Can One Size Fit All: A Multi-group Analysis of Indian Corporates

Abstract

This study aims to investigate the relationship among the various institutional pressures, corporate social responsibility, legitimacy, and reputation in the context of Indian corporates. It also provides an in-depth industry-wise analysis for the said relationship. BSE 500 listed companies form the population for this pan-India primary research. PLS-SEM is the statistical technique adopted to examine the relationship between research constructs. The overall industry analysis established a positive association between all six institutional pressures and CSR activities, but the comprehensive multigroup industrial analysis revealed that kind of institutional pressures that are important to corporates vary across industries. Multi-group analysis points out that—local communities, peers, and media are statistically significant for high as well as low impact industries. But government and NGOs are not statistically significant pressure groups for low impact industries. However, industry associations are not important as a pressure group for both high impact as well as low impact industries. Furthermore, legitimacy is seen to fully mediate the relationship between CSR and reputation for the national level survey.

Keywords

CSR India institutional pressures legitimacy multi-group analysis reputation

I. Introduction

Several countries including Sweden, Norway, the Netherlands, Denmark, France and Australia had introduced mandatory corporate social responsibility (CSR) reporting requirements in the first decade of 21st century. The governments, particularly, in Asia Pacific countries such as India, Indonesia and Mauritius, have gone a step further and introduced regulations relating to mandatory spending (Bansal et al., 2019).

The laws made by the governments across the world—whether relating to reporting requirements or mandated spending—are not customized according to the type of industry. The Companies Act, 2013, through Section 135, mandates that specified firms having threshold revenue must spend a minimum of 2% of reported income on CSR initiatives (MCA, 2013). The regulation has changed the status of CSR in India from voluntary engagement to mandatory requirement for many corporates (Dixit et al., 2021). In the financial year 2019–2020, 22,155 companies spent ₹24,063 crores on 34,291 CSR projects as compared to ₹10,066 crores spent by 16,545 companies on 9,365 projects during 2014–2015 (MCA, 2021). Though the introduction of CSR regulation has led to increase in CSR spend, the mandated CSR in India, too, does not factor into account industry specific requirements, and thus, provides the perfect background against which we have carried our study. In Indian context researchers such as Aswani, Chidambaran, and Hasan, 2021 have found out that one-size-fits all approach towards CSR legislation is sub optimal and has negatively impacted the value of companies incurring high capital expenditure but FMCG sector has been able to derive the benefit of mandated CSR. Further, many firms spending more than 2% of profits on CSR before 2013 have curtailed their CSR spend to statutory limit and this may be because reputational advantage associated with CSR spending stands diluted in Indian context as almost all large firms are mandatorily doing this now (Mukherjee & Bird, 2016).

The governments of various countries have ignored isomorphic pressures due to which CSR practices converge in same industry, and has not, therefore, adopted the sector specific approach to CSR legislation (Beschorner et al., 2013). Scholars and practitioners have found differences in CSR practices at an industry level (Jackson & Apostolakou, 2010; Jain et al., 2017). However, research on industry specific CSR practices is still in its infancy stage (Dabic et al., 2016). The Journal of Business Ethics brought out a special issue on ‘Industry Specific Corporate Social Responsibility’ in 2017 to highlight the importance of the subject. In order to bridge the gap between the government’s approach to the CSR legislation and the outlook of the corporates, our study emphasizes through empirical survey that regulations by the governments should factor into institutional differences across industries in CSR legislation. The corporates can also understand how to strategize their mandatory CSR spend better and also how to increase its impact on the stakeholders.

For the purpose of present research, the institutional theory is the explanatory theory. This theory suggests that organizations differ among themselves due to the institutional context in which they are operating and conform to institutional expectations to obtain legitimacy (Morgan & Kristensen, 2006). Most of the researchers apply institutional theory to the study of organizational legitimacy (Doh et al., 2010; Suddaby, 2013) Organizations engage in legitimacy seeking behaviour to understand what, according to institutional actors that exert institutional pressures, is acceptable corporate behaviour (Hamann & Acutt, 2003). Di Maggio and Powell (1983) contend that within a particular field all organizations become similar because of isomorphic forces that is a result of three types of institutional pressures—coercive, mimetic and normative (Jamali & Neville, 2011). Coercive pressures emanate from forces outside the organizations and compel an organization to adopt certain practices or make structural changes (Di Maggio & Powell, 1983). Mimetic pressures refer to the process of organizations modelling or imitating the practices of those in the same organizational field that are perceived to be more successful (Roy & Séguin, 2000). According to Han and Yang (2016, p. 366), ‘the normative pillar of institutional theory refers to legitimacy pressures from informal values, rules, and habits or social expectations on organizations’. Scott (1995) refers to them parallelly as—regulative pressures, cognitive pressures and normative pressures (Chua & Rahman, 2011).

The application of institutional theory to CSR is a recent phenomenon. Many scholars (Brammer et al., 2012; Campbell, 2007) have explored the institutional embeddedness of CSR. Furthermore, the manner in which CSR is being approached by the corporates all across the world is changing rapidly due to the institutional pressures emerging from two important contexts—country and industry (Jain et al., 2017). Very few researchers have studied relationship between specific measurement dimensions of different type of institutional pressures and CSR (Han & Yang, 2016). This research is an attempt to fill this gap in industry context in India. Further, these companies use CSR activities to seek legitimacy from institutional actors resulting in enhanced reputation. Corporate reputation is an important outcome of the CSR activities and influence of industry effects on their relationship has been well established in the literature (Tetrault Sirsly & Lvina, 2019; Melo & Garrido-Morgado, 2012). More polluting companies such as oil and gas and chemicals are known to enhance their corporate legitimacy through enhanced reporting of CSR performance resulting in better corporate reputation (Kolk, 2010). Compliance to king code of governance by South African companies helped companies to enhance their legitimacy and thus, reputation (Ackers & Eccles, 2015). It is clear that legitimacy links CSR and reputation and industry type has an impact on their relationship (Aqueveque et al., 2018). The relationship between CSR, legitimacy and reputation has not been explored empirically much (Abdullah & Aziz, 2013).

Accordingly, our research objectives are as follows:

How various institutional pressures influence the CSR activities in industry context?

How does legitimacy link CSR and reputation in industry context in India?

To achieve our research objectives, we propose a model that integrates different dimensions of institutional pressures, CSR and organizational legitimacy and reputation within the framework provided by institutional theory and helps to understand how industry effects affect relationship among these variables. There is no study that has done an in-depth industrial analysis for the concerned CSR phenomena in India.

We have summarized our literature review and have proposed hypotheses based on the same. After validating the model, we present the multi group analysis in an industry context.

II. Literature Review and Hypothesis Formulation

Industry Context, CSR and Institutional Theory

Cottrill (1990) was one of the first researchers to emphasize that industry level realities should be incorporated in the study of CSR and asserted that the government and other pressure groups are important influences on CSR profile of companies. Many researchers such as Moore (2001); and Simpson and Kohers (2002) concluded that CSR-initiatives across industries differ in their nature and form. Godfrey et al. (2010) have emphasized the need for large-scale studies or industry specific case studies to understand the relationship between unique characteristics of industry and CSR. Though researchers are using framework provided by institutional theory to study relationship between a specific type of industry and CSR and some of these works are tabulated in Table 1, large scale studies have not been reported so far. Our study attempts to fill this gap.

In order to understand industry context, Jackson and Apostolakou (2010) in their study of relationship between country and industry context, and CSR in Western European firms classified industry into two categories based on the sectoral impact on stakeholders—high impact and low impact industries. They assumed that environmental footprint is similar to economic and social impact. According to them, ‘ecological footprint is an imperfect but reasonable proxy for sectoral-level pressures for institutionalization’ (Jackson & Apostolakou, 2010, p. 377). The high impact industries (industries with the highest ecological foot print) include automobiles, basic resources, chemicals, construction and materials, and so on, that are likely to put more focus on CSR activities in order to gain legitimacy and mitigate their negative social and environmental footprint. The low impact industries that include consumer goods industries and service sector industries like financials, media, technology and insurance besides others have less ecological foot print as compared to high impact industries.

