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How IT executives create organizational benefits by translating environmental strategies into Green IS initiatives: Organizational benefits of Green IS strategies and practices

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Organizations increasingly recognize that environmental sustainability is an urgent problem. Green information systems (Green IS) initiatives can assist organizations in reaching their environmental goals by providing the ability to reduce the environmental impacts of information technology (IT) manufacturing, operations and disposal; facilitate transparency and enhance the efficiency of organizational resources and business processes; and foster eco-products through technological innovation. However, the nature and type of benefits such initiatives can accrue remain poorly understood, and accordingly, IT executives struggle to integrate environmental aspects in the corporate strategy and to launch Green IS initiatives. This paper clarifies the mechanisms that link organizational beliefs about environmental sustainability to Green IT and Green IS actions undertaken, and the organizational benefits that accrue from these actions. Using data from a global survey of 118 senior-level IT executives, we find that Green IS strategies mediate the relationship between environmental orientation and the implementation of Green IT practices and Green IS practices, which in turn lead to organizational benefits in the form of cost reductions, corporate reputation enhancement and Green innovation capabilities. Our findings have implications for the potential of IS to enable organizations' environmental sustainability and also for the differentiation of Green IT and Green IS practices.
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Information Systems Journal
Special Issue on: Information Systems addressing the Challenges of Environmental
Sustainability
Title of paper:
How IT Executives Create Organizational Benefits by Translating Environmental Strategies into
Green IS Initiatives
Suggested running headline:
Organizational Benefits of Green IS Strategies and Practices
Authors:
Fabian Loeser (corresponding author), Institute of Information Systems Management, Technische
Universitaet Berlin, Strasse des 17. Juni 135, 10623 Berlin, Germany, email:
Fabian.Loeser@gmail.com
Jan Recker, QUT Business School, Brisbane, Queensland University of Technology, Queensland,
Australia, email: j.recker@qut.edu.au
Jan vom Brocke, Hilti Chair of Business Process Management, Institute of Information Systems,
Universitaet Liechtenstein, Fürst-Franz-Josef-Strasse, 9490 Vaduz, Liechtenstein, email:
jan.vom.brocke@uni.li
Alemayehu Molla, PhD School of Business Information Technology & Logistics, RMIT University,
400 Swanston Street, Melbourne VIC 3000, Australia, email: alemayehu.molla@rmit.edu.au
Ruediger Zarnekow, Institute of Information Systems Management, Technische Universitaet Berlin,
Strasse des 17. Juni 135, 10623 Berlin, Germany, email: Ruediger.Zarnekow@tu-berlin.de
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How IT Executives Create Organizational Benefits
by Translating Environmental Strategies into
Green IS Initiatives
ABSTRACT
Organizations increasingly recognize that environmental sustainability is an urgent problem. Green
Information Systems (Green IS) initiatives can assist organizations in reaching their environmental
goals by providing the ability to reduce the environmental impacts of Information Technology (IT)
manufacturing, operations, and disposal; facilitate transparency and enhance the efficiency of
organizational resources and business processes; and foster eco-products through technological
innovation. However, the nature and type of benefits such initiatives can accrue remain poorly
understood, and accordingly IT executives struggle to integrate environmental aspects in the corporate
strategy and to launch Green IS initiatives. This paper clarifies the mechanisms that link
organizational beliefs about environmental sustainability to Green IT and Green IS actions undertaken,
and the organizational benefits that accrue from these actions. Using data from a global survey of 118
senior-level IT executives, we find that Green IS strategies mediate the relationship between
environmental orientation and the implementation of Green IT practices and Green IS practices, which
in turn lead to organizational benefits in the form of cost reductions, corporate reputation
enhancement, and Green innovation capabilities. Our findings have implications for the potential of IS
to enable organizations’ environmental sustainability and also for the differentiation of Green IT and
Green IS practices.
KEYWORDS
Environmental Sustainability, Environmental Orientation, Green IT, Green IS, Strategy, Practices,
Belief-Action-Outcome, Organizational Benefits, Survey, PLS-SEM
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INTRODUCTION
The information systems (IS) discipline has been challenged to determine how IS can contribute to
environmentally responsible human activity (Watson et al., 2010; Elliot, 2011). Researchers claim that
IS can be a key enabler, assisting individuals, organizations, governments, and society to transform
towards environmentally sustainable practices. In this context, the IS discipline has started to
systematically explore the role that IS might play (e.g., Melville, 2010; Elliot, 2011; Seidel et al.,
2013; Hedman & Henningsson, 2016; Hasan et al., in press).
An increasing number of studies that examine the role of IS for environmental sustainability have
appeared in response to this global challenge. These studies broadly fall into two categories: abstract
and substantive. Abstract-level studies, for instance, investigate factors that influence the adoption of
any type of Green IS (e.g., Chen et al., 2011; Molla et al., 2011; Thongmak, 2013), while substantive-
level studies conceptualize requirements for some type of Green IS, such as energy systems (Watson et
al., 2010) or examine particular systems for specific environmental challenges, such as energy
consumption (Loock et al., 2013), greenhouse gas emissions (Hilpert et al., 2013), or organizational
initiatives (e.g., Bengtsson & Ågerfalk, 2011; Butler, 2011; Seidel et al., 2013).
Both types of studies are important, but it appears that most Green IS research to date is substantive-
level in nature. The limitation of substantive-level studies is that they develop models that pertain only
to specific cases (e.g., Seidel et al., 2013), so they are limited in providing insights about the benefit of
Green IS in general. The second key limitation of the research to date is the absence of empirical
studies that evaluate consequences. Malhotra et al.’s (2013) review shows that the majority of research
articles published in the domain of Green IS are conceptual or analytical studies, as opposed to impact
studies that analyze organization-level outcomes empirically.
Our objective is to clarify both the antecedents and benefits of Green IS initiatives. Specifically, we
ask a) how environmental orientation and strategy influence Green IS initiatives and (b) whether
Green IS initiatives yield organizational benefits in general. The scope of our study to address this
objective is to understand the organizational beliefs about environmental sustainability, the actions
that organizations undertake by formulating Green IS strategies and translating these into Green IT
practices and Green IS practices, and the organizational benefits they generate as outcomes. Based on
a literature review, we show that no empirical study has yet contributed this knowledge, even though it
is an important problem to address: CIOs are cautious about investments in Green IS when the
investments’ business value is unclear (Corbett, 2010; Dedrick, 2010; Bengtsson & Ågerfalk, 2011).
Moreover, while a wide range of Green IT practices helps to reduce IT energy consumption in data
centers and office environments, thus decreasing operational costs, the business case for enterprise-
level Green IS initiatives that enhance the resource efficiency of business and production processes is
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more difficult to determine (Molla & Abareshi, 2012). This is because the long-term payoffs of Green
innovations at the product level are often even less tangible because of ambiguous customer
preferences and the uncertain development of future markets (Michaud & Llerena, 2010).
Our research provides three unique contributions: Theoretically, we establish a new model of
organizational benefits accruing from Green IS investments. The model, based on Melville’s (2010)
Belief-Action-Outcome framework, postulates how environmental orientation shapes the formulation
of Green IS strategies and the implementation of Green IT practices and Green IS practices that reduce
costs and generate reputational and innovation benefits. The model also demonstrates the types of
Green IT and Green IS practices and the specific organizational benefits being associated with them.
Empirically, we provide the first (to the best of our knowledge) general-level study of the
organization-level benefits of Green IS initiatives that builds on data from senior-level IT executives.
We also provide new and validated measurement instruments for novel Green IS concepts such as
Green IS strategy, Green IT practices and Green IS practices. In doing so, we add useful and original
knowledge to the emergent field of Green IS research. Practically, our research provides senior-level
IT executives with content, scope and measures for the design of Green IS strategies. We show that
companies that execute Green IS initiatives must make substantial changes to their orientation to
climate change, such as by revisiting their internal values and standards of ethical behavior. We also
present the first empirical insights on the expected outcomes of such initiatives, which are important
because of the prevailing uncertainty concerning the business benefits of Green IS (Elliot, 2013),
particularly regarding their strategic long-term benefits (Shrivastava et al., 2013).
The rest of the paper is organized as follows. First, we review the literature on Green IS research,
focusing on the contributions to empirical knowledge to date. Then we provide a brief review of
Melville’s (2010) Belief-Action-Outcome framework, which is the starting point for our theorizing.
Next, we develop our research model and discuss the research method. Then we describe our
empirical results and the analyses we conducted on the data. Finally, we discuss our findings and their
implications before reviewing our research’s limitations and contributions.
BACKGROUND
To provide a background to our study, we review the literature on the theoretical relationships between
IS and environmental sustainability, the empirical findings generated to date, and the potential benefits
that might accrue from Green IS initiatives.
Information Systems and Environmental Sustainability
Environmental sustainability issues have come to the societal and governmental forefront because it is
widely believed that the future of our ecosystem and society depends on our collective ability to limit
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or, ideally, reverse human-initiated environmental degradation and the effects of global climate change
(Bansal, 2005). A survey of chief executive officers (CEOs) in 2013 indicates that 70 percent see
environmental sustainability as a significant business issue (Kiron et al., 2013). While the
management literature typically sees institutional and resource-based perspectives as triggers for
environmental innovation (Berrone et al., 2013) and mechanisms for improving environmental
outcomes (Bansal, 2005), DeGarmo et al. (2011) argue that corporate sustainability is primarily an
information challenge. New dimensions of environmental performance must be integrated into
measurement systems to facilitate transparency and allow for responsible decision-making as well as
accountability to internal and external stakeholders.
