Why Do Firms Participate in Voluntary Environmental Programs? A Meta-Analysis of the Role of Institutions,Resources,and Program Stringency

Abstract

We meta-analyze research on why firms join voluntary environmental programs (VEPs) to assess the impact of program stringency, or the extent to which they have rigorous, enforceable standards on these decisions. Stringency creates trade-offs for firms by affecting programs’ effectiveness, legitimacy, and adoption costs. Most research considers singular programs and lacks cross program variation needed to analyze program stringency’s impact. Our meta-analysis addresses this by sampling 127 studies and 23 VEPs. We begin by identifying common institutional and resource-based drivers of participation in the literature, and then analyze how program stringency moderates their impacts. Our results suggest that strictly governed VEPs encourage participation among highly visible and profitable firms, and discourage it when informal institutional pressures are higher, and firms have prior experience with other VEPs or quality management standards. We demonstrate that VEP stringency has nuanced effects on firm participation based on the institutional and resource-based factors facing them.

Keywords

voluntary environmental programs meta-analysis institutional theory resource-based view

Introduction

Environmental governance is a central topic in research on organizations and the natural environment because market failures continue to allow firms to degrade ecological systems at alarming rates (Hoffman & Jennings, 2018; Rockström et al., 2009). While regulations are the centerpiece of environmental governance efforts, a number of factors have limited their effectiveness, including political opposition, monitoring costs, and the absence of global authorities (Aragon-Correa et al., 2020; Hoffman, 2011). In this context, voluntary environmental programs (VEPs) have emerged as alternative market-driven policy instruments. VEPs are environmental self-regulatory institutions that provide competitive incentives to improve their environmental performance beyond what is legally mandated (Berchicci & King, 2007; Videras & Alberini, 2000). They include environmental management systems (e.g., ISO 14001), environmental performance mandates (e.g., Forest Stewardship Council), codes of conduct (e.g., UN Global Compact), and written commitments (e.g., Climate Challenge Program; Darnall et al., 2009), which can be sponsored by governments, trade associations, and nongovernmental organizations (NGOs; Darnall et al., 2017). To attract participants, VEPs provide firms their seal of approval, which they use to signal their legitimacy-conferring stakeholders their commitment to environmental protection (Borck & Coglianese, 2009; Prakash & Potoski, 2007).

Scholars have taken a keen interest in studying why firms join VEPs because of their potential to reduce negative environmental impacts, with numerous studies considering both contextual and firm-level factors that drive firms to adopt these programs (e.g., Babakri et al., 2003; Darnall et al., 2009; Delmas & Montes-Sancho, 2010; King et al., 2005). Furthermore, a number of reviews and commentaries have provided comprehensive assessments of this research showing that participation is often driven by regulatory threats, stakeholder pressures, and programs’ provision of reputational benefits, public recognition, and improved branding (e.g., Khanna, 2001; Koehler, 2007; Lyon & Maxwell, 2007). We build on this research by studying how VEP stringency affects firms’ propensity to join. VEP stringency refers to a program’s ability to ensure that firms make substantive efforts to improve environmental performance (Castka & Corbett, 2016). A program’s stringency depends on its governance design, which can involve strict environmental performance standards and enforcement mechanisms or lenient standards and weak enforcement, leading to symbolic participation (Carmin et al., 2003; Darnall et al., 2017). To date, scholars have relied mainly on anecdotal evidence to examine the role of VEP stringency, and developed conflicting accounts of how it drives firm participation, with some suggesting that firms favor stringent programs because of their external legitimacy (Castka & Corbett, 2016; Prakash & Potoski, 2007), and others arguing that they prefer lenient programs because of their lower adoption costs (Ahmed, 2012; Fischer & Lyon, 2014; Koehler, 2007).

To help settle this debate, we meta-analyze research on VEP participation in 23 VEPs from 127 studies. This method helps us overcome a limitation of nearly every study on the topic that has prevented them from considering the role of stringency, which is that they sample single programs and therefore lack the cross-program variation needed to study its effect (Aragon-Correa et al., 2020; Castka & Corbett, 2015; Tuczek et al., 2018). Meta-analytical techniques overcome this limitation by allowing us to aggregate the results from single program studies, code the stringency of each program in the metasample, and model the interaction effect of stringency and the antecedents of interest on firms’ participation in VEPs (Stanley & Doucouliagos, 2012).

To date, most research on the drivers of VEP participation relies on institutional theory or resource-based view (RBV) explanations (Tuczek et al., 2018). Institutional perspectives have focused on how sociopolitical pressures influence firm decisions to join VEPs as a legitimacy-seeking strategy (Darnall et al., 2009; Delmas & Montes-Sancho, 2010; King et al., 2005). RBV approaches most often consider how VEP participation depends on firms’ possession of complementary resources that optimize the benefits that VEPs can provide (Baek, 2017; Darnall, 2006). Our results suggest that stringency diminishes firms’ propensity to join VEPs as a result of informal institutional expectations, strengthens their likelihood of joining when they have high visibility and strong financial performance, and weakens their motivation for joining when firms already participate in other VEPs or other quality management standards.

Our study makes several contributions to the literature on VEPs. First, as discussed above, it generates more conclusive evidence about the effect of VEP stringency on firms’ adoption of these programs. We show that this effect is nuanced and enhances the power of some drivers and diminishes others. In doing so, we answer the call to conduct “a simultaneous investigation of multiple standards [that] allows researchers to answer questions that are impossible to answer in single-standard studies” (Castka & Corbett, 2015, p. 301). Our meta-analysis also helps us consolidate and corroborate some of the main findings in the existing literature about the role of institutional and resource-based drivers of firms’ participation in these programs. Finally, we extend research on the resource-based motivations for joining VEPs by showing that the “coupling effect” (Castka & Corbett, 2015) of joining multiple VEPs and/or quality management standards can drive firms into lenient programs that enable symbolic participation.

The rest of the article proceeds as follows: first, we review research on VEP stringency and its ability to drive substantive corporate participation by establishing “institutional rules” (Darnall et al., 2018), as well as competing views on how it affects firm participation. We then outline existing research on the antecedents of VEP participation, and develop hypotheses for how VEP stringency moderates the effects of these antecedents. Next, we describe our meta-analytical procedures and results. We conclude by discussing the study’s results, implications for research and practice, and limitations.

Theory and Hypotheses

VEPs seek to improve environmental outcomes by encouraging rather than mandating their participants to adopt better practices. If they become widely diffused, they can also create mimetic and normative pressures on other firms to follow suit (Prakash & Potoski, 2012). Their emergence coincides with the industry’s view that traditional command and control environmental regulations are costly and inefficient “one-size-fits-all” prescriptions that stifle environmental and technological innovation, as well as efforts by governments to reduce their own monitoring and compliance costs (Rivera & de Leon, 2008). Firms join VEPs to gain their seal of approval, which they can use to signal legitimacy-conferring stakeholders (including communities, customers, and regulators) their commitment to the environment (Berchicci & King, 2007). This helps stakeholders make sense of firms’ environmental performance because they frequently lack evaluative information, or the capacity to inspect and monitor business practices that are known to cause negative environmental impacts (Darnall, 2006). Because of this information void, skeptical stakeholders often falsely stereotype firms as poor environmental performers regardless of their actual performance. VEPs mitigate these problems for stakeholders by warranting firms’ efforts (Barnett & King, 2008; Berchicci & King, 2007). Still firms’ decision to participate in VEPs is not always straightforward. Some VEPs have high adoption costs that outweigh their potential competitive benefits (Ahmed, 2012). Others with lower adoption costs are seen as greenwashing tools (Castka & Corbett, 2016). Both issues are related to VEP stringency, which affects whether they offer firms the right balance of carrots and stick for improving environmental performance and improving legitimacy with key stakeholders (Darnall et al., 2017).

VEP Stringency

A VEP’s stringency is a function of its governance design (Berchicci & King, 2007; Darnall & Carmin, 2005; Prakash & Potoski, 2012). Governance design refers to the set of criteria or rules that VEPs institute to induce participating firms to meet their objectives (Borck & Coglianese, 2009). Three particular rules have emerged that have significant influence on VEP effectiveness: type of environmental standards, type of monitoring criteria, and presence of sanctions for participating firms that fail to comply with a program’s standards (Darnall & Carmin, 2005; Darnall et al., 2017; King & Lenox, 2000). We discuss each next.

Environmental performance standards are the environmental outcomes that VEPs require members to achieve (Carmin et al., 2003). They can be environmental performance targets, which require firms to achieve measurable outcomes for protecting ecosystems, reducing natural resource consumption or mitigating pollution; environmental management systems, where firms are required to adopt practice systems that purport to reduce environmental impacts; codes of conduct that aim to normalize sustainable values in organizations; and, written commitments by firms to improve their environmental practices or performance (Berchicci & King, 2007; Darnall & Carmin, 2005; Darnall et al., 2017). Environmental performance targets are most likely to induce substantive efforts from firms because they provide unambiguous expectations that are rooted in actual ecological outcomes. Other standards are either open to interpretation or to policy-practice decoupling, which can lead to symbolic adoption by firms (Darnall et al., 2017; Wijen, 2014).

Monitoring criteria refer to how programs conduct oversight of firm compliance with VEP environmental performance standards (King & Lenox, 2000). Stringent monitoring prevents firms from claiming compliance without substantiating the claim and involves third party oversight by accredited bodies or VEPs with no connections to industry. Lenient monitoring involves industry or trade associations conducting oversight (for industry-sponsored VEPs), self-monitoring, or absence of oversight. Only third-party oversight mitigates moral hazard that leads to shirking and symbolic participation by ensuring that neutral bodies oversee firms (Berchicci & King, 2007).

Finally, sanctions refer to a VEP’s ability to penalize firms that are out of compliance with its environmental performance standards. The most stringent penalty is expulsion from the VEP, which allows a program to withhold the benefits that programs offer to participating firms (Koehler, 2007). Programs can also attempt to shame their participants by publicizing their noncompliance; however, this penalty is less stringent because it allows firms to continue to publicize their VEP membership (Darnall & Carmin, 2005; Delmas & Burbano, 2011).

It is important to note that these different components of a VEP’s governance work as a system. VEPs with strong monitoring and penalties and weak environmental performance standards are likely to induce strong compliance, albeit on trivial improvements. Similarly, exceptional standards may mean little if weak oversight and/or sanctions enable symbolic effort (Darnall et al., 2017).

