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

[1] G. G. James, J. E. Asuquo, N. A. Michael & E. O. Etim, “Adaptive predictive model for post Covid’19 health-care assistive medication adherence system”, in Contemporary Discourse on Nigeria’s Economic Profile, Ayandel et al. (Ed.), A Festschrift in Honour of Prof. Ukpabio Ndaeyo Nyaudoh, A Publication of Universit of Uyo, Uyo. Akwa Ibom State, Nigeria, 2023, pp. 622-631. https://www.researchgate.net/profile/James-G/publication/380941779.

[2] G. James, I. Umoren, A. Ekong, S. Inyang & O. Aloysius, “Analysis of support vector machine and random forest models for classification of the impact of technostress in COVID and post-covid era”, Journal of the Nigerian Society of Physical Sciences 6 (2024) 2102. https://doi.org/10.46481/jnsps.2024.2102.

[3] C. T. Cuervo, M. N. Orviz, G. S. Aree & S. I. Femandez, “Tecnostres en la sociedad de la tecnologya y la comunicacion: revision bibliografica a partir de la web of science”, Archivos de Prevencion de Riesgos Laborales 21 (2018) 25. https://dx.doi.org/10.12961/aprl.2018.21.01.4.

[4] M. M. Rahman, F. Khatun, A. Uzzaman, S. S. Sami, M. A. Bhuiyan & T. S. Kiong, “A comprehensive study of artificial intelligence and machine learning approaches in confronting the coronavirus (COVID-19) pandemic”, International Journals of Health Services 51 (2021) 446. https://doi.org/10.1177/00207314211017469.

[5] R. Riedi, H. Kindermann, A. Auinger & A. Javor, “Technostress from a neurobiological perspective - system breakdown increases the stress hormone cortisol in computer users”, Business & Information Systems Engineering 4 (2012) 61. https://aisel.aisnet.org/bise/vol4/iss2/2.

[6] Y. K. Lee, C. T. Chang, Z. H. Cheng & Y. Lin, “Helpful-stressful cycle? Psychological links between type of mobile phone user and stress”, Behaviour & Information Technology 35 (2016) 1055800. https://doi.org/10.1080/0144929X.2015.1055800.

[7] C. Liu, T. Cheng & C. Chen, “Exploring the factors that influence physician technostress from using mobile electronic medical records”, Informatics for Health and Social Care 44 (2017) 1364250. https://doi.org/10.1080/17538157.2017.1364250.

[8] P. Galluch, V. Grover & J. Thatcher, “Interrupting the workplace: examining stressors in an Information technology context”, Journal of the Association for Information Systems 16 (2015) 47. https://open.clemson.edu/all_dissertations/448.

[9] M. Monica, E. Ingusci, F. Signore, A. Manuti, M. L. Giancaspro, V. Russo, M. Zito & C. G. Cortese, “Wellbeing costs of technology use during covid’19 remote working: an investigation using the Italian translation of the technostress creators scale”, Sustainability 12 (2020) 5911. https://doi.org/10.3390/su12155911.

[10] N. D. Derra, Machine learning and technostress as important aspects for improving the performance of data scientists in contemporary marketing contexts, Ph.D. thesis, Business and Economics, Faculty of Law, University of Bayreuth, 2021. https://doi.org/10.15495/EPub_UBT_00005398.

[11] N. Norhisham, “Understanding technostress during the era of Covid’19: a conceptual paper”, International Journal of Academic Research in Business and Social Sciences 11 (2021) 1936. https://doi.org/10.6007/IJARBSS/v11-i8/10628.

[12] M. Tarafdar, C. I. Cooper & J. F. Stich, “The technostress trifecta – techno eustress, techno distress, and design: theoretical directions and agenda for research”, Information Systems Journal 29 (2019) 12169. https://doi.org/10.1111/isj.12169.

[13] C. Liu, T. Cheng & C. Chen, “Exploring the factors that influence physician technostress from using mobile electronic medical records”, Informatics for Health and Social Care 44 (2017) 1364250. https://doi.org/10.1080/17538157.2017.1364250.

