EXPLAINING THE LEVELS OF KNOWLEDGE … · 1Department of Management, Kerman Branch, Islamic Azad...

Indian Journal of Fundamental and Applied Life Sciences ISSN: 2231– 6345 (Online)

An Open Access, Online International Journal Available at www.cibtech.org/sp.ed/jls/2014/04/jls.htm

2014 Vol. 4 (S4), pp. 574-591/Kiani et al.

Research Article

© Copyright 2014 | Centre for Info Bio Technology (CIBTech) 574

EXPLAINING THE LEVELS OF KNOWLEDGE PROCESSING IN

KHUZESTAN TELECOMMUNICATIONS DEPARTMENT USING THE

ROUGH SETS THEORY

Masoud PourKiani1,

*Siavash Rezaei

1 and Farhad Gheisari

2

1Department of Management, Kerman Branch, Islamic Azad University, Kerman, Iran

2Department of Management, Baghmalek Branch, Islamic Azad University, Baghmalek, Iran

*Author for Correspondence

ABSTRACT

This study has investigated the processing of knowledge in Khuzestan Telecommunications Department

and examined its relationship with the critical determinant in the organization such as task (task variety,

task analyzability and task interdependency), organization culture (solidarity and autonomy) and

information technology. The study population included 510 employees who are aware of the knowledge

management. Based on Cochran's formula, the sample size was 200 people. To collect the data, a

questionnaire designed by Prof. Seok Woo Sung with the reliability coefficient of 0.93 was used. In this

study, in order to reduce the data and draw conclusions from them, the Rough set theory (RST) was used.

After determining the maximum and minimum points of responders and decision variables, the decision

table is established and then it was standardized. The compatible and incompatible cases were identified

at a later stage. Then the resolution matrix was formed. The results show that if the task analysis is at a

low level, the process of knowledge will be at a low level. When the variable of task diversity, integration

and application of information technology of organization are at an intermediate level, the process of

knowledge of organization will be at an intermediate level. If the task dependency and independency of

organization are at a high level, the process of knowledge of organization will be at a high level.

Keywords: Information Technology, Knowledge Management, Knowledge Processing Styles

INTRODUCTION

In recent years, knowledge is considered as an important strategic resource of companies and

organizations. Their growth, development and continued survival in competitive arena depend on

processing and management of intellectual capital and its application in order to create value in

organization. Today, the use of traditional management methods has been expired. A new perspective has

been emerged on "the knowledge organization” and "knowledge-based economy" in the life of

organizations. The Knowledge organization in which people learn how to learn together and each

individual is a creative human being as well as a knowledge-creator and the knowledge-based economy

(unlike traditional economies in which the capital and labor were important), the intellectual capital or

knowledge capital, which are a set of skills, experiences, talents and capabilities of the employees of an

organization, are the most important part. This knowledge brings about value for organization and causes

the organization growth and development.

In recent years, much attention has been paid to the use of knowledge effective strategies to support

knowledge management processes within the organization. But, little attention has been paid to the

selection and implementation of such strategies in organizational units which play a vital role in

knowledge management processes (acquisition, creation, transfer and application of knowledge). Since

the organizational units are different in terms of the type and nature of tasks, the prevailing culture of

their environment, and their technology, it is natural that they use different ways of knowledge processing

to be match with their construction and activities. Most companies and organizations do not regard the

fact that different work units require their own special style to process the knowledge according to the

nature of their task and employ a style and an overall strategy in all of their business units. This leads to

complications and irreparable harm such as the loss of knowledge as a result of intellectual capital

withdrawal, loss of organizational learning, decision-making speed reduction, low speed of responding




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the needs, environmental changes and reduced productivity and profitability of the organization. Despite

the large investments of companies and organizations such as Alborz Insurance Company to manage the

knowledge effectively and maintain the intellectual capital in organizations, the lack of a proper style of

knowledge processing in working units leads to the loss of these precious investments.

The purpose of this study is to help organizations especially the Khuzestan Telecommunication

Department to have a correct knowledge processing by determination of the appropriate level for each of

the factors influencing it.

Research Literature

Knowledge Processing Styles

A strategic knowledge shows the organization's overall approach to coordinate resources and knowledge

talents in accordance to its strategic thinking needs. With this method, the gap between what companies

need to know about strategies implementation and what they really know and act upon can be reduce.

