ECO-CONTROL CHANGE AND ENVIRONMENTAL PERFORMANCE:
A LONGITUDINAL PERSPECTIVE
ABSTRACT
Purpose - The aim of this longitudinal study is to quantitatively examine the impact of
changes in the mix of eco-controls. More specifically, the purpose of this study is twofold.
First, it investigates the nature of change occurring in eco-controls by analyzing three
attributes of change, namely (i) direction of change, (ii) scope of change, and (iii) scale of
change. Second, this study investigates the impact of changes in eco-controls by examining
to what extent the three attributes of change specifically explain environmental
performance.
Design/methodology/approach - Longitudinal survey approach is used to collect data
from a sample of manufacturing firms at two points in time.
Findings - The results suggest three main conclusions: (i) changes leading to more (less)
importance devoted to eco-controls within the organization contribute positively
(negatively) to environmental performance; (ii) concerted changes on all aspects of the mix
of eco-controls contribute more to environmental performance than piecemeal changes on
specific aspects of the mix; and (iii) the aspect which contributes to environmental
performance is not the scale of that change, but the mere presence of a credible signal
which reflects the seriousness of the intentions.
Originality/value – This paper contributes to management accounting change literature by
breaking down the nature of change of management control practices in attributes
(direction, scope, and scale) and examining their specific impact on performance.
Keywords: eco-control, environmental performance, change, longitudinal
Paper type: Research paper
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1. INTRODUCTION
For the last twenty-five years, increasing attention has been devoted to sustainability and
sustainable development in the accounting discipline (Gray, 1992; Hopwood, 2009; Gray
and Bebbington, 2000; Jones and Solomon, 2013; Milne and Grubnic, 2011; Bebbington
and Thomson, 2013). Although an extensive body of research has examined issues related
to environmental disclosure and reporting practices, the field of environmental
management accounting is still relatively new. Despite recent developments in that field
(e.g., Arjaliès and Mundy, 2013; Ferreira et al., 2010; Gond et al., 2012; Pondeville et al.,
2013; Rodrigue et al., 2013; Guenther et al., 2016), we are just beginning to emphasize the
importance of addressing the issues related to the integration of environmental matters
within management control practices, namely eco-control.1
Notably, one informative stream of research, dominated by qualitative studies, has
longitudinally examined changes in eco-controls based on a process view (Bouten and
Hoozée, 2013; Contrafatto and Burns, 2013; Arroyo, 2012; Moore, 2013). However, those
studies are collectively limited in their ability to provide a broader picture of eco-controls
across a group of organizations (Arjaliès and Mundy, 2013). Also, they do not provide
evidence related to the consequences of eco-control in terms of environmental
performance. On the other hand, another emerging stream of research, dominated by
quantitative studies, has examined sample of firms and provided empirical evidence of the
positive influence of components of eco-controls on environmental performance (Henri
and Journeault, 2010; Epstein and Wisner, 2005; Henri et al., 2014; Wisner et al., 2006).
However, because of their cross-sectional design, those studies present a static view, i.e.
they do not incorporate the evolution of eco-controls and environmental performance over
time, and thus raise concerns in terms of the existence of causality. Also, because of the
limited number of studies in that stream of research and its focus on components of ecocontrol, it remains unclear whether a variation in the mix of eco-controls has an effect on
1
Following the definition of management control system from Simons (1987,1990), eco-control is defined
as the formalized procedures and systems that use financial and ecological information to maintain or alter
patterns in environmental activity. The concept of eco-control has also been referred to as sustainability
control systems (SCS) (Gond et al., 2012), environmental management control systems (EMCS) (Pondeville
et al., 2013), and environmental management accounting (EMA) tools (Bouten and Hoozée, 2013).
2
performance. Therefore, the aim of this study is to reconcile these two streams of research
by examining a sample of firms longitudinally and study the changes in the mix of ecocontrols and the influence of those changes on environmental performance. Assuming that
eco-controls contribute to environmental performance (which our longitudinal design will
aim to validate), little is known about the way in which eco-controls change and to what
extent it influences, or not, environmental performance. Do firms change their eco-controls
in concert or in a piecemeal fashion? Do changes be substantive or modest to be effective?
Based on survey data collected on a sample of manufacturing firms at two points in time,
the purpose of this exploratory paper is twofold. First, it investigates the nature of change
occurring in eco-controls. In other words, our goal is to analyse three attributes of change,
namely (i) direction of change, (ii) scope of change, and (iii) scale of change. Second, this
study investigate the impact of changes on eco-controls. Put differently, it aims to examine
to what extent the three attributes of change explain specifically environmental
performance.
The remainder of this paper is organized as follows. The next section describes the
conceptual framework, and presents our hypotheses. The following section presents the
methodology, including a sample definition, data collection and measurement of
constructs. The results of our analyses are next described, followed by a discussion and the
conclusion of this study.
2. CONCEPTUAL FRAMEWORK
2.1 Change in (eco) controls
Although change in eco-controls has not yet been examined quantitatively, such change
has been investigated more globally for management control practices (Libby and
Waterhouse, 1996; Williams and Seaman, 2001; Moores and Yuen, 2001; Sulaiman and
Mitchell, 2005; Williams and Seaman, 2002; Baines and Langfield-Smith, 2003; Henri,
2010). Past quantitative research examining change in management control practices has
3
mainly addressed issues related to the motives, effects, and circumstances conducive to
change while less attention has been devoted to the nature of the changes (Sulaiman and
Mitchell, 2005). As suggested by Quattrone and Hopper (2001), the majority of the studies
depict change as the passage of a management control practice from one status to another
without providing a formal definition of that concept. Although those studies suggest that
the nature of change is far from homogeneous, limited evidence has been provided to
examine the nature of change in management control practices and whether those changes
explain performance (Baines and Langfield-Smith, 2003; Sulaiman and Mitchell, 2005).
Globally, this stream of research mainly reflects a generic view of change and does not
address specifically the attributes of change. Based on the organizational change literature,
three basic attributes of change in eco-controls are examined, namely (i) direction (i.e., the
trajectory of the change in terms of importance devoted to eco-control), (ii) scope (i.e., the
range of specific eco-controls targeted by the change), and (iii) scale (i.e., the magnitude
of the change) (Greenwood and Hinings, 1996; Miller and Friesen, 1982; Plowman et al.,
2007). The impact of these attributes on environmental performance, namely the
development of competitive benefits following the use of environmental practices and
actions (Sharma and Vredenburg, 1998), are discussed in the next section.
Figure 1 illustrates the conceptual framework of the study. The mix of eco-controls is
comprised
of
various
components,
namely
environmental
strategic
planning,
environmental performance indicators, environmental budget, and environmental
incentives. It is worth mentioning that this paper does not differentiate among the causes
of change as it is not its goal to draw conclusions about the context under which different
changes in eco-controls are required. Instead, the objective is to break down the nature of
change in its various attributes and examine to what extent they influence, or not,
environmental performance.
-- Insert figure 1 --
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2.2 The influence of attributes of change of eco-controls on environmental
performance
Direction of change
The direction of change refers to a vector mirroring the importance devoted to control
practices in terms of design and use. The trajectory of a change can be positive or negative
in that it can reflect a more or less sophisticated design and more or less intensity in the use
of control practices in comparison to the current state2. Most studies examining change in
management control practices have described the direction of change based on their design
while neglecting the importance of their use. For instance, some studies conceptualize
change as the number of changes adopted in management control practices regardless of
the implication in terms of an increase or decline in the use of the control practices within
the organization (Libby and Waterhouse, 1996; Williams and Seaman, 2001; 2002). Other
studies categorize change based on the technical level of change in control practices, such
as addition, removal or modification (Sulaiman and Mitchell, 2005; Chanegrih, 2008;
Henri, 2010) but they overlook the implications of those technical changes in terms of
growth or falling-off of the use of controls within the organizations.
Up to now, we do not know much about the extent and impact on performance of change
in the importance devoted to control practices (notably their use). One stream of research
has provided quantitative evidence suggesting that change in management control practices
has positive effects within organizations (Williams and Seaman, 2002; Sulaiman and
Mitchell, 2005; Baines and Langfield-Smith, 2003; Henri, 2010). Another group of studies
has specifically provided evidence that eco-controls have a positive influence on
environmental performance (Henri and Journeault, 2010; Epstein and Wisner, 2005; Henri
et al., 2014; Wisner et al., 2006). Based on the combined results of those two set of studies,
it can be argued that change conducted within eco-controls will influence environmental
performance.
