STUDY ON THE KNOWLEDGE -SHARING NETWORK OF

INNOVATION TEAMS USING SOCIAL NETWORK ANALYSIS

Zhihong Tian, Zhenji Zhang

Beijing Jiaotong University, Beijing, China

Dongpo Xiao

China Agriculture Film and Television Center, Beijing, China

Keywords: Social network analysis, Knowledge network, Knowledge–sharing.

Abstract: Under the conceptual framework of social network theory, we study the knowledge-sharing network of

innovation teams by using the social network analysis methods. This paper puts particular emphasis on how

does the structure of knowledge-sharing network impact on the knowledge flows at the overall level. We

expect to find the key man and small groups in knowledge-sharing activities. Comparing with the actual

organizational structure, we could improve the efficiency of knowledge flows within the organization.

1 INTRODUCTION

Team is a formal group composed of individuals

who make great efforts and cooperate for common

goals. Members of team committee common goals,

and maintain the mutual responsible relationship.

The differences between innovation teams and

traditional teams are as follows: innovation team

members may be more loosely organized, but they

are brought together in a series of research and

development projects or tasks. The main work of

innovation teams is knowledge innovation.

Innovation teams can be defined as: they are

collaborative groups which are composed with

members who have complementary skills and

common mission objectives, and their main tasks are

science research and development projects.

From the definition of innovation teams we can

see that the efficient knowledge innovation is the

most important goal of such teams. Therefore, the

knowledge sharing within the team and

collaboration capabilities determine the

effectiveness of the task (Fu and Liu, 2008).

Knowledge flows within the team is a transfer and

diffusion process of knowledge. By the action of

technology networks and social networks,

knowledge flows within the organization exhibit

network-like structure which is called

knowledge-sharing network. From the perspective

of occurrence process, the flow of knowledge

exhibit the nature of the connection group, and that

is called the feature of "network ". From the

perspective of the essence, knowledge flows are

strongly influenced by the behaviors of the actors,

and that is called the feature of "social" (Chau and

Xu, 2007).

Social network theory provides a theoretical

basis which embed the actors of knowledge and

knowledge-sharing activities into social networks

(social structure). Social network theory was first

proposed by Simmel. In the sixties and seventies of

the 20th century, a series of mid-level theory

formed, and at the same time, social network

analysis (SNA) method was proposed to generate

testable model. Therefore, from the perspective of

social relations, under the conceptual framework of

social network theory, we research innovation

team's knowledge sharing network with the social

network analysis method.

2 SOCIAL NETWORK ANALYSIS

Social network analysis views social relationships in

terms of network theory consisting of nodes and ties

(also called edges, links, or connections). Nodes are

438

Tian Z., Zhang Z. and Xiao D..

STUDY ON THE KNOWLEDGE -SHARING NETWORK OF INNOVATION TEAMS USING SOCIAL NETWORK ANALYSIS.

DOI: 10.5220/0003590804380443

In Proceedings of the 13th International Conference on Enterprise Information Systems (KMKSSC-2011), pages 438-443

ISBN: 978-989-8425-54-6

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

the individual actors within the networks, and ties

are the relationships between the actors. The

resulting graph-based structures are often very

complex. There can be many kinds of ties between

the nodes. Research in a number of academic fields

has shown that social networks operate on many

levels, from families up to the level of nations, and

play a critical role in determining the way problems

are solved, organizations are run, and the degree to

which individuals succeed in achieving their goals.

There are two types of social network which are

ego-centered network and whole network. Since we

expect to find the key man and small groups in

knowledge-sharing activities, we use the whole

network theory.

Metrics (measures) in social network analysis are

as follows.

2.1 Centrality

This measure gives a rough indication of the social

power of a node based on how well they "connect"

the network. "Betweenness", "Closeness", and

"Degree" are all measures of centrality.

1) Degree Centrality

The first, and simplest, is degree centrality. Degree

centrality is defined as the number of links incident

upon a node. Degree is often interpreted in terms of

the immediate risk of node for catching whatever is

flowing through the network. If the network is

directed (meaning that ties have direction), we

usually define two separate measures of degree

centrality, namely indegree and outdegree.

