Generating Business Models for Digitalized Ecosystems
Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
Andreas Pfeiffer
Information Systems 5, RWTH Aachen University, Ahornstr. 55, 52064 Aachen, Germany
Keywords: Digital Transformation, Digitization, Business Model, Business Model Development, Emobility.
Abstract: For various industries, Lucas et al. (2013) have recently described how intensely organizations, industries,
society and the economy are transforming through digital technology implementation in products, services
and institutions. Research is asked to find answers to the question of how to identify digitization opportunities,
risks and costs. Furthermore, the leverage of digitalization opportunities with regard to customer’s
value-in-use, network perspectives, flexible remodeling of business operations and enlarging business model
scope and scale needs to be addressed. Answers can only be found by respecting the distinct nature of
digitality, which is a sound basis for generativity as well as evoking high complexity in product, services and
network partnerships. The ongoing emobility as well as development of the smart home market can currently
be seen as fields excellently demonstrating the enormous and creative potential of digital transformation. This
makes it an ideal field of investigation to find answers to the proposed research questions. Taking the
requirements of digitalization into account, the paper presents an approach for business model development
evolved and tested in the field of emobility and smart homes. This approach is based on the principles of
Service-Oriented Architectures (SOA) in combination with ideas of well-proven business modeling methods.
Digital transformation is a phenomenon that has
enormously changed the way we interact with our
environment within the last decades. For various
industries, Lucas et al. (2013) have recently described
how intensely organizations, industries, society and
the economy are transforming through digital
technology implementation in products, services and
institutions. As Lusch and Nambisan (2015) point
out, digital technology plays a dual role as an enabler
(operand) and initiator (operant) within the
transformation process. It enables the creation of new
value networks and facilitates the exchange of
resources and knowledge within the network. In
addition, digital technology is becoming increasingly
part of new offerings through digitization of products
and service. Even more digitization of products
enables value creation through additional services
having a combinatorial effect on the digital service
ecosystems (Barrett et al., 2015). Based on these
facts, some significant scientific attempt has been
made to explain the challenges of digitalization and
some steps have been taken to suggest how these
challenges can be addressed. Nonetheless, there is
still a lack of guidance on how to manage digital
transformation successfully and the adoption or
radical change of existing business models in the era
of digitalization. Simultaneous digital transformation
is attracting managerial attention and thereby digital
business strategies are being set up, new business
models are developed and tested to ensure
companies’ long-term survival (Bharadwaj et al.,
Not surprisingly, information systems science ranks
research on business models and the impact of
information and communication technology (ICT) on
business models as a priority task. This task includes
questions on ICT’s transformative nature, the
subsequent impact on industrialization as well as new
product and service models. Furthermore, IT support
for developing and managing business models is
addressed by means of substantiation of conceptual
models, graphical representations and the design of
software tools for supporting business model
development (Veit et al., 2014). Managerial
Pfeiffer, A.
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach.
In Doctoral Consortium (DCSMARTGREENS 2016), pages 3-16
perspective research should answer the question of
how to identify digitization opportunities, risks and
costs. Furthermore, the leverage of digitalization
opportunities with regard to customer value
propositions, remodeling business operations and
enlarging business model scope and scale by
identifying new customer channels and entering new
markets needs to be addressed. Answers can only be
found by respecting the distinct nature of
digitalization, which is a sound basis for generativity
as well as evoking high complexity in products,
services and network partnerships.
The ongoing emobility as well as development of
the smart home market can currently be seen as fields
excellently demonstrating the enormous and creative
potential of digital transformation. This makes it an
ideal field of investigation to find answers to the
proposed research questions. Taking into account the
requirements of digitalization, a proposal for a new
development approach for business model generation
will be developed and evaluated in the field of
emobility and smart homes. This approach is based
on the principles of Service-Oriented Architectures
(SOA) in combination with ideas of well-proven
business modeling approaches (Chung and Chao,
2007; Newcomer and Lomow, 2005; Osterwalder and
Pigneur, 2010).
The theoretical basis of this research is enabled by an
extensive review of the literature on digital artifacts,
digital transformation and business models. This
supports the conceptual arguments and addresses the
objective to derive insights into digital nature and its
influence on business model development. Therefore,
as an important precondition, the state of the art
regarding the nature of digital artifacts and digital
technology has been analyzed. Furthermore, the
literature on digital transformation is evaluated to
work out relations, impacts and opportunities for
business models and business model generation. A
general definition of a business model and its fields
of application will be a conceptual basis for the case
study research and the elaboration of a service-
oriented business modeling approach. The presented
outcomes of the first case study thereby (see section
6.1) focuses on the impact of digitization on business
models and rely on digitization and digital
transformation related literature. It utilizes a simple
but effective business model classification scheme to
evaluate digital technology’s impact on development
of business model opportunities within the emobility
market. Second case study’s outcome (see section
6.2) takes the findings of the first case study into
account and presents based on the relevant literature
a service-oriented business modeling approach.
3.1 Digital Artifacts and the Nature of
In this section, the distinct characteristics of digital
artifacts, the layered modular architecture (LMA) of
digital technology, the core design principles of
digital technology as well as a definition for digital
infrastructures will be introduced. This will be the
basis for the definition of “digitalization” and help to
back the understanding of digitalization of business
models by providing an understanding and
facilitating possibilities through form-giving
structures of digitized physical products and services.
