Do CSR Activities Influence Corporate Reputation? Evidence from India

Abstract

Many firms today value corporate social responsibility (CSR) because it can boost their reputation in a competitive market. Many studies have shown that CSR practices affect a company’s reputation, but few have examined specific CSR elements affect Indian corporate reputation. Thus, this study explores how economic, governance/legal, social/ethical, and environmental CSR variables affect business reputation. A mailed survey of mid-level and senior managers at 403 Bombay Stock Exchange-listed companies in six major industry categories was used to gather data. Using the Likert scale with five points, 51 items were scored on the four CSR dimensions and five items were scored on corporate reputation dimension. Descriptive statistics, reliability tests (using Cronbach’s alpha), principal components/factor analysis, Pearson correlation analysis, and multiple linear regression were used to examine the survey data. Governance, legal, social (including ethical), and environmental CSR factors boost firm reputation. The four dimensions of CSR (economic, governance/legal, social/ethical, and environmental) are positively and significantly predictive of corporate reputation. The environmental factor of CSR (β = 0.425; t = 7.935; p < 0.001) was the strongest predictor of business reputation, while the economic dimension (β = 0.119; t = 2.378; p = 0.018) was the weakest predictor, but still statistically significant. This research will add to strategic management literature by showing how CSR dimensions affect corporate reputation in a developing economy and giving managers advice on how to execute effective CSR programs.

1. Introduction

In academic and business circles alike, one of the hot topics being discussed is corporate social responsibility (CSR). People all over the globe now expect that businesses will think about their social and environmental impact while operating; consumers, workers, investors, and government officials expect to see these issues included in what they perceive as core to companies’ business (Carroll, 1979; McWilliams et al., 2006). In addition to all the people who expect this, CSR is increasingly urgent to implement around the world because of the rapid escalation of worldwide environmental problems (e.g., climate change, disparity between rich and poor areas, and bad government) and the increasing severity of these problems (Van Beurden & Gössling, 2008; Wood, 2010).

The growing focus internally on legal obligations related to CSR as well externally on background events influencing companies in India to implement CSR practices has led to the increasing importance of CSR within India today. An illustration of this rise in significance is that India has become one of the first countries in the world to pass legislation mandating that firms that meet certain criteria must devote a specific propration of their net profits to corporate social responsibilty (Companies Act, 2013, section 135); nevertheless, there is still limited research examining how companies can strategically implement CSR and subsequently impact their positions in the marketplace. Most of the literature linking CSR and reputation pertains only to developed nations, which suggests that CSR awareness and CSR-to-reputation relationships may vary across countries due, in part, to differences in their institutional settings (i.e., India currently has a smaller amount of news coverage focused on CSR-related content than do more developed countries; CSR-related practices have existed for less time in India; and the different types of stakeholders associated with CSR will also differ between nations).

Corporate reputation—generally viewed by stakeholders to mean the overall reputation a company has earned based upon its economics, social behavior, and environmental performance overall as a group—is an important intangible asset and will help create long-lasting competitive advantages (C. J. Fombrun et al., 2000; Roberts & Dowling, 2002). In previous study, there appears to be little consensus regarding the manner in which corporate social responsibility (CSR) programs generate a business reputation. Some research studies support a strong, positive association (Melo & Garrido-Morgado, 2012; J. Park et al., 2014; E. Park, 2019), whereas others find little to no association, or only a contextual association (Walker et al., 2019). There is a gap in the existing literature regarding the differential influence of the various dimensions of CSR (i.e., economic, legal/good governance, social/ethical, and environmental) on a corporation’s reputation and as a result business success. This gap is especially prevalent with regard to BSE-listed companies in India.

This study will investigate how the four dimensions of corporate social repsonsibilty (CSR) established by Carroll (1979, 1991, 2016) impact upon corporate reputation of BSE-listed companies in India from the perspective of the companies’ mid- and top-level managers. Focusing on individuals at the managerial level will complement existing research that has been conducted from the perspective of customers or investors (Farooq et al., 2014), and provide further understanding about how organizations understand, and operationalize, their CSR strategy.

This paper has been organized in the following manner. The theoretical foundation of this investigation is discussed in Section 2 of this paper. Section 3 includes a review of previous literature and the hypotheses developed for this study. The research methodology used to conduct this study is discussed in Section 4, which includes details about the sample, the instruments used (both in terms of design and data collection), and analysis procedure(s) employed. The empirical results will be presented and discussed in Section 5. A discussion of both theoretical and practical ramifications can be found in Section 6. Section 7 comes to close with a discussion on potential future paths of research as well as list of limitations.

2. Theoretical Background

2.1. Corporate Social Responsibility: Concept and Dimensions

Multiple conceptual traditions have approached the theoretical construct of CSR. The most cited and used framework for CSR is Carroll’s (1979) foundational pyramid model, which encompasses a firm’s four CSR responsibilities: economic, legal, ethical, and philanthropic. Carroll’s model states that the layering of these responsibilities involves the economic viability (i.e., financial performance) of the firm being the basis for legal compliance, ethical conduct, and voluntary contributions to society (i.e., philanthropy). In some instances, philanthropy is reconceptualized as a firm’s social responsibility; however, the basic structure of Carroll’s model continues to provide a basis for empirical CSR research (Schwartz & Carroll, 2003).

Dahlsrud (2008) added a fifth dimension of CSR by adding an environmental responsibility dimension to bolster emphasis on firms’ responsibilities to be ecologically responsible in response to increasing stakeholder demands for accountability on ecological stewardship. The present study adopts this extended framework by treating the following four pillars of CSR engagement as the basis for CSR engagement: economic CSR; governance and legal CSR; social and ethical CSR; and environmental CSR.

The resource-based view (RBV) of the corporation serves as the theoretical basis for the connection between corporate social responsibility (CSR) and the reputation of the organizations that are involved in the business. According to Barney (1991), a company that has an ongoing competitive advantage has valuable, rare, and imperfectly imitable resources that are specific to the company itself, and that cannot be substituted. Corporate image satisfies these conditions, as it is developed gradually because of consistent actions over a period, cannot be duplicated very easily, and provides both monetary and non-monetary benefits to the corporation (Roberts & Dowling, 2002). An organization’s reputation as an intangible asset that is of strategiv importance will be improved as a result of investments in corporate social responsibility (CSR), which can serve to communicate to stakeholders about the organization’s commitment to ethical business practices. (McWilliams et al., 2006; Branco & Rodrigues, 2006).

