An Evaluation Fuzzy Model and Its Application for Knowledge-Based Social Network

Part of the Studies in Computational Intelligence book series (SCI, volume 526)


Knowledge-based organizations (KBOs) such as universities, research institutes, and research centers at businesses and industries manage their projects in pursuit of the goals of their organizations. It is well understood that collaboration becomes one of the most important factors for successful completion of these projects. In performing a project jointly, it is important for project team members to know who has the required knowledge. Thus it is imperative to assess what and how much the employees know. Using a knowledge-based social network and its basic approach, a new method is proposed to analyze the knowledge and collaboration collectively possessed by the employees of a KBO. This study first deal with the methodologies of evaluating knowledge and collaboration possessed both by individuals and the organization. Since the quantitative evaluation is essential in developing a series of evaluating methods, the measures of knowledge and collaboration are derived. Then, the knowledge network types of KBO and the network roles of KBO members are discussed. Four types of knowledge-based social network and four roles of network members are also discussed respectively. An evaluation fuzzy model is proposed to test the feasibility of the knowledge-based social network and its measures. A case study is used to demonstrate effectiveness of the proposed model.


Knowledge-based social network Knowledge competence Collaboration competence Knowledge evaluation fuzzy model Familiarity evaluation fuzzy model 



This work was supported by the 2008 Research Fund (No. 1.080008.01) of UNIST, Ulsan 689–798, Korea.


