Email this article MachineLearningAlgorithm to predict and improve efficiency of employee performance in organizations
Abstract
Employee performance has been identified as a critical problem for companies because of its negative effect on operational productivity and long period evolution plans. To solve this problem, companies use machine learning algorithms to anticipate workplace efficiency. Precise forecasts enable organizations to act on preservation or succession planning of employees. However, the data for the modeling issue originates from HR Information Systems; It is generally less in relation to other areas of the companies information systems and is clearly relevant to its objectives This contributes to the presence of redundant values in the data that makes predictive models vulnerable to over-fitting and thus unreliable. This is the central subject based on in this article, and one that has not been discussed conventionally. Using HRIS data from a global retailer, XGBoost is calculated against six widely used supervised classification method and reveals its considerably higher precision for employee performance estimation.
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S. Jahan, “Human Resources Information System (HRIS): A Theoretical Perspective”, Journal of Human Resource and Sustainability Studies, Vol.2 No.2, Article ID:46129,2014.
M. Stoval and N. Bontis, “Voluntary performance: Knowledge management– Friend or foe?”, Journal of Intellectual Capital, 3(3), 303- 322,2002.
J. L. Cotton and J. M. Tuttle, “Employee performance: A meta- analysis and review with implications for research”, Academy of management Review, 11(1), 55-70,1986.
L. M. Finkelstein, K. M. Ryanand E.B. King, “What do the young (old) people think of me? Content and accuracy of age-based metastereotypes”, European Journal of Work and Organizational Psychology, 22(6), 633-657,2013.
B. Holtom, T. Mitchell, T. Lee, and M. Eberly, “Performance and retention research: A glance at the past, a closer review of the present, and a venture into the future”, Academy of Management Annals, 2: 231-274,2008
C. von Hippel, E. K. Kalokerinos and J. D. Henry, “Stereotype threat among older employees: Relationship with job attitudes and performance intentions”, Psychology and aging, 28(1), 17,2013.
S. L. Peterson, “Toward a theoretical model of employee performance: A human resource development perspective”, Human Resource Development Review, 3(3), 209-227,2004.
J. M. Sacco and N. Schmitt, “A dynamic multilevel model of demographic diversity and misfit effects”, Journal of Applied Psychology, 90(2), 203-231,2005.
D. G. Allen and R. W. Griffeth, “Test of a mediated performance – Performance relationship highlighting the moderating roles of visibility and reward contingency”, JournalofAppliedPsychology,86(5),1014-1021,2001.
D. Liu, T. R. Mitchell, T. W. Lee, B. C. Holtom, and T. R. Hinkin, “When employees are out of step with coworkers: How job satisfaction trajectory and dispersion influence individual-and unit-level voluntary performance”, Academy of Management Journal, 55(6), 1360-1380,2012.
B. W. Swider, and R. D. Zimmerman, “Born to burnout: A meta- analytic path model of personality, job burnout, and work outcomes”, Journal of Vocational Behavior, 76(3), 487-506,2010.
T. M. Heckert and A. M. Farabee, “Performance intentions of the faculty at a teaching-focused university”, Psychological reports, 99(1), 39-45,2006.
H. Jantan, A. R. Hamdan, and Z. A. Othman, “Towards Applying Data Mining Techniques for Talent Managements”, 2009 International Conference on Computer Engineering and Applications, IPCSIT vol.2, Singapore, IACSIT Press,2011.
V. Nagadevara, V. Srinivasan, and R. Valk, “Establishing a link between employee performance and withdrawal behaviours: Application of data mining techniques”, Research and Practice in Human Resource Management, 16(2), 81-97, 2008.
W. C. Hong, S. Y. Wei, and Y. F. Chen, “A comparative test of two employee performance prediction models”, International Journal of Management, 24(4), 808,2007.
L. K. Marjorie, “Predictive Models of Employee Voluntary Performance in a North American Professional Sales Force using Data-Mining Analysis”, Texas, A&M University College of Education,2007.
D. Alao and A. B. Adeyemo, “Analyzing employee attrition using decision tree algorithms”, Computing, Information Systems, Development Informatics and Allied Research Journal, 4,2013.
V. V. Saradhi and G. K. Palshikar, “Employee churn prediction”, Expert Systems with Applications, 38(3), 1999- 2006,2011.
D. Michie, D. J. Spiegelhalter, and C. C. Taylor, Machine Learning, Neural and Statistical Classification. Ellis Horwood Limited,1994.
G. King and L. Zeng, “Logistic regression in rare events data”, Political Analysis, 9(2), 137–163,2001.T. Mitchell, Machine learning. 2nd ed. USA: McGraw Hill, 1997.
H. A. Elsalamony (2014), “Bank direct marketing analysis of data mining techniques”, International Journal of Computer Applications,85(7).
A. Liaw and M. Wiener, “Classification and regression by randomForest”, R news, 2(3), 18-22,2002.
L. Breiman, Random forests. Machine Learning, 45(1), 5–32, 2001.
P. Cunningham and S. J. Delany, “k-Nearest neighbour classifiers”, Multiple Classifier Systems, 1-17,2007.
C. Cortes and V. Vapnik, Support-vector networks. Machine learning, 20(3), 273-297,1995.
Y. Freund and R. E. Schapire, “A decision-theoretic generalization of on-line learning and an application to boosting”, Journal of computer and system sciences, 55(1), 119-139,1997.
J. H. Friedman, “Greedy function approximation: a gradient boosting machine”, Annals of statistics, 1189-1232,2001.
S. Lessmann and S. Voß, “A reference model for customer- centric data mining with support vector machines”, EuropeanJournalofOperationalResearch199,520–530,2009.T. Fawcett, “An introduction to ROC analysis”, Pattern Recognition Letters 27 (8), 861–874,2006.
DOI: https://doi.org/10.26483/ijarcs.v11i0.6534
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