Assessing Chatbot Acceptance in Policyholder’s Assistance Through the Integration of Explainable Machine Learning and Importance–Performance Map Analysis

Companies are increasingly giving more attention to chatbots as an innovative solution to transform the customer service experience, redefining how they interact with users and optimizing their support processes. This study analyzes the acceptance of conversational robots in customer service within the insurance sector, using a conceptual model based on well-known new information systems adoption groundworks that are implemented with a combination of machine learning techniques based on decision trees and so-called importance–performance map analysis (IPMA). The intention to interact with a chatbot is explained by performance expectancy (PE), effort expectancy (EE), social influence (SI), and trust (TR). For the analysis, three machine learning methods are applied: decision tree regression (DTR), random forest (RF), and extreme gradient boosting (XGBoost). While the architecture of DTR provides a highly visual and intuitive explanation of the intention to use chatbots, its generalization through RF and XGBoost enhances the model’s explanatory power. The application of Shapley additive explanations (SHAP) to the best-performing model, RF, reveals a hierarchy of relevance among the explanatory variables. We find that TR is the most influential variable. In contrast, PE appears to be the least relevant factor in the acceptance of chatbots. IPMA suggests that SI, TR, and EE all deserve special attention. While the prioritization of TR and EE may be justified by their higher importance, SI stands out as the variable with the lowest performance, indicating the greatest room for improvement. In contrast, PE not only requires less attention, but it may even be reasonable to reallocate efforts away from improving PE in order to enhance the performance of the more critical variables.