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A Taxonomy of Carsharing Business Models

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Carsharing clubs that grant members temporary access to vehicles have existed for more than half a century. Only recently, however, have advances in digital technologies such as the mobile Internet begun to foster new carsharing business models, thereby increasing the attractiveness of carsharing for both operators and users. Thus far, these new business models have typically been classified as roundtrip, point-to-point, nonprofit/cooperative, or P2P carsharing. However, not all operators fit neatly into these rather broad groups. Moreover, significant differences exist among the business models of operators within the same group. Therefore, we complement these archetypes by developing a taxonomy of carsharing business models. This classification scheme translates the aforementioned technological advances into the creation of economic value and can be used for a more accurate analysis of existing operators as well as the systematic discovery of new business models.
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A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 1
A Taxonomy of Carsharing Business Models
Completed Research Paper
Gerrit Remane
University of Göttingen
Chair of Information Management
Humboldtallee 3
37073 Göttingen, Germany
gremane@uni-goettingen.de
Robert C. Nickerson
San Francisco State University
Department of Information Systems
1600 Holloway Avenue
San Francisco, CA 94132, USA
rnick@sfsu.edu
Andre Hanelt
University of Göttingen
Chair of Information Management
Humboldtallee 3
37073 Göttingen
ahanelt@uni-goettingen.de
Jan F. Tesch
University of Göttingen
Chair of Information Management
Humboldtallee 3
37073 Göttingen
jtesch@uni-goettingen.de
Lutz M. Kolbe
University of Göttingen
Chair of Information Management
Platz der Göttinger Sieben 5
37073 Göttingen
lkolbe@uni-goettingen.de
Abstract
Carsharing clubs that grant members temporary access to vehicles have existed for
more than half a century. Only recently, however, have advances in digital technologies
such as the mobile Internet begun to foster new carsharing business models, thereby
increasing the attractiveness of carsharing for both operators and users. Thus far, these
new business models have typically been classified as roundtrip, point-to-point,
nonprofit/cooperative, or P2P carsharing. However, not all operators fit neatly into
these rather broad groups. Moreover, significant differences exist among the business
models of operators within the same group. Therefore, we complement these archetypes
by developing a taxonomy of carsharing business models. This classification scheme
translates the aforementioned technological advances into the creation of economic
value and can be used for a more accurate analysis of existing operators as well as the
systematic discovery of new business models.
Keywords: Carsharing; sharing economy; taxonomy; business models
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 2
Introduction
In 1998, after having analyzed the European and North American carsharing markets, Susan Shaheen
made the following observation:
Virtually all existing carsharing programs and businesses manage their services and operations
manually. Users place a vehicle reservation in advance with a human operator, obtain their
vehicle key through a self-service, manually controlled key locker, and record their own mileage
and usage data on forms that are stored in the vehicles, key lockers, or both. As carsharing
programs expand beyond 100 vehicles, manually operated systems become expensive and
inconvenient, subject to mistakes in reservations and billing, and vulnerable to vandalism and
theft. (Shaheen et al. 1998, p. 35-36)
Since then, these problems in carsharing have mostly disappeared because digital technologies have
automated the manual steps described by Shaheen et al. (1998) and facilitated the innovation of
completely new business models. This enabling role of digital technologies can also be observed in other
segments of the sharing economy that have long been in existence but only recently experienced digital
technology-enabled business model innovations, including peer-to-peer (P2P) accommodation,
crowdsourcing, and crowdfunding.
The domain of carsharing is particularly suitable for better understanding the role of digital technologies
in the sharing economy for several reasons. First, carsharing is a key sector in the sharing economy and
more mature than most other segments (PWC 2014). Second, a broad range of companies from highly
varied sectors have started to operate carsharing services, including automotive vehicle manufacturers
(e.g., Daimler with car2go), car rental companies (e.g., Avis with Zipcar), transportation service providers
(e.g., Deutsche Bahn with Flinkster), insurance companies (e.g., BCAA with Evo), energy companies (e.g.,
eni with Enjoy), and numerous startups (e.g., Turo). Third, carsharing business models are highly diverse,
with some organizations operating purely as digital platforms (e.g., Turo) while others depend on a
significant amount of assets, such as parking stations and vehicle fleets (e.g., Zipcar). For these reasons,
we assume carsharing to be more mature, professionalized, and diverse than most other segments of the
sharing economy.
Carsharing has existed for over half a century and provides individuals with the benefits associated with
owning a private vehicle while removing the responsibilities of ownership (Shaheen and Cohen 2007).
The users benefit from cost savings, convenient locations, and guaranteed parking (Shaheen and Cohen
2007), whereas society benefits because one shared vehicle replaces 9 to 13 regular vehicles (Martin et al.
2010). One of the first documented carsharing operators, SEFAGE, was founded in Switzerland in 1948
(Harms and Truffer 1998). SEFAGE employed a neighborhood business model and was operated by the
members of a shared housing cooperative (Harms and Truffer 1998). However, it was only since the
2000s that advances in digital technologies began to significantly increase the attractiveness of carsharing
for operators and users alike. Technological advances allowed for automated reservations and smartcard
vehicle access, as well as real-time vehicle monitoring and tracking (Shaheen and Cohen 2013). Such
developments have made carsharing an important alternative means of transportation in many regions of
the world (Shaheen and Cohen 2013).
As the business model concept is a useful tool for understanding how technological advances lead to the
creation of economic value (Al-Debei and Avison 2010), future research should systematically transfer its
ideas to contribute to a better understanding of the sharing economy (Knote and Blohm 2016). In the
domain of carsharing, researchers have grouped existing operators into different business model
archetypes (e.g., Barth and Shaheen 2002; Cohen and Kietzmann 2014; Shaheen and Cohen 2013). The
classification system propounded by Cohen and Kietzmann (2014) is similar to most other categorizations
and groups carsharing business models into point to point (e.g., car2go), roundtrip (e.g., Zipcar),
nonprofit/cooperative (e.g., Modo), and P2P (e.g., Turo). These archetypes allow for a quick
understanding of the primary differences among carsharing operators. For example, Zipcar requires
vehicles to be returned to the pickup location (i.e., “roundtrip”), whereas car2go allows its users to park
vehicles at a different location (i.e., “point to point”). In contrast to these two for-profit operators, Modo is
member owned and therefore not profit driven (i.e., “cooperative”). Alternatively, Turo’s business model
differs from the three aforementioned operators in that Turo does not lend its own vehicles; instead, it
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 3
serves as an intermediary between car owners that rent out their vehicles and carsharing users (i.e.,
“P2P”).
Not all operators fit into these rather broad groups, however; for instance, the carsharing company
CiteeCar adopts elements of both the roundtrip and P2P archetypes. Cars owned by CiteeCar can only be
used for roundtrips (i.e., roundtrip archetype), but a private individual provides a parking space for the
car and performs several maintenance duties (i.e., P2P archetype). Furthermore, this classification
neglects to highlight important differences among the various design options for operators belonging to
the same archetype. For example, some of the largest P2P carsharing companies such as Getaround and
Turo employ widely different business models. Getaround has invented automatic access kits that allow
the renter to access the vehicle without the owner’s presence, but Turo does not make use of this
technology. Thus, they address different use cases and customers. Whereas the classification of carsharing
business models into a few generic archetypes provides a significant reduction in complexity, we argue
that for some purposes, such as the design of new business models, a more detailed perspective is
necessary. Though some researchers have listed the dimensions according to which carsharing services
differ (e.g., Alfian et al. 2014; Boyacı et al. 2015; Correia and Antunes 2012), they have not been
systematically derived and have rarely been directly linked to the business model concept. Furthermore,
the increasing importance of digital technologies for today’s carsharing business models (Shaheen and
Cohen 2013) is only sporadically reflected by these classifications.
