Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era

Authors

  • Gabriel James
    Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Anietie Ekong
    Department of Computer Science, Akwa Ibom State University, Ikot Akpaden, Nigeria
  • Aloysius Akpanobong
    Department of Computer Networks, Faculty of Science and Information Technology, UPM, Serdang, Malaysia
  • Enefiok Etuk
    Department of Computer Science, Michael Okpara University of Agriculture, Umudike, Nigeria
  • Saviour Inyang
    Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Samuel Oyong
    Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Ifeoma Ohaeri
    Department of Computing, Topfaith University, Mkpatak, Nigeria
  • Chikodili Orazulume
    Department of Electrical Electronics Engineering, Topfaith University, Mkpatak, Nigeria
  • Peace Okafor
    Department of State Service, Ebonyi State Command, Abakaliki, Nigeria

Keywords:

COVID-19 era, Technostress, Machine Learning Models, Machine Learning, AI, Deep Learning

Abstract

The global crisis caused by the coronavirus outbreak and other diseases has significantly changed daily life, work, and education, forcing individuals and organizations to adapt to evolving virtual environments. These challenges have led to conditions induced by an inability to process information effectively with computer technologies. This study models a system that employs a Random Forest algorithm for prediction and classification, using age, gender, hours spent, and technological experience as parameters to categorize stress into high, moderate, and low levels. Data were collected via a questionnaire during the COVID-19 and post-COVID eras using a non-probabilistic sample of knowledgeable respondents. The model achieved 90% accuracy, demonstrating its prediction efficiency. Additionally, an interactive user interface was developed to facilitate real-time evaluation of technostress’s impact on technology use. This work contributes a novel machine learning framework for technostress assessment, providing a practical tool for organizations and policymakers to better understand and mitigate technology-induced stress.

Dimensions

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Published

2025-04-03

How to Cite

Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era. (2025). African Scientific Reports, 4(1), 277. https://doi.org/10.46481/asr.2025.4.1.277

Issue

Volume 4, Issue 1, April 2025

Section

MATHEMATICAL SCIENCE SECTION
Copyright & Licensing

Copyright (c) 2025 Gabriel James, Anietie Ekong, Aloysius Akpanobong, Enefiok Etuk, Saviour Inyang, Samuel Oyong, Ifeoma Ohaeri, Chikodili Orazulume, Peace Okafor

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era. (2025). African Scientific Reports, 4(1), 277. https://doi.org/10.46481/asr.2025.4.1.277

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