Keywords

Financial Performance, CSR Activities, Economical Factors.

Introduction

Corporate Social Responsibility (CSR) has garnered increasing attention as businesses recognize their responsibility to not only pursue profits but also contribute positively to society and the environment. This literature review examines the relationship between CSR activities and their impact on economic, legal, and ethical factors within organizations.

Numerous studies have explored the economic implications of CSR activities on firms' financial performance. Scholars such as McWilliams and Siegel (2001) have found a positive association between CSR and financial performance, suggesting that companies engaged in responsible practices may experience improved profitability and enhanced long-term sustainability. Furthermore, Sen and Bhattacharya (2003) argue that CSR can lead to increased consumer loyalty and trust, leading to higher customer retention rates and a competitive advantage in the marketplace. However, other researchers have highlighted potential concerns regarding the cost of implementing CSR initiatives and their impact on short-term profitability (Margolis & Walsh, 2003). These conflicting viewpoints emphasize the need for a nuanced understanding of the economic consequences of CSR activities.

The relationship between CSR activities and legal factors centers on compliance with regulations and the potential for shaping public policies. CSR initiatives often go beyond mere legal compliance to address environmental, labor, and consumer protection concerns (Gunningham & Rees, 1997). Companies embracing CSR may be more proactive in addressing legal issues and may also influence the development of regulations through active engagement with policymakers and advocacy groups (Branco & Rodrigues, 2008). Such engagement can lead to a mutually beneficial environment where companies' ethical commitments align with the legal framework.

CSR is fundamentally rooted in ethical considerations, emphasizing responsible business conduct, and ethical decision-making. Ethical factors are essential drivers of CSR initiatives, as companies aim to uphold integrity, transparency, and fairness in their practices (Matten & Moon, 2008). Researchers have shown that CSR activities positively influence a firm's ethical reputation and enhance stakeholder perceptions, fostering trust and goodwill with customers, employees, and investors (Godfrey et al., 2009). Moreover, ethical considerations in CSR practices extend to supply chain management, ensuring fair treatment of workers and adherence to labour standards (Locke et al., 2013). Integrating ethical values into CSR activities reinforces an organization's commitment to responsible business practices.

To achieve a comprehensive understanding of the impact of CSR activities, it is essential to consider the integration of economic, legal, and ethical factors. Companies that adopt a proactive approach to CSR, aligning their economic objectives with legal compliance and ethical considerations, may realize long-term benefits (Vogel, 2005). Integrating CSR into the core business strategy can create a virtuous cycle where positive financial performance supports the allocation of resources for further CSR initiatives (Porter & Kramer, 2006). By aligning economic interests with ethical behaviour, firms can build a sustainable competitive advantage and enhance stakeholder trust.

RQ1: Is there any impact of Economic, Legal and Ethical factors on Corporate Financial Performance?

RQ2: Is there any impact of Age and size on Corporate Financial Performance?

Output of Company and CSR Activities of Stockholder

The output of a company is a crucial indicator of its financial performance and overall success. Traditional financial metrics, such as revenue, profitability, and return on investment, are commonly used to measure a firm's output. Several studies have investigated the impact of CSR activities on a company's financial performance. For instance, research by Margolis and Walsh (2003) suggests that socially responsible companies tend to experience improved financial performance over time, attributed to increased customer loyalty and trust. Similarly, Waddock and Graves (1997) conducted a meta-analysis that revealed a positive association between CSR and financial performance, indicating that companies with robust CSR initiatives may achieve better financial outcomes in the long run.

Stockholders play a crucial role in shaping a company's CSR initiatives. As shareholders, they have the power to influence corporate decision-making processes through voting rights and voice their concerns during annual general meetings. Stockholders can advocate for socially responsible practices and encourage companies to integrate CSR into their core business strategies (Berrone et al., 2017). Moreover, stockholders' increasing focus on ethical and sustainable investment practices has driven companies to pay more attention to CSR activities, as they seek to attract socially responsible investors (Clark & Knight, 2020).

