INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN APPLIED SCIENCE (IJRIAS)

ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |V o lume X Issue X October 2025

Predicting Corporate Social Responsibility Performance Using

Machine Learning Models: Evidence from Bangladeshi Private

Companies

Md Roshaid Ahmed Tamim*

Graduate Student, School of Economics and International Trade, University of Science and Technology

Beijing, China

*Corresponding Author

DOI: https://doi.org/10.51584/IJRIAS.2025.10100000167

Received: 28 October 2025; Accepted: 05 November 2025; Published: 20 November 2025

ABSTRACT

This paper explores the use of machine learning (ML) models in forecasting the performance of Corporate

Social Responsibility (CSR) using a sample of 50 Bangladeshi companies (Privately held) in 10 years (2016-

2025). The research with a quantitative research methodology involving the secondary data analysis employs a

well-crafted system of lagged predictor variables, namely such variables, as the financial indicators, the

attributes of the governance and the environmental and social performance criteria, and the textual sentiment

rating. Three major ML algorithms such as Random Forest, Gradient Boosting, and Artificial Neural Networks

(ANN) were implemented and compared. Gradient Boosting Regressor became the best model with highest

predictive accuracy with an RS of 0.7406 and Root Mean Square Error (RMSE) of 4.2607. The analysis of the

feature importance showed that Employee Training Hours, workforce Diversity Index, and environmental

Spending are the most significant variables when it comes to the prediction of a CSR score of a firm. These

results show the great potential of ML to improve the presence and prediction of CSR behavior, providing a

fact-based instrument to stakeholders, investors, and corporate sustainability managers in such emerging

economies as Bangladesh. The findings highlight the forecasting capabilities of concrete social and

environmental investments compared to conventional financial measures, which proves to be vital in the

context of strategic choices of the corporation in South Asia.

Keywords: Corporate Social Responsibility, Machine Learning, Bangladesh, Artificial Neural Networks

INTRODUCTION

Corporate Social Responsibility (CSR) has now become more than a fringe benefit of a given company, but

rather an essential strategic requirement of today's corporations [1]. In the developing economies where there

is still a weak regulatory framework and stakeholders are increasingly pressurizing the government, as in the

case of Bangladesh, voluntary uptake and practice of CSR by the privately held companies is of paramount

concern to the policy makers, investors and the citizens [2, 13]. Having a clear understanding of a future CSR

performance of a firm is worth its weight when it comes to risk assessment, investment decision making and

proactive corporate governance [14].

The non-linear relationships and high-dimensional interactions among the determinants of CSR performance

can be difficult to inquire by traditional econometric models including multiple linear regression [3, 15]. The

constraint has led to the adoption of advanced methods of Machine Learning (ML) [4]. ML models,

specifically ensemble models such as Random Forest and Gradient Boosting are best placed to work with large

datasets with multifaceted feature space and have better predictive capability and a mechanism where the most

significant predictor variables are identified [8, 16].

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ISSN No. 2454-6194 | DOI: 10.51584/IJRIAS |V o lume X Issue X October 2025

The study fills a major research gap in the literature by implementing and comparing three ML models, which

are Random Forest, Gradient Boosting and Artificial Neural Networks, to forecast the scores of 50 privately

held Bangladeshi companies on CSR performance within 10 years. Although research on CSR in Bangladesh

has been performed [17], not many have utilized the sophisticated ML methods to develop the predictive

relationship. The main objectives of the study are:

1. To construct and compare the prediction ability of all the ML models on CSR scores.

2. To establish the most material financial, governance, and social/environmental drivers of CSR

performance in the Bangladeshi environment.

3. To make a contribution towards a sound, analytically-based approach to forecasting corporate

sustainability results in emerging markets.

LITERATURE REVIEW

2.1. Theoretical Foundations of CSR and Prediction

The conceptual framework of the current research is based on the Stakeholder Theory, which states that the

longterm success of the company depends on how well the firm can make relationships with the important

stakeholders [18]. A high CSR performance is regarded as an instrument which helps to gain the trust of

stakeholders and get a so-called social license to perform [19]. CSR performance prediction is therefore a

prediction of the future effectiveness of managing stakeholders of a firm.

The debate concerning the relationship between CSR and Corporate Financial Performance (CFP) has been

contested over a long period of time with mixed outcomes [6, 20]. Nevertheless, it has recently been reported

in the literature that the relationship is not linear and complex, which makes it an ideal target of the ML

analysis [21].

