Chatbots in Logistics : A Field Experiment on Intelligent Freight Dispatching

Fueled by the widespread adoption of algorithms and artificial intelligence (AI), the use of chatbots has become increasingly popular in various business contexts. In this paper, we study how to effectively and appropriately use chatbots in logistics, particularly in dispatching freights automatically. Specifically, this paper seeks to understand the effects of two voice chatbot design features (i.e., identity disclosure and anthropomorphism) on the operational performance of freight dispatching. In collaboration with a large truck-sharing platform, we conducted a field experiment that randomly assigned over 10,000 truck drivers to receive outbound calls from the voice chatbot dispatcher of our focal platform. Our empirical results suggest that chatbot identity disclosure at the beginning of the conversation significantly reduces the operational performance in terms of response rate. However, humanizing the voice chatbot by adding our proposed anthropomorphism features (i.e., interjections and filler words) significantly improves response rate, conversation duration, and order acceptance intention. Moreover, interestingly, humanizing the voice chatbot along with its identity disclosure can still improve operational outcomes, and its magnitude is similar to the improvement brought about by humanizing the chatbot without identity disclosure. This finding indicates that improving anthropomorphism may potentially mitigate the negative effects of chatbot identity disclosure. Finally, we propose one plausible explanation related to the enhanced trust between humans and algorithms for the performance improvement and empirically show that drivers are more likely to disclose information to chatbot dispatchers with anthropomorphism features. Our proposed anthropomorphism improvement solutions are now being implemented and used by our collaborator platform