THE INFLUENCE
OF INTELLECTUAL CAPITAL ON FIRM VALUE WITH INSTITUTIONAL OWNERSHIP AS A
MODERATION VARIABLE
Anindya Nurmalita Dewi
Faculty of Economics and Business,
Universitas Diponegoro,
Indonesia
Email: [email protected]
KEYWORDS intellectual capital; institutional
ownership; firm value |
ABSTRACT The benchmark for the success of a company can be seen in the resources
that support and support the company's activities. This is expected to be
able to improve financial performance from time to time, so that the company
is able to achieve targets to maintain the company's survival. Therefore, the
purpose of this study is to examine and analyze how Intellectual Capital (IC)
affects Firm Value, examines and analyzes how Institutional Ownership
influences Intellectual Capital (IC) to Firm Value, examines and analyzes how
Institutional Ownership moderates the influence of Intellectual Capital (IC)
on company value. For this research, the population is all conventional
general banking listed on the Indonesia Stock Exchange (IDX) from 2017 –
2021. The sample of this study uses banking companies that have been listed
on the Indonesia Stock Exchange (IDX) for the period 2017 – 2021. The
conclusion in this study is: Intellectual Capital (VAIC) has a positive
effect on Firm Value (TOBINS_Q), Institutional Ownership (KI) has a positive
effect on Firm Value (TOBINS_Q), Institutional Ownership can moderate the
effect of Intellectual Capital on Firm Value. |
INTRODUCTION
The
benchmark for the success of a company can be seen in the resources that
support and support the company's activities (Leonita, 2020).
This is expected to be able to improve financial performance from time to time,
so that the company is able to achieve targets to maintain the company's
survival. Therefore the success achieved by the company is not solely
determined by the results of the work achieved and is calculated by the
company's current financial ratios (Prapaska & Siti, 2012).
The main thing that determines the survival of a company is intangible assets,
namely assets in the form of human resources (HR) which play an important role
in carrying out the company's tangible assets (Nova,
2023).
Intellectual
Capital (IC) is an indicator that can be used in weighing and estimating
knowledge assets (Najah, 2021).
Intellectual Capital (IC) refers to intangible assets related to the knowledge
and expertise that the company uses (Kusumowati & Meiranto, 2013).
Intellectual Capital (IC) is believed to play a role in maximizing company
value.
According
to Sumiati and Indrawati (2019)
maximizing or increasing the value
of the company for shareholders is the goal of a company. But maximizing the
value of the company is the end goal. Before maximizing the value of the
company, managers must first create a value. Because if the maximum company
value will also increase the pleasure or satisfaction of the shareholders so
that they are able to maximize the welfare level of the shareholders and it is
also more appropriate than maximizing profits
(Wijaya & Sedana, 2015).
Based on the description above, the
objectives of this study are (1) to examine and analyze how intellectual
capital (IC) influences firm value. (2) Reviewing and analyzing how
Institutional Ownership influences Intellectual Capital (IC) on company value.
(3) Review and analyze how Institutional Ownership moderates the influence of
Intellectual Capital (IC) on company value.
HYPOTHESIS DEVELOPMENT
The Effect of Intellectual Capital on Firm
Value
In research by
Rahmita et al. (2020) intellectual
capital is proven to increase company value. The effect of intellectual capital
on increasing firm value is also found in Lestari (2017) and
Simarmata and Subowo (2016) which
shows that intellectual capital has a positive impact on firm value. Based on
the explanation above, the authors formulate the hypothesis as follows:
H1: Intellectual Capital has a
positive effect on Firm Value.
Effect of Institutional Ownership on Company Value
According to Tamrin and
Maddatuan (2019) defines institutional ownership as the percentage of shares
owned by institutions such as investment companies, banks, insurance companies,
or other companies. One of the forms of distribution of shares among outside
shareholders is institutional ownership.
In Lestari (2017) shows institutional ownership has a supervisory or
monitoring function in increasing firm value. This is in line with the research
conducted by Aditya and Supriyono (2015).
