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Balancing
excellence
A digital future
Are you ready?
Measuring value
Tips and challenges
Volume 6 Issue 2
Providing insight and analysis for business professionals
02
Strategy execution in Africa
Creating focus and capability
in a complex environment
12
Measuring open innovation
A toolkit for successful
innovation teams
24
Anti-corruption practices
Reinforcement opportunities
via internal controls
30
A new paradigm of Business
Intelligence
How in-memory computing can change
the analytical landscape
36
Key performance indicators
Winning tips and common challenges
44
The secret behind mechatronics
Why companies will want to be part
of the revolution
54
Embracing a digital future
62
Making the best of
a difcult trend
68
Getting user buy-in
to new IT systems
24
68
12
44
54
30
36
62
02
Contents
Measuring open
innovation
A toolkit for successful
innovation teams
Even experienced managers still go
blank when asked how to assess, control
and measure the performance of open
innovation (OI) activities. To address this,
we will discuss a general framework for
an OI performance measurement system
and present a metrics-based management
toolkit that provides a suite of KPIs for a
specic set of OI methods.
12 Volume 6 Issue 2
Authors
Marc Erkens
Consultant, Performance Improvement
Advisory, EY, Germany
Dr. Susanne Wosch
Senior Manager, Performance
Improvement, Advisory, EY, Germany
Prof. Dr. Frank Piller
Chair Technology and Innovation
Management Group, RWTH Aachen
University, Germany
Dr. Dirk Lüttgens
Assistant Professor Technology and
Innovation Management Group, RWTH
Aachen University, Germany
13
Measuring open innovation. A toolkit for successful innovation teams
T
hanks to loads of compelling
research studies
and best practice cases in OI
carried out over
the last decade, several
companies have begun
to embrace and partially apply the new
principles and methods OI offers. Yet,
while the development of innovation
metrics, in general, is still an emerging
discipline, there is absolutely no clear
guidance on how companies should
approach them in order to measure
the success of their OI initiatives.
Upcoming challenges in
measuring
today’s OI practices
While, in the past, traditional problem-
solving processes led to perhaps a few
hundred ideas, these days, a successful
ideation contest — if it is directed to an
external network — can easily generate
thousands of insights. Teamwork will
span across companies, universities,
governments, suppliers, customers and
individuals. And it will involve numerous
online tools, such as search engines,
databases, podcasts, websites and other
toolkits.1
The incorporation of such a large
1. H. Habicht and K. M. Möslein, “Open Innovation Maturity: Ein
Reifegradkonzept zum Controlling von Open Innovation,“
Controlling, Zeitschrift Unternehmenssteuerung, 2011.
number of diverse insights can be
challenging, confusing and appear
uncontrollable.
It is easy to see how the level of
complexity of initiatives driven by OI far
exceeds that which corporate innovation
teams normally deal with in traditionally
executed innovation projects. Deploying
OI requires not just access to nancial
resources and the clear allocation of
responsibilities. The untapped secret
lies in a company’s ability to successfully
measure the huge amount of knowledge
being gathered.
Does your company
measure up?
The need for OI metrics
Studies have shown that around 90% of a
company’s innovation efforts never result
in commercialized products or services.2
This has led to a suspicion that innovation
still seems to rely on fairly random
incidents, rather than being the result of
clearly dened performance measurement
procedures.3 Other research conrms this,
pointing especially to the shortcomings
of coordination and underestimation of
the complexity that arises in the context
2. R. G. Cooper, Top oder Flop in der Produktentwicklung,
2002.
3. R. Reichwald and F. Piller, Open Innovation: Kunden als
Partner im Innovationsprozess, 2005.
“The level of complexity
of initiatives driven by OI
far exceeds that which
corporate innovation
teams normally deal with
in traditionally executed
innovation projects.”
14 Volume 6 Issue 2
Figure 1
Framework for an OI performance measurement system
Outcome
Output
Process
Input Instrumental use
Source: Open Innovation KPI Study 2012, EY.
