G Model ARTICLE IN PRESS YMARE-552; No. of Pages 18 Management Accounting Research xxx (2015) xxx–xxx Contents lists available at ScienceDirect Management Accounting Research journal homepage: www.elsevier.com/locate/mar Translating environmental motivations into performance: The role of environmental performance measurement systems Irene Eleonora Lisi ∗ Università Cattolica del Sacro Cuore, largo A. Gemelli 1, 20123 Milan, Italy a r t i c l e i n f o Keywords: Environmental performance measurement systems Environmental performance Eco-efficiency Business case Stakeholders Survey a b s t r a c t Although corporate environmentalism has achieved great momentum and the literature has examined both its motivations and performance outcomes, relatively little is known about the specific managerial processes whereby companies may translate their motivational factors into improved performance. In this respect, the environmental accounting literature suggests the introduction of specific control mechanisms such as environmental performance measurement systems. Yet, in the environmental domain, driving performance through measurement may be less straightforward than often realized because of various technical and motivational challenges. To examine further the theoretically questionable role of performance measurement in the environmental context, this study proposes a model in which the use of environmental performance measures for a variety of decision-making and control purposes mediates the links between firms’ environmental motivations and corporate performance. The results from a survey of 91 Italian companies provide support for the hypothesized relationships, while offering several insights into the differential strength of business-oriented, stakeholders-oriented and ethical motivations and their implications for environmental performance measurement systems. The paper concludes with some avenues for future research revealed by this work. © 2015 Elsevier Ltd. All rights reserved. 1. Introduction Over recent decades, companies in every sector have been confronted with increasing pressures to control and improve their operations’ impacts on the natural environment (Burnett and Hansen, 2008). Corporate environmental proactivity, in turn, is claimed to be associated with favorable internal outcomes such as reduced waste and discharges, increased efficiency, reduced energy and resource costs, lower risk and better reputation, and ∗ Correspondence to: via Bigiogera 13, 20128 Milan, Italy. Tel.: +39 3497613213. E-mail address: ireneeleonora.lisi@unicatt.it reduced compliance costs (Sharma and Vredenburg, 1998). As managers recognize these advantages, they in many cases commit substantial resources toward environmental protection initiatives (Roewer, 2008). To date, environmental management research has extensively examined environmental motivations as a basic trigger of organizations’ environmental proactivity. Those motivations can basically reflect a mix of businessoriented, stakeholders-oriented and ethical motivations (Bansal and Roth, 2000). However, this literature is relatively silent on which specific managerial processes may translate such motivational factors into improved performance (Wisner et al., 2006). The management control literature has started to analyze this issue by discussing the potential role of specific http://dx.doi.org/10.1016/j.mar.2015.06.001 1044-5005/© 2015 Elsevier Ltd. All rights reserved. Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 2 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx environmental performance measurement and control systems in supporting companies’ environmental initiatives (Gond et al., 2012). As a particular application of management control systems, they are expected to foster the translation of companies’ environmental motivations into improved performance by better aligning organizational and behavioral structures with firms’ objectives and underlying value drivers (Henri and Journeault, 2010). More specifically, it is suggested that environmental performance measurement and control systems are important to: identify emerging threats and opportunities, facilitate environmental decision-making and coordination by managers, promote goal and value congruence between the individual and the organization, and facilitate learning (see, e.g., Arjaliès and Mundy, 2013; Henri and Journeault, 2010; Virtanen et al., 2013). Yet, these alleged positive effects might be less straightforward than often realized, for at least two reasons. First, similar to performance measurement systems in general, environmental performance measurement systems are also fraught with commensuration problems, which may hamper their effectiveness. Indeed, the technical challenges implied in the measurement of many environmental impacts – such as those associated with carbon emissions or water use – are well recognized (Unerman and Chapman, 2014). If environmental performance measures are perceived to have low controllability or technical validity, their use – particularly when linked with rewarding – can have dysfunctional effects (Virtanen et al., 2013). Second, the environmental domain represents a particularly challenging decision-making setting, in which ethical motivations play a crucial role (Bansal and Roth, 2000), but they may sometimes conflict with economic considerations (Figge and Hahn, 2013). In such a context, the introduction of ad hoc systems aimed at quantifying the environmental actions of an organization and at formally integrating the environmental concerns into the organizational routines may even be counterproductive, as the risk exists of undermining employees’ intrinsic motivation to work toward environmental goals (Virtanen et al., 2013). Based on such arguments, it therefore seems possible to question the supposedly unproblematic role of environmental performance measurement systems as a mechanism for translating companies’ environmental motivations into performance. To address this puzzle, this paper develops a comprehensive model in which the use of Environmental Performance Measures (EPM) for a wide variety of decision-making and control purposes acts as an intervening variable among business motivations, perceived stakeholders’ pressures and top management’s environmental commitment on the one hand, and environmental and economic performance on the other hand. The theoretical perspective underlying this model leverages the concept of environmental performance measurement systems as tools to deal with various forms of uncertainty (Davila, 2000). Building on Galbraith (1973), the different types of uncertainty under consideration in this study refer to the differences between the amount of information required to meet the various environmental concerns and expectations held by managers and external stakeholders and the amount of information already possessed by the organization. By supplying the information required to deal with uncertainty related to the environmental implications of a range of decision-making and control contexts (e.g., capital expenditures’ approval, suppliers’ selection or product decisions), EPM address Galbraith’s (1973) information gaps and foster the translation of companies’ environmental motivations into improved performance. The model is tested using survey data from a sample of 91 Italian firms. The empirical results, based on Partial Least Squares (PLS) structural equation modeling (Chin, 1998), confirm the hypothesized mediation role for the use of EPM. In particular, the use of EPM for decision-making and control appears to partially mediate the relationship among firms’ business motivations and environmental performance and to fully mediate the links between the other two motivations (i.e., perceived stakeholders’ pressures and top management’s environmental commitment) and environmental performance. The results also show that EPM use positively influences economic performance indirectly through environmental performance. This study contributes to the literature by developing insights into the link between environmental motivations and performance. More specifically, it investigates the processes through which companies may translate their motivational factors into enhanced performance. The use of EPM for decision-making and control is shown to represent an effective mechanism in that respect. In so doing, this paper also contributes to the performance measurement literature, in which the empirical evidence supporting the link between performance measurement systems and economic performance is limited and conflicting (see, e.g., Franco-Santos et al., 2012; Melnyk et al., 2014). This study’s results contribute to this stream of research by suggesting that the influence of a performance measurement system on economic performance is mediated by one intervening variable (environmental performance) that depicts the operational consequences of the actions induced by the system. Finally, this work reinforces the initial insights on the economic effects of environmental performance measurement and control systems (Henri et al., 2014; Henri and Journeault, 2010; Wisner et al., 2006) by using objective – rather than subjective – economic performance data. The remainder of the paper is organized as follows. The next section develops the theoretical model. Section 3 clarifies the research method, including sample selection and variable measurement. This is followed by a presentation of the results. The final section discusses the results and concludes the paper by raising implications for theory and practice, acknowledging limitations of the study, and offering directions for further research. 2. Theoretical framework and hypotheses 2.1. Environmental motivations, corporate performance and environmental performance measurement systems The literature provides various explanations for corporate environmental proactivity, reflecting a combination of business-oriented, stakeholders-oriented and ethical motivations (see, e.g., Banerjee et al., 2003; Bansal and Roth, 2000). Some authors adopt an economics-based paradigm Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx and claim that companies engage in proactive green management because of its potential financial benefits. Other academics agree on a sociopolitical approach and maintain that firms respond to external green-oriented stakeholders to demonstrate that their operations are legitimate and congruent with societal expectations. Finally, other studies emphasize the role of ethical motivations, based on the idea that environmental actions are grounded in moral values and are a reflection of top management’s sensibility toward environmental ills. Recent contributions have also argued that these perspectives are complementary and their combination can create a more comprehensive view of corporate environmental proactivity (Ervin et al., 2013). Proactive environmental management, in turn, has been linked with improved corporate performance through various operational, reputational and competitive benefits (Sharma and Vredenburg, 1998). Within the environmental management literature, however, the concept of environmental proactivity remains somewhat abstract (Wisner et al., 2010). Environmentally proactive firms have been described, for example, as those in which environmental management is a priority for top management (Hunt and Auster, 1990), which commit a substantial amount of resources to environmental management and integrate it into strategic planning (Judge and Douglas, 1998), or which actively manage their processes to minimize environmental impacts beyond regulatory requirements (Aragòn-Correa and Sharma, 2003). As a result, relatively little is still known on which specific managerial processes may translate companies’ environmental motivations into improved performance (Wisner et al., 2006). Useful insights in this respect may be offered by the emerging stream of research on environmental performance measurement systems, which have attracted growing attention in recent years as tools supporting the integration of environmental concerns into firms’ business processes (see, e.g., Bonacchi and Rinaldi, 2007, and references therein). Environmental performance measurement systems represent an important component of environmental management control systems (Henri and Journeault, 2010) and a specific application of performance measurement systems. They can be used to supply information for decision-making (Burritt et al., 2002; Burritt and Schaltegger, 2010) as well as to support the attainment of environmental objectives through performance evaluation and rewarding (Gabel and Sinclair-Desgagné, 1993; Perego and Hartmann, 2009). By fostering the alignment of management processes with firms’ objectives and underlying value drivers (Ittner et al., 2003), environmental performance measurement systems may help companies translate their environmental motivations into improved performance. More specifically, they are expected to play such a mediation