Using the word ‘positive’ or ‘negative’ change neither implies that the change was required, nor that the
change has been well executed. It is simply used in a geomatrical way to describe the trajectory of the vector
of change. Positive (negative) change refers to more (less) attention devoted to eco-controls in period II than
in period I.
2
5
More specifically, the importance devoted to eco-controls within the organization can
increase or decrease depending on changes conducted to either the design of eco-controls
(e.g., introduction or removal of environmental performance indicators, explicit
consideration or not of environmental concerns within the firm’s mission statement, the
specific budgeting or not of environmental expenses, revenues and investment, etc.) or
their use (e.g., more or less intensity in the use of environmental performance indicators
for purposes of monitoring, decision-making and external reporting, the involvement or
not of environmental personnel in firm’s strategic planning process, etc.). Based on results
found in past eco-control literature, it is argued that technical improvements to the design
of eco-controls and / or more intensity in their use will result in improvement in the
environmental performance.
In fact, eco-controls are accounting systems which influence decisions and express
accountability (Gray, 1992). Like other management control practices, eco-controls are a
primary source of information for both ‘decision-facilitating’ and ‘decision-influencing’
activities (Demski and Feltham, 1976; Williams and Seaman, 2002). They are used to
guard against undesirable behaviour and to encourage desirable actions (Merchant, 1982).
Eco-controls allow for the quantification of environmental actions and the integration of
environmental concerns within organizational routines. By providing appropriate financial
and ecological information, eco-controls support effective resource management and
environmental performance (adapted from Baines and Langfield-Smith, 2003). Indeed, by
clarifying and translating environmental vision and strategy, eco-controls direct managers
to critical areas of environmental matters, communicate the associations between
employees' actions and environmental goals, improve the allocation of resources, and
encourage the establishment of priorities based on such environmental goals (Epstein,
1996; Epstein and Birchard, 2000). In other words, more (less) importance devoted to ecocontrols is expected to improve (reduce) environmental performance in various ways,
notably: (i) by providing more (less) feedback, (ii) by providing more (less) information
for decision-making, and (iii) by focusing organizational attention to a greater (lesser)
6
extent (Henri and Journeault, 2010). This leads to our first hypothesis related to the
direction of change:
Hypothesis 1: Change leading to more (less) importance devoted to eco-controls
contributes positively (negatively) to environmental performance.
Scope of change
The scope of change refers to the range of specific control practices targeted by change.
Regardless of the direction, this change can be conducted on specific components of the
system (piecemeal change) or performed on all components of the system (concerted
change). Most accounting studies examining change have investigated localized changes
to specific management control practices such as costing, performance measurement or
budgeting (e.g., Gosselin, 1997; Chenhall and Euske, 2007; Baines and Langfield-Smith,
2003; Henri, 2010; Frow et al., 2010; Bogsnes, 2009), or generic sub-systems such as
planning, controlling, decision making, and directing (e.g., Libby and Waterhouse, 1996;
Williams and Seaman, 2001). This focus on specific aspects of management control
practices and the lack of attention devoted to their interrelationships contrasts with the idea
of controls operating as a system (Grabner and Moers, 2013). The notion of
interdependence is at the heart of the distinction between management control as a system
or a package. Grabner and Moers (2013) suggest that management controls form a system
if these practices are interdependent, complements or substitutes, and the design choice has
taken this interdependence into account. The management control package refers to the
complete set of management controls and does not account specifically for possible
interdependencies between them (Malmi and Brown, 2008). Considerable effort has been
devoted in recent literature to examining management control as a package (Bedford and
Malmi, 2015; Chenhall and Langfield-Smith, 1998), as a system (Indjejikian and Matejka,
2012; Friis et al., 2015), or as a combination of both a package and a system (Bedford et
al., 2016).
Although changes might initially occur for a specific control practice, there are
implications for the other components of the system that might be adjusted or replaced
7
afterwards. Considering that the components of a system can be interdependent, the
examination of the impact of change of one specific control practice without considering
the rest of the system can at best lead to incomplete findings. In other words, failing to
consider simultaneous links among management control practices can lead to serious
model under specification and reporting of spurious effects (Chenhall, 2007). The literature
has been silent thus far concerning the scope of change in controls operating as a system.
We first build on the notion of tightly coupled structure to conceptualize the performance
effect of the scope of change. Weick (1982) defines tightly (loosely) coupled structure as
situations in which elements affect each other continuously (suddenly), constantly
(occasionally), significantly (negligibly), directly (indirectly), and immediately
(eventually).
Organizational theorists argue that in the context of tightly coupled
structures, changing only one or two elements might upset the balance and destroy
complementarities whereas in the context of loosely coupled structures, it has the
advantage of being less disruptive, costly and cognitively demanding (Miller and Friesen,
1982). In other words, an approach of concerted changes (i.e. changes performed on all
components of the mix of controls) is more consistent with tightly coupled structures,
whereas a piecemeal approach (i.e. change conducted on individual components of the mix
of controls) is more consistent with loosely coupled structures whereby one control can
change independently without importantly influencing the other controls. Although both
change approaches may contribute to performance, their impact could differ depending on
whether the mix of controls is tightly or loosely coupled. The question is whether the mix
of eco-controls is considered to be tightly or loosely coupled.
As a component of the global mix of management control practices, it is argued that ecocontrol is an application of a tightly coupled structure. Clearly, in order to translate
environmental intentions throughout the organization, and given that environmental
concerns can sometimes be considered as a distraction from economic concerns (Porter and
Linde, 1995), the individual eco-controls are mutually dependent in order to send a strong
and consistent message. More specifically, the environmental message is more consistent
if simultaneously (i) the environmental concerns are integrated into the objectives ensuing
8
from the strategic planning process; (ii) those environmental objectives are mirrored in the
forecasts and environmental aspects are visible throughout the budgeting process
(expenses, revenues, investments); (iii) environmental performance indicators are used to
provide feedback and focus organizational attention on environmental objectives; and (iv)
the environmental performance indicators are integrated into the performance evaluation
and incentive process. This view of eco-controls as tightly coupled structures has been
supported in past studies. Epstein (1996) argues that in order to be effective in translating
environmental strategy into organizational routines, organizations should successively
integrate environmental concerns within strategic planning, performance measurement,
and incentives. Gond et al. (2012) propose a configurational view to capture the
interrelations among practices of eco-control. Based on levers of control framework
Simons (1990, 1995), Arjaliès and Mundy (2013:287) suggest that “the full potential of the
four levers of eco-control is realized when they are mobilized together”.
Given the fact that eco-controls are perceived as tightly coupled structures, this paper
builds on the notions of complementary effects and off-balance to conceptualize the
performance effect of the scope of change (Milgrom and Roberts, 1995; Kristensen and
Israelsen, 2014). In the context of tightly coupled structures, changes in one element will
severely compromise performance unless adjustments are also made to many other
elements (Roberts, 2004). Kristensen and Israelsen (2014) suggest the notion of balance to
refine this complementary effect. Off-balance, which occurs when one or more control
form deviates from the ideal distance to the other control forms, obstructs the
complementary performance effects, especially with tightly coupled structures.
It is argued that changes performed on specific eco-control practices are expected to have
at least a minimum performance effect (Kristensen and Israelsen, 2014). This is referred to
as the individual effects.3 However, because of the complimentary effect, change
conducted on all components of the mix of eco-controls is expected to lead to synergies
from balancing the eco-controls. In a situation of off-balance between control practices,
In their study, Kristensen and Israelsen (2014) use the term ‘additive effect’ to describe the sum of effects
of individual bundles of control practices on performance irrespective of the synergies from balancing the
control practices.
3
9
the individual effects can be thwarted (Kristensen and Israelsen, 2014). More specifically,
having some eco-controls at a low level could potentially reduce the performance effect of
having other eco-controls at a high level. Because of the off-balance ensuing from
piecemeal change which reduces the individual effects, it is expected that concerted change
to all components of the eco-control mix will benefit from the complimentary effect which
increases the individual effects, and thus leads to greater environmental performance.