Table 1.
Some Studies in Industry Context

Authors Year Industry Context Findings

Burdon & Sorour 2020 UK financial service sector Similarity of responses to the regulator’s sanction is due to interplay of all three types of isomorphic forces.

Carrigan, McEachern, Moraes & Bosangit 2017 Jewellery industry picked from Birmingham Jewellery Quarter Institutional pressures are resulting into a more responsible agenda

Shnayder, Van Rijnsoever & Hekkert 2016 Sixteen multinationals belonging to packaged food industry Cultural-cognitive pressures are indeed a strong motivator

Beddewela & Fairbrass 2016 MNCs operating in Sri Lanka MNCs face both regulative and normative pressures from Sri Lanka’s key institutional actors such as the government, NGOs and trade associations.

Zuo, Schwartz & Wu 2015 Chinese food industry Normative pressure is the only significant pressure

Young & Makhija 2014 The global apparel industry spanning over 23 countries Employee CSR is affected by regulative and normative pressures.

O’Connor & Gronewold 2013 Petroleum refining multinationals MNCs and SMEs engage in similar CSR practices indicating that normative pressure is well defined for this sector.

Pedersen, Neergaard, Pedersen & Gwozdz 2013 Nordic fashion industry (covering Denmark, Norway, Sweden, Finland and Iceland) The Nordic fashion industry adopts conformance as a strategy to withstand institutional CSR pressures.

Hamann 2004 Platinum and chrome mining companies in South Africa Regulative pressures (state’s legislated transformation programme) is the most important driver of CSR

Authors	Year	Industry Context	Findings
Burdon & Sorour	2020	UK financial service sector	Similarity of responses to the regulator’s sanction is due to interplay of all three types of isomorphic forces.
Carrigan, McEachern, Moraes & Bosangit	2017	Jewellery industry picked from Birmingham Jewellery Quarter	Institutional pressures are resulting into a more responsible agenda
Shnayder, Van Rijnsoever & Hekkert	2016	Sixteen multinationals belonging to packaged food industry	Cultural-cognitive pressures are indeed a strong motivator
Beddewela & Fairbrass	2016	MNCs operating in Sri Lanka	MNCs face both regulative and normative pressures from Sri Lanka’s key institutional actors such as the government, NGOs and trade associations.
Zuo, Schwartz & Wu	2015	Chinese food industry	Normative pressure is the only significant pressure
Young & Makhija	2014	The global apparel industry spanning over 23 countries	Employee CSR is affected by regulative and normative pressures.
O’Connor & Gronewold	2013	Petroleum refining multinationals	MNCs and SMEs engage in similar CSR practices indicating that normative pressure is well defined for this sector.
Pedersen, Neergaard, Pedersen & Gwozdz	2013	Nordic fashion industry (covering Denmark, Norway, Sweden, Finland and Iceland)	The Nordic fashion industry adopts conformance as a strategy to withstand institutional CSR pressures.
Hamann	2004	Platinum and chrome mining companies in South Africa	Regulative pressures (state’s legislated transformation programme) is the most important driver of CSR

Source: Authors’ manifestation.

Institutional Pressures and CSR

As already stated, there are three kinds of institutional pressures that exert pressure on the organizations to take up CSR—regulative, cognitive and normative (Scott, 1995). In the following sub-section relationship between each of these with CSR is examined.

Regulative Pressure and CSR

Coercive processes operating at the level of industry create regulatory pressures and constrain and guide behaviour. Two important institutions that exert formal and informal pressures on the business are—government and industry associations (Han & Yang, 2016). Governments all across the world are introducing CSR regulations in some form or the other and CSR decisions are not being left to the discretion of managers (Knudsen & Brown, 2015; Steurer, 2010). Self-regulation in the form of soft laws is an important priority area of industry associations (Wooten & Hoffman, 2016). Some associations such as UN Global Compact and the Confederation of Indian Industry (CII) are pan industries while some make soft laws for specific industries such as Food and Drug Administration (FDA), USA and The National Association of Software and Service Companies (NASSCOM), India. High impact industries have a greater need to obtain legitimacy and hence are expected to be more socially responsible (Hoffman, 2001) as compared to low impact industries that face less institutional pressures to act responsibly (Gardberg & Fombrun, 2006). The industry regulation pushes high impact industries to adopt CSR practices (Kollman & Prakash, 2002) to evade bad publicity, penalities and governmental regulation (Kagan et al., 2003).

On the basis of the literature, thus, we can formulate the hypotheses as follows:

H₁(a): Governmental regulation positively affects the CSR policies of industries.

H₁(b): Governmental regulation positively affects the CSR policies of high impact industries.

H₁(c): Governmental regulation positively affects the CSR policies of low impact industries.

H₂(a): Industry regulation positively affects the CSR policies of industries.

H₂(b): Industry regulation positively affects the CSR policies of high impact industries.

H₂(c): Industry regulation positively affects the CSR policies of low impact industries.

Cognitive Pressures and CSR

Cognitive pressures are a result of mimetic isomorphism that occurs because firms in the same industry mimic/copy the practices and patterns of those that they perceive to be successful (Di Maggio & Powell, 1983). This is done to be ‘no better or worse than any organisation’ in the industry and this results in positive perception of the organization amongst the shareholders (Kondra & Hinings, 1998, p. 745). Local communities and peers are important cognitive institutional pressures influencing the corporates in India (Jha & Aggrawal, 2019). High impact industries such as oil and gas engage more with local communities (Frynas, 2005). In a study of CSR initiatives in fifty Indian manufacturing sector companies across automobile, cement, chemical, pharmaceutical and textile industries, Shanmugam et al. (2015) found that local community has more influence on CSR policies than any other stakeholder. Institutional scholars have argued that many companies engage in CSR activities under peer effect to avail reputational benefits, to address the concerns of stakeholders if other firms in the industry are doing it and to adhere to new norms with uncertain benefits among other reasons (Durand & Jacqueminet, 2015; Malik et al., 2019). Peer pressure differs among industries and exerts a positive pressure on firms in the industry to take up CSR activities, the extent of which depends upon the nature of activity of the industry (Dupire & M’Zali, 2016).

On the basis of the literature, thus, we can formulate the hypotheses as follows:

H₃(a): Local communities positively affects the CSR policies of industries.

H₃(b): Local communities positively affects the CSR policies of high impact industries.

H₃(c): Local communities positively affects the CSR policies of low impact industries.

H₄(a): Peer pressure positively affects the CSR policies of industries.

H₄(b): Peer pressure positively affects the CSR policies of high impact industries.

H₄(c): Peer pressure positively affects the CSR policies of low impact industries.

Normative Pressures and CSR

Normative systems regulate by means of social sanctions and obligations (Sá de Abreu et al., 2015). Normative pressures created by these systems ‘introduce a prescriptive, evaluative, and obligatory dimension into social life’ (Scott, 1995, p. 37). Two important institutional actors that take note of outcomes of an organization’s activity and evaluate them against certain norms are non-government organizations (NGOs) and media (Jamali, 2010; Vashchenko, 2017). NGOs can be defined as ‘social, cultural, legal, and environmental advocacy and/or operational groups that have goals that are primarily non-commercial’ (Kourula & Laasonen, 2010, p. 36)¹ NGOs enjoy the status of important institutional actors influencing policies of the organizations including CSR initiatives (Mathews, 1997) and are source of social pressure at the level of industry (Levendis et al., 2006). In their cross industry study in Indian context, Jain et al. (2017) found that high impact industries such as metal and mining and cement feel the pressure from NGOs more than other type of industries.