Conceptual use
Symbolic use
Upstream
e.g., lead
user method
Ideation
e.g., ideation
contest
Downstream
e.g., broadcast
search
Phase of innovation
OI method
Principle 1
Type of measurement
Principle 2
Type of utilization
Principle 3
KPI
of OI processes.4 However, if companies
approach OI in a more organized and
systematic way — e.g., through the
application of new innovation metrics
they could raise their return on innovation
at no or small additional costs.
Among those companies that do
measure innovation, we found that
most still use very generic innovation
metrics that are primarily based on
R&D and product-development metrics
solely (i.e., the number of patents led
in the past year or the number of ideas
submitted by employees). Though
somewhat useful, these metrics provide
only minimal support for organizations
on their innovation journey, since they
do not map performance measures that
instantly drive, impact or completely
indicate a company’s (open) innovation
performance. Why do innovation
departments still not have access to the
right tools and metrics to enable them to
successfully control and measure their
OI projects?
4. S. Hagenhoff, “Innovationsmanagement für Kooperationen.
Eine instrumentenorientierte Betrachtung,“ Niedersächsische
Staats- und Universitätsbibliothek, 2008.
In our experience, what seems to be a
real challenge for companies is nding the
relevant metrics for their OI activities and
the discipline to make measurement a
priority as part of a standardized process.
Appropriate tools and metrics are needed
that empower innovation teams to properly
measure OI in order to be able to promote
the best innovation ideas and solutions
and turn new knowledge into successful
commercialized products or services.
If our clients could raise their return
on innovation by just 10%–20% through
controlled and measured OI practices, this
would give them a signicant competitive
advantage and the potential to be true
game changers.
Framework for an OI
performance measurement
system
Using our experience in performance
measurement and the ndings of desk
research, we singled out three quite
distinct principles that companies
must consider in order to successfully
implement a metrics-based performance
measurement system for their OI projects.
Figure 1 outlines this simple
framework, including our three principles
on OI metrics. It gives a suite of KPIs and
provides a better idea of how to properly
set up a performance measurement
system that will help you to assess, control
and measure your OI activities.
Principle 1
Use unique metrics for each OI
method
Measuring OI greatly depends on your
desired innovation goals and the underlying
OI method with its fundamental features,
characteristics and resources that you are
going to use in your OI project. In other
words, method-specic metrics or KPIs
are needed in order to be able to properly
assess and measure the progress and
success of each of these activities.
We deep dived into the three most
prominent methods of OI that cover both
the early, as well as the later, stages of the
innovation process:
15
Measuring open innovation. A toolkit for successful innovation teams
►The lead user method identies
innovative users who are at the leading
edge of important trends and benet
greatly from obtaining a solution to
their needs. Thus, they are motivated to
discuss and tackle their innovation needs
and ideas in workshops.
In an ideation contest, a company
seeking innovation-related information
posts a task-specic challenge to a
population of independent, competing
agents (e.g., customers or suppliers)
who then submit ideas within a given
time frame. The company rewards the
participants that generated the best
solutions.
Boadcast search involves contests that
seek technical solutions rather than just
ideas. Online broker companies, so-called
intermediaries, such as InnoCentive or
Nine Sigma, provide companies access to
a global pool of scientists, engineers and
other professionals to help them solve,
primarily, R&D problems they have been
unable to solve through internal methods.
The problem has a stipulated time frame
and cash prize for the winning solution.
With the help of the intermediary, the
company denes the problem and develops
criteria for picking a solution.
It is quite obvious that measuring the
innovation success of a lead user project
requires a different set of KPIs than
those required for broadcast search.
Whereas the focus of a lead user project
lies primarily on evaluating the identied
new needs and trends provided by
innovative users, measuring the success
of broadcast search requires metrics
that map the potential performance of a
technical solution.
Principle 2
Consider different types of
measures: input, process, output
and outcome (IPOO)
The second principle concerns the
different types of measures that need
to be tracked by a holistic performance
measurement system. The framework
should be designed to link the outputs or
outcomes of an OI initiative to the inputs.
Input KPIs measure the input elements
within a project, such as human or nancial
resources.
