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Journal of Operations Management 30 (2012) 69–84

Contents lists available at ScienceDirect

Journal of Operations Management

jo ur nal home page: www.elsev ier .com/ locate / jom

he competitive determinants of a firm’s environmental management activities:vidence from US manufacturing industries

hristian Hofera,∗, David E. Cantorb, Jing Daic

Sam M. Walton College of Business, WCOB 475, University of Arkansas, Fayetteville, AR 72701, United StatesCollege of Business, Iowa State University, 3119 Gerdin Business Building, Ames, IA 50011, United StatesCollege of Business, Iowa State University, 3132 Gerdin Business Building, Ames, IA 50011, United States

r t i c l e i n f o

rticle history:eceived 25 August 2010eceived in revised form 1 June 2011ccepted 14 June 2011vailable online 28 June 2011

a b s t r a c t

Environmental management (EM) issues have received substantial attention in operations management.While the link between EM practices and firm performance has been well studied, little is known aboutthe competitive drivers of a firm’s EM activities. In this research, a Schumpeterian economics perspectiveis adopted to investigate competitive interactions among leader and challenger firms in the domain of EM,with a particular focus on operational EM activities. Using econometric methods, the empirical analysis of

eywords:nvironmental managementompetitionS manufacturing industries

panel data from a broad cross-section of US manufacturing firms reveals that such rivalry does exist andthat the effect of a rival’s past EM activity on a focal firm’s EM activity is greater for more profitable andsmaller firms. In addition, firm characteristics such as market leadership, firm size and firm profitabilityare found to significantly affect the magnitude of a firm’s EM activities. This study presents theoreticaland empirical evidence of rivalrous behaviors in the domains of EM and OM and, thus, has interestingimplications for operations management research and practice.

. Introduction

Environmental management (EM) is an important operationsanagement (OM) topic (Klassen and McLaughlin, 1996; Gattiker

nd Carter, 2010; Sarkis et al., 2010). As stakeholder pressuresount (Rondinelli and Vastag, 1996), firms adopt EM activities toonitor and control the impact of their operations on the natural

nvironment. Montabon et al. (2007) suggest that firms evaluatehe impact of their EM activities across operational, tactical, andtrategic EM activities. At the operational level, firms seek ways toeduce the amount of emissions and waste generated while savingosts at the same time. Hence, firms incorporate environmentalonsiderations into plant-level operations by recycling produc-ion materials, replacing environmentally problematic materials,nd using returnable or reusable packaging and pallets, for exam-le. From a tactical perspective, environmental considerations are

mportant to OM because customers demand products that arenvironmentally sustainable from design to distribution. Thus, theM function makes product design and sourcing decisions based onnvironmental criteria. Likewise, environmentally oriented reverse

ogistics activities have received significant attention in OM litera-ure and practice (Sarkis et al., 2010). From a strategic perspective,rms are under pressure from key stakeholders to develop strate-

∗ Corresponding author. Tel.: +1 4795756154.E-mail address: chofer@walton.uark.edu (C. Hofer).

272-6963/$ – see front matter © 2011 Elsevier B.V. All rights reserved.oi:10.1016/j.jom.2011.06.002

© 2011 Elsevier B.V. All rights reserved.

gies, policies and practices that are aligned with the organization’senvironmental goals (Klassen, 1993). The resulting changes in afirm’s strategic orientation and resource allocations have affectedthe practice of OM and the way operational performance is assessed(Kleindorfer et al., 2005).

A burgeoning amount of OM research has shown that imple-menting EM activities may result in improved firm performance(Klassen and McLaughlin, 1996; Melnyk et al., 2003; Montabonet al., 2007). For example, Klassen and McLaughlin (1996) foundthat EM announcements are positively correlated with a firm’smarket valuation. Similarly, Montabon et al. (2007) presented acontent analysis of EM practices and concluded that EM activitiesare associated with product innovation, process innovation, andsales growth. Hence, there is evidence that the implementation ofEM activities is associated with competitive advantage.

The aforementioned studies, among many others, have madesignificant contributions to the OM literature. Our study addsto this stream of research by examining the firm-level compet-itive determinants of EM activities among market leaders andchallengers. In so doing, we analyze a firm’s overall EM activityas well as the subset of operational EM activities as defined byMontabon et al. (2007). Specifically, this study addresses the fol-lowing research questions: Does a rival firm’s past EM activity

impact a focal firm’s EM activity? Do firm characteristics such asmarket leadership, size and profitability moderate this relation-ship? To address these questions, this study applies theoreticaland empirical concepts from both the fields of Schumpeterian

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conomics and OM. Within the strategic management literature,he Schumpeterian perspective is a well-established theoreticalens that is used to provide insight into why firms engage in com-etitive behaviors (Chen et al., 1992; Ferrier et al., 1999; Grimmnd Smith, 1997; Basdeo et al., 2006; Young et al., 1996). Likewise,he Schumpeterian view has been applied in prior OM research.yerson and Pilkington (2005), for example, draw on Schumpete-

ian arguments to study large incumbent firms’ advantages overmaller entrants in light of disruptive technological change broughtbout by the introduction of electric vehicles in California. Grimaud1999) develops an analytical model grounded in Schumpeterianconomics to study the trade-off between environmental qualitynd economic growth and proposes policy instruments that leado optimal growth paths. Lavie (2006), an example of a concep-ual paper, draws on the Schumpeterian perspective to discuss howncumbent firms respond to technological change by reconfiguringrganizational capabilities.

With its focus on competitive moves and countermoves, thechumpeterian perspective is well-suited for our study of theompetitive determinants of a firm’s overall and operational EMctivities. The central tenet of Schumpeterian economics is that arm’s competitive activity instigates a rival’s competitive response,ltimately leading to cycles of “creative destruction” of competitivedvantage (Grimm and Smith, 1997). Firms implement EM activ-ties in an effort to create efficiencies and to obtain stakeholderpproval and, thus, gain competitive advantage and realize supe-ior returns (Klassen and McLaughlin, 1996; Christmann, 2000;elnyk et al., 2003; Montabon et al., 2007). Rival firms will often

opy or otherwise counteract such competitive moves. For exam-le, by 2007, Coca-Cola had lowered the weight of its Dasani PETottles by 35% as part of its efforts to “meet [. . .] economic, socialnd environmental needs” (The Coca-Cola Company, 2007). By thend of 2008, Pepsico, Coca-Cola’s principal rival, announced that itad reduced the bottle weight of its competing Aquafina product by0% (Pepsi Bottling Ventures, 2008). Clearly, the OM function of therm serves as a critical enabler of these activities for Coca-Cola andepsico.

Our empirical analysis is directly related to the Schumpete-ian framework in several ways. First, we examine competitiveoves and countermoves in the domain of EM between rival firms

ver time, since such actions are not synchronous. Rather, theres a time lag between the observation of a firm’s EM activity andhe competitor’s reaction (Ferrier et al., 1999). While prior studiesave focused on competitive interactions in terms of market-basedctions such as price reductions, new product introductions, andicensing activities (Ferrier et al., 1999; Smith et al., 1991; Youngt al., 1996), our research is focused on both overall and opera-ional EM actions. Second, and in line with prior Schumpeterianesearch, our empirical analysis focuses on market leader and chal-enger firms, i.e. the top two competing firms as defined by their

arket shares (e.g., Ferrier et al., 1999). Previous Schumpeterianesearch emphasized that no leadership position is secure or sus-ainable per se and that market leaders and challengers are engagedn constant rivalry (D’Aveni, 1994). Third, similar to prior Schum-eterian research, we examine how firm-level characteristics mayffect the competitive behavior of leaders and challengers in theontext of EM. For example, Schumpeter (1934) noted that largerms are in a better position to design and implement competitivections due to greater financial and human resource availability.ence, we investigate how firm characteristics such as firm sizeffect a firm’s EM activity and the magnitude of its response to aival’s EM activity.

This study makes several contributions to the OM literature.mportantly, this is the first study to investigate the competitiveeterminants of market leader and challenger firms’ EM activi-ies at both the overall and operational levels. In so doing, this

Management 30 (2012) 69–84

research contributes to the literature by applying the theoreticalconcepts from Schumpeterian economics to the study of opera-tions and environmental management, thus further highlightingthe strategic implications of OM activities for the firm (Boyeret al., 2005; Berry and Taggart, 1994; Lavie, 2006). Moreover,only a limited amount of OM research has empirically analyzedmulti-year data on firm-level EM activity. Building on the work ofMontabon et al. (2007), this study fills that gap by constructing aunique panel data set of overall and operational level EM activ-ity, extracted from firms’ annual corporate environmental reports,spanning the 2006–2009 time period. The use of corporate envi-ronmental reports responds to the call for the use of innovativedata sources in OM (Boyer and Swink, 2008) and is consistent withprior research in the OM area (Montabon et al., 2007; Tate et al.,2010). A centering resonance analysis software tool is used to ana-lyze these reports (Corman and Dooley, 2006), and the resulting EMactivity data are augmented with secondary financial data. Finally,this study applies rigorous econometric techniques and presentscompelling evidence in support of the theoretical framework andresearch hypotheses.