The absolute value of the degree centrality is

defined as follows:

∑∑

===

ijiiD

xxndnC )()(

(1)

The value of

is 0 or 1, which means whether

there is relationship between the actor j and i or not.

The standardized value is defined as follows:

)1/()()(

−

′

gndnC

(2)

The definition of centrality can be extended to

graphs. The graph

G (group degree centrality) is

defined as follows:

[]

∑

−

iDD

nCnC

))((max

)()(

(3)

This formula means a gap between the largest

degree centrality and the degree centrality of any

other actor in the network. The greater this

difference is, the higher the group degree centrality

of the entire network is. The extreme is the star

network.

The situation that the group degree centrality is

too high means that the allocation of relationships is

uneven in the group. Only several key men

participate in the interaction, and this action will

lead to the atrophy of knowledge sources and low

efficiency of knowledge sharing. However, too low

group degree centrality will affect the knowledge

sharing within the team.

2) Betweenness

Betweenness is a centrality measure of a vertex

within a graph. Vertices that occur on many shortest

paths between other vertices have higher

betweenness than those that do not.

The betweenness is defined as follows:

∑

jkijkiB

gngnC /)()(

(4)

The group betweenness is defined as follows:

[]

)2()1(

)()(2

−−

−

∑

nCnC

iBB

(5)

This formula means a gap between the largest

betweenness and the betweenness of any other actor

in the network. The greater this difference is, the

higher the group betweenness of the entire network

is.

In 1992, Burt proposed the concept of structural

holes: in the network, if an actor links the other two

who have no direct connection between them, then

the actor’s location is called structural hole, and the

actor can control the flow of resources and thus

place a profit. Structural holes mark the interest of

the location in a network: when a member of an

innovation team is on the structural hole, he has

chances to access to two types of heterogeneous

information flow, and at that time, across the

structural holes, the redundancy of the information

obtained is very low (Gammelgard et al., 2004).

In topology, a cut-point is a point of a connected

space such that its removal causes the resulting

space to be disconnected. For example, every point

of a line is a cut-point, while no point of a circle is a

cut-point. Cut-points are useful in the

characterization of topological continua, a class of

spaces which combine the properties of compactness

and connectedness and include many familiar spaces

such as the unit interval, the circle, and the torus.

In accordance with the Burt 's view, there will be

STUDY ON THE KNOWLEDGE -SHARING NETWORK OF INNOVATION TEAMS USING SOCIAL NETWORK

ANALYSIS

439

small groups in the network which has excessive

structural holes. If the network which the actor

embeds is a reciprocal relationship network, he will

pass information between two small groups which

have no strong relationship, and the actor's role

become a "bridge "—a position that can stimulate

the circulation and sharing of knowledge. In a large

creative team, the bridges are essential. For example,

some key members of different groups play a bridge

role. Through the bridge, small groups can get some

overlapping research knowledge to promote

innovation activities.

2.2 Small Group

Small group (subgroup or cliques) is a sub-group in

which the relationships of the members are

particularly close. Small groups can match the

factions, which is the overall structure indicators of

network.

There are two kinds of methods for the

calculation of small groups. First, calculate the node

degree, and view a group of connected nodes as a

small group. Second, nodes that can be achieved in

the distance will be viewed as a small group. In this

paper, we select the most commonly used method—

K-plex.

K-plex is a Sub-graph that contains g

nodes. In

the graph, each node is connected with g

-k nodes in

the same sub-graph.

The presence of small groups of

knowledge-sharing activities has both positive and

negative effects. On the one hand, the small group

members can maintain a high level of strong ties, to

strengthen knowledge sharing effect, and to

stimulate knowledge innovation within small groups.

In a large innovation team, members of different

sub-groups easily form small groups, which is

conducive to concentrate their limited forces and

improve the stage activities. Another aspect is that if

a small group is too self-closing, the knowledge of

outside groups can not enter, and the knowledge

within the group can not be shared. Extreme cases

are: in a team that lacks of a common vision, each

member come together only for research or

development, and even the division of labor and

cooperation can not be fully realized. In such team,

knowledge sharing will not be able to achieve, and

accumulation of knowledge and innovation also can

not be achieved for the whole team.

