Online reviews in e-commerce: A bibliometric analysis of consumer decision-making

Over the past two decades of digitalization, Internet technology has reconfigured the information ecosystem of consumer decision-making. Nowadays, consumers actively obtain product information through multi-touch interaction networks, where online reviews have become a core element influencing their purchase decisions. This phenomenon presents enterprises with a dual challenge: to address the issue of processing massive online reviews and to establish a dynamic trust mechanism in virtual consumption scenarios. Based on the Web of Science database literature, this study employs bibliometric analysis to systematically examine the mechanism by which online reviews influence consumers' purchasing decisions. It is found that existing studies can be integrated into seven theoretical dimensions, ranging from the basic layer of sentiment analysis and product feature mining to the behavioral driving layer of online trust construction, and ultimately to the decision-making ecosystem encompassing consumer psychological mechanisms, behavioral modeling, and the identification of false reviews. The study not only establishes an interdisciplinary theoretical framework for analysis but also highlights the crucial role of the balance between real-time data processing technology and privacy protection in e-commerce practice. The paper suggests that we should explore the intelligent analysis system of reviews driven by large language models and the dynamic decision prediction model in cross-cultural scenarios, thereby providing a new research paradigm for precision marketing in the digital economy era.