The objective of this research is to demonstrate how the business model concept aids in better
understanding the sharing economy by systematically transferring its ideas to the domain of carsharing.
We aim to take an integrative perspective by interlinking business model components, types, and
instances. To do so, we proceed in five phases: First, we review existing research on the classification of
carsharing business models. Second, we create a comprehensive database of global carsharing operators.
Third, we deconstruct the operators’ business models into a classification scheme that provides decision
rules to categorize the data. In accordance with Doty and Glick (1994), such a classification scheme can be
referred to as a taxonomy. Fourth, we empirically identify business model archetypes from this taxonomy
and compare them to existing theory. Finally, we discuss the implications, limitations, and future research
opportunities before we conclude.
Literature Review
A dual approach was employed to identify prior literature dealing with the classification of carsharing
business models. First, a search for existing literature reviews on carsharing was conducted , revealing
four review articles: While Degirmenci and Breitner (2014) analyze 93 articles to identify the specific
impact of carsharing on information systems research, Ferrero et al. (2015) assess 95 articles to classify
carsharing research in general. Jorge and Correia (2013) cover 26 articles to identify research gaps in
demand modeling, and Sonneberg et al. (2015) evaluate 35 articles on optimization approaches. Second,
further potentially relevant articles were identified using a keyword search with different combinations of
the terms “carsharing” or “car sharing” and “classification,” “types,” “typology,” “taxonomy,” or “business
models.This search was conducted in common databases, including EBSCO, Web of Science, and Google
Scholar. To ensure proper quality, the scope was limited to articles from peer-reviewed journals and
conferences.
In total, eight relevant articles with concepts that fit into two groups were identified (Table 1). The first
group of concepts describes different types of carsharing business models, such as the aforementioned
point-to-point, roundtrip, nonprofit/cooperative, and P2P archetypes proposed by Cohen and Kietzmann
(2014). Other types of carsharing business models include business, college/university,
government/institutional fleets, public transit, and vacation/resort (Shaheen and Cohen 2013). The
second group contains concepts that list the dimensions according to which existing carsharing business
models differ. These range from generic decision criteria (e.g., Boyacı et al. 2015) to more detailed lists
containing up to eight dimensions (Barth and Shaheen 2002).
As mentioned previously, archetypes provide a quick understanding of important differences, but they fail
to classify some operators (e.g., CiteeCar) and neglect key distinctions among operators employing the
same type of business model (e.g., Turo and Getaround). These shortcomings could be mitigated by
supplementing the archetypes with a classification scheme i.e., taxonomy listing the most important
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 4
dimensions along which carsharing operators differ. However, existing research on these dimensions
presents several limitations resulting from the widely varied research objectives of previous studies. First,
instead of being systematically derived, most taxonomies have been developed ad-hoc and thus fail to
provide details on how each dimension and the associated characteristics originated. Second, with the
exception of the rather generic dimensions of Cohen and Kietzmann (2014), these dimensions have not
been directly linked to the business model concept and thereby neglect important components. For
instance, different options for carsharing business models to capture value remain largely unexplored.
Thus, we argue that a taxonomy listing the most relevant dimensions and characteristics of carsharing
business models provides value for both theory and practice through enabling the adequate classification
of existing carsharing operators and creating a pathway for the systematic discovery of new business
models.
Author
Proposed archetypes
Proposed taxonomy dimensions
Alfian et al.
2014
36 logical dimensions from a combination of
taxonomy dimensions
Returning time: specified vs. open ended
Destination service: roundtrip vs. one-way vs.
undeclared
Relocation technique: static inventory balancing vs.
static shortest time vs. rebalancing
Barth and
Shaheen
2002
Neighborhood carsharing
Station cars
Multi-nodal, shared-use vehicles
Hybrid models
Definition of basic objective
Linkages with other travel modes
Size of target area and target group served
Organization, services offered, business models
Vehicles
Customer service
Technological sophistication
Sources of support
Boyacı et al.
2015
-
Strategic decisions: location, number, size of stations
Tactical decisions: fleet size
Operational decisions: vehicle relocation, pricing
Cohen and
Kietzmann
2014
B2C point to point
B2C roundtrip
Nonprofit/cooperative
P2P
Value proposition
Supply chain
Customer interface
Financial model
Correia and
Antunes 2012
-
Organizational goals: non-profit vs. commercial
Geographical scope: community level to national
Depot location: at transit stations vs. independent
Trip configuration: one-way vs. roundtrip
Hildebrandt
et al. 2015
-
Reservation: phone vs. online
Vehicle location: fixed stations vs. dispersed
Vehicle access: key vs. smartphone/card
Metering and accounting: fixed hourly and mileage
rate vs. automatic usage-based accounting
Online account: no online account vs. online account
Incentive scheme: no incentive scheme vs. incentives
for cautious driving
Interoperability: account for one city vs. account
usable in various cities
Nourinejad
and Roorda
2015
One-way
Two-way
Hybrid
-
Shaheen and
Cohen 2013
Neighborhood residential
Business
College/university
Government and institutional fleets
Public transit
One-way
Personal vehicle sharing
Vacation/resort
Market segments
Parking
Vehicles and fuels
Insurance
Technology
Table 1. Literature Classifying Carsharing Business Models
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 5
Methodology
The development of taxonomies has already provided a better understanding of new, digital technology-
enabled business models in different domains. For instance, Peters et al. (2015) develop a taxonomy for
telemedicine services and use it to derive three patterns, while Labes et al. (2013; 2015) derive a taxonomy
of cloud business models, identifying four types of platforms. Still further, Haas et al. (2014) create a
taxonomy for crowdfunding business models and uncover three underlying archetypes. We adopted
central elements from these studies for our research design, resulting in three distinct phases (Table 2).
Phase 1:
Set up database
Phase 2:
Develop taxonomy
Phase 3:
Perform cluster analysis
Steps
Search for carsharing operators
and store information in a
database
Filter for bankrupt operators
Pre-classify operators
Request additional information
where necessary
Define meta-characteristic for the
taxonomy
Run through several iterations of
the taxonomy-development
method until all ending conditions
are fulfilled
Identify a useful number of
clusters
Specify the companies belonging
to each cluster
Method
Literature review, desk research
Taxonomy development
Combination of different clustering
algorithms
Source
Carsharing literature, Carsharing
Association, carsharing blogs,
CrunchBase, Wikipedia, practice
reports, e-mail
Carsharing literature (for theoretical
concepts), carsharing database (for
empirical data)
Carsharing business model
taxonomy
Results
Database with 94 independent
carsharing organizations
Taxonomy of carsharing business
models with 15 dimensions
Seven empirically derived carsharing
business model archetypes
Table 2. Research Phases
Specifically, we employed the Nickerson et al. (2013) taxonomy-development method (Haas et al. 2014)
and applied empirical clustering methods to identify the archetypes (Haas et al. 2014; Labes et al. 2013;
2015). Peters et al. (2015) and Labes et al. (2013; 2015) use business model frameworks for their
taxonomy development, serving as checklists for the systematic identification and organization of all
relevant dimensions. Both these papers develop completely new business model concepts for their
research. For our research, however, an extensive review of the business model literature (omitted here
due to space limitations) uncovered El Sawy and Pereira's (2013) VISOR framework, which met our
research design needs. Most importantly, it is one of the few business model frameworks developed
explicitly for digital business models (other frameworks include Al-Debei and Avison 2010, Turber and
Smiela 2014, as well as Weill and Woerner 2013). This was particularly important as today’s carsharing
business models (as well as many others from the sharing economy, such as P2P accommodation or
crowdfunding) rely heavily on digital technologies to create and capture value and can therefore be
considered digital business models (Fichman et al. 2014). The VISOR framework emphasizes the
importance of the customer interface, the central role of digital platforms, and the need to orchestrate
complex ecosystems by decomposing digital business models into five components: value proposition,
interface, service platforms, organizing model, and revenue model.