role through their influence on people’s behavior and on organizational capabilities (Franco-Santos et al., 2012). Concerning the behavioral impacts, the use of performance measures may help to concentrate the efforts of executives on what is important for the organization, to foster the cooperation and coordination among people, and to improve employees’ motivation toward the achievement of the firm’s objectives. Regarding the influence on organizational capabilities, performance measurement 3 systems may facilitate both single- and double-loop learning through the routines they stimulate. Yet, performance measurement is not without its critics (Melnyk et al., 2014), and there are reasons to expect that, in the environmental context, the introduction of formal performance measurement systems may be particularly problematic or even counterproductive. First, commensuration problems are likely to create challenges, as sufficiently established measurement practices are still lacking for many environmental impacts such as those associated with carbon emissions or water use (Hartmann et al., 2013; Unerman and Chapman, 2014). If environmental performance measures are perceived to have low controllability or technical validity, their use – particularly when linked with rewarding – can negatively affect motivation and other job-related attitudes, ultimately endangering performance (Franco-Santos et al., 2012). Alternatively, companies could focus their efforts primarily on easily quantifiable environmental measures – such as energy consumption – and overlook other aspects potentially of more critical importance but harder to quantify – for example, carbon emissions or biodiversity (Arjaliès and Mundy, 2013). A second challenge with formal performance measurement systems, which may be particularly severe in the environmental context, is their potentially negative effect on intrinsic motivation (Adler and Chen, 2011). Indeed, improving environmental performance can provide significant personal rewards for many employees (Virtanen et al., 2013). In this setting, a focus on the use of informal systems and the role of behavioral aspects could be critical in providing employees with the intrinsic motivation to achieve environmental goals (Epstein, 2010). On the contrary, an excessive economic rationalization of environmental concerns through formal performance measurement systems could have negative motivational consequences (Virtanen et al., 2013). With the aim of further exploring the (theoretically unclear) role of performance measurement in the environmental context, this paper defines environmental performance measurement systems as the extent to which Environmental Performance Measures (EPM) are used by managers for a variety of different purposes pertaining to both the ‘decision-making’ and decision-control (‘decisioninfluencing’) roles of management accounting information (Luft and Shields, 2003).1 A comprehensive model is thus developed in which the use of EPM mediates the relationships among firms’ environmental motivations and corporate performance (see Fig. 1). More specifically, it is proposed that EPM use is positively influenced by expected competitive advantage (H1a), perceived stakeholders’ concern (H1b), and top management’s environmental commitment (H1c). EPM 1 In so doing, this study adopts a more specific and comprehensive operationalization than those employed in previous research. For example, Perego and Hartmann (2009) focus on decision-control exclusively, Passetti et al. (2014) consider only decision-making, whereas Henri and Journeault (2010) refer to a broad, single item for internal decision-making together with other generic uses of EPM (namely, monitoring compliance with policies and regulations, motivating continuous improvement, and providing data for external reporting). Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model ARTICLE IN PRESS YMARE-552; No. of Pages 18 4 I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Expected competitive advantage H1a Perceived stakeholders’ concern H2a H1b EPM use Environmental performance H2b Economic performance H1c Top management’s environmental commitment Fig. 1. General theoretical model. use, in turn, enhances a company’s environmental performance (H2a) and, through this, its economic performance (H2b) as well. Control paths in Fig. 1 are shown as dotted lines to represent a test for the possibility of a direct association between environmental motivations and environmental performance. Such direct links would be observed if environmentally motivated firms adequately aligned their organizational and behavioral structures to the goals of environmental protection even in the absence of specific environmental performance measurement systems, for example by relying on their traditional control systems or through more soft and informal mechanisms such as cultural controls (Epstein, 2010). 2.2. Environmental motivations and EPM use (H1a, H1b, H1c) Expected competitive advantage is defined here as the belief that proactive environmental initiatives represent a source of competitive advantage and improve long-term profitability. The search for competitive advantage has been repeatedly recognized as a fundamental, businessoriented motivation for environmental proactivity (see, e.g., Banerjee et al., 2003; Bansal and Roth, 2000; Paulraj, 2009). Empirical findings from the environmental management literature have generally confirmed that companies adhering to this ‘business case’ rationale (Laine, 2005) tend to adopt voluntary and innovative environmental management activities aimed at improving their environmental performance (Sharma, 2000). However, this traditionally positive relationship between a firm’s economic motivations and its environmental proactivity has been recently called into question by Boiral and colleagues (2012), who find that – in their sample of Canadian firms – the possible economic benefits that could result from environmental initiatives are negatively related with their commitment to reduce greenhouse gas emissions. In the management accounting literature, however, various arguments suggest that a competitive advantage rationale provides a strong incentive for managers to invest in environmental management accounting and control systems that quantify the costs, benefits, and operational outcomes of proactive environmental management (Burnett and Hansen, 2008). Because management control systems are adopted to assist managers in achieving some desired organizational outcomes, organizations adhering to the business case rationale for corporate environmentalism should develop environmental management control systems to support the implementation of their environmental initiatives (Pondeville et al., 2013). This expectation can be expected to hold, in particular, with respect to firms’ performance measurement systems, which play a major role in aligning management processes with the achievement of the firm’s objectives (Otley, 1999). More specifically, firms that perceive corporate environmentalism as key value driver leading to enhanced firm value should make more extensive use of EPM for decisionmaking to align their goals and resource allocation with this value driver and to ensure that the expected economic benefits actually materialize (Ittner et al., 2003). In addition, the business case rationale for corporate environmentalism should also positively affect EPM use for control (i.e., performance evaluation and rewarding) purposes. Indeed, incentive systems should encompass environmental criteria if top management wants to align employees’ efforts toward environmental protection as this is perceived to have positive consequences on profit (Gabel and SinclairDesgagné, 1993). Based on the aforementioned arguments, the following hypothesis is proposed: H1a. EPM use for decision-making and control is positively influenced by expected competitive advantage. As shown in Fig. 1, the second determinant of EPM use is represented by perceived stakeholders’ concern, which has been variously recognized as an influential, external motivation for corporate environmentalism (Buysse and Verbeke, 2003; Henriques and Sadorsky, 1999). Perceived stakeholders’ concern is defined here as the perceived degree of concern a company’s stakeholders demonstrate toward the natural environment. Indeed, managers’ subjective evaluations of stakeholders’ pressures – not stakeholders’ pressures as such – determine the role of stakeholders in the adoption of environmental initiatives (Ervin et al., 2013). Stakeholders-oriented arguments have been extensively applied within the environmental management literature to explain why firms may voluntarily adopt environmental protection initiatives that are not required by law (Plaza-Úbeda et al., 2009). On the contrary, they have not been subject to thorough investigation in management control research (Pondeville et al., 2013). Yet, from a management control perspective, management must consider and weigh stakeholders’ concerns when designing and implementing performance measurement systems (Ferreira and Otley, 2009). Therefore, organizations should be expected to reinforce their environmental performance measurement mechanisms when perceived stakeholders’ pressures intensify (Songini and Pistoni, 2012). In this respect, Rodrigue et al. (2013) provide some preliminary evidence regarding stakeholders’ nuanced influences on the choice of EPM in their case-study organization. In particular, organizations should make more extensive use of EPM for decision-making purposes as a means to improve their alignment with stakeholders’ growing environmental concerns (Henriques and Sadorsky, 1999) as well as to achieve legitimacy in the stakeholders’ eyes (Bansal and Roth, 2000). Organizations should also make more extensive use of EPM for performance evaluation and rewarding Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx to further direct managerial effort toward stakeholders’ environmental priorities (Berrone and Gomez-Mejia, 2009a). However, in the absence of specific mandatory regulations, it could also be argued that perceived pressures from stakeholders are quite weak and lead to superficial responses that are detached from companies’ internal functioning (Boiral et al., 2012; Ervin et al., 2013).2 According to this skeptical view, companies could, for example, measure and report EPM to promote a legitimate image outside the organization, but at the same time ignore such information in relation to their internal decision-making and control processes. Such a ceremonial response, while theoretically plausible, is nevertheless considered to be rather myopic and possibly counterproductive when stakeholders’ environmental pressures are particularly strong, as key stakeholders are unlikely to be fooled by imageenhancing activities (Berrone and Gomez-Mejia, 2009b). On the contrary, achieving legitimacy in such a context is likely to require relatively substantial practices (Berrone and Gomez-Mejia, 2009a). Based on the preceding discussion, the following hypothesis is formulated: H1b. EPM use for decision-making and control is positively influenced by perceived stakeholders’ concern. The third determinant of EPM use (see Fig. 1) is top management’s environmental commitment, a motivation which stems from the concern that top executives have for their social obligations as well as the social good (Bansal and Roth, 2000). The literature has long recognized that one of the main drivers to the adoption of environmental initiatives by firms is represented by top management’s ethical values and attitudes toward environmental ills (see, e.g., Banerjee et al., 2003; Bansal and Roth, 2000; Keogh and Polonsky, 1998; Paulraj, 2009). In this respect Keogh and Polonsky (1998), by leveraging on the organizational commitment literature (Meyer and Allen, 1991), advance the notion of affective environmental commitment, which encompasses the individual’s emotional attachment to, identification with and involvement in supporting environmental concerns. The authors argue that, by nature of its deep emotional attachment, such an affective commitment provides the impetus to embrace as global a view as possible and expend great effort in the pursuit of the environmental goals that view engenders. Managers affectively committed to the natural environment will constantly seek to explore any and all opportunities that present themselves and to craft alternative solutions to capitalize on such opportunities. Environmentally committed managers can therefore be expected to follow through on their commitment to the natural environment by – amongst other things – promoting EPM use to ensure the business is operating in accordance with their environmental concerns and priorities. 2 The possibility of a disconnect between the measures adopted by companies in response to external pressures and firms’ internal practices has been highlighted by various schools of thought, and particularly by institutional research (Baxter and Chua, 2003). 