Data Science, AI, and e-Commerce Analytics

The digital transformation of commerce has ushered in a new era driven by data, algorithms, and intelligent systems. The Data Science, AI, and e-Commerce Analytics section of the Journal of Theoretical and Applied Electronic Commerce Research (JTAER) serves as a platform for cutting-edge research at the intersection of data-driven technologies and electronic commerce. This section welcomes contributions that explore the use of big data, machine learning, and artificial intelligence to better understand and shape consumer behavior, enhance personalization through recommendation systems, and improve decision-making across e-business environments.

We are particularly interested in studies addressing predictive and prescriptive analytics, algorithmic decision-making, text mining, social network analysis, and the ethical and secure use of data in e-commerce. With the growing relevance of generative AI and real-time analytics, we aim to highlight research that advances theory and offers practical implications for digital platforms, marketplaces, and service providers.

We invite interdisciplinary and methodologically rigorous submissions that offer novel insights, scalable solutions, and theoretical contributions to this fast-evolving field.