=Paper=
{{Paper
|id=Vol-2574/short12
|storemode=property
|title=Extending e3tools to Assess Adoption Chain and Co-Innovation Risks (short paper)
|pdfUrl=https://ceur-ws.org/Vol-2574/short12.pdf
|volume=Vol-2574
|authors=Alejandro Arreola González,Jens Wittenzellner,Helmut Krcmar
|dblpUrl=https://dblp.org/rec/conf/vmbo/GonzalezWK20
}}
==Extending e3tools to Assess Adoption Chain and Co-Innovation Risks (short paper)==
https://ceur-ws.org/Vol-2574/short12.pdf
Extending e3tools to Assess Adoption Chain and Co-
Innovation Risks
Alejandro Arreola González1, Jens Wittenzellner2 and Helmut Krcmar3
1 Business Model and Service Engineering, fortiss GmbH, Munich, Germany
gonzalez@fortiss.org
2 Technical University of Munich, Munich, Germany
jens.wittenzellner@tum.de
3 Chair of Information Systems, Technical University of Munich, Munich, Germany
helmut.krcmar@tum.de
Abstract. Digital platforms form ecosystems enabling value co-creation and cre-
ating structures of interdependence. The success of innovations in such ecosys-
tems can largely depend on adoption chains, or on co-innovation. Theory sug-
gests that the assessment of these ecosystem risks increases the odds of success
in such cases. We present an extension to support the assessment of adoption
chain and co-innovation risks using the value modelling tool e3tools. We demon-
strate the implementation of ecosystem risk logics and a dashboard using exam-
ples from literature.
Keywords: Ecosystem Risk; Value Modelling; Tool; Risk Assessment
1 Problem Identification and Motivation
The ecosystems that form around digital platforms often determine their value creation
and innovation [1]. A good design of a platform’s business model should be explicit in
how it approaches the risks that ecosystem actors deviate from envisioned roles and
positions. When innovations depend on other actors, a focal firm’s (i.e. platform oper-
ator) strategic approach to ecosystem risks will increase the odds of success [2]. Sup-
porting the assessment of the risks that (1) partners cannot co-innovate, and that (2)
partners do not adopt an innovation can lead to better platform designs.
One framework available for the analysis of value co-creation in ecosystems is
e3value [3]. Researchers have so far discussed, further developed and extended the
framework in tens of scientific papers. Within this framework, an open source software
tool called e3tools [4] is available offering graphical value modelling and supporting
the explorative analysis of value co-creation and ecosystem design. Among other qual-
ities, the tool allows the modelling of interdependence structures, the simulation of
value exchanges between different actors and automated net cash flow analysis. Fur-
ther, e3tools supports fraud risk and revenue sensitivity analyses. However, software
tool support for ecosystem risk analysis is not available [5, 6].
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Copyright © 2020 for this paper by its authors. Use permitted under Creative Commons License
Attribution 4.0 International (CC BY 4.0).
In this paper, we present extensions to the value modelling tool e3tools. The pro-
posed enhancements support the assessment of co-innovation and adoption chain risks
(i.e., ecosystem risks) on value models. In addition, we show how a dashboard could
summarize information about the impact of these ecosystem risks and support decision
making. We use theoretical examples [7] to show that the extension has the required
effects.
2 Methodology
In platform ecosystems, value propositions largely depend on ecosystem partners as-
suming positions and roles envisioned by the platform provider. In such settings, eco-
system risk can threaten the success of innovations. Software tool support could be
useful to assess ecosystem risks when designing platform value models, and could thus
increase the odds of success. This work aims at contributing with an artefact using the
design science research (DSR) methodology of [8] as summarized in Table 1.
Table 1. This work’s DSR activities following [8] (source: own research)
Problem identifi- The failure to assess adoption chain and co-innovation risks threatens
cation and moti- the success of platforms. Assessing these ecosystem risks refers to a
vation class of value modelling problems for assessing business risks. It is
classified as a semi-quantitative risk assessment approach.
Definition of so- A value modelling tool extension is required to assess these ecosystem
lution objectives risks. The tool extension should enable the assessment of co-innovation
and adoption chain risks based on a value model.
Design and de- A class of solution extension is designed to enable the assessment of
velopment ecosystem risks. The class of extension is instantiated in e3tools to sup-
port the assessment of co-innovation and adoption chain risks.
