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Designing Business Models in the Era of the Internet of Things

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The increasing pervasiveness of digital technologies, also refered to as "Internet of Things" (IoT), offers a wealth of business model opportunities, which often involve an ecosystem of partners. In this context, companies are required to look at business models beyond a firm-centric lens and respond to changed dynamics. However, extant literature has not yet provided actionable approaches for business models for IoT-driven environments. Our research therefore addresses the need for a business model framework that captures the specifics of IoT-driven ecosystems. Applying an iterative design science research approach, the present paper describes (a) the methodology, (b) the requirements, (c) the design and (d) the evaluation of a business model frame- work that enables researchers and practitioners to visualize, analyze and design business models in the IoT context in a structured and actionable way. The identified dimensions in the framework include the value network of collaborat- ing partners (who); sources of value creation (where); benefits from collabora- tion (why). Evidence from action research and multiple case studies indicates that the framework is able to depict business models in IoT.
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M.C. Tremblay et al. (Eds.): DESRIST 2014, LNCS 8463, pp. 17–31, 2014.
© Springer International Publishing Switzerland 2014
Designing Business Models in the Era of Internet
of Things*
Towards a Reference Framework
Stefanie Turber1, Jan vom Brocke2, Oliver Gassmann3, and Elgar Fleisch4
1 Chair of Innovation Management, University of St Gallen, Switzerland
stefanie.turber@unisg.ch
2 Hilti Chair of Business Process Mgt., University of Liechtenstein, Liechtenstein
jan.vom.brocke@uni.li
3 Chair of Innovation Management, University of St Gallen, Switzerland
oliver.gassmann@unisg.ch
4 Chair of Information Management, ETH Zurich, Switzerland
efleisch@ethz.ch
Abstract. The increasing pervasiveness of digital technologies, also refered to
as "Internet of Things" (IoT), offers a wealth of business model opportunities,
which often involve an ecosystem of partners. In this context, companies are
required to look at business models beyond a firm-centric lens and respond to
changed dynamics. However, extant literature has not yet provided actionable
approaches for business models for IoT-driven environments. Our research
therefore addresses the need for a business model framework that captures the
specifics of IoT-driven ecosystems. Applying an iterative design science
research approach, the present paper describes (a) the methodology, (b) the
requirements, (c) the design and (d) the evaluation of a business model frame-
work that enables researchers and practitioners to visualize, analyze and design
business models in the IoT context in a structured and actionable way. The
identified dimensions in the framework include the value network of collaborat-
ing partners (who); sources of value creation (where); benefits from collabora-
tion (why). Evidence from action research and multiple case studies indicates
that the framework is able to depict business models in IoT.
Keywords: Internet of Things, business model, value networks, digitization,
service-dominant logic, collaboration, digital ecosystem, architecture.
1 Introduction
Today companies are exposed to highly dynamic business environments, driven
by rapid developments and ever-increasing pervasiveness of digital technologies.
* An earlier version of this manuscript appeared in the Proceedings of the 22nd European
Conference on Information Systems as “A Business Model Type for the Internet of Things",
Research in progress, S. Turber and C. Smiela.
18 S. Turber et al.
A driving force is that digital technology gets increasingly weaved in previously non-
digital products, such as bikes, clothes and everyday household appliances. This phe-
nomenon, referred to as "Internet of Things" (IoT) [1], is expected to have a major
influence on the nature of products and services, and in consequence on overarching
business models (BM) [2, 3], i.e. the overarching logic of how businesses work [4].
The "Nest", a digitized thermostat for private homes, is a popular, recent example
to demonstrate how IoT is changing market dynamics: Equipped with sensors and
connected to the internet, the "Nest" can be controlled remotely via a mobile app and
can track the energy use of a household over time. These features open up numerous
opportunities for novel services and business models within an emerging ecosystem
of new collaborators. A current campaign for example includes energy providers as
partners to reward users, when they let their "Nest" switch off the HVAC1 during
peak times2. From this lens "Nest" itself serves as platform, which brings multiple
partners together to (co-) create and exchange valuable services (conf. [5]).