Zack (1999) suggests that the organizations and businesses have recognized that, to consolidate their

presence in the competitive, they should clearly manage their knowledge capital and talent and select the

knowledge management tools for its implementation. Such organizations should have a broad and global

view and a tendency towards joint management of knowledge. Knowledge processing styles of working

units are different depending on the type of knowledge (implicit, explicit), a unit of knowledge

management (such as different parts of creation, transmission, storage and use of knowledge) deals with

it. The unit that deals with explicit knowledge prefers changing its style from the unit coding process

which deals with tacit knowledge toward personalization style and using its own employee’s tacit

knowledge to solve the problems (Song, 2008). Hansen et al., (1999) have been classified the

organizational general approaches toward knowledge management practices into two strategies;

codification and personalization.

Codification Style

The codification strategy emphasizes the use of written knowledge by individual registration approach. In

this method, the knowledge has been obtained from its owners and it has been recorded and stored in the

database to be reused. Companies which care about the codification strategy, invest heavily in

information technology. In this style, people retrieve and used the encoded Knowledge which has been

recorded in databases, electronic cabinets, manuscripts and manuals, without any contacts with the

knowledge owners. Therefore, the codification style creates an intellectual capital by transforming the

individual knowledge into the structured capital. By providing tools for effective storage and retrieval of

the knowledge, the codification style promotes the knowledge recording and reusing and provides an easy

access to knowledge at all organization borders (Gamlgard and Reiter, 2005; Scholes and Job, 2001, Song

and Tang, 2006). The codification style largely facilitates the searching and retrieval of the knowledge.

By effective implementation of "routine and repetitive operations" in a written form (such as manuals,

databases, repositories, etc.), the organizational units can benefit from the advantages of knowledge

codification. The purpose of codification is to transform the organizational knowledge in such a way that

to be accessible for those who need it. In the simplest sense, the knowledge must be encoded to be

organized, explicit, portable and easy to understand as much as possible.

Personalization Style

Personalization process style simulates the learning and knowledge creation through the collective

interaction with experts and colleagues. This style strongly depends on individuals’ ability and their

willingness for interaction and sharing their knowledge. Personalization includes various learning process

such as face to face communication, informal conversations and accumulated collective experiences

(Principe and Tel, 2001; Sung and Tang, 2006; Zola and Winter, 2002). This approach is appropriate

when a unit is constantly dealing with knowledge that cannot be coded (Hansen et al., 1999). Each unit

commonly relies on the extensive interactions between members to share and exchange the knowledge

(Song, 2008). This strategy does not focus on registered knowledge of database, but focuses on dialogue

between individuals. Knowledge can be exchanged in brainstorming session and interactions of

individuals. In this approach, the knowledge transferring happens not only through face-to-face




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interactions but also through the electronic communication such as networks of individuals. The company

that follows this style or strategy attempts to communicate and transfer knowledge by an individual to

others. The purpose of these companies is to take advantage of this knowledge to be remained in the

minds of employees.

Companies that use this strategy invest less in the field of information technology compared to firms that

create a database. The purpose of Information Technology is to facilitate the conversation and exchanging

the Knowledge. Thus, the knowledge does not become the structural capital, but it enjoys the

unconditional freedom in the organization. The companies, which benefit from this style of processing,

concentrate on individuals’ direct conversation and try to build a social network for individuals to

communicate with each other in a normal manner. Knowledge which is not coded or cannot be coded can

be transmitted by other methods such as negotiations or personal networks. This style also makes it easier

to transfer the tacit knowledge since the statement of this kind of knowledge is difficult in the handbook

and databases but can be transmitted over the personal relationships easier. Furthermore, this style is

coordinated with the ideals propounded by people like Chris Argyris and Peter Senge on organizational

learning and inclusive organizations. These two people emphasize the importance of giving employees

more freedom and responsibility. To improve learning, Senge emphasizes the focus on decentralization

and free communications which were limited due to the organization. Personalization strategy supports

these kinds of learning in which the individual negotiations and communications have enough space for

new diverse ideas (Fisher, 2001). A proper understanding of the knowledge processing concept and styles

can occur through bilateral cooperation and interaction.

The Organizational Factors affecting the Knowledge Processing

Task

Task is a specified work activity to achieve the target. To overcome the uncertainty and provide the

information processing needs, the organizational units may implement formal and informal procedures.

Some internal units of the organization may choose different styles. This depends on the type of

knowledge (implicit or explicit) these units need in doing their tasks. The units’ tasks can be evaluated in

the following three aspects: “task variety”, “task analyzability” and “task interdependency” (Daft and

Lengel, 1986, Daft and Macintosh, 1981; Peru, 1967).

