Ali Rezaeian
Shahid Beheshti University, Iran
E-mail: A_Rezaeian@Sbu.ac.ir
Rouhollah Bagheri
Shahid Beheshti University, Iran
E-mail: R.bagheri@aut.ac.ir
Submission: 13/07/2017
Accept: 02/03/2018
ABSTRACT
The current research has been done with the aim of knowledge network
interpretive structural modeling in car industry’s R&D centers. The key
factors for implementing a knowledge network in car industry’s R&D centers
have been determined and then the final graphical model has been drawn by
Interpretive Structural Modeling (ISM) approach.
The method of the current applied research includes a survey of experts and
then the variables extracted through investigating research background, after
that the MATLAB R2013 software is used for making compatible matrix as well as
drawing graphical relations of the model by Interpretive Structural Modeling
approach.
After studying related works & interviewing with under-studied firms’
managers, interpretive structural modeling (ISM) & MICMAC analysis was used
to generate a model for knowledge network.
Previous
studies had not investigated the knowledge network in car industry’s R&D
centers; however, the present study implemented the knowledge network model in
R&D Centers.
Keywords: Knowledge Network,
Knowledge Management, Interpretive Structural Modeling, R&D Centers
1. INTRODUCTION
Nowadays,
knowledge networks are among the new efficient concepts in organizations for
knowledge sharing process, which create knowledge interaction and communication
among individuals with knowledge bases. This helps organizations use their
internal and external knowledge resources in the form of a single logical
network.
Network
opinion refers to a form of organization with structural priorities regardless
of its form as a mediator between market and hierarchy. According to Seufert et al. (1999), the dominant spirit on firms
and research centers connected together through knowledge networks are hidden
in knowledge flow of different knowledge
bases.
Knowledge
flow always moves inside knowledge networks from dense parts of knowledge
to parts with low density and results in synergy and
multiple knowledge creation in co-organizations which are connected to each
other through knowledge networks. Knowledge is created, codified, categorized
and stored in knowledge networks to use in whole organization for different
applications. But, what is important for next step is mainstreaming knowledge
in the veins of the organization as its blood. Knowledge networks help the
knowledge flow in the organization body as blood in veins.
Knowledge
networks should direct knowledge flow from different parts to its application
place. However, one of the main challenges in knowledge networks is to
encourage individuals to take effective and continuous actions in
organization’s knowledge sharing systems.
Many
studies in Iranian organizations confirmed that the largest challenge for
having successful knowledge-based management systems was low tendency of
individuals for documenting and knowledge sharing. The issue of knowledge
networks plays an important role for sharing individuals’ knowledge in
different R&D centers of car industry.
Since,
knowledge networks are of the most efficient and the most effective solutions
for knowledge sharing among individuals and knowledge bases; this research
investigated the role of this issue as a
tool for knowledge sharing and increasing the rate of knowledge flow in order
to reproduce knowledge and also implement knowledge management in car industry
so that reworks would be minimized.
The main
question of this research is to determine the structure of car industry’s
R&D centers knowledge network. It also determines the constituting elements
of car industry’s R&D centers knowledge network. In this research, first,
the effective variables are identified and then proper interpretive structural
modelling is developed in knowledge network of R&D centers.
2. NECESSITY OF
KNOWLEDGE NETWORKING IN CAR INDUSTRY’S R&D CENTERS
Currently, the primary issue in car
industry is not knowledge sharing or absence of effective communication,
information and knowledge among different parts of the industry. These kinds of
knowledge are separately circulating in the body of each different R&D
centers in the optimistic state and there are no related and integrated
knowledge bases in different car industry’s R&D centers so that knowledge
sharing happens among various centers.
Formal structures of Iran’s car
industry do not present real flow of the knowledge. Besides formal
organizational structures knowledge, informal networks are sharing and
circulating knowledge; this has directed attentions of some managers to provide
necessary guideline and planning for using this potential in order to increase
knowledge flow rate and knowledge sharing (TAVALLAEE et al., 2012).
