The effect of factors on degree of disclosing accounting information: Evidence from food industry

dependent audit (KT), Solvency 
(TT), Firm Size (QM), Asset efficiency (VQ). The relationship between the level of disclosure and some independent variables 
is closely related: Assets efficiency (VQ) (Pearson Correlation index is 0.317), Solvency (TT) (Pearson Correlation index is 
0.262). This allows us to see an indication of the influence of these factors on the level of accounting information disclosure of 
companies in the food industry listed on Vietnam's stock market. In addition, the two variables Number of Subsidiaries (CTC) 
and Firm Size (QM) have a high correlation coefficient of 0.692 (significance level of 5%). However, the Number of 
subsidiaries (CTC) has no correlation with the dependent variable of the disclosure index (significance level of 5%). The 
relationship between the Ownership of foreign shareholder (SH) and Firm size (QM) variable has a Pearson Correlation 
coefficient of 0.480. However, this correlation coefficient is not worrisome because if this index is above 0.5, it is necessary to 
reconsider and over 0.8 to determine whether there are multiple collinear, so it can temporarily be retained for the next steps. 
After eliminating the independent variables that have no correlation with the dependent variables, we get Person correlation 
coefficients in Table 2. 
Regression model analysis 
The study uses a stepwise selective analysis method (a combination of gradual inclusion and exclusion to re-examine the factors 
that really affect) to introduce independent variables: Number of members of the Administrative Council (QT), Chairman of the 
Board concurrently General Director (KN), The Percentage of non-executive board members (TL), Ownership of foreign 
shareholder (SH), Profitability (SL), Independent audit (KT), Solvency (TT), Firm size (QM), Asset efficiency (VQ). The 
 530
analysis results show 6 models reflecting the relationship between the disclosure index and the independent variables and the 
results are summarized in See Table 3. 
Table 2 
Result of correlation analysis 
Correlations ID QT KN TL SH SL KT TT VQ QM 
ID Pearson Correlation 1 .187* -.227** -.195** .152* -.165* -.225 .265* -.317** .161** Sig. (2-tailed) .012 .002 .009 .041 .027 .002 .000 .000 .030 
QT Pearson Correlation .187* 1 .076 .042 .315** -.058 .060* -.085 -.135 .226 Sig. (2-tailed) .012 .308 .575 .000 .439 .421 .258 .070 .002 
KN Pearson Correlation -.227** .076 1 .386** -.001 .066 .189** -.125 .236 .029** Sig. (2-tailed) .002 .308 .000 .987 .379 .011 .094 .001 .696 
TL Pearson Correlation -.195** .042 .386** 1 .012 .016 .184** -.007 .024** .045 Sig. (2-tailed) .009 .575 .000 .872 .827 .013 .927 .751 .547 
SH Pearson Correlation .152* .315** -.001 .012 1 -.005 .178* -.075** -.007 .480 Sig. (2-tailed) .041 .000 .987 .872 .949 .017 .316 .922 .000 
SL Pearson Correlation -.165* -.058 .066 .016 -.005 1 .016* -.118 .106 .099 Sig. (2-tailed) .027 .439 .379 .827 .949 .829 .115 .155 .188 
KT Pearson Correlation -.225** .060 .189* .184* .178* .016 1** -.077 -.006* .373* Sig. (2-tailed) .002 .421 .011 .013 .017 .829 .302 .935 .000 
TT Pearson Correlation .265** -.085 -.125 -.007 -.075 -.118 -.077** 1 -.062 -.065 Sig. (2-tailed) .000 .258 .094 .927 .316 .115 .302 .405 .385 
VQ Pearson Correlation -.317** -.135 .236** .024 -.007 .106 -.006** -.062 1** -.028 Sig. (2-tailed) .000 .070 .001 .751 .922 .155 .935 .405 .707 
QM Pearson Correlation .161* .226** .029 .045 .480** .099 .373* -.065** -.028 1 Sig. (2-tailed) .030 .002 .696 .547 .000 .188 .000 .385 .707 
(Source: Results of running SPSS 22) 
Table 3 
Models reflecting the relationship between the level of disclosure and the independent variables 
Model R R2 Adjusted R square Std. Error of the Estimate Durbin - Watson 
1 .317a .101 .096 .0670418 
2 .401b .161 .151 .0649448 
3 .452c .204 .191 .0634207 
4 .524d .275 .258 .0607274 
5 .543e .295 .275 .0600227 
6 .559f .313 .289 .0594366 1.882 
(Source: Results of running SPSS 22) 
According to the analysis results, the model with the largest adjusted R square is 0.289, showing that the above independent 
variables explained 28.9% for the dependent variable of the information disclosure index. 
