Multidimensional Performance Tracking
Commenced in January 2007
Frequency: Monthly
Edition: International
Paper Count: 32918
Multidimensional Performance Tracking

Authors: C. Ardil


In this study, a model, together with a software tool that implements it, has been developed to determine the performance ratings of employees in an organization operating in the information technology sector using the indicators obtained from employees' online study data. Weighted Sum (WS) Method and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method based on multidimensional decision making approach were used in the study. WS and TOPSIS methods provide multidimensional decision making (MDDM) methods that allow all dimensions to be evaluated together considering specific weights, allowing employees to objectively evaluate the problem of online performance tracking. The application of WS and TOPSIS mathematical methods, which can combine alternatives with a large number of dimensions and reach simultaneous solution, has been implemented through an online performance tracking software. In the application of WS and TOPSIS methods, objective dimension weights were calculated by using entropy information (EI) and standard deviation (SD) methods from the data obtained by employees' online performance tracking method, decision matrix was formed by using performance scores for each employee, and a single performance score was calculated for each employee. Based on the calculated performance score, employees were given a performance evaluation decision. The results of Pareto set evidence and comparative mathematical analysis validate that employees' performance preference rankings in WS and TOPSIS methods are closely related. This suggests the compatibility, applicability, and validity of the proposed method to the MDDM problems in which a large number of alternative and dimension types are taken into account. With this study, an objective, realistic, feasible and understandable mathematical method, together with a software tool that implements it has been demonstrated. This is considered to be preferable because of the subjectivity, limitations and high cost of the methods traditionally used in the measurement and performance appraisal in the information technology sector.

Keywords: Weighted sum, entropy ınformation, standard deviation, online performance tracking, performance evaluation, performance management, multidimensional decision making.

Digital Object Identifier (DOI):

Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 1084


[1] Hennessy, J., Patterson, D. (2011). Computer Architecture: A Quantitative Approach.The Morgan Kaufmann Series in Computer Architecture and Design. USA
[2] Gerrish, Ed. (2015). The Impact of Performance Management on Performance in Public Organizations: A Meta-Analysis. Public Administration Review 76 (1):48–66.
[3] Torrington, D., Hall, L.(1995). Personel Management. HRM in Action (3e).
[4] Pugh, D. (1991). Organizational Behaviour. Prentice Hall Interneational (UK) Ltd.
[5] Bolton, T. (1997). Human Resource Management: An introduction. Massachusetts: Blackwell Publishers.
[6] Armstrong, M., (1996). Employee Reward, Institute of Personnel and Development IPD House, London.
[7] Schermerhorn, J.R. (1989). Management and Productivity, (Third Ed.), New York, John Wiley and Sons Inc.
[8] Barkey, J.B. (2002). Gaining and Sustaining Competitive Advantage. New Jersey: Prentice Hall.
[9] Knouse, S.B. (1996). Management Perspectives on TQM Concepts and Practices. ASQ Quality Press, (January 1996).
[10] de Waal, A. A., Counet, H. (2009). Azons Learned from Performance Management Systems Implementations. International Journal of Productivity and Performance Management, 58(4), 367–390.
[11] Paauwe, J., Boselie, P. (2005). HRM and Performance: What Next? Human Resource Management Journal, 16.34
[12] Tung, A., Baird, K., Schoch, H. P. (2011). Factors influencing the effectiveness of performance measurement systems. International Journal of Operations & Production Management, 1287-1310.
[13] Mabey, Christopher; Salaman, Graeme; Storey, John. (1999). Human Resource Management: A Strategic Introduction, 2nd Edition. Blackwell Publishers Ltd.
[14] Locke E A, Saari L M, Shaw K N, Latham G P.(1981). Goal setting and task performance: 1969-1980. Psychol. Bull. 90:125-52.
[15] Vroom, V.H., Deci, E.L., (1983). Management and Motivation, Penguin (first published 1970)
[16] Saaty, Thomas L.(1986). Axiomatic Foundation of the Analytic Hierarchy Process. Management Science, Volume 32, Issue 7, pp. 841 - 855.
[17] Zeleny, M. (1982). Multiple Criteria Decision Making. USA: McGraw-Hill Book Company.
[18] 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, Volume 107, Issue 3, 16 June 1998, pp. 507-529.
[19] Yu, P.L. (1985). Multiple-criteria Decision Making: Concepts, Techniques, and Extensions, Plenum Publishing Corporation, New York.
[20] Liu, X. (2004). On the methods of decision making under uncertainty with probability information, Int. J. Intel. Syst. 19, 1217–1238.
[21] 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.
[22] Jahanshahloo, G.R., Lotfi, F. H., Izadikhah, M. (2006).An algorithmic method to extend TOPSIS for decision-making problems with interval data, Applied Mathematics and Computation, Volume 175, Issue 2, 15 April 2006, pp. 1375-1384.
[23] Hwang, C. L., Yoon, K. (1981). Multiple Attributes Decision Making Methods and Applications, Springer-Verlag, Berlin, Heidelberg.
[24] Ribeiro, R.A. (1996). Fuzzy multiple attribute decision making: a review and new preference elicitation techniques, Fuzzy Sets Syst. 78, 155–181.
[25] Saaty, T.L. (1983). Priority Setting in Complex Problems, IEEE Transactions on Engineering Management, vol. 30, no. 3,, pp. 140-155.
[26] Triantaphyllou, E., S.H. Mann (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.
[27] Scheubrein, R., Zionts, S. (2006). A problem structuring front end for a multiple criteria decision support system, Computers and Operations Research 33, 18 – 31.
[28] Tzeng, G.H., Huang, J.J. (2011). Multiple Attribute Decision Making Methods and Applications. USA: CRC Publishers.
[29] Triantaphyllou, E., Shu, B., Nieto Sanchez, S., Ray T. (1998). Multi-Criteria Decision Making: An Operations Research Approach, Encyclopedia of Electrical and Electronics Engineering, 15: 175-186.
[30] Guarnieri, P. (2015). Decision Models in Engineering and Management, Springer International Publishing AG.
[31] 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 (2006) 1–22.
[32] Bernroider, W.N., Mitlöhner, J. (2005). Characteristics of the Multiple Attribute Decision Making Methodology in Enterprise Resource Planning Software Decisions. Communications of The IIMA. 5(1): 49-57.
[33] Triantaphyllou, E. (2000). Multi-criteria Decision Making Methods: A Comparative Study, Kluwer Academic Publishers, Dordrecht.
[34] Choo, E.U., Schoner, B., Wedley, W.C., (1999). Interpretation of Criteria Weights in Multicriteria Decision Making. Computers and Industrial Engineering. 37: 527-541.
[35] Malczewski, J. (1999). GIS and Multi-Criteria Decision Analysis. JohnWiley & Sons, Inc., New York.
[36] Al-Ahmari, A. M. A. (2008). A methodology for selection and evaluation of advanced manufacturing technologies, International Journal of Computer Integrated Manufacturing, Volume 21, 2008, Issue 7, pp. 778-789.
[37] Várhelyi, A.,Kaufmann, C., Persson, A. (2015). User-related assessment of a Driver Assistance System for Continuous Support – A field trial. Transportation Research Part F: Traffic Psychology and Behaviour, Volume 30, April 2015, pp. 128-144.
[38] Huesmann, A., Farid, M., Muhrer, E. (2016). From Controllability to Safety in Use: Safety Assessment of Driver Assistance Systems,Automated Driving, pp 495-518.