Formally stated:
Hypothesis 2: Concerted change on eco-controls has a greater contribution to
environmental performance than piecemeal change.
Scale of change
In this study, the scale refers to the magnitude of the change in the control practices.
Regardless of direction, greater magnitude of change is reflected by the difference between
the level of eco-controls before and after the change. The change can be modest or
substantive depending on the importance of that difference. Regardless of the scope, a
substantive change implies that at least one component of the mix has been the subject of
a major change. Indeed, as documented in various case studies, the implementation of one
control practice can represent a major organizational change with serious and numerous
implications in terms of resources, risk, and political expedience (Miller and Friesen, 1982;
Plowman et al., 2007). For instance, the adoption of activity-based costing (Chenhall and
Euske, 2007; Briers and Chua, 2001), beyond budgeting (Frow et al., 2010; Bogsnes,
2009), and balanced scorecard (Papalexandris et al., 2004; Hoque, 2014) constituted all
substantive change for these organizations. In past quantitative studies, the scale of change
has mainly been addressed as a methodological concern rather than a conceptual matter. In
other words, more emphasis has been devoted to explaining the measurement of the
magnitude of changes while neglecting a theoretical discussion regarding the intensity of
the signals transmitted by the scale of change and the implications of that change. In terms
of the mix of control, little is known about the extent and impact on performance of minor
changes sending weak signal and ensuing small implications as compared major changes
sending strong signal and ensuing large implications.
10
This section builds on the notion of signalling to conceptualize the performance effect of
the scale of change. Signalling is the use of structures (notably management control
practices) to convey information throughout the organization which could not (or hardly)
be disseminated effectively through other means (Meyer, 1979). More specifically, a signal
is an alterable, observable attribute of an organization which is informative especially when
something about the signaller is unobservable to the receiver, and this something affects
the way the receiver responds to the signaller. It is generally insufficient to reveal that the
organization intends to change without investing in the necessary resources needed to
execute this change (Meyer, 1979). Thus, it is probably fair to say that substantive change
calls for stronger signals whereas modest change involves weaker signals. It is argued that
stronger signals contribute more to performance because of the (i) seriousness of the
intentions conveyed by the change, (ii) limited attention of the organizational members,
and (iii) ensuing implications.
First, a substantive change sends a strong signal demonstrating the seriousness of the
intentions conveyed by the organization. Greater magnitude of change in eco-controls is a
convincing signal to organizational members that the organization is effectively becoming
more (or less) attentive to environmental concerns, and employees will be more (or less)
rewarded for environmental behaviours. A stronger signal will affect the way the
organizational members will respond. It is argued that the intensity of the signal influences
the level of integration of environmental concerns within the organizational routines and
decision-making process afterwards. A stronger signal will influence the actions chosen as
well as the balance between environmental and economic criteria in the decision-making
process more than a weaker signal, which ultimately will lead to greater environmental
performance.
Second, it is well-established in the literature that organizational members have neither the
time nor the capacity to process all the information available to them, notably because of
bounded rationality and concurrent activities (Simons, 1990). In order to offset this ‘limited
attention’ of organizational members, a strong signal is necessary to get noticed. Change
in eco-controls represents a convincing way to communicate variation in a firm’s
11
environmental motivation. As environmental concerns represent only one subset of
organizational matters, a substantive change in eco-controls sends a clear and sound signal
of the preferences and priorities of the organization towards environmental concerns.
Compared to a weaker signal sent by a more modest change that faces the risk of being
neglected, a stronger signal may be better received by organizational members, and thus,
may lead to greater environmental performance.
Third, depending on the intensity of the signals diffused by the change in eco-controls, the
ensuing implications will also vary (Miller and Friesen, 1982; Plowman et al., 2007).
Regardless of the direction, strong signals associated with substantive change may lead to
(i) more variation in the involvement of organizational members from various departments
or functions within the organization (e.g., accounting and finance, human resources
management, operations, research and development, etc.), (ii) more variation in the
resources dedicated to environmental concerns (e.g., financial, human, technological, etc.),
and (iii) more variation in the level of discussion, debate, and exchanges of information
among organizational members and level of learning throughout the firm (Arjaliès and
Mundy, 2013; Bouten and Hoozée, 2013; Contrafatto and Burns, 2013). Thus, the ensuing
implications following substantive change in eco-controls may have a greater contribution
to environmental performance compared to the implications following a modest change.
Considering the previous three arguments, our final hypothesis is posited:
Hypothesis 3: Substantive change to eco-controls has a greater contribution to
environmental performance than modest change.
3. METHOD
3.1 Research Design
In order to test the effect of change of eco-controls on environmental performance, this
study uses a longitudinal survey design. This approach has the advantage of assessing
change in variables over time as well as providing more credible evidence of causality (Van
der Stede, 2014). As part of larger research project, data has been collected at two different
12
points in time, namely 2005 and 2010. One important issue with longitudinal survey design
is to determine the time lag over which the effect is expected to have taken place (Van der
Stede, 2014). Although there is no “right” time, this study has followed the work of Miller
and Friesen (1982) on structural change and have chosen a five-year period. This period is
sufficiently long to reflect a sequence of change in the various components of the ecocontrol mix in response to environmental motivations and let the effects happen.
Considering that during this time period some changes in eco-control might be completed
while others are in progress, it becomes important to breakdown change in attributes
(direction, scope, scale) in order to capture the progress of the change and its effect on
environmental performance. Furthermore, as other events might influence environmental
performance during the period, this study controls for three important variables
documented in past studies, namely variation in size, stakeholder pressures and financial
resources (Al-Tuwaijri et al., 2004; Henri and Journeault, 2010; Ferreira et al., 2010;
Pondeville et al., 2013). These variables have been chosen because they capture a broad
range of internal and external phenomena that might have occurred during the time period
and that might have influenced environmental performance.
In 2005, data from a survey administered to a random sample of 1500 Canadian
manufacturing firms from Scott’s Manufacturing database have been collected4. The
survey was sent to the CEO or another member of the top management team (COO or
senior vice-president). A total of 303 usable questionnaires has been received, for a
response rate of 20.9%. The common analysis of the non-response bias has been conducted
and it appears that non-response bias was not a major concern in this sample5. In 2010, we
another survey containing a group of identical questions to the same 303 firms that had
In this study, ‘firm’ is a fully autonomous entity or a subunit of a larger firm. In all cases, they appeared as
separate entities in the database. Organizations with 100 employees or more, and reporting sales of over $20
million have been selected. These criteria are intended to ensure that organizations are large enough for
organizational variables to apply (Miller, 1987) and that management control systems are sufficiently
developed (Bouwens and Abernethy, 2000). The final sample comprised 1447 organizations (considering
wrong addresses, organizations that moved, etc). To improve the response rate, the guidelines of the Total
Design Method have been followed (Dillman, 2000).
5 Initially, the comparison between respondents and non-respondents with respect to size, industry and
geographical region did not reveal any significant differences. Moreover, the comparison between the first
and last 10% of respondents (the latter being used as a proxy for the non-respondents) did not reveal any
significant differences in the responses obtained for the main constructs of the study.
4
13
completed the questionnaire in 2005 has been sent. After three follow-ups, 78 usable
questionnaires have been received, for a response rate of 25.7%. The common analysis of
the non-response bias (vs. the 303 firms in the sample using the same procedure depicted
in footnote 6) has been conducted a second time and it appears that non-response bias was
not an issue. Although the global response rate of 5.4% may not be enough to claim external
validity (displaying the difficulties of longitudinal survey design), it still represents rich
longitudinal data providing strong internal validity. Hence, the dataset is considered to
provide exploratory evidence of the impacts of attributes of change of eco-controls.
The 78 firms are distributed within twenty manufacturing industries (3-digits NAICS
code). Those industries represent between 1.3% and 11.5% of the sample with on average
4 firms per industries. Of the 78 firms in the sample, 18 have the same respondents for the
two periods, 27 have different respondents (considering departures, transfers, promotions,
etc.) and 33 did not reveal the name of the respondents for one of the two periods. In
addition, for 60.3% of those 78 firms, the two respondents hold the same position.