The Impact of Stockholders' CSR Activities on the Company

Stockholders' engagement in CSR activities can have significant implications for a company's reputation, stakeholder relationships, and long-term financial performance. Engaging in CSR can help companies build a positive image and enhance their brand reputation (Bhattacharya et al., 2009). This can lead to increased customer loyalty, higher consumer trust, and ultimately, improved sales and revenue (Sen & Bhattacharya, 2001). Furthermore, stockholders' involvement in CSR initiatives can positively influence the company's relationship with other stakeholders, including employees, suppliers, and local communities (Jones, 1995).

However, it is essential to recognize that the impact of stockholders' CSR activities on a company's output is not unidirectional. Companies must strike a balance between responding to stockholders' CSR concerns and maintaining their financial stability and profitability (Ioannou & Serafeim, 2015). Overemphasis on CSR activities without careful consideration of financial implications could potentially affect the company's financial performance negatively.

Corporate governance practices play a pivotal role in mediating the relationship between CSR activities and financial performance. An effective governance structure encourages responsible decision-making and ensures that companies prioritize long-term sustainable growth (Khan et al., 2020). Strong governance practices promote transparency and accountability, aligning the interests of stockholders and the company in achieving both financial and CSR objectives (Ioannou & Serafeim, 2012).

Customer Loyalty and CSR Activity of Society

Customer loyalty is a crucial determinant of a company's long-term success and profitability. Research has shown that CSR activities can positively impact customer loyalty by influencing customers' perceptions of the company (Homburg et al., 2017). When customers perceive a company to be genuinely engaged in CSR initiatives, they are more likely to develop an emotional connection with the brand, leading to increased loyalty and repeat purchases (Lichtenstein et al., 2004). CSR initiatives foster a positive brand image and reputation, which is crucial factors driving customer loyalty (Bhattacharya et al., 2008).

Society's collective CSR initiatives can also have a significant influence on customer loyalty. Customers today are increasingly socially conscious and expect companies to play an active role in addressing social and environmental challenges (Sen & Bhattacharya, 2001). When society is actively involved in CSR activities, customers tend to view the company more favorably, perceiving it as an integral part of a socially responsible community (Du et al., 2010). Society's CSR engagement can positively impact customers' perceptions of the company's values, which, in turn, enhances customer loyalty.

Customers' loyalty is closely linked to their alignment with a company's CSR initiatives and values. When customers identify with the social causes and values promoted by a company's CSR activities, they are more likely to support and remain loyal to the brand (Bhattacharya & Sen, 2003). Customers who feel that a company's CSR initiatives resonate with their own personal values are more emotionally connected to the brand, resulting in higher levels of brand loyalty (Maignan & Ferrell, 2004). CSR initiatives that align with customer values create a sense of shared purpose, fostering a deeper sense of loyalty and commitment.

In competitive markets, CSR activities can serve as a differentiation strategy that sets a company apart from its competitors. Customers often consider a company's CSR reputation when making purchase decisions (Creyer & Ross, 1997). Companies that are actively engaged in meaningful CSR initiatives are perceived as more trustworthy, reliable, and socially responsible, which can give them a competitive advantage (Porter & Kramer, 2006). This differentiation through CSR can lead to increased customer loyalty, as customers are more likely to choose socially responsible brands over alternatives.

Effective communication of CSR activities can significantly influence customer loyalty. Companies need to communicate their CSR efforts transparently and authentically to customers (Morsing & Schultz, 2006). Transparent communication fosters customer trust, leading to enhanced loyalty and positive brand associations (Bhattacharya et al., 2008). Engaging customers through CSR communication channels, such as social media and cause-related marketing, can strengthen the emotional bond between the brand and customers, resulting in increased customer loyalty (Peloza & Shang, 2011).

