Determinants of Food-Delivery Timeliness: An Empirical Test Using Multiple Linear Regression

Baiyang Zhang ()
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Baiyang Zhang: Nanjing Agricultural University, School of Information Management

A chapter in Proceedings of the 2025 7th International Conference on Economic Management and Cultural Industry (ICEMCI 2025), 2025, pp 1053-1065 from Springer

Abstract: Abstract With the rapid development of the food delivery industry, delivery efficiency has become a key factor influencing user experience and platform competitiveness. This study employs a multiple linear regression model to conduct an in-depth analysis of factors affecting delivery time, aiming to provide theoretical foundations and empirical support for optimizing delivery efficiency. The research selected multiple independent variables including delivery personnel’s age, service ratings, weather conditions, order type, and vehicle type, constructing a multiple linear regression model with delivery time as the dependent variable to investigate the impact and significance of each factor on delivery duration. Data were sourced from real operational operations of an Indian food delivery platform, covering order information across different cities and time periods. The findings reveal that regional traffic conditions, average service ratings, and weather are the most significant factors affecting delivery time. Notably, there is a strong negative correlation between average service ratings and delivery time, while delivery personnel’s age shows a positive correlation with delivery time. In contrast, order type and time periods have relatively weaker impacts on delivery duration. This study not only provides data-driven insights for optimizing platform operations but also offers new perspectives and methodologies for academic research in related fields.

Keywords: Takeout delivery time; Multiple linear regression model; Influencing factors (search for similar items in EconPapers)
Date: 2025
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DOI: 10.2991/978-94-6463-888-2_103

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