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

Maria Cristina Morariu
<strong>the</strong> variance in MB (adjusted R 2 is .912). Looking at <strong>the</strong> significance <strong>of</strong> each variable, we can observe
that except for VACA, all <strong>the</strong> variables are significant; that SCVA and companies size are both
positively correlated with MB while VAHU and industry type are negatively correlated with MB.
However, contrary to <strong>the</strong>oretical expectations, <strong>the</strong> results exhibited in Table 11 indicate a non
significant association between VACA and MB and appears to reject H4a. Similar to <strong>the</strong> results
obtained by correlation analysis, <strong>the</strong> results reported in Table 11, show that H3a is verified and
corroborate prior studies conducted by Firer and Williams (2003), Chen et al. (2005). However,
because <strong>the</strong> correlation between VAHU and MB is negative means that H2a is rejected. This finding
(similar to Firer and Williams, 2003) implies that Romanian investors appreciate <strong>the</strong> importance <strong>of</strong> SC
in favour <strong>of</strong> HC.
Table 11: Regression analysis_M4
Coefficients a
Unstandardized Standardized
Coefficients Coefficients
Model
B Std. Error Beta
t Sig.
1 (Constant) .082 .186 .442 .670
LN_SALES .013 .004 .544 3.356 .010
INDUSTRY -.073 .014 -.851 -5.204 .001
VAHU_LnVA -.463 .126 -.970 -3.683 .006
SCVA_Rec 1.251 .338 .820 3.705 .006
VACA_rec .132 .208 .120 .636 .543
a. Dependent Variable: MB_Rec; R Squared = .946; F (5,8) = 28.037; p < .05
Table 12 shows <strong>the</strong> results <strong>of</strong> regression coefficients for all explanatory variables, using pr<strong>of</strong>itability as
<strong>the</strong> dependent variable. The model is significant (F (5, 8) = 6.463, p < 0.05) and explains 67.8.2% <strong>of</strong>
<strong>the</strong> variance in pr<strong>of</strong>itability (adjusted R 2 is .678). Looking at <strong>the</strong> significance <strong>of</strong> each variable into <strong>the</strong>
model, we can observe that none is statistically significant. Accordingly, H2b, H3b and H4b are
rejected. This finding partially confirm <strong>the</strong> findings from Joshi et al. (2010), Muhammad and Ismail,
2009; Kamath (2008).
Table 12: Regression analysis_M5
Coefficients a
Unstandardized Standardized
Coefficients Coefficients
Model
B Std. Error Beta
t Sig.
1 (Constant) -1.353 .553 -2.446 .040
LN_SALES .004 .012 .098 .314 .762
INDUSTRY -.037 .042 -.282 -.898 .396
VAHU_LnVA -.115 .374 -.155 -.306 .767
SCVA_Rec 1.600 1.004 .676 1.593 .150
VACA_rec .887 .618 .519 1.434 .189
a. Dependent Variable: ROE_rec; R Squared = .802; F (5,8) = 6.463; p < 0.05
Table 13 exhibits <strong>the</strong> results <strong>of</strong> <strong>the</strong> regression coefficients for all explanatory variables, using
productivity as <strong>the</strong> dependent variable. The model is statistically significant (F (5,8) = 6.962, p < 0.05)
and explains 69.6% <strong>of</strong> <strong>the</strong> variance in productivity (adjusted R 2 = .696). Table 13 shows a significant
positive correlation between VACA and productivity. No significant correlation is identified between
SCVA, VAHU and productivity. These results support H4c and confirm that CA plays a major role in
productivity variation. Moreover, this finding agrees with previous studies conducted by Zeghal and
Maaloul, 2010. However, contrary to <strong>the</strong>oretical expectations, <strong>the</strong> results indicate a non-significant
association between <strong>the</strong> both VAHU and SCVA and productivity. This result partially confirms <strong>the</strong>
findings <strong>of</strong> Ghosh and Mondal, 2009 and appears to reject <strong>the</strong> H2c, H3c.
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