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

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

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

    Google Scholar 

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

    Google Scholar 

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

    MathSciNet  Article  Google Scholar 

  4. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    MathSciNet  Article  Google Scholar 

  8. 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 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  11. 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 

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

    Google Scholar 

  13. 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 

  14. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  20. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  24. 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 

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

    Google Scholar 

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

    Google Scholar 

  27. 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 

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

    Article  Google Scholar 

  29. 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 

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

    Google Scholar 

  31. 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 

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

    Article  Google Scholar 

  33. 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 

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

    Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

  38. 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 

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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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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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Keywords

  • Organization of knowledge
  • Keyword network
  • Small-world network
  • Power-law distribution
  • Hierarchy