Process KPIs are used to transform
inputs into outputs and to improve the
efciency of the innovation process: time
variances, budget variances, error ratio, etc.
Output KPIs measure the results
of the development activities within
an innovation process: number of
ideas, number of patents, number of
publications, etc.
Outcome KPIs aim to determine
the value of an innovation in terms
of economic and market-oriented
performance indicators.
Only the combination of both input and
output (outcome) metrics can provide a
meaningful understanding of the cause-
effect relationships of a project.
Moreover, since the real value of the
output (outcome) of an OI initiative is the
result of more than just the resources
invested (input), various measures of the
processing or transformation procedures
should also be integrated into the
framework.
Principle 3
Think about how to utilize your OI
metrics effectively
The mere provision of a performance
measurement system through the
collection of appropriate management
information is itself no guarantee of
successful innovations.
Pelz5 proposes that metrics can
be utilized on three different levels:
instrumental, conceptual and symbolic.
Instrumental use refers to the
application of information or metrics used
directly for decision-making. For instance,
5. D. C. Pelz, “Some expanded perspectives on use of social
science in public policy,” M. Yinger and S. Cutler, Major social
issues: a multidisciplinary view, 1978.
“Method-specic metrics
or KPIs are needed
in order to be able to
properly assess and
measure the progress
and success of each
of these activities.”
16 Volume 6 Issue 2
when the OI project is canceled because
the metric “expected sales” is below a
specic threshold, the metric was used
instrumentally.
►A more indirect use is the conceptual
one. The use of the information or metric
does not directly lead to a concrete action,
but rather provides general enlightenment
and understanding. For example, when a
manager recognizes that the lead time of
OI projects is on average 30% lower than
for conventionally run innovation projects,
they are using the metric “lead time”
conceptually.
►Metrics can also be used after decisions
have already been made to legitimize
and justify them. This kind of use is called
symbolic. If an OI project is canceled due
to cost overruns, the ofcial reason for
its termination is “quality of ideas” — this
metric is used symbolically.
The way in which metrics should be
utilized greatly depends on your desired
project goals. For instance, if you are
following long-term goals rather than
short-term success with your OI project,
i.e., to facilitate a sustainable innovation
culture, hard measures such as expected
sales should be used conceptually for
providing general enlightenment and
understanding, and less for decision-
making purposes.
Outlook
So far, a simple and easy-to-apply
framework for OI performance
measurement has been outlined. However,
there is still no answer to what we should
actually be measuring. What are the
relevant KPIs behind that framework?
This question was the focus of our Open
Innovation KPI 2012 study, in which we
identied the most relevant KPIs from the
perspective of innovation managers and
performance measurement consultants.
17
Measuring open innovation. A toolkit for successful innovation teams
Open Innovation KPI 2012
study6
We rst had to decide which of the existing
methods and tools for integrating external
knowledge into the OI process should be
applied to our performance measurement
toolkit (principle one). As described above,
the decision was made to take a closer
look at the three most prominent methods
of “inbound OI”: lead user method,
ideation contest and broadcast search,
which cover both the early and later
stages of the innovation process.
The framework was enriched with
a number of meaningful performance
measures for each OI method. However,
there was no indication that the
information and metrics collected from
literature would offer a wide enough
range of application for practical decision-
making in business corporations.
In order to close this gap, experts from
corporate functions and management
consulting were asked to participate
in a survey to assess the relevance of
an assembled set of KPIs. Our sample
included large European companies
from a range of different industries (e.g.,
chemical and pharmaceutical, automotive
and mechanical engineering, consumer
goods and professional services) with
6. The survey, conducted by EY in cooperation with the
Technology and Innovation Management Group at RWTH
Aachen University, Germany, aimed to identify the most
relevant KPIs for measuring OI.
annual revenues in excess of €200m
and with 1,000 employees or more. We
received usable responses from 117
consultants and industry practitioners.
Making better use of metrics
to drive improvement in OI
projects
The study’s rst question explored
how metrics were being used by both
practitioners and consultants to monitor
the performance and predict the return of
their OI projects.
