THE AGILE-ENTERPRISE INNOVATION PLANNING

How to Align Self-organization Processes for Innovation Management

Mixel Kiemen

Vrije Universiteit Brussel, Pleinlaan 2, Brussels, Belgium

Keywords: Innovation management, Self-organizing complexity, Design science research.

Abstract: The Agile-Enterprise Innovation Planning System (A-EIP) is build to manage breakthrough innovation

based on best practices in the innovation literature. From the literature a novelty paradox is recognized:

knowledge is both a barrier as a source for innovation. The goal of the A-EIP system is suppress the former

and amplify the later. Theory on self-organizing feedback mechanism are needed to understand how to

overcome the novelty paradox. The A-EIP system contains four management systems (MS): Group-MS,

Business-MS, Strategic-MS, Learning-MS. Each management system will be important for the basic three

stages of innovation: incubation phase, growth phase and maturity phase. The management systems will

create a flow over the three phases and make emergence and aggregation manageable. The practical

development and validation of the research is done in respect to Internet innovation. In contrast to

innovation cases, a new emerging approach is pursued. Currently experiments have been done with a course

that can be extended to a full Master program to create micro-spinoffs, such a program is considered the

easiest way to create a test bed for the A-EIP system.

1 INTRODUCTION

History has the habit to repeat itself. This is why the

Agile-Enterprise Innovation Planning (A-EIP)

system is named after the Enterprise Resource

Planning (ERP) system. Before the ERP system

existed, only the super-accountants of a company

had a holistic view of the company’s resources.

Now, more people may acquire a strategic

understanding of the resources via an ERP system.

Today, we see visionary leaders who facilitate

innovation in a similar position as super-accountants

before ERP.

The problem with not having a way to

understand how some CEO create value from

innovation is that it results quickly in personal cult

and celebrity status, which are not a guaranty for

success. What is more, celebrities make market very

nervous. Probably the best example of this is how

Steve Jobs health has a direct influence on the stock

of Apple. Notice that ERP systems did not replace

super-accountants but made the system more

accountable. In this way the ERP systems made the

market less autocratic and more democratic. We

need a similar democratization movement for

innovation management.

Using IT-support for resources is simple

compared to support for innovation. It is not

impossible to build support for innovation, but it

requires a profound understanding of innovation

management and understanding of complicated

feedback mechanisms. In previous research a theory,

based on system and cybernetics research, has been

developed to explain a complicated feedback

mechanism that overcomes the novelty paradox

(Kiemen 2008). According to the novelty paradox

knowledge is both a barrier as a source for novelties.

The novelty theory will be used in this paper for

defining the main structure of the A-EIP system. To

avoid the novelty paradox, the A-EIP will create

tools to support the innovation. The tools are

designed to rewire knowledge, by disconnecting it

from its historical context and connecting it onto the

emerging novelty.

The A-EIP contains four interacting

Management Systems (MS): Group-MS, Business-

MS, Strategic-MS, Learning-MS. The Group-MS is

needed to support small agile teams that will explore

an idea. The Business-MS is needed to transform the

idea into a spinoff. The Strategic-MS is needed to

get a holistic view about the innovation strategy. The

Kiemen M. (2011).

THE AGILE-ENTERPRISE INNOVATION PLANNINGHow to Align Self-organization Processes for Innovation Management.

In Proceedings of the Second International Conference on Innovative Developments in ICT, pages 47-53

DOI: 10.5220/0004471500470053

 SciTePress

Learning-MS is needed to create learning processes

for all the emerging novelties.

The A-EIP system is considered an IT support

for next generation Technology Transfer Office

(TTO). According to Koenraad (2010) a next

generation TTO would be fully imbedded in the

university functioning. Early experiments have been

done with a course. The course could get extended

to a program to pursue micro-spinoff opportunities.

By creating such a program the required condition to

validate the A-EIP system can be possible.