IoT in general inspires a wealth of new business models, which frequently involve
diverse partners of thereby arising cross-industry ecosystems [6, 2]. This fact requires
companies to rethink their firm-centered lenses in order to stay ahead in IoT driven
market environments [5]. However, many companies have difficulties to capture and
tap into the unprecedented ecosystem complexity around products and services in a
structured way. Burkhardt [6] generally identifies the "absence of formalized means
of representations (..) to allow a structured visualization of business model" as a ma-
jor research gap. We applied existing methods for business modeling in workshops
with companies, and found that the important characteristics of IoT ecosystems can-
not sufficiently be addressed by these methods. Such characteristics, for instance,
include multi-partner collaborations on digital platforms or the customers' enhanced
role as value co-creator by providing user data [7, 8].
Our research addresses the need for a business model framework in IoT-driven
market environments, which recognizes the specific impact of above-mentioned digi-
tization. We chose a design science research (DSR) approach for our study to design a
"framework for IoT business models" as the intended artifact. The artifact's design
requirements build upon sources of justificatory knowledge across different domains:
Marketing, strategic management and information systems.
The overarching research process is guided by the method described by Peffers et
al [9]. All in all, the business model framework shall provide researchers with a
framework to readily analyze business models in complex, IoT driven ecosystems.
Practitioners are provided with an understandable and consistent framework to depict
their organization's current and envisioned business models within complex IoT eco-
systems.
In the following section we begin by outlining the method and procedure of our
study in more details. We then set out related work and the requirements for the in-
tended artifact. In section 4 we explicate the design of our business model framework
by describing each dimension, including a brief rationale and an illustrative real-
1 HVAC: Heating, ventilating, air conditioning.
2 https://nest.com/thermostat/life-with-nest-thermostat/
Designing Business Models in the Era of Internet of Things 19
world instantiation. The next section describes aspects of the evaluation to test and
improve the design, as well as insights on the performance of the proposed artifact.
We conclude by outlining key features and limitations of the artifact, as well as impli-
cations and an outlook on future research.
2 Research Design
As our primary goal is to create a new artifact, we chose a design science research
approach. In this paper, the artifact, which we describe as business model framework
for the Internet of Things, is an approach for visualizing, envisioning and analyzing
complex business models in digital market environments. Our study mostly applies
the method suggested by Peffers [9] and includes six iterative activities. Table 1 pro-
vides an overview of how we applied the method in our research. The first column
outlines each activity A1 to A6. The second column provides details about applied
methods and evaluation per activity. The last column includes outcome and status.
Important is that each activity is linked with an appropriate evaluation method to
reach at the intended outcome, and less visible, that activity A1-6 rather iterative than
strictly subsequent. So we iterated in particular the prototyping and evaluation activi-
ties (A3) - the core activities of DSR - several times to continuously determine and
improve the performance of the progressing artifact [10]. After several completed
iterations we are approaching at the end of A3 to continue with a cross-industry busi-
ness model workshop as final proof-of-concept demonstration in A4. At this point we
see the artifact advanced to a level to share it with the wider scientific community.
The present paper describes the artifact prototype prior to the proof-of-concept activi-
ty (A4) of the research process.
3 Background
Applying a DSR approach, we build our artifact upon relevant, extant work [11],
which we find in three domains:
Information Systems (IS) research provides us with essential insights regarding the
nature of digital technology and digitized objects (3.1).
Service-dominant (S-D) logic as part of recent marketing research provides a valu-
able extract about new market dynamics in the light of increasing digitization (3.2).
Business Model (BM) research provides insights into useful building blocks by a
large number of previous modeling approaches for different purposes [6] (3.3).
We proceed with a compact outline of each knowledge source and extract the relevant
"bites" to inform the design of our business model artifact.
20 S. Turber et al.
Table 1. Application of DSR for developing the IoT business model artifact [9]
Outcome
A 1
Outlining the problem
situation
A 2
Analyzing extant research
for ideas and definition of
solution requirements
A 3
Prototyping solutions &
testing in practice
A 4
Proof-of-concept demon-
stration of the applicabil-
ity of the proposed
framework
A 5
Summary evaluatio n
A 6
Communication
Method/Stimulus:
Real-world BM workshops with companies revealed the di fficul-
ty to visualize, develop and analyze business models in IoT
driven business environments with extant BM approaches.
