Crowdshipping the last-mile delivery - an empirical investigation into the crowd's willingness to participate as crowdshipping drivers

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Crowdshipping the last-mile delivery - an empirical investigation into the crowd's willingness to participate as crowdshipping drivers

Auteur : Dietmann, Kathrin Promoteur(s) : Limbourg, Sabine

Faculté : HEC-Ecole de gestion de l'Université de Liège

Diplôme : Master en ingénieur de gestion, à finalité spécialisée en Supply Chain Management and Business Analytics

Année académique : 2019-2020

URI/URL : http://hdl.handle.net/2268.2/8913

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CROWDSHIPPING THE LAST-MILE DELIVERY -

AN EMPIRICAL INVESTIGATION INTO THE CROWD’S WILLINGNESS TO PARTICIPATE AS CROWDSHIPPING

DRIVERS.

Jury:

Promoter:

Sabine LIMBOURG Reader(s):

Herbert MEYR Thierry PIRONET

Dissertation by

Kathrin DIETMANN

For a Master in Business Engineering with a specialization in Supply Chain Management and Business Analytics Academic year 2019/2020

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iii

List of Figures

2.1 Research model . . . 31

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vii

List of Tables

4.1 Acceptance level Cronbach’s Alpha . . . 42

4.2 Results of Cronbach’s Alpha . . . 42

4.3 Results of factor loadings . . . 44

4.4 Pearson correlation coefficient values and interpretation . . . 48

4.5 Effect strength categories and interpretation . . . 54

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Abbreviations

ICT Information and Communications Technology

VRPRDL vehicle routing problem with roaming delivery locations CEP Courier Express Parcel

w. willing to participate uw. unwilling to participate B2C business-to-consumer C2C consumer-to-consumer SD Standard deviation

CC coefficient of contingency ANOVA Analysis of Variance

CSR Corporate Social Responsibility

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1

1 Introduction

1.1 Context

The growth of the parcel delivery market continues to accelerate. The global parcel delivery market was valued at more than 240 billion euro in 2018. The share for the last-mile delivery was about 103 billion euro. A further steady growth is assumed (MC Kinsey & Company, 2019; Orbis Research, 2019).

In logistics the last-mile refers to the last stage in the delivery process where the customer receives its parcel (Macharis & Melo, 2011). With the increasing number of shipments, the capacity of last-mile delivery systems will have to expand enormously and the burden on infrastructure will increase. This causes the community in cities to face an increase in congested streets, air pollution and noise pollution (Deloison et al., 2020). As an illustration, delivery vehicles spend an average of 90% of their operating time parked. Considering a tour duration of 5 hours, every delivery vehicle causes 80 minutes of congestion a day. This is mainly reinforced by parking in the second row (Seeck & Göhr, 2018). The number of delivery vehicles on the road may increase by 36% for the 100 biggest cities worldwide until 2030. As a result CO2-Emissions could rise by 25 million tons (Deloison et al., 2020). Moreover, delivery vehicles partially are running down the road empty, this is due to poor capacity utilization (Kaup & Demircioglu, 2017). To overcome air pollution, traffic and greenhouse gases, cities like Berlin or London have established low emission zones, regulating vehicles with higher emissions (Sadler Consultants Ltd, 2020). These issues challenge the last-mile delivery and cause the transportation systems to become more efficient and sustainable.

The main driver of the rising shipment volumes is primarily the increasing national and cross-border e-commerce, besides the urbanization contributes to the increase (Bundesverband Paket und Expresslogisitc e.V., 2019; Kaup & Demircioglu, 2017).

For the worldwide e-commerce market, a continuous growth of 10% per year is estimated up to 2030. This means that e-commerce will account for one third of global trade (MC Kinsey & Company, 2019). Considering this trend on the consumer level, a study of Capgemini Research Institute (2019) found that about 40% of consumers

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use delivery services once or a few times a week. To express this in the number of parcels, on average every citizen in Germany orders 24 parcels a year (MC Kinsey &

Company, 2019). E-commerce giants such as Amazon and Alibaba use the dynamic growth of the online trade to enter the delivery market and use the last-mile delivery as a key differentiator. Due to their benefits in the economies of scale they represent a major competition for established Courier Express Parcel (CEP) service providers (Briest et al., 2019). The CEP service providers can hardly keep up with the cost saving potentials of e-commerce giants. Furthermore, the competition is driven by the variety of delivery options and the perceived quality of the delivery service. These factors make it more difficult for incumbent companies to compete with new entrants (Briest et al., 2019; Joerss et al., 2016).

With regard to the entire logistics chain, the last-mile delivery is not only the most polluting but the most expensive section. When comparing the last-mile delivery costs with other supply chain stages like warehousing or parceling, it is evident that the last-mile delivery costs with a share of about 41% are by far the highest costs (Capgemini Research Institute, 2019). For this purpose a need for transformation is required in the last-mile (MC Kinsey & Company, 2019). New last-mile delivery concepts as well as innovative solutions and improvements in information and communication technologies offer huge potential for improvement. The improvement of quality and efficiency as volume in the parcel market grows, causes high costs of investment. The fact, that customers have a low willingness to pay and a high expec- tation towards the delivery service, as they do not value the service, further challenges the last-mile delivery. Consumers’ expectations cover convenient time-window deliveries as well as cheap deliveries. Furthermore, fast and environmentally friendly deliveries are relevant for the customers (Prümm et al., 2017). Another challenge is the fact, that the delivery success is low. As a consequence the recipient needs to pick up the parcel at a pick-up station and the delivery is not as convenient as expected (Seeck & Göhr, 2018). A challenge on the service provider side is the market density, as some regions may be not profitable for CEP service providers to serve (Gevaers et al., 2014). In order to meet customer needs and the challenges in the last-mile, not only an improvement of the cost structure, but also further solutions and technologies are necessary (Capgemini Research Institute, 2019; Gevaers et al., 2014).

The sharing economy follows the principle of sharing rather than owning things.

Since its emergence, the sharing economy has been experiencing a steadily growing economic importance. The principle has been well established in the private sector for instance for the market of media and entertainment, accommodation and passenger transportation. Established companies are by way of example Spotify,

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1.1. Context 3

Airbnb and Uber (Beutin, 2017). Implications from the private sector can be drawn for the industrial sector and especially for logistics. Taking into account the access of private space sharing conducted by Airbnb, a similar approach for the sharing of industrial space for logistics purposes is conceivable. A further area of the sharing economy is the access to freelancers and on-demand labor. Considering the flexible and short-term access to workers this concept can be transformed to other areas like the logistics sector (Gesing, 2017). It has already become apparent that benefits in the area of logistics can be achieved. Areas of implementation are warehousing, transport capacity and logistics data (Bundesvereinigung Logistik e. V, 2018). The DHL Trend Research (2019) already stated that the sharing economy is a relevant field of improvement for the logistics sector in the next few years.

