Complexity theory: dynamics and non-linearity are the only reason for knowledge management to exist


Nov 6, 2013 (5 years and 4 months ago)



Complexity theory: dynamics and non
linearity are the only
reason for knowledge management to exist

Prof dr Walter Baets

Euromed center for Knowledge Management (E

Euromed Marseille

Ecole de Management (F) and Nyenrode University (NL)


1. Introduction

A lot has been said and written about knowledge management, probably starting
with the proponents of the learning organization on the one hand, and Nonaka’s view
on knowledge management on the other hand.

Increasingly, authors have added the
subject to their vocabulary and the more that the ‘general management thinkers’
have got involved (Leonard
Barton, Drucker, etc.) the more knowledge management
has acquired the status of a major buzzword. In the 1999

European Conference on
Information Systems (Copenhagen) the ‘best research paper award’ was given to a
paper that argued that knowledge management would be the next hype to forget
people (Swan et al., 1999). This choice appeared to me to represent a publ
ic act of
masochism on behalf of the IS community, given that IS experts, more than any
other people, should have a clear idea of why knowledge management is here to

This chapter attempts to provide a broad framework for the subject, highlighting
e different aspects (including the human ones) which should be considered when
talking about knowledge management. This ‘taxonomy in brief’ is of course based on
a particular paradigm (as any other taxonomy) that is known as the complexity
paradigm. Lo
oking through the lenses of complexity theory, we can see why
knowledge management is a new and fundamental corporate activity. Complexity
theory allows us to understand why knowledge is a corporate asset and why and how
it should be managed. The lenses
of complexity theory allow us to say that
knowledge management is not just another activity of importance for a company.

A number of knowledge management projects, based on this taxonomy, were
researched over the last 5 years within Notion (The Nyenrode I
nstitute for
Knowledge Management and Virtual Education), a research center fully sponsored
by Achmea (second largest Dutch insurance holding; the fifth largest within its


European network), Atos/Origin, Philips, Sara Lee/DE and Microsoft. Full detail of
hose research projects can be found in Baets (2004a)

This chapter attempts to present the complete picture of KM, starting with the
paradigm, covering the infrastructure and process, with the aim of clarifying the
subject of study. Both the corporate and

the academic perspectives appear in this

2. The knowledge era

An important and remarkable evolution in what we still call today the industrial
world is that it is no longer industrial. We witness a rapid transition from an
industrial society in
to a knowledge society. The knowledge society is based on the
growing importance of knowledge as the so
called fourth production factor. Many
products and certainly all services have a high research and development cost,
whereas the production cost itsel
f is rather low. Developing and launching a new
operating system like Windows costs a huge amount of investment for Microsoft,
which makes the first copy very expensive, but any further copies have a very low
production cost. Having a number of consultan
ts working for a company is a large
investment for a consulting company, so when they are actively working on a
project, their marginal cost is close to zero. Having the knowledge base, which
means having the consultants available is expensive. Their rea
l work for a client is
relatively cheaper. Even the best example of industrial production in the Western
part of the world, which is car manufacturing, became increasingly knowledge
based. More than 40% of the sales price of a car is due to research, dev
and marketing.

We still talk about the industrialized countries, since most of our thinking is still
based on concepts of industrial production dating back to the earlier parts of the
previous century (the 20
, if not even the end of the 19

ntury). What we have
observed, though, is that increasingly companies get involved in optimizing supply
chains and that those supply chains evolve into demand and supply chains. The
following step consists of supporting those chains with information tec
hnology (IT)
in order to increase efficiency. The strange thing that happens in a next stage is
that a progressive use of IT puts pressure on the existence itself of the chain.
The better a chain is integrated based on IT, the more a pressure gets create
which makes the chain explode into a network. Particularly in such circumstances,
the ‘owner’ of the knowledge base manages the process. Network structures evolve
around knowledge centers. Companies manage brands and outsource most of the
chain itself
. Extreme examples of this approach are probably Calvin Klein,
Benetton and Nike. Again, knowledge and particularly the capacity to manage,


create and share knowledge is becoming the center of the scope of the successful
company. This can be translated
via brand management, direct marketing to
targeted clients, etc. but it is the visual part of the evolution from an industrial
market into a knowledge based market. Knowledge becomes yet another attribute
of the changing economic reality.

