Abstract

Digital transformation produces uneven organizational outcomes because technological investment interacts with capabilities, governance arrangements, and ecosystem relationships. This study develops an integrated framework explaining these relationships through dynamic capability theory, the resource-based view, institutional theory, organizational ambidexterity, and digital ecosystem perspectives. The research design combines bibliometric analysis, a longitudinal enterprise panel, executive surveys, structural equation modeling, and interviews. The supplied study reports 2,347 publications, 612 publicly listed enterprises across 28 countries, 7,344 firm-year observations within a stated 2012–2025 window, 1,412 valid executive questionnaires, and 36 interviews. Reported estimates associate digital transformation capability with strategic performance, organizational innovation, operational responsiveness, and competitiveness. Organizational adaptability provides a proposed transmission mechanism, while governance quality and ecosystem participation help explain differences in transformation outcomes. Survey responses also indicate a gap between recognizing transformation as strategically important and integrating it into organizational culture. The findings support an interpretation of digital transformation as coordinated organizational renewal rather than technology acquisition alone. However, the available manuscript does not provide the underlying datasets, estimation outputs, complete measurement instruments, or sufficient information to resolve inconsistencies in panel coverage and construct labeling. Accordingly, the numerical findings are presented as reported evidence rather than independently verified causal estimates. The framework contributes a clearer account of resource complementarity, institutional embeddedness, and interorganizational value creation. Its managerial implications emphasize capability development, accountable governance, and deliberate ecosystem participation, while its research implications concern transparent measurement, stronger identification, and reproducible comparative analysis.