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

3.2.4 Knowledge-sharing
Huei-Fang Chen and Yi-Wen Lin
This section included three items on knowledge-sharing modified from <strong>the</strong> ideas <strong>of</strong> Bock and Kim
(2002). The values for <strong>the</strong>se items were measured using a 7-point Likert-type scale (1 = completely
disagree to 7 = completely agree). One sample item for repatriate knowledge-sharing is ‘I <strong>of</strong>ten
actively tell my colleagues about <strong>the</strong> working knowledge <strong>of</strong> overseas.’ The Cronbach’s α is 0.88 for
<strong>the</strong> three items. Because <strong>the</strong> answers to this survey were self-reported, we used Harman’s one-factor
test and scale item trimming, as suggested by Podsak<strong>of</strong>f and Organ (1986), to address <strong>the</strong> issue <strong>of</strong>
common method variance. Confirmatory factor analysis was conducted to test whe<strong>the</strong>r <strong>the</strong> four-factor
model was superior to <strong>the</strong> one-factor model. The results show that <strong>the</strong> four-factor model (GFI = 0.97;
CFI = 0.99; NFI = 0.98; RMSEA = 0.049) better fits <strong>the</strong> data than <strong>the</strong> single-factor model (GFI = 0.72;
CFI = 0.77; NFI = 0.76; RMSEA = 0.27). Thus, common method variance was not a serious problem
in this study.
3.3 Reliability and validity
A measurement model was used to test <strong>the</strong> reliability and <strong>the</strong> convergent and discriminant validity <strong>of</strong>
<strong>the</strong> instrument items. The results show that all reliability measures (Cronbach’s α) are above <strong>the</strong>
recommended level <strong>of</strong> 0.70 (0.88~0.95), thus indicating adequate internal consistency (Nunnally,
1978). Convergent validity is demonstrated when items load high (factor loading > 0.50) on <strong>the</strong>ir
associated factors (Hair, Black, Babin, Anderson and Tatham 2006). The results <strong>of</strong> <strong>the</strong> confirmatory
factor analysis show that all measures have significant factor loadings (λ) (0.67~0.97) and in fact
loaded higher than <strong>the</strong> suggested threshold. Convergent validity was also adequate when <strong>the</strong>
constructs had an average variance extracted (AVE) <strong>of</strong> at least 0.50 (Hair et al. 2006). Table 1 lists
<strong>the</strong> correlation matrix, which indicates <strong>the</strong> correlations among <strong>the</strong> constructs and <strong>the</strong> square root <strong>of</strong>
AVE on <strong>the</strong> diagonal (0.76~0.94); all AVEs were above <strong>the</strong> recommended level <strong>of</strong> 0.50.
Discriminant validity is used to assess <strong>the</strong> extent to which constructs differ. Based on Anderson and
Gerbing’s (1988) recommendations, we assessed discriminant validity for two estimated constructs by
limiting <strong>the</strong> estimated correlation parameter (φi, j) to 1.0 and <strong>the</strong>n performing a chi-squared difference
test on <strong>the</strong> values obtained for <strong>the</strong> constrained and unconstrained models.
In accordance with Bagozzi, Yi and Phillips (1991) and Anderson and Gerbing (1988), we tested
discriminant validity by comparing six constrained models with <strong>the</strong> unconstrained model using a chisquared
difference test for each data set. The results showed that nei<strong>the</strong>r <strong>the</strong> chi-squared values nor
<strong>the</strong> fit indices <strong>of</strong> <strong>the</strong> six constrained models were significantly superior to those <strong>of</strong> <strong>the</strong> unconstrained
model. These four constructs thus exhibit adequate discriminant validity.
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