Knowledge in a
company has different forms and most commonly one regroups these
forms into three categories of knowledge. Tacit knowledge is mainly based on lived
experiences while explicit knowledge refers to the rules and procedures that a
company follows. Cultural k
nowledge then is the environment in which the company
and the individual (within the company) operate.

Different forms of knowledge are crafted by various different activities.
Conversion of knowledge takes place based on the tacit and explicit knowledge

a person possesses or has access to. The creation of knowledge very often takes
place during joint work sessions, such as brainstorms, management meetings, etc.
Equally important but more difficult to capture is knowledge processing via
n. Very often, assimilation is based on cultural knowledge as a first
input, reinforced with tacit knowledge that quit often collapses with explicit rules
and regulations. It seems important to stress, however, that knowledge
management is only the ‘suff
icient’ condition. The ‘necessary’ condition in order to
deal with new economic realities is the boundary condition for knowledge
management and that is the learning culture of the company. On top of the mere
fact that the most interesting knowledge is i
mplicit and therefor ‘stored’ in people,
it is the dynamics of the knowledge creation and sharing activity (let us for
easiness call this learning) where the people come a second time in the picture.

Above all, knowledge management and learning is an atti
tude and a way of working
with management. It is an overall approach that goes beyond the addition of a
number of functional tactics. One could even say that it is a kind of philosophy of
management, rather than a science. This process is one of redefin
ing the target of
the company from a profit making or share
value increasing entity to a knowledge
creating and sharing unit. The first type of organization has a rather short
focus, whereas the latter type has a more visionary and long
term one.

The aim of the company is no longer purely growth as such, but rather it becomes
sustainable development and renewal. Hence, organizations not only need

they also need the skills and competencies to dynamically update and put

knowledge into pra
ctice. This results in the need for organizations to learn
continuously and to look for continuous improvement

in their actions

through the


acquired knowledge. Hence, organizations should embrace the philosophy of the
learning organizations, the process be
ing organizational learning (Baets, 1998).

A learning organization enables each of its members to continually learn and helps
to generate new ideas and thinking. By this process, organizations continuously
learn from their own and others experience, adap
t and improve their efficiency
towards the achievement of their goal. In a way, learning organizations aim to
convert themselves into "knowledge
based" organizations by creating, acquiring and
transferring knowledge so as to improve their planning and act

In order to build a learning organization, or a corporate learning culture, companies
should be skilled at systematic problem solving, learning from their own experience,
learning from the experiences of others, processing knowledge quickly and
ficiently through the organization and experimenting with new approaches.
Developments in information and knowledge technologies make it increasingly
possible to achieve these competitive needs and skills.

3. The complexity paradigm

In the past, ident
ifiable when market change moved slower, we got used to thinking
in terms of reasonably linear behavior as markets and industries appeared to be
more stable or mature. Concretely, one thought one could easily forecast future
behavior based on past observa
tions and in many respects we developed complex
(and sometimes complicated) methods to extrapolate linear trends (Prigogine and
Stengers, 1988; Nicolis and Prigogine, 1989). But in reality, markets do and did not
behave in a linear way. The future is not

a simple extrapolation of the past. A
given action can lead to several possible outcomes ("futures"), some of which are
disproportionate in size to the action itself. The "whole" is therefore not equal to
the sum of the "parts". This contrasting perspe
ctive evolved from complexity and
chaos theory. Complexity theory challenges the traditional management
assumptions by embracing non
linear and dynamic behavior of systems, and by
noting that human activity allows for the possibility of emergent behavior
(Maturana and Varela, 1984). Emergence can be defined as the overall system
behavior that stems from the interaction of many participants

behavior that
cannot be predicted or even "envisioned" from the knowledge of what each
component of a system does.

Organizations, for example, often experience change
processes as emergent behavior. Complexity theory also tells corporate executives
that beyond a certain point, increased knowledge of complex, dynamic systems does
little to improve the ability to exten
d the horizon of predictability for those
systems. No matter how much one knows about the weather, no matter how
powerful the computers, specific long
range predictions are not possible. Knowing


is important, not predicting, thus there is no certainty (S
tewart, 1989; Cohen and
Stewart, 1994)).