Evaluation:
BM workshops in various ind ustries, e. g. heating (5/13), home
security (6/13), smart lighting (6/13), mobility (8/13), industry
4.0 (8/13), smart city (11/13) etc.
Literature review, review with researchers (IS, Management
sciences), interview with practictioners (strategy, C-level)
Method:
Review of extant research at the intersection of man agement
sciences, marketing and information systems research
Review of extant busines s model approaches
Derivation of requirements from theory
Evaluation:
Cross-check w. experts and practictioners, test w. simple real-
world IoT-business model instances (Nest)
Method:
Prototyping by emp loying design principles [12] as interdiscipli -
nary research team (IS, Strategy Management et al)
Several times: T esting and revis iting prototypes of the new
artifact through 1. mult iple case stu dies (cases: B M of startups
and incu mbents in the I oT context, in th e smart home and
smart city context specifically. 2. Action research: Business
model work shops in IoT conte xt (sma rt city)
Evaluation:
As part of each t esting. Evaluation criteria equals the criteria in A5
Method:
Action research: Cross-industry BM workshop wit h several
companies , which are eco system partners, i .e. startups an d
incumbents in the overarching IoT context. Ideal: Wide range of
industries represented
Evaluation:
Equals evalua tion in A5
By expert and practictioner s
Method:
Semi-str uctured interviews wi th BM workshop partic ipants after
cross-i ndustry workshop (A 4).
Review with experts from re search and practice
Analysing
Evaluation:
Structured evaluat ion according to following sets of criteria
Set 1: to evalua te DSR process by Hevner’s Guide lines
Set 2: to evalua te DSR output (artifact)
Method:
Four levels of communication
Academic conferen ce / journal c ontributions (IS, Strat. Mgt)
Articles in pract ictioners outlet
Workshop concept to operationalize & apply the BM artifact in
firms
Evaluation:
Feedback of wider IS research and BM community
Feedback by practic e partners
Method & Evaluation Activity
Clear design objec-
tive: A “BM for IoT
context”
Justified research gap
of high r elevance
Preliminary assum p-
tions on artifact re-
quirements
Status: done
(see: 1 Intro)
Relevant research
streams identified, i.e.
(1) IS: Digitized objects
research; (2) BM re-
search; (3) S-D logic
Justified artifact
requirements
Status: done
(see: 3 Background)
Validated a rtifact
instances, in particular
in smart home and
smart city context
Status: done
(see: 4 Artifact)
Validated a rtifact
instance in the overall
IoT cont ext
Status:
planned in 2014
Field teste d, actionable
and just ified artifact ,
ready to use for and
research ers and prac-
tictioners.
Status:
planned in 2014
Peer reviewed p ublica-
tions
Status: ongoing
Designing Business Models in the Era of Internet of Things 21
3.1 IS: The Nature of Digitized Objects as Nucleus of Business Models in IoT
The Internet of Things, as stated, includes the universe of products and services,
which are enabled by digital technology. They are internet-connected and able to
directly communicate with each other [12]. According to Yoo et al [2] the incorpora-
tion of digital material causes physical objects to adopt all characteristics of digital
technology, i.e. e. they become programmable, addressable, sensible, communicable,
memorable, traceable, and associable. Yoo et al [3] further theorize that all digitized
objects feature a layered architecture, which includes four layers (Fig. 1): The device
layer comprises hardware, which can be any kind of devices, and an operating system
to control the hardware; the network layer involves both the logical transmission plus
network standards, and the physical transport; the service layer features direct interac-
tion with the users through application programs, e.g. as the user create or consume
content; the content layer hosts the data, such as texts, images or meta-data like geo-
time stamps.
A key feature in the context of IoT business models is, that these four modular lay-
ers of digitized objects can be de-coupled. This way the digitized object represents a
combination of elements across these layers, which are solely loosely interconnected
through specified interfaces. "De-couplebility" of content, devices and information
infrastructures allows multiple stakeholders to contribute across the four layers in an
unforeseen way - interoperability provided [13, 14]. In the final analysis, the layers
can be regarded as sources of value creation by multiple ecosystem partners [15, 16]
and lay the foundation of business models, which distributively exist in multiple sites.