5 These arguments lead to the following hypothesis: H1c. EPM use for decision-making and control is positively influenced by top management’s environmental commitment. 2.3. EPM use and corporate performance (H2a, H2b) As shown in Fig. 1, the two performance dimensions considered in the theoretical model are environmental performance and economic performance. Indeed, this study investigates the direct effect of EPM use on environmental performance (H2a), as well as the indirect effect of EPM use on economic performance through environmental performance (H2b). With respect to environmental performance, there is no clear agreement concerning the definition and operationalization of this construct (Ilinitch et al., 1998) and its most appropriate boundaries (Gray and Milne, 2004). Definitions have ranged from quite narrow ones focused on environmental impacts, such as waste or toxic releases (AlTuwaijri et al., 2004; Burnett and Hansen, 2008), to broader conceptualizations based either on rating agencies’ scores (Cho et al., 2012) or on multidimensional, subjective assessments directly made by sample respondents (Henri and Journeault, 2010). At the extreme, it has even been argued that environmental performance at the organizational level should be abandoned altogether in favor of regional and eco-system levels (Gray and Milne, 2004). In this study, environmental performance is defined as “a firm’s effectiveness in meeting and exceeding society’s expectations with respect to concerns for the natural environment” (Judge and Douglas, 1998, page 245). This definition was chosen as it does not restrict the scope of this multidimensional construct to environmental impacts only, but it adopts a societal perspective that also considers the capacity of the organization to establish harmonious relationships with external green-oriented stakeholders (Henri and Journeault, 2010). Concerning the link between EPM use and environmental performance, a few empirical studies show a positive relationship between some aspects of management control systems and environmental performance (e.g., Henri et al., 2014; Henri and Journeault, 2010; Judge and Douglas, 1998; Wisner et al., 2006). Among management control mechanisms, environmental performance measurement systems could also be expected to be positively associated with environmental performance. On the one hand, the use of EPM for decision-making purposes allows for the integration of environmental concerns within organizational routines and processes. This enhances environmental performance by clarifying expectations, reducing the ambiguity associated with tasks related to achieving environmental strategies, and providing a coherent reflection of environmental priorities (Chenhall, 2005). Indeed, managers need a considerable amount of information from their environmental accounting systems to support decision-making in relation to cost reduction, process and production efficiency, regulatory compliance, and product improvement (Burritt et al., 2002). By revealing the cause-and-effect Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 6 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx relationships among environmental operations, strategies and goals, or between environmental and organizational issues (Chenhall, 2005), environmental performance measurement systems improve the decision-making process (Burritt and Schaltegger, 2010) and contribute to environmental performance. On the other hand, the use of EPM for control purposes (such as performance evaluation and rewarding) promotes environmental goal congruence between individuals and the organization (Flamholtz et al., 1985). It motivates people to align their behavior with the environmental goals of the organization and to exert additional effort (Bonner et al., 2000), which in turn should improve environmental performance. Indeed, if performance is rewarded based only on economic parameters, managers quickly recognize that eventual trade-offs on the environment are acceptable and, thus, environmental performance will be negatively affected (Epstein, 1996). On the contrary, the use of EPM for performance evaluation and rewarding sends a clear message from top-management that environmental performance is important to the firm, forcing managers to focus on both profit-related activities and activities related to environmental performance (Gabel and Sinclair-Desgagné, 1993). The aforementioned arguments lead to the following hypothesis: H2a. Environmental performance is positively influenced by EPM use for decision-making and control. Concerning the other performance dimension considered in the model (Fig. 1), the literature reports contrasting arguments for a direct effect of environmental performance measurement and control systems on economic performance, that is performance expressed in financial metrics. On the one hand, such mechanisms are used to encourage desirable actions (Merchant, 1998), which can have major economic consequences in relation to environmental issues through, e.g., cost reductions, improved product pricing, attraction of human resources and reputational improvements (Ferreira et al., 2010). On the other hand, environmental performance measurement and control systems may also have some costs that offset their benefits, such as promoting information overload, spreading managerial efforts over too many objectives, reducing motivation by including multiple goals that are inconsistent in the short term, and increasing administrative costs relative to simpler systems (Henri and Journeault, 2010). Empirical evidence suggesting there is no direct relationship between environmental control systems and economic performance is provided by Henri and Journeault (2010) and by Henri et al. (2014). Yet, if EPM use has no direct effect on economic performance, it may indirectly influence it through environmental performance. Such a mediation effect may be expected based on the ‘eco-efficiency’ thesis (Burnett and Hansen, 2008). Eco-efficiency challenges the traditional economic view according to which improving environmental performance inevitably contributes to penalties, such as increased lead times, reduced quality or increased costs, ultimately decreasing returns to stockholders (Melnyk et al., 2003). On the contrary, proponents of the eco-efficiency thesis argue that pollution is a form of economic inefficiency and, thus, reductions in pollution actually increase productive efficiency and thereby reduce costs (Porter and van der Linde, 1995). Therefore, in contrast with the traditional ‘win-lose’ model of pollution control and cost management, eco-efficiency reflects an underlying ‘win–win’ paradigm (Burnett and Hansen, 2008). Robust empirical support for this thesis is provided by Al-Tuwaijri et al. (2004). The authors, by controlling for endogeneity concerns through a simultaneous equations approach, are able to conclude that higher environmental performance is significantly associated with higher economic performance. Similarly, Burnett and Hansen (2008) provide both cross-sectional and longitudinal evidence suggesting that lower polluting plants are also more efficient. The use of EPM for both decision-making and control has been linked to environmental performance (H2a). As argued above, environmental performance positively affects economic performance. Therefore, EPM use is expected to have indirect implications for economic performance by influencing an intermediary level of performance – environmental performance – which in turn influences economic performance. The following hypothesis is thus proposed: H2b. Economic performance is positively influenced by EPM use for decision-making and control through environmental performance. 2.4. Control variables To control for the potentially spurious effects of other factors on the relationships under investigation, the model includes as control variables: (i) size, (ii) industry, (iii) performance measurement quality, and (iv) the presence of a certified environmental management system. These factors have been chosen as their influence has been documented in past environmental management and accounting research (Banerjee et al., 2003; Ferreira et al., 2010; Henri and Journeault, 2010). As larger firms are more likely to adopt sophisticated management accounting techniques (Bouwens and Abernethy, 2000), size is likely to affect companies’ environmental performance measurement systems. In addition, size may influence the link among EPM use and economic and environmental performance, as the potential cost savings or revenue improvements related to environmental matters may be more important for larger firms (Henri and Journeault, 2010). Industry is an important variable driving the type and degree of external pressures organizations are facing with respect to environmental issues (Banerjee et al., 2003); thus, industry can alter organizations’ responses to such issues and, consequently, their environmental performance measurement systems as well. In addition, organizations that operate in industries that have a greater impact on the environment are more likely to use environmental management control systems because they will enjoy more material benefits (Ferreira et al., 2010). The perceived quality of EPM is also included as a control. Indeed, if the quality of EPM is perceived to Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx be particularly low, it is likely that their use is hindered (Abernethy et al., 2004). Finally, the model controls for the presence of a ISO 14001 or EMAS certified environmental management system (Melnyk et al., 2003). 3. Research method 3.1. Sample selection and data collection Data were collected using a web-based questionnaire administered to a target sample of Italian organizations from a variety of industries. Survey data were also complemented with archival data whenever possible. The survey was administered with the contribution of the Italian branches of two of the world’s leading bodies in the field of management systems certification services – Bureau Veritas and DNV GL Business Assurance – who acted as the ‘legitimate authority’ (Dillman, 2000) to increase the response-rate. A non-random purposive sampling strategy was applied as it was considered better suited than a fully random sampling approach given the novelty of the field under investigation. In particular, only companies with more than a hundred employees – as listed in the sponsors’ client databases – were selected because they were expected to have more developed environmental performance measurement systems (Perego and Hartmann, 2009). A total of 443 potential respondents were comprised in the final target sample. It was requested that the company’s Sustainability/Corporate Responsibility manager (or, in absence, the person most responsible for environmental aspects within the firm) be involved in the survey. Such a profile, indeed, emerged as the most knowledgeable respondent about the topics under investigation during the pre-tests of the questionnaire with several professionals in the field.3 The web-survey was administered using a slightly modified version of the implementation strategy suggested by Dillman (2000): a pre-notice mailing to alert about the study; a first mailing containing the link to access the web-based questionnaire; and three follow-ups. To encourage completion of the questionnaire, participants were promised a summary of the results and assured confidentiality (Dillman, 2000). Of the 443 distributed questionnaires, 124 were received (28%). Of these returned questionnaires, nineteen were dismissed because the respondent declared the issues investigated were not applicable to the company. Moreover, fourteen questionnaires with multiple missing values on dependent variables were excluded from hypothesis testing to avoid any artificial increase in relationships with independent variables (Hair et al., 2010). For the remaining 3 In some instances, respondents were senior officers from the Sustainability/Corporate Responsibility or environmental functional areas, and in others they were general managers, quality, HR, manufacturing or financial managers. Respondents were generally members of the top management team, as only 1.56 hierarchical levels separated – on average – respondents from their companies’ CEOs, and their mean company tenure was 12.45 years. Such a high-level profile seems appropriate to obtain valid and reliable subjective evaluations of companies’ characteristics and policies. 