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

Key

words: 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.

1 RESEARCH PROBLEM

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.,

2013).

2 OUTLINE OF OBJECTIVES

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

perspective research should answer the question of

how to identify digitization opportunities, risks and

Pfeiffer, A.

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach.

In Doctoral Consortium (DCIT 2016), pages 19-31

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).

3 STATE OF THE ART

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

Digitality

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

technological objects, like computers or other

nonmaterial objects, like operating systems, to be

DCIT 2016 - Doctoral Consortium on Internet of Things

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

Architecture

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

artifact, which is seen as a structured and organized

arrangement of material and nonmaterial

technological objects consisting of computing,

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach

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

notions.

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

technologies infrastructural” (Tilson et al., 2010).

Consistently, Yoo et al. (2010) point out that by

digitalization is meant “the transformation of

DCIT 2016 - Doctoral Consortium on Internet of Things

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 dot.com 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).

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 deﬁned as a

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach

speciﬁc customer’s (or a group of customers')

contribution to a focal firm's proﬁt. 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.,

2014).

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).

4 METHODOLOGY

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

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,

DCIT 2016 - Doctoral Consortium on Internet of Things

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

ecosystems.

5 EXPECTED OUTCOME

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

evolution.

6 STAGE OF THE RESEARCH

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

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.

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach

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

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

DCIT 2016 - Doctoral Consortium on Internet of Things

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,

loose coupling and standards foster digital technology

capabilities: reusability, distributiveness and

interoperability (Luthria and Rabhi, 2015; Mueller et

al., 2010).

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach

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.

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

DCIT 2016 - Doctoral Consortium on Internet of Things

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)

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

Figure 5: Core Business Model–own diagram.

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

contribution into account it additionally encompasses

a network perspective on value creation and

capturing.

Further on, the SoBM concept presents a

comprehensive, reusable and digitization-oriented

Generating Business Models for Digitalized Ecosystems - Service-oriented Business Modeling (SoBM) - A Structured Modeling Approach

way of business modeling in digital transformed

ecosystems.

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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