2. Literature review

Environmental management is part of the broader realm ofsustainability that has received increasing attention in operationsmanagement (Kleindorfer et al., 2005; Linton et al., 2007). WithinOM, most of the sustainability literature focuses on EM (White andLee, 2009). However, several studies also examine issues of socialresponsibility in a supply chain context (e.g., Carter and Jennings,2002, 2004) and examine how social and environmental manage-ment activities interact to affect performance outcomes (Pullmanet al., 2009; Carter and Rogers, 2008; Pagell and Gobeli, 2009).

In line with the purpose of this study, this literature reviewprimarily focuses on EM within the OM literature. Indeed, a signif-icant amount of research has examined the performance outcomesand antecedents of environmental management practices (Angelland Klassen, 1999). Numerous articles on these topics have beenpublished in a variety of OM and operations research journals.In this section, we summarize this body of research and reviewthe theoretical bases that have been applied to the study of EMissues.

2.1. The performance outcomes of EM

One stream of research examines the relationship between EMand organizational or environmental performance outcomes. Forexample, Melnyk et al. (2003) and Zhu and Sarkis (2004) con-ducted survey studies and found that firms adopting EM practicestend to see improved environmental and operational performance.Montabon et al. (2007) analyzed a cross-section of 45 corporateenvironmental reports and found evidence of a positive relation-ship between EM practices and firm performance. Klassen andMcLaughlin (1996) conducted an event study and found that pos-itive environmental events are correlated with a firm’s marketvaluation. However, Jacobs et al. (2010) collected data from thedaily business press and found mixed results regarding the linkbetween EM and the market value of the firm.

2.2. The antecedents of EM

There is also an increasing amount of interest in examiningthe antecedents of EM in the field of operations management.

Prior research has investigated how external, organizational andindividual level factors contribute to EM activity. External factorsinclude legislation and regulation (Foster et al., 2000), as well asstakeholder pressures and involvement (Delmas, 2001; Delmas

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C. Hofer et al. / Journal of Opera

nd Toffel, 2008; Reuter et al., 2010; Sarkis et al., 2010; Simpsont al., 2007). At the organizational level, prior research has high-ighted the importance of top management commitment (Angell,001; Branzei et al., 2004; Gattiker and Carter, 2010; Klassen, 2001),esource and management system availability (Aragón-Correa andharma, 2003; Sroufe, 2003), and communication and trainingChinander, 2001; Sarkis et al., 2010), for example. Individual levelttitudes, experiences, and preferences have also been identifieds important predictors of environmental commitment (Corbettnd Kirsch, 2001; Klassen, 2001; Pagell and Gobeli, 2009; Sharma,000).

While these studies have made significant contributions to theiterature, there is a paucity of research on the competitive deter-

inants of EM. Prior research has acknowledged that competitiveressure, i.e. the pursuit of competitive advantage, contributes to arm’s EM activity (Bansal and Roth, 2000), but the specific effectsf the magnitude of a rival’s past EM activities on a focal firm’s EMfforts remain unexplored. This study contributes to existing liter-ture by examining the extent to which leader and challenger firmsmplement EM activities in response to their rivals’ EM efforts.

.3. Theoretical bases of EM research

From a theoretical perspective, several studies have adoptedtakeholder theory to study the determinants of EM prac-ices. Stakeholders include workers, customers, shareholders,overnment and nongovernmental organizations, as well ashe community (e.g., Bansal and Roth, 2000; Sharma, 2000).uilding on stakeholder theory, Sarkis et al. (2010) suggesthat stakeholders pressure the firm to adopt environmentalractices, and that this relationship is mediated by trainingrograms.

Institutional theory has also been applied to the study of EMHoffman, 1999; Brown et al., 2006; Fowler and Hope, 2007; Tatet al., 2010). According to institutional theory, the social, political,nd economic environments influence firms’ strategies and organi-ational decision-making (North, 1996). Hence, institutional theorys helpful in explaining how changes in social values, technologicaldvancements, and regulations affect decisions regarding EM activ-ties. For example, Delmas and Toffel (2008) draw on institutionalheory to examine how different organizational strategies leadacilities to adopt EM practices. Related to our study, the conceptf mimetic isomorphism (DiMaggio and Powell, 1983), a centrallement of institutional theory, helps to explain how a rival’s EMerformance creates peer pressure on firms to increase their EMctivity.

In this study, we are particularly interested in examining theompetitive drivers of EM activity. There are, of course, multipleheories of competition and competitive advantage in the man-gement and economics literature (Conner, 1991). For example,everal scholars have examined competition from a resource-basediew which contends that valuable, rare, inimitable, and non-ubstitutable resources allow firms to gain competitive advantagesBarney, 1986). Proponents of transaction cost economics theory,n turn, argue that a firm’s competitive advantage is rooted ints ability to minimize transaction costs by selectively outsourc-ng some activities while organizing others in-house, dependingn the characteristics of the transaction (Williamson, 1975). Whilehe aforementioned theoretical perspectives represent importantontributions to the study of competition, their central focus is onhe sources of competitive advantage rather than on the process ofompetition, i.e. the determinants of competitive activity. There-ore, we draw on the Schumpeterian perspective to further our

nderstanding of the competitive determinants of a firm’s EM activ-

ties and how various firm-level characteristics drive the intensityf rivalrous exchanges in the domain of EM.

Management 30 (2012) 69–84 71

3. Theoretical background and hypothesis development

Schumpeterian economics is an important theoretical lens thathas been widely applied to the study of the determinants of com-petitive activity (Young et al., 1996). The Schumpeterian view isbased on the contention that competitive actions trigger compet-itive responses by rivals (Schumpeter, 1934, 1942) and providesdetailed insights into the competitive dynamics among rival firms.According to Schumpeter, firms and markets evolve through pat-terns of competitive actions and reactions that create and erodecompetitive advantage (Grimm and Smith, 1997). Firms that takesuccessful actions can reap performance advantages over those thatdo not act. However, no permanent equilibrium is ever reachedbecause the visible profit of the acting firm motivates the rivalto respond (Grimm and Smith, 1997). The range of possible com-petitive actions available to firms varies from tactical moves thatrequire limited resources, such as price cuts, new advertising pro-motions, and service improvements, to strategic moves that requiremore substantial commitments of specific resources and are moredifficult to reverse, such as domain changes, facilities expansions,strategic alliances, acquisitions, and new product or service intro-ductions (Chen and MacMillan, 1992; Miller and Chen, 1994; Smithet al., 1991).

Given the previously discussed performance-enhancing effectsof EM activities, the Schumpeterian perspective is a suitable theo-retical framework for our study of the competitive determinantsof a firm’s EM efforts. Indeed, the Schumpeterian perspectiveis an important theoretical perspective that has been appliedto the study of technological innovation and change (e.g., Berryand Taggart, 1994; Lavie, 2006), green innovation and technol-ogy (Phillimore, 2001; Dyerson and Pilkington, 2005), as well asenvironmental permitting (Grimaud, 1999).

Based on the Schumpeterian perspective, we develop a model ofthe competitive determinants of EM activity. Specifically, we pro-pose that a rival’s past EM activity affects a focal firm’s EM activity.In addition, firm characteristics such as market leadership, firmsize, and firm profitability are hypothesized to affect firm’s EMactivities. It is also suggested that these firm characteristics moder-ate the effect of a rival firm’s EM activity on a focal firm’s EM activity.The resulting research model is outlined in Fig. 1 and each of thehypothesized relationships is further discussed in this section.

To enhance our understanding of the Schumpeterian per-spective, insights from signaling theory are integrated into thisframework. As Porter (1980) explains, “a signal is any action bya competitor that provides a direct or indirect indication of itsintentions, motives, goals, or internal situation” (p. 75). In essence,signals convey information about product quality (Engers, 1987;Kihlstrom and Riordan, 1984; Nelson, 1974; Riley, 1975), firmreputation, or the competitive intentions of rivals (Fombrun andShanley, 1990; Heil and Robertson, 1991) to market participants.Thus, competitive actions and reactions signal to the market andto stakeholders the value that a firm is creating for them (Clarkand Montgomery, 1998; Rindova and Fombrun, 1999; Basdeo et al.,2006). Competitive actions serve as signals of a firm’s competi-tive aggressiveness (e.g., Clark and Montgomery, 1998; Chen et al.,1992), capabilities (Grimm and Smith, 1997), and market position(Ferrier et al., 1999; Grimm et al., 2005). Clark and Montgomery(1998) applied the signaling perspective in a game theory simula-tion to examine how the actions of a firm influence the perceptionsof market participants regarding its capabilities as a competitor.Their study provides empirical support for the contention that firmactions serve as signals that instigate responses from its rivals.

above, the central tenet of this theoretical framework is that com-petitive actions are signals that lead to competitive responses, thus

72 C. Hofer et al. / Journal of Operations Management 30 (2012) 69–84

Focal FirmEM Activity

Focal Firm Characteristics:• Market Leadership• Firm Size• Firm Profitability

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nstigating cycles of competitive interactions between rival firms.M practices are actions that are implemented to satisfy stake-older pressures, create operational efficiencies, seize strategicpportunities, and, thereby, gain competitive advantage. Therefore,t is expected that a rival’s past EM activity will impact the naturend magnitude of a focal firm’s EM activity.

