The Influence of Trust on Crowd Logistics Enterprise’s Operational Performance: A SEM-PLS Model

With the development of information communication and satellite positioning, crowd logistics has gradually emerged as a popular urban distribution mode for logistics enterprises. Trust and synergy between the crowd workers and the crowd logistics enterprises have become key issues in urban logistics development. Based on commitment-trust theory and logistics synergy, this paper developed a theoretical model exploring ability trust, goodwill trust, logistics synergy, and crowd logistics operational performance. We used SmartPLS software to analyze the questionnaire survey from 50 senior managers in crowd logistics enterprises. The results show that ability trust has a significant positive impact on logistics synergy and operational performance. Logistics synergy was also found to increase the operational performance of crowd logistics enterprises and plays a mediating role in the impact of ability trust and goodwill trust on operational performance. We recommend that logistics enterprises evaluate their crowd workers’ service abilities, promote trust relationships with them, strengthen the construction of crowd logistics mechanisms, and invest in crowd worker training and development.

Coordinating Demand and Supply for Crowd Logistics Platforms with Network Effect

The crowd logistics platforms connect stochastic demand with uncertain delivery supply which is provided by independent service providers. Considering direct-network effects and cross-network effects between the demand and supply side, a dynamic surge pricing model for crowd logistics platforms is built. The pricing strategy is derived to coordinate the supply with demand to equilibrium. Furthermore, the pricing strategy minimizing cumulative delivery orders is analyzed. The numerical simulation results show that the dynamic surge pricing strategies can stimulate the uncertain delivery supply for maximizing platforms’ revenue. And, direct-network effects pose a positive impact on the dynamic surge pricing strategy. In contrast, the cross-network effects have a negative impact on the pricing strategy. However, direct-network effects and cross-network effects negatively influence platforms’ revenue.

Towards a taxonomy of crowdsourced delivery business models

PurposeThe paper aims to develop (1) a comprehensive framework for classifying crowdshipping business models and (2) a taxonomy of currently implemented crowdshipping business models.Design/methodology/approachThe business models of 105 companies offering crowdsourced delivery services are analysed. Cluster analysis and principal component analysis are applied to develop a business model taxonomy.FindingsA detailed crowdsourced delivery business model framework with 74 features is developed. Based on it, six distinct clusters of crowdshipping business models are identified. One cluster stands out as the most appealing to customers based on social media metrics, indicating which type of crowdshipping business models is the most successful.Research limitations/implicationsDetailed investigations of each of the six clusters and of recent crowdshipping business model developments are needed in further research in order to enhance the derived taxonomy.Practical implicationsThis paper serves as a best-practices guide for both start-ups and global logistics operators for establishing or further developing their crowdsourced delivery business models.Originality/valueThis paper provides a holistic understanding of the business models applied in the crowdshipping industry and is a valuable contribution to the yet small amount of studies in the crowd logistics field.

Bibliometric analysis of sharing economy logistics and crowd logistics

Purpose This study aims to review the literature on sharing economy logistics and crowd logistics to answer the three following questions: How is the literature on sharing economy logistics structured? What are the main trends in sharing economy logistics and crowd logistics? What are the future research options? Design/methodology/approach Bibliometric analysis is used to evaluate 85 articles published over the past 12 years; it identifies the top academic journals, authors and research topics contributing to the field. Findings The sharing economy logistics and crowd logistics literature is structured around several disciplines and highlights that some are more scientifically advanced than others in their subject definitions, designs, modelling and innovative solutions. The main trends are organized around three clusters: Cluster 1 refers to the optimal allocation of costs, prices, distribution and supplier relationships; Cluster 2 corresponds to business related crowdsourcing and international industry practices; and Cluster 3 includes the impact of transport on last-mile delivery, crowd shipping and the environment. Research limitations/implications The study is based on data from peer-reviewed scientific journals and conferences. A broader overview could include other data sources such as books, book chapters, working papers, etc. Originality/value Future research directions are discussed in the context of the evolution from crowd logistics to crowd intelligence, and the complexities of crowd logistics such as understanding how the social crowd can be integrated into the logistics process. Our results are part of the crowd science and engineering concept and provide some answers about crowd cyber-system questions regarding crowd intelligence in logistic sector.

An Efficient Algorithm for Crowd Logistics Optimization

Crowd logistics is a recent trend that proposes the participation of ordinary people in the distribution process of products and goods. This idea is becoming increasingly important to both delivery and retail companies, because it allows them to reduce their delivery costs and, hence, to increase the sustainability of the company. One way to obtain these reductions is to hire external drivers who use their own vehicles to make deliveries to destinations which are close to their daily trips from work to home, for instance. This situation is modelled as the Vehicle Routing Problem with Occasional Drivers (VRPOD), which seeks to minimize the total cost incurred to perform the deliveries using vehicles belonging to the company and occasionally hiring regular citizens to make just one delivery. However, the integration of this features into the distribution system of a company requires a fast and efficient algorithm. In this paper, we propose three different implementations based on the Iterated Local Search algorithm that are able to outperform the state-of-art of this problem with regard to the quality performance. Besides, our proposal is a light-weight algorithm which can produce results in small computation times, allowing its integration into corporate information systems.