Managers
of car industry should become more responsible towards using new ways and
methods of knowledge management in car industry.
In the following items, the
necessities of paying attention of car industry to knowledge networks are
briefly stated;
·
Creating
value added: By
implementing knowledge networks among R&D centers of car industry, value
added is created for each centers.
·
Human
resources: Because of
the rise in the age of the employees and experts of this industry and the
resulting increase of the risk of knowledge and experience exit from the
organization as well as necessity of using younger employees and transferring
knowledge and experience of more experienced employees to new employees, a
mechanism is needed to provide knowledge and experience transfer from employees
with high job experience to employees with low job experience in knowledge
network.
·
Integration
level being less: Due
to being separated from different units of car industry’s R&D centers
around the country, solutions which deal with new managerial tools have less
integration level. It is hoped that these solutions have proper integration
level through implementing knowledge networks among these structures.
·
Imbalance in knowledge & information
flow: Due to high geographical dispersion of R&D centers, there is no
proper balance in knowledge & information flow in these centers.
·
Separated
implementation of knowledge management: In recent years, knowledge management despite its
importance has been ignored from senior managers’ point of view, which costs
significant amount of money. With macro and strategic perspective, if these
solutions are performed correctly with integrated programs and accurate
strategies they will result in formation of a strong knowledge network, which
causes a synergy in car industry.
·
Global competition: Car industry practice in the
international level and compete with other international firms. Therefore, it
should be able to use knowledge of its experts to the highest level for
attendance in international competition level and taking international markets
in different countries. But, because of inability for optimum and accurate
usage of managerial new tools it could not use its experts’ knowledge and
experience optimally to create competitive advantage for itself through
increasing productivity and decreasing finished-price of its products. Iran car
industry should pay attention to knowledge management since it has essential
role on globalization of Iranian organizations & industries.
·
Sharing
successful activities: The
possibility of the best activities & experiences circulation throughout the
network and their transition to different units of car industry will be
provided through implementing network knowledge.
3. KNOWLEDGE NETWORKS
Main
function of knowledge network is to acquire and share knowledge and makes it
accessible inside and outside the organization (TAVALLAEE et al., 2012).
According to Easton (1992) an approach to
network is to consider it as a set of communicative units (EASTON, 1992). The
proccess of network implementation is related to a complex network of
activities, institutions and diffusion (KLIMASAUSKIENE, 2003).
Networking
can help organizations find essential knowledge and use them for successful
innovation performing (SEUFERT et al., 1999). The
process of knowledge sharing is knowledge distribution inside the organization
among employees and even outside of the organization. Knowledge sharing is one
of the main factors in organization success because it can result in knlowledge
expansion to those parts of the organization which are able to explore it.
Knowledge
sharing results in idea sharing. Knowledge network is a good solution for exchanging
individual and group knowledge. So, creating group knowledge network can be a
good solution for facilitating knowledge exchange and availability.
Infrastructures of IT and computer networks are the most important
infrastructures of knowledge network implementation (MONGE et al., 1998;
TAVALLAEE et al., 1998).
Researchers
know knowledge network as a key factor for understanding the process of
knowledge creation. Therefore, relations among people in the knowledge network
facilitate knowledge creation. Since, knowledge is placed in the existing
relations of knowledge network, as communication gets stronger, the density of
knowledge in network increases and higher volume of knowledge is included
within the network.
Also,
knowledge network increases the chance of collaboration; this results in
sharing and integration of different mental models (JAYRAMA; AYVARI, 2005). Individual knowledge which is circulating
through the knowledge network can result in knowledge application in the body
of R&D centers. This will cause to transfer the individual knowledge to
group & organizational network. Individual knowledge is the knowledge which has been
embedded in people and the organization tries to transfer it to groups and
organizational network in the context of knowledge network to be embedded in
the organization; it results in creation of value added for the organization.