A way of implementing the sharing economy into the transportation sector is to uti- lize the free capacity of passenger transportation. In addition to the poor capacity utilization of the delivery vehicles, transportation of passengers is underutilized. As an example cars on the German streets are occupied on average by 1.46 individuals per vehicle (Biallas, 2020). Furthermore, on a daily basis the residents are more than three billion kilometers on the way of which more than 50% is done by car. The rest includes public transportation, carpooling, bike and walking (Bundesministerium für Verkehr und digitale Infrastruktur, 2020). Already under consideration of the car use and capacity utilization, there exists great potential for unused capacity. Logistics could make use of the poor capacity utilization by means of the sharing economy and thus crowdshipping.

Crowdshipping is a sharing mobility service that uses the crowd for the delivery of goods. The idea is to match parcels which need to be shipped from an origin to a destination, with individuals travelling anyway, for instance to work, on the same route. This shifts the transportation and delivery of parcels by postman to private individuals, the delivery is therefore crowdsourced. Potential benefits to society, companies and consumers are possible (Buldeo Rai et al., 2017).

In the best case transportation in the last-mile can be rationalized. This will be the situation if crowdshipping journeys cover those journeys which are already part of the existing traffic. Meaning crowdshipping drivers deliver parcels while on their way, without any extra trip. In this way traffic utilization can be improved while empty runs can be decreased (Buldeo Rai et al., 2017; Kaup & Demircioglu, 2017).

Furthermore, a limitation of traffic growth as well as congestion and greenhouse gas emissions may be realized through crowdshipping (Kaup & Demircioglu, 2017; Le

& Ukkusuri, 2018b).

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1.2 Research Motivation

1.2.1 Managerial Motivation

The number of pilot projects and start-ups in the field of crowdshipping is increasing. The examples of the local food delivery service Postmates (US) or the consumer to consumer service Peer (Netherlands) show the implementation of successful crowdshipping concepts (Peer, 2015; Postmates INC, 2020). Besides concepts for e-commerce are present, for instance the startup Hytchers focuses on the concept of on the way delivery in Belgium (Hytchers, 2017). Also established companies like Amazon with its concept Amazon flex are active on the crowdshipping market (Amazon.de, 2020). However, the implementation faces a number of challenges which causes some firms to fail or even avoid implementation. As a matter of fact, crowdshipping companies face barriers like the loss and deterioration of physical goods, safety and privacy concerns as well as the risk of illegal or hazardous items (Mckinnon, 2016; Mladenow et al., 2015). Additionally, to achieve an efficient crowdshipping system as well as cost savings, a critical mass of crowdshipping drivers and customers is fundamental (Chandler & Kapelner, 2013; Hossain & Kau- ranen, 2015). To beat these obstacles, restrictions by the provider, review systems and legal regulations can provide a basis. These may include a check of the driving licenses, identity cards and police certificates as well as instructional videos (Kunze, 2016; Mckinnon, 2016; Mladenow et al., 2015; Rougès & Montreuil, 2014; J. Wang et al., 2016).

In comparison to the operating costs of traditional CEP service providers, savings for a crowdshipping concept are conceivable for instance due to a reduction in double- parking ticket costs (Le & Ukkusuri, 2018a). In addition, crowdshipping offers the possibility to do without an investment-intensive infrastructure. This results in an advantageous cost structure of crowdshipping companies compared to traditional logistics providers, whereby the last-mile service is offered at a lower price. This enables new market segments to be developed. Particularly delivery systems for small market segments like local retailers, convenience markets or restaurants open up possible areas of implementation for crowdshipping concepts, especially when focusing on urban areas (Ködel & Von Danwitz, 2017; Punel et al., 2018). The fact that 63% of consumers prefer e-commerce than retail stores due to convenience allows crowdshipping to regain consumers who are migrating from retail to online commerce by offering the service of conventional logistics providers at reduced prices and shorter transportation distances (Capgemini Research Institute, 2019; Ködel &

Von Danwitz, 2017). However, the cost advantage could be to the detriment of the

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1.2. Research Motivation 5

drivers, which may see crowdshipping as a possibility to substitute travel costs. In any case this depends on the compensation system and the underlying motivation of the crowdshipping drivers (Devari et al., 2017). In addition to cost savings, crowdshipping could open up the possibility of sustainable transportation as the trips are made anyway (Kaup & Demircioglu, 2017; Le & Ukkusuri, 2018b). However, this presupposes that a sufficient number of individuals are willing to participate and therefore a critical mass of drivers is achieved. This leads to the fact that before crowdshipping drivers are introduced to the last-mile delivery market as new players their underlying motivation should be understood. By means of this the strategy of the crowdshipping system can be adjusted to the needs and expectations of the drivers and thus the critical mass can be reached (Chandler & Kapelner, 2013; Hossain &

Kauranen, 2015).

1.2.2 Academic Motivation

Previous work has focused mainly on motivational factors in the context of crowdsourcing of virtual tasks. These tasks are done via the internet and include marketplaces offering the possibility to outsource machine learning development (Arslan et al., 2019; Kaufmann & Schulze, 2011). Crowdshipping and crowdsourcing are based on the same concept. However, crowdshipping outsources physical tasks. For this reason research on the motivation in crowdsourcing of virtual tasks is beneficial, but can not precisely be transferred to the context of crowdshipping. Thus, it is meaningful to undertake more research on the crowd’s motivation to participate as a crowdshipping driver.

In the context of crowdshipping the last-mile delivery few relevant publications have been found. Previous work has mainly focused on the crowdshipping business models (Buldeo Rai et al., 2017; Carbone et al., 2017; Chen & Pan, 2016; Devari et al., 2017; Frehe et al., 2017; Ködel & Von Danwitz, 2017; Mladenow et al., 2015; Palo- heimo et al., 2016; Rougès & Montreuil, 2014). Another field of investigation was the scaling up of crowdshipping concepts (Mckinnon, 2016; J. Wang et al., 2016).

Limited research has focused on the receivers’ behavior (Le & Ukkusuri, 2019a, 2019b; Punel et al., 2018; Punel & Stathopoulos, 2017). To the author’s best knowledge, very few publications can be found in the literature that discuss the willingness of potential crowdshipping drivers and the shipping conditions (Le & Ukkusuri, 2018a, 2018b, 2019a; Paloheimo et al., 2016). In this move only little publications focused on the behavior of stakeholders for crowdshipping the last-mile delivery (Le

& Ukkusuri, 2018a, 2019a, 2019b). It has not yet been widely understood which factors influence travelers’ willingness to participate as crowdshipping drivers while on

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the way. Most studies tended to focus on demographical factors of potential crowdshipping drivers. It is not yet known whether, and if so, which intrinsic and extrinsic factors influence individuals willingness to participate as crowdshipping drivers.

Further, there has been little discussion of the connection between potential drivers’

accepted shipping conditions like the detour tolerance and their willingness to participate. The same applies to the connection of drivers’ habits and lifestyles by way of example the mode of transport used and the willingness to participate. In the light of the available literature and recent trends in the last-mile delivery, there is considerable evidence for further research into the factors influencing a participation as crowdshipping driver (Frehe et al., 2017; Le & Ukkusuri, 2018b, 2019a).

