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.
Digital transformation has expanded the risk exposure of firms beyond conventional operational, financial, and compliance categories. As organizations adopt artificial intelligence, cloud infrastructure, digital platforms, data-intensive business models, and automation, they face risks that are technically embedded, strategically consequential, and socially visible. These risks often emerge simultaneously across technology architectures, data practices, organizational routines, labor systems, and stakeholder relationships. Despite the growing importance of digital risk, existing business risk frameworks frequently classify these risks in fragmented ways. Some frameworks treat privacy as a legal compliance matter, bias as a technical problem, platform dependence as a sourcing issue, and workforce disruption as a human resource concern. This fragmentation limits managerial understanding of how digital risks interact, accumulate, and escalate across the firm. This article develops an original taxonomy of digital business risks. It classifies the risk landscape into six categories: strategic lock-in, data privacy, algorithmic bias, platform dependence, workforce displacement, and reputation loss. The taxonomy is designed to distinguish categories clearly while also showing how they may interact in practice. The article follows a conceptual taxonomy development approach based on synthesis of peer-reviewed journal articles published. It applies formal classification criteria related to risk source, impact domain, time horizon, controllability, regulatory exposure, and organizational response capability. The resulting taxonomy provides definitions, sub-dimensions, comparative criteria, and managerial use cases. The contribution of the article is a systematic classification framework for digital business risk identification and assessment. It offers a shared language for researchers, managers, boards, and risk professionals seeking to govern digital transformation more effectively. The taxonomy also creates a foundation for future empirical validation and integration into enterprise risk management and digital strategy processes.
Institute for Management, Business, and Accounting Studies