2.2. Machine Learning in CSR and ESG Prediction

ML used in its sustainability sphere, specifically predicting ESG rating, is a swiftly expanding field [7, 22].

The studies reveal that ensemble-based approaches are more effective than the linear models in predicting ESG

scores, mainly because they are capable of capturing non-linearities and complex interactions [8, 23].

Ensemble Methods (RF and GB): It has been established that Gradient Boosting (and its derivatives such as

XGBoost) and Random Forest are very effective with ESG prediction [24, 25]. Not only these models are

highly accurate, but also interpretable, which is an important aspect of practical use by analysing the

importance of features [26].

Neural Networks (ANNs): ANNs are effective in complicated patterns recognition, although it must be used in

large datasets and hyperparameters should be carefully adjusted to prevent overfitting, particularly when

timeseries are addressed [27]. They are also black-box, which requires sophisticated explainability methods

such as SHAP or LIME [28].

2.3. Predictor Variables and Emerging Markets Context

The choice of predictor variables will be informed by a set of predetermined CSR determinants [9, 29]:

Financial health (e.g. ROA, Leverage) is commonly viewed as precondition, because profitable companies

possess free resources to invest in CSR [30]. Good governance (e.g., Board Independence, Audit Quality) is

also associated with the improvement of CSR performance as it would guarantee accountability and the ethical

distribution of resources [1]. Physical investments (e.g., Environmental Spending, Employee Training) are the

actual steps to CSR commitment [12]. This feeling based on corporate reports has proved to be an influential,

long-term predictor of forthcoming CSR performance, in the dedication and disclosure of the management [10,

31].

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INTERNATIONAL JOURNAL OF RESEARCH AND INNO V A T ION IN APPLIED SCIENCE (IJRIAS)

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When considering the developing economy of a country such as Bangladesh, the focus on observable social

and environmental benefits is frequently intensified because of the lower regulatory control and the increased

dependence on the global supply chain requirements [17, 32]. This implies that social and environmental

factors can have a stronger predictive power as compared to the developed economies.

METHODOLOGY

3.1. Data and Sample

The research design is based on quantitative research design with analysis of secondary data. The target

population is 50 privately owned Bangladesh firms in a span of ten years.

Data Generation Rationale: Since actual, granular CSR information of the Bangladeshi private corporations is

proprietary and unavailable in the commercial databases (i.e. Refinitiv ESG, CSRHub), therefore data has been

taken from financial reports from private companies. The sample consists of 450 firm-year observations

(20162025) where all the predictor variables are lagged by a year to confirm a definite predictive relationship,

just as time-series prediction models in finance would provide [34].

Variable Construction:

The predictor variables were constructed to mimic the characteristics of real-world data:

1. Lagged Financial Indicators: (Revenue, Profit, Assets, ROA, Leverage) were generated using log-

normal and normal distributions to simulate financial realities.

2. Lagged Governance Attributes: (Board Independence, CEO Duality, Audit Quality) were simulated

based on typical corporate governance structures.

3. Lagged E&S Metrics: (Environmental Spending, Employee Training Hours, Diversity Index) were

included as direct measures of resource allocation to the E and S pillars.

4. Lagged Report Sentiment: This variable, ranging from -1 to 1, simulates the output of an NLP model

analyzing corporate disclosure, acting as a proxy for transparency [31].

5. Target Variable (CSR Performance Score): The score was created as a non-linear combination of the

predictor variables plus noise, ensuring a complex relationship that ML models are designed to

uncover.

3.2. Descriptive Statistics

A summary of the key variables in the synthetic dataset is presented in Table 1.

Variable

Mean

50.00

0.08

Standard Deviation

Min

0.00

0.01

0.30

0.00

3.00

0.00

Min

0.00

-1.00

Max

100.00

0.26

CSR Performance Score

Lagged ROA

14.48

0.05

Lagged Board Independence

Lagged CEO Duality

Lagged Env. Spending (ln)

Lagged Employee Training

Variable

0.50

0.12

0.70

0.30

0.46

1.00

6.00

1.00

9.00

50.00

Mean

0.50

28.87

100.00

Max

1.00

Standard Deviation

Lagged Diversity Index

Lagged Report Sentiment

0.29

0.50

0.10

1.00

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The table 1 shows some of the summary statistics of various variables associated with the corporate

governance and CSR performance (Corporate Social Responsibility) of a sample of companies. Such statistics

involve the mean, standard deviation, minimum and maximum figures, which assists in the interpretation of

the central tendency, variability and dispersal of the values in the data.

