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The Human Resource Management challenge of predicting employee turnover using machine learning

  • Full or part time
  • Application Deadline
    Monday, May 25, 2020
  • Funded PhD Project (Students Worldwide)
    Funded PhD Project (Students Worldwide)

Project Description

Context
Applications of machine learning have the potential to significantly impact upon the role of Human Resource Management by automating both novel and existing objectives that are difficult or costly to achieve currently. One such example is the ability to predict employee attrition and identify its underlying causes. This has the potential to not only improve employee retention but also productivity, well-being and robustness. However, this potential also harbours additional challenges and demands. The successful application of a machine learning approach is reliant on appropriate quantity and quality of data. This could lead to increased monitoring and tracking of quantified employee performance and engagement. This may involve data gathering perceived as intrusive by employees such as location, activity and behaviour monitoring. Conversely, without appropriate data machine learning may be more prone to increased error which could have significant potential impact on individuals in the form of improper predictions.

Aims
This research will explore the above challenges through applied research. It aims to examine the practicalities of applying machine learning to turnover prediction, the identification of key indicators in attrition and how this might inform data-driven retention strategies.

Objectives
To Design, build and evaluate an employee turnover prediction tool
To examine the performance, capabilities, limitations and challenges of such a system applied in a real-world context.

Indicative methodology
Evaluate predictive performance of a range of supervised machine learning approaches to a real-world data set. Use statistical methods to identify and describe correlated variables from the real-world data. Use qualitative/quantitative methods to measure impact of related factors and how they influence decision making from either employee and employer perspective.

Essential Criteria:
Applicants should have or be able to evidence:
• Education to Masters Degree level in a relevant area.
• A First or Upper Second (2.1) Honours Degree

A sound understanding of, and interest in several of the following areas:
• Machine Learning / Artificial Intelligence
• Human Computer Interaction
• Qualitative / Quantitative Research Methods
• Ability to contribute to research study design
• Computer literacy
• Proficiency in oral and written English
• Ability to organise and meet deadlines
• Good interpersonal skills and ability to work independently and contribute to a team
• Commitment and an enthusiastic approach to completing a higher research degree

Desirable Criteria:
• Existing connections/links with appropriate communities/groups
• Human Resource Management

For further information please contact:
Dr Chris Bowers, Head of Dept. Computing, Worcester Business School ()
Dr Lynn Nichol, Head of Dept. Mgmt. and Finance, Worcester Business School ()

Funding Notes

During the period of your studentship you will receive the following:
• a tax free bursary of £15,009 for a period of 3 years
• a fee-waiver for 4 years
• a budget to support your project costs for the first 3 years of the project
• a laptop
• use of the Research Student Study Space in the Research School

You will play an active role in the life of both the Research School and of the School. You will be given opportunities to gain experience in learning and teaching under the guidance of your Director of Studies.

References

Harrison, T. Nichol, L. Gatto, M. Wai M. Cox. A. Gold, J (2018) What will be the Surprises for HRD in 2028? A Futures Scenario. International Journal of HRD Practice, Policy and Research. Vol 3 No 2 63-71. doi 10.22324/ijhrdppr.3.113.

García, D.L., Nebot, À., & Vellido, A. (2016). Intelligent data analysis approaches to churn as a business problem: a survey. Knowledge and Information Systems, 51, 719-774.

Zhao Y., Hryniewicki M.K., Cheng F., Fu B., Zhu X. (2019) Employee Turnover Prediction with Machine Learning: A Reliable Approach. In: Arai K., Kapoor S., Bhatia R. (eds) Intelligent Systems and Applications. Advances in Intelligent Systems and Computing, vol 869. Springer

Tambde, A. Motwani, D. (2019) Employee Churn Rate Prediction and Performance Using Machine Learning. International Journal of Recent Technology and Engineering (IJRTE) 8(2S11):824-826

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