Enhancing National Security Through AI-Driven Crowdsourced Intelligence Systems in Nigeria: Public Awareness and Adoption Analysis

Abstract

Nigeria's security landscape is increasingly challenged by complex threats such as terrorism, cybercrime, insurgency, and communal violence. Traditional intelligence systems are often reactive and insufficient to address these dynamic security risks. This study explores the awareness and adoption of AI-driven crowdsourced intelligence systems (AI-DCRIS) as a strategic tool for enhancing national security in Nigeria. Drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model, the study integrates additional constructs such as digital literacy, trust in AI, perceived risk, and reward mechanisms. Data were collected through a structured survey of 204 respondents across Nigeria’s six geopolitical zones and analyzed using Structural Equation Modeling (SEM). Findings reveal that perceived usefulness, digital literacy, and trust in AI significantly influence adoption intention, while awareness strategies moderate adoption outcomes. However, reward systems and perceived accessibility showed weaker effects. The study proposes a validated framework and offers policy and design recommendations to improve public engagement with AI-DCRIS platforms. These findings underscore the importance of building trust, enhancing digital competencies, and developing inclusive infrastructure to realize the full potential of AI in participatory national security.