VEP Stringency and Firm Participation

There has been limited research on how VEP stringency affects firm decisions to join a program, with several viewpoints emerging. First, there is some evidence that more stringent VEPs are attractive to firms because they weed out members that may shirk programs obligations and undermine their legitimacy (Darnall et al., 2018). Conversely, other research suggests that firms prefer lenient programs because of their lower adoption costs. Ahmed (2012) demonstrates that more lenient VEPs attract participants for this reason, while stringent programs with higher adoption costs fail to provide sufficient participation incentives. Similarly, Fischer and Lyon (2014) demonstrate that, in a competition for participants, lenient VEPs should be able to seize market share away from stringent ones because they provide more optimal competitive benefits. Finally, some have argued or demonstrated that stringency has diminishing returns for participating firms. In particular, they argue that firms may prefer well-governed VEPs with respect to monitoring and penalties, because external stakeholder value strong oversight and compliance mechanisms. At the same time, they expect that firms may wish to avoid VEPs with stringent environmental performance standards, which have the highest compliance costs and may not be as critical for external stakeholders (Prakash & Potoski, 2007). This view has support from a study by Castka and Corbett (2016) that relies on survey evidence from experts.

To develop more conclusive evidence on the role of VEP stringency vis-à-vis firm participation, we use meta-analytical techniques to aggregate the existing body of single-program studies, and model how cross program variation in stringency affects the results of these studies. To this end, we followed standard practices in meta-analytical research designs by surveying the literature for key factors leading to VEP participation and theorizing how our variable of interest, program stringency, shapes their impacts (Heugens et al., 2009; Lipsey & Wilson, 2001; Stanley et al., 2013). Our search confirmed prior reviews of the topic finding that institutional theory and the RBV were predominant theoretical perspectives for explaining how factors that drive firms to join VEPs (Tuczek et al., 2018). Thus, we chose those theoretical perspectives as the starting point for developing our conceptual model. We then narrowed our selection of antecedents to those receiving substantial enough empirical consideration in the literature to include in our meta-analysis (Kirca et al., 2012). This process led to the identification of three institutional and three resource-based factors that drive VEP participation in ways that should depend on program stringency: formal institutional pressures, informal institutional pressures and firm’s visibility; and, firm’s prior financial performance, prior environmental performance and prior experience adopting other VEPs or quality management standards (see Figure 1).

Figure 1.

Summary of the hypotheses.

VEP Stringency, Institutional Pressures, and VEP Participation

The most widely used lens for explaining why firms join VEPs is institutional theory (Tuczek et al., 2018). In this area, scholars have focused on how VEPs help firms maintain or protect their “corporate environmental legitimacy” (Berchicci & King, 2007; Darnall, 2006; Delmas & Montes-Sancho, 2010; King & Lenox, 2000), or “the generalized perception or assumption that a firm’s corporate environmental performance is desirable, proper or appropriate” (Bansal & Clelland, 2004, p. 94). Corporate environmental legitimacy is a strategic issue for firms because their performance and survival depend on whether stakeholders, including communities, customers, investors, regulators and suppliers, believe that their actions are a net benefit to society. Otherwise, they may withhold critical resources (Frooman, 1999).

Stakeholders exert environmental legitimacy pressures on firms through environmental institutions, or “humanly devised constraints that structure human interaction” (North, 1990, p. 3) for protecting the environment. Broadly, there are two types of institutions, formal institutions, which include policies, regulations, laws, and formal contracts; and informal institutions such as norms, widely held values, and preconscious cultural belief systems (Peng, 2002). Formal institutions work through legal systems and regulators that are tasked with ensuring firms’ compliance with government mandates. Informal institutions work through nongovernmental stakeholder groups, such as community and advocacy groups, industry associations, and academic institutions (North, 1990). Their influence capacity is derived from their ability to mobilize social pressures on firms to uphold moral obligations, norms, and customs (Hoffman, 1999). Firms can have varying levels of sensitivity to institutional pressures involving their environmental legitimacy because stakeholders face extensive information search costs when evaluating firms’ environmental performance (Darnall & Carmin, 2005; Oliver, 1991). Thus, environmental legitimacy pressures that firms face can also depend on their visibility, or the extent to which they have well-known brands or operations, which may reduce stakeholder search costs and/or incentivize them to incur those costs (Rivera & de Leon, 2004). Below, we discuss how each of these institutional factors interacts with VEP stringency to drive firms to join programs.

Research has demonstrated that formal institutions drive firms to join VEPs by generating regulatory scrutiny of corporate environmental performance, especially when firms operate in polities or industries with stringent environmental regulations (Barnett & King, 2008; Mazur & Welch, 1999). In turn, regulatory scrutiny generates coercive pressures on firms in the form of higher incidence of inspection, fines, court proceedings, and/or new regulatory actions if an industry’s collective industrial impacts on the environment are deemed too high (Blackman, 2012; Rivera & de Leon, 2004). In response, many firms join VEPs to signal regulators that they are proactively trying to meet their environmental expectations (Darnall et al., 2009).

We expect firms to prefer stringent VEPs when managing formal environmental institutional expectations. Generally, firms want to preclude actions from regulatory stakeholders involving the enforcement of formal institutional expectations because they can involve costly sanctions or new regulations (Darnall et al., 2009). While VEPs can help firms assuage this concern, not every program is necessarily legitimate with regulatory stakeholders, as they have the tools and resources of government that allow them to evaluate a VEP’s governance design and stringency, as well as its subsequent impact on corporate environmental performance (Darnall et al., 2017). Thus, they often can distinguish between VEPs that promote symbolic or substantive behavior, which should increase their skepticism toward lenient VEPs. This gives firms incentives to join more stringent programs as formal environmental institutional pressures become stronger because such programs are more likely to assuage regulators. More formally, we predict that

Hypothesis 1: VEP stringency positively moderates the relationship between formal environmental institutional pressures and firms’ participation in VEPs.

Informal institutions can also drive firms to join VEPs to manage their environmental legitimacy. Environmental norms, value systems, and cultural expectations generate pressures on firms when nongovernmental stakeholders are able to mobilize customer support through media campaigns, whether end consumers or business customers who may be concerned about their suppliers’ environmental performance (Frooman, 1999; King et al., 2005). Accordingly, VEP participation is higher in polities with more “individuals participating in formalized organizations or groups with the primary focus of protecting the natural environment” (Darnall et al., 2009, p. 288), or in industries where peers are concerned about being negatively stereotyped because of the environmental behavior of “a few bad apples” (Barnett & King, 2008).

We expect VEP stringency to weaken firms’ tendency to join VEPs in response to informal environmental institutional pressures. Nongovernmental stakeholders who champion such pressures can find it more difficult to distinguish between stringent and lenient VEPs than regulators because they have fewer resources for evaluating programs and less agency in the policy process (Darnall et al., 2009; Darnall et al., 2017). Furthermore, they do not directly sanction firms for environmental illegitimacy; rather, they must mobilize consumers and business customers to do so through their purchasing behavior. This indirect influence mechanism limits nongovernmental stakeholders’ power over firms as they must rely on customers to discern VEP’s stringency, who are less likely to have that level of attention for the task. This provides firms leeway to join lenient VEPs to protect or maintain their environmental legitimacy (Fischer & Lyon, 2014), or conversely. fewer incentives for firms to join stringent VEPs. Thus, we more formally predict that

Hypothesis 2: VEP stringency negatively moderates the relationship between informal environmental institutional pressures and firms’ participation in VEPs.

A number of studies have shown that firms’ visibility motivates higher participation in VEPs (Arora & Cason, 1996; Moon & de Leon, 2007; Rivera & de Leon, 2004). Visibility is driven by public listings (Rivera & de Leon, 2004), strong brand recognition (Rivera, 2002), and/or the production of final goods that are consumer facing as opposed to intermediate goods (Arora & Cason, 1996; Moon & de Leon, 2007). High visibility motivates VEP participation because it can seize the attention of legitimacy-conferring stakeholders, whether governments, trading partners, or the general public, and motivate them to scrutinize firms’ environmental records. Such scrutiny, in turn, exposes firms to potential blowback from important stakeholders, and motivates them to join a VEP as a way to mitigate that risk (Khanna et al., 2007).

We expect VEP stringency to strengthen visible firms’ tendency to join such programs. Visible firms may prefer stringent VEPs to lenient ones because the heightened scrutiny facing them applies not only to their existing environmental problems but also to how they subsequently choose to manage them (Tashman et al., 2019). Thus, legitimacy-conferring stakeholders are more likely to evaluate a VEP’s stringency to ensure that the program does not allow the firm to shirk their commitments to improving their environmental performance. Accordingly, if managers believe their choice of VEPs is also being more carefully evaluated, they should be more wary of lenient VEPs, which may be recognized as ineffective programs (Darnall et al., 2009). Stringent VEPs, on the other hand, send clearer signals to legitimacy-conferring stakeholders that firms are tackling their environmental responsibilities substantively. This should increase their attractiveness to managers of firms when their legitimacy challenges stem from visibility. Based on these arguments, we predict that

Hypothesis 3: VEP stringency positively moderates the relationship between firms’ visibility and their participation in VEPs.

VEP Stringency, Resource-Based Advantages, and VEP Participation

The second most used perspective for explaining why firms join VEPs is the RBV (Tuczek et al., 2018). It is well known that the RBV argues that the source of competitive advantage for firms are their “resources,” or their unique assets, capabilities, skills, knowledge, and routines, which allow them to develop, produce, and commercialize superior products and services (Barney, 1991). This can also apply to resources that make firms more environmentally sustainable, which can increase their efficiency and innovativeness, improve their brands, and enhance their stakeholder relationships (Hart, 1995). Since VEPs aim to help firms develop such resources, it follows that they should offer firms some avenue for developing resource-based sources of competitive advantage (Rivera, 2002). One impediment to this, however, is that these programs provide “club” benefits to all firms who join (Potoski & Prakash, 2005b), and sometimes generate reputational spillovers to nonparticipants in the same industry (Borck & Coglianese, 2009). Thus, VEP participation is not a uniquely valuable resource on its own. Furthermore, VEPs have compliance and certification costs that can outweigh any advantages they create for some firms (Bansal & Hunter, 2003; Kollman & Prakash, 2001).