In the context of studying digital artifacts and
technology, it is important to distinguish them from
physical artifacts. With their theory on the nature and
identity of technological objects, Faulkner and Runde
(2013) presented a well-proven sound basis for
identifying digital artifacts, their distinct attributes
and design principles. They argue that objects are
beside others, such as events and properties, basic
kinds of entities. Regarding them as “structured
continuants,” they see objects as structured and
composed of distinct elements. Technological objects
are seen as a subset of objects that is specified by the
function assigned to it by members of the human
community. Technological objects can be separated
into two categories, material and nonmaterial
technological objects. The first possesses a physical
mode of being, like office chairs and flipcharts, which
have properties of location, mass, shape and volume.
Nonmaterial technological objects have a
nonphysical mode of being and thus are “aspatial.”
Nonmaterial, nonhuman technological objects are
called syntactic entities and are composed of symbols
that are formed by syntactic and semantic rules of the
language in which they are couched. Examples of
syntactic entities are research articles, product
designs and bitstrings, such as computer files.
In sum, Faulkner and Runde (2013) present three
criteria for nonmaterial technological objecthood:
continuants combined with structure, an agentive
function imposed by human communities and a
nonphysical mode of being.
An important implication of nonmaterial
technological objects is that they may be distinct from
material and other nonmaterial “bearers”. For
instance, bitstrings as a collection of 1s and 0s as such
have no spatial attributes and rely on material
DCSMARTGREENS 2016 - Doctoral Consortium on Smart Cities and Green ICT Systems
technological objects, like computers or other
nonmaterial objects, like operating systems, to be
usable. However, they possess a particular technical
identity like material objects. Technical identity
thereby depends on the community in which it is
“used and/or appropriately referenced if (1) it has
assigned to it the function associated with that
technical identity, and (2) its structure is such that it
is generally able to perform that function” (Faulkner
and Runde, 2013).
Kallinikos et al. (2013) introduce four significant
attributes of these technological nonmaterial objects
that they describe as “digital artifacts qua objects”.
These attributes describe the specific nature of digital
objects. Examining the ambivalent ontology of digital
artifacts, Kallinikos et al. give a broad overview of
the existing literature on the ontology and properties
of digital artifacts within IS research, concluding that
“digital artifacts are intentionally incomplete and
perpetually in the making” and “[…] they lack the
plenitude and stability afforded by traditional items
and devices” (Kallinikos et al., 2013). Kallinikos et
al. elaborate through their studies that digital artifacts
can be distinguished from physical objects by their
editability, interactivity, reprogrammability/openness
and distributedness. The first three attributes concern
the operations by which digital objects are put
together (editability, interactivity,
reprogrammability) and the last two the ecology of
relations within which these operations are embedded
(openness, distributedness).
Editability thereby concerns the possibility to
change a digital object constantly by reorganizing the
constituent elements, by deleting or adding new
elements or by modifying individual elements of the
object. Hereby, the logical structure that governs the
object and the mechanisms of information production
and processing are not interfered with.
Digital artifacts are interactive in the sense of
offering alternative possibilities of a contingent
nature to activate their embedded functions or to
discover the encapsulated information items.
Interaction does not need to invoke change or
modification of the object. This is facilitated by the
“responsive and loosely bundled nature of the items
that make up digital objects” (Kallinikos et al., 2013).
Openness and reprogrammability of digital
artifacts describe the accessibility and modifiability
by other digital objects that are not the ones governing
their own behavior. This means that the logical
structure of digital artifacts can be modified by other
objects than the ones that govern and manage the
mechanisms of information production and
processing. Thereby, openness is closely tied to the
interoperable character of digital artifacts.
As the result of openness and interoperability,
digital artifacts are hardly ever contained within a
single source or institution. Thus, they are classified
as distributive in the sense that they are transient
assemblies of functions, information items or
components disseminated over digital ecosystems.
Insofar as they are not bonded to an obvious entity
and in being distributed the existence of various
combinations of digital objects of the same kind is
possible. By this they are borderless, fluid and
crucially transfigurable (Kallinikos et al., 2013).
Kallinikos et al. further argue that digital artifacts
“are further supported by the modularity and
granularity of the ecosystems in which digital objects
are embedded”. In this context, digital artifacts are
from Kallinikos et al.’s point of view associated with
the concept of modularity in means of objects being
relatively independently organized in blocks that
constitute a system by “a wider yet loosely coupled
network of functional relationships”. These blocks
are mediated through interfaces that can serve a broad
spectrum of functions. The granularity of digital
objects refers to the ingredients from which blocks
are made and describes “the minute size and
resilience of the elementary units or items by which a
digital object is constituted” (Kallinikos et al., 2013).
3.2 Digitization, Digital Technology,
and the Layered Modular
Based on the presented theory of digital artifacts,
digital technology can be divided into digitized and
digital artifacts. The second one stands for
nonmaterial, nonhuman technological objects that
fulfill all mentioned characteristics of nonmaterial
technological objecthood. Nonetheless, the
combination of nonmaterial and material
technological objects in the sense of e.g., an iphone
application used on an iphone is a digital technology
insofar as nonmaterial objects can be embedded into
material technological objects. This technical process
of embedding digital artifacts is called “digitization”
and the results are called digitized artifacts.