3 BACKGROUND AND DATA

PROCESSING

In this paper, we select a university innovation

research team as a knowledge-sharing networks

quantitative study object. The purpose of the study is

to discover the team's knowledge sharing structure

and the status, to identify and solve problems in the

knowledge flow and to improve the flow efficiency

within the organization.

In the team, there are 14 members engaged in the

development of an information system project. We

use letters A ~ N to indicate the members. The

team's organizational structure is shown as figure 1.

A is the leader of the team, while leading a team

contained of B, K and H. C and D are assistants of A

and they both lead a research team. C’s group is the

largest, and responsible for the most expensive part

of the project. The organizational structure will be

compared to the knowledge-sharing network in

order to discover problems in the knowledge flow.

Figure 1: The team's organizational structure.

In accordance with the requirements of the

whole network analysis, we first determine the

analysis unit and social network boundary. This

network is a closed social network composed of 14

members. Then, we determine the important

relationship dimension to be analyzed. In this paper

we use the 1 - mode network, which only measure

the exchange of knowledge among the team

members. Then, we design the questionnaire. There

are 3 survey questions to answer, including "When

confronted with the knowledge and technology

difficulties, who would you ask for help?", “Who do

you often get the most substantive help from? " and

" who do you discuss the situation of the project

with?". Three questions are designed to get more

knowledge sharing status. Based on the analysis,

ultimately the relationship between members is a

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

440

binary data case. There is a relationship of

knowledge sharing or not between any two

members.

After gathering up the questionnaire, we must

analyze the validity. First, check the questionnaire to

delete the failure respondents. In the whole social

network analysis, all names of the network will be

given to select by the respondents. The answers of

different members will be compared. For example, if

A selects B in the 3rd title, B does not choose A.

This means that the validity of the A’s answer is

doubt.

After confirming the validity of the questionnaire,

we input the data. We use network analysis software

UCINET. UCINET (University of California at

Irvine NETwork) is a powerful social network

analysis software, and originally developed by

Linton Freeman who is a scholar in University of

California in Irvine, and then maintained by Steve

Borgatti in Boston University and Martin Everett in

University of Westminster.

Enter the data into UCINET. The

knowledge-sharing relationship matrix is shown as

figure 2.

Figure 2: Knowledge-sharing relationship matrix.

4 SNA-BASED

KNOWLEDGE-SHARING

NETWORK ANALYSIS

4.1 Drawing of Network Diagrams

In UCINET, according to the relations matrix, the

software can draw a network diagram, which is

shown as Figure 3. As knowledge exchange and

sharing is a two-way relationship, the arrows in the

diagram are two-way. You can put the diagram as an

undirected graph. This diagram can clearly show the

knowledge flow within the team and propagation

condition. However, in order to descript the problem

more accurately, we must analyze the structural

parameters of the network specifically.

Figure 3: Network diagrams.

4.2 Analysis of Centrality

From the figure 3, we can see that A, C and D have

more ties, and they are at the center of the network.

Using UCINET, We calculate the degree centrality

and betweenness centrality of each member.

The data of degree centrality is shown as Table 1.

From the data we can see that, the degree centrality

of C is the highest. The absolute value is 5, while the

relative value is 38.462%. A and D follow C by the

absolute value 4 and the relative value 30.769%.

Table 2 shows the overall statistical parameters of

degree centrality of the network. The average degree

centrality of network nodes is 2.714, and overall

relative degree centrality of the network is 20.51%.

Table 1: Degree centrality.

Then, we calculate the value of betweenness.

The data of betweenness is shown as Table 3. From

the data we can see that, the betweenness of C is the

highest. D and A follow C. Table 4 shows the overall

statistical parameters of betweenness of the network.

The average betweenness of network nodes is

51.68%.

STUDY ON THE KNOWLEDGE -SHARING NETWORK OF INNOVATION TEAMS USING SOCIAL NETWORK

ANALYSIS

441

Table 2: Statistical parameters of degree centrality.

Table 3: Betweenness.

Table 4: Sstatistical parameters of betweenness.

