Online User Behavior in the Context of Big Data

Dear Colleagues,

With the widespread adoption of the Internet and mobile devices, there has been an exponential increase in the amount of user behavior data that can be collected. The vast array of user data includes not only basic information such as browsing history, but also extends to user interactions such as purchasing patterns, commenting behavior, and social media sharing trends. Fortunately, with advancements in technology, we can utilize techniques such as machine learning, natural language processing, and data mining to sift through the massive amounts of data and uncover valuable insights about user behavior. Through user behavior analysis, we can gain a deep understanding of users, including their interests, needs, preferences, and behavior patterns. Armed with this knowledge, businesses can tailor their services to provide better user experiences, accurate identification of user portraits, and personalized recommendations that accurately meet user demands. For example, through text-mining analysis of linguistic data collected from streamers, we can identify the specific linguistic characteristics that are strongly correlated with sales performance in livestreaming e-commerce. This insight can be used to create tailored marketing strategies that aim to improve livestreaming performance (Liu et al., 2023). By using insights obtained through user behavior analysis, businesses can make informed decisions about critical areas of operation such as product design, marketing strategies, and more. Based on this background, this Special Issue mainly focuses on user behavior in the context of big data. Both qualitative and quantitative empirical studies are welcome. Examples of the topics of interest include but are not limited to:

• Change and innovation management in the context of big data.
• The intersection of big data, artificial intelligence, user behavior, and business value.
• Analytics and challenges related to user behavior in the context of big data.
• Marketing and management decision-making using big data analytics.
• Analysis of user behavior in Africa and Latin America using big data techniques.
• Mining user preferences and patterns through big data-driven user behavior.
• Exploratory studies of user behavior analytics using big data techniques.
• The emergence and evolution of user behavior analytics through big data.
• Consumer psychology and decision-making in the context of human–AI interactions.
• Livestreaming e-commerce and social e-commerce.
• Digital transformation, digital operation, and enterprise-level big data applications.
• Management and operation of online platforms and markets based on big data.
• User online reviews and fake reviews identification.
• Consumer privacy rights and protection in the context of big data analytics and AI applications.
• Ethical considerations related to big data analytics and AI applications.
• Generative AI security, data security and privacy, and corporate digital responsibility.

Dr. Jiaming Fang
Dr. Chao Wen
Dr. Benjamin George
Topic Editors