An Empirical Analysis of Last-Mile Delivery Efficiency: Evaluating Operational and Demographic Determinants in E-commerce Logistics
DOI:
https://doi.org/10.53983/ijmds.v15n03.003Keywords:
Last-Mile Delivery, Operational Efficiency, Operational Friction, Order Cancellations, Return to Origin (RTO), Rider demographicsAbstract
The rapid expansion of the e-commerce and food delivery sectors has placed unprecedented pressure on last-mile logistics. This study investigates the factors influencing delivery rider efficiency through a comprehensive statistical lens. Utilizing a dataset of monthly performance metrics, the research employs descriptive analysis, paired t-tests, independent t-tests, ANOVA, and multiple linear regression to identify productivity drivers. Key findings reveal a significant "delivery gap" between orders attempted and orders delivered (t = 26.99, p < 0.001), primarily attributed to operational friction such as customer cancellations and Return to Origin (RTO) incidents. The regression model (R2 = 0.89) identifies Orders Attempted and Working Hours as the strongest positive predictors of success, while Cancelled Orders and RTOs significantly impede performance. Notably, the study finds that demographic factors, including gender (p = 0.77) and education level, have no significant impact on delivery productivity. The study concludes that last-mile efficiency is predominantly determined by operational variables rather than rider demographics. Recommendations focus on technological interventions to reduce order cancellations and optimize route density to bridge the attempt-delivery gap.
References
[1] Castillo, V. E., Bell, J. E., Rose, W. J., & Rodrigues, A. M. (2018). Crowdsourcing last mile delivery: Strategic implications and future research directions. Journal of Business Logistics, 39(1), 7-25. https://doi.org/10.1111/jbl.12173 DOI: https://doi.org/10.1111/jbl.12173
[2] Cheng, Y., et al. (2024). The impact of delivery intensity on gig worker health and productivity. Journal of Transport & Health, 34, 101732. DOI: https://doi.org/10.1016/j.jth.2023.101732
[3] Dong, J., Zhang, G., & Wu, L. (2025). Life against algorithmic management: A study on burnout and its influencing factors among food delivery riders. Frontiers in Public Health, 13, 1531541. DOI: https://doi.org/10.3389/fpubh.2025.1531541
[4] Edirisinghe, L., Shen, L., & Wijeratne, A. W. (2024). Gender imbalance and trends in tertiary logistics education: A global case study. Maritime Policy & Management
[5] Gevaers, R., Van de Voorde, E., & Vanelslander, T. (2019). Cost modelling and simulation of last-mile characteristics and innovations. Euro-Asian Journal of Management, 24(1/2), 35-58.
[6] He, Z., Hoffmann, T., Netschert, M., & Shao, A. (2021). The last mile delivery in the era of e-commerce: A logistics framework and customer-oriented analysis. Sustainability, 13(21), 11634. https://doi.org/10.3390/su132111634 DOI: https://doi.org/10.3390/su132111634
[7] Miko, S., & Abbas, M. (2024). Determinants of efficient last-mile delivery in the healthcare sector. EPRA International Journal of Multidisciplinary Research, 11(6), 109-120.
[8] Nguyen-Phuoc, T. Q., et al. (2023). Factors influencing job stress and risky driving behaviors among food delivery riders. Safety Science, 158, 105973.
[9] Papakostopoulos, V., & Nathanael, D. (2021). Work and demographic characteristics of food delivery riders in the gig economy. Traffic Injury Prevention, 22(sup1), S56-S61.
[10] Raj, A., et al. (2024). An integrated analytical framework for quick-commerce order fulfillment. International Journal of Production Economics, 268, 109123.
[11] Sun, L., & Wang, Y. (2022). Algorithmic management and the gender productivity gap in the gig economy. Computers in Human Behavior, 128, 107084. https://doi.org/10.1016/j.chb.2021.107084 DOI: https://doi.org/10.1016/j.chb.2021.107084
[12] Visser, J., Nemoto, T., & Browne, M. (2014). Home delivery and the impacts on urban freight transport: A review. Procedia - Social and Behavioral Sciences, 125, 15-27. https://doi.org/10.1016/j.sbspro.2014.01.145 DOI: https://doi.org/10.1016/j.sbspro.2014.01.1452