Watson et al. (2010) argue that IS have been the greatest force for productivity improvement in the
last half century, and it is expected that such systems can also help with the global environmental
challenge (vom Brocke et al., 2013) – more than 60 percent of CEOs (Gadatsch, 2011) expect IS to
enable organizations to become more environmentally sustainable. To respond to the increased social,
cultural, and legislative pressures, business firms increase their attention to environmental concerns
(Mintzberg et al., 2002). The problem is that IS, as technological artifacts, contribute to the
environmental sustainability challenge themselves by consuming vast amounts of electricity, thereby
placing heavy burdens on power grids and contributing to greenhouse gas emissions and other
environmental problems during their production, use, and disposal (Murugesan, 2008).
To investigate these challenges, two sub-fields of research—Green IT and Green IS—have emerged as
areas of IS research that address environmental sustainability issues regarding technology-based
systems. Green IT is “the study and practice of designing, manufacturing, using, and disposing of
computers, servers, and associated subsystems efficiently and effectively with minimal or no impact
on the environment” (Murugesan, 2008, p. 25), while the concept of Green IS captures “IS-enabled
organizational practices and processes that improve environmental and economic performance”
(Melville, 2010, p. 2).
Some scholars argue that the concept of Green IT has a restricted view of technological issues (e.g.,
Dao et al., 2011), whereas the concept of Green IS is more comprehensive and includes people,
processes, and capabilities that address environmental sustainability in a holistic way. Watson et al.
(2010, p. 24), suggest that Green IT is part of the more far-reaching concept of Green IS, which
examines the possible ability of IT-based systems to make significant contributions to reducing
greenhouse gas emissions and mitigating the effects of global climate change and other environmental
problems. The key assumption is that, while IT creates negative environmental impacts because of the
electricity required for its operation and the problem of disposing of obsolete hardware, innovative IS
can be used to reduce environmental problems by changing processes and practices (Loos et al.,
2011). The key allure of IS in this regard is their potential to assist individuals and organizations to
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make better—that is, more environmentally sustainable—decisions and to facilitate environmentally
sustainable (rather than environmentally unsustainable) work practices.
We contend that the environmental sustainability initiatives of organizations that are attempting to
decrease their environmental footprint invariably involve some Green IT and some Green IS practices
because the isolated implementation of Green IT practices, such as energy-efficient server farms and
cloud solutions, are limited to the boundaries of the IT function and do not leverage IS’ potential to
decrease enterprise-wide environmental impacts. Accordingly, we understand Green IS initiatives as a
wide range of IS-related environmental actions, such as the formulation of Green IS strategies and the
translation of these strategies into concrete environmental practices that affect IT infrastructure
resources, organizational processes, and even end-users’ products and services. Therefore, we define
Green IS initiatives as investments in IS and its deployment, use, and management in order to
minimize the negative environmental impacts of IT, business operations, and end-users’ products and
services.
Empirical Research on Green IS
To position the contribution that we make in this study, we considered the state of knowledge in Green
IS research and Green IT research, particularly empirical studies. Malhotra et al.’s (2013) review
showed that twenty-nine of thirty Green IS research articles describe conceptual or analytical case
studies, rather than being quantitative empirical studies that evaluate Green IS initiatives’
environmental and economic impacts on performance.
To determine whether this body of knowledge has changed substantially since that review, we
reviewed all issues of the AIS basket of eight journals (EJIS, ISJ, ISR, JAIS, JIT, JMIS, JSIS, and
MISQ) published in 2013, 2014, and 2015, as these journals are widely acknowledged to represent
leading IS research. This review identified five relevant articles (Table 1). Then we searched on
Google Scholar using the keywords “Green IS” and “Green IT” from 2013 to 2015 to identify
additional empirical studies that focus on organizations and that were published in journals ranked
A*/A by the Australian Business Deans Council (ABDC). This process resulted in ten additional
articles. Table 1, which summarizes our selective review, classifies the contributions in the literature
as substantive or abstract, lists whether the studies operated on an individual (micro) or organizational
(macro) level, and provides details about the basis of the empirical evidence reported.
Table 1. Summary of main recent empirical contributions to Green IS research.
Reference Element
of study Focus of study Level of study Empirical Evidence
Henfridsson
& Lind
(2014)
Green IS Micro: Sustainability
strategizing
Substantive:
Communities, processes, and activities to
develop sustainability strategy
Single case study
Corbett
(2013a)
Green IS Micro:
Design and use of carbon-
Substantive:
How to develop a Green IS to persuade
Three case studies
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management system employees to develop eco-friendly behavior.
Loock et al.
(2013)
Green IS Micro:
Decisions by consumers
Substantive:
Goal-setting and energy-efficient behavior in
private households.
1791 electricity
consumers
Marett et al.
(2013)
Green IS Micro:
System use
Substantive:
Antecedents of drivers’ continuous use of a
bypass system
Survey of 212 drivers
Seidel et al.
(2013)
Green IS Macro and micro:
Organizational and individual
sensemaking and practices
Substantive:
Duration of one transformation initiative
Single case study
Chuang &
Huang
(2015)
Green IT Macro:
Green IT capital
Abstract:
How Green IT’s human, structural. and
relational capital contribute to business
competitiveness
A survey of 148
Companies in Taiwan
Cooper &
Molla (2014)
Green IT Macro:
Green IT assimilation
Abstract:
Absorptive capacity and contextual
influences of Green IT assimilation
International survey of
148 large
organizations
Stiel &
Teuteberg
(2014)
Green IT Macro:
Modeling IT’s environmental
impact
Substantive:
IT lifecycle analysis
Simulation
Bai & Sarkis
(2013)
Green IT Micro:
Modeling tools
Substantive:
Green IT strategic decision-making
Simulation
Cai et al.
(2013)
Green IT Macro:
Adoption of Green IT
Abstract:
Drivers of public concern, regulation, cost
reduction, and differentiation related to
adoption
A survey of 70
respondents in China
Corbett
(2013b)
Green IS Micro:
Smart meters
Substantive:
The energy-efficiency value of demand-side
management through smart metering
Secondary data from
the US Energy
Information
Administration
Gholami et
al. (2013)
Green IS Micro:
Individual decision-makers’
beliefs and actions
Abstract:
Antecedents and consequences of a firm’s
adoption of Green IS
405 senior managers
from Malaysian
businesses
Hertel &
Wiesent
(2013)
Green IS Micro:
Modeling tools
Substantive:
Optimal IS investment for energy efficiency
Simulation
Molla (2013) Green IT Macro:
IT firms’ environmental
innovation
Abstract:
An instrument to measure environmentally
sustainable IT performance
A survey of 133
Australian IT firms
Ryoo & Koo
(2013)
Green IS Micro:
Alignment of Green practices
and IS
Abstract:
The environmental and economic value of
aligning IS with Green manufacturing and
marketing practices
A survey of 77
manufacturing
employees from South
Korea
This
research Both
Green IT
and
Green IS
Macro: Organization-level
beliefs, actions, and outcomes
related to Green IS initiatives
Abstract:
Orientation, strategy, practices, and benefits
of Green IS initiatives in general
Cross-sectional,
global, senior: 118
senior-level IT
executives
Our interpretation of this literature review is that substantive studies (9 out of 15) dominate general-
level studies (6; Table 1). There are also more micro-level studies (9) than macro-level studies (5, with
one study addressing both levels). Among studies like ours that are macro and general-level in nature,
the four related studies (Cai et al., 2013; Molla, 2013; Chuang & Huang, 2014; Cooper & Molla,
2014) all examine Green IT but not Green IS practices. Three of these four studies examine cross-
sectional organization-level data from organizations in one country (Australia, China, Taiwan), with
only Cooper and Molla (2014) collecting data from multiple countries (viz., Australia, New Zealand,
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the US). The present study adds a study that examines elements associated with both Green IT and
Green IS on an abstract, macro level using data from a global survey of IT executives.
Benefits of Green IS Initiatives
Many outcomes, both positive and negative, might accrue from Green IS initiatives. Consistent with
our objective and scope, we focus on organizational benefits alone. Scholars argue that Green IS
initiatives can transform a firm’s sustainability, leading to a variety of organizational benefits and
advantages (Dao et al., 2011; Seidel et al., 2013; vom Brocke et al., 2013) but without being clear as
to the nature of these benefits and their origin in specific actions undertaken. We take this step. To
identify the possible organizational benefits of Green IS initiatives, we broadly searched for Green IS
literature that discusses the potential benefits of Green IS, independent from how benefits were
interpreted specifically (e.g., as positive impact, value or advantage).