Consequently, digitized artifacts can be defined as the
assemblages of digital and physical artifacts that are
recognized as an end product to meet customer needs.
Examples of digitized artifacts are everyday
consumer products like mobiles and ebooks, but also
a full range of industrial equipment, textile or car
production robots. Digital technology will be
furthermore understood as both digital and digitized
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
artifact, which is seen as a structured and organized
arrangement of material and nonmaterial
technological objects consisting of computing,
communication, interaction and information
technologies (Henfridsson et al., 2014). Furthermore,
digital technology can be used as an enabling medium
for designing and providing digital services offerings
(Chowdhury, 2015).
It should be emphasized that according to Yoo et
al. (2010) the incorporation of digital objects causes
physical objects to adopt the characteristics of digital
artifacts (Yoo et al., 2010), whereby these digitized
objects are characterized by distinct trajectories of
material and digital artifacts, meaning that the entity
no longer follows one unified line of development.
Insofar understanding digital, nondigital systems as
well as the management of decoupled systems
increases the complexity of the development and
maintenance of business models within digitalized
ecosystems (Henfridsson et al., 2014).
Key enabler for digitization of technological
objects is their so-called “layered modular
architecture” (LMA) of digital technology. LMA
facilitates the separation of material and nonmaterial
entities. It maintains an interoperability among the
components by a hierarchical dependence between
the layers. The LMA can be described by four loosely
connected but interdependent layers: device,
network, service and content. The device layer
contains two kinds of technological objects. First,
physical hardware units like computer hardware.
Second, nonmaterial objects like operating systems
providing control and maintenance of the physical
machine functionality as well as connecting
interfaces to the network layer. Similar to the device
layer, the network layer consists of material as well
as nonmaterial technological objects, providing a
sublayer for physical transport like cables and radio
spectrum as well as a sublayer for logical
transmission with nonmaterial objects like network
standards. The service layer enables direct interaction
with users through application programs featuring
functionality, like creating or consuming content. The
highest layer comprises data like text, sounds or
images as well as metadata and directory information
about e.g., content’s origin and ownership (Yoo et al.,
2010). Following Yoo et al., predigital technology is
featured by tightly coupled entities (such as books,
analog telephone), or as in the case of purely physical
or mechanical products (such as mechanical timers,
powerlines, sockets) layers do not even exist. Digital
technology facilitates through the separation of the
four layers a free and individual design in between the
different layer levels (Nylén, 2015). Digital
technology is delivered intentionally incomplete with
temporary bindings across the four layers. It is
thereby following the procrastination principle,
holding that a digital artifact “should not be designed
to do anything that can be taken care of by its users”
(Zittrain, 2008).
The open and dynamic breeding ground of digital
technology, their catalyzing LMA, the fluid character
of digital content and a rapid diffusion through the
internet triggers unprecedented opportunities of
generativity (Kallinikos et al., 2013; Zittrain, 2006).
Generativity here refers to the “overall capacity of a
technology to produce unprompted change driven by
large, varied, and uncoordinated audiences” (Zittrain,
2006), which creates abundant opportunities for
innovating products, services (Boland et al., 2007;
Tilson et al., 2010; Yoo et al., 2010; Zittrain, 2006)
and business models carrying out these innovations
(Chesbrough, 2007) and themselves being influenced
by the nature of digitality.
3.3 Digitalization
After having emphasized the distinct characteristics
of digital artifacts, digitization and thereby the nature
of digital technology as well as the generativity that
is created by digital technology, there is a solid
conceptual basis for understanding the impact and
challenges for an industry facing digitalization. This
phenomenon has recently been intensively discussed
in applied managerial literature and science but
surprisingly enough a commonly accepted or clear
definition and understanding are still missing
(Bounfour, 2016; Hanelt et al., 2015). Besides being
mistakenly used as a synonym for digitization–which
is, as already shown, a technical process of
embedding digital technology into technological
objects–it is often discoursed in context to digital
transformation and digital innovation without
clarifying the precise relationship between the
Applied managerial literature tries simply to
describe digital transformation as “the use of new
digital technologies (social media, mobile, analytics
or embedded devices) to enable major business
improvements (such as enhancing customer
experience, streamlining operations or creating new
business models)” (Fitzgerald et al., 2013).
For sure, this is a shortcut in characterizing the
effect of digital transformation by the underlying
targets. More precisely, Tilson et al. (2010)
characterize digitalization as “a sociotechnical
process of applying digitized technology to broader
social and institutional contexts that render digital
DCSMARTGREENS 2016 - Doctoral Consortium on Smart Cities and Green ICT Systems
technologies infrastructural” (Tilson et al., 2010).