1. Task Variety

The task variety is related to the number and frequency of exceptions, unexpected and non-predictable

events that take place over time on a task (Daft and Macintosh, 1981). In a work unit with a high level of

work diversity, it is expected that the individuals devote much of their time to learn and exchange the

knowledge, as they do for searching and learning the knowledge, to be able to deal with unexpected

events (Daft and Lengel, 1986, Daft and Macintosh, 1986).

2. Task Analyzability

The task analyzability is related to the extent to which a task can be broken into smaller components

(Ahuja and Karli, 1999). The analyzable tasks have a lot of rules and procedures especially in the fixed

environment where the tasks are considerably routine and repetitive and individuals can organize their

activities and tasks efficiently.

3. Task Interdependency

The task interdependency is related to the extent to which individuals require other people information

and support to do their work. With the increased need to assist colleagues in carrying out tasks, the task

interdependency increases. The task interdependency may differ between members of a single

organization or between different units. In any case, the task interdependency is closely associated with

the transmission of information (Song, 2008). A unit with a high degree of task interdependency indicates

that its members can do their tasks perfectly by sharing the knowledge and resource with their colleagues.

Organization Culture

The organization culture is a set of beliefs, values and assumptions common to the members of an

organization which shape the way of thinking, decisions and actions. The organization culture is an

important factor in the success of knowledge management, because it does not only specify the valued




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knowledge, but also it determines the kind of knowledge which should be kept in order to maintain the

competitive advantage. The cultural variables in this study consist of "solidarity" and "autonomy" which

play a critical role in the success of knowledge management functions.

1. Solidarity

In this study, the solidarity is related to the ability of a unit to “follow the common goals without

considering the social relations”. Solidarity among members of similar organizations which follow the

common goals is based on shared experiences. A powerful form of mass culture and knowledge sharing

can increase the probability of the knowledge management functions success in working units by

encouraging the members to participation, interrelation and collaboration (Ahuja, 2000; Alavi et al.,

2006; Lee and Choi, 2003). Solidarity among members of a unit or between different units creates a

strong feeling of confidence and facilitates the knowledge exchange through an extensive interaction such

as formal and informal communication.

2. Autonomy

The autonomy is related to the freedom of an individual or group of individuals in planning their tasks

and establishing the rules and procedures for implementing them. Previous studies indicate that the

autonomy has a strong relationship with the success of knowledge management functions. High level of

autonomy facilitates the development of decentralized networks. It also encourages open communication

between colleagues. The workers with knowledge autonomy often try to propose new ideas and utilize

new skills to develop their competencies. They tend to express their thoughts, experiences and beliefs

(Song, 2008).

Information Technology

The information technology plays a key role in facilitating knowledge flow in organizations. By

improving the knowledge codification and reducing the communication costs, IT can improve

organizational efficiency and effectiveness. Employees can use IT in order to have easy access to prior

knowledge, finding and absorbing new knowledge and easy receiving the expertise in specific areas. In

the firms that rely heavily on knowledge registration and reuse, information technology can facilitate the

acquisition, storage, retrieval, sharing and exchange of knowledge resources (Hansen et al., 1999).

The Research Model

This study has investigated the processing of knowledge in Khuzestan Telecommunications Department

and examined its relationship with the critical determinant in the organization such as task (task variety,

task analyzability and task interdependency), organization culture (solidarity and autonomy) and

information technology.

Figure 1: The research model (Song, 2008(




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Figure 2: Basic model of research considering the decision table

Research Question

How do the organizational factors determine the level of knowledge processing in Khuzestan

Telecommunications Department?

MATERIALS AND METHODS This research is an applied research in terms of the purpose and it is descriptive in terms of the method of

data collection. In this study, two types of variables; decision and conditional, have been considered. The

conditional variables include task variety, task analyzability and task interdependency, solidarity and

autonomy and information technology. The decision variable is the level of knowledge processing. The

Rough Sets Theory was founded in the early 1980s by Professor Zdzislaw Pawlak. This theory deals with

the analysis of the data table. The main objective of Rough set analysis is to obtain the approximate

concepts of acquired data. This method is a powerful tool for mathematical reasoning in cases of

uncertainty. It provides some methods for eliminating or mitigating irrelevant and surplus information and

knowledge of databases. This process eliminates redundant data without loss of essential data of the

database. As a result of data reduction, a set of summarized and meaningful rules can be obtained which

make the decision much easier for decision maker (Ziarko, 1993). To run Rough Sets, the information is

usually displayed in the form of a flat sheet. The columns present the features, the rows present the

objects and the cells contain features values for each object. These types of tables are called information

systems or decision tables. So, in each decision table, the features can be divided into two categories:

1. Decision features (D)

2. Conditional or positional features (C)

Accordingly, the decision tables can be revealed as DC=TU, =S , where Result =D ,

an,…ai,,…a2,a1, =C and xn,…x3,x2,x1, = U (World Series) (Jarvinen, 2004).

