Content uploaded by Alexia Athanasopoulou
Author content
All content in this area was uploaded by Alexia Athanasopoulou on Mar 23, 2019
Content may be subject to copyright.
Designing digital tooling for business model exploration
for the Internet-of-Things
Alexia Athanasopoulou1 Mark de Reuver1, and Timber Haaker2
1 Delft University of Technology, Delft, The Netherlands
{A.Athanasopoulou, G.A.deReuver}@tudelft.nl
2 Saxion University of Applied Sciences, Enschede, The Netherlands
t.i.haaker@saxion.nl
Abstract. As digital technologies are transforming enterprises, the interest in
business models is increasing. Technological disruptions like the Internet of
Things (IoT) drive enterprises to redefine their business models to create and
capture value, and eventually, to stay competitive. The need for business model
innovation may be urgent, yet it is not always clear what to change in a business
model. In these cases, business model exploration is needed. Within academia
and practice, business model tools are mainly focused on formalizing single
business model designs rather than facilitating systematic exploration of alter-
native business models. In this study, we present the design and prototype of a
digital tool created to facilitate business model exploration. We use Design Sci-
ence Research (DSR) as our research approach. In this paper, we present the re-
sults from the first cycle evaluation of the design and prototype.
Keywords: Business models, Design Science Research, Business model tool-
ing, Internet of Things, Business Model Exploration, Design, Prototype
1 Introduction
New digital technologies are radically changing enterprises [1]. As technologies affect
enterprises and the business environment is changing, enterprises need to reconsider,
reinvent and redesign their existing business models to stay competitive [2, 3].
Business models is an emerging topic in information systems (IS) (e.g., [8, 9, 10,
11, 12, 13]. More recently, studies focus on business model tooling [4], but the poten-
tial benefits of business model tooling are still understudied [7]. Existing tooling
mainly facilitates specifying and illustrating single business model designs (e.g. Busi-
ness Model Canvas), rather than supporting the exploration of alternative business
models in a structured way. Additionally, existing tooling is, in many cases, generic
without considering digital technology innovations and disruptions.
One solution to support enterprises with radical changes is to do business model
exploration. With business model exploration, enterprises can discover new business
model opportunities [4]. A systematic approach to business model exploration and
experimentation enables enterprises to get new business model ideas [5,6] creating
competitive advantage [7,2].
2
In many publications, the process towards designing a business model is presented
linearly. However, in practice business managers face the uncertainty of the evolving
markets [14]. During business model exploration entrepreneurs are engaged in an
iterative process where they create and test business models until they reach a revised,
alternated, and assumed viable business model [2]. Business model exploration can be
important when new opportunities require the rethinking of the business model. De-
fining business model exploration involves creating alternative business models, and
suggesting changes, [15], conceptualizing the changes, and hence conceptualizing the
business models, [2], and assessing what could happen under a range of different
decision choices and alternatives [16,52]. For this study, we focus on business model
exploration triggered by a technology disruption. More specifically, we focus on IoT
since this is a major technology innovation that has the potential to fundamentally
change business models.
Hence, the research question for this study is: what are the functionalities needed
of a digital tooling to support business model exploration for businesses that face
disruptive technology innovation? We use Design Science Research (DSR) as an
approach for designing, prototyping and evaluating the tool.
With this paper, we aim to contribute to the business model literature by designing
and testing tooling features as well as providing a step-by-step approach to the devel-
opment of new business model tooling. Furthermore, we aim at a practical contribu-
tion with the development of an easy to use tool with minimum complexity, and high
automation that supports enterprises with their business model exploration and inno-
vation process.
The paper is structured as follows. In section 2 we provide a background on busi-
ness models, business model innovation theory, as well as work related to IoT and
business model tooling. Section 3 provides a description of the Design Science Re-
search (DSR) approach we followed. In section 4 we discuss the design of the artifact
and the main functional design principles. Section 5 gives a short description of the
first version of the developed prototype. In section 6 we present the first cycle of the
evaluation and the results. In section 7 we conclude with the discussion, limitations
and our future steps.
2 Background
A business model is defined as the core logic of how an enterprise creates and cap-
tures value [17, 18]. Some researchers view business models as the reflection of the
strategy of an enterprise [19, 20, 21]. Magretta argues that ‘a good business model
remains essential to every successful organization, whether it’s a new venture or an
established player’ [23, p. 3]. Business models should change over time in response
to internal or external drivers [3].