Consistently, Yoo et al. (2010) point out that by
digitalization is meant “the transformation of
sociotechnical structures that were previously
mediated by non-digital artifacts or relationships into
ones that are mediated by digitized artifacts and
relationships. Digitalization goes beyond a mere
technical process of encoding diverse types of analog
information in digital format (i.e., ‘digitization’) and
involves organizing new sociotechnical structures
with digitized artifacts as well as the changes in
artifacts themselves” (Yoo et al., 2010). Hence, the
notion of digitalization includes the transformational
nature of digitality as “a marked change in form,
nature, or appearance” affecting individuals, firms,
economies and societies (Lucas et al., 2013; Yoo et
al., 2010) in part or as a whole by transformation of
individual habits, organizational as well as
operational structures through digital technology,
including digital artifacts themselves. This can be
characterized by a significant change in nature and
focus of the business activities needed to acquire new
capabilities or markets, and fundamental changes in
tasks to leverage competitive advantages (Bounfour,
2016; Lucas et al., 2013).
Following this view, digitalization and digital
transformation should be understood synonymously.
By this, the differentiation between digitalization and
digitization is underlined by highlighting the
sociotechnical perspective, the processual character
and the impact on social entities (consumers and
producers) and institutions (organizations and
markets). In addition, a thorough understanding of
digitalization’s influence on processes,
organizational forms, relationships, user’s product or
service experience, market coverage, customers and
the overall disruptive impact of digitalization is
covered (Lucas et al., 2013).
Surely, conjured up by the distinct nature of
digital artifacts, digitalization evokes generativity and
thereby unpredictable combinations of products,
services, ways of operating businesses as well as
business models carrying out these combinations into
a market, creating a good seed ground for innovation
(Chesbrough, 2007; Henfridsson et al., 2014; Yoo et
al., 2010; Yoo et al., 2012).
Innovation is “a new idea, device, or method,” as
well as “the act or process of introducing new ideas,
devices, or methods” (Meriam-Webster dictionary).
Insofar as one can follow that by digital innovation of
a new idea, device or method enabled by digital
technologies or the process of introduction, just these
through digital technologies is meant (Yoo et al.,
2012). However, an innovational character is a
sufficient, but not necessary, condition to
digitalization. This means that, from the perspective
of the sociotechnical microsystem, every digital
transformation is conjunct with a kind of novelty due
to introduction of new technological artifacts,
changing value propositions, operational processes or
business model architecture. Nevertheless, an
innovation has to cover novelty characteristics to the
macrolevel. Digital transformation thereby is not
forced to cover the characteristics of innovation.
Digital transformation can also be performed by a
sociotechnical process of introducing well-known
digital technology or digitized processes into new
fields of application.
3.4 Business Model Concept
With the era came a discussion about and on
the concept of business models in science and applied
science literature popular. Management scholars tried
to find out how business works and how value is
created, especially because billions of dollars had
been spent on “business models” that later turned out
to fail (DaSilva et al., 2014). Since then, researchers
and practitioners have made a considerable number of
attempts to define, describe and operationalize the
business model concept (Fielt, 2014; Petrikana et al.,
2015). Nevertheless, there does not exist a commonly
accepted definition of business models and their
conceptual components. Furthermore, the concept
boundaries of application differ according to context
and conditions (Fielt, 2014).
Following Fielt’s comprehensive study on
business model definitions and concept elements, a
business model can be defined out of a generic and
holistic point of view in the way that “[it] describes
the value logic of an organization in terms of how it
creates and captures customer value and can be
concisely represented by an interrelated set of
elements that address the customer, value
proposition, organizational architecture and
economics dimensions” (Fielt, 2014). This definition
follows major and well-accepted focal firms’ oriented
research and practitioner streams (e.g., Chesbrough,
2007; Johnson, 2010; Osterwalder and Pigneur, 2010)
explicitly focusing on customer value creation. It
understands value delivery included in the value
creation process, because “[the] separation of creating
value and delivering value [is seen] as a supply-side
perspective focusing on producers adding value.
Customer (use) value cannot be created without
involving the user and considering the use context”
(Fielt, 2014).
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
Business model concept pays high attention on
customer value creation and capturing while this
value is viewed at least from two angles. From a
supplier-centric point of view it is dened as a
specic customer’s (or a group of customers')
contribution to a focal firm's prot. The value
capturing elements of a business model include this
view on ‘value-in-exchange’. It is the value
embedded in the product itself by adding value during
the production process at the point of the exchange
process. Briefly, it describes what the vendor gets
from the customer.
From a customer-centric point of view value is
defined by customers, based on their perceptions of
the usefulness of the product or service on offer. This
‘value-in-use’ is set up through the execution of the
value creation elements. It defines what the customer
gets from the purchase in sense of the generated value
with and determined by the user during the
consumption process. Following this perspective,
value-in-use is created by focal companies offering a
“value proposition” and customers accepting the
proposal and thereby realizing the proposal or
value-in-use (Bowman and Ambrosini, 2000).
In general, a value proposition can be defined as
a focal firm’s promise to provide resources that create
potential value within customer’s activities. These
resources can be a conjunction of economic,
functional, emotional, social or technical components
which usefulness depends on customers or
beneficiary’s perception (Mele and Polese, 2011).
As an instrument for strategic analysis and
planning, business models are used to explain value
chains or lately even more value networks from the
perspective of a focal firm in an aggregated form.
