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SNA study. In practice, it is difficult to achieve this goal. Because of some ignored
firms' communication, it might result in insignificant. Second, maybe there still
exist some moderate effects that were not found. Third, the sample is too small for
multiple-regression. But we still believe these independent variables have
significant affect to innovation performance.
4.2 Network Analysis
The following section, five hypotheses about network position variables (degree,
closeness, betweenness, structural holes, coreness etc.) and innovation performance
will be tested in detail. The results of correlation analysis are shown in Table 9.
There are significantly positive correlations among independent variables. This is
because our independent variables are all network position variables, although they
capture different position concepts. However, high correlations among independent
variables could lead to collinearity in multiple regressions. In addition, our research
respectively focuses on the effect of different network position variables on
innovation performance and seeks to examine which network position variable
could explain innovation performance most. Hence, this section will adopt simple
linear regression to test our hypotheses.
Table 9
Means, standard deviations, and correlations
Structural
Holes
Innovation
Performance
Mean
S.D
Degree
Closeness
Betweenness
Coreness
Degree
11.44
6.35
Closeness
0.55
0.09
0.975**
Betweenness
0.06
0.09
0.917**
0.875**
Structural Holes
0.63
0.20
0.718**
0.804**
0.550*
Coreness
0.21
0.11
0.937**
0.942**
0.861**
0.671**
Innovation
Performance
14.28
2.49
0.486*
0.509*
0.487*
0.337
0.621**
Four graphs represent network structures of optoelectronic industry in the STSP
and in different scope. Code A represents LCD industry companies. Code B
represents other optoelectronic industry companies which settle in the STSP and
support/compete with Code A firms. Code C represents research institution units
which settle in the STSP and cooperate with Code A firms.
Figure 2 shows the LCD industry in the STSP (LCDnet), which are our research
main targets. The size of nodes is weighted by degree centrality. It can be observed
A3 and A4 are key players in this network structure. Besides, the evidence indicates
that the LCD cluster is a close cluster. The ties maintained by these two key players
almost make up the whole LCD cluster, especially for firm A4 that almost has
collaboration relationships with all other LCD firms. In a close network, through
frequent social interaction, actors are likely to have shared values, shared norms and
even collective behavior (Nooy, et al., 2005). Moreover, LCD industry is a cluster
that places importance on supply chain's collaboration. Hence, the two key players
would play a role of industrial leader to lead LCD firms to achieve a better overall
industry performance.