Furthermore, the stakeholder theory provides additional support for the connection between CSR and reputation. Freeman’s (1984) stakeholder framework suggests that all a companies’s stakeholders including customers, employees, suppliers, communities, and regulators, who are directly and indirectly impacted by the operations of the company. When CSR activities are developed to fulfil the needs and address the concerns of a firm’s stakeholders, they will build a favorable reputation for the firm through goodwill and trust from its various stakeholders (Bhattacharya & Sen, 2004; J. Park et al., 2014). Branco and Rodrigues (2006) also note that there will be variations in the reputational consequences that are associated with corporate social responsibility activites. Additionally, the company’s capacity to successfully convey these CSR initatives may be restricted due to the limited communication channels that are available in emerging countries.

2.2. Corporate Reputation

According to C. Fombrun and Shanley (1990) and Barnett et al. (2006), the term “Corporate reputation” refers to an overall evaluation of the opinions held by various stakeholder regarding the actions taken by a business in the past in comparison to those of its primary competitiors. According to Miles and Covin (2000), the perceptions of stakeholders regarding the credibility, trustworthiness, reliability, and accountability of a company are included in the concept of corporate reputation. Reputation is generally regarded as a strategic intangible asset because it facilitates differentiation between a firm and its competitors, enhances customer loyalty, helps attract human capital, and allows an organization to weather crises (Roberts & Dowling, 2002; C. J. Fombrun, 2005).

The researchers evaluated the reputation from the manager’s perspectives, without regard to how external stakeholders rate the organization. As a result, operationalized reputation, which is based on how managers perceive the reputation of the company in connection to its external stakeholders, is a more accurate method of capturing the manager’s perception of the company’s reputation. Managers can, in turn, create a self-serving bias in their company reputation by overestimating the reputation of the organization. Other research prior to this study also used manager informants to define firm level constructs (Farooq et al., 2014; J. Park et al., 2014), and thus the results of the study must be viewed considering this limitation.

3. Literature Review and Hypothesis Development

3.1. Economic CSR and Corporate Reputation

Economic Corporate Social Responsibility (CSR) is the commitment of a corporation to deliver products and/or services in a cost-effective manner; to return a profit to its shareholders; to stimulate job creation; and to enhance economic development. The CSR framework is used to measure a corporation’s reputation, as a corporation’s perceived financial stability will impact the level of trust from all that company’s stakeholders (Carroll, 2016). Studies also show that companies are rewarded with increased stakeholder reputation value for meeting their economic obligations to stakeholders (J. Park et al., 2014). Saeidi et al. (2015) demonstrated that economic performance is a mediator in the CSR and corporate reputation relationship.

Evidence from developing countries provides a different perspective. In some markets, stakeholders place a higher emphasis on social and environmental performance than on generating profits; therefore, some companies may not derive substantial reputational benefits from solely focusing on their economic CSR. This study will examine whether ECSR is a significant predictor of corporate reputation in Indian firms listed on the Bombay Stock Exchange (BSE).

H₁. 

Economic CSR responsibility has a positive and significant impact on corporate reputation.

3.2. Governance and Legal CSR and Corporate Reputation

The legal and governance component of CSR includes compliance with laws and regulations, good governance practices, and compliance with records of employment, taxes, and reporting (Carroll, 1991). Companies that work inside a legal framework let stakeholders know the company is credible and reliable, which enhances its reputation (J. Park et al., 2014). Corporate Governance which is characterized by board accountability, transparency, and responsiveness to stakeholders may be positively correlated with the reputational capital of a corporation in both developed and developing markets (Cochran & Wood, 1984; Shum & Yam, 2011). In India, where companies listed on the Bombay Stock Exchange (BSE) are subject to rigorous scrutiny of their compliance with the Companies Act and the Securities and Exchange Board of India (SEBI) Guidelines, CSR that includes governance may be an especially important reputation-enhancing factor.

H₂. 

Governance and legal CSR responsibility has a positive and significant influence on corporate reputation.

3.3. Social and Ethical CSR and Corporate Reputation

CSR is the cooperation or agreement to uphold fairness, respect for workers, and abiding by community interests, as well as conducting oneself ethically beyond what is required by law (Carroll, 1979; Turker, 2009). Stakeholders have been shown to reward companies thought to engage in CSR activities with increased trust and loyalty, which can translate into greater reputation (Brammer & Millington, 2005; Bhattacharya & Sen, 2004). Customers’ perceptions of a company’s ethics significantly enhance that company’s overall reputation, as demonstrated by Bendixen and Abratt (2007). Since community welfare is a major concern to stakeholders in India, along with cultural values, socially and ethically responsible CSR will most likely resonate with stakeholders through CSR activities, such as charitable acts, employee assistance programs, and paying respectful wages.

H₃. 

Social and ethical CSR responsibility has a positive and significant effect on corporate reputation.

3.4. Environmental CSR and Corporate Reputation

As part of its Environmental CSR components, businesses are expected to proactively reduce their negative impact on the environment through sustainable practices, utilizing clean energy resources, develop products that naturally harm the environment, and promote environmental stewardship (Dahlsrud, 2008; Carroll, 2016). In recent years, environmental concerns such as climate change, depletion of natural resources, and loss of biodiversity have come to dominate the agenda for all stakeholders globally, making Environmental CSR critical to many stakeholders’ perception of how organizations behave within the context of Environmental CSR. In fact, researchers Khojastehpour and Johns (2014) find that Environmental CSR is a more significant contributor to the development of corporate reputation than other CSR dimensions due to the importance of environmental issues when consumers and investors evaluate an organization’s behavior. E. Park (2019) found this was also true in the airline industry. It is anticipated that the growing awareness of water and air pollution, water scarcity, and vulnerability to climate change will boost the potential of Environmental CSR as a means for business to build their credibility (reputation) in the global marketplace.

H₄. 

Environmental CSR has a positive and significant influence on corporate reputation.