H2: Institutional Ownership has an
effect positive on Company Value.
The Effect of Intellectual Capital on Firm Value with
Institutional Ownership as a moderating variable
In Siddik and Chabacib (2017) shows that institutional ownership functions as a
supervisory tool to increase firm value. This research is the same as that
conducted by Aditya and Supriyono (2015) and Fadlun (2016) the result is that institutional ownership is able to
improve the relationship between intellectual capital on firm value. Based on
the explanation that has been described, the authors formulate the hypothesis
as follows:
H3: Institutional Ownership
moderates the effect of Intellectual Capital on Firm Value.
METHOD RESEARCH
Data Types and
Sources
This study uses secondary
data adopted from banking companies listed on the Indonesia Stock Exchange
(IDX).
Population and
Sample
For this research, the population is
all conventional general banking listed on the Indonesia Stock Exchange (IDX)
from 2017 – 2021.
This research sample uses banking
companies that have been listed on the Indonesia Stock Exchange (IDX) for the
2017 - 2021 period.
Method of collecting data
The data used in this study is
secondary data and uses a sampling technique.
Data analysis method
In this study the data analysis
methods consisted of: (1) Descriptive Statistics, (2) Normality Test, (3)
Classical Assumption Test including Autocorrelation Test, Heteroscedasticity
Test, Multicollinearity Test, and Linear Regression Analysis with moderating
variables. The regression equation is as follows:
NP = α1+β1IC+β2Size+β3Leverage+β4
Growth+e1 (1)
NP
= α2+β5IC +β6 IO+β7 Size+β8 Leverage+β9 Growth+e2 (2)
NP
= α3+β10 IC+β11 IO+β12 (IC.IO)+β13 Size+β14 Leverage+β15 Growth+e3 (3)
Information:
NP: Firm Value
ICs: Intellectual
Capital
IOs: Institutional
Ownership
α: Constant
β1 … β15: Regression coefficient
e: Error
The model
feasibility test consists of:
F test
This test is
to find out how the independent variable influences the dependent variable
using SPSS Ghozali (2009).
Determination Coefficient Test (R2)
Coefficient of Determination
(Goodness of fit)
Intend to estimate how much percent
of the independent variables have an effect to
the dependent variable. Mark R2 proves
how many comparisons between the total of various dependent
variables which can be interpreted by the
explanatory variable.
Hypothesis testing
Test the hypothesis using mutual
test shows
how big the influence of one independent
variable or explanatory variable personally
when explaining variables dependent on
Ghozali (2009).
RESULT AND DISCUSSION
Table 1. Descriptive Statistics
Source:
Processed secondary data (2022)
Based on table 1. It is known that for the
Corporate Value variable (Tobins Q) the average is 1.208347, the minimum value
is 1.028870, namely PT Bank KB Bukopin Tbk (BBKP) in 2019 and the maximum is1.486880namely PT Bank Danamon Indonesia Tbk (BDMN)
in 2018 with a standard deviation of0.112001.
So based on the average value of 1.208347, it indicates that the company's
average PBV is 1.21%.
Tabel
2. Initial normality test results 1
Source:
Processed secondary data (2022)
Seen from table 2. It is
known that the test for final normality can be seen from the Kolmogorof-Smirnov
sig. of 0.000 <0.05 it can be said that the data in this study are not
normal. Then do the removal of abnormal data or outliers with the following
results:
Tabel
3. Final normality test results 1
Source:
Processed secondary data (2022)
Based on table 3 above it is
known that the test for final normality can be seen from the Kolmogorof-Smirnov
sig. of 0.200 > 0.05 it can be said that the data in this study are normal.
