Intention to use determinants of AI chatbots to improve customer relationship management efficiency

Abstract

AI chatbots are the key technology that embraces the technology in service. Nevertheless, the use of AI chatbots intention is not visible in most companies; as a result, they are unable to maintain customer relationships with Generation Z. This study aims to examine how user experience and satisfaction (UES), perceived utility and ease of use (PUEU), communication effectiveness (CE), and user acceptance and trust (UAT) relate to the development of effective customer relationship management (CRM), and how these factors affect users’ intentions to use chatbots (IUAC). This study collected data from medium-sized businesses and larger corporations in Asia, Europe, and Africa. A quantitative survey method was also used, followed by self-administered questionnaires. This study used a cross-sectional research design to investigate the impact of multiple factors on enhancing customer relationship management using structural equation modelling partial least squares (SEM-PLS). These findings indicate a strong correlation between variables. The study shows strong correlations between communication effectiveness and intention to use AI chatbots; the intention to employ AI chatbots and their perceived utility and ease of use; the intention to use an AI chatbot and acceptance and trust; and experience, satisfaction, and intention to use AI chatbots. The results of this study extend beyond customer relations to include other areas such as business operations, suppliers, distributors, and emerging economies. Therefore, this study provides a solid basis for understanding the relationships between innate traits and propensity to use AI chatbots for future advice.

Keywords

• User experience and satisfaction
• perceived utility and ease of use
• communication effectiveness
• user acceptance and trust
• customer relationship management
• Customer Relationship Management (CRM)