Research has shown that resource-based advantages from participating in VEPs tend to accrue to firms with complementary resources (Bansal & Hunter, 2003; Darnall, 2006; Darnall & Edwards, 2006; King et al., 2005; Tuczek et al., 2018). Complementary resources are those that are necessary for firms to earn rents from a strategy or technology (Teece, 1986). They are generally developed during the course of other productive activities, but create incidental synergies with those strategies or technologies (Christmann, 2000). Accordingly, studies have shown that resources that complement environmental strategies drive firms to join VEPs to gain these synergies (Tuczek et al., 2018). Here, we consider three complementary resources receiving significant research attention: strong prior financial performance, strong prior environmental performance, and prior experience with VEPs or other quality management standards.

Superior prior financial performance gives firms excess capital, and in turn, strategic flexibility to adopt longer time horizons for realizing improvements in efficiency and/or branding that could result from implementing VEP standards (Hart, 1995; Rivera, 2002). It also can provide firms with the ability to deploy knowledge gained from VEPs for innovative activities such as novel pollution control or prevention technologies (Darnall, 2006). Finally, it allows firms to absorb costs associated with adapting business processes to fit program’s standards or certify compliance, if required (Arora & Cason, 1995; Moon & de Leon, 2007).

We argue that firms with superior prior financial performance will gravitate toward stringent VEPs, because these programs have higher levels of resource complementarity for them. First, because more stringent VEPs are better at inducing substantive environmental improvements, they provide firms stronger incentives to pursue proactive environmental strategies, which yield longer run financial benefits to firms that have the financial flexibility to adopt them (Bansal, 2005; Hart, 1995; Russo & Fouts, 1997; Sharma & Vredenburg, 1998). Weaker programs with less influence over firm behavior are less likely to help firms become strategic with their environmental practices. Second, stringent VEPs are exclusive “clubs” that have higher levels of credibility with external stakeholders. In turn, they provide better branding and reputational benefits to firms that can afford to meet their requirements (Prakash & Potoski, 2007). Since lenient VEPs attract poorer performers who can undermine the program’s reputation, they offer less complementary value in this way. Based on these arguments, we predict that

Hypothesis 4: VEP stringency positively moderates the relationship between firms’ prior financial performance and their participation in VEPs.

Research has also demonstrated that firms with strong prior environmental performance are also more likely to join VEPs for several reasons. First, they have fewer VEP adoption and compliance costs because they can more easily meet or exceed the program’s standards (Darnall, 2006). They also find it easier to incorporate new knowledge from VEPs into their existing practices because they have better experience with environmental practices (Sharma & Vredenburg, 1998). Finally, firms with superior prior environmental performance also use VEPs to differentiate themselves from firms with environmental legitimacy problems, or to develop “greener” brands (Arora & Cason, 1996; Potoski & Prakash, 2005a; Rivera, 2002).

We expect such firms to prefer stringent VEPs. First, they should want to join more exclusive VEPs to avoid being associated with poor environmental performers who could tarnish the program’s credibility (Prakash & Potoski, 2007). As discussed earlier, lenient VEPs sometimes allow poor environmental performers to participate in purely symbolic ways, which can harm the credibility of those programs. Strong environmental performance should also decrease compliance costs of stringent VEPs for firms (Darnall et al. 2009). Firms with strong environmental performance are also in better positions to benefit more from stringent programs because they facilitate learning networks with other high-performing participants, where high environmental performers can collaborate on extending their competitive advantages (Berliner & Prakash, 2012). In light of these considerations, we hypothesize that

Hypothesis 5: VEP stringency positively moderates the relationship between firms’ prior environmental performance and their participation in VEPs.

Finally, there is significant research on how the prior adoption of other VEPs or quality management standards drives firms to enroll in new VEPs (Castka & Corbett, 2015; Corbett & Kirsch, 2001; Darnall, 2006). Castka and Corbett (2015) refer to this as the “coupling” effect, where multiple VEPs and/or quality management standards are coadopted, so firms can leverage capabilities learned from one program into others. Some research also explores how the coupling effect can lead to standard-specific “competencies,” where firms develop unique knowledge on implementing standards that can be embedded throughout organizations (De Vries et al., 2018). Such competencies can allow firms with prior experience in such programs to easily implement new VEP standards (Darnall, 2006). For example, numerous studies have considered how the quality management standard ISO 9001 coexists with the ISO 14001 environmental management system based on their similar process requirements (e.g., Castka & Balzarova, 2018; Castka & Corbett, 2015; Darnall, 2006; Heras-Saizarbitoria & Boiral, 2013; King & Lenox, 2001).

We expect VEP stringency to negatively moderate the relationship between participation and the prior adoption of other VEPs or quality management standards. As discussed above, stringent VEPs are more likely to induce substantive improvements, often by using environmental performance targets. Such targets can require firms to develop specific practices that are capable of solving the idiosyncratic environmental issues under the purview of the program (Wijen, 2014). However, because of their level of specificity, they may be incompatible with other standards that focus on commonality, as opposed to idiosyncratic environmental problems (Castka & Corbett, 2015; Smith & Fischlein, 2010). We also expect stringent monitoring and sanctions to make new VEPs less complementary with existing ones or quality management standards, as they can constrain firms’ flexibility in how they choose and/or install practices in a way that complies with stringent VEP mandates (Wiegmann, 2019). Based on these considerations, we predict that

Hypothesis 6: VEP stringency negatively moderates the relationship between firms’ prior experience with other VEPs or quality management standards and new VEP participation.

Method

We use meta-analytical techniques to aggregate and study the existing empirical literature on the drivers of VEP participation from single-program studies (Hunter & Schmidt, 2004; Stanley et al., 2013) and the moderating role of VEP stringency. This research method is appropriate because (a) there have been many empirical studies on the topic, but this research has generally not been generalizable because of its predominant focus on single programs; (b) it allows us to identify heterogeneity in effect sizes due to study or methodological characteristics; and (c) it allows us to explore moderators that are not part of primary studies (in our case, VEP stringency; Albertini, 2013; Hedges & Olkin, 1985; Lipsey & Wilson, 2001).

Literature Search and Sample

To identify studies for the meta-analytic sample, we first searched the literature for potential articles using a five-step search strategy that follows established norms in the management and economics disciplines for meta-analytical research (Heugens et al., 2009; Stanley et al., 2013). First, we read several prominent review articles about VEPs (e.g., Berchicci & King, 2007; Borck & Coglianese, 2009; Darnall & Sides, 2008; Tuczek et al., 2018) to develop the list of keywords to guide our literature search. This list included terms related to VEPs¹ and the names of 46 high profile programs.² Second, we conducted searches in Google Scholar and Business Source Premier for those keywords and programs. Third, we manually searched 21 economics, management, policy, and political science journals based on our initial article searches to ensure that key publications were not omitted.³ Fourth, we used a “snowballing” technique that backward-tracks references of the retrieved articles and forward-tracks the articles that cited them by using Google Scholar (Duran et al., 2016). Finally, we solicited unpublished empirical studies fitting our search criteria from five Academy of Management’s listservs (i.e., Organization and the Natural Environment; Social Issues in Management; International Management; Organization and Management Theory; Business Policy and Strategy).

To be included in our sample, studies had to measure variables associated with our constructs of interest, calculate bivariate and/or partial correlation coefficients between those variables and VEP participation, and provide statistical information for calculating coefficient significance (i.e., t test, z test, and/or standard errors). If statistical information was missing, we contacted the authors to obtain that information. We identified variables for constructs in our model by reviewing retrieved papers grounded in institutional theory and the RBV and recording their measures. Other papers were included if they contained the coefficients and statistical information described above, even if when they did not use institutional theory or the RBV. We chose this approach because there are numerous important atheoretical studies on this topic, including seminal works by Arora and Cason (1995, 1996) and Khanna and Damon (1999), and we wanted to ensure that we were not restricting our sample to papers from the management discipline and its focus on organizational theory. Last, we included papers that modelled VEP participation in first-stage selection models that ultimately predicted VEP outcomes. It is important to note that, while our process represents recent best practices in meta-analytical research (Buckley et al., 2013; Duran et al., 2016; Stanley et al., 2013), it excluded many quantitative, qualitative, and conceptual papers lacking the necessary coefficients and statistical information. We explore this important limitation in more detail in the final section of the article.

The search process yielded a final sample of 127 primary studies (111 published and 16 unpublished papers) covering 23 programs. The years included in these studies range from 1988 to 2019. The sample is global, but the majority (71%) of primary studies are based on U.S. data. Table 1 contains a list of the VEPs included in sample as well as their levels of stringency based on our conceptualization and coding approach, which we further illustrate in the measures section below. One author extracted and coded effects from each study, while a second coded a subsample of 200 randomly selected effect sizes to assess the level of agreement in coding practices (Stanley et al., 2013). The two coders had a high degree of interrater agreement (Cohen’s kappa: 0.98).

Table 1.

VEPs Included in the Analyses and their Stringency Scores.

VEP name	Environmental performance target score	Monitoring score	Sanctioning score	Total stringency score
33/50	0	1	0	1
ARET	1	0	0	1
Business for Social Responsibility	0	0	0	0
Certification for Sustainable Tourism	1	1	0	2
Clean Industry	1	1	0	2
Climate Challenge	0	0	0	0
Climate Wise	0	0	0	0
Ecological Blue Flag Program	1	1	1	3
EMAS	0	1	1	2
Energy Star Buildings	1	0	1	2
Global Compact	0	0	1	1
Global Reporting Initiative	0	0	0	0
Green Lights	0	0	0	0
ISO 14001	0	1	1	2
LEED	1	1	1	3
Oeko-Tex 100 Program	1	1	1	3
Responsible Care	0	0	0	0
Metal Finishing Strategic Goals Program	1	0	0	1
State Voluntary Cleanup Program (Colorado, Oregon)	1	0	1	2
Sustainable Slopes	0	0	0	0
Voluntary Challenge and Registry	0	0	0	0
Voluntary Reporting of Greenhouse Gases	0	0	0	0
Waste Wise	0	0	0	0

Measures in Primary Studies

VEP Participation

Our primary studies included three operationalizations of participation in VEPs from the sampled literature: (a) most studies (95%) used a dummy variable, where 1 denotes a firm’s membership, and 0 denotes nonmembership; some used (b) percentage probability of participation in a VEP (e.g., Christmann & Taylor, 2001); and (c) extent of a firm’s VEP certified production (e.g., Passetti & Tenucci, 2016).

