The Effect Of Company Size, Industry Type And Research And Development Intensity On Intellectual Capital Disclosure


 
 
This study aims to determine the effect of company size, industry type, and the intensity of research and development on intellectual capital disclosure. This research uses secondary data in conducting analysis. The dependent variables are intellectual capital and independent variables, namely company size, industry type, research and development intensity. The population of this research is companies that are included in Kompas100 index on the IDX in 2018. The sample used in this study is 100 samples. Testing the hypothesis of this study using multiple linear regression test. The results showed that: 1) firm size had an effect on intellectual capital disclosure, 2) the type of industry had no effect on intellectual capital disclosure, 3) the intensity of research and development had no effect on intellectual capital disclosure. 
 
 



INTRODUCTION
Business development is growing rapidly, knowledge ( knowledge based business ) is a strategy to keep pace with it. Company development can be seen from the growth in financial and physical capital together with growth in intellectual capital . Sawarjuwono and Kadir (2003), the creation of transformation and capitalization of knowledge by applying knowledge management. Technological advances are now based on science by applying knowledge management for these companies (Sri Iswati, 2006). In financial reporting, companies generally only focus on the company's finances. But to create useful and transparent company performance reporting, companies need to disclose non-financial reports that are owned by the company. Nonfinancial reporting is expected to attract investors. Disclosures of intellectual capital are part of the company's non-financial disclosures. Intangible assets are non-monetary assets that are not physically visible (Ulum, 2015). Expenditures of liabilities and resources can increase the resources of non-financial assets that can be categorized as assets. The revolution in information technology, knowledge and the economy is increasingly knowledge-based, and changes in the views of individuals and society are some of the important factors in disclosing intellectual capital. Rima Aprisa (2016), a large company will reveal a wider range of intellectual capital due to stakeholder demands. The higher the demands for disclosure of non-financial assets compared to micro companies. A high science company will disclose a wider intellectual capital than a lower science company (Woodcock and Whiting, 2011) . Research and development is used to find new knowledge and insights related to the creation of new products according to consumer needs (Ni Made and Dewa Gede, 2016).

Basic concepts a. Intellectual Capital
Intellectual Capital is a non-financial asset in the form of knowledge and information sources that function to compete and improve company performance. Intellectual capital is disclosed to get a bigger profit margin. According to Stewart, to increase the competitiveness of companies, it requires disclosure of intellectual capital which includes knowledge, innovation and information used to create added value for intangible assets and the ability to compete. In measuring intellectual modsl using the formula:

RESULTS AND DICUSSION Descriptive statistics
Descriptive statistical analysis is useful for providing a general description of the data that has been collected and becomes material for research. This analysis is described in terms of maximum value, minimum value, average, standard deviation, median, and frequency. The dependent variable described in this variable is the disclosure of intellectual capital by companies listed on the Kompas100 index for the period August 2018 -January 2019 Indonesian stock exchange. While the independent variables in this study include; company size, industry type, and research and development (R&D) intensity . The sample description based on descriptive statistical analysis is described in table 4.1 below: Source: researcher data, 2020 The size of the company in this study can be seen from the total assets owned by the company. From the table of descriptive statistical analysis, it can be seen that from the overall data collected company size, the highest company size is 16.01 and the lowest company size is 11.88. The standard deviation is 7.07 and the mean company size is 13.42.
Industry types are grouped into two, namely high IC intensive industry and low IC intensive industry . The score is 0 for low IC intensive industry and 1 for high IC intensive industry . In this study, the results of the analysis of companies included in the compass index of August 2018 -January 2019 show companies with high IC intensive industry by 49% and low IC intensive industry by 51%.
Research and development intensity shows how much research and development costs to support sales. The highest level of research and development intensity was 3.12, while the lowest value was 0.04. The standard deviation value of 0.56 is greater than the calculated average value of 0.45. Shows that there is a variation in the sample value due to a fairly wide deviation of data.
Overall, the score index for voluntary intellectual capital disclosure (ICD) by companies has the highest value of 68.51 and the lowest score of 24.07. While the standard deviation value is 38.89, and the average value is 50.94. This shows that the average disclosure of intellectual capital (ICD) in companies that are included in the Kompas100 index is still minimal.