Table 4 
ANOVA analysis 
Model Sum of Squares Df Mean Square F Sig. 
1 
Regression .090 1 .090 19.922 .000b 
Residual .800 178 .004 
Total .890 179 
2 
Regression .143 2 .072 16.955 .000c 
Residual .747 177 .004 
Total .890 179 
3 
Regression .182 3 .061 15.056 .000d 
Residual .708 176 .004 
Total .890 179 
4 
Regression .244 4 .061 16.555 .000e 
Residual .645 175 .004 
Total .890 179 
5 
Regression .263 5 .053 14.584 .000f 
Residual .627 174 .004 
Total .890 179 
6 
Regression .278 6 .046 13.135 .000g 
Residual .611 173 .004 
Total .890 179 
(Source: Results of running SPSS 22) 
The results of testing the suitability of the model show that the significance level of the test F <0.05 (Sig. = 0.000) reflects that 
regression analysis is appropriate. The model fits the data set and can be extrapolated to the whole reflection. The test results 
on the regression coefficients of model 6 show the significance level of the t-test on regression coefficients <0.05, reflecting the 
regression coefficients of independent variables with statistical significance. Measurements of multicollinearity phenomenon 
T. T. Tuan et al. /Accounting 6 (2020) 531
shows that VIF values (Asset efficiency: 1,025; Solvency: 1,019; Independent audit: 1,206; Enterprise size: 1,222; The 
Percentage non-executive board members: 1,038; Number of members of the Administrative Council: 1.083) <5 reflects no 
multicollinearity among these six independent variables. 
Table 5 
The sixth model by stepwise method 
Model B Std. Error Standardized Coefficients Beta T 
Sig. 
 Tolerance VIF 
(Constant) .650 .028 23.156 .000 
VQ -.025 .006 -.274 -4.291 .000 .975 1.025 
TT .007 .002 .253 3.973 .000 .981 1.019 
KT -.041 .010 -.281 -4.056 .000 .829 1.206 
QM 1.904E-009 .000 .250 3.594 .000 .818 1.222 
TL -.001 .000 -.513 -2.376 .019 .963 1.038 
QT .008 .004 .138 2.109 .036 .923 1.083 
(Source: Results of running SPSS 22) 
Self-correlation test is performed by using the Durbin-Watson test to check for autocorrelation in residuals of an estimation 
analysis. The results in Table 3 show that the coefficient of the Durbin-Watson test is 1,882 in the range of 1-3, reflecting no 
autocorrelation phenomenon between the residuals e. Regression analysis reflects the relationship between the independent 
variables and the level of disclosure as follows: 
ID=0.65 – 0.274×VQ + 0.253×TT – 0.281×KT + 0.250×QM – 0.513×TL + 0.138×QT 
Thus, the regression model with six independent variables: Number of members of the Administrative Council (QT), The 
Percentage non-executive board members (TL), Independent audit (KT), Solvency (TT), Firm size (QM), Assets efficiency 
(VQ) satisfies all the assumptions for regression models multiples thus can be used to explain the volatility of the accounting 
information disclosure of listed companies in Vietnam stock market of food industry. From the regression equation and the 
estimated results showed that three factors including: Assets efficiency (VQ), Independent audit (KT) and The Percentage non-
executive board members (TL) have negative relationship with the level of information disclosure. The rate of which factors 
The Percentage non-executive board members (TL) have the most negative impact with the regression coefficient is -0.513, this 
is not consistent with previous studies Chen and Jaggi (2000), Haniffa and Cooke (2002), Barako (2007), Nguyen Trong Nguyen 
(2016. Conversely, factors Solvency (TT), Firm size (QM) and the Number of members of the Administrative Council (QT) 
have a positive impact on the level of disclosure accounting of enterprises of food industry. In particular, the impact of Solvency 
(TT) is the most powerful with the regression coefficient is +0.253 i.e. The solvency of the business is the higher the level of 
disclosure of business as short. This is in line with the market due to increase the confidence of lenders of capital as well as 
other stakeholders. 
5. Conclusion 
Through this research, we may have noticed that the disclosure level of the public listed company of the food industry in Vietnam 
is not high, the enterprises of the food industry should focus on complying with the principles, accounting standards to increase 
the degree of disclosure. The result of research indicates factors: Assets efficiency (VQ), Solvency (TT), Independent audit 
(KT), Firm size (QM), The Percentage of non-executive board members (TL) and Number of members of the Administrative 
Council (QT) have a significant impact on the disclosure index of public listed companies of the food industry in Vietnam's 
stock market. The state agencies should provide more specific guidelines on the requirements of disclosure of the public listed 
companies in general and the public listed companies of the food industry in particular to ensure the transparency of Vietnam's 
market. 
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