In each row of the decision table, the decision rules can be achieved in the form of if ...... then....... . In

this way, for example if: a1=1,a2=1,a3=2,a4=1,a5=2,a6=1 then d=1.

Accordingly, two types of rules are applicable in a decision table:

1. Incompatible rules (conflicting rules): These are the rules with the same positional features but

different decision features.

2. Compatible rules (similar rules): These are the rules which are not incompatible.

Based on these two rules, the position and decision equivalence classes can be written. After the

formation of resolution matrix, the reduced set can be inferred.

The study population included 510 employees, of Khuzestan Telecommunications Department, who are

aware of the knowledge management. Based on Cochran's formula, the sample size was 200 people. To

collect the data, a questionnaire designed by Prof. Seok Woo Sung with the reliability coefficient of 0.93




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was used. The questionnaire consists of 8 general questions and 21 specialized questions. Each question

was encoded based on the 5-point Likert scale.

Table 1: Cronbach's alpha test for each of variables

Row Variable Cronbach's alpha values

1 Task variety 0.7008

2 Task analyzability 0.7342

3 Task interdependency 0.8391

4 Solidarity 0.7118

5 Autonomy 0.7237

6 information technology supports 0.9573

Data Analysis

In this study, to extract the logical rules to check the level of knowledge processing of Khuzestan

Telecommunications Department employees, the Rough set theory (RST) was used. Since 21 items

measure the factors affecting the knowledge processing and valuation is based on the 5-point Likert scale,

the minimum score for a respondent is 21 and the maximum one is 105.

On this basis:

If the respondent score is between 21 and 49, the knowledge processing is at a low level. It means:

21 ≤ X ≤48

If the respondent score is between 50 and 78, the knowledge processing is at an intermediate level. It

means: 49 ≤ X ≤76

If the respondent score is between 79 and 105, the knowledge processing is at a high level. It means:

77 ≤ X ≤105

On the other hand, these six aspects influencing the knowledge processing, regarding the number of

questions of each component, have the minimum and maximum scores as the following table:

Table 2: The score of organizational factors influencing the knowledge processing of decision table

Row

Components of

decision table

Organizational factors and

aspects

Number of

items

The

minimum

score

The

maximum

score

1 a1 Task variety 3 3 15

2 a2 Task analyzability 3 3 15

3 a3 Task interdependency 3 3 15

4 a4 Solidarity 3 3 15

5 a5 Autonomy 4 4 20

6 a6 information technology 5 5 25

According to Table (3), the range of each organizational factor affecting knowledge processing can be

stated as follows:

V (a1) = {3, 4, 5, …, 14, 15}

V (a2) = {3, 4, 5, … , 14, 15}

V (a3) = {3, 4, 5,… , 14, 15 }

V (a4) = {3, 4, 5, …, 14, 15}

V (a5) = {4, 5, 6, …, 19, 20}

V (a6) = {5, 6, 7, …, 24, 25}




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In other words, the component of task variety can have a score between 3 and 15, the component of task

analyzability can have a score between 3 and 15, the component of task interdependency can have a score

between 3 and 15, the component of solidarity can have a score between 3 and 15, the component of

autonomy can have a score between 4 and 20 and the component of information technology can have a

score between 5 and 25.

In the next step, in the columns a1, a2, a3, a4, a5 and a6 (position features) in column d (decision

features), the listed numbers are replaced by the items codes. In other words, they are standardized. Thus,

in Table (3), the codes number 1, 2, and 3 indicate the low level, the intermediate level and the high

respectively for each variable, respectively.

Table 3: Standardization of organizational factors influencing the knowledge processing

Feature Name of organizational factor

and knowledge processing

The value of

each dimension

Maximum and

minimum

Standards

Code

X≤6≥3 1

a1 Task variety X≤15≥3 X≤10≥7 2

X≤15≥11 3

X≤6≥3 1

a2 Task analyzability X≤15≥3 X≤10≥7 2

X≤15≥11 3

X≤6≥3 1

a3 Task interdependency X≤15≥3 X≤10≥7 2

X≤15≥11 3

X≤6≥3 1

a4 Solidarity X≤15≥3 X≤10≥7 2

X≤15≥11 3

a5 Autonomy X≤20≥4

X≤9≥4 1

X≤15≥10 2

X≤20≥16 3

a6 information technology X≤25≥5

X≤11≥5 1

X≤17≥12 2

X≤25≥18 3

X≤49≥21 1

d knowledge processing X≤105≥21 X≤78≥50 2

X≤105≥79 3




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At this stage, the decision table is rewritten and standardized according to the table (3).

