Digital transformation has emerged as a pervasive force reshaping how organizations operate, compete, and create value in technology-driven environments. This conceptual research agenda article synthesizes the evolution of scholarly inquiry into organizational adaptation to digital transformation, with particular emphasis on strategic reconfiguration, cultural shifts, and structural redesign. Drawing on a curated set of peer-reviewed publications, the analysis traces major theoretical milestones—from early recognition of digital disruption as a trigger for strategic responses to multidisciplinary frameworks distinguishing digitization, digitalization, and full-scale transformation. Scholarship has progressively shifted from technology adoption to holistic organizational change, highlighting the interplay among dynamic capabilities, leadership roles, and business model innovation as critical mechanisms for adaptation. Emerging phenomena such as digitally enabled agility, platform-based ecosystems, AI-augmented decision-making, and cultural ambidexterity are examined as they challenge traditional organizational paradigms. A conceptual roadmap is proposed to visualize the interconnected evolution of strategic, cultural, and structural mechanisms and their linkages to unresolved theoretical tensions. The article identifies persistent gaps, including the under-theorized role of contextual contingencies in adaptation processes and the long-term sustainability of cultural transformations. By proposing a forward-looking agenda, this work aims to guide future research toward more integrated, multilevel, and process-oriented understandings of how organizations can thrive amid continuous technological upheaval. Ultimately, successful adaptation requires not merely implementing digital tools but orchestrating profound shifts across strategy, culture, and structure to foster resilience and innovation in volatile digital economies.
Artificial intelligence has emerged as a transformative force in business strategy research, reshaping how organizations conceptualize competitive advantage, reconfigure capabilities, and restructure decision architectures. This integrative review synthesizes peer-reviewed studies to map the evolving role of AI in strategic management. Drawing on literature from leading journals in strategy, information systems, and innovation, the analysis examines how AI is conceptualized—as both a decision-support tool and an autonomous strategic actor—and evaluates its organizational implications across adoption, governance, and transformation processes. Key findings reveal convergences around AI’s augmentation of dynamic capabilities and competitive positioning, yet persistent tensions exist regarding automation-augmentation paradoxes, managerial role erosion, and ethical governance challenges. The review introduces the AI Strategic Organizational Integration Model, a novel synthesis framework comprising five interconnected domains that organize prior research and highlight pathways toward emerging theoretical directions. By classifying studies along dimensions of strategic cognition, capability transformation, organizational redesign, governance tensions, and market-level outcomes, the model illuminates gaps in longitudinal evidence and cross-level theorizing. This work advances an integrative understanding of AI’s strategic significance while offering a structured foundation for future research on intelligent systems in dynamic business environments.
Management research has increasingly recognized data as a critical organizational resource, yet its precise theoretical status remains fragmented across competing scholarly traditions. This narrative review examines how the literature conceptualizes data’s role in strategy, capability development, and competitive advantage, focusing on the transition from raw data to strategic value. We synthesize key theoretical perspectives, including the resource-based view, dynamic capabilities, information processing theory, and organizational learning, to analyze how data is positioned as a resource, a capability input, and an infrastructural condition. The review identifies three central themes: the conceptual progression from data to strategic value, the organizational enablers of data-driven capability, and persistent theoretical ambiguities regarding data’s ontological status. We argue that data’s strategic value is neither inherent nor automatic but emerges through complex processes of governance, interpretation, and integration with organizational routines. The analysis highlights unresolved debates about whether data constitutes a primary resource, a foundational capability, or a dynamic asset whose value is contingent on context. This review offers a synthesized framework to guide future research on data-centric strategy and informs managerial understanding of the organizational conditions required to convert data into sustainable competitive advantage.
Institute for Management, Business, and Accounting Studies