4. Research Methodology

4.1. Research Objective and Conceptual Framework

The purpose of this study to investigate the impact of four different aspects of corporate social responsibility (CSR) as follows: Economic, governance and legal, social and ethical, and environmental), on the reputation of BSE-listed firms in India as perceived by managers. Specifically, the study tests four directional hypotheses (H₁–H₄) which predict a positive relationship between each of the four CSR dimensions and the corporate reputation.

The conceptual framework will establish one dependent variable, corporate reputation, in addition to four independent variables, which will be economic CSR, governance and legal CSR, social and ethical CSR, and environmental CSR; and. Carroll’s (1979, 1991, 2016) pyramid framework will serve as the basis for the study. In addition, environmental responsibility (Dahlsrud, 2008) will be incorporated into the framework, and resource based theory theory and stakeholder theory will be utilized as theoretical underpinnings.

4.2. Measurement Instruments and Scale Sources

The survey was organized into two separate sections. The first section collected demographic data from respondents as follows: their position, type of industry they worked within, company size (being small, medium, or large), annual revenue, and where their registered business office is located. The second section consisted of items that asked respondents to rate four different dimensions of CSR (Corporate Social Responsibility) along with how they feel about their company’s reputation.

Items were all taken from previously published and established measures, then further tested for content validity within an Indian context via pilot testing with both academics and practitioners. The Economic CSR scale consisted of seven items and was taken from Alvarado-Herrera et al. (2017), while the Governance and Legal CSR scale included 11 items and the Social and Ethical CSR scale included 10 items—both were taken from El-Garaihy et al. (2014) and Turker (2009). Lastly, the Environmental CSR scale consisted of 13 items sourced from Mai (2013) and Turker (2009). In relation to measuring corporate reputation, the research study used a modified five-item scale developed by El-Garaihy et al. (2014); thus all items were rated on a five-point Likert-type scale, where 1 = Strongly Disagree and 5 = Strongly Agree.

4.3. Sample, Scope, and Data Collection

The sample was made up of mid- and senior-level managers working in BSE-listed companies in the National Capital Region (NCR) of Delhi and adjoining industrial areas of Northern India. The geographical scope was purposefully chosen because it is one of the most industrially concentrated and CSR-active locations of India and yet, realistically accessible for mailing survey administration. Respondents were expected to have knowledge about CSR frameworks, as they were all affiliated with BSE-listed firms, which required them to comply with regulated disclosure standards for CSR under Indian legislation. Banking and financial services firms were excluded from the scope of the research since their organizations and governance structures vary substantially from nonfinancial sectors (Faccio & Lasfer, 2000).

A purposive sampling technique was used to ensure that the six distinct industrial categories (as well as an “Others” category covering electricity generation, health care, telecommunications, diversified firms’ chemicals and petrochemicals, and the transportation sectors) are represented within the sample. Industries under the “Others” category (power, healthcare, telecom, diversified conglomerates, chemical and petrochemical, and transport) were added to capture the BSE-listed universe broadly and to avoid any selection bias from excluding CSR-active firms that would have otherwise fallen below the threshold for a separate category. All these industries are associated with the mandated declaration of CSR under the Companies Act (2013). The total presence of these industries (30.3%) in the sample represents the heterogeneous industrial mix of the firms listed on the BSE and operating in the Delhi NCR region. Having adequate quantity of sample sources per category allows for the successful completion of the research project. A sample of at least one manager from each company listed on the BSE provided their responses via the questionnaire. Of the 500 mailings sent to BSE-listed firms, 403 completed questionnaires were received in response, for a response rate of 80.6%. Data collection occurred from June through August 2025 and covered the NCR of Delhi and the adjacent industrial zones of Northern India, along with secondary data collected from firm-specific websites and annual reports for context. Since the sample used for this study is a geographically limited sample and not representative of all firms that are listed on the BSE in terms of a national level across India, interpreting the results must be done accordingly.

Table 1. Sector-wise Distribution of Sample Firms (N = 403).

S. No.	Sector	No. of Firms	Percentage (%)
1	Oil & Gas	60	14.9
2	Information Technology	60	14.9
3	Steel and Metal Industries	49	12.2
4	Metal, Metal Products & Mining	40	9.9
5	Capital Goods	36	8.9
6	Fast Moving Consumer Goods (FMCG)	36	8.9
7	Others (Power, Healthcare, Telecom, Diversified, Chemical & Petrochemical, Transport)	122	30.3
	Total	403	100.0

Source: Author’s compilation. Banking and finance sector excluded (Faccio & Lasfer, 2000).

presents further details of the sample distribution by sector.

4.4. Model Specification

In order to test the hypotheses, the research makes use of the multiple linear regression model that is described below:

CR = b₀ + b₁(EcoCSR) + b₂(GovCSR) + b₃(SocialCSR) + b₄(EnvCSR) + ε

(1)

where CR denotes Corporate Reputation (dependent variable); EcoCSR, GovCSR, SocialCSR, and EnvCSR denote the four CSR dimension scores (independent variables); constant term is denoted by b₀; the regression coefficients are denoted by b₁ to b₄; and the error term is denoted by ε.

4.5. Analytical Procedure

Data analysis was conducted in three successive steps, with each stage immediately informing the next, as stated below.

Stage 1—Reliability Assessment: In order to assess the internal consistency of each a priori CSR scale and the corporate reputation scale, Cronbach’s α, was utilized, with a minimum threshold of 0.70, as stated by Nunnally in 1978). All of the scales that were able to meet this requirement were transferred to Stage 2.

Stage 2—Variable Construction and Construct Validation Using PCA: The entire collection of 41 CSR items was subjected to a Principal Components Analysis (PCA) that was rotated with Varimax method. Both the Kaiser-Meyer-Olkin (KMO) measure of sample adequacy and Bartlett’s test of sphericity were computed in order to determine whether the correlation matrix was suitable for the factor analysis. Items were retained if they simultaneously satisfied both of the following criteria: (a) communality has to be more than or equal to 0.50, which indicates that the extracted factors account for at least fifty percent of the variance for each item; and (b) rotated factor loading must be greater than or equal to 0.50 on one component, indicating that an item was clearly associated with a single component. Items not meeting either condition were dropped. The eigenvalue-greater-than-one criterion confirmed by visual inspection of the scree plot extracted four components. These criteria are in line with the guidelines established by Hair et al. (2006) for samples of more than 200 respondents.

