A Data-Driven Model to Construct the Influential Factors of Online Product Satisfaction

Abstract

Online shopping is becoming more prevalent, with consumers turning to e-commerce platforms to search for information about the goods and services they need. Users will usually check other consumer reviews on the platform as a reference while shopping. Online retailers can collect and analyze these online reviews to monitor consumer opinions about product quality, logistics services, packaging and other attributes to provide an accurate basis for product improvement and service optimization. This paper applies the Latent Dirichlet Allocation (LDA) algorithm to extract the critical factors that affect consumer satisfaction. More than 30,000 reviews of seven kinds of 3C (computer, communication, and consumer electronic) product categories obtained by crawler technology are analyzed. Then, the DEMATEL-ANP (DANP) method is applied to the extracted framework to build a cause-and-effect diagram of 3C product satisfaction model. The innovative LDA-DANP hybrid model clarifies the causal influence of the evaluation dimensions for 3C products sold online. The results show that brand value is the most important dimension affecting consumer online product satisfaction. Appearance design, logistics awareness service and product performance also have a positive influence on perceived service and brand value. Finally, some management implications and practical suggestions are proposed.