Modelling and Analyzing the Employees’ Engagement in Workplace using Machine learning Tools

Abstract

In a new economy where immaterial capital is crucial, companies are increasingly aware of the necessity to efficiently manage human capital by optimizing its engagement in the workplace. The accession of the human capital through its engagement is an efficient leverage that leads to a real improvement of the companies’ performance. Despite the staple attention towards human resource management, and the efforts undertaken to satisfy and motivate the personnel, the issue of engagement still persists. The main objective of this paper is to study and model the relation between eight predictors and a response variable given by the employees’ engagement. We have used different models to figure out the relation between the predictors and the dependent variable after carrying out a survey of several employees from different companies. The techniques used in this paper are linear regression, ordinal logistic regression, Gradient Boosting Machine learning and neural networks. The data used in this study is the results of a questionnaire completed by 60 individuals. The results obtained show that the neural networks perform slightly the rest of models considering the training and validation error of modelling and also highlight the complex relation linking the predictors and the predicted.