Maximización de los beneficios del aprendizaje automáticomejora de la teoría de efectos de redes de datos para mejorar la creación de valor y la apropiación

  1. Ricardo Costa Climent 1
  1. 1 Uppsala University y University of Economics and Human Sciences of Warsaw
Revista:
ESIC Digital Economy & Innovation Journal

ISSN: 2792-8721

Año de publicación: 2023

Volumen: 2

Número: 1

Tipo: Artículo

DOI: 10.55234/EDEIJ-2-062 DIALNET GOOGLE SCHOLAR lock_openAcceso abierto editor

Otras publicaciones en: ESIC Digital Economy & Innovation Journal

Resumen

La teoría recientemente propuesta de los efectos de la red de datos tiene como objetivo explicar cómo se crea el valor del usuario a partir del uso de la tecnología de aprendizaje automático. La teoría explica la capacidad de aprendizaje única del aprendizaje automático, que utiliza grandes conjuntos de datos para hacer predicciones y mejorar la toma de decisiones. Este artículo ofrece una evaluación de la teoría de los efectos de la red de datos, identificando algunas de sus fortalezas y limitaciones. En cuanto a las fortalezas, contribuye al éxito de las empresas, explica las características únicas de las tecnologías de ML y es un avance del cuerpo de la teoría de los efectos de red. Sus limitaciones luego se transforman en un conjunto de preguntas de investigación interrelacionadas que se centran en la relación del uso del aprendizaje automático y cuestiones tales como: captura de valor, una visión co-evolutiva, una perspectiva de múltiples actores y la dinámica de bases de datos. Este artículo describe un enfoque multiteórico para estudiar la creación de valor y la captura que permite el uso de tecnologías de aprendizaje automático.

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