Phase 1: Set Up Database
The objective of the first phase was to create a database of carsharing operators that were operational
between December 2015 and January 2016. We proceeded in three steps: First, we combined a variety of
sources to assemble a fairly complete picture of worldwide carsharing operators. We examined all articles
from the literature review and searched the database of the Carsharing Association (CSA), the world's
largest startup database (CrunchBase), several carsharing blogs (e.g., www.carsharing-blog.de),
Wikipedia, and practice reports (e.g., Roland Berger 2013). Second, we excluded those operators that
went bankrupt or did not have an English or German homepage, as the language barrier would hinder the
adequate classification of the operators’ business models. Third, to reduce complexity, we pre-classified
operators according to the four business model archetypes provided by Cohen and Kietzmann (2014) and
added a fifth category for operators that did not fit one of these categories. Where necessary, we requested
further information on some operators business models. This step actually overlapped with the second
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 6
research phase because the necessity for information became clearer as we attempted to classify the
business models. All the information gathered on the final set of 94 carsharing operators was stored in a
database. With more than 100,000 vehicles and 4 million customers, these operators make up the vast
majority of the global carsharing market (Shaheen and Cohen 2016).
Phase 2: Develop Taxonomy
The objective of the second phase was to systematically develop a taxonomy of carsharing business
models that contains the most important dimensions along which the business models differ, aided by
Nickerson et al.’s (2013) taxonomy-development method. The method is rigorous as it clearly defines the
necessary steps and ending conditions. It has also been proven through numerous applications (e.g.,
Geiger et al. 2012; Nakatsu et al. 2014; Remane et al. 2017). Furthermore, the method allows for the
combination of theoretical knowledge and empirical findings, making it particularly suitable for our
purposes. While existing carsharing business model literature has already identified many dimensions
along which carsharing business models differ (see Table 1, column “Proposed taxonomy dimensions”),
the comprehensive database of global carsharing operators developed in Phase 1 might reveal additional
dimensions.
Nickerson et al.’s (2013) taxonomy-development method proceeds in several steps: First, the meta-
characteristic is defined, guiding the development of the dimensions. Second, the ending conditions must
be outlined. Third, the method allows for iteration through two distinct cycles. One cycle is empirical-to-
conceptual, which means that a subset of the objects to be classified must be evaluated for common
characteristics and dimensions, which are then added to the taxonomy. The other cycle is conceptual-to-
empirical, which means that the dimensions and characteristics may be derived from the literature but
must be evaluated by real-world examples afterwards. Finally, the method ends when the ending
conditions are met.
For this research, we defined the meta-characteristic as the components of carsharing business models.
All dimensions must be a consequence of this meta-characteristic and aid in describing the structural
differences of carsharing business models. As we found the aforementioned VISOR concept developed by
El Sawy and Pereira (2013) to be a compelling framework for guiding this process, we adopted their
framework and cast our meta-characteristic within it. Hence, each dimension must relate to one of
VISOR’s five components. Next, we adopted the eight objective and five subjective ending conditions from
Nickerson et al. (2013). Afterwards, we ran through five iterations until all carsharing operators from the
database were classified and the ending conditions were fulfilled (Figure 1).
We first opted for a conceptual-to-empirical iteration and integrated the taxonomy dimensions identified
during the literature review. During this iteration, we added nine dimensions: destination (Alfian et al.
2014; Cohen and Kietzmann 2014; Correia and Antunes 2012; Shaheen and Cohen 2013), additional
benefits (Barth and Shaheen 2002; Shaheen and Cohen 2013), vehicle booking (Alfian et al. 2014;
Shaheen and Cohen 2013), vehicle access (Hildebrandt et al. 2015; Shaheen and Cohen 2013), parking
infrastructure (Barth and Shaheen 2002; Correia and Antunes 2012; Hildebrandt et al. 2015), vehicle
ownership (Cohen and Kietzmann 2014; Shaheen and Cohen 2013), price structure (Hildebrandt et al.
2015), transaction-based revenues (Cohen and Kietzmann 2014), and organizational ownership (Barth
and Shaheen 2002; Cohen and Kietzmann 2014; Correia and Antunes 2012). The second, third, fourth,
and fifth iterations were empirical-to-conceptual and led to the successful classification of all carsharing
operators in the data sample. We started with operators that were pre-classified as roundtrip and added
three further dimensions along which the operators differed: vehicle types, booking platform, and
continuous revenues. In the next cycle, we included the operators that offered point-to-point carsharing
and added minimum duration and vehicle refueling as new dimensions along which these operators
differed from the previous ones. The following iteration was conducted on companies with a P2P business
model and led us to include the differential dimension of vehicle maintenance. Finally, we added the
nonprofit/cooperative operators as well as the operators pre-classified as ones for which adding new
dimensions was unnecessary. At this point, all operators from the database were classified and all
objective and subjective ending conditions were fulfilled, thereby ending the iterations. We further
elaborate on each taxonomy dimension in the results section.
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 7
Figure 1. Development of Dimensions for the Carsharing Business Model Taxonomy
Phase 3: Perform Cluster Analysis
The third phase targeted the empirical identification of carsharing archetypes from the taxonomy by
conducting a cluster analysis. The objective of a cluster analysis is to form groups of objects whereby
objects in the same group are as similar as possible and objects in different groups are as dissimilar as
possible (Kaufman and Rousseeuw 2005). Deciding on the number of clusters to use is one of the greatest
challenges of such an analysis (Anderberg 1973). Punj and Stewart (1983) compare the performance of
different clustering methods and find that a two-stage approach delivers the best results. First, the
number of clusters must be defined using Ward’s method. Then the clusters must be further specified
using an iterative partitioning procedure.
Following this recommendation, we first applied Ward’s method (all analyses were conducted in SPSS,
version 23). Ward’s method is an agglomerative clustering procedure, which means that it starts by
combining the two closest objects into one group and repeats this step until at the final stage all objects
belong to the same group (Landau and Everitt 2004). As we had 94 carsharing organizations in our data
sample, it took 93 iterations of Ward’s method until all organizations belonged to the same group. The
similarity between two organizations was calculated by their number of identical characteristics along the
taxonomy dimensions and measured as squared Euclidean distance, which is suitable for binary data.
