Towards More Convenient Services: A Text Analytics Approach to Understanding Service Inconveniences in Digital Platforms

Abstract

In today’s fast-paced world, where time is our most valuable asset, convenience is on the rise. This trend has led to a huge growth in digital on-demand services, which target convenience-oriented consumers. Using big data and text analytics, we examine the impact of service inconveniences on customer satisfaction in the context of on-demand food delivery. Building on the Model of Service Convenience and Attribution Theory, we analyze 235,147 user-generated reviews through a combination of keyword-assisted topic modelling and cumulative link model analysis. We introduce the concept of Remote support inconvenience and identify the key topics related to each inconvenience. We find that all service inconveniences negatively influence satisfaction (especially cancelled orders and remote support incidences), and the effects are exacerbated when moderated by stability or controllability attributions. These insights contribute to our theoretical understanding of service inconvenience and can help platforms identify and improve critical areas of their services.