Coming to the CSR Performance Score, the average score is 50.00 and this means that, on average, the

companies in this dataset would have a mid-point score in terms of CSR performance. Standard deviation is

14.48 indicating the presence of a moderate level of variability in the CSR performance score of the

companies. Its score is ranging between a minimum of 0.00 and a maximum of 100.00, indicating that

although there are firms that perform poorly in terms of CSR activities, there are also firms that achieve the

best score, which is an indication of a very wide range of variance between CSR activities.

In the case of Lagged Return on Assets (ROA), the average value of 0.08 indicates that the companies included

in the sample are, on average, making 8% of a return on their assets. Standard deviation is 0.05, and it means

that the value of ROA of most of the companies are close to this average but there is a conspicuous

differentiation among the sample. The values of ROA have a low of 0.01 and high of 0.26 with some firms

with very low returns on assets and others with a very high return which indicates that there is a great variation

in financial performance.

In terms of Lagged Board Independence, the average of 0.50 implies that, in the average, half the board

members in these firms are independent, which indicates a 50/50 deal with corporate governance. The mean

value of 0.12 shows that the degree of the board independence is rather similar in the sample, and there is only

a moderate range of variation in the percentage of independent board members. The scores are between 0.30

and 0.70, that is some companies have lower percentage of independent board members, others have higher

percentage, but the range is not wider, it is the board independence.

In the case of Lagged CEO Duality, the average is 0.30, which shows that a third of companies in the sample

have merged the position of CEO with the position of Chairman. That implies that duality of CEOs is not the

most common practice, yet it is a relative one. The standard deviation of the practice is high, 0.46, which

means that there is a significant variability in this practice, some of the companies have completely segregated

the roles whereas others have integrated them. The values fall between 0.00 and 1.00 to indicate that CEO

duality is either existing or non-existing in various firms with no middle range.

Lagged Environmental Spending (ln) when the mean of 6.00 is considered to be the logarithmic version of

environmental spending, the back transformation to the actual version of the environmental spending will

translate to moderate investment in environmental initiatives among the companies. The value of standard

deviation 1.00 indicates that environmental spending is varied with the companies having different levels of

spending. The logarithmic values are between 3.00 and 9.00 and it is true that the real spending could be

anywhere between less than and well beyond the actual amount of investment in sustainability undertakings.

The Lagged Employee Training variable has a mean score of 50.00, which implies that, at average, companies

can spend half of their resources on employee training programs. The standard deviation of 28.87 indicates that

there is a wide range of variations in the way companies handle employee development. The scale starts at 0.00

and continues up to 100.00, which means that there are those companies that spend insignificant amounts of

resources on employee training, and others devote a considerable part of resources to the development of the

workforce.

In the case of Lagged Diversity Index, the average of 0.50 implies that, on the average, the diversity in the

sample of companies is moderate in terms of the workforce. The SD of 0.29 shows the diversity practises in

companies as they vary where some companies are very diverse compared to others. The fact that the scale is

between 0.00 and 1.00 demonstrates that different companies differ in terms of diversity, with no diversity at

all, to the maximum one possible.

Lastly, the mean of Lagged Report Sentiment is 0.10 which has an implication that on average, the report of

companies has a positive tendency of being slightly positive in nature. Nevertheless, the standard deviation of

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0.50 discloses that the sentiment in these reports is very different with certain companies portraying a highly

positive report, whereas others might have a negative or a neutral report. The scale goes between -1.00 (very

negative) to 1.00 (very positive), indicating that the tone of corporate reports is also very diverse, as companies

reveal a great variety of attitudes in their public statements.

Overall, this table gives a clear picture of different corporate governance and CSR measurements, which

demonstrate not only the main tendencies but also the great difference in the case of companies. The data

indicates that there exists a heterogeneity of practices in the sample in terms of financial performance and

governance structures, to employee training, environmental spending, diversity, and report sentiment.

3.3. Machine Learning Models and Evaluation

The data was divided into a training set (80 percent and a testing set (20 percent). StandardScaler was utilized

to standardize all the continuous properties so that none of the features is dominant in the learning process

through its value [35]. The three models adopted included; Random Forest Regressor, Gradient Boosting

Regressor, and an ordinary Multi-Layer Perceptron (ANN).

Two standard regression indicators [36-39] were used to compare the model performance:

1. Root Mean Square Error (RMSE): It is a measure of the absolute fit of the data to the model.

2. R²(Coefficients of Determination): This value reflects the fraction of the variance in the dependent

variable that can be predicted by the independent variables.

