An Inconvenient Truth: Understanding Service Inconvenience in Digital Platforms

Abstract

In today’s busy world, convenience is on the rise. On-demand services (e.g., food delivery services) promise swift solutions to our daily needs. However, limited research explores how service inconveniences (e.g., order cancelations and delays) impact consumer satisfaction, and which factors exacerbate such impact. This study addresses this gap by leveraging text analytics on a dataset of 222,371 user-generated reviews in food delivery platforms. Building on the Model of Service Convenience and Attribution Theory, we hypothesize that when consumers experience an inconvenience, it is not only what happened that matters to them, but also why they think it happened (causal attributions). Given that these two models have not been jointly tested, it is unclear how attributions moderate the effect of different service inconveniences on satisfaction. We present a scalable approach to measure service inconvenience attributions, allowing us to identify not only critical inconveniences but also a new construct: remote support inconvenience. Our results show that when stability or responsibility attributions are present, the effect of inconveniences on satisfaction can be over four or eleven times stronger (−426% and −1,140% from baseline, respectively). These insights contribute to the theoretical understanding of service inconveniences and offer actionable guidance for platforms to improve their services.