After deleting items, four composite mean scores were calculated, one for each factor retained, by averaging the scores of only those items that passed the screening requirements and loaded cleanly on that component. These composite scores are the predictor variables utilized in all subsequent analyses (Pearson correlations and multiple regressions). The four composites are directly related to the four variables listed further (Factor Reconstruction) and are designated as (1) Economic and Profitability, (2) Governance and Ethical, (3) Society and Community Development, and (4) Environmental and Sustainability. These labels reflect the empirical item content of each factor retained and map directly onto the four theoretical CSR dimensions hypothesized in H₁–H₄. The renaming does not change the constructs being tested, but rather refines their descriptive precision based on the items that best represented each dimension empirically.

Stage 3—Testing the hypothesis: The manager’s perception of the company’s reputation served as the dependent variable in the multiple liner regression model, which was constructed with the four composite factor scores serving as the independent variables. (see model specification, Section 4.4). Pearson product-moment correlations were used to investigate bivariate associations between all variables prior to regression. The Variance Inflation Factor (VIF) and tolerance statistics were utilized in order to examine the presence of multicollinearity inside the regression model. Prior to interpretation of individual predictor coefficients, an omnibus F-test was performed to test overall model significance. Analyses were conducted using SPSS version 26.

4.6. Ethical Considerations

This research received ethical approval from the SGTB Khalsa College, Delhi University, under its ethical standards, and from the Ethics Committee of Delhi University, per the Helsinki Declaration (GL-EAC–05-10, dated 25 April 2025). We used mail-in survey instruments instead of conducting interviews with subjects and provided potential Subjects with a Participant Information Sheet (PIS) so that they could complete questionnaires and return them back to us. The PIS contained details concerning the research objectives, all rights which Subjects had as a necessary part of giving informed consent to be researched, our intent for using their data, how/where/how long their data is to be stored, and other relevant information to enable Subjects to give themselves informed consent. All participation in the research was voluntary, and after collecting the data from Questionnaire Returns, no single subject’s response could be linked to that subject until all the data from the subjects had been statistically analyzed.

5. Results

5.1. Reliability Assessment

Table 2. Reliability Assessment (Cronbach’s Alpha).

Construct	No. of Items	Cronbach’s Alpha
Economic CSR	7	0.824
Governance CSR	11	0.796
Social and Ethical CSR	10	0.740
Environmental CSR	13	0.756
Corporate Reputation	5	0.778

Source: Author’s analysis.

presents the reliability coefficients for each of the constructs that were researched. As a result of the fact that the Cronbach’s alpha values for all constructs were greater than the common criteria of 0.70 (Nunnally, 1978) and ranged from 0.740 to 0.824; it can be concluded that all of the scales exhibited satisfactory levels of internal consistency.

5.2. Descriptive Statistics

Table 3. Descriptive Statistics (N = 403).

Variable	Mean	Std. Deviation
Economic CSR (EC)	4.091	0.426
Governance & Legal CSR (G&L)	4.142	0.352
Social & Ethical CSR (S&E)	4.148	0.393
Environmental CSR (ENV)	4.035	0.314
Corporate Reputation (CR)	3.275	0.455

Source: Author’s analysis.

presents descriptive statistics for the factors that were investigated in the study. All four CSR dimensions were rated with an average greater than 4.00, which means that the average score from managers indicates a high level of perceived CSR engagement. The average score of corporate reputation (3.275) was lower than CSR dimensions, meaning that managers perceive their firm’s reputation to have a moderate level of performance relative to CSR dimensions. Environmental CSR has the lowest standard deviation (0.314), indicating that managers have the greatest level of agreement or consensus about environmental responsibility, while corporate reputation has the highest standard deviation (0.455), indicating that managers do not have a high level of agreement or consensus regarding their company’s reputation.

5.3. Factor Analysis

To simplify many intercorrelated measures into a few representative constructs or factors is the goal of factor analysis (Ho, 2006). This is accomplished through the process of factor analysis. It makes it possible for a researcher to reduce many variables to a smaller number of variables. Therefore, factor analysis is a method of collecting and analyzing data. When conducting factor analysis, it is presumed that all variables are connected to one another to a certain extent. Consequently, it follows that variables that have dimensions that are similar should have a high degree of correlation, whereas variables that have dimensions that are different should have a low degree of correlation. These high and low correlation coefficients become very clear in the correlation matrix as variables with similar dimensions “hang” together (Ho, 2006, p. 203). This is because the correlation matrix is very clear.

A researcher may choose to use either the Q-mode or the R-mode for factor analysis after giving it a lot of thought. The rows are what a Q-mode factor analysis looks at, which cuts down on the amount of data. The R-mode factor analysis, on the other hand, looks at the columns, which means there are fewer variables. Some people say that R-mode factor analysis is the most popular method because most researchers want to cut down on the number of variables in a study so that it is easier to handle (Abiola & Udofia, 2011). The R-mode factor analysis was used for this study.

The intercorrelations between variables are sufficiently strong for factor analysis is demonstrated by the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy value which is 0.850 which is presented in the

Table 4. KMO and Bartlett’s Test of Sphericity.

Test	Value
Kaiser-Meyer-Olkin Measure of Sampling Adequacy	0.850
Bartlett’s Test of Sphericity—Approx. Chi-Square	7842.31
Bartlett’s Test of Sphericity—Degree of Freedom (df)	820
Bartlett’s Test of Sphericity—Sig.	<0.001

Source: Author’s analysis. Extraction Method: Principal Component Analysis.

. There is significant difference between the observed and expected values of correlation coefficients, which indicates that the correlation matrix is not an identity matrix and that factor analysis is appropriate. This conclusion is supported by the Barlett’s Test of Spehericity, which also yields a very significant statistic (p < 0.001).

According to the findings of the KMO and Bartlett’s test (Table 4), the data were sufficient for factor analysis. The high KMO value demonstrates that the sample is adequately representative, and the results of Bartlett’s test demonstrate that the variables are sufficiently connected to identify the factors that lie beneath the surface. Therefore, you can confidently proceed with or interpret the factor analysis results, knowing that your data is well-suited for this type of analysis.