  1. 1.
    Pheng, L.S., Chuan, Q.T.: Environmental factors and work performance of project managers in the construction industry. Int. J. Project Manage. 24, 24–37 (2006)CrossRefGoogle Scholar
  2. 2.
    Wi, H., Jung, M.: Modeling and analysis of project performance factors in an extended project-oriented virtual organization (EProVO). Expert Syst. Appl. 37, 1143–1151 (2010)CrossRefGoogle Scholar
  3. 3.
    Borgatti, S.: Identifying sets of key players in a social network. Comput. Math. Organ. Theory 12, 21–34 (2006)CrossRefMATHGoogle Scholar
  4. 4.
    Huemann, M., Keegan, A., Turner, J.R.: Human resource management in the project-oriented company: a review. Int. J. Project Manage. 25, 315–323 (2007)CrossRefGoogle Scholar
  5. 5.
    Pettersen, M.: Selecting project managers: An integrated list of predictors. Project Manage. J. 22, 21–26 (1991)Google Scholar
  6. 6.
    Alavi, M., Tiwana, A.: Knowledge integration in virtual teams: The potential role of KMS. J. Am. Soc. Inform. Sci. Technol. 53, 1029–1037 (2002)CrossRefGoogle Scholar
  7. 7.
    Akgün, A.E., Byrne, J., Keskin, H., Lynn, G.S., Imamoglu, S.Z.: Knowledge networks in new product development projects: a transactive memory perspective. Inf. Manage. 42, 1105–1120 (2005)CrossRefGoogle Scholar
  8. 8.
    Büchel, B., Raub, S.: Building knowledge-creating value networks. Eur. Manage. J. 20, 587–596 (2002)CrossRefGoogle Scholar
  9. 9.
    Wi, H., Oh, S., Mun, J., Jung, M.: A team formation model based on knowledge and collaboration. Expert Syst. Appl. 36, 9121–9134 (2009)CrossRefGoogle Scholar
  10. 10.
    Higson, C., Briginshaw, J.: Valuing Internet business. Bus. Strategy Rev. 11, 10–20 (2000)CrossRefGoogle Scholar
  11. 11.
    Brainard, W.C., Tobin, J.: Pitfalls in financial model building. Am. Econ. Rev. 58, 99–122 (1968)Google Scholar
  12. 12.
    Luthy, D.H.: Intellectual capital and its measurement. In: Proceedings of the Asian Pacific Interdisciplinary Research in Accounting Conference (APIRA), Osaka, Japan, pp. 16-17. Citeseer, (1998)Google Scholar
  13. 13.
    Wilkins, J., Van Wegen, B., De Hoog, R.: Understanding and valuing knowledge assets: overview and method. Expert Syst. Appl. 13, 55–72 (1997)CrossRefGoogle Scholar
  14. 14.
    Sveiby, K.E.: The New Organizational Wealth: Managing & Measuring Knowledge-Based Assets. Berrett-Koehler Publishers, San Francisco (1997)Google Scholar
  15. 15.
    Edvinsson, L., Malone, M.S.: Intellectual Capital: Realizing Your Company’s True Value by Finding its Hidden Brainpower. Harper Business, New York (1997)Google Scholar
  16. 16.
    Dekker, R., De Hoog, R.: The monetary value of knowledge assets: a micro approach. Expert Syst. Appl. 18, 111–124 (2000)CrossRefGoogle Scholar
  17. 17.
    Liebowitz, J., Wright, K.: Does measuring knowledge make ‘‘cents’’? Expert Syst. Appl. 17, 99–103 (1999)CrossRefGoogle Scholar
  18. 18.
    Wu, L.-C., Ong, C.-S., Hsu, Y.-W.: Knowledge-based organization evaluation. Decis. Support Syst. 45, 541–549 (2008)CrossRefMATHGoogle Scholar
  19. 19.
    Wi, H., Mun, J., Oh, S., Jung, M.: Modeling and analysis of project team formation factors in a project-oriented virtual organization (ProVO). Expert Syst. Appl. 36, 5775–5783 (2009)CrossRefGoogle Scholar
  20. 20.
    Pereira, S.C., Soares, A.L.: Improving the quality of collaboration requirements for information management through social networks analysis. Int. J. Inf. Manage. 27, 86–103 (2007)CrossRefGoogle Scholar
  21. 21.
    Marsden, P.V.: Egocentric and sociocentric measures of network centrality. Soc. Netw. 24, 407–422 (2002)CrossRefGoogle Scholar
  22. 22.
    Wasserman, S., Faust, K.: Social Network Analysis: Methods and Applications. Cambridge university press Cambridge (1994)Google Scholar
  23. 23.
    Chen, L., Gable, G., Hu, H.: Communication and organizational social networks: a simulation model. Comput. Math. Organ. Theor. 18, 1–20 (2012)Google Scholar
  24. 24.
    Zou, G., Yilmaz, L.: Dynamics of knowledge creation in global participatory science communities: open innovation communities from a network perspective. Comput. Math. Organ. Theor. 17, 35–58 (2011)CrossRefGoogle Scholar
  25. 25.
    Beaudry, C., Schiffauerova, A.: Impacts of collaboration and network indicators on patent quality: the case of Canadian nanotechnology innovation. Eur. Manage. J. 29, 362–376 (2011)CrossRefGoogle Scholar
  26. 26.
    Ynalvez, M.A., Shrum, W.M.: Professional networks, scientific collaboration, and publication productivity in resource-constrained research institutions in a developing country. Res. Policy 40, 204–216 (2011)CrossRefGoogle Scholar
  27. 27.
    Juran, J., Godfrey, A.B.: Quality Handbook. McGraw-Hill, New York (1999). RepublishedGoogle Scholar
  28. 28.
    Ahire, S.L.: Management science-total quality management interfaces: an integrative framework. Interfaces 27, 91–105 (1997)CrossRefGoogle Scholar
  29. 29.
    Tennant, G.: Six Sigma: SPC and TQM in Manufacturing and Services. Gower Publishing Ltd, Aldershot (2001)Google Scholar
  30. 30.
    Yang, S.J.H., Chen, I.Y.L.: A social network-based system for supporting interactive collaboration in knowledge sharing over peer-to-peer network. Int. J. Hum. Comput. Stud. 66, 36–50 (2008)CrossRefGoogle Scholar
  31. 31.
    Kruglinski, D., Wingo, S., Shepherd, G.: Inside Visual C++ 6.0. Microsoft press (2005)Google Scholar
  32. 32.
    Fuzzy Logic Design Tools; Xfuzzy 3, IMSE-CNM (2012)
  33. 33.
    Zhang, Y., Qiao, L.: Virtual Instrument Software Development Environment: Labwindows/Cvi 6.0 Programming Guide. Mechanical industry press, Beijing (2002)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  1. 1.Technology Commercialization GroupResearch Institute of Industrial Science and Technology (RIST)Pohang-siRepublic of Korea
  2. 2.School of Technology ManagementUlsan National Institute of Science and Technology (UNIST)UlsanRepublic of Korea

Personalised recommendations