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Remote and hybrid work have become enduring features of contemporary organizational life. What initially appeared to be an emergency response to pandemic disruption has increasingly developed into a structural transformation of how work is designed, coordinated, supervised, and experienced. This shift has created new managerial challenges that extend beyond questions of where employees work. The central problem addressed in this review is that remote and hybrid work are still often discussed as flexible work arrangements rather than as digitally mediated management systems. Such a narrow framing underestimates how digital platforms, communication routines, monitoring practices, and performance expectations reshape the relationship between employees, managers, teams, and organizations. The implications are particularly significant for coordination, trust, productivity, and employee well-being. The objective of this integrative review is to synthesise peer-reviewed evidence on remote and hybrid work as digital management systems. The review brings together literature from management, information systems, human resource management, organizational behaviour, and organizational psychology. It focuses on how remote and hybrid work reconfigure managerial practice through technology-mediated coordination, altered trust relations, changing productivity assumptions, and new well-being risks. The review finds that remote and hybrid work function as complex digital management systems rather than simple location choices. They require intentional design of coordination mechanisms, explicit communication norms, trust-based accountability, careful use of monitoring technologies, and active protection of employee well-being. The review concludes that digital managers must adopt a systemic approach that treats coordination, trust, productivity, and well-being as interdependent rather than separate managerial concerns.

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.