Demonstration Examples from literature are used to show that the tool extension mod-
els the impact of these risks as proposed in theory.
Contribution The main contributions are the description of a class of value modelling
tool extensions for modelling ecosystem risks and an implemented ex-
tension for assessing co-innovation and adoption chain risks.
3 Definition of Solution Objectives
3.1 Representing the Logic of Ecosystem Risks
Adoption chain risks are related to the partners’ willingness to undertake the activities
required for a value proposition, raising questions of priorities and incentives for par-
ticipation [2]. An adoption chain is the path of a product or service from scratch to the
end consumer. This path is critical when the success of an innovation depends on spe-
cific ecosystem structures. Ecosystem partners only co-create if they are rewarded with
an appropriate value. The extension must be able to represent the logic of minimums
embedded in adoption chain risks [7]. If an actor is worse off with an innovation (i.e.
the actor ha as deficit), the adoption chain should be broken.
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Co-innovation risk is defined as the challenge partners face in developing the ability
to undertake the new activities that underlie their planned contributions [2]. Co-inno-
vation risks depend on the joint probability that each ecosystem partner involved will
be able to deliver on their innovation commitments within a specific time frame [7].
Accordingly, the extension must be able to represent the logic of multiplications em-
bedded in co-innovation risks [7]. The probabilities of success of all the ecosystem
partners along a dependency path should be multiplied in order to estimate the chances
of joint success. This requires probabilities to be propagated throughout a dependency
path.
The e3value modelling element AND is needed in case the partners need to work
together to realise a service which satisfies the customer need [9]. The OR element is
needed if an actor can decide which offer he will choose, for example, if two actors
provide the same product and the actor takes the one with the better conditions [9]. The
AND and OR elements also have two different variants of how they are used in a model.
A fork is used when a path is split into several paths. After a fork, the following paths
are dependent on this one element. A join is used when several paths merge into one.
A path is dependent on the previous incoming connections [9]. Accordingly, modifica-
tions to four different variants are needed: the OR-join, OR-fork, AND-join and the
AND-fork.
3.2 Dashboard
Dashboards are useful to manage growing complexity [10], which characterizes digital
platform ecosystems [11]. Dashboards can be used to analyse current states and possi-
ble future scenarios as well as support the managers in decision making [10]. Charts
and colours are helpful to explain factual connections much faster and to highlight es-
sential facts [12]. A dashboard should provide an overview of the most important as-
pects of an ecosystem that are required to assess and manage ecosystem risks. It should
also provide an insight into ecosystem risk-related dynamics of the ecosystem, which
are essential to realize a value proposition.
Risks need to be ranked and prioritized in order to identify areas for immediate im-
provement and, thus, focus best efforts on dealing with threatening risks [13]. A risk
level matrix [13] could enable a quick overview of the risk in each value exchange. The
columns in the risk level matrix describe the percentage of the probability. The rows
classify the impact of a value exchange.
4 Design and Development
To enable the analysis of ecosystem risks, we first modified the value exchanges.
e3tools already supports formulas for value exchanges, actors and value activities. To
enable risk modelling, it was essential to add the values Probability and Impact to the
property Formula. We integrated the formulas into every value exchange. The value
Probability describes the probability that a value offering is successfully realized.
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To enable joint probabilities, it is necessary to propagate the probability of each
value offering through a dependency path up to a boundary element. To allow this, we
made changes to the Traverse function. The function Traverse is initiated by the func-
tion Enhance, which searches for every element after a start stimulus and forwards it to
the function Traverse where it traverses through a dependency path. Traverse always
takes the next element, checks its type and decides which steps are necessary to get the
next element. If it gets the next element, it recalls itself and repeats the same steps as
before until every boundary is reached. The elements must be forwarded through the
path to allow each probability on the path to be multiplied with the probability of the
next value exchange. The function traverse forwards to the next element the current
probability in the graph until all end stimuli are reached.
To add a probability, we need to verify if the OR join was visited before because the
the node´s default probability is 1. If we do not distinguish between the first and later
visits, it is impossible to know why the likelihood of the node is 1. It could either be an
unvisited node, or a visited node where every incoming path had a probability of 1. The
OR join always saves the highest possible probability. Once all incoming paths have
been considered, the current probability of the node is requested. This probability is
then forwarded to an outgoing path.
