Study on the Standardization of Knowledge Management Facing the Open
Innovation
Yang Ping1,2
2
1
Institute of Standard and Specification Research, Naval Academy of Armament, Shanghai, China
Institute of Finance and Economics Research, Shanghai University of Finance & Economics, Shanghai, China
(ocean.py@163.com)
Abstract - Knowledge management is becoming a key
factor in determining its success or failure as the most
important resource of innovative activities. But it is a
problem that how to establish a close contact between open
innovation and knowledge management to improve the
efficiency of knowledge management and to promote open
innovation. This paper systematically analyzes the
standardization operation mode of knowledge input,
extraction, curing, accumulation, transfer and diffusion in
open innovation with the help of cloud models and multiattribute decision theory. And get the conclusion that: the
standardization work optimize the structure of knowledge
management, reduce innovation risk, promote open
innovation through effective flow of knowledge in the whole
process of innovation by standard as a transfer carrier;
standards is not only the most effective connecting link
between open innovation and knowledge management, but
also the multiplier to promote the common development of
both.
Keywords - open innovation, standardization, knowledge
management, cloud model, multiple attribute decision
making
I. INTRODUCTION
Since 2003, the concept of open innovation raised by
Professor Henry Chesbrough [1] had become a mainstream
to improve research efficiency and reduce research costs
[2]
, it could fundamentally change the innovation model,
help cultivate a deeper ability to innovate, and enhance
core competitiveness, achieve cost-effective model of
economic growth changes and have great significance to
maintaining economic growth[3]. In United States and
Europe the studies and discussions on open innovation
model are more and more in depth, and open innovation
has been gradually integrated into the national innovation
policy. Knowledge is the most important resources of
open innovation activities, its production, creation and
application is an evolution of complex process which run
through the whole process of innovation [4], and is always
accompanied by the development, test, mature and
diffusion of the technology, so knowledge has been
become a key factor in determining its success or failure.
In the practice of open innovation, knowledge
management has become a new model to replace the
traditional product innovation management [5]. The
standard is not only the input of open innovation, but also
the output; not only the extraction and accumulation of
knowledge, but also the optimization and reorganization;
not only a means of knowledge management, but also the
knowledge management support. With the help of cloud
models and multi-attribute decision theory, this paper
systematically analyzes the standardization operation
mode of knowledge input, extraction, curing,
accumulation, transfer and diffusion in the open
innovation.
II. EXTRACTION AND ACCUMULATION OF
INNOVATION KNOWLEDGE BY
STANDARDIZATION
A. Theoretical analysis
Open innovation is an innovative model which could
rewrite the map of innovation activities, break through the
organizational boundaries, use complementary and
knowledge resources outside the organization to have
multi-angle dynamic cooperation with a variety of
partners in various stages in the innovation chain.
Compared with traditional high-cost "closed
innovation" model, open innovation is an “innovation
paradigm shift” which could get higher innovation output
by less scientific research, is an inevitable choice to solve
the lack of internal resources under the situation of
scientific and technological innovation in constant
acceleration. It has no fundamental changes in the internal
ductility of innovation, but form a breakthrough in
stretched chains and innovative ways, Yang Wu believes
that it could make the organization's innovative resources
recombined quantity and quality in time and space, to
achieve seamless integration and overall optimization [6].
The essence of this innovative system is the alliance
between the internal and external knowledge base [7], in
this process of innovation, knowledge is sustained
absorbed, created, and accumulated, and continued to be
reassembled [8]. The range of knowledge sources is
expanding and the number of knowledge generated is
increasing.
Typically, the classification of tacit knowledge and
explicit knowledge is the most important classification
structure in the field of knowledge management [9]. It is
mainly based on the degree that knowledge can be
presented to the division. In fact, tacit knowledge and
explicit knowledge are interrelated and transformed into
each other, while the standard is the most effective way of
refining and sharing of tacit knowledge. Explicit
knowledge is transformed from the tacit knowledge, but it
also becomes the premise of the new tacit knowledge
bourgeoning. Tacit knowledge deep in the innovation
individual is of great value, but is difficult to exchange by
market; second a lot of explicit knowledge of the various
innovation units cannot fully share, resulting that this part
of the knowledge may have a trend to be hidden, so that
tacit knowledge content would be further increasing in the
entire open innovation process. Standardization can
promote the conversion from tacit knowledge to explicit
knowledge. From the view of knowledge management
point, these standards make individual tacit knowledge
explicit essentially in the process of innovation, store it in
the knowledge base, but also to ensure this knowledge
does not disappear in the transfer between innovative
individuals, in order to achieve knowledge share.