For our artifact we adopt the four layers to naturally structure and organize value
creation across multiple partners in digital ecosystems. We regard this as the nucleus
of our business model framework in IoT.
3.2 Service-Dominant Logic Translates into Key Artifact Requirements
The increasing pervasiveness of digital technology is closely linked with the increas-
ing ability to separate service and information from physical goods [3]. This special
affordance of digital technology is a major reason for the emergence of new market
Fig. 1. The modular layered architecture of digital technology [5]
NETWORK LAYER
CONTENTS L AYER
SERVICE LAYER
Logical Transmission
Ph
y
sical Trans
p
ort
DEVICE LAYER
Logical Capability
Physical Machinery
22 S. Turber et al.
dynamics and complex webs of activities between market partners. In this line, the S-
D logic has evolved, as a new marketing paradigm seeking to describe the principles
of these transformations [8]. As the S-D logic describes a type of market environ-
ments, which we envision our business model framework to operate, the S-D logic
provides us with valuable input to define our artifact's requirements3.
A first important cornerstone of S-D logic is the network-centric view. The focus is
put on relationships between market partners and customers, which together build
"value creation networks". The single firm appears, in the first place, as "organizer of
value creation" [18]. In this light a firm's collaborative competence becomes a core
premise for competitive advantage [19]. For our artifact we state the first requirement:
R1: Provide a network-centric view to reflect multi-partner collaborations
Another distinctive aspect is the assumed role of the customer. While traditional
value creation models regard firms as the only value creators due to their production
and distribution activities, S-D logic ties in with the opposing literature stream, which
conceives the customer as an indispensible part in the value creation process: The
customer as co-creator and co-producer of value [20]. The traditional produc-
er/consumer divide becomes consequently obsolete [21]. The reason for customers –
and entities in general - to contribute to the value creation process differ [22]. For the
purpose of this study we classify the reasons as monetary and non-monetary benefits
and derive further requirements:
R2: Reflect customer's role as co-producer in the value network
R3: Reflect monetary as well as non-monetary reasons to collaborate
The concept of customer as co-creator leads also to a revised notion of offerings in
S-D logic, by which offerings are no longer conceived as output of a manufacturing
process. Instead, offerings are seen as input feeding into the value co-creation process,
or what Normann calls "artifacts designed to more effectively enable and organize
value co-production" [20]. Offerings can be composed of a variety of artifacts, such as
services or goods. In abstract terms, these artifacts represent "carriers" of certain
competences [20], and ideally serve all as "a service platform that enables service
exchange and value co-creation" [21]. In this light, physical products are conceived as
medium to provide service. The traditional distinction between goods and services is
finally transcended [21].
This view on artifacts features an important parallel with Yoo et al's layer model of
digital innovation (2.1): In S-D logic the "artifacts" serve as platform to create value
upon, which perfectly corresponds to the layer model, by which each single layer
serves as platform on which other actors can build modules in other layers [23] and
with each layer can be seen as source of value creation [conf. 15].
R4: Reflect layer architecture to structure sources of value creation
3 Normann's approach is here framed as part of the S-D logic stream for proven similarities
[20]
Designing Business Models in the Era of Internet of Things 23
The S-D logic offers a fresh view on resources: The fact that firms always co-
create value with the external environment implies that not only internal resources
shall be regarded as relevant – as the prevalent resource-based view suggests [24] -
yet also external resources that the firm can draw upon. Instead of an internal/external
categorization, S-D logic therefore classifies resources as "operant" or "operand". The
primacy is put on operant resources. They are dynamic and able to cause effects, such
as knowledge, skills and technologies, and usually intangible. Operant resources are
employed to act on other resources, while operand resources are acted on [21]. The
latter are static and tangible, and include raw materials and goods. [7, 21]. Finally, in
S-D logic a firm's external environment, its "ecosystem" of co-creating actors, is
therefore seen as operant resource and important source of competitive advantage. It
delivers the last requirement:
R5: Reflect ecosystem and value network partner as operant resource
To summarize: There is a need for a business model framework featuring five solu-
tion requirements R1-5, which can be derived from S-D logic (Table 2). These re-
quirements guide the building process of the artifact in A3. For the evaluation activi-
ties, the requirement serve as criteria the artifact has to meet.