Institutional Drivers

We examined the institutional drivers of VEP participation through the concepts of formal environmental expectations, informal environmental expectations, and visibility. The sampled studies measured formal environmental expectations at the industry-, state- and country-levels of analysis. Industry-level formal institutional expectations reflect the strength of industry-specific regulations such as the Metal Products and Machinery Point Source Category, which imposes specific operating standards for wastewater discharges in the metal finishing industry (e.g., Brouhle et al., 2009). State-level formal institutional expectations were captured by indices such as the U.S. state-based League of Conservation Voters score (e.g., Vidovic & Khanna, 2012) or Mazur and Welch’s (1999) index of state environmentalism. Country-level regulatory formal institutional expectations reflect the stringency of national environmental regulations and are captured by survey-based measures of perceived stringency of national environmental regulations (e.g., Passetti & Tenucci, 2016).

Our sample captured informal environmental expectations through several measures: (a) size of industry association membership, (b) NGOs’ membership per capita, and (c) various socioeconomic characteristics of local communities. Size of industry association was used as a proxy of the strength of informal environmental expectations because it captures firms’ exposure to peer expectations to protect an industry’s environmental reputation (e.g., Montiel & Husted, 2009). NGOs’ environmental presence was measured in several ways in the sample studies, including Sierra Club membership dues collected in a given U.S. state (e.g., Delmas & Montiel, 2009), the percentage of a given U.S. state’s population belonging to the Sierra Club (e.g., Vidovic & Khanna, 2012), the number of environmental NGOs in a given country (e.g., Kayser et al., 2014), and survey-based measures capturing the perceived intensity of NGOs’ environmental pressures (e.g., Darnall & Kim, 2012). Studies in our sample proxied community-driven expectations with several socioeconomic indicators, including income per capita (e.g., Ullah et al., 2014), percentage of population living in poverty (e.g., Gamper-Rabindran & Finger, 2013), percentage of people who did not graduate from secondary school (e.g., Potoski & Prakash, 2005a), population density (e.g., Antweiler & Harrison, 2007), and community contributions to environmental NGOs (e.g., Brouhle et al., 2009).

Visibility

This independent variable was operationalized in several ways by studies in our sample, including self-reported survey measures about businesses’ brand recognition (e.g., Delmas & Montes-Sancho, 2010; King & Lenox, 2000), counts of news items mentioning a given business (e.g., Albers & Günther, 2010), criticism and praise in news coverage (e.g., Berrone et al., 2017), and dummy variables measuring whether businesses were publicly traded (e.g., Rivera & de Leon, 2004), and produced consumer-facing goods (e.g., Innes & Sam, 2008).

Resource-Based Drivers

Sample studies measured the resource-based drivers of VEP participation in terms of prior financial performance, prior environmental performance, and prior experience adopting other VEPs or quality management standards. Sampled studies measured prior financial performance using (a) accounting measures, such as return on assets (e.g., Moon & de Leon, 2007); (b) sales growth (e.g., Heras-Saizarbitoria et al., 2011); (c) sales (e.g., Khanna & Damon, 1999); (d) average profit margin (e.g., Chapple et al., 2001) and (e) market measures such as Tobin’s Q (e.g., King & Lenox, 2001). Prior environmental performance was measured as (a) the number of environmental enforcement actions facing a given business (e.g., Innes & Sam, 2008); (b) size of environmental fines (e.g., Blackman, 2012); (c) the amount of toxic and greenhouse gas emissions (e.g., Antweiler & Harrison, 2007), and (d) the number of accidental spills (e.g., Brouhle et al., 2013). In coding these effect sizes, we ensured that higher values of these variables reflected better environmental performance, which required changing some of the coded coefficients’ signs. Finally, sampled studies measured prior experience adopting other VEPs and quality management standards using dummy variables capturing whether businesses had already adopted ISO 9000, or different VEPs before (e.g., Darnall, 2006; King et al., 2005).

Coding of Moderators

VEP Governance Stringency

We measured this variable by coding each VEP in our sample for the presence of governance characteristics identified by Carmin et al. (2003), Darnall and Carmin (2005), and Darnall et al. (2017). The resulting measure is a count score with a range of 0 to 3 that is based on whether the VEP has stringent (a) environmental performance standards, (b) monitoring criteria, and/or (c) and sanctions. Our coding sources were descriptions of VEP governance designs in primary studies and VEP websites. Two authors conducted the coding, and worked together to identify and resolve coding differences. To construct the overall stringency score, we used the following method: first, programs received 1 point if their environmental performance standards involved establishing specific environmental performance targets (e.g., emissions reductions, toxic release reductions, reductions in natural resource consumption) and 0 points if they had other requirements; second, they received 1 point if they required third party oversight by accredited bodies or VEPs with no connections to industry, and 0 points if they were not subjected to such oversight; and third, they received 1 point if they had a policy of expelling businesses that failed to comply with their mandates, and 0 points if they lacked such a policy.

Methodological and Study Artifacts

We controlled for several methodological and study artifacts in our moderator analyses using meta-analytic regression. Specifically, we controlled for whether coded coefficients were partial or bivariate (reference group) with a dummy variable. We controlled for whether studies used panel or cross-sectional designs (reference group) with a dummy variable. We controlled for the median year of sample window of each study to allow for the possibility that the focal relationship is changing over time. We controlled for whether VEPs applied to firms or facilities (reference group) with a dummy variable. We also included a dummy variable capturing whether the sample is U.S.- or non-U.S.-based (reference group). Finally, we controlled for whether studies were published or unpublished (reference group) with a dummy variable since publication bias is an issue in meta-analyses (Gonzalez-Mulé & Aguinis, 2018).

Meta-Analytic Procedures

HOMA Procedure

We used Hedges–Olkin type meta-analysis (HOMA) to estimate the direct relationship between institutional and resource-based VEP drivers and VEP participation, in order to establish baseline evidence for the existence of generalizable effect sizes across the extant literature. HOMA allowed us to calculate meta-analytical mean correlations using Pearson product-moment correlations (r) and partial correlation coefficients (r_xy.z) as effect sizes in separate analyses to allow comparing results. While r exhibits scale-free linearity between our variables of interest, it ignores the possible effects of other variables that might be included in the analysis as controls. HOMAs using the partial correlations r_xy.z address this constraint because these effects exclude the influence of other variables on the dependent variable (Doucouliagos & Ulubaşoğlu, 2008). The HOMA procedure we used also specified random effects in line with recent methodological guidelines (Geyskens et al., 2009). To account for sample differences across effect sizes in different studies, we weighted each effect size by its inverse variance weight (w) (the inverse of the squared standard error; Hedges & Olkin, 1985). These weights were then used to compute the standard error of the mean effect size and its corresponding 95% confidence interval.

MARA Procedure

To test our hypotheses, we rely on meta-analytic regression analysis (MARA). MARA is a weighted least squared-based technique that estimates the effect of study-level moderator variables on primary study effect sizes. Similar to multiple regression analysis, MARA constructs a linear regression model involving a set of predictors, which in this case are the potential moderators (VEP stringency and various methodological and study artifacts) on the dependent variable (i.e., the effect size from primary studies). We followed several norms in the management and economics literatures, including using both (r) and (r_xy.z) as our effect estimates for the MARA procedure (Stanley & Doucouliagos, 2012), weighting effect sizes by their inverse variance weight (w) to capture the differences in the precision of the information contained in them, and estimating random effects (Aguinis et al., 2011; Geyskens et al., 2009; Gonzalez-Mulé & Aguinis, 2018). Our MARAs were based on the following regression equation:

R_{i} = y_{0} + y_{m} D_{i} + b_{m} S_{i} + u_{i},

where R_i is the partial and bivariate correlation between our antecedents of interest and VEP participation, y₀ is the constant term, D is the VEP stringency score, S is a vector of methodological and study artifacts and u_i is the random component.

Results

HOMA Results

Table 2 contains HOMA results for institutional and resource-based drivers of VEP participation. Overall, these results show strong evidence that the examined institutional factors motivate VEP participation and moderate evidence that the examined resource-based factors drive VEP participation. Specifically, formal institutional expectations (r = .10, p < .01; r_xy.z = .03, p < .01), informal institutional expectations (r = .05, p < .01; r_xy.z = .02, p < .01) and visibility were all consistent drivers of VEP participation (r = .20, p < .05; r_xy.z = .02, p < .01). Resource-based factors showed a more mixed picture. There was moderate support for the notion that prior financial performance (r = .05, p < .05; r_xy.z = .01, p < .10) and experience with other VEPs and quality management standards (r = .11, ns, r_xy.z = .08, p < .01) drive firms to join VEPs. However, results for prior environmental performance were mixed with bivariate analyses suggesting a negative relationship (r = −.07, p < .01), while partial analyses indicate that firms with better prior environmental performance are more likely to enroll in a VEP (r_xy.z = .11, p < .01). We explore the implications of these results in more detail in the discussion section.

Table 2.

HOMA Meta-Analytic Results of VEP Antecedents’ Impacts on VEP Participation.

Predictor	Pearson product-moment correlation (r)						Partial correlation coefficient (r_xy.z)
Predictor	k	N	M	SE	Q test	I²	k	N	M	SE	Q test	I²
Institutional drivers: VEP participation
Formal institutional expectations	68	514210	0.10***	0.02	10250.67	99%	292	2117634	0.03***	0.00	3762.98	92%
Informal institutional expectations	34	274424	0.05***	0.01	1088.98	97%	258	1254252	0.02***	0.00	1661.73	85%
Visibility	17	46964	0.20**	0.10	5721.39	99%	139	265929	0.02***	0.01	1263.11	89%
Resource-based drivers: VEP participation
Prior financial performance	27	47079	0.05**	0.01	151.27	83%	109	415850	0.01*	0.00	457.30	76%
Prior environmental performance	60	545507	−0.07***	0.02	11033.19	99%	422	2908531	0.11***	0.01	134260.91	99%
Other VEPs and quality management standards	3	48229	0.11	0.07	58.83	97%	35	148543	0.08***	0.01	273.68	88%

Note. k = Number of effect sizes; N = total sample size; M = mean effect sizes; SE = the standard error of mean correlation; Q test = Cochran’s homogeneity test statistic; I² = scale-free index of heterogeneity; HOMA = Hedges–Olkin type meta-analysis; VEP = voluntary environmental program.

p < .10. ^**p < .05. ^***p < .01.