5.3.1. Communalities

The communality of each variable is presented in

Table 5. Communalities.

Items	Initial Value	Extraction
Economic CSR 1	1	0.517
Economic CSR 2	1	0.791
Economic CSR3	1	0.721
Economic CSR4	1	0.941
Economic CSR5	1	0.765
Economic CSR6	1	0.724
Economic CSR7	1	0.772
Governance & Legal CSR 1	1	0.817
Governance & Legal CSR 2	1	0.725
Governance & Legal CSR 3	1	0.595
Governance & Legal CSR 4	1	0.764
Governance & Legal CSR 5	1	0.837
Governance & Legal CSR 6	1	0.525
Governance & Legal CSR 7	1	0.555
Governance & Legal CSR 8	1	0.542
Governance & Legal CSR 9	1	0.705
Governance & Legal CSR 10	1	0.502
Governance & Legal CSR 11	1	0.541
Social & Ethical CSR1	1	0.738
Social & Ethical CSR2	1	0.594
Social & Ethical CSR3	1	0.552
Social & Ethical CSR4	1	0.553
Social & Ethical CSR5	1	0.664
Social & Ethical CSR6	1	0.553
Social & Ethical CSR7	1	0.504
Social & Ethical CSR8	1	0.567
Social & Ethical CSR9	1	0.836
Social & Ethical CSR10	1	0.779
Environmental CSR1	1	0.511
Environmental CSR2	1	0.589
Environmental CSR3	1	0.674
Environmental CSR4	1	0.565
Environmental CSR5	1	0.564
Environmental CSR6	1	0.505
Environmental CSR7	1	0.52
Environmental CSR8	1	0.465
Environmental CSR9	1	0.409
Environmental CSR10	1	0.762
Environmental CSR11	1	0.467
Environmental CSR12	1	0.567
Environmental CSR13	1	0.753

. This refers to the percentage of variance in each variable that can be attributed to the factors that are shared by all respondents (Ho, 2006). By calculating the communalities, one can determine the extent to which the extracted factors have been able to account for the variance in the variable.

The proportion of variance that can be assigned to the common factors is equal to one for each variable, as can be shown in Table 5. This is the case for all variables simultaneously. A cut-off point of 1 was utilized by the researcher in the process of identifying relevant variables. This was done in order to ensure that the process was carried out correctly. A consequence of this is that variables that have an eigen value that is higher than 0.50 are considered to be significant factors for the purpose of conducting additional analysis in this study. (Note: Items were evaluated using two criteria to evaluate similar features for both communalities at least 0.50 and for at least one of the rotated factor loadings of the items onto one of the component factors as being 0.50 or greater according to the PCA (principal components analysis) procedure. Of the 41 items entered the PCA, 23 did not meet one or both criteria and so were dropped from the analysis. Only 18 items remained in each of the three composites, as follows: Economic (6), Governance and Ethical (5), Society and Community (4), and Environmental and Sustainability (3) dimensions; these were used in the development of composite scores and for hypothesis testing. The individual retained items and their associated rotated factor loadings can be viewed further.)

The conclusion of the factor analysis, which is based on Principal Component Analysis (PCA), offers information about the percentage of variance that can be attributed to the factor solution for each variable. This means that all the variance of a variable can be assigned to at least one of the extracted factors. The initial communalities have been set at one, which indicates that this is the case. The extraction communalities provide an indication of the percentage of the total variance for a certain variable that can be attributed to the factor solution.

Many variables are consistently associated with each other—meaning they will have common variance—meaning extracted factor scores will provide a clear representation of those variables. For instance, G&L CSR1 has an extraction communality of 0.817 or 81.7% of the variance in G&L CSR1; this is explained by the factor model and G&L CSR5 the highest level of extraction communality at 0.837 or 83.7% of the variance in G&L CSR4; this is explained by the factors that make up the model. Other good examples include Eco CSR 4 with an extraction communality at 0.941, and S&E CSR1 with an extraction communality of 0.738, both showing a strong connection or correspondence to the underlying factor structure.

Alternatively, several variables, such as G & L CSR 6 (0.525), Eco CSR 1 (0.517), G & L CSR 10 (0.502), S & E CSR 7 (0.507), and ENV CSR 9 (0.409), show lower communalities, which indicates that these variables do not strongly relate to the extracted factors. As a result, there could be large amounts of unique variance or variance from outside the present model for these variables.

Using PCA as the analytic procedure, the dataset of 41 items produced four components, each with eigenvalues above 1.0, which supports the Kaiser criterion (Kaiser, 1974) and the scree plot inflection point. Collectively, these four components account for 77.071% of the total variance of the dataset as presented in

Table 6. Total Variance Explained.

	Initial Eigenvalues			Extraction Sums of Squared Loadings			Rotation Sums of Squared Loadings
		% of	Cumulative		% of	Cumulative		% of	Cumulative
Component	Total	Variance	%	Total	Variance	%	Total	Variance	%
1	14.592	54.306	54.306	14.592	54.306	54.306	5.833	42.26	42.26
2	5.381	8.633	62.939	5.381	8.633	62.939	5.597	12.438	54.698
3	4.465	7.825	70.763	4.465	7.825	70.763	5.196	11.546	66.244
4	3.885	6.308	77.071	3.885	6.308	77.071	4.872	10.827	77.071
5	0.993	4.65	81.721
6	0.824	4.277	85.998
7	0.707	3.503	89.501
8	0.693	3.095	92.596
9	0.617	2.261	94.857
10	0.585	1.966	96.823
11	0.502	1.555	98.378
12	0.451	1.003	99.382
13	0.278	0.618	100

, suggesting a high degree of reliability and a parsimonious factor solution. Prior to rotation, the variance explained by Component 1 (54.306%) dominated the data. This is commonly observed with attitudinal data, where a general factor is released from an analysis. Following application of the Varimax rotation, the variance explained is more evenly dispersed across the four components as referred in Table 6; Component 1 was explained 42.260%, Component 2 = 12.438%, Component 3 = 11.546%, and Component 4 = 10.827%. Not only did the total variance (77.071%) remain the same following the Varimax rotation, but the cumulative variance explained by each component indicates that they have meaning as distinct conceptual dimensions and are not artifacts of the solution.

