Main Article Content
Employee performance has been identified as a critical problem for companies because of its negative effect on operational productivity and long period evolution plans.Â 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 companiesÂ 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.
Submission of a manuscript implies: that the work described has not been published before, that it is not under consideration for publication elsewhere; that if and when the manuscript is accepted for publication, the authors agree to automatic transfer of the copyright to the publisher.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgment of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work
- The journal allows the author(s) to retain publishing rights without restrictions.
- The journal allows the author(s) to hold the copyright without restrictions.
S. Jahan, â€œHuman Resources Information System (HRIS): A Theoretical Perspectiveâ€, Journal of Human Resource and Sustainability Studies, Vol.2 No.2, Article ID:46129,2014.
M. Stoval and N. Bontis, â€œVoluntary performance: Knowledge managementâ€“ Friend or foe?â€, Journal of Intellectual Capital, 3(3), 303- 322,2002.
J. L. Cotton and J. M. Tuttle, â€œEmployee performance: A meta- analysis and review with implications for researchâ€, Academy of management Review, 11(1), 55-70,1986.
L. M. Finkelstein, K. M. Ryanand E.B. King, â€œWhat do the young (old) people think of me? Content and accuracy of age-based metastereotypesâ€, European Journal of Work and Organizational Psychology, 22(6), 633-657,2013.
B. Holtom, T. Mitchell, T. Lee, and M. Eberly, â€œPerformance and retention research: A glance at the past, a closer review of the present, and a venture into the futureâ€, Academy of Management Annals, 2: 231-274,2008
C. von Hippel, E. K. Kalokerinos and J. D. Henry, â€œStereotype threat among older employees: Relationship with job attitudes and performance intentionsâ€, Psychology and aging, 28(1), 17,2013.
S. L. Peterson, â€œToward a theoretical model of employee performance: A human resource development perspectiveâ€, Human Resource Development Review, 3(3), 209-227,2004.
J. M. Sacco and N. Schmitt, â€œA dynamic multilevel model of demographic diversity and misfit effectsâ€, Journal of Applied Psychology, 90(2), 203-231,2005.
D. G. Allen and R. W. Griffeth, â€œTest of a mediated performance â€“ Performance relationship highlighting the moderating roles of visibility and reward contingencyâ€, JournalofAppliedPsychology,86(5),1014-1021,2001.
D. Liu, T. R. Mitchell, T. W. Lee, B. C. Holtom, and T. R. Hinkin, â€œWhen employees are out of step with coworkers: How job satisfaction trajectory and dispersion influence individual-and unit-level voluntary performanceâ€, Academy of Management Journal, 55(6), 1360-1380,2012.
B. W. Swider, and R. D. Zimmerman, â€œBorn to burnout: A meta- analytic path model of personality, job burnout, and work outcomesâ€, Journal of Vocational Behavior, 76(3), 487-506,2010.
T. M. Heckert and A. M. Farabee, â€œPerformance intentions of the faculty at a teaching-focused universityâ€, Psychological reports, 99(1), 39-45,2006.
H. Jantan, A. R. Hamdan, and Z. A. Othman, â€œTowards Applying Data Mining Techniques for Talent Managementsâ€, 2009 International Conference on Computer Engineering and Applications, IPCSIT vol.2, Singapore, IACSIT Press,2011.
V. Nagadevara, V. Srinivasan, and R. Valk, â€œEstablishing a link between employee performance and withdrawal behaviours: Application of data mining techniquesâ€, Research and Practice in Human Resource Management, 16(2), 81-97, 2008.
W. C. Hong, S. Y. Wei, and Y. F. Chen, â€œA comparative test of two employee performance prediction modelsâ€, International Journal of Management, 24(4), 808,2007.
L. K. Marjorie, â€œPredictive Models of Employee Voluntary Performance in a North American Professional Sales Force using Data-Mining Analysisâ€, Texas, A&M University College of Education,2007.
D. Alao and A. B. Adeyemo, â€œAnalyzing employee attrition using decision tree algorithmsâ€, Computing, Information Systems, Development Informatics and Allied Research Journal, 4,2013.
V. V. Saradhi and G. K. Palshikar, â€œEmployee churn predictionâ€, 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, â€œLogistic regression in rare events dataâ€, Political Analysis, 9(2), 137â€“163,2001.T. Mitchell, Machine learning. 2nd ed. USA: McGraw Hill, 1997.
H. A. Elsalamony (2014), â€œBank direct marketing analysis of data mining techniquesâ€, International Journal of Computer Applications,85(7).
A. Liaw and M. Wiener, â€œClassification and regression by randomForestâ€, R news, 2(3), 18-22,2002.
L. Breiman, Random forests. Machine Learning, 45(1), 5â€“32, 2001.
P. Cunningham and S. J. Delany, â€œk-Nearest neighbour classifiersâ€, 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, â€œA decision-theoretic generalization of on-line learning and an application to boostingâ€, Journal of computer and system sciences, 55(1), 119-139,1997.
J. H. Friedman, â€œGreedy function approximation: a gradient boosting machineâ€, Annals of statistics, 1189-1232,2001.
S. Lessmann and S. VoÃŸ, â€œA reference model for customer- centric data mining with support vector machinesâ€, EuropeanJournalofOperationalResearch199,520â€“530,2009.T. Fawcett, â€œAn introduction to ROC analysisâ€, Pattern Recognition Letters 27 (8), 861â€“874,2006.