Driven by digitalization and accelerated by the COVID-19 pandemic, e-commerce has experienced strong growth, especially in the last four years. This transformation has reshaped consumer behavior, business models, and workplace dynamics, where digitalization such as artificial intelligence and automation have improved operational efficiency, personalization, and market reach. This study explores these dynamics and provides an overview of e-commerce in the U.S. through a time series approach, analyzing five key variables: sales, employment, hours worked, costs, and the producer price index. It also models and forecasts sales and the producer price index using classic, deep learning, and hybrid methods. The results show that while sales have increased, employment and labor hours have fallen, alongside stable production costs and a reduction in the producer price index over the past two years. In forecasting, deep neural networks offer superior predictive performance, although classic methods provide similarly accurate results in series with clear trends and seasonality, making them a more computationally efficient alternative. This research contributes to decision making in e-commerce by exploring the relationships between sales growth and labor market dynamics, evaluating the effectiveness of different forecasting methods, and highlighting the need for strategic adaptability in a digitalized sector.

Filipe R. Ramos acknowledges funding by national funds through FCT – Fundação para a Ciência e a Tecnologia under the project UIDB/00006/2020. DOI: 10.54499/UIDB/00006/2020.

E-commerce,Trends,Digitalization,Time series,Deep learning,Hybrid models,Forecasting,Prediction error

• Mathematics - Natural Sciences
• Economics and Business - Social Sciences
• Other Social Sciences - Social Sciences