S-D Logic (extract) Requirements (R) for the artifact:
Collaboration is essential R1: Network-centric, rather than firm-centric
Customer and partners are operant
resource and co
-
producer of value
R2: Reflects customer as co-producer, rather
than solel
y
receive
r
Incentives to participate in the ecosystem
can be monetary and non-monetary
R3: Take monetary and non-monetary benefit
from collaborating into account
Artifacts (=Yoo's "layers") are source
of value creation
R4: Reflect four layers of digital innovation as
source of value creation
Ecosystem is operant resource R5: Explicates all (potential) IoT ecosystem
participants of the external environment
3.3 Business Model Research Delivers the Main Building Blocks
So far literature does not provide a commonly acknowledged definition of "business
model" and what elements it consists of [2, 28]. In general terms, as stated, the con-
cept refers to the overarching logic of how a business works [4], or put differently,
represents "a holistic picture of the business by combining factors located inside and
outside the firm" [26]. A review of the extant literature by Mason [27] moreover has
yet revealed a shift over time: Initially, the business models were intended to describe
the roles of various network actors, especially in the narrow context of early internet
and e-commerce businesses. Among them, Timmers' approach might be the most
popular example [28]. As the business model concept became more widely applied
beyond the context of digital businesses, the network-centric perspective has largely
Table 2. S-D logic translated into requirements for the business model artifact
24 S. Turber et al.
given way to a firm-centric view conceiving business models as undivided "property
of the firm" [27]. Today, as digitization reaches all kinds of business and industries -
vividly illustrated by the "Nest" example (section 1), we intend to revitalize the net-
work centric view and tie in with early business model research [27, 28]. Not least
this parallels with the first solution requirement R1.
Moreover, we analyzed the extant business model approaches as of 1996 against
the identified set of solution requirements R1-5 as outlined in section 3.2. Our conclu-
sion is that none of the prior studies found met all criteria for mainly two reasons: The
approaches conceive business models as concept at firm level rather than network
level, or are meant to explicate business models on a generic level and so are not sup-
portive in capturing specifics of IoT ecosystems. As an exception can be seen the
approach by El Sawy et al [2], emphasizing the evolutionary dimension of digital
business models.
Despite the variety of business model approaches, it is noticeable that some conti-
nually recurring components exist although named differently [4, 26]: These essential
elements can be summarized by the following dimensions (Fig. 2): "Who" defines the
target customer to be addressed, "What" refers to the value proposition towards the
customer, "How" addresses the value chain needed to deliver the value proposition.
"Why" finally describes the underlying economic model to capture value. This basic
approach traces back to Peter Drucker [4] and builds the foundation of business model
research to this day [20]. For its archetypal character we elected this conceptualization
as starting point to build a specialized business model artifact upon.
4 Artifact
In this section, we describe our artifact, a business model framework for IoT contexts,
which we reached at after several iterations along the path of six activities as outlined
in section 2. In general our research has led to a network-centric, 3-D framework
consisting of three dimensions:
Who: Collaborating partners who build the value network
Where: Sources of value co-creation rooted in the layer model of digitized objects
Why: Benefits for partners from collaborating within the value network
Fig. 2. Archetypal Business Model [32]
How?
Why?
What?
Who?
Revenue
Model Value
Chain
Value
Proposition
Designing Business Models in the Era of Internet of Things 25
We explicate each dimension of the artifact including a short rationale and by re-
ferring to the requirements. We illustrate the dimension by the "Nest" case, as intro-
duced in section 1, which also serves as instantiation in the evaluation section.
4.1 Dimension "Who": Value Network of Collaborators
The first dimension "Who" encompasses all participants of an IoT ecosystem circling
around digitized products. This includes partners, customers and all remaining stake-
holders, which we refer to as "collaborators" in a wider sense and which are listed one
by one. They can be specified at the intended level of abstraction.