1.3 Problem statement and contributions

As shown in the previous section, the crowd’s underlying motivation for a participation as a crowdshipping drivers is of major importance for the implementation of a crowdshipping concept (Chandler & Kapelner, 2013; Hossain & Kauranen, 2015).

In order to investigate the drivers underlying motivation, expectations and habits, this work pursues the question, what factors influence the crowd’s willingness to participate as last-mile crowdshipping drivers, while they are on the way.

The main objective of this study is to propose factors that encourage the crowd to participate as crowdshipping drivers. Against this background, motivational factors as well as shipping conditions and habits of the potential drivers are studied. The results will provide insights on influencing factors for participation, these can then be used to design the crowdshipping system including a strategy to reach a critical mass.

The findings of this study serve entrepreneurs as well as established companies interested in a crowdshipping activity as a basis to identify potential crowdshipping drivers and to build up a strategy according to drivers needs and expectations. In the context of crowdshipping the corpus of implemented concepts focus on national or international transportation. Crowdshipping for the last-mile delivery is a relatively new concept that is not yet established on a wide range. The concepts that have already been implemented are very heterogenous. This leads to a lack in operational data and to the difficulty to draw conclusions from real world examples. The present research aims to close the gap of missing information of the crowds underlying motivational factors. The results help crowdshipping companies to identify reasons why individuals may accept a participation as crowdshipping drivers. Based on the results crowdshipping companies can identify and target potential drivers specifically.

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1.4. Approach 7

The comprehension of the drivers’ compensation expectations, habits and motivation factors serve as an insight to develop business strategies. Based on the results of the intrinsic and extrinsic motivational factors, companies will be able to address the customers in an appropriate way and set incentives and compensations accordingly.

The identification of shipping conditions helps to identify necessary frameworks.

Furthermore, the habits and lifestyle of the crowd serves as insight for areas of implementation.

This work addresses the factors that influence the crowd’s willingness to participate as crowdshipping drivers. In this way it complements the sparse literature on the crowdshipping drivers underlying motivation. By incorporating intrinsic and extrinsic factors, a deeper understanding of the underlying motivational factors is given.

A combination with the existing literature on the business models opens up a better understanding of the possible successful business strategies.

1.4 Approach

The remainder of the thesis is organized as follows: section two outlines the current trends in the last-mile delivery. It further examines the concept of crowdshipping for the last-mile delivery. The already existing approaches as well as possible implications are considered. In order to identify factors relevant for the willingness to participate as crowdshipping drivers, the already existing literature as well as motivation theory is reviewed. With the aim to answer the research question, a model was developed based on the existing literature. Section three is devoted to the research design. The procedure used to test the hypotheses by means of an online survey is described here. Section four shows the results of the online survey and the statistical analysis. The discussion of the results follows in chapter five. Finally, Section six includes a summary, implications as well as limitations and an outlook for further research.

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2 Literature Review

This chapter presents the current state of research and the research model of the previous thesis. The current last-mile delivery trends are followed by a review of the state of research on the concept of crowdshipping. In addition, this chapter deals with the willingness to participate as crowdshipping drivers. Finally, the hypotheses are presented on the basis of the literature.

2.1 Last-mile delivery

The following section deals with the current state of research in the last-mile delivery.

The definition of the last-mile delivery is followed by a discussion of current trends appearing in the last-mile delivery.

2.1.1 Definition of last-mile delivery

The term ’last-mile’ refers to the last stage in e-commerce, where cargo is transported from a depot based at the border of a metro area to customers in the city (Kafle et al., 2017). Focusing on the B2B delivery the last-mile delivery is defined as ’the final leg in a business-to-consumer delivery service whereby the consignment is delivered to the recipient, either at the recipient’s home or at a collection point’ (Macharis &

Melo, 2011, p. 57).

2.1.2 Trends in last-mile delivery

Today’s global logistics system enables the transport of huge amounts of cargo over long distances across the globe. Nevertheless, oftentimes the biggest issue is the last-mile of the delivery. The last-mile issue, already discussed in chapter 1, con- stantly presents logistics and e-commerce with new challenges. There have been several approaches attempting to overcome these challenges and to improve the last- mile delivery. The various innovations differentiate in terms of the presence of the customer, the deliverer or the means of transport used for the delivery.

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Focusing on sustainable alternatives for the last-mile delivery, two concepts are predominantly proposed in the literature. First, the use of bicycles or tricycles powered by humans or electric energy. Second, the use of light commercial vehicles powered by diesel or electric energy (Oliveira et al., 2017). With regard to power, the corpus of literature focus on the use of electric energy (Oliveira et al., 2017). Compared to traditional logistics service, the concepts of bicycles, tricycles and light commercial vehicles offer the possibility to deliver parcels to truck restricted areas (Conway et al., 2012). These two concepts are implemented by Deutsche Post AG in Germany with its bicycle, tricycle and electric light commercial vehicle fleets (Deutsche Post AG, 2020). The trend of bicycles and tricycles is further transposed in the Nether- lands and Denmark, followed by more and more cities, implementing cargobikes. By way of example, the largest national parcel services providers in Germany, by name DHL, DPD, GLS and UPS launched cargo bikes in German cities like Munich and Hamburg (Klein, 2018). The implementation of cargobikes in densely built-up urban areas is accompanied by a need for micro depots for temporary storage of consign- ments. This opens up the possibility to reduce costs and social externalities (Conway et al., 2012; Klein, 2018). However, this entails the restriction that sufficient space and appropriate permits for the installation of micro depots are present. Further con- straints for an implementation of cargobikes are the availability of suitable areas and vehicles (Conway et al., 2012; Ninnemann et al., 2017). Especially the transformation of the bicycle trend to countries with a high car dependence may be hindered due to the missing networks of cycle ways, as they exist for instance in the Netherlands, where personal travel by bike is common (Mckinnon, 2016). With regard to the vehicles, it is problematic that innovative vehicle technologies are not yet marketable due to the high acquisition costs and road traffic regulations (Ninnemann et al., 2017).

Through the use of bicycles, tricycles or light commercial vehicles, environmental benefits such as a reduction of CO2 emissions and atmospheric pollutant may be realized (Oliveira et al., 2017).