The computer industry provides an example of competitivections and responses in the domain of EM. Apple Corporation onlyegan to reveal the amount of carbon emissions associated withhe manufacture and use of its products when it discovered that theack of environmental disclosure, especially relative to its competi-or Dell, hurt the company as reflected in its poor environmentalatings (Engardio et al., 2007). At the same time, Apple announcedhat it was ending the use of environmentally problematic materi-ls such as polyvinyl chlorides (PVCs) and bromide flame retardantsBFRs) in its devices. This announcement put Apple well ahead ofts rival Dell, which had previously set the same goal but had notet achieved it (Burrows, 2009). Thus, there is both theoretical andnecdotal evidence in support of the key contention of this study: aival’s past EM activities, both at the overall and operational levels,ill spur a focal firm’s EM activity.

ypothesis 1. The greater the rival firm’s past EM activity, thereater the focal firm’s EM activity.

Consistent with prior Schumpeterian economics research, its expected that several firm-level characteristics, including mar-et leadership, firm size, and firm profitability, will impact theocal firm’s EM efforts and affect the magnitude of the focal firm’sesponse to a rival firm’s EM activity. In the subsequent paragraphs,hese concepts are discussed and applied to the EM research model,nd associated hypotheses are presented.

The first firm-level determinant of a firm’s EM activity is mar-et leadership. A market leader is defined as the firm with theargest share in a given market, while a challenger is defined ashe firm with the second largest market share. As noted by D’Aveni1994), no leadership position is secure or sustainable per se sinceompetitive advantage is short lived and firms must undertake aeries of actions to ensure long-term viability and market leader-hip. Yet, Miller and Chen (1994) found empirical evidence thatood past performance contributed to competitive inertia and lackf competitive aggressiveness among leading firms. At the sameime, challengers recognize the opportunity to improve their mar-et position by competing more aggressively (Ferrier et al., 1999).necdotal evidence in support of this contention in the context ofM exists in the paperboard mills industry. Smurfit-Stone, marketeader at the time, fully implemented and certified sustainable fiberourcing practices nearly three years after Meadwestvaco, its main

ival, had done so (MeadWestvaco, 2008; Smurfit-Stone, 2011). Inact, Meadwestvaco, while being the challenger firm in its indus-ry, advertises itself as a “leader in sustainability” (MeadWestvaco,008) based on its achievements in terms of operational EM

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activities including emissions and waste reduction, resource con-servation, materials sourcing, and product design.

Hypothesis 2. The market leader’s EM activity is lower than thatof its challenger.

Firm size is another important determinant of a firm’s compet-itive behavior (Schumpeter, 1942). Firms that are large in terms ofabsolute size have vast resources that can be used to invest in amyriad of actions including EM activities. Indeed, there is a longline of empirical work that provides mixed evidence of the rela-tionship between the size of the firm and its competitive behavior(Grimm and Smith, 1997). One viewpoint suggests that large firmssuffer from bureaucratic inertia, which can stifle innovation andoverall firm productivity (Hannan and Freeman, 1984). However,the Schumpeterian view is that large firms are useful and valuablebecause they are more capable of producing competitive actionsand reactions. Schumpeter (1934) argues that large firms providea more suitable arrangement for the utilization of the factors ofproduction than smaller firms. Large firms benefit from specializedhuman and physical resources required to promote and implementEM activities within the boundaries of the firm. For example, theinvestment in environmental management systems (EMS) repre-sents a tremendous burden to firms since such systems requiresubstantial investments in information technology.

Furthermore, large firms are typically engaged in multiple EMactivities (Montabon et al., 2007; Tate et al., 2010). Hence, largefirms may be able to benefit from the economies of scale advantagesassociated with larger portfolios of EM projects. In addition, largefirms have knowledge management systems in place that facilitatesharing lessons learned and ideas across EM projects (Aiken andHage, 1970; Damanpour, 1996).

Hypothesis 3. The larger the firm, the greater the firm’s EM activ-ity.

The profitability of the firm serves as an important contribut-ing factor to a firm’s competitive behavior (Schumpeter, 1942). EMactivities may require significant financial investments (Tate et al.,2010). Further, the financial viability of EM projects impacts thestrategic decisions to pursue them. Given that there is a significantperceived cost associated with EM activities and that the long-termpayoff of EM initiatives is uncertain, more profitable firms withgreater financial and slack resources may have a greater ability toinvest in EM activities (Tate et al., 2010). Hence, it is expected thatmore profitable firms can commit more resources and longer timehorizons to the development and implementation of EM activities.

Hypothesis 4. The greater a firm’s profitability, the greater its EM

activity.

The firm-level variables that are hypothesized to impact thedegree of a focal firm’s EM activity – market leadership, firm size,and firm profitability – are also expected to moderate the relation-

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1 The six-digit NAICS level represents the most detailed level of industry defini-tions. We contend that this level most closely reflects market definitions and, thus,allows us to best define the top two firms in that market. The use of higher-level

C. Hofer et al. / Journal of Opera

hip between a rival firm’s past EM activity and a focal firm’s EMctivity.

First, it is expected that the magnitude of a firm’s response tots rival’s EM activity will differ between market leaders and chal-engers. While we expect that market leading firms will tend toe complacent in terms of EM activity relative to challengers, welso expect a leader firm to react more aggressively upon observinghe challenger’s increased EM activity. As noted above, inactivityill inevitably lead to the destruction of the competitive status

D’Aveni, 1994). Hence, a challenger’s increased EM activity weak-ns the leading firm’s advantage and may threaten to dethrone theeader (Ferrier et al., 1999). Leading firms, therefore, undertake fur-her EM actions as part of their effort to keep their competitivedvantage and maintain market leadership.

Moreover, the challenger’s actions are signals that compete forhe same stakeholder attention and favorable interpretations as aeading firm’s actions. Accordingly, leading firms will desire to haveheir EM actions more visible to key stakeholders than the chal-enger’s actions. However, since firms that compete in the samendustry are viewed as market substitutes, the coexistence of EMctivities reduces the leader’s relative value-added effect to stake-olders (Basdeo et al., 2006; Brandenburger and Nalebuff, 1996).onsistent with Basdeo et al. (2006), it is expected that leadingrms will increase their EM activities to a greater extent than chal-

enger firms in response to increased rival EM activity as a way toeduce the likelihood that stakeholders will make favorable infer-nces about the challenger’s EM actions.

ypothesis 5. For market leaders, a rival’s past EM activity willave a greater positive effect on the focal firm’s EM activity com-ared to challengers.

Similarly, firm size moderates the effect of a rival’s past EM activ-ty on a focal firm’s EM activity. Large firms have the resourcesnd expertise necessary to respond to a rival’s competitive behav-or (Ferrier, 2001; Sambamurthy et al., 2003). However, prioresearch has suggested that larger firms often respond to compet-tive threats of a rival more rigidly and slower than smaller firmsChattopadhyay et al., 2001; Hayes and Upton, 1998). Hannan andreeman (1984) note that this rigidity is due to larger firms’ bureau-ratic procedures and hierarchical structures that render swift andfficient decision-making more difficult. Following this logic, firmshat are larger in absolute size are expected to be less nimble andess responsive when faced with increased rival EM activity. Statedifferently, the effect of a rival’s EM activity on a focal firm’s EMctivity is expected to be of smaller magnitude for larger focal firms.

ypothesis 6. The greater the focal firm’s size, the lower the pos-tive effect of a rival firm’s past EM activity on the focal firm’s EMctivity.

Finally, the extent of a focal firm’s profitability is hypothesized toffect the magnitude of a rival firm’s EM activity on a focal firm’s EMctivity. Profitability provides the financial buffer necessary to give

firm leeway in managing responses to competitive pressures andubsequently permits the firm to experiment with a greater num-er of competitive moves (Ferrier, 2001). Organizational theoryuggests that slack financial resources can buffer the firm’s techni-al core from environmental upheavals (Cyert and March, 1963) orllow it to pursue riskier strategies (Hambrick and D’Aveni, 1988).ow levels of profitability, in turn, inhibit the firm’s ability to mobi-ize the necessary resources and constrain the firm in its abilityo engage in competitive moves and countermoves (Ferrier, 2001).

ccordingly, profitable firms are in a position to debunk the com-etitive behavior of challengers because they can organize their

nternal resources to either introduce new EM activities or enhancexisting EM efforts.

Management 30 (2012) 69–84 73

Hypothesis 7. The greater the focal firm’s profitability, the greaterthe positive effect of a rival firm’s past EM activity on the focal firm’sEM activity.