According to what has been said,
network can be defined as follow: a complex of main members who share a set of
information, resources, etc. in a unique system or do common activities while
their emphasis is on facilitating information expansion & relating
organization & various individuals to each other in regional, local,
national and international level in the form of a specific program for example
due to the activity field, the geographic location, and the organizational
affiliation and for definite or indefinite period of time, a set of information
or resources and so on (CHINSOMBOON, 2000).
Knowledge-oriented
relations among individuals, organizational bases & organizations, based on
knowledge, are the new and applied achievements in the field of knowledge
management.
Previous
researches associated with knowledge sharing often need to implement
communicative and interactive processes due to implicit nature of the main part
of knowledge (iceberg metaphor). Explicit knowledge is codifying and
categorizing easily and is transferable and shareable indirectly through
different communicative and informative technologies; but implicit knowledge is
complicated and is transferable through informal networks and interactions
among people. Not only do these networks
indicate relations among members but also they are essential for knowledge
creation and sharing process (JAYRAMA; AYVARI,
2005).
These factors can be divided into
two categories; Individual and group. The existence of these factors is
incentives for knowledge sharing and their non-existence will impede from
knowledge sharing (YORTCHI, 2010). Two
researchers of this field have presented a framework which indicates general
dimensions of knowledge sharing as follows (WANG;
NOE, 2010):
·
Organizational framework
·
Individual & group characteristics
·
Cultural features
·
Personal features of HR
·
Encouraging factors
In this
view, the cultural dimension has been considered as a subdivision of human, and
organizational dimensions, individual, group characteristics and encouraging
factors as subdivisions of human dimensions.
In this
field, knowledge networks, as the most effective and efficient solution for
knowledge sharing, have tools such as knowledge base, video conferences,
multimedia e-mails,
joint plaster boards group and applied sharing software, and so forth which
have the duty of making knowledge communicable and intractable among people
& different knowledge bases inside and outside of the organization and give
the possibility of using internal & external knowledge resources seamlessly
despite island being nature of the
organization. Knowledge network is one infrastructures of knowledge
management implementation which implementation reasons are knowledge effective
flow, sharing and synergy through effective combination of knowledge bases of
R&D centers.
As a
result, it is expected that knowledge bases of interrelated firms to be
expanded because expansion of knowledge bases for a firm results in expansion
of knowledge bases for the other firms. These firms exchange the best solutions
simultaneously which results in recreation of knowledge in the organization and
creates the capabilities, based on new knowledge as well as causing to
facilitate sharing and knowledge-based affairs of the interrelated firms in
whole the network (JOHNSON, 2009).
According
to existing definitions in valid scientific resources, knowledge networks are
mainly focused on intra-organizational knowledge sharing and integration with
external knowledge instead of mere concentration on knowledge creation. In
fact, knowledge network concept is a response for the necessity of human center
or pole existence as well as for knowing that what the employees know &
what they get from the organization (EARL, 2001).
Knowledge networks are tools for communicating between knowledge workers &
experts of the organization in order to exchange knowledge for achieving
predetermined specific aims. Knowledge network is a tool for knowledge
dispersion & creation.
Since knowledge network has created
organizations with the capability of access to knowledge, resources &
technology, it has been identified as the main factor for achieving competitive
achievements (JOHNSON, 2009). The flows
of knowledge are other important features which indicate the knowledge network.
Knowledge flows are mainly one-sided or two-sided (FREEMAN, 1991); (HARGADON, 1998);
(NOOTEBOOM, 1999). The flows of knowledge
are in relation with specific kind of agreements i.e. one-sided flow about
issuing a license & two-sided flows about joint R&D (HARGADON, 1998).
4. RESEARCH METHODOLOGY
The
method of current applied research is a quantitative method including surveys
from experts and variables to be extracted through investigating research
background and surveys from experts, then software of MATLAB R2013 b2 is used
for making compatible matrix then graphic relations of the model are drawn by
Interpretive Structural Modeling approach.
