Level of AI Usage in Mining Regions: The Case of The City of Kolwezi, Democratic Republic of Congo, Lualaba Province
DOI:
https://doi.org/10.47941/japsy.3388Keywords:
Artificial Intelligence, Mining Regions, Kolwezi, Digital Divide, Technology AdoptionAbstract
Purpose: This study assesses the level, determinants, and perceived impacts of Artificial Intelligence (AI) adoption across industrial mining workers, artisanal miners, and administrative personnel in Kolwezi, Democratic Republic of Congo.
Methodology: Based on a mixed-methods design involving 250 survey respondents and 3 focus groups collected between February and May 2025, the analysis reveals pronounced disparities in AI literacy and use.
Findings: Industrial employees—75% of whom possess tertiary education—reported significantly higher AI familiarity (Mean = 3.94/5) and usage intentions (Mean = 4.12/5) than artisanal miners (Mean familiarity = 2.21/5), reflecting strong educational and digital divides. Correlation tests show positive associations between digital experience and AI behavioral intention (r = .63, p < .001), while fear of automation negatively predicts acceptance (r = –.41, p < .01). Focus-group insights highlight perceptions of increased safety, improved planning, and production optimization among industrial users, contrasted with concerns over job displacement among artisanal miners. These findings indicate that AI adoption in Kolwezi is advancing but remains uneven, shaped by socioeconomic status, digital exposure, and institutional context.
Unique Contribution to Theory, Practice and Policy: The study contributes evidence for designing equitable, context-specific digital strategies in African mining cities.
Downloads
References
Aker, J., & Mbiti, I. (2010). Mobile phones and economic development in Africa. Journal of Economic Perspectives, 24(3), 207–232.
Avle, S., Quartey, A., & Hutchful, D. (2018). Researching Africa and AI futures. AI & Society, 33(3), 483–496.
Banza-Larco, A., Tshikomba, M., & Kabuya, P. (2021). Remote sensing applications in copper–cobalt mining regions of DR Congo. Journal of Mining Geoscience, 14(2), 55–69.
Bessière, P., Laugier, C., & Siegwart, R. (2019). Probabilistic robotics. MIT Press.
Biloslavo, R., Bagnoli, C., & Edgar, D. (2020). Digital transformation and business model innovation. Journal of Business Research, 110, 193–202.
Botha, D., Nel, J., & Van Zyl, D. (2019). Digital mining frameworks in Southern Africa. Resources Policy, 61, 250–260. https://doi.org/10.1016/j.resourpol.2018.12.003
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Brynjolfsson, E., & McAfee, A. (2014). The second machine age. Norton.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton.
Brynjolfsson, E., & McAfee, A. (2017). Machine, platform, crowd: Harnessing our digital future. W.W. Norton.
Campbell, B. (2020). Mining, sustainability and power in Africa. Routledge.
Crawford, K. (2021). Atlas of AI. Yale University Press.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). Sage Publications.
Cuvelier, J. (2011). Men, mines and masculinities in Congo. Leuven University Press.
Cuvelier, J., Miranda, A., & Mushagalusa, E. (2021). Governance and contestation in cobalt mining. African Affairs, 120(480), 567–590.
D’Souza, D. (2021). Digital transitions in Central African mining regions. Extractive Industries and Society, 8(4), 100–112.
Davis, F. (1989). Perceived usefulness, perceived ease of use, and acceptance of IT. MIS Quarterly, 13(3), 319–340.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Diallo, O., et al. (2022). Mining technologies and labor transformation in Africa. Resources Policy, 75, 102497.
Durrant-Whyte, H., Geraghty, R., Pavez, A., & Rogers, P. (2015). Autonomous systems in mining. IEEE Transactions on Automation Science, 12(3), 755–768. https://doi.org/10.1109/TAS.2015.2430921
Ferguson, J. (2006). Global shadows. Duke University Press.
Friemel, T. N. (2016). The digital divide has grown old. New Media & Society, 18(2), 313–331.
Gärtner, D., & Schönherr, N. (2021). AI and sustainability transitions in extractive industries. Journal of Cleaner Production, 299, 126–152.
Geenen, S. (2012). A dangerous bet: Artisanal miners and mining companies in the DRC. Resources Policy, 37(3), 322–330.
Geenen, S. (2018). Hybrid governance in mining regions of the DRC. Third World Quarterly, 39(2), 319–335. https://doi.org/10.1080/01436597.2017.1380381
Geenen, S. (2018). Hybrid governance in mining regions of the DRC. Third World Quarterly, 39(2), 319–335. https://doi.org/10.1080/01436597.2017.1380381
Geenen, S. (2018). Hybrid governance in mining regions of the DRC. Third World Quarterly, 39(2), 319–335.