4. Sample, data and variables

4.1. Sample

Our research examines how a rival firm’s past EM activityimpacts a focal firm’s EM activity. Since competitive interactionswill likely be most pronounced between the top two competi-tors in an industry (Ferrier et al., 1999), the empirical analysisfocuses on market leaders and challengers, i.e. the top two firms inan industry only. Hence, we sampled only those (six-digit NAICS)manufacturing industries that had clearly identifiable sets of twoindustry-leading firms.1 In addition, the analysis was limited toindustries in which publicly traded companies account for at least70%2 of total industry sales as indicated in the Annual Survey ofManufacturers and the 2007 Economic Census (Basdeo et al., 2006;Derfus et al., 2008). This selection ensured that the majority ofthe economic activity was captured in the firm-level observationsincluded in the Compustat database of publicly listed firms in theUS. Thus, we have a sufficient level of confidence that the firmsidentified were truly the market-leading firms in their respectiveindustries. Moreover, we followed the approach of Ferrier et al.(1999) and sampled only those firms with specialization ratiosgreater than 70%. This ratio is measured as the proportion of totalrevenues that is generated in the firm’s primary six-digit NAICSindustry. This restriction ensured that the financial and operationalsecondary data of included firms primarily pertain to the firms’activities in the industries in which they are identified as leadersand challengers.

The resulting data set includes 96 leader and challenger firmsfrom 48 six-digit NAICS manufacturing industries. The majority ofthese industries are in the computer and electronics, machinery,transportation equipment, chemical, and food manufacturing sec-tors. The sample comprises a total of 288 firm-level observationsfrom 2007, 2008, and 2009 (2006 data are used only to provideinformation on firms’ lagged EM activities). Due to missing datapoints, 14 observations are excluded from the analysis, bringingthe effective sample size to 274 observations.

4.2. Data

Data on EM activities and financial characteristics at the firmlevel were compiled for the 2006–2009 time period. This relativelyrecent time period was selected to ensure the greatest availabilityof data on EM activities. Data were collected from multiple sec-ondary data sources, including firm-level financial data from theCompustat database and EM activity data from electronic copies ofcorporate environmental reports. An advantage of secondary datais that it eliminates concerns such as key informant bias and com-mon methods bias, which are often associated with survey research(Roth, 2007; Gattiker and Parente, 2007).

NAICS aggregations is problematic in that the two largest firms in an industry seg-ment may actually operate in different six-digit industries and, thus, not competein the same product markets.

2 We note that the sample selection is largely robust to changes in the cutoffdefinition.

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4 C. Hofer et al. / Journal of Opera

le guidelines exist for the reporting of firms’ EM activities. Hence,ome researchers have relied on a variety of databases such ashe Measuring the Environmental Performance of Industry (MEPI)roject, the Toxics Release Inventory (TRI) database and the KLDtats database. However, limitations in terms of sample sizes andcope of such data have inhibited their widespread use. Montabont al. (2007, p. 1009), therefore, proposed the use of corporate envi-onmental reports as a primary source of information on a firm’s EMctivities and indeed call for future studies to “gather the hardcopyersion of the report” and to use “multiple years of environmentaleports” in subsequent EM studies. Increasingly, firms publish theseeports as a means to communicate their EM efforts and achieve-ents to stakeholders and the general public (Jones et al., 1999;heeler and Elkington, 2001; Adams and Frost, 2006; Bernhart

nd Slater, 2007). While these reports are neither standardized norubject to reporting guidelines (Cerin, 2002), recent research onnvironmental issues in the operations and supply chain literatureas relied on such reports to assess firms’ EM activities (Montabont al., 2007; Tate et al., 2010). Following these examples, EM activ-ty data for the sampled firms were collected from electronic copiesf corporate environmental reports as recommended by Montabont al. (2007). These reports were obtained from ResponsibilityRe-orts.com, company websites, or from the respective companies’ublic relations departments.3

.3. Structured content analysis

Structured content analysis techniques were employed toxtract data on a firm’s EM activities4 from its annual corporatenvironmental reports. Structured content analysis is an appropri-te methodology to analyze archival documents such as corporatenvironmental reports and has been widely used in various dis-iplines (Unerman, 2000), including operations and supply chainanagement (Montabon et al., 2007; Tate et al., 2010). Human

aters are often used to code the information contained in textse.g., Montabon et al., 2007; Wilmshurst and Frost, 2000; Grayt al., 1995). Human coding is desirable because raters can assesshe presence and intensity of implementation of an activity byeviewing the contextual information that is provided in the text.owever, human coding is inherently subjective, which may give

ise to systematic bias in a coder’s interpretation of factual infor-ation (Oliveira and Murphy, 2009; Wilmshurst and Frost, 2000;ilne and Adler, 1999). Moreover, time and resource constraints

imit the usability of human coders for the analysis of a large num-er of comparatively long texts, as is the case here.

Automated (objective) coding using specialized software pro-rams is a viable alternative to human coding (Neuendorf, 2001;cPhee et al., 2002). Specifically, centering resonance analysis

CRA), an automated coding algorithm, allows researchers to math-

matically assess the centrality of a topical theme within a text.his approach does not merely count the frequency of occurrencef a keyword or a string of keywords, but rather assesses its inter-onnectedness in the text based on network analysis (McPhee

3 Because there could be concerns that corporate environmental reports may notccurately reflect the true degree of a firm’s EM activities, we compared reportshat are registered with the Global Reporting Initiative (GRI) to those that are notegistered with GRI. We found no evidence of systematic differences between thesewo groups of reports (p = 0.68). Additionally, we found a positive and statisticallyignificant correlation among our measures of EM activities and the environmentalanagement variable reported in KLD Stats (r = 0.59, p < 0.05). Finally, we surveyed

perations managers and correlated their survey responses with our measures ofM activity. Based on a sample of 25 observations, we found that these measuresre positively and significantly correlated (r = 0.38, p < 0.10).4 Detailed information on the definition and measurement of these EM activities

s provided further below and in Appendices A and B.

Management 30 (2012) 69–84

et al., 2002). Numerous studies published in leading journals haveemployed this methodology (e.g., Lee and James, 2007; Canary andJennings, 2008; Tate et al., 2010).

Following the example of Tate et al. (2010), a CRA software pro-gram (Crawdad) was used to measure the extent of a firm’s EMactivities. To that end, positively correlated keyword combinations(themes) representing the EM activities listed by Montabon et al.(2007) were identified (see Appendix A). The prevalence of each ofthese themes was then assessed for each corporate environmentalreport. The resulting theme intensity scores form the basis for thecalculation of the measures of a firm’s EM activity. Further detail onthe measurement of EM activity is provided in Section 4.4 below aswell as in Appendix B.

In addition to automated content analysis, subjective codingwas performed by research assistants who were unfamiliar withthe research questions under investigation on a randomly selectedsubsample of 58 reports to assess the validity of the measuresobtained through CRA analysis.5 Specifically, the intensity of imple-mentation of each of the 33 EM activities identified by Montabonet al. (2007) was scored on a five-point scale, ranging from zero(no implementation) to five (full implementation). Analogous tothe CRA analysis, measures of EM activity were calculated. Furtherinformation on the human coding exercise is provided in AppendixB. The Pearson product-moment correlation between the measuresobtained via CRA analysis and the measures generated after humancoding is � = 0.46, which is positive and significant at the one per-cent level. This result provides evidence that the measures obtainedthrough centering resonance analysis are valid measures of firms’reported EM activities.

4.4. Measurement of variables

4.4.1. Dependent variablesThe magnitude of a firm’s EM activity is the dependent vari-

able of interest in this study and is calculated using the Crawdadsoftware. This program identifies positively correlated keywordcombinations (themes) across corporate environmental reportsand computationally evaluates the centrality of each theme for eachreport. The measure of EM activity (EM-Activity) is the sum of allrelevant and statistically significant EM themes in the firm’s cor-porate environmental report and, thus, represents the degree of afirm’s overall EM activity. Similar measures of the degree of a firm’scompetitive activity have been used in prior research (Ferrier et al.,1999; Basdeo et al., 2006).

Since we are particularly interested in examining the role ofoperational EM activities that are implemented at the plant level,we define a set of operational EM activity measures that are a sub-set of the overall EM activity measures. Specifically, Montabon et al.(2007) classify 13 of the total of 33 EM activities as operationalin nature. These operational activities include efforts in terms ofrecycling of supplies and materials, proactive and reactive wastereduction, remanufacturing, resale or internal consumption ofbyproducts, substituting environmentally problematic materials,reducing packaging, conserving energy, accounting for environ-mental costs (e.g., the monetary cost of emissions), managingenvironmental risks, and providing incentives for environmentalinnovation. The measure of the degree of operational EM activi-

ties (Ops EM-Activity) is defined analogously to the broader overallEM-Activity variable discussed above and is used as an alternativemeasure of EM activity to verify the robustness of the empirical

5 Time and resource constraints limited the number of reports we could scoresubjectively given that it took raters an average of more than 3 h to score a singlereport.

C. Hofer et al. / Journal of Operations

Table 1Descriptive statistics (N = 274).