Further on, business models describe how activities
are combined to execute a firm’s strategy (Petrikina
et al., 2014). Understanding a business model in this
way, they can be seen as “reflections of the realized
strategy” (Casadesus-Masanell and Ricart, 2010) and
as what a company is actually delivering at a certain
time. Therefore, business strategy and business
models are closely interlinked as business models are
part of the strategy work and execution (Demil and
Lecocq, 2010). It is commonly accepted that a firm
not only can use the business model concept for
reasoning about different business models. Even
more one or more different business models can be
executed in coexistence within a company’s strategic
portfolio (Trkman et al., 2015). Thereby, a “business
model as a model” is a relevant and useful
“manipulable instrument” to help scholars and
managers in reflecting what a firm does or could do
to create and capture value. Furthermore, it can be
used to change and manage its existing models to fit
with changes in technology or market conditions
(Baden-Fuller and Haeflinger, 2013; Spieth et al.,
Representations of business models are widely
used tools for analyzing and developing new
products, services as well as value creation and
capturing in detail. Besides other modeling tools, the
Business Model Canvas (BMC) of Osterwalder and
Pigneur (2010) can be considered as a popular and
well-known representative business model. It is a
holistic and easily applicable tool to develop, analyze
and innovate business models of new and existing
businesses. However, BMC fails to capture essential
aspects of digital technology, such as recombination,
interoperability and distributiveness. Furthermore, it
is applicable for development of new business models
but conceptually misses reusability and further
utilization through recombination of existing value
creating and capturing services. The business model
description often remains superficial and detailed cost
structures are rare. This makes continued use of BMC
in operationalization of business model integration
impossible or considerably more difficult. Last but
not least, business model evolution in the sense of
building new business on existing models seems not
to be appropriately considered within the BMC
concept (Fielt, 2014; Zolnowski et al., 2014).
The research is based on design science (Hevner et
al., 2004) and the design science research
methodology (Peffers et al., 2007). Therefore, a series
of case studies will be conducted to gain insights into
the research problem and to evaluate the developed
artifacts. Two have already been executed. The first
is with a provider of electric vehicle services, the
other with a provider of a smart home platform.
Following a problem-oriented approach, the first case
study is based on an enhanced business modeling
approach to identify the relation between digitization
and digitalization within the emobility market setting.
Furthermore, influences on business model
development and possible modeling support in
digitized ecosystems are considered.
The case study was conducted in autumn 2015,
including a set of workshops. These were focused on
investigating new business models for emobility
products and services deployment based on electric
vehicle supply equipment (EVSE) and digitalization
opportunities. In the first step, the deployment of
EVSE at the industry-partner side was analyzed from
DCSMARTGREENS 2016 - Doctoral Consortium on Smart Cities and Green ICT Systems
early 2009 up to mid-2015 utilizing an adoption of the
BMC method (Osterwalder and Pigneur, 2010). The
used adoption of BMC was focused on an elaboration
of general services and infrastructure (physical,
personnel and digital) relations as well as the related
value proposition evolution over time. This was
meant to be the starting point for future business
model development.
Utilizing an intensive literature review, the study
was designed to test the idea of digital technology
enhanced service identification and the derivation of
business services fulfilling “unknown” customer
needs in business model development. Within the
demonstration phase and the following evaluation,
key ideas of utilizing the SOA concept for enhancing
business modeling were identified and transferred to
a design update. This was fundamental for the next
research phase and a case study with a provider of
smart home platform services.
Taking the findings of the first case study into
account, the second case study was conducted in
winter 2015 based on an enhanced business modeling
approach using the SOA concept as a significant
methodological improvement. The results are under
analysis and will be presented later this year.
Nevertheless, a first comprehensive outlook will be
given in Section 6 presenting a Service-oriented
Business Modeling (SoBM) approach as a solution
proposal for business modeling in digitized
Within the ongoing emobility and smart home market
development, a research approach has been set up to
deliver results regarding five aspects: First, to analyze
digital transformation by studying the influence of
digitized technology within development of
emobility and smart home markets. Second, to
identify thereby a structured approach for developing
business models within digitalized ecosystems based
on IT-enabled service detection. Third, to provide an
adaptive business modeling toolset enabling business
development and visualizing business models as a
decision support tool. Fourth, to elaborate a process
model for continued business development
supporting running businesses. Fifth, to apply this
approach in the form of case studies within the
emobility and smart home markets, testing usability
and performance of the approach in business model
As described above, the research has already passed
two iteration phases. Therefore, the results of the first
case study can be presented with regard to identifying
derived requirements of a digital nature on business
modeling. As the case study had an exploratory
character, the study also delivers results regarding the
development of the emobility market and the
influence of digitality. These will also be presented.
As an outlook in the second step, the SoBM as an
outcome of the second case study will be presented
comprehensively as a basis for discussion.
6.1 Digitization and Business Models
As a first research outcome a generic classification of
digital artifact integration in EVSE (see figure 1) and
resulting possibilities for business model generation
as emobility service provider (EMSP) was presented
(Pfeiffer and Jarke, 2016).
Figure 1: Electric Vehicle Supply Equipment (EVSE)
Layered Modular Architecture–own diagram.
Reflecting the emobility market situation up to
2015, EMSP business is defined as a combination of
charging-services operators’ (CSO) and charging-
services providers’ (CSP) business. An EMSP
thereby is a company running its own EVSE network
and providing charging and information services for
EVs regardless of whether they provide these services
within their own or in foreign EVSE networks. By
delivering these services, they create value for EV
B2C and B2B users and in value chains through
B2B2C network business. The study results strongly
support the assumption that within digitalized market
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
settings, EMSP value capturing and business model
sustainability is highly reliant on the grade of
digitized technology used and digitalization within
the business model itself.