While the literature suggests a positive association between customer loyalty and CSR activity of society, some challenges and limitations must be considered. Not all customers may prioritize CSR initiatives when making purchasing decisions (Kim et al., 2019). The impact of CSR on customer loyalty may vary across different customer segments and product categories (Brammer & Millington, 2006). Additionally, companies must be cautious of engaging in token CSR efforts merely for promotional purposes, as customers can perceive inauthenticity and react negatively (Hartmann & Apaolaza-Ibáñez, 2020).

Brand Awareness and CSR Activity of Investors

Brand awareness is a critical component of a company's marketing strategy, as it represents the extent to which consumers and investors are familiar with and recognize a company's brand. Research suggests that CSR activities can significantly impact brand awareness. When companies engage in socially responsible practices and communicate their CSR efforts effectively, they are more likely to build a positive brand image, leading to increased brand awareness (Mishra & Suar, 2010). CSR initiatives create a narrative around the brand, reinforcing its values, mission, and commitment to social and environmental responsibility (Ailawadi et al., 2014). Investors, as key stakeholders, play a vital role in shaping brand awareness through their perceptions of a company's CSR activities.

Investors' perception of a company's CSR activities influences their decision-making processes and investment strategies. Research indicates that investors are increasingly considering a company's CSR performance when making investment decisions (Oikonomou et al., 2014). CSR initiatives are viewed as indicators of a company's long-term sustainability and commitment to ethical practices (Flammer, 2015). Positive investor perception of a company's CSR activities can contribute to higher brand awareness as investors actively share and advocate for their investment choices.

Brand awareness has a direct impact on a company's financial performance. High brand awareness can lead to increased customer loyalty, repeat purchases, and positive word-of-mouth marketing, ultimately driving revenue growth (Yoo et al., 2000). Studies have found a positive association between brand awareness and financial performance indicators, such as sales revenue, market share, and stock prices (Aaker & Joachimsthaler, 2012). Investor perception of a company's CSR activities can enhance brand awareness and, in turn, positively influence financial performance by attracting more investors and enhancing stakeholder confidence in the company's future prospects (Ailawadi et al., 2014).

CSR activities can contribute to building stronger relationships with stakeholders, including investors. When companies prioritize CSR and communicate their initiatives transparently, it fosters a sense of trust and credibility among stakeholders (Sen & Bhattacharya, 2001). Investors who perceive a company as socially responsible are more likely to develop a positive perception of the brand, leading to stronger investor-brand relationships (Rupp et al., 2013). These positive relationships can lead to greater investor support, including increased investment and advocacy for the company, contributing to higher brand awareness.

Effective communication of CSR activities plays a significant role in shaping investors' perception and brand awareness. Companies need to communicate their CSR initiatives transparently and consistently to investors (Branco & Rodrigues, 2008). Transparent CSR reporting and communication create a narrative that highlights the company's commitment to sustainability and ethical practices, thereby enhancing brand awareness among investors (Oikonomou et al., 2014). Regularly sharing updates and outcomes of CSR initiatives further reinforces the brand's image and positively influences investor perception.

Brand awareness can also act as a risk mitigation strategy for companies. In times of crisis or reputation-damaging events, companies with strong brand awareness are more likely to recover faster due to higher investor and stakeholder confidence (Lichtenstein et al., 2004). Positive investor perception of a company's CSR activities can serve as a buffer during challenging times, protecting the brand's reputation and reducing potential negative impacts on financial performance.

While the literature highlights the positive relationship between brand awareness and the CSR activity of investors, some challenges and limitations must be considered. Investors' awareness and perception of a company's CSR initiatives may vary based on their access to information and level of interest in CSR (Yoo et al., 2000). Moreover, the influence of CSR on brand awareness and financial performance may be context-specific and influenced by industry dynamics (Brammer & Millington, 2006). Additionally, companies must be cautious of engaging in green washing or merely using CSR initiatives for promotional purposes, as investors can discern in authenticity and react negatively (Hartmann & Apaolaza-Ibáñez, 2020).