2 DESIGN OF INNOVATION

The innovation management literature can be

organized according to their conceptual design trend.

The most important conceptual design for

breakthrough innovation is rooted with

Schumpeter’s (1975) creative destruction.

Schumpeter noticed how the market had competition

from within: new players were capable of

overthrowing incumbents. It seems to be particularly

the knowledge barriers that requires breakthrough

innovation management. By identifying a simple and

a breakthrough problem, scholars have identified

different barriers: component and architectural

(Henderson and Clark 1990), continuous and

discontinuous (Hamel and Prahalad 1994),

incremental and radical (Freeman and Soete 1997)

and sustaining and disruptive (Christensen 1995).

Another conceptual design has its origin in

Porter’s (1980) competitive advantage. This design

focused on identifying the strategic business value.

It was further developed as to the resource-based

view (Wernerfelt 1984; Barney 1991) and then

shifted to the dynamic capabilities framework

(Teece, Piasno and Shuen 1997; Eisenhardt and

Martin 2000). A third, more fragmented conceptual

design, is based on alliances. Some related theories,

amongst others, are absorptive capacities (Cohen

and Levinthal 1990), ambidextrous organizations

(Thusman 1996) and open innovation (Chesbrough

2003).

The creation of value from breakthrough

innovation is an ad-hoc activity. Organizing these

activities in an existing business will create a

knowledge barrier. The breakthrough innovation has

to emerge openly as a whole complex and it should

only be reintegrated into existing business when it

becomes stable.

The goal to create an IT-support structure inline

with such a view is not new, but the issue has been

scarcely mentioned. Applegate and Co. (2003, p

232) intruded a big-small design in their 6th edition

of their book. The design is proposed as a hybrid

between a big company and a small agile company

as to overcome the novelty paradox. Although the

7th edition builds on the previous big-small design,

it is now called an agile enterprise with an on

demand control (Applegate and Co. 2006, p 58-71),

but by adding the detail the general big-small design

gets lost.

Christensen’s elaborated studies on disruptive

innovation come very close to the core of the

novelty paradox. Christensen (1997, p 96-97)

illustrates why management cannot understand

breakthrough innovation: they have no values for

measuring emergence and aggregation. Christensen

(2003, p 237-242) also illustrate how not being able

to value such innovation leads to the downfall of the

company on longer term. He even suggests a

disruptive growth engine (ibid, p 278), but more as

formal business processes than as an IT-system.

Clearly our A-EIP system will be closely related to

the disruptive growth engine.

3 THE NOVELTY MODEL

In cognitive studies of the mind, much effort has

gone to understand the information processes for

learning. The basic mechanism is feedback, but in

some case feedback is not an option, because the

feedback would be too late. One extreme example is

to test if one more step would make you fall from a

cliff. In many ways a feed-forward process is more

interesting, but feed-forward is only possible if a

good internal model exist to make the prediction. In

a complex dynamic environment the feed-forward

would not be a good option too.

A compromise is created, a feed-forward process

that is corrected by a feedback process, which we

call an anticipation process. Hawkins (2004) predicts

that we should find anticipatory cells in all areas of

cortex. With such anticipatory cells Hawkins create

convincing hierarchical structure for our sensory

system as a micro-management of anticipation.

Simon (1962) defines such a structure as a

hierarchical architecture of structural complexity.

Dehaene et al. (1998) uses four such hierarchical

structures as entries to the global workspace of the

brain. Little did they know that they just discovered

the novelty model. Kiemen (2008) claims that such a

novelty model has been constructed in three in

depended analyses.

The four anticipation processes lift the novelty

paradox by creating a complicated bootstrapping

INNOV 2011 - Second International Conference on Innovative Developments in ICT

relationship. Two “things” A and B can be said to

stand in a bootstrapping relationship if A is used to

develop, support or improve B, while B is used to

develop, support or improve A. The internalizing

and externalizing process build up a knowledge

model inline with what is in the environment. The

third anticipation process, called directing, will add

focus to amplify rear events. With three processes

the system has novelty, but does not understand it

yet. The last process is the actual learning process

that anticipates what is relevant in the rear events.