Media² acts as an institutional pressure for organizations ‘by subjecting them to the constant threat of public exposure’ (Campbell, 2007, p. 957). Media exerts pressure on most of the aspects of organizational behaviour (Margolis & Walsh, 2003; Pollock & Rindova, 2003) including CSR initiatives (Dickson & Eckman, 2008). Firms that require a social license to operate, face diverse demands and therefore, engage in higher levels of CSR to get legitimacy (Zyglidopoulos et al., 2012). Based on literature, we can formulate hypothesis as:

H₅(a): NGOs positively affects the CSR policies of industries.

H₅(b): NGOs positively affects the CSR policies of high impact industries.

H₅(c): NGOs positively affects the CSR policies of low impact industries.

H₆(a): Media positively affects the CSR policies of industries.

H₆(b): Media positively affects the CSR policies of high impact industries.

H₆(c): Media affects the CSR policies of low impact industries.

CSR, Organizational Legitimacy and Reputation

Organizational legitimacy can be defined as ‘a generalized perception or assumption that the actions of an entity are desirable, proper, or appropriate within some socially constructed system of norms, values, beliefs, and definitions’ (Suchman, 1995, p. 574).

Different authors have identified a number of sub types of organizational legitimacy (Aldrich & Fiol, 1994; Zimmerman & Zeitz, 2002) but Suchman’s three way classification remains the most popular because ‘legitimacy as an individual perception is best conceptualized and measured as a three-dimensional construct’ (Alexiou & Wiggins, 2019, p. 3). Suchman (1995) classified organizational legitimacy into pragmatic, moral and cognitive. Pragmatic legitimacy is rooted in the self-interest of audience (institutions here) that expect a return from the organization for granting legitimacy. Moral or normative legitimacy deals with organization’s compliance with social norms, ethics, and values. Cognitive legitimacy represents the extent to which an organization’s activities can be ‘classified, understood, and integrated’ by the institutional actor (Humphreys & Latour, 2013, p. 774).

Reputation can be defined as a collective perception of various stakeholders (Fombrun, 1996) or perception of a single stakeholder (Wartick, 1992) about the organizational action and results. For the purpose of our research, we are studying only managerial perception. Thus, reputation is, ‘the aggregation of a single stakeholder’s perception of how well organizational responses are meeting the demand and expectations of many organizational stakeholders’ (Wartick, 1992, p. 34). Institutional pressure emanates from these stakeholders. Reputation is a positive outcome of CSR (Brammer & Pavelin, 2006). Further, ‘institutional theory can be used to provide a deeper understanding of the relationship between CSR and reputation’ (Aksak et al., 2016, p. 81) as this theory explains how through norm-governed behaviour organizations acquire legitimacy leading to good reputation (Abdullah & Aziz, 2013). Thus, reputation is a potential consequence of legitimacy (King & Whetten, 2008; Lucchini & Moisello, 2017). As reputation is an outcome of both CSR and legitimacy, legitimacy mediates between CSR and reputation (Abdullah & Aziz, 2013). On the basis of literature, we can build the following hypothesis:

H₇(a): Legitimacy mediates CSR initiatives on reputation in industries.

H₇(b): Legitimacy mediates CSR initiatives on reputation in high impact industries.

H₇(c): Legitimacy mediates CSR initiatives on reputation in low impact industries.

III. Methodology

Research Design

An explanatory research design is adapted for the current study due to the presence of causal relationships among the research constructs (Cooper & Schindler, 2014). A cross-sectional approach is adopted for taking a snapshot of the CSR practices and its relationship with legitimacy and reputation.

Research Variables and Their Operationalization

The identified research variables are logically woven in the proposed research framework and their standardized scales were identified from literature for empirical testing. The questionnaire so developed was given for vetting to five academic experts and also to equal number of CSR professionals. To measure institutional pressures, scale developed by Jha and Aggarwal (2019) was used, Turker’s (2009) scale was used to operationalize CSR, scale developed by Alexiou and Wiggins (2018) was utilized to measure legitimacy and Taghian et al.’s (2012) scale helped to measure reputation. Out of seventeen items of Turker’s scale, seven items relating to employees were dropped on the basis of the suggestion of experts (money spent on employee welfare is no longer counted towards CSR spend by the companies in India) and two items that contained terms like ethical behaviour and moral, were dropped from legitimacy scale given by Alexiou and Wiggins as these, in the opinion of experts, do not go well with measuring our independent variable—CSR. All items—but for the personal information questions—were measured on a seven-point Likert-type scale anchored by strongly disagree and strongly agree.

Research Population

The population for the study includes the BSE 500 listed companies as they are obliged to follow the mandatory CSR rules laid down by the government. The BSE 500 list contains industries belonging to various industries, thus, making this study a pan India cross-industry study.

Sampling Technique

Non-probabilistic purposive sampling technique is deployed in the current research because of the objective data collection from corporate executives. Mostly the upper echelon of the companies is approached for the concerned information regarding CSR activities, legitimacy and company reputation linkages. As the study is spread across large geographical area, both online and offline mode of data collection are used resulting in a total of 191 respondents—98 from high impact industries and 93 from low impact industries. This culminates to a response rate of 57.35% (a total of 333 managers—176 from high impact industries and 157 from low impact industries—were provided with the final questionnaire). In order to assess sufficiency of sample size, we applied the ten times rule, which states that when applying PLS SEM for data analysis, sample size should be at least equal to ten times the largest number of arrows going into any one construct (Barclay et al., 1995). In our case, CSR is the construct into which maximum number of arrows (ten) are going into (refer Figure 1). The minimum sample size is, thus, 100 (10×10) as per the above stated rule. Our sample size of 191 is way above and, thus, sufficient.

Figure 1.

Source: Data analysis of study data through smart PLS-3.

Data Analysis Technique

As the population of the study is limited to only 500 companies, PLS-SEM was expected to result in more robust research outcomes as compared to CB-SEM (Hair et al., 2019). Moreover the presence of a formative construct (i.e., CSR) in the research model demanded the PLS-SEM approach of data analysis for meaningful conclusions (Hair et al., 2017). PLS-SEM is run in the software SMART-PLS-3 for the assessment of measurement and structural research models and hypotheses testing. Further, multi-group analysis (MGA) is run to obtain some deep insights in this cross-industrial study.

Preliminary Data Analysis

The preliminary data analysis was done before running the path model analysis. Common method bias is assessed through Harman’s single factor test and it accounted for much less variance than the benchmarked limit of 50% (Podsakoff et al., 2003). Normality of data is evaluated through the skewness and kurtosis values obtained in SMART-PLS-3 which came out to be within the lenient range of ±2 (Cooper & Schindler, 2014). This concludes the methodological approach and preliminary analysis leading to the path analysis.

IV. Data Analysis

The path model analysis through PLS-SEM comprises of measurement model assessment and structural model evaluation followed by hypotheses testing. As the present research model incorporates both reflective and formative constructs, the assessment of measurement model is of two folds as explained below.

Reflective Measurement Model

The reflective constructs are analysed for their reliability and validity. Internal consistency reliability is measured through the Cronbach’s Alpha values or the composite reliability values as shown in Figure 2. Values of both the reliability measures are greater than the prescribed value of 0.7 for all the constructs (Hair et al., 2020), thus establishing the internal consistency reliability.

Figure 2.

Source: Data analysis of study data through smart PLS-3.

Validity of the constructs are assessed in two parts: convergent validity and discriminant validity. Factor loadings and average variance explained values (AVE) are the measures of convergent validity (Hair et al., 2013). Factor loadings of all the items of the constructs are greater than 0.7 and AVE values are greater than 0.5 (Table 2), acceptable as per the PLS-SEM guidelines (Hair et al., 2019).

Table 2.

