A Graph Theoretic Approach for Employee Performance Appraisal

Document Type : Original Article

Authors

1 Assistant Professor, Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

2 PhD Student, Faculty of Mathematical Sciences, Sharif University of Technology, Tehran, Iran

Abstract

In an organization, employees are often evaluated (ranked) by multiple supervisors (or co-workers) in different time periods. Providing a single ranking based on these evaluations in such a way that it has the most compatibility and consistency with the provided evaluations is called rank aggregation. This rank aggregation problem, which has wide applications in various sciences, has been studied extensively theoretically in mathematics and computer science. In this paper, we study for the first time, the application of rank aggregation in the field of performance appraisal. In this application, the performance rankings of employees are given by a number of evaluators and the goal is to provide a final ranking for the employees that has the most similarity to the given evaluations. Other than obtaining the most consistent rankings, we show that more inference results like the quality of the evaluators and informal relationships between individuals can be obtained as by-product of this process of rank aggregation.

Keywords

References

[1]          Dwork C., Kumar R., Naor M., Sivakumar D., Rank aggregation revisited, 2001
[2]          Jackson B.N., Schnable P.S., Aluru S., Consensus genetic maps as median orders from inconsistent sources, IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2008, 5, 161–171
[3]          Fagin R., Kumar R., Sivakumar D., Efficient similarity search and classification via rank aggregation, In: Proceedings of the 2003 ACM SIGMOD International Conference on Management of Data, 2003, 301–312
[4]          Dwork C., Kumar R., Naor M., Sivakumar D., Rank aggregation methods for the web, In: Proceedings of the 10th International Conference on World Wide Web, 2001, 613–622
[5]          Borda J.-C. de, Mémoire sur les élections au scrutin: Histoire de l’, Paris, France, 1781, 12
[6]          de Caritat M.J.A.N., De Condorcet M., Essai sur l’application de l’analyse à la probabilité des décisions rendues à la pluralité des voix, 1785
[7]          Emond E.J., Mason D.W., A new rank correlation coefficient with application to the consensus ranking problem, Journal of Multi-Criteria Decision Analysis, 2002, 11, 17–28
[8]          Cook W.D., Kress M., Seiford L.M., An axiomatic approach to distance on partial orderings, RAIRO-Operations Research, 1986, 20, 115–122
[9]          Diaconis P., Graham R.L., Spearman’s footrule as a measure of disarray, Journal of the Royal Statistical Society: Series B (Methodological), 1977, 39, 262–268
[10]        Coppersmith D., Fleischer L., Rudra A., Ordering by weighted number of wins gives a good ranking for weighted tournaments, In: Proceedings of the Seventeenth Annual ACM-SIAM Symposium on Discrete Algorithm, 2006, 776–782
[11]        Demetrescu C., Finocchi I., Combinatorial algorithms for feedback problems in directed graphs, Information Processing Letters, 2003, 86, 129–136
[12]        Ailon N., Charikar M., Newman A., Aggregating inconsistent information: ranking and clustering, Journal of the ACM (JACM), 2008, 55, 1–27
[13]        Karp R.M., Reducibility among combinatorial problems, In: Complexity of Computer Computations, Springer, 1972, 85–103
[14]        Shaout A., Yousif M.K., Performance evaluation–Methods and techniques survey, International Journal of Computer and Information Technology, 2014, 3, 966–979
[15]        Rahmati A., Noorbehbahani F., A new hybrid method based on fuzzy AHP and fuzzy TOPSIS for employee performance evaluation, In: 2017 IEEE 4th International Conference on Knowledge-Based Engineering and Innovation (KBEI), 2017, 165–171
[16]        Mehrabanpour, M., Raei Ezabadi, M. E., Akhlaghi FeyzAsar, R. Designing a Decision Support System for Ranking the Subsidiaries in Multi-disciplined Holding Companies. Modern Research in Decision Making, 2019,4, 36-70.
[17]        Nahid Titkanlu, H., Keramati, A. Applying Evidence Theory to Aggregate Feedbacks in 360 Degree Feedback Model. Modern Research in Decision Making, 2018, 3, 275-299.
[18]        Torkashvand, A., Azar, A. Assessing the Teaching Researching Performance With the Help of Data Envelopment Analysis Model: Teaching Groups of Humanity Sciences Faculty, Tarbiat Modares University. Management Research in Iran, 2006, 10, 1-23.
[19]        Anvary Rostamy, A. A., Ghodratian Kashan, S. A. Designing a comprehensive model to evaluate performance and rank of a company. Management Research in Iran, 2004, 8, 109-135.
[20]        Jafari M., Bourouni A., Amiri R.H., A new framework for selection of the best performance appraisal method, European Journal of Social Sciences, 2009, 7, 92–100
[21]        Dessler G., Human resource management, Prentice Hall, 2000
[22]        Kemeny J.G., Mathematics without numbers, Daedalus, 1959, 88, 577–591
[23]        Kemeny J., Snell J., Mathematical models in the social sciences. Blaisdell, New York, 1962, 1972
[24]        Sigmund P.E., Nicholas of Cusa and medieval political thought, 1963
[25]        Black D., others, The theory of committees and elections, 1958
[26]        Kendall M.G., A new measure of rank correlation, Biometrika, 1938, 30, 81–93
[27]        Lapata M., Automatic evaluation of information ordering: Kendall’s tau, Computational Linguistics, 2006, 32, 471–484
[28]        Signorino C.S., Ritter J.M., Tau-b or not tau-b: Measuring the similarity of foreign policy positions, International Studies Quarterly, 1999, 43, 115–144
[29]        Kemeny J.G., Snell L.J., Preference ranking: an axiomatic approach, Mathematical Models in the Social Sciences, 1962, 9–23
[30]        Bartholdi J., Tovey C.A., Trick M.A., Voting schemes for which it can be difficult to tell who won the election, Social Choice and Welfare, 1989, 6, 157–165
 

Files

Share

How to cite

Statistics