The focus on non
linear behavior of markets collides with the traditional positivist
and Cartesian view of the world. That positivist perspective translated in the
traditional management literature

the stuff th
at most MBAs are taught

describes "
" world in terms of variables and matrices, and within a certain
system of coordinates. Exact and objective numbers are needed in order to create
models while simulations can offer a ‘correct’ picture of what to exp
Particularly business schools have welcomed this ‘scientific’ way of dealing with
management problems as the one which could bring business schools up to the
"scientific" level of the beta sciences. It is clear that much of the existing
management p
ractice, theory, and "remedies" based on the positivist view are
limited by their dependence on several inappropriate assumptions as they don't
reflect business and market behavior. Linear and static methods are the ones that
are taught in business school
s. Therefore, markets have to be linear and static.
As we know they are not (Arthur, 1990).

It seems important to elaborate a little more on positivist thinking as we want to
propose later a different paradigm.

A major aspect of positivism is the div
ision between object and subject. This
means that the outer world (e.g. an industry) is pre
given, ready to be "truthfully"
represented by organizations and individuals. The mind is able to create an inner
representation that corresponds to the outer wor
ld, be it an object, event or state.
Translated to knowledge, positivism considers that knowledge exists independent
of the human being that uses it, learns it, transfers it. Knowledge reflects and
represents “the world in itself” and can be built up ind
ependent of the observer,
the “knower”. What if the universal knowledge that is transferred is mainly a
theoretical framework, a form which is of little use in the non
linear and dynamic

Another premise of positivist thinking is based on a str
ict belief in (absolute)
causality and (environmental) determinism. As there exist clear
cut connections
between cause and effect, managerial actions lead to predictable outcomes and
thus to control. Successful systems are driven by negative feedback pro
toward predictable states of adaptation to the environment. The dynamics of
success are therefore assumed to be a tendency towards stability, regularity, and
predictability. The classic approach to strategy illustrates this reductionism. The
lexities of industries are reduced in terms of maturity, continuity and
stability so that a single prediction of an organization's future path can be
described. As a consequence, the better the environmental analysis according to a


number of dimensions, t
he better the course (strategy) can be defined and
implemented (Baets and Van der Linden, 2000, 2003).

My own research over the last years, and currently undertaken in the E
suggests that instead of searching for causality, the concept of synchronicit
(being together in time), often referred to as a quantumstructure, allows much
more insight in business dynamics (Baets, 2004b). Indeed that quantumstructure
is a holistic concept of management, based on interacting “agents”. Those networks
of agents/p
eople create emergent behavior and knowledge.

Positivism is the prevailing scientific view in the Western world, since it perfectly
coincides with the Cartesian view of the world: the over
riding power of man as a
fact of nature. Nature gives man the pow
er to master nature, according to laws of
nature. In 1903 however, Poincaré, a French mathematician, brought some doubt in
this positivist view. Without really being able to prove, or even to gather evidence,
he warned:

"Sometimes small differences in t
he initial conditions generate very
large differences in the final phenomena. A slight error in the
former could produce a tremendous error in the latter. Prediction
becomes impossible; we have accidental phenomena."

It suggested that with the approache
s used, man was not always able to control
their own systems. Hence, there's the limit to the Cartesian view of the world.

It took quite a number of years until, in 1964, Lorenz showed evidence of the
phenomenon. Lorenz, an American meteorologist, was i
nterested in weather
forecasting. In order to produce forecasts, he built a simple dynamic non
model. Though it only consisted of a few equations and a few variables, it showed
"strange" behavior. A dynamic model is one where the value in a given

period is a
function of the value in the previous period. For example, the value of a particular
price in a given period is a function of its value in the previous period. Or, the
market share for product A in a given period is a function of the market
share in
the previous period. In other words, most if not all, economic phenomena are
dynamic. Such a dynamic process that continuously changes can only be simulated
by a stepwise procedure of very small increments. It is an iterative process. Once

value of the previous period is calculated, it is used as an input value for the
next period, etc.

