Factors influencing users' adoption and use of conversational agents: A systematic review

As artificially intelligent conversational agents (ICAs) become a popular customer service solution for businesses, understanding the drivers of user acceptance ICAs is critical to ensure its successful implementation. To provide comprehensive review factors affecting consumers' adoption and use ICAs, this study performs systematic literature extant empirical research on topic. Based search performed in July 2019 followed by snowballing approach, 18 relevant articles were analyzed. Factors found influence human-machine cognitive engagement categorized into usage-related, agent-related, user-related, attitude evaluation, other factors. This proposed collective model users' whereby driven mainly usage benefits, which are influenced agent characteristics. The emphasizes model's context-dependency, as depend settings, provides several strategic business implications, including design, personalization, relationship management.