Tabel
4. Initial normality test results 2
Source:
Processed secondary data (2022)
Based
on table 4 above it is known that the test for final normality can be seen from
the Kolmogorof-Smirnov sig. of 0.000 <0.05 it can be said that the data in
this study are not normal. Then do the removal of abnormal data or outliers
with the following results:
Tabel
5. Final normality test results 2
Source:
Processed secondary data (2022)
Based on table 5 above it is known that the test for
final normality can be seen from the Kolmogorof-Smirnov sig. of 0.098 > 0.05
it can be said that the data in this study are normal.
Tabel
6. Initial normality test results 3
Source:
Processed secondary data (2022)
Based
on table 6 above it is known that the test for final normality can be seen from
the Kolmogorof-Smirnov sig. of 0.000 <0.05 it can be said that the data in
this study are not normal. Then do the removal of abnormal data or outliers
with the following results:
Tabel
7. Final normality test results 3
Source:
Processed secondary data (2022)
Based on table 7 above it is known that the test for final
normality can be seen from the Kolmogorof-Smirnov sig. of 0.071 > 0.05 it
can be said that the data in this study are normal.
Table
8. Multicollinearity test results 1
Source:
Processed secondary data (2022)
Based on table 8 it can be
seen that the test results for multicollinearity have a tolerance value for
each independent variable > 0.1 and for VIF values < 10 so that it can
be said that multicollinearity does not occur or is free from multicollinearity
in this study.
Table 9. Multicollinearity test results 2
Source:
Processed secondary data (2022)
Based
on table 9, it can be seen that the test results for multicollinearity have a
tolerance value for each independent variable > 0.1 and for VIF values
< 10 so that it can be said that multicollinearity does not occur or is
free from multicollinearity in this study.
Table 10. Multicollinearity test results 3
Source:
Processed secondary data (2022)
Based on table 10, it can be seen that the test results for
multicollinearity have a Tolerance value for each independent variable > 0.1
and for VIF values < 10 so that it can be said that multicollinearity does
not occur or is free from multicollinearity in this study.
Table 11. Multicollinearity test results 4
Source:
Processed secondary data (2022)
Based on table 11, it can be
seen that the test results for multicollinearity have a Tolerance value for
each independent variable > 0.1 and for a VIF value < 10 so that it can
be said that multicollinearity did not occur or was free from this study.
Table 12. Multicollinearity test results 5
Source:
Processed secondary data (2022)
Based on table 12, it can be seen that the test results for
multicollinearity have a Tolerance value for each independent variable > 0.1
and for VIF values < 10 so that it can be said that multicollinearity does
not occur or is free from multicollinearity in this study.
Table 13. Multicollinearity test results 6
Source:
Processed secondary data (2022)
Based on table 13, it can be seen that the test results for multicollinearity
have a Tolerance value for each independent variable > 0.1 and for a VIF
value < 10 so that it can be said that multicollinearity does not occur or
is free from multicollinearity in this study.
Table 14. Autocorrelation test result 1
Source:
Processed secondary data (2022)
Based on table 14 above it can be seen that the test results
for autocorrelation of 1.883 are between 1.5 and 2.5 meaning that
autocorrelation does not occur or is free in this study.
Table 15. Autocorrelation test result 2
Source:
Processed secondary data (2022)
Based on table 15 above it can be seen that the test results
for autocorrelation of 2.306 are between 1.5 and 2.5 meaning that
autocorrelation does not occur or is free in this study.
Table 16. Autocorrelation test result 3
Source:
Processed secondary data (2022)
Based on table 16 it can be seen that the test results for
an autocorrelation of 1.975 are between 1.5 and 2.5, meaning that there is no
autocorrelation in this study.
Table 17. Autocorrelation test result 4
Source:
Processed secondary data (2022)
Based on table 17 it can be seen that the test
results for an autocorrelation of 2.453 are between 1.5 and 2.5, meaning that
there is no autocorrelation in this study.
Table 18. Autocorrelation test result 5
Source:
Processed secondary data (2022)
Based on table 18 it can be seen that the test
results for an autocorrelation of 2.019 are between 1.5 and 2.5, meaning that
there is no autocorrelation in this study.