A computer allowed Lorenz to show what could happen with non
linear dynamic
systems. As is known, he observed that very small differences in starting value


caused chaotic behavior after a number of iterations. The observed difference
became larger than the signal itself. Hence, the predictive value of the model
became zero (Stewart, 1989). Lorenz's observation caused a real paradigm shift in
sciences. Lo
renz showed what Poincaré suggested, namely that non
linear dynamic
systems are highly sensitive to initial conditions. Complex adaptive systems are
probabilistic rather than deterministic, and factors such as non
linearity can
magnify apparently insignif
icant differences in initial conditions into huge
consequences, meaning that the long term outcomes for complex systems are
unknowable. Today we know, thanks to the integration of ideas of the two main
scientific revolutions of the last century (relativit
y and quantummechanics), that
another underlying problem, aggravating the complex structure, is the structure of
synchronicity in the “business nature”.

Translated to management, this advocates that companies and economies need to
be structured to encour

an approach that embraces flux and competition in
complex and chaotic contexts rather than a rational one. Mainstream approaches
popularized in business texts, however, seldom come to grips with non
phenomena. Instead, they tend to model pheno
mena as if they were linear in order
to make them tractable and controllable, and tend to model aggregate behavior as
if it is produced by individual entities which all exhibit average behavior.

Positive feedback has been brought into the realm of economi
cs by Brian Arthur
(Arthur, 1990), who claims that there are really 2 economies, one that functions on
the basis of traditional diminishing returns, and one where increasing returns to
scale are evident due to positive feedback. Marshall introduced the co
ncept of
diminishing returns already in 1890. This theory was based on industrial
production, where one could chose out of many resources and relatively little
knowledge was involved in production. Production then seemed to follow the law of
returns, based on negative feedback in the process and this led to a
unique (market) equilibrium. Arthur's second economy includes most knowledge
industries. In the knowledge economy, companies should focus on adapting,
recognizing patterns, and building

webs to amplify positive feedback rather than
trying to achieve "optimal" performance. A good example is VHS becoming a
market standard, without being technically superior. A snowball effect ensued
which made VHS the market standard, even though Betama
x offered better
technology at a comparable price.

Arthur also specified a number of reasons for increasing returns that particularly
fits today's economy. Most products, being highly knowledge intensive, with high
front costs, network effects, and cu
stomer relationships, lead to complex
behavior. Let us take the example of Windows. The first copy of Windows is quite
expensive due to huge research costs. Microsoft experiences a loss on the first


generation. The second and following generations cost

very little comparatively,
but the revenue per product remains the same. Hence, there is a process of
increasing returns.

Two more interesting developments have consequences for our argument. Recent
neurobiological research, e.g. by Varela (Maturana an
d Varela, 1984), has revealed
the concept of self
organization and the concept that knowledge is not stored, but
rather created each time over and again, based on the neural capacity of the brain.
Cognition is enacted, which means that cognition only exis
ts in action and
interpretation. This concept of enacted cognition goes fundamentally against the
prevailing idea that things are outside and the brain is inside the person. The
subject can be considered as the special experience of oneself, as a process
terms of truth. By identifying with objects, the individual leaves the opportunity
for the objects to "talk". In other words, subject and object meet in interaction,
in hybrid structures. Individuals thus become builders of facts in constructing
ts of knowledge which relate to events, occurrences and states. Knowledge
is concerned with the way one learns to fix the flow of the world in temporal and
spatial terms. Consequently, claims of truth are transposed on objects; the subject
is "de
ivised". There is not such subdivision between the object and the
subject. Cognition is produced by an embodied mind, a mind that is part of a body,
sensors and an environment (Baets, 1999; Baets and Van der Linden, 2000).

Research in artificial life g
ave us the insight that instead of reducing the complex
world to simple simulation models, which are never correct, one could equally define
some simple rules, which then produce complex behavior (Langton, 1989). This is
also a form of self
like the flock of birds that flies south. The first
bird is not the leader and does not command the flog. Rather, each bird has a
simple rule e.g. to stay 20 cm away from its two neighbors. This simple rule allows
us to simulate the complex behavior of
a flock of birds.

Probabilistic, non
linear dynamic systems are still considered deterministic. That
means that such systems follow rules, even if they are difficult to identify and
even if the appearance of the simulated phenomenon suggests complete cha
os. The
same complex system can produce at different times, chaotic or orderly behavior.
The change between chaos and order cannot be forecast, nor can the moment in
which it takes places, either in magnitude or direction. Complexity and chaos refer
the state of a system and not to what we commonly know as complicated, i.e.
something that is difficult to do. The latter depends not on the system, but more
on the environment and boundary conditions. Perhaps for a handicapped person,
driving a car is m
ore complicated. In general, building a house seems more
complicated than sewing a suit, but for some other people building a house would be


less complicated than sewing a suit. This depends on the boundary conditions for
each individual person.