Comparing with the actual organization chart,

we can see that A, C and D who have the highest

degree centrality are just the team leaders. They are

in the heart of knowledge-sharing network as the

central figures which are consistent with their roles

and responsibilities of work characteristics. They

occupy the knowledge of the team, while they

coordinate the members to start work and to

exchange of views.

The actual position of B is not a leader, but his

centrality is very high. He follows three leaders.

This shows that B is very active in activities of

knowledge sharing. The members N and M whose

centralities are at the last two should arouse our

attention. They belong to the group led by C, but the

ties with other members are very less. This situation

may due to the special nature of their tasks or to the

design of the organizational structure, the character

and ability of members. Thus, we need to analyze

the actual situation.

4.3 Analysis of Small Group

In the analysis of small group, the parameter k and

the Minimum Set Size require repeated attempts to

obtain a reasonable classification. After several

attempts, we choose k to 2, and specify the

Minimum Set Size to 4. After calculation, we find

that there are 3 knowledge sharing small groups:

ABCD, ABHK and DEFJ. Small-group relationship

matrix histogram is shown as Figure 4.

Figure 4: Small-group relationship matrix histogram.

The above words compare the centrality data

with the organizational structure, and we analyze the

status of knowledge sharing from the perspective of

the individual. Now, we compare the

knowledge-sharing small groups with the actual

organizational structure to analyze the problems in

the knowledge-sharing network.

First of all, A, B, C and D form a small group. A,

C and D are leaders of the team. B is a very active

member in knowledge sharing activities, who is

likely to be involved in leadership group as a

technical authority. A, B, H and K form a small

group. Comparing with the actual organizational

structure, we can see that the group is led by A. In

this group, only B contact with other members

outside the small group. D, E, F and J is just the

small group led by the D. Except the leader D, other

members have no contact with other small group

ICEIS 2011 - 13th International Conference on Enterprise Information Systems

442

members. D is the "bridge" of the team. Knowledge

sharing within the group is conducive to the

knowledge application, but desolation from the

whole team will be conducive to failure.

The team led by C has not yet formed small

groups, and members tend to conflicts. First,

because the members of the group are too many.

Second, partly because the work of members is low

similar. Both reasons are the organizational structure

design problems. As a leader, C should shoulder the

task to strengthen knowledge sharing within the

group. M and N are at the edges of the entire

organization, who must improve their knowledge

and communication skills.

5 CONCLUSIONS

Research on the knowledge-sharing networks in

innovative team has important significance for

improving the efficiency of the team. In this paper,

under the conceptual framework of social network

theory, using social network analysis methods, we

quantitatively analyze the network structure of

innovative team, identify the central figures and

small groups, and find the knowledge-sharing

network flows. Comparing with the actual

organizational structure, we discover the defects of

the organizational structure. These studies contribute

to optimize the knowledge flow within the team.

REFERENCES

Fu F, Liu L, W. I. Empirical analysis of online social

networks in the age of Web2.0[J]. Physica A:

Statistical Mechanics and its Applications, 2008(3):

675-684.

Chau M, Xu J. Mining communities and their

relationships in blogs: a study of online hate groups[J].

International Journal of Human-Computer Studies,

2007, 65(1): 57-70．

Jens Gammelgard, Kenneth Husted, Snejina Michailova.

Knowledge- Sharing Behavior and Post-Acquisition

Integration Failure[J]. CKGWP, 2004(6)：24-41.

ROY L．Tacit knowledge and knowledge management: the

keys to sustainable competitive advantage

[J]． Organizational Dynamics．2001．29 (3): 164．

The Hybrid Vigor Institute．A multi-method analysis of

the social and technical conditions for

interdisciplinary collaboration (2003) [R/OL].

[2009-5-10]. http:// hybridvigor.org/

Kochikar V. P. Suresh. J. K. Towards a knowledge-sharing

organization: some challenges faced on the infosys

journey[R]. India: hfosys Technologies Limited, 2004.

Hansen M. T. The search-transfer problem: the role of

weak ties in sharing knowledge across organization

subunits[J]. Administrative Science Quarterly, l999,

44 (1): 82-111.

STUDY ON THE KNOWLEDGE -SHARING NETWORK OF INNOVATION TEAMS USING SOCIAL NETWORK

ANALYSIS

443