How Chatbot Language Shapes Consumer Perceptions: The Role of Concreteness and Shared Competence

Abstract

In service settings, chatbots frequently are associated with substandard care, depersonalization, and linguistic misunderstandings. Drawing on assemblage theory (i.e., the examination of how heterogeneous parts, through their ongoing interaction, create an emergent whole with new capacities that the parts themselves do not have), the authors investigate how chatbots’ language concreteness—the specificity of words used during interactions with consumers—can help improve satisfaction, willingness to use the chatbot, and perceived shopping efficiency. Across three experiments, the findings reveal a psychological mechanism driven by concrete chatbot language that makes chatbots seem competent and reinforces consumer self-competence, in turn boosting satisfaction, willingness to use the chatbot, and perceived shopping efficiency. This pattern of results contributes to consumer behavior by providing evidence of the chatbot language concreteness effect on consumer–chatbot interactions. For practitioners, the authors outline conversational designs that could help optimize implementation of chatbots in customer service.