Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era
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 e...
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| Main Authors: | , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nigerian Society of Physical Sciences
2025-04-01
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| Series: | African Scientific Reports |
| Subjects: | |
| Online Access: | https://asr.nsps.org.ng/index.php/asr/article/view/277 |
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| _version_ | 1849421036759547904 |
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| author | Gabriel James Anietie Ekong Aloysius Akpanobong Enefiok Etuk Saviour Inyang Samuel Oyong Ifeoma Ohaeri Chikodili Orazulume Peace Okafor |
| author_facet | Gabriel James Anietie Ekong Aloysius Akpanobong Enefiok Etuk Saviour Inyang Samuel Oyong Ifeoma Ohaeri Chikodili Orazulume Peace Okafor |
| author_sort | Gabriel James |
| collection | DOAJ |
| description | 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.
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| format | Article |
| id | doaj-art-bd1eac7764804321b25cd85460ffbe64 |
| institution | Kabale University |
| issn | 2955-1625 2955-1617 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Nigerian Society of Physical Sciences |
| record_format | Article |
| series | African Scientific Reports |
| spelling | doaj-art-bd1eac7764804321b25cd85460ffbe642025-08-20T03:31:34ZengNigerian Society of Physical SciencesAfrican Scientific Reports2955-16252955-16172025-04-014110.46481/asr.2025.4.1.277Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID eraGabriel JamesAnietie EkongAloysius AkpanobongEnefiok EtukSaviour InyangSamuel OyongIfeoma OhaeriChikodili OrazulumePeace OkaforThe 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. https://asr.nsps.org.ng/index.php/asr/article/view/277COVID-19 eraTechnostressMachine Learning ModelsMachine LearningAIDeep Learning |
| spellingShingle | Gabriel James Anietie Ekong Aloysius Akpanobong Enefiok Etuk Saviour Inyang Samuel Oyong Ifeoma Ohaeri Chikodili Orazulume Peace Okafor Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era African Scientific Reports COVID-19 era Technostress Machine Learning Models Machine Learning AI Deep Learning |
| title | Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era |
| title_full | Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era |
| title_fullStr | Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era |
| title_full_unstemmed | Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era |
| title_short | Enhanced machine learning model for classification of the impact of technostress in the COVID and post-COVID era |
| title_sort | enhanced machine learning model for classification of the impact of technostress in the covid and post covid era |
| topic | COVID-19 era Technostress Machine Learning Models Machine Learning AI Deep Learning |
| url | https://asr.nsps.org.ng/index.php/asr/article/view/277 |
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