The four anticipation processes, internalizing,

externalizing, directing and learning, are connected

to a working memory that functions like a

blackboard. If one process adds a concept the other

three will react on it, which can strengthen the

concept, break it down or create alternatives. This

quite complex interaction can, under good

conditions, solve the novelty paradox. Although the

novelty model is mostly based on cognitive studies,

another novelty model was found by studying how

science emerges (Latour 1999, see Kiemen 2008).

4 A-EIP SYSTEM

Kiemen (2009) designed a novelty model for

innovation management, which turns out to be a

hyper-novelty model. A hyper-novelty model is a

model where each of the four hierarchical structures

is itself a novelty model. One interpretation of this

effect is that innovation is the novelty of novelties.

The four hierarchical structures of the innovation

model are Management System (MS) of A-EIP:

Group-MS, Business-MS, Strategic-MS, and

Learning-MS. Each MS relates to one of the four

anticipation processes, but each MS is also a novelty

model. The Group-MS will internalize the

innovation, as the group becomes a new actor to

explore the innovation. The Business-MS will

externalize the innovation by creating a spinoff.

Between creating a group and having a spinoff we

have the innovation project. The Strategic-MS will

give direction by identifying values that emerge

when the project grows. The Learning-MS will

create knowledge about how the project can grow.

As each of the four management systems is a

novelty model, they all have anticipation processes,

which get named after their key indicators. The

subsections describe how these 16 indicators

determine the core functionalities of the four MS’s.

However as this is a hyper-novelty model the MS

are part of a meta-level model, which also adds four

key indicators. So in total 20 key indicators are

recognized, as illustrated by table 1 in the appendix.

The goal of the A-EIP system is to have a

general IT-support for all activities of the innovation

project and to design the coordination between

activities so that they suppress knowledge as barrier

and amplify knowledge as source for innovation. It

is claimed that the table enforced such a rewiring of

knowledge.

4.1 The Core of A-EIP

The core of the A-EIP is a project management

system. The project core is needed to communicate

issues and plan event. The key of any innovation is

to take actions as to transform an idea into a reality.

Of course the problem of such a system would be

the novelty paradox, but this is why the core

interacts with the Group-MS, Business-MS,

Strategic-MS, and Learning-MS.

Managing agile project is a topic well discussed

in literature around IT management. Such knowhow

has to be applied to the project management, like

scrum methodology and group decision support.

What is different is that the interaction via the four

management systems should create a cognitive

landscape in relation to the novelty model.

Innovation is often represented as three phases of an

S-curve, which will have their effect on four

management systems. The three phases of an S-

curve are: the incubation phase, the growth phase

and the maturity phase.

Each user should know in what phase the

development is as to understand the difference in the

game. In the incubation phase many things are

unknown and uncertain. In this phase variety and

collaboration is needed to explore the novelty. To

manage all the disconnected parts an issue queue

like structure is needed. Such a structure contains

meta-information or tags, which is abstract and

allows bundling associations between the

disconnected parts.

During the growth phase a selection will occur

by merging projects. Now it will be relevant to see

friendly competition to make the fittest projects

absorb other project and thus include their assets. In

the maturity phase the assets are known. Business

processes and a knowledge system allow the project

to get continued by other people than the creative

teams.

4.2 Group Management System

Innovative groups are agile groups they have a core

THE AGILE-ENTERPRISE INNOVATION PLANNING - How to Align Self-organization Processes for Innovation

Management

of people and an extended social network to allow

the group to explore the innovation. In the

incubation phase there may be several subjective

reasons for the team to form. The teams would be

small and one member would be in several groups.