Normality Test
Normality testing is carried out to determine whether in the regression modal, the residual variables have a normal distribution. The method used in the normality test is to look at the Kolmogorov-Smirnov (KS) value. Following are the results of the normality test:

Multicollinearity Test
The multicollinearity test aims to determine whether there is a correlation between the independent variables (independent), if there is a correlation between these variables it can be said that the regression model is not good. According to Ghozali (2013) multicollinearity can be seen from the tolerance and inflation factor (VIF) values . The results of the multicollinearity test are presented in the following table. Based on table 4.3, the multicollinearity test results can be concluded that the tolerance value for the company size variable is 0.957, the industrial type variable is 0.946, and the research and development intensity variable is 0.984. Meanwhile, the VIF value for the company size variable was 1.044, the industry type variable was 1.057, and the research and development intensity value was 1.016. The tolerance value for all variables is more than 0.1 (> 0.1) and the VIF value for all variables is less than 10 (<10), it can be concluded that the results of this test show that the analyzed data does not occur multicollinearity.

Autocorrelation Test
Autocorrelation testing aims to test whether in the linear regression model there is a correlation between the disturbing error in period t and the confounding error in period t-1 (Ghozali, 2013). The test model uses the Durbin-Watson (DW-test). The criteria for testing with the DW-test is if the DW is located between Du and 4-Du. The results of the analysis show in the following table:

Durbin -watson
Terms Information There is no autocorrelatio n Source: researcher data, 2020 Based on table 4. It can be seen that the DW value is 1.993 for the dU and dL values can be seen from the DW table at a significance of 0.05 where n (amount of data) = 100 and k (number of independent variables) = 3, the dL value is 1.613 and the dU value is 1,736. The value 4 -dU is 2.007. DW values are in the dU <DW <4 -dU area so it can be concluded that the regression model is free from autocorrelation problems and is feasible to use. The results of the analysis show that the DW value must be between 1.736 (dU) and 2.007 (4 -dU), so as not to experience autocorrelation problems. The DW value is 1.993 between the dU and 4 -dU values, so there is no autocorrelation and is feasible to use.

Heteroskedasticity test
Heterocodasticity testing aims to test whether there is an inequality of variance in the regression capital, if heteroscedasticity occurs, the regression model can be said to be good. According to Ghozali (2013), heteroscedasticity testing can be seen from the presence or absence of a scaterrplot chart pattern between SPRESID (residual) and ZPRED (dependent variable). The following are the results of the heteroscedasticity test:

Gambar 4.1 Graph of Heteroscedasticity Test Results
Source: researcher data, 2020 Based on Figure 4.1, the scatterplot graph of the dependent variable is disclosure of intellectual capital. The graph can be seen that the data points are spread above and below or around the number 0, the data distribution does not form a wavy wavy pattern, then narrows and widened again. It can be concluded that heeroscedasticity does not occur in the regression model, so the regression model is suitable to be used to predict the dependent variable based on the input of the independent variable.

Hypothesis Test (t test)
The t test is used to determine the effect of each independent variable on the dependent variable. Partially test, the independent variable on the dependent variable. If the significance value is smaller <0.05 and the t-value is greater> t-table, the hypothesis is accepted. The significance value (Sig)> 0.05 and the tcount value is smaller <t-table, the hypothesis is rejected.

CONCLUSION
The results of this study conclude that the firm size variable affects intellectual capital disclosure. the firm size variable affects intellectual capital disclosure. This research is in line with previous research including Ming Chen (2019) with the method of multiple linear regression analysis showing that company size has a partial effect on intellectual capital disclosure. Ni Made Ari Astuti and Dewa Gede Wirama's research (2016) used multiple linear regression analysis which showed the same results. A large company size will increase the attention and pressure from stakeholders. Larger companies will budget a lot of money in disclosing a wider range of voluntary information about intellectual capital. Thus, stakeholder information needs will be met.