Digital marketing model innovation and Generation Z as consumer decision-making style. Making predictions with an artificial neural network, in Mexico

• Juan Mejía Trejo ^[1]
  1. [1] Universidad de Guadalajara

     Universidad de Guadalajara

     México

     
• Localización: Contaduría y administración, ISSN 0186-1042, ISSN-e 2448-8410, Vol. 66, Nº. 4, 2021
• Idioma: inglés
• Títulos paralelos:
  • Modelo de innovación de mercadotecnia digital y la Generación Z como estilo de toma de decisiones de consumidor. Haciendo predicciones con una red neural artificial en México
• Enlaces
  • Texto completo (pdf)
• Dialnet Métricas: 2 Citas
• Resumen
  • español

    El presente trabajo se orienta a evaluar las capacidades de predicción entre el Modelo de Innovación de Mercadotecnia Digital (DMMI), basado en el Manual de Oslo 4ª.ed. y el Modelo de Estilos para Toma de Decisiones del Consumidor (CDMS). La metodología implicó una red neural basada en el sofware SPSS para analizar los datos recolectados de 400 jóvenes estudiantes mexicanos (Generación Z) pertenecientes a diez universidades locales de la ciudad de Guadalajara, Jalisco, de Enero a Junio de 2019. Esto es muy importante para varias organizaciones interesadas en reconocer las condiciones de cómo colectar y medir los datos de innovación del DMMI relacionados con diferentes CDMS bajo el comportamiento de internet, para ser más competitivos. Los resultados sugieren mejoras en cada una de las relaciones estratégicas del modelo DMMI-CDMS con alto nivel de predicción en un Perceptrón Multicapa (MLP) como red neural predictiva sobre diferentes variables comparada con una regresión Binaria Logística (BLR) para evaluar y explicar el alcance de tales predicciones del modelo DMMI-CDMS.

  • English

    This research aims to assess the predictive capabilities between the Digital Marketing Model Innovation (DMMI), based on the Oslo Manual 4th ed., and the Consumer Decision-Making Style (CDMS) model.

    The methodology involved an artificial neural network based on SPSS software to analyze data collected from 400 young Mexican students (Generation Z) belonging to ten local Guadalajara city universities from January to June 2019. The above mentioned are essential for several organizations interested in recognizing how to collect and measure innovation data of DMMI related to different CDMS internet behavior to increase competitiveness. The results suggest improvements on each one of the strategic relationships at the DMMI-CDMS model. Such improvements involving a high prediction level based on Multilayer Perceptron (MLP) as a predictive neural network on different variables compared with a Binary Logistic Regression (BLR) to assess and explain the scope of such predictions of the DMMI-CDMS model

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