Table 4: Standardized decision table N

Frequency

d =

knowledge

processing

a6 =

information

technology

a5 =

Autonomy

a4=

Solidarity

a3= Task

interdependency

a2= Task

analyzability

a1=

Task

variety

u

6 1 1 1 1 1 1 1 u1

12 2 2 2 2 2 2 2 u2

5 1 2 1 2 1 2 1 u3

6 1 1 1 1 1 1 1 u4

9 1 2 1 2 1 1 1 u5

12 2 1 2 1 2 2 1 u6

6 1 2 2 2 2 1 1 u7

12 3 1 1 1 1 1 2 u8

10 2 2 1 2 1 2 2 u9

12 1 2 1 2 1 1 2 u10

9 3 3 3 3 3 3 3 u11

11 2 1 1 1 1 2 1 u12

9 2 2 2 2 2 1 2 u13

11 3 3 3 3 3 2 3 u14

7 1 1 1 1 1 1 2 u15

12 1 1 2 1 2 2 1 u16

8 1 1 2 1 2 1 1 u17

12 3 2 3 2 3 2 3 u18

3 1 2 1 2 1 2 2 u19

10 2 2 1 2 1 2 2 u20

12 2 2 1 2 1 2 1 u21

6 3 3 3 3 3 3 2 u22

In the next step, the compatible and incompatible tables are created according to Table 4

Table 5: Incompatible components (similar components) in the decision table

u a1= Task

variety

a2= Task

analyzability

a3= Task

interdependency

a4=

Solidarity

a5=

Autonomy

a6= information

technology

d= knowledge

processing

u3 1 2 1 2 1 2 1

u21 1 2 1 2 1 2 2

u6 1 2 2 1 2 1 2

u16 1 2 2 1 2 1 1

u9 2 2 1 2 1 2 2

u19 2 2 1 2 1 2 1




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Table 6: Compatible components in the decision table

u a1= Task

variety

a2= Task

analyzability

a3= Task

interdependency

a4=

Solidarity

a5=

Autonomy

a6=

information

technology

d= knowledge

processing

u1 1 1 1 1 1 1 1

u2 2 2 2 2 2 2 2

u4 1 1 1 1 1 1 1

u5 1 1 1 2 1 2 1

u7 1 1 2 2 2 2 1

u8 2 1 1 3 1 3 1

u10 2 1 1 2 1 2 1

u11 3 3 3 3 3 3 3

u12 2 2 2 2 2 2 2

u13 2 1 2 2 2 2 2

u14 3 3 3 2 3 2 3

u15 2 1 1 1 1 1 1

u17 1 1 2 1 2 1 1

u18 3 2 3 2 3 2 3

u20 2 2 1 2 1 2 2

u22 2 3 3 3 3 3 3

Table 7: The arranged compatible table

u a1 a2 a3 a4 a5 a6 d

u1 1 1 1 1 1 1 1

u4 1 1 1 1 1 1 1

u5 1 1 1 2 1 2 1

u7 1 1 2 2 2 2 1

u8 2 1 1 3 1 3 1

u10 2 1 1 2 1 2 1

u15 2 1 1 1 1 1 1

u17 1 1 2 1 2 1 1

u2 2 2 2 2 2 2 2

u12 2 2 2 2 2 2 2

u13 2 1 2 2 2 2 2

u20 2 2 1 2 1 2 2

u11 3 3 3 3 3 3 3

u14 3 3 3 2 3 2 3

u18 3 2 3 2 3 2 3

u22 2 3 3 3 3 3 3

Minimal Set of Features

Since the decision variable (d) has three statuses (low, medium and high), therefore, according to Table

(7), respondents who have the score of 1, 2 and 3 are placed in three different groups. These three groups

are called the equivalence classes of decision or the conceptual sets:

17,15,10,8,7,5,4,111 uuuuuuuudUxX

20,13,12,222 uuuudUxX

22,18,14,1133 uuuudUxX

Based on three sets of X1, X2, X3, the lower and upper approximations are calculated for each of the

three sets. It should be noted that A is defined as the following set:




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654321 ,,,,, aaaaaaA

Table 8: Approximation of three sets of X1, X2, and X3

11 XxUxXAA

17,15,10,8,7,5,11 uuuuuuuXA

22 XxUxXAA

20,13,22 uuuXA

33 XxUxXAA

22,18,16,113 uuuuXA

11 XxUxXA A

17,15,10,8,7,5,4,11 uuuuuuuuXA 22 XxUxXA A

20,13,12,22 uuuuXA

33 XxUxXA A

22,18,16,113 uuuuXA

The precision of Rough set can be determined with the following statement:

