Political Leadership and Public Trust in Nigeria: Review of the COVID-19 Global Pandemic Lockdown and End SARS Protest

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Abstract

Public trust is fundamental to good governance, particularly during national crises that require political legitimacy, institutional responsiveness, and public cooperation. This study examines political leadership and public crisis-management strategies during Nigeria’s 2020 COVID-19 lockdown and considers their relationship with the subsequent EndSARS mass protests. Grounded in Authentic Leadership Theory, the study investigates citizens’ perceptions of governmental trustworthiness by assessing the antecedents, statements, and practices of political leaders during the pandemic. It employs qualitative content analysis of selected speeches delivered by political leaders during the national lockdown and interprets them comparatively in relation to selected global experiences. The analysis suggests that deficiencies in political leadership and crisis communication contributed to declining public trust and intensified public dissatisfaction, which formed an important context for the EndSARS protests. The study therefore reinforces the proposition that the quality of leadership substantially determines the effectiveness of crisis governance and the public’s response to governmental actions. It concludes that strengthening public trust in Nigeria requires reforms addressing the institutional and political-cultural conditions surrounding leadership emergence, supported by constitutional amendments and genuine judicial independence. The study contributes to scholarship on authentic leadership, crisis governance, and political trust by connecting governmental responses during the COVID-19 pandemic with broader patterns of citizen mobilization and demands for accountable governance.


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Article Details

How to Cite
Biu M, I., Joji, B. G., K, R. J., & Sani, Y. (2026). Political Leadership and Public Trust in Nigeria: Review of the COVID-19 Global Pandemic Lockdown and End SARS Protest. African Multidisciplinary Journal of Sciences and Artificial Intelligence, 3(3), 324-344. https://doi.org/10.58578/amjsai.v3i3.11640

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