Considering that no item of the questionnaire contains static data, the stability or validity
of the answers for the 18 firms having the same respondents cannot be assessed. However,
for the two periods of time, the profile of the respondents remains similar. The respondents
had on average 16.1 (2005) and 16.24 (2010) years of experience working for their
organization. Furthermore, the positions of the respondents for 2005 and 2010 are
respectively distributed as follows: (i) CEO / general manager 29%-28%, (ii) senior vicepresidents 40%-42%, (iii) environmental representatives 19%-15%, and (iv) other
positions 12%-15%. The important issue of inter-rater reliability will be discussed in the
next section as well as in the results section.
3.2 Measurement of Constructs
Appendix 1 presents the instruments used to measure the main constructs. The same
instruments were used in 2005 and 2010. Descriptive statistics of the main constructs and
correlation matrix for both years are presented in Table 16. Five instruments were used to
6
It is worth mentioning the high level correlation among eco-controls, which support the view that the mix
of eco-control represents a tightly coupled system.
14
measure the mix of eco-controls, namely environmental strategic planning, the design and
use of environmental performance indicators, environmental budgeting, and environmental
incentives.
First, the environmental issues integration in strategic planning was measured using an
instrument developed by Judge and Douglas (1998) containing four items ranging on a
seven-point Likert-type scale. A higher mean score indicates more integration of
environmental issues in strategic planning.
-- Insert table 1 --
Second, the design of environmental performance indicators was measured using an
instrument developed based on the ISO 14031 standard. The instrument included 13 items,
each on a seven-point Likert-type scale. The respondents were asked to rate the extent to
which thirteen performance indicators is measured. To identify the underlying dimensions
of this construct, an exploratory factorial analysis (EFA) was carried out.. The final
factorial analysis revealed that three dimensions explained a total of 67.6% of the variance.
The three dimensions are labelled respectively as input indicators, output and impact
indicators, and managerial indicators. Higher mean scores of the three dimensions indicate
a more sophisticated design of environmental performance indicators.
Third, the use of environmental performance indicators was measured using an instrument
developed by Bennett and James (1998) containing four items ranging on a seven-point
Likert-type scale. The respondents were asked to indicate to what extent the organization
relies on environmental performance indicators for different uses. A higher mean score
indicates a greater use of performance indicators.
Fourth, a three-item instrument has been developed to measure the integration of
environmental issues into the budget. The instrument contains three questions asking the
respondents to rate the extent to which (i) environmental expenses, (ii) environmental
investment, and (iii) incomes from material scrap or recycled waste are detailed in the
15
budget of the organization, each on a seven-point Likert-type. A higher mean score
indicates a more detailed integration of environmental matters into the budgeting of the
organization.
Fifth, the integration of environmental criteria into the incentive system was measured
using an instrument developed by Sharma (2000) containing three items ranging on a
seven-point Likert-type scale (1=not at all, 7=to a very great extent). A higher mean score
indicates more integration of environmental criteria into the incentive system.
Environmental performance is measured using a perceptual instrument. As several authors
argue, in terms of consistently providing valid and reliable performance assessment, neither
objective nor subjective measures are superior (e.g., Venkatraman and Ramanujam, 1987;
Dess and Robinson, 1984; Boulianne, 2007). Environmental performance is adapted from
the instrument developed by Sharma and Vredenburg (1998) in order to focus on one
specific dimension, namely operational benefits ensuing from environmental practices.
This internal and process dimension constitutes one aspect of the environmental
performance matrix (Ilinitch et al., 1998) and is aligned with eco-practices. The
respondents were asked to indicate the extent to which environmental practices have led to
various types of benefits. The questionnaire contains six items ranging on a seven-point
Likert-type scale. A higher mean score indicates better environmental performance. In
order to establish the validity of the answers provided by the respondents in 2005 and 2010,
as well as the inter-rater reliability, the mean score of this construct was compared with
objective data obtained from a public database. The results (not tabulated but available
upon request) support the validity of the construct as well as the inter-rater reliability.
Lastly, the control variables are measured as follows. Size is measured using the natural
log of the number of employees. Stakeholder pressures are measured using the instrument
of Buysse and Verbeke (2003). To identify the underlying dimensions of this construct, an
exploratory factorial analysis (EFA) was carried out. The final factorial analysis revealed
that three dimensions explained a total of 69.6% of the variance. The three dimensions are
labelled respectively as business stakeholders, financial stakeholders, and institutional
16
stakeholders. A higher mean score indicates more pressure from specific group of
stakeholders. Financial resources are measured with an instrument using three indicators,
namely return on investment, operating profits, and cash flow from operations. The
respondents were asked to indicate the performance of their organization over the past
twelve months compared to their leading competitors based on a seven-point Likert-type
scale. A higher mean score indicates more financial resources available.
3.3 Reliability of constructs
To establish the reliability of each construct, the Cronbach Alpha and composite reliability
have been examined. The constructs must exceed the recommended cut-off point of 0.70
to reflect an acceptable level (Nunnally, 1967; Fornell and Larcker, 1981). Moreover, to
verify convergent validity, the variance extracted has been analysed and we have
performed first-order confirmatory factor analyses (CFA) for all constructs (except for the
design of environmental performance indicators for which we have conducted secondorder CFA). The variance extracted must exceed the recommended cut-off point of 0.50 to
reflect acceptable validity (Hair et al., 1998). Three main elements were examined for the
CFA, namely the significance of the standardized factor loading and the R2 for each item,
and the overall acceptability of the measurement model using chi-square statistics and three
fit indices. Those indices, namely NNFI (non-normed fit index), CFI (comparative-fit
index), and RMSEA (root mean square error of approximation) reflect two complementary
types of indices (absolute fit and incremental fit measures) and they are among the most
frequently reported.7 Lastly, discriminant validity has been assessed by comparing the
variance extracted from each individual construct with the squared correlation between
latent constructs (Fornell and Larcker, 1981). To support discriminant validity, the variance
extracted for each construct must exceed the squared correlations.
Appendix 1 presents the statistics of the measurement analysis. Considering the presence
of longitudinal data, no respecification has been made to preserve the constructs in their
entirety through time. All constructs exceed the recommended cut-off point for the
7
The threshold values recommended are (i) NNFI > 0.90 (Tabachnick and Fidell, 2001), (ii) CFI > 0.95
(Hu and Bentler, 1995), and (iii) RMSEA < 0.l0 (Browne and Cudeck, 1993).
17
Cronbach Alpha, composite reliability and variance extracted, exhibit acceptable model
fit8, and all factor loadings are statistically significant (p<0.01). All comparisons between
the variances extracted and the squared correlations support the discriminant validity of the
constructs. This supports to the validity of the constructs.
4. RESULTS AND DISCUSSION
4.1 Coding process and preliminary evidence
Figure 2 illustrates the global coding process in five successive steps. The results of coding
1 and 2 are used as preliminary and descriptive evidence. Coding 3, 4, and 5 will be used
later for hypotheses testing. It is worth mentioning that for those five coding schemes, the
breakdown of firms within each category is not statistically different for the three following
sub-samples: (i) the firms having the same respondents for the two periods (n=18), (ii) the
firms having different respondents for the two periods (n=27), and (iii) the firm for which
the name of the respondent is not provided for one of the period (n=33).
--Insert figure 2 --
Coding 1: Cluster membership for 2005 and 2010
Based on cluster analyses9, the aim of coding 1 is to establish patterns of relationships
among firms in regards to their mix of eco-controls. Using three different methods (see
8
Notable exceptions include the RMSEA of environmental performance indicators in 2010 which is
slightly above the thresholds. Also, the CFI of environmental performance indicators in 2005 is slightly
above the thresholds.
9
This statistical technique sorts observations into similar sets or groups for which variance among elements
grouped together is minimized and at the same time between-group variance is maximized (Ketchen and
Shook, 1996). A two-stage procedure is used to gain benefits from both hierarchical and non-hierarchical
methods (Hair et al., 1998; Punj and Stewart, 1983). First, a hierarchical algorithm is used to identify the
number of clusters and cluster centroids. To increase confidence in the results, this step is conducted using
three different methods, namely Ward’s and the average linkage method (between and within groups). The
results of this first step are used as starting points for the non-hierarchical clustering. This second step allows
for the fine-tuning of the results by permitting the switching of cluster membership. One of the biggest
challenges with cluster analysis is determining the final number of clusters. The use of multiple methods is
suggested in the literature to deal with this issue. Two techniques based on the agglomeration coefficient are
used in this study (Aldenderfer and Blashfield, 1984): (i) a graph reflecting the number of clusters against
the agglomeration coefficient (the appropriate number of clusters is found at the ‘elbow’ of the graph), and
18
footnote 9), a three-cluster solution is the most appropriate classification for both periods.
