An Integrative Model to Predict Product Replacement Using Deep Learning on Longitudinal Data
Título del documento: An Integrative Model to Predict Product Replacement Using Deep Learning on Longitudinal Data
Revista: BAR - Brazilian Administration Review
Base de datos: CLASE
Número de sistema: 000494851
ISSN: 1807-7692
Autores: 1
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Instituciones: 1Universidade Federal do Rio Grande do Sul, Porto Alegre, Rio Grande do Sul. Brasil
Año:
Volumen: 17
Número: 2
País: Brasil
Idioma: Inglés
Tipo de documento: Artículo
Enfoque: Analítico, descriptivo
Resumen en inglés Past research on product upgrades has focused either on understanding who and when will upgrade or on figuring out why consumers will upgrade, but seldom on all. It has also neglected the interplay between these matters with decision context and timing. This manuscript depicts a comprehensive approach where, for the first time, product characteristics, individual differences, process, and contextual variables are analyzed on a predictive model of real product upgrades, identified through the systematic collection of primary data from a panel of smartphone consumers. We tested one traditional linear logistic regression model and two types of non-linear, state-of-the-art machine-learning models (extreme gradient boosting and deep learning) to explain upgrading behavior. Results provide an integrative, yet parsimonious, product-upgrade model showing the importance of resources; news about the smartphone brand; sentimental value; predicted, current, and remembered enjoyment; update capacity; and how much the smartphone meets the user’s current needs as the most relevant variables to determine which consumers are more prone to upgrade their smartphones. Our findings advance upgrade decision theory by taking a holistic approach to the phenomenon and bridging different theoretical accounts of the replacement decision literature
Disciplinas: Economía
Palabras clave: Econometría,
Reemplazo de productos,
Datos longitudinales,
Consumo,
Decisiones de compra,
Aprendizaje automático
Keyword: Econometrics,
Product replacement,
Longitudinal data,
Consumption,
Buying decisions,
Machine learning
Texto completo: https://biblat.unam.mx/hevila/BARBrazilianadministrationreview/2020/vol17/no2/6.pdf Texto completo (Ver HTML) Texto completo (Ver PDF)