MODELING THE IMPACT OF RECOMMENDATION ALGORITHMS ON GEN Z E-COMMERCE CONSUMPTION BEHAVIOR

Abstract

The consumptive behavior of Generation Z (Gen Z) in e-commerce platforms is strongly influenced by recommendation algorithms, which often drive impulsive purchasing decisions. This issue is further exacerbated by low levels of financial literacy and the widespread availability of Buy Now Pay Later (BNPL) services, which increase the risk of a recurring debt cycle. This study aims to model and quantitatively estimate the level of impulsive behavior using a deep learning approach. Two neural network architectures were tested and compared. The first architecture, an Artificial Neural Network (ANN), was employed as a preliminary analytical model to map the nonlinear relationships between preprocessed static variables and impulsivity levels. The second architecture, a hybrid model combining a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM), was specifically designed to capture temporal patterns and the dynamic evolution of impulsive behavior over time. Quantitative evaluation results demonstrate that the RNN-LSTM hybrid model achieved superior performance with exceptionally high estimation accuracy, as indicated by a Mean Absolute Error (MAE) of 0.0821 and a coefficient of determination (R²) of 0.9767. In comparison, the static ANN model achieved only an MAE of 0.2078 and an R² of 0.8924. These findings explicitly confirm that impulsive behavior is a dynamic phenomenon, and thus, the hybrid RNN-LSTM architecture proves significantly more effective in analyzing sequential behavioral patterns.