The results of Table 2 suggest that for each period, each eco-control differs significantly
(p<.01) among the clusters. A similar pattern of relationships is observed in 2005 and 2010
in which cluster 1 (C1) refers to the firms devoting low importance to eco-control (overall
score of 2.48 / 2.22), cluster 2 (C2) refers to the moderate importance devoted to ecocontrol (overall score of 4.59 / 3.82), and cluster 3 (C3) refers to the high importance
devoted to eco-control (overall score of 5.92 / 5.48). In 2005 (2010), 24% (13%) of the
firms pertain to C1, 40% (31%) of the firms pertain to C2, and 36% (56%) pertain to C3.
- Insert table 2 -
At this moment, one point merits further consideration. The variation between the mix of
eco-controls in 2005 and 2010 might result from systematic changes in institutional
practices (for instance, a rise of eco-controls in the manufacturing industry following new
regulations) and / or voluntary changes in the practices of individual firms. To examine
this issue, the mean score of each eco-control between 2005 and 2010 for the sample of
firms (regardless of their cluster membership) have been compared. No significant
difference is observed between the two time periods for each eco-control.10 This suggests
that the changes observed in the clusters membership do not result from systematic changes
in institutional practices. Instead, the changes emanate from ‘positive’ and ‘negative’
movements of firms between and within clusters. In other words, the global absence of
variation between 2005 and 2010 for each eco-control suggests the absence of a uniform
movement of firms but rather a voluntary mix of specific increase and decrease of ecocontrols within the firms. The understanding of these movements between and among
clusters is the purpose of coding 2.
Coding 2: Movement among and within clusters between 2005 and 2010
(ii) an examination of the incremental changes in the agglomeration coefficient (the appropriate number of
clusters is found at the step before a sudden jump occurs). The results tabulated are those using Ward’s
method and the Eucledian distance.
10
Not tabulated but available upon request.
19
Based on the results of coding 1, the aim of coding 2 is to capture the movement of each
firm with regards to its cluster membership between 2005 and 2010. Two main scenarios
are conceivable: (i) a firm remains in the same cluster, or (ii) a firm jumps from one cluster
to another. In the first scenario, the firm makes some ‘positive’ or ‘negative’ changes but
remains in (i) cluster 1 (coded M3), (ii) cluster 2 (coded M4), or (iii) cluster 3 (coded M5).
In the second scenario, the firm performs changes and jumps from one cluster to another.
Those jumps are coded based on two sub-scenarios: (i) the firm jumps back, or (ii) the firm
jumps forward. In the first case, the firm can (a) jump back 2 clusters, i.e. from C3 to C1
(coded M1), or (b) jumps back 1 cluster, i.e. from C3 to C2, or C2 to C1 (coded M2). In
the second case, the firm can (a) jumps forward 1 cluster, i.e. from C1 to C2, or C2 to C3
(coded M6), or (b) jumps forward of 2 clusters, i.e. from C1 to C3 (coded M7).
Table 3 presents the descriptive statistics of coding 2. For each of the seven movements
(M1 to M7), the table presents the number of firms, the mean score of each eco-control for
2005 and 2010, the significance of the difference between the two periods, and the Cohen’s
distance. 40% of the firms (n=31) have remained in the same cluster (M3;M4;M5) while
60% (n=47) have jumped from one cluster to another (M1;M2;M6;M7)11. It is worth
mentioning that for the firms that have made a jump from one cluster to another (back or
forward), all components of the mix of eco-control have been significantly changed.
Conversely, for the firms that have remained in the same cluster, significant changes have
been made only to some components.
- Insert table 3 -
Validation of coding 1 and 2 using profile deviation analysis
In order to validate and provide robustness to the classification suggested by the cluster
analysis, a profile deviation analysis has been conducted. The results (not tabulated but
available upon request) provides support for coding 1 and 2.
11
The firms remaining in cluster 1 (M3) will be removed from further analysis for two main reasons. First,
as no component of the mix of eco-controls has been significantly changed, this group of firms is not
considered to have experimented change. Second, the number of observation in that group is too small (n=2).
20
4.2 Coding process and hypotheses testing
As illustrated in Figure 2, the results of coding 2 are used to establish coding 3, 4, and 5
that will be used for hypotheses testing. Coding 3 refers to the direction of change, coding
4 to the scope of change, and coding 5 to the scale of change. Those three codings will be
used to respectively test hypothesis 1, 2 and 3.
Coding 3 (Direction of change) and test of hypothesis 1
Based on the results of coding 2, the aim of coding 3 is to capture the direction of change
by classifying the seven movements of eco-controls (M1-M7) into two categories: (i) the
movements involving an increase in the importance devoted to eco-controls, and (iii) the
movements involving a decrease in the importance devoted to eco-controls. To determine
the direction of the change, this study refers to the sign of the variation between 2005 and
2010 (see column V in table 3): a plus-sign “+” signifies more importance devoted to ecocontrols whilst a minus-sign “-” signifies less importance devoted to eco-controls. For 55%
of the firms, less importance has been devoted to eco-control whilst for 45% of the firms,
more importance has been devoted. In order to examine the influence of the direction of
change on variation of environmental performance, the following OLS regression equation
is tested:
Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + ε
Eq. (1)
where
Y = Variation of environmental performance between 2005 and 2010
X1 = Direction of change – dichotomous variable whereby 0 = less importance devoted to ecocontrols (M1;M2;M4;M5) and 1 = more importance devoted to eco-controls (M6;M7)
X2 = Variation of size between 2005 and 2010
X3-4-5 = Variation of stakeholder pressures (group 1,2,3) between 2005 and 2010
X6 = Variation of financial resources between 2005 and 2010
Part A of table 4 presents the results of equation 1. The only significant variable is the
direction of change (1.66; p<01). This result suggests that, regardless of the scope and
scale, changes involving more (less) importance devoted to eco-controls have a positive
(negative) influence on the variation of environmental performance. This supports
21
hypothesis 1 and to the previously discussed cross-sectional studies providing evidence of
the link between eco-control and environmental performance.
- Insert Table 4 -
Coding 4 (Scope of change) and test of hypothesis 2
Based on the results of coding 2, the aim of coding 4 is to capture the scope of change by
classifying the seven movements of eco-controls (M1-M7) into two categories: (i) the
movements involving a piecemeal change on specific components of the mix of ecocontrol, and (ii) the movements involving a concerted change on all components of the mix
of eco-control. To determine the scope of the change, this study refers to the statistical
meaning of the variation between 2005 and 2010 (see column VI in table 3): to be classified
as a concerted change, all components of the eco-control mix must have changed
significantly; otherwise we consider facing piecemeal change. Concerted changes
represent 62% of our sample compared to 38% for piecemeal changes. In order to examine
the influence of the scope of change on the variation of environmental performance, the
following OLS regression equation is tested:
Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + ε
Eq. (2)
where
Y = Absolute variation of environmental performance between 2005 and 2010 12
X1 = Scope of change – dichotomous variable whereby 0 = piecemeal changes (M4;M5) and 1 =
concerted changes (M1;M2;M6;M7)
X2 = Absolute variation of size between 2005 and 2010
X3-4-5 = Absolute variation of stakeholder pressures (group 1,2,3) between 2005 and 2010
X6 = Absolute variation of financial resources between 2005 and 2010
12
The absolute value of the variation of environmental performance is used as a dependent variable because
piecemeal and concerted change vary in the amount of importance devoted to eco-control (a ‘positive’ or
‘negative’ change). Considering that we want to isolate the effect of the scope of the change on the
environmental performance, regardless of its direction, the absolute value was necessary to control for the
effect of the direction of change.
22
Part B of table 4 presents the results of equation 2. The only significant variable is the
scope of change (.58; p<05). This result suggests that, regardless of the direction and scale,
concerted changes on eco-controls influence the variation of environmental performance
more than piecemeal change. This supports hypothesis 2 and suggests that because of the
complimentary effect, change conducted on all components of the mix of eco-controls is
expected to lead to synergies from balancing the eco-controls, and thus lead to higher
performance effect than individual effects. In a situation of off-balance between control
practices resulting from piecemeal change, the individual effects appear to be thwarted.