5.3.2. Rotated Component Matrix

The factor loadings for all 41 items used in the PCA are included in the rotated component matrix (

Table 7. TOTAL ROTATED COMPONENT MATRIX (Varimax Rotation).

Items	Component
	1	2	3	4
Economic CSR1	0.417
Economic CSR2	0.726
Economic CSR3	0.772
Economic CSR4	0.811
Economic CSR5	0.695
Economic CSR6	0.684
Economic CSR7	0.672
Governance & Legal CSR1		0.744
Governance & Legal CSR2		0.696
Governance & Legal CSR3		0.495
Governance & Legal CSR4		0.69
Governance & Legal CSR5		0.776
Governance & Legal CSR6		0.425
Governance & Legal CSR7		0.455
Governance & Legal CSR8		0.342
Governance & Legal CSR9		0.62
Governance & Legal CSR10		0.402
Governance & Legal CSR11		0.441
Social & Ethical CSR1			0.706
Social & Ethical CSR2			0.494
Social & Ethical CSR3			0.352
Social & Ethical CSR4			0.453
Social & Ethical CSR5			0.646
Social & Ethical CSR6			0.453
Social & Ethical CSR7			0.404
Social & Ethical CSR8			0.467
Social & Ethical CSR9			0.727
Social & Ethical CSR10			0.738
Environmental CSR1				0.411
Environmental CSR2				0.489
Environmental CSR3				0.624
Environmental CSR4				0.465
Environmental CSR5				0.464
Environmental CSR6				0.305
Environmental CSR7				0.42
Environmental CSR8				0.365
Environmental CSR9				0.409
Environmental CSR10				0.731
Environmental CSR11				0.467
Environmental CSR12				0.467
Environmental CSR13				0.698

). Two sequential screening criteria were used to identify the final set of retained items. First, any item that had a communality below 0.50, meaning less than half of its variance could be attributed to the factor solution, was flagged for removal. Second, all remaining items with rotated factor loadings below 0.50 on their primary component, or with cross-loadings of similar magnitude on more than one component, were eliminated because they do not produce a sufficiently distinct signal for any one factor.

Applying these criteria to the whole 41-item pool, 23 things were eliminated and 18 items were retained in the four components as follows: Factor 1 (Economic and Profitability): 6 items maintained from original 7 (Economic CSR1 was eliminated; communality = 0.517, loading = 0.417); Factor 2 (Governance and Ethical): 5 items retained from original 11 (G&L CSR3, 6, 7, 8, 10, 11 were eliminated due to loadings below 0.50); Factor 3 (Society and Community Development): 4 elements retained from an original 10 (S&E CSR2, 3, 4, 6, 7, 8 were deleted due to loadings below 0.50); Factor 4 (Environmental and Sustainability) Retained: 3 items from 13 (ENV CSR1, 2, 4, 5, 6, 7, 8, 9, 11, 12 removed due to low communalities or loadings (<0.50)).

Table 8. Factor Reconstruction.

Factor Name	Variables	Rotated Factor Loadings
Factor 01 Economic & Profitability aspect	Economic CSR2	0.726
	Economic CSR3	0.772
	Economic CSR4	0.811
	Economic CSR5	0.695
	Economic CSR6	0.684
	Economic CSR7	0.672
Factor 02 Governance & Ethical Aspect	Governance & Legal CSR1	0.744
	Governance & Legal CSR2	0.696
	Governance & Legal CSR4	0.69
	Governance & Legal CSR5	0.776
	Governance & Legal CSR9	0.62
Factor 03 Society & Community Development aspect	Social & Ethical CSR1	0.706
	Social & Ethical CSR5	0.646
	Social & Ethical CSR9	0.727
	Social & Ethical CSR10	0.738
Factor 04 Environmental & Sustainability aspect	Environmental CSR3	0.624
	Environmental CSR10	0.731
	Environmental CSR13	0.698

illustrates the 18 items that have been retained and their associated rotated factor loadings (Factor Reconstruction). After that, the mean composite scores for each of the factors were computed by using just the items that were maintained. These items were used as the independent variables for the correlation and regression analyses that were provided in Section 5.4 and Section 5.5. The relabeling of factors from the theoretical a priori labels (Economic, Governance and Legal, Social and Ethical, Environmental) to the empirically derived labels (Economic and Profitability, Governance and Ethical, Society and Community Development, Environmental and Sustainability) is indicative of the item content of each retained set and does not indicate an alteration in the theoretical constructs being tested in H₁-H₄.

5.4. Correlation Analysis

Before providing the correlation and regression results, it is crucial to clarify the construction of variables used in

Table 9. Pearson Inter-Correlations among CSR Dimensions and Corporate Reputation (N = 403).

Variable	E&P	G&E	S&CD	E&S	Corporate Reputation
Economic & Profitability (E&P)	1	0.667 **	0.622 **	0.667 **	0.234 **
Governance & Ethical (G&E)	0.667 **	1	0.784 **	0.631 **	0.141 **
Society & Community Dev. (S&CD)	0.622 **	0.784 **	1	0.632 **	0.123 *
Environmental & Sustainability (E&S)	0.667 **	0.631 **	0.632 **	1	0.500 **
Corporate Reputation	0.234 **	0.141 **	0.123 *	0.500 **	1

** Correlation significant at the 0.01 level (2-tailed); * Correlation significant at the 0.05 level (2-tailed). E&P = Economic & Profitability; G&E = Governance & Ethical; S&CD = Society & Community Development; E&S = Environmental & Sustainability.

,

Table 10. Regression Model Summary.

R	R2	Adjusted R2	Std. Error of Estimate	Durbin-Watson
0.747	0.558	0.553	0.305	2.347

Predictors: (Constant), Environmental & Sustainability, Economic & Profitability, Society & Community Development, Governance & Ethical. Dependent Variable: Corporate Reputation.

and

Table 11. Regression Coefficients—Impact of CSR Dimensions on Corporate Reputation.

