Is AI chatbot recommendation convincing customer? An analytical response based on the elaboration likelihood model.

Area of Science:

• Information Systems
• Consumer Behavior
• Artificial Intelligence

Background:

• While AI in e-commerce is gaining attention, research on AI chatbots specifically is limited.
• Understanding consumer adoption of AI chatbot recommendations is crucial for e-commerce development.

Purpose of the Study:

• To evaluate how AI chatbots persuade users to adopt their recommendations in online shopping.
• To present a framework for factors influencing consumer readiness to adopt AI chatbot recommendations.

Main Methods:

• Utilized the theory of elaboration likelihood to develop an analytical framework.
• Collected questionnaire data from 411 Chinese consumers of AI chatbots.

Main Results:

• Chatbot recommendation reliability and accuracy positively influenced AI technology trust and reduced perceived self-threat.
• AI technology trust positively correlated with adoption intention, while perceived self-threat negatively correlated.
• Perceived dialogue quality moderated these relationships, strengthening trust and weakening self-threat effects.

Conclusions:

• AI chatbot trustworthiness and perceived safety are key drivers of adoption intention in e-commerce.
• Enhancing chatbot dialogue and reliability can significantly boost consumer trust and willingness to adopt AI recommendations.