MARA Results

Tables 3 and 4 contain results for hypotheses predicting the moderating effect of VEP stringency on the relationship between institutional- and resource-based factors and VEP participation. These tables also report results about the effects of methodological and study artifacts on the focal relationships. Models 1 to 3 in Table 3 pertain to the institutional drivers of VEP participation, while Models 4 to 6 in Table 4 pertain to the resource-based drivers. Model 1 indicates that stringency does not moderate the relationship between formal environmental expectations and VEP participation (β = −.00; ns). Thus, we do not find support for Hypothesis 1, which predicted that VEP stringency positively moderates the relationship between formal environmental institutional pressures and firms’ enrollment in VEPs. Model 2 supports our prediction (Hypothesis 2) that program stringency weakens the effect of informal environmental expectations on VEP participation (β = −.02, p < .01). Finally, Model 3 supports Hypothesis 3, which predicted that program stringency strengthens the relationship between visibility and VEP participation (β = .03, p < .01). Overall, results provide partial support for our hypotheses on the moderating role of VEP stringency on institutional antecedents of VEP participation.

Table 3.

MARA Meta-Analytic Results of Program Stringency’s Moderation Effects on the Relationships Between Institutional Drivers and VEP Participation.

Variable	Model 1	Model 2	Model 3
Variable	Formal environmental expectations	Informal environmental expectations	Visibility
VEP stringency	−0.00	−0.02***	0.03***
Methodological and study artifacts
Published study	−0.02	0.06***	−0.13***
Median year of sample window	−0.01***	0.00***	−0.01***
Partial correlation	−0.07***	−0.03***	−0.18***
Panel design	−0.02*	−0.03***	−0.06***
USA	−0.03**	−0.02*	0.01
Firm-level	0.02*	−0.03***	0.00
k	354	283	153
R ²	.07	.10	.23
Q_model(p)	53.76	82.60	97.67
Q_residual(p)	69.46	752.15	321.86
V	0.01	0.00	0.01

Note. MARA = meta-analytic regression analysis; VEP = voluntary environmental program.

p < .10. ^**p < .05. ^***p < .01.

Table 4.

MARA Meta-Analytic Results of Program Stringency’s Moderation Effects on the Relationships Between Resource-Based Drivers and VEP Participation.

Variable	Model 4	Model 5	Model 6
Variable	Prior financial performance	Prior environmental performance	Other VEPs and quality management standards
VEP stringency	0.01*	0.02	−0.05***
Methodological and study artifacts
Published study	−0.04***	−0.12***	0.12***
Median year of sample window	0.00	−0.00	−0.01**
Partial correlation	−0.04***	0.18***	−0.01
Panel design	−0.01	0.03	0.01
USA	0.05***	−0.08**	−0.16***
Firm-level	0.03*	0.07***	0.16***
k	136	473	38
R ²	.14	.17	.84
Q_model(p)	42.64	117.11	178.19
Q_residual(p)	269.19	574.91	33.27
V	0.00	0.04	0.00

Note. MARA = meta-analytic regression analysis; VEP = voluntary environmental program.

p < .10. ^**p < .05. ^***p < .01.

Model 4 supports our prediction that program stringency magnifies the relationship between prior financial performance and VEP participation (β = .01, p < .10) in Hypothesis 4. Model 5 fails to support Hypothesis 5, which predicted that program stringency positively moderates the relationship between prior environmental performance and VEP participation (β = .02, ns). Finally, Model 6 supports Hypothesis 6, which predicted that program stringency weakens the likelihood of firms with experience with other VEPs or quality management standards joining VEPs (β = −.05, p < .01). Overall, results demonstrate moderate support for hypotheses on how VEP stringency moderates the relationships between RBV factors and VEP participation.

Tables 3 and 4 also report results for control variables. In four out of the six models, the effect of a study being published is negatively related to the focal relationship, meaning that we did not find clear evidence of a “file drawer problem” (Rosenthal, 1979). The significant negative effects of median sample year in Model 1, Model 3, and Model 6 suggest that these particular drivers of VEP enrollment become weaker over time; in the remaining models the effect of time is not relevant. Partial effect sizes have a significant and negative moderating effect in five out of six models (here the reference groups was a study’s reliance on bivariate effects), suggesting that partial correlations yield weaker relationships between the VEP drivers and VEP participation. Similarly, studies that rely on panel design show weaker relationships between institutional factors and VEP participation (here cross-sectional designs were the reference group); however, they were not relevant for resource-based factors. The U.S. sample dummy indicates that U.S. studies had weaker effects in four out of the six examined VEP participation drivers. Finally, with the exception of Models 2 and 3, the effects of firm-level VEPs strengthen the impact of VEP drivers on VEP participation (here facility-level was the reference group).

Robustness Tests

We employed several sensitivity analysis techniques to examine the effects of outliers and publication bias on our results. To identify outliers, we used the one-sample removed analysis (Borenstein et al., 2009) and the multivariate multidimensional influence diagnostics (Viechtbauer & Cheung, 2010). We found similar results as those presented in Table 2. We also performed the triangulation method to test for potential publication bias in our HOMA analyses (Harrison et al., 2017). This method consists of three tests: (a) Duval and Tweedie’s (2000) trim and fill, (b) cumulative meta-analysis (Borenstein et al., 2009), and (c) selection models (Hedges & Vevea, 2005). The results of these tests were consistent with our main analyses, except in the case of prior finance performance (partial effect size only), which is not significantly related VEP participation after controlling for publication bias. These results are available from the corresponding author on request.

We also performed one additional set of MARA analyses as part of our robustness tests. Results of these analyses are included in the appendix (Tables 5 and 6). Specifically, we performed MARA with separate scores for stringency and governance as conceptualized by Castka and Corbett (2016) and Smith and Fischlein (2010). The first set of authors use the terms “stringency to refer to the substantive requirements associated with a label, and governance for the processes by which those requirements are set and enforced” (1507) via “auditing methods and their ability to sanction violators” (1505). These concepts fit closely with strength of environmental performance standards (i.e., stringency) and strength of monitoring criteria and sanctions (i.e., governance). To capture this, we measured stringency with a dummy variable that equals to 1 if a VEP relied on environmental performance targets (e.g., emissions reductions, toxic release reductions, reductions in natural resource consumption), and 0 otherwise. We then measured governance as a sum of two dummy variables capturing whether the VEP had third party oversight for monitoring and expulsion mechanisms for sanctions (1 if it had them, 0 otherwise, respectively). When summed together, the governance score had a range from 0 to 2. Finally, we entered the separate stringency and governance variables into our MARA models.

These results confirm that institutional and resource-based drivers of VEP participation depend on program’s governance design. They also suggest that the effect is nuanced, and that stringency and governance (as defined by Castka & Corbett, 2016) may play distinctive roles in driving VEP participation, depending on a firm’s primary motivation for joining one. We unpack these results and their implications in the Discussion.

Discussion

Our meta-analysis considered how VEP stringency affects firm decisions to join programs in conjunction with known institutional and resource-based drivers from existing single program studies. We chose this approach because “a simultaneous investigation of multiple standards allows researchers to answer questions that are impossible to answer in single-standard studies” (Castka & Corbett, 2015, p. 301). Prior research has already developed strong evidence that firms join VEPs to preempt regulations, appease stakeholders, and seek access to resource-based benefits including “marketing and other financial benefits to firms [that] rely on the ability of firms to gain a competitive advantage through participation by signaling to stakeholders” (Matisoff, 2015, p. 111; see also Khanna, 2001; Koehler, 2007; Lyon & Maxwell, 2007; Smith & Fischlein, 2010). Our study builds on these cumulative findings to make two main contributions. First, it addresses an unresolved debate about whether VEP stringency motivates or discourages firms from joining programs. Second, our HOMA results provide more systematic evidence on how institutional and resource-based factors influence firm participation decisions. We discuss each contribution in turn.

First, we help clarify how VEP stringency affects firms’ participation. Some researchers have suggested that firms prefer more stringent programs because they send stronger signals to stakeholders about their efficacy, while others have developed evidence that firms prefer lenient programs that have lower adoption costs (Ahmed, 2012; Castka & Corbett, 2016; Fischer & Lyon, 2014; Koehler, 2007; Prakash & Potoski, 2007). Our results suggest instead that stringency’s effect on firm participation in VEPs is nuanced, and contingent on different institutional and resource-based factors. To start, they suggest that the impact of stringency on VEP participation varies depending on the firm’s institutional context. In particular, the absence of a significant interaction between formal institutional expectations and stringency suggests that firms do not see the latter as a signaling mechanism that helps mitigate regulator concerns. Since regulators may be more preoccupied with firms’ ultimate environmental performance than VEP governance, firms may likewise focus on improving performance in a substantive way to appease those pressures, and be less concerned with VEP governance design. At the same time, the negative interaction between informal institutional expectations and stringency suggests that firms are prone to leveraging asymmetric information about lenient VEPs’ efficacy with nongovernmental stakeholders to manage those environmental legitimacy pressures in symbolic ways. In such cases, joining VEPs appears to be a strategic effort to protect environmental legitimacy without incurring the costs of meeting stakeholder expectations in substantive ways (Oliver, 1991). The positive interaction between stringency and visibility on VEP participation suggests that firms prefer programs that motivate substantive action on environmental performance when they have stakeholders capable of scrutinizing them. This is encouraging because more visible firms are larger, powerful corporate actors with stronger brands and greater potential to generate mimetic effects that attract other less visible firms into more stringent VEPs (Aragon-Correa et al., 2020).

In addition, our results help explain how stringency affects VEPs’ complementarity with firms’ resource base. First, in finding that prior financial performance and stringency positively interact to affect VEP participation, our results suggest that financial success breeds a willingness to take more substantive action on environmental performance, which could ultimately feedback to firms in ways that improve their financial performance (Berchicci & King, 2007). Prior research demonstrates that substantive environmental commitments help firms develop proactive environmental strategies, where firms profit from their sustainability efforts (Sharma & Vredenburg, 1998). With this in mind, economically successful firms may opt into stringent VEPs to pursue virtuous pays-to-be-green cycles. Second, and surprisingly, stringency conditions did not interact with prior environmental performance to affect VEP participation, even though sustainable firms have incentives to gravitate to stringent programs to differentiate themselves from poor performers. It is possible that the tendency for strong environmental performers to join VEPs (King & Lenox, 2000; Rivera, 2002), and poor environmental performers to avoid them (Short & Toffel, 2010) smooths out stringency as a contextual factor. Finally, in finding that stringency weakens the relationship between prior VEP/quality management standard engagement and new VEP adoption, we help establish that leniency gives firms the flexibility to implement the new program in a way that fits with their approach to existing standards in their resource base. As we theorized, more stringent programs can be less compatible with each other because they can require high levels of specificity and rigor, and by extension, limited flexibility. Stringent programs take this approach because they govern environmental issues that are idiosyncratic to their socioecological contexts (Wijen, 2014). We believe this result suggests that the “coupling” effect discussed by Tuczek et al. (2018) can ironically lead to decoupling substance from practice since firms tend to adopt multiple programs when they are lenient. Future research should investigate whether there are common microfoundations of stringent VEPs that could increase their compatibility if they are known to managers. Overall, our results suggest that stringency significantly affects firms’ ability to leverage their resource base to address environmental matters.