Variable	B	Std. Error	Beta (β)	T	Sig.	Tolerance	VIF
(Constant)	0.459	0.210		2.183	0.030
Environmental & Sustainability (E&S)	0.615	0.078	0.425	7.935	0.000	0.388	1.576
Society & Community Dev. (S&CD)	0.163	0.066	0.141	2.481	0.014	0.346	1.888
Governance & Ethical (G&E)	0.196	0.076	0.152	2.598	0.010	0.327	1.628
Economic & Profitability (E&P)	0.127	0.053	0.119	2.378	0.018	0.444	1.253

Dependent Variable: Corporate Reputation. Note: All VIF values are above 1.0, confirming the absence of multicollinearity concerns.

. The PCA shown in Section 5.3 was followed by the removal of items that failed to meet the communality (<0.50) or factor-loading (<0.50) criteria (see Table 8 for the remaining item set). A composite mean score for each of the four experimentally derived factors was then computed by averaging the responses to the retained items within that component. These four composite scores, Economic and Profitability (6 items), Governance and Ethical (5 items), Society and Community Development (4 items), and Environmental and Sustainability (3 items), were used as independent variables in the Pearson correlation matrix (Table 9) and multiple regression analysis (Table 10 and Table 11). The dependent variable, corporate reputation as perceived by managers, was calculated as the mean of the five items on corporate reputation. All five items were kept, as they had good reliability (α = 0.778) and were not subject to PCA screening. Such a variable creation process guarantees a perfect fit between the factor analytic results and the regression model and allows the hypotheses (H₁–H₄) to be verified by means of empirically purified, theoretically grounded composite measures. All CSR dimensions positively correlated to corporate reputation, where the strongest bivariate correlation was environmental sustainability (E&S) with corporate reputation (r = 0.500, p < 0.01), the next strongest bivariate correlation was between economic profitability and corporate reputation (r = 0.234, p < 0.01), and the weakest bivariate correlation was between governance-ethics and corporate reputation (r = 0.141, p < 0.01). The CSR dimensions also correlated with one another, where the strongest correlation was between governance-ethics and society-community development (r = 0.784, p < 0.01).

5.5. Regression Analysis

This study explored the link between the four Composite Corporate Social Responsibility (CSR) dimensions and the corporate reputation as rated by managers. Table 10 provides a summary of the statistics for the multiple regression analysis that was conducted. The overall model was statistically significant (F(4, 398) = 125.643, p < 0.00001), suggesting that the four Composite CSR dimensions explain a statistically significant degree of variance in the corporate reputation rating made by managers. The multiple correlation coefficient, R, was 0.747 and the coefficient of determination (R²) was 0.558, indicating that the four Composite CSR predictor variables account for approximately 55.8% of the variance in the corporate reputation ratings made by managers. The fact that this outcome is still accurate after considering the total number of predictors that were incorporated into the model is demonstrated by the adjusted R² value of.553. Given that the Durbin-Watson statistic was calculated to have a value of 2.347, it may be concluded that the residuals did not exhibit any significant association. All the statistical findings in this study agree with the existence of statistically significant relationships between CSR dimensions and the corporate reputation rating that was provided by managers. This is because the design of the study was cross-sectional, and the data that was used in the analysis came from a single source; no findings should be interpreted as establishing a causal direction for these CSR dimensions relative to a corporate reputation rating from managers. Since there was only one group of respondents providing data on all our key variables through one survey, there could be a common method bias (CMB) present. We referenced Podsakoff et al. (2003) on ways to prevent CMB and used several techniques in this study to prevent any kind of bias from occurring, including splitting up CSR (Corporate Social Responsibility) and reputation measures into different sections of the survey; respondent anonymity was assured throughout the questionnaire process.

As soon as we had finished collecting the data, we used Harman’s single factor test (Harman, 1967) to determine whether there was a significant risk of CMB. The fact that only 35 percent of the total variance could be described by a single factor implies that there is no large risk for CMB (Podsakoff et al., 2003), which lends credence to the notion that this research is both valid and free of bias.

Regression analysis results support all four hypotheses (H₁–H₄), indicating that all four CSR dimensions have a statistically significant and positive relationship with how managers perceive their company’s reputation. Due to the nature of this study being cross-sectional and relying on a single-source survey design, it can be said that these results are significant in nature rather than indicating a causal relationship. Of the four CSR Dimensions, Environmental and Sustainability (H₄) had the highest statistical relationship to manager-perceived corporate reputation (β = 0.425; t = 7.935; p < 0.001), demonstrating that managers perceive companies that engage in more active environmental stewardship activities to have a stronger reputation than those who do not. Governance and Ethics (H₂) (β = 0.152; t = 2.598; p = 0.010) and Society and Community Development (H₃) (β = 0.150; t = 2.481; p = 0.014) achieved statistically significant positive relationships at the p = 0.05 level. Economic Profitability (H₁) also achieved a statistically significant positive relationship to manager-perceived corporate reputation but it was the weakest relationship in the model, demonstrating that CSR activities focused on economic performance are less strongly related to how managers perceive the reputation of companies when compared to the other 3 dimensions of CSR in this study (β = 0.119; t = 2.378; p = 0.018). All VIF values were below 5.0 and all Tolerance Values exceeded 0.10, therefore no multicollinearity problem.

6. Discussion

6.1. Theoretical Implications

There are a variety of ways in which the findings presented by this research contributes to the literature on CSR and reputation. First, by being an empirical study from a developing country, it has shown that all four CSR dimensions (economic, governance/legal/social/ethical, and environmental) show statistically significant positive associations with manager-perceived corporate reputation, however, we can see that the strongest positive association is observed for environmental CSR. The existing literature has proved that all four sub-dimensions of CSR are positive and statistically significant predictors of reputation (e.g., Khojastehpour & Johns, 2014; E. Park, 2019). Previous research has suggested that the environmental aspect of corporate social responsibility (CSR) has the largest association with reputation, but economic CSR has a smaller association with manager-perceived corporate reputation. The findings of this study are consistent with those findings.