Table 19. Autocorrelation test result 6
Source:
Processed secondary data (2022)
Based
on table 19 it can be seen that the test results for an autocorrelation of
2.452 are between 1.5 and 2.5, meaning that there is no autocorrelation in this
study.
Table 20. Heteroscedasticity test
result 1
Source: Processed secondary
data (2022)
Based on table 20
it can be seen
that the results of the heteroscedasticity test for each independent variable
have a significance value above 0.05 (sig>0.05) so that it can be said that
there is no heteroscedasticity.
Table 21. Heteroscedasticity test
result 2
Source: Processed secondary
data (2022)
Based on table 21,
it can be seen
that the results of the heteroscedasticity test for each independent variable
have a significance value above 0.05 (sig>0.05) so that it can be said that
there is no heteroscedasticity.
Table 21. Heteroscedasticity test
result 3
Source: Processed secondary
data (2022)
Based on table 22, it can be seen that the
results of the heteroscedasticity test for each independent variable have a
significance value above 0.05 (sig > 0.05) so that it can be said that there
is no heteroscedasticity.
Table 21. Heteroscedasticity test
result 4
Source:
Processed secondary data (2022)
Based
on table 23, it can be seen that the results of the heteroscedasticity
test for each independent variable have a significance value above 0.05 (sig
> 0.05) so that it can be said that there is no heteroscedasticity.
Table 21. Heteroscedasticity test
result 5
Source:
Processed secondary data (2022)
Based on table 24, it can be seen that the results of
the heteroscedasticity test for each independent variable have a significance
value above 0.05 (sig > 0.05) so that it can be said that there is no
heteroscedasticity.
Table 21. Heteroscedasticity test
result 6
Source:
Processed secondary data (2022)
Based on table 25, it can be seen that the results of
the heteroscedasticity test for each independent variable have a significance
value above 0.05 (sig > 0.05) so that it can be said that there is no
heteroscedasticity.
Table 26. Fit model
test result 1
Source:
Processed secondary data (2022)
From table 26, it is known that the sig. F = 0.000 <0.05,
it can be said that the fit model, or in this independent variable, can be used
to predict the dependent.
Table 27. Fit model
test result 2
Source:
Processed secondary data (2022)
From the table 27, it is known that the sig. F
= 0.125 > 0.05, it can be said that the model is not fit, or the independent
variables cannot be used to predict the dependent.
Table 28. Fit model
test result 3
Source:
Processed secondary data (2022)
From table 28, it is known that the sig. F
= 0.000 <0.05, it can be said that the model is fit, and the independent
variables can be used to predict the dependents.
Table 29. Fit model
test result 4
Source:
Processed secondary data (2022)
From table 29, it is known that the
sig. F = 0.055> 0.05, it can be said that the model is not fit, and the
independent variable cannot be used to predict the dependent.
Table 30. Fit model
test result 4
Source:
Processed secondary data (2022)
From table 30, it is known that the sig. F
= 0.000 <0.05, it can be said that the model is fit, and the independent
variables can be used to predict the dependents.
Table 30. Fit model
test result 4
Source:
Processed secondary data (2022)
From the table 31,
it is known
that the sig. F = 0.083> 0.05, it can be said that the model is not fit, and
the independent variable cannot be used to predict the dependent.
Table
32. Coefficient of determination test result 1
Source:
Processed secondary data (2022)
From the table 32,
above it is
known that the Adjusted R Square value is 0.609, meaning that the independent
variable affects the dependent by 60.9% while the remaining 39.1% is influenced
by other variables.
Table
33. Coefficient of determination test result 2
Source:
Processed secondary data (2022)
From the table 33, it is known that the
Adjusted R Square value is 0.018, meaning that the independent variable affects
the dependent by 1.8% while the remaining 98.2% is influenced by other
variables.
Table
34. Coefficient of determination test result 3
Source:
Processed secondary data (2022)
From the table 34, it can be seen that the
Adjusted R Square value is 0.470, meaning that the independent variable affects
the dependent by 47% while the remaining 53% is influenced by other variables.
Table
35. Coefficient of determination test result 4
Source:
Processed secondary data (2022)
From the table 35, it can be seen that the
Adjusted R Square value is 0.046, meaning that the independent variable affects
the dependent by 4.6% while the remaining 95.4% is influenced by other
variables.
Table
36. Coefficient of determination test result 5
Source:
Processed secondary data (2022)
From the table 36, it can be seen that the
Adjusted R Square value is 0.465, meaning that the independent variable affects
the dependent by 46.5% while the remaining 53.5% is influenced by other
variables.
Table
37. Coefficient of determination test result 6
Source:
Processed secondary data (2022)
From the table 37, it can be seen that the
Adjusted R Square value is 0.045, meaning that the independent variable affects
the dependent by 4.5% while the remaining 95.5% is influenced by other
variables.
Table
38. T-test results of the effect of intellectual capital on firm value with
variable size and leverage variable control
Source: Processed secondary
data (2022)
Table
39. T-test results of the effect of intellectual capital on firm value with
variable size and leverage variable control
Source: Processed secondary
data (2022)
Table
40. T-test results of the effect of intellectual capital on institutional
owbership through with variable size and leverage variable control
Source: Processed secondary
data (2022)
Table
41. T-test results of the effect of intellectual capital on institutional
owbership through with variable size and leverage variable control
Source: Processed secondary
data (2022)
Table
42. T-test results of the effect of intellectual capital in moderating
institutional owbership through with variable size and leverage variable
control
Source: Processed secondary
data (2022)
Table
43. T-test results of the effect of intellectual capital in moderating
institutional owbership through with variable size and leverage variable
control
Source: Processed secondary
data (2022)
Table
44. Hypothesis test results 1
Source:
Processed secondary data (2022)
The significance value of t for the
Intellectual Capital (VAIC) variable <0.05 with a positive coefficient value
means that Intellectual Capital (VAIC) has a positive effect on Firm Value
(TOBINS_Q). The results of this study are in accordance with the
Resources-Based theory. This theory assumes that a company has competitiveness
with competing companies if the company is able to manage and process its own
resources commensurate with the capabilities of an office.
Table
44. Hypothesis test results 2
Source:
Processed secondary data (2022)
The significance value of t for the
variable Institutional Ownership (KI) <0.05 with a positive coefficient
value means that Institutional Ownership (KI) has a positive effect on Firm
Value (TOBINS_Q)
Table
45. Hypothesis test results 3
Source: Processed secondary
data (2022)
The significance value of t for the
Intellectual Capital (VAIC) x Institutional Ownership (KI) variable is <0.05
with a positive coefficient value so that it means that Institutional Ownership
can moderate the effect of Intellectual Capital on Firm Value.
Table
46. Hypothesis test results before utilizing control variable
Source:
Processed secondary data (2022)
Table
47. Hypothesis test results before utilizing control variable
Source:
Processed secondary data (2022)
CONCLUSION
The
conclusions in this study are: (1) Intellectual Capital (VAIC) has a positive
effect on Firm Value (TOBINS_Q). (2) Institutional Ownership (KI) has a
positive effect on Firm Value (TOBINS_Q). (3) Institutional Ownership can
moderate the influence of Intellectual Capital on Company Value. Suggestions
for this study are as follows: (1) In future research, other variables may be
added that may affect firm value, for example funding decisions and dividend
policies. (2) In further research, it can expand the research sample, not only
onp.sbanking companies that have been listed on the Indonesia Stock Exchange
(IDX) but use all companies that have been listed on the Indonesia Stock
Exchange (IDX) so that the resulting sample is larger and can be
generalized.(3) In further research, it is also possible to add a range of
research periods.
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Copyright holders:
Anindya Nurmalita Dewi (2023)
First publication right:
Devotion - Journal of Research and Community
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