The results demonstrated that
consultants and practitioners share a
slightly different opinion on how best to
apply metrics to OI. Figure 2 shows 39% of
the EY consultants reported that decisions
should be made directly on the basis of an
indicator score (instrumental use), while
almost the same proportion of innovation
managers prefer to use metrics tactically
or symbolically to delay or spur action on
an OI issue.
To some extent, this difference in focus
is explained by the different interests and
perspectives of the two groups.
Innovation managers tend to assume
that their OI projects are subject to
signicant uncertainty, particularly in
the early stages of development, thus,
for them, concrete targets and measures
don’t seem to be denable or detectable.
Consultants, in turn, frequently
experience innovation projects going
out of control, because no or too few
suitable measures have been determined
in advance. That fact gives rise to a
more instrumental use of metrics, where
generated data is incorporated directly
into the decision-making processes,
thereby leading to improved results.
We also explored the primary role that
metrics play when tracking different types
of measures. Instrumental use seems to
be more prominent at the very beginning
(input) and at the end of the performance
measurement process (outcome), while
conceptual and symbolic use dominate
output measures (see Figure 3).
In conclusion:
Depending on the innovation problem:
a dedicated focus on increasing radical
innovation should involve a conceptual use
of OI metrics.
Depending on the innovation culture:
if companies tend increasingly to lax
treatments concerning deadlines and
budget, then an instrumental use of
measures is recommended.
Depending on the types of measures:
while input and outcome measures
should rather follow an instrumental use,
output measures should follow a more
conceptual use.
Figure 2
The application of OI metrics by practitioners and
consultants
Source: Open Innovation KPI Study 2012, EY.
Number of innovation management (IM) practitioners was 12.
Number of EY consultants was 80.
Instrumental
22% IM practioners 40% IM practioners
39% EY consultants 38% EY consultants
Conceptual
38% IM practioners
23% EY consultants
Symbolic
“Only the combination of both
input and output (outcome)
metrics can provide a meaningful
understanding of the cause-effect
relationships of your project.”
18 Volume 6 Issue 2
Measures for OI: are there
any suitable measures?
To investigate the usefulness of different
metrics for measuring OI, we provided our
respondents with an assembled set of KPIs
for each OI method. In general, industry
rms and management consultants view
these indicators as important. Thus,
they are somewhat condent that these
measures are getting it right and helping
rms to improve their OI activities.
In general, respondents seem to have
a stronger tendency toward nancial
outcome measures, and prefer less those
indicators that, by nature, are more
difcult to attract. Interestingly, measures
that relate to an economic outturn seem
to be more promising than measures that
address empirically proven critical success
factors of OI. Why isn’t it common to
integrate a prevailing empirically validated
OI enabler as part of a performance
measurement system?
One reason is that the new methods
of OI are relatively young and are
still maturing. The “old” systems for
measuring innovation are, at best, slightly
adjusted to external inuences but do
not capture or quantify critical success
factors of OI. Another reason could be
that outcome measures are usually more
meaningful than high-risk intermediate
results.
Qualitative indicators, such as the
radicalism and novelty of ideas, are
hard to attract, since they have to be
collected through painstaking qualitative
assessment procedures.
Figure 3
Preferred types of use of OI metrics
Source: Open Innovation KPI Study 2012, EY.
46%
18% 19% 37% 20%
46% 41% 30%
36%
Instrumental use
35% 22% 50%
Input Process Output Outcome
Conceptual use
Symbolic use
19
Measuring open innovation. A toolkit for successful innovation teams
OI scorecards: what are the
most important measures?
In order to help organizations identify and
determine a coherent portfolio of the right
metrics, we propose three OI scorecards.
These scorecards, shown in Tables 1, 2
and 3, were created based on our three
key principles on how to measure OI
(see Figure 1) and represent the highest
priority measures selected by our survey
respondents.
The scorecards are structured around
two phases: initiation and implementation.