The paper examined the opportunities of digitzed
technology for business model development and
business transformation (see figure 2). The basis was
an in-depth analysis of the historical development of
ICT-enhanced infrastructure in the emobility
charging market. Originating from the digitization of
EVSE, five generic business model types were
conducted and analyzed. In a first step, the LMA
service layer’s digital technology-based services
were transferred into a business model description.
Value creation as a core element was described by
core business process service elements. Further on,
value-capturing opportunities and business model
evolution prospects were deduced based on the
elaborated business process services. Value capturing
was therefore categorized into a revenue model,
business scope and scale, as well as OPEX and
CAPEX of the business model.
Figure 2: Generic Emobility Service Provider business
models-own diagram.
The analysis showed that basic customer needs–
charging services–can be fulfilled by any of the
EVSE-based EMSP business model approaches.
However–from the customers’ perspective–the
quality of service and value-added services (e.g., real-
time geoinformation) as well as the flexibility of
payment and contractual models rises by using
digitized EVSE equipment. These effects are ceteris
paribus accompanied by higher CAPEX for
investment into ICT (EVSE and backend systems).
Further, by implementing EVSE type 4 and higher
technology, lower OPEX can be achieved through
digitally optimized manual processes, e.g., by
preventive maintenance or remote assistance.
From the industry partners’ perspective, the
higher investment in digital EVSE technology and
ICT backend systems thereby can be significantly
compensated by minimizing manual services
processes in the field. In addition to the just
mentioned values for customers’ quality and
flexibility perception and business models’ cost
structures, further benefits can be achieved. Digital
technology-based service enhancement enables
higher flexibility of the revenue model (e.g., usage-
based tariffs, geoinformation services for third
parties) as well as a higher scalability and scopability
of the business model itself. As EMSP business
models are operating in the emobility market, there
are various opportunities for promoting value-added
services in the transport and energy market. This
underlines the assumption that the digital nature
makes product and service boundaries become fluid
(Yoo et al., 2010). In the current case, it descends as
the digital offspring of EVSE type 5’s digital nature.
This type of “charging system” is creating
unprecedented possibilities for product and service
innovation e.g., by promoting services in the energy
and transport system (e.g., information services and
smart-grid services). The later stages of developing
type 5 technology enable an enrichment of EMSP
business models by promoting new services based on
already existing technology in the field. Furthermore,
it has to be stated that the digital nature obviously can
make its generativity significantly stronger through
implementation of open, accessible, interoperable
and interconnected technology following the LMA
architecture model bringing the layers’ borders. To
safeguard the business model’s sustainability and
create a future-proof setup, industry partners’
experience suggests strongly that ICT should be
embedded at least with state-of-the-art technology
acknowledging the LMA. This means to force layer
independence, which is not regarded within EVSE for
types 1 to 3. In the current case, it is an interconnected
infrastructure setup as in types 4 and 5 EVSE based
on webservice technology. It has been experienced
that it is highly costly and inhibits quick-to-market
strategies with solid blocks of soft- and hardware.
Even more practice has shown that dump EVSE as
well as closed-shop infrastructure systems (up to
EVSE type 3) lower business model development
possibilities by forcing high changing cost at EVSE
DCSMARTGREENS 2016 - Doctoral Consortium on Smart Cities and Green ICT Systems
deployment sites accompanied by high complexity of
managing the different trajectory paths of digital and
physical technologies in the field.
This study is exploratory in three senses. Being
based on the expert knowledge and experiences of a
pioneering company in the young field of emobility,
it provides an overview on digital technology and
business development since 2009. Thereby, insights
from digital technology’s influence on business
deployment over time are gained. This provides a
fertile ground for deducing learning for future
business generation in the emobility market at a
highly digitized point of intersection between smart
transport and energy markets.
Furthermore, it demonstrates the generative
character of digital technology and the exploratory
design of LMA utilized as a basis for an advanced
business modeling in digitized market settings.
Applying the LMA’s “service layer” within
businesses’ value proposition design, unprecedented
possibilities are generated by digitality becoming
visible. Thus, e.g., already identified customers’
issues can be solved (e.g., “Is the EVSE I’m heading
for available?,” “I want to pay-as-I-go!”) or
customers’ needs that they do not even know (e.g.,
“Energy price optimization by energy market
optimized charging”) can be addressed by digital
technology-based services. Using EVSE type 5
technology, existing digital services from other fields
of application can easily be involved to solve these
issues, which brings time-to-market and cost-
structure advantages (e.g., use of Google maps and
integration of PayPal payment services). Moreover,
other fields of application and customers can be
addressed, expanding the scope and scale of EMSP
business models by detection of EVSE-based
business services.
Last but not least, the observations indicate that
business modeling approaches within digitized
market setups should facilitate LMA and a service
architecture-based approach to identify profitable
value creation and capturing opportunities. Thereby,
the generative nature of digitality can be leveraged
and transferred into business models carrying digital
artifacts into reality. Therefore, future directions of
research will lead to the application of the “Service-
Oriented Architecture” (SOA) concept into business
modeling approaches to facilitate value identification
through service-oriented business modeling. Taking
advantage of thinking in “service repositories,” value
creation and capturing in existing and new fields of
business seem to be promising approaches in
digitalized market setups. The SOA concept is strictly
based on the principles of modularity and granularity.
