  Google Scholar 

  • Wright, S. (1932). The Roles of mutation, inbreeding, crossbreeding and selection in evolution. Proceedings of the VI International Congress of Genetics, 356–366.

  • Wright, S. (1964). Stochastic processes in evolution. In J. Gurland (Ed.), Stochastic models in medicine and biology (pp. 199–241). USA: University of Wisconsin Press.

    Google Scholar 

Download references

Acknowledgments

This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea Government (MEST) (No. 2009-0070359).

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Authors and Affiliations

  1. Department of Marketing and Management, University of Southern Denmark, Campusvej 55, 5230, Odense M, Denmark

    Sangyoon Yi

  2. School of Business, Sejong University, 98 Gunja-dong, Gwangjin-gu, Seoul, 143-747, Korea

    Jinho Choi

Authors
  1. Sangyoon Yi
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  2. Jinho Choi
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Correspondence to Jinho Choi.

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

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