Digital platforms have become central orchestrators of value creation in contemporary markets, yet their governance remains a fragmented but critical domain of inquiry. This narrative literature review synthesizes peer-reviewed studies published to examine how institutional structures, strategic coordination mechanisms, and ecosystem owner dynamics shape platform-based competition. Drawing from leading journals in management, information systems, and innovation, the analysis identifies five core research streams: governance architectures, coordination mechanisms, platform leadership and orchestration, power asymmetries, and regulatory-institutional challenges. Key findings reveal persistent tensions between openness for innovation and control for value capture, evolving governance practices that balance cocreation with cost management, and growing power imbalances between platform owners and complementors. The analysis culminates in a conceptual synthesis model that illustrates the interconnected flows of coordination, control, and value within ecosystems. This review demonstrates that effective governance is not merely technical but fundamentally institutional, influencing complementor participation, ecosystem stability, and market outcomes. By integrating disparate perspectives, this work highlights unresolved tensions in multi-sided markets and proposes directions for future scholarship on global platform regulation and adaptive governance in volatile digital environments. The synthesis offers practical guidance for managers and policymakers navigating the complexities of platform-dominated economies.
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.
Algorithmic decision systems (ADS) are rapidly transforming organizational decision-making by automating routine and complex processes across strategy, operations, human resources, and customer management. Drawing on peer-reviewed literature, this research agenda article examines the evolution of ADS from traditional human-centric models to hybrid human–algorithm configurations and increasingly autonomous systems. It highlights substantial opportunities, including enhanced efficiency, scalability, and data-driven precision, alongside critical risks such as algorithmic bias, opacity, reduced accountability, and erosion of human judgment. Governance challenges—encompassing fairness, transparency, explainability, and ethical oversight—remain unresolved and demand new theoretical and managerial frameworks. The paper first traces the historical and technological trajectory of algorithmic integration in organizations, then analyzes emerging dynamics, including bias amplification, tensions in human–AI interaction, and dependence risks. A conceptual roadmap visualizes these interrelationships and pathways for governance intervention. By synthesizing insights from leading journals in management, information systems, and strategy, the article identifies persistent theoretical gaps in organizational adaptation, legitimacy, and long-term societal impact. It concludes with a structured future research agenda comprising twelve targeted questions to guide scholars and practitioners toward responsible ADS deployment. This work contributes a comprehensive foundation for advancing theory and practice in digital business and management studies.
Generative artificial intelligence has emerged as a transformative force in business and management, fundamentally altering how organizations create value, make decisions, and manage knowledge. This narrative review synthesizes contemporary scholarship to examine how generative AI functions as a new layer of organizational capability, creating strategic opportunities while simultaneously introducing significant managerial, ethical, and governance challenges. The analysis reveals that generative AI differs fundamentally from prior forms of artificial intelligence through its capacity for content generation, contextual reasoning, and human-like interaction, enabling unprecedented applications in innovation management, strategic decision support, knowledge work transformation, and business model experimentation. However, these capabilities generate corresponding vulnerabilities, including algorithmic hallucination, embedded bias, operational opacity, and organizational over-dependence. The review identifies three major tensions: augmentation versus automation, efficiency versus reliability, and innovation acceleration versus governance lag. Drawing on scholarly sources, this article proposes an integrative framework situating governance and human oversight as essential mediating mechanisms between generative AI capabilities and organizational outcomes. The findings suggest that successful generative AI adoption requires organizations to balance opportunity exploitation with risk mitigation through structured accountability systems, human-in-the-loop protocols, and adaptive governance architectures.
The governance of artificial intelligence has emerged as a critical organizational capability as firms increasingly deploy AI systems that shape strategic outcomes, raise ethical concerns, and face expanding regulatory requirements. This article reviews the literature on organizational AI governance across management, information systems, and interdisciplinary scholarship. The analysis reveals that effective AI governance requires integration of three interconnected dimensions: ethical governance addressing bias, fairness, transparency, and accountability; strategic governance encompassing competitive positioning, oversight structures, and control mechanisms; and regulatory governance responding to evolving compliance mandates. The review identifies tensions between innovation imperatives and responsible governance, highlighting the need for organizations to balance rapid AI deployment with robust control systems. Key governance mechanisms include transparency frameworks, explainability tools, human oversight protocols, and risk management processes. The analysis concludes that AI governance must evolve from fragmented technical and compliance approaches toward integrated organizational systems that embed accountability across leadership, management, and operational levels.
Platform-based business models have become central to digital competition because they reorganise how firms create, coordinate, and appropriate value across multi-sided ecosystems. Unlike traditional pipeline models, platforms depend on interactions among users, complementors, developers, advertisers, and other external actors. This shift has made platform competitiveness a strategic phenomenon that cannot be explained only by internal resources or product-market positioning. This systematic review examines how platform-based business models generate and sustain firm competitiveness through three interrelated mechanisms: network effects, ecosystem governance, and value capture. The objective is to synthesise fragmented evidence from strategy, information systems, innovation, and management research. The review focuses on how these mechanisms operate individually and how their interaction shapes platform performance and durability. The findings show that platform competitiveness is supported by architectural design, multi-sided participation, network effects, ecosystem orchestration, and monetization choices. However, the evidence also reveals tensions between openness and control, growth and quality, value creation and value capture, and network expansion and strategic vulnerability. Five tables summarise the review method, platform typology, network effects, governance mechanisms, and value capture models. The review concludes that platform competitiveness should be understood as a dynamic system rather than as the automatic result of scale. Network effects require governance, governance shapes value capture, and value capture can either strengthen or weaken ecosystem health. Future research should therefore examine platform success and failure through longitudinal, comparative, and context-sensitive designs.