Our second contribution is that our HOMA synthesizes findings from existing research on individual VEPs on the institutional and resource-based drivers of firms’ participation. Prior research has yielded strong anecdotal evidence around these factors (Khanna, 2001; Koehler, 2007; Lyon & Maxwell, 2007). We provide another layer of evidence that these results hold in a cross-program study (Aragon-Correa et al., 2020; Tuczek et al., 2018). In particular, we show that environmental institutional pressures, whether formal or informal, consistently drive VEP participation, along with visibility or susceptibility to institutional pressures, while VEPs are less able to attract firms in weaker regulatory contexts where they are most needed (Schepers, 2010).

Our HOMA results also corroborate prior research on the resource-based drivers of VEP participation and provide additional evidence that firms are interested in leveraging prior experience in these programs through the “coupling” effect. First, the positive effect of prior financial performance on participation is consistent with single program studies that find it gives firms patient capital needed to use VEP adoption strategically (e.g., Arora & Cason, 1995; Darnall, 2006; King & Lenox, 2001, Moon & de Leon, 2007). The tentative support from partial correlations for a positive relationship between prior environmental performance and VEP participation suggests that VEPs do in fact offer low-hanging competitive fruit for firms that already have strong environmental performance (e.g., Bansal & Hunter, 2003; Prakash & Prakash, 2005a). This result also has implications for studies of VEP effectiveness that use selection models to control for prior environmental performance in first stage analyses. In particular, it supports prior research suggesting that selection models may overcontrol for prior environmental performance in the first analytical stage given its likely correlation with post hoc environmental performance. As a result, they may fail to detect benefits that VEPs create such as positive spillovers of environmental practices to nonparticipating firms (Matisoff, 2015). Second, results from our partial correlations analyses provide tentative support for prior research showing that firms join multiple VEPs or quality management standards to pursue the coupling effect, where firms attempt to leverage the knowledge gained from implementing multiple standards, and even develop that knowledge into standards competencies (Castka & Balzarova, 2018; De Vries et al., 2018). Still harmonizing standards from multiple VEPs and/or quality management programs is resource intensive and challenging (Wiegmann, 2019). Furthermore, as discussed earlier, firms taking this approach seem to join lenient VEPs that promote symbolic environmental practices.

Finally, our robustness tests add to the early evidence about whether specific rules of VEPs governance are more influential in attracting firms, and in doing so they answer calls to investigate whether “certain rules may play a stronger role than others” (Darnall et al., 2017, p. 448). One other study by Castka and Corbett (2016) addressed this question, finding that firms prefer programs with strong monitoring and enforcement criteria, but had no preference on the environmental performance standards. Thus, our results are confirmatory, but also provide new granularity by configuring the effect of stringency with institutional and resource-based drivers of participation.

Limitations and Future Research

Our study has several limitations that offer opportunities for future research. First, our measures of VEP stringency are summative of the presence of stringent attributes in a program’s governance design, which values all three attributes as equal in their impact on VEP participation, when they are actually likely to vary. Furthermore, the dichotomous measures that make up this variable fail to capture nuanced differences in the quality of environmental performance targets and practices in environmental management systems across VEPs. We operationalized stringency in this way because more robust independent measures have yet to be developed. However, future research should consider developing measures of stringency that capture not only the design of the program’s governance mechanisms but also the actual rigor through which its rules are implemented and enforced. One possibility is to use VEP effects on corporate environmental performance as a proxy for VEP stringency. Second, our study’s database excluded many quantitative (e.g., Castka & Corbett, 2016), qualitative (e.g., Boiral, 2007) and conceptual papers (e.g., Smith & Fischlein, 2010) with critical insights about the institutional and resource-based drivers of VEP participation. Thus, even though we have the largest meta-analytical sample on VEP participation to date (to our knowledge), its insights are limited to the focal research question on how VEP stringency impacts firm participation. Thus, we leave open many research questions that could be examined empirically from this broader literature. For example, researchers could build on Smith and Fischlein’s (2010) arguments that VEP attractiveness to firms depends in part on the relational advantages of their leaders and sponsors. Third, our methodology required us to only sample single-program studies, meaning that we were unable to include several multiprogram studies of the drivers of VEP participation. Fourth, several high profile VEPs, such as the Forest Stewardship Council and Marine Stewardship Council, were not in our sample because of a lack of quantitative research on them. We believe that this is an important research opportunity for scholars interested in this topic. Finally, the HOMA contains lower than ideal numbers of observations since we relied on subsamples of institutional- and resource-based drivers of VEP participation. Future research could address this limitation after more studies of individual programs have been conducted by replicating our meta-analysis.

Footnotes

Appendix

Table 6.

MARA Meta-Analytic Results of Program Stringency on Resource-Based Drivers of VEP Participation.

Variable	Model 4	Model 5	Model 6
Variable	Prior financial performance	Prior environmental performance	Other VEPs and quality management standards
Stringency	−0.05	0.06	0.01
Governance	0.02**	0.02	−0.05***
Methodological and study artifacts
Published study	−0.04***	−0.12***	0.10**
Median year of sample window	0.00	−0.00	−0.00**
Partial correlation	−0.04***	0.18***	−0.01
Panel design	−0.01	0.02	0.01
USA	0.06***	−0.08**	−0.14***
Firm-level	0.03**	0.07***	0.14***
k	136	473	38
R ²	.14	.17	.87
Q_model(p)	44.86	115.89	204.22
Q_residual(p)	266.78	571.72	31.05
V	0.00	0.04	0.00

Note. MARA = meta-analytic regression analysis; VEP = voluntary environmental program.

p < .10. ^**p < .05. ^***p < .01.

Acknowledgements

Peter A. Tashman would like to acknowledge generous support from the Donahue Center for Business Ethics and Social Responsibility at the University of Massachusetts Lowell. Svetlana Flankova would like to acknowledge generous support from the Swiss National Science Foundation (SNSF - P1SGP1_188071).

Declaration of Conflicting Interests

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

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Peter Tashman

Notes

Author Biographies

Peter Tashman (PhD, George Washington University) is an Assistant Professor of Management in the Manning School of Business and a Fellow with the Donahue Center for Business Ethics and Social Responsibility at the University of Massachusetts Lowell. His research focuses on how institutional complexity and natural resource dependence affect corporate social and environmental performance.

Svetlana Flankova is a PhD Candidate and Research Associate at the University of St. Gallen. Her research interests are at the intersection of strategy, corporate governance and environmental sustainability.

Marc van Essen (PhD, Erasmus University) is a Professor of International Business at the Darla Moore School of Business, University of South Carolina. His research interests include comparative corporate governance, international business, family business, and meta-analytic research methods.

Valentina Marano (PhD, University of South Carolina) is an Associate Professor in the International Business and Strategy Group at Northeastern University’s D’Amore-McKim School of Business. She explores the practice adoption, organizational legitimacy and performance of multinational corporations from both emerging and advanced economies, comparative corporate governance, and corporate social responsibility.

References

Aguinis

Gottfredson

Wright

(2011). Best-practice recommendations for estimating interaction effects using meta-analysis. Journal of Organizational Behavior, 32(8), 1033-1043. https://doi.org/10.1002/job.719

Ahmed

(2012). The design of voluntary environmental programs: Competition and incentives to participate. Strategic Behavior and the Environment, 2(3), 217-238. https://doi.org/10.1561/102.00000016

Albers

Günther

(2010). Disclose or not disclose: Determinants of social reporting for STOXX Europe 600 firms. Zeitschrift für Planung & Unternehmenssteuerung, 21(3), 323-347. https://doi.org/10.1007/s00187-010-0113-4

Albertini

(2013). Does environmental management improve financial performance? A meta-analytical review. Organization & Environment, 26(4), 431-457. https://doi.org/10.1177/1086026613510301

Antweiler

Harrison

(2007). Canada’s voluntary ARET program: Limited success despite industry co-sponsorship. Journal of Policy Analysis and Management, 26(4), 755-774. https://doi.org/10.1002/pam.20284

Aragon-Correa

J. A.

Marcus

A. A.

Vogel

(2020). The effects of mandatory and voluntary regulatory pressures on firms’ environmental strategies: A review and recommendations for future research. Academy of Management Annals, 14(1), 339-365. https://doi.org/10.5465/annals.2018.0014

Arora

Cason

(1995). An experiment in voluntary environmental regulation: Participation in EPA′ s 33/50 program. Journal of Environmental Economics and Management, 28(3), 271-286. https://doi.org/10.1006/jeem.1995.1018

Arora

Cason

(1996). Why do firms volunteer to exceed environmental regulations? Understanding participation in EPA’s 33/50 program. Land Economics, 74(4), 413-432. https://doi.org/10.2307/3146906

Babakri

Bennett

Franchetti

(2003). Critical factors for implementing ISO 14001 standard in United States industrial companies. Journal of Cleaner Production, 11(7), 749-752. https://doi.org/10.1016/S0959-6526(02)00146-4

10.

Baek

(2017). The diffusion of voluntary environmental programs: The case of ISO 14001 in Korea, 1996-2011. Journal of Business Ethics, 145(2), 325-336. https://doi.org/10.1007/s10551-015-2846-3

11.

Bansal

(2005). Evolving sustainably: A longitudinal study of corporate sustainable development. Strategic Management Journal, 26(3), 197-218. https://doi.org/10.1002/smj.441

12.

Bansal

Clelland

(2004). Talking trash: Legitimacy, impression management, and unsystematic risk in the context of the natural environment. Academy of Management Journal, 47(1), 93-103. https://doi.org/10.2307/20159562

13.

Bansal

Hunter

(2003). Strategic explanations for the early adoption of ISO 14001. Journal of Business Ethics, 46(3), 289-299. https://doi.org/10.1023/A:1025536731830

14.

Barnett

King

(2008). Good fences make good neighbors: A longitudinal analysis of an industry self-regulatory institution. Academy of Management Journal, 51(6), 1150-1170. https://doi.org/10.5465/amj.2008.35732609

15.

Barney

(1991). Firm resources and sustained competitive advantage. Journal of Management, 17(1), 99-120. https://doi.org/10.1177/014920639101700108

16.