Afterwards, we determined the appropriate number of clusters by evaluating the descriptive data on these
iterations, namely the dendrogram, the scree plot (using the elbow rule), and the distance between the
coefficients. These statistics indicated that four and seven clusters would be most useful.
Second, we applied the k-means method for the four- and the seven-cluster solution and compared the
outcomes. The k-means method is an iterative partitioning procedure that goes through several rounds of
optimization for an a priori defined number of clusters until each object is closer to the mean vector of its
group than that of any other group (Landau and Everitt 2004). In both cases the k-means method ran
through three iterations until no significant enhancements were achieved during the last iteration.
Afterwards, we manually evaluated the resulting clusters of each solution for their explanatory power
(similar to, e.g., Malhotra et al. 2005). The four carsharing clusters were mostly congruent with the four
carsharing business model types proposed by Cohen and Kietzmann (2014). In contrast, the seven
clusters were more fine grained and revealed important differences among operators that belonged to the
Destination
Minimum duration
Vehicle types
Additional benefits
Vehicle booking
Vehicle access
Booking platform
Parking infrastructure
Vehicle ownership
Vehicle maintenance
Vehicle refueling
Iteration 2 Iteration 3 Iteration 4 Iteration 5
Dimen-
sions
Iteration 1
Sum
Approach
9
Conceptual-to-empirical
12
Empirical-to-conceptual
14
Empirical-to-conceptual
15
Empirical-to-conceptual
15
Empirical-to-conceptual
Legend: = New dimension from current iteration = Dimension from previous iteration
Price structure
Transaction-based rev.
Continuous rev.
Org. ownership
Destination
Minimum duration
Vehicle types
Additional benefits
Vehicle booking
Vehicle access
Booking platform
Parking infrastructure
Vehicle ownership
Vehicle maintenance
Vehicle refueling
Price structure
Transaction-based rev.
Continuous rev.
Org. ownership
Destination
Minimum duration
Vehicle types
Additional benefits
Vehicle booking
Vehicle access
Booking platform
Parking infrastructure
Vehicle ownership
Vehicle refueling
Price structure
Transaction-based rev.
Continuous rev.
Org. ownership
Destination
Vehicle types
Additional benefits
Vehicle booking
Vehicle access
Booking platform
Parking infrastructure
Vehicle ownership
Price structure
Transaction-based rev.
Continuous rev.
Org. ownership
Destination
Additional benefits
Vehicle booking
Vehicle access
Parking infrastructure
Vehicle ownership
Price structure
Transaction-based rev.
Org. ownership
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 8
same business model type in the four-cluster solution. For instance, the P2P carsharing companies
Getaround and Turo, as explained before, employ very different business models; however, they belong to
the same cluster in the four-cluster solution while belonging to different clusters in the seven-cluster one.
In other words, the seven-cluster solution holds more explanatory power, as it adds important
distinctions to the archetypes identified by other authors. Therefore we will only explain the results of this
solution in the following.
Results
Database
The 94 carsharing operators in our database stem from around the world, including South America, North
America, Europe, Asia, and Australia. We pre-classified them according to the carsharing business model
archetypes proposed by Cohen and Kietzmann (2014). The business models of these operators could be
classified as follows: 31 roundtrip, 18 point to point, 21 nonprofit/cooperative, 20 P2P, and 4 were not
assignable to any of these categories. The complete list of operators can be found in Appendix 1.
Taxonomy
The resulting taxonomy contains 15 dimensions, each with two to six distinct characteristics (Figure 2).
Each of the 94 carsharing operators from the database is described by exactly one characteristic for each
dimension. It is important to note that the taxonomy contains the most important dimensions along
which the carsharing operators differ, which means that business model components that are identical for
all operators are not listed here. For instance, all operators in the data sample require upfront
registration, allow vehicle booking via the Internet, and offer an automatic payment solution. In the
following we explain each dimension as well their corresponding characteristics in greater detail. As the
dimensions were assigned to the components of the VISOR framework for digital business models (El
Sawy and Pereira 2013), we discuss them along the framework’s five components.
Value Proposition
Destination: What is the destination of the trip under consideration? Some carsharing operators only
offer roundtrips, i.e., the vehicle must be returned to the pickup location, whereas others allow the vehicle
to be dropped off at another location.
Minimum duration: What is the shortest amount of time for which a vehicle can be booked? The
minimum required booking time ranges from one minute (e.g., to account for the last mile of a trip), to
about an hour (e.g., for shopping), to one day or longer (e.g., for vacation).
Vehicle types: What types of vehicles are available for booking? Whereas some operators offer only
specific vehicles (e.g., car2go offers only Smarts), others allow a wide range of vehicles to be booked for
different purposes (e.g., Zipcar offers various vehicle classes).
Additional benefits: What additional benefits are included in the carsharing service? These benefits may
be free or discounted parking or the delivery of the vehicle to another destination.
Interface
Vehicle booking: When must the vehicles be booked and returned? At some carsharing services, the
vehicles can only be booked in advance with a fixed return time, whereas other services allow instant
booking but require upfront specification of the return time or allow instant, open-ended bookings.
Vehicle access: How are the vehicles accessed? Some carsharing services still depend on manual key
handover, meaning that car owner and renter must meet in person, whereas others have developed lock
boxes for keys that can be access by pin codes and others have developed fully automatic solutions with
smartcards, fobs, or smartphones.
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 9
Dimension
Characteristics
Value
proposition
Destination
Roundtrip
One-way
Roundtrip with option for one-
way
Minimum duration
At least 1 day or longer
Hourly
By the minute
Vehicle types
Identical or very similar vehicles available
Very different vehicles available
Additional benefits
Free/discounted parking
Delivery by owner
No additional benefits
Inter-
face
Vehicle booking
Reservation and fixed return
time
Instant access and fixed
return time
Instant access and open ended
Vehicle access
Manual key handover
Lock box for key
Automatic
Service
platform
Booking
platform
Proprietary
Open for other providers
Parking
infrastructure
Dedicated carsharing
stations
Only attached to other
stations
Street parking
Private homes
Organizing
model
Vehicle ownership
Operator owned
Private customers
Vehicle maintenance
Maintained by operator
Maintained by private customer
Vehicle refueling
Refueled and paid by owner
Refueled by driver and paid by
owner
Refueled and paid by driver
Revenue
model
Price structure
By duration only
Combination of duration and distance
Transaction-based
revenues
Service fee (including insurance)
Commission and/or insurance
Continuous revenues
Membership
fee from
drivers
Service fee
from car
owners
Subsidies
Advertising
Combination
of multiple
sources
No continuous
revenues
Organizational
ownership
Private company
Cooperative
Government owned
Figure 2. Taxonomy of Carsharing Business Models
Service Platform
Booking platform: Can the booking platform be used to book vehicles from other carsharing operators?
Some carsharing operators have partnered with others (e.g., car2go and Flinkster), allowing their users to
book the vehicles of their partners without the need to register with the other operator.
Parking infrastructure: Where are the vehicles parked? Some operators have their vehicles parked at
dedicated carsharing stations around the city, whereas others only offer the vehicles attached to other
stations such as airports. Still other services have vehicles distributed around the city and parked on
streets, with others parking at private homes.
Organizing Model
Vehicle ownership: Who owns the vehicle? In today’s carsharing services, the vehicles are owned either by
the carsharing operator or by private customers.
