I, Chatbot: Modeling the determinants of users' satisfaction and continuance intention of AI-powered service agents

By Muhammad Ashfaq, Jiang Yun, Shubin Yu, Sandra Maria Correia Loureiro. Telematics and informatics, 2020

840 citations recorded by the source corpus.

Abstract Chatbots are mainly text-based conversational agents that simulate conversations with users. This study aims to investigate drivers of users’ satisfaction and continuance intention toward chatbot-based customer service. We propose an analytical framework combining the expectation-confirmation model (ECM), information system success (ISS) model, TAM, and the need for interaction with a service employee (NFI-SE). Analysis of data collected from 370 actual chatbot users reveals that information quality (IQ) and service quality (SQ) positively influence consumers’ satisfaction, and that perceived enjoyment (PE), perceived usefulness (PU), and perceived ease of use (PEOU) are significant predictors of continuance intention (CI). The need for interaction with an employee moderates the effects of PEOU and PU on satisfaction. The findings also revealed that satisfaction with chatbot e-service is a strong determinant and predictor of users’ CI toward chatbots. Thus, chatbots should enhance their information and service quality to increase users’ satisfaction. The findings imply that digital technologies services, such as chatbots, could be combined with human service employees to satisfy digital users.

Subfields: Artificial Intelligence, Information Systems and Management, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/8e592af041f5f1c11f2976fd8ed24020a36c4c7b