Reliability and Validity Measures

Constructs	Items	Factor Loadings	AVE	Composite Reliability	Cronbach’s Alpha
Government	Govt1	0.821	0.637	0.84	0.715
	Govt2	0.841
	Govt3	0.729
Industry associations	IA1	0.890	0.826	0.934	0.896
	IA2	0.935
	IA3	0.901
Legitimacy	L1	0.663	0.51	0.912	0.894
	L10	0.668
	L2	0.746
	L3	0.757
	L4	0.670
	L5	0.701
	L6	0.772
	L7	0.766
	L8	0.693
	L9	0.688
Local communities	LC1	0.891	0.733	0.891	0.818
	LC2	0.895
	LC3	0.777
Media	Media1	0.823	0.807	0.846	0.728
	Media2	0.812
	Media3	0.779
NGO	NGO1	0.906	0.611	0.893	0.762
NGO	NGO2	0.891	0.611	0.893	0.762
Peers	P1	0.858	0.584	0.824	0.701
	P2	0.811
	P3	0.664
Reputation	R1	0.717	0.51	0.894	0.857
	R2	0.798
	R3	0.804
	R4	0.719
	R5	0.755
	R6	0.785

Source: Data analysis of study data through smart PLS-3.

Discriminant validity is another crucial parameter of reflective measurement model assessment. Table 3 shows the values of discriminant validity as per the suggested HTMT criterion (Hair et al., 2019). As shown in Table 4, all the HTMT values are less than the threshold value of 0.85 (Hair et al., 2013). Thus, establishing the discriminant validity for the reflective measurement model. This concludes the assessment of reflective measurement model with validity and reliability measures.

Table 3.

Formative Measurement Model

CSR Items	VIF	Outer Weights	T Statistics	p-Values
C1	1.690	0.262	3.375	.001
C10	1.253	0.259	4.311	.000
C2	1.892	0.342	2.473	.011
C3	1.552	0.141	2.058	.040
C4	1.334	0.189	3.526	.000
C5	1.474	0.168	2.626	.009
C6	1.420	0.118	2.016	.044
C7	1.311	0.148	2.608	.009
C8	1.346	0.179	3.544	.000
C9	1.596	0.241	3.885	.000

Source: Data analysis of study data through smart PLS-3.

Table 4.

HTMT Values

	Government	Industry Associations	Local Communities	Media	NGO	Peers	Repu-tation	legitimacy
Government
Industry associations	0.159
Local communities	0.227	0.643
Media	0.477	0.123	0.126
NGO	0.183	0.087	0.194	0.092
Peers	0.239	0.100	0.096	0.363	0.095
Reputation	0.313	0.193	0.312	0.173	0.423	0.155
Legitimacy	0.287	0.221	0.322	0.149	0.378	0.190	0.564

Source: Data analysis of study data through smart PLS-3.

Formative Measurement Model

As the path model incorporates CSR construct as a formative scale, the evaluation of VIF (variance inflation factor) values and significance of all its items becomes necessary before moving on to the structural model assessment. The first column of Table 3 shows the VIF values for all the formative indicators of CSR for identifying the collinearity issues in the path model (Becker et al., 2012). All the VIF values for the formative indicators are less than the threshold value of 5, thus indicating no issue related to collinearity (Hair et al., 2012).

Another important parameter of formative construct is significance of its outer weights. The outer weights of all the items of CSR construct are presented in the second column of Table 3 with t-statistics and the related p-values. All the outer weights have come out to be significant for the CSR construct with p-value less than 0.05 (at 5% level of significance), establishing its validity as a formative construct.

Structural Model Assessment

As per the PLS-SEM guidelines, the assessment of measurement model is to be followed by structural model evaluation. Figure 2 demonstrates the structural model. This analysis includes evaluation of coefficient of determination (R²), Model fit index (SRMR value), effect size (f²), predictive relevance (Q²), and hypotheses testing for the proposed relationships (Hair et al., 2011).

The major values of structural model are displayed in Table 5. The R² value for the dependent variable of the path model, that is, reputation is 0.889, portraying high coefficient of determination for the research framework. 88.9% of variance in corporate reputation can be explained with the help of endogenous factors incorporated in the study. The global model fit index, SRMR value is 0.052, much below the suggested threshold limit of 0.08 (Sarstedt et al., 2019).

Table 5.

Structural Model Assessment

	R Square
Reputation	0.889

	SRMR
Model fit	0.052
	f Square	Q Squared
Legitimacy	0.803	0.509
Reputation	4.138	0.215

Source: Data analysis of study data through smart PLS-3.

In contrast to the coefficient of determination for the whole research model the effect size helps in identifying the individual impact on the dependent variables. It can be seen from Table 5 that both legitimacy (f² = 0.803) and reputation (f² = 4.138) are having large effect sizes when compared to the suggested threshold values (f² > 0.35 implies large effect size) (Hair et al., 2013).

To study the out-of-sample predictive relevance of the proposed research model, the predictive relevance is evaluated through the blindfolding process of SMART-PLS at the omission distance of 7 (Hair et al., 2013). The Q² values obtained through the blindfolding process is shown in Table 5. Legitimacy has a Q² value of 0.509 indicating a high predictive relevance and reputation has a Q² value of 0.215 indicating a moderate predictive relevance according to the thumb-rule values, that is, small predictive relevance till the value of 0.02, moderate predictive relevance till 0.35 and high predictive relevance for Q² values greater than 0.35 (Aggarwal & Kapoor, 2020; Stone, 1974).

Hypotheses Testing

The structural model assessment concludes with the hypotheses testing for the proposed relationships in the path model with the help of bootstrapping process of PLS-SEM at 5% significance level with 5,000 sub-samples. Table 6 represents the significant values for each predicted hypotheses.

Table 6.

Hypotheses Testing

Hypotheses No	Relationship	Path Coefficient	T Statistics	p-Values
H₁(a)	Government → CSR	0.231	3.579	0
H₂(a)	Industry associations → CSR	0.164	2.403	.016
H₃(a)	Local communities → CSR	0.244	4.375	0
H₄(a)	Peers → CSR	0.229	3.873	0
H₅(a)	NGO → CSR	0.257	4.802	0
H₆(a)	Media → CSR	0.31	5.462	0

Source: Data analysis of study data through smart PLS-3.

Mediation Analysis

H7 has proposed a crucial mediation relationship between CSR, legitimacy and reputation to be investigated through the primary data. The mediation analysis involves a two-step process, that is, investigating the specific indirect effect from the independent variable to the dependent variable through the mediator (if significant) which is to be followed by the direct effect assessment between the dependent and independent variable (Shariq et al., 2019).

Table 7 shows the specific indirect effect of CSR to reputation through legitimacy, which came out to be significant with t-value = 16.884 (p-value = 0). It is to be followed by the direct effect evaluation as shown in the same table. The direct effect between CSR and reputation is nullified in the presence of the mediator, that is, legitimacy with t-value = 1.489 (p-value = .137), at 5% significance level. Thus, the research model is experiencing a case of full mediation, that is, the relationship between CSR and reputation is fully mediated through the legitimacy.

Table 7.

Mediation Analysis

Specific Indirect Effect
CSR → legitimacy → Reputation	0.605	16.884	0.000
Direct Effect
CSR → Reputation	0.045	1.489	0.137

Source: Data analysis of study data through smart PLS-3.

Multi-group Analysis

To get a deeper insight in this cross-industry study, multi-group analysis is performed on the collected data according to the intensity of industries. The responses are clubbed together on the basis of their industry-type and they were then subjected to the multi-group analysis in SMART-PLS-3 software to get industry-wise insights. For our study, we used macro approach and the classification adopted by Jackson and Apostolakou (2010).

Table 8 represents the bootstrapping results obtained for the multi-group analysis with 5,000 sub-samples at 5% level of significance. The relationship between local communities and CSR, media and CSR, peers and CSR, and legitimacy and CSR are significant for both the high and low impact industries at 5% level. The relationship between CSR and reputation is not significant for both low and high impact industries.

Table 8.