Each phase is split into the relevant stages
of the performance measurement process,
i.e., input, process, output, outcome. KPIs
are then allocated to all of the identied
processes within both the initiation and
implementation phases. There is also a
third part to the scorecard: overall KPIs.
Again, this is split into the stages of the
performance management process and
KPIs are allocated accordingly. It is worth
noting that, unlike in the initiation and
implementation phases, these overall KPIs
are identical across the three scorecards.
On average, we observed the following:
All the metrics taken from relevant
literature play an important role in
measuring success across all three
scorecards.
Both lead user and ideation contest
are particularly complex methods, and
broadcast search is semi-complex.
All three methods require more than
a few KPIs.
Table 1
OI scorecard for lead user integration
I-P-O-O Category Measurement of KPI Survey results*
Arithmetic mean
A. Initiation phase
Input Top
management
commitment
Degree of freedom Freedom given by top management to establish search
elds outside of the core business
1.6
Outcome Market
potential Customer potential Degree to which the lead user represents the mass market
that the company is targeting for the future
1.5
B. Implementation phase
Input Staff Diversication Number of lead users participating in workshop relative to
internal staff members
1.0
Quality Heterogeneity Degree of heterogeneity of the lead users, e.g., variation in
interests and expertise of the lead user
1.3
Process Quantity Adaptation effort Number of times feedback is gathered from lead users for
each developed prototype
1.1
Output Quality Strategic t Compatibility of solutions with existing business strategy 1.2
Knowledge
generation By-product Additional number of interesting suggestions and ideas that
emerge during the workshop
1.0
Customer
loyalty Lead user network Percentage of participating lead users with whom you
establish contact for potential future collaborations or
full-time employment
1.0
Outcome Protability Prot ratio Ratio of expected prots from the lead user innovation
compared with those generated by projects run with more
traditional internal innovation processes
1.2
Overall KPIs
Input Top management commitment Degree of top management commitment to open
innovation initiative
2.6
R&D Cost to market Cost to market of development using open innovation 1.1
Process Time Time to market Time to market of the innovation 1.3
Risk Intellectual property Degree of protection of intellectual property in cooperation
with external partners
1.3
Output Sustainability Culture Increase in corporate-wide open innovation culture through
the open innovation activity
1.2
Outcome Creativity Originality Customers benet from the innovation provided (t to
market)
1.9
Protability Revenues Expected increase in revenue from new customers as a
percentage of total sales
1.7
* Base of data collection of arithmetic mean: 3 (very important), 1 (important), 0 (neutral), -1 (unimportant) and -3 (very unimportant).
n=87 (August 2012).
20 Volume 6 Issue 2
Output measures appeared to be
relatively less promising across all three
methods.
KPIs that are used to measure process
efciency of transforming inputs into
outputs are rated lowest in importance.
Input and outcome KPIs follow a more
instrumental use.
Findings specic to each method are
as follows:
Lead user integration: rms seem
to have the strongest tendency toward
metrics that relate on an input and
outcome perspective at all stages (i.e.,
initiation and implementation).
Ideation contest: measures that aim
to determine the value of an innovation
in terms of outcome KPIs are signicantly
important throughout all stages (i.e.,
initiation and implementation phase).
Interestingly, only input measures that
appear at the initiation phase scored
signicantly high.
Broadcast search: input and outcome
measures that appear at the initiation
phase are considered to be of low
importance, since they do not show up in
our scorecard.
Table 2
OI scorecard for ideation contests
I-P-O-O Category Measurement
of
KPI Survey results*
Arithmetic mean
A. Initiation phase
Input Costs IT platform Cost of implementing the IT platform 1.2
Quality IT platform Number of available communication channels on the IT
platform (e.g., chat function, forum, private message,
commenting and rating abilities)
1.1
User friendliness of the IT platform or web page (e.g.,
measured by the number of complaints per test person)
1.8
Problem
formulation Scalability of the task (is the task description broad
enough to engage a large number of participants?)
