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In algorithmically mediated environments, traditional managerial authority is undergoing profound reconfiguration as data-driven systems assume decision rights previously reserved for human hierarchies. This theory-development article synthesizes insights from algorithmic management, AI-driven organizational decision systems, digital control mechanisms, and governance of algorithmic oversight to reconceptualize how authority, control, and accountability are redistributed in contemporary digital business organizations. Drawing on peer-reviewed studies, we identify critical gaps in existing frameworks—particularly the insufficient theorization of hybrid human-algorithmic authority relations and the emergence of distributed governance structures. We advance a novel theoretical model that positions algorithmic systems as active co-holders of organizational authority rather than mere tools. Five formal propositions articulate the causal dynamics of authority delegation, feedback loops, and accountability shifts in data-driven contexts. Figure 1 presents a conceptual architecture that illustrates bidirectional flows among algorithmic cores, managerial interfaces, and organizational actors. The framework contributes to digital business and management studies by offering a coherent lens for understanding managerial control in algorithmically governed systems, with implications for theory, practice, and policy in AI-augmented organizations.

Algorithmic management refers to the use of algorithmic systems to direct, monitor, evaluate, reward, and discipline workers. It has become especially visible in platform-mediated work, where task allocation, pricing, performance scoring, and access to work are increasingly organized through digital systems. Its influence, however, is no longer confined to gig work, because similar logics of monitoring, ranking, prediction, and automated evaluation are spreading into conventional organizations. The central problem addressed in this article is that management theory has not yet fully treated algorithmic management as a distinct managerial control logic. Much existing work explains algorithms as technological tools that intensify established forms of supervision, measurement, or coordination. This interpretation is useful but incomplete because it underestimates how algorithmic systems alter the basic architecture of control itself. The objective of this article is to develop a theory-driven account of algorithmic management as a new control logic. The article argues that algorithmic management reconfigures three core dimensions of work: employee autonomy, performance measurement, and accountability. It therefore requires a theoretical model that explains not only what algorithms do to workers, but how algorithmic systems reorganize the relations among workers, managers, data, and responsibility. The article distinguishes algorithmic management from traditional managerial control, explains the autonomy paradox created by algorithmic systems, analyses the density of digital performance measurement, and theorises the displacement of accountability in algorithmically managed work. It proposes a model linking algorithmic control intensity, autonomy suppression, performance measurement density, and accountability displacement. The central conclusion is that algorithmic management is not a neutral managerial technology but a distinctive control logic that demands new theoretical and practical responses.

Digital self-service systems have become central to contemporary digital business, reshaping how customers access, perform, and evaluate service tasks. Across retail, hospitality, banking, healthcare, transport, and platform-based services, organisations increasingly rely on kiosks, mobile apps, chatbots, online portals, and automated interfaces to move routine activities from employees to customers. This shift changes the service encounter from an employee-led interaction into a digitally mediated process in which customers are expected to participate more actively. The objective of this scoping review is to map the literature on digital self-service business systems through three interconnected dimensions: customer autonomy, service efficiency, and managerial control. Rather than treating self-service technologies only as technical tools, the review frames them as business systems that redistribute work, responsibility, and decision-making among customers, employees, and managers. The review therefore focuses on how digital self-service systems create value, where they create friction, and how firms attempt to govern service quality at scale. The review follows a scoping synthesis approach informed by PRISMA-ScR principles. Some peer-reviewed journal articles were included to capture recent developments in self-service technology, service automation, customer participation, chatbot service, frontline technology, service robots, and scoping review methodology. The synthesis charts dominant technology types, service contexts, customer outcomes, operational claims, and managerial control mechanisms. The review contributes a structured map of the digital self-service literature and identifies gaps in how autonomy, efficiency, and control are studied together. Existing research is rich but fragmented, with many studies concentrating on customer acceptance, service failure, or specific technologies rather than integrated business-system governance. Future research should examine how firms can design self-service systems that balance customer agency, operational productivity, ethical responsibility, and service quality assurance.