Navigating the Future: Examining Sustainable and Resilient Drivers Shaping the Integration of Crowdshipping in E‐Commerce Logistics

Abstract

The last mile is often subjected to unique challenges such as congested urban areas, unpredictable customer availability and the need for personalized services. To address these complexities, and to be competitive in the market, companies are increasingly turning towards innovative solutions. One such innovative solution is crowdshipping (CS). The concept of CS was first introduced by Walmart in 2013, where store customers may act as crowdshippers and fulfill the demands of online retailers or shops by transporting packages on their way home with guaranteed compensation. To understand the factors influencing CS and its potential to motivate logistics firms for delivery, this research work determines the 20 key sustainable and resilient factors affecting the adoption of CS in the e‐commerce logistics sector. Furthermore, the Neutrosophic Best‐Worst Method (NBWM) is employed to prioritize these factors, followed by the application of Neutrosophic Interpretive Structural Modeling (NISM) with cross‐impact matrix multiplication applied to classification (MICMAC) analysis to assess the interrelationships amongst the factors. The results of the NBWM reveal that technology for the successful implementation of CS, rating and reward system, delivery cost–time efficiency, CO2 emissions and service quality are the top five highly weighted factors. The results of NISM dictate the technology for the successful implementation of CS, integration of CS with the mainstream (operations research models), government interventions and the usage of back‐up drivers as the factors with the highest driving power. The proposed NBWM and NISM give direction to practitioners and policy managers in planning sustainable and resilient strategies for addressing CS implementation factors in the e‐commerce logistics sector. Further, competent strategies can be developed by the managers based on the prioritization and the driving and dependence power of CS adoption factors.