Digital business research has generated a wide range of capability concepts to explain how firms compete, innovate, adapt, and learn in technology-intensive environments. Yet these concepts often appear under overlapping labels, including digital capabilities, analytics capabilities, platform capabilities, operational capabilities, and dynamic capabilities. This proliferation has enriched the field but has also made it difficult to compare findings across studies. The central problem addressed in this article is the absence of a coherent taxonomy for digital business capabilities. Without clear classification rules, researchers may treat different capabilities as equivalent, while managers may invest in digital resources without understanding which capability category they are actually developing. This ambiguity weakens conceptual precision, measurement design, and strategic capability development. The objective of the article is to develop an original taxonomy that classifies digital business capabilities into four distinct categories. These categories are Customer Intelligence, Operational Agility, Platform Coordination, and Strategic Learning. Each category is defined by a distinct value-creation logic, resource orientation, temporal focus, and managerial purpose. The resulting taxonomy identifies four mutually exclusive and collectively exhaustive categories of digital business capabilities. It defines the sub-dimensions of each category, explains how the categories differ, and provides tables that support classification, comparison, and managerial application. The taxonomy offers a shared language for scholars and practitioners seeking to analyse, measure, and develop digital business capabilities with greater precision.
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.
Digital business has expanded the number and variety of revenue models through which firms capture value from software, platforms, data, digital services, and online ecosystems. Subscription, freemium, marketplace, pay-per-use, licensing, and data-enabled models are now widely used across sectors, but their conceptual boundaries are often blurred. This makes it difficult for researchers and managers to compare models systematically. The problem addressed in this article is the lack of a clear taxonomy for distinguishing digital revenue models in business management. Existing terminology often mixes pricing mechanisms, business model architectures, customer access rights, platform intermediation, and data monetization under overlapping labels. As a result, revenue model analysis may become imprecise, fragmented, or overly case-specific. The objective of this article is to develop a rigorous taxonomy of digital revenue models based on explicit classification criteria. The taxonomy classifies digital revenue models into six categories: subscription, freemium, marketplace, pay-per-use, licensing, and data-enabled models. These categories are differentiated according to revenue generation logic, payment structure, value unit, customer relationship, scalability mechanism, and governance requirement. The resulting taxonomy provides definitions, sub-types, comparative criteria, and governance implications for each model. It contributes a shared vocabulary for studying digital revenue strategies and offers managers a practical tool for designing, combining, and governing revenue portfolios. The article concludes that digital revenue model selection should be treated not only as a monetization choice but also as a strategic governance decision.
Institute for Management, Business, and Accounting Studies