Vehicle maintenance: Who maintains the vehicle? Duties such as regular inspections, repairs, and tire
change are conducted either by the carsharing operator or by private customers.
Vehicle refueling: Who is responsible for refueling the vehicle? Some operators do not require drivers to
refuel the vehicle, while others require the driver to refuel when the fuel is below a certain level in which
case the operator will typically refund the costs and others require the driver to return the vehicle with
the same level of fuel as it had when it was picked up.
Revenue Model
Price structure: How are service prices calculated? Pricing could be determined by rental duration,
typically with a maximum travel distance, or it could be calculated as a combination of rental duration
and distance traveled.
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 10
Transaction-based revenues: What revenues does the operator receive from each transaction? A
transaction in carsharing means that a user rents, drives, and returns a vehicle. The operator charges
either a service fee for the vehicle rental or commission on a service fee that is payed to a third party.
Continuous revenues: Which continuous revenues does the carsharing operator receive? In addition to
transaction-based revenues, operators generate continuous revenues in the form of membership fees from
drivers, service fees from car owners for renting the automatic access technology, subsidies, advertising, a
combination of several of the aforementioned sources, or no continuous revenues.
Organizational ownership: Who owns the carsharing operator and thus determines the profit motive?
Carsharing organizations can be owned by private companies (for profit), cooperatives (serving its
members), or the government (serving the citizens).
Archetypes
The seven clusters identified each cover between 4 and 26 of the 94 operators from the data sample. Each
of the clusters has different centers along the dimensions and characteristics of the carsharing business
model taxonomy (Table 3). As the characteristics were mutually exclusive and collectively exhaustive, the
data can be read as percentages. For instance, 92% of the operators in Cluster 1 offer only roundtrips as
destination, whereas 8% of operators offer an additional option allowing for one-way trips. The color
shade represents the proportions, i.e., the darker the color, the higher the percentage of companies in the
cluster belonging to a characteristic for the corresponding dimension. In the following sections, we
describe each cluster in greater detail by highlighting the most typical characteristics and providing
illustrative examples.
Cluster 1: Roundtrip, Multiple Vehicle Types
The first cluster describes carsharing operators that offer roundtrips with a large variety of vehicle types.
The German operator Flinkster, which is owned by the national rail company Deutsche Bahn, is a typical
representative of this cluster. Flinkster vehicles can be booked for roundtrip journeys with a minimum
duration of one hour. Users can request vehicles ranging from small city cars such as the Smart to large
transporters such as the Ford Transit. The vehicles can be booked instantly, but the return time must be
specified at the time of the booking. Customers may use a smartcard or their smartphones to access the
vehicles. In contrast to the majority of other companies from this cluster, Flinkster has teamed up with
some of its competitors, allowing its users to also book vehicles from other carsharing operators,
including Cambio, FORD carsharing, and car2go. Flinkster vehicles are parked at various stations around
the city. Flinkster owns the vehicles and covers all maintenance activities as well as the cost for fuel. Prices
are calculated as a combination of duration and kilometers driven. For each rental, Flinkster charges a
service fee that also includes insurance. Unlike some other companies in the cluster, Flinkster does not
charge a monthly membership fee, but just as all others in this cluster Flinkster is a for-profit
company. Other well-known operators from this cluster include GoGet (Australia), Cambio (Germany),
Stadtmobil (Germany), Enterprise CarShare (USA), and Zipcar (USA).
Cluster 2: Roundtrip, Single-Purpose Vehicles
The second cluster is similar to the first cluster in many aspects, as it also describes operators offering
roundtrip carsharing. However, operators from this cluster only rent identical or very similar vehicles
rather than providing a wide range of vehicle types for different purposes. These vehicles can be
exclusively electric (e.g., Zen Car, Belgium), transporters (e.g., Hertz 24/7, Germany), or the Toyota Prius
(e.g., O2 Autocomparte, Mexico). Except for minor deviations, the other cluster centers, i.e., the most
frequent characteristics of the dimensions, are comparable to those of the first cluster. An interesting
exception to some typical characteristics for this cluster, however, is the company CiteeCar. Despite
recently running into financial difficulties, the company has developed a remarkable business model
innovation. While the vehicles are company owned, they are parked at private customers’ homes (“the
hosts”) who are compensated with, e.g., free driving hours for maintaining the vehicle (e.g., taking it for
inspection). Thus, on the dimensions parking infrastructure and vehicle maintenance, CiteeCar’s business
model compares more closely to P2P business models, which we explain below in Clusters 6 and 7.
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Thirty Seventh International Conference on Information Systems, Dublin 2016 11
Dimensions
Characteristics
Clus-
ter 1
Clus-
ter 2
Clus-
ter 3
Clus-
ter 4
Clus-
ter 5
Clus-
ter 6
Clus-
ter 7
Number of operators per cluster
26
11
21
12
4
14
6
Destination
Roundtrip
92%
82%
100%
0%
0%
100%
100%
One-way
0%
18%
0%
100%
100%
0%
0%
Round trip with option for one-way
8%
0%
0%
0%
0%
0%
0%
Minimum
duration
At least 1 day or longer
0%
0%
0%
0%
0%
43%
0%
Hourly
100%
100%
100%
25%
0%
57%
100%
By the minute
0%
0%
0%
75%
100%
0%
0%
Vehicle types
Identical or very similar vehicles available
0%
100%
19%
83%
75%
7%
0%
Very different vehicles available
100%
0%
81%
17%
25%
93%
100%
Additional
benefits
Free/discounted parking
12%
9%
5%
100%
0%
0%
0%
Delivery by owner
4%
0%
5%
0%
0%
36%
0%
No additional benefits
85%
91%
90%
0%
100%
64%
100%
Vehicle booking
Reservation and fixed return time
4%
0%
5%
0%
0%
86%
33%
Instant access and fixed return time
96%
91%
95%
0%
25%
14%
67%
Instant access and open ended
0%
9%
0%
100%
75%
0%
0%
Vehicle access
Manual key handover
0%
0%
0%
0%
0%
100%
0%
Lock box for key
19%
0%
19%
0%
0%
0%
17%
Automatic
81%
100%
81%
100%
100%
0%
83%
Booking
platform
Proprietary
69%
100%
100%
92%
100%
100%
100%
Open for other providers
31%
0%
0%
8%
0%
0%
0%
Parking
infrastructure
Dedicated carsharing stations
96%
82%
100%
0%
100%
0%
0%
Only attached to other stations
4%
9%
0%
0%
0%
21%
0%
Street parking
0%
0%
0%
100%
0%
0%
0%
Private homes
0%
9%
0%
0%
0%
79%
100%
Vehicle
ownership
Operator owned
100%
100%
100%
100%
100%
0%
0%
Private customers
0%
0%
0%
0%
0%
100%
100%
Vehicle
maintenance
Maintained by operator
100%
91%
100%
100%
100%
0%
0%
Maintained by private customer
0%
9%
0%
0%
0%
100%
100%
Vehicle refueling
Refueled and paid by owner
0%
9%
0%
75%
0%
0%
0%
Refueled by driver and paid by owner
100%
91%
0%
25%
100%
0%
50%
Refueled and paid by driver
0%
0%
100%
0%
0%
100%
50%
Price structure
By duration only
12%
45%
19%
75%
100%
86%
50%
Combination of duration and distance
88%
55%
81%
25%
0%
14%
50%
Transaction-
based revenues
Service fee (including insurance)
100%
100%
100%
100%
100%
0%
0%
Commission and/or insurance
0%
0%
0%
0%
0%
100%
100%
Continuous
revenues
Membership fee from drivers
35%
45%
57%
33%
0%
0%
0%
Service fee from car owners
0%
0%
0%
0%
0%
0%
100%
Subsidies
0%
0%
0%
0%
25%
0%
0%
Advertising
0%
0%
0%
8%
0%
0%
0%
Combination of multiple sources
4%
0%
5%
0%
0%
0%
0%
No continuous revenues
62%
55%
38%
58%
75%
100%
0%
Organizational
ownership
Private company
96%
100%
0%
100%
75%
100%
100%
Cooperative
4%
0%
0%
0%
25%
0%
0%
Government owned
0%
0%
100%
0%
0%
0%
0%
Table 3. Results of Crosstab Analysis
Cluster 3: Roundtrip, Cooperative
The third cluster covers cooperative carsharing operators that exclusively offer roundtrip carsharing
(similar to the first two clusters). A typical business model from this cluster is operated by Modo, a
cooperative from Canada that operates 400 vehicles in and around Vancouver. While Modo operates
under a business model that in many aspects is comparable to those of the first cluster, there are also
some differences. Modo does not cover the costs for refueling, instead requiring users to return the vehicle
with the same level of fuel as it had when it was picked up. In addition, while the majority of operators
from this cluster charge monthly fees, this fee can be waived at Modo if shares are purchased. Finally, all
operators from this cluster are cooperatives, i.e., their purpose is not to make profits but to serve their
members. Some of the other larger cooperatives are Mobility CarSharing (Switzerland), Co-wheels (UK),
and City CarShare (USA).
