Magazines | Journals | Books and Articles

The IUP Journal of Information Technology

An Ensemble Learning Approach to Predict Employees' Preference for E-Working in the Post-Pandemic World

Article Details

Pub. Date	: Dec, 2022
Product Name	: The IUP Journal of Information Technology
Product Type	: Article
Product Code	: IJIT011222
Author Name	: S A D D Abesiri and R A H M Rupasingha
Availability	: YES
Subject/Domain	: Engineering
Download Format	: PDF Format
No. of Pages	: 18

Price

Download

Abstract

The Covid-19 pandemic has forced a large segment of the global workforce to shift to e-working. The pandemic has convinced many organizations that e-working has benefits for a successful business. As a result, it is critical to identify employees' suggestions and evaluate their motivation to continue the e-working concept in the post-pandemic world. The study was conducted by randomly surveying employees using various Machine Learning algorithms, including Naive Bayes, Decision Tree, Random Forest, Multilayer Perceptron (MLP), Support Vector Machine (SVM) and logistic regression. The ensembling algorithm uses 66% of the percentage split method in the Waikato Environment for Knowledge Analysis (WEKA) tool. Accuracy, precision, recall, f-measure values and error rates were used to compare the results. The ensemble learning algorithm shows the best results with 90% accuracy, making it easier to predict employees' preference for e-working and accordingly take decisions.

Introduction

The Covid-19 pandemic and the subsequent lockdown has had disruptive impacts, changing how people go about their daily lives and conduct themselves (Kaur, 2021). E-working has suddenly become important in the changed scenario. An unparalleled rate of change in the workplace and the workforce's use of technology is being driven by this urgent requirement for remote work. Since the advent of the pandemic, widespread acceptance of e-working has emerged as a critical corporate reform (Savia, 2020).