The selection of academic leaders, particularly Deans, plays a critical role in shaping institutional governance, research direction, and administrative effectiveness. However, decision-making processes in many higher institutions remain largely subjective, often influenced by informal biases and non-transparent voting practices. The study demonstrates how a structured MCDM framework can operationalize transparent and auditable academic leadership selection within a higher education institution. A case study was conducted at the Federal Polytechnic, Ile-Oluji, Nigeria, involving four candidates evaluated across four criteria: Leadership, Intelligence, Quality of Publication, and Candidate Availability. Criteria weights were rigorously derived using AHP through pairwise comparison matrices, yielding priority weights of 0.25, 0.30, 0.25, and 0.20, respectively, with a consistency ratio (CR = 0.022) indicating acceptable judgment consistency. Candidate performance scores obtained from 20 respondents were normalized and aggregated using both AHP-derived weights and a linear weighted-sum model. The results consistently identified Chief Lecturer B as the most suitable candidate, achieving the highest ranking across arithmetic and geometric mean evaluations. Sensitivity analysis confirmed the robustness of the ranking under moderate variations in criteria weights. The study demonstrates that integrating AHP into institutional voting processes enhances the decision operationalization framework, transparency, and defensibility of leadership selection. While the findings are context-specific, the proposed framework provides a replicable decision-support model for improving merit-based governance in higher education institutions.
- Open Access
- Article
Voting Decision-Making in Nigerian Higher Institutions: A Hierarchical Decision Model Approach
Author Information
Received: 24 Mar 2026 | Revised: 05 Jun 2026 | Accepted: 27 Jul 2026 | Published: 31 Jul 2026
Abstract
Keywords
hierarchical decision model | voting decision-making | academic leadership | higher education | polytechnics
References
- 1.
Geschwind, L.; Aarrevaara, T.; Berg, L.N.; et al. The changing roles of academic leaders: Decision-making, power, and performance. In Reforms, Organizational Change and Performance in Higher Education; Pinheiro, R., Geschwind, L., Hansen, H.F.; et al., Eds.; Palgrave Macmillan: London, UK, 2019; pp. 95–123. https://doi.org/10.1007/978-3-030-11738-2_6.
- 2.
Taherdoost, H.; Madanchian, M. Multi-criteria decision making (MCDM) methods and concepts. Encyclopedia 2023, 3, 77–87. https://doi.org/10.3390/encyclopedia3010006.
- 3.
Moreira, F.R.; Carvalho Georg, M.A.; Ribeiro Júnior, L.A.; et al. Binary decision support using AHP: A model for alternative analysis. Algorithms 2025, 18, 320. https://doi.org/10.3390/a18060320.
- 4.
Caporale, D.; Rinaldi, A. The application of analytical hierarchy process to assess adaptation strategies for flood and landslides risks: A case study of a multi-risk area community. Environ. Sci. Policy 2024, 163, 103959. https://doi.org/10.1016/j.envsci.2024.103959.
- 5.
Olabanjo, O.; Honenberger, P. MCDA4AI: A framework for managing n > 2 criteria problems in decisions about artificial intelligence. Array 2026, 29, 100723. https://doi.org/10.1016/j.array.2026.100723.
- 6.
Stepanenko, V.; Kashevnik, A. Competence management systems in organizations: A literature review. In Proceedings of the 2017 20th Conference of Open Innovations Association (FRUCT), St. Petersburg, Russia, 3–7 April 2017. https://doi.org/10.23919/FRUCT.2017.8071344.
- 7.
Miranda, S.; Orciuoli, F.; Loia, V.; et al. An ontology-based model for competence management. Data Knowl. Eng. 2017, 107, 51–66. https://doi.org/10.1016/j.datak.2016.12.001.
- 8.
Black, S.A. Qualities of effective leadership in higher education. Open J. Leadersh. 2015, 4, 54–66. https://doi.org/10.4236/ojl.2015.42006.