In the case of the AND join, it is not necessary to check if the node was already
visited because the node has a probability of 1 and the first incoming path will only be
multiplied by it. Therefore, this multiplication does not sophisticate our result. Contrary
to the OR-join, all incoming paths are included for the probability calculation. This
probability is then forwarded to the outgoing path. There is no difference between an
AND or OR node when forwarding the probability of the fork. The difference shows
up at the following elements or at the end of the path, where the cumulative probability
is calculated. Only at this point one option could turn out as the better one.
We implemented a risk level matrix following [13] to enable a quick overview of
the probability of each value exchange. The columns denote the probability while the
rows classify the impact of the value exchange. It is either a benefit or a threat. The
legend shows the occurring risk level. A value exchange could either be “High profita-
ble”, “Profitable”, “Negligible”, “Unacceptable” or “Critical”. The risk levels “Unac-
ceptable” or “Critical” should be avoided.
5 Artefact Description and Demonstration
In order to demonstrate that the implemented extension artefact successfully allows the
assessment of ecosystem risks, we modelled two examples from literature [7] as well
as two synthetic examples. For both examples from the literature, we generated e3value
models of the situations presented in two chapters of the work to test the logics imple-
mented. The synthetic examples were value models designed ad-hoc to test if the risks
are propagated through dependency paths and to test if the changes to the OR element
are performed as designed.
First, as shown in Figure 1, we modelled an adoption chain where an innovation
needs to pass through two intermediaries before reaching the end customer [7]. In this
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example, the innovation is highly profitable for the innovator (surplus of +4), creates
high margins and low handling costs for the distributor (surplus of +3), higher up-front
costs, retraining and after-sales service issues, despite slightly higher margins for the
retailer (a deficit of –1), and very high value for the end customer (surplus of +5). The
net system surplus created by innovation 11 (4 + 3 – 1 + 5).
NCF: +4 NCF: +3 NCF: -1 NCF: +5
Path Direction
NCF: Net Cash Flow
Fig. 1. Adoption chain risk example (Source: example based on [7], created with extended
e3tools).
Then, as shown in Figure 2, we modelled a co-innovation risk where complementors
(or supplier) have an eight-in-ten chance of succeeding independently [7]. In this ex-
ample, the chance that they will all jointly succeed at the end of the year is the product
of their independent probabilities (0.85 × 0.85 × 0.85 × 0.85). We include the probabil-
ity and impact of each value exchange as well as the joint or cumulative probability at
each step of the dependency path.
P:85%
I:-63%
P:85%
P:100% I:-25%
I:-18%
CP: 52,2% P:85%
I:100%
P:85%
CP: Cumulative Probability CP: 52,2% I:100%
I: Impact
P: Probability Path Direction
Fig. 2. Co-innovation risk example (source: example based on [7], created with extended
e3tools).
To test the propagation of risk, we used a synthetic example were a path starts at an
Innovator and ends at an End Customer. The example is shown in Figure 3. The joint
probability that the value proposition will be materialized for the “End Customer” is
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0.432 (0.8 × 0.6 × 0.9). The calculation considers every value exchange throughout the
path. Accordingly, to calculate the probability of 0.48 for the actor “Retailer” the ex-
tended function traverse multiplies 0.8 × 0.6 of the two previous value exchanges.
P:80% P:60% P:90%
I:17% I:-19% I:-24%
CP: 80% CP: 48% CP: 43,2%
CP: Cumulative Probability
I: Impact
P: Probability Path Direction
Fig. 3. Co-innovation risk example to test risk propagation (source: own example, created with
extended e3tools).
Figure 4 shows the synthetic example used to test the modified OR-join. If the path
from Comp. 1 is the first path, it will be saved in the node with 0.6. When the next
connection from Comp. 2 with 0.5 appears at the node, the highest probability is deter-
mined. Since 0.6 is the higher probability, the following path will be calculated with
this probability, because it is the better option. Afterwards, it requests the outgoing
connection element and forwards the new probability along the path. With the better
option, the value proposition would be materialized with a probability of 0.48. Other-
wise, the probability would be 0.4.
