A multi-criteria decision analysis model for selecting an optimum customer service chatbot under uncertainty

Chatbots are increasingly integrated into customer support systems due to recent developments in artificial intelligence. Chatbots can provide quick and tailored services to users, including employees and consumers. This paper presents a decision-making framework for chatbot selection in the telecommunication industry. The most suitable chatbot is chosen using a new combination strategy combining Combined Compromise Solution (CoCoSo) and analytic hierarchy process (AHP) in the context of single-valued neutrosophic sets (SVNSs). The proposed method uses the AHP to generate attribute weights (collected from previous studies), and CoCoSo ranks the options and selects the best chatbot. The AHP-CoCoSo is an efficient multi-criteria decision-making model for solving complex under uncertainty. The usefulness and viability of the proposed model are statistically demonstrated by solving an explanatory case study of chatbot selection in telecommunication. The method's resilience and strength are further demonstrated through sensitivity analysis and comparisons with previously proposed approaches. The findings of this study reveal that the approach used might lead to more realistic results when dealing with uncertain information.