To form
alize in a simplified way the findings of complexity theory, we could state
three characteristics. First, complex systems are highly dependent on the initial
state. A slight change in the starting situation can have dramatic consequences in a
later perio
d of time caused by the dynamic and iterative character of the system.
Second, one cannot forecast the future based on the past. Based on the
irreversibility of time principle (of Prigogine), one can only make one step ahead at
a time, scanning carefully

the new starting position. Third, the scaling factor of a
linear system causes the appearance of "strange attractors", a local minimum
or maximum around which a system seems to stay for a certain period of time in
quasi equilibrium. The number of at
tractors cannot be forecasted, neither can it
be forecasted when they attract the phenomenon.

There are a myriad of insights we gain from complexity theory and its applications
in business and markets for knowledge management (Baets, 1998; Baets and Van
der Linden, 2000).

The ‘irreversibility of time’ theorem suggests that there is no best solution. There
are "best" principles of which one can learn, but no best solutions or practices that
one could copy. There are even no guaranteed solutions that co
uld be used in most
circumstances. This fact deems the need for a different way of organizing the
process of knowledge creation and knowledge management.

4. What should be understood by Knowledge Management: the corporate

Allow recalling that this

chapter attempts to present the complete picture of
knowledge management, starting from the paradigm, covering the infrastructure
and process, with the aim of clarifying the subject of study. Though the corporate
and the academic perspectives are at time
s a little different, they both appear in
figure 1.


Asset Mgmt.
Dynamic process
Learning metaphors
Multi disciplinary
Subject of

Figure 1: A taxonomy of knowledge management

Any managerial concept is based on a particular paradigm and according to the view
developed in this paper, the paradigm of

complexity (non
linear and dynamic
systems behavior) sheds interesting and refreshing light on the nature of
knowledge management. Earlier in this chapter we have explained why the
complexity paradigm positions knowledge at the center of a knowledge
company and it does so increasingly with virtual or extended companies.

The left side of the figure shows the corporate logic in understanding knowledge
management. The paradigm serves as the glasses through which we look to the
corporate purpose (gai
ning sustainable competitive advantage, or expressed
simpler, survival) and what we observe then is the means to achieve the purpose,
i.e. asset management. The chosen glasses allow to identify (observe) the way
ahead in reaching the goal. The immediate
‘next’ step is the ‘infrastructure’ or
stakeholders necessary for knowledge management:

Human resources management and management development;

Information and communication technology, in particular artificial intelligence;

Business education and (virtua
l) learning.


The corporate purpose remains to create sustainable competitive advantage, and
the means for realizing that is (and has always been) asset management. However,
for knowledge intensive companies this means that knowledge management moves
o the picture. A translation (a filter) above and beyond the necessary
integration of infrastructure and stakeholders is necessary in order to combine the

infrastructure in knowledge management. That filter is a dynamic process, in which
the ‘learner’ sh
ould be given responsibility. Pedagogical metaphors give us an
insight into this filter process (Baets, 1999).

The prevailing pedagogical metaphor is the transfer metaphor. Knowledge in
general and, more specifically, subject matters, are viewed as tr
commodities. A student (a learner) is seen as a vessel positioned alongside a
loading dock. ‘Knowledge’ is poured into the vessel until it is full. Whereas the
student is the empty vessel, the teacher is a crane or a forklift. The teacher
livers and places knowledge into the empty vessel. Courses applying the transfer
theory would be very much lecture
based, would include talks from leading figures
in the relevant fields (the more the better) and would provide students with
duplicated cour
se notes. Once the vessel is filled, a ‘bill of loading’, which is the
diploma, certifies the content of the vessel. IT improves the speed of the loading
(with high tech cranes). Nobody can guarantee that in the next harbor, the cargo
is not taken out o
f the ship. Monitoring a student means monitoring the process of
filling the vessel and sometimes sampling the quality of the contents. This same
metaphor became the prevailing one while talking about (virtual) knowledge
management approaches (Baets and
Van der Linden, 2000; 2003).

