Chatbots and machine learning in e-commerce: Research on the consumer experience

This study examines the use of machine learning and chatbots in e-commerce. The growing prevalence of online shopping has driven businesses to adopt new technologies, with artificial intelligence–based applications standing out in this field. Machine learning is employed to understand consumer preferences and provide appropriate recommendations, while chatbots establish direct communication with customers, respond to their inquiries, and assist in solving problems. These methods make the shopping process more comprehensible and user-friendly. In this research, applications and websites based on machine learning and chatbots were analyzed within the framework of the Unified Theory of Acceptance and Use of Technology (UTAUT), focusing on individuals' adoption and usage processes. Participants' tendencies to accept and use these technologies were evaluated according to the dimensions of the model, and their levels were examined using the Friedman test. In addition, whether the dimensions of the unified technology acceptance and use model differed significantly according to participants' demographic characteristics (such as age, gender, and education level) was analyzed through difference tests (independent samples t-test and ANOVA). In this way, the study both revealed general tendencies toward the adoption of technological innovations and assessed whether these tendencies varied by demographic variables. As a result, this study demonstrates that machine learning and chatbots enhance user experience in e-commerce. For users, shopping becomes easier and more personalized, while for businesses, these technologies serve as a factor that increases competitiveness. Ultimately, this research aims to contribute to both academic studies and the industry on this subject.