Digital Transformation: A Theoretical and Methodological Approach, Drivers and Effects of Implementation

Objective. The study aims to develop a theoretical and methodological approach to digital transformation based on integrating platform, competence, and ecosystem approaches and to empirically assess its quantitative effects on Russian companies during 2018–2023. Method. Systemic and institutional analysis, mathematical modeling (modified Cobb–Douglas function), case studies (250 companies with actual data for 2018–2023 plus 50 companies with forecast data for 2024–2025), regression analysis of panel data (Rosstat, HSE), and technical analysis of architectures (microservices, IoT, CI/CD). Result. An integrated model of digital transformation is proposed. Based on actual data from 250 companies (2018–2023), systemic transformation increases labor productivity by 18–27%, profitability by 12–19%, and reduces transaction costs by 22–35%. The contribution of engineering solutions (platforms, digital twins, ML models) is quantified for Russia. Forecast data (2024–2025) indicate potential productivity effects up to 28–37% with GenAI and 5G adoption, pending verification. Conclusion. Digital transformation is an engineering-organizational process dependent on IT architecture maturity, DevOps/ML skills, and ecosystem integration. Findings support evidence-based digital strategies.