5th European International Conference on Industrial Engineering and Operations Management

Types of HR Analytics used for the prediction of Employee Turnover in different Strategic Firms with the use of Enterprise Social Media

Sonal Gupta & R.R.K. Sharma
Publisher: IEOM Society International
0 Paper Citations
Track: Industry 4.0

In the era of data science and big data analytics, HR analytics help organizations and their human resources (HR) managers to predict reason behind the employee exits and reduce attrition rate and employee turnover at an early stage. Organization invests lot of time and money in hiring and training their workforce. When they leave the job, the reduction of cost of capital is borne by the company. In this context, employee turnover presents a serious problem and a big peril for organizations as it affects not only their productivity but also their planning continuity.  To overcome this, HR Analytics is the most important tool for organization to gain insights out of big data, collected from different sources (e.g. public social media, enterprise social media, Internet of Things etc.) to reduce employee turnover. From static to descriptive, descriptive to diagnostic, diagnostic to predictive, predictive to prescriptive type of HR analytics has come a long way.

This research attempts to explore that organization apply different types of HR Analytics for different business strategy with the use of Social media / Enterprise social media (ESM) for the prediction of employee turnover. Michael Porter competitive business strategy included Cost leadership Strategy, Differentiator Strategy and Focus. By using statistical tool chi-square we concluded that there is significant difference between various strategy types and levels/ types of HR Analytics used in the organization for prediction of Employee turnover with the use of ESM.

Published in: 5th European International Conference on Industrial Engineering and Operations Management, Rome, Italy

Publisher: IEOM Society International
Date of Conference: July 26-28, 2022

ISBN: 978-1-7923-9161-3
ISSN/E-ISSN: 2169-8767