Coding 5 (Scale of change) and test of hypothesis 3
Based on the results of coding 2, the aim of coding 5 is to capture the scale of change by
classifying the seven movements of eco-controls (M1-M7) into two categories: (i) the
movements involving a modest change to eco-controls, and (iii) the movements involving
a substantive change to eco-controls. To determine the scale of the change, this study refers
to the Cohen’s indice of the variation between 2005 and 2010 (see column VII in table 3).
To be classified as a substantive change, one criterion must be respected; otherwise we
consider facing a modest change. This criterion requires that the Cohen’s indice of at least
one component of the mix of eco-controls must be superior or equal to the median of all
Cohen’s indices, namely 1.3713. 59% of the changes in our sample are modest in
comparison to 41% which are substantive. In order to examine the influence of the scale of
change on the variation environmental performance, the following OLS regression
equation is tested:
Y = β0 + β1 X1 + β2 X2 + β3 X3 + β4 X4 + β5 X5 + β6 X6 + ε
Eq. (3)
where
Y = Absolute variation of environmental performance between 2005 and 2010 14
As sensibility analysis, the criterion has been reformulated as follows: the Cohen’s indice of at least one
component of the mix of eco-controls must be superior or equal to the mean of all Cohen’s indices, namely
2.05. The results and conclusions described remain unchanged.
14 The absolute value of the variation of environmental performance is used as a dependent variable because
modest and substantive change can involve more or less importance devoted to eco-control (a ‘positive’ or
‘negative’ change). Considering that this study wants to isolate the effect of the scale of the change on the
environmental performance, regardless of its direction, the absolute value was necessary to control for the
effect of the direction of change.
13
23
X1 = Scale of change – dichotomous variable whereby 0 = modest changes (M5;M6) and 1 =
substantive changes (M1;M2;M4;M7)
X2 = Absolute variation of size between 2005 and 2010
X3-4-5 = Absolute variation of stakeholder pressures (group 1,2,3) between 2005 and 2010
X6 = Absolute variation of financial resources between 2005 and 2010
Part C of table 4 presents the results of equation 3. The effect of the scale of change is not
significant. This result suggests that, regardless of the direction and scope, the
implementation of substantive as compared to modest changes in eco-controls do not
influence the variation of environmental performance. Thus, hypothesis 3 is not supported.
This suggests that contrary to our expectations, it might not be the intensity of signal per
se which is informative, but instead the mere presence of a signal. The simple integration
of environmental concerns within the eco-controls might be sufficient to send a signal
about the seriousness of the intentions. Considering the limited attention of organizational
members, the notion of individual environmental commitment is tentatively proposed to
explain why even a weaker signal is noticed. The individual environmental commitment
results from intrinsic desires to support environmental issues, accept environmental goals
and values, as well as alternative interpretations of external forces (Douglas and Polonsky,
1998). Considering the importance of sustainable development for government, business
and communities around the world, the organizational members might be more sensitive
to environmental signals in comparison to other organizational signals. This sensitivity
could potentially be translated into three types of environmental commitment (Meyer and
Allen, 1991; Douglas and Polonsky, 1998): (i) affective commitment (i.e. emotional
attachment of managers to, identification with and involvement in supporting
environmental concerns), (ii) continuance commitment (the perceived economic and social
costs that managers associate with disregarding environmental concerns), and (iii)
normative commitment (sense of obligation by managers to continue supporting
environmental concerns). Therefore, both stronger and weaker signals might lead to
similar actions ensuing similar implications.
24
Validation of coding 3,4 and 5 using profile deviation analysis
As previously mentioned, the Euclidian distances have been computed for each firm based
on a profile deviation analysis. In sum, the results based on the profile deviation analysis
strongly supports the results based on the groups proposed by the cluster analysis.
5. CONCLUSION
The aim of this paper was to examine, longitudinally and quantitatively, to what extent
attributes of change in the mix of eco-controls influence environmental performance.
Based on two surveys of manufacturing firms collected at two points in time, the results
suggest three main conclusions:
1. Changes leading to more (less) importance devoted to eco-controls within the
organization, notably through variation in the use or improvement in the design,
contribute positively (negatively) to environmental performance. Not only does
this conclusion support the few cross-sectional studies suggesting that eco-controls
contribute positively to environmental performance, but it also expands on this
research by suggesting that a reduction in the importance devoted to eco-controls
will lead to a reduction in environmental performance. In other words, once
engaged in the process of integrating environmental concerns within control
practices, backtracking might lead to negative consequences.
2. In order to contribute more extensively to environmental performance, concerted
changes on all aspects of the mix of eco-controls are preferable to piecemeal
changes made on specific aspects of the mix. Considering the complimentary
effects from balancing the eco-controls, synergies will contribute to amplify the
individual effects of change on each eco-control.
3. In the process of change of eco-control, the aspect which contributes to
environmental performance is not the scale of that change but the mere presence
of a credible signal which reflects the seriousness of the intentions. This signal,
25
modest or substantive, mirrors the increase or decrease of the importance devoted
to eco-controls.
This study contributes specifically to three streams of research, namely (i) environmental
management accounting, (ii) management accounting change, and (iii) management
control as a system. First, the results of this paper support the small number of crosssectional studies that have provided empirical evidence suggesting the positive impacts of
eco-controls on environmental performance. This longitudinal quantitative study bridges
the gap between this emerging stream of research and the qualitative studies examining the
process of change of eco-controls by demonstrating which aspect of change of eco-control
contributes to environmental performance. Secondly, this paper contributes to management
accounting change literature by breaking down the nature of change of management control
practices in attributes (direction, scope, and scale) and examining their specific impact on
performance. Past quantitative studies have provided limited evidence in regards of the
nature of changes in management control practices and whether those changes explain
performance. Thirdly, the results of this study contribute to the emerging literature devoted
to the interdependence among management control practices. More specifically, this study
suggests that, because of the interdependence among management controls, synergies
ensuing from change conducted on all components of the mix of controls are beneficial for
organizations.
This study is subject to potential limitations in terms of internal and external validity. First,
this study does not investigate all the potential components of the mix of eco-control. Other
aspects could be added in future studies to provide a more complete picture, such as
environmental costing, project management, and capital investment. Secondly, considering
that data have been collected quantitatively at two periods of time, this study is not able to
examine the pace of the change, i.e. whether the change is swift and episodic or gradual
and continuous. Also, as previously mentioned, there is not perfect time lag. Although it is
argued that a five-year period was adequate to capture change in the mix of eco-controls
and its impacts, some might argue that this period is too short or too long. In both cases,
the breakdown of change in its various attributes to capture the components of change and
26
the presence of broad control variables to capture the influence of external and internal
phenomena contribute to alleviate these concerns. Lastly, despite the efforts to collect data
from a larger set of firms, the sample size remains relatively low. This might have
influenced the power of the models. That is why, despite the richness of the data set, this
paper should be considered as an exploratory study.
27
Figure 1 Conceptual framework
Mix of eco-controls
Direction of change
More vs. less
importance devoted
to eco-controls
H1
Scale of change
H2
Environmental performance
Modest vs.
substantive
H3
Scope of change
Piecemeal vs.
concerted
28
Figure 2 Coding process
Code
C1
C2
C3
TOTAL
Coding 1 : Cluster membership
for 2005 and 2010
Initial clusters
2005
Low importance devoted to eco-control
19
Moderate importance devoted to eco-control 31
High importance devoted to eco-control
28
78
2010
10
24
44
78
Coding 2 : Movement among
and within clusters
between 2005 and
2010
Code Direction
0 Less importance
1
More importance
Coding 2
M1-M2
M4-M5
M6
M7
TOTAL
Coding 3 : Direction of change
N
42
Code Scope
0 Piecemeal
34
1
Concerted
76
TOTAL
Coding 2
M4
M5
M1-M2
M6-M7
Coding 4 : Scope of change
N
29
Code Scale
0 Modest
47
1
76
TOTAL
Substantive
Coding 2
M5
M6
M1-M2
M4-M7
N
45
31
76
Coding 5 : Scale of change
29
Table 1 Descriptive statistics and correlation matrix of the main constructs
Panel A: Sample 1 - 2005
Descriptive statistics
No. of items
Theoretical range
Minimum
Maximum
Mean
Standard deviation
Median
Strategic
planning
Performance
indicators design
Performance
Budget
indicators - use
Incentives
Environmental
performance
4
1-7
1.0
7.0
4.86
1.80
5.25
13
1-7
1.0
7.0
4.96
1.36
5.06
4
1-7
1.0
7.0
5.18
1.55
5.63
3
1-7
1.0
7.0
3.45
1.74
3.67
6
1-7
1.0
6.50
3.46
1.64
3.75
3
1-7
1.0
7.0
4.31
1.96
4.67
Correlation matrix (Pearson)
Strategic planning
Performance indicators – design
Performance indicators – use
Budget
Incentives
Environmental performance
1.0
.62**
.66**
.57**
.58**
.38**
1.0
.71**
.74**
.56**
.40**
1.0
.73**
.56**
.39**
1.0
.69**
.42**
1.0
.58
1.0
Note 1: * Significant at the .05 level ** Significant at the .01 level.
