Digital Transformation in SMEs: Governance Performance Mediated by AI-Enabled Analytics and Process Integration
Abstract
Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation.
Fields of Study & Subjects
Digital Transformation in Industry
Big Data and Business Intelligence
Organizational and Employee Performance
Publication details
- Journal / source
- Systems
- Publisher
- MDPI AG
- Volume
- 14
- Issue
- 3
- Pages
- 324–324
- Publication date
- 18 March 2026
- Language
- EN