Research on intelligent strategies for enhancing user experience in China’s import cross-border E-commerce platforms - Wang, Xia; Salleh, Kalsom; Siang, Liew Cheng (Edelweiss Applied Science and Technology, 2025)
This study aims to address user experience problems on China's import cross-border e-commerce platforms through the implementation of smart technologies. A mixed methods approach was employed, comprising a survey (n=385), in-depth interviews, and comprehensive platform analysis. The research identified five major pain areas: product authenticity concerns (27.3%), logistical inefficiency (24.5%), payment security issues (18.7%), language barriers (16.2%), and inadequate after-sale service (13.3%). Five smart enhancement measures were developed: search and recommendation systems utilizing user profiling and cross-cultural semantics; multilingual NLP-powered customer service; predictive analytics and blockchain-driven logistics; trust frameworks with product validation systems; and personalized experience design for Chinese consumers. Implementation of these measures yielded significant improvements in conversion rate (77.8%), customer satisfaction (35.9%), delivery time (43.0%), and return rates (42.5%). The study establishes a strong correlation between platform intelligence and user satisfaction (r=0.79, p<0.01), confirming that integrated application of various intelligent algorithms substantially enhances cross-border e-commerce experiences. The proposed smart technology framework provides practical solutions for e-commerce platforms seeking to overcome cross-cultural challenges and optimize user experience in the Chinese import market.