Robots may not be ready to replace human drivers on the-last mile delivery. However, there exist different approaches, coming along with the advancement of Information and Communications Technology (ICT) (Prümm et al., 2017). A growing business sector is autonomous driving delivery robots. Prototypes of these robots are already tested for the last-mile delivery. These robots have the potential to be particularly useful in the field of food, flower and grocery deliveries. Autonomous delivery robots operate on a lower cost level than traditional delivery services. However, there are still technical hurdles, as the robots can not work completely autonomous. For instance the charge and unload process needs to be executed by humans. Furthermore, robots restrain pedestrians and other traffic, when they use the sidewalks. For this

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2.1. Last-mile delivery 11

reason the implementation is only reasonable in suburban or low-density traffic areas, whereby the robots can only supplement existing delivery services (Hoffmann

& Prause, 2018). Quite recently, considerable attention has been paid to delivery drones. According to Brunner et al. (2019) a delivery by drones is efficient in terms of using energy. Moreover, drones offer a quiet and safe delivery. Another advantage is the traffic independency and the possibility to access areas which are difficult to reach for traditional logistic services (Indap, 2019). Nonetheless, the delivery process by drones is not fully mature. Especially the handover process is critical. Also safety precautions against manipulation attempts are required. In addition, to ensure sufficient coverage, an adequate size of the drones fleet is significant (Brunner et al., 2019).

In general, challenges facing the implementation of robots in the last-mile delivery are legal regulations. These are by way of example the weight of delivery drones, the liability in case of accidents and traffic laws. Additionally, data regulation pro- tection is a critical factor, as the data which is necessary to operate a delivery robot, is not only saved on the robot itself, but also needs to be transferred over the internet (Hoffmann & Prause, 2018).

To deal with the problem of busy urban areas, underground delivery was developed.

Cargo is transported in caps using the underground pipeline system (Slabinac, 2015).

This approach has been implemented in Dresden (Germany), where the ’CarGo tram’

operates on the same railway’s system as the light rail transit (Jacyna & Szczepa´nski, 2013). This concept is sustainable, reliable and energy efficient. From a logistical standpoint, the bigger maximum load is beneficial when compared to other transportation systems. Furthermore, just in time delivery is feasible on a competitive transportation price level. However, market players may not be interested in the system, as the transportation is carried out on pallets or on roll boxes (Slabinac, 2015).

In addition, it is difficult to estimate the real project costs (Jacyna & Szczepa´nski, 2013).

A quite new concept is the delivery to the trunk of the customer’s car (Groß et al., 2017). The car of the customer is equipped with a tracking device. This provides the postman with the possibility to deliver the parcel to the customer’s car trunk (Reyes et al., 2017). Several providers discuss and evaluate this concept (Groß et al., 2017).

Currently, the German car manufacturer Daimler, tests the service ’chark’ which is an in-car delivery service for online shopping and other services in Stuttgart. This service is based upon the delivery to the customer’s trunk and may be expanded to other geographical areas as well as to other car manufacturers (Daimler AG, 2020). In the same move the Austrian post tested this delivery concept (Nigl & Bröckl, 2019). In

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the field of the delivery to the customers’ trunk, Reyes et al. (2017) conducted a com- putational study modelling the delivery to the customer’s trunk by vehicle routing problem with roaming delivery locations (VRPRDL). The reduction in the distance travelled was specified as main benefit (Reyes et al., 2017).

The reception and delivery box concept, which are the main approaches to handle the problem of unattended delivery was examined by Punakivi et al. (2001). The reception box concept is a customer specific refrigerated box, which is installed at the customers home for instance at the garage or home yard. A delivery box provides a flexible insulated box which includes a docking mechanism (Punakivi et al., 2001). Compared to the delivery box, the reception box concept offers a more efficient home delivery transportation, while the delivery box concept gains its advantage in a smaller investment. Over all the concepts offer the possibility to achieve savings for e-grocer’s or distribution service provider’s. Regardless, due to the high investment for the customer, an implementation of the concept may be seen as critical (Punakivi et al., 2001).

The efficiency of parcel lockers for the last-mile delivery was analyzed by Iwan et al.

(2016). Parcel lockers are located at highly frequented places and offer customers the possibility to receive and send parcels around-the-clock. For customers, it is possible to choose the delivery to a parcel locker in e-commerce shops and to track their con- signments during the delivery. When the parcel is delivered, the customer receives a code to pick-up the parcel (Iwan et al., 2016). For the implementation of parcel lockers the location is of major importance. For this reason Deutsch and Golany (2018) mathematically analyzed the possibility of a parcel locker network, considering the optimal number, location and size of parcel locker facilities. Overall parcel lockers are a potential way to make the last-mile more sustainable (Iwan et al., 2016).

In addition, advantages for several stakeholders can be realized. Consolidation op- portunities offer cities the possibility to reduce the city logistics flow. The delivery around-the-clock to specific locations and the consolidation of deliveries helps logistics carriers to reduce the number of failed deliveries as well as vehicles for the delivery. Retailers can offer their customers a better service and customers can pick- up their deliveries at any time (Deutsch & Golany, 2018; Faugère & Montreuil, 2016;

Morganti et al., 2014). A similar concept to parcel lockers are pick-up points. They offer customers the possibility to pick-up their parcels at a storage place, provided by logistic services or merchants (Mangiaracina et al., 2019; X. Wang et al., 2014).

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2.2. Crowdshipping the last-mile delivery 13

2.2 Crowdshipping the last-mile delivery

This section broaches the issue of crowdshipping the last-mile delivery. The definition of crowdshipping is followed by the research investigations for the last-mile delivery. Alongside the impacts of the implementation of a crowdshipping concept and existing business models are presented.

2.2.1 Definition of crowdshipping

Crowdshipping originates the concept of crowdsourcing and the sharing economy also known as collaborative consumption. Crowdsourcing is the act of outsourcing a function or problem, which is otherwise performed by internal employees, to the crowd (Howe, 2006). Hamari et al. (2016) defines collaborative consumption as a ’peer-to-peer-based activity of obtaining, giving, or sharing the access to goods and services, coordinated through community-based online services’ (Hamari et al., 2016, p. 2047). The concepts of crowdsourcing and collaborative consumption com- bined with the rise of digitalization change the allocation of the business participants.

While the crowd is demanding or providing a service, the role of the company is changing towards a mediator which is responsible for the coordination and the pro- vision of an IT platform for the exchange of services (Frehe et al., 2017). Following the upgrowth of crowdsourcing and the sharing economy, human knowledge sharing opens the possibility to establish encyclopedias like Wikipedia as well as open source software. Furthermore, businesses like eBay and MySpace are established (Howe, 2006; Mladenow et al., 2015).

Urban transportation is disrupted by these developments as well. This occurs in two forms. First the sharing of transportation capacity for passengers like the carsharing concept Share Now, the Ridesharing concept BlaBlaCar or the RidePooling concept Moia which is located in Germany (Comuto SA, 2020; Moia GmbH, 2020; Share now GmbH, 2020). Second the sharing of transportation capacity for parcels introduced by established companies as well as start-ups (Frehe et al., 2017; Le &

Ukkusuri, 2019a). For the sharing of parcels, examples are given in the subsection 2.2.4.

The trend of collaborative consumption finds its way into the logistics sector as collaborative logistics (Carbone et al., 2015). Collaborative logistics can be distinguished by four types, depending on the logistics management system and the function of logistics. First, the peer-to-peer logistics, second the business logistics, third the crowd-party logistics and fourth the crowd-driven logistics. This thesis deals with crowd-party logistics, where logistics is the purpose of the collaboration

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and the logistics management is organized decentralized (Carbone et al., 2015). The implementation of crowd-party logistics to the delivery of parcels in the last-mile offers the opportunity to let the crowd become a deliverer (Dörrzapf et al., 2017). This leads to the concept of crowdshipping.

