Proceedings

ICD_Index
Company_size
a. Listwise N=21
3.2.3 Testing hypothesis H3
Table 9. Pearson correlation results
ICD_Index Company_size
Pearson Correlation 1 0.373
Sig. (2-tailed) 0.096
Pearson Correlation 0.373 1
Sig. (2-tailed) 0.096
H3: There is no relation between the combination of company size and industry
type and IC disclosure
In the case of hypothesis H3 we have one normally distributed scale dependent
variable (ICD_Index) and two independent variables from which one is nominal
(Industry_type) and the other is scale (Company_size). Accordingly we are using
multiple regression analysis to test the hypothesis H3. The results of multiple
regression analysis are presented in Table 10, Table 11, and Table 12 below.
Table 10. Multiple regression results: Model summary
Model R R Square Adjusted R Square Std. Error of the Estimate
1 0.445 0.198 0.109 0.12424
a. Predictors: (Constant), Industry_type, Company_size
Table 11. Multiple regression results: ANOVA
Model Sum of Squares df Mean Square F Sig.
1
Regression 0.069 2 0.034 2.220 0.137
Residual 0.278 18 0.015
Total 0.346 20
a. Predictors: (Constant), Industry_type, Company_size
b. Dependent Variable: ICD_Index
Table 12. Multiple regression results: Coefficients
Unstandardized Coefficients Standardized Coefficients
Model
B Std. Error Beta
t Sig.
1
(Constant) -0.208 0.278 -0.746 0.465
Company_size 0.022 0.014 0.337 1.579 0.132
Industry_type
a. Dependent Variable: ICD_Index
-0.063 0.055 -0.245 -1.146 0.267
Simultaneous multiple regression was conducted to investigate the best predictors of
ICD_Index. When the combination of variables to predict IC disclosure included
company size and industry type F (2, 18) = 2.220, p = 0.137 > 0.05. The adjusted R
squared is 0.109 which means that 11% of the variance in ICD_Index was explained
by the model. According to the results obtained it means that we can not reject
~ 412 ~