Advertisement

Simplifying the Manager Competency Model by Using the Rough Set Approach

  • Wei-Wen Wu
  • Yu-Ting Lee
  • Gwo-Hshiung Tzeng
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3642)

Abstract

It is now a leading company strategy to apply competency models for identifying and developing capabilities of their managers. However, a competency model usually contains too many intended competencies to be implemented. Recently, some scholars and experts argue that eight is the maximum for managers to assess. Hence, how to simplify the manager competency model is becoming an important issue. Well known as data mining techniques, the rough sets theory is a relatively new approach and good at data reduction in qualitative analysis, so that the rough set approach is suitable for dealing with the qualitative problem in simplifying the competency model. The aim of this paper is to mining the minimal set of competencies through using the rough set approach to help companies for better utilizing the competency model. The results show that the “self-management” competency is the most indispensable portion to a manager competency model.

Keywords

Middle Manager Decision Table Competency Model Decision Class Covering Index 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Athey, T.R., Orth, M.S.: Emerging Competency Methods for the Future. Human Resource Management 38(3), 215–226 (1999)CrossRefGoogle Scholar
  2. 2.
    Beynon, M.J., Peel, M.J.: Variable precision rough set theory and data discretisation: an application to corporate failure prediction. Omega: International Journal of Management Science 29(6), 561–576 (2001)CrossRefGoogle Scholar
  3. 3.
    Boyatzis, R.E.: The competent manager: A model for effective performance. John Wiley & Sons, New York (1982)Google Scholar
  4. 4.
    Curry, B.: Rough sets: current and future developments. The International Journal of Knowledge Engineering and Neural Networks 20(5), 247–250 (2003)Google Scholar
  5. 5.
    Dimitras, A.I., Slowinski, R., Susmaga, R., Zopounidis, C.: Business failure prediction using rough sets. European Journal of Operational Research 114(2), 263–280 (1999)zbMATHCrossRefGoogle Scholar
  6. 6.
    Dive, B.: Education Management. New Zealand Management, Auckland (2004)Google Scholar
  7. 7.
    Doumpos, M., Zopounidis, C.: Rough Sets and Multivariate Statistical Classification: A Simulation Study. Computational Economics 19(3), 287–301 (2002)zbMATHCrossRefGoogle Scholar
  8. 8.
    Dubois, D., Prade, H.: Putting rough sets and fuzzy sets together. In: Slowinski, R. (ed.) Intelligent Decision Support: Handbook of Applications and Advances of the Rough Sets Theory, pp. 203–232. Kluwer Academic Publishers, Dordrecht (1992)Google Scholar
  9. 9.
    Goh, C., Law, R.: Incorporating the rough sets theory into travel demand analysis. Tourism Management 24(5), 511–517 (2003)CrossRefGoogle Scholar
  10. 10.
    Greco, S., Matarazzo, B., Slowinski, R.: A new rough set approach to evaluation of bankruptcy risk. In: Zopounidis, C. (ed.) Operational Tools in the Management of Financial Risks, pp. 121–136. Kluwer Academic Publishers, Dordrecht (1998)Google Scholar
  11. 11.
    Greco, S., Matarazzo, B., Slowinski, R.: Rough sets methodology for sorting problems in presence of multiple attributes and criteria. European Journal of Operational Research 138(2), 247–259 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  12. 12.
    Hellriegel, D., Jackson, S.E., Slocum, J.W.: Management: A Competency-Based Approach. South-Western College Pub, Cincinnati (2004)Google Scholar
  13. 13.
    Japanese Style Competency Study Group: A Proposal for Japanese Style Competency Model. Tokyo: Japan Productivity Center for Socio-Economic Development (2000)Google Scholar
  14. 14.
    JPC-SED: The Fifth Survey on Changes in the Japanese-style Personnel System. Tokyo: Japan Productivity Center for Socio-Economic Development (2002)Google Scholar
  15. 15.
    Kelner, S.P.: A Few Thoughts on Executive Competency Convergence. Center for Quality of Management Journal 10(1), 67–72 (2001)MathSciNetGoogle Scholar
  16. 16.
    Krusinska, E., Slowinski, R., Stefanowski, J.: Discriminat versus rough set approach to vague data analysis. Applied Stochastic Models and Data Analysis 8(1), 43–56 (1992)zbMATHCrossRefGoogle Scholar