30
Panel B: Sample 2 - 2010
Descriptive statistics
No. of items
Theoretical range
Minimum
Maximum
Mean
Standard deviation
Median
Strategic
planning
Performance
indicators design
Performance
indicators use
Budget
Incentives
Environmental
performance
4
1-7
1.5
7.0
4.87
1.41
5.0
13
1-7
1.0
7.0
4.91
1.34
5.18
4
1-7
1.0
7.0
4.91
1.60
5.25
3
1-7
1.0
7.0
4.41
1.61
4.67
3
1-7
1.0
6.33
3.66
1.57
4.0
6
1-7
1.0
6.67
4.00
1.41
4.00
1.0
.73**
.71**
.71**
.27**
1.0
.60**
.60**
.30**
1.0
.58**
.27**
Correlation matrix (Pearson)
Strategic planning
Performance indicators – design
Performance indicators – use
Budget
Incentives
Environmental performance
1.0
.69**
.77**
.57**
.63**
.41**
1.0
.49**
1.0
Note 1: * Significant at the .05 level ** Significant at the .01 level.
31
Table 2 Initial cluster analyses (coding 1)
2005
Clusters
Clustering variables
Strategic planning
Performance indicators – design
Performance indicators – use
Budget
Incentives
(1)
2.97
3.43
3.13
1.46
1.43
(2)
4.68
4.76
5.40
4.59
3.50
(3)
6.33
6.24
6.34
5.93
4.76
Overall score
2.48
4.59
Number of cases
(%)
19
(24%)
Scheffe’s
pairwise
comparison
1-2
1-3 2-3
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
2010
Clusters
(1)
2.65
2.24
2.63
2.20
1.37
(2)
4.27
4.45
3.93
3.71
2.76
(3)
5.71
5.77
5.96
5.29
4.66
5.92
2.22
3.82
5.48
31
28
10
24
44
(40%)
(36%)
(13%)
(31%)
(56%)
Scheffe’s
pairwise
comparison
1-2 1-3 2-3
**
**
**
**
**
**
**
**
**
**
**
**
**
**
**
Note 1: The Wards’s method is used with the Eucledian distance.
Note 2: Cluster 1: Low importance devoted to eco-control; Cluster 2: Moderate importance devoted to ecocontrol; Cluster 3: High importance devoted to eco-control
Note 3: * Significant at the .05 level ** Significant at the .01 level
32
Table 3 Movements among and within clusters between 2005 and 2010 (coding 2-5)
I
Movements
M1 Firms
jump back
– 2 clusters
(N=4)
M2 Firms
jump back
– 1 cluster
(N=9)
M3 Firms
remaining
in cluster 1
(N=2)
M4 Firms
remaining
in cluster 2
(N=10)
M5 Firms
remaining
in cluster 3
(N=19)
M6 Firms
jump
forward – 1
cluster
(N=26)
M7 Firms
jump
forward – 2
clusters
(N=8)
II
Mix of eco-controls
III
2005
IV
2010
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
Strategic planning
Performance indicators - design
Performance indicators – use
Budget
Incentives
5.94
6.61
6.31
5.38
4.75
5.72
5.47
5.89
5.67
4.93
2.63
2.83
3.13
1.33
1.0
4.98
4.71
5.90
4.57
3.10
6.34
6.14
6.29
5.95
4.65
4.03
4.47
4.47
3.37
2.79
2.91
3.24
3.13
1.83
1.48
1.94
1.86
1.94
2.25
1.17
3.50
3.66
3.86
2.33
2.15
2.88
2.81
3.0
2.67
1.17
4.55
4.36
3.70
3.33
3.03
5.95
5.75
5.99
5.23
4.65
5.12
5.26
5.28
4.96
4.04
5.44
5.94
5.78
5.83
4.38
V
Sign of
change
+
+
+
=
+
+
+
+
+
+
+
+
+
+
VI
Stats
sig.
**
**
**
**
*
**
**
**
**
*
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
n.s.
**
n.s.
n.s.
n.s.
*
n.s.
**
n.s.
**
**
*
**
**
**
**
**
**
**
VII
Cohen
5.52
6.29
5.20
7.92
4.90
2.22
1.37
2.03
3.34
2.73
0.24
0.04
0.10
2.85
1.42
0.30
0.41
2.08
0.96
0.05
0.57
0.68
0.44
0.79
0.01
0.77
0.86
0.57
1.03
0.87
2.07
3.27
2.21
4.21
3.37
VIII
Coding
Direction: Negative
Scope: Concerted
Scale: Substantive
Direction: Negative
Scope: Concerted
Scale: Substantive
Direction: n/a
Scope: n/a
Scale: n/a
Direction: Negative
Scope: Piecemeal
Scale: Substantive
Direction: Negative
Scope: Piecemeal
Scale: Modest
Direction: Positive
Scope: Concerted
Scale: Modest
Direction: Positive
Scope: Concerted
Scale: Substantive
Note 1: * p < 0.05 ** p < 0.01
Note 2: The distance of Cohen is an indice measuring the magnitude of a treatment effect. Unlike significance
tests, this indice is independent of sample size. This indice is calculated based on the difference between the
means, divided by the pooled standard deviation.
Note 3: The group M3 has been removed from the analyses for two main reasons. First, as no component of
the mix has been significantly changed, this group of firm is not considered having experimented change in
eco-controls. Second, the number of observation in that groups is too small (n=2).
33
Table 4 Influence of attributes of change on the variation of environmental
performance
Part A: Influence of direction of change (coding 3)
Independent variables
(n=76)
Constant
Direction of change
Variation of business stakeholders’ pressures
Variation of financial stakeholders’ pressures
Variation of institutional stakeholders’ pressures
Variation of size
Variation of financial resources
R2 = .316
Adjusted R2 = .258
Variation of environmental performance
Coefficient
Standard
t
error
-.64
.35
1.85
1.66
.54
3.08
.12
.15
.81
.11
.16
.73
.03
.17
.20
.37
.39
.95
.09
.12
.71
Sig.
.069
.003
.420
.470
.845
.343
.479
Note 1: The direction of change is a dichotomous variable whereby 0 = less importance devoted to ecocontrols and 1 = more importance devoted to eco-controls
Note 2: The variation of stakeholder pressures and variation of financial resources are continuous variables.
The variation of size is measured using the natural log of the number of employees.
Note 3: The dependent variable is the variation of environmental performance between 2005 and 2010.
Part B: Influence of scope of change (coding 4)
Independent variables
(n=76)
Constant
Scope of change
Variation of business stakeholders’ pressures
Variation of financial stakeholders’ pressures
Variation of institutional stakeholders’ pressures
Variation of size
Variation of financial resources
R2 = .086
Adjusted R2 = .09
Variation of environmental performance (absolute value)
Coefficient
Standard
t
Sig.
error
1.30
.37
3.49
.001
.58
.30
1.98
.050
.14
.14
1.01
.316
.12
.14
.86
.391
-.05
.16
.34
.732
-.01
.38
.04
.972
-.02
.12
.20
.840
Note 1: The scope of change is a dichotomous variable whereby 0 = piecemeal changes and 1 = concerted
changes.
Note 2: The dependent variable is the absolute value of the variation of environmental performance between
2005 and 2010.
Note 3: The variation of stakeholder pressures and variation of financial resources are continuous variables.