Corporate Financial Performance (CFP) is Depends on Age of the Company and a Size of the Company

The age of a company is a key determinant of its financial performance. Numerous studies have examined the relationship between company age and CFP, with mixed findings. Some research suggests that older companies tend to exhibit more stable and consistent financial performance due to their established market position, customer base, and experience in navigating business cycles (Chandler, 1990). Mature companies may have a competitive advantage in terms of brand recognition and access to financial resources, contributing to more favourable CFP outcomes (Foster & Learmonth, 2013).

However, the relationship between company age and CFP is not always linear. Younger companies may also demonstrate higher growth potential and innovation, leading to above-average financial performance in their early stages (Kuratko, 2007). Newer companies often have greater flexibility to adapt to market changes and technological advancements, which can positively influence CFP (Davidsson & Honig, 2003). Conversely, older companies may face challenges related to organizational inertia and resistance to change, potentially hindering their financial performance (Chandler, 1990).

Company size is another crucial factor that significantly affects CFP. Extensive research has explored the relationship between company size and financial performance, with notable findings. Larger companies tend to benefit from economies of scale, allowing them to lower costs and increase operational efficiency, which can positively impact their CFP (Chatterjee & Samuelson, 2015). Greater financial resources and access to capital also provide larger companies with more opportunities for investment and expansion, leading to potential growth in revenue and profitability (Barney & Arikan, 2001).

It is essential to consider the moderating effects of industry and market conditions on the relationship between company age, size, and CFP. Different industries and market environments can influence the performance of companies. For instance, in rapidly evolving industries or technology sectors, younger and smaller companies may thrive due to their adaptability and innovation (Kuratko, 2007). On the other hand, in more established and mature industries, larger and older companies may have a competitive edge in terms of resources and market presence (Foster & Learmonth, 2013).

Furthermore, market volatility and economic conditions can also influence CFP. During economic downturns or recessionary periods, smaller and younger companies may face greater financial challenges due to limited access to capital and reduced consumer spending (Barney & Arikan, 2001). In contrast, larger and more established companies may have stronger financial positions to weather economic storms (Chatterjee & Samuelson, 2015).

In addition to industry and market conditions, the effectiveness of management and strategy can moderate the relationship between company age, size, and CFP. Competent management teams with strong leadership and strategic vision can significantly impact a company's financial performance (Finkelstein & Hambrick, 1996). Effective management can lead to better resource allocation, operational efficiency, and strategic decision-making, enhancing CFP irrespective of the company's age or size (Barnett & Carroll, 1995).

Furthermore, strategic choices made by companies can influence their financial performance. For instance, companies that pursue growth strategies through diversification or international expansion may experience different financial outcomes compared to companies focused on cost leadership or differentiation (Porter, 1980). Strategic alignment with market opportunities and customer needs can drive financial success, regardless of company age or size (Hitt et al., 2007).

It is crucial to differentiate between short-term and long-term financial performance when considering the impact of company age and size. While younger and smaller companies may exhibit higher growth rates and financial performance in the short term due to innovation and agility, older and larger companies may demonstrate greater stability and consistent financial results over the long term (Hitt et al., 2007). Long-term financial performance is often influenced by a combination of strategic decisions, industry dynamics, and economic conditions, providing a more comprehensive understanding of a company's sustainability and value creation potential.

Regardless of a company's age or size, there are various strategies that can positively impact CFP. For younger and smaller companies, prioritizing innovation, market research, and strategic partnerships can help leverage their agility and adaptability to capitalize on emerging opportunities (Gelderen et al., 2011). Implementing effective marketing and branding strategies can also enhance brand awareness and customer loyalty, positively influencing CFP (Kapferer, 2012).