7 sample of 91 cases, the level of randomness in missing values was tested with Little’s missing completely at random (MCAR) test and the result was found to be acceptable (2 = 231.837, DF 227, and Sig. 0.399). Therefore, in a small number of other cases individual missing values were replaced with mean values (Hair et al., 2010). This resulted in a complete data set of 91 responses, for a final response rate of 20.5%, which compares favorably with prior environmental management accounting survey-based studies (see, e.g., Ferreira et al., 2010; Pondeville et al., 2013). Nevertheless, the possibility of non-response bias was also investigated. In particular, early and late responses were compared in paired samples of 45, 30 and 15 using both an independent samples t-test and its non-parametric equivalent, the Mann–Whitney U-test. The results (not reported) show that there are no significant differences on any of the study variables – including demographic and control variables – with the sole exception of economic performance, which is higher for late respondents compared to early respondents when paired samples of 30 are considered.4 In addition, during some follow-up phone calls, I discussed with approximately 40 non-respondents their reason(s) for not completing the questionnaire. These reasons were mainly time pressures and receiving too many surveys, which are similar to those reported in other studies (Chenhall, 2005; Hall, 2008). Overall, these tests indicate that there is no significant non-response bias in the sample. I also estimated the extent to which the common method variance affects the results by performing two statistical tests: Harman’s (1976) one factor test and partialling out a ‘marker variable’ (Lindell and Whitney, 2001). According to the first test, if a substantial amount of common method variance exists in the data, then either a single factor will emerge out of an exploratory factor analysis or one factor will account for the majority of the variance in the measurement items used in the model. The un-rotated exploratory factor analysis, using the eigenvalue-greater-than-one criterion, revealed five distinct factors that accounted for 70.18% of the variance, with the first factor capturing 39.03% of the variance in the data. According to the second test, if a variable can be identified that is theoretically unrelated to at least one other variable in a study (preferably the dependent variable), then it can be used as a marker variable in controlling for the common method variance (Lindell and Whitney, 2001). Following the approach by Elbashir et al. (2011), I used respondents’ age as the unrelated marker variable as a surrogate for the common variance and examined the PLS structural model both with and without the marker variable. The findings (not reported) show the marker variable is not statistically significant and the original results are 4 Indeed, when applied to paired samples of 30, the independent samples t-test shows that economic performance is higher for late respondents (X̄ = 3.03) as compared to early respondents (X̄ = −9.36) (t = −2.214, p = 0.031). However, a statistically significant difference is not confirmed by the Mann–Whitney U-test. In addition, no statistically significant differences emerge when comparing paired samples of 15 and 45 according to both tests. Therefore, it seems possible to conclude that the overall sample is not systematically biased in this respect. Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 8 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Table 1 Demographic variables and sample composition (n = 91). Variable Minimum Panel A: Descriptive statistics for demographic variables 1 Job tenure (years) 2 Company tenure (years) 30 Age (years) a 0 Hierarchical level Maximum Mean SD 25 34 62 5 8.49 12.45 43.83 1.56 5.43 8.13 7.81 0.86 Frequency % Panel B: Respondents by functional area Sustainability/Environmental General management Quality HR Other 33 9 25 7 11 38.8 10.6 29.4 8.2 12.9 Panel C: Respondents by education High school University degree Master degree Doctorate degree 26 36 21 5 29.5 40.9 23.9 5.5 Panel D: Respondents by gender Male Female 55 33 62.5 37.5 Panel E: Respondents by industry category (SIC codes) Agriculture, mining and construction (01–19) Manufacturing (20–39) Transportation and utilities (40–49) Wholesale and retail (50–59) Services (60–89) 10 47 7 7 20 11.0 51.6 7.7 7.7 22.0 a Measured by asking respondents how many hierarchical levels separate them from their companies’ CEOs. not affected by its inclusion in the model. Together these procedures suggest that the common method bias does not seriously affect the results of this study. Demographic information was collected from respondents regarding role, job tenure, company tenure, hierarchical level, education level, age, gender and main industry. Table 1 reports the descriptive statistics and frequencies for these variables. factorability of the items,5 the design of all measurement instruments was based on the results of principal components analysis and Cronbach alpha statistics of internal reliability (Nunnally, 1978). Table 2 contains an overview of the wording of items in the final analysis together with the results of the factor and reliability analysis. Descriptive statistics, based on the average scores of multi-item variables, are presented in Table 3. 3.2. Variable measurement The questionnaire solicited respondents’ subjective appreciation regarding: the use of EPM for decision-making and control, expected competitive advantage, perceived stakeholders’ concern, top management’s environmental commitment and environmental performance. Established scales were used whenever available. Economic performance was measured through archival data. An initial survey draft was circulated among four academic scholars with substantive or psychometric expertise and was pre-tested by four professionals from the two survey sponsors and three managers (not part of the sample) for clarity, understandability, ambiguity, and face validity (Dillman, 2000). This resulted in minor changes to the wording of some items and to the questionnaire’s layout. The questionnaire was then translated into Italian by applying the back-translation procedure proposed by Behling and Law (2000). The psychometric properties of the measurement scales were also assessed prior to including them in the PLS measurement model. In particular, after checking the 3.2.1. EPM use EPM can be defined as “specific expressions that provide information about . . . [the] results of an organization’s management of its environmental aspects” (ISO, 1999). To enhance the understandability and uniformity of interpretation on the part of survey respondents, a definition of EPM based on the Global Reporting Initiative (GRI) G3 guidelines was provided in the introductory section of the study’s questionnaire. Indeed, because the GRI guidelines represent the de facto global standard for corporate responsibility reporting (KPMG, 2011), they are supposed to be familiar to most organizations. In particular, GRI G3 EPM cover the performance related to inputs (e.g., materials, energy, water), outputs (e.g., emissions, effluents, waste), biodiversity, environmental compliance, and other 5 The Bartlett test of sphericity showed that nonzero correlations existed at a significance level of 0.000 for all the variables. The Kaiser–Meyer–Olkin measures of sampling adequacy were above 0.7 in all cases (Hair et al., 2010). Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS 9 I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Table 2 Principal component analysis and Cronbach alphas for the main variables (n = 91). Factors and Cronbach alphas Factor loadings Panel A: EPM use (˛ = 0.895, Eigenvalue = 4.31, 61.51% of variance) Evaluate managers’ performance (Item 1) Incentivize and reward managers (Item 2) Establish formal strategic objectives (Item 3) Evaluate and approve capital expenditures (Item 4) Make product decisions, e.g.: product price, product mix (Item 5) Select/assess external suppliers (Item 6) The daily management and operational decisions, e.g.: assess make-or-buy alternatives, assess the manufacturing process to use (Item 7) 0.764 0.723 0.854 0.751 0.825 0.760 0.806 Panel B: Expected competitive advantage (˛ = 0.864, Eigenvalue = 2.85, 71.21% of variance) Being environmentally conscious can lead to substantial cost advantages for our firm (Item 1) Our firm can enter lucrative new markets by adopting environmental strategies (Item 4) Our firm can increase market share by making our current products more environmentally friendly (Item 5) Reducing the environmental impact of our firm’s activities will lead to a quality improvement in our products and processes (Item 6) 0.725 0.859 0.936 0.842 Panel C: Perceived stakeholders’ concern (˛ = 0.834, Eigenvalue = 2.69, 67.38% of variance) Our stakeholders feel that environmental protection is a critically important issue facing the world today (Item 1) The public is very concerned about environmental destruction (Item 2) Our customers are increasingly demanding environmentally friendly products and services (Item 3) Our stakeholders expect our firm to be environmentally friendly (Item 4) 0.763 0.796 0.853 0.867 Panel D: Top management’s environmental commitment (˛ = 0.963, Eigenvalue = 2.81, 93.74% of variance) The top management team in our firm is committed to environmental preservation (Item 1) Our firm’s environmental efforts receive full support from our top management (Item 2) Our firm’s environmental strategies are driven by the top management team (Item 3) 0.973 0.976 0.956 Panel E: Environmental performance (˛ = 0.896, Eigenvalue = 3.07, 76.75% of variance) Complying with environmental regulations (i.e., emissions, waste disposal) (Item 1) Limiting environmental impact beyond compliance (Item 2) Preventing and mitigating environmental crises (i.e., significant spills) (Item 3) Educating employees and the public about the environment (Item 4) 0.902 0.895 0.866 0.840 Table 3 Descriptive statistics (n = 91). Variable Mean SD Theoretical range Actual range Panel A: Descriptive statistics (for scale variables) EPM use Expected competitive advantage Perceived stakeholders’ concern Top management’s environmental commitment Environmental performance Economic performance Size (ln n. employees) EPM perceived quality 3.66 5.18 5.69 5.72 5.37 −3.92% 5.92 5.22 1.12 1.05 0.83 1.10 0.91 18.15 1.44 1.17 1.00–7.00 1.00–7.00 1.00–7.00 1.00–7.00 1.00–7.00 NA NA 1.00–7.00 1.00–6.00 2.00–7.00 1.75–7.00 1.00–7.00 2.00–7.00 −79% to 77% 4.28–11.29 1.00–7.00 Variable Frequency % Panel B: Frequencies (for dummy variables) Industry = 1 (manufacturing) Industry = 0 (non-manufacturing) Certification = 1 (certified environmental management system) Certification = 0 (no certified environmental management system) 47 44 46 45 51.60 48.40 50.50 49.50 relevant information such as environmental expenditures and the impacts of products and services (GRI, 2006). As previously mentioned, this paper investigates the extent to which EPM are used within organizations6 for a variety of different purposes pertaining to both the decision-making and decision-control roles of management accounting information (Luft and Shields, 2003). 6 The corporate level of analysis employed in the study is consistent with prior environmental management accounting literature (e.g., Henri and Journeault, 2010; Perego and Hartmann, 2009). Given the absence of an established scale simultaneously capturing the extent to which EPM are used by managers for both decision-making and control, an instrument was developed by adapting to the environmental context items from Ittner and Larcker (2001), Perego and Hartmann (2009) and Gerdin (2005). The instrument consists of seven items measured over a seven-point fully anchored Likert scale, asking the respondent to rate to what extent (ranging from 1 = not at all to 7 = totally) his/her firm uses EPM for a variety of purposes. In particular, for decision-making, two items (namely, establishing formal strategic objectives and evaluating capital expenditures) were derived Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 10 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx from Ittner and Larcker (2001) and three items (regarding product decisions, suppliers’ selection and operational decisions) were adapted from Gerdin’s (2005) list. For decision-control, one item (i.e., evaluating managerial performance) was adapted from Ittner and Larcker (2001) and one item (incentivizing and rewarding managers) was taken from Perego and Hartmann (2009). As reported in Table 2, the results of an exploratory factor analysis show that the seven-item scale is onedimensional; indeed, each item loads on the same factor above 0.723.7 This factor explains 61.51% of the variation. The Cronbach alpha for the scale is 0.895, well above the conventional lower limit of 0.7. However, because the scale has not been used in prior research, I performed additional tests to examine the extent to which it converged with alternative measures of EPM use. First, respondents were asked whether there were any environmental targets amongst the objectives formally assigned to managers within their firms (yes/no). A dichotomous variable was then obtained by coding 1 for affirmative answers and 0 for negative ones. I deliberately chose an alternative measure that was different in format (forced choice) from the seven-point Likert type scale to be consistent with the