Variable Mean Std. deviation

EM-Activity 31.63 39.55Ops EM-Activity 12.92 18.10Rival EM-Activity lagged 27.87 38.15Rival Ops EM-Activity lagged 10.61 16.34Market Leadership 0.50 0.50Firm Size (sales, million $) 20,890.55 53,268.27Firm Profitability (ROE) 0.07 0.46EM-Activity lagged 25.75 36.89Ops EM-Activity lagged 10.13 16.30Market Concentration 4018.08 2010.80

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escriptive statistics for sector-specific fixed effects are not shown due to spaceonstraints.

esults in a more narrowly defined operations management con-ext.

Similarly, we group the remaining EM activities that are moretrategic in nature and replicate the analyses using the associatedtrategic EM-Activity variable. It is noted that the Ops EM-Activitynd Strategic EM-Activity measures are mutually exclusive andollectively exhaustive. While the subsequent discussion focusesn overall and operational EM activities, we report the empiricalesults for Strategic EM-Activity in Appendix C and note that theesults are consistent.

.4.2. Independent variablesA rival firm’s past EM activity, Rival EM-Activity lagged, is the first

ey independent variable of interest in this research. The rival’s EMctivity enters the regression model in lagged form to allow for aemporal sequence of competitive EM interactions (e.g., Baum andorn, 1996). As highlighted above, because we are also interested

n examining operational EM activities, Rival Ops EM-Activity laggeds specified in the operations-level models.

Three additional firm-level measures are also included in theodel to test the hypotheses set forth in this study. First, the Mar-

et Leadership dummy variable identifies whether the firm is thearket leader (Market Leadership = 1) or the leader’s key competi-

or, i.e. the challenger (Market Leadership = 0). The Market Leadershipariable is defined based on market shares. Firm size and firm prof-tability are measured by the value of the firm’s sales (Firm Size) andhe firm’s return on equity6 (Firm Profitability), respectively.

Turning to the control variables, lagged measures of the degreef a firm’s EM activity, EM-Activity lagged and Ops EM-Activity

agged, respectively, are included to control for a firm’s EM activityn the previous time period. These measures are defined as outlinedn Section 4.4.1 above and lagged by one year.7 In line with prioresearch, we also control for the degree of market concentration,hich is measured by means of the Herfindahl-Hirschmann Index

Market Concentration) and calculated as the sum of squared markethares of all the firms operating in the industry (e.g., Viadiu et al.,006; Henley, 1987; Joglekar and Hamburg, 1989). In addition,

inary variables identifying three-digit NAICS sectors are includedo account for aggregate industry fixed effects. Descriptive statisticsre provided in Table 1.

6 The empirical results are substantively the same when return on sales is useds an alternative measure of firm profitability.7 Although the time lag decision is somewhat arbitrary, we believe that a one

ear lag is appropriate. First, a shorter lag (e.g., six months) is not possible sincehe corporate environmental reports are published on an annual basis only. Second,iven the dynamic nature of industries, the use of longer lags risks the possibilityhat other changes (e.g., productivity improvements, economic cycles, etc.) wouldonfound our results. We also note that the use of one-year lags is consistent withrior research (e.g., Basdeo et al., 2006). However, the empirical results are robusthen a two-year lag is applied.

Management 30 (2012) 69–84 75

As shown in Table 1, the average firm in our sample had$20,890 million in annual sales and operated in markets that weremoderately concentrated (mean Market Concentration = 4018).Because the Firm Size and Market Concentration variables violateskewness and kurtosis assumptions, these measures enter themodel in logarithmic form. Turning to the bivariate correlationsin Table 2, the large correlation coefficients between the measuresof EM activity are expected. It is noted that the measures of EM-Activity and Ops EM-Activity do not enter the estimation modelssimultaneously. Rather, these variables are used as alternate mea-sures of EM activity.

5. Empirical analysis and results

5.1. Estimation methodology

A negative binomial generalized estimating equations (GEE)model is used to investigate the competitive determinants of afirm’s EM activity. Wooldridge (2003), Gittelman and Kogut (2003),and Shane (2000, 2002) note that when the dependent variable isdefined in terms of non-negative count data, as is the case withour measure of EM activity, negative binomial regression is anappropriate methodology. In some cases, a Poisson regression maybe used to estimate the model. However, an assumption of thePoisson model is that the conditional variance of the dependentvariable equals its mean (Greene, 2003). Accordingly, we examinedwhether this assumption is violated and found that the data is over-dispersed (Greene, 2003; Hausman et al., 1984). Specifically, theover-dispersion statistics (alpha), as shown in Table 3 below, rangefrom 4.02 to 5.39 and, thus, are greater than the acceptable thresh-old of 1.0. Additionally, a Poisson goodness-of-fit test (�2 = 9159.47,p < 0.01) further supported the conclusion that a Poisson modelis inappropriate. A negative binomial model, in turn, relaxes themean and variance assumptions of a Poisson model (Greene, 2003).Therefore, a negative binomial generalized estimating equations(GEE) procedure is used to investigate the determinants of EMactivity.8

Greene (2003), Di Gregorio and Shane (2003), Sine et al.(2003), and Liang and Zeger (1986) note that GEE regression isan appropriate methodology to analyze panel data due to poten-tial autocorrelation and unobserved cross-sectional heterogeneity.Arellano-Bond tests suggest the presence of autocorrelation ofresiduals that results from the serial correlation of a firm’s EMactivity over time. Likewise, unobserved firm-level heterogeneitylikely explains some variability in the dependent variables. The GEEmodel corrects for autocorrelation of residuals and also controlsfor systematic firm-specific differences in EM activity through theinclusion of firm fixed effects.

Because our data set includes observations from leader andchallenger firms in each six-digit NAICS industry, the residualsof firm-level observations within an industry may be non-independent. To address this potential issue, we included in ourmodel the lagged value of a rival firm’s EM activity, the Leaderdummy variable, and aggregate industry fixed effects (e.g., Dowelland Killaly, 2009). Furthermore, we randomly sampled a singlefirm from each six-digit NAICS industry and estimated the empir-ical model using this subsample of cross-sectionally independentobservations. The results are largely consistent with the full-sampleestimation results and provide evidence that any residual non-

independence that may exist in the data does not bias the empiricalfindings.

8 We note that the results are consistent, and the coefficient estimates tend to havehigher significance levels when a generalized Poisson model (instead of a negativebinomial model) is estimated.

76 C. Hofer et al. / Journal of Operations Management 30 (2012) 69–84

Table 2Bivariate correlations (N = 274).

1 2 3 4 5 6 7

1 Rival EM-Activity lagged2 Rival Ops EM-Activity lagged 0.953 Market Leadership −0.04 −0.024 Firm Size 0.27 0.25 0.285 Firm Profitability 0.02 −0.01 −0.07 0.206 EM-Activity lagged 0.19 0.19 0.08 0.31 0.087 Ops EM-Activity lagged 0.19 0.19 0.05 0.29 0.07 0.958 Market Concentration −0.11 −0.10 −0.01 −0.41 −0.03 −0.06 −0.06

Correlation coefficients significant at p < 10% are printed in bold.

Table 3Negative binomial GEE regression results (N = 274).

All EM activities Operational EM activities

Model 1 Model 2 Model 3 Model 4Coef. Coef. Coef. Coef.

Constant −7.288** −13.052** −5.627** −10.589**

(1.768) (1.889) (1.746) (1.832)Rival EM-Activity lagged 0.004* 0.113** 0.008† 0.230**

(0.002) (0.015) (0.005) (0.035)Market Leadership −0.389* −0.513* −0.299* −0.300

(0.155) (0.203) (0.153) (0.190)Firm Size 0.881** 1.427** 0.735** 1.164**

(0.076) (0.101) (0.075) (0.093)Firm Profitability −0.248† −0.493* −0.154 −0.441*

(0.145) (0.195) (0.153) (0.194)Market Leadership × Rival EM-Activity lagged 0.002 −0.007

(0.004) (0.009)Firm Size × Rival EM-Activity lagged −0.012** −0.024**

(0.002) (0.004)Firm Profitability × Rival EM-Activity lagged 0.009† 0.036**

(0.005) (0.013)EM-Activity lagged 0.016** 0.014** 0.035** 0.035**

(0.002) (0.002) (0.004) (0.004)Market Concentration 0.206 0.333† 0.063 0.197

(0.177) (0.178) (0.175) (0.174)

Wald �2 382.51** 439.76** 295.02** 377.20**

�D 53.7** 44.3**

Over-dispersion (Alpha) 5.39 4.98 4.38 4.02

Std. errors are shown in parentheses; sector-specific fixed effects are not shown due to space constraints.

5

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* Sig. at 5% level.** Sig. at 1% level.† Sig. at 10% level.