The variation of size is measured using the natural log of the number of employees. For all those independent
variables the absolute value is used.
34
Part C: Influence of scale of change (coding 5)
Independent variables
(n=76)
Constant
Scale of change
Variation of business stakeholders’ pressures
Variation of financial stakeholders’ pressures
Variation of institutional stakeholders’ pressures
Variation of size
Variation of financial resources
R2 = .056
Adjusted R2 = .02
Variation of environmental performance (absolute value)
Coefficient
Standard
t
Sig.
error
1.28
.40
3.17
.002
.39
.34
1.16
.249
.12
.14
.83
.412
.13
.14
.93
.356
-.05
.16
.29
.775
.03
.39
.08
.940
-.02
.12
.12
.903
Note 1: The scale of change is a dichotomous variable whereby 0 = modest changes, and 1 = substantive
changes.
Note 2: The dependent variable is the absolute value of the variation of environmental performance between
2005 and 2010.
Note 3: The variation of stakeholder pressures and variation of financial resources are continuous variables.
The variation of size is measured using the natural log of the number of employees. For all those independent
variables the absolute value is used.
35
Appendix 1 Questionnaire Items and Statistics of Measurement Analysis
Environmental issues integration in strategic planning
Please indicate your agreement with the following statements:
Scale: 1=Strongly disagree to 7=Strongly agree
2010
Standardized
loadings
Environmental issues are explicitly considered within the 0.75**
company’s strategic planning process.
Consideration for the natural environment is addressed 0.61**
within the company’s mission statement or statement of
business principles.
When environmental issues are considered within the 0.91**
strategic planning process, the top management team makes
proactive, forward-thinking decisions.
Environmental personnel participate in the company’s 0.81**
strategic planning process.
Items
Goodness-of-fit of the model:
Cronbach Alpha:
Composite reliability:
Variance extracted:
0.57
2005
Standardized
loadings
0.88**
0.37
0.87**
0.76
0.83
0.89**
0.79
0.65
0.85**
0.72
R2
χ2 (2) = 2.049 p=0.36;
NNFI= 1.0; CFI=1.0; RMSEA
= 0.0
0.84
0.85
0.60
R2
0.77
χ2 (2) = 0.06 p=0.97;
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.0
0.93
0.93
0.76
Environmental performance indicators - use
Please indicate the extent to which your organization (or business unit) relies on environmental performance
indicators to:
Scale: 1= not at all to 7=to a great extent
Items
Monitor internal compliance with environmental policies
and regulations
Provide data for internal decision-making
Motivate continuous improvement
Provide data for external reporting
Goodness-of-fit of the model:
Cronbach Alpha
Composite reliability:
Variance extracted:
2010
Standardized
loadings
0.88**
R2
0.77
0.99**
0.99
0.76**
0.57
0.67**
0.45
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
0.88
0.90
0.70
2005
Standardized R2
loadings
0.87**
0.75
0.93**
0.87
0.83**
0.69
0.71**
0.50
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
0.90
0.90
0.70
36
Environmental performance indicators - design
2nd order model
Please rate the extent to which each of the following environmental performance indicators is measured by
your organization (or business unit):
Scale: 1=Not at all to 7=To a great extent
2010
Standardized
loadings
Group 1: Input indicators
0.80**
Inputs of raw materials indicators (e.g. wood, metals, 0.77**
synthetics, etc.)
Inputs of auxiliary materials (e.g. glue, adhesives, chemicals, 0.67**
solvents, etc.)
Inputs of energy (e.g. electricity, fuel, gas, oil, etc.)
0.75**
Inputs of water (e.g. municipal water, spring water, etc.)
0.78**
Items
0.64
0.59
2005
Standardized R2
loadings
0.71**
0.51
0.60**
0.36
0.46
0.57**
0.32
0.56
0.61
0.70**
0.67**
0.48
0.45
R2
Group 2: Output and impact indicators
Outputs of solid waste (e.g. oil, chemical, metal, etc.)
Outputs of wasted water (e.g. emission of wasted water
containing organic materials, etc.)
Outputs of air emissions (e.g. CO2, SO2, dust, solvents, etc.)
Financial impacts (e.g. costs and benefits of environmental
actions, etc.)
Indicators providing information about the local, provincial
or national conditions of the environment (e.g. contaminant
concentrations in ambient air, number of dead fish in a
specific watercourse, etc.)
Installation, operation and maintenance of the physical
facilities and equipments (e.g. hours of preventive
maintenance, average fuel consumption of vehicle fleet, etc.)
0.97**
0.88**
0.71**
0.95
0.77
0.51
0.75**
0.57**
0.56**
0.57
0.33
0.31
0.59**
0.82**
0.35
0.67
0.61**
0.70**
0.37
0.49
0.72**
0.52
0.72**
0.51
0.52**
0.27
0.68**
0.46
Group 3: Managerial indicators
Implementation of environmental policies and programs (e.g.
% of environmental targets achieved, number of employees
trained, etc.)
Conformance with requirements or expectations (e.g. number
of fines or violations, number of environmental incidents,
number of audits, etc.)
Community relations (e.g. number of complaints from public
or employees, number of enquiries from stakeholders, etc.)
0.81**
0.76**
0.65
0.58
0.86**
1.0**
0.74
1.0
0.59**
0.34
0.53**
0.28
0.72**
0.51
1.0**
1.0
Goodness-of-fit of the model:
χ2 (60) = 137.77 p< .001;
NNFI= .919; CFI=.948;
RMSEA = 0.105
0.91
0.90
0.75
Cronbach Alpha:
Composite reliability:
Variance extracted:
χ2 (60) =124.55 p< .001;
NNFI= .903; CFI=.925;
RMSEA = 0.087
0.88
0.82
0.60
37
Environmental budget
Please indicate the extent to which the following items are detailed in the budget of your organization (or
business unit).
Scale: 1=Not detailed at all to 7=very detailed
Items
Environmental expenses
Environmental investment
Incomes from material scrap or recycled wastes
Goodness-of-fit of the model:
Cronbach Alpha
Composite reliability:
Variance extracted:
2010
Standardized R2
loadings
0.94**
0.88
0.90**
0.80
0.52**
0.27
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
0.81
0.84
0.65
2005
Standardized R2
loadings
0.92**
0.84
0.98**
0.96
0.55**
0.30
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
0.85
0.87
0.70
Environnemental incentives
Concerning the integration of environmental performance indicators in employee evaluation, please indicate
to what extent:
Scale: 1= not at all to 7=to a very great extent
2010
Standardized R2
loadings
Environmental indicators are important in reward systems
0.78**
0.61
Environmental performance objectives are included in the 0.86**
0.74
planning systems
Environmental performance indicators are weighted on par 0.89**
0.79
with economic performance indicators
Goodness-of-fit of the model:
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
Cronbach Alpha
0.88
Composite reliability:
0.88
Variance extracted:
0.71
Items
2005
Standardized R2
loadings
0.87**
0.75
0.91**
0.84
0.85**
0.72
χ2 (0) = 0 p> .001
NNFI=
1.0;
CFI=1.0;
RMSEA = 0.00
0.91
0.91
0.77
38
Environmental performance
Please indicate the extent to which the organization’s (or business unit) environmental practices and actions
have led to any of the following competitive benefits:
Scale: 1=No contribution to 7=Very large contribution
2010
Standardized R2
loadings
Increased process/production efficiency
0.78**
0.56
Increased in productivity
0.94**
0.88
Increased knowledge about effective ways of managing 0.75**
0.56
operations
Improved process innovations
0.99**
0.99
Improved product quality
0.63**
0.39
Improved product innovations
0.65**
0.41
Goodness-of-fit of the model:
χ2 (6) = 8.847 p=.182;
NNFI= 0.985; CFI=0.994;
RMSEA = 0.078
Cronbach Alpha
0.91
Composite reliability:
0.91
Variance extracted:
0.64
Items
2005
Standardized
loadings
0.86**
0.94**
0.82**
R2
0.75
0.89
0.68
0.86**
0.73
0.83**
0.69
0.74**
0.55
χ2 (6) = 10.870 p=.144;
NNFI= 0.986 CFI=0.994;
RMSEA = 0.079
0.94
0.94
0.71
Note: * Significant at the .05 level ** Significant at the .01 level.
39
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