Proceedings of the 8th International Conference on Intellectual ...

4.2 Correlation analysis
Maria Cristina Morariu
Correlation analysis is <strong>the</strong> initial statistical technique used to analyse <strong>the</strong> association between <strong>the</strong>
dependent and <strong>the</strong> independent variables. Table 7 shows <strong>the</strong> findings from Pearson correlation matrix
analysis. As a start, <strong>the</strong> Pearson’s correlation coefficients were analysed to check for <strong>the</strong> absence <strong>of</strong>
multicollinearity problems. First we can observe that a significant positive correlation exists between
two independent variables VAHU and SCVA, r (12) = .844. This multicolinearity problem was
eliminated after finding <strong>the</strong> results <strong>of</strong> collinearity test that we ran (Table 5 & 6; see VIF < 3).
Table 5: Multicollinearity VAHU
Table 6: Multicollinearity SCVA
Coefficients a
Collinearity Statistics
Model
Tolerance VIF
1 SCVA_Rec .725 1.379
VACA_rec .670 1.493
LN_SALES .517 1.935
a. Dependent Variable: VAHU_LnVA
Coefficients a
Collinearity Statistics
Model Tolerance VIF
1 VACA_rec .698 1.433
LN_SALES .551 1.815
VAHU_LnVA .667 1.499
a. Dependent Variable: SCVA_Rec
Considering <strong>the</strong> correlations between independent variables and dependent variables, only nine are
statistically significant. VACA is significantly positively associated with ROE, r (12) = .615, p < .05 and
with MB, r (12) = .858, p < .01.This means that companies with relatively high VACA were likely to
have high ROE and MB. No significant correlation is identified between VACA and ATO. SCVA is
significantly positively correlated with ROE, r (12) = .563, p < 0.05 but negatively correlated with ATO,
r (12) = -.570, p < 0.05. This means that companies that have efficiently used <strong>the</strong>ir SC were likely to
have recorded a high ROE but a small ATO. No significant correlation is between SCVA and MB.
VAHU is significantly negatively correlated with ATO, r (12) = -.746, p < 0.01. VAHU is not
significantly correlated with <strong>the</strong> remaining two dependent variables (ROE and MB). This means that
when companies used efficiently <strong>the</strong>ir HC <strong>the</strong>y recorded a small ATO. Regarding VAIN, we notice a
negative significant correlation with ATO, r (12) = -.724, p < 0.01 and no significant correlation
between this variable and MB or ROE. Considering VAIC <strong>the</strong> findings show a significant positive
correlation with ROE, r (12) = .621, p < 0.05, but a negative significant correlation with ATO, r (12) =
.666, p < 0.01. No significant correlation is between this independent variable and MB. Overall,
correlation results imply sample firms with a higher-level VACA were associated with higher levels <strong>of</strong>
productivity and higher levels <strong>of</strong> market valuation. Fur<strong>the</strong>r, sample firms with higher levels <strong>of</strong> VAHU
were associated with lower levels <strong>of</strong> productivity and, sample firms with higher levels <strong>of</strong> SCVA were
associated with higher levels <strong>of</strong> pr<strong>of</strong>itability but lower level <strong>of</strong> productivity. Consequently, in
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