Artificial intelligence is becoming increasingly embedded in business management, influencing decisions in strategy, operations, marketing, human resources, finance, and organizational control. Its managerial significance no longer lies only in its capacity to process information faster than humans, but in its growing ability to recommend, rank, predict, allocate, and sometimes decide. This shift raises important questions about how organizations should govern AI when it begins to affect managerial judgment itself. The central problem addressed in this review is that management research has often treated AI as a performance-enhancing tool while giving less sustained attention to its governance consequences. Three tensions remain particularly fragmented: the delegation of decision authority to algorithmic systems, the maintenance of organizational accountability in distributed human-machine arrangements, and the conditions under which managers trust or distrust AI-assisted decisions. These issues are analytically distinct but practically interdependent. The objective of this critical review is to synthesize literature on AI in business management through the integrated lenses of authority, accountability, and trust. Rather than presenting AI adoption as an inevitable route to efficiency, the review interrogates the organizational assumptions behind AI-enabled decision-making. It asks how AI changes managerial discretion, responsibility, oversight, and confidence in organizational decisions. The review concludes that AI governance in management must move beyond technical performance and address the institutional conditions under which AI-assisted decisions are authorized, explained, contested, and trusted. Authority, accountability, and trust should not be treated as separate implementation concerns but as a connected governance triad. Future management research should therefore conceptualize AI not merely as a tool, but as a socio-technical actor that reshapes managerial responsibility and organizational control.
Digital business and management research has expanded rapidly since 2017, reflecting the growing influence of digital transformation, platform ecosystems, analytics capabilities, and artificial intelligence in organizational settings. This expansion has produced a diverse and increasingly fragmented body of scholarship across strategy, information systems, innovation management, marketing, and organizational theory. Although several reviews have clarified specific subfields, the broader intellectual evolution of digital business research remains insufficiently mapped from a bibliometric perspective. This bibliometric review examines the evolution of digital business and management research. Its objective is to identify publication trends, intellectual foundations, thematic clusters, and emerging research fronts across four focal domains: digital strategy, platform-based business models, data analytics, and AI governance. By integrating performance analysis with co-citation and keyword-oriented interpretation, the article provides a structured view of how the field has developed. The analysis indicates that digital business research has moved from broad discussions of digital transformation toward more specialized debates on strategy formation, ecosystem governance, analytics-driven value creation, and responsible AI-enabled management. It also shows that the field is increasingly shaped by interdisciplinary connections between strategic management, information systems, innovation studies, and organizational governance. Five tables summarise the data source, publication trends, keyword clusters, leading contributors, and emerging research gaps. The review concludes that digital business and management research is becoming more mature, but also more complex. The next stage of scholarship requires stronger integration across platform strategy, analytics capabilities, organizational accountability, and AI governance. Future research should move beyond technological adoption narratives and examine how digital technologies reshape authority, coordination, value capture, and managerial responsibility.
Digital disruption has become a persistent condition of contemporary business rather than an exceptional crisis event. Firms now confront cyber incidents, platform failures, data integrity problems, supply chain shocks, algorithmic dependencies, and rapid shifts in digital markets. These disruptions challenge not only operational continuity but also the strategic capacity of organizations to maintain direction, value creation, and stakeholder confidence. The literature on resilience has expanded significantly, yet digital business resilience remains conceptually fragmented. Studies of organizational agility, data governance, cybersecurity, digital transformation, and business continuity often develop in parallel rather than as an integrated body of work. This fragmentation limits the ability of scholars and managers to understand how digital resilience is actually produced across interconnected systems. This critical review examines digital business resilience through four interdependent pillars: organizational agility, data governance, cyber risk management, and strategic continuity. It argues that resilience should not be reduced to rapid response, technical recovery, or compliance-based continuity planning. Instead, digital business resilience is better understood as a systemic capability that enables firms to anticipate, absorb, respond to, recover from, and adapt to digitally mediated disruptions. The review concludes that digital business resilience emerges from alignment rather than from isolated investment in tools, controls, or agile routines. Organizations need governance architectures that connect adaptive responsiveness with reliable data, cyber preparedness, and strategic discipline. A critical and integrative perspective is therefore essential for future research and managerial practice.
Generative artificial intelligence has moved rapidly from experimental use to practical adoption across digital business and management contexts. Its diffusion has been accelerated by large language models, image generators, code generators, and conversational systems that can support content creation, analysis, automation, and decision support. This systematic review examines the evidence on Generative AI in business and management studies, with particular attention to productivity, decision quality, governance, and organizational risk. The review addresses the need for a balanced synthesis that recognises both the performance promise of Generative AI and the risks created by its probabilistic, opaque, and adaptive nature. The findings show that Generative AI can improve productivity by reducing task completion time, expanding output volume, supporting creative work, and assisting knowledge workers. However, the evidence also indicates uneven benefits across tasks, expertise levels, organizational contexts, and governance conditions, while decision quality remains vulnerable to hallucination, bias, over-reliance, and weak accountability. The review concludes that Generative AI should be understood not merely as a productivity technology but as an organizational transformation phenomenon. Its business value depends on the co-development of human oversight, governance structures, risk controls, workforce capabilities, and context-sensitive implementation practices.
Institute for Management, Business, and Accounting Studies