The Missing Middle: Algorithmic Control, Legitimacy, and Worker Resistance in the Nigerian Financial Sector

This research challenges techno-utopian discourses around automation by examining experiences, negotiations, and resistance to augmented algorithmic control in the highly regulated, underexamined Nigerian financial sector in the Global South.
Employing a qualitative, interpretivist methodology in Lagos, we conducted 49 semi-structured interviews with expert professionals and 8 managers) across two distinct entities, namely a licensed commercial bank and a Bureau de Change (BDC), between February and May 2024. Data were analysed using Braun and Clarke's reflexive thematic analysis.
Contrary to assumptions of post-hierarchy, participants regarded dashboards and risk-scoring systems as manifestations of neo-hierarchy, serving as proxy management that is both Taylorist in operation and algorithmic in design, sanctioned by the Central Bank of Nigeria's regulatory intensity and fiduciary requirements. Control failures arose not from excessive monitoring but from insufficient elucidation, resulting in information disparity, a lack of feedback, and preemptive apprehension.
The resistance was infrapolitical, as it was neither obvious nor explicit, and took three forms: non-cooperation through analogue manipulation, gaming through data manipulation and use of clients, and negotiation through collaborative algorithmic interpretation. Notably, both frontline staff and managers rejected both full automation and full autonomy, converging on a preference for augmentable control.
The theory of this convergence is the missing middle: an explanation is necessary for the relationship contract, including human feedback and contestation learning. We extend the Ouchi (1979) taxonomy to demonstrate how bureaucratic and clan logic exist simultaneously in algorithmic workspaces.

algorithmic control, augmentation, resistance, neo-hierarchy, financial sector, Nigeria, feedback vacuum, missing middle

1. Arner, D. W., Zetzsche, D. A., Buckley, R. P., & Barberis, J. N. (2017). Fintech and Regtech: enabling innovation while preserving financial stability. Georgetown Journal of International Affairs, 18, 47–58. https://doi.org/10.1353/gia.2017.0036 [Google Scholar] [Crossref]

2. Anwar, M. A., & Graham, M. (2020). Between a rock and a hard place: Freedom, flexibility, precarity and vulnerability in the gig economy in Africa. Competition & Change, 25(2), 102452942091447. https://doi.org/10.1177/1024529420914473 [Google Scholar] [Crossref]

3. Barricelli, B. R., Virpi Roto, Clemmensen, T., Campos, P., Lopes, A., Gonçalves, F., & Abdelnour-Nocera, J. (2019). Human Work Interaction Design. Designing Engaging Automation. Springer. [Google Scholar] [Crossref]

4. Braun, V., & Clarke, V. (2021). Braun, V. and Clarke, V. (2021) thematic analysis a practical guide. Sage, London. - References - scientific research publishing. [Google Scholar] [Crossref]

5. Bronowicka, J., & Ivanova, M. (2020). Resisting the algorithmic boss: Guessing, gaming, reframing and contesting rules in app-based management. SSRN Electronic Journal, 8(2). https://doi.org/10.2139/ssrn.3624087 [Google Scholar] [Crossref]

6. Dharamsi, S., & Charles, G. (2011). Ethnography: Traditional and criticalist conceptions of a qualitative research method. Canadian Family Physician Medecin de Famille [Google Scholar] [Crossref]

7. Dégallier-Rochat, S., Kurpicz-Briki, M., Endrissat, N., & Yatsenko, O. (2022). Human augmentation, not replacement: A research agenda for AI and robotics in the industry. Frontiers in Robotics and AI, 9(5). https://doi.org/10.3389/frobt.2022.997386 [Google Scholar] [Crossref]

8. Financial Services Authority. (2012). Final Notice: UBS AG. Financial Services Authority. https://www.fca.org.uk/publication/final-notices/ubs-ag-2012.pdf [Google Scholar] [Crossref]

9. Financial Stability Board. (2022). Artificial intelligence and machine learning in financial services. [Google Scholar] [Crossref]

10. Flamholtz, E. G., Das, T. K., & Tsui, A. S. (1985). Toward an integrative framework of organizational control. Accounting, Organisations and Society, 10(1), 35–50. https://doi.org/10.1016/0361-3682(85)90030-3 [Google Scholar] [Crossref]

11. Haight, J. M. (2007). Automated control systems do they reduce human error and incidents? Professional Safety, 52(5), 119–126. [Google Scholar] [Crossref]

12. Haight, J. M., & Kecojevic, V. (2005). Automation vs. human intervention: What is the best fit for the best performance? Process Safety Progress, 24(1), 45–51. https://doi.org/10.1002/prs.10050 [Google Scholar] [Crossref]

13. Håkansson, H., & Lind, J. (2004). Accounting and network coordination. Accounting Organisations and Society, 29(1), 51–72. https://doi.org/10.1016/s0361-3682(02)00058-2 [Google Scholar] [Crossref]

14. Kellogg, K. C., Valentine, M. A., & Christin, A. (2020). Algorithms at Work: The New Contested Terrain of Control. Academy of Management Annals, 14(1), 366–410. [Google Scholar] [Crossref]

15. Kinnie, N., Hutchinson, S., & Purcell, J. (2000). “Fun and surveillance”: The paradox of high commitment management in call centres. The International Journal of Human Resource Management, 11(5), 967–985. https://doi.org/10.1080/095851900422375 [Google Scholar] [Crossref]

16. Lammi, I. J. (2020). Automating to control: The unexpected consequences of modern automated work delivery in practice. Organisation, 3(2), 135050842096817. [Google Scholar] [Crossref]