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Thirty Seventh International Conference on Information Systems, Dublin 2016 12
Cluster 4: One-Way, Free-Floating
The fourth cluster is fundamentally different from the previous three in many aspects. The cars do not
have to be returned to their original locations but can be left somewhere on the street in the service area.
Owned by Daimler and Europcar, car2go launched one-way carsharing in Ulm, Germany, in 2008. Today,
car2go is the world’s largest one-way carsharing operator, offering more than 10,000 vehicles in many
large cities worldwide. As of January 2016, the company exclusively offers Smarts, which are distributed
over a certain area within the city. The vehicles can be booked by the minute without specifying the return
time or the return location while car2go staff refuel the vehicles. Other operators from this cluster include
Evo (Canada), DriveNow (Germany), and Enjoy (Italy).
Cluster 5: One-Way, Stationary
Companies from the fifth cluster mostly employ a hybrid business model combining aspects of the second
(roundtrip, single-purpose vehicles) and fourth clusters (one-way, free-floating). The largest operator
from this cluster is Autolib’, which is operated by the Bolloré industrial group in Paris, France. The
company informed us that they have 3,750 electric vehicles serving more than 270,000 users. As with the
fourth cluster, these cars can be booked by the minute and returned at locations different from the pickup
site. All destinations, however, are dedicated stations providing charging infrastructure.
Cluster 6: P2P, Manual Access
The sixth cluster consists of companies that do not possess their own fleet. These companies act as
intermediaries between customers who rent their vehicles to each other, i.e., P2P carsharing. Probably the
largest company from this cluster is Turo, which has already received approximately 100 million USD
funding, according to the startup database CrunchBase (2016). Turo provides a platform allowing a
customer (the renter) to borrow a vehicle from another customer (the owner). The company provides an
insurance package for the duration of the rental and charges a commission for each successful
intermediation. The majority of vehicles on the Turo platform cannot be booked instantly, as the owner is
required to confirm the request from the potential renter in advance. The renter and the owner must then
meet in person to hand over the keys. After the rental, the renter must return the vehicle to the same
location and hand the keys back. Due to the effort required for both parties in this process, Turo only
allows for rentals lasting one day or longer. An atypical and interesting company from this cluster is
Flightcar, which offers carsharing exclusively at airports rather than at customers’ homes. The idea
behind this company is that car owners make extra money while traveling by renting their cars to
incoming travelers instead of paying for airport parking. The incoming travelers can rent these cars at
lower rates than those of traditional car rental agencies at airports.
Cluster 7: P2P, Automatic Access
Like the sixth cluster, the seventh cluster includes P2P companies that facilitate rentals between two
customers. However, the business models of these operators are fundamentally different from those of the
previous cluster, as vehicles are equipped with an automatic access technology. For instance, Getaround,
which is the second major P2P carsharing platform in the US, installs automatic access technology
(Getaround Connect) in each owner’s car, allowing for GPS tracking, tamper detection, and engine lock.
In San Francisco alone, there are currently more than 500 vehicles with this technology. This model has
several implications: First, the vehicles can be rented by the hour because handing over keys and
returning them is no longer necessary. Second, the vehicles can be booked instantly without the owner
needing to confirm the booking. Third, Getaround receives additional revenue by charging the owners a
monthly fee for renting the automatic access technology. Other operators offering similar technology for
P2P carsharing include Car Next Door (Australia), Sharoo (Switzerland), Drivy (France), iCarsClub
(Singapore), and JustShareIt (USA).
Discussion
The cross-cluster comparison of the seven clusters reveals some interesting insights into carsharing.
Whereas for all clusters there are companies whose sole business model is carsharing, the companies
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 13
operating carsharing in addition to their traditional business models present sharp differences. For
instance, car rental companies such as Avis (which acquired Zipcar), Enterprise, and Starcar seem to
focus on the first cluster, i.e., roundtrip, multiple vehicle types. This is unsurprising, as this business
model is very similar to traditional car rental in many aspects. In contrast, the largest carsharing systems
that are operated by automotive manufacturers (car2go by Daimler and DriveNow by BMW) are one-way,
free-floating models. In these systems the trip duration is much shorter, but the number of trips is much
greater. Hence, the vehicle manufacturers also benefit from the many customers experiencing their
vehicles. With Evo and Enjoy, companies from the insurance and energy sector have also entered the one-
way, free-floating cluster. Furthermore, the comparison reveals that the archetypes address very different
use cases. The one-way carsharing services (Clusters 4 and 5) can be booked by the minute and used for
unidirectional trips, thus complementing or competing with public transport and taxis. In contrast,
services from the roundtrip clusters (1, 2, and 3) as well as the P2P, automatic-access cluster (7) only
allow hourly rental and returning the vehicle to the same location. Therefore, they are typically used for
trips that do not justify the effort of renting from a car rental company (e.g., shopping). A further
distinction is that operators from the P2P, manual-access cluster (6) only allow vehicles to be booked for
longer durations and require manual key handover and return. For a customer, the use case of renting a
car from such a platform is rather similar to renting a car from a traditional car rental company.
