When Chatbot Fails: Expectancy Violation and Intergroup Bias in Human-AI Interactions

Chatbots, based on artificial intelligence power, has been common across many industries and deeply embedded in customers' daily lives. However, it's been reported that chat bots fail when interacting with customers. This research investi gates the impact of chatbot-made failure on customers' purcha se intention and the underlying mechanisms. Based on expectan cy violation theory and social identity theory, the authors prop ose that erring chatbots (vs. humans) trigger a greater extent of negative expectancy violation, which can be explained by custo mers' intergroup biases, which in turn negatively impact custo mers' purchase intention. The results of our experiment (N=13 2) validate our hypotheses. By exploring mental models and int ergroup bias held by customers, these findings deepen our unde rstandings of customers' perceptions of AI, and provide insight s for alleviating algorithm aversion in a human-machine symbi otic society.