X1set X2set X3set

4,141 uuuu AA 12,2122 uuuu AA 1111 uu A

55 uu A AA uu 1313 1616 uu A

77 uu A 2020 uu A 1818 uu A

88 uu A 2222 uu A

1010 uu A

1515 uu A

1717 uu A




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Table: ???????

u

u1

u4

u5

u7

u8

u1

0

u1

5

u1

7

u2

u1

2

u1

3

u2

0

u1

1

u1

4

u1

8

u2

2

u1

λ

a4,a

6

a3,a

4,a

5,a

6

a3,a

4,a

5,a

6

a1,a

4,a

6

a1

a4,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

3,a

4,a

5,a

6

a1,a

2,a

4,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

u4

λ

a4,a

6

a3,a

4,a

5,a

6

a1,a

4,a

6

a1,a

4,a

6

a1

a3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

3,a

4,a

5,a

6

a1,a

2,a

4,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

u5

a4,a

6

a4,a

6

a3,a

4,a

5,a

6

a1,a

4,a

6

a1

a1,a

4,a

6

a3,a

4,a

5,a

6

a3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

3,a

5

a1,a

2

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

u7

a3,a

4,a

5,a

6

a3,a

4,a

5,a

6

a3,a

5

a1,a

3,a

4,a

5,a

6

a1,a

3,a

5

a1,a

3,a

4,a

5,a

6

a4,a

6

a1,a

2

a1,

a2

a1

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

u8

a1,a

4,a

6

a1,a

4,a

6

a2

a1,a

4,a

6

a4,a

6

a4,a

6

a1,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

a2,a

4,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a2,a

3,a

5

u10

a1,a

4,a

6

a1,a

4,a

6

a1

a1,a

3,a

5

a4,a

6

a4,a

6

a1,a

2,a

3,a

5

a2,a

3,a

5

a2,a

3,a

5

a3,a

5

a2

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a2,a

3,a

4,a

5,a

6

u15

a1

a1

a1,a

4,a

6

a1,a

3,a

4,a

5,a

6

a4,a

6

a4,a

6

a1,a

3,a

5

a2,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

a3,a

4,a

5,a

6

a2,a

4.a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

u1

7

a3,a

5

a3,a

5

a3,a

4,a

5,a

6

a4,a

6

a1,a

3,a

4,a

5,a

6

a1,a

3,a

4,a

5,a

6

a1,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

4,a

6

a1,a

4,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

لودج

10

: کیکفت سیرتام

یریذپ




Research Article


u2

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2

a2,a

3,a

4,a

5,a

6

a2,a

3,a

5

a2,a

3,a

4,a

5,a

6

a1,a

2,a

4,a

6

λ

a2

a3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

3,a

5

a2,a

3,a

4,a

5,a

6

u1

2

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2

a2,a

3,a

4,a

5,a

6

a2,a

3,a

5

a2,a

3,a

4,a

5,a

6

a1,a

2,a

4,a

6

a1

a2

a3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

3,a

5

a2,a

3,a

4,a

5,a

6

u1

3

a1,a

3,a

4,a

5,a

6

a1,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

6

a1,a

3,a

5

a3,a

4,a

5,a

6

a3,a

5

a3,a

4,a

5,a

6

a1,a

4,a

6

a2

a2

a2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a2,a

3,a

4,a

5,a

6

u2

0

a1,a

2,a

4,a

6

a1,a

2,a

4,a

6

a1,a

2

a1,a

2,a

4,a

6

a2

a2

a2,a

4,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1

a3

a2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

3,a

5

a2,a

3,a

4,a

5,a

6

u1

1

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a4,a

6

a2,a

4,a

6

a1

u14

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

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a1,2

,a3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a4,a

6

a2

a1,a

4,a

6

u18

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

5

a1,a

2,a

3,a

4,a

5,a

6

a1,a

2,a

3,a

4,a

5,a

6

a1,a

3,a

5

a1,a

3,a

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a1,a

2,a

3,a

5

a1,a

3,a

5

a2,a

4,a

6

a2

a1,a

2,a

4,a

6

u2

2

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2,a

3,a

4,a

5,a

6

a1,a

2,a

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a1,a

2,a

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a2,a