.2. Empirical results

The negative binomial GEE model was estimated in Stata using

he xtgee command. The estimation results for Models 1–4 and thessociated model fit statistics are reported in Table 3. All modelsield highly significant model fit statistics. Model 1 is the base-ine model with EM-Activity as the measure of EM activity. Model 3

10

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uses operations-level measures of EM activity (Ops EM-Activity) butis otherwise equivalent to Model 1. In the interest of brevity, thesubsequent discussions focus on the degree of firms’ overall EM

activities (EM-Activity). It is noted, however, that the estimationresults are largely consistent in terms of the direction and statisti-cal significance of the coefficient estimates when Ops EM-Activityis used as a measure of EM activity.

001

Rival EM-ActivityLagged

-Activity lagged interaction.

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The hypotheses were tested using hierarchical regression anal-sis. Following the approach outlined by Cohen (2003) and mostecently employed by Li et al. (2011), the variables were enterednto the model in sequential steps. Model 1 (Model 3) includes the

ain effect variables (Rival EM-Activity lagged, Market Leadership,irm Size, and Firm Profitability) only and is used to test Hypotheses–4. The moderating effect variables are added in Model 2 (Model). Following the hierarchical regression approach, we examine the

ncremental improvement in terms of overall model fit betweenodel 1 and Model 2 (likewise, between Model 3 and Model 4) byeans of a likelihood-ratio chi-square test (McCullagh and Nelder,

989). These test statistics (�D = 53.7 and �D = 44.3, respectively)ere found to be significant at the less than one percent level in

oth instances, indicating that the interaction effects collectivelydd significantly to the quality of the empirical models. Therefore,t is appropriate to examine Models 2 and 4 to test Hypotheses 5–7.

detailed discussion of the hypothesis testing results is providedelow.

.3. Hypothesis testing results

Hypothesis 1 states that the greater the rival firm’s past EMctivity, the greater the focal firm’s EM activity. The positive andtatistically significant coefficient of the Rival EM-Activity laggedariable ( ̌ = 0.004, p ≤ 0.05) provides evidence in support of thisypothesis. Hypothesis 2, which states that the leader’s EM activity

s lower than that of its challenger, is also supported. The coefficientstimate of the Market Leadership variable is negative ( ̌ = −0.389)nd significant at p < 0.05 in Model 1. Thus, there is evidence thateader firms are less active than challengers in terms of their EMfforts. Hypothesis 3 is supported as well. The positive and sig-ificant coefficient of the Firm Size variable ( ̌ = 0.881, p ≤ 0.01)

ndicates that the larger the firm, the greater its EM activity. Theest of Hypothesis 4 produces an unexpected result. The coefficientstimate of the Firm Profitability variable is negative and signifi-ant, indicating that greater profitability is associated with a loweregree of EM activity, all else equal. A possible explanation maye that comparatively profitable companies see less of a need to

mplement EM activities in an effort to gain competitive advantagend, ultimately, improve their financial performance.

The moderating effects of market leadership, firm size, and firmrofitability on the effect of a rival’s lagged EM activity on a focalrm’s EM activity are explored in Hypotheses 5–7 and tested inodels 2 and 4, respectively. Hypothesis 5 contends that for mar-

et leaders, a rival’s past EM activity will have a greater positiveffect on the focal firm’s EM activity than for challengers. How-ver, the interaction term (Market Leadership × Rival EM-Activity

agged) carries a statistically insignificant coefficient estimate. Thus,ypothesis 5 is not supported.

In line with Hypothesis 6, we find that the greater the focal firm’size, the lower the positive effect of a rival firm’s past EM activ-

able 4ummary of hypothesis testing results.

Hypothesis Independent variable

H1 Rival EM-Activity lagged

H2 Market Leadership

H3 Firm Size

H4 Firm Profitability

H5 Market Leadership × Rival EM-Activity lagged

H6 Firm Size × Rival EM-Activity lagged

H7 Firm Profitability × Rival EM-Activity lagged

.s.—not statistically significant.

Management 30 (2012) 69–84 77

ity on the focal firm’s EM activity. The coefficient estimate of theFirm Size × Rival EM-Activity lagged interaction term is negative andsignificant ( ̌ = −0.012, p ≤ 0.01) as expected. To provide furtherinsight on this finding, we provide an illustration of this moder-ating relationship in Fig. 2 following the procedure suggested byAiken and West (1991). The dashed line represents the positivemarginal effect of a rival’s past EM activity on focal firm EM activ-ity (Hypothesis 1) across low, medium, and high levels of rival EMactivity and with firm size held constant at its mean. Hypothesis6 states that firm size attenuates this effect. Hence, it is expectedthat the marginal effect of rival EM activity on focal firm EM activ-ity will be greater for small firms (25th percentile in size) than forlarge firms (75th percentile in size). Accordingly, the lower andupper lines in Fig. 2 graph the marginal effect of rival EM activityon focal firm EM activity for small and large firms, respectively. Thedifference in the levels of EM activity between small and large firmsreflects the finding that larger firms are more active in terms of EM,all else equal (Hypothesis 3). However, while an increase in rivalEM activity accelerates small firms’ EM efforts, larger firms are lessresponsive, and their EM activity levels may even decrease. Thisfinding provides support for Hypothesis 6.

Hypothesis 7 is supported as well. The more profitable the focalfirm, the greater the effect of the rival firm’s lagged EM activity onthe focal firm’s EM activity. The interaction term (Firm Profitabil-ity × Rival EM-Activity lagged) is positive as expected ( ̌ = 0.009,p ≤ 0.10) and marginally significant.

The hypothesis testing results for both measures of EM activ-ity (overall and operational) are summarized in Table 4 below.Hypotheses 1–3 are supported both for the overall measure of EMactivity as well as for operationally focused EM activities. However,there is no support for Hypotheses 4 and 5. Hypothesis 6, in turn,is strongly supported for both the overall and operational mea-sures of EM activity, and Hypothesis 7 receives at least marginalsupport.

6. Discussion

6.1. Theoretical implications

The purpose of this study is to examine how a rival firm’s pastEM activity impacts a focal firm’s EM activity in US manufacturingindustries. In so doing, this study draws on Schumpeterian eco-nomics (Schumpeter, 1934, 1942) and signaling theory (Clark andMontgomery, 1998; Rindova and Fombrun, 1999; Basdeo et al.,2006). The empirical results are consistent with these theoreticalbases, thus highlighting the theoretical contribution of Schum-peterian economics to the study of EM within the operations

management literature. Specifically, this study presents both the-oretical and empirical evidence that competitive pressures by arival firm contribute to the magnitude of a focal firm’s EM activ-ities. Thus, this study contributes to the EM literature, which is part

Hypothesis testing results

Expected sign All EM-Activity Ops EM-Activity

+ + +− − −+ + ++ − n.s.+ n.s. n.s.− − −+ + +

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f the broader realm of sustainability that has received increasingttention in OM (Kleindorfer et al., 2005; Linton et al., 2007).

Prior EM research has adopted institutional and stakeholder the-ries in explaining a firms’ EM efforts (e.g., Delmas and Toffel, 2004,008; Sarkis et al., 2010). This study further explores EM activityrom a competitive dynamics perspective by focusing on rival firmehavior as a key determinant of EM actions. The view that indus-ry rivals place peer pressure on a firm to increase EM activity isonsistent with institutional theory. However, the Schumpeterianerspective offers further insight into the dynamics of actions andeactions that constitute the process of competition. Specifically, arm’s announcement of EM activity can be viewed as a competitiveignal that triggers a competitive response, which, in turn, insti-ates further cycles of rivalrous activity that aim at (re-)establishingompetitive advantage. Thus, these actions collectively contributeo the proliferation of EM within firms and industries, as evidencedy the empirical results presented in this study.

The Schumpeterian perspective also complements the resource-ased view (RBV) which proposes that a firm’s valuable, rare,

nimitable, and non-substitutable resources are the sources ofompetitive advantage (Barney, 1986). Indeed, Schumpeter (1942)rgues that larger and more profitable firms that have greaterccess to financial and intellectual resources should be relativelyore active in terms of competitive behaviors and, ultimately,

njoy greater success. The empirical findings confirm that firm sizes a significant driver of EM activity. However, the results withespect to the effect of profitability on EM activity are inconsis-ent with Schumpeter’s (1942) expectation. RBV may offer a betternd more detailed perspective on the resources needed to com-ete effectively in the domain of EM and why the possession of suchesources may ultimately be associated with superior performance.herefore, it is apparent that the Schumpeterian perspective doesot replace other theories of competition and competitive advan-age. Rather, Schumpeter complements these theories by studyinghe drivers and outcomes of competitive interactions betweenrms.

While the Schumpeterian perspective has received support inrior research (e.g., Ferrier et al., 1999; Smith et al., 1991), itspplication to the study of competitive dynamics in EM – andM research, in general – is rare. Yet, it is well-documented that

uperior EM capability is associated with significant performanceenefits (e.g., Klassen and McLaughlin, 1996; Melnyk et al., 2003)nd, therefore, a relevant competitive variable. The Schumpeterianerspective offers a basis for the exploration of inter-firm rivalryhat gives rise to competitive advantage in the areas of EM andM. Likewise, this research presents evidence that rivalrous behav-

ors are not limited to market-based or product-related issues, suchs pricing and product technology. Indeed, most prior Schumpete-ian research has focused on demand-side aspects of competitione.g., Ferrier et al., 1999; Basdeo et al., 2006). This study demon-trates that firms also compete in terms of EM and OM activitiesnd, thus, it highlights the strategic and competitive importance ofhese fields.