Figure 1: Research Plan
This research has been done in two
main phases:
First phase: Identifying and extracting
indicators; in this phase in addition, research
literature investigation criteria have been identified and its indicators have
been determined through surveying from industrial and academic experts.
Interpretive Structural Modeling starts by providing a list from variables
which are related to the subject or issue. These variables have been resulted
from investigating literature, interviewing with experts or though
questionnaires.
Second
Phase: Determining relationship between variables & their types (modeling);
In this phase
the questionnaire of determining relationship for Interpretive Structural
Modeling method completed by the experts. Then, by creating relations matrix and creating compatibility in
relations matrix, ISM graph has been drawn as relations graphic modeling and different types of variables
have been determined through MICMAC analysis.
The approach of Interpretive
Structural Modeling has been used in this research which has been used for
creating a qualitative-quantitative model as well as is an effective and efficient methodology for
subject in which qualitative variables have mutual effect on each other in
different levels of importance. (RUIZ-BENITEZ;
CAMBRA-FIERRO, 2011).
Through using this technique we can
find relations between qualitative variables of the issue (RUIZ-BENITEZ; CAMBRA-FIERRO, 2011).This model
makes it possible to organize a set of various & interrelated factors
in a comprehensive organized model as well as to explain the complicated
pattern of conceptual relations among a set of variables by using some main
concepts of the graph theory.
This method is interpretive because
judgments of a group of people determine whether there is any relationship
between these elements or not. ISM is a tool for integrating perception of
different participatory groups & is used while trying to apply a coherent
and systematic thinking on a complicated under-study discussion. Also, this is
both interpretive and structural which means it decides which variable to use
and how they are linked together.
According to the experts’ judgment,
it extracts a general structure from a set of variables according to
communication and as well as it is a modeling technique which displays
variables specific relations and a general structure in a graphic model. Interpretive
structural modeling process consists of six basic steps.
First step: Achieving structural self-interaction matrix; this is the
matrix to the dimension of variables which variables are brought in its first
column and row respectively. The
pairwise relations of variables are specifying through notations.
Self-interaction matrix is formed by discussions and ideas of experts group (THAKKAR; DESHMUKH; GUPTA; SHANKAR, 2007).
This
matrix indicates interaction
between model elements. Each of experts fills out a questionnaire through which
the type of the relations between the two variables can be identified.
Table 2: Conceptual relations in formation of
structural self-interaction matrix
Notation |
Notation Definition |
V |
i causes to j(row causes to j) |
A |
j causes to i(column causes to row) |
X |
Bilateral relations of i&j |
O |
No valid relation |
Source:
Thakkar, Deshmukh, Gupta and Shankar, (2007)
As it has been referred, this matrix
is completed through filled questionnaires by experts according to table 2.
Resulted information has been collected by structural imperative modeling
method and the final
self-interaction matrix is formed. For determining the type of suggested
relations, viewpoints of experts based on managerial different techniques such
as brain storming, nominal group technique & so on is used. For determining
the relation type notations in table 2 can be used.
Second step: achieving accessibility matrix;
accessibility matrix can be achieved through converting notations of Structural
Self-Interaction Matrix relations to zero and one. These
rules have been shown in table 3.
Table 3: conversion of conceptual
relations to numbers
Conceptual notation |
i to j |
j to i |
V |
1 |
0 |
A |
0 |
1 |
X |
1 |
1 |
O |
0 |
0 |
Source:
Thakkar, Deshmukh, Gupta and Shankar (2007)
Third step: Compatibility of Accessibility
Matrix; in this step the transitive state among factors should be investigated;
if i causes j & j causes k, then i
must cause k (110).
Huang et.al have used
mathematical rules for adaptation so that, Accessibility Matrix they have
exponentiated to k+1 and K>1. Of course, the operation of matrix
exponentiation must be according to Boolean logic. For achieving the final
compatible matrix M-file coding structure in MATLAB R2013b version is done.