In literature, there is no single formal definition of the term crowdshipping (Punel

& Stathopoulos, 2017). This thesis defines crowdshipping the last-mile delivery as the activity of outsourcing a delivery service for parcels to an undefined crowd. The crowd offers free capacity in terms of time and space as well as the willingness to transport parcels for others as crowdshipping driver. The term crowdshipping driver is referred to an individual of the crowd that transports parcels while on the way.

Crowdshipping drivers are considered as occasional, volunteer drivers, and they are coordinated by a technical platform managed by a crowdshipping provider (Buldeo Rai et al., 2017; Frehe et al., 2017; Punel & Stathopoulos, 2017).

Crowdshipping can be classified as a pick-up and delivery problem with the objective to minimize costs for the transportation of goods from an origin to a destination (Arslan et al., 2019). The purpose is to match parcels which need to be shipped from an origin to a destination with individuals travelling along the same route. For example, the individuals who may be travelers or commuters, pick-up and drop-off the parcels on their way home or to work. This shifts the transportation and delivery of parcels from a postman to a private individual. By transporting parcels for others in return for a reward, the unused capacity in the vehicle while on the way to the grocery store, for example can be utilized and transportation on the last-mile can be rationalized (Gatta et al., 2019; Mckinnon, 2016; Punel et al., 2018). Crowdshipping drivers are individuals from the crowd, registered to an online platform and willing to deliver parcels during their commute. The registration neither offers an employee contract, workers’ rights nor privileges normally granted to employees (Mckinnon, 2016). To coordinate the supply and demand of the delivery, a technical platform is used. This may be accessed in multiple ways for instance via a mobile app or the web browser (Carbone et al., 2017).

2.2.2 Investigations

This section reviews the literature related to the diverse set of crowdshipping concepts.

The papers of Frehe et al. (2017) and Dörrzapf et al. (2017) serve as an introduction into the crowdshipping business model concepts. To guarantee a sustainable implementation of crowdshipping services, Frehe et al. (2017) evolved a business

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model concept. Based on interviews with existing companies located in Germany, crowd logistics models were reviewed. The authors advocate, that for the development of a crowd logistics business model concept it is important to include the perspective of logistics, business administration, and information systems (Frehe et al., 2017). Another investigation was conducted by Dörrzapf et al. (2017). In order to demonstrate the recruiting and establishment of a crowd-community for the last- mile delivery a field test was conducted. Focusing on the motivation and function of the crowd-community, financial incentives are excluded to get a better insight into the motivational factors apart from economical incentives. Dörrzapf et al. (2017) distinguishes between four crowd delivery concepts. First, the delivery from one or more shops to the customers home. Second, from one or more shops to a pick-up point e.g. a train station. Third, from a pick-up point for instance a store, to the customers home. Fourth and finally, from point-to-point e.g. departments of universities (Dörrzapf et al., 2017). Furthermore, an analysis of start-ups in the crowdshipping delivery industry in 2014 was conducted by Rougès and Montreuil (2014). The implications for management and science practice given by the authors, are taken up in the subsection 2.2.4.

The different crowd delivery concepts can be applied to a diverse set of context, for instance friendship (Devari et al., 2017), library (Paloheimo et al., 2016) or taxi (Chen & Pan, 2016) deliveries.

Devari et al. (2017) reveals the concept of crowdshipping the last-mile delivery by the use of friendship networks for the pick-up and delivery of small retail store orders in urban areas. The results show that the deliveries carried out by a social network can resolve last-mile delivery problems like the not-at-home syndrome, high delivery costs as well as emissions and the need for a quick and solid delivery. Furthermore, the authors found, that supposing a ten-minute detour of crowdshipping drivers from their original way, the total truck mileage of retailers may be decreased by 57%, which is equivalent to about 8.600 USD per day. The privacy concerns emerging in the context of crowdshipping can be neglected, due to the level of friendship between the deliverer and the receiver of the parcels (Devari et al., 2017).

In his investigation into a crowdshipping concept for library deliveries in Finland, Paloheimo et al. (2016) showed that a crowdshipping concept can be successfully implemented for the delivery of books to and from a library. It was adapted from the results, that an implementation of similar concepts for other services like food services for elderly individuals are conceivable. The authors put the aspect that crowdshipping promotes social solidarity, which is promoted by the interaction between the crowdshipping peers and the sense of satisfaction after a successful delivery (Palo- heimo et al., 2016).

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Chen and Pan (2016) proposed a methodological approach for a taxi crowdshipping system. The approach is a two-phase decision model including the offline taxi path movement and the online package routing and taxi scheduling. It is based on a taxi fleet, a road network and customer self-pickup features located in stores in an urban area. The authors aim to test the implementation of the established framework in Hangzhou in China (Chen & Pan, 2016).

Initial work on the perspective of crowdshipping stakeholders was conducted (Ködel

& Von Danwitz, 2017; Le & Ukkusuri, 2019b). Sender’s choice of shipping services for different goods, given a logistics market including crowdshipping and traditional carriers was analyzed by Le and Ukkusuri (2019b). For example, the research has provided evidence that for food, beverages and groceries crowdshipping is preferable compared to traditional carriers (Le & Ukkusuri, 2019b).

In order to get insights into the possible impacts of a crowdshipping implementation for the German CEP market Ködel and Von Danwitz (2017) conducted a survey with potential crowdshipping customers and couriers. By means of the expectations and attitudes of the potential stakeholders, perspectives and implications for traditional logistics, start-ups and retailers were derived (Ködel & Von Danwitz, 2017).

The literature on crowdshipping concepts shows a variety of approaches. Kunze (2016) investigated a vision of future logistics by an assessment of existing as well as arising transport logistics operations. Differences of the crowdshipping concept compared to traditional logistics were discussed in Carbone et al. (2017) based on an exploratory case study approach. Mladenow et al. (2015) studied location based crowdshipping concepts. Mckinnon (2016) and J. Wang et al. (2016) observed the potential of scaling up the crowdshipping concept. In the light of the future logistics, crowdshipping may offer the possibility of a win-win-win-business trend, benefiting customers, crowd members and service organizations. By the implementation of a drop-box infrastructure and local multi-channel cross-docking centers for last-mile transports, an easy pick-up and drop-off of parcels as well as a reduction in detours can be ensured for crowdshipping concepts (Kunze, 2016). Comparing traditional logistics and crowdshipping services around the globe, Carbone et al. (2017) proposed a rating of the available services and gave recommendations for prospective crowdshipping implementations. Focusing on location based crowdshipping, Mlade- now et al. (2015) studied the categorization of crowdshipping applications under the light of classic and information logistics. A foundation for further research was laid out (Mladenow et al., 2015). Considering the scaling up of the crowdsourcing concept, the network participants play a critical role according to (Mckinnon, 2016).

