ARTIFICIAL INTELLIGENCE–ENABLED DECISION SUPPORT SYSTEMS AND THEIR IMPACT ON STRATEGIC AGILITY IN MODERN ORGANIZATIONS

Abstract Purpose: This paper examines the technological, organizational and psychological determinants that are important in determining user acceptance and decision making performance in artificial intelligence-based decision support systems (AI-DSS). In particular, it explores the effectiveness of system quality, trust towards AI, organizational support, and technological self-efficacy as a combination in increasing the dependence of AI-enabled decision tools among users. Design/Methodology/Approach: The survey design was quantitative, cross-sectional and used 300 participants who are actively involved in organizational settings by using the AI-based decision support systems. The validated Likert-scale instruments were used to obtain data and analysed through reliability testing, validity assessment, Pearson correlation, multiple regression, and mediation, moderation analysis via PROCESS macro. The Shapiro-Wilk test has been used to be sure that the assumptions of normality are met. Findings: Findings reveal that the quality of the system, the trust in AI, and organizational support are significant predictors of the user acceptance of AI-DSS. AI trust is also a strong factor that affects decision-making performance. The mediation analysis has shown that the relationship between the quality of the system and user acceptance is partly mediated by trust, and the moderation results have shown that the impact of AI usage on performance is enhanced by technological self-efficacy. All the hypotheses were accepted, and it proved that there is a strong and unified model of AI-DSS adoption. Implications: The research expands the AI adoption theory by identifying trust and self-efficacy as the key factors determining AI related behaviors. In practice, to make AI decision tools the most effective, organizations are encouraged to invest in the quality of the systems, open communication, and training and capability-building efforts. Originality/Value: The paper presents an empirical framework that unites system-level, organization, and psychological elements and presents new information on the dynamics of human-AI interaction.