Berchicci

King

(2007). 11 Postcards from the edge: A review of the business and environment literature. Academy of Management Annals, 1(1), 513-547. https://doi.org/10.5465/078559816

17.

Berliner

Prakash

(2012). From norms to programs: The United Nations Global Compact and global governance. Regulation & Governance, 6(2), 149-166. https://doi.org/10.1111/j.1748-5991.2012.01130.x

18.

Berrone

Fosfuri

Gelabert

(2017). Does greenwashing pay off? Understanding the relationship between environmental actions and environmental legitimacy. Journal of Business Ethics, 144(2), 363-379. https://doi.org/10.1007/s10551-015-2816-9

19.

Blackman

(2012). Does eco-certification boost regulatory compliance in developing countries? ISO 14001 in Mexico. Journal of Regulatory Economics, 42(3), 242-263. https://doi.org/10.1007/s11149-012-9199-y

20.

Boiral

(2007). Corporate greening through ISO 14001: A rational myth? Organization Science, 18(1), 127-146. https://doi.org/10.1287/orsc.1060.0224

21.

Borck

J. C.

Coglianese

(2009). Voluntary environmental programs: Assessing their effectiveness. Annual Review of Environment and Resources, 34, 305-324. https://doi.org/10.1146/annurev.environ.032908.091450

22.

Borenstein

Hedges

Higgins

Rothstein

(2009). Introduction to meta-analysis. John Wiley. https://doi.org/10.1002/9780470743386

23.

Brouhle

Graham

Harrington

D. R.

(2013). Innovation under the Climate Wise program. Resource and Energy Economics, 35(2), 91-112. https://doi.org/10.1016/j.reseneeco.2012.12.002

24.

Brouhle

Griffiths

Wolverton

(2009). Evaluating the role of EPA policy levers: An examination of a voluntary program and regulatory threat in the metal-finishing industry. Journal of Environmental Economics and Management, 57(2), 166-181. https://doi.org/10.1016/j.jeem.2008.07.006

25.

Buckley

Devinney

Tang

(2013). Meta-analytic research in international business and international management. In Devinney

T. M.

Pedersen

Tihanyi

(Eds.), Philosophy of science and meta-knowledge in international business and management (pp. 263-297). Emerald Group Publishing.

26.

Carmin

Darnall

Mil-Homens

(2003). Stakeholder involvement in the design of US voluntary environmental programs: Does sponsorship matter? Policy Studies Journal, 31(4), 527-543. https://doi.org/10.1111/1541-0072.00041

27.

Castka

Balzarova

M. A.

(2018). An exploration of interventions in ISO 9001 and ISO 14001 certification context: A multiple case study approach. Journal of Cleaner Production, 174(February), 1642-1652. https://doi.org/10.1016/j.jclepro.2017.11.096

28.

Castka

Corbett

C. J.

(2015). Management systems standards: diffusion, impact and governance of ISO 9000, ISO 14000, and other management standards. Foundations and Trends in Technology, Information and Operations Management, 7(3-4), 161-379. https://doi.org/10.1561/0200000042

29.

Castka

Corbett

C. J.

(2016). Adoption and diffusion of environmental and social standards. International Journal of Operations & Production Management, 36(11), 1504-1529. https://doi.org/10.1108/IJOPM-01-2015-0037

30.

Chapple

Cooke

Galt

Paton

(2001). The characteristics and attributes of UK firms obtaining accreditation to ISO 14001. Business Strategy and the Environment, 10(4), 238-244. https://doi.org/10.1002/bse.289

31.

Christmann

(2000). Effects of “best practices” of environmental management on cost advantage: The role of complementary assets. Academy of Management Journal, 43(4), 663-680. https://doi.org/10.2307/1556360

32.

Christmann

Taylor

(2001). Globalization and the environment: Determinants of firm self-regulation in China. Journal of International Business Studies, 32(3), 439-458. https://doi.org/10.1057/palgrave.jibs.8490976

33.

Corbett

C. J.

Kirsch

D. A.

(2001). International diffusion of ISO 14000 certification. Production and Operations Management, 10(3), 327-342. https://doi.org/10.1111/j.1937-5956.2001.tb00378.x

34.

Darnall

(2006). Why firms mandate ISO 14001 certification. Business & Society, 45(3), 354-381. https://doi.org/10.1177/0007650306289387

35.

Darnall

Carmin

(2005). Greener and cleaner? The signaling accuracy of US voluntary environmental programs. Policy Sciences, 38(2-3), 71-90. https://doi.org/10.1007/s11077-005-6591-9

36.

Darnall

Edwards

(2006). Predicting the cost of environmental management system adoption: The role of capabilities, resources and ownership structure. Strategic Management Journal, 27(4), 301-320. https://doi.org/10.1002/smj.518

37.

Darnall

Potoski

(2017). Institutional design of ecolabels: Sponsorship signals rule strength. Regulation & Governance, 11(4), 438-450. https://doi.org/10.1111/rego.12166

38.

Darnall

Vázquez-Brust

(2018). Third-party certification, sponsorship, and consumers’ ecolabel use. Journal of Business Ethics, 150(4), 953-969. https://doi.org/10.1007/s10551-016-3138-2

39.

Darnall

Kim

(2012). Which types of environmental management systems are related to greater environmental improvements? Public Administration Review, 72(3), 351-365. https://doi.org/10.1111/j.1540-6210.2011.02503.x

40.

Darnall

Potoski

Prakash

(2009). Sponsorship matters: Assessing business participation in government- and industry-sponsored voluntary environmental programs. Journal of Public Administration Research and Theory, 20(2), 283-307. https://doi.org/10.1093/jopart/mup014

41.

Darnall

Sides

(2008). Assessing the performance of voluntary environmental programs: Does certification matter? Policy Studies Journal, 36(1), 95-117. https://doi.org/10.1111/j.1541-0072.2007.00255.x

42.

Delmas

M. A.

Burbano

V. C.

(2011). The drivers of greenwashing. California Management Review, 54(1), 64-87. https://doi.org/10.1525/cmr.2011.54.1.64

43.

Delmas

M. A.

Montes-Sancho

(2010). An institutional perspective on the diffusion of international management system standards: The case of the environmental management standard ISO 14001. Business Ethics Quarterly, 21(1), 1-31. https://doi.org/10.5840/beq20112115

44.

Delmas

M. A.

Montiel

(2009). Greening the supply chain: When is customer pressure effective? Journal of Economics & Management Strategy, 18(1), 171-201. https://doi.org/10.1111/j.1530-9134.2009.00211.x

45.

De Vries

Jakobs

Egyedi

T. M.

Eto

Fertig

Kanevskaia

Klintner

Koch

Mijatovic

Mirtsch

Morone

Orviska

Riillo

Scaramuzzino

. (2018). Standardization: Towards an agenda for research. International Journal of Standardization Research, 16(1), 52-59. https://doi.org/10.4018/IJSR.2018010104

46.

Doucouliagos

Ulubaşoğlu

(2008). Democracy and economic growth: A meta-analysis. American Journal of Political Science, 52(1), 61-83. https://doi.org/10.1111/j.1540-5907.2007.00299.x

47.

Duran

Kammerlander

Van Essen

Zellweger

(2016). Doing more with less: Innovation input and output in family firms. Academy of Management Journal, 59(4), 1224-1264. https://doi.org/10.5465/amj.2014.0424

48.

Duval

Tweedie

(2000). Trim and fill: A simple funnel-plot-based method of testing and adjusting for publication bias in meta-analysis. Biometrics, 56(2), 455-463. https://doi.org/10.1111/j.0006-341X.2000.00455.x

49.

Fischer

Lyon

T. P.

(2014). Competing environmental labels. Journal of Economics & Management Strategy, 23(3), 692-716. https://doi.org/10.1111/jems.12061

50.

Frooman

(1999). Stakeholder influence strategies. Academy of Management Review, 24(2), 191-205. https://doi.org/10.5465/amr.1999.1893928

51.

Gamper-Rabindran

Finger

(2013). Does industry self-regulation reduce pollution? Responsible care in the chemical industry. Journal of Regulatory Economics, 43(1), 1-30. https://doi.org/10.1007/s11149-012-9197-0

52.

Geyskens

Krishnan

Steenkamp

Cunha

(2009). A review and evaluation of meta-analysis practices in management research. Journal of Management, 35(2), 393-419. https://doi.org/10.1177/0149206308328501

53.

Gonzalez-Mulé

Aguinis

(2018). Advancing theory by assessing boundary conditions with meta-regression: A critical review and best-practice recommendations. Journal of Management, 44(6), 2246-2273. https://doi.org/10.1177/0149206317710723

54.

Harrison

Banks

Pollack

O’Boyle

Short

(2017). Publication bias in strategic management research. Journal of Management, 43(2), 400-425. https://doi.org/10.1177/0149206314535438

55.

Hart

(1995). A natural-resource-based view of the firm. Academy of Management Review, 20(4), 986-1014. https://doi.org/10.5465/amr.1995.9512280033

56.

Hedges

Olkin

(1985). Statistical methods for meta-analysis. Academic Press.

57.

Hedges

Vevea

(2005). Selection method approaches. In Rothstein

Sutton

Borenstein

(Eds.), Publication bias in meta-analysis: Prevention, assessment, and adjustments (pp. 145-174). John Wiley. https://doi.org/10.1002/0470870168.ch9

58.

Heras-Saizarbitoria

Boiral

(2013). ISO 9001 and ISO 14001: Towards a research agenda on management system standards. International Journal of Management Reviews, 15(1), 47-65. https://doi.org/10.1111/j.1468-2370.2012.00334.x

59.

Heras-Saizarbitoria

Molina-Azorín

Dick

(2011). ISO 14001 certification and financial performance: Selection-effect versus treatment-effect. Journal of Cleaner Production, 19(1), 1-12. https://doi.org/10.1016/j.jclepro.2010.09.002

60.

Heugens

P. P.

van Essen

van Oosterhout

J. H.

(2009). Meta-analyzing ownership concentration and firm performance in Asia: Towards a more fine-grained understanding. Asia Pacific Journal of Management, 26(3), 481-512. https://doi.org/10.1007/s10490-008-9109-0

61.

Hoffman

(1999). Institutional evolution and change: Environmentalism and the US chemical industry. Academy of Management Journal, 42(4), 351-371. https://doi.org/10.5465/257008

62.

Hoffman

(2011). Talking past each other? Cultural framing of skeptical and convinced logics in the climate change debate. Organization & Environment, 24(1), 3-33. https://doi.org/10.1177/1086026611404336

63.