Chuang and Huang (2015) suggest that the development of Green IT-related human, structural, and
relational capital can contribute to business competitiveness. Harmon and Demirkan (2011) explain
that IT-related environmental measures can reduce costs, whereas innovative IS services can create
customer and societal benefit, thus changing the competitive landscape, although they do not provide
empirical data in support of this assertion. In a study of sixty-three firms, Benitez-Amado and
Walczuch (2011) find that IT-management capabilities facilitate proactive environmental strategies,
thus leading to organization-level cost savings and improved corporate reputation. In this context,
Ziegler et al. (2011) suggest that environmental technologies can have a positive effect on corporate
reputation, an intangible resource that positively influences financial performance (Orlitzky et al.,
2003). Bengtsson and Ågerfalk’s case study (2011) indicates that IT can be a change agent in
sustainability innovations, changing the behavior of employees through sustainability initiatives.
Thambusamy and Salam (2010) present a preliminary case study that brings these organizational
benefits together and demonstrate that organizations can reduce costs, build reputation, and innovate
to create new growth trajectories using IT-enabled environmental sustainability strategies.
We conclude from this literature review that Green IS initiatives can generate at least three types of
benefits: 1) Green IS initiatives can reduce costs by increasing the resource efficiency of IT
infrastructure resources (Murugesan, 2008; Corbett, 2010) and organization-wide business processes
(Watson et al., 2008). 2) Green IS initiatives can also enhance corporate reputation by decreasing the
organization’s environmental footprint while providing tools for environmental performance tracking
and reporting (El-Gayar & Fritz, 2006; Thambusamy & Salam, 2010). 3) Green IS initiatives can
facilitate and improve organizational capabilities for Green product and process innovations, which
can result in long-term organizational advantages (Albino et al., 2009; Bengtsson & Ågerfalk, 2011;
Besson & Rowe, 2012; vom Brocke et al., 2012).
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THEORETICAL FOUNDATION
To pursue our objective of identifying the antecedents and benefits of Green IS initiatives in
organizations, we require a framework that draws attention to organization-level intentions and
behaviors, establishes relevant links from intentions to concrete actions in the context of
environmental sustainability, and mechanisms that lead to organizational-level benefits as outcomes.
Melville’s (2010) Belief-Action-Outcome framework provides such a basis. It differs from other
theories that provide a framework for the factors and forces that influence organizational Green IS
initiatives, such as motivational theory (Molla & Abareshi, 2012), institutional theory (Butler, 2011),
and the technology-organization-environment framework (Dao et al., 2011), because these theories are
useful in clarifying the antecedents to Green IS initiatives but not their outcomes (Gholami et al.,
2013).
Melville’s (2010) framework suggests that organizational behaviors are the result of beliefs and
actions on the macro and micro levels. It covers three areas: Beliefs capture how psychic states
(beliefs, desires, orientations, etc.) related to the natural environment are formed. On the macro level,
these states include how an organization coordinates and divides labor and defines its agents’
environment-related expectations. These expectations could include the managerial interpretation of
environmental issues in light of corporate identity (Sharma, 2000). On the micro level, beliefs capture
environment-related attitudes in the form of norms and beliefs. For instance, individual
environmentalism depends on ecological worldviews, awareness of consequences, and ascription of
responsibility (Steg, 2000; Steg et al., 2005).
Actions describe how psychic states related to the natural environment translate into actions. On the
macro level, these actions include those an organization undertakes to affect its agents’ actions. For
instance, organizations deploy IS to allow for sensemaking of environmental issues and use the
enterprise’s social networks to democratize sustainability information and its employees’ critical
environmental decisions (Seidel et al., 2013). On the micro level, actions describe what individuals do
to improve behavioral environmentalism. For instance, individuals may choose to use web portals that
minimize energy consumption by setting individual goals (Loock et al., 2013) or to delocalize work
practices by relying on file-sharing and conferencing systems rather than physical travel (Seidel et al.,
2013).
Outcomes describe the consequences of the actions on the macro and/or micro levels, as a measure of
the organizations’ (or other social systems’) environmental functioning. Outcomes in this framework
can be as both positive and negative for both business and the environment. For example, they could
include environmental impacts on the behavior of organizations (or other social systems) or such
systems’ environmental performance. Outcomes may also be environmentally negative, e.g., IT
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investments in server farms that increase electricity demand and, thus, greenhouse gas emissions (Cho
et al., 2007).
Melville’s (2010) framework provides a sound conceptual basis on which to differentiate
organizational from individual actions and to classify behavior in terms of beliefs, actions, and
outcomes. While previous research examined micro-level beliefs, actions, and outcomes (Table 1), we
examine these elements on the macro—that is, organizational—level. Our literature review in Table 1
shows that this focus is unique in the literature.
RESEARCH MODEL
In developing a research model based on Melville’s (2010) framework, we start by describing how we
instantiated the core categories of belief formation, sustainability actions, and organizational
outcomes. The BAO framework describes both positive and negative outcomes for businesses and the
environment. In line with our study’s scope, our instantiation of outcomes is limited to the reported
positive environmental and economic benefits at an organizational level. However, the context of
these benefits is the environmental context within which organizations seek benefits by improving the
environment (Porter & van der Linde, 1995).
In developing constructs for each category, we followed the extant literature on construct development
(Lewis et al., 2005; Urbach & Ahlemann, 2010; MacKenzie et al., 2011). We reviewed the literature
on our major constructs of environmental orientation, Green IS strategy, Green IT practices, Green IS
practices, and organizational benefits. Next, we identified candidate measurements and conducted
suggested tests to focus and improve our measures. More information on this process is provided in
the research method section. We discuss each category of constructs in turn.
Organizational Belief Formation: Environmental Orientation
Environmental belief formation on the organization level relates to the attention an organization pays
to environmental issues. Because environmentalism is increasingly important for a firm’s
competitiveness, corporate environmentalism has evolved from being a complementary management
task to an integral part of strategic management activities (Schaltegger et al., 2013). The creation of
competitive advantage is highly context-dependent, and uncoordinated environmental sustainability
initiatives without strategic coherence are ineffective (Orsato, 2006).
Some studies examine how an organization’s environmentalism is formed. For example, Chen et al.
(2010a), Butler (2011) and Molla and Abareshi (2012) analyze the organizational motivations for
adopting Green IS or Green IT. External pressures shape executives’ personal beliefs and result in
sustainability actions (Melville, 2010; Gholami et al., 2013). The phenomenon of corporate
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environmentalism has been studied through the concept of environmental orientation, which is defined
as managers’ recognition of the importance of the environmental issues that their firms are confronted
with (Banerjee et al., 2003). A firm’s historical development, organizational culture, top management
commitment, and executives’ personal experiences influence its environmental orientation (Barney,
1986; Banerjee et al., 2003). This environmental orientation of the firm, in turn, shapes executives’
beliefs about the environment, decision-making processes, and the initiation of environmental actions
(Gholami et al., 2013). Hence, we conceptualize environmental orientation (Table 2) as an antecedent
of Green IS strategies, Green IT practices, and Green IS practices.
Table 2: Conceptualization of environmental orientation.
Construct Definition Description
Environmental
Orientation
Executives'
recognition of the
importance of the
environmental
issues that face
their firm (Banerjee
et al., 2003, p. 106).
A company’s environmental orientation reflects its internal values, standards of ethical
behavior, commitment to environmental protection, and relationships with external
stakeholders (Banerjee et al., 2003, p. 106). This concept is closely linked to
organizational culture, which refers to the complex set of values, beliefs, and
assumptions that define how a firm conducts its business (Barney, 1986).
Environmental orientation guides executives’ beliefs and actions and influences how a
firm interacts with key stakeholders on issues related to the environment (Rugman &
Verbeke, 1998).
Organizational Sustainability Actions: Green IS Initiatives
We use three constructs to describe and measure the IS-related sustainability actions an organization
undertakes: Green IS strategy delineates environmental IS strategies from an organization level and a
function level; Green IT practices refer to environmental actions implemented in the domain of the IT
department while focusing on reducing IT-based environmental impacts; and Green IS practices,
which cover environmental actions, such as process innovations that use IS to decrease the
organization’s environmental footprint, or environmental technologies, which facilitate Green product
innovations that decrease the environmental impacts of end-user products and services. We discuss
each construct in turn.
Green IS strategy
Banerjee (2002, p. 182) emphasizes that “environmental concerns need to be translated into strategy if
corporate greening is to occur” and explains that environmental strategies at a function level are
limited to the reduction of waste and emissions. Organizational strategies, on the other hand, can
enhance business performance by facilitating low-cost or differentiation advantages (Porter, 1980;
Orsato, 2006).
Typically, corporate, business, and functional strategies are differentiated (Andrews, 1971), a
distinction that corporate sustainability research also applies (Stead et al., 2004). For environmentally
sustainable management practices to be strategically significant, sustainability must be integrated into
strategies on each of these levels (Aragón-Correa, 1998). Chen et al. (2010b, p. 237) suggest that
“while IS strategy is part of a corporate strategy, conceptually it should not be examined as part of a
12
business strategy. Rather, it is a separate perspective from the business strategy that addresses the
scope of the entire organization to improve firm performance.” Therefore, the consistent and holistic
translation of an organization's environmental orientation into IS-related sustainability actions requires
that Green IS strategies are not being restricted to the functional management level of the IT domain
but also being considered in organizational IS strategies (Loeser et al., 2012). Accordingly, we
conceptualize Green IS strategy by means of two sub-constructs, organizational Green IS strategy and
functional Green IS strategy (Table 3).