These are fundamental elements of digital nature’s
generative matrix, enabling better maintenance and
development of existing business models as well as
the exploration of business network partnership by
using well-proven SOA methods.
Because of its exploratory character, the study
was limited in several aspects by focusing on EVSE
technology and analyzing EMSP business models.
Thereby, simplifications regarding automotive and
energy market integration were applied. For instance,
the analysis was conducted reflecting the grid and EV
as “black boxes” with interfaces to use EVSE as a
physically connected point of grid and EV to deliver
and acquire possible services and vice versa. Besides
this digital technology, like battery management
systems, smart grid management systems, navigation
systems or mobile smartphone applications were
assumed as ways to interact with the infrastructure
but not being part of the investigation. Furthermore,
customer’s willingness to pay for quality of service
and value-added services was not part of the
investigation as well as strategic issues regarding
customer accountability in B2B2C relationships were
neglected. Last but not least, data security and privacy
as well as regulatory requirements should be
examined in further research.
6.2 Service-oriented Business
Modeling - Proposal for a Solution
of Business Modeling in Digital
Transformed Ecosystems
As described above, digital technology offers
unprecedented opportunities for value creation and
capturing within digitally transformed ecosystems.
Furthermore, the development of new products,
services and product service systems has to follow the
distinct nature of digitality. This nature has been
characterized as interactive, interoperable,
networked, borderless and fluid. Digital
infrastructure creates a highly flexible, complex and
networked ecosystem. Following the concept of a
LMA, digital technology’s data and service layers can
be utilized as a starting point for business
development by being transformed into new business
services carrying digital technologies’ value
proposition into sociotechnical systems.
Taking the nature of digitality into account, the
SOA concept seems to deliver compatible
components for analyzing, developing and executing
business models in digitally transformed ecosystems.
Core elements of SOA are highly reliant on digital
artifacts’ characteristic modularity and granularity.
Furthermore, SOA’s design principles of modularity,
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
loose coupling and standards foster digital technology
capabilities: reusability, distributiveness and
interoperability (Luthria and Rabhi, 2015; Mueller et
al., 2010).
Based on this perception the idea was born to
propose a business model development concept that
takes the advantages of SOA into account. Thereby a
practical-oriented concept should be developed to
exploit the opportunities of LMAs as well as to master
chances and issues arising from the digital
transformation of ecosystems. Following this thought
a case study was conducted to test, to further
elaborate and to improve the approach. The resulting
procedure model, development cycle and finally the
SoBM concept will be subsequently presented.
SoBM Procedure Model. The development
approach (see figure 3) is structured as follows. Based
on the conviction that digital product and service
development in particular has to be derived from
customer’s value-in-use, partner-related expertise
and IT-enabled perspectives, an analysis of the
ecosystem and technology opportunities is the
starting point for the procedure model. Needless to
say, a good market knowledge, the ability to identify
and involve network partnership and an intense
knowledge of information systems and processes is
key to sustainable digital transformation of business
models. Therefore, the analysis elaborates distinct
market structures, value chains and networks, as well
as market partners’ objectives.
Figure 3: SoBM Procedure model–own diagram.
Either based on the ecosystem analysis or on a
digital technology’s LMA service layer exploration,
a company assessment is iteratively conducted to
identify possible market positions and value
propositions. The company assessment is based on an
existing structured business model (SoBM)
description identifying possible value creation
opportunities by utilizing business service
identification based on either digital technology
(LMA) or customer needs as well as competitors’
value proposition-based identification methods.
In the next stage, the deriving portfolio of
business models is assessed by its probabilities and
profits. Afterwards, high-ranked business models
undergo the SoBM development cycle (see figure 4),
which will be described later as part of the SoBM.
One key outcome of this process is a concrete service
repository (see figure 5) related to the focal firm’s
value proposition and external customer as well as
partner services. Having an elaborated SoBM based
on service repository and a set of identified business
partners, a second business model assessment stage
can be conducted. Herein a proof of concept helps to
assess business model’s prospects. With a positive
decision, the business model can be transferred by
business model integration into the operational stage.
Taking new or redesigned business into operations is
starting point for a continuous business model
improvement process.
Development Cycle and the Service-oriented
Business Model. The core of the presented business
model development concept is the layered SoBM.
Essential element of the SoBM is a layered business
model architecture. This is formed by a set of
business, service and infrastructure layers for all
business partners in different detail level. It takes
shape within the SoBM development stages (see
figure 4) by being completed and optimized through
an iterative process defining layered business models
for the customer structure, the focal firm and the
value network partners.
Figure 4: SoBM development cycle–own diagram.
DCSMARTGREENS 2016 - Doctoral Consortium on Smart Cities and Green ICT Systems
Customer’s layered business model development
is focused on one or more customers’ values-in-use,
the associated revenue opportunities and customer’s
value context. Latter is formed by involved
customers’ services and infrastructures. These are
formulated on specific layers to support the
value-in-use focused focal business model
development in the next stage.
