THE INFLUENCE OF ENVIRONMENTAL.
SOCIAL AND
GOVERNANCE (ESG) ON INVESTMENT DECISION MAKING IN
INVESTORS
Nabila Rahmadania1.
Ummu Salma Al Azizah2*.
Ash Shoffi Hana Fadhilah3 Sumardi4
1,2,3,4
Management.
Faculty of Economics and Business.
Universitas Muhammadiyah Prof.
Dr.
Hamka.
Indonesia rahmadanianabila709@gmail.
ummusalma@uhamka.
Abstract This study aims to examine the influence of Environmental.
Social, and Governance (ESG) factors on investment decision-making, with intention used as a control variable.
This study is motivated by the increasing attention to sustainability issues in the investment world, particularly in the context of investors in Indonesia.
Data was collected through a survey of 204 active investors and analyzed using quantitative methods with a Partial Least Squares Structural Equation Modeling (PLS-SEM) approach via the SmartPLS 4 software.
The analysis results indicate that the Social and Governance dimensions of ESG have a positive and significant influence on investment decisions, while the Environmental dimension does not show a significant influence.
Additionally, the intention variable as a control also proves to have a positive influence on investment decisions, consistent with the Theory of Planned Behavior (TPB) framework.
These findings indicate that in the context of the Indonesian investment market, investors consider social and corporate governance aspects more than environmental aspects in their investment decisions.
This study contributes empirically to the literature on ESG and investment behavior, and provides implications for market participants, regulators, and companies in designing communication strategies and investment policies that are more responsive to relevant ESG dimensions.
Keywords: Environmental.
Social, and Governance (ESG).
Investment Decision.
Intention Introduction The notion of Environmental.
Social, and Governance (ESG) has appeared as a core element issue in the transformation of global investment practices.
ESG is no longer viewed merely as a sustainability indicator but also as a tool for assessing long-term risks that impact a companyAos reputation and performance (Global Sustainable Investment Alliance, 2.
From Ariasinta et al.
, .
in Indonesia, attention toward ESG has increased along with regulatory commitments, such as the Financial Services Authority (OJK) mandating sustainability reporting and promoting the establishment of the SRI-KEHATI index, which benchmarks companies consistently applying ESG principles (Nugroho et al.
, 2.
Nevertheless, participation by individual investors in ESG-based instruments remains relatively low, particularly in regions like Bali, which according to (Putra & Cipta, 2.
, accounts for only 3.
22% of the total domestic investors.
ESG stands for Environmental.
Social, and Governance Ai non-financial criteria employed by investor to evaluate a companyAos sustainability, ethics, and governance practices.
It serves as an important framework for aligning business and investment strategies with sustainability values and has become a long-term trend in the investment world (Azizah & Haron, 2.
The Environmental aspect includes emissions, waste, and energy management.
the Social aspect addresses social responsibility, employee welfare, and ethical supply chains.
while the Governance aspect focuses on board structure, transparency, and corporate ethics (Paranita et al.
, 2.
Although the benefits of ESG have been widely recognized, its effectiveness in influencing investor decisions has shown inconsistent empirical results (Demiraj et al.
, 2.
This suggests that investor decisions are shaped not only by ESG characteristics but also by internal factors such as intention.
According to the Theory of Planned Behavior proposed by Ajzen, .
, intention serves as the main predictor of actual actions, including in the area of investment decisionmaking.
A personAos intention is considered to be shaped by their attitudes, social expectations, and perceived behavioral control.
However, the Theory of Planned Behavior (TPB) does not explicitly incorporate external dimensions such as Environmental.
Social, and Governance (ESG) components in its conceptual model.
In practical application.
Regenerative aspects are increasingly recognized as rational determinants in guiding investment choices (Alhamis.
Hence, it becomes essential to combine intention and ESG within a unified behavioral framework that is better aligned with the current market environment.
Previous studies have shown varying results.
Research by .
ariroh et al.
, .
It was inferred that ESG exerts a substantial effect on investment choices, while Zaky & Hendrawati, .
found that ESG does not significantly affect investment efficiency.
Moreover, very few studies consider intention as a controlling factor in the linkage within the nexus of ESG and investment decision-making, especially in the context of individual investors in Indonesia.
These inconsistencies indicate a research gap that must be addressed empirically.
Based on this identified gap, this research provides an original contribution through the incorporation of ESG and intention into a single analytical framework grounded in TPB.
The focus on individual investors provides added value, as most prior studies have concentrated on institutional investors.
A statistical method employing Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to analyze the structural associations among variables 2 | Page in a more comprehensive and precise manner (Husnah et al.
, 2.
This approach is expected to address both empirical and theoretical questions regarding the significance of ESG and intention in the process of making investment decisions.
Consequently, this investigation puts forward hypotheses that each component of ESGAiEnvironmental.
Social, and GovernanceAi has a considerable effect on how investment decisions are made.
Intention is additionally assumed to serve a notable function as a controlling factor.
The objective of this investigation is to empirically examine the influence of ESG on the investment choices of individual investors, with intention regarded as a behavioral factor.