Multi-Group Analysis

	Path Coefficient (High-Impact Industry)	T-Value (High-Impact Industry)	p-Value (High-Impact Industry)	Path Coefficient (Low-Impact Industry)	T-Value (Low-Impact Industry)	p-Value (Low-Impact Industry)
CSR → Reputation	0.068	1.710	.087	0.026	0.623	.533
CSR → legitimacy	0.708	15.176	.000	0.599	8.580	.000
Government → CSR	0.288	3.765	.000	0.132	1.436	.151
Industry associations → CSR	0.162	1.715	.086	–0.004	0.025	.980
Local communities → CSR	0.143	2.186	.029	0.338	3.377	.001
Media → CSR	0.298	3.745	.000	0.257	3.214	.001
NGO → CSR	0.412	5.572	.000	0.112	1.106	.269
Peers → CSR	0.235	2.469	.014	0.348	4.073	.000
Legitimacy → Reputation	0.902	32.453	.000	0.913	34.430	.000

Source: Data analysis of study data through smart PLS-3.

V. Results and Discussion

In this section, discussion on major results is presented.

All the hypotheses from H1 to H6 are supported through the data analysis of Indian corporates with t-values greater than 1.96 at 5% level of significance (refer Table 6) indicating that all six institutional pressures exert influence on CSR policies of the corporates. The study revealed that all institutional pressures are statistically significant. Multi-group analysis points out that—local communities, peers, and media are statistically significant at 0.05 % level for high as well as low impact industries. But government and NGOs are not statistically significant pressure groups for low impact industries (refer Table 9). However, industry associations are not important as a pressure group for both high impact as well as low impact industries.

Table 9.
Hypotheses Testing for Multi-group Analysis

Hypotheses High-Impact Industries Low-Impact Industries

Government → CSR H₁(b): Supported H₁(c): Not-supported

Industry associations → CSR H₂(b): Not-supported H₂(c): Not-supported

Local communities → CSR H₃(b): Supported H₃(c): Supported

Peers → CSR H₄(b): Supported H₄(c): Supported

NGO → CSR H₅(b): Supported H₅(c): Not-supported

Media → CSR H₆(b): Supported H₆(c): Supported

CSR → Legitimacy→ Reputation H₇(b): Supported H₇(c): Supported

Hypotheses	High-Impact Industries	Low-Impact Industries
Government → CSR	H₁(b): Supported	H₁(c): Not-supported
Industry associations → CSR	H₂(b): Not-supported	H₂(c): Not-supported
Local communities → CSR	H₃(b): Supported	H₃(c): Supported
Peers → CSR	H₄(b): Supported	H₄(c): Supported
NGO → CSR	H₅(b): Supported	H₅(c): Not-supported
Media → CSR	H₆(b): Supported	H₆(c): Supported
CSR → Legitimacy→ Reputation	H₇(b): Supported	H₇(c): Supported

Source: Data analysis of study data through smart PLS-3.

Firms in different industries adopt different CSR approaches towards handling local community pressure depending upon community embeddedness of the business (Boutin-Dufresne & Savaria, 2004). But in context of India every corporate to whom CSR Rules apply, has to take up mandated CSR spend in local community (Act, 2013). This probably is the reason that local community is an important institutional pressure for Indian corporates (Jha & Aggrawal, 2019).

Peer pressure is one of the important pressures driving CSR in India in both eras—pre mandated spending as well as mandated spending (Bansal et al., 2019; Sood & Arora, 2006). Mimetic isomorphism pushes firms in the industry to take up CSR activities of similar nature. For example, Royal Dutch/ Shell and BP, two leading oil and gas companies of the World, have taken major CSR initiatives to develop renewable energy and have committed themselves to combat carbon dioxide emissions in order to mitigate their negative impact to climate change (Frynas, 2009). Many oil and gas companies across the World including India have followed suit. In order to give push to renewable energy business, the oil-to-retail Indian conglomerate—Reliance Industries—recently announced a wave of partnerships with REC, NexWafe, Sterling and Wilson, Stiesal and Ambri for total costs of $1.2 billion.³

Media provides visibility to the corporates and hence, influences its reputation (Wartick, 1992). Visibility attracts attention and this is, especially, true for listed companies and multinational companies (Garcia-Sanchez et al., 2014). Our sample consists of managers from top listed companies in India. Therefore, as results indicate, media pressure is statistically significant in our study. Even the amount of disclosure on CSR related disclosure in Annual Report of corporates in India has increased significantly in India after passing of the Companies Act, 2013. Enhanced disclosure on CSR can also be attributed to increased media pressure (Deegan et al., 2000; Reverte, 2009).

The government as an institutional pressure has come out to be statistically significant for high impact industries in our study. According to some researchers such as Govindan et al. (2014), and Jha and Aggrawal (2018) the government is the main driver of CSR initiatives in high impact industries such as manufacturing, mining and oil and gas companies. High impact industries depend heavily on mandate of government. Yin (2017), in a mixed method study of Chinese corporates, concluded that a firm that is politically embedded faces more pressure from regulators to take up CSR activities than low impact industries.

Results have indicated that NGO is also an important pressure group. But interestingly, multi-group analysis has revealed that its influence on low impact industries is not statistically significant. In Indian context, high impact industries such as capital goods, cement, power, oil and gas, and metal and mining have attracted adequate social activism and hence, face greater pressure from NGOs (Ananthanarayanan, 2015). Some high impact companies such as oil and gas face more pressure from NGOs as they are perceived to be ‘highly risky in terms of environmental issues as well as employees’ (Jackson & Apostolakou, 2010, p. 374) and this may explain the fact that pressure from NGOs to carry out CSR is not significant on low impact industries.

Industry associations impose regulative pressures besides the government on corporates. But in Indian context, the situation is different. Our results also show that industry associations do not exert pressure upon high as well as low impact industries to take up CSR. India’s two most important industry associations—Confederation of Indian Industry (CII) and Federation of Indian Chambers of Commerce and Industry (FICCI)—pursue developmental activities with the government at both centre and state level (Sinha, 2005). She has pointed out that ‘over time, other state actors have continued to use business associations to initiate and publicize difficult policy change’ (Sinha, 2005, p. 11). Thus, their basic philosophy revolves around partnering with the government rather than influencing the activities of the members in a big way.

As revealed by Table 7, the relationship between CSR and reputation is being fully mediated by legitimacy in our model. There can be relationship between CSR and reputation in the absence of legitimacy but as soon as legitimacy is introduced in the research model (in Indian context only) the relationship between CSR and reputation vanishes. It implies that legitimacy is a very strong factor in the relationship of CSR and reputation and the whole impact of CSR on reputation can be explained through legitimacy. The full mediation also supports the assumption of institutional theory that corporates design CSR activities to get legitimacy for their actions.

VI. Conclusion

The study has used institutional theory framework in a developing country context. It provides a holistic view of influence of institutional pressures on CSR and how it, in turn, influences reputation through legitimacy. The study has achieved the research objectives framed. The first research objective was to determine how various institutional pressures influence the CSR activities in industry context.

The overall industry analysis established a positive association between all six institutional pressures and CSR activities, but the comprehensive multigroup industrial analysis revealed that kind of institutional pressures that are important to corporates vary across industries. The study highlights the regulatory role played by the government in a developing country context especially for high impact industries. The Indian Government should take cognisance of this fact and customize mandatory CSR laws accordingly. The government can look for more public private partnerships with high impact industries and give them an opportunity to create ‘shared value’ and make strategic use of CSR (Lindorff et al., 2012). It can give discretion to low impact industries to choose their CSR activities rather than forcing them to choose from mandated under the law. In order to make Industry Associations more relevant, it is important that they should encourage collaborations among members to ensure there is no duplication of CSR activities in the area in which they are operating and providing holistic solutions. For example, a construction company can build toilets, a FMCG can provide toilet cleaners and soap for hand wash and NGOs working in the area of sanitation can be roped in to run and maintain the toilets on pay per user basis. This will improve CSR impact and sustainability of CSR activities. To remain relevant, NGOs should grow in size, possibly through mergers or affiliations with bigger NGOs. Internationally, NGOs such as Global Reporting Initiative and Green Peace have put pressure on corporates to improve disclosure of Sustainability issues and to adopt best manufacturing practices.