Organizational Green IS strategy is characterized by business and IT executives’ mutual
understanding concerning future opportunities and challenges and by collaborative, cross-functional
strategic planning processes. Organizational Green IS strategy articulates a shared vision by top
management and IT executives and describes the fundamental role of Green IS in achieving
organization-wide, long-term environmental objectives. This conception relates to corporate
environmental sustainability strategies, defined as the long-term vision formulated by top management
that outlines the organization’s attitude toward stakeholders and the natural environment (Klassen &
McLaughlin, 1996; Stead et al., 2004).
A functional Green IS strategy facilitates effective and efficient IT operations and IS-based processes
through a resource-efficient IT infrastructure that supports environmental goals. Concrete policies
defined at the function level result in the effective implementation of Green IT practices and Green IS
practices. Functional strategies are important in creating internal Green-IS-related resources and
capabilities over time. These firm-specific assets, both tangible and intangible, lay the foundation for a
company's productivity and innovation capacities (Barney, 1991), so the development of IS-based
environmental management systems and the establishment of environmental management practices are
key factors at this level (Klassen & McLaughlin, 1996). Green IS strategies at the function level
determine concrete action plans and affect business and production processes, and since they can
increase the resource efficiency of internal operations, they enhance the firm’s competitiveness (Grant,
1991).
For the conceptualization of these two domains of Green IS strategy, we refer to Chen et al. (2010b),
who review and consolidate the literature on IS strategy from leading IS journals to identify
fundamental IS strategy concepts. Our research uses two of these concepts: “IS strategy as the master
plan of the IS function” and “IS strategy as the shared view of the role of IS within the organization”
(see Chen et al., 2010b, p. 239). These conceptualizations refer to strategy as a plan and as a
perspective (Mintzberg, 1987) and represent two specific facets of a Green IS strategy (Table 3).
Table 3: Conceptualization of Green IS strategy (with reference to Chen et al., 2010b, p. 239).
Second-
order First-
order Conceptual
domain Definition Description
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construct constructs
Green IS
strategy
Organizational
Green IS
strategy
Organization-
wide role of
Green IS;
organization-
centric
Shared view of
the role of
Green IS in the
organization
An organizational Green IS strategy describes a
perspective: What is our view toward Green IS in the
organization?
Desired strategic impact: Provide a shared understanding
of the potential of Green IS throughout the organization and
guide fundamental Green IS investment decisions.
Functional
Green IS
strategy
Intended
course of
action;
IS-centric
Master plan of
the Green IS
function
A functional Green IS strategy describes a plan: What
assets (staff, processes, infrastructure, applications,
budget, etc.) are required for Green IS implementation, and
how should existing assets be allocated efficiently?
Desired strategic impact: Give direction for the effective
and efficient management of IS resources and capabilities.
Green IT practices
Green IT practices decrease the negative environmental effects of the manufacturing, operation, and
disposal of IT equipment and infrastructure (Murugesan, 2008; Dao et al., 2011). Green IT practices,
such as considering eco-labels when purchasing IT hardware, the consolidation and virtualization of
servers and storage devices, the deployment of free cooling, the use of thin clients, and the
refurbishing of computers to extend their lifecycle, are directly related to IT components, devices, and
infrastructure. Building on this definition, the Green IT practices construct covers three kinds of IT
environmental impacts: the resource requirements of manufacturing IT equipment like desktop
computers, notebooks, servers, printers, and network devices; the power consumption by all of the
organization’s IT devices, particularly the servers running in data centers and the desktop computers
and peripheral IT equipment in office environments; and electronic waste generated by disposing of
outdated IT equipment.
To capture these three kinds of environmental impacts, we conceptualize Green IT practices by means
of the sub-constructs IT sourcing, IT operations, and IT disposal (Park et al., 2012) (Table 4). The
basis for our conceptualization was a comprehensive catalogue of seventy exemplary measures
developed by Loeser (2013) to give guidance to practitioners on how to decrease IT-related
environmental impacts using Green IT practices.
Table 4: Conceptualization of Green IT practices.
Second-
order
constructs
First-
order
constructs
Definition Description
Green IT
practices
IT sourcing Environmentally-friendly
sourcing practices for IT
hardware and services
Green IT initiatives that focus on the environmental assessment
and auditing of suppliers and the selection of IT hardware and ser-
vices according to predefined environmental criteria.
IT
operations Green IT practices to
decrease IT operations’
energy consumption
Implementation of Green IT measures in data centers (e.g., server
consolidation and virtualization, energy monitoring, air-flow
optimization) and office environments (e.g., installing energy man-
agement software, raising users’ awareness of environmental
issues, deploying energy-efficient desktop computers) to decrease
IT operations’ energy consumption.
IT disposal End-of-IT-life manage-
ment
Green IT practices that reduce e-waste by repairing, re-deploying,
or disposing of outdated IT hardware in an environmentally friendly
14
manner.
Green IS practices
In contrast to Green IT practices, which are restricted to the domain of IT departments and so are
limited to IT’s environmental impacts, organization-wide Green IS practices refer to the positive
environmental impacts that can be achieved by decreasing the negative environmental effects of
business operations and advancing corporate sustainability (Butler, 2011). Green IS practices relate to
organizational processes that can be enhanced using IS solutions, including those that facilitate the
tracking and improvement of energy and resource flows, industry 4.0 technologies that support smart
factories through cyber-physical processes and the Internet of Things, and environmental technologies
that contribute to eco-products (e.g., building automation, smart grids, traffic-management systems).
Business and production processes’ resource efficiency can be enhanced through IS-enabled process
re-engineering (Seidel et al., 2013), and environmental management systems can quantify emissions
and track resource flows (Corbett, 2010; Malhotra et al., 2013), thereby uncovering opportunities to
reduce business and production processes’ consumption of resources (Benitez-Amado et al., 2010).
Green IS initiatives can also foster innovations that decrease resource consumption, waste, and
emissions during the use phase of end users’ products and services (Albino et al., 2009), thereby
reducing their environmental footprint. The integration of IS functionalities into a company’s
processes can generate innovative end products and infrastructure solutions, such as building
automation, smart-grid technologies, engine-control units, intelligent traffic management systems, and
dematerialization initiatives that substitute physical products with digital services (e.g., books, music)
(GeSI, 2008; Dangelico & Pujari, 2010; Butler, 2011). To capture these kinds of Green IS practices,
we conceptualize Green IS practices by means of the sub-constructs process reengineering,
environmental management systems, and environmental technologies (Table 5).
Table 5: Conceptualization of Green IS practices.
Second-
order
construct
First-
order
constructs
Definition Description
Green IS
practices
Process re-
engineering IS-enabled reengi-
neering of business and
production processes
Green IS practices that enhance the resource efficiency of business
and production processes through IS-enabled process re-engineering
and business transformation.
Environ-
mental
management
systems
Use of IS-based
environmental
management systems
to control resource
flows, waste, and
emissions
Use of IS-based environmental management systems that track
resource flows, waste, and emissions (to provide information for
environmental control and sustainability-oriented decision-making);
enhance transparency; and provide aggregated information for
external stakeholders through environmental reports.
Environ-
mental
technologies
IS-enabled
environmental
technologies that
reduce the footprints of
products and services
Improvement of the environmental characteristics of end products and
services with the help of Green IS, such as smart buildings, traffic
management systems, smart grids, engine control units, and
dematerialization through digital services.
15
Outcomes: Perceived Organizational Benefits
In keeping with the BAO framework, we conceptualize the outcome construct of the present research
as perceived organizational benefits. Building on previous studies, we discussed possible outcomes of
Green IS initiatives: Central benefits can be cost reductions from enhanced resource efficiency of
internal operations, increased revenues from a positive corporate reputation, and technological
innovations that result in eco-products that support competitive differentiation and/or the creation of
new markets (Klassen & McLaughlin, 1996; Chen et al., 2010a; Thambusamy & Salam, 2010; Ziegler
et al., 2011). A company’s ability to differentiate itself from its competitors through innovative eco-
products can increase profit margins if customers perceive and value the products’ superior
environmental characteristics (Aragón-Correa & Sharma, 2003; Albino et al., 2009; Dangelico &
Pujari, 2010). Accordingly, we define organizational benefits as consisting of three dimensions: cost
reductions, corporate reputation, and Green innovation capabilities (Table 6).
Table 6: Conceptualization of organizational benefits.
Second-
order
construct
First-
order
constructs Definition Description
Organi-
zational
benefits
Cost reductions Reduction of
operational costs
through superior
resource
efficiency
Firms’ competitiveness depends on their operational costs. Effective
environmental management systems can track and analyze the flow of
organizational material and resource consumption in order to help
executives identify the optimization opportunities that can be realized with
the aid of environmental process technologies (Klassen & Whybark, 1999).
As a consequence, the raw material requirements and energy
consumptions of business and production processes can be reduced
(Porter & van der Linde, 1995). Internal operations’ enhanced resource
efficiency reduces costs (Ambec & Lanoie, 2008).
Corporate
reputation Positive corporate
image resulting
from effective
environmental
management
Environmental management systems and environmental technologies
enhance the efficiency of internal resources, decreasing resource
requirements, corporate waste, and emissions. Firms that act in an ethical
and environmentally responsible manner (corporate citizenship) can
improve their reputations with internal and external stakeholders. A positive
corporate image increases existing customers’ loyalty and attracts new
ones, increasing sales volumes and profits. A good reputation can also
improve employee retention rates and the firm’s attractiveness to talented
workers, which can also enhance competitiveness (Sharma & Vredenburg,
1998; Bansal & Roth, 2000; Bansal, 2005; Ziegler et al., 2011).
