Analysis of Data on Staff Turnover Using Association Rules and Predictive Techniques
Abstract
Purpose: The purpose of this paper is to present the results of an analysis and evaluation of data on employee turnover based on deep data mining using association rules and decision trees in a specific organisation.
Methodology/Approach: For the analysis, we chose deep data mining methods, primarily a search for association rules using the Apriori algorithm in the R programming language. For the sake of supplementation and comparison of results, data were also analysed using the predictive decision trees method, applying the C5.0, rpart and ctree algorithms in the R program.
Findings: The results of the analyses showed that observing the basic principles of correct communication from the beginning of an employment relationship, or during hiring, is justified. Communication and regular conversations between a superior and employees can help identify problems earlier, address them and reduce the number of people leaving the company. The results of the analysis helped the organisation to set measures to reduce the number of an employee leaving.
Research Limitation/implication: A limiting factor in performing such analyses is the availability of quality data in the required quantity. Our most significant advantage when performing our analysis was that quality data were available. To create the final structure of the required data set, we used data from the organisation’s internal information systems.
Originality/Value of paper: This contribution offers a new approach to analysing data on employee turnover, whose essence is that we need to find the most interesting and frequent correlations in a significant amount of data.
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References
Agrawal, R., Imielinski, T. and Swami, A., 1993. Mining Association Rules Between Sets of Items in Large Databases. In: P. Buneman and S. Jajaodia eds., Proceedings of the 1993 ACM SIGMOD international conference on Management of data. Washington, DC, USA, 25-28 May 1993. New York, USA: ACM.
Bardessono, D., 2016. 5 Signs You’re A “Unicorn” Employee, Benchmark Consulting, [online] 10 October. Available at: < http://www.benchmarkhr.com/5-signs-youre-a-unicorn-employee/ > [Accessed 05 March 2018].
Berikov, V., Litvinenko, A. and Lbov, G.S., 2008. Methods for statistical data analysis with decision trees. [pdf] s.l.: Sobolev Institute of Mathematics. Available at: < http://www.math.nsc.ru/AP/datamine/eng/context.pdf > [Accessed 05 March 2017].
Berson, A., Smith, S. and Thearling, K., 1995. An Overview of Data Mining Techniques. [pdf] s.n. Available at: < http://weber.itn.liu.se/~jimjo94/courses/TNM048/documents/DM-Techniques.pdf > [Accessed 05 March 2017].
Janice, L., 2014. New Perspectives on Staff Turnover in the IT Field. Knowledge HEC, [online] 11 December. Available at: < http://www.hec.edu/Knowledge/Strategy-Management/Human-Resources-Management/New-Perspectives-on-Staff-Turnover-in-the-IT-Field > [Accessed 08 January 2017].
Markulik, Š., Cehlár, M. and Kozel, R., 2018. Process approach in the mining conditions. Acta Montanistica Slovaca, 23(1), pp.46-52.
Methot, J.R., Lepak, D., Shipp, A.J. and Boswell, W.R., 2017. Good Citizen Interrupted: Calibrating a Temporal Theory of Citizenship Behavior. ACAD MANAGE REV, [e-journal] 42(1), pp. 10-31. http://dx.doi.org/10.5465/amr.2014.0415.
Mueller, R., 2017. How Can An Organization Transform Its Culture To Become More Data Driven?. MacroSoft, [online] 13 October. Available at: < https://www.macrosoftinc.com/blog/how-organization-transform-culture-more-data-driven.html > [Accessed 02 March 2018].
Ongori, H., 2007. A review of the literature on employee turnover. African Journal of Business Management, [e-journal] 1(2), pp.46-54.
Parali?, J., 2003. Discovering knowledge in databases. Košice: Elfa.
Peterson, B., 2017. Travis Kalanick lasted in his role for 6.5 years — five times longer than the average Uber employee. Business Insider, [online] 20 August. Available at: < http://www.businessinsider.com/employee-retention-rate-top-tech-companies-2017-8 > [Accessed 05 March 2018].
Smith B. and Rutigliano T., 2002. The truth about turnover. Business Journal, [online]. Available at: < http://www.gallup.com/businessjournal/316/truth-about-turnover.aspx > [Accessed 16 January 2017].
Sullivan, J., 2017. The Ideal Turnover Rate. Monster, [online]. Available at: < http://hiring.monster.ca/hr/hr-best-practices/recruiting-hiring-advice/strategic-workforce-planning/employee-turnover-rate-canada.aspx > [Accessed 15 February 2017].
Wilkonson, J., 2014. Employee Turnover Definition. The Strategic CFO, [online] 7 April. Available at: < https://strategiccfo.com/employee-turnover/ > [Accessed 05 March 2018].
Zgodavova, K., Hudec, O. and Palfy, P., 2017. Culture of quality: insight into foreign organisations in Slovakia. Total Quality Management & Business Excellence, [e-journal] 28(9-10), pp.1054-1075. http://dx.doi.org/10.1080/14783363.2017.1309120.
Zulla Consulting & Partners, 2017. Should I stay or should I go – Why your employees have this doubt?. Zulla Consulting & Partners. [online] 26 April. Available at: < http://zulla-consulting.com/employee-fluctuation > [Accessed 15 February 2018].
Authors
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