Paper Type
Complete
Abstract
As AI-driven digital transformation advances, secure behavior in public institutions depends not only on technology but also on employee motivation. This study examines how Openness to AI (AIES-AI) and Perceived Development Barriers (AIES-DEV) relate to secure behavioral intentions (BIS-ACT) in public-sector organizations. Drawing on self-determination theory and protection motivation theory, we introduce and validate the CAIMOS model using survey data from 221 Polish public-sector employees. Partial Least Squares Structural Equation Modeling (PLS-SEM) reveals that openness to AI is positively, though marginally, associated with secure intentions, while perceived development barriers show a small but significant positive relationship. The model explains 44% of the variance in secure intentions, indicating moderate explanatory power. Findings suggest that motivational dynamics in AI-enabled environments may operate through both activating and mobilizing mechanisms. The study provides a context-sensitive measurement tool and advances understanding of human-centered cybersecurity in the digital transformation of public organizations.
Paper Number
1328
Recommended Citation
Kowal, Jolanta; Konopka, Lech; Ziobrowska, Monika; and Zaremba, Dariusz, "Motivating Secure Behavior in AI-Enabled Public Organizations: Ethical, Inclusive, and Sustainable Transformation" (2026). AMCIS 2026 Proceedings. 1.
https://aisel.aisnet.org/amcis2026/ccris/sig_ccris/1
Motivating Secure Behavior in AI-Enabled Public Organizations: Ethical, Inclusive, and Sustainable Transformation
As AI-driven digital transformation advances, secure behavior in public institutions depends not only on technology but also on employee motivation. This study examines how Openness to AI (AIES-AI) and Perceived Development Barriers (AIES-DEV) relate to secure behavioral intentions (BIS-ACT) in public-sector organizations. Drawing on self-determination theory and protection motivation theory, we introduce and validate the CAIMOS model using survey data from 221 Polish public-sector employees. Partial Least Squares Structural Equation Modeling (PLS-SEM) reveals that openness to AI is positively, though marginally, associated with secure intentions, while perceived development barriers show a small but significant positive relationship. The model explains 44% of the variance in secure intentions, indicating moderate explanatory power. Findings suggest that motivational dynamics in AI-enabled environments may operate through both activating and mobilizing mechanisms. The study provides a context-sensitive measurement tool and advances understanding of human-centered cybersecurity in the digital transformation of public organizations.
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