- 9.
Khatri, P.; Duggal, H.K.; Lim, W.M.; et al. Student well-being in higher education: Scale development and validation with implications for management education. Int. J. Manag. 2024, 22, 100933. https://doi.org/10.1016/j.ijme.2024.100933.
- 10.
Ibrahim, M.A.; Abdullah, A.; Ismail, I.A.; et al. Leadership at the helm: Essential skills and knowledge for effective management in Islamic Economics and Finance schools. Heliyon 2024, 10, e36696. https://doi.org/10.1016/j.heliyon.2024.e36696.
- 11.
Gibney, R.; Shang, J. Decision making in academia: A case of the dean selection process. Math. Comput. Model. 2007, 46, 1030–1040. https://doi.org/10.1016/j.mcm.2007.03.024.
- 12.
Li, J.; He, R.; Wang, T. A data-driven decision-making framework for personnel selection based on LGBWM and IFNs. Appl. Soft Comput. 2022, 126, 109227. https://doi.org/10.1016/j.asoc.2022.109227.
- 13.
Afshari, A.; Yusuff, R.M.; Hong, T.S.; et al. A review of the applications of multi criteria decision making for personnel selection problem. Int. J. Innov. Manag. Technol. 2011, 2, 80–86.
- 14.
Blachowski, J.; Hajnrych, M.; Trybała, P.; et al. Multi-criteria methodology for evaluating university campus facilities using the AHP approach. Zesz. Nauk. Politech. Poznańskiej. Organ. Zarządzanie 2022, 86, 57–72. https://doi.org/10.21008/j.0239-9415.2022.086.04.
- 15.
Özbek, D.; Yaralioğlu, K.; Karagöz, E. The effective personnel selection via multi-criteria decision-making method Analytic Hierarchy Process (AHP): A web-based application. KnE Soc. Sci. 2018, 233–249. https://doi.org/10.18502/kss.v3i10.3541.
- 16.
Danışan, T.; Özcan, E.; Eren, T. Personnel selection with multi-criteria decision making methods in the ready-to-wear sector. Teh. Vjesn. 2022, 29, 1339–1347. https://doi.org/10.17559/TV-20210816220137.
- 17.
Luo, S.; Xing, L. A hybrid decision making framework for personnel selection using BWM, MABAC and PROMETHEE. Int. J. Fuzzy Syst. 2019, 21, 2421–2434. https://doi.org/10.1007/s40815-019-00745-4.
- 18.
Saaty, T.L. Decision making with the analytic hierarchy process. Int. J. Serv. Sci. 2008, 1, 83–98. https://doi.org/10.1504/IJSSCI.2008.017590.
- 19.
Alharairi, M.; Amin, S.H.; Zolfaghari, S.; et al. Fuzzy analytic hierarchy process: A comprehensive literature review. Int. J. Anal. Hierarchy Process 2025, 17, 1–27. https://doi.org/10.13033/ijahp.v17i3.1311.
- 20.
Sun, S.; Gong, Z.; Wei, G.; et al. Multi-criteria probabilistic sorting method: Interval utility regression within the regularization framework. Inf. Sci. 2026, 743, 123333. https://doi.org/10.1016/j.ins.2026.123333.
- 21.
Auguściak, I.; Więckowski, J.; Sałabun, W. Personnel selection under Intuitionistic Fuzzy Multi-Criteria Decision Analysis evaluation. Procedia Comput. Sci. 2024, 246, 3840–3850. https://doi.org/10.1016/j.procs.2024.09.157.
- 22.
Maurya, A.P.; Suri, P.K. Towards evidence-based governance: A review of data analytics adoption in government agencies. Adv. Consum. Res. 2026, 6, 1855–1865.
This work is licensed under a Creative Commons Attribution 4.0 International License.
Suite 4002 Level 4, 447 Collins Street, Melbourne, Victoria 3000, Australia
General Inquiries: info@sciltp.com