1.2
Output Scope Heterogeneity Heterogeneity (diversity) of external contest participants
(e.g., customers, suppliers)
1.2
Outcome Market potential Customer
potential Degree to which contest participants represent the
mass market that the company is targeting for the future
1.3
B. Implementation phase
Process Quality Degree of
interaction Depth of contestant community interactions
(e.g., number and intensity of messages exchanged
within the community)
1.1
Output Quantity Productivity Percentage of winning ideas that become
company projects
1.7
Sustainability Reputation
and image Increase in company reputation among participants
(e.g., duration of membership or frequency of use of
the platform)
1.1
Outcome Commercialization Imitability Difculty for competitors to imitate winning ideas 1.0
Overall KPIs
Input Top management commitment Degree of top management commitment to open
innovation initiative
2.6
R&D Cost to market Cost to market of development using open innovation 1.1
Process Time Time to market Time to market of the innovation 1.3
Risk Intellectual
property Degree of protection of intellectual property in
cooperation with external partners
1.3
Output Sustainability Culture Increase in corporate-wide open innovation culture
through the open innovation activity
1.2
Outcome Creativity Originality Customers benet from the innovation provided (t to
market)
1.9
Protability Revenues Expected increase in revenue from new customers as a
percentage of total sales
1.7
* Base of data collection of arithmetic mean: 3 (very important), 1 (important), 0 (neutral), -1 (unimportant) and -3 (very unimportant).
n=86 (August 2012).
21
Measuring open innovation. A toolkit for successful innovation teams
Table 3
OI scorecard for broadcast search
I-P-O-O Category Measurement
of
KPI Survey results*
Arithmetic mean
A. Initiation phase
Process Time Delivery date
variations Average delay in meeting deadlines (due to failed contract
negotiations) in relation to projects run with more traditional
internal innovation processes
1.0
Output Scope Size of target
group Number of accessible problem solvers via the intermediary
compared with the rm's own R&D employees
1.4
Heterogenetic Degree of heterogeneity of the solver community, e.g.,
variation in interests and expertise of the solvers
1.4
B. Implementation phase
Input Time The ratio between the number of days the problem is open to
solvers and the average number of days for similar problems
initiated by other rms seeking solutions
1.0
Quality Problem
formulation Specicity of the problem (is the task or issue broad enough to
attract a relatively large number of solvers?)
1.0
Process Quality Adaptation
effort Number of times feedback is gathered from intermediary in
the development of the problem statement
1.0
Output Quantity Trafc Number of individuals or solvers opening the problem per
submitted solution
1.0
Outcome Protability Cost saving Estimated savings from using crowdsourcing initiative relative
to costs of a similar in-house problem-solving process
1.4
Market
potential Technological
potential Anticipated technological lead over competitors from utilizing
external solution processes
1.6
Feasibility Compatibility of solution with the company’s internal
innovation processes (ease with which solution is integrated
into subsequent phases of the development process)
1.3
Overall KPIs
Input Top management commitment Degree of top management commitment to open innovation
initiative
2.6
R&D Cost to market Cost to market of development using open innovation 1.1
Process Time Time to market Time to market of the innovation 1.3
Risk Intellectual
property Degree of protection of intellectual property in cooperation
with external partners
1.3
Output Sustainability Culture Increase in corporate-wide open innovation culture through the
open innovation activity
1.2
Outcome Creativity Originality Customers benet from the innovation provided (t to market) 1.9
Protability Revenues Expected increase in revenue from new customers as a
percentage of total sales
1.7
* Base of data collection of arithmetic mean: 3 (very important), 1 (important), 0 (neutral), -1 (unimportant) and -3 (very unimportant).
n=83 (August 2012).
22 Volume 6 Issue 2
When, in the process, to use
the scorecards
When looking at it from a process
perspective, the developed OI scorecards
can be applied to the different phases of
the innovation process as follows (see
also Figure 4): in the early stages, both
the lead user method and idea contests
are helpful tools for identifying customer
needs and rst solution approaches.
Broadcast search, however, is particularly
useful in the later stages of the
innovation process to generate suitable
knowledge for technological solutions or
to identify potential solution providers.
Depending on the chosen method, the
individual scorecards can then be used to
monitor and predict the success of the OI
campaign.