The second research objective was to assess how does legitimacy links CSR and reputation in industry context in India. The study contributes to the literature on relationship between CSR and reputation by empirically testing legitimacy as a mediating variable in Indian context. Establishing such a relationship reinforces the fact that any effort by a corporate to build reputation without taking cognizance of specific institutional context to get legitimacy may not yield desired result. The researchers can test our model in context of developing as well as developed world.

The study emphasis the fact that the managers need to integrate their CSR activities with the management system of the organization in a harmonious and mature way to ensure pressure groups are managed and value created for all the stakeholders. Our research can help them to understand which stakeholders they should target while designing CSR activities as per industry practises. Managers should design CSR activities keeping legitimacy in the background because if they want to make strategic use of CSR to gain reputation, obtaining legitimacy for their actions is the route in Indian context.

The research on CSR within institutional theory framework is in nascent stage in India (Jha & Aggarwal, 2019). As more research is undertaken in this area, the understanding of theoretical dimension and practicalities of CSR in India will become better. The initial analysis substantiated that positive associations between all six institutional pressures and CSR activities, but the comprehensive industrial analysis revealed numerous enigmas. This study has unravelled the policy flaws, that is, a uniform CSR mandate may not provide the desired sustainable results and these policies and laws must be redesigned by taking into account intricacies of particular industries in consideration. Further, the mediation analysis between CSR, legitimacy, and reputation corroborates the idea that no matter what social responsibilities a firm fulfils to improve its reputation, it tries to obtain legitimacy for sustainable results. Thus, more such research work should be taken up in the Indian context to validate these findings.

This study is one of the few empirical studies undertaken within the framework provided by institutional theory in the Indian context. There are certain limitations with regard to inclusion of types of institutional pressures. More pressure groups such as employees, international buyers and vendors may be added. This study has taken only reputation has outcome variable. Other variables such as operational performance or financial performance may be considered. Scholars can expand the theoretical framework to include resource dependence theory, social exchange theory, and social identity theory to name a few as these also have a profound impact on CSR. Contextual factors have a bearing on form and kind of CSR initiatives chosen by the corporate (Arya & Zhang, 2009). In India, CSR is mandatory for specified companies. Institutional pressures, therefore, may differ in other developing countries due to this. This limits the generalisability of the findings to some extent.

Footnotes

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Notes

References

Abdullah

, & Aziz

Y. A.

(2013). Institutionalizing corporate social responsibility: effects on corporate reputation, culture, and legitimacy in Malaysia. Social Responsibility Journal, 9(3), 344–361.

Ackers

, & Eccles

N. S.

(2015). Mandatory corporate social responsibility assurance practices: The case of king III in South Africa. Accounting, Auditing and Accountability Journal, 28(4), 515–550. 10.1108/AAAJ-12-2013-1554

Aggarwal

V. S.

, & Kapoor

(2020). Multigroup analysis of higher-order model of knowledge variables in the context of global ventures through PLS-SEM. South Asian Journal of Business Studies. 10.1108/SAJBS-02-2020-0037

Aksak

E. O.

, Ferguson

M. A.

, & Duman

S. A.

(2016). Corporate social responsibility and CSR fit as predictors of corporate reputation: A global perspective. Public Relations Review, 42(1), 79–81.

Aldrich

H. E.

, & Fiol

C. M.

(1994). ). Fools rush in? The institutional context of industry creation. Academy of Management Review, 19(4), ), 645–670.

Alexiou

, & Wiggins

(2019). Measuring individual legitimacy perceptions: Scale development and validation. Strategic Organization, 17(4), 470–496. 10.1177/1476127018772862

Ananthanarayanan

(2015, April 2). Contract workers make up 46% of workforce of India’s largest industrial companies. Livemint. https://www.livemint.com/Money/zSFof4LqBrTfX3dxvp8Y2I/Contract-workers-make-up-46-of-workforce-of-Indias-largest.html

Aqueveque

, Rodrigo

, & Duran

I. J.

(2018). Be bad but (still) look good: Can controversial industries enhance corporate reputation through CSR initiatives. Business Ethics: A European Review, 27(3), 222–237.

Arya

, & Zhang

(2009). Institutional reforms and investor reactions to CSR announcements: Evidence from an emerging economy. Journal of Management Studies, 46(7), 1089–1112.

10.

Aswani

, Chidambaran

N. K.

, & Hasan

(2021). Who benefits from mandatory CSR? Evidence from the Indian Companies Act 2013. Emerging Markets Review, 46, 100753. 10.1016/j.ememar.2020.100753

11.

Bansal

, Khanna

, & Sydlowski

(2019). Incentives for corporate social responsibility in India: Mandate, peer pressure and crowding-out effects. SSRN Electronic Journal, 1–47. 10.2139/ssrn.3370838

12.

Barclay

, Higgins

, & Thompson

(1995). The partial least squares approach to casual modeling: Personal computer adoption and use as an illustration. Technology Studies, 2, 285–309.

13.

Becker

J. M.

, Klein

, & Wetzels

(2012). Hierarchical latent variable models in PLS-SEM: Guidelines for using reflective-formative type models. Long Range Planning, 45(5–6), 359–394. 10.1016/j.lrp.2012.10.001

14.

Beddewela

, & Fairbrass

(2016). Seeking legitimacy through CSR: Institutional pressures and corporate responses of multinationals in Sri Lanka. Journal of Business Ethics, 136(3), 503–522.

15.

Beschorner

, Hajduk

, & Simeonov

(2013). Corporate responsibility in Europe: Government involvement in sector-specific initiatives. Verlag Bertelsmann Stiftung.

16.

Boutin-Dufresne

, & Savarin

(2004). Corporate social responsibility and financial risk. The Journal of Investing, 13(1), 57–66.

17.

Brammer

, Jackson

, & Matten

(2012). Corporate social responsibility and institutional theory: New perspectives on private governance. Socio-Economic Review, 10(1), 3–28. 10.1093/ser/mwr030

18.

Brammer

S. J.

, & Pavelin

(2006). Corporate reputation and social performance: The importance of fit. Journal of Management Studies, 43(3), 435–455.

19.

Burdon

W. M.

, & Sorour

M. K.

(2020). Institutional theory and evolution of ‘a legitimate’ compliance culture: The case of the UK financial service sector. Journal of Business Ethics, 162. 10.1007/s10551-018-3981-4

20.

Campbell

J. L.

(2007). Why would corporations behave in socially responsible ways? An institutional theory of corporate social responsibility. Academy of Management Review, 32(3), 946–967. 10.5465/AMR.2007.25275684

21.

Carrigan

, McEachern

, Moraes

, & Bosangit

(2017). The fine jewellery industry: Corporate responsibility challenges and institutional forces facing SMEs. Journal of Business Ethics, 143(4), 681–699. 10.1007/s10551-016-3071-4

22.

Chua

, & Rahman

(2011). Institutional pressures and ethical reckoning by business corporations. Journal of Business Ethics, 98(2), 307–329. 10.1007/s10551-010-0551-9

23.

Cooper

D. R.

, & Schindler

P. S.

(2014). Business research methods. The McGraw−Hill Companies.

24.

Cottrill

M. T.

(1990). Corporate social responsibility and the marketplace. Journal of Business Ethics, 9(9), 723–729.

25.

Dabic

, Colovic

, Lamotte

, Painter-Morland

, & Brozovic

(2016). Industry-specific CSR: Analysis of 20 years of research. European Business Review, 28(3), 250–273. 10.1108/EBR-06-2015-0058

26.