However, this classification with tacit knowledge
and explicit knowledge is not entirely reasonable and
sufficient for all the knowledge, which have been divided
into eight by economists Max. H. Boesot with two
dimensions such as knowledge diffusion and coding,
concrete and abstract (see Fig.1 and 2) [10]. We note that
the property of such knowledge is complex, may be
overlap, and has a great uncertainty. In this case, the role
of standards is not only to extract new generated
knowledge, or convert tacit knowledge into explicit
knowledge, but also screen on all kinds of knowledge
according to needs of the various stages in open
innovation, and classify knowledge according to the
different objects.
abstract
Secret
Scientific
Knowledge Knowledge
between probability theory and fuzzy mathematical theory.
The cloud model not only reflects the uncertainty of the
concept of the natural language, but also reflects the
relationship between randomness and fuzziness, and
constitutes a mutual mapping between the qualitative and
quantitative[12]. In knowledge management practice, we
try to adopt the cloud model to identify and extract
knowledge of open innovation.
F. Bin, L. Daoguo, W. Mukuai were systematically
summarized the research work of the cloud theory [13],
here will not repeat them. But we must know that normal
cloud model by the specific structure of expectations,
entropy and hyper entropy generator to generate the
qualitative concept and quantitative conversion value,
reflecting the uncertainty of the concept. This particular
structure is not only to relax a prerequisite for the
formation of a normal distribution, but also relax from
accurately determining the membership function to
constructing expectation function of normal distribution
membership, therefore is more general applicability, and
is more simple and direct to complete interaction
conversion process between qualitative and quantitative
[14]
.
Normal cloud model can be expressed as follows:
Let U be quantitative domain of a precise numeric
representation, C is the qualitative concept of U , if the
quantitative value x U , and x is a random realization of
a qualitative concept C , if x satisfies: x ~ N Ex,  En2 .
Among
it, En ~ N  En, He2  ,

and
x
satisfy

the
relationship for the degree of certainty C as in (1):
Specific
Local
Knowledge
Non-spread
Thematic
Knowledge
spread
Fig.1 knowledge Type Ⅰ
Data from: Max. H. Boesot. Information Space (pp.169)
coded
uncoded
Special
Knowledge
Public
Knowledge
Personal
Knowledge
General
Knowledge
Non-spread
spread
Fig.2 knowledge Type Ⅱ
Data from: Max. H. Boesot. Information Space (pp.204)
B. Model analysis
Uncertainty is one of the basic properties of the
objective world. The generalized uncertainty includes five
aspects as ambiguity, randomness, incompleteness,
inconsistency and instability. In which fuzziness and
randomness are essential [11]. This is precisely the basic
characteristics of knowledge application, generation and
accumulation in open innovation process. The cloud
model is the conversion model between qualitative
concept and its quantitative expression, formed by the
specific structure algorithm on the basis of the interaction
 e

 x  Ex 2
2
2 En '
(1)
The distribution of x in the domain U is called the
normal cloud. Cloud droplets which contribute to
qualitative concept C in U have 99.7% fall on the
interval of  Ex  3En, Ex  3En , and contribution to
qualitative concept of the cloud droplets outside the
interval of  Ex  3En, Ex  3En is a small probability
event, could be negligible, which is the “3 En rules” of
normal cloud [15].
L. Changyu, L. Deyi and P. Lili have proved that
using normal cloud model to represent uncertain
knowledge is rationality and effectiveness (see Fig.3) [16].
Here in the practice of open innovation, use normal cloud
model to analyze the standard of knowledge extraction.
We could extract the standard from knowledge, and make
standard divided into four groups, that is (applied standard,
theoretical standard), (technical standard, system
standard), (special standard, public standard), (specific
standard, general standard) (see Fig.4 and Fig.5), which in
accordance with two dimensions of the eight knowledge
properties corresponding to the foregoing. Then, we can
classify the knowledge of the standard extraction and
store into standard framework or standard system, to
provide an initial demonstration for the formation of
standards and standard system [17] (note: classification of
standard in innovation knowledge here is different from
the former, and the nouns of these standard categories are
not very standardized, for an instance, “specific standard”
should be “detail specification”, do so because that this
paper would like to make classification of standard
correspond with innovation knowledge, in order to help
everyone to understand and apply).
