A Research Study on Evaluating the Impact of AI-Powered Chatbots on Customer Satisfaction and Operational Costs in E-Commerce SMEs

Abstract

To see how AI is changing customer satisfaction and business costs for SMEs in the e-commerce industry, this study explores the adoption of AI chatbots. As we see advances in natural language processing (NLP) and machine learning, small businesses now depend on chatbots to give 24/7 service, handle simple requests and improve their business flow. An online questionnaire was sent out to 400 people working in four SMEs: electronics, fashion, home goods and organic products. Using the Extended Technology Acceptance Model (TAM), the research team analyzed how PEOU and PU affect CSQ and, as a result, CSAT, OCR and reuse intention by customers. Researchers observed that PEOU and PU both strongly explain more than 48% of the quality seen in chatbot services. The level of satisfaction for customers increased and operational costs decreased a lot as chatbot service quality rose (P-value = 0.000 for both). TechGiant Electronics cut its workforce expenses by 25% thanks to using chatbots, but fashion which needs attention to detail, only saw slight reductions. Customers are more willing to use chatbots again when they are happy with the service which benefits sustainability in small and medium companies. Chatbots responded to usual questions in a timely manner, but failed to deal with complicated or personal questions, so having a human agent step in became necessary. Additionally, the report points out that some of the problems for SMEs with chatbots are security, trust and the expense of setting them up. Recommendations involve introducing chatbots one step at a time, making regular system upgrades and upgrading with advanced NLP and machine learning features. The study can help small e-commerce companies that want to use chatbots to connect better with customers and cut back on expenses. By highlighting usability and usefulness in chatbot adoption, the study identifies opportunities to examine future models that use people and artificial intelligence together in customer service and customize chatbots for particular industries.