√ P:60%
I:100%
P:80%
CP: 48% I:100%
P:50%
I:100%
CP: Cumulative Probability CP: 60%
I: Impact
P: Probability Path Direction
Fig. 4. OR join example (Source: own example, created with extended e3tools. Numbers added
manually for better understanding.)
To test the dashboard (Figure 5) , we used the previous co-innovation risk example
shown in Figure 2. The component Results is an extension of the already existing Prof-
itability Table. This panel presents the profitability table and the profitability table after
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showing the impact of the probabilities which are connected to the actors. The digital
platform has a negative result when considering the joint probability. The reason for
this is the redistribution of income and loss. The digital platform is connected to the
innovations with 0.85 and connected to the end customer with a 0.52 probability of
success. So relatively more will be deducted from the profit. This leads to a negative
result for the digital platform. The joint probability of the complementors leads to such
a low probability of success. The next column shows the possibility to redistribute in-
come to compensate the negative result of the digital platform. The actor with the high-
est result compensates the highest percentage of the offset value. The column Comp.
shows the shares of the compensated amount of every actor. The last column indicates
the adjusted results, after the compensation of the values. Now, every actor has a posi-
tive result (except the end customer, which is not considered), which ensures the reali-
sation of the value proposition.
Fig. 5. Dashboard (Source: own implementation)
The component Paths shows the available paths in the value model. This example
shows only one path. Further paths would be listed successively. The buttons above are
useful to colour each path according to the minimum probability of each path. The used
colours to colourize the value exchanges of each path are red (0 – 0.33), orange (0.33 –
0.66) and green (0.66 – 1). In the component Actors, the list field allows the selection
of one actor to show which paths are arriving at it from the start point with which in-
coming probability. The component Value Exchanges shows which actors are con-
114
nected and the direction of the path including probability and cumulated joint probabil-
ity of each value exchange. The last column shows the impact of the value exchange,
thus, how beneficial or detrimental it is when this particular value exchange takes place
or not. The buttons enable the colouring of the value exchanges in the value model. The
button With Probability Colouring colours the value exchanges according to the speci-
fied probability and impact. The button “With Cumulated Probability Colouring” col-
ours the value exchanges according to the cumulated joint probability and the entered
impact. The colouring follows the risk level matrix component, which assigns the prob-
ability and impact to a specific risk level. The numbers in the matrix represent the IDs
of each value exchange. The buttons above the risk level matrix allow to show the value
exchanges with the entered probability or with the cumulated joint probability. There-
fore, it is possible to see which connections are critical from the beginning or only
critical because of the joint probability of all actors which occur before the relationship.
The Decision Support component was created using a policy and points to uncertain
actors or paths, or to how a value proposition could be realized through compensation
in case a partner has a deficit.
6 Discussion
The tool extension to analyse adoption chain and co-innovation risks presented in this
work enables tool support for the assessment of and decision making regarding these
risks. The extension artefact supports the analysis of ecosystem risks as proposed in
theory [2, 7]. Our approach relies on concepts, elements, functions and other function-
alities of e3tools [4] which we could extend to implement tool support for the analyses
described in the literature [7]. Further, we present a dashboard that presents rich infor-
mation for decision makers at a glance. Our work evidences the applicability and ex-
tensibility of the value modelling framework e3value [3], while showing some tool-
based ecosystem risk analyses.
As mentioned above, value modelling tools available, such as e3tools, already sup-
port some analyses of certain business risks. However, the logics of ecosystem risks
differ substantially from the implementations available. The logic of adoption chain
risks follows a logic of minimums (instead of surplus) while the logic of co-innovation
risks follows a logic of multiplication (instead of averages) [7]. Our extension artefact
provides novel tool functionalities grounded in theory to assess ecosystem risks, and
support decision making regarding distribution of income. This can enable the design
of better ecosystem or alignment strategies, which in turn can lead to better platform
ecosystem designs.
In this work we only dealt with co-innovation an adoption chain risks. Further eco-
system risks, especially those specific to digital platform ecosystems, may follow other
logics than the ones discussed and implemented here. Further ecosystem risks were not
part of the scope of this work. We hope that other researchers can extend the proposed
class of tool extension and the instantiated artefact to enable tool-based analyses of
further ecosystem risks. We demonstrated the utility of the tool based on examples.
This means that an empirical evaluation is still needed to evidence the utility of the tool.
115
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