Assessing Employee Satisfaction in the Context of Covid-19 Pandemic

Abstract

The actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adapt new working methods in order to avert/minimize infection. Monitoring employee satisfaction is a very difficult task, but one that is paramount in the current pandemic crisis. To respond to this challenge, a workable problem-solving methodology had to be developed and tested that examined the dynamics between Artificial Intelligence, Logic Programming, and Entropy for Knowledge Representation and Reasoning. Such formalisms are in line with an Artificial Neural Network approach to computing, where the ultimate goal is to assess the satisfaction of employees in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real world data collected through questionnaires that had an overall accuracy of greater than 90%.