One major external driver is a new technology. Bower and Christensen character-
ize a new technology as disruptive when it lacks refinement, has performance prob-
lems, disrupts an existing market or creates a new one, and eventually leads to new
products [45]. For instance, one major new technology is emerging is the Internet of
3
Things (IoT). ‘IoT is a network that connects uniquely identifiable things to the Inter-
net. The things have sensing/actuation and potential programmability capabilities’
[24]. IoT can transform enterprises in many ways such as to deliver innovation, im-
prove customer experiences [25], and increased cost efficiency, process agility [26]
and more accurate forecasting of stock situations [27]. While the IoT is spreading, the
traditional and well-known business model frameworks might not be in line with the
IoT needs. Rethinking of the value creation and capture will fundamentally change
the business models. However, research on IoT and business models is relatively
underdeveloped [28].
The existing literature on business model tooling is mainly focused on how to de-
sign and evaluate a business model (e.g. [29,30,54]), or how an enterprise can move
from an old to a new business model [31]. The existing tooling is available in differ-
ent formats, such as in book [29], physical cards [32], web-based app (e.g. [33]) and
mobile app (e.g. [34, 35]), printed cards (e.g. [36, 37]), computer-based (e.g. [38]) or
web-based (e.g. [39]). However, to the best of our knowledge, business model tools
designed for technology disruption are not widely available. More specific existing
business models do not take into consideration technology disruption as a separated
part of the business model design, (e.g. in business model CANVAS [29] technology
is not a separate building block). Even in cases that technology is one of the basic
building blocks (e.g. STOF model [11]) how business models can be affected by
technology disruption is not analyzed.
Sosna et al. argue that most business models have not ‘gone straight from the
drawing board into the implementation […] in reality new business models rarely
work the first time around since decision makers face difficulties in both exploratory
and implementation stages’ [2, p. 384]. However, tools for business design, testing,
and implementation are emerging, tools for systematic business model exploration are
lacking, especially in relation to disruptive technology innovations.
3 Design Science Research
For this study, we follow a DSR approach because it focuses on ‘producing and ap-
plying knowledge of tasks or situations in order to create effective artifacts’ [40 p.
253] In other words, DSR allows us to produce innovative artifacts as an answer to
unsolved problems [41, 42, 43]. For our study, we want to create an innovative arti-
fact that can contribute to the business model tooling literature. DSR allows the crea-
tion of an artifact as a solution for the gap within the literature and practice. There-
fore, we argue that DSR is the most appropriate approach to be followed for our
study. Figure 1 presents our DSR approach adapted by Gregor and Hevner [44]. Al-
terations on their approach are made based on the identified needs of the research.
The activities followed during this approach are presented below. This paper only
concerns and communicates the results of the first cycle iteration (design and proto-
type).
For the first activity (Background), we reviewed the literature of business model
ontologies, business model innovation, business model exploration and business mod-
4
el tooling. The purpose of this activity was to understand the main theories, and the
practical problem and to realize what a potential solution to the practical problem
could be.
Fig. 1. The DSR approach we followed for this study (adapted by [44]).
For the second activity (Method) we reviewed potential research approaches. We
realized that a potential solution to the lack of business model tooling for disruptive
technologies is to design and develop a digital-based artifact. Reviewing the literature
we identified some research methods that could be adopted such as the Action Design
Research (ADR), Soft Systems Methodology (SSM), and DSR. However, we con-
cluded that DSR is the most appropriate research approach for our study as it supports
the creation of a theory-based artifact that contributes to the theory.
For the third activity (Design) we focused on the design of the artifact. To do so we
took specific actions: Firstly, we used a Q-methodology approach (removed for re-
view process, under review) to identify perspectives regarding the business models
after a technology disruption. This study allowed us to understand how technology
disruption affects existing business models design and needs. We focused on the large
mobility ecosystem as we identify it as an industry with major IoT disruption where
many enterprises are affected Then, we did an action research where. We investigate
how business model exploration is facilitated by business model tooling and what are
the gaps in the current state-of-the-art tooling in supporting business model explora-
tion as part of the BMI process. Based on the obstacles we identify during this re-
search we derived specific recommendations. This recommendations shaped the de-
sign principles and later the development of the prototype.
In activity 4 (Development), we develop an interactive prototype. Initially a paper-
based navigation plan was designed. Then, we convert it to a software-based, clicka-
ble artifact.
For our fifth activity (Evaluation) we evaluate the developed prototype to draw
conclusions regarding the satisfactory or unsatisfactory functionality of the artifact
[43].
Method
Back-
ground
Design
Devel-
opment
Evalua-
tion
Discus-
sion
Communication
5
In the sixth activity (Discussion), we interpret the results.
For the seventh and last activity (Communication), we communicate some conclu-
sions, the contribution to the scientific community [41]. In this publication, we com-
municate the used research approach and the results from the first evaluation cycle of
the developed prototype. Feedback from the communication together with feedback
from the evaluation activity will be used in our future studies as inputs for the next
iteration and improvements of the study and the artifact.
4 Design
In this DSR activity, we focus on the creation of the design principles (DPx) we want
to test and that will inform the artifact development. (Section 5 Development). To
formulate the design propositions we follow the structure given by [55]: ‘Provide the
system with [material property—in terms of form and function] in order for users to
[activity of user/group of users—in terms of action], given that [boundary condi-
tions—user group’s characteristics or implementation settings]’ ([22], p. 4045).
Entrepreneurs are often not only unaware of the concept of business model but also
even that they have a business model. Even enterprises that invest in new ideas often
have little ability to do business model innovation incorporating these changes [47,
48]. Entrepreneurs that are unaware of business model concepts and ontologies are
likely to struggle to identify and revise their business models in a systematic way. At
the same time, recent studies have identified so-called patterns of business model
designs, which reoccur [53,54]. Hence, we suggest preloading a business model can-
vas sheet with common patterns as opposed to the common ‘fill-the-blank’ approach
of existing business model tools templates (e.g. business model canvas). Hence, the
first principle for designing our prototype is:
DP1: Using pre-filled business model templates facilitates the user’s understand-
ing of the components of the current business model given a specific business case.
Generating ideas on how to change different business model components is im-
portant for business model exploration [14, 49]. However, when there is technology
disruption, such as with the IoT, scholars argue that they do not have a holistic view
on which aspects of the business model will be affected [50]. We argue that a solution
that solves that problem is the use of solution based business model patterns. Thus,
the second design principle on which we based we based the design for the prototype
is:
DP2: Using templates with solution-based patterns improves idea-generation on
how to change different components of the current business model, given a specific
technology disruption.
Making a decision about whether adapting the components in the business model is
a prerequisite to business model implementation. A deliberate evaluation of intended
business model changes helps to avoid investments in unfruitful paths [46]. However,
existing business model tooling focuses mainly on designing or re-designing new
business models but they do not offer opportunities for assessing the changes before
they get implemented [51]. Studies suggest that a way to do this is by using
6
assessment methods (e.g. critical success factors, key performance indicators) to
assess the feasibility of the generated ideas and potential changes [38, 51, 10].
However, busi-ness model assessment tooling is not widely available. Such
assessment requires ‘a clear and structured description of the business model’ [38].
That might not be possi-ble in the case of the reshape of a business model due to a
digital disruption. We ar-gue that assessing and understanding the feasibility of a
business model change, is a prerequisite to decision-making about business model
adaptation and thus, to consider when doing business model exploration. Hence, the
third design principle for the prototype is:
DP3: Using a tool with assessment features improves user’s decision making about
whether to adapt components in the business model, given that the users have already
identified potential changes on the current business model.
While our contribution is focused on the three design principles, we need to have
non-functional requirements too in order to have a user-friendly prototype that can be
evaluated. In our previous study, we identify eight non-functional characteristics that
business model tooling should have (i.e., have a structure, stimulate the user, be
adaptive to the abilities of the users, have low barriers, be simple, provide specific
results, be enjoyable, and easy interface [4].
The three developed principles together with the non-functional requirements
allowed as getting an understanding on how the artifact should be. In the next activity
we present the prototype as it was derived from the above principles and
functionalities.
5 Development
For the development of the prototype, we used the commonly used program Microsoft
Excel. We choose this program as it allows us to facilitate and implement the design
principles described in section 4 (Design). We created the prototype based on the
identified design principles and the functional and not functional requirements in a
three-step approach. Each of the steps reflects one of the design propositions we de-
scribed in the design section. That allows us to apply and test the design principles
independently from each other. More specific the three steps are:
Step 1: The creation of a description of components of the existing business model;
Step 2: The exploration and identification of IoT opportunities and potential changes
that might contribute towards an IoT business model;
Step 3: Assessment of the changes defined in the previous step. In that step, users,
based on critical factors [10] assess the changes. The outcome of this step is a list with
the selected changes, which, according to the user, might have a positive, negative or
unknown contribution to their business.
The tool contains three parts, each of which reflects one of the design principles from
Section 4. Figure 2 presents a screenshot from the developed prototype. Due to the
page limitations a clickable prototype of the tool can be accessed via
https://invis.io/VFGQMD0GHZC.
7
Fig. 2. Screenshot of the developed prototype. The screenshot presents the first step of the
process (description of the existing business model).
6 First Cycle evaluation and Discussion
In this paper, we present the first cycle evaluation (figure 3) approach and the results.
For the evaluation we collect data from interviews, short questionnaires, and pre- and
post use surveys.
6.1 Evaluation approach
Initially, we asked software developers to alpha test the prototype (1). With the alpha
testing, we wanted to identify any major or minor technical issue. The alpha testers
used and tested the prototype, and then fill out an online accessible questionnaire. We
asked questions regarding major and minor mistakes (e.g. bugs), time estimation, and
the response of the tool in different actions. We immediately implement their feed-
back. Then, we pilot test the evaluation approach with junior researchers. The pilots
provide feedback for the improvement of the business model tooling and the evalua-
tion process (2).
For the beta testing, we asked the opinion of four consultants experienced with
business models and technology disruption (we refer to them as beta testers) (3). The
researchers presented the prototype to the beta testers (physical or online). During the
discussion, the beta testers provided their comments.
8
Fig. 3. Overview of first cycle evaluation outline.
As the next step, we did workshops (between November 2017 and January 2018) with
23 master level students with an entrepreneurship interest as participants to use the
prototype and provide comments (4). For the purposes of these workshops, we used a
specific business scenario (the case of a car-renting company in the IoT era) in order
to illustrate as much as possible a real case that the business model could be used and
have some impact. Also, the use of a specific case allowed us to increase the validity
of our evaluation because all the participants will follow the same tasks for one spe-
cific situation.
The participants of the workshops used the prototype for this specific case and
filled out a pre- and post- questionnaire including questions regarding both the func-
tional and non-functional requirements. For ethical reasons, we asked facilitators,
uninvolved with this research, to be part of the workshops as facilitators of the work-
shops. The facilitators provided some observations from the workshops that were
used for the validity of the evaluation and for future improvements.
Finally, we analyzed the collected data (5) from the previous steps for the first cy-
cle evaluation. The results will be used as inputs for next iterations. Figure 3 presents
the steps of our evaluation while Table 1 summarizes the data collection approach.
Table 1. Details of the data collection for the first cycle evaluation of the prototype.
Actions
Informants
Duration and
Location
Focus of the feedback
Data collection
methods
Alpha test-
ing (1)
Software de-
velopers
~30΄; Online
Technical requirements
Shot question-
naire
(2)
Pilots
(1) Alpha testing
(3) Beta test-
ing
(5) First
cycle evalu-
ation results
Prototype
(4) Workshop
Improvements for next cycle
Improvements
Results are presented in this paper
Internal evaluation only
Next cycle evaluation
9
Internal pilot
testing (2)
PhD research-
ers
~1h, workshop
setting (the
results from this
workshop were
used only for
piloting the
evaluation ap-
proach)
Informal testing of the
prototype and the meth-
odology for improve-
ments
Internal use only
Beta testing
(3)
Technology
consultant, and
business con-
sultants
~30΄; Online
(via Skype); face
to face meeting
Principles, Functional and
non-functional require-
ments
Interviews
Workshop
(4)
Students with
entrepreneur-
ship focus
~1.5h (Lab
setting)
Use of the tool in practice
Post- and Pre-
questionnaire,
observations
6.2 Findings from the first cycle evaluation
The DSR actions described above allow us to collect informative feedback for the
developed prototype. This section discusses the results of the first cycle evaluation.
We firstly discuss the main comments we received from all the informants and then
we explicitly focus on findings regarding the three design principles. We analyzed the
data collected from the beta testing and the workshops.
Beta testing
All the beta testers acknowledged that the tool will be useful and that the pre-filled
option is an interesting feature. Three beta testers suggested that at the next version of
the tool, and more specific the second step if the IoT transformation needs to include
more options regarding the disruption in order to support the users with the business
model transformation, and offer more value to the users and support them more with
their next actions regarding their business model exploration.
It is interesting that all the beta testers extensively commented on the last step (as-
sessment of the changes). They argued that this step requires a lot of time and the
users might not find that appealing. Additionally, they argued that we cannot use the
same Key Performance Indicators (KPI) for each potential change. Finally, three of
the beta testers suggested that the tool needs to provide a final recommendation to the
users in the form of a prioritization list of the order the identified changes should be
implemented. One of the beta testers argues that this will contribute more on the us-
ers’ decision making because they will have some concrete actions to do in the near
future. All the beta users argued that the tool needs to be more automated (e.g., to
give suggestions, to prioritize without requesting the users to do it on their own etc.).
One of the beta testers point out that the open-type answers should be eliminated be-
cause users prefer to brainstorm and decide upon specific recommendations. The
dynamic nature of IoT requires constant updates on the functionalities of the business
model tooling.
10
Regarding the non-functional requirements, the layouts, color codes were well re-
ceived but some improvements can be made, as the informants got confused in some
situations. Two of the beta testers argued that the illustration of the existing business
model (step 1 of the tool) should be always visible so the users can identify potential
changes to their existing business model.
Workshops
For the first cycle evaluation, we did three workshops with master level students with
an entrepreneurship focus in their studies. Regarding the outcomes from the work-
shops, a common feedback we received from the majority of the participants was that
a clear description of the purpose of the business model tool was needed in order to be
able to work with it. Additionally, some of the participants find that the third step
(where they had to assess the potential changes) was too demanding and they ex-
pected that the tool would require fewer inputs from them.
Finally, we analyzed the observations of the facilitators. From the observations, it was
clear that the participants were engaged in the process and the use of the tools. How-
ever, the observers noticed that the informants had some issues with the understand-
ing of the wording, the process was time-consuming and the layout of the prototype
could be improved.
Regarding the non-functional requirements, some of the participants mentioned that
some of the used words and definitions in the excel sheets (i.e. the prototype) were
difficult to understand.
Findings regarding the design principles
While this paper presents the first cycle evaluation iteration, it was of our interest to
identify if the findings indicate that, in some extent, the design principles (discussed
in section 4) that informed our prototype, contribute to the business model explora-
tion. The alpha testers did not focus on the functionalities of the prototype and hence,
the findings from this activity do not provide any indication regarding the design
principles. Table 2 summarizes findings from the beta users, both supporting the
choices regarding the design of the prototype, and points of improvements (i.e. find-
ings, suggestions).
Table 2. Initial findings and suggestions form the informants regarding the design principles
(DPx) from the first cycle evaluation.
DP1: Using pre-filled
business model tem-
plates facilitates the
user’s understanding
of the components of
the current business
model.
DP2: Using templates with
solution-based patterns im-
proves idea-generation on
how to change different com-
ponents of the current busi-
ness model.
DP3: Using a tool with
assessment features im-
proves user’s decision
making about whether to
adapt components in the
business model
Findings
‘Pre-filled is a perfect
‘What I don’t like: you put
‘If it is designed well then it
11
idea’ (beta tester 1).
The pre-filled option
is interesting as it is
not a common feature
on the design of exist-
ing business model
tooling’ (beta tester 2).
the user to think the transfor-
mation. This is the job of a
consultant Also, I am not sure
if they will understand that
you give them inspiration and
not a specific solution. How-
ever, they will get inspired’
(beta tester 1).
can really help the users to
make decisions’ (beta tester
3).
Sugges-
tions for
improve
prove-
ments
‘Where are questions
regarding the costs?’
(beta tester 3 and 4).
‘Support them by making
questions. Provide an over-
view, for example: Do you
think that it will be profita-
ble?’ (beta tester 4).
Prioritization of the as-
sessed changes is needed so
users can make decisions
(overall comment).
For this evaluation cycle, we collected data from 23 participants. While 23 partici-
pants are not statistically significant, the collected data allowed us to make some ini-
tial conclusions.
Table 3. Quantitative findings from workshops (N=23).
Design
Principles
Statements
Test statistics
DP1
I have a solid interpretation of what the business model
components are.
t(16)=2.432, p<.05
I am able to apply my knowledge on business model on a
new context/case/industry.
t(16)=2.281, p<.05
DP2
I am able to generate qualitative ideas on how business
models components can be changed.
t(15)=2.112, p<.05
I am able to estimate how unexpected my generated ideas
are.
t(15)=2.449, p<.05
DP3
When it comes to a decision regarding a business model
change I prefer to keep everything as it is.
t(15)=0.557 p<.05
In the workshops, we collected quantitative data to evaluate the impact of the proto-
type on business model exploration. We did so by asking the participants to fill out
the same questionnaire before and after the use of the prototype. The questionnaires
were divided into three sections, each containing statements related to one of the three
design principles. Then, we ran paired t-tests to measure differences before and after
using the prototype. Out of the 17 pairs of statements, five were significantly in-
creased (p< .05), see Table 3.
7 Conclusion
In this paper, we presented the design and development of a digital tooling for busi-
ness model exploration for the IoT. During the design, we developed three design
principles that established the requirements of the tooling. The prototype served as an
instrument to evaluate which of these principles contributes to the business model
12
exploration process. Various informants evaluate the prototype of the digital tooling
prototype. Overall, the informants appreciated the functionalities of the tooling and
had a positive feedback regarding the design principles. On the other hand, they point
out improvements for a future version of the tooling. The quantitative support to an
extent that the developed prototype improves the business model exploration.
The limitation of this study is related to the participants and the size sample. For
this paper, participants were 23 Master level students. While the participants provided
us with fruitful insights, in the future we plan to revise the prototype based on the
feedback we received and replicate the workshops (following an experimental design)
to a larger scale and ground our findings from a larger and more representative group
of informants (i.e. practitioners).
This study contributes to the business model innovation theory with the three de-
veloped design principles, with the overall aim to contribute to the business model
exploration process. Additionally, this study contributes in the form of an artifact (i.e.,
the prototype) as an improved solution for a specific problem (business model explo-
ration) [44]. In practice, our study contributes to the development of an easy to use
tool with minimum complexity, and high automation that supports enterprises with
their business model exploration and innovation process.
The results of this paper in addition to future studies can improve the functionali-
ties of the artifact and it can deliver an updated version of the artifact. This paper is
focusing on the IoT as an instance of disrupting technology. However, future studies
could examine if the proposed design knowledge is applicable for other kinds of dis-
rupting technology. Additionally, future studies could test the business model tooling
in different industries (e.g. automotive, healthcare). These studies could contribute to
the understating if the design knowledge suggested in this design knowledge is gener-
alizable.
Acknowledgments
This work is part of Envision project. Envision has received funding from the Euro-
pean Union’s Horizon 2020 research and innovation program under grant agreement
645791. We also thank all the informants for their feedback, and the facilitators for
their contribution.
References
1. Bharadwaj, A., El Sawy, O. A., Pavlou, P. A., Venkatraman V.N. Digital business strate-
gy: towards a next generation of insights. MIS Quartely 37(2), 471–482. (2013).
2. Sosna, M., Trevinyo-Rodríguez, R. N., Velamuri, S.R. Business Model Innovation through
Trial-and-Error Learning: The Nature house Case. Long Range Planning, 43 (2), 383–407
(2010). http://doi.org/https://doi.org/10.1016/j.lrp.2010.02.003.
3. De Reuver, M., Bouwman, H., Maclnnes, I. Business model dynamics: a case survey.
Journal of Theoretical and Applied Electronic Commerce Research 4 (1), 1–11 (2009).
doi.org/10.4067/S0718-18762009000100002.
13
4. De Reuver, M., Athanasopoulou, A., Roelfsema, M., Riedl, A. Designing an ICT tooling
platform to support SME business model innovation : Results of a first design cycle. In:
Proceedings of BLED 2016. 7, (2016).
5. Baden-Fuller, C., Morgan, M. S. Business models as models. Long Range Planning 43(2–
3), 156–171 (2010). doi.org/10.1016/j.lrp.2010.02.005.
6. McGrath, R. G. Business models: A discovery driven approach. Long Range Planning,
43(2–3), 247–261 (2010). http://doi.org/10.1016/j.lrp.2009.07.005.
7. Eppler, M. J., Hoffmann, F., Bresciani, S. New Business Models Through Collaborative
Idea Generation. International Journal of Innovation Management 15 (6), 1323–1341
(2011). doi.org/10.1142/S1363919611003751.
8. Cosenz, F., Noto, G. A dynamic business modelling approach to design and experiment
new business venture strategies. Long Range Planning In press. (2017)
doi.org/10.1016/j.lrp.2017.07.001.
9. Roelens, B., Poels, G. The Development and Experimental Evaluation of a Focused Busi-
ness Model Representation. Business & Information Systems Engineering 57 (1), 61–71.
doi.org/10.1007/s12599-014-0363-z (2015).
10. Bouwman, H., De Vos, H., Haaker, T. Mobile service innovation and business models.
Springer-Verlag Berlin Heidelberg (2008). doi.org/10.1007/978-3-540-79238-3.
11. Fritscher, B., Pigneur, Y. Computer Aided Business Model Design: Analysis of Key Fea-
tures Adopted by Users. In: Proceedings of the 47th Hawaii International Conference on
System Sciences. 3929–3938 (2014). doi.org/10.1109/HICSS.2014.487.
12. Kyriazis, D., Varvarigou, T. Smart, autonomous and reliable Internet of Things. In Proce-
dia Computer Science 21, 442–448 (2013). doi.org/10.1016/j.procs.2013.09.059.
13. El Sawy, O., Pereira, F. Business Modelling in the Dynamic Digital Space - An Ecosystem
Approach. Springer Briefs Series in Digital Spaces 68 (2013). doi.org/10.1007/978-3-642-
31765-1.
14. Berends, H., Smits, A., Reymen, I., Podoynitsyna, K. Learning while (re-) configuring:
Business model innovation processes in established firms. Strategic Organization (1)
(2016). doi.org/10.5465/AMBPP.2013.15477abstract.
15. Cavalcante, S., Kesting, P., Ulhøi, J. Business model dynamics and innovation:
(re)establishing the missing linkages. Management Decision, 49(8), 1327–1342, (2011).
doi.org/10.1108/00251741111163142.
16. Bisbe, J., Malagueño, R. How Control Systems Influence Product Innovation Processes:
Examining the Role of Entrepreneurial Orientation. Accounting and Business Research 44
(0), 1–46 (2012).
17. Kallio, J., Tinnilä, M., Tseng, A. An international comparison of operator-driven business
models. Business Process Management Journal 12 (3), 281, (2006).
doi.org/10.1108/14637150610667962.
18. Linder, J., Cantrell, S. Changing Business Models : Surveying the Landscape. Accenture
Institute for Strategic Change, 1–15 (2000). doi.org/10.4018/978-1-59904-939-7.
19. Solaimani, S., Bouwman, H. A framework for the alignment of business model and busi-
ness processes: A generic model for trans-sector innovation (2012).
20. Casadesus-Masanell, R., Ricart, J. E. From strategy to business models and onto tactics.
Long Range Planning 43(2–3), 195–215 (2010). doi.org/10.1016/j.lrp.2010.01.004.
21. Leem, C. S., Suh, H. S., Kim, D. S. A classification of mobile business models and its ap-
plications. Industrial Management and Data Systems, 104 (1), 78–87 (2004).
doi.org/10.1108/02635570410514115.
14
22. Chandra, L., Seidel, S, and Gregor, S, (2015). Prescriptive Knowledge in IS Research:
Conceptualizing Design Principles in Terms of Materiality, Action, and Boundary Condi-
tions, H, USA.
23. Magretta, J. Why business models matter. URL: https://hbr.org/2002/05/why-business-
models-matter (2002) (visited on 25 /11/2017).
24. Minerva, R., Biru, A., Rotondi, D. Towards a definition of the Internet of Things (IoT).
Technical report, Politecnico di Torino (2–86), (2015).
25. Brody, P., Pureswaran, V. The next digital gold rush: how the internet of things will create
liquid, transparent markets. Strategy & Leadership Vol. 43 (1), 36-41 (2015).
doi.org/10.1108/SL-11-2014-0094.
26. Uckelmann, D., Harrison, M., Michahelles, F. Architecting the Internet of Things. Archi-
tecting the Internet of Things. Springer. doi.org/10.1007/978-3-642-19157-2. (2011).
27. Geerts, G. L., O'Leary, D. E. A Supply Chain of Things: The EAGLET Ontology for
Highly Visible Supply Chains. Decision Support Systems 63 (1), 3–22 (2014).
28. Ju, J., Kim, M., Ahn, J. Prototyping Business Models for IoT Service. Procedia - Procedia
Computer Science 91, 882–890 (2016). http://doi.org/10.1016/j.procs.2016.07.106.
29. Osterwalder, A., Pigneur, Y. Business Model Generation: A Handbook for Visionaries,
Game Changers, and Challengers. A handbook for visionaries, game changers, and chal-
lengers. John Wiley en Sons Ltd, (2010). doi.org/10.1523/jneurosci.0307-10.2010.
30. Bouwman, H., De Vos, H., Haaker, T., Faber, E., Fielt, E., Haaker, T., De Reuver, M.
STOF Model: Critical Design Issues and Critical Success Factors. Mobile Service Innova-
tion and Business Models. In: Bouwman, H. De Vos, H. and T. Haaker ed Mobile service
innovation and business models 71-88 (2008), 81–88. doi.org/10.1007/978-3-540-79238-3.
31. De Reuver, M., Bouwman, H., Haaker, T. Business Model Roadmapping: a Practical Ap-
proach To Come From an Existing To a Desired Business Model. International Journal of
Innovation Management 17 (1). 1340006, (2013). doi.org/10.1142/S1363919613400069.
32. Foresight cards URL: Available at https://foresightcards.com/workshops/business-models/
(visited on 04 /04/2018).
33. Leenstack. URL: https://leanstack.com/ (visited on 04 /04/2018).
34. Business Model Canvas app. URL:
https://play.google.com/store/apps/details?id=com.thirdmobile.modelcanvas&hl=en.
35. SWOT Analysis app. URL:
https://play.google.com/store/apps/details?id=com.v1_4.learning4managersswotanalysis.c
om&hl=en (visited on 04 /04/2018).
36. Gassmann, O., Frankenberger, K., Csik, M. The St. Gallen Business Model Navigator.
Working paper. University of St. Gallen. (2014).
37. Business Innovation kit. URL: http://www.uxberlin.com/businessinnovationkit/ (visited on
04 /04/2018).
38. Foresight cards URL: Available at https://foresightcards.com/workshops/business-models/
(visited on 04 /04/2018).
39. E3 value. URL: https://www.e3value.com/ (visited on 04 /04/2018).
40. March, S.T, Smith, G.F., Design and natural science research on information technology,
Decision Support Systems, 15 (4), 251-266, (1995). https://doi.org/10.1016/0167-
9236(94)00041-2.
41. Hevner, A. R., March, S. T., Park, J., Ram, S. Design Science in Information. MIS Quar-
terly, 28(1), 75–105, (2004).
42. Peffers, K., Tuunanen, T., Rothenberger, M. A., Chatterjee, S. A Design Science Research
Methodology for Information Systems Research. Journal of Management Information Sys-
tems 24 (3), 45–77 (2007). doi.org/10.2307/40398896.
15
43. Verschuren, P., Hartog, R. Evaluation in design-oriented research. Quality and Quantity,
39 (6), 733–762 (2005), doi.org/10.1007/s11135-005-3150-6.
44. Gregor, S., Hevner, A. R. Positioning Design Science Research for Maximum Impact.
Management Information Systems Quarterly, 37, 337–355 (2013).
45. Bower, J. L., Christensen, C. M. Disruptive Technologies: Catching the Wave, (1995).
URL: https://hbr.org/1995/01/disruptive-technologies-catching-the-wave (visited on 04
/04/2018).
46. Christensen, C. M., Bartman, T., Van Bever, D. (2016). The hard truth about business
model innovation. MIT Sloan Management Review, 58(1), 31.
47. Chesbrough, H. Business model innovation: Opportunities and barriers. Long Range Plan-
ning 43 (2–3), 354–363 (2010). doi.org/10.1016/j.lrp.2009.07.010.
48. Demil, B., Lecocq, X. Business model evolution: In search of dynamic consistency. Long
Range Planning 43 (2–3), 227–246 (2010). doi.org/10.1016/j.lrp.2010.02.004.
49. Achtenhagen, L., Melin, L., Naldi, L. Dynamics of Business Models – Strategizing, Criti-
cal Capabilities and Activities for Sustained Value Creation. Long Range Planning (46)
(2013). doi.org/10.1016/j.lrp.2013.04.002.
50. Krotov, V. The Internet of Things and new business opportunities. Business Horizons 60
(6), 831–841 (2017). doi.org/https://doi.org/10.1016/j.bushor.2017.07.009.
51. Heikkilä, M., Bouwman, H., Heikkilä, J., Solaimani, S., Janssen, W. Business model met-
rics: an open repository. Information Systems and E-Business Management, 14(2), 337–
366 (2016). doi.org/10.1007/s10257-015-0286-3.
52. Sterman, J. Systems Thinking and Modeling for a Complex World. McGraw-Hill Higher
Education (2000).
53. Gassmann, O., Frankenberger, K., Csik, M. The St. Gallen Business Model Navigator.
Working paper. University of St. Gallen (2014).
54. Remane, G., Nickerson, R., Hanelt, A., Tesch, J., Kolbe, L. A taxonomy of carsharing
business models In: Proceedings of 37th International Conference on Information Systems
(ICIS 2016) (2016).