A group would contain fulltime equivalent

(FTE) between 1 to 7 people. The number is not

absolute, it depends on the complexity of the

problems. In the incubation phase groups are

unstable and can easy change. During the growing

phase the groups are stable and should grow.

Depending on the complexity of the project the

group is expected to grow to 25 to 75 FTE. This can

happen by merging of project.

The Group-MS contains elements that allow a

project to take action. As such Group-MS makes an

actor emerge. From the model of emerging science

(Latour 1999), four types of actors are identified:

resources, audiences, allies and peers. Resources

(e.g. time, money, human resources, etc.) can help to

make the group act. Audiences can make sure that

the actions of the groups stay relevant. By finding

allies the resources and audiences can become

bundled, this will create leverage. The sustainability

and growth of the group will depend on the available

peers. Peers are colleagues in the incubation phase,

competitors in the growth phase and external expert

in the maturity phase. While allies have

complementary goals and assets, peers have the

same goals and assets.

The Group-MS can be best compared as the

merging of a social networking site with a Human

Resource (HR) system. The social networking will

contain extended profiles to formalize and document

the different actors, which allow the emerging of

social interaction, like knowledge sharing and idea

generation. Not every person will want to be part of

every phase in the project as such the HR part of the

system should simplify transition between groups.

4.3 Business Management System

Just as the Group-MS has its effect on each of the

three phases, so will the Business-MS. This is

evident for the maturity phase, but does require

explanation for the incubation phase. Spread over

the many innovation management cases, one can

recognize the importance of feedback form early

business development. It allows the identification of

emerging markets; elaborate the change needed in

supply and defines the business culture.

Many new businesses use Strengths,

Weaknesses, Opportunities and Threats (SWOT)

analyses to explore business opportunities. In

relation to the novelty model, the first two aspects

(SW) are considered internal features, while the last

two (OT) are considered directional features. The

external and learning features are related to action

planning, which follows the SWOT analysis. The

external feature is the action planning that will test

how to serve a market. The action planning will

result in business process to regulate the operations,

which is considered the learning features. By

creating more agile iterations of SWOT analysis and

action planning it is expected to improve the

business development during each phase.

During the incubation phase the iterations will

need to be short, just a few days, and plenty. The

goal is to increase diversity and find unlikely

business opportunities. During the growth phase the

iterations can take weeks, but only few iterations are

created, now the focus is to find advantages over the

competing projects. For the maturity phase there

would hardly be more than one iteration, but it could

take months, now it is directed to possible

acquisitions or spinoff.

The IT-support for the Business-MS will become

the intranet knowledge base. For the A-EIP the

intranet is the externalizing of innovation, as it allow

to spinoff the novelty to better know expert methods,

which only work if there is knowledge. While the

variation in the incubation phase would have created

disconnected bits of assets, the growth phase would

result in standardization of that knowledge and the

maturity phase would align the standard with

particular markets or firm.

4.4 Strategic Management System

Strategy is normally focused on the core business of

a company. However innovation often redefines

strategy and so strategy will need to be discovered

too. Xerox PARC is a classic example. While Xerox

PARC has invented many technologies, it often

failed to create value, so an open strategy is needed

(Chesbrough 2003).

From the analysis about innovation strategy (see

section 2) four key indicators are suggested:

bundles, brands, networks and cultures. The bundles

refer to how the aggregation of business assets, like

resources, result in unique values that need to fit a

particular business strategy. For brands, we can look

at case of near bankruptcy. In such cases companies

get divided, but this was not the case for Apple or

IBM. So the brand has its influence on the strategy.

Imagining an idea is easy, but putting it in

practice will depend on networks. An idea can

become unrealistic because no network can be

INNOV 2011 - Second International Conference on Innovative Developments in ICT

created. The strategy will as such adapt depending

on the actual network that is being build. Then there

is the business culture that facilitates the growth of

companies’ assets without the need to organizational

control. So culture automates the emergence of

business values.