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3,a

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5,a

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a2,a

3,a

4,a

5,a

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2,a

3,a

4,a

5,a

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a2,a

3,a

4,a

5,a

6

a2,a

3,a

4,a

5,a

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a2,a

3,a

4,a

5,a

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a2,a

3,a

4,a

5,a

6

a1

a1,a

4,a

6

a1,a

2,a

4,a

6




Research Article


Table 10: Table of features frequency compared to decision feature

N2 N1 d A :Conditional features

u a6 a5 a4 a3 a2 a1

- - 12 1 1 1 1 1 1 1 u1

- - 12 2 2 2 2 2 2 2 u2

- - 6 1 2 2 2 2 1 1 u3

- - 9 1 2 1 2 1 1 1 u4

- - 8 1 1 2 1 2 1 1 u5

7 2 5 1,2 2 1 2 1 2 1 u6

- - 12 1,2 1 2 1 2 2 1 u7

- - 11 2 1 1 1 1 2 1 u8

- - 12 1 2 1 2 1 1 2 u9

- - 9 2 2 2 2 2 1 2 u10

17 2 3 1,2 2 1 2 1 2 2 u11

5 3 7 1,3 1 1 1 1 1 2 u12

- - 6 3 3 3 3 3 3 2 u13

- - 12 3 2 3 2 3 2 3 u14

- - 11 3 3 3 3 3 2 3 u15

- - 9 3 3 3 3 3 3 3 u16

Where:

yaxaAaAIND yx,

yINDxUyydxd &

Now suppose: V is a non-empty set.

Definition 1: function (1) is a Sugeno measure function if the following conditions are true:

1) 01 vg 12 vg

2) VVV 21 , 2121 vgvgVV

3) If a sequence of ascending or a descending sequence is a subset of V, then:

3)

iiii vgvg limlim

Obviously, if a finite ascending or descending sequence is a subset of V, then (3) is as follows:

For ascending sequence:

3’)

i

n

ii

n

i

vgvg11

For descending sequences:

3’’)

i

n

ii

n

i

vgvg11

Definition 2: The belief function (Bel): a Sugeno function which is true in the conditions (4) is a belief

function:

4) For every finite sequence of subsets of V, the following equation is satisfied:

k

kji

kji

k

i

i

k

i

i vvBelvvBelVBelVBel ...1... 1

1

1

11

Definition 3: The exponential function (Pl): a Sugeno function which is true in the conditions (4’) is a

power-like function.




Research Article


k

kji

kji

k

i

i

k

i

i vvPlvvPlVPlvPl ...1... 1

1

1

11

The Sugeno measure function is defined as follows on 3,2,12 SubvSubv :

1,2,3 2,3 1,3 1,2 3 2 1 iV

0 0 173

7

173

20

173

43

173

56

173

47 0 ivm

Obviously, the exponential and belief functions can be easily calculated by this function especially for

incompatible rules.

Definition 5: The basic probability assignment function (BPAF):

The function of 1,0: VSubm is a basic probability assignment function if it is true in the following

conditions:

1) 10: xmvx

2)

1 xm

vx

Note that:

ymxBel

xy

ymxPl

xy

As can be seen, using data/ decision table No. 5, the agreement and conditional probability tables can be

obtained as follows:

Table 11: The agreement table of decision and feature values

sum 3 2 1 d

a4 sum 3 2 1

d

a1

74 12 23 39 1 87 0 35 52 1

100 12 53 35 2 81 18 41 22 2

26 26 0 0 3 32 32 0 0 3

200 50 76 74 sum 200 50 76 74 sum

sum 3 2 1 d

a5 sum 3 2 1

d

a2

103 12 43 48 1 75 12 9 54 1

59 0 33 26 2 110 23 67 20 2

38 38 0 0 3 15 15 0 0 3

200 50 76 74 sum 200 50 76 74 sum

sum 3 2 1 d

a6 sum 3 2 1

d

a3

74 12 23 39 1 103 12 43 48 1

100 12 53 35 2 59 0 33 26 2

26 26 0 0 3 38 38 0 0 3

200 50 76 74 sum 200 50 76 74 sum




Research Article


Using the agreement table of decision and feature values, the probability distributions of features

(assuming independency of the features) and late conditional probability for each value of the decision

variable (d) (the condition of having known amounts of Features) can be calculated:

In terms of late conditional probability, there is jdd and rj 1 when dd vCardvr :

kk vavava ...2211

It is formulated in a general form as follows:

kkr

ikkrir

kkirvavavap

ddvavavapddpvavavaddp

...

......