principle of maximally dissimilar forms of ratings, urged in the literature on convergent validation (Hall, 2008). The point-biserial correlation between the multi-item measure and the dichotomous measure is 0.497 (p < 0.001), providing reasonably strong support for the convergent validity of the seven-item measure used in the study.8 In addition, respondents answering affirmatively to this same question were also asked to indicate: the percentage of managers to whom such environmental targets were formally assigned, and what percentage (if any) of managers’ variable compensation depended, on average, upon the achievement of such environmental targets. The Pearson correlation coefficients among the seven-item scale for EPM use and these two percentages are, respectively, 0.486 (p < 0.001) and 0.521 (p < 0.005), providing additional support for the convergent validity of the seven-item measure. Finally, as a test of discriminant validity, I examined the relationship among the multi-item measure of EPM use for internal decision-making and control and a measure of EPM use for external accountability purposes, i.e., a dichotomous variable equal to 1 if the company publishes an Environmental/Sustainability report and 0 otherwise. As expected, the point-biserial correlation between the multi-item 7 Therefore, at first glance respondents do not seem to clearly distinguish among the decision-making and the decision-control use of EPM. To examine more in depth whether the theoretical distinction between these two different uses of EPM could be supported by the empirical data, a further factor analysis (with oblique Oblimin rotation) was run imposing two factors. Results provide evidence for the existence of two dimensions as expected. Indeed, the five items for decision-making load on the first factor, whereas the two items for decision-control load on the second factor. However, due to the problem of justifying the number of factors imposed, the analyses that follow are based on the one-dimensional solution. Yet, the PLS model was also tested by distinguishing between the two dimensions of EPM use. Results from these additional analyses are presented in Section 4.3. 8 I calculated the score for each respondent on the seven-item scale as an average of the seven items. measure and the dichotomous measure is not significant (0.141; p > 0.1), providing support for the divergent validity of the seven-item measure used in the study. 3.2.2. Expected competitive advantage, perceived stakeholders’ concern and top management’s environmental commitment The items to measure expected competitive advantage, perceived stakeholders’ concern and top management’s environmental commitment were drawn from the corresponding scales developed by Banerjee et al. (2003).9 Specifically, expected competitive advantage was measured by asking respondents their agreement (ranging from 1 = completely disagree to 7 = completely agree) on six statements concerning the competitive advantage benefits (in terms of cost savings, quality improvements and growth opportunities) perceived to be derived from environmental initiatives. Exploratory factor analysis supported the use of only four items. These items load, as expected, on a single factor, explaining 71.21% of the variation. The Cronbach alpha is 0.864. Perceived stakeholders’ concern was measured by asking respondents their agreement (ranging from 1 = completely disagree to 7 = completely agree) on four statements concerning their perceptions of importance assigned by the company’s stakeholders to protecting the environment and the potential customer demand for environmentally friendly products and services. The results of an exploratory factor analysis show that the four-item scale is one-dimensional, with each item loading on the same factor above 0.76. This factor explains 67.38% of the variation. The Cronbach alpha is 0.834. Finally, top management’s environmental commitment was measured by asking respondents their agreement (ranging from 1 = completely disagree to 7 = completely agree) on three items concerning their evaluation of top management’s commitment to and support for environmental initiatives. The results of an exploratory factor analysis show that the three-item scale is one-dimensional, with each item loading on the same factor above 0.95. This factor explains 93.74% of the variation. The Cronbach alpha is 0.963. The measures described above are congruent with the nature of the study’s independent variables, which refer to management’s subjective appreciation of environmental motivations rather than to their objective strength, consistent with prior studies (Ervin et al., 2013; Pondeville et al., 2013). Nevertheless, to verify the accuracy of the respondents’ subjective evaluations, whenever possible I checked the extent to which they converged with more objective, proxy measures used in the literature. The results provide support for the validity of the subjective assessments employed in the study.10 9 These scales have also been applied by another study in a different country (Fraj-Andrés et al., 2009), providing evidence of their robustness. 10 In particular, industry membership was used as a proxy measure for expected competitive advantage because companies operating in highly polluting industries, being exposed to higher future environmental costs, are more likely to foresee potential cost savings in relation to environmental matters (Ervin et al., 2013; Henri and Journeault, 2010). Consistent Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx 3.2.3. Environmental and economic performance Consistent with prior literature (e.g., Boiral et al., 2012; Henri and Journeault, 2010; Judge and Douglas, 1998), environmental performance is measured using a perceptual instrument. As several authors argue, in terms of consistently providing a valid and reliable performance assessment, neither objective nor subjective measures are superior (Chenhall, 2003). In this study, the items to measure environmental performance were drawn from the scale developed by Judge and Douglas (1998). In particular, four questions asked the respondents to rate their firms’ performance in 2010, compared to other competitors across the industry, on several environmental dimensions (such as compliance with environmental regulations and limitation of environmental impact beyond compliance). Answer categories ranged from 1 = much worse to 7 = much better, such that a higher score indicates better environmental performance. The results of an exploratory factor analysis show that the four-item scale is one-dimensional, with each item loading on the same factor above 0.84. This factor explains 76.75% of the variation. The Cronbach alpha for the scale is 0.896. To establish the validity of the subjective appreciations made by the respondents, the mean score of the items was compared with more objective data collected through some questions positioned elsewhere in the questionnaire.11 Such tests provide some support for the validity of the subjective measure used in the study. Economic performance is measured by relying on archival data. Prior environmental studies have used both accounting-based and market-based measures to represent economic performance. For example, Spicer (1978) used both accounting-based and market-based metrics (profitability and the price-earnings ratio). King and Lenox (2002) used accounting-based measures, whereas AlTuwaijri et al. (2004) preferred market-based measures. In this study, because the majority of firms in the sample are with such arguments, a positive and significant correlation has been established between the mean score of the four items as provided by survey respondents and a dummy variable equal to one for companies operating in the chemical industry (0.19; p < 0.05), a sector characterized by high environmental impact (Banerjee et al., 2003; Perego and Hartmann, 2009). On the contrary, the mean score of the four items was negatively and significantly correlated with membership in the service industry (−0.18; p < 0.05), a relatively low polluting industry in which cost advantages are generally not foreseen (Ervin et al., 2013). Concerning perceived stakeholders’ pressures, company size and ownership (i.e., listed status) were used as proxy measures of companies’ public visibility and, therefore, of the strength of stakeholders’ pressures with respect to environmental issues (Ervin et al., 2013; Henri and Journeault, 2010). As expected, a positive and significant correlation has been established between the mean score of the four items as provided by survey respondents and (i) the natural log of the firm’s employees (0.24; p < 0.05), and (ii) a dummy variable equal to one for listed companies (0.17; p < 0.1). 11 More specifically, respondents were asked to indicate the total quantity of waste – both produced and recycled – by their firms throughout 2010. Respondents answered such questions in 27 cases. Positive and slightly significant correlations have been established between the mean score of environmental performance as provided by survey respondents and (i) the percentage of recycled waste over produced waste (0.25; p < 0.1), and (ii) the quantity of recycled waste divided by the firm’s operating revenues (0.27; p < 0.1). Therefore, the firms that reported having good environmental performance are those that recycle more. A similar proxy of environmental performance has been employed by Al-Tuwaijri et al. (2004). 11 not publicly quoted, I used the return on capital employed (ROCE), a standard accounting measure of operating profitability. ROCE data were collected from Amadeus, a database developed by Bureau Van Dijk Electronic Publishing that contains financial data about European companies. To control for industry influences, I subtracted from each firm’s ROCE the average industry ROCE, based on a firm’s dominant four-digit SIC code. This method of controlling for industry effects has been used frequently (Agle et al., 1999). 3.2.4. Control variables Size is measured using the natural log of the number of employees (Henri and Journeault, 2010), as derived from the Amadeus database. Industry is measured as a dummy variable, distinguishing among manufacturing (SIC codes 20–39 inclusive) and non-manufacturing firms, because manufacturing firms are considered to be more environmentally sensitive (Melnyk et al., 2003). Companies’ industrial codes were also derived from the Amadeus database. The perceived quality of EPM is measured through a single-item instrument drawn from Abernethy et al. (2004). Finally, the presence of a certified environmental management system is measured through a dummy variable based on the respondents’ answers to a question asking them whether the majority of their firms’ facilities are certified according to the ISO 14001 or EMAS standards. 3.3. Partial Least Squares (PLS) structural equation modeling PLS was used to test the research model.12 PLS is a component-based structural equation modeling technique that simultaneously tests the psychometric properties of the scales used to measure the constructs (i.e., measurement model) and examines the strength of the relations between the constructs (i.e., structural model) (Chin, 1998). Over the last few years, a growing number of accounting studies using PLS have been published (e.g., Chapman and Kihn, 2009; Elbashir et al., 2011; Pondeville et al., 2013). PLS was chosen for this study because it is suitable for the causal-predictive analysis of complex models with multiple independent and dependent variables (Chin and Newsted, 1999). In addition, unlike covariance-based approaches to structural equation modeling, PLS does not require multivariate normal data, places minimum requirements on measurement levels, and is suitable for small samples (Chin, 1998).13 In this study, PLS was 12 I used Smart PLS 2.0 (Ringle et al., 2005). Chin’s (1998) rule of thumb suggests that the sample size for a PLS study should be 5 to 10 times for either: (1) the largest number of formative indicators for a particular construct in the measurement model; or (2) the largest number of structural paths directed at a particular construct in the structural model. In this study, the dependent latent variable with the largest number of structural paths directed at it is EPM use, with seven independent variables (namely, expected competitive advantage, perceived stakeholders’ concern, top management’s environmental commitment, and the four control variables). Thus, the sample size of 91 cases satisfies this requirement. I also employed power analysis to investigate the issue more in depth. In particular, under the assumption that the 13 Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 12 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx used to test reflective links between constructs and measures (indicators), signifying that indicators are believed to reflect the unobserved, underlying construct (Chapman and Kihn, 2009). As previously mentioned, PLS comprises a measurement model and a structural model, which are estimated simultaneously. However, to maximize the interpretability of both models, the PLS model is interpreted in two stages: first, the reliability and validity of the measurement model is assessed, and then the structural model is assessed. 