Forecasting performance and determinants of household expenditure on fruits and vegetables using an artificial neural network model

Kyoung Jin Kim1   Hong Sung Mun1   Jae Bong Chang2,*   

1Department of Livestock Business and Marketing Economics, Konkuk University, Seoul 05029, Korea
2Department of Food Marketing and Safety, Konkuk University, Seoul 05029, Korea


Interest in fruit and vegetables has increased due to changes in consumer consumption patterns, socioeconomic status, and family structure. This study determined the factors influencing the demand for fruit and vegetables (strawberries, paprika, tomatoes and cherry tomatoes) using a panel of Rural Development Administration household-level purchases from 2010 to 2018 and compared the ability to the prediction performance. An artificial neural network model was constructed, linking household characteristics with final food expenditure. Comparing the analysis results of the artificial neural network with the results of the panel model showed that the artificial neural network accurately predicted the pattern of the consumer panel data rather than the fixed effect model. In addition, the prediction for strawberries was found to be heavily affected by the number of families, retail places and income, while the prediction for paprika was largely affected by income, age and retail conditions. In the case of the prediction for tomatoes, they were greatly affected by age, income and place of purchase, and the prediction for cherry tomatoes was found to be affected by age, number of families and retail conditions. Therefore, a more accurate analysis of the consumer consumption pattern was possible through the artificial neural network model, which could be used as basic data for decision making.

Figures & Tables

Fig. 1. Structure of artificial neural network for strawberry. O1, output node; B1-B2, bias node; H1-30, hidden node;I1-29, input node.