Fig.3 Normal cloud model examples of analysis
Data from: L. Changyu, L. Deyi and P. Lili (2004)
abstract
Specific
Applied
Standard
Theoretical
Standard
Technical
Standard
System
Standard
Non-spread
spread
Fig.4 standard Type Ⅰ of innovation knowledge
Data from: author drew
coded
uncoded
Special
Standard
Public
Standard
​Specific
Standard
General
Standard
Non-spread
in the process of research and innovation, that is,
knowledge transfer to the next generation of R&D
through the standard. In the open innovation process, a lot
of knowledge must be filtered and structured by
standardization principles which would be detailed and
fixed to enhance the quality of intellectual capital.
For explicit knowledge, the standardization process is
designed so that knowledge of the user could quickly
search for the required knowledge, for instance, we could
standardize the product design process knowledge by
standardization, unify concept and its relationship
statements, provide a standardized language for different
knowledge background and different level of staff to
reduce or eliminate the confusion of the concepts and
terminology,
thereby
reducing
friction
and
misunderstanding costs in the process of knowledge
transfer, and accelerating the design between the effective
exchange, sharing and reusing of experience knowledge.
Therefore, to the accumulation of existing knowledge,
standardization is an important means which has been
confirmed in practice.
But for tacit knowledge, knowledge transfer will
generate more uncertainties, and even greater risk to
innovation. More importantly, the environmental
conditions and maturity may be not clear to the later stage,
application feasibility and realization way of tacit
knowledge may be unpredictable to the previous stage.
This is information incomplete of standardization existing
in the knowledge transfer. In deeper level, this uncertainty
and incomplete is due to the changes of knowledge
property. Specifically, in the process of knowledge
transfer, regardless of the tacit knowledge or explicit
knowledge, knowledge properties continue to change,
which include the increase, reduce or turnover of the
property. Therefore, we would like to solve this problem
by using of multi-attribute decision theory in practice.
spread
Fig.4 standard Type Ⅱ of innovation knowledge
Data from: author drew
III. OPTIMIZATION AND TRANSFER OF
INNOVATION KNOWLEDGE BY
STANDARDIZATION
A. Theoretical analysis
Companies involved in the process of technical
standardization is the process to understand and master
the development technology of product, is a process to
solve technical problems by its own knowledge
accumulation and organizational resources, so the
companies which propose standard can put the
development of new technology system into technical
track, thereby more likely to gain an advantage in the later
product competition.
Standardization reflects the accumulation of the
knowledge base, and they, in turn, will generate feedback
to R&D to promote innovation output. Standard
application and a variety of feedback loops play a key role
B. Model analysis
On the basis of previous research, taking into
account open innovation process, knowledge management
and standardization work has emerged features as multiattributes and information incomplete. In practice, we use
multi-attribute decision making under the conditions of
incomplete information, the standard extraction of
knowledge for sorting, filtering and structured. At present,
the multi-attribute decision making problems under
complete information is almost complete. However, in
complex systems engineering management practice, most
of the information has the property of inaccurate,
incomplete and vague, coupled with the limitations of
managers understanding of the problem or their own lack
of knowledge of other reasons, program attribute values
and attribute weighting coefficient information which
managers are given or acquired is incomplete. Especially
a lot of technical and management elements are uncertain,
even subject to change at any time. Therefore, based on
previous research results, we could apply multi-attribute
decision-making method in the innovation knowledge
with incomplete information [18].
IV. MANAGEMENT AND DIFFUSION OF
INNOVATION KNOWLEDGE BY
STANDARDIZATION
A. Theoretical analysis
Through standardization of technical experience will
be accumulated to form the basis of the emergence of new
technologies, to promote technological innovation. Multitechnology competition will lead to the uncertainty of the
future, resulting in technology in the market cannot be
quickly accepted by consumers, although a number of
technical co-exist, but had not made great progress in the
plight of. Technical standards can reduce this diversity
through its coordinating role, greatly reducing the
technology of friction between the social benefits of the
huge loss. But also by enhancing consumer confidence, to
become the standard technology quickly dominate the
market, so as to promote the development of the
technology and technical standards as a mature
technology system, can make better technology products
compatible with to further promote the development of
complementary or compatible products. Companies
involved in the technical standardization process at the
same time understand and master the technology of
product development process is the accumulation of
knowledge and organizational resources to solve technical
problems, the proposed standard to the development of
new technology system included in the technical track,
thus more likely to gain an advantage in the later product
competition.
