Trust Evaluation Method of E-Commerce Enterprises with High-Involvement Experience Products

Abstract

Purpose: High-involvement experience products (HIEP) are generally characterized by a high value and difficult purchasing decision for customers, and a wrong decision will bring large losses to consumers, severely affecting their trust in enterprises. The purpose of this paper is to solve the problem of trust evaluation of HIEP e-commerce enterprises. Tasks and research methods: First, given the heterogeneity of trust information in the big data context, this paper collects the reputation data of HIEP enterprises and the trust big data of enterprises in industry, commerce and justice by a crawler program. Next, we use the dictionary and pattern matching methods to extract the trust features in text big data and construct the trust evaluation feature set integrating judicial information. Then, based on machine learning methods, we propose a LAS-RS model, which aims to solve the problem of trust evaluation in an imbalanced and high-dimensional data context. Finally, by introducing signal theory, this paper reveals the differential influence mechanism of big data feature variables on the trust of HIEP e-commerce enterprises. Originality: This study further enriches the relevant theories and methods of e-commerce trust evaluation research and is conducive to a better understanding and control of potential trust risks.