By the use of a network min-cost flow problem and pruning techniques, J. Wang

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et al. (2016) demonstrated that crowdshipping can be applied to a large-scale mobile crowdshipping problem, whereat a large pool of individuals deliver parcels in the last-mile. When scaling up a crowdshipping concept, it should be considered, that in case of a low participation, detours and additional trips are indispensable to fulfill the deliveries. As a result, margin costs and mileage may be not as low as expected (Mckinnon, 2016).

2.2.3 Impact

All stake- and shareholders may earn economic benefits by applying the concept of crowdshipping (Frehe et al., 2017).

Compared to traditional CEP service providers, which offer in most cases only one delivery tour per day, crowdshipping offers the possibility of an immediate delivery, as long as a crowdshipping driver is flexible. In the same move the individual assignment of a package to a driver allows the personalization of the delivery (Rougès &

Montreuil, 2014). According to Mckinnon (2016) benefits in the area of express deliveries and failed deliveries may be realized by crowdshipping. Expensive van fleets are unessential and crowdshipping offers flexibility by an adaption of the delivery to customers as well as drivers needs. By including personal interaction between the crowdshipping driver and the recipient of the parcel, a diminution in the degree of failed deliveries may be realized (Mckinnon, 2016). End users benefit from lower transportation costs and rewards for the transportation of parcels (Mladenow et al., 2015). Crowdshipping offers customers the possibility of an easy delivery by adding a new level of delivery between service provider and customers (J. Wang et al., 2016).

Mckinnon (2016) stated, that the participation as crowdshipping driver offers citizens the possibility to save on travel costs or to earn extra money due to a relatively high net margin for a delivery. This is owed to the low additional costs for a delivery.

The strength presented on the company side, is the image of a customer- and environmentally friendly company (Mladenow et al., 2015). Arslan et al. (2019) mathematically analyzed the delivery using the crowds free capacity, while on the way. The findings corroborate the reduction of costs and vehicle-km for the last-mile delivery by crowdshipping, preferably with a combination of ad-hoc drivers and a dedicated vehicle fleet as backup (Arslan et al., 2019). For retailers this means a reduction in delivery costs (Rougès & Montreuil, 2014). B. Cohen and Muñoz (2016) points out, that by an optimization of the delivery route, consolidation can be simplified when using crowdshipping.

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By the implementation of crowdshipping concepts for areas with scarce margins and small return on investments, CEP service providers may be released of costs, con- straints and hassles (Ködel & Von Danwitz, 2017; Mckinnon, 2016). In this move it is possible to enlarge the market segment due to a reduction of delivery prices.

This may benefit especially local retailers and the convenience market, because flexible deliveries and short delivery distances boost the competitiveness of local retails compared to big e-commerce companies (Ködel & Von Danwitz, 2017). For these reasons, a cooperation with crowdshipping service providers especially in inner city areas may be beneficial for CEP service providers (Ködel & Von Danwitz, 2017).

Furthermore, by the use of a crowdshipping network, fewer investments into a logistics infrastructure are possible benefits (Carbone et al., 2017).

Using the crowds free transportation capacity, either in their own vehicle on foot or with public transport, extends the available capacity for parcel transport and reduces the specifically needed transportation of parcels. The consolidation of transport volume implies a reduction in traffic density as well as a saving of energy resources and CO2 emissions (Carbone et al., 2017; DHL Customer Solutions & Innovation, 2013;

Dörrzapf et al., 2017; Mladenow et al., 2015; J. Wang et al., 2016). Over all the environmental footprint can be minimized (Rougès & Montreuil, 2014). In the short term, cost savings for cities and the national account deficit may be realized due to a reduction of the amount of imported oil. On the long run, savings of vehicles and infrastructure can be realized (Paloheimo et al., 2016).

Emerging problems of a crowdshipping service concern the loss and deterioration of physical goods, safety and privacy concerns (Mladenow et al., 2015). Compared to the traditional last-mile logistics with CEP service providers, crowdshipping shows a higher probability for the delivery of stolen, lost or damaged parcels or goods (Mckinnon, 2016). Besides, in the worst case, crowdshipping drivers are exposed to the risk of illegal or hazardous goods or even to the undesired participation in a terroristic act. As the roles of the typical business has changed, it can be hard to define the accountable party in cases of losses or damages. A clear allocation can be difficult as the company operates only as intermediate and the drivers work without a contract. These doubts can be oppressed by specific restrictions for deliveries, insurances and reviews of the trustworthiness of crowdshipping drivers conducted by the crowdshipping service provider (Mckinnon, 2016; Mladenow et al., 2015).

Furthermore, process improvements, feedback systems and digitalization may be an opportunity to deal with these problems (Kunze, 2016; Rougès & Montreuil, 2014).

Data privacy and confidentiality are of major importance, especially in case of home deliveries, where some customers may be afraid to share their addresses. Moreover,

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trust between sender’s and couriers can be a critical issue (Rougès & Montreuil, 2014). Therefore, legal regulations are needed (Mladenow et al., 2015; J. Wang et al., 2016). Over all, general requirements, penalty rules and obligations for the use of the service are necessary (J. Wang et al., 2016).

In the connection between crowdshipping companies and CEP service providers a problematic point may be the competitive environment. Apart, crowdshipping offers the possibility for a collaboration between crowdshipping companies and CEP service providers. Nevertheless, the collaboration may be difficult in terms of adequate service quality (DHL International GmbH, 2013; Mckinnon, 2016).

From an economic standpoint, four points need to be taken in critical consideration.

First, additional costs which may appear for insurances, the development and implementation of software, customer and employee training, routing instructions or GPS devices (Mladenow et al., 2015). Second, depending on the applied business model, in case of additional trips, for crowdshipping drivers an appropriate payment compared to the costs for the delivery may occur. This problem does not apply to deliveries on the way with small detours (Mckinnon, 2016). Third, the significant share of the crowdshipping concept in urban areas, which implies reaching a critical mass of customers, drivers and deliveries (Ködel & Von Danwitz, 2017; Mckinnon, 2016). Fourth, according to some authors, the actual environmental impact should be viewed critically (Chen & Pan, 2016; Paloheimo et al., 2016).

2.2.4 Business models

The business models available on the crowdshipping market are extremely heteroge- neous besides the mutuality of providing a service for crowdshipping drivers (Mck- innon, 2016). The general delivery process for a delivery with crowdshipping is described by Rougès and Montreuil (2014) as follows. First, the delivery to be made is characterized by the customer. Second, on the service providers platform a task is generated. Third, the sender and the courier are matched. Fourth, the price is fixed.

