Influência dos fatores socioeconômicos e do acesso ao varejo tradicional nas entregas do comércio eletrônico : análise econométrica e espacial para Belo Horizonte, Uberlândia e Patos de Minas

Abstract

The growth of online purchases is associated with the benefits of online shopping, such as greater convenience and lower prices, which generates an increase in the number of deliveries. This phenomenon has negative consequences for urban mobility since urban planning often does not include urban freight transport (UFT). The negative externalities of UFT include growth in the number of freight vehicle trips, environmental and noise pollution, congestion, increased risk of traffic accidents, and damage to the road infrastructure. Knowing the characteristics related to deliveries contributes to an UFT inclusive urban planning, which helps to implement public policies for greater city sustainability. Previous studies have indicated a relationship between socioeconomic factors and ecommerce in a particular region. However, there is a lack of studies that explore spatial factors and their relationship with ecommerce, such as access to shopping opportunities in traditional retail and the geographic distribution of demographic profiles of econsumers. In this context, it is relevant to study the influence of socioeconomic factors and access to purchase opportunities in ecommerce deliveries. Therefore, the objective of this dissertation was to identify the relationship between socioeconomic factors and the opportunity to access traditional retail and the number of ecommerce deliveries in Belo Horizonte, Uberlândia, and Patos de Minas. To do so, firstly, the influence of these factors on the number of deliveries was verified using negative binomial regression models. To analyze the spatial correlation between deliveries and socioeconomic factors and purchase opportunity, Geographically Weighted Regression was used in Belo Horizonte, where, among the three cities analyzed, spatial dependency of the number of deliveries was detected. Results for the econometric models indicate the influence of income and age as being a common point between the three cities. The spatial model disclosed the influence of income and age, as well as gender, number of retail shops, and race.