Green
innovation
capabilities
Superior R&D
leads to Green
product and
process
innovations that
differentiate the
firm from
competitors
If managers fundamentally rethink the internal processes that are in place,
they can innovate to enhance company-wide operations’ resource
efficiency (Klassen & Whybark, 1999). Environmental research and
development (R&D) can give rise to Green product innovations that
influence a product’s entire lifecycle, from design to manufacturing to use
and disposal. Customers’ appreciation of products and services with small
environmental footprints is increasing, and they are willing to pay a price
premium for them. The environmental characteristics of these products,
such as lower fuel consumption during the use phase, can differentiate
them from competitors, increasing the firm’s competitiveness, profit
margins, and sales volumes (Klassen & McLaughlin, 1996; Albino et al.,
2009; Chang, 2011).
Proposition Development
Having specified the research constructs that compose our research model, we offer seven propositions
describing the links between environmental orientation, Green IS strategy, Green IT practices, Green
16
IS practices, and organizational benefits. Figure 1 visualizes the propositions, which we discuss in
turn.
Figure 1: Research model.
Congruent with Melville’s (2010) BAO framework, we first suggest that environmental actions on an
organization level (viz., the implementation of Green IS strategy, Green IT practices, and Green IS
practices) are driven by the formation of organizational sustainability beliefs. Specifically, in line with
Banerjee et al. (2003), we argue that IT executives' environmental orientation drives their
sustainability-related actions, such as the formulation of environmental strategies and the
implementation of concrete environmental practices. Organizations develop responses to
environmental issues when executives identify economic opportunities, legislation, and eco-
responsibility as salient issues (Bansal & Roth, 2000), at which time the formation of a Green IS
strategy translates its executives’ beliefs about environmental issues (i.e., their orientation) into both
an organizational perspective and a master plan. If executives recognize the importance of the
environmental issues that face their firm, it is more likely that a corresponding strategy will be
formulated in response (Sharma, 2000). Therefore:
P1: Environmental orientation is positively associated with the formulation of a Green IS
strategy.
Similarly, the implementation of Green IT practices and Green IS practices depends on executives’
recognition of the need to respond to environmental issues because action occurs only when the actor
recognizes that an event requires response. For example, the adoption of a social networking site that
encourages energy conservation depends on the presence of a belief that reducing greenhouse gas
emissions is critical to sustainability (Bottrill, 2007) and that sustainability is a desirable outcome.
Seidel et al. (2013) show that sustainability-related action in an organization is supported when
environmental beliefs are shared through systems that facilitate the democratization of information and
reflective disclosure. Therefore, we contend that similar influences motivate the implementation of
both Green IT practices and Green IS practices in an organizational initiative:
Environmental
Orientation
Beliefs
Green IT
Practices
Green IS
Strategy
(+)
P2
(+)
P3
Actions
(+)
P1 Organizational
Benefits
Outcomes
(+)
P7
(+)
P6
Green IS
Practices
(+)
P5
(+)
P4
(+)
P7
17
P2: Environmental orientation is positively associated with the implementation of Green IT
practices.
P3: Environmental orientation is positively associated with the implementation of Green IS
practices.
Uncoordinated environmental sustainability initiatives that involve the implementation of new
practices that have no strategic coherence are ineffective (Orsato, 2006) because such implementations
rely on managerial interpretation, strategy, and policy definition (Bansal & Roth, 2000). The
formulation of environmental strategy has been shown to positively influence the execution of an
organization’s eco-friendly initiatives (Ramus & Steger, 2000). In the same manner, Green IT
practices and Green IS practices effectively describe change resources in the form of dynamic
capabilities that help a company to become more environmentally sustainable, so they describe
capabilities that facilitate the effective and efficient use of IS and the firm’s assets (Watson et al.,
2008). A Green IS strategy features and defines the characteristics of such capabilities and facilitates
the effective deployment, combination, and efficient management of the firm’s technological
infrastructure (Green IT practices), as well as IS-based new environmental practices that change
processes, management, and/or the environmental characteristics of technology use. Therefore, we
expect that:
P4: Green IS strategy is positively associated with the implementation of Green IT
practices.
P5: Green IS strategy is positively associated with the implementation of Green IS
practices.
Finally, we explore the links between organizational actions and outcomes. Green IT practices can
decrease the electricity costs of IT operations, whereas Green IS practices can reduce business and
production processes’ consumption of resources through efficiency enhancements facilitated by
process re-engineering. Moreover, Green IS practices can lower compliance costs by delivering
necessary information through environmental management systems. Green IS can also deliver tools
that help a firm to implement environmental management that improves the firm’s image and
reputation. Finally Green IS practices can lead to environmental technology innovations that alter the
firm’s products and services (Corbett, 2010). Therefore, we expect the implementation of Green IT
practices and Green IS practices to have organizational benefits as perceived by the IT executives.
Accordingly, we suggest that:
P6: The implementation of Green IT practices is positively associated with organizational
benefits.
P7: The implementation of Green IS practices is positively associated with organizational
benefits.
18
Like our research model, these seven propositions remain on an abstract level of theorizing and
encapsulate our general expectations of links. However, our construct definitions, most of which
involve multiple dimensions, allow us to evaluate the propositions empirically, which is important in
our effort to ascertain which Green IT or Green IS practices in particular depend most and least on a
Green IS strategy and which practices lead to which types of organizational benefits. We report on the
detailed evaluations of our propositions in the results section below.
RESEARCH METHOD
Design
To evaluate the propositions in our research model, we designed a global, cross-sectional survey
targeting senior-level IT executives as respondents. This choice was motivated by three main
conclusions from our literature review: The need in research and practice for abstract rather than
substantive-level contributions, a lack of global Green IS studies in IS research that rely on
quantitative data, and the need for an organization-level research model that builds on practice-
oriented concepts and delivers meaningful insights. In addition, we recognize that senior-level IT
executives have sophisticated knowledge regarding the formulation of Green IS strategies, the
implementation of Green IT practices and Green IS practices, and outcomes at an organization level,
so these respondents are a suitable target population for our research model.
The survey method is appropriate when there are clearly identified independent and dependent
variables and a model that theorizes the relationships between the variables (Pinsonneault et al., 1993).
Such is the case in our study. We collected data using a web-based instrument because of the
advantages of low cost, no geographical restrictions, and fast responses (Klassen & Jacobs, 2001).
However, web-based surveys also have disadvantages, such as low response rates, as was an issue in
our study.
In designing the survey, we followed Fowler’s (2009) recommendations in using Dillman’s (2007)
Tailored Design Method. In particular, we sought to create valuable rewards for the respondents by
assembling an executive-oriented management summary and an extensive catalogue of
implementation measures and keeping the efforts required to participate at a minimum by developing a
user-friendly online survey with a clear structure, graphic elements, and completion time of less than
ten minutes. We also emphasized the study’s importance in advancing Green IS research and practice,
pointing out the benefits that organizations could achieve by implementing Green IS following a
strategic approach.
19
Measurement
Because measurements for our thirteen theoretical constructs were not always available or suitable, we
created new measurement instruments, where needed, using Lewis et al.’s (2005) and MacKenzie et
al.’s (2011) guidelines. Because of methodological considerations (Gefen et al., 2000;
Diamantopoulos & Siguaw, 2006) and the availability of some empirically validated measures from
prior research, all thirteen first-order constructs where measured reflectively using a common 7-point
Likert scale, anchored between “strongly disagree” and “strongly agree.”
In developing the measures, we first identified potential items for the instrument by reviewing
empirical studies that included similar research constructs, as recommended by Urbach and Ahlemann
(2010). Although we could not adapt complete measurement scales to our constructs, 248 fragments
and single items suited the research model. Next, we analyzed the initial list of 248 items and found
that they did not cover all sub-dimensions of the constructs’ domains. In particular, several aspects of
the Green IS strategy, Green IT practices, and Green IS practices constructs were missing. Therefore,
we developed 22 new items based on our construct definitions and descriptions (Lewis et al., 2005;
MacKenzie et al., 2011). Then we analyzed the quality and appropriateness of the 270 selected items;
three researchers from non-IS disciplines, each with a profound knowledge of quantitative studies and
significant experience with SEM research, provided critical feedback, in response to which we revised
several items. Next, a panel of five IS researchers, all of whom were familiar with the key subject
areas, participated in a rating procedure (MacKenzie et al., 2011) to reduce the number of items to 89.
Then we pretested the measurement instrument with eleven researchers and practitioners who were
familiar with the research topic to evaluate its appropriateness (Lewis et al., 2005). The pretest and
subsequent feedback helped to improve the structure and the design of the survey. Because the
participants in the pretest criticized the length of the survey, we undertook another round of item
screening in which the five IS research panelists once again evaluated the items’ relevance using the
content-validity-ratio method Lewis et al. (2005) propose. Based on this assessment, the final
instrument contained 50 items (Appendix A, Table 10). Next, we discuss how we measured each
construct.