Conclusion
OI is not an automatic success but one
that demands appropriate tools and
metrics that enable you to change
your strategy before mistakes become
expensive or great ideas are refused. To
this end, a performance measurement
toolkit exists, empowering decision-
makers and innovation teams — especially
in technology-based industries — to
properly assess, control and measure the
performance of their OI activities.
Contrary to many other OI indicator
studies, a toolkit has, in this case, been
realized, not only in terms of secondary
data sources, but also through an
empirical evaluation. This allowed us to
reduce the initial amount of indicators to
reach a much smaller, though statistically
signicant, set of relevant metrics
provided by our three OI scorecards.
Thus, these scorecards might help you
to identify and determine a coherent
portfolio of right metrics directly
associated to your OI strategy, as they
reect only those measures that were
rated signicantly important by almost 90
innovation experts and consultants.
Once identied, the measures have to
be utilized or initiated by the responsible
actors within your company. As our study
reveals, input and outcome measures
should rather follow an instrumental
use, while output and process KPIs were
dominated by a conceptual use.
However, a successful application of
indicators also depends on the innovation
challenge (degree of innovation), as well
as on a company’s ability and sincerity
to appropriately plan and manage an OI
campaign (corporate culture). A dedicated
focus on increasing radical innovation
should involve a conceptual use of OI
metrics. Nevertheless, if companies
tend increasingly to lax treatments
concerning deadlines and budget, then
an instrumental use of measures is
recommended.
Figure 4
OI scorecards along the innovation value chain
Upstream
Ideation Downstream
Lead user method
Ideation
contest
Broadcast
search
“The way in which
metrics should be
utilized greatly
depends on your
desired project goals.”
23
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EYG No. AU2346
Performance 6.2 May 2014.
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... Another topic that is being discussed is culture as a barrier to integrate and apply external knowledge (Chesbrough & Brunswicker, 2013;Mosig et al., 2017). This phenomenon originates from the previously mentioned fundamental difference of corporate management (design for efficiencies, risk aversion and stability) and innovation characteristics (unknown, risky) as well as the fact that most innovations are never commercialized (Erkens et al., 2014). The differences result in an omni-present conflict of OI. ...
... In the OI world trust becomes an important element as it can help overcome uncertainty and make pre-investment without planned outcomes more likely (Hasche et al., 2017). Trust is also needed as evaluating metrics for OI are not yet existing due to the fact that OI creates outputs fluently and informally (Erkens et al., 2014). Therefor corporates must manage their expectations well when it comes to OI. ...
Thesis
The post-acceleration phase of corporate accelerator programs includes all activities after acceleration of a start-up and is a topic that attracts limited interest in scientific literature so far. Neither practical nor academic guidance on how to structure the post-acceleration phase currently exist. This omission is judged to be negligent since acceleration outcomes are utilized during the post-acceleration phase, when the acceleration has come to an end. Actions taken during the post-acceleration phase will therefore determine the overall success of the corporate accelerator program , which should elicit interest of researchers. The existing gap in literature and missing recommendations for implementation in practice are the main motivators for this thesis. A multiple case study analysis with four cases was performed to collect insights on how the post-acceleration phase is managed in practice and to provide recommendations for a structured post-acceleration phase management approach. The main result of the thesis is that a structured post-acceleration phase is recommended , if corporates aim for long-term collaborative partnerships with external start-ups. Areas that must be covered during post-acceleration are a joint evaluation of the acceleration success, the structured integration of acceleration outcomes into the corporate organization and the creation of entrepreneurial network structures to drive cultural change and to maintain long term relationships. The thesis demonstrates that the post-acceleration phase is an integral part of corporate acceleration and that it must be managed properly to achieve acceleration objectives. Corporates that follow the recommendations outlined in this thesis will have more long-term acceleration outcomes, will be able to improve future accelerations and will enable cultural transformation towards more entrepreneurship. This thesis lays explorative groundwork and functions as a starting point for additional research so that corporate accelerator programs can be further improved ben-efitting of corporates and participating start-ups.
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