Deegan

, Rankin

, & Voght

(2000, March). Firms’ disclosure reactions to major social incidents: Australian evidence. Accounting forum, 24(1), 101–130.

27.

Dickson

M. A.

, & Eckman

(2008). Media portrayal of voluntary public reporting about corporate social responsibility performance: Does coverage encourage or discourage ethical management? Journal of Business Ethics, 83(4), 725–743. 10.1007/s10551-008-9661-z

28.

Di Maggio

P. J.

, & Powell

W. W.

(1983). The iron cage revisited: Institutional isomorphism in organizational fields. American Sociological Review, 48(2), 147–160. http://www.jstor.org/stable/2095101

29.

Dixit

S. K.

, Verma

, & Priya

S. S.

(2021). Corporate social responsibility motives of Indian firms. Journal of Modelling in Management. 10.1108/JM2-07-2020-0190

30.

Doh

J. P.

, Howton

S. D.

, Howton

S. W.

, & Siegel

D. S.

(2010). Does the market respond to an endorsement of social responsibility? The role of institutions, information, and legitimacy. Journal of Management, 36(6), 1461–1485. 10.1177/0149206309337896

31.

Dupire

, & M’Zali

(2016). CSR strategies in response to competitive pressures. Journal of Business Ethics, 1–21. 10.1007/s10551-015-2981-x

32.

Durand

, & Jacqueminet

(2015). Peer conformity, attention, and heterogeneous implementation of practices in MNEs. Journal of International Business Studies, 46(8), 917–937. 10.1057/jibs.2015.21

33.

Fombrun

C. J.

(1996). Reputation. Harvard Business School Press.

34.

Frynas

J. G.

(2009). Corporate social responsibility in the oil and gas sector. The Journal of World Energy Law & Business, 2(3), 178–195. 10.1093/jwelb/jwp012

35.

Frynas

J. G.

(2005). The false developmental promise of Corporate Social Responsibility: Evidence from multinational oil companies. International Affairs, 81(3), 17–19. 10.1111/j.1468-2346.2005.00470.x

36.

Garcia-Sanchez

I. M.

, Cuadrado-Ballesteros

, & Sepulveda

(2014). Does media pressure moderate CSR disclosures by external directors? Management Decision, 52(6), 1014–1045. 10.1108/MD-09-2013-0446

37.

Gardberg

N. A.

, & Fombrun

C. J.

(2006). Corporate citizenship: Creating intangible assets across institutional environments. Academy of Management Review, 31(2), 329–346. 10.5465/AMR.2006.20208684

38.

Godfrey

P. C.

, Hatch

N. W.

, & Hansen

J. M.

(2010). Toward a general theory of CSRs: The roles of beneficence, profitability, insurance, and industry heterogeneity. Business and Society, 49(2), 316–344. 10.1177/0007650308315494

39.

Govindan

, Kannan

, & Shankar

K. M.

(2014). Evaluating the drivers of corporate social responsibility in the mining industry with multi-criteria approach: A multi-stakeholder perspective. Journal of Cleaner Production, 84(1), 214–232. 10.1016/j.jclepro.2013.12.065

40.

Hamann

(2004). Corporate Social Responsibility, partnerships, and institutional change: The case of mining companies in South Africa. Natural Resources Forum, 28(4), 278–290. 10.1111/j.1477-8947.2004.00101.x

41.

Hamann

, & Acutt

(2003). How should civil society (and the government) respond to ‘corporate social responsibility’? A critique of business motivations and the potential for partnerships. Development Southern Africa, 20(2), 255–270. 10.1080/03768350302956

42.

Han

, & Yang

(2016). Influences of institutional pressures on corporate social performance: Empirical analysis on the panel data of Chinese power generation enterprises. The China Business Review, 15, 361–378.

43.

Hair

, Hult

G. T. M.

, Ringle

C. M.

, & Sarstedt

(2013). A primer on partial least squares structural equation modeling. SAGE Publications.

44.

Hair

J. F.

, Howard

M. C.

, & Nitzl

(2020). Assessing measurement model quality in PLS-SEM using confirmatory composite analysis. Journal of Business Research, 109, 101–110. 10.1016/j.jbusres.2019.11.069

45.

Hair

J. F.

, Ringle

C. M.

, & Sarstedt

(2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice, 19(2), 139–152. 10.2753/mtp1069-6679190202

46.

Hair

J. F.

, Sarstedt

, Ringle

C. M.

, & Mena

J. A.

(2012). An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science, 40(3), 414–433. 10.1007/s11747-011-0261-6

47.

Hair

J. F.

, Risher

J. J.

, Sarstedt

, & Ringle

C. M.

(2019). When to use and how to report the results of PLS-SEM. European Business Review, 31(1), 2–24. 10.1108/EBR-11-2018-0203

48.

Hair

, Sarstedt

, Ringle

C. M.

, & Gudergan

S. P.

(2017). Advanced issues in partial least squares structural equation modeling. SAGE Publications.

49.

Hoffman

A. J.

(2001). Linking organizational and field-level analyses: The diffusion of corporate environmental practice. Organization and Environment, 14(2), 133–156.

50.

Humphreys

, & Latour

K. A.

(2013). Framing the game: Assessing the impact of cultural representations on consumer perceptions of legitimacy. Journal of Consumer Research, 40(4), 773–795.

51.

Jackson

, & Apostolakou

(2010). Corporate social responsibility in Western Europe: An institutional mirror or substitute? Journal of Business Ethics, 94(3), 371–394. 10.1007/s10551-009-0269-8

52.

Jain

, Aguilera

R. V.

, & Jamali

(2017). Corporate stakeholder orientation in an emerging country context: A longitudinal cross industry analysis. Journal of Business Ethics, 143(4), 701–719. 10.1007/s10551-016-3074-1

53.

Jamali

(2010). The CSR of MNC subsidiaries in developing countries: Global, local, substantive or diluted? Journal of Business Ethics, 93(2), 181–200.

54.

Jamali

, & Neville

(2011). Convergence versus divergence of CSR in developing countries: An embedded multi-layered institutional lens. Journal of Business Ethics, 102(4), 599–621. 10.1007/s10551-011-0830-0

55.

Jha

, & Aggrawal

V. S.

(2018). Community-level CSR implementation through the lens of institutional theory: An empirical study. The Qualitative Report, 23(10).

56.

Jha

, & Aggrawal

V. S.

(2019). Institutional pressures for corporate social responsibility implementation: A study of Indian executives. Social Responsibility Journal, 16(4). 10.1108/SRJ-11-2018-0311

57.

Kagan

R. A.

, Gunningham

, & Thornton

(2003). Explaining corporate environmental performance: How does regulation matter? Law and Society Review, 37(1), 51–89.

58.

King

B. G.

, & Whetten

D. A.

(2008). Rethinking the relationship between reputation and legitimacy: A social actor conceptualization. Corporate Reputation Review, 11(3), 192–207.

59.

Knudsen

J. S.

, & Brown

(2015). Why governments intervene: Exploring mixed motives for public policies on corporate social responsibility. Public Policy and Administration, 30(1), 51–72. 10.1177/0952076714536596

60.

Kolk

(2010). Trajectories of sustainability reporting by MNCs. Journal of World Business, 45(4), 367–374.

61.

Kollman

, & Prakash

(2002). EMS-based environmental regimes as club goods: Examining variations in firm-level adoption of ISO 14001 and EMAS in UK, US, and Germany. Policy Sciences, 35(1), 43–67.

62.

Kondra

A. Z.

, & Hinings

C. R.

(1998). Organizational diversity and change in institutional theory. Organization Studies, 19(5), 743–767.

63.

Kourula

, & Laasonen

(2010). Nongovernmental organizations in business and society, management, and international business research: Review and implications from 1998 to 2007. Business & Society, 49(1), 35–67.

64.

Levendis

, Block

, & Morrel

(2006). Nuclear power. Journal of Business Ethics, 67, 37–49.

