Search

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.

Platform ecosystems represent a distinctive organizational form in the digital economy, characterized by interdependent actors coordinated through digital infrastructure. This systematic integrative review synthesizes peer-reviewed studies to examine three core themes in management scholarship: governance structures, strategic leadership, and innovation dynamics. Drawing on targeted literature from leading journals, the analysis reveals how platform owners balance openness and control to sustain generativity while mitigating power asymmetries. Strategic leadership emerges as a dynamic capability for ecosystem orchestration, enabling platform firms to align complementor incentives and drive value co-creation. Innovation processes are shown to depend on complementor participation, selective promotion of complements, and evolving coordination mechanisms that address tensions between autonomy and collective performance. The review traces the evolution of research from an early emphasis on governance mechanisms to a later focus on leadership, power dynamics, and the ecosystem lifecycle. An original synthesis model—the Platform Ecosystem Governance-Leadership-Innovation Synthesis Model—is introduced to integrate fragmented insights into five interconnected layers. By classifying the literature thematically and highlighting persistent tensions, this review provides a unified architecture for future platform research and offers actionable insights for ecosystem managers.

The rapid proliferation of big data and advanced analytics has fundamentally altered how organizations develop analytical capabilities, execute strategic decisions, and undergo structural transformation. This integrative review synthesizes peer-reviewed studies published to map the evolving landscape of data-driven organizations in management research. By classifying extant work into thematic domains, the review traces the progression from foundational analytical competencies to their integration within strategic processes and, ultimately, to broader organizational change. Key insights reveal that analytical capabilities serve as critical enablers of data-informed decision-making, yet persistent tensions arise between algorithmic outputs and managerial intuition. Governance structures and cognitive shifts further mediate the translation of analytics into sustainable transformation. The study introduces the D3O Framework (Data-Driven Decision and Organizational Evolution Framework) as a novel synthesis architecture that organizes the literature into six interconnected layers, highlighting feedback mechanisms and inter-layer dynamics. This structured integration clarifies fragmented insights, underscores the shift from intuition-based to evidence-driven management, and offers a roadmap for future scholarship. The findings hold significant implications for theory and practice, emphasizing how organizations can harness analytics for competitive advantage while navigating human–data tensions.

Algorithmic management has rapidly emerged as a dominant form of technology-mediated organizational control in the digital economy, reshaping how work is allocated, monitored, evaluated, and coordinated across platforms and traditional firms. This systematic integrative review synthesizes peer-reviewed studies to examine the mechanisms, implications, and tensions of algorithmic control. Drawing on literature from management, information systems, and organizational studies, the review identifies core themes including automated monitoring and surveillance, the automation of managerial functions, worker autonomy and behavioral responses, governance and accountability challenges, and broader effects on organizational design. A novel integrative architecture—the algorithmic management control ecosystem (AMCE) model—is introduced to organize the fragmented research into five interconnected layers. The synthesis reveals persistent tensions between efficiency gains and issues of fairness, transparency, and autonomy, while tracing the evolution of the field from early conceptualizations of big-data-driven control to more recent examinations of platform-specific governance and resistance. Findings highlight how algorithms embed power asymmetries and create new forms of digital Taylorism, yet also open avenues for hybrid human–algorithmic systems. The review concludes by offering a structured foundation for future scholarship on technology-mediated organizational control in digitally transformed workplaces.