With the developing technology and society’s growing concern over the environment, academic and industrial communities are rethinking the way we organize production [1
], transportation [2
], and logistics [3
]. The development of novel methods and technologies provides previously unforeseen solutions to better management, operations and planning of industrial activities towards improved efficiency and sustainability. One example is tracing [4
] and decision support systems [5
] in perishable goods logistics.
Among the emerging conceptual solutions, crowdsourced delivery, or crowdshipping, is receiving increasing attention. Crowdsourced delivery can be defined as a delivery service, a business mode that designates the outsourcing of logistics to a crowd, while achieving economic benefits for all parties involved [6
]. By making use of crowdsourced transportation capacities, deliveries of goods are performed without having to deploy dedicated logistics services. Instead, they are transported in a non-dedicated context. This means a reduced delivery cost for the owners of products and a decreased impact on the environment. Crowdshipping can take the form of almost all transportation modes: passenger cars [7
], public transport [8
], and taxis [9
]; it can also be implemented in different ways in terms of network layout: point-to-point [10
], hub-and-spoke [11
], and multi-hop relays [12
We narrow down our scope to city logistics, which by itself is complex and can be improved on many dimensions [13
]. Literature sees crowdshipping as the more sustainable solution to city logistics [14
], because of its potential in terms of economic and environmental benefits [15
]. Pilot projects of crowdshipping platforms are increasingly reported in scientific studies and by industrial sectors. Rougès et al. [16
] analyze 26 businesses run by companies and start-ups that provide platforms for crowdsourced delivery and point out that the potential power of crowdshipping could be one of the alternative transportation solutions. In particular, a multi-segment multi-carrier delivery mode called “TwedEx” is discussed in [12
]: people carry packages secondary to their daily lives, e.g., commuting or shopping. In this context, each package is handled from person to person as relay based on overlaps in time and space until the package is delivered. This business model is further researched in simulated numerical studies in [17
]. The analysis shows great potential in this business model, as it has remarkable speed and coverage. The authors call for “constructing and fielding (such) services”, which could provide new insights for crowdsourced activities and business models.
2. Literature Review
Literature on crowdshipping mainly focuses on 3 aspects, namely supply, demand, and platform (readers are referred to [18
] for a comprehensive literature review on these three parts of crowdshipping). The supply side focuses mainly on crowdshippers, who, with some compensation, provide casual delivery service according to their availability and willingness. For instance, Miller et al. [19
] study commuters’ behavior by sending out surveys to understand how willing people are to participate as workers in crowdsourced logistics. Chi et al. [20
] use surveys and reveal that being recognized by an organization or a community is an important motivating reason to participate in crowdshipping. Le and Ukkusuri [21
] suggest that socio-demographic characteristics, freight transportation experience, and social media usage significantly influence people’s decision in participating as crowdshippers. Ermagun and Stathopoulos [22
] carry out a comprehensive analysis of factors that can contribute to the response from the supply side.
The demand side concerns mainly parcel senders and receivers regarding topics such as characteristics of demand and customer trust. Frehe et al. [23
] show that the usability of crowdshipping platforms and customer trust are important factors that influence customer acceptance of crowdshipping. Dablanc et al. [24
] carry out a survey of crowdshipping platforms and find that prepared meals, groceries, retailing goods, and laundry are the most common delivery items. Gatta et al. [15
] use a state preference survey to estimate demand for crowdshipping and evaluate its economic and environmental impacts. Punel et al. [25
] use a survey to study motivations of people making use of crowdshipping services. They find that saving money is not the dominating factor that motivates customers to choose for crowdshipping services. Rather, users tend to be driven by environmental concerns.
The performances of crowdshipping platforms have been analyzed in abundance at a system level, mostly by optimizing the overall system performance with mathematical approaches in matching or task assignments. Chen et al. [26
] develop a method for recommending tasks to mobile crowdworkers with the aim of maximizing the expected total rewards collected by all agents. Soto Setzke et al. [27
] develop a matching algorithm that assigns items to drivers for delivery, with the objective of minimizing the additional travel time apart from planned routes. Chen et al. [9
] use Taxi data in a city as a reference to develop a strategy to minimize package delivery time by assigning paths to each package request. Arslan et al. [10
] study a dynamic pickup and delivery problem in order to match the delivery requirements to existing traffic flow. These articles investigate crowdsourced delivery at a system level, mostly to optimize the overall performance of the system by improving matching or task assignments.