[39] Tamke, A., Dang, T., Breuel, G. (2011). A flexible method for criticality assessment in driver assistance systems,Intelligent Vehicles Symposium (IV), 2011 IEEE, 5-9 June 2011, Baden-Baden, Germany.
[40] Yu, D. (2017). Hesitant fuzzy multi-criteria decision making methods based on Heronian mean, Technological and Economic Development of Economy, Volume 23, 2017, Issue 2, pp. 296-315.
[41] Michalke, T.P., Nagarathinam, A., Schafers, L. (2011). A Dynamic Approach for Ensuring the Functional Reliability of Next-Generation Driver Assistance Systems, 14th International IEEE Conference on Intelligent Transportation Systems (ITSC 2011), October 5-7, 2011, Washington DC, US.
[42] Beg, I., Rashid, T. (2017). Modelling Uncertainties in Multi-Criteria Decision Making using Distance Measure and TOPSIS for Hesitant Fuzzy Sets, Journal of Artificial Intelligence and Soft Computing Research, Volume 7, Issue 2 (Apr 2017).
[43] Shannon, C. (1948). A Mathematical Theory of Communication, Bell System Technical Journal 27, 379-423.
[44] Xiaoxing L., Krishnan, A., Mondry, A. (2005). An Entropy-based gene selection method for cancer classification using microarray data, BMC Bioinformatics 2005, 6:76, pp.1-14.
[45] Wang, J.-q., Wu, J.-t., Wang, J., Zhang, H.-y., Chen,X.-h. (2016). Multi-criteria decision-making methods based on the Hausdorff distance of hesitant fuzzy linguistic numbers, Soft Computing, April 2016, Volume 20, Issue 4, pp. 1621–1633.
[46] 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.
[47] Shih H. S., Shyurb, H. J., Lee, E. S. (2007). An Extension of TOPSIS for Group Decision Making. Mathematical and Computer Modelling, 45(7-8): 801–813.
[48] Chen, M. F., Tzeng, G. H. (2004). Combining grey relation and TOPSIS concepts for selecting an expatriate host country. Mathematical and Computer Modelling, 40, 1473-1490.
[49] Abo-Sinna, M. A. Amer, A. H. (2005). Extensions of TOPSIS for multi-objective large-scale nonlinear programming problems, Applied Mathematics and Computation, vol. 162, pp. 243-256.
[50] Cheng, S., Chan, C. W., Huang, G. H. (2002). Using Multiple Criteria Decision Analysis For Supporting Decisions of Solid Waste Management. Journal of Environmental Science and Health, 37(6): 975-990.
[51] Zitzler, E., Thiele, L. (1999). Multiobjective Evolutionary Algorithms: A Comparative Case Study and the Strength Pareto Approach, in IEEE Transactions on Evolutionary Computation, Vol. 3, No. 4. pp. 257-271.
[52] Zadeh, L. (1963). Optimality and non-scalar-valued performance criteria, in IEEE Transactions on Automatic Control.
[53] Zadeh, L.A., (1965). Fuzzy sets, Information and Control, 8, 338-353.
[54] Zimmermann, H.J. (1990). Fuzzy Set Theory and its Application, Kluwer Academic Publishers, Boston, 35-85.
[55] Yu, V. F., Hu, K. J. (2010). An Integrated Fuzzy Multi-Criteria Approach for The Performance Evaluation of Multiple Manufacturing Plants. Computers and Industrial Engineering, 58(2), 269–277.
[56] 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 (ISSN 1613-0073).
[57] Myers, J.L, Well, A.D. (2003). Research Design and Statistical Analysis (2.ed.), Lawrence Erlbaum.
[58] Chen P Y, Popovich P M. (2002). Correlation: Parametric and Nonparametric Measures. California: Sage Publications, 2002.
[59] Kendall M G, Babington-Smith B. (1939). The Problem of m Rankings. The Annals of Mathematical Statistics, 1939; 10 (3): 275- 287.
[60] Siegel S. (1956). Nonparametric Statistics for the Behavioral Sciences. New York: McGraw- Hill, 1956.
[61] Mitchell, R. K., Agle R., B., Wood, D. J. (1997). Toward a Theory of Stakeholder Identification and Salience: Defining the Principle of Who and What Really Counts. The Academy of Management Review, Vol. 22, No. 4 (Oct., 1997), pp. 853-886.
[62] Churchman, C.W., Ackoff, R.L. (1954). An Approximate Measure of Value.Journal of Operations Research Society of America 78, 367-379.
[63] MacCrimmon, K.R. (1968).Decision making among multiple – attribute alternatives: A Survey and Consolidated Approach. RAND Memorandum,RM-4823-ARPA.
[64] Klee, A.J. (1971). The Role of Decision Models in the Evaluation of Competing Environmental Health Alternatives. Management Science 18(2),52-67.
[65] Miettinen, K. M. (1998). Nonlinear Multiobjective Optimization. Kluwer Academic Publishers, Boston, Massachusetts, USA, 1998.
[66] Deng H, Yeh CH, Willis RJ. (2000). Inter-company comparison using modified TOPSIS with objective weights. Comput.Oper. Res. 27 963-973.
[67] Diakoulaki D, Mavrotas G, Papayannakis L (1995). Determining objective weights in multiple criteria problems: The CRITIC method. Comput. Oper. Res. 22 763-770.
[68] Ying-Ming W., Ying L.(2010).Integration of correlations with standard deviations for determining attribute weights in multiple attribute decision making. Mathematical and Computer Modelling 51 (2010) 1–12.
[69] Weitendorf, D. (1976). Beitrag zur Optimierung der räumlichen Struktur eines Gebäudes. Dissertation A, Hochschule für Architektur und Bauwesen, Weimar.
[70] The Model View Controller (MVC) Pattern
[71] The Model-View-Controller (MVC)
[72] Vujičić, Momčilo D.,Papić, Miloš Z., Blagojević, Marija D.(2017) Comparative Analysis of Objective Techniques for Criteria Weighing in Two MCDM Methods on Example of an Air Conditioner Selection, Tehnika – Menadžment 67 (2017) 3, pp.422-429.