[14] G. James, A. Ekong, E. Abraham, E. Oduobuk & P. Okafor, “Analysis of support vector machine and random forest models for predicting the scalability of a broadband network”, Journal of the Nigerian Society of Physical Sciences 6 (2024) 2093. https://doi.org/10.46481/jnsps.2024.2093.

[15] C. Ituma, G. G. James & F. U. Onu, “Implementation of intelligent document retrieval model using neuro-fuzzy technology”, International Journal of Engineering Applied Sciences and Technology 4 (2020) 65. http://dx.doi.org/10.33564/IJEAST.2020.v04i10.013.

[16] G. G. James, E. G. Chukwu, P. O. Ekwe, E. C. Asogwa & C. H. Darlington, “Design of an intelligent-based system for the diagnosis of lung cancer”, International Journal of Innovative Science and Research Technology 8 (2023) 796. https://www.research-gate.net/profile/James-G/publication/372338202.

[17] G. James, A. Ekong & H. Odikwa, “Intelligent model for the early detection of breast cancer using fine needle aspiration of breast mass”, International Journal of Research and Innovation in Applied Science (IJRIAS) 9 (2024) 348. https://doi.org/10.51584/IJRIAS.2024.90332.

[18] G. G. James, A. Ejaita, C. Ituma & J. E. Asuquo, “Development of hybrid intelligent based information retrieval technique”, International Journal of Computer Applications 184 (2022) 1. https://doi.org/10.5120/ijca2022922401.

[19] G. Zhang, F. Zhou, F.Wang & J. Luo, “Knowledge creation in marketing based on data mining”, 2008 International Conference on Intelligent Computation Technology and Automation (ICICTA), Changsha, China, 2008, pp. 782-786. https://doi.org/10.1109/ICICTA.2008.45.

[20] Z. L. Ping & S. Q. Liang, “Data mining application in banking-customer relationship management”, 2010 International Conference on Computer Application and System Modeling (ICCASM 2010), Taiyuan, China, 2010, pp. 340-342. https://doi.org/10.1109/ICCASM.2010.5619002.

[21] E. W. T. Ngai, L. Xiu & D. C. K. Chau, “Application of data mining techniques in customer relationship management: a literature review and classification”, Expert Systems with Applications 36 (2009) 2592. https://doi.org/10.1016/j.eswa.2008.02.021.

[22] A. L. Roth, M. Huff & K. Bendib, Censored 2018: press freedoms in a “Post-Truth” world, The Top Censored Stories and Media Analysis of 2016-17, New York: Seven Stories Press, New York, 2017. https://www.lighthousebookshop.com/book/9781609807818.

[23] H. Timonem & J. Vuori, “Visibility of work: how digitalization changes the workplace”, Proceedings of the 51st Hawaii International Conference on System Sciences, Waikoloa Village, HI, USA, 2018. http://hicss.hawaii.edu/.

[24] G. G. James, G. P. Oise, E. G. Chukwu, N. A. Michael, W. F. Ekpo & P. E. Okafor, “Optimizing business intelligence system using big data and machine learning”, Journal of Information Systems and Informatics 6 (2024) 631. https://doi.org/10.51519/journalisi.v6i2.631.

[25] C. Ituma, G. G. James & F. U. Onu, “A neuro-fuzzy based document tracking & classification system”, International Journal of Engineering Applied Sciences and Technology 4 (2020) 414. http://www.ijeast.com.

[26] G. G. James, A. Ekong, E. Abraham, E. Oduobuk, N. Michael, V. Ufford & O. Ebong, “An enhanced control solution for efficient urban waste management using deep learning algorithms”, African Scientific Reports 3 (2024) 183. https://doi.org/10.46481/asr.2024.3.3.183.

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

Similar Articles

61-70 of 79

You may also start an advanced similarity search for this article.

Most read articles by the same author(s)