An important role of standardization is to make
chaotic technological innovation into a system of
technological innovation activities, and the formation of
new markets. The success of innovation depends on the
match and synergy between the factors, the important role
of technical standards is to coordinate the business
independently complete a variety of technological
innovation and by given a comprehensive and systematic
framework, making the chaotic innovation to a systematic
way into the system of science, technology and industry
play a joint role, provide a useful service for end users,
and can open up new markets for the participants or
partners to bring changes in the vitality to the industrial
structure. Open innovation, standardization can ensure the
integration of technology and innovative modular,
generic, serialized, you can ensure that the technology and
innovation interface interoperability, interconnection,
complementary, you can ensure that the overall standard
of the independent innovation in the system progressive
realization within the system framework, to ensure a
variety of innovative sources of the final integration into
the scientific research achievements with independent
intellectual property rights. Therefore, the coordinated
development of open innovation needs to be in close
connection with standardization, and to form systemic
innovation by internal and external technical modular,
innovative synchronized and coordinated to improve the
role and status of China's technical standards.
B. Model analysis
According to the multi-attribute decision making, we
can get specific innovative stage, sort of the main series of
standards for different object structure model. As
mentioned earlier, if we integrate these standards and their
elements together, then due to the different standard
attribute assignment of different objects in different stages
of innovation, leading to various standard elements may
overlap, conflicting and uncoordinated .Therefore, we
can, consistent iterative model, making and group
decision-making matrix between acceptable similarity of
individual decision-making matrix is constantly being
adjusted until acceptable similarity between the group
decision-making matrix, in order to amend the Multiple
Attribute Decision Making matrix[19].
Typically, the selected principal component analysis
and factor analysis is to establish and optimize an
effective way of standard systems and standard
development frameworks. Correlation coefficient matrix
of the starting point of these two methods are variable, in
less loss of information under the premise of multiple
variables (these variables requires the presence of strong
correlation, in order to guarantee the principal
components extracted from the original variables )
integrated into a few variables to study all aspects of the
overall multivariate statistical methods, and this small
number of several variables represent the information can
not overlap, that is variable between unrelated. The
principal component analysis is the use of dimensionality
reduction techniques using the few variables instead of
the original multiple variables, variable focus most of the
information of the original variables; scientific evaluation
function score by calculating the integrated principal
component, the objective economic phenomenon;
information contribution to the influence of the
comprehensive evaluation focused on the application.
Factor Analysis is not the choice of the original variables,
but according to the information of the original variables
to regroup, to identify common factors affecting the
variable, the simplification of data, abandoning the special
factor [20].
Open innovation and technology diffusion process in
a variety of needs of the standard preliminary
classification of the eight attributes of the previous
standard for measuring the actual situation of the
technology development and application of experimental
and market demand, the needs of the different criteria in
observations
in
these
eight
attributes,
is
X ij  i  1, 2,
, n; j  1, 2,
p
. As a result, we can get the
following matrix as in (2):
 x11
x
21
X 


 xn1
x12
x22
xn 2
x1 p 
x2 p 
  X1, X 2 ,


xnp 
XP 
(2)
X  x1i , x2i ,, xni ' , i  1,2,, p .
Among it 1
Then, the application of factor analysis based on
multi-attribute decision-making information, various
standard view re-combination to identify common factors
affecting the variable defined in the standard division of
clear criteria for classification, to improve the standard
system, so as to open innovation, knowledge management
more optimized knowledge structure and knowledge
level.
V. CONCLUSION
In summary, open innovation can receive a higher
innovation output by less investment and lower costs in
research; but also it may face a huge risk, and risk
investment of market operation. Knowledge is becoming
a key factor in determining its success or failure as the
most important resource of innovative activities. In the
practice of open innovation, knowledge management has
become a new model to replace the traditional product
innovation management. Standardization work is not only
an effective way of knowledge extraction and
accumulation in innovation management, but also to
provide a development framework for knowledge
management, and to optimize the structure and level of
the knowledge management, to improve management
efficiency. On the other hand, standardization work could
not only provide knowledge input for open innovation,
but also promote open innovation through knowledge
transfer in the whole process of innovation by standard as
a media; not only curing the innovative technological
achievements, but also promote innovation technology
diffusion. This article asserts: according to the needs of
open
innovation
and
knowledge
management
characteristics, through the reasonable, advanced
scientific modeling tools integrated into standardization
work, then standards is not only the most effective
connecting link between open innovation and knowledge
management, but also the multiplier to promote the
common development of both.
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