Research Methodology

This study will adopt a quantitative research approach to analyze the relationship between CSR activities and Corporate Financial Performance (CFP). A cross-sectional research design will be employed to collect data at a single point in time from a sample of companies in the selected states. A stratified random sampling technique will be used to select 500 companies from various industries in Gujarat, Maharashtra, Rajasthan, and Madhya Pradesh. Structured questionnaires will be administered to company representatives responsible for CSR activities and financial performance. Financial reports, CSR disclosures, and other relevant data will be collected from publicly available sources such as annual reports, company websites, and databases.

Structured questionnaires will be designed to collect data on CSR activities, financial performance, and other relevant variables. Data on financial performance will be extracted from financial reports, including metrics like Return on Assets (ROA), Return on Equity (ROE), and Profit Margin. Utilizing SPSS, descriptive statistics such as means, standard deviations, and frequencies will be calculated for the variables. Structural Equation Modelling (SEM) will be performed using Smart PLS to examine the relationships between CSR activities, mediating variables, and CFP. Hypotheses will be tested, and path analysis will be conducted to determine the strength and significance of these relationships Figures 1 & 2.

Figure 1 Research Model

Figure 2 Research Model

Ethical guidelines will be followed throughout the research process. Informed consent will be obtained from participants, and data confidentiality and anonymity will be ensured.

Objectives

1. To know the impact of Economic, Legal and Ethical factors on Corporate Financial Performance.

2. To know the impact of Age and size on Corporate Financial Performance. HO1: CSR activities in all sectors of Company are depending on Economic, Legal and Ethical factors

3. HO2: There is correlation between output of Company and CSR activities of stockholder

4. HO3: There is correlation between satisfaction of employee and CSR activity of employee

5. HO4: There is correlation between public image and CSR activity of community

6. HO5: There is correlation between sales increasing and CSR activity of customers

7. HO6: There is correlation between customer loyalty and CSR activity of society

8. HO7: There is correlation between brand awareness and CSR activity of investors

9. HO8: Corporate Financial Performance (CFP) is depending on age of company and size of company.

Analysis

Table 1 presents the results of two statistical tests used in the context of factor analysis to assess the suitability of data for such an analysis. These tests are the Kaiser-Meyer-Olkin (KMO) Measure of Sampling Adequacy and Bartlett's Test of Sphericity.

The KMO test is used to determine whether the data is appropriate for factor analysis. It assesses the adequacy of the sample size by measuring the proportion of variance among variables that might be common variance. The KMO value ranges between 0 and 1. A KMO value close to 1 indicates that the data is suitable for factor analysis, whereas a value closer to 0 suggests that the data is not suitable.

In this case, the KMO value is 0.926, which is close to 1. This indicates that the data used in the paper is highly suitable for factor analysis, as there is a strong amount of common variance among the variables.

In this case, the approximate chi-square value is 24923.001, and the degrees of freedom (df) is 1128. The significance level (Sig.) is reported as 0.000, which is less than 0.05. As a result, we reject the null hypopaper and conclude that the data shows significant correlations among the variables, supporting the suitability of factor analysis.

In summary, Table 1 suggests that the data used in the paper is suitable for factor analysis, as indicated by the high value of KMO (0.926) and the statistically significant result of Bartlett's Test of Sphericity (Sig. = 0.000). These results provide confidence in the application of factor analysis to the dataset.

Table 2 presents the results of reliability statistics, specifically Cronbach's Alpha, for the items used in the paper. Cronbach's Alpha is a measure of internal consistency reliability, which assesses how well the items in a scale or instrument consistently measure the same underlying construct.

The Cronbach's Alpha coefficient is a numerical value that ranges from 0 to 1. It quantifies the degree of internal consistency among the items in the scale. A higher value indicates greater internal consistency, meaning that the items are strongly related and collectively measure the construct well. A Cronbach's Alpha value of 1 would represent perfect internal consistency, while a value of 0 would indicate no internal consistency.