.2. Managerial implications

The results from this study have multiple implications for EMithin the field of OM. First, this study’s findings clearly indicate

hat the magnitude of a firm’s (operational) EM activities is, ateast in part, driven by its rival’s activities in this regard. Thus, thisesearch underlines the strategic importance of environmentallyriendly operations, indicating that firms design and leverage their

nvironmental efforts to gain competitive advantage (Berman et al.,999; Bosse et al., 2009; Harrison et al., 2010). Accordingly, man-gers should carefully monitor competitors’ EM activities and berepared to act or react accordingly in an effort to gain or retain

Management 30 (2012) 69–84

a competitive advantage. Moreover, our study highlights to policymakers that competitive considerations drive a firm’s commitmentto EM above and beyond legislative or regulatory motives.

It should be noted that competitive advantages derived fromEM activities are likely to be short-lived since innovation (in termsof EM) spurs imitation by rival firms—unless such innovation isprotected. To the extent that the development of environmentallyfriendly operations practices involves intellectual property (e.g.,manufacturing processes, methods, and materials), patent protec-tion is needed to prevent or at least slow down the process of EMactivity imitation and, thus, erosion of competitive advantage.

This study also examined differences in EM activity betweenleader and challenger firms and, in line with prior research (Ferrieret al., 1999), found that a leader firm’s EM activity is generally lowerthan that of its challenger. This finding is consistent with anecdotalevidence: Meadwestvaco, at the time the number two firm in thepaper mills industry, implemented sustainable fiber sourcing prac-tices and claimed environmental leadership long before the marketleader (Smurfit-Stone) paid attention to EM (MeadWestvaco, 2008;Smurfit-Stone, 2011). Clearly, challengers recognize the opportu-nity to improve their competency in EM activity by competing moreaggressively (Ferrier et al., 1999). This finding is important to leaderfirms who need to pursue EM activities continually in an effort toestablish and maintain leadership in EM, an area of significant andincreasing stakeholder interest (Rondinelli and Vastag, 1996).

The finding that firm size is an important determinant ofEM activity is also interesting. In line with Schumpeter (1934,1942), this result highlights the fact that large firms are critical tothe development of environmentally friendly operations practicessince larger firms have the resources and expertise necessary toinvest in EM activities. Inter-firm rivalry, in turn, is the vehicle thatpromotes the diffusion of such practices to small and medium-sizedfirms and, ultimately, throughout an industry.

While firm size is an important determinant of a firm’s EM activ-ities, we also find that the effect of the rival firm’s past EM activityon the focal firm’s EM activity is lower for larger firms. As we dis-cussed earlier, larger firms often respond to competitive threatsmore rigidly than smaller firms (Chattopadhyay et al., 2001; Hayesand Upton, 1998). Given our central finding that inter-firm rivalryextends to EM, it is important that larger firms develop organiza-tional practices to mitigate bureaucracy as a way to respond moreswiftly to competitive threats that emanate from a rival’s increasedEM activity.

Our findings with respect to the effect of firm profitability on EMactivity are statistically insignificant or even contrary to our expec-tation that more profitable firms should be more active in termsof EM. This empirical result reflects the contention by Tate et al.(2010) that firms may perceive EM to be a risky investment withpotentially high upfront costs and uncertain payoffs and, therefore,refrain from allocating excess liquidity to EM activities. However,we do find support for the contention that more profitable firmswill respond more aggressively to a rival’s increased EM efforts.When faced with competitive threats, firms leverage their financialresources to implement countermoves and engage in increased EMactivity. To be able to do so effectively and swiftly, however, man-agers need to ensure that they have top-level strategic and financialsupport.

7. Conclusion

This study investigates the competitive determinants of

EM activity in US manufacturing firms from a Schumpete-rian economics perspective. While prior research has shownthat EM practices positively affect firm performance (Klassenand McLaughlin, 1996; Christmann, 2000; Melnyk et al., 2003;

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ontabon et al., 2007), the central tenet of this research is that arm’s EM activities are, in part, driven by the competitor’s EM activ-

ties. Using econometric methods, the empirical analysis of panelata from a broad cross-section of US manufacturing firms revealshat such rivalry in the domain of EM does exist and that the effectf a rival’s EM activity on a focal firm’s EM activity is greater forore profitable and smaller firms. Thus, this study provides both

heoretical and empirical evidence that competitive pressures con-ribute to the intensity of a focal firm’s EM activities. In addition,rm characteristics such as market leadership, firm size, and firmrofitability are found to significantly affect the magnitude of arm’s (operational) EM activities. Specifically, market leaders arehown to be generally less active in terms of EM than challengers.irm size, in turn, is positively associated with the degree of EMctivity.

While our study makes significant contributions to the litera-ure, there are some limitations that can be addressed in futureesearch. First, we imposed some restrictions during the samplingrocess that may limit the degree to which the industries included

n the sample are representative of the general population of manu-acturing industries. Specifically, we sampled only those industriesn which public firms generated at least 70% of total industry output.

oreover, only industries with clearly defined and non-diversifiedeader–challenger pairs were considered. As such, future researchhould examine the competitive exchanges among privately heldrms, highly diversified firms, or firms that are not among the two

argest firms within their respective industries. We also used one-ear lags to examine the effects of past rival firm EM activity on aocal firm’s EM activity. However, it is conceivable that the time thatlapses between a rival’s actions and a focal firm’s response maye shorter or longer in at least in some instances. Clearly, moreesearch is needed to further explore competitive dynamics in theomain of environmental management.

There are also several opportunities for future Schumpeterianconomics research in OM beyond the topic of EM. While this study

ocuses on EM activities, it is plausible that patterns of innovationnd imitation may be observed in other areas of operations. Forxample, the adoption of JIT practices is often widely publicizede.g., Huson and Nanda, 1995), and the financial performance effects

able 5ist of EM activities.

# Activities

Operational1 Recycling—office supplies

2 Recycling—production materials

3 Waste reduction (proactive)

4 Waste reduction (reactive)

5 Remanufacturing

6 Creating a market for waste products7 Consume waste/scrap internally

8 Substituting env. problematic materials

9 Packaging

10 Energy conservation efforts

11 Environmental cost accounting

12 Spreading risk of environmental problems

13 Rewards as incentive for env. project

Tactical14 Supplier selection

15 Environmental standards for suppliers

16 Environmental audits of suppliers

17 Early supplier involvement

18 Environmental awards/recognition to the focalcompany

Management 30 (2012) 69–84 79

of these practices are well documented (e.g., Kinney and Wempe,2002). Therefore, an interesting future research question is: Does afirm’s JIT adoption lead to similar actions in rival firms? If so, will therival firm attempt to increase its JIT implementation efforts relativeto its competitor? Likewise, an investigation of rivalrous behaviorsin the broader realm of sustainability management including, forexample, social and charitable activities, may be a topic of inter-est for future research. Thus, there are numerous opportunities toinvestigate competitive interactions in the domains of operationsand sustainability management.

Another interesting endeavor is the examination of the linksbetween EM activities and a firm’s competitive position and per-formance. While a generally positive relationship between EM andperformance has been suggested in prior research, a more detailedunderstanding of the effects of EM activities on profitability orother performance measures, such as market shares, for example,is missing. Such studies are suggested for future research. Lastly,from a methodological perspective, this study utilized centeringresonance analysis to extract contextual information from largeamounts of archival documents. Future research can employ thistechnique in lieu of subjective coding.

Acknowledgments

We gratefully acknowledge the guidance provided by theanonymous referees, the Associate Editor, and Ken Boyer, the Co-Editor-In-Chief. Moreover, we appreciate the advice and supportprovided by Martin Dresner, Curt Grimm, Jim McElroy, and MattWaller. We would also like to thank our student research assistantsfor their work on this project. Likewise, the editorial assistance pro-vided by Allison Lukens is much appreciated. We are also gratefulfor the financial support provided by the Supply Chain Manage-ment Research Center at the University of Arkansas and the IowaState University College of Business.

Appendix A. Environmental management activities (basedon Montabon et al., 2007)

See Table 5.

Explanation

Does the company recycle its office papers, plastic bottles, etc.?Does the company recycle its production materials, etc.?Elimination of waste before it is produced, waste preventionReduction of waste through use of scrubbers, incinerators, and treatment ofwaste after it is producedRebuilding a product where some of the parts or components are recoveredTreating waste as an input to another product that can be sold at a profitRecycling waste into other productsReplacing a material that can cause environmental problems with anothermaterial which is not problematicReturnable packaging, reduced packaging, recyclable packaging,environmentally responsible packagingInstalling energy efficient equipment, equipment that can capture previouslyreleased energy (e.g., steam) or finding ways to reduce energy consumptionAccounting for environmental costs, attempting to put a cost on env. programsUsing a third party or expert to deal with environmental issuesIncentive programs that reward ideas for env. improvement

Are supplier selected based on a sourcing criteria based on env. dimensions?Are suppliers required to adhere to certain env. performance standards?Are suppliers audited on environmental dimensions?Are suppliers involved in new product design?Awards to the focal company for environmental achievement (by governmentbodies, magazines, env. groups, etc.)