65.

Lindorff

, Prior Jonson

, & McGuire

(2012). Strategic corporate social responsibility in controversial industry sectors: The social value of harm minimisation. Journal of Business Ethics, 110(4), 457–467. 10.1007/s10551-012-1493-1

66.

Lucchini

, & Moisello

(2017). CSR disclosure, visibility and media pressure: International evidence from the apparel and textile industry. European Journal of Economics, Finance and Administrative Sciences, 7(93), 1450–2275.

67.

Malik

, Al Mamun

, & Amin

(2019). Peer pressure, CSR spending, and long-term financial performance. Asia-Pacific Journal of Accounting and Economics, 26(3), 241–260.

68.

Margolis

J. D.

, & Walsh

J. P.

(2003). Misery loves companies: Rethinking social initiatives by business. Administrative Science Quarterly, 48(2), 268–305.

69.

Mathews

J. T.

(1997). Power Shift. Foreign Affairs, 76, 50–66.

70.

MCA. (2013). The Companies Act 2013. Ministry of Corporate Affairs India. https://www.mca.gov.in/Ministry/pdf/CompaniesAct2013.pdf.

71.

MCA. (2021). National CSR portal. Ministry of Corporate Affairs India. https://www.csr.gov.in/

72.

Melo

, & Garrido-Morgado

(2012). Corporate reputation: A combination of social responsibility and industry. Corporate Social Responsibility and Environmental Management, 19(1), 11–31. 10.1002/csr.260

73.

Moore

(2001). Corporate social and financial performance: An investigation in the UK supermarket industry. Journal of Business Ethics, 34(3–4), 299–315.

74.

Morgan

, & Kristensen

P. H.

(2006). The contested space of multinationals: Varieties of institutionalism, varieties of capitalism. Human Relations, 59(11), 1467–1490. 10.1177/0018726706072866

75.

Mukherjee

, & Bird

(2016). Analysis of mandatory CSR expenditure in India: A survey. International Journal of Corporate Governance, 7(1), 32. 10.1504/ijcg.2016.077982

76.

O’Connor

, & Gronewold

K. L.

(2013). Black gold, green earth: An analysis of the petroleum industry’s csr environmental sustainability discourse. Management Communication Quarterly, 27(2), 210–236. 10.1177/0893318912465189

77.

Pedersen

E. R. G.

, Neergaard

, Pedersen

J. T.

, & Gwozdz

(2013). Conformance and deviance: Company responses to institutional pressures for corporate social responsibility reporting. Business Strategy and the Environment, 22(6), 357–373. 10.1002/bse.1743

78.

Podsakoff

P. M.

, MacKenzie

S. B.

, Lee

J. Y.

, & Podsakoff

N. P.

(2003). Common method biases in behavioral research: A critical review of the literature and recommended remedies. Journal of Applied Psychology, 88(5), 879–903. 10.1037/0021-9010.88.5.879

79.

Pollock

T. G.

, & Rindova

V. P.

(2003). Media legitimation effects in the market for initial public offerings. Academy of Management Journal, 46(5), 631–642.

80.

Reverte

(2009). Determinants of corporate social responsibility disclosure ratings by Spanish listed firms. Journal of Business Ethics, 88(2), 351–366.

81.

Roy

, & Seguin

(2000). The institutionalization of efficiency-oriented approaches for public service improvement. Public Productivity & Management Review, 23(4), 449–468.

82.

Sá de Abreu

M. C.

, da Cunha

L. T.

, & Barlow

(2015). Institutional dynamics and organizations affecting the adoption of sustainable development in the United Kingdom and Brazil. Business Ethics: A European Review, 24(1), 73–90.

83.

Sarstedt

, Ringle

C. M.

, Cheah

J. H.

, Ting

, Moisescu

O. I.

, & Radomir

(2019). Structural model robustness checks in PLS-SEM. Tourism Economics. 10.1177/1354816618823921

84.

Scott

W. R.

(1995). Institutions and organisations. SAGE Publications.

85.

Shanmugam

, Lakshmi

, & Visalakshmi

(2015). Employing multi-criteria decision making in examining CSR initiatives. International Journal of Business Innovation and Research, 9(1), 115–132. 10.1504/IJBIR.2015.065967

86.

Shariq

S. M.

, Mukhtar

, & Anwar

(2019). Mediating and moderating impact of goal orientation and emotional intelligence on the relationship of knowledge oriented leadership and knowledge sharing. Journal of Knowledge Management, 23(2), 332–350. 10.1108/JKM-01-2018-0033

87.

Shnayder

, Van Rijnsoever

F. J.

, & Hekkert

M. P.

(2016). Motivations for corporate social responsibility in the packaged food industry: An institutional and stakeholder management perspective. Journal of Cleaner Production, 122, 212–227. 10.1016/j.jclepro.2016.02.030

88.

Simpson

W. G.

, & Kohers

(2002). The link between corporate social and financial performance: Evidence from the banking industry. Journal of Business Ethics, 35(2), 97–109.

89.

Sinha

(2005). Understanding the rise and transformation of business collective action in India. Business and Politics, 7(2). 10.2202/1469-3569.1112

90.

Sood

, & Arora

(2006). The political economy of corporate responsibility in India. UNRISD.

91.

Steurer

(2010). The role of governments in corporate social responsibility: Characterising public policies on CSR in Europe. Policy Sciences, 43(1), 49–72.

92.

Stone

(1974). Cross-validatory choice and assessment of statistical predictions. Journal of the Royal Statistical Society: Series B (Methodological), 36(2), 111–133.

93.

Suchman

M. C.

(1995). Managing legitimacy: Strategic and institutional approaches. Academy of Management Review, 20, 571–610.

94.

Suddaby

(2013). Institutional theory. In Encyclopedia of management theory (pp. 379–384). SAGE Publications.

95.

Taghian

, D’Souza

, & Polonsky

(2012). A study of older Australians’ volunteering and quality of life: Empirical evidence and policy implications. Journal of Nonprofit & Public Sector Marketing, 24(2), 101–122.

96.

Turker

(2009). Measuring corporate social responsibility: A scale development study. Journal of Business Ethics, 85(4), 411–427.

97.

Tetrault Sirsly

C. A.

, & Lvina

(2019). From doing good to looking even better: the dynamics of CSR and reputation. Business and Society, 58(6), 1234–1266. 10.1177/0007650315627996

98.

Vashchenko

(2017). An external perspective on CSR: What matters and what does not ? Business Ethics, 26(4), 396–412. 10.1111/beer.12162

99.

Wartick

S. L.

(1992). The relationship between intense media exposure and change in corporate reputation. Business & Society, 31(1), 33–49.

100.

Wooten

, & Hoffman

A. J.

(2016). Organizational fields past, present and future. SSRN Electronic Journal. 10.2139/ssrn.2767550

101.

Yin

(2017). Institutional drivers for corporate social responsibility in an emerging economy: A mixed-method study of Chinese business executives. Business & Society, 56(5), 672–704.

102.

Young

S. L.

, & Makhija

M. V.

(2014). Firms’ corporate social responsibility behavior: An integration of institutional and profit maximization approaches. Journal of International Business Studies, 45(6), 670–698. 10.1057/jibs.2014.29

103.

Zimmerman

M. A.

, & Zeitz

G. J.

(2002). Beyond survival: Achieving new venture growth by building legitimacy. Academy of Management Review, 27(3), 414–431.

104.

Zuo

, Schwartz

M. S.

, & Wu

(2017). Institutional forces affecting corporate social responsibility behavior of the Chinese food industry. Business & Society, 56(5), 705–737.

105.

Zyglidopoulos

S. C.

, Georgiadis

A. P.

, Carroll

C. E.

, & Siegel

D. S.

(2012). Does media attention drive corporate social responsibility? Journal of Business Research, 65(11), 1622–1627. 10.1016/j.jbusres.2011.10.021