Our research contributes to theory in several ways. First, products and services in the sharing economy
rely heavily on digital technologies. The business model concept is a useful lens for understanding how
these technological advances can be linked with the creation of economic value (Al-Debei and Avison
2010). As carsharing is one of the largest and most mature sectors of the sharing economy (PWC 2014),
the observations from this domain might also be relevant for other, younger segments. To our knowledge,
however, the business model concept has not yet been systematically transferred to the carsharing domain
on a level that goes beyond textual description. Therefore, we have integrated existing research on
carsharing business models (Table 1) and recent developments from practice (the database of carsharing
operators) with the VISOR concept (El Sawy and Pereira 2013) that is particularly fitting for the
characteristics of digital business models. The taxonomy allows for a systemization and synthesis of
research on carsharing business models. For instance, researchers analyzing specific aspects of
carsharing, such as vehicle relocation algorithms, can list their underlying assumptions more precisely by
using the taxonomy to specify which business model configuration they have analyzed. Furthermore, we
have demonstrated the usefulness of the taxonomy structure by empirically deriving seven carsharing
business model types. We not only confirmed the archetypes described by previous research (Barth and
Shaheen 2002; Cohen and Kietzmann 2014; Nourinejad and Roorda 2015; Shaheen and Cohen 2013) but
also outlined their typical characteristics and identified three new archetypes that should be added to
existing collections: roundtrip, single-purpose vehicle (Cluster 2); one-way, stationary carsharing (Cluster
5); and P2P, automatic-access carsharing (Cluster 7).
Second, the concept of the business model is most powerful when the three hierarchical layers i.e., the
elements belonging to a business model, the archetypes describing frequently observable configurations of
the elements, and instances of real companies are used in combination (Osterwalder et al. 2005).
However, existing business model research tends to analyze the business model on just one of the three
layers, often neglecting the others. Notable exceptions to this trend include Haas et al. (2014), Labes et al.
(2013; 2015), and Peters et al. (2015), from which we derived our research design, combining the most
useful methods from each study. We performed an important extension of their approaches by combining
them with the VISOR concept from El Sawy and Pereira (2013), which already reflects the most important
elements of digital business models. This allows the methodology to be easily transferred to any other
domain in which digital technologies have enabled the development of new business models.
Furthermore, Schneider and Spieth (2013) highlight another opportunity for enhancing business model
research by calling for more tools supporting managers with business model innovations. The taxonomy
that we have developed is such a tool, as it counteracts cognitive bias from sticking to business model
configurations that are already known (Bohnsack et al. 2014). Such cognitive bias might occur when
looking solely at the archetypes of carsharing business models; however, our taxonomy structure avoids
this by listing the most important dimensions of carsharing business models and options for their
specification. This allows new business models to be systematically innovated by recombining existing
elements in new ways, as done by CiteeCar and Getaround. In the past, CiteeCar adapted selected
elements of P2P carsharing (i.e., the host concept) to roundtrip carsharing, while Getaround transferred
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 14
car access technology from roundtrip carsharing to P2P carsharing. In the future, such white spots, i.e.,
new configurations, can be systematically identified when comparing the results of the cross-tab analysis
(Table 3). In looking at the analysis, a few examples that may serve as inspiration to stimulate future
innovation become clear. For example, roundtrip, single-purpose vehicle operators (Cluster 2) have not
yet been combined with advertising, while no roundtrip cooperative (Cluster 3) offers one-way carsharing.
Furthermore, one-way, free-floating operators (Cluster 4) do not yet offer vehicle delivery or long-term
upfront reservation, and P2P, manual access (Cluster 6) has not been combined with discounted parking,
membership fees for drivers, advertising, or subsidies.
Third, our research sheds some light on the under-researched phenomenon of how the digital
transformation affects traditional industries (Yoo et al. 2010). Yoo et al. (2010) as well as Porter and
Heppelmann (2014) explain how the increasing diffusion of digital technologies into physical products
promotes the development of new digital business models. The effects of increasing digitization have often
been discussed using cars as a narrative example (e.g., Bharadwaj et al. 2013; Porter and Heppelmann
2014; Yoo 2010; Yoo et al. 2012). The carsharing domain the focus of our research is particularly
suitable for understanding several aspects of the digital transformation. For instance, the majority of
carsharing business models are enabled by cars becoming smart, connected products (Porter and
Heppelmann 2014, p. 3): they can be accessed by smartphone, duration and distance are reported and
billed automatically, and the embedded navigation system guides the driver along the fastest route.
Furthermore, the convergence of industries (Yoo et al. 2012) is clearly visible. This new era is witnessing
companies from formerly separate sectors offering carsharing services themselves. At the same time, the
value proposition of carsharing can be similar to that of car ownership, car rental, taxis, or public
transport, meaning that operators from these traditional markets also have to compete with carsharing
services.
Our research makes important contributions for managerial practice, of which we highlight two. First, the
combination of our results, i.e., the database of carsharing operators, the business model taxonomy, and
the archetypes, provide a comprehensive market overview. The taxonomy and the archetypes allow for a
better understanding of important aspects of different business models by abstracting from single
instances to constituent elements. We acknowledge that the market overview might be known by
managers currently working for carsharing operators but assume it to be very useful for companies
considering entering the carsharing market or partnering with current operators. Furthermore, Cohen
and Kietzmann (2014) discuss how local governments can mitigate principalagent conflicts in carsharing
by facilitating merit models. Therefore, we also consider local governments to be important profiteers
from the market overview, allowing them to develop more accurate regulatory frames. Second, the
taxonomy we developed is a ready-to-use tool for business model innovation. Therefore, current
carsharing operators can analyze their own business models by using the taxonomy to systematically
compare their models to those of their competitors and identify white spots, i.e., combinations of
characteristics that have not yet been employed. Although the taxonomy will not reveal the perfect
business model combination, our experience has shown that it is a valuable tool for stimulating creativity,
communicating ideas, and integrating these into a complete business model.
Limitations and Future Research Opportunities
Our study is not free of limitations. We describe the limitations here and outline future research activities
that might address some of these constraints. First, Nickerson et al. (2013) argue that taxonomies are
never perfect but in the best case useful. We would argue that our taxonomy is useful in gaining a better
understanding of the most important aspects underlying current carsharing business models. This value
has been demonstrated by applying the taxonomy to empirically identify carsharing business model
archetypes. Again, archetypes are also never perfect there is even an entire research stream discussing
different algorithms to determine the ideal number of clusters based on different thresholds (e.g., Mojena
1977). However, we would also argue that there is no perfect number of archetypes; instead, the clusters
in the best case are useful for a specific purpose. We have shown that four of the seven empirically
identified archetypes can be found in prior literature, giving them some theoretic validity. The overall
usefulness of our research, however, will become clearer when future research starts to use the taxonomy
and the archetypes.
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 15
A second limitation of the taxonomy is that some dimensions might mutually exclude one another. We
refrained from systematically identifying these relationships within this research, but think that such an
analysis might be an interesting opportunity for future research. For instance, some limitations might
logically exclude each other whereas others have not been implemented for other reasons such as
technological constraints. In particular combinations from the second group might result in valuable
ideas for future business model innovation.
Third, our research is built on a database covering the carsharing operators and their business models at
the time of this research. The carsharing market is a dynamic environment with new operators regularly
entering the market and existing players frequently innovating their business models. Therefore, the
carsharing database and the classification of individual operators will soon be outdated. However, the
taxonomy of carsharing business models as well as the carsharing business model archetypes reflect the
constituent elements of these instances, making them relevant for a longer period. Nonetheless, with
further advances in digital technologies and business models, regular updates of the taxonomy will be
necessary, which would provide interesting research opportunities.