4. Results 4.1. Measurement model The preliminary analyses of dimensionality and reliability of multi-item constructs (i.e., the factor analyses and Cronbach alphas) were presented above (see Table 2). The output from PLS in relation to the measurement model (see Appendices A and B) confirms these preliminary tests by showing high (greater than 0.70) loadings of all items on their respective latent variables. In addition, the high composite reliability measures for all latent variables (from 0.89 to 0.98) confirm the alpha scores by demonstrating satisfactory reliability (Nunnally, 1978). The convergent validity of constructs is assessed by examining the average variance extracted (AVE) statistics. As Appendix A shows, the AVE for each variable is well above 0.50, which demonstrates adequate convergent validity (Hair et al., 2014). Finally, concerning discriminant validity, Appendix B shows that the square roots of the AVEs (diagonal) are all greater than the respective correlations between constructs, in support of the measures’ discriminant validity (Chin, 1998). An additional test of discriminant validity assesses each measurement item to ensure that it has a higher loading on its assigned factor than on the other factors (Chin, 1998). As Appendix A indicates, each measurement item loads higher on the appropriate construct than on any other construct. Overall, the results from the PLS measurement model indicate that each construct exhibits satisfactory reliability and validity. 4.2. Structural model To test the hypotheses, a PLS structural model was estimated. PLS uses an iterative estimation algorithm, with a series of simple or multiple OLS regression analyses (Chin, 1998); therefore, the path coefficients in the structural model can be interpreted as standardized regression coefficients.14 Because PLS makes no distributional regression of EPM use is a regular OLS regression (Abernethy et al., 2010) and setting ˛ to 5% (two-tailed) and power to 80%, my sample is able to detect a true effect size of 0.17, which can be considered a medium effect size according to the operational definition suggested by Cohen (1988). 14 Because the estimation of path coefficients in the PLS structural model is based on OLS regressions of each endogenous latent variable on its corresponding predecessor constructs, these path coefficients might be biased in the presence of high levels of collinearity among the predictor assumptions, bootstrapping (5000 samples with replacement) is used to evaluate the statistical significance of each path coefficient (Hair et al., 2014).15 Because the objective of PLS is to maximize the variance explained rather than fit, the overall incidence of significant relationships between constructs and the explained variance of the dependent variables (i.e., the R2 measures) are used to evaluate the PLS model instead of goodness-of-fit measures (Chin, 1998). Another assessment of the structural model involves the model’s capability to predict, as expressed by the Stone-Geisser’s Q2 measure of predictive relevance (Hair et al., 2014). The R2 and Q2 for the study’s endogenous variables, together with the path coefficients and the corresponding t-statistics, are shown in Table 4 and, graphically, in Fig. 2. Overall, the results suggest the model has good predictability. As Table 4 indicates, the coefficients for the five hypothesized paths in the model are all statistically significant at the 0.1 level or better. The results also show that 50 percent of EPM use, 37 percent of environmental performance and 3 percent of economic performance are explained by the model. In addition, Stone-Geisser’s Q2 is greater than zero for all endogenous latent variables, providing support for the predictive relevance of the corresponding explanatory variables (Hair et al., 2014). In particular, the results suggest that the use of EPM for decision-making and control is positively influenced by expected competitive advantage (ˇ = 0.279, p < 0.01), perceived stakeholders’ concern (ˇ = 0.189, p < 0.05) and top management’s environmental commitment (ˇ = 0.349, p < 0.01), in support of H1a, H1b and H1c. The proposed positive association between EPM use and environmental performance (H2a) is also supported with a strong and significant path coefficient (ˇ = 0.335, p < 0.01). Taken together, these results indicate that the use of EPM for decision-making and control purposes acts as a mediating variable in the relationships between a company’s environmental motivations and its environmental performance. More specifically, the results provide evidence of partial mediation with reference to expected competitive advantage, which significantly affects environmental performance both directly (ˇ = 0.287, p < 0.01) as well as indirectly through EPM use. A pattern of full mediation emerges instead with respect to the other two motivations. Indeed, the direct path among perceived stakeholders’ concern and environmental performance is negative but not significant (ˇ = −0.014, p > 0.1), whereas the direct link between top management’s environmental commitment and environmental performance is positive but still not significant (ˇ = 0.108, p > 0.1). To provide a more thorough analysis of these mediating effects, the significance of the indirect paths was also tested by bootstrapping their sampling distribution, following the procedure suggested by constructs. Therefore, a preliminary test of the structural model involves an assessment of collinearity diagnostics (Hair et al., 2014). The VIF values for all the predictor constructs are well below the threshold value of 5, suggesting collinearity does not threaten the robustness of the study’s findings. 15 Statistical significance is determined using the reported original PLS estimates and bootstrapped standard errors (Hair et al., 2014). Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model ARTICLE IN PRESS YMARE-552; No. of Pages 18 13 I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Table 4 PLS structural model: path coefficients, t-statistics, R2 and Q2 (n = 91). Paths from Paths to EPM use Environmental performance Economic performance Expected competitive advantage Perceived stakeholders’ concern Top management’s environmental commitment EPM use Environmental Performance Size Industry EPM perceived quality Certification 0.279 (3.073)*** 0.189 (2.142)** 0.349 (3.642)*** – – −0.071 (1.043) 0.113 (1.443) 0.099 (1.332) 0.125 (1.643) 0.287 (2.395)*** −0.014 (0.202) 0.108 (1.253) 0.335 (3.135)*** – −0.146 (1.528) 0.007 (0.118) – 0.108 (1.441) – – – −0.035 (0.474) 0.178 (1.602)* 0.079 (1.085) – – 0.005 (0.072) R2 Stone-Geisser’s Q2 0.502 0.313 0.377 0.264 0.031 0.041 Each cell reports the path coefficient (t-value). Bold style denotes hypothesized paths, while italic style denotes control paths. *, ** and *** denote significance at the 0.1, 0.05 and 0.01 levels using a one-tailed test for hypotheses with predicted sign and a two-tailed test for control paths. Expected competitive advantage 0.287*** 0.279*** Perceived stakeholders’ concern EPM use 0.335*** 0.178* Environmental performance Economic performance R2= 0.502 Q2 = 0.313 R2= 0.377 Q2 = 0.264 R2= 0.031 Q2 = 0.041 0.189** 0.349*** Top management’s environmental commitment Fig. 2. PLS structural model with significant path coefficients (n = 91). *, ** and *** denote significance at the 0.1, 0.05 and 0.01 levels. Preacher and Hayes (2008).16 The results (not reported) provide support for the significance of the three indirect effects acting through EPM use. Finally, the results also suggest that EPM use positively influences economic performance indirectly through environmental performance (H2b). Indeed, the direct path between environmental performance and economic performance is positive and slightly significant (ˇ = 0.178, p < 0.1). Combined with the positive and significant path coefficient between EPM use and environmental performance on the one hand, and with the insignificant direct link between EPM use and economic performance (ˇ = −0.035, p > 0.1) on the other hand, this result provides confirmatory evidence for a full mediating effect of environmental performance on the relationship between EPM use and economic performance. In other words, even if EPM use does not directly enhance a firm’s bottom line, it does so indirectly through its positive influence on environmental performance. The significance of this indirect effect was also tested by applying the bootstrapping procedure suggested by Preacher and Hayes (2008), obtaining confirmatory results (not reported). 16 As noticed by Hair et al. (2014), this approach is particularly suited for PLS settings as it makes no distributional assumption, unlike the Sobel (1982) test. It also exhibits higher levels of statistical power. 4.3. Sensitivity and additional analyses To validate the robustness of the overall model, several sensitivity analyses were run. Firstly, the PLS structural model was tested with alternative measures of economic performance, namely cash flow divided by operating revenues and ROE. These data were also collected from the Amadeus database, like ROCE, and were similarly adjusted for industry by subtracting the dominant four-digit industry average from their respective firm counterparts. The findings are qualitatively similar, as all of the results that were previously significant are still significant and the paths that were not significant remain unchanged. Secondly, to control for potential respondent’s bias – and particularly for the endogeneity concern that the respondent’s function systematically influences his/her subjective evaluations with regard to both the independent and dependent variables in the theoretical framework – a robustness check was performed by adding to the PLS structural model a control variable equal to one if the respondent came from the Sustainability/Corporate Responsibility or Environmental functional areas, and zero otherwise. The results are the same as those obtained by estimating the model without such a control variable. Finally, some additional analyses were run by distinguishing between the two underlying dimensions of EPM use. More specifically, three different PLS models were Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 14 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx tested in which the EPM use variable was (i) exclusively composed of the items referring to decision-making purposes, (ii) exclusively composed of the items referring to decision-control purposes, and (iii) split into two separate variables, one for decision-making and one for control. The results are generally in line with those of the principal model, confirming the robustness of the identified relationships. As a unique exception, the path coefficient between perceived stakeholders’ concern and EPM use for control purposes is approximately zero and statistically insignificant for both the model exclusively focused on this dimension (ˇ = 0.080, p > 0.1) as well as for the one in which both dimensions are modeled separately (ˇ = 0.072, p > 0.1). These unexpected findings are discussed in the next section. 