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Title: Assessing Employee Satisfaction in the Context of Covid-19 Pandemic
Authors: Fernandes, Ana
Lima, Rui
Figueiredo, Margarida
Ribeiro, Jorge
Neves, José
Vicente, Henrique
Keywords: COVID–19
Human Resources Management
Organizational Performance
Artificial Intelligence
Logic Programming
Knowledge Representation and Reasoning
Artificial Neural Networks
Issue Date: 2020
Publisher: ITI Research Group
Citation: Fernandes, A., Lima, R., Figueiredo, M., Ribeiro, J., Neves, J. & Vicente, H., Assessing Employee Satisfaction in the Context of Covid-19 Pandemic. Paradigmplus, 1(3), 23–43, 2020.
Abstract: The actual COVID-19 pandemic crisis brought new challenges for all companies, forcing them to adopt new working methods to avert/minimize infection. Monitoring employee satisfaction is a challenging task, but one that is paramount in the current pandemic crisis. A workable problem-solving methodology has been developed and tested to respond to this challenge 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. The ultimate goal is to assess employees’ satisfaction in Water Analysis Laboratories while considering its development and management. The model was trained and tested with real-world data collected through questionnaires. The proposed supervised exercise yielded an overall accuracy of 92.1% and 90.5% for both, training and testing sets.
ISSN: 2711-4627 (electronic)
Type: article
Appears in Collections:CIEP - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica
QUI - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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