Hoffman

Jennings

(2018). Institutional-political scenarios for anthropocene society. Business & Society, 60(1), 59-94. https://doi.org/10.1177/0007650318816468

64.

Hunter

J. E.

Schmidt

F. L.

(2004). Methods of meta-analysis: Correcting error and bias in research findings. Sage.

65.

Innes

Sam

(2008). Voluntary pollution reductions and the enforcement of environmental law: An empirical study of the 33/50 program. Journal of Law and Economics, 51(2), 271-296. https://doi.org/10.1086/589659

66.

Kayser

S. A.

Maxwell

J. W.

Toffel

M. W.

(2014). Supply chain screening without certification: The critical role of stakeholder pressure. Harvard Business School (Working Paper, 15-009).

67.

Khanna

(2001). Non-mandatory approaches to environmental protection. Journal of Economic Surveys, 15(3), 291-325. https://doi.org/10.1111/1467-6419.00141

68.

Khanna

Damon

(1999). EPA’s voluntary 33/50 program: Impact on toxic releases and economic performance of firms. Journal of Environmental Economics and Management, 37(1), 1-25. https://doi.org/10.1006/jeem.1998.1057

69.

Khanna

Koss

Jones

Ervin

(2007). Motivations for voluntary environmental management. Policy Studies Journal, 35(4), 751-772. https://doi.org/10.1111/j.1541-0072.2007.00246.x

70.

King

Lenox

(2000). Industry self-regulation without sanctions: The chemical industry’s responsible care program. Academy of Management Journal, 43(4), 698-716. https://doi.org/10.2307/1556362

71.

King

Lenox

(2001). Does it really pay to be green? An empirical study of firm environmental and financial performance: An empirical study of firm environmental and financial performance. Journal of Industrial Ecology, 5(1), 105-116. https://doi.org/10.1162/108819801753358526

72.

King

Lenox

Terlaak

(2005). The strategic use of decentralized institutions: Exploring certification with the ISO 14001 management standard. Academy of Management Journal, 48(6), 1091-1106. https://doi.org/10.5465/amj.2005.19573111

73.

Kirca

A. H.

Hult

G. T. M.

Deligonul

Perryy

M. Z.

Cavusgil

S. T.

(2012). A multilevel examination of the drivers of firm multinationality: A meta-analysis. Journal of Management, 38(2), 502-530. https://doi.org/10.1177/0149206310369177

74.

Koehler

D. A.

(2007). The effectiveness of voluntary environmental programs: A policy at a crossroads? Policy Studies Journal, 35(4), 689-722. https://doi.org/10.1111/j.1541-0072.2007.00244.x

75.

Kollman

Prakash

(2001). Green by choice? Cross-national variations in firms’ responses to EMS-based environmental regimes. World Politics, 53(3), 399-430. https://doi.org/10.1353/wp.2001.0010

76.

Lipsey

M. W.

Wilson

D. B.

(2001). Practical meta-analysis. Sage.

77.

Lyon

T. P.

Maxwell

J. W.

(2007). Environmental public voluntary programs reconsidered. Policy Studies Journal, 35(4), 723-750. https://doi.org/10.1111/j.1541-0072.2007.00245.x

78.

Matisoff

(2015). Sources of specification errors in the assessment of voluntary environmental programs: Understanding program impacts. Policy Sciences, 48(1), 109-126. https://doi.org/10.1007/s11077-014-9204-7

79.

Mazur

Welch

(1999). The geography of American environmentalism. Environmental Science and Policy, 2(4-5), 389-396. https://doi.org/10.1016/S1462-9011(99)00033-7

80.

Montiel

Husted

(2009). The adoption of voluntary environmental management programs in Mexico: First movers as institutional entrepreneurs. Journal of Business Ethics, 88(2), 349-363. https://doi.org/10.1007/s10551-009-0282-y

81.

Moon

S.-G.

de Leon

(2007). Contexts and corporate voluntary environmental behaviors: Examining the EPA’s green lights voluntary program. Organization & Environment, 20(4), 480-496. https://doi.org/10.1177/1086026607309395

82.

North

(1990). Institutional change and economic performance. Cambridge University Press. https://doi.org/10.1017/CBO9780511808678

83.

Oliver

(1991). Strategic responses to institutional processes. Academy of Management Review, 16(1), 145-179. https://doi.org/10.5465/amr.1991.4279002

84.

Passetti

Tenucci

(2016). Eco-efficiency measurement and the influence of organizational factors: Evidence from large Italian companies. Journal of Cleaner Production, 122(May), 228-239. https://doi.org/10.1016/j.jclepro.2016.02.035

85.

Peng

M. W.

(2002). Towards an institution-based view of business strategy. Asia Pacific Journal of Management, 19(2-3), 251-267. https://doi.org/10.1023/A:1016291702714

86.

Potoski

Prakash

(2005a). Covenants with weak swords: ISO 14001 and facilities’ environmental performance. Journal of Policy Analysis and Management, 24(4), 745-769. https://doi.org/10.1002/pam.20136

87.

Potoski

Prakash

(2005b). Green clubs and voluntary governance: ISO 14001 and firms’ regulatory compliance. American Journal of Political Science, 49(2), 235-248. https://doi.org/10.1111/j.0092-5853.2005.00120.x

88.

Prakash

Potoski

(2007). Collective action through voluntary environmental programs: A club theory perspective. Policy Studies Journal, 35(4), 773-792. https://doi.org/10.1111/j.1541-0072.2007.00247.x

89.

Prakash

Potoski

(2012). Voluntary environmental programs: A comparative perspective. Journal of Policy Analysis and Management, 31(1), 123-138. https://doi.org/10.1002/pam.20617

90.

Rivera

(2002). Assessing a voluntary environmental initiative in the developing world: The Costa Rican Certification for Sustainable Tourism. Policy Sciences, 35(4), 333-360. https://doi.org/10.1023/A:1021371011105

91.

Rivera

de Leon

(2004). Is greener whiter? Voluntary environmental performance of western ski areas. Policy Studies Journal, 32(3), 417-437. https://doi.org/10.1111/j.1541-0072.2004.00073.x

92.

Rivera

de Leon

(2008). Voluntary environmental programs: Are carrots without sticks enough? Policy Studies Journal, 36(1), 61-63. https://doi.org/10.1111/j.1541-0072.2007.00253.x

93.

Rockström

Steffen

Noone

Persson

Å.

Chapin

F. S.

III Lambin

E. F.

Lenton

T. M.

Scheffer

Folke

Schellnhuber

H. J.

Nykvist

Cynthia

de Wit

C. A.

Hughes

van der Leeuw

Rodhe

Sörlin

Snyder

P. K.

Costanza

. . . Foley

J. A.

(2009). A safe operating space for humanity. Nature, 461, 472-475. https://doi.org/10.1038/461472a

94.

Rosenthal

(1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638-641. https://doi.org/10.1037/0033-2909.86.3.638

95.

Russo

M. V.

Fouts

P. A.

(1997). A resource-based perspective on corporate environmental performance and profitability. Academy of Management Journal, 40(3), 534-559. https://doi.org/10.2307/257052

96.

Schepers

(2010). Challenges to legitimacy at the Forest Stewardship Council. Journal of Business Ethics, 92(2), 279-290. https://doi.org/10.1007/s10551-009-0154-5

97.

Sharma

Vredenburg

(1998). Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities. Strategic Management Journal, 19(8), 729-753. https://doi.org/10.1002/(SICI)1097-0266(199808)19:8<729::AID-SMJ967>3.0.CO;2-4

98.

Short

J. L.

Toffel

M. W.

(2010). Making self-regulation more than merely symbolic: The critical role of the legal environment. Administrative Science Quarterly, 55(3), 361-396. https://doi.org/10.2189/asqu.2010.55.3.361

99.

Smith

T. M.

Fischlein

(2010). Rival private governance networks: Competing to define the rules of sustainability performance. Global Environmental Change, 20(3), 511-522. https://doi.org/10.1016/j.gloenvcha.2010.03.006

100.

Stanley

Doucouliagos

(2012). Meta-regression analysis in economics and business (Vol. 5). Routledge. https://doi.org/10.4324/9780203111710

101.

Stanley

Doucouliagos

Giles

Heckemeyer

Johnston

Laroche

Nelson

J. P.

Paldam

Poot

Pugh

Rosenberger

R. S.

Rost

(2013). Meta-analysis of economics research reporting guidelines. Journal of Economic Surveys, 27(2), 390-394. https://doi.org/10.1111/joes.12008

102.

Tashman

Marano

Kostova

(2019). Walking the walk or talking the talk? Corporate social responsibility decoupling in emerging market multinationals. Journal of International Business Studies, 50(2), 153-171. https://doi.org/10.1057/s41267-018-0171-7

103.

Teece

(1986). Profiting from technological innovation: Implications for integration, collaboration, licensing, and public policy. Research Policy, 15(6), 285-305. https://doi.org/10.1016/0048-7333(86)90027-2

104.

Tuczek

Castka

Wakolbinger

(2018). A review of management theories in the context of quality, environmental and social responsibility quality management standards. Journal of Cleaner Production, 176(March), 399-416. https://doi.org/10.1016/j.jclepro.2017.12.161

105.

Ullah

Wei

Xie

(2014). ISO certification, financial constraints, and firm performance in Latin American and Caribbean countries. Global Finance Journal, 25(3), 203-228. https://doi.org/10.1016/j.gfj.2014.10.003

106.

Videras

Alberini

(2000). The appeal of voluntary environmental programs: Which firms participate and why? Contemporary Economic Policy, 18(4), 449-460. https://doi.org/10.1111/j.1465-7287.2000.tb00041.x

107.

Vidovic

Khanna

(2012). Is voluntary pollution abatement in the absence of a carrot or stick effective? Evidence from facility participation in the EPA’s 33/50 program. Environmental and Resource Economics, 52(3), 369-393. https://doi.org/10.1007/s10640-011-9533-3

108.

Viechtbauer

Cheung

(2010). Outlier and influence diagnostics for meta-analysis. Research Synthesis Methods, 1(2), 112-125. https://doi.org/10.1002/jrsm.11

109.

Wiegmann

P. M.

(2019). Managing innovation and standards: A case in the European heating industry. Palgrave Macmillan.

110.

Wijen

(2014). Means versus ends in opaque institutional fields: Trading off compliance and achievement in sustainability standard adoption. Academy of Management Review, 39(3), 302-323. https://doi.org/10.5465/amr.2012.0218