CATALYZING ENTREPRENEURSHIP AND INDUSTRIAL DEVELOPMENT THROUGH TECHNOLOGY DRIVEN SAFETY AND MAINTENANCE PRACTICES
Keywords:
entrepreneurship, industrial development, maintenance practices, safety, skills gapAbstract
The mismatch between workforce competencies and the technical demands of modern industrial systems continues to limit entrepreneurship and industrial development. This study examines how technology-driven safety and maintenance practices, including predictive maintenance systems, digital monitoring tools, and automated safety protocols, can help close this skills gap and support entrepreneurial growth within industrial ecosystems. A mixed-methods design combines survey data from industrial practitioners and entrepreneurs with qualitative case studies of enterprises that have adopted such solutions. Quantitative data are analyzed with descriptive and inferential statistics, while qualitative data are examined through thematic analysis to identify recurring patterns and institutional dynamics. Survey and case-study data collection for this study is complete. The findings show that technology adoption shapes entrepreneurial capacity, workforce readiness, and industrial performance, while also revealing barriers such as limited training infrastructure, restricted access to technology, and weak institutional support. These findings inform a proposed framework linking skills development, technology integration, and safety-conscious entrepreneurship. The study contributes to discourse on sustainable industrial development and offers recommendations for policymakers, industry stakeholders, and educational institutions working to connect entrepreneurship, technology, and industrial safety for long-term economic growth.
References
Abidi, M. H., Mohammed, M. K., & Alkhalefah, H. (2022). Predictive maintenance planning for Industry 4.0 using machine learning for sustainable manufacturing. Sustainability, 14(6), 3387. https://doi.org/10.3390/su14063387
Adepoju, O., & Aigbavboa, C. (2020). Assessing knowledge and skills gap for construction 4.0 in a developing economy. Journal of Public Affairs. https://doi.org/10.1002/pa.2264
Alenjareghi, M. J., Sekkay, F., Dadouchi, C., & Keivanpour, S. (2025). Wearable sensors in Industry 4.0: Preventing work-related musculoskeletal disorders. Sensors International. https://doi.org/10.1016/j.sintl.2025.100343
Attah, E., Onwe, C., & Obi-Anike, H. (2025). Bridging the skills gap: Enhancing employability through university-industry collaborations in Nigeria. Industry and Higher Education. https://doi.org/10.1177/09504222251397493
Braun, V., & Clarke, V. (2021). Thematic analysis: A practical guide. SAGE Publications.
Chiamogu, A. P., & Chiamogu, U. P. (2025). Bridging skills mismatch in Nigeria's TVET system: Integrating Industry 4.0 competencies for entrepreneurial readiness. Journal of Science Innovation and Technology Research. https://doi.org/10.70382/ajsitr.v9i9.032
Chiamogu, A. P., & Chiamogu, U. P. (2026). Bridging the skills gap in Nigeria's technical education sector: The role of industry partnerships in polytechnic curriculum development. Journal of African Innovation and Advanced Studies. https://doi.org/10.70382/ajaias.v11i2.094
Çınar, Z., Nuhu, A. A., Zeeshan, Q., Korhan, O., Asmael, M. B. A., & Safaei, B. (2020). Machine learning in predictive maintenance towards sustainable smart manufacturing in Industry 4.0. Sustainability, 12(19), 8211. https://doi.org/10.3390/su12198211
Creswell, J. W., & Plano Clark, V. L. (2021). Designing and conducting mixed methods research (4th ed.). SAGE Publications.
Damilos, S., Saliakas, S., Karasavvas, D., & Koumoulos, E. (2024). An overview of tools and challenges for safety evaluation and exposure assessment in Industry 4.0. Applied Sciences, 14(10), 4207. https://doi.org/10.3390/app14104207
Iordanova, A., Kirilchuk, I., Gladilin, D., & Persidskaya, K. (2023). Industrial and environmental safety management in the Russian Federation using Industry 4.0 technologies. Russian Journal of Resources, Conservation and Recycling. https://doi.org/10.15862/44inor123
Jiboku, J. O. (2021). Skills development within Nigeria's multinational corporations. African Identities. https://doi.org/10.1080/14725843.2021.1932413
Kachalla, U. M., & Adamu, H. (2024). Adoption of Human Resource Technology (HRT): Its implication on entrepreneurs and micro scale industries' (MSI) employee performance in Yobe State. Journal of Advanced Research and Multidisciplinary Studies. https://doi.org/10.52589/jarms-lwpt6sh9
Mallioris, P., Aivazidou, E., & Bechtsis, D. (2024). Predictive maintenance in Industry 4.0: A systematic multi-sector mapping. CIRP Journal of Manufacturing Science and Technology. https://doi.org/10.1016/j.cirpj.2024.02.003
Memon, M. A., Ting, H., Cheah, J. H., Thurasamy, R., Chuah, F., & Cham, T. H. (2020). Sample size for survey research: Review and recommendations. Journal of Applied Structural Equation Modeling, 4(2), i–xx. https://doi.org/10.47263/JASEM.4(2)01
Niu, Y., Fan, Y., & Ju, X. (2024). Critical review on data-driven approaches for learning from accidents: Comparative analysis and future research. Safety Science. https://doi.org/10.1016/j.ssci.2023.106381
O'Connor, C., & Joffe, H. (2020). Intercoder reliability in qualitative research: Debates and practical guidelines. International Journal of Qualitative Methods, 19, 1–13. https://doi.org/10.1177/1609406919899220
Oghuvbu, E. A., Gberevbie, D., & Oni, S. (2022). Technology policy and sustainable development in Nigeria. Vestnik RUDN. International Relations. https://doi.org/10.22363/2313-0660-2022-22-2-385-396
Oladejo, D. A., Obadare, G. O., & Olayemi, O. J. (2026). Adoption of artificial intelligence and human resource upskilling in emerging markets: Evidence from small and medium enterprises in Oyo State, Nigeria. Acta Economica. https://doi.org/10.63356/ace.2026.004
Onwusa, S. (2021). The issues, challenges and strategies to strengthen Technical, Vocational Education and Training in Nigeria. International Journal of Research and Innovation in Social Science. https://doi.org/10.47772/ijriss.2021.5502
Pittri, H., Godawatte, G. A. G. R., Atibila, D. W., Agyekum, K., Amartey, P. A., Botchway, E., & Dompey, A. M. A. (2025). Knowledge, training, and skills gaps for emerging construction technologies adoption in the construction industry of a developing country. International Journal of Construction Education and Research. https://doi.org/10.1080/15578771.2025.2587943
Rabiu, A. A., Bawa, K., & Saminu, S. (2025). Artificial intelligence adoption for skills development in Nigeria: A systematic review and roadmap for TVET transformation. International Journal of Research and Innovation in Social Science. https://doi.org/10.47772/ijriss.2025.908000051
Santos, C. N. D., Costa, A. C. F., Francisco, F. E., & Oliveira, O. (2025). Driving the development and improvement of occupational health and safety through Industry 4.0 technologies. IEEE Access. https://doi.org/10.1109/access.2025.3567188
Zhan, X., Wu, W., Shen, L., Liao, W.-H., Zhao, Z., & Xia, J. (2022). Industrial internet of things and unsupervised deep learning enabled real-time occupational safety monitoring in cold storage warehouse. Safety Science. https://doi.org/10.1016/j.ssci.2022.105766
Zonta, T., & Li, G.-P. (2020). Predictive maintenance in the Industry 4.0: A systematic literature review. Computers & Industrial Engineering. https://doi.org/10.1016/j.cie.2020.106889
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