Focal firm’s layered business model is the core
element of SoBM concept (see figure 5). On the
business layer it defines focal firm’s value
propositions as an answer to the identified customers’
needs. Furthermore, it includes firm’s key activities
corresponding to the value propositions. Means and
methods required to carry out these activities are
described on the service and infrastructure layer. The
service layer is formed by a focal service repository.
This value-in-exchange-oriented tool fulfills the
function of organizing and utilizing all key activity
bonded and value proposition relevant services.
These services are categorized by business process
services, as high-level abstract services, coordination
services, atomic business services, whereby all
service types can also be business or business-
enabling IT services. Focal firm’s services are of
public domain when they interact with value
network’s or customers’ services. Private services are
executed within the firm. Services utilize firm’s
resources which are described on the infrastructure
layer. These resources can be intangible,
tangible/physical and personnel infrastructures each
described with relation to the supported services.
Intangible infrastructures are resources like IT-
applications, business relations, (digitalized)
Figure 5: Core Business Model–own diagram.
information. These are enabler and initiator resources
having high impact on business success in digitalized
ecosystems. Physical resources are e.g. machines,
IT-hardware, communication channels. Personnel
infrastructures cover firm’s organizational setup and
human capital. Personnel infrastructure thereby
models people’s roles and contribution to the service
repository with different skills and knowledge in their
intra-organizational and inter-organizational
boundaries (Mele and Polese, 2011).
In network partners’ layered business models
value propositions, services and infrastructure are
represented on specific layers. These are derived
through identification of relevant network partners’
skills and contribution based on the focal service
needs. Partner’s business model elements are directly
mapped to relevant components on focal firm’s
business and services layers. By this all necessary
activities for fulfilling the value propositions are
bundled within an interrelated service repository.
Finally, the financial aspects of the focal business
model are analyzed in a cost-benefit analysis (Brent,
2007; Farbey et al., 1992). This analysis is based on
the cost-architecture which can be derived from the
service repository in conjunction with the value-in-
use benefits described by specific revenue models
within customers’ layered business models. By
applying financial values to the service repository,
including external service costs, the cost structure of
value creation and capturing is caught in a structured
way. Benefits are modeled according to the customer
structure by addressing financial values to customer’s
demand-side needs. Not measured within the service
repository are nonfinancial costs and benefits. These
are applied on the business layer level according to
the value propositions. Thereby, an overall criterion
for decisions on business model implementation is
given. This is not solely built upon financial, but
further on other economic, ecological or social
components. Other economic reasons are, e.g., the
future-proof technological setup of a business model
investment. A cost–benefit-oriented SoBM scenario
analysis based on service out- or insourcing as well as
on service-specific digitization degrees support
business model decision making within the business
model assessment stage.
It has been shown that the SoBM concept is
covering customers, value proposition, organizational
architecture and economic dimensions. Thereby it is
qualified to be a business model concept describing
all relevant value creating and capturing elements
(Fielt, 2014). By taking market/customer needs
(value-in-use), focal propositions and service
architecture as well as market/value partnership
Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach
contribution into account it additionally encompasses
a network perspective on value creation and
Further on, the SoBM concept presents a
comprehensive, reusable and digitization-oriented
way of business modeling in digital transformed
The modeling approach is comprehensive
because value creation and capturing can be described
on a holistic basis for the whole value network
connected over all SoBM layers and being
orchestrated within a coherent service repository. In
addition, through the connection of value
propositions and service repository elements, value
creation and capturing within the business model is
underpinned with clarifying content. Last but not
least, service as well as infrastructure layer provide an
interconnected value context.
The modeling approach is reusable because
SoBM concept is not only covering business
development aspects but even more it is a basic tool
for supply-side and demand-side tendering
procedures and for creation of new value network
partnerships. Besides this, existing internal or
partner’s public service repositories can be used as a
starting point for new business development based on
existing business models and business partnerships
(Löhe and Legner, 2009). Moreover, SoBM enables
the execution of business models by providing an
elaborated “ready-to-use” service repository with
clear and measurable preconditions.
The modeling approach is digitization-oriented
because it utilizes digital technology’s LMA within
the SoBM development process to assess and
generate new value-in-use applications. This fosters
the use of digital technology as an operant in new
business development. Further on, it enriches the
service repository with optimized IT-enabled services
and thereby unlocks operand capabilities in new or
redesigned business models. On top of that the
utilization of the SoBM concept warrants alignment
with business partners service (IT- and non-IT
related) fulfillment from the scratch by taking
partners service repositories into account. Within the
SoBM development cycle this allows to use “uniform
means to offer, discover and interact with, and use
capabilities to produce desired effects consistent with
measurable preconditions and expectation”
(Demirkan and Goul, 2006). Within the
implementation phase the usage of a service
repository allows an IT-oriented implementation
based on existing SOAs. By this SoBM proposes to
enable a more flexible, faster, flawless and cheaper
implementation of IT-related services.
Overall SoBM structure facilitates digital artifacts
intentionally incomplete and perpetually nature to
trigger unprecedented possibilities of business value
generation. By following the nature of digitality and
it transposes the concepts of modularity, granularity,
interactivity, openness and distributiveness into
business model’s architecture utilizing the SOA
concept and focusing on network effects as well as on
the value-in-use at the customer side.
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