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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM53654
Volume:
9 |
11 |
Nov 2025
Pages:
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
The e-commerce sector has grown quickly, which has made competition tougher and raised customer expectations for service quality. This research paper examines the transformative impact of artificial intelligence, specifically chatbots and AI-driven tools, on the enhancement of customer service operations and satisfaction within the e-commerce sector. This study investigates the influence of AI chatbots on customer satisfaction scores, operational efficiency, and business outcomes through a comprehensive survey of 250 e-commerce customers across major platforms such as Amazon, Flipkart, Shopify, and Alibaba, supplemented by secondary data analysis. Employing the Technology Acceptance Model (TAM) as the theoretical framework, the study examines perceived ease of use and perceived usefulness, along with their impact on customer satisfaction and future usage intentions. The main findings show that AI chatbots take an average of 42.9 seconds to respond to a customer’s first question and solve 77.97% of customer problems without any help from a human. This leads to big improvements in operational efficiency. The study shows that perceived usefulness (correlation: 0.578) and perceived ease of use (correlation: 0.501) are good indicators of how happy customers are. Also, 72.4% of customers who talk to a chatbot end up buying something, which has a big effect on sales. The best way to balance efficiency and customer satisfaction is to use a hybrid model that combines AI automation with human agent intervention. This research offers significant insights for e-commerce enterprises aiming to enhance their customer service strategy via AI implementation, while preserving quality and a customer-centric approach.
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Publication Date
10/10/2025
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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Conflict of Interest
The authors declare no conflict of interest related to this research work.
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