In the paper, the Cronbach's Alpha value is reported as 0.978. This is an exceptionally high value, close to 1, suggesting that the items used in the scale demonstrate very strong internal consistency. Such a high value is generally desirable as it indicates that the items are reliably measuring the intended construct. This indicates the number of items that were included in the scale or instrument to measure the construct. In this case, the scale consists of 48 items.

Given the Cronbach's Alpha value of 0.978 and the 48 items in the scale, the results indicate that the items used in the paper have very high internal consistency. This suggests that the items are measuring the same underlying construct in a highly reliable manner. Researchers and readers can have confidence in the scale's ability to consistently assess the targeted construct.

Having a reliable scale is crucial for research as it ensures that the data collected is consistent and trustworthy, allowing for more accurate and valid conclusions to be drawn from the analysis. With such a high Cronbach's Alpha, the paper demonstrates that the instrument used to measure the construct is robust and suitable for analysis and interpretation. In conclusion, Table 2 provides strong evidence that the scale employed in the paper has excellent internal consistency, making it a reliable tool for assessing the construct of interest.

Table 3 provides information on various factors, their factor loadings, Cronbach's alpha, composite reliability (rho_a), composite reliability (rho_c), and average variance extracted (AVE) for the paper. These measures are commonly used in the context of factor analysis and structural equation modeling to assess the reliability and validity of the factors or constructs used in the study.

Cronbach's alpha is a measure of internal consistency reliability for each factor. It indicates how well the items within a factor are interrelated or correlated. A high Cronbach's alpha value (close to 1) suggests strong internal consistency, indicating that the items in the factor are measuring the same underlying construct reliably. Composite reliability (rho_a) and composite reliability (rho_c) are alternative measures of internal consistency, similar to Cronbach's alpha. They also assess the reliability of the items within each factor. High values for composite reliability indicate strong internal consistency.

AVE represents the average amount of variance captured by each factor's items. It assesses the convergent validity of the construct. AVE values should ideally be above 0.5 to demonstrate good convergent validity, indicating that the items in the factor are capturing a significant amount of variance in the construct they represent.

In summary, Table 3 provides a comprehensive assessment of the reliability and validity of the factors used in the paper. Factors with high reliability and validity are crucial for ensuring the accuracy and robustness of the study's results and conclusions. Researchers can use this information to confidently interpret the findings related to the various constructs included in the analysis.

Table 4 presents the results of the HTMT (Heterotrait-Monotrait) matrices for the factors used in the paper. The HTMT is a measure used to assess discriminant validity in structural equation modeling (SEM) or factor analysis. Discriminant validity indicates whether different factors or constructs in the study are distinct and not highly correlated with each other.

In the table, each row and column represents a factor (e.g., AC, BA, CCU, CFP, etc.). The numbers in the cells of the table represent the HTMT values between the factors. The HTMT value compares the correlations between factors (heterotrait correlations) to the average correlations within factors (monotrait correlations).

Interpretation of HTMT Values

1. If the HTMT value is less than 0.85, it indicates that the factors have discriminant validity, meaning that they are distinct and not highly correlated.

2. If the HTMT value is close to or greater than 0.85, it suggests potential lack of discriminant validity, and the factors may be highly correlated, which could lead to issues in the analysis.

The diagonal cells of the table (where factors are compared to themselves) are left blank or contain dashes. This is expected, as factors will always have perfect correlation with themselves (HTMT = 1), and they are not compared to others.

For example, consider the cell in the row BA and the column CCU. The HTMT value is 0.754. Since this value is less than 0.85, it indicates that the factor Brand Awareness (BA) and the factor CSR towards Customers (CCU) have discriminant validity, and they are distinct constructs not highly correlated.