Related to the novelty model, the bundles are

internal features, the brands are external features, the

networks are directional features and the cultures are

learning features. Together each feature makes a

holistic view of all the values emerge. Mapping and

modelling tools can give IT-support to make the

holistic view emerge for the Strategy-MS.

The Strategy-MS values would also be

disconnected bits during the incubation phase. In the

growth phase, strategies are aligned and the values

they envision will regulate the activities in the

Group-MS and Business-MS. During the maturity

phase the Strategy-MS has to regulate the

overshooting effect.

Overshooting often happens by focusing on a

minority of high demanding customers. Such

customers will have demands that are totally

irrelevant for the minority of customers. If a

company asks the majority of its customers to pay

for a service that only a minority requires, the

company is creating an opening for low-end

competition. Instead we expect the overshooting to

identify innovation opportunities for new projects.

4.5 Learning Management System

Learning is so important for breakthrough

innovation that it requires a separate management

system. Learning is indeed about knowledge, but the

goal of the Learning-MS is not the intranet of the

Business-MS. The goal is to understand how to learn

related to the innovation. After all, once a

breakthrough innovation is understood there will be

incremental growth. In educational environments a

course is defined to teach the students particular

skills. The Learning-MS is to identify the learning

skills, and create learning material, processes and

validation methods.

Leaning is essential to get trough the incubation

phase and the four key indicators are related to it:

associations, tags, challenges and experiences.

Associations are used to relate existing disconnected

issues in any of the management systems. As

associations connected fragmented information they

internalize a cognitive landscape. The tags identify

external information related to any internal issue. As

such tags externalize the cognitive landscape and

makes in embedded in the environment. The

challenges are meta-information, recognized

between associations and tags. Challenges will give

direction to all the different issues and result in

alignment. Challenges will define goals that allow

directing the system to amplify the novelty. The

experiences are concrete cases that are identified as

containing value to be learned. Such value can be

both positive, which contain experience in favour to

be repeated, and negative, which contain experience

best to be avoided.

During the incubation phase the IT-support looks

like a blackboard, which has the ability to connect

all the disconnected bits of the other management

systems. During the growth phase the alignment

will result in a repository containing training

syllabus and tutorials to train people the required

skills. During the maturity phase the Learning-MS

can identify spillover effects that would create new

opportunities and so new groups to explore them.

Such spillover effects would again be disconnected

and they would be the first entries to the blackboard.

Such spillover effects are expected to stimulate basic

group creation.

4.6 Emerging and Aggregating Novelty

Christensen (2003) illustrate why management

cannot understand the incubation phase. Basically it

is because they do not have measurable values for

emergence and aggregation. By the four

management systems such values can become

measurable without constraining the innovation, but

by understanding the connectivity between the

information.

The transformation of each phase is hidden in the

control of the different management systems. Once a

strategy is clear enough the incubation phase ends

and the growth phase begins. Once the intranet can

unequivocally state the business values the growth

phase ended and the maturity phase begins. During

the maturity phase the spillover effects can start new

projects creating a recursion in the A-EIP system.

The Group-MS has social networking features

that can facilitate the incubation phase. So the

Group-MS will dominate the incubation phase.

During the growing phase the Strategy-MS will

dominate and it will regulate the Group-MS and

Business-MS. The Business-MS will dominate the

maturity phase. The intranet is expected to grow

much more by including the outcomes of the

Strategy-MS and Learning-MS.

The other systems still have their effect on the

maturity phase, but not on the knowledge base of the

intranet. The Group-MS will be most concerned

THE AGILE-ENTERPRISE INNOVATION PLANNING - How to Align Self-organization Processes for Innovation

Management

about transferring knowledge to the new team. The

Strategy-MS will identify the overshooting effects

and the Learning-MS will identify spillover effects,

both are used to kickstart new projects. Of course we

expect new projects to emerge also spontaneously.

The Learning-MS is the drive behind the phases

and the glue between the management systems.