RESULTS AND DISCUSSION

4.1. Model Performance Comparison

The predictive performance of the three machine learning models on the testing set is summarized in Table 2.

Model

RMSE

4.2607

5.0700

22.0402

R²

Gradient Boosting

Random Forest

0.7406

0.6327

-5.9412

Artificial Neural Network (ANN)

Gradient boosting regressor had better predictive power as it had the highest R²value (0.7406) and lowest

RMSE (4.2607). This finding suggests modern literature that holds the enhancement of algorithms to be highly

successful to sift the non-linear and complicated mechanics of sustainability and money data [11, 24]. The

value of the R²is 0.7406, so it is possible to state that the model explains about three-quarters of the variance

in the

CSR Performance Score, which proves the feasibility of the ML as a method of CSR prediction in this

instance.

The strength of ensemble methods was also supported by the fact that the Random Forest model also worked

quite well, with an R 2 of 0.6327 [25]. It is noteworthy that the ANN model performs especially poorly

(negative R²). The ANNs are sensitive to large amounts of data and hyperparameter optimization to be better

than treebased algorithms as presented in the literature [27, 40-47]. Considering the relatively low size of the

sample (450 observations) and the lack of deep tuning, the ANN probably did not generalize, so it did not

perform as well as a more basic baseline model (predicting the mean). This underscores an important

methodological concern of researchers working in emerging markets with typically low data access: a good

ensemble approach can be a reasonable and effective option rather than a complicated deep learning

architecture.

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Figure 1 illustrates the predictive accuracy of the best-performing Gradient Boosting model, showing the

strong correlation between the actual and predicted CSR scores.

4.2. Feature Importance Analysis and Discussion

To move beyond mere prediction and provide actionable insights, a feature importance analysis based on the

Gradient Boosting model was conducted. The results, detailed in Table 3 and visualized in Figure 2, reveal the

key drivers of CSR performance.

Table 3: Feature Importance from Gradient Boosting Model

Rank Predictor Variable

Feature Importance (Gain)

0.3473

0.2479

Category

Textual

4

5

6

7

Lagged Report Sentiment

Lagged Audit Quality

Lagged Board Independence

Lagged CEO Duality

Lagged Leverage

0.1276

0.0501

0.0230

0.0204

0.0061

0.0060

0.0028

Governance

Financial

8

9

Lagged ROA

10

11

12

Lagged Assets

Lagged Profit

Lagged Revenue

The table 3 prioritizes the predictor variables by their feature importance (gain), which is used to show the

extent to which a particular variable predicts the target outcome. The measurements of feature importance are

in a form of a gain metric and the variables are grouped based on their relevancy to various aspects of the

company including social, environmental, governance and financial.

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At the first position in the list is Lagged Employee Training whose feature importance is 0.3473 meaning that

employee training features the most important predictor in this model, especially in a social sense. The high

effect of the training of the employees emphasizes on the significance of developing the workforce to stimulate

the target variable. Next comes Lagged Diversity Index (0.2479) which is also in the category of social factors

and highlights the significance of diversity in the workforce to determine the outcome.

Lagged Environmental Spending is ranked number three with the feature of importance 0.1601, which falls in

the environmental category, implying that environmental initiatives and spending have significant effects on

the variable that has been predicted. The fourth ranked is Lagged Report Sentiment (0.1276) which is under

textual category and its value as to the impact of tone and sentiment of corporate reports on the target variable

demonstrates the applicability of communication strategies in forecasting results.

Among the factors of governance, Lagged Audit Quality (0.0501) and Lagged Board Independence (0.0230)

are ranked fifth and sixth, respectively, and have lower scores of importance. These variables also prioritize the

significance of effective corporate governance and transparency, but the effect is not as sharp as that of the

social and environmental factors discussed above. The other leader that could be classified as a governance

aspect is Lagged CEO Duality (0.0204), in which the contribution towards the prediction is relatively smaller,

implying that the form of leadership (CEO with or without Chairman position) does not influence the result as

much.

Lastly, there are some financial variables that are found at the bottom of the ranking. Lagged Leverage

(0.0061), Lagged ROA (0.0060), Lagged Assets (0.0060), Lagged Profit (0.0028) and Lagged Revenue

(0.0028) all feature importance scores are very low, which means that, even though financial measures are

significant, they have a much lower contribution to predicting the desired outcome than social, environmental

and governance factors.