Rationale: The explicit itemizing of all participants reflects the service-dominant
logic's view that a company's external environment represents an "operant resource"
offering the inherent opportunity for each participant to co-create value with other
external participants as collaborators [19]. Moreover, customers are listed together
with other collaborators on a single dimension, which conveys the philosophy, that
value is always co-created with the customer, often even co-produced, especially in
the digital context [7]. A distinction between partners and customers reflected by
different dimensions was therefore redundant. Requirements considered: R1, R2, R5.
Instantiation "Nest": In the "Nest" case the collaborating partners, i.e. value creators,
are the following: (1) Nest Labs, a company which provides home owners with the
"Nest", i.e. a learning thermostat plus an app, to remotely control the device (2) The
"Nest" user, who contributes first in a monetary way by purchasing the "Nest" and
later by using it as "Nest" feedbacks real-time data about the user's heating habits to
Nest Labs (data layer). Nest Labs processes the data to customize the "Nest", i.e.
adjusts it to the user's habits, to increase the overall user experience. So far (1) and (2)
build a bilateral relationship. As Nest “owns” valuable data due to this relationship,
Fig. 3. Artifact: Framework design for a business model framework in the IoT context
Network
Collaborator 3
Collaborator 1
Collaborator 2
Device Service Contents Where
Who
Why
Monetary benefits
Non-monetary b enefits
26 S. Turber et al.
also other partners are interested to collaborate and enhance the value-creation net-
work: (3) Energy providers, who reward Nest users based on individual consumption
data (data layer). E.g. if users run their “Nest” in the "rush hour reward" mode, so that
the HVAC gets switched off during peak times. (4) Finally Google, who has recently
joined the ecosystem by acquiring Nest Labs. Google’s contribution is not clear at this
point. It is assumed they are enabled to offer new services by access to behavioral
data beyond the Web. In our artifact, all four collaborators are listed one by one on
the dimension "Who". Depending on the desired level of abstraction the collaborators
can be displayed abstract as "Nest users in California” and "Energy companies" or
more precisely, such as “Green Mountain Energy Ltd" and "Nest users in San Fran-
cisco, CA 94104”4
4.2 Dimension "Where": Sources of Value Creation
The dimension "Where" features the four-layered modular architecture of digitized
products, which includes the device, connectivity, services and contents layer (3.1).
Each layer represents a distinct source of opportunities for collaborators to contribute
to the value creation process [15, 16].
Rationale: We exposed the four-layer architecture in the artifact by an extra dimen-
sion, as the nucleus of business models in the IoT context (3.1). The layers naturally
structure the collaborators according to their kind of contribution in the value creating
process. Another benefit is, that the four layers are able to depict "co-opetition" as-
pects within the ecosystem landscape: Two players can be partners at one layer and
compete on another layer in the same ecosystem [5]. Requirements considered: R4
Instantiation "Nest": Along the four-layered structure, Nest Labs contributes on the
device layer with the "Nest" thermostat, on the service layer by providing the app as
interface to the "Nest" thermostat, and finally on the data layer by providing valuable
user data. The user contributes on the device layer by purchasing the "Nest", the con-
tent layer by feedbacking real time data. Concerning co-opetition: Playing on differ-
ent layers, Nest Labs and the energy provider are complimentary in the described
scenario. Would the energy provider come up with an own internet-connected ther-
mostat, they may still partner on the service layer, yet compete on the device layer.
4.3 Dimension "Why": Benefit for Collaborators
The dimension "Why" outlines each collaborator's "reason" to participate in the eco-
system. It is meant to depict all monetary as well as non-monetary benefits, which
attracts collaborators to participate in the ecosystem [19].