80 C. Hofer et al. / Journal of Operations Management 30 (2012) 69–84

Table 5 (Continued)

# Activities Explanation

19 Participation in environmental initiatives, certificationprograms

e.g., ISO 14000, Eco-Management and Audit Scheme, EPA, Green Lights, GreenSeal, OSHA

20 Product development and innovation Is the company investing in environmental R&D?21 Design of products Are products being designed to meet env. standards and objectives?22 Specific design targets Does the firm quantify environmental design goals?23 Environmental risk analysis Does the firm assess the risks of materials to the env. or to people?24 Use of life cycle analysis or design for environment Does the company analyze the full env. impact from design to production,

consumption and disposal?25 Environmental management systems Does the firm use environmental management systems?26 Communication Communications with stakeholders as to the env. impacts of the firm’s ops.Strategic27 Integration with long-term business strategy Are environmental considerations integrated in strategic planning?28 Surveillance of the market for environmental issues Does the company look for env. friendly opportunities in the market place?29 Strategic alliances Alliances with other firms to jointly work on environmental projects?30 Corporate policies and procedures Detail and extent of consideration of env. issues throughout organization’s

policies/procedures31 Environmental mission statement Leadership/mission/vision statement32 Environmental department/teams Are there teams dedicated to integrating environmental initiatives? How high

are these teams in the corporate hierarchy?33 Employee environmental training programs Does the report mention environmental training programs for employees?

A

i

ttieneirkmwiCoiaiccrM(3giaest(

tr

tY

ppendix B. Data collection and analysis process

The sample selection and data collection process is summarizedn Fig. 3 and further explained below.

The automated content analysis of the corporate environmen-al reports first requires each report to be converted to a text filehat can be processed by the Crawdad software. Once uploadednto Crawdad, each text was analyzed individually. Specifically,ach report was parsed into noun phrases and tokenized.9 Theseoun phrases are then linked into a CRA network with more influ-ntial words being located in the center of the network and lessnfluential words at the fringes of the network. In the next step, alleports were analyzed simultaneously, and the 500 most influentialeywords that occurred in at least half the reports were deter-ined. At the same time, a correlation matrix of these keywordsas calculated. A positive and significant correlation coefficient

ndicates that a given pair of keywords tends to co-occur in texts.onversely, a negative correlation suggests that as one keywordccurs, the other keyword is generally not found in its vicinity. Todentify themes, i.e. combinations of two relevant keywords suchs, for example, “waste” and “reduction” we first identified all pos-tively correlated keyword combinations. Out of 250,000 keywordombinations (500 × 500), approximately 53,000 were positivelyorrelated. A top-down approach was then used to filter out theelevant themes that best identify the 33 EM activities defined byontabon et al. (2007). Through this process, a set of 314 themes

i.e. positively correlated keyword combinations) representing the3 EM activities was identified. Next, the influence value for aiven theme was calculated for each report as the sum of thentensity values of the keywords that make up the theme (Cormannd Dooley, 2006). Corman et al. (2002) suggest that theme influ-nce values greater than 0.02 are significant. Hence, the count ofignificant themes occurring within a given corporate environmen-al report was used as the measure of the degree of EM activityEM-Activity).

Of the 314 themes identified by the authors (see above), 54hemes were significant within the Johnson Controls Inc. 2007eport. Hence, this number is used as a measure of the magnitude

9 Tokenization involves light stemming of word forms (e.g., “activi-ies” ⇒ “activity”) and grouping of well known word combinations (e.g., “Nework” ⇒ “new york”).

of Johnson Controls’ environmental management activity. As canbe seen in Fig. 4, these themes are not spread equally across all 33activities identified by Montabon et al. (2007). For example, thereare twelve significant themes associated with activity 10 (energyconservation), suggesting that the firm assigns great importance toenergy conservation. Yet, no evidence of implementation of severalother activities is found, indicating that Johnson Controls focusesits environmental management efforts in some select areas. Specifi-cally, 20 of 33 activities are observed with at least some significancein this report. This number can be interpreted as the breadth orscope of Johnson Controls’ environmental management activities.While not reported in this manuscript, we note that the empiricalresults of interest remain the same when EM activity is measuredin terms of the scope of EM activities rather than in terms of theoverall magnitude of a firm’s EM activities. The scope of EM activ-ity is defined as the number of distinct EM activities with at leastone associated significant theme. In the Johnson Controls exampleshown above, the score for the scope of EM activity would, thus, be20.

As an alternative to the automated text analysis describedabove, human coders were used to assess the intensity of imple-mentation of the 33 EM activities identified by Montabon et al.(2007). This determination was made based on the frequencywith which an activity is mentioned in a report and the degreeof implementation of this activity as indicated in the report. Afive-point Likert scale, ranging from zero (no implementation) tofive (full implementation), was employed. The reports were codedby several raters. To ensure consistency, all raters were providedwith detailed instructions including comprehensive definitionsand a step-by-step coding process outline. In addition, all raterscompleted a training exercise that was reviewed by one of theauthors of this study to ensure conformance to the instructionsand guidelines given. Since human coding was used for valida-tion purposes only and due to resource constraints, 58 (out of162) reports were randomly sampled for human coding. A mea-sure of the degree of firms’ EM activities was generated as the sumof the intensity scores across all activities assigned by the rater(EM-ActivityHuman).

It is noted that the objective (automated) and subjective

(human) approaches to analyzing corporate environmental reportswill, naturally, yield different results with respect to the assess-ment of the implementation of EM activities. However, it isexpected that these measures will be positively correlated, thereby

C. Hofer et al. / Journal of Operations Management 30 (2012) 69–84 81

Sample Selection

Data Collection

SecondaryDatabases

Analysis of Corp. Env. Reports

Conversion of reports to text filesAnalysis of

individual reports using

Centering Resonance

Analysis

Statistical identification of recurring themes (across reports)

Identification of relevant themes

Calculation of theme influence values for

each report

Corp. Env.Reports

Primary Analysis:Automated Coding

Secondary Analysis:Subjec�ve Coding

Coding of individual reports: assessment of

degree of implementation of relevant activities

Training of coders

Preparation of instruction and

training materials

Verification

Calculation of theme influence values for

each reportReconciliation

Fig. 3. Overview of data collection procedure.

1 10

3

0 0 0

21

12

0 0

3

10 0 0

3

6

12

0 01

4

10

2 2

0

34

1

0

2

4

6

8

10

12

14

1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33

Sig.themecount

Env. mgmt.activities(Montabon et al.,2007)EM-Acti vit y = sum of sig. theme co unts = 54

Example: Johnson Controls Inc.“2007 Business and Sustainability Report”

Ops EM-Activit y = 23 Strategi c EM-Acti vit y = 31

of EM

pstrs

Fig. 4. Calculation

roviding evidence of the accuracy and validity of the mea-

ures of firms’ environmental management activities. To assesshe latter, the Pearson product-moment correlation between theespective measures of firms’ EM activities was calculated for theubsample of 58 reports for which both objective and subjective

activity measures.

data were available. The correlation coefficient �(EM-Activity, EM-

ActivityHuman) = 0.46 is positive and significant at the one percentlevel. This result provides at least some evidence that the measuresobtained through centering resonance analysis are valid measuresof firms’ EM activities.

8 tions

A

TG

St

R

A

A

A

A

A

A

B

B

B

B

B

B

B

B

B

B

2 C. Hofer et al. / Journal of Opera

ppendix C. Empirical results for Strategic EM-Activity

See Table 6.

able 6EE negative binomial regression results (N = 274).

Strategic EM activities

Model 1Coef.

Model 2Coef.

Constant −7.323** −11.997**

(1.956) (2.021)Rival EM-Activity lagged 0.006† 0.159**

(0.003) (0.027)Market Leadership −0.441** −0.628**

(0.169) (0.218)Firm Size 0.876** 1.323**

(0.084) (0.110)Firm Profitability −0.320* −0.571**

(0.141) (0.201)Market Leadership ×

Rival EM-Activity lagged0.009(0.007)

Firm Size ×Rival EM-Activity lagged

−0.017**

(0.003)Firm Profitability ×

Rival EM-Activity lagged0.013†

(0.007)EM-Activity lagged 0.022** 0.020**

(0.003) (0.004)Market Concentration 0.178 0.277

(0.196) (0.190)

Wald �2 261.37** 321.79**

�D 36.7**

td. errors are shown in parentheses; sector-specific fixed effects are not shown dueo space constraints.

* Sig. at 5% level.** Sig. at 1% level.† Sig. at 10% level.

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