2211

2211

2211

jvjarpk

j

iddjvjarpk

j

k

j

k

j

ir ddp

1

1

1

1

In terms of research question, using the agreement tables, probability distributions of features and the

conditional probability of these features can be calculated if there is a number of specific decisions:

Table 12: Probability distributions of features and the conditional probability of these features if d

is known

a1,a2,…,a6 1 2 3 Sum

1apr 0.435 0.405 0.16

1

2apr 0.35 0.55 0.75

3apr 0.515 0.295 0.19

4apr 0.37 0.50 0.13

5apr 0.55 0.295 0.19

6apr 0.37 0.50 0.13

11 dapr 0.703 0.297 0

12 dapr 0.73 0.27 0

13 dapr 0.649 0.351 0

14 dapr 0.527 0.473 0

15 dapr 0.649 0.351 0

16 dapr 0.527 0.473 0

21 dapr 0.461 0.539 0




Research Article


22 dapr 0.118 0.882 0

23 dapr 0.566 0.434 0

24 dapr 0.303 0.697 0

25 dapr 0.566 0.434 0

26 dapr 0.303 0.697 0

31 dapr 0 0.36 0.64

32 dapr 0.24 0.46 0.30

33 dapr 0.24 0 0.76

34 dapr 0.24 0.24 0.52

35 dapr 0.24 0 0.76

36 dapr 0.24 0.24 0.52

dpr 0.37 0.38 0.25 1

In Table (12), dj icon is used to display the item (d = j) for j = 1, 2, 3.

For example; jdapdap irjjr , i=1,2,3,4,5,6, shows the conditional distribution of ai if the

decision variable d is equal to j. In addition,

3,2,1 ii a

For example, for the following decision rule:

2222212 654321 daaaaaa

Late conditional probability of event:

222212

2222222221222

654321

654321

apapapapapap

dapdapdapdapdapdapdp

rrrrrr

rrrrrrr

71712.0380030839484.0

510022115633.0

50.0295.035.0405.0

697.0434.0697.0434.0118.0539.038.0

RESULTS AND DISCUSSION

1. Given the set of 1XA , it can be concluded that the speakers of this set are sure that the knowledge

processing and the organizational factors influencing the knowledge processing of their organization are

at a low level. Given the set of 1XA , it can be stated that some of the speakers of this set expressed that the

knowledge processing may be at a low levels in their organization and the organizational factors

influencing the knowledge processing of their organization is at a low or intermediate level.

2222122 654321 aaaaaadpr




Research Article


2. Given the set of 2XA , it can be stated that the speakers of this set are sure that the knowledge


at an intermediate level. On the other hand, given the set of 2XA , it can be stated that some of the

speakers of this set expressed that the knowledge processing may be at an intermediate levels in their

organization and the organizational factors influencing the knowledge processing of their organization is

at an intermediate level.

3. Given the set of 3XA , it can be concluded that the speakers of this set are sure that the knowledge


at a high level. Given the set of 3XA , it can be stated that some of the speakers of this set expressed that

the knowledge processing may be at a high levels in their organization and the organizational factors

influencing the knowledge processing of their organization is at a high level.

4. According to the rules if-then we can conclude that:

Table 13: Decision Rule

IF a1=1,a2=1,a3=1,a4=1,a5=1,a6=1 THEN Result=1
















5. According to data reduction as well as reduction table, we can have a description of d = 1 constraint by

a2 = 1 which is called the value reducer.

IF a2=1 THEN Result=1

So, if the organizational factors affecting the knowledge processing are at a low level, then the knowledge

processing will be at a low level. Using the same argument, the above sixteen rules can be summarized as

follows:

IF a1=2, a4=2, a6=2 THEN Result=2

IF a3=3, a5=3 THEN Result=3

ACKNWLEDGEMENT

We are grateful to Islamic Azad University, Kerman branch authorities, for their useful collaboration.




Research Article


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performance. Information and Management 40(5) 403-417.

Daft RL and Makintosh NB (1981). A tentative exploration into the amount and equivocality of

information processing hn organizational work units. Administrative Science Quarterly 26 207-224.

Hansen MT, Nohria N and Tierney T (1999). What's your Strategy for managing knowledge?. Harvard

Business Review 77(2) 106-116.

Gammelgaard J and Ritter T (2005). The knowledge retrieval matrix: Codification and personalization

as separate strategies. Journal of Knowledge Management 9(4) 133-14351.

Song S (2008). Work Unit’s knowledge Processing style. International Journal of Knowledge

Management 4(219).

Van der vegt G, Van der vliert E and Oosterhof A (2003). Information dissimilarity and organizational

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Management Journal 46 715-727.

Zack MH (1999). Developing a knowledge strategy. California Management Review 41(3) 125-145.

EXPLAINING THE LEVELS OF KNOWLEDGE … · 1Department of Management, Kerman Branch, Islamic Azad...

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