Performance Evaluation for a Balanced Scorecard System by Group Decision Making with Fuzzy Assessments

Chi-Bin Cheng^*

Department of Information Management, Tamkang University 151 Ying-chuan Road, Tamsui, Taipei County, Taiwan.

REFERENCES

1. [1] Kaplan, R. S., and D. P. Norton. 1992. The balanced scorecard as a strategic management system. Harvard Business Review, January-February: 61-66.
   
   
2. [2] Kaplan, R. S., Kaplan, R. D. P., and Norton, D. 1993. Putting the Balanced Scorecard to Work. Harvard Business Review, Sep-Oct, 134-147.
   
   
3. [3] Ittner, C. D., and Larcker, D. F. 1998. Innovations in performance measurement: Trends and research implications. Journal of Management Accounting Research, 10: 205-238.
   
   
4. [4] Ittner, C. D., Larcker, D. F. Meyer, M. W. 2003. Subjectivity and the Weighting of Performance Measures: Evidence form a Balanced Scorecard, The Accounting Review, 78, 3: 725-758.
   
   
5. [5] McKenzie, F. C., and Shiling, M. D. 1998. Avoiding performance measurement trap: Ensuring effective incentive design and implementation. Compensation and Benefits Review, 30: 57-65.
   
   
6. [6] S. Baiman., and Rajan, M. V. 1995, The informational advantages of discretionary bonus schemes, The Accounting Review, 70, 4: 557-579.
   
   
7. [7] Prendergast, C., and Topel, R. 1993. Discretion and bias in performance evaluation, European Economic Review, 37, 2-3: 355-365.
   
   
8. [8] Kaplan, R. S., and Norton, D. P. 1996. “The Balanced Scordcard”, Boston, MA: Harvard Business School Press.
   
   
9. [9] Baddeley, A. 1994. The magical number seven: still magic after all these years? Psychological Review, April, 353-356.
   
   
10. [10] Saaty, T. L. 1980. “The Analytic Hierarchy Process”, McGraw-Hill, New York.
    
    
11. [11] Lipe, M. G., and Salterio, S. E. 2000. The balanced scorecard: judgmental effects of common and unique performance measures, The accounting Review, 75, 3: 283-298.
    
    
12. [12] Zadeh, L. A. 1975. The concept of a linguistic variable and its application to approximate reasoning, Information Science, 8: 199-249.
    
    
13. [13] Syau, Y. R., Hsieh, H. T., and Lee, E. S. 2001. Fuzzy numbers in the credit Rating of Enterprise Financial Condition, Review of Quantitative Finance and Accounting, 17: 351-360.
    
    
14. [14] Dubois, D., and Prade, H. 1980. “Fuzzy Sets and Systems: Theory and Applications”, Academic Press, New York, U.S.A.
    
    
15. [15] Chang, P. T. 1994. “Fuzzy Regression analysis”, Ph.D. dissertation, Department of Industrial and Manufacturing Engineering, Kansas State University.
    
    
16. [16] Jang, J. S., Sun, C. T., and Mizutani, E. 1997. “Neuro-Fuzzy and Soft Computing: A Computational Approach to Learning and Machine Intelligence”, Prentice Hall, Upper Saddle River, N. J. U.S.A.
    
    
17. [17] Zanakis, S., Mandakovic, T., Gupta, S., Sahay, S., and Hong, S. 1995. A review of program evaluation and fund allocation methods within the service and government sectors, Socio-Economic Planning Sciences, 29:59-79.