A Study on Gen Z-Focused Customer-Centric Risk Assessment Model to Predict Delivery Delays in E-Commerce Supply Chains

Fathima Saniya P P; Honey Poothullil Johnson

A Study on Gen Z-Focused Customer-Centric Risk Assessment Model to Predict Delivery Delays in E-Commerce Supply Chains

Author(s)	Ms. Fathima Saniya P P, Ms. Honey Poothullil Johnson
Country	India
Abstract	This study focuses on the problem of delivery delays in e-commerce and how they influence customer satisfaction, especially among Gen Z consumers. With the rapid growth of online shopping, customers now expect faster and more reliable delivery services. When these expectations are not met, it often leads to dissatisfaction, reduced trust, and customers choosing alternative platforms. This study aims to understand the reasons behind delivery delays and explore ways to reduce them effectively. The research is based on data collected from 261 respondents using a structured questionnaire. It examines three important areas, namely operational factors, AI-based technology monitoring, and service measures. The data was analysed using statistical tools in Jamovi along with machine learning models developed through the Orange Data Mining tool. Techniques such as descriptive statistics, reliability analysis, correlation, logistic regression, and independent samples t-test were used to understand the relationships between variables, while machine learning methods like Random Forest and Gradient Boosting were applied to improve prediction of delivery delays. The findings indicate that delivery delays are quite common, with more than half of the respondents experiencing delays in their most recent orders. Among the factors studied, operational issues such as inefficient inventory management, slow order processing, and challenges in last-mile delivery were found to be the most significant causes of delays. Technology-based monitoring showed a positive but limited impact on its own, suggesting that it becomes more effective when combined with strong operational systems. Service measures also played a role, as customers with higher expectations were more sensitive to delays. The study also shows that machine learning models can help in predicting delivery delays in advance. Even though the accuracy of these models is moderate, they are capable of identifying high-risk orders, allowing businesses to take preventive actions instead of reacting later.
Keywords	Delivery Delay, E-Commerce, Gen Z, Supply Chain, Machine Learning, Random Forest, Logistic Regression, Customer Satisfaction
Field	Business Administration
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-05

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CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

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A Study on Gen Z-Focused Customer-Centric Risk Assessment Model to Predict Delivery Delays in E-Commerce Supply Chains

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