Gillwald, A., Mothobi, O., & Rademan, B. (2022). After Access: ICT access and use in Africa. Research ICT Africa.
Hartman, H. L., & Mutmansky, J. M. (2016). Introductory mining engineering (3rd ed.). Wiley.
Hilson, G. (2020). Artisanal mining and sustainable development. The Extractive Industries and Society, 7(4), 145–156.
Hilson, G. (2022). Artisanal mining and digital technologies. Resources Policy, 76, 102168. https://doi.org/10.1016/j.resourpol.2022.102168
Johnstone, H., Haddud, A., & Barros, A. (2022). Automation risks in extractive industries. Production & Manufacturing Research, 10(1), 52–72.
Jowitt, S. M., Werner, T. T., & Weng, Z. (2020). Critical minerals and global supply chain governance. Nature Resources Policy, 5(1), 12–25.
King, W., & He, J. (2006). TAM meta-analysis. Information & Management, 43(6), 740–755.
Kitula, A. (2006). Environmental and socioeconomic impacts of mining. Journal of Cleaner Production, 14(3), 405–414.
Klemens, P., & Batuecas, A. (2023). AI-enabled ESG monitoring for mining operators. Mining Technology Review, 9(1), 44–59.
Korinek, A., & Stiglitz, J. (2021). Steering AI toward shared prosperity. NBER Working Paper.
Manyika, J., et al. (2017). Harnessing automation for the future of work. McKinsey Global Institute.
Marangunić, N., & Granić, A. (2015). Technology acceptance model: Review. Universities and Informatics, 9(4), 1–18.
McNab, K., et al. (2021). Large-scale automation in mining. Mining Technology, 130(2), 104–118.
Mitchell, R., & Esterhuizen, G. (2022). Automation and data analytics in copper mining operations. Mining Engineering, 74(6), 22–30.
Mthembu-Salter, G. (2020). The political economy of cobalt in the DRC. Oxford University Press.
Parreira, A., et al. (2020). Risks of digital technologies in industrial mining. Safety Science, 129, 104828.
Patton, M. Q. (2015). Qualitative research & evaluation methods (4th ed.). Sage Publications.
Radley, B., & Vogel, B. (2020). Resource extraction and labour dynamics in the DR Congo. Review of African Political Economy, 47(166), 256–271. https://doi.org/10.1080/03056244.2020.1710315
Rajak, D. (2011). In good company: Mining and corporate responsibility. Stanford University Press.
Reiman, T., & Rollenhagen, C. (2014). Human–technology–organization interactions. Safety Science, 70, 472–479.
Rogers, E. M. (2003). Diffusion of innovations (5th ed.). Free Press.
Runge, I. (2021). Applications of drones and AI in mineral operations. International Journal of Mining Science, 10(3), 115–128.
Sadowski, J. (2020). Too smart: AI and surveillance capitalism. MIT Press.
Smith, M., & Neupane, S. (2020). AI and the global south. UNESCO.
Sovacool, B. K. (2019). Sustainable minerals and the energy transition. Energy Research & Social Science, 58, 101–113. https://doi.org/10.1016/j.erss.2019.101254
Sovacool, B. K. (2019). Sustainable minerals and the energy transition. Energy Research & Social Science, 58, 101–113.
Susskind, R. (2020). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Susskind, R. (2020). The future of the professions: How technology will transform the work of human experts. Oxford University Press.
Tashakkori, A., & Teddlie, C. (2010). SAGE handbook of mixed methods in social & behavioral research (2nd ed.). Sage Publications.
Tschakert, P., & Singha, K. (2007). Contested technologies in mining areas. Geoforum, 38(3), 721–740.
UNECA. (2021). AI for Africa: Policy roadmap. United Nations Economic Commission for Africa.
van Deursen, A., & Helsper, E. (2015). A nuanced understanding of digital inequalities. New Media & Society, 17(9), 1507–1526.
van Dijk, J. (2020). The digital divide. Polity Press.
Venkatesh, V., Morris, M., Davis, G., & Davis, F. (2003). UTAUT. MIS Quarterly, 27(3), 425–478.
Venkatesh, V., Thong, J. Y., & Xu, X. (2016). Unified theory of acceptance and use of technology: A synthesis and the road ahead. Journal of the Association for Information Systems, 17(5), 328–376. https://doi.org/10.17705/1jais.00428
Wyche, S., & Olson, J. (2018). Gender and ICTs in Africa. Information Technologies & International Development, 14, 1–17.
Zhou, Y. (2021). AI literacy and acceptance in low-income contexts. Technology in Society, 67, 101747.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 Pauline Ngenyibungi Mulamba

This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution (CC-BY) 4.0 License that allows others to share the work with an acknowledgment of the work's authorship and initial publication in this journal.