On the other hand, consider the cell in the row CFP and the column CL. The HTMT value is 0.909. Since this value is also less than 0.85, it suggests that the factor Corporate Financial Performance (CFP) and the factor Customers Loyalty (CL) have discriminant validity. The table shows HTMT values for all possible combinations of factors, and researchers can use these values to assess the discriminant validity of the constructs in the study.

Assessing discriminant validity is crucial in structural equation modeling and factor analysis, as it ensures that the factors or constructs being studied are distinct and independent from each other. If discriminant validity is not established, it may lead to issues in the analysis, such as inflated or misleading results.

For the paper, Table 4 provides evidence of whether the factors used in the study have discriminant validity. Researchers can review the HTMT values to determine if the factors are adequately distinct or if there are potential problems related to the correlations between the factors. If any lack of discriminant validity is observed, researchers may need to reconsider the measurement model or explore potential relationships between the factors more thoroughly. Overall, Table 4 helps to validate the measurement model used in the paper and provides insights into the independence of the different factors, enhancing the credibility of the study's findings and conclusions.

Table 5 presents the results of the Fornell-Larcker criterion, which is used to assess the discriminant validity of the factors in the paper. The Fornell-Larcker criterion is based on the concept of comparing the square root of the AVE (Average Variance Extracted) of each factor with the correlation between that factor and other factors. It helps determine whether the factors are adequately distinct from each other and have discriminant validity. In the table, each row and column represents a factor (e.g., AC, BA, CCU, CFP, etc.). The numbers in the cells of the table are the correlations between the factors.

To apply the Fornell-Larcker criterion, we compare the square root of the AVE of each factor (located on the diagonal of the table) with the correlations between that factor and other factors. If the square root of the AVE of a factor is greater than the correlation with other factors, then discriminant validity is established. Conversely, if the square root of the AVE is smaller than or equal to the correlation with other factors, there may be a lack of discriminant validity.

The diagonal cells (where the row and column factors are the same) contain the square root of the AVE for each factor. For example, the value 0.916 in the cell for factor AC indicates the square root of the AVE for the factor AC.

For each off-diagonal cell, the value represents the correlation between the factors. For example, the cell at the intersection of row BA and column AC shows a correlation of 0.768 between the factors BA and AC. To assess discriminant validity, we compare the square root of the AVE (diagonal cells) with the correlations (off-diagonal cells) for each factor.

1. If the square root of the AVE for a factor is greater than the correlations with all other factors, then the factor has discriminant validity. This means that the factor is distinct from other factors, and it is not highly correlated with them.

2. If the square root of the AVE for a factor is equal to or smaller than the correlation with any other factor, it indicates a potential lack of discriminant validity. In such cases, there might be significant overlap or shared variance between the factors, raising concerns about the uniqueness of the constructs.

The Fornell-Larcker criterion is essential for assessing the quality of the measurement model used in the paper. Establishing discriminant validity ensures that the factors or constructs being studied are distinct and independent from each other. This is crucial for obtaining reliable and valid results in structural equation modeling and factor analysis.

For the paper, Table 5 provides valuable insights into the discriminant validity of the factors used in the study. Researchers can review the correlations and the square root of the AVE values to determine if the factors are adequately distinct or if there are potential issues related to the overlap between the factors. If any lack of discriminant validity is observed, researchers may need to revise the measurement model, reconsider the inclusion of certain factors, or explore potential relationships between the factors more thoroughly.

Overall, Table 5 helps researchers validate the measurement model and ensure that the factors are effectively distinct and independent, enhancing the robustness and credibility of the study's findings and conclusions.

Table 6 provides a detailed comparison between the "Original sample (O)" and the "Sample mean (M)" for each pair of variables in the paper. It also includes the "Standard deviation (STDEV)" of the original sample, "T statistics (|O/STDEV|)", "P values", and a "Decision" column indicating whether the relationship between the variables is supported or not supported based on the statistical analysis.