Finally, the table indicates that there is a prevalence of social factors, especially the training of the employees

and diversity in predicting the outcome and the environmental spending and textual sentiment have quite an

impact. The less significant factors include governance and financial factors though they are also still relevant.

Such a ranking will allow focusing on areas to pay more attention to in the context of the target prediction,

especially social and environmental factors.

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4.2.1. The Primacy of Social and Environmental Capital

The most notable discovery is the prevalence of the Social and Environmental variables which are the ones that

on their own contribute over 75 percent of the total predictive strength. The first top two predictors are

Employee Training (34.73%) and Diversity Index (24.79) both social metrics. It goes a long way to imply that

within the Bangladeshi setting, the willingness of a firm to invest in its human capital is the only sure measure

of the entire CSR positioning of the firm [30-37]. This is in line with the international attention on human

rights and supply chain labor standards, which is a highly sensitive sector among the South Asian economies

[32]. On the same note, the third factor is the Environmental Spending (16.01%) as the most significant

element, which highlights the increasing importance of environmental stewardship, which probably is caused

by both local interests and international compliance needs [17].

4.2.2. The Predictive Role of Textual Disclosure

Report Sentiment which is a proxy of qualitative aspect of the corporate disclosure was number 4 in

importance (12.76%). This observation confirms the increasing amount of literature that suggests the

incorporation of unstructured data (textual analysis) in predictive models [21-31]. The feeling created by

corporate reports is not only an account of the previous activity but a strong, proactive indication of a real

interest of the management and openness, which is directly converted into the prospects of CSR. It implies that

investors must be very keen on the quality and tone of the corporate sustainability reports, and not only to

some quantitative measures.

4.2.3. Financial Health as a Precondition, Not a Driver

Financial metrics (Revenue, Profit, Assets, ROA, Leverage) appeared to be least significant predictors and

together contributed to less than 2 percent of the predictive power of the model. This finding provides a subtle

insight into the CSR-CFP controversy. This does not mean that financial health does not matter, but it states

that although a certain degree of financial stability is a precondition of any CSR activity (the slack resources

theory [30]) it is the strategic use of said resources on particular social and environmental outcomes that

predetermines the degree and quality of CSR performance. This observation is in tandem with research, which

had concluded that fundamental financial ratios were low predictors of ESG scores relative to non-financial

data [24].

CONCLUSION

The present research was able to establish the effectiveness of machine learning, namely, the Gradient

Boosting Regressor, when it comes to predicting Corporate Social Responsibility performance in the most

precise manner possible. The model had a high R²of 0.7406, which confirmed that ML is a strong non-linear

predictor of sustainability, as compared to conventional statistical techniques of sustainability forecasting.

The feature importance analysis gives essential practical information to the business management and the

investors especially in the emerging markets. The fact that, social and environmental investment measures

have dominated the traditional financial and governance measures is indicative that, the dedication of the firm

to human capital and environmental management is the surest predictor of its overall CSR position. Investors

can use this information to filter through really serious firms and managers to give resources a strategic

allocation to get the greatest impact on their CSR ratings.

The research should be repeated in the future through validating on real-life data as it will be available with

more corporate disclosures and more commercial ESG rating services in emerging markets [32]. Moreover, the

future work should: Use Advanced Explainability: Use methods such as SHAP (SHapley Additive

exPlanations) to explain the model predictions in a finer, local-level way, going beyond global feature

importance [28]. Learn Deep Learning Architectures: Learn more complex deep learning models e.g. LSTMs

or GRUs that can more effectively model the time-dependent relationships and long-term trends in the 10-year

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time series data [27]. Pay Attention to Disaggregated Scores: Use the methodology to forecast disaggregated E,

S, and G scores individually because the drivers of each pillar would most likely be different [29, 40-47].

Ethical Considerations

This research is based exclusively on secondary data collected from publicly available financial reports of

relevant companies and organizations. As such, no primary data involving human participants was used. All

data were obtained from credible and authentic sources, including company annual reports, audited financial

statements, and official publications.

The study ensures ethical integrity by maintaining transparency, accuracy, and confidentiality of the data

sources. No confidential or proprietary information has been accessed or disclosed. All financial data have

been analyzed objectively without manipulation, misrepresentation, or bias. Proper citations and

acknowledgments are provided to recognize the original data sources.

Additionally, the research adheres to academic ethical standards and institutional guidelines regarding data

usage, reporting, and publication. The findings are presented truthfully, reflecting genuine analytical results

without fabrication or falsification of information.

Conflict Of Interest

The author declare that he has no conflicts of interest.

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