Fourth
Step: determining
levels of variables; in order to determine the level & priority of
variables accessibility set and prerequisite set for each variable are
determined. Accessibility set of each variable includes variables which can be
achieved through this variable and prerequisite set includes variables through
which these variables can be achieved. Then, intersection of accessibility and
prerequisite sets for all factors are determined and factors will be
considered as high level if accessibility set is equal to intersection set of
those factors. To achieve to other levels, previous levels should be separated
from the matrix and process to be repeated. After re-determining the levels,
the achieved matrix is settled respectively. The new matrix is called cone
matrix (THAKKAR;
DESHMUKH; GUPTA; SHANKAR, 2007).
Fifth step: drawing graphs; at first, the criteria
are sorted by levels and according to achieved priority from up to down. Then
structural model is drawing through nodes and lines according to the achieved
matrix from categorized received matrix by levels. If there is any relation
between i to j, it will be shown by an arrow from
i to j. (THAKKAR; DESHMUKH;
GUPTA; SHANKAR, 2007).
Sixth
step: MICMAC analysis (Figure 2); in this part, model variables
are analyzed and are categorized by two criteria; influence and dependence to
determine that which variable has the most significant effect on the others. In
the following, also, it is identified by interpreting variables that what the
dependency of each of the model’s variables is like.
Figure 2: MICMAC interpretation
The aim of
this analysis is identifying and analyzing influence and dependency of the
variables. In this analysis all variables are divided to 4 categories by
influence and dependency power.
·
Autonomous variables which have weak influence and
dependence. These variables are partly unlinked to the system as well as have
less and weak communication with the system.
·
Dependent variables which have weak influence and strong
dependency.
·
Relational variables which have strong influence and
dependency. These variables are dynamic because any changes in them can affect
the system as well as system feedback may change them too.
·
Independent
variables which have strong influence & weak dependency (RAVI; SHANKAR; TAIWARI, 2005).
5. DATA ANALYSIS & FINDINGS
After studying related research, 25
variables have been identified. According to a survey from experts of this
field in car industry’s R&D centers, 12 main variables have been identified
in designing the knowledge network pattern of car industry according to table
4.
Table 4: Key variables of knowledge network pattern designation
No |
Variable |
Reference |
1 |
National macro environment |
(ZHOU; BROWN; DEV, 2009; PEREZ; PABLOS, 2003;
MALHOTRA, 2003; REZAEEAN; DANAEEFARD; ZANKOEENEJAD, 2011; FARSHAD;
KHODADADHOSEINI, 2006) |
2 |
Industry environment |
(ZHOU; BROWN; DEV, 2009; PEREZ; PABLOS, 2003; MALHOTRA, 2003; REZAEEAN;
DANAEEFARD; ZANKOEENEJAD, 2011; FARSHAD; KHODADADHOSEINI, 2006) |
3 |
Organizational Internal environment |
(ANDREA; VON KROGH; SEUFERT, 2005) |
4 |
Explicit knowledge |
(MIRKAMALI; HOSEINGHOLINEJAD, 2010) |
5 |
Implicit knowledge |
(MIRKAMALI; HOSEINGHOLINEJAD, 2010) |
6 |
Organizational culture |
(ZAHRA; NEUBAUM; LARRAÑETA, 2007; POURSERAJEAN;
OLIA; SOLTANI, 2013; ALVANI; ZAREEMATIN; PASHAZADEH, 2009) |
7 |
Social culture |
(ZAHRA; NEUBAUM; LARRAÑETA, 2007; POURSERAJEAN;
OLIA; SOLTANI, 2013; ALVANI; ZAREEMATIN; PASHAZADEH, 2009) |
8 |
IT Software systems |
(ZAHRA; NEUBAUM; LARRAÑETA, 2007; GHANI, 2009; ALIPOUR,
