Time Evolution Analysis of Riders’ Preference Attention and Satisfaction on Real-Time Crowdsourcing Logistics Platform

Abstract

This study examines the evolutionary trends in preference focus and satisfaction among real-time crowdsourced logistics platform riders (hereafter referred to as riders) in China since the outbreak of the COVID-19 pandemic. By analyzing real-time comments from 5,652 riders and applying the Latent Dirichlet Allocation (LDA) model to identify riders’ preferences and calculate their attention levels, combined with riders’ actual ratings to infer satisfaction levels (ranging from very dissatisfied to satisfied), this research for the first time explores the changing patterns of riders’ preferences and their attention and satisfaction trends in the pandemic context from a long-term dynamic perspective. The findings reveal that riders prioritize interaction quality; the range of fluctuation in average attention levels is large when high and small when low; attention to platform management, system utility, and rider relationships is on the rise, whereas interest in other preferences is declining; there is a significant correlation between rider attention and platform policies; and social media and related public opinion influence riders’ preferences. These insights are instrumental for the platform to adjust the policy direction duly and meet the core demands of riders according to the limited priority.