Integrating Artificial Intelligence into Management Information Systems to Enhance Public Sector Decision-Making Quality; Mediating role of Trust in AI and moderating Effect of Computer Self-Efficacy between AIMIS and Trust in AI

Abstract

The integration of Artificial Intelligence into Management Information Systems has become increasingly important for enhancing decision-making quality in the public sector, where efficiency, transparency, and accuracy are critical. The present study examined the role of AI Integration into Management Information Systems in enhancing public sector decision-making quality, with a specific focus on the mediating role of Trust in Artificial Intelligence and the moderating effect of Computer Self-Efficacy on the relationship between AI integration and trust in AI. Grounded in technology acceptance and socio-technical system perspectives, this study adopted a quantitative, cross-sectional research design. Data were collected from 200 public sector employees holding managerial, administrative, and IT-related positions across multiple departments. Standardized self-report instruments were used to measure AI integration into MIS, trust in AI, computer self-efficacy, and decision-making quality. Statistical analyses were conducted using SPSS, including descriptive statistics, independent sample t-tests, ANOVA, correlation analysis, and mediation and moderated-mediation testing using PROCESS macro. The results indicated that AI integration into Management Information Systems positively influences public sector decision-making quality, demonstrating that AI-enabled systems support more informed, timely, and effective decisions. Although trust in automated systems showed a significant association with decision-making quality, mediation analysis revealed that the indirect effect of trust was not statistically significant. This indicates that trust in automated systems does not function as a mediator in the relationship between AI integration into Management Information Systems (AIMIS) and decision-making quality. However, the moderation analysis revealed that Computer Self-Efficacy did not significantly moderate the relationship between AI integration into MIS and trust in AI, indicating that confidence in computer usage alone may not be sufficient to strengthen trust in AI-driven systems. Additionally, independent sample t-test results showed no significant gender differences in trust or decision-making outcomes, highlighting the consistency of AI-related perceptions across male and female employees. The findings contribute to the growing body of literature on AI adoption in the public sector by integrating technological and psychological factors into a single explanatory framework. The study offers valuable implications for policymakers, system developers, and public administrators aiming to leverage AI-enabled MIS for improved governance and decision-making.