2014; PAHLEVANI; PIRAYESH; ALIPOUR; BASHKOH, 2010; FAZOLLAHI; NOUROZI, 2011) |
9 |
IT & network hardware systems |
(ZAHRA; NEUBAUM; LARRAÑETA, 2007; GHANI, 2009; PAHLEVANI;
PIRAYESH; ALIPOUR; BASHKOH, 2010; FAZOLLAHI; NOUROZI, 2011) |
10 |
Managerial mechanisms |
(ASKARANY; SMITH; YAZDIFAR, 2007; LIN, 2008; PALMIÉ;
2012; TAGHIZADEH; ZEAEE, 2013; HASAANZADEH; TEYMORITABEE, 2015) |
11 |
Structural mechanisms |
(PAHLEVANI; PIRAYESH; ALIPOUR; BASHKOH, 2010; ALVANI;
ZAREEMATIN; PASHAZADEH, 2009; SHAHBANDZADEH; HASSANNIAZI, 2014) |
12 |
Relational mechanisms |
(FIGALLO; RHINE, 2002; PAHLEVANI; PIRAYESH; ALIPOUR;
BASHKOH, 2010; ALVANI; ZAREEMATIN; PASHAZADEH, 2009; ALIPOUR, 2014; MOZAFARI;
SAADAT, 2009; KAZEMI; VAHIDIMOTLAGH; VAHIDIMOTLAGH,
2015) |
Performing 6 steps of
Interpretive Structural Modeling
First step is achieving Structural Self Interaction Matrix Table
5. In this research, the Structural Self Interaction Matrix has been achieved
under the supervision of 9 industrial and academic experts.
Table 5: Structural Self Interaction Matrix
Variable |
National Macro Environment |
Industry Environment |
Organizational Internal Environment |
Explicit Knowledge |
Implicit knowledge |
Individual culture |
Organizational culture |
IT Software systems |
IT & Network Hardware systems |
Managerial mechanisms |
Structural mechanisms |
Relational mechanisms |
1 |
|
V |
V |
O |
O |
O |
V |
O |
V |
V |
O |
V |
2 |
|
|
V |
V |
V |
O |
V |
O |
V |
V |
V |
V |
3 |
|
|
|
V |
V |
X |
V |
V |
V |
V |
V |
V |
4 |
|
|
|
|
X |
O |
V |
A |
O |
V |
V |
X |
5 |
|
|
|
|
|
V |
A |
A |
O |
X |
V |
A |
6 |
|
|
|
|
|
|
A |
O |
O |
V |
O |
X |
7 |
|
|
|
|
|
|
|
V |
O |
X |
V |
V |
8 |
|
|
|
|
|
|
|
|
V |
A |
A |
X |
9 |
|
|
|
|
|
|
|
|
|
O |
O |
O |
10 |
|
|
|
|
|
|
|
|
|
|
V |
V |
11 |
|
|
|
|
|
|
|
|
|
|
|
V |
12 |
|
|
|
|
|
|
|
|
|
|
|
|
Second step is
achieving the accessibility matrix which can
be achieved through converting notation of Structural Self Interaction Matrix
relations to 0 and 1. It has been shown in table 6.
Table 6: accessibility matrix
Variable |
National Macro Environment |
Industry Environment |
Organizational Internal Environment |
Explicit Knowledge |
Implicit knowledge |
Individual culture |
Organizational culture |
IT Software systems |
IT & network hardware systems |
Managerial mechanisms |
Structural mechanisms |
Relational mechanisms |
1 |
1 |
1 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
2 |
0 |
1 |
1 |
1 |
1 |
0 |
1 |
0 |
1 |
1 |
1 |
1 |
3 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
4 |
0 |
0 |
0 |
1 |
1 |
0 |
1 |
0 |
0 |
1 |
1 |
1 |
5 |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
0 |
0 |
1 |
1 |
0 |
6 |
0 |
0 |
1 |
0 |
0 |
1 |
0 |
0 |
0 |
1 |
0 |
1 |
7 |
0 |
0 |
0 |
0 |
1 |
1 |
1 |
1 |
0 |
1 |
1 |
1 |
8 |
0 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
1 |
0 |
0 |
1 |
9 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
10 |
0 |
0 |
0 |
0 |
1 |
0 |
1 |
1 |
0 |
1 |
1 |
1 |
11 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
1 |
1 |
12 |
0 |
0 |
0 |
1 |
1 |
1 |
0 |
1 |
0 |
0 |
0 |
1 |
Third Step is making the accessibility matrix
compatible so that in this step it should be noted that if it is achieved from
A to B then from B to C; as a result, it can be achieved from A to C directly (THAKKAR; DESHMUKH; GUPTA; SHANKAR, 2007). The
matrix of the table (6) is multiplied to itself to some extent that product is
equal to last step matrix; so the compatible matrix is achieved. In this step
MATLAB R2013b software has been used for computing (its source code of
computation has been attached). In table 7 compatible resulted matrix of this
software has been shown.