Fourth, we only analyzed carsharing operators with English or German homepages and excluded various
operators that were identified initially, including Fleety (Brazil), uucars (China), 24Rent (Finland),
Carsonar (France), Rentip (Israel), ICS Carsharing (Italy), Choimobi Yokohama (Japan), SoCar (Korea),
Carrot (Mexico), MyWheels (Netherlands), Bildeleringen (Norway), FleetPoland (Poland), Mob
Carsharing (Portugal), Anytime (Russia), Car4Use (Serbia), SocialCar (Spain), Bil.coop (Sweden),
garajyeri (Turkey), and several others from these countries. Therefore, a classification of the omitted
operators using our taxonomy could make an important future contribution. For instance, it would be
interesting to see whether new dimensions and characteristics need to be added. Further, a comparison of
carsharing business models across countries and regions using the seven archetypes of this research
might reveal interesting insights.
A research domain beyond carsharing that directly results from this research is a transfer of the
methodology to other sectors. The methodology has already proven useful for telemedicine services
(Peters et al. 2015), cloud business models (Labes et al. 2013; 2015), and crowdfunding business models
(Haas et al. 2014). We have further enhanced the methodology by combining it with the VISOR concept
(El Sawy and Pereira 2013), making it applicable to any industry whose business models are being
transformed by digital technologies.
Conclusion
To date, theory and practice have mostly classified carsharing business models into a few archetypes. As
these archetypes neglect important differences and commonalities among operators, we supplemented
them by developing a more thorough taxonomy. This taxonomy does not replace existing archetypes; it is
to be seen as complementary, as we have taken an integrated perspective that connects business model
components (i.e., the taxonomy), business model types (i.e., the archetypes), and business model
instances (i.e., the operator database). Whereas the archetypes quickly reveal the most important
similarities and differences, the taxonomy can be used for a more profound analysis of individual
operators and for the systematic innovation of new business models. As carsharing is one of the most
mature sectors of the sharing economy, some of our findings might also be relevant for better
understanding the future development in other, less mature segments.
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 16
Appendix 1
Business model
archetype
Carsharing operators (country)
Roundtrip
book-n-drive (DEU)
Cambio (DEU)
CarShare HFX (CAN)
CarSharing Ansbach,
Autoverleih Muhr e.K. (DEU)
Carvient Club (UK)
cityhop (NZL)
Communauto (CAN)
Drive Carsharing (DEU)
Enterprise CarShare (USA)
eThos (USA)
Flexicar (AUS)
Flinkster (DEU)
FORD Carsharing (DEU)
GoCar (IRE)
GoGet (AUS)
GreenShareCar (AUS)
GreenWheels (DEU)
Hertz 24/7 (DEU)
IsaCar (DEU)
Joycar (BRA)
O2 Autocomparte (MEX)
Share a Starcar (DEU)
Stadtmobil (DEU)
Stattauto Kassel (DEU)
Stattauto München (DEU)
teilAuto (DEU)
UhaulCarShare (USA)
VRTUCAR (CAN)
Zazcar (BRA)
Zen Car (BEL)
Zipcar (USA)
Point to point
Autolib' (FRA)
BlueIndy (USA)
Bluely (FRA)
book-n-drive cityFlitzer (DEU)
car2go (DEU)
CityBee (LIT)
Communauto Auto-mobile
(CAN)
DriveNow (DEU)
EkoRent (FIN)
Enjoy (ITA)
Evo (CAN)
MultiCity (DEU)
Pogo CarShare (CAN)
Smove (SGP)
Stadtmobil stadtflitzer (DEU)
Stattauto München Flexy
(DEU)
WaiveCar (USA)
YourCar (DEU)
Nonprofit/
cooperative
Ameranger Auto-Gemeinschaft
(DEU)
Capital CarShare (USA)
Carma City CarShare (USA)
CarShare Vermont (USA)
CarShareNL (CAN)
Coast Car Co-Op (CAN)
Community CarShare (CAN)
Community CarShare
Quantum Station (CAN)
Co-wheels (UK)
eGo CarShare (USA)
Hourcar (USA)
Ithaca Carshare (USA)
Kootenay (CAN)
Mobility CarSharing (CHE)
Modo (CAN)
OGO Car Share Co-op (CAN)
Options for Cars (CAN)
Peg City Car Co-op (CAN)
Regina Car Share (CAN)
Saskatoon CarShare (CAN)
Trondheim Bilkollektiv
MoveAbout (NOR)
P2P carsharing
Car Next Door (AUS)
Carhopper (USA)
carsharing24/7 (AUT)
CarUnity (DEU)
Drivy (FRA)
easyCar Club (UK)
Enalux LLC (USA)
FlightCar (USA)
Getaround (USA)
iCarsClub (SGP)
JustShareIt (USA)
Mobocars (LVA)
Rentecarlo (UK)
Sharoo (CHE)
Snappcar (NLD)
Tamyca (DEU)
Tripndrive (FRA)
Turo [formerly RelayRides]
(USA)
VikingCars (ISL)
YourDrive (NZL)
Others
Bluemove (ESP)
CiteeCar (DEU)
City Car Club (FIN)
City of Aspen Car To Go (USA)
A Taxonomy of Carsharing Business Models
Thirty Seventh International Conference on Information Systems, Dublin 2016 17
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... With the rapid growth of carsharing, a broad range of enterprises from highly varied sectors have started entering in the carsharing market, including car producers, insurance companies, transportation service providers, and energy companies [4]. Carsharing organizations operating throughout the world in various forms have sprung up. ...
... CS systems are either business-to-consumer or peer-to-peer models (Münzel et al., 2018;Nansubuga & Kowalkowski, 2021;Remane et al., 2016). Business-to-consumer are commercial and most widely used models. ...
Thesis
The fight against climate change requires rapid global action to decarbonize both industry and society. Among the largest emitters of climate-damaging greenhouse gases, changes in indi-vidual road mobility are necessary to substantially reduce emissions. Several promising measures can be promptly implemented to reduce emissions from individual mobility. Three of the most important are the electrification of individual road mobility with electric vehicles, the integration of electric vehicles as an essential part of the electricity system, and new concepts of mobility to reduce car ownership. However, the decarbonization potential in individual measures can be leveraged only through the efficient use of digital technologies and compre-hensively designed information systems. Consequently, a great need exists for research at the intersection of information systems, including digital technologies and sustainable mobility. The information systems community must help address the global challenge of decarbonizing individual mobility. This work includes seven research papers addressing the decarbonization and digitalization of transportation and energy to enable sustainable individual mobility. Therefore, this thesis first addresses the management of electric vehicle charging to accelerate the expansion of charging options. Second, it addresses the integration of electric vehicles into the electricity system, including in combination with renewable energy sources and the provi-sion of flexibility enabled by digital technologies. Third, it describes the need for innovation in shared mobility solutions, such as carsharing, to reduce car ownership. This thesis positions itself at the intersection of green information systems and information systems for innovative mobility business models. It bridges research into sustainable energy systems and the devel-opment of new business models and services in the mobility sector.
... Business model taxonomies are a prevailing artifact that supports managers and researchers in understanding, analyzing, and designing new business models (e.g., [La15]). [Mö21] provide an overview of existing business model taxonomies based on the domains they cover (e.g., logistics [Mö20] or car-sharing [Re16]) and the technologies they use (e.g., digital platforms [FRP20b]). Subsequently, while business model taxonomies exist, there is yet none that sheds light on an increasingly relevant field, i.e., data-driven business models in ecosystems. ...
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