We operationalized environmental orientation as a reflective first-order construct that we measured
reflectively (Mode A, according to Becker et al., 2012) with four items (Table 10 in Appendix A).
These items capture, in particular, organizational (executives’ and employees’) positions, goals,
values, and identities regarding environmental protection.
Our definition of the concepts Green IS strategy (Table 3), Green IT practices (Table 4), Green IS
practices (Table 5), and organizational benefits (Table 6) featured sub-dimensions. Operationalizing
each concept as a multidimensional, hierarchical construct allowed us to retain a relatively
parsimonious research model while maintaining a high level of detail for supplementary analysis. In
20
modeling our four higher-order constructs, we followed the guidelines Becker et al. (2012) propose.
We applied the recommended repeated-indicator approach, using measurement Mode B, for our four
reflective-formative second-order constructs. We evaluated our construct models with PLS-SEM,
using the inner-path weighting scheme, as Wetzels et al. (2009) recommended.
We modeled Green IS strategy as a reflective-formative second-order construct with the underlying
dimensions organizational Green IS strategy and functional Green IS strategy (Table 3). Each
dimension describes one defining characteristic of the overarching Green IS strategy construct but
they represent two distinct facets of strategy (i.e., as an organization-wide perspective and as a
functional plan). Both dimensions, which we measured with five reflective items each, influence the
second-order construct and were modeled through formative relationships accordingly (Petter et al.,
2007; Urbach & Ahlemann, 2010).
We also modeled Green IT practices as a reflective-formative second-order construct (Becker et al.,
2012). It consists of practices that address four different IT management areas and thus facets of the
construct (viz., IT sourcing, data center operations, IT operations in the office environment, IT
disposal). We measured each of the four first-order constructs using three reflective items.
In the same way, we modeled the construct Green IS practices as a reflective-formative second-order
construct that is influenced through the three reflectively measured first-order constructs of process
reengineering (5 items), environmental management systems (4 items), and environmental
technologies (3 items). For this construct, we used the repeated indicator approach with an inner path
weighting scheme and Mode B measurement because it is robust in models that have an unequal
number of items (Becker et al., 2012).
Finally, we modeled organizational benefits also as a reflective-formative second-order construct. We
defined it through the three sub-dimensions cost reductions, corporate reputation, and Green
innovation capabilities. We measured the underlying first-order constructs empirically with three
reflective items each (Petter et al., 2007; Becker et al., 2012).
Procedures
We conducted the online survey using the open-source software LimeSurvey. We defined our target
population as large companies from highly developed countries, so we invited CIOs and similar
senior-level IT executives from companies with more than 250 employees in the US, Canada,
Germany, Australia, and New Zealand to participate in the survey. This range of countries ensured that
we had data from North America, Europe, and the Asia-Pacific region.
We used a database of 6,546 contact records for CIOs and senior-level IT executives that we acquired
from the Top IT Executives Database (5,899 records) (Applied Computer Research, Inc.), OneSource
21
Australia (384 records), and our own research on CIOs of large German enterprises (263 records).
This sample features a distribution of companies in terms of size that is similar to the target population
(Table 11 in Appendix B). After sending the initial invitation, we followed up with four rounds of
reminders, each with different formulations of the invitation text, to improve the response rate (Sivo et
al., 2006). Our emails were undeliverable to 29.3 percent of the email addresses. Of the 4,628
invitations that were delivered, we received 169 responses for a response rate of 3.65 percent.
Although this response rate is low, the number of responses is comparable to the number of responses
used in previous Green IS and Green IT research published in top-tier IS journals (Table 1). To put our
sample size in context, we performed a ten-year analysis of the sample sizes of IS studies that focus on
organizational benefits and were published in the basket of eight IS journals. We identified twenty-
five articles with numbers of responses ranging from 59 to 372 (median 144 and mean 162; Table 13
in Appendix B), suggesting that the number of observations in our study is within the norm of similar
IS studies. We also performed analyses of statistical power to ensure our sample size was sufficient to
run the analyses required.
To show that our study is comparable to other organizational performance-related IS studies in spite of
the limitations and future enhancements that we discuss in the Limitations section, we reviewed the
empirical base of the twenty-five articles we identified (Table 13) and found four primary features:
First, some studies include a wide variety of industries in their sample frames (Ravichandran &
Lertwongsatien, 2005); only a few studies focus on a specific industry segment (Pavlou & El Sawy,
2006; Ray et al., 2005). Second, most of the firms investigated were medium-sized or large. Third,
nearly all studies are conducted in North America, especially in the US. Fourth, previous researchers
follow both single- and multiple-respondent designs. By contrast, in our study we examine the
concepts of Green IS strategies and practices and their potential to deliver organizational benefits in
general. Because these concepts are recognized as being important to all industries (Melville et al.,
2010), we included all industries in the sample selection. However, similar to previous studies, we
focused on medium-sized to large organizations.
One of the advantages of the sampling frame used in the current study was that, unlike some previous
studies that mix single-informant IT and non-IT respondents, we exclusively address IT executives.
We focus on senior IT executives like CIOs because they tend to be knowledgeable about the issues
with which we are concerned. In addition, they tend to be well-versed not only in the organizational
capabilities and benefits that pertain to IT but also in managing customer and enterprise processes to
improve business performance, including environmental initiatives and corporate responsibility (Weill
& Woerner, 2013). Most CIOs are also involved in processes related to formulating organizational and
business strategy. Many of them report directly to CEOs, and most interact with business managers
while maintaining a focus on the overall business (Kappelman et al., 2014). These commonalities
indicate that CIOs are knowledgeable about questions of organizational benefits and business
22
performance. Our focus on IT executives also helps us avoid conflicts caused by multiple responses
from the same company and alleviates complexity during data analysis. Because our study design
might raise questions about common method bias, we sought to determine whether common method
bias is a major problem in interpreting our results.
RESULTS
In reporting our results, we proceed in three steps. First, we report on measures taken to screen and
purify the data and to assess potential sources of bias. Second, we report on measurement and
structural model estimation using structural equation modeling (SEM) (Hair et al., 2011). Third, we
report on selected supplementary analyses in order to examine parts of our results in more detail.
We opted for partial least squares structural equation modeling (PLS-SEM), which is an established
technique in IS and strategic management research (Hair et al., 2013a). PLS-SEM is particularly
useful in exploratory research settings, where the identification of relationships is the central purpose
(Goodhue et al., 2012; Ringle et al., 2012). This focus was particularly relevant to our goals of
evaluating our propositions in general and exploring important links between core concepts in detail.
In addition, as a component-based approach, PLS-SEM is appropriate and often used to test higher-
order constructs and complex research models (Urbach & Ahlemann, 2010; Ringle et al., 2012).
PLS-SEM is comprised of two levels of analysis: the measurement model, which evaluates the latent
constructs’ measurement scales, and the structural model, which assesses the direction and strength of
the relationships between the constructs (Gefen et al., 2000). Our PLS-SEM evaluation and our
detailed report of the analysis’ statistics and quality criteria follow Gefen et al.’s (2011) and Hair et
al.’s (2013a) established guidelines.
Data Screening
We received 169 survey responses, of which 48 were incomplete. Analysis of the standard deviation
of the responses revealed 3 more data sets that included numerous arbitrary answers and so were
invalid. We searched for multivariate outliers by calculating the Mahalanobis d-squared values with
SPSS and found that the remaining datasets were all within an acceptable range, resulting in a final
sample of 118 valid datasets. Table 7 summarizes the descriptive statistics of the organizations whose
employees participated in the survey.
23
Table 7: Characteristics of respondents’ organizations [n = 118].
Annual company revenues
[million USD] Annual IT budget [million
USD] Number of
employees Number of
IT staff
< 50 22% < 1 13% 251 – 1,000 40% < 10 13%
50 – 250 32% 1 – 5 36% 1,001 – 5,000 25% 11 – 50 42%
251 – 1,000 17% 5.1 – 25 20% 5,001 – 25,000 27% 51 – 250 20%
1,001 – 5,000 17% 25.1 – 100 21% 25,001 – 100,000 5% 251 – 1,000 20%
5,001 – 25,000 8% 100.1 – 500 8% > 100,000 3% > 1,000 5%
> 25,000 4% > 500 2%
Our response rate of 3.65 percent is low, although we followed Sivo et al.’s (2006) suggestions to
increase response rates by considering feedback from colleagues and practitioners, continually
improving invitation mails, sending several rounds of reminders, guaranteeing confidentiality, and
providing an incentive for survey participants in the form of a management summary and an extensive
catalogue of Green IT/IS measures. As Abareshi and Martin (2008) explain, it is often difficult to get
top managers to respond to survey requests. One reason for the low response rate is likely to have been
our sending the invitations and follow-ups via email. Ranchhod and Zhou (2001) emphasize that
online surveys tend to have lower response rates than mail surveys do. Senior-level executives are
unlikely to respond to outside emails from unknown addresses and response rates to reminder emails
are likely to decrease (Baruch & Holtom, 2008). In short, low response rates are not unusual for
surveys that address senior executives (Anseel et al., 2010; Messerschmidt & Hinz, 2013).
