Data Monitoring, Policy Classification, and User Behavior in Public Tertiary Institutions

Vollständiger Abstract

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This study examined the influence of data monitoring and policy classification on user behaviour in public tertiary institutions in Rivers State, Nigeria, and assessed the moderating role of regulatory oversight on the overall relationship between data classification practices and security compliance. The study was grounded in the General Deterrence Theory, the Protection Motivation Theory, and the Theory of Planned Behaviour, and adopted a quantitative cross-sectional survey design. Data were obtained from 409 academic and non-teaching staff across five selected public tertiary institutions using a validated structured questionnaire. Multiple regression analysis was employed to test the directional hypotheses, while hierarchical moderated regression analysis was used to test the moderation hypothesis, all at a 0.05 level of significance. Descriptive statistics revealed grand means of 4.10 (data monitoring), 4.10 (policy classification), 4.17 (user behaviour), and 4.12 (regulatory oversight), all exceeding the criterion mean of 3.0. Regression 1. Introduction analyses confirmed that data monitoring significantly predicted user behaviour (β = 0.761, t = 20.331, p < 0.05, R² = 0.579) and policy adherence (β = 0.748, t = 19.826, p < 0.05, R² = 0.559). Policy classification significantly influenced user behaviour (β = 0.716, t = 18.633, p < 0.05, R² = 0.513) and policy adherence (β = 0.721, t = 18.925, p < 0.05, R² = 0.520). Hierarchical moderated regression analysis confirmed that regulatory oversight significantly moderated the relationship between data classification practices and security compliance (ΔR² = 0.063, F change = 96.332, p < 0.05). The study concludes that data monitoring, policy classification, and regulatory oversight are critical institutional and governance mechanisms for improving information security compliance in Nigerian public tertiary institutions.