Rationale: We find it essential to not only depict one company's revenue model, which
"Why" is usually meant for (3.2), yet to consider all collaborators' benefits in a wider
sense from their participation in the ecosystem. The reason is, that the collaborators
4 In compliance with the prevailing privacy code of conduct.
Designing Business Models in the Era of Internet of Things 27
in sum build the external ecosystem, i.e. e. an essential "operant resource" [7]. In con-
sequence a healthy ecosystem features a competitive advantage, whose overall stability
depends on each collaborator's satisfaction. Moreover, as the customer is likewise re-
garded as collaborator, it is no longer necessary to feature a customer-specific value
proposition (in the traditional BM: "What", see 3.2), yet can be covered by the same
dimension, "Why", which outlines all benefits occurring in the ecosystem. These can
be monetary as well as non-monetary (fun, ethic reasons etc.) [19]. Requirements con-
sidered: R1, R3
Instantiation "Nest": Nest Labs derives first of all monetary benefits from being part
of the ecosystem, i.e. e. revenues by selling the "Nest" device and by selling meaning-
ful data. The "Nest" user's benefits from using "Nest" in the ecosystem context are
varied, and may include haptic benefits (pleasant temperature), ethical benefits (sav-
ing energy), economic benefits (saving money, getting rewarded) or psychic benefits
(benefits). The energy providers are attracted by the possibility to reduce the risk of
energy shortage by influencing customers' behavior by monetary incentives. Google
may benefit from new insights into consumers' behavior beyond the Web to leverage
its data analytics competences into the internet of things5.
5 Evaluation
In the first place, the new artifact should be useful and an effective solution to the
problem of depicting IoT-driven business models (cf. "goal" in table 3). To assess
whether we have reached at an artifact, which is equally rigor and relevant, we con-
ducted evaluations at two levels: We evaluated (a) the artifact as research output and
(b) the underlying research process. For the latter, we compared our overall DSR
study with Hevner et al's suggested guidelines for building and evaluating design
science research [29]. The following section outlines (a) the output evaluation, with
regard to the overall evaluation scheme applied as well as major findings.
As performance is closely related to the intended use, we specifically compared the
progressing artifact prototype with the initial goal, i.e. the effective depiction of IoT
business models. We operationalized our goal by two sets of criteria: Criteria set 1
analysis whether and to what extent the artifact features good model properties, in-
spired by March et al [10]. Criteria set 2 examines whether and how well the solution
requirements, we derived from S-D logic (section 3) are incorporated in the artifact.
To use appropriate methods for the evaluation of our framework artifact, we con-
sulted prior DSR work specifying the evaluation of models and frameworks [10, 29–
31]. We finally gathered a wealth of insights and evidence especially by using case
studies and action research, enriched by expert and practitioner evaluations operatio-
nalized by questionnaires. Table 3 summarizes the applied evaluation scheme. In the
following, we use first and foremost the "Nest" example (detailed in section 4) as
instantiation to representatively indicate evidence in a concise way.
5 http://www.wired.com/business/2014/01/googles-3-billion-nest-
buy-finally-make-internet-things-real-us/
28 S. Turber et al.
Table 3. Criteria and methods to evaluate the artifact's performance
Goal of our DSR study Criteria sets based on goal Methods for gathering evidence
An effective solution...
Set 1 Good model properties
M1: Fidelity with the real world
M2: Comp leteness (=R1-R6)
M3: Level of detail
M4: Robustness Interviews / expert evaluation
multiple case studies,
action research,
instantiation
...which is able to depict
business models in IoT envi-
ronments
Set 2 Justified solution requirements
R1: Network-ce ntric view
R2: Customer as co-producer
R3: (Non-) monetary reasons to participate
R4: Value creation across four laye rs
R5: Ecos ystem as operant reso urce
Concerning criteria set 2 we may refer to the elaboration on the dimensions includ-
ing the "Nest" case, which demonstrated that all solution requirements R1-5 are incor-
porated in the artifact. Concerning set 1: The criteria "Fidelity with the real world" is
seen reflected as the framework is able to describe the partner constellations and the
value creation logic of "Nest" and other analyzed ecosystems, despite its strong sim-
plification. The criteria "completeness" is inherent to the artifact by transitive relation:
The requirements R1-5, which are built in the artifact, reflect the central concepts of
the S-D logic. The S-D logic itself is recognized to comprehensively depict digital
market dynamics. Hence, we may argue R1-5 justifies completeness. Regarding the
criteria "level of detail" evaluation reveals that overall the artifact's dimension help to
depict the core of an IoT ecosystem without getting lost in details. Except for dimen-
sion "Why", which carries the benefits for each partner from participating: Here the
classification of "monetary" and "non-monetary" benefits helps to clarify on a generic
level why partner collaborate. A more fine-grained dimension involving metrics could
reveal further useful insights, such as the degree of partner's satisfaction and insights
on the ecosystem' overall stability. The artifact's "robustness" we see reflected by the
flexibility to work smoothly from several angles, e. g. in the Nest case, it is irrespec-
tive whether one looks at it from the energy provider's or Nest Labs' perspective.
