
The rapid growth of e-commerce has significantly changed the retail landscape, prompting businesses to prioritize customer service improvement. To achieve this, organizations are increasingly using technology, particularly artificial intelligence. One of the most prominent advancements in this sphere is the extensive adoption of chatbots alongside human support to assist customers. While both chatbots and human agents offer diverse advantages and face unique challenges, this study aims to explore customer preferences when seeking assistance in e-commerce. The study focuses on understanding how customers perceive the effectiveness of chatbots and human support in resolving their queries. It also intends to measure customer satisfaction levels based on their experiences with both chatbots and human agents and identifying key areas for improvement to enhance customer interactions and service quality. In order to analyse these aspects, both primary and secondary data were collected. A total of 155 responses were gathered through an e-survey using the Convenience Sampling technique. The findings highlight the necessity for businesses to strike a balance between automation and human intervention. While chatbots provide efficiency and instant responses, human support remains crucial for handling complex issues and personalized interactions. To enhance customer experience, businesses must focus on improving chatbot accuracy, reliability, and personalization while ensuring seamless collaboration between AI-driven solutions and human agents.
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 0 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Average | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Average | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Average |