The results are anticipated to provide theoretical contributions to the advancement of a more comprehensive model of investment behavior and deliver practical insights for policymakers, corporations, and investors in IndonesiaAos capital market.
Literature Review 1 Theory of Planned Behavior (TPB) The Theory of Planned Behavior (Ajzen, 1.
explains that an individualAos behavior is driven by intention, which is influenced by attitude, subjective norms, and perceived behavioral control.
In investment contexts.
TPB has been used to understand how psychological factors shape investorsAo decisions, such as confidence, social influence, and perceived ability to manage risks (Besri et al.
, 2.
However.
TPB does not explicitly include sustainability elements like ESG.
Therefore, this study extends TPB by integrating ESG as an external factor influencing investorsAo intentions and decision-making.
Material and Method This investigation statistical method employing with an explanatory design analyze the impact of Environmental.
Social, and Governance (ESG) aspects on the investment decisions of individual investor, with intention serving as a control factor (Budiyanto & Sari, 2.
The quantitative method highlights objective evaluation and statistical testing of the connections among variables (Ali et al.
, 2.
The study population comprises active individual investors in Indonesia, both from the Java region and other areas outside Java.
The main data were gathered by means of an online questionnaire distributed through the use of Google Forms (Arya Setiawan, 2.
The survey included structured items for each construct, assessed employing a five-point Likert scale that spans from Austrongly disagreeAy .
to Austrongly agreeAy .
The investigation employed purposive sampling, a technique that selects participants according to predetermined criteria: novice investors with less than one year of investing experience, within the age range of under 25 to over 45 years, possessing at least an undergraduate degree (S.
, and earning a monthly income (Gusriyanti & Ananda, 2.
stated by Hair et al.
, .
, the required least number of respondents in PLS-SEM is calculated by multiplying the total number of indicators by five.
Since this study employed 40 indicators, the minimum required sample is 200 respondents.
The operational definitions of variables were classified into exogenous and endogenous factors.
The exogenous variables include Environmental (X.
Social (X.
, and Governance (X.
, which are not influenced by other variables within the model (Hair et al.
, 2.
The endogenous variable is investment decision 3 | Page (Y), representing the dependent outcome of the model.
Additionally, intention was introduced as a control variable, measuring the respondentAos motivation or readiness to invest.
The dataset was analyzed employing Structural Equation Modeling (SEM) through the Partial Least Squares (PLS) method.
This method is frequently utilized because of its flexibility in managing intricate models and non-normal data distributions (Irwan & Adam, 2.
The assessment was conducted in two primary phases, consisting of the evaluation of the measurement construct .
uter mode.
and analysis of the structural construct .
nner mode.
The measurement construct was assessed by applying convergence validity .
oading factor Ou .
Average Variance Extracted (AVE Ou 0.
, discriminant validity, composite reliability, and CronbachAos alpha (Nurhalizah et al.
, 2.
Conversely, the structural construct was tested using collinearity statistics (VIF < .
, the R-squared value (RA), magnitude of effect .
A), and hypothesis verification through bootstrapping.
A hypothesis was regarded as statistically meaningful when the t-value was greater than 1.
96 or the p-value was below 0.
05 (Hair et al.
The data gathering process took place in 2024, focusing on a varied pool of participants with differences in age, education level, gender, and investment background.
This research provides empirical evidence regarding the effect of ESG elements on individual investorsAo choice behavior, along with the moderating role of intention as a control variable.
Result and Discussion RESULTS Outer Model In accordance with the guidelines of Hair et al.
, .
, an indicator is considered valid when it possesses an outer loading value Ou 0.
All indicators on the Intention and Environmental variables meet these criteria.
Some indicators on the Social.
Governance, and Investment Decision variables have values between 0.
70, but are still acceptable because they still support overall construct validity, or if the indicator has an important theoretical contribution and does not reduce overall construct reliability.
Thus, all indicators in this model are considered valid.
Table 1 Table Average Variance Extrackted (AVE) Variabel Averagae Variance Extrackted (AVE) Sumber: SmartPLS 4.
Ket.
Valid Valid Valid Valid Valid According to the outcomes of the Average Variance Extrackted (AVE) assessment, all constructs within this model have met the criteria for convergent validity, indicated by the AVE value exceeding 0.
50, in accordance with the recommendations of (Hair et al.
, 2.
The Intention concept records the highest AVE value, namely 0.
646, signifying that the indicators 4 | Page forming this concept can account for more than 64% of the divergence it contains.
The Environmental concept also showed good results with an AVE value of 0.
626, followed by Governance at 0.
571, and Investment Decision at 0.
Meanwhile, the Social construct has the lowest AVE value of 0.
532, but is still above the required minimum limit.
Thus, all variables within this model have fulfilled the requirements set.
convergent validity, which means that the indicators used are able to adequately represent their constructs.
Based on the testing outcomes, the data is considered to have satisfied the criteria for convergent validity.
This is evidenced through the external loading and AVE scores that align with the established standards, indicating that the model is valid and can advance to the stage of structural analysis.
