The nexus between AI-driven capabilities and knowledge systems in digital business environments
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DOIhttp://dx.doi.org/10.21511/kpm.10(1).2026.09
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Article InfoVolume 10 2026, Issue #1, pp. 122-136
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Type of the article: Research article
The increasing development of digital business environments has contributed to the diversification of knowledge sources globally, making smart knowledge management crucial for enhancing the accuracy of decision-making processes. This study aims to investigate the impact of AI-driven capabilities, including adaptive learning, intelligent analytics, automation capability, integration capability on knowledge systems, and the role of smart knowledge management as a mediating factor within the context of the Federal Civil Service Council in Baghdad, Iraq. The study employed a quantitative method to collect data between April 2025 and August 2025 from 161 employees with at least three years of experience in knowledge management, organizational content and records, data, and machine learning. This sample included knowledge management managers, knowledge management specialists, data analysts, knowledge support technicians, and operations managers at the Federal Civil Service Council. The findings indicate that enhancing AI-driven capabilities across the four dimensions of adaptive learning, intelligent analytics, automation capability, and integration capability contributes to organizational success. This is evident from the correlation between adaptive learning (p = 0.012, < 0.279), analytical intelligence (p = 0.018, < 0.213), automation capabilities (p = 0.02, < 0.05), and knowledge systems. The study found that intelligent knowledge management plays a crucial mediating role in the relationship between AI capabilities and knowledge systems, contributing to the success of digital organizations and the accuracy of decision-making. This is further demonstrated by the positive correlation between the dimensions of AI capabilities and knowledge systems.
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JEL Classification (Paper profile tab)M15, O32, O33, D83
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References40
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Tables8
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Figures2
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- Figure 1. Research model
- Figure 2. PLS-SEM measurement and structural model for smart knowledge management in digital business environments
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- Table 1. Demographic variables
- Table 2. Indicator loadings by construct (smart knowledge management model)
- Table 3. Reliability and convergent validity
- Table 4. HTMT ratios
- Table 5. Fornell-Larcker criterion
- Table 6. R² adjusted
- Table 7. Hypothesis testing estimates (total effect)
- Table A1. Research questionnaire
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