To ensure that our dataset still demonstrated external validity, we examined our data for nonresponse
bias using three established post-hoc techniques to assess the possibility of a nonresponse bias (Sivo et
al., 2006). First, we compared the responses from the first wave of participants to those from the last
wave of respondents using a two-tailed test (Armstrong & Overton, 1977; Rogelberg & Stanton, 2007;
Gefen et al., 2011) and found that the test was not significant at the 0.05 level. Second, we compared
the demographic characteristics of the respondents’ organizations to those of the overall sample from
our contact record database (Sivo et al., 2006) using a chi-squared test of homogeneity, which did not
indicate a significant difference in company-size distributions between the expected observations and
the observed responses at the 0.05 level (Table 11 in Appendix B). Third, we contacted 100 randomly
selected non-respondents to learn their reasons for not participating in the survey, which is an
established method for determining whether relevant patterns of nonresponse reasons emerge
(Ravichandran & Rai, 2000; Rogelberg & Stanton, 2007). If dominant reasons for nonresponse are
related to the topic of the survey (e.g., systematic disregard of environmental sustainability issues),
participants’ responses would differ from non-respondents, indicating a biased sample. Table 12 in
Appendix B shows that 96 percent of the reasons for nonresponse were unrelated to the topic of the
survey. Based on the results of these tests, there is no indication of a nonresponse bias, and we
assumed that the dataset has external validity, despite a low response rate.
24
Next, we determined whether our sample of 118 datasets is large enough to test our structural model.
Leading researchers and statisticians (Marcoulides & Saunders, 2006; Goodhue et al., 2012; Hair et
al., 2013b) recommend conducting an assessment to ensure that the statistical power of the sample is
sufficient to ensure statistical validity of the conclusions reached, which “concerns the power to detect
relationships that exist and determine with precision the magnitude of these relationships” (Sivo et al.,
2006, p. 354). Wetzels et al. (2009) explain that the convention for behavioral research is to use a
value of 0.80 for power. Marcoulides et al. (2009) advise using Cohen’s (1988) power tables to
evaluate the number of predictors and the effect size of each of the structural model’s multiple
regression analyses to calculate the statistical power. Following this method, we calculated that, with n
= 118 and a maximum of seven predictors, we achieve the required statistical power of 80 percent for
effect sizes larger than or equal to 0.18, with an error probability of less than 1 percent (GPower
calculator, as suggested by Hair et al., 2013b). Therefore, our sample has sufficient statistical power
for our conclusions to be valid for all effect sizes that are larger than or equal to 0.18. We also
conducted a more rigorous test that takes additional parameters of the entire model into account
(http://www.danielsoper.com, as proposed by Gefen et al., 2011). This test is based on the work of
Westland (2010, p. 476), who proposes “two lower bounds on sample size in SEM, the first as a
function of the ratio of indicator variables to latent variables, and the second as a function of minimum
effect, power and significance.” According to this sophisticated SEM evaluation of sample size, we
achieve statistical power of 80 percent for effect sizes larger than or equal to 0.25 in our model with
fifty observed and thirteen latent variables. Therefore, we can conclude that the sample has adequate
power to detect medium to large effects, which is acceptable for exploratory research that seeks to
identify the relationships between theoretically derived constructs (Pinsonneault & Kraemer, 1993;
Gefen et al., 2011; Goodhue et al., 2012). Our post-hoc power analysis simulation, reported in the
structural model estimation section, supports this assertion.
To assess the potential for common method bias, which would indicate a systematic error of
measurement, we conducted Harman’s single-factor test, which involves performing an exploratory
factor analysis in SPSS with all independent and dependent variables and analyzing the unrotated
solution (Podsakoff et al., 2003). The first factor that emerged explained 21.98 percent of the total
variance. Since the first factor does not explain the majority of the variance, a common method bias is
unlikely (Gefen et al., 2011). Because Harman's single factor test has several methodological
shortcomings (Podsakoff et al., 2003), we also conducted a second test by including a common
method factor in the PLS model, as Liang et al. (2007) describe. This test revealed that the average
substantively explained variance of the indicators is 0.72, whereas the method-based variance is only
0.014, making the ratio of substantive variance to method variance 51:1. This high value suggests the
absence of significant common method bias.
25
Measurement Model Estimation
We used PLS-SEM, as implemented in SmartPLS 2.0 (Ringle et al., 2005), to assess both the
measurement model and the structural model. We first evaluated the properties of the first-order
constructs, followed by the properties of the second-order constructs (Jarvis et al., 2003).
We discarded four measurement items because their loadings were lower than 0.707 (Chin, 1998)
(ITC1: 0.683; ISR5: 0.596; IST1: 0.666; OBR5: 0.560; Table 10 in Appendix A). It was possible to do
so without significantly affecting our results because all constructs but one (organizational benefits –
corporate reputation) retained three or more items, and all constructs showed improved reliability and
validity after we deleted the items. Next, we analyzed the cross-loadings of the remaining forty-six
measurement items and found that all items exhibited higher loadings on the constructs they were
intended to measure than they did on any other constructs (Table 14 in Appendix C). The AVEs are
higher than 0.6 for all constructs, pointing to a high convergent validity. With a minimum of
composite reliability of 0.84, all constructs’ composite reliability was well above the 0.7 threshold,
which indicates internal consistency reliability (Table 15). To determine discriminant validity, we
checked whether each construct shared more variance with its assigned measurement items than did
any other construct (Table 16). The Fornell-Larcker criterion, which requires the average variance
explained (AVE) of each latent construct to be greater than the construct’s highest squared correlation
with any other construct, was met for all constructs (Table 16).
Next, we evaluated the higher-order constructs (Hair et al., 2013a). As Becker et al. (2012)
recommend, we used the repeated-indicator PLS-SEM approach to model the second-order reflective-
formative constructs in SmartPLS 2.0. To evaluate the constructs, we assessed the path coefficients
between the lower-order latent variables and the higher-order constructs (PLS algorithm, path
weighting scheme, bootstrapping with 118 cases and 1,000 re-samples). All paths from the first- and
second-order sub-constructs to the higher-order constructs showed weights considerably above the 0.2
threshold (Chin, 1998; Urbach & Ahlemann, 2010), and the positive relationships were significant at
the 0.001 level (Table 17). To identify possible multicollinearity between the formative indicators, we
evaluated the Variance Inflation Factor (VIF) statistics with SPSS (Table 17). All VIFs were 2.3 or
smaller, well below recommended cut-off of 3.3 (Diamantopoulos & Siguaw, 2006; Petter et al., 2007;
Hair et al., 2011). The significance of the paths from lower-order to higher-order constructs and the
low multicollinearity between the indicators demonstrate that the chosen lower-order constructs
represent distinct facets of the higher-order constructs (Becker et al., 2012).
Structural Model Estimation
Next, we evaluated the structural model using a PLS algorithm (SmartPLS 2.0, path weighting
scheme) to estimate the predictive power of the model and analyzed the significance of the path
26
coefficients with a bootstrapping procedure (118 cases and 1,000 re-samples). The results are shown in
Figure 2.
Figure 2: Assessment of the structural model with PLS-SEM (n = 118).
The model explains 40.9 percent of the variance of the dependent outcome variable organizational
benefits through the endogenous latent variables Green IT practices and Green IS practices. 41
percent of the variance in Green IS strategy is explained by environmental orientation. More than 52
percent of the variance of Green IT practices and more than 62 percent of the variance of Green IS
practices are explained through Green IS strategy and the exogenous latent variable environmental
orientation. These R2 values fall within the range of moderate to substantial power to explain the
endogenous variables.
Using one-tailed tests based on a bootstrapping procedure, we evaluated our propositions by
examining the significance and weights of the paths in the structural model. P1, P3, P4, P5, and P7
receive significant support from the data at the p < 0.001 level. The path proposed in P2 is significant
at the p < 0.05 level. We rejected only P6 because the path from Green IT practices to organizational
benefits is not significant (p > 0.05).
We estimated the effect sizes (f²) to determine the relative contributions of each path (Liang et al.,
2007). According to Cohen (1988), f² values from 0.02 to 0.13 are small effect sizes, those from 0.13
to 0.26 are moderate effect sizes, and values greater than 0.26 are large effect sizes (Wetzels et al.,
2009). The results show that environmental orientation has a large effect on Green IS strategy (f² =
0.69) but only a small effect on Green IT practices (f² = 0.04) and Green IS practices (f² = 0.10).
According to the PLS model, Green IS strategy has a large effect on Green IT practices (f² = 0.41) and
on Green IS practices (f² = 0.55). Green IS practices have a large effect on the creation of
organizational benefits (f² = 0.19), but Green IT practices have a minor effect (f² = 0.04).
In order to establish the adequacy of our sample size (n = 118) to detect the effect sizes obtained in the
PLS analysis with acceptable power (0.80), we conducted a post-hoc power-analysis simulation
Environmental
Orientation
Beliefs
Green IT
Practices
R² = 0.526
Green IS
Strategy
R² = 0.410
0.202*
0.258***
Actions
0.640*** Organizational
Benefits
R² = 0.409
Outcomes
(+)
P7
0.182
ns
Green IS
Practices
R² = 0.621
0.598***
0.579***
0.497***
Significance level
*0.05
** 0.01
*** 0.001
27
following Aguirre-Urreta and Rönkkö’s (2015) simulation procedure using R. We obtained the factor
loadings for the items measuring each of the constructs, the path coefficients, and residual values from
the PLS run in Figure 2. We used a sample size of 118, 1000 converged replications, and 500
bootstrapping resamples for the simulation. The simulation analysis assumed a normal distribution to
generate the sample data. The results are summarized in Table 8.
