How Does Amazon's Al Chatbot, Rufus, Impact Customer Satisfaction?
Category
Sciences and Technology
Department
Technology
Student Status
Graduate
Research Advisor
Dr. Tatiana Goris
Document Type
Event
Location
Governors
Start Date
10-4-2025 10:20 AM
End Date
10-4-2025 10:20 AM
Description
This study examines how Amazon's Al-powered chatbot, Rufus, influences customer satisfaction in e-commerce. As Al-driven customer support becomes more common, it is essential to assess whether features like accuracy, response time, and personalization contribute to a positive user experience. Using a quantitative research approach, this study incorporates descriptive and correlational analysis to measure how these factors impact user satisfaction.
Data will be collected through a structured survey of 40+ participants who have used Rufus at least five times. The survey will assess response time, accuracy, personalization, and overall satisfaction levels using a 5-point scale. The study will test three key hypotheses: (1) higher accuracy increases trust and satisfaction; (2) faster response times improve user experience; and (3) personalized responses lead to higher engagement.
Findings from previous research indicate that Al chatbots can enhance or hinder customer interactions depending on their design. However, existing studies focus on general Al use in customer service, with limited insights into e-commerce-specific bots like Rufus. This study aims to fill that gap by providing data-backed insights into Al chatbot performance.
The results will help Amazon and other e-commerce businesses refine chatbot features to better serve customers. By identifying strengths and limitations, this research contributes to improving Al-driven customer interactions, making online shopping more efficient, engaging, and user-friendly.
How Does Amazon's Al Chatbot, Rufus, Impact Customer Satisfaction?
Governors
This study examines how Amazon's Al-powered chatbot, Rufus, influences customer satisfaction in e-commerce. As Al-driven customer support becomes more common, it is essential to assess whether features like accuracy, response time, and personalization contribute to a positive user experience. Using a quantitative research approach, this study incorporates descriptive and correlational analysis to measure how these factors impact user satisfaction.
Data will be collected through a structured survey of 40+ participants who have used Rufus at least five times. The survey will assess response time, accuracy, personalization, and overall satisfaction levels using a 5-point scale. The study will test three key hypotheses: (1) higher accuracy increases trust and satisfaction; (2) faster response times improve user experience; and (3) personalized responses lead to higher engagement.
Findings from previous research indicate that Al chatbots can enhance or hinder customer interactions depending on their design. However, existing studies focus on general Al use in customer service, with limited insights into e-commerce-specific bots like Rufus. This study aims to fill that gap by providing data-backed insights into Al chatbot performance.
The results will help Amazon and other e-commerce businesses refine chatbot features to better serve customers. By identifying strengths and limitations, this research contributes to improving Al-driven customer interactions, making online shopping more efficient, engaging, and user-friendly.
