The role of natural language processing in improving customer service and support in E-Commerce

Kuek, Shu Hui (2023) The role of natural language processing in improving customer service and support in E-Commerce. Final Year Project, UTAR.

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Abstract

This research investigates the factors The study centers on the role of Natural Language Processing (NLP) in improving customer service and support in Ecommerce. The study utilizes quantitative surveys to comprehensively explore the role of Natural Language Processing (NLP) in enhancing customer service and support in E-commerce. Drawing upon a theoretical framework grounded in the Technology of Acceptance Model (TAM) and Social Cognitive Theory(SCT), the research identifies and analyzes key factors such as customer experience, customer satisfaction, perceived ease of use, perceived usefulness, social influence, self�efficacy and observational learning. The process of collecting data took place using online platforms, such as WhatsApp, Facebook, Instagram, and Microsoft Teams. The study is to offer useful insights for marketers, policymakers, and stakeholders in the cosmetics industry who want to comprehend and take advantage of the role of Natural Language Processing in improving customer service and support in E-commerce through data analysis and interpretation. The findings clearly show the structural model results provide support for H1, H3, H5 and H6, indicating that customer experience exerts a positive influence on customer satisfaction while observational learning, perceived ease of use and social influence exerts a positive influence on customer experience. Specifically, the results reveal that customer experience emerges as the most significant predictor in improving customer service and support in e-commerce, followed by perceived ease of use, social influence and observational learning. There was a discussion of the implications, limitations, and suggestions for additional research.

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