Proceedings of the 12th European Conference on Knowledge ...

Zoltán Gaál et al
Knowledge Sharing which contains questions regarding <strong>the</strong> extent <strong>of</strong> availability and usefulness <strong>of</strong>
knowledge based on a 5-point Likert scale.
The participants <strong>of</strong> <strong>the</strong> research can be found in various working areas and industries <strong>the</strong> data <strong>of</strong>
which are presented in Figure 2.
Figure 2: Distribution <strong>of</strong> participant <strong>of</strong> <strong>the</strong> survey according to industries and working areas
3.4 Results
In this part <strong>of</strong> <strong>the</strong> Empirical study <strong>the</strong> results using PCA will be presented.
3.4.1 Results <strong>of</strong> KMO and Bartlett’s Tests
To determine <strong>the</strong> appropriateness <strong>of</strong> <strong>the</strong> data set for PCA Kaiser-Meyer-Olkin (KMO) measure <strong>of</strong>
sampling adequacy and Bartlett’s test <strong>of</strong> sphericity is used. By using correlations and partial
correlations for testing whe<strong>the</strong>r <strong>the</strong> variables used are adequate to correlate <strong>the</strong> KMO statistic is
calculated, while Bartlett’s test is used for revealing <strong>the</strong> relationship between <strong>the</strong> variables by testing
<strong>the</strong> null hypo<strong>the</strong>sis that <strong>the</strong> variables are uncorrelated in <strong>the</strong> population (Hinton et al 2004; Foster et al
2006; Székelyi, Barna 2002). Although <strong>the</strong> values <strong>of</strong> KMO statistic can vary from 0 to 1, Kaiser (1974)
recommended values greater than 0.5 to be accepted. If <strong>the</strong> significance value <strong>of</strong> Bartlett’s test is less
than 0.05, <strong>the</strong>n this test is significant, and thus <strong>the</strong> analysis is appropriate (Field 2005; Sajtos, Mitev
2007).
The results <strong>of</strong> both tests can be found in Table 1.
Table 1: The KMO and Bartlett values <strong>of</strong> maturity <strong>of</strong> knowledge sharing
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure <strong>of</strong> Sampling Adequacy .740
Bartlett's Test <strong>of</strong> Sphericity Approx. Chi-Square 1105.361
df 28
Sig .000
Table 1 shows that <strong>the</strong> KMO test with <strong>the</strong> value <strong>of</strong> 0.740 has been above <strong>the</strong> accepted limit <strong>of</strong> 0.5. In
addition, <strong>the</strong> Bartlett test yields a high Chi-square value <strong>of</strong> 1105.361, and a significance level <strong>of</strong> 0.000
which is also under <strong>the</strong> accepted limit <strong>of</strong> 0.05. Thus both tests have verified that <strong>the</strong> data are
appropriate for PCA.
3.4.2 Results <strong>of</strong> Total Variance Explained
The table <strong>of</strong> Total Variance Explained lists <strong>the</strong> eigenvalues associated with each component before
extraction, after extraction and after rotation. In social science <strong>the</strong> total cumulative variance explained
above 60 % is considered acceptable (Sajtos, Mitev 2007).
Table 2 shows <strong>the</strong> result <strong>of</strong> Total Variance Explained.
309