Factor Analysis of AI Chatbot Continuance Use: An Extended Expectation Confirmation Model Perspective

Abstract

The adoption of technology in the workplace has grown rapidly alongside advancements in information and communication technology. One key innovation is the adoption of AI-based chatbots to support work activities. In the digital era, the success of the new technology relies heavily on user perception and response. This study aims to identify the factors influencing users’ intention to continue using AI chatbots at work using the Extended Expectation Confirmation Model (E-ECM), with variables including confirmation, perceived usefulness, satisfaction, continuance intention and with additional variables being perceived ease of use and trust. A quantitative method was used by distributing questionnaires to 400 respondents.  The respondents are workers who have used AI chatbots for their work.  This research adopts a quantitative method and employs data analysis using the PLS-SEM technique. The findings reveal that perceived ease of use and trust significantly affect continuance intention, while confirmation, perceived usefulness, and trust significantly affect satisfaction. However, perceived usefulness and satisfaction did not significantly influence continuance intention. These insights can help stakeholders and users focus on key factors to optimize AI chatbot adoption in the workplace. The results of this study are expected to serve as a reference for developers and users to pay attention to what factors affect the intention of continuous use of AI chatbots so as to increase the effectiveness of using AI chatbots.