5. Discussion and conclusions This study aimed to improve our understanding on the role that environmental performance measurement systems may play in translating companies’ environmental motivations into performance. To this end, a theoretical model was developed in which the use of EPM for a variety of decision-making and control purposes acts as an intervening variable among expected competitive advantage, perceived stakeholders’ pressures and top management’s environmental commitment on the one hand, and environmental and economic performance on the other hand. This model was tested using PLS on data collected from a survey of 91 Italian firms. The results generally confirm the hypothesized relationships. Indeed, all of the three motivational factors significantly influence the use of EPM for decision-making and control, which in turn positively affects environmental performance. However, while EPM use appears to fully mediate the relationship between perceived stakeholders’ concern and environmental performance, as well as the link between top management’s environmental commitment and environmental performance, a pattern of partial mediation emerges instead with reference to expected competitive advantage. This may suggest that the introduction of specific environmental performance measurement systems is crucial to align organizational conduct with the goals of environmental protection in relation to stakeholders-oriented and ethical motivations, but this is not necessarily the case with respect to business-oriented motivations. Indeed, it may be that firms’ economic arguments for corporate environmentalism are already – although partially – channeled through the conventional management control systems, which are traditionally focused on the economic goals of organizations (Gond et al., 2012). Alternatively, it could be argued that the business case rationale represents – if compared with the other two factors – a relatively stronger motivation, whose full deployment requires the mobilization of a more extensive set of managerial mechanisms than those investigated here. Soft or informal control mechanisms, such as cultural controls (Epstein, 2010), could represent examples of other translating devices. Additional insights on the differential strength of the three environmental motivations and their implications for environmental performance measurement systems are offered by the sensitivity analyses, in which the two theoretical dimensions of EPM use (namely, decision-making and control) are considered separately. Such tests confirm the robustness of the identified relationships with respect to business-oriented and ethical motivations, as these two factors significantly influence both the decision-making and the decision-control use of EPM. Perceived stakeholders’ concern, instead, is found to positively influence only the decision-making use, but not the control use. One possible explanation may be related to the peculiarities of the control use in comparison with the decision-making one, and specifically to the lower extent to which companies’ performance evaluation and rewarding mechanisms can be observed and judged by external stakeholders. As these practices tend to be maintained in highly confidential terms (Kolk and Perego, 2014), it may be that stakeholders’ pressures represent a relatively weak incentive toward the integration of environmental criteria into performance evaluation and rewarding processes because stakeholders are less likely to easily distinguish superficial responses from more substantive ones with respect to these practices. These findings seem to provide partial support for the skeptical view according to which perceived pressures from stakeholders may be relatively weak and lead to superficial, legitimacy-enhancing responses detached from companies’ internal processes (Boiral et al., 2012; Ervin et al., 2013). Future work could investigate these initial thoughts in greater depth. Finally, the results also suggest that EPM use positively influences economic performance indirectly through environmental performance. This finding partly contrasts with Henri and Journeault’s (2010) results, according to whom an indirect effect of eco-control on economic performance is supported only under certain circumstances (namely, higher environmental exposure, higher public visibility, higher environmental concern, and larger size). In the present study, this indirect effect is found to hold for the overall sample. This contrasting result may be due to the different operationalization of the economic performance variable. Indeed, Henri and Journeault (2010) measure this construct subjectively. The present study, instead, employs an objective and industry-adjusted measure of economic performance, thus avoiding eventual respondent’s bias in this respect. This study is not free of limitations, which have to be taken into consideration when interpreting the findings. First, a new instrument was developed to measure the use of EPM for decision-making and control purposes. The instrument exhibited satisfactory psychometric properties, apart from some minor concerns regarding its ability in distinguishing between the two types of use, as noticed in footnote 7. Yet, future research is warranted to refine and further validate the instrument. Second, no clear evidence of causality can be established with survey-data obtained from cross-sectional analyses. Rather, the evidence must be considered consistent with theoretical arguments and predicted hypotheses. Third, although tests indicate an absence of non-response bias, the relatively low response rate is a shortcoming of the study. A final weakness is the lack of explicit convergence between the outcomes of the Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model ARTICLE IN PRESS YMARE-552; No. of Pages 18 15 I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx exploratory factor analyses and the final PLS measurement model. Notwithstanding these limitations, this study is believed to contribute to the literature in several ways. In general terms, it builds insights into the link between environmental motivations and performance by investigating the role played by environmental performance measurement systems in this respect. In so doing, this paper also contributes to the performance measurement literature, which has to date provided conflicting results on the performance consequences of performance measurement systems (Franco-Santos et al., 2012; Melnyk et al., 2014). Finally, this study strengthens the preliminary insights on the economic consequences of environmental management control systems available in the literature (see, e.g., Henri et al., 2014; Henri and Journeault, 2010; Wisner et al., 2006) by using objective – rather than subjective – economic performance data. This study also has implications for practice. Its results concerning the differential strength of companies’ environmental motivations could be of particular interest to policy makers and business advice organizations. These findings suggest that public policy and business advice in this area should particularly emphasize the business case and economic arguments to foster the integration of environmental concerns into companies’ decision-making and control processes. This study is also of practical significance for management accountants, environmental managers and top management in general. Indeed, its findings concerning the performance effects of EPM use provide additional confirmatory evidence on the ‘eco-efficiency’ thesis (Burnett and Hansen, 2008) and, thus, encourage managers to adopt initiatives aimed at improving their companies’ environmental performance as a way to contribute to corporate economic well-being. The use of EPM for internal decision-making and control is shown to represent an effective mechanism in this respect. This study also opens up avenues for future research. In particular, it could be interesting to further examine the relationship among perceived stakeholders’ concern and EPM use by adopting a more fine-grained operationalization of the first variable (see, for example, Buysse and Verbeke, 2003), to test whether the strength of such link vary according to the specific stakeholders’ categories considered. An additional extension of the model could be related to the inclusion of other, more informal control systems such as cultural ones, which can play an important role in relation to environmental issues (Epstein, 2010). Finally, it could be interesting to replicate such an investigation by shifting the focus of analysis from the environmental to the social dimension of the sustainability concept (Bebbington and Thomson, 2013). Indeed, starting from the end of the 1980s, environmental accounting emerged as the prime focus of attention of the sustainability accounting literature, to the detriment of the social dimension (Owen, 2008). Therefore, it could be interesting to adapt this study’s model to investigate the role of social performance measurement systems, to test whether the identified relationships also hold in that context or if differences arise. Acknowledgements This paper is based on my PhD thesis completed at Bocconi University, Milan. I am grateful for the support and advice of my PhD supervisor Professor Angelo Ditillo. I would like to thank the Editor and the two anonymous reviewers for their insightful comments and suggestions. Thanks also to Tony Davila, Andrea Dossi, Mario Molteni, Carlos Larrinaga, and participants at the EAA Doctoral Colloquium (2010) and at the EABIS PhD Conference (2012). Finally, I thank Bureau Veritas Italia, DNV GL Business Assurance Italia and SDA Bocconi School of Management for their operational support in survey administration. Appendix A. Item loadingsa and Cross Loadings; Composite Reliability and AVE statistics for the main variables (n = 91). EPM use Expected competitive advantage Perceived stakeholders’ concern Top management’s environmental commitment Environmental performance Economic performance 0.771 0.731 0.850 0.748 0.820 0.754 0.807 0.429 0.326 0.436 0.319 0.406 0.311 0.375 0.403 0.329 0.513 0.487 0.398 0.374 0.442 0.316 0.455 0.411 0.344 0.392 0.471 0.505 0.491 0.430 0.387 0.399 0.456 0.369 0.426 0.102 0.171 −0.053 0.117 0.076 −0.025 −0.008 0.766 0.821 0.917 0.855 0.398 0.384 0.297 0.406 0.213 0.109 0.062 0.241 0.496 0.325 0.396 0.299 0.024 0.144 0.119 0.052 Perceived stakeholders’ concern 0.492 0.353 Item 1 Item 2 0.416 0.276 0.370 0.483 Item 3 0.795 0.788 0.828 0.646 0.220 0.291 0.306 0.237 0.287 0.106 0.056 0.132 EPM use Item 1 Item 2 Item 3 Item 4 Item 5 Item 6 Item 7 Expected competitive advantage 0.432 Item 1 0.305 Item 4 0.370 Item 5 Item 6 0.455 Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model ARTICLE IN PRESS YMARE-552; No. of Pages 18 16 I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Appendix A (Continued) Item 4 EPM use Expected competitive advantage Perceived stakeholders’ concern Top management’s environmental commitment Environmental performance Economic performance 0.463 0.363 0.865 0.397 0.263 0.106 Top management’s environmental commitment 0.519 0.203 Item 1 0.516 0.197 Item 2 Item 3 0.494 0.171 0.501 0.521 0.402 0.973 0.976 0.955 0.291 0.324 0.315 0.178 0.198 0.242 Environmental performance Item 1 Item 2 Item 3 Item 4 Economic performance 0.465 0.503 0.508 0.410 0.068 0.405 0.415 0.396 0.379 0.093 0.235 0.300 0.394 0.236 0.122 0.220 0.358 0.270 0.270 0.212 0.900 0.896 0.871 0.836 0.157 0.121 0.134 0.126 0.172 1.000 COMPOSITE RELIABILITY AVE 0.918 0.615 0.906 0.708 0.891 0.672 0.978 0.937 0.930 0.767 NAb NAb a All loadings in this table are statistically significant at p < 0.001. Composite reliability and AVE will only be suitable to use for multiitem constructs. b Appendix B. Inter-Construct Correlations and Square Root of AVE statisticsa for the main variables (n = 91). EPM use EPM use Expected competitive advantage Perceived stakeholders’ concern Top management’s env. commitment Environmental performance Economic performance 0.784 0.476 0.537 0.527 0.540 0.068 Expected competitive advantage Perceived stakeholders’ concern 0.841 0.446 0.197 0.455 0.093 0.820 0.492 0.335 0.122 Top management’s env. commitment Environmental performance Economic performance 0.968 0.320 0.212 0.876 0.157 1.000 a Diagonal elements are the square roots of the AVE statistics. Offdiagonal elements are the correlations between the latent variables calculated in PLS. AVE will only be suitable to use for multi-item constructs. Appendix C. Supplementary data Supplementary data associated with this article can be found, in the online version, at http://dx.doi.org/10. 1016/j.mar.2015.06.001. References Abernethy, M.A., Bouwens, J., van Lent, L., 2004. Determinants of control system design in divisionalized firms. Account. Rev. 79 (3), 545–570. Abernethy, M.A., Bouwens, J., van Lent, L., 2010. Leadership and control system design. Manage. Account. Res. 21 (1), 2–16. Adler, P.S., Chen, C.X., 2011. Combining creativity and control: understanding individual motivation in large-scale collaborative creativity. Account. Org. Soc. 36 (2), 63–85. Agle, B.R., Mitchell, R.K., Sonnenfeld, J.A., 1999. Who matters to CEOs? An investigation of stakeholder attributes and salience, corporate performance, and CEO values. Acad. Manage. J. 42 (5), 507–525. Al-Tuwaijri, S.A., Christensen, T.E., Hughes, I.I.K.E., 2004. The relations among environmental disclosure, environmental performance, and economic performance: a simultaneous equations approach. Account. Org. Soc. 29 (5–6), 447–471. Aragòn-Correa, J.A., Sharma, S., 2003. A contingent resource-based view of proactive corporate environmental strategy. Acad. Manage. Rev. 28 (1), 71–88. Arjaliès, D., Mundy, J., 2013. The use of management control systems to manage CSR strategy: a levers of control perspective. Manage. Account. Res. 24 (4), 284–300. Banerjee, S.B., Iyer, E.S., Kashyap, R.K., 2003. Corporate environmentalism: antecedents and influence of industry type. J. Market. 67 (2), 106–122. Bansal, P., Roth, K., 2000. Why companies go green: a model of ecological responsiveness. Acad. Manage. J. 43 (4), 717–736. Baxter, J., Chua, W.F., 2003. Alternative management accounting research – whence and whither. Account. Org. Soc. 28 (2–3), 97–126. Bebbington, J., Thomson, I., 2013. Sustainable development, management and accounting: boundary crossing. Manage. Account. Res. 24 (4), 277–283. Behling, O., Law, K.S., 2000. Translating Questionnaires and Other Research Instruments: Problems and Solutions. Sage Publications, Thousand Oaks, CA. Berrone, P., Gomez-Mejia, L.R., 2009a. Environmental performance and executive compensation: an integrated agency-institutional perspective. Acad. Manage. J. 52 (1), 103–126. Berrone, P., Gomez-Mejia, L.R., 2009b. The pros and cons of rewarding social responsibility at the top. Hum. Resour. Manage. 