Fifth, the courier manages the task. Sixth, communication between the courier, the sender and the recipient is possible. Seventh, the courier is rated by the recipient and the sender (Rougès & Montreuil, 2014). This general process can be modified in several ways. The access to the platform, may be via an app or a website. The offer is made by the customer either on a website of an e-commerce company, which offers crowdshipping as delivery option or on a platform where a delivery task for specific products in retail stores is created. Apart from the two main situations, some companies match individuals who want to send a parcel to a destination with individuals who travel to the same destination. The matching of the delivery can be automated

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or carried out manually. For the revenue model, the most used case are fixed prices, but other methods are conceivable. The drivers may be commuters or travelers, non- professional dedicated couriers or professional couriers. Furthermore, the possibility of parcel tracking can be applied optional (Ködel & Von Danwitz, 2017; Rougès &

Montreuil, 2014).

For a successful implementation of the mentioned processes, resources are of major importance, These can be distinguished in long and short term and internal and external resources. Predominantly important resources are, for the short-term internal an ICT infrastructure and external a carrier network. For the long-term internal the ICT infrastructure needs to be adapted to the growing number of customers and the business needs to achieve profit. Major important factors for the long term external are for instance having a critical mass of customers or achieving trust (Frehe et al., 2017).

As reported by Ködel and Von Danwitz (2017), the drives can be distinguished with regard to the factors, employment status, delivery area and business relationship.

The authors distinguish between the on-demand-courier, the occasional courier and the peer-to-peer courier. The on-demand-courier operates as sideline deliverer, in intra-urban areas and on a business to consumer or business to business relationship. The occasional courier, operates as leisure courier, in intra-urban areas and based on a business to consumer relationship. The peer-to-peer courier operates as leisure deliverer, either on a national or international basis (Ködel & Von Danwitz, 2017). Rougès and Montreuil (2014) reported the business-to-consumer (B2C) intra- urban model which refers to the on-demand-courier and the occasional courier, as a commanding concept. For the German market Ködel and Von Danwitz (2017) rec- ommended on-demand and occasional drivers.

On the crowdshipping market several start-ups as well as established companies ap- peared. In the following an overview is given. The emerging crowdshipping start-ups are classified according to the drivers distinction previously introduced. The concept of occasional, leisure couriers is implemented by Hytchers (Belgium), Postmates (US), Packator (Germany) and Peer (Netherlands). The concept of on-demand- couriers who work as part-time couriers is implemented by Deliveroo (Great Britain), Liefery (Germany), Quiqup (Great Britain) and Max (Nigeria). National peer-to-peer deliveries are executed by Roadie (US). International peer-to-peer deliveries are carried out by Piggybee (Belgium) and Bistip (Indonesia) (Ködel & Von Danwitz, 2017;

Mckinnon, 2016; Punel & Stathopoulos, 2017). Apart from the start-up scene, established companies developed crowdshipping concepts (Punel & Stathopoulos, 2017).

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2.3. Willingness to participate 21

Deutsche Post DHL piloted the trial MyWays in Sweden. This project stipulates individuals the opportunity to ship parcels for others for a small fee. The project never went beyond the test phase (DHL International GmbH, 2013; Mckinnon, 2016). In 2013, Walmart enabled store customers to deliver parcels from the shop to online customers home on the same day (Arslan et al., 2019; Savelsbergh & van Woensel, 2016; Serafini et al., 2018). In 2018, Walmart announced a new delivery pilot project, based on crowdshipping deliveries. An in-house platform is used for the assignment of orders, including delivery time windows, grocery delivery orders and navigation assistance. The delivery service has been applied to 50 markets (Walmart Inc., 2018).

Apart from the in-house platform, further platforms of external providers are used by Walmart to implement crowdshipping delivery for their customers. For instance, they collaborate with the platforms Roadie and Postmates (Gottlieb, 2020; Nas- sauer, 2016; Postmates INC, 2018). Uber extended their taxi business and entered the crowdshipping market with UberRush (Mckinnon, 2016). The service was shut down, but there is no official statement about the reasons (Dickey, 2018). Being the world’s heaviest user of last-mile delivery service, Amazon entered the crowdshipping market with Amazon Flex. Initiated in Seattle, the service has been expanded to the US and Europe (Mckinnon, 2016).

2.3 Willingness to participate

This section deals with the willingness to participate as crowdshipping drivers. The description of investigations into the willingness to participate as crowdshipping drivers is followed by the motivational theory.

2.3.1 Willingness to participate as crowdshipping drivers

Before stating the current investigation in the field of the willingness to participate as crowdshipping drivers. A definition of the willingness is given. The Oxford Learner’s Dictionaries (n.d.) defines willingness as ’the quality of being happy and ready to do something’. Transferred to the context of the present study this means that the willingness is stated as the quality of being happy and ready to participate as crowdshipping driver.

To the author’s knowledge, crowdshipping the last-mile delivery has been scarcely investigated from the standpoint of factors influencing potential crowdshipping drivers willingness to participate as crowdshipping drivers. The main corpus of literature focuses on sociodemographic characteristics, financial compensation and delivered goods.

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Initial work conducted by Le and Ukkusuri (2019a) in this field focused on changing shipping behaviors, potential crowdshipping drivers willingness to work and stakeholders characteristic. The authors conducted a survey to investigate the sender’s and couriers behaviors. Given the opportunity to transport parcels for somebody, the participants were asked their willingness to work as crowdshipping driver and their desired level of compensation. Furthermore, investigations regarding the delivery situation, the accepted detour and the payment were conducted. Using random util- ity maximization and random regret minimization, they modeled the crowdshipping demand for different products. Results show that about 80% of respondents were interested in participating as a crowdshipping driver. The analysis of the reasons for rejecting a participation showed that the main reasons were time and interest.

The payment expectations of $11.70 were significantly below the cost for traditional carriers. In general demographic factors were analyzed as reasons for agreeing or rejecting a participation. Factors that significantly influenced the willingness where age, number of children, car ownership and payment. A high potential to attract crowdshipping drivers offers social media advertising. Regarding the products, it was astonishing that perishable products like groceries are highly attractive for the crowdshipping concept (Le & Ukkusuri, 2019a).

Crowdshipping B2C deliveries in urban areas, by the use of mass transit networks was analyzed by Gatta et al. (2019) and Serafini et al. (2018) for the city of Rome.

They suppose that passengers pick-up and drop off parcels to automated parcel lockers situated in transit stations or their neighborhood. Gatta et al. (2019) evaluated the environmental and economic impact as well as the willingness of the customers to buy a crowdshipping service. Serafini et al. (2018) on the other hand illuminates the willingness of the crowd to work as crowdshipping driver. The authors stated the concept of crowdshipping based on non-dedicated trips, as the most environmentally- friendly service. The findings show a reduction in the amount of particulates. Con- cordant with Le and Ukkusuri (2019a), food and grocery deliveries are stated the most common concepts for the implementation. The best target group for crowdshipping drivers are commuters using the metro. For the city of Rome the estimated number of potential crowdshipping drivers was higher than the one for crowdshipping consumers (Gatta et al., 2019; Serafini et al., 2018).

Considering the delivery on the last mile by friends, a study of Devari et al. (2017) indicated, that individuals would be willing to transport parcels for their friends.