Table 8: Power analysis results based on Aguirre-Urreta and Rönkkö’s (2015) methodology.
Path Parameter R
Simulation
Statistical power greater
than 0.8 (n = 118)?
P1: Environmental Orientation Green IS Strategy 0.640 1.000 Yes
P2: Environmental Orientation Green IT Practices 0.202 0.678 No
P3: Environmental Orientation Green IS Practices 0.258 0.929 Yes
P4: Green IS Strategy Green IT Practices 0.579 1.00 Yes
P5: Green IS Strategy Green IS Practices 0.598 1.00 Yes
P6: Green IT Practices Organizational Benefits 0.182 0.536 No
P7: Green IS Practices Organizational Benefits 0.497 1.000 Yes
The result of the simulation shows that our sample size is adequate to detect all but two paths with
adequate power. For P2 and P6, the powers were only 0.678 and 0.536, respectively. The non-
significant finding about P6 should be interpreted with caution since our sample lacks sufficient power
to identify a true relationship if it existed.
Supplementary Analyses
We carried out four additional analyses to examine our results in greater detail.
First, we examined the suggested mediations in our model by assessing the type of mediation between
environmental orientation and Green IT/IS practices using Zhao et al.’s (2010, p. 201) decision tree
for establishing and understanding mediation and non-mediation. Our original empirical results
indicated a minor effect between environmental orientation and Green IT practices, although the path
between the two is significant (β = 0.202; p < 0.05; f² = 0.04). By comparison, the influence of
environmental orientation on Green IS strategy is much more pronounced (β = 0.640; p < 0.001; f² =
0.69), so environmental orientation, which influences executives’ beliefs, has a strong effect on the
formulation of Green IS strategies. The impact of Green IS strategy on the implementation of Green
IT practices is large (β = 0.579; p < 0.001; f² = 0.41).
Equally, our data suggest only a minor effect of environmental orientation on Green IS practices (β =
0.258; p < 0.001; f² = 0.10) while Green IS strategy has a large effect on the implementation of Green
IS practices (β = 0.598; p < 0.001; f² = 0.55).
28
These results suggest a linkage from environmental orientation to Green IS strategy through to Green
IT practices and Green IS practices respectively. We used Zhao et al.’s (2010) procedure, which is
based on Preacher and Hayes’ (2004) syntax, to examine the proposed mediation effects. Results are
summarized in Table 9.
Table 9: Mediation tests based on Zhao et al. (2010).
Mediation test Unstandardized
coefficient a
Unstandardized
coefficient b
Unstandardized
coefficient c
Mean
value a × b 95% CI
Environmental Orientation
Green IS Strategy
Green IT Practices
0.640 0.579 0.573 0.370 Lower Bound: 0.255
Upper Bound: 0.499
Environmental Orientation
Green IS Strategy
Green IS Practices
0.640 0.598 0.641 0.383 Lower Bound: 0.275
Upper Bound: 0.514
For the mediation path environmental orientation Green IS strategy Green IT practices, the
results show that, before Green IS strategy is introduced as a mediator, environmental orientation has
a significant total effect on Green IT practices (coefficient = 0.573; t = 7.52; p < 0.001). When Green
IS strategy is introduced as the mediator, environmental orientation does not have a significant direct
impact on Green IT practices (coefficient = 0.202; t = 2.42). At the same time, the indirect effect of
environmental orientation on Green IT practices through Green IS strategy is 0.370 with 95 percent
bootstrap confidence intervals (CI) of 0.255 and 0.499. Since this CI does not contain zero, the indirect
effect is significantly different from zero. In addition, because the original direct path between
environmental orientation and Green IT practices is significant and the product a × b × c is positive,
the type of mediation can be classified as complementary (Zhao et al., 2010).
For the mediation path environmental orientation Green IS strategy Green IS practices, the
results show that the direct effect of environmental orientation on Green IS practices is significant
(coefficient = 0.641; t = 8.988; p < 0.001). When the mediator is included, environmental orientation
retains a significant direct impact on Green IS practices (coefficient = 0.258; t = 3.457; p < 0.001).
The indirect effect of environmental orientation on Green IS practices through Green IS strategy is
0.383. Because the CI does not contain zero and the product a × b × c is positive, the type of mediation
can also be classified as complementary (Zhao et al., 2010). Both types of mediation are consistent
with the proposed research model. These results suggest that executives’ environmental orientation is
more likely to lead to environmental actions in the form of Green IT practices and Green IS practices
when Green IS strategies have been formulated than when they have not.
The second of the four additional analyses we carried out was an examination of a deconstructed first-
order structural model. While hierarchical construct models are established in quantitative IS research
(Ringle et al., 2012), they are also criticized because detailed information may be lost when constructs
29
are aggregated to a higher level (Wright et al., 2012), so we also examined the deconstructed first-
order structural model. Table 20 and Table 21 in Appendix D summarize these results, which are
consistent with our main results (Figure 2).
Third, given the notable scarcity of empirical research on Green IS initiatives and outcomes, we
relaxed the assumption that there is only one model that fits the data and tested meaningful variants of
our structural model (Evermann & Tate, 2011). In comparing our proposed model to alternative
models, we evaluated the predictive relevance of the structural model by comparing the cross-
validated redundancies of the latent variables (Ringle et al., 2012) through the Q-square statistic
(Sharma & Kim, 2012). In the original model, the Q-square values of all endogenous latent variables
are considerably larger than zero (Green IS strategy: Q² = 0.2681; Green IT practices: Q² = 0.2098;
Green IS practices: Q² = 0.3373; organizational benefits: Q² = 0.1943). That the values in the
alternatively tested models are considerably lower indicates that the proposed research model is
preferable.
Fourth, we compared the variations in our model results to organization-level variations. In particular,
we sought to determine whether responses about environmental orientation, Green IS strategy, Green
IT practices, Green IS practices, and reported organizational outcomes varied between organizations
of differing sizes. To that end, we compared the variance in the latent variable scores for all first- and
second-order constructs between respondents, grouped by reported number of employees and reported
annual IT budget, two indicators of an organization’s size. Results from this test are summarized in
Table 23. No significant variances were detected for either grouping, suggesting that the results
obtained are robust against variations in number of employees and IT budget size.
Detailed Exploration of Propositions
Having formulated our propositions on a general level between our higher-order constructs, we now
explore the links in our structural model in more detail.
First, we examine the paths between the first-order sub-constructs of Green IS strategy (organizational
Green IS strategy and functional Green IS strategy) and the implementation of Green IT practices and
Green IS practices. Figure 3 shows a detailed view of the links in this part of the structural model
(overview in Figure 2). We omitted the insignificant paths in the interest of clarity.
30
Figure 3: Detailed view of the links between Green IS strategy and Green IT/IS practices.
As Figure 3 illustrates, organizational Green IS strategy has a significant effect on the implementation
of Green IS practices, whereas functional Green IS strategy significantly impacts Green IT practices.
Functional Green IS strategies address primarily issues that are bound to the IT domain, so these
strategies articulate plans that seek to decrease the direct environmental impacts of IT manufacturing,
IT operations, and IT disposal. The relevant implementation measures in this area refer to Green IT
practices. On the other hand, organizational Green IS strategies refer to high-level understanding of
the potential of IS to decrease organization-wide and product-related emissions through the use of
Green IS. The empirical results confirm that organizational strategies promote the implementation of
cross-functional Green IS practices; apparently, functional strategies are not appropriate for the
consistent implementation of Green IS since these strategies are restricted to the boundaries of the IT
domain. This conclusion underscores the importance of organizational strategies, which are a necessity
if the enterprise-wide potential of Green IS is to be realized.
Next, we examine in detail the effects of Green IT practices and Green IS practices on the creation of
organizational benefits. Figure 4 provides a more detailed view of the results in Figure 2. Green IT
practices have a moderate positive effect on cost reductions (β = 0.412; p < 0.001; f² = 0.11), while
their impact on corporate reputation and Green innovation capabilities is not significant. By contrast,
Green IS practices do not have a significant relationship with cost reductions but have a large effect
on corporate reputation (β = 0.572; p < 0.001; f² = 0.25) and a moderate effect on Green innovation
capabilities (β = 0.459; p < 0.001; f² = 0.14).
Organizational
Green IS
Strategy
Green IS
Practices
R
2
= 0.622
Green IT
Practices
R
2
= 0.541
0.321**
Significance level: not significant / * = 0.05 / ** = 0.01 / *** = 0.001
Functional
Green IS
Strategy
0.468***
31
Figure 4: Detailed view of the links between Green IT/ IS practices and organizational benefits.
For both detailed views, we ran additional R simulations (Aguirre-Urreta & Rönkkö, 2015) to
determine the power of the sample size to detect each path shown. The sample was adequate to detect
nine of the paths (Table 22 in Appendix D) including the two significant paths illustrated in Figure 3
and the three in Figure 4
Overall, our empirical analysis supports the proposition that Green IS initiatives can have
organizational benefits (e.g., Brooks et al., 2010; