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portada Using Recursive Informational System Feedback in Management. Research (en Inglés)
Formato
Libro Físico
Año
2026
Idioma
Inglés
N° páginas
40
Encuadernación
Tapa Blanda
Dimensiones
28x21.6x0.2 cm
ISBN13
9798254096795

Using Recursive Informational System Feedback in Management. Research (en Inglés)

Alonzo Curry (Autor) · Independently published · Tapa Blanda

Using Recursive Informational System Feedback in Management. Research (en Inglés) - Alonzo Curry

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Reseña del libro "Using Recursive Informational System Feedback in Management. Research (en Inglés)"

In a fast-paced, data-driven business environment, organizations increasingly seek methods to improve decision-making, operational efficiency, and adaptability to changing market conditions. Traditional management models, while effective to a degree, often struggled with the complexity and speed at which modern businesses had to operate. This research is where management, using recursive information systems, came into play-offering a way to drive continuous improvement through data and automated feedback mechanisms (Hamel & Prahalad, 1994). At its core, a recursive system was a self-perpetuating process that continuously refined itself by learning from feedback and iterating previous actions or decisions. In the context of management, recursive systems utilize feedback loops-where outputs of a system are fed back into the system as inputs-allowing the system to adjust its behavior over time based on new data (Mintzberg et al., 2009). This created a dynamic learning environment where decisions were constantly updated, and outcomes were continually optimized (Schilling, 2021). This iterative process was supported by systems theory, which emphasized the interconnection of various components within an organization, shifting the focus from isolated decisions to a Progressive approach (Jones & Taylor, 2022). Just as feedback loops in natural systems (like ecosystems or the human body) allowed organisms to adapt and evolve, recursive systems in management facilitated adaptive decision-making, where each iteration led to a more refined approach (Terry et al., 2023).

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