While one system is more dominating at a particular

phase, they all have their influence and the

Learning-MS can help at every step. The Group-MS

transition during the maturity phase will make the

creative team external expert and allow an

operational team to take over, but this is only

possible because the Learning-MS has created

training packages to do so. The Business-MS is less

clear during the incubation phase, but tags can guide

the early development. The Strategy-MS can only

give a holistic view because of association and it can

only have direction because of challenges.

5 CONCLUSIONS

This paper has been examining the A-EIP system

and its four management systems to support the

development of breakthrough innovations. While the

system is complicated, it does seem to give a

reasonable answer to innovation management

problems. The A-EIP system is both a tool to

support innovation as a way to make emergence and

aggregation measurable values. The different

management systems have a natural interaction that

allows a fluid transformation of each innovation

phase.

The A-EIP system is however just a design. At

the moment the system is only described on an

abstract level, but even on that level it has complex

structure. Attempt to use the A-EIP system for

existing innovation mediums all failed for their own

reason and because the large amount of creative

people needed for a test (probably more than 200).

Consider the way the A-EIP system is designed; it is

itself a breakthrough innovation. Thus it needs to

follow the breakthrough innovation method. Let us

consider the four steps in Christensen’s (2003, p

278) Disruptive Growth Engine:

1. Start before you need to

2. A senior management in charge

3. An expert team of movers and shapers

4. Train the troops

Notice that step 3 is part of the incubation phase and

step 4 is part of the maturity phase in the A-EIP

system. It is expected that first steps 1 & 2 have to

be taken before it is possible to develop the A-EIP

system itself. Kiemen (2010) has created controlled

tests with course over a period of four year to

address step 1. In that course business students learn

about future Internet development by developing

their own projects. This controlled environment only

allows for understand how to approach step 2.

Step 2 seems to be a tricky issue. Christensen

considers that the breakthrough innovation emerges

in a large company. As step 1 has emerged in

academic environment, it may well be needed to

have step 2 also in the same environment. Instead of

senior management a Dean or even a vice-rectors

may be needed and a bigger research/education

project to investigate the next generation of

Technology Transfer Office (TTO). Koenraad

(2010) elaborate the evolution if TTO in three

stages:

1. It started as an isolated operation next to the

university

2. It became a professional service supporting the

third mission activities of the university

3. It is emerging towards a strategically embedded

and fully diffused activity throughout the university

Koenraad’s 3

stage seems a natural extension to the

controlled experiments with the course. The outputs

of the current course are IT prototypes for an

Internet business. So far one student has created a

business plan to take the prototype to a next level. If

the course could be extended to full Master program

on Internet Business, it would allow the further

support of micro-spinoffs. The micro-spinoffs would

not be a disruption of regular academic spinoffs as

PhD spinoffs are breakthrough projects. Instead the

support for micro-spinoffs can be a test bed for the

A-EIP support, which could later become valuable

to support PhD spinoffs.

To finish this paper, we like to notice that the

whole A-EIP system is being tested for Internet

application, as the Internet makes many parts

emerge fluently. This is not to say that the A-EIP

system is only usable for Internet, but it allows us to

understand the basic functioning. Once the A-EIP

system has a proof of concept for Internet

innovations, studies can look at how to use the A-

EIP for other domains.

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APPENDIX

Table 1: The A-EIP system and its 20 key indicators.

General terms

Group-MS Business-MS Strategic-MS Learning-MS A-EIP system

Internalizing

(1)Resources (5)Strength (9)Bundles (13)Associations (17)Actors

Externalizing

(2)Audiences (6)Market (10)Brands (14)Tags (18)Spinoffs

Directing

(3)Allies (7)Opportunity (11)Networks 15)Challenges (19)Values

Learning

(4)Peers (8)Processes (12)Cultures (16)Experiences (20)Knowledge

Novelty

Actors Spinoffs Values Knowledge Innovation

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