Understanding automated conversational agent as a decision aid: matching agent's conversation with customer's shopping task

To provide better services to customers, especially immediate responses and 24/7 availability, businesses are implementing text-based automated conversational agents, i.e. chatbots on their social platforms and websites. Chatbots are required to not only provide customers with necessary consultancy and guidance but also communicate friendly and socially. Based on the cognitive fit theory, this study attempts to examine the role of chatbot as a decision aid and how the match between information presentation in forms of decisional guidance and communication style and the shopping task influences consumers' perceived cognitive fit and decision performance outcomes.

A 2 x 2 x 2 between subject online experiment was conducted to identify which kind of decisional guidance (suggestive and informative guidance) and communication style (task-oriented vs social-oriented style) are the most appropriate for each type of shopping task (searching vs browsing task).

The findings show that when customers interact with chatbots, they will perceive higher cognitive fit if the chatbots provide them with suggestive guidance and communicate in a friendly style especially when they perform a searching task.

This study is the first attempt to understand the role of chatbots as a decision aid to customers using the communicative language. This study also tries to explore the cognitive fit theory in a novel way, and we propose the information presentation in forms of communicative language rather than matrices, tables and graphs.