Table 7:
Final compatible matrix
Variable |
National Macro Environment |
Industry Environment |
Organizational Internal Environment |
Explicit Knowledge |
Implicit knowledge |
Individual culture |
Organizational culture |
IT Software systems |
IT & network hardware systems |
Managerial mechanisms |
Structural mechanisms |
Relational mechanisms |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
2 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
3 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
4 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
5 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
6 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
7 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
8 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
9 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
0 |
1 |
0 |
0 |
0 |
10 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
11 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
12 |
0 |
0 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
1 |
Forth step in
determining the level of variables. Each level is identified when intersection of accessibility and prerequisite sets is
equal to accessibility set. Accessibility set is equal to the row in front of
each criterion and prerequisite set is equal to the column in front of
each criterion.
After determination of the higher level
variable, this variable is deleted from the variables’ list, and then this should be done for other
variables until each variable is placed in its specific level. The level
numbers are equal to the numbers of repetitions. In this research, the level numbers
were equal to 4. The final result of determining levels of variables has been
shown in table 8.
Table 8 levels of model
variables
Model level |
variables |
1 |
9 |
2 |
3,4,5,6,7,8,10,11,12 |
3 |
2 |
4 |
1 |
Fifth step is drawing
a graph, to sort criteria by levels and insert them in the
final model. At the end, the relations between them according to the compatible
matrix are identified. This final model of the research has been shown in
figure 3.
Sixth step is MICMAC analysis (Figure 4) in which variables has
been categorized to 4 by 2 influence and dependency power.
Figure 4: The diagram of
influence & dependency power
To compute influence power sum of row
̓ s numbers for each variable and to compute dependency power sum of column ̓ s
numbers for each variable is used which has been shown in table 9 based on
variables.
Table 9: the degree of variables influence & dependency power
Variables |
1 |
2 |
3 |
4 |
5 |
6 |
7 |
8 |
9 |
10 |
11 |
12 |
influence power |
12 |
11 |
10 |
10 |
10 |
10 |
10 |
10 |
1 |
10 |
10 |
10 |
dependency power |
1 |
2 |
11 |
11 |
11 |
11 |
11 |
11 |
12 |
11 |
11 |
11 |
6. Conclusion
Identifying important and effective
factors for creating knowledge network in R&D centers is very important.
Thus, this research tries to identify important variables from other research
for implementing knowledge network. As a result, 12 important and effective
variables which had the most proportionality with the population and were
considered more by managers and experts in car industry’s R&D centers have
been chosen.
Then, their relations and sequences
have been obtained by ISM technique. Results have indicated that national macro
environment variable is the cornerstone of the knowledge network in Iran car
industry’s R&D centers. It means that, this variable should be used and its
potentials and capacities in national level should be considered for starting
the knowledge network.
As a result, the field for the next variable
i.e. industrial environment which considers existing potentials and capacities
of the industry is provided; then all
other variables; organizational internal, explicit knowledge, individual
culture, organizational culture, managerial, structural and relational
mechanisms are placed in the same level of importance.
IT and network hardware systems are
the last ones which are as the context of installing knowledge networks in car
industry’s R&D centers as well as it can be called as backbone of the
knowledge network in R&D centers which all configuration of the knowledge
network is mounted on.
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