Enterprise Architecture: Enabler of Organizational Agility and Digital Transformation

Enterprise Architecture (EA) has changed as a strategic competency that helps organizations to align technologi-cal resources with business aims and, thus, achieve organizational flexibility and support digital transfor-mation. This research is an attempt to analyze the EA aspect as an enabler of agility and a generator of successful initiatives of having a digital transformation in various contexts of organizations. The study based the cross-sectional research design to collect primary data in 212 organizations of the mid and large size in the fields of finance, healthcare, and manufacturing within OECD countries. Structural equation model framework (SEM) quantitative analysis shows that a positive association exists between the mature EA implementation and the improvement in organizational agilities (SEM: 0.72, p < 0.001) with highly significant gains in digital transformation met-rics, notably; IT-business alignment (67% increase), decision-making speed (42% improvement) and operational efficiency (38% gain). The findings further show that EA maturity moderates the association between agility and transformation and imply that it plays a central role in agitating adaptive capacity and innovation. The contribution of this paper to the literature is that the gap between the theory and practice is filled by means of the empirical validation of the effects of EA. The novelty of the research is that the analytical framework is integrated with the focus on enterprise architecture maturity, agility enablers, and digital transformation outcomes as well as it provides academically-grounded idea and practical suggestions that entail the role of the chief information officer (CIO), enterprise architects, and digital strategy leaders. The research establishes the strategic necessity of integrating EA into core business planning in order to generate sustainable competitive advantage in the turbulent digital world. The results are reliable and are generalizable because ethical data collection and rigorous analysis by statistics are carried out.