Applying kansei engineering and text mining to design fresh food logistics services

Abstract

ABSTRACT The study focuses on identifying the key drivers that can meet consumers’ perceptual demands for fresh food logistics services and how e-retailers balance changing demands. In this light, a service quality improvement approach integrating Kansei engineering and text mining is proposed to span the property and semantic space. The data from the Chinese e-commerce platform is used as 288 service scenarios. Meanwhile, GeoDetector and Kano model are used to build models and find priorities of services. The results show that consumers’ demands are fine-grained and hierarchical. Fresh, fast, affordable, reliable and satisfied demands are the rational. Convenient, thoughtful, pleasant, persistent and surprised demands are the perceptual. Most logistics attributes have a ‘1 + 1 > 2’ effect on consumers’ perceptual demands during the emergency period, and exposure safety and logistics persistence are most significantly affected, having a ‘weak + weak = strong’ effect. Additionally, must-be, one-dimensional, and attractive quality based on the kano model provide more targeted strategies to improve the input-output utility.