Abstract

This paper describes the conceptual framework, development process, and theoretical structure for an online performance tracking system. The principle factors influencing online performance tracking are described using the weighted sum model as computational method on measures of performance. Input data for the computational model were obtained directly from a realtime system in an actual organization that directly measured staff performance. In this multicriteria decision-making approach, the criteria weights are computed using the entropy information method and ranking of 15 alternatives (employees) is computed using the weighted sum model. Computational results obtained using the online performance appraisal system are evaluated and discussed relative to the weighted sum model.

Keywords: performance appraisal, performance evaluation, weighted sum model, entropy information method, online performance tracking, multicriteria decision making, multicriteria analysis, reference objective theory.

JEL Classification: C53.

Cite as: Ardil, C., Bilgen, S. (2017). Online Performance Tracking. SocioEconomic Challenges, 1(3), 58-72. DOI: 10.21272/sec.1(3).58-72.2017

References

1. DeNisi, A. S., Pritchard, R. D. (2006). Performance appraisal, performance management and improving individual performance: a motivational framework. Management and Organization Review, 2(2), 253-277.
2. Murphy, K. R., Cleveland, J. N. (1991). Performance Appraisal: An Organizational Perspective. Allyn and Bacon, Boston.
3. Yu, P.L. (1985). Multiple-criteria Decision Making: Concepts, Techniques, and Extensions. Plenum Publishing Corporation, New York.
4. Hwang, C. L., Yoon, K. (1981). Multiple Attribute Decision Making Methods and Applications. Springer, Berlin Heidelberg.
5. Triantaphyllou, E. (2000). Multi-criteria Decision Making Methods: A Comparative Study. Kluwer Academic Publishers, Dordrecht.
6. Zadeh, L. A. (1965) Fuzzy sets. Information and Control, 8 (3) 338–353.
7. Wang, H. F. (2000). Fuzzy Multicriteria Decision Making – an Overview, J. Intel. Fuzzy Syst., 9, 61-83.
8. Chen, S. J., Hwang, C. L. (1991). Fuzzy Multiple Attribute Decision Making. Springer Verlag, Berlin.
9. Ribeiro, R. A. (1996). Fuzzy multiple attribute decision making: a review and new preference elicitation techniques, Fuzzy Sets Syst. 78, 155-181.
10. Kall, P., Wallace, S.W. (1994). Stochastic Programming. Wiley, Chichester.
11. Sengupta, J. K. (1981). Optimal Decision under Uncertainty. Springer, New York.
12. Vajda, S. (1972). Probabilistic Programming. Academic Press, New York.
13. Liu, X. (2004). On the methods of decision making under uncertainty with probability information. Int. J. Intel. Syst., 19, 1217-1238.
14. Saaty, T. L. (1980). The Analytic Hierarchy Process. McGraw-Hill, New York.
15. Saaty, T. L. (1983). Priority Setting in Complex Problems. IEEE Transactions on Engineering Management, 30(3), 140-155.
16. Zanakis, S. H., Solomon, A., Wisharta, N., Dublish, S. (1998) Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507-529.
17. Oraee, K., Bakhtavar, E. (2010). Selection of Tunnel Support System by Using Multi Criteria Decision-Making Tools, 29th International Conference on Ground Control in Mining.
18. Abo-Sinna, M. A., Amer, A. H. (2005). Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems. Applied Mathematics and Computation, 162, 243-256.
19. Jahanshahloo, G. R., Lotfi, F. H., Izadikhah, M. (2005). An algorithmic method to extend TOPSIS for decision-making problems with interval data. Applied Mathematics and Computation, 175(2), 1375-1384.
20. Zitzler, E. and Thiele, L. (1999). Multiobjective Evolutionary Algorithms. In IEEE Transactions on Evolutionary Computation, 3(4), November.
21. Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria. In IEEE transactions on Automatic Control.
22. Helff, F., Gruenwald, L., d’Orazio, L. (2016). Weighted Sum Model for Multi-Objective Query Optimization for Mobile-Cloud Database Environments, in the Workshop Proceedings of the EDBT/ICDT 2016 Joint Conference (March 15, 2016, Bordeaux, France) on CEUR-WS.org (ISSN 1613-0073).
23. Triantaphyllou, E., Mann, S. H. (1989). An Examination of the Effectiveness of Multi-Dimensional Decision-Making Methods: A Decision-Making Paradox. International Journal of Decision Support Systems, 5, 303-312.
24. Mendoza, G. A., Martins, H. (2006). Multi-criteria decision analysis in natural resource management: A critical review of methods and new modelling paradigms. Forest Ecology and Management, 230, 1-22.
25. Malczewski, J. (1999). GIS and Multi-Criteria Decision Analysis. John Wiley & Sons, Inc., New York.
26. Shannon, C. (1948). A Mathematical Theory of Communication. Bell System Technical Journal, 27, 379-423.
27. Xiaoxing, L., Krishnan, A., Mondry, A. (2005). An Entropy-based gene selection method for cancer classification using microarray data. BMC Bioinformatics, 6(76), 1-14.
28. Fishburn, P. C. (1967). Additive Utilities with Incomplete Product Set: Applications to Priorities and Assignments. Operations Research Society of America (ORSA), Baltimore, MD, U.S.A.
29. Karlin, S., Studden, W. J. (1966). Tchebycheff Systems: with Applications in Analysis and Statistics. New York, Interscience Publishers.
30. Ignizio, J. P. (1978). A review of goal programming: a tool for multiobjective analysis. J. Opl. Res. Soc., 29(11), 1109-1119.
31. Zeleny, M. (1974). Linear multiobjective programming. Springer-Verlag, Heidelberg, Berlin, New York.