However, since knowledge appears to be dynamic and learning non
linear (based on
our paradigm), another paradigm is necessary. Here again educational science
provides us with a valid illustration. The travelling metaphor is
one by which the
teacher initiates and guides the students through an unknown terrain that needs to
be explored. The student is the explorer and the teacher/tutor is the experienced
and expert travelling companion and counselor. The guide not only points

out the
way, but also provides travelling maps and a compass. The ‘teaching methods’ (if one
can still call them such) which are most used in applying this theory are experiential
methods: simulations, projects, exercises with unpredictable outcomes (as i
n some
case studies), discussions and independent learning. In courses applying this
theory, monitoring means regularly comparing each other’s travelling notes.
Experiments have shown that this theory is particularly effective in adult
education, since ad
ults are better equipped in order to deal with the increased
responsible that the ‘learner’ has in this paradigm. One step on from the travelling
theory is the growing metaphor. In many respects, this theory does not differ
greatly from the previous one.

Rather, it is an extension of it, which focuses more


on the self
initiative of the student. Subject matters are a set of experiences
that each student should incorporate into his/her personality. The aim for the
student is to develop his/her personalit
y. This latter paradigm (be it the travelling
metaphor or the growing one) perfectly fits complexity theory (our overall
paradigm or glasses). It allows us to integrate the infrastructure into asset
management. It introduces the rational for work place
learning, and the necessary
integration of the latter with knowledge management. This makes knowledge
management different from and value adding to information management.

5. Research perspective on Knowledge Management

The combination of infrastructu
re (with its different stakeholders and/or
disciplines) and the learning process (filter) makes knowledge management what it
should be. Most existing knowledge management theories either do not get much
further than a discussion of means and purposes, or
they overstress one of the
infrastructural aspects, ignoring the unity and necessity of all the three elements
together. In our view, knowledge management, knowledge creation and knowledge
sharing (via virtual learning platforms) are integral parts of the

same model.

From a research perspective, we consider complexity theory as the basic science(s)
involved. In particular the following concepts are of importance for the correct
understanding of the paradigm and its consequences for knowledge management:

Sensitivity of the complex system to initial conditions

Existence of (many) strange attractors in complex systems

Irreversibility of time principle (Prigogine)

Behavior of complex systems far away from equilibrium (Prigogine)

Learning behavior of systems

Autopoeisis (Varela)

Embodied mind (Varela)

Enacted cognition (Varela)

Artificial life research and its applications (Langton)

Law of increasing returns (Arthur)

Quantumstructure of business

All these aspects need a good explanation and a clear link to ma
consequences is necessary.

As already mentioned earlier, and visualized in figure 1, the disciplines involved in
knowledge management are human resources management and management


development, ICT and particularly artificial intelligence (AI), an
d business
education, increasingly virtual education. The management development function
should be the driver in this knowledge creation, knowledge sharing, learning
process, ensuring that each individual receives at the pace that s/he can process.
MD s
hould provide further the learning conditions. It is unavoidable that ICT and
AI are necessary in order to support the knowledge management process (Baets
and Venugopal, 1998; Venugopal and Baets, 1995). Building IT platforms, extracting
knowledge via A
I and virtual education are only some of the aspects where IT is of
help. Business education, and increasingly this includes virtual education, is
responsible for creating some input in the learning process but equally to make
some of the extracted knowle
dge accessible for each individual. Business
education in this respect has also to do with the content. The aspect of knowledge
sharing is an educational one too. Knowledge management, therefore, needs to
integrate successfully disciplines like human res
ources management, organizational
sciences, educational sciences, artificial intelligence and cognitive sciences, etc,
implicitly defining a knowledge management research agenda.

It is my firm belief that in the decade to come, we will see a breakthrough
in the
understanding of the underlying theory justifying the (corporate) necessity for
knowledge management, in line with the agenda set out in this chapter. As
suggested earlier, the consequence of the concepts developed here and its logical
extension is

an unavoidable ontological discussion about causality versus
synchronicity. In my work (2004b) I call this the quantum structure of business
(or in particular, in the reference, of innovation), which provides an integrated and
applicable theoretical and
conceptual framework in order to understand and manage
consequently dynamic processes, knowledge management only being one of them.
The first research projects undertaken confirm this potential understanding and
its application in business. It is the acc
eptance of the ontological evidence for
synchronicity that drives the research agenda of E


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