Table 1 Table Heterotrait-Monotrait Ratio Variabel Sumber: SmartPLS 4.
Based on the discriminant validity test, it is also reinforced by the HTMT (HeterotraitMonotrait Rati.
Based on the criteria from Hair et al.
, .
, a good HTMT value must be below 0.
The HTMT values between constructs obtained from the analysis results, such as Environmental-Social .
Environmental-Governance .
, and GovernanceInvestment Decision .
, are all fall beneath the suggested limit.
This demonstrates that the model meets the discriminant validity requirements based on the HTMT method.
Therefore, these findings demonstrate that the model possesses fulfilled discriminant validity, where each construct can be empirically distinguished from the other constructs.
The fulfilled discriminant validity strengthens the reliability of the structural model in depicting the associations among the latent variables being examined.
Table 1 Table Composite Reliability & CronbachAos alpha Variabel CronbachAos alpha Sumber: SmartPLS 4.
Composite Reliability The reliability testing results reveal that all constructs obtained CronbachAos Alpha and Composite Reliability .
values exceeding 0.
70, which signifies an acceptable degree of internal reliability.
The CronbachAos Alpha values range from 0.
795 to 0.
853, whereas the Composite Reliability values lie among 0.
859 and 0.
These findings comply with the 5 | Page standards suggested by Hair et al.
, .
, indicating that each indicator within every construct demonstrates strong consistency and reliability in representing the measured latent variable.
Inner Model The R-square (R.
value for variabel Y of 0.
603 shows that 60.
3% of the variation in investment choices .
ariable Y) is explained by the independent factors incorporated in this research model.
The residual 39.
7% is accounted for by otherfactors beyond the scope of the This result demonstrates that the model possesses a relatively strong explanatory power in describing the relationships among the variables under investigation.
Table 1 Table Path Coefficients Variabel Original (O) C_ -> Y_ X1_ -> Y_ X2_ -> Y_ X3_ -> Y_ C_ -> Y_ Sumber: SmartPLS 4.
Sample (M) Standard
(STDEV)
T statistics P values (|O/STDEV|) Figure 2.
Inner Model Source: SmartPLS 2025 From the outcomes of pathway analysis employing the Partial Least Squares approach (PLS) technique, it was discovered that several independent variables significantly affect the investment decision variable.
Variable C presents an influence coefficient of 0.
246 with a tstatistic of 2.
566 and a p-value of 0.
These findings demonstrate that variable C exerts a favorable and meaningful impact on investment decisions at the 5% significance level.
Put differently, the higher the magnitude of variable C.
The larger the likelihood that investors will make investment decisions.
In contrast, variable X1 records a coefficient of 0.
105 with a tstatistic of 1.
120 and a p-value of 0.
263, signifying that its impact on investment decisions is not statistically significant since the p-value is greater than 0.
Furthermore, the X2 variable exhibits a coefficient of 0.
211 with a t-statistic of 2.
and a p-value of 0.
016, which indicates a favorable and statistically significant influence on investment decisions.
This indicates that an increase in the X2 variable will also be followed by an increase in the tendency to make investment decisions.
The X3 variable has the largest coefficient, which is 0.
322, with a t-statistic of 2.
893 and a p-value of 0.
004, which shows the 6 | Page strongest and most significant favorable influence on investment choices.
Thus, of the four variables, variables C.
X2, and X3 significantly influence investment decisions, while X1 does not exert a substantial impact.
CONCLUSION
This research aims to analyze how Environmental.
Social, and Governance (ESG) dimensions together with investment intention affect the decision-making processes of individual investors in Indonesia.
By applying the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique, the study reveals several essential results that contribute to a broader understanding of sustainable investment behavior.
First, intention has been shown to yield a beneficial and statistically significant impact on investment decisions.
This result strengthens the Theory of Planned Behavior theory Ajzen, .
which posits that intention serves as the primary determinant of real behavior, including within the field of investment decision-making.
The stronger an individualAos intention to invest, the higher the likelihood of translating that intention into actual investment actions.
Second, of the three ESG dimensions tested.
Social and Governance aspects demonstrate a beneficial and significant impact on investment choices.
This outcome implies that attention to Responsible business practices and the quality of good governance are important factors in determining investors' investment preferences.
The Social dimension reflects concern for social issues such as employee welfare and contributions to society, while Governance shows the importance of structure and integrity in corporate management.
In contrast, the Environmental variable does not exhibit a meaningful effect on investment choices.
This may be due to the low awareness or access to adequate information related to environmental issues among individual investors, as well as the perception that environmental issues do not have a direct impact on investment returns in the short term.
Overall, the model applied in this research demonstrates a relatively strong explanatory power, with an R-square value of 0.
603, indicating that 60.
3% of the variance in investment decisions is accounted for by the intention variable and the ESG dimensions included in the This investigation a theorical contribution to the advancement of investment decisionmaking frameworks that are responsive to sustainability principles, while also delivering practical implications for investors, corporations, and policymakers in promoting responsible investment practices.
REFERENCE