For each pair of variables (e.g., AC -> CFP, BA -> CFP, CCU -> IS, etc.), the "Original sample (O)" column provides the correlation coefficient between the two variables in the original data, while the "Sample mean (M)" column gives the correlation between the variables based on the sample mean.

For example, consider the first row: AC -> CFP. The original sample correlation between AC and CFP is 0.678, while the sample mean correlation is 0.659. The standard deviation for this pair of variables is 0.177. The T statistics is |0.678/0.177| = 3.825, resulting in a very low P value of 0.000. Therefore, the relationship between AC and CFP is "Supported," indicating that there is a significant correlation between the two variables in the population

On the other hand, consider the second row: BA -> CFP. The original sample correlation is -0.132, the sample mean correlation is -0.157, and the standard deviation is 0.2. The T statistics is |(-0.132)/0.2| = 0.66, resulting in a relatively high P value of 0.509. As the P value is greater than the typical significance level of 0.05, the relationship between BA and CFP is "Not Supported," suggesting that there is no significant correlation between these variables in the population.

Table 6 is crucial for evaluating the strength and significance of the relationships between the variables in the paper. It helps researchers determine which relationships are supported by the data and which are not, based on the statistical analysis. The information in this table aids in drawing conclusions and making inferences about the relationships between different constructs in the study. Researchers can use the results to confirm or refute their hypotheses and to gain insights into the relationships between various factors or variables. Overall, contributes to the validity and reliability of the findings in the paper and provides a solid basis for interpreting and discussing the results in the context of the research questions and objectives.

Conclusion and Discussion

The paper conducted a comprehensive investigation into various factors related to Corporate Social Responsibility (CSR) and its impact on different business outcomes. The study examined the relationships between multiple constructs, such as Brand Awareness, CSR towards Customers, Corporate Financial Performance, Customers Loyalty, CSR towards Community, CSR towards Employees, CSR towards Investors, CSR towards society, CSR towards Stakeholders, Increases in sales, Company Output, Public image, and Size and Age of Company.

The analysis involved rigorous statistical techniques, including Kaiser-Meyer-Olkin Measure of Sampling Adequacy, Bartlett's Test of Sphericity, Reliability Statistics (Cronbach's Alpha), R-square, R-square adjusted, and various fit indices like SRMR, d_ULS, d_G, Chi-square, and NFI. These measures helped ensure the validity and reliability of the results.

The findings from the study revealed significant correlations between several constructs, providing valuable insights into the relationships between CSR and different business outcomes. Notably, CSR was found to have a positive and significant impact on Brand Awareness, Corporate Financial Performance, Customers Loyalty, Public image, and other key business indicators.

However, some relationships did not exhibit statistically significant associations, suggesting that further investigation and refinement of the model may be necessary. Factors such as Employees' Perception, Community Engagement, and Stakeholder involvement may require deeper exploration to better understand their effects on business outcomes.

In conclusion, the paper provides essential contributions to the field of CSR and its implications for various aspects of business performance. The results underscore the importance of integrating responsible practices into corporate strategies, as it positively influences customer perception, financial performance, and other critical metrics. These findings hold implications for businesses aiming to enhance their competitiveness and sustainable growth by adopting socially responsible practices. Despite its contributions, the study acknowledges certain limitations, including the use of cross-sectional data and a specific sample size. Future research should consider longitudinal data and broader sample sizes to validate and extend the findings.

Overall, this paper represents a valuable contribution to the literature on CSR and its impact on business outcomes, providing valuable insights for academics, practitioners, and policymakers seeking to promote responsible business practices and sustainable growth in contemporary business environments.

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Received: 09-Jan-2025, Manuscript No. AMSJ-25-15602; Editor assigned: 10-Jan-2025, PreQC No. AMSJ-25-15602(PQ); Reviewed: 28- Jan-2025, QC No. AMSJ-25-15602; Revised: 20-Feb-2025, Manuscript No. AMSJ-25-15602(R); Published: 14-Mar-2025