Journal Press India^®

Author Details ( * ) denotes Corresponding author

Purpose: The present study is an attempt to study the underlying emotional drivers of customer satisfaction in the rapidly expanding Indian e-commerce sector. The study focuses on identifying fundamental affective factors embedded in customer reviews that shape user experiences across multiple product categories. Design/Methodology/Approach: To achieve the research objectives, we use a research framework; more than 10,000 verified customer reviews posted on leading Indian e-commerce platforms during the period of 2022 to 2024 were analyzed. Lexicon-based sentiment analysis techniques, namely VADER (Valence Aware Dictionary and Sentiment Reasoner) and the NRC (National Research Council (Canada)) emotion lexicon, were used to extract emotional and affective patterns from unstructured textual data. Findings: The findings indicate that platform choice explains less than 5% of the variability in consumer sentiment. Delivery dependability is identified as the most significant contributor to positive sentiment, resulting in 68% of favorable evaluations, while concerns regarding product authenticity account for 72% of negative reviews. Research Limitations: The present research has its own limitations. For instance, it uses lexicon-based sentiment analysis algorithms, which ignore many variations, such as sarcasm or regionally specific phrases found in customer reviews. Managerial Implications: The results suggest that e-commerce companies should emphasize supply chain reliability, last-mile delivery efficiency, and quality assurance processes rather than superficial platform enhancements. Originality/Value: This study provides a scalable, evidence-based approach for obtaining actionable insights from unstructured consumer feedback. It examines traditional assumptions in e-commerce research and presents practical recommendations.

Keywords

Consumer Behavior, Customer Satisfaction, Data-Driven Decision Making, Emerging Markets, Indian E-commerce, Last-Mile Delivery, Lexicon-Based NLP, Online Reviews, Operational Drivers, Product Authenticity, Sentiment Analysis, Text Mining