From Algorithm to Doorstep: Consumer Acceptance of Chatbots in Last-mile Logistics

Failed deliveries are a major source of inefficiency in last-mile logistics that could be addressed by AI-enabled chatbots that facilitate real-time interaction between parcel recipients and Logistics Service Providers. This study examines the factors driving parcel recipient intention to use AI-enabled chatbots in this context, drawing on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) model and a scenario-based experiment with 214 university students. Three chatbot conditions were compared: a well-functioning chatbot, an error-prone chatbot, and a privacy-invasive chatbot. Results show that chatbot errors indirectly reduce future usage intentions through lower user satisfaction. Furthermore, expected performance and social influence are the primary predictors of intention to use. These findings extend UTAUT to a utilitarian, post-purchase last-mile logistics context and offer practical guidance for Logistics Service Providers seeking to improve last-mile performance through AI-enabled chatbots.