17. https://doi.org/10.1177/1350508420968179 [Google Scholar] [Crossref]

18. Lei, Y.-W., & Kim, R. (2024). Automation and Augmentation: Artificial Intelligence, Robots, and Work. Annual Review of Sociology, 50(8). https://doi.org/10.1146/annurev-soc-090523-050708 [Google Scholar] [Crossref]

19. Lincoln, Y. S., & Guba, E. G. (1985). Naturalistic inquiry. International Journal of Intercultural Relations, 9(4), 289–331. https://doi.org/http://dx.doi.org/10.1016/0147-1767(85)90062-8 [Google Scholar] [Crossref]

20. Lorenz, L., Meijer, A., & Schuppan, T. (2021). The algocracy as a new ideal type for government organisations: Predictive policing in Berlin as an empirical case. Information Polity, 26(1), 71–86. https://doi.org/10.3233/ip-200279 [Google Scholar] [Crossref]

21. Manjaly, J., Varghese, R. M., & Varughese, P. (2021). Artificial Intelligence in the Banking Sector – A Critical Analysis. International Journal of Management, 8(S1-Feb), 210–216. [Google Scholar] [Crossref]

22. https://doi.org/10.34293/management.v8is1-feb.3778 [Google Scholar] [Crossref]

23. Monteiro, P., & Adler, P. S. (2022). Bureaucracy for the 21st Century: Clarifying and Expanding Our View of Bureaucratic Organisation. Academy of Management Annals, 16(2), 427–475. [Google Scholar] [Crossref]

24. Mumby, D. K., Thomas, R., Martí, I., & Seidl, D. (2017). Resistance Redux. Organisation Studies, 38(9), 1157–1183. https://doi.org/10.1177/0170840617717554 [Google Scholar] [Crossref]

25. Myers, M. D. (2019). Qualitative Research in Business & Management (3rd ed.). Sage Publications Ltd. [Google Scholar] [Crossref]

26. Möhlmann, M., & Zalmanson, L. (2017). Hands on the wheel navigating algorithmic management and Uber drivers’ autonomy. In proceedings of the international conference on Information Systems (ICIS) (pp. 10-13). - references - scientific research publishing. [Google Scholar] [Crossref]

27. Nicolini, D. (2012). Practice theory, work, and organisation: An introduction. Oxford University Press. [Google Scholar] [Crossref]

28. Orlikowski, W. J., & Scott, S. V. (2008). Sociomateriality: Challenging the Separation of Technology, Work and Organisation. The Academy of Management Annals, 2(1), 433–474. [Google Scholar] [Crossref]

29. https://doi.org/10.1080/19416520802211644 [Google Scholar] [Crossref]

30. Ouchi, W. G. (1979). A Conceptual Framework for the Design of Organisational Control Mechanisms. Management Science, 25(9), 833–848. https://doi.org/10.1287/mnsc.25.9.833 [Google Scholar] [Crossref]

31. Pink, S., & Morgan, J. (2013). Short-term ethnography: Intense routes to knowing. Symbolic Interaction, 36(3), 351–361. [Google Scholar] [Crossref]

32. Rahimnia, F., & Karimi Mazidi, A. R. (2015). Functions of control mechanisms in mitigating workplace loafing; evidence from an Islamic society. Computers in Human Behaviour, 48(7), 671–681. https://doi.org/10.1016/j.chb.2015.02.035m [Google Scholar] [Crossref]

33. Rahman, H. A. (2020). Invisible cages: how opaque control tactics influence worker behaviour. Academy of Management Proceedings, 2020(1), 20920. [Google Scholar] [Crossref]

34. https://doi.org/10.5465/ambpp.2020.20920abstract [Google Scholar] [Crossref]

35. Rahman, H. A. (2021). The Invisible Cage: Workers’ Reactivity to Opaque Algorithmic Evaluations. Administrative Science Quarterly, 66(4), 945–988. https://doi.org/10.1177/00018392211010118 [Google Scholar] [Crossref]

36. Raisch, S., & Krakowski, S. (2021). Artificial Intelligence and Management: The Automation-Augmentation Paradox. Academy of Management Review, 46(1). https://doi.org/10.5465/2018.0072 [Google Scholar] [Crossref]

37. Schneider, B., & Vipond, H. (2023). The Past and Future of Work: How History Can Inform the Age of Automation. SSRN Electronic Journal, 10(4). https://doi.org/10.2139/ssrn.4643947 [Google Scholar] [Crossref]

38. Serebryakova, T. Y., & Fatkhullin, E. R. (2024). Problems of Development of Control Models and Mechanisms in Modern Ecosystems. Accounting Analysis Auditing, 5(2). [Google Scholar] [Crossref]

39. https://doi.org/10.26794/2408-9303-2024-11-3-32-46 [Google Scholar] [Crossref]

40. Thornberg, R., & Charmaz, K. (2014). Grounded theory and theoretical coding. The SAGE Handbook of Qualitative Data Analysis, 5(1), 153–169. https://doi.org/10.4135/9781446282243.n11 [Google Scholar] [Crossref]

• A Study on Customer Awareness on Green Banking Intiatives
• International Financial Reporting Standards and Earnings Management: A Global Research Landscape Analysis
• Analysis of Financial Performance and Operational Efficiency of State Bank of India Using the CAMEL Framework
• Roles and Performance of India Post Payment Bank (IPPB) With Special References to Shivamogga District
• Enhancing Transactional Security in U.S. Banking Systems by Implementing OTP-Based Two-Factor Authentication to Mitigate Debit and Credit Card Fraud