Second, economic CSR (β = 0.119) has been shown to have a lesser effect on corporate reputation than environmental CSR (β = 0.425). This finding is contrary to Carroll’s (1979) pyramid model, which implied that economic responsibility would have been the most built-upon basis for building corporate reputation. In India, however, it appears that the pattern of associations observed in this study suggests that environmental stewardship and governance CSR may resonate more strongly with managerial perceptions of reputation than economic performance-oriented CSR, though causal interpretation is not warranted given the cross-sectional design. This study is also in agreement with the findings of Melo and Garrido-Morgado (2012), who discovered that measures of social responsibility have a more significant influence on reputational evaluation in emerging markets when compared to indices of economic performance.

In the third instance, rather than coming from the point of view of customers or investors, this study contributes to the existing body of literature on corporate social responsibility and corporate reputation. It also extends the CSR-reputation literature by giving findings from managers’ internal evaluations of their firm’s reputation throughout CSR studies that utilized external evaluations of stakeholders, indicating strong convergent validity of both forms of research design. Some differences exist; self-enhancement may also exist among managerial respondents to the self-reported data. Therefore, this must be considered when interpreting results.

In the fourth instance, the significant positive coefficients of every single one of the four CSR dimensions is in agreement with the reasoning behind the resource-based view where CSR investments generate intangible reputational assets that result in competitive advantage (Barney, 1991; Roberts & Dowling, 2002), providing more empirical support to this theoretical linkage in an emerging-market setting. Moreover, positive regression coefficients across all dimensions provide additional support for the conceptual linkage between CSR participation and reputational results via a direct, cross-sectional design. Although this study provides direct relationships, no estimates were made about indirect relationships or mediation; thus, claims about indirect or mediated relationships should be appropriately qualified.

6.2. Practical Implications

According to the conclusions of this study, corporate managers and government officials will likely discover that they have access to several interesting options because of the findings of the study. The strongest observed association was between environmental CSR (β = 0.425) and manager-perceived corporate reputation for companies on the BSE in India; therefore, the findings suggest that managers seeking to strengthen their firm’s reputational standing may benefit from prioritising environmental initiatives, though the cross-sectional design precludes definitive causal recommendations (between clean energy, developing eco-friendly products, recycling, and reducing their carbon footprint) rather than on only providing economic CSR. This is consistent with Khojastehpour and Johns (2014), who found that increased awareness among stakeholders of the ecological threats posed by business operations is a significant factor that contributes to the significance of environmental corporate social responsibility (CSR).

In addition, managers should consider both governance and legal CSR (β = 0.152) and socially ethical CSR (β = 0.141) to be significantly associated with manager-perceived corporate reputation. It is important for managers to build transparency through good governance practices, comply with the governing rules, and conduct themselves in an ethically responsible manner as they develop the company’s reputation strategy. In India, this includes complying with the requirements of SEBI and the Companies Act but also ensuring that they are communicating their commitment to good governance to both their internal and external stakeholders. Activities that are socially oriented, such as charitable donations, employee welfare, and social inclusion, contribute to the goodwill that stakeholders have toward the firm and promote the reputation of the company.

Thirdly, our study findings indicate that the findings suggest that firms focusing exclusively on economic aspects of CSR(β = 0.119) show weaker reputational associations than those engaging across all four CSR dimensions; this finding supports current research (Branco & Rodrigues, 2006; Melo & Garrido-Morgado, 2012) that shows businesses that limit their CSR initiatives to those associated with finance or investor returns will receive fewer reputational benefits than those companies that develop a balanced CSR strategy by investing time and money into supporting all four dimensions of CSR. Thus, it is recommended that Indian businesses develop a balanced multi-dimensional CSR strategy to enhance their reputational status.

Finally, companies’ ability to communicate their CSR programs effectively with interested parties is critical in helping companies turn their CSR expenditures into reputational improvements, particularly in developing nations where there have been fewer developed institutional communication systems (Branco & Rodrigues, 2006; Slack et al., 2015).

7. Conclusions

The objective of this study was to investigate the potential connection between four aspects of corporate social responsibility and the reputation of a cross-section of respondents (i.e., 403 Managers) from companies that are listed on the Bombay Stock Exchange (BSE) and are situated in the National Capital Region (NCR) of New Delhi and Northern India. To do this, the researchers used numerous statistical methods (e.g., PCA, correlation, and multiple regression) and, discovered that every aspect of corporate social responsibility (CSR) has a direct and significant relationship with the reputation of the company, as seen by managers, which provides support for each of the four hypotheses; H₁–H₄—the existence of a favorable relationship between the four aspects of CSR -Environment/Sustainability, Economic/Profitability; whereas, the positive relationship for CSR dimension of Environmental/Sustainability was the highest at 0.425, whereas the CSR dimension of Economic/Profitability was the lowest at 0.119. Since this study utilized a “cross-sectional research methodology” and utilized a single source of perceptual data, it is most fair to regard these data as evidence of statistically significant correlations rather than causal relationships. To verify both directionality and magnitude of any potential cause and effect relationships, future research should use either longitudinal or experimental research designs. A total of 55.8 percent of the variation in corporate reputation could be attributed to each of the four aspects of corporate social responsibility (R² = 0.558). In light of the findings of this study, it appears that managers in India are being subjected to more strict environmental regulations and demands, they should develop their reputational corporate social responsibility strategy based on solid environmental stewardship, effective governance, social responsibility and, sound economic practices; and that adopting a holistic approach to corporate social responsibility will produce more favorable results than pursuing a narrowly defined economic approach.

8. Limitations and Future Research

In addition, this research includes a great deal of shortcomings. The design of the study, which is cross-sectional, prevents the discovery of causal inferences. As a result, the observed relationships between corporate social responsibility (CSR) dimensions and corporate reputation are correlational. This indicates that reverse causality is possible, which means that highly regarded companies may invest more money into CSR. Longitudinal or experimental methods would give stronger support for the claim of a causal relationship.

The geographic area represented in the sample is Northern India and includes only BSE-listed corporations. Therefore, before generalizing the findings of this study to unlisted small and medium firms, companies from other regions of India, and companies from other developing nations with distinct institutional and cultural contexts, it is important to exercise care and apply reasonable caution. A potential study of mediating variables, which may influence the link between corporate social responsibility and corporate reputation, is not included in the research. These variables include firm size, industry, communication intensity, and stakeholder salience. The impact of these variables could be investigated in subsequent study to achieve a more comprehensive understanding of the connection between corporate social responsibility and the reputation of private companies.