Moreover, the framework is evaluated applicable across different IoT themes and
industries, e.g. to smart home, smart city and to any other IoT-driven context. In addi-
tion, what we learned as side effect from business model workshops with practitioners
is that a method or instruction is desirable to complement the artifact and facilitate
using it. A final proof of the value of the artifact is provided by a cross-industry busi-
ness model workshop and summary evaluation, which are both still to come.
6 Conclusion
Although many business model approaches exist, there is no actionable business
model framework to effectively depict business models in IoT ecosystems. We see
this gap in sharp contrast to the overall importance and omnipresence of the topic6
6 http://www.weforum.org/sessions/summary/new-digital-context
Designing Business Models in the Era of Internet of Things 29
and in essence, our research approach attempts to address this need. This section is
meant to summarize core features of the artifact and how it contributes to research
and practice. We outline limitations and give an outlook on future research.
The specific features of our business model framework can be seen in three diffe-
rentiating elements incorporated in the artifact: (a) IoT-driven market principles are
recognized by applying solution requirements rooted in S-D logic, (b) the sources of
value creation in IoT environments are recognized by applying the four layer model.
(c) the relevance of the external environment is recognized by strictly applying a net-
work-centric view. Another benefit can be seen in the applied design science research
method [9] ensuring that the artifact is closely linked with theory and practice.
Our project is currently approaching at the proof of concept demonstration in A4.
Several "prototype and test" iterations in A3 along defined criteria enabled us to de-
termine whether both good model properties and solution requirements are
represented in the artifact, and to refine accordingly. In a nutshell, we find the artifact
as is well performing in both regards for IoT business models across industries. How-
ever, we see some limitations concerning the criteria "level of detail": In the present
state the dimension "Why" allows only for a rough picture on each collaborators ben-
efit, which restricts the artifact to solely manual use. To serve as basis for a business
model software solution, as requested [6], the dimension needs to be further enhanced
e.g. by an underlying metric. Moreover, the artifact works well as tool to depict busi-
ness models in IoT, yet would benefit from a complementary method to facilitate its
application. Furthermore, we tested the artifact so far in ecosystems involving IoT.
We yet assume the artifact likewise applicable to digital ecosystems in general, which
is another area of future research.
Our DSR study at its completion represents a business model framework, which
contributes to both theory and practice: For theory, our work adds to the current busi-
ness model research in the emerging context of Internet of Things by providing a both
theoretically founded and field-tested business model framework. In this way re-
searchers can readily use the framework to for example analyze IoT business model
patterns in an efficient and structured way. Our paper also demonstrates, how DSR
can be applied for developing a framework at the interface of three different domains:
Strategic management, marketing and information systems. So far DSR has been
commonly employed in IS research [11], yet is rarely used in management sciences.
For practitioners the artifact serves as tool for depicting, analyzing and envisioning
business models in IoT. By making recent IoT-driven market dynamics and specifics
of digitized goods explicit, the artifact is able to decidedly support business model
development in complex IoT ecosystems. This is relevant, as without a clear view on
market dynamics and collaborative value creation logic, it is hard to create sustainable
IoT ecosystems and be a competitive part of it, which is the situation today for many
companies, with roots in manufacturing in particular. Not least, resulting instance
business models, specific to a certain IoT ecosystem, can be seen as mean of commu-
nication between current and future ecosystem partners.
30 S. Turber et al.
Acknowledgements. The present work is supported by the Bosch IoT Lab at St
Gallen University, Switzerland. An earlier version of this manuscript appeared in
the Proceedings of the 22nd European Conference on Information Systems. The
authors are grateful to the anonymous reviewers for thoughtful comments and helpful
suggestions.
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