A few studies also report the joint effect of these aspects, which are essential steps to comprehensive analysis that can support crowdshipping initiatives from multiple aspects. Marcucci et al. [8
] conduct a survey that estimates people’s willingness of participating in and paying for a crowdshipping service to analyze market potentials of crowdshipping. Rai et al. [28
] reveal the relation between a crowdshipping platform and the crowdshippers. They recognize that having a “happy crowd” is important for a crowdshipping platform. In addition, a few studies focus on supply-platform relations. Zheng and Chen [29
] investigate a crowdsourced task-assigning problem considering the possibility that participants may reject a task. They measure the willingness of participation using a probability function of rejection. Gdowsk et al. [30
] aim to minimize the total cost in matching and routing with a crowdshipping platform. They use a probability function to consider the crowdshippers’ willingness to participate/reject jobs. Kim et al. [31
] introduce a “Hit-or-Wait” approach in order to balance the timing when participants are matched with tasks with minimal disruptions of their existing route. These studies consider either only supply’s (that is, crowdshippers’) influence on platforms [29
] or platform’s influence on the supply aspect [31
]. A gap is identified here, as the two-way interaction between supply aspects and platforms is not fully investigated.
In this study, we take a unique standpoint investigating the two-way interaction between the supply side and platform aspect of crowdshipping. We particularly focus on the load of tasks imposed by the platform on crowdshippers and how participants mentally perceive this load. Unlike many other studies with data gathered from questionnaires, we use real-world experiments to reveal crowdshippers’ behavior. As mentioned in [12
], crowdshippers are not dedicated employees of delivery companies, thus the delivery tasks are only perceived as a “side-objective” apart from their daily life. Carrying a parcel and giving it to someone will for sure introduce some degree of disruption to their normal living patterns. Naturally, the more disruption the system imposes to each of the participants, the more likely it will affect the willingness of participants in a negative way. On the other hand, a more demanding load can increase the overall performance of the crowdshipping platform. Therefore, understanding the relations among overall performance, the degree of disruption that tasks impose, and how this disruption is perceived, will no doubt help better design crowdshipping platforms.
Our objective for this study is to investigate the relations between system-level performance and individual-level intrusiveness by means of real-world experiments. We conducted a case study in a small area in the Dutch city The Hague. Volunteers were invited to cycle in this area. Meanwhile, they were asked to perform ad-hoc relays to deliver small mango parcels. The parcels were tracked by GPS (the Global Positioning System) trackers.
The rest of the paper is organized as follows. In Section 3
, we briefly look at the elements of crowdsourced systems and explain the experiment design for the case study. In Section 4
, the results of the experiments are analyzed. We also discuss the experiment results and the potential of this form of crowdsourced logistics. Section 5
concludes this article and points out future research directions.
Rarely does scientific research focus on the mutual influence between supply aspects and crowdshipping platform design. This paper takes a first—yet preliminary—step to analyze the two-way relations between these two important pillars of crowdshipping systems by means of real-world experiments. In particular, the investigation seeks to understand how the designing of a crowdshipping system may influence the interactions between the complexity of rules, level of intrusiveness, and overall system effectiveness. We recruited volunteers to participate as bicycle crowdshippers in the case study, where they simulate their daily commuting actions. In the meantime, they formed a grid of ad-hoc relay system to move small parcels of mangoes and eventually deliver them to certain locations. Each parcel was equipped with a GPS tracker. The experiments were performed in an area in The Hague, Netherlands. Two scenarios with rules of different complexity were applied. Results from the GPS were retrieved and analyzed.
From the analysis, it is revealed that the way a crowdshipping platform is designed has influences on both the system level and individual level for crowdshippers. On one hand, more complex rules impose a higher level of intrusiveness. Thus participants, apart from their primary goals (i.e., their daily lives), may need to take extra steps, mentally and in practice, to follow the instructions given by the logistics system. On the other hand, more complex rules may contribute to better overall system performance (in this study, efficiency and reliability). In addition, our analysis indicates that when rules become more complex, the impact on selected indicators may not be of the same amount with the increase of the level of intrusiveness. Crowdshipping platform designers need to analyze the impact of a change of task load on system-wise performance as well as on participants’ behaviors, in order to adjust the design in the best way. This is especially noteworthy as it can illustrate the significance of the design of rules of the crowdsourced systems: a well-designed system can accomplish much without being too intrusive and having to require more than necessary from participants. The study also provides a reference from an economic perspective, as more intrusiveness could be paired with higher rewards, which keeps the logistics activity attractive to participants.
There are certain limitations to this study. Firstly, the experiments are only with two comparing groups with relatively fewer participants at a smaller scale, which makes it difficult to conduct more thorough, quantitative studies. Subsequently, the participants in the experiments might have seen these tasks as their primary goal rather than the secondary, as the simulated commuting behavior is not their actual commuting behavior. Nevertheless, it does not diminish the value of this study, as it reveals the importance of system design in balancing system-wise performance and intrusiveness of individual participant in a crowdsourced logistics system. It also points directions for further and more thorough study on crowdshipping. In future research, it is worthwhile to conduct larger size experiments from people’s real daily activities. It is also interesting to quantify the level of intrusiveness and/or complexity of rules/tasks with a more explicit form, which may serve as a step to providing analytical support for better crowdshipping system design.