Furthermore, for this special case they would be willing to accept a detour as far as 15 minutes and a non rewarded delivery (Devari et al., 2017).

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Based on an online survey, Punel et al. (2018) investigated the attitude of crowdshipping users and non-users. They found that the typical crowdshipping user is characterized as a young, male and full-time employed with a low-income individual. Moreover, most users are individuals with a strong sense of community and environmental concerns. The users may have concerns regarding trust, privacy and safety, which can be mastered by the possibility to gain a new experience. The area of implementation is mainly urban and the transportation distance is a medium distance (Punel et al., 2018).

A study investigation by Ködel and Von Danwitz (2017) provided insights into requirements for a crowdshipping concept on the customer and the deliverer side.

Overall results show that the awareness level for crowdshipping among the participants was low. Compared to the service of CEP service providers, the customers estimated crowdshipping as more flexible and comfortable. On the deliverer side, the motivating factors were mostly auxiliary income and an ecological contribution.

From a customer standpoint, e-commerce was mentioned for the main reason of using crowdshipping. Important features for customers were for instance the delivery during desired time windows, an insurance for the delivered goods, live updates of the delivery and certified drivers. Matching the wishes of the customers, drivers agreed to a verification, but at the same time they expected a consideration like an economic incentive or preferential access to deliveries. They furthermore agreed to transportation insurance. However, GPS tracking of the delivery was mentioned as critical, justified by privacy issues. Regarding the payment of drivers, a payment on the base of the delivery distance was desirable (Ködel & Von Danwitz, 2017).

Le and Ukkusuri (2018b) studied individuals behavioral assessment for a participation in a crowdshipping market. Sociodemographic factors like gender, age, race, income or the education level as well as the experience with transporting parcels and social media usage were identified as influencing factors for a participation. When it comes to the implementation of a business model, crowdshipping drivers behaviors, perceptions, demographics, payment expectations and the context for the readiness to accept a detour are of major importance (Le & Ukkusuri, 2018b).

Paloheimo et al. (2016) studied a pilot crowdsourcing service for library deliveries.

The trial used a crowdsourcing delivery service to transport books to and from a library in Finland. Factors influencing the motivation to participate were identified.

The prime reason for customers was the idea to make everyday life and transport routines easier. The driver’s motivation was driven by the desire to try out something new. Interestingly environmental issues were not of major importance for the participants. As the drivers mainly used bicycles for the delivery, health benefits may be a

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further motivation for the participants (Paloheimo et al., 2016).

An investigation into the willingness of potential crowdshipping drivers was conducted by Le and Ukkusuri (2018a). They conducted a survey in Vietnam and the US.

The majority of the participants were willing to take part as crowdshipping drivers.

As incentive economic factors were mentioned. In contrast, factors which led to a refusion were non-economical. In terms of incentives, the government may offer for instance tax cuts on the income earned from crowdshipping, free priority parking in some areas or free congestion pricing fee (Le & Ukkusuri, 2018a).

Punel and Stathopoulos (2017) analyzed the factors influencing the acceptability and choices for crowdshipping. The findings show that for short distance deliveries, senders prefer transparency regarding the performance and speed of the driver.

For long distance deliveries, delivery conditions, driver training and experience are postulated (Punel & Stathopoulos, 2017).

2.3.2 Motivation theory

The literature review of the section 2.3 showed that environmental factors, a sense of community as well as economic factors influence the willingness of the crowd to participate as crowdshipping drivers (Ködel & Von Danwitz, 2017; Punel et al., 2018). These factors can be classified into the categories of intrinsic and extrinsic motivation. The topic of motivational theory was included into the present thesis, in order to categories the variables regarding participants underlying motivational factors and to identify further possible factors.

According to Ryan and Deci (2000) an individual who is active and energized, is an individual who is motivated. The counterpart is an unmotivated individual who feels no impetus or inspiration to act. Individuals motivated can be distinguished in their level of motivation and in the kind of motivation. Therefore, when analyzing motivation, it is not merely important, how much an individual is motivation, but also the nature and focus of the motivation. Different types of motivation can be distinguished based on the Self-Determination Theory. These types of motivation are linked to the occasion or objectives on which an activity is based on. The two main types of motivation are intrinsic and extrinsic motivation. Intrinsic motivation, describes an activity based on inherently interest or enjoyment. It is a volitional activity that offers the experience of freedom and autonomy. These activities are representative of one’s sense of self (Ryan & Deci, 2000). For instance an individual who’s working motivation is intrinsically, is working for enjoyment or community goals (Amer-Yahia & Roy, S., B., 2016). Extrinsic motivation, describes an activity

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based on inducement of a separable outcome. The activity may be guided by the experience of pressure and control. As counterpart to intrinsic motivation, extrinsic motivation effuses not from one’s sense of self (Ryan & Deci, 2000). An aspired outcome in the context of working is for instance earning money (Amer-Yahia &

Roy, S., B., 2016).

As previously mentioned, crowdshipping originates to the concept of crowdsourcing and collaborative consumption, for this reason literature investigating the motivational factors for a participation in collaborative consumption and crowdsourcing activities was included into the literature basis for the present thesis. Against the background that crowdshipping deals with physical tasks and some crowdsourcing activities deal with virtual tasks, the literature was reviewed with caution. A number of existing studies in the literature have examined motivational theories for crowdsourcing and collaborative consumption like online crowdsourcing marketplaces (Chandler &

Kapelner, 2013; Hamari et al., 2016; Kaufmann & Schulze, 2011), innovation communities (Ståhlbröst & Bergvall-Kåreborn, 2011) and online communities (Brabham, 2010; Pilz & Gewald, 2013).

Motivational factors influencing the willingness to participate in the establishment of online crowdsourcing markets, were discussed by a great number of authors in literature. The motivation theory was applied to collaborative consumption by Hamari et al. (2016). The authors investigated the motivation of individuals for a participation in the online collaborative consumption hub Sharetribe. Derived from Self- Determination Theory and the moving factors for online collaboration, social commerce and online sharing, Hamari et al. (2016) proposed dimensions for intrinsic and extrinsic motivation. The dimensions for intrinsic motivation were enjoyment and sustainability, for extrinsic motivation economic benefits and reputation (Hamari et al., 2016). For online collaboration, enjoyment, economic incentives, reputation and self-fulfillment were the moving factors. Comparatively, social commerce and online sharing was actuated by collaboration and the factor’s enjoyment, economic incentive and reputation (Hamari et al., 2016). Further research was conducted on the microtask crowdsourcing platform Amazon Mechanical Turk. It was reported in literature that the meaningfulness of the task influences the willingness to participate (Chandler & Kapelner, 2013). Furthermore, it was shown that intrinsic motivation positively influences the correctness of the task and that a higher payment correlates with the work output (Rogstadius et al., 2011). The importance of intrinsic motivation factors was accentuated by Kaufmann and Schulze (2011).

In the context of innovation communities, Ståhlbröst and Bergvall-Kåreborn (2011) stated the intrinsic motivation factors learning, nosiness and amusement as strongest.