48 (6), 959–971. Boiral, O., Henri, J.-F., Talbot, D., 2012. Modeling the impacts of corporate commitment on climate change. Bus. Strat. Environ. 21 (8), 495–516. Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Bonacchi, M., Rinaldi, L., 2007. DartBoards and clovers as new tools in sustainability planning and control. Bus. Strat. Environ. 16 (7), 461–473. Bonner, S.E., Hastie, R., Sprinkle, G.B., Young, S.M., 2000. A review of the effects of financial incentives on performance in laboratory tasks: implications for management accounting. J. Manage. Account. Res. 12 (1), 19–64. Bouwens, J., Abernethy, M.A., 2000. The consequences of customization on management accounting system design. Account. Org. Soc. 25 (3), 221–241. Burnett, R.D., Hansen, D.R., 2008. Ecoefficiency: defining a role for environmental cost management. Account. Org. Soc. 33 (6), 551–581. Burritt, R.L., Hahn, T., Schaltegger, S., 2002. Towards a comprehensive framework for environmental management accounting – links between business actors and environmental management accounting tools. Aust. Account. Rev. 12 (27), 39–50. Burritt, R.L., Schaltegger, S., 2010. Sustainability accounting and reporting: fad or trend? Account. Audit. Account. J. 23 (7), 829–846. Buysse, K., Verbeke, A., 2003. Proactive environmental strategies: a stakeholder management perspective. Strat. Manage. J. 24 (5), 453–470. Chapman, C.S., Kihn, L.A., 2009. Information system integration, enabling control and performance. Account. Org. Soc. 34 (2), 151–169. Chenhall, R.H., 2003. Management control system design within its organizational context: findings from contingency-based research and directions for the future. Account. Org. Soc. 28 (2–3), 127–168. Chenhall, R.H., 2005. Integrative strategic performance measurement systems, strategic alignment of manufacturing, learning and strategic outcomes: an exploratory study. Account. Org. Soc. 30 (5), 395–422. Chin, W.W., 1998. The partial least squares approach to structural equation modeling. In: Marcoulides, G.A. (Ed.), Research Methods for Business Research. Lawrence Erlbaum Associates, Mahwah, NJ, pp. 295–336. Chin, W.W., Newsted, P.R., 1999. Structural equation modeling analysis with small samples using partial least squares. In: Hoyle, R.H. (Ed.), Statistical Strategies for Small Sample Research. Sage Publications, Thousand Oaks, CA, pp. 307–341. Cho, C.H., Guidry, R.P., Hageman, A.M., Patten, D.M., 2012. Do actions speak louder than words? An empirical investigation of corporate environmental reputation. Account. Org. Soc. 37 (1), 14–25. Cohen, J., 1988. Statistical Power Analysis for the Behavioral Sciences. Lawrence Erlbaum Associates, Hillsdale, NJ. Davila, T., 2000. An empirical study on the drivers of management control systems’ design in new product development. Account. Org. Soc. 25 (4–5), 383–409. Dillman, D.A., 2000. Mail and Internet Surveys: The Tailored Design Method. John Wiley & Sons, New York. Elbashir, M.Z., Collier, P.A., Sutton, S.G., 2011. The role of organizational absorptive capacity in strategic use of business intelligence to support integrated management control systems. Account. Rev. 86 (1), 155–184. Epstein, M.J., 1996. Measuring Corporate Environmental Performance. Irwin, Burr Ridge, IL. Epstein, M.J., 2010. The challenge of simultaneously improving social and financial performance: new research results. In: Epstein, M.J., Manzoni, J.-F., Davila, T. (Eds.), Performance Measurement and Management Control: Innovative Concepts and Practices. Emerald, Bingley, UK. Ervin, D., Wu, J., Khanna, M., Jones, C., Wirkkala, T., 2013. Motivations and barriers to corporate environmental management. Bus. Strat. Environ. 22 (6), 390–409. Ferreira, A., Moulang, C., Hendro, B., 2010. Environmental management accounting and innovation: an exploratory analysis. Account. Audit. Account. J. 23 (7), 920–948. Ferreira, A., Otley, D., 2009. The design and use of performance management systems: an extended framework for analysis. Manage. Account. Res. 20 (4), 263–282. Figge, F., Hahn, T., 2013. Value drivers of corporate eco-efficiency: management accounting information for the efficient use of environmental resources. Manage. Account. Res. 24 (4), 387–400. Flamholtz, E.G., Das, T.K., Tsui, A.S., 1985. Toward an integrative framework of organizational control. Account. Org. Soc. 10 (1), 35–50. Fraj-Andrés, E., Martínez-Salinas, E., Matute-Vallejo, J., 2009. Factors affecting corporate environmental strategy in Spanish industrial firms. Bus. Strat. Environ. 18 (8), 500–514. Franco-Santos, M., Lucianetti, L., Bourne, M., 2012. Contemporary performance measurement systems: a review of their consequences and a framework for research. Manage. Account. Res. 23 (2), 79–119. Gabel, H.L., Sinclair-Desgagné, B., 1993. Managerial incentives and environmental compliance. J. Environ. Econ. Manage. 24 (3), 229–240. Galbraith, J., 1973. Designing Complex Organizations. Addison-Wesley, Reading, MA. 17 Gerdin, J., 2005. The impact of departmental interdependencies and management accounting system use on subunit performance. Eur. Account. Rev. 14 (2), 297–327. Gond, J.-P., Grubnic, S., Herzig, C., Moon, J., 2012. Configuring management control systems: theorizing the integration of strategy and sustainability. Manage. Account. Res. 23 (3), 205–223. Gray, R.H., Milne, M., 2004. Towards reporting on the triple bottom line: mirages, methods and myths. In: Henriques, A., Richardson, J. (Eds.), The Triple Bottom Line: Does It All Add Up? Earthscan, London, pp. 70–80. GRI, 2006. Sustainability Reporting Guidelines. GRI, Amsterdam. Hair, J.F., Black, W.C., Babin, B.J., Anderson, R.E., 2010. Multivariate Data Analysis, seventh ed. Pearson Education, Upper Saddle River, NJ. Hair, J.F., Hult, G.T.M., Ringle, C.M., Sarstedt, M., 2014. A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications, Thousand Oaks, CA. Hall, M., 2008. The effect of comprehensive performance measurement systems on role clarity, psychological empowerment and managerial performance. Account. Org. Soc. 33 (2–3), 141–163. Harman, H.H., 1976. Modern Factor Analysis. University of Chicago Press, Chicago. Hartmann, F., Perego, P., Young, A., 2013. Carbon accounting: challenges for research in management control and performance measurement. ABACUS 49 (4), 539–563. Henri, J.-F., Boiral, O., Roy, M.-J., 2014. The tracking of environmental costs: motivations and impacts. Eur. Account. Rev. 23 (4), 647–669. Henri, J.-F., Journeault, M., 2010. Eco-control: the influence of management control systems on environmental and economic performance. Account. Org. Soc. 35 (1), 63–80. Henriques, I., Sadorsky, P., 1999. The relationship between environmental commitment and managerial perceptions of stakeholder importance. Acad. Manage. J. 42 (1), 87–99. Hunt, C.B., Auster, E.R., 1990. Proactive environmental management: avoiding the toxic trap. Sloan Manage. Rev. 31 (2), 7–18. Ilinitch, A.Y., Soderstrom, N.S., Thomas, T.E., 1998. Measuring corporate environmental performance. J. Account. Public Policy 17 (4–5), 383–408. ISO, 1999. ISO 14031 Environmental Management – Environmental Performance Evaluation – Guidelines. ISO, Geneva. Ittner, C.D., Larcker, D.F., 2001. Assessing empirical research in managerial accounting: a value-based management perspective. J. Account. Econ. 32 (1–3), 349–410. Ittner, C.D., Larcker, D.F., Randall, T., 2003. Performance implications of strategic performance measurement in financial services firms. Account. Org. Soc. 28 (7–8), 715–741. Judge, W.Q., Douglas, T.J., 1998. Performance implications of incorporating natural environmental issues into the strategic planning process: an empirical assessment. J. Manage. Stud. 35 (2), 241–262. Keogh, P.D., Polonsky, M.J., 1998. Environmental commitment: a basis for environmental entrepreneurship? J. Org. Change Manage. 11 (1), 38–49. King, A., Lenox, M., 2002. Exploring the locus of profitable pollution reduction. Manage. Sci. 48 (2), 289–299. Kolk, A., Perego, P., 2014. Sustainable bonuses: sign of corporate responsibility or window dressing? J. Bus. Ethic 119 (1), 1–15. KPMG, 2011. KPMG International Survey of Corporate Responsibility Reporting 2011. KPMG, Amsterdam. Laine, M., 2005. Meanings of the term ‘sustainable development’ in Finnish corporate disclosures. Account. Forum 29 (4), 395–413. Lindell, M.K., Whitney, D.J., 2001. Accounting for common method variance in cross-sectional research designs. J. Appl. Psychol. 86 (1), 114–121. Luft, J., Shields, M.D., 2003. Mapping management accounting: graphics and guidelines for theory-consistent empirical research. Account. Org. Soc. 28 (2–3), 169–249. Melnyk, S.A., Bititci, U., Platts, K., Tobias, J., Andersen, B., 2014. Is performance measurement and management fit for the future? Manage. Account. Res. 25 (2), 173–186. Melnyk, S.A., Sroufe, R.P., Cantalone, R., 2003. Assessing the impact of environmental management systems on corporate and environmental performance. J. Oper. Manage. 21 (3), 329–351. Merchant, K.A., 1998. Modern Management Control Systems: Text and Cases. Prentice Hall, Upper Saddle River, NJ. Meyer, J.P., Allen, N.J., 1991. A three-component conceptualization of organizational commitment. Hum. Resour. Manage. Rev. 1 (1), 61–89. Nunnally, J.C., 1978. Pscychometric Theory, second ed. McGraw-Hill, New York. Otley, D., 1999. Performance management: a framework for management control systems research. Manage. Account. Res. 10 (4), 363–382. Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001 G Model YMARE-552; No. of Pages 18 18 ARTICLE IN PRESS I.E. Lisi / Management Accounting Research xxx (2015) xxx–xxx Owen, D., 2008. Chronicles of wasted times? A personal reflection on the current state of, and future prospects for, social and environmental accounting research. Account. Audit. Account. J. 21 (2), 240–267. Passetti, E., Cinquini, L., Marelli, A., Tenucci, A., 2014. Sustainability accounting in action: lights and shadows in the Italian context. Br. Account. Rev. 46 (3), 295–308. Paulraj, A., 2009. Environmental motivations: a classification scheme and its impact on environmental strategies and practices. Bus. Strat. Environ. 18 (7), 453–468. Perego, P., Hartmann, F., 2009. Aligning performance measurement systems with strategy: the case of environmental strategy. ABACUS 45 (4), 397–428. Plaza-Úbeda, J.A., Burgos-Jiménez, J., Vazquez, D.A., Liston-Heyes, C., 2009. The ‘win–win’ paradigm and stakeholder integration. Bus. Strat. Environ. 18 (8), 487–499. Pondeville, S., Swaen, V., De Rongé, Y., 2013. Environmental management control systems: the role of contextual and strategic factors. Manage. Account. Res. 24 (4), 317–332. Porter, M.E., van der Linde, C., 1995. Green and competitive: ending the stalemate. Harv. Bus. Rev. 73 (5), 120–134. Preacher, K.J., Hayes, A.F., 2008. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behav. Res. Methods 40 (3), 879–891. Ringle, C.M., Wende, S., Will, A., 2005. SmartPLS, 2.0 (beta) ed. Germany, Hamburg. Rodrigue, M., Magnan, M., Boulianne, E., 2013. Stakeholders’ influence on environmental strategy and performance indicators: a managerial perspective. Manage. Account. Res. 24 (4), 301–316. Roewer, U., 2008. Environmental Protection Expenditure by Industry in the European Union 1997–2004. Eurostat, Luxembourg. Sharma, S., 2000. Managerial interpretations and organizational context as predictors of corporate choice of environmental strategy. Acad. Manage. J. 43 (4), 681–697. Sharma, S., Vredenburg, H., 1998. Proactive corporate environmental strategy and the development of competitively valuable organizational capabilities. Strat. Manage. J. 19 (8), 729–753. Sobel, M.E., 1982. Asymptotic confidence intervals for indirect effects in structural equation models. Sociol. Methodol. 13, 290–312. Songini, L., Pistoni, A., 2012. Accounting, auditing and control for sustainability. Manage. Account. Res. 23 (3), 202–204. Spicer, B.H., 1978. Investors, corporate social performance and information disclosure: an empirical study. Account. Rev. 53 (1), 94–111. Unerman, J., Chapman, C., 2014. Academic contributions to enhancing accounting for sustainable development. Account. Org. Soc. 39 (6), 385–394. Virtanen, T., Tuomaala, M., Pentti, E., 2013. Energy efficiency complexities: a technical and managerial investigation. Manage. Account. Res. 24 (4), 401–416. Wisner, P.S., Epstein, M.J., Bagozzi, R.P., 2006. Organizational antecedents and consequences of environmental performance. Adv. Environ. Account. Manage. 3, 143–167. Wisner, P.S., Epstein, M.J., Bagozzi, R.P., 2010. Environmental proactivity and performance. Adv. Environ. Account. Manage. 4, 105–127. Please cite this article in press as: Lisi, I.E., Translating environmental motivations into performance: The role of environmental performance measurement systems. Manage. Account. Res. (2015), http://dx.doi.org/10.1016/j.mar.2015.06.001