Predicting the Antecedents of Quality of Life from the Use of Smart Technologies in Supermarket Retail: An in-depth Investigation Using Artificial Neural Networks

Abstract

This article aims to identify the determining factors for advances in smart technologies in supermarket retail that influence citizens’ quality of life in the context of the COVID-19 crisis. We developed a theoretical model using Artificial Neural Networks (ANN) from a sample of 469 users of smart technologies in supermarket retail. In contrast, a multivariate Exploratory Factor Analysis (EFA) approach organized the input data to an artificial neural network (ANN) model to predict factors of importance to the quality of life. Based on the results, this study revealed that the coexistence of the proposed predictive variables is analyzed to understand the quality of life using smart technologies and analyzing the users through generational classification. Subjective safety proved to be the construct with the most important predictive power of data analysis, and, at the same time, it is one of the main concerns of consumers.

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