  17. 17.
    Mansfield, R.S.: Building Competency Models: Approaches for HR Professionals. Human Resource Management 35(1), 7–18 (1996)CrossRefGoogle Scholar
  18. 18.
    McClelland, D.C.: Testing for competence rather than for intelligence. American Psychologist 28(1), 1–24 (1973)CrossRefGoogle Scholar
  19. 19.
    Mi, J.S., Wu, W.Z., Zhang, W.X.: Approaches to knowledge reduction based on variable precision rough set model. Information Sciences 159(3-4), 255–272 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  20. 20.
    Mirable, R.: Everything You Wanted to Know About Competency Modeling. Training and Development 51(8), 73–77 (1997)Google Scholar
  21. 21.
    Mori, N., Tanaka, H., Inoue, K.: Rough sets and KANSEI: knowledge acquisition and reasoning from KANSEI data. Kaibundo, Japan (2004)Google Scholar
  22. 22.
    Pawlak, Z.: Rough sets. International Journal of Computer and Information Science 11(5), 341–356 (1982)zbMATHCrossRefMathSciNetGoogle Scholar
  23. 23.
    Pawlak, Z.: Rough Classification. International Journal of Man-Machine Studies 20(5), 469–483 (1984)zbMATHCrossRefGoogle Scholar
  24. 24.
    Pawlak, Z.: Rough Sets: Theoretical Aspects of Reasoning about Data. Kluwer Academic Publishers, Norwell (1992)Google Scholar
  25. 25.
    Pawlak, Z.: Rough Sets. In: Lin, T.Y., Cercone, N. (eds.) Rough Sets and Data Mining: Analysis for Imprecise Data, Kluwer Academic Publishers, Norwell (1997)Google Scholar
  26. 26.
    Pawlak, Z.: Rough sets, decision algorithms and Bayes’ theorem. European Journal of Operational Research 136(1), 181–189 (2002)zbMATHCrossRefMathSciNetGoogle Scholar
  27. 27.
    Quinn, J.B., Anderson, P., Syndey, F.: Managing professional intellect: Making the most of the best. Harvard Business Review 74(2), 71–80 (1996)Google Scholar
  28. 28.
    Rodriguez, D., Patel, R., Bright, A., Gregory, D., Gowing, M.K.: Developing Competency Models to Promote Integrated Human Resource. Human Resource Management 41(3), 309–324 (2002)CrossRefGoogle Scholar
  29. 29.
    Schoonover, S.C., Schoonover, H., Nemerov, D., Ehly, C.: Competency-Based HR Applications: Results of a Comprehensive Survey. Andersen/Schoonover/SHRM (2000)Google Scholar
  30. 30.
    Sinnott, G.C., Madison, G.H., Pataki, G.E.: Competencies: Report of the Competencies Workgroup, Workforce and Succession Planning Work Groups. New York State Governor’s Office of Employee Relations and the Department of Civil Service (2002)Google Scholar
  31. 31.
    Skowron, A., Grzymala-Busse, J.W.: From the rough set theory to the evidence theory. In: Fedrizzi, M., Kacprzyk, J., Yager, R.R. (eds.) Advances in the Dempster–Shafer Theory of Evidence, pp. 295–305. John Wiley & Sons, New York (1993)Google Scholar
  32. 32.
    Slowinski, R., Zopounidis, C.: Application of the rough set approach to evaluation of bankruptcy risk. International Journal of Intelligent Systems in Accounting, Finance and Management 4(1), 27–41 (1995)Google Scholar
  33. 33.
    Spencer, L.M., Spencer, S.M.: Competence at work: Model for superior performance. John Wiley & Sons, New York (1993)Google Scholar
  34. 34.
    Tay, F.E.H., Shen, L.: Economic and financial prediction using rough sets model. European Journal of Operational Research 141(3), 641–659 (2002)zbMATHCrossRefGoogle Scholar
  35. 35.
    Walczak, B., Massart, D.: Rough sets theory. Chemometrics and Intelligent Laboratory Systems 47(1), 1–16 (1999)CrossRefGoogle Scholar
  36. 36.
    Wang, Y.F.: Mining stock price using fuzzy rough set system. Expert Systems with Applications 24(1), 13–23 (2003)CrossRefGoogle Scholar
  37. 37.
    Works Institute: What is the competency? Works. 57 (1), 1-47, Japan, Recruit (2003)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Wei-Wen Wu
    • 1
  • Yu-Ting Lee
    • 2
  • Gwo-Hshiung Tzeng
    • 3
  1. 1.International Trade DepartmentTa Hwa Institute of TechnologyHsin-ChuTaiwan
  2. 2.Distinguished Chair Professor G.-H. Tzeng, Institute of Management of TechnologyNational Chiao Tung UniversityHsinchuTaiwan
  3. 3.Department of Business AdministrationKainan UniversityTaoyuanTaiwan

Personalised recommendations