Accounting Research Festival 2025 P-ISSN: x-x.
E-ISSN: x-x Website: https://publikasiilmiah.
id/fra4 Redefining Accounting Education: Balancing Technological Innovation with Ethics and Sustainability Beyond Compliance: The Synergy of ESG and Green Process Innovation as Determinants of Firm Value Yeti Ery Listia 1* .
Diah Arum Muarifah 2 .
Sri Retnoningsih 3 1 Accounting.
Wahid Hasyim University.
Semarang.
Indonesia.
Email: arifarhiewise@gmail.
2 Accounting.
Wahid Hasyim University.
Semarang.
Indonesia.
Email: arifarhiewise@gmail.
3 Accounting.
Wahid Hasyim University.
Semarang.
Indonesia.
Email: arifarhiewise@gmail.
ABSTRACT
This study aims to examine the impact of ESG Disclosure (X.
Green Process Innovation (GPI) (X.
, and Company Size (X.
on Company Value (Y) in the energy sector on the Indonesia Stock Exchange for the period 2021-2024.
The method used in this study is quantitative by utilizing secondary data from 144 samples analyzed through purposive sampling.
For data analysis, panel data regression with a Random Effects Model (REM) was used.
The research findings indicate that all proposed hypotheses are rejected.
Through the t-test .
, the variables ESG Disclosure.
Green Process Innovation, and Company Size do not show a significant impact on their respective Company Values.
The results of the F-test .
also support that the three independent variables do not have a significant effect when tested simultaneously on Company Value.
Keywords: ESG1.
Innovation2.
Green Process Innovation3.
Corporate Values4.
Energy Sector5
INTRODUCTION
The growing global awareness of sustainability issues has made it imperative for companies worldwide to adopt responsible business practices, both environmentally and socially.
In this context.
Environmental.
Social, and Governance (ESG) disclosure has become a crucial factor that not only influences reputation but also directly impacts a company's market value .
The transparency provided by ESG information can increase trust among investors and consumers, which in turn contributes positively to financial ESG disclosure serves as a strategic signal that confirms a company's commitment to sustainability.
This transparent reporting is crucial for increasing stakeholder trust and reducing information asymmetry between management and investors .
Companies with superior ESG performance will choose to make more comprehensive disclosures as an indication of better and longer-term risk management .
This positive and credible signal ultimately contributes positively to the company's market value.
In addition to the communication function through disclosure (X-.
, commitment to sustainability must be realized through concrete actions, namely Green Process Innovation.
Investment in GPI .
uch as the adoption of environmentally friendly technologies or recycling processe.
serves as a costly signal to the market that the company has superior management quality and the ability to create operational excellence and cost efficiency in the future .
Empirical evidence confirms these economic benefits.
example, the use of renewable energy or recycling processes has been shown to reduce energy costs and increase competitiveness .
In Indonesia, the energy transition practices carried out by PT Pertamina show that GPI is a tangible signal of the company's commitment to ensuring compliance with environmental regulations while strengthening the signal of competitiveness in the eyes of investors .
It is important to consider the intrinsic factor of the company, namely Company Size (X_.
, because it acts as a signal of credibility and stability for ESG and GPI signals.
Large companies are inherently perceived by the market as more stable, liquid, and better resourced entities to bear signaling costs .
uch as GPI investment costs and ESG disclosur.
than smaller companies.
In addition, due to the high level of Proceedings Accounting Research Festival | 4 13 scrutiny by regulators and the public .
, large companies are forced to send more comprehensive transparency signals, which further strengthens the Company's Value in the capital market.
Although awareness and recognition of the significance of ESG Disclosure practices have increased globally, the empirical relationship between ESG disclosure and Company Value in the capital market still leaves inconsistent results ( research gap ), especially in the context of developing countries such as Indonesia.
Previous research shows inconsistent results regarding the influence of ESG on Company Value in Indonesia, such as the results between findings that show no influence .
and positive influence.
Furthermore, the findings .
indicate that corporate environmental commitment needs to be mediated by Green Innovation.
This strengthens the argument that GPI (X_.
is very important as a signal of real actions that are valued by the market.
No research has simultaneously examined the three variables of ESG Disclosure.
Green Process Innovation, and Company Size on Company Value in the Indonesian energy sector during the 2021-2024 The energy sector, which is highly vulnerable to environmental issues, provides an ideal context for examining how sustainability signals translate into market valuations.
Therefore, this research seeks to fill this gap.
The study aims to analyze the influence of ESG Disclosure (X-.
Green Process Innovation (GPI) (X-.
, and Company Size (X-.
on the Company Value of the energy sector on the Indonesia Stock Exchange in This research has practical and theoretical benefits, where the theoretical benefits contribute to the development of Signaling Theory in emerging markets and the practical benefits provide practical guidance for energy sector company management to prioritize investments that are proven to increase Company Value in the eyes of investors.
This study tests four main hypotheses developed from the Signaling Theory framework.
Partially, it is hypothesized that ESG Disclosure has a significant effect on firm value (H-.
Green Process Innovation has a significant effect on firm value (H-.
, and Firm Size has a significant effect on firm value (H-.
addition, the simultaneous hypothesis (H_.
states that the three independent variables together will have a significant effect on Firm Value.
This hypothesis testing will be conducted using empirical data from energy sector company reports on the Indonesia Stock Exchange (IDX) by applying multiple linear regression analysis methods.
Signal Theory Signaling Theory , proposed by Ross .
, provides a foundation for analyzing how firm value is determined in situations of information asymmetry .
This condition occurs when management has superior access to information regarding the company's internal conditions, quality, and prospects compared to investors.
Therefore, management is motivated to send convincing positive signals to the market to reduce uncertainty.
In the context of sustainability.
ESG disclosure and investment in Green Process Innovation serve as strong non-financial signals regarding this prospective quality.
Transparent ESG reporting demonstrates to investors sound risk management and a company's commitment to longterm stability and good governance .
Meanwhile, green process innovation signals increased operational efficiency and sustainable competitive advantage , as it has the potential to reduce production costs while ensuring compliance with increasingly stringent environmental regulations .
Because of Proceedings Accounting Research Festival | 72 these quality signals, investors are expected to place a higher value on the company.
However, the effectiveness of these signals is influenced by company size .
Larger companies have higher visibility , making the signals they send more readily accepted by the market.
In addition, signals from large entities are considered more credible because they are assumed to have sufficient financial resources ( deep pockets ) to carry out large investments in ESG and innovation, so their promises of sustainable performance are more trustworthy .
RESEARCH METHOD
This study uses a quantitative method.
Quantitative research is research to examine the relationship of one variable with another variable .
The data analyzed is secondary data sourced from official documents, financial reports, and sustainability reports of Energy Sector Companies for 2021-2024 and has complete data for variable measurement needs.
This study will use two types of variables:
independent variables and dependent variables.
The independent variables in this study are ESG Score.
Green Process Innovation, and Company Size.
The dependent variable is company value, which is obtained through the Indonesia Stock Exchange website and the website.
Data collection was conducted by thoroughly examining the report's contents, particularly the sections containing disclosures related to ESG.
Green Process Innovation, and company size.
This study aimed to determine the influence of ESG disclosure (H.
Green Process Innovation (H.
, company size (H.
, and all X variables (H.
on company value (Y).
The population of this study is all energy companies listed on the Indonesia Stock Exchange (IDX) for the period 2021-2024.
This study selected companies in the energy sector .
ncluding coal, oil, and ga.
, which has the most significant environmental impact and is the largest contributor to carbon emissions.
This sector combines the highest environmental pressures with the need for large capital innovation amidst the acceleration of global sustainability trends and the enforcement of OJK Regulation (POJK) No.
51/POJK.
03/2017.
Therefore, researchers are relevant to accurately measure whether ESG Disclosure practices.
Green Process Innovation (GPI), and company size truly impact company value in the energy The sample in this study used a purposive sampling method, a technique for selecting samples based on specific criteria tailored to the research objectives.
The criteria used in this study were:
Table 1.
Population and Sample Criteria Publishing financial reports Incomplete data on ESG.
GPI.
Company Size Number of sample data used Total sample Amount ESG Disclosure ESG disclosure is a strategic management action to communicate internal quality .
isk management, longterm commitment, superior performanc.
to the market, thereby improving investor perception and logically increasing company value.
To measure ESG Disclosure, we refer to research .
, .
, and .
This measuring tool is measured using the Eikon Revinitiv score by assigning a score of 0 to undisclosed GRI items and a score of 1 to disclosed items.
ENV = Total items disclosed by the company / Total GRI disclosure items Social = Total items disclosed by the company / Total GRI disclosure items GVN = Total items disclosed by the company / Total GRI disclosure items Proceedings Accounting Research Festival | 4 13 Green Process Innovation Green innovation is an effort made by an organization to demonstrate environmental commitment through the development and implementation of better processes, techniques, and management systems .
To measure Green Process Innovation we refer to research .
This measurement is carried out using the method .
dummy variables give a score of 0 if they do not have an ISO 14001 certificate and give a score of 1 if they have an ISO 14001 certificate.
This is also explained by Qi et al.
, .
that previous studies used ISO 14001 as evidence of a company's commitment to environmental management practices into the company's operational activities.
Waste, emissions, material use, energy use and noise pollution were reduced after ISO 14001 certification.
Company Size Company size is the size of the company.
Sujarweni, 2015, said that company size is a reflection of the total amount of assets controlled by the entity to run its daily operations.
Simply put, the higher the value of the company's assets, the larger the scale of its size.
Company size refers to research .
with the method .
of total assets/size of company assets by using the calculation of the value of the logarithm of total assets .
r Natural Logarithm/Ln Total Asset.
UK = LN (Total Asset.
Company Values In financial literature.
Firm Value (Y) is a representation of the success and prospects of an entity in the eyes of the public, which is reflected by its stock market price .
This value is formed by investor perceptions of the company's ability to achieve long-term success .
The measurement of this variable refers to research (Zulfikar et al.
, 2.
using the PBV method as a tool to measure firm value, as disclosed .
which states that the measurement of the Firm Value (Y) variable often refers to the Price to Book Value (PBV) ratio, because PBV effectively reflects the market's assessment of the company's PBV = Closing Stock Price / Book Value of Stock Research methods This study applies a quantitative method with a panel data regression approach to examine the impact of independent variables on the dependent variable.
The selection of the most appropriate model and hypothesis testing are carried out through a series of statistical tests supported by EViews 13 software .
The regression equation can be formulated as follows:
Information :
Y = Company Value C.
= Constant C.
= Regression coefficient for ESG Disclosure X1 = ESG Disclosure C.
= Regression coefficient for Green Process Innovation X2 = Green Process Innovation C.
= Regression coefficient for Firm Size Proceedings Accounting Research Festival | 74 X3 = Company Size [CX=R] = Specific error component for the Random Effects model The data was tested using classical assumption tests, including tests for normality .
esidual distributio.
, multicollinearity .
orrelation between independent variable.
, and heteroscedasticity .
niform residual Once the classical assumptions were met, model selection was performed using the Chow and Hausman tests to determine the most appropriate panel data model.
Then, a Model Test (F-Tes.
is conducted to verify the model's collective feasibility.
The final step is a Hypothesis Test .
-Tes.
, which aims to measure the specific and individual contribution of each independent variable to the dependent variable, allowing the decision to accept or reject the research hypothesis based on its significance level.
RESULTS AND DISCUSSION
Chow Test Results Table 2.
Chow Test Effects Test Statistics Prob.
Cross-section F Cross-section Chi-square .
Based on the results of the Chow Test presented in Table 2, the Cross-section F probability value is This probability value is smaller than the significance level .
0000 < 0.
accordance with the Chow Test decision-making criteria, if the probability value is < 0.
05, then the null hypothesis (H.
stating that there are differences in effects between individuals .
ompanies/region.
is Therefore, a more appropriate and efficient model to use in panel data regression analysis in this study is the Fixed Effect Model (FEM).
Hausman Test Results Table 3.
Hausman Test Test Summary Chi-Sq.
Statistic Chi-Sq.
Prob.
Random cross-section Based on the results of data processing in table 3, the Random Cross-Section Probability value is 0.
this value is significantly greater than the significance level of 0.
In accordance with the decisionmaking criteria, because the Prob.
> .
, then H_0 fails to be rejected, this indicates that the Random Effect Model (REM) is more appropriate to use.
Proceedings Accounting Research Festival | 4 13 Lagrange Multiplier Test Results Table 4.
Lagrange Multiplier Test Hypothesis Test Cross-section Time Both Breusch-Pagan .
Honda .
King Wu .
Standardized Honda .
Standardized King Wu 8.
Gourieroux, et al.
Based on the data processing results in table 4, the Breusch-Pagan Cross-section probability value is This value is significantly smaller than the significance level .
In accordance with the decision-making criteria, because the probability value .
<0.
05, the null hypothesis (H.
is Based on these three tests, the most appropriate and selected model for use in the panel data regression analysis in this study is the Random Effect Model (REM).
Descriptive Statistical Test Table 5.
Descriptive Statistical Test Mean Median Maximum Minimum Std.
Dev.
Skewness Kurtosis 08E 09
01E 08
1,000,000
1,000,000
1,000,000
1,000,000
31E 08
51E 09
40E 08
Jarque-Bera Probability Sum Sum Sq.
Dev.
78E 10
40E 19
231,0000
82E 10
08E 20
Observations Proceedings Accounting Research Festival | 76 Based on the results of the descriptive test in table 5, it shows that the number of valid data for each variable is 296 originating from energy sector companies listed on the Indonesia Stock Exchange for the 2020-2024 period.
The results of the descriptive statistical test for the ESG variable show a minimum value of 0.
0000, including PT.
BYAN 2021, and a maximum value of 1.
0000, one of which is at PT.
ABMM
2021, a mean .
798 and a standard deviation of 0.
The results of the descriptive statistical test for the Green Process Innovation variable show a minimum value of 0.
000 in several companies including PT.
MCOL 2023, 2024 and PT.
RMKE 2021, a maximum value of 1.
00, one of which is at PT.
ABMM 2021-2024, a mean .
780 and a standard deviation of 0.
The results of the descriptive statistical test of the Company Size variable show a minimum value of 0.
00 at PT.
SUNI 2021
and PT.
CUAN 2021, a maximum value of 7,510 at PT.
PGAS 2021, a mean of 2,310, and a standard deviation of 8,400.
For the final statistical results, namely the Company Value variable, which shows a minimum value of -204097.
0 at PT.
SURE 2022, a maximum value of 4,080 at PT.
UNIQ 2024, a mean of 9,404,4298, and a standard deviation of 5,010.
Multicollinearity Test Table 6.
Multicollinearity Test
1,000,000
1,000,000
1,000,000
Based on the guidelines proposed by Napitupulu et al.
1: .
, if the correlation coefficient value between independent variables is less than 0.
85, then the regression model can be concluded to be free from multicollinearity problems.
These correlation coefficient values .
261831693, 0.
are smaller than the specified limit of 0.
Thus, it can be concluded that there is no serious multicollinearity problem between variables X1 and X2 in this research's regression model.
These results indicate that the regression model has passed the multicollinearity test and is suitable for use in further analysis.
Heteroscedasticity Test Table 7.
Heteroscedasticity Test Variable Coefficient Std.
Error t-Statistic Prob.
21E 08 60545937
-44079146 51594277
The results of the heteroscedasticity test show that Prob.
X1: 0.
Prob.
X2: 0.
3936 and Prob.
X3:
Because all Prob.
values are > .
, then H_0 fails to be rejected.
This indicates that the variance of the residuals is constant .
, so the model is free from heteroscedasticity problems.
Proceedings Accounting Research Festival | 4 13 Panel Data Regression Equation The panel data regression equation in this study aims to test the influence of ESG variables.
Green Process Innovation and Company Size on ethical decision making, so that the multiple linear regression equation model can be formulated as:
Based on the results of the panel data regression analysis above, it is distributed in the following equation Y = C.
*X1 C.
*X2 C.
*X3 Y = 54913031.
3484 - 11717692.
3874*X1 60627844.
9496*X2 0.
00509518200497*X3
It can be concluded that:
Constant .
,823,630.
: This is the predicted value of Y if all independent variables (X1.
X2, and X.
are zero.
Coefficient X1 (-11717692.
: Indicates a negative relationship between X1 and Y.
Every 1 unit increase in X1 will predict a decrease in the value of Y by 11717692.
3874 units, assuming variables X2 and X3 are constant .
eteris paribu.
X2 coefficient ( 60627844.
: Indicates a positive relationship between X2 and Y.
Every 1 unit increase in X2 will predict a decrease in the value of Y by 60627844.
9496 units, assuming variables X1 and X3 are constant .
eteris paribu.
X3 coefficient ( 0.
: Indicates a positive relationship between X3 and Y.
Every 1 unit increase in X3 will predict an increase in the value of Y by 0.
00509518200497 units, assuming variables X1 and X2 are constant .
eteris paribu.
Determinant Coefficient Test Table 8.
Determinant Coefficient Test R-squared Adjusted R-squared SE of regression F-statistic Prob(F-statisti.
86E 08 Table 8 shows that the Adjusted R-squared is -0.
009062 <0.
5, indicating that the model is not optimal, leaving ample room for improvement.
Model strengthening can be achieved through refinement of variable selection, exploration of additional variables, or model restructuring.
Proceedings Accounting Research Festival | 78 F Test (Simultaneou.
Table 9.
F Test R-squared Adjusted R-squared SE of regression F-statistic Prob(F-statisti.
86E 08 Based on the results of the F test in table no.
9 with an F-statistic value of 0.
116934, this means that all independent variables (X1.
X2.
tested simultaneously do not have a significant influence.
Partial T-Test Table 10.
Partial T-Test Variable Coefficient Std.
Error t-Statistic Prob.
11E 08
-11717692 99414800
07E 08
Based on the results of the partial T test in Table 10.
Based on the results of the T test in table no.
10 it can be explained that the X1 coefficient is -11,717,692 .
Probability 0.
9063 so that the Prob.
> 0.
05, then H0 fails to be rejected.
The X1 variable has no significant effect on Y.
Academically, the negative coefficient cannot be interpreted because it is not statistically significant.
Coefficient 60,627,845 .
Probability: 0.
5721, because the value of Prob.
> 0.
05, then H_0 fails to be rejected.
Variable X2 has no significant effect on Y.
Coefficient: 0.
9211, because the value of Prob.
> 0.
05, then H_0 fails to be rejected.
Variable X3 has no significant effect on Y.
References and Use of Reference Management Software In this study, the data processing process took place in stages using a combination of two main software The first was Microsoft Excel, which was used in the initial stage as a tool to search, extract, and organize secondary data .
inancial reports and sustainability report.
from a sample of companies in the energy sector.
Excel played a crucial role in structuring the panel data and initial variable calculations, such as measuring Company Size using the Natural Logarithm of Total Assets and the Firm Value ratio using the Price to Book Value (PBV) method.
The structured data was then imported into EViews 13 (Econometric View.
EViews 13 serves as the primary software for more complex econometric analyses, including selecting the most appropriate panel data regression model .
sing the Chow.
Hausman, and LM test.
, checking classical assumptions .
ulticollinearity and heteroscedasticit.
, and finally, estimating the selected regression model (REM) and testing hypotheses (F-test and t-tes.
to reach statistical In this way.
MS Excel supported the data preparation phase, while EViews 13 supported the statistical inference and hypothesis validation phases.
Proceedings Accounting Research Festival | 4 13
CONCLUSION
Based on the results of panel data regression research on energy sector companies listed on the IDX for the 2021-2024 period, where the Random Effects Model (REM) was selected, it was found that all proposed hypotheses were rejected, indicating that the independent variables did not have a significant influence on Company Value.
Partially.
ESG Disclosure (X.
was insignificant because, in the context of a fossil fuel-dominated sector, investors tend to be skeptical and view ESG reports as greenwashing or merely regulatory compliance, so this information fails to be an effective quality signal to increase value in the eyes of the market.
The insignificance of Green Process Innovation (GPI) (X.
indicates that investments in decarbonization and energy efficiency projects undertaken during this period likely still require a long period of time to be reflected as an increase in value (PBV), or the innovation is still smallscale and has not had a substantive impact on the company's core financial performance.
Furthermore.
Company Size (X.
is also insignificant, implying that amidst the disruption of the energy transition, the size of company assets in the energy sector can actually be considered a liability due to the risk of stranded assets .
otentially obsolete asset.
, which negates the traditional advantages of business scale.
Collectively, these findings (F-test insignifican.
indicate that in the 2021-2024 IDX energy industry.
Company Value is still dominated by macroeconomic and non-model factors, such as global commodity price fluctuations, domestic energy policies, and short-term market sentiment, rather than the internal efforts related to sustainability and innovation studied.
REFERENCES