Text Meets Task: Unveiling Consumer Responses to Chatbot Interactions in E-Commerce

Abstract

This study aims to explore and understand consumer responses to chatbot interactions in e-commerce settings, focusing on key factors such as chatbot speed, accuracy, friendliness, availability, ease of use, and helpfulness. With the rapid integration of AI-driven chatbots in online shopping platforms, it is crucial to examine how these elements influence consumer satisfaction, trust, and engagement. The research seeks to provide insights into the effectiveness of chatbots in enhancing the overall e-commerce experience, thereby offering valuable information for businesses looking to optimize their digital customer service strategies. Data for this study will be collected through an online survey targeting e-commerce consumers who have interacted with chatbots during their shopping experiences. The survey will employ a structured questionnaire with Likert-scale items designed to measure the impact of each of the identified factors on consumer satisfaction and trust. A sample size of 268 respondents will be targeted, ensuring a diverse representation across different demographic segments. The data will be analyzed using multiple regression analysis and structural equation modelling (SEM) to identify the relationships between chatbot features and consumer satisfaction levels. Preliminary findings suggest that chatbot speed and accuracy significantly influence consumer satisfaction and trust, while chatbot friendliness and helpfulness enhance consumer engagement and overall satisfaction. Chatbot availability and ease of use also play critical roles in shaping positive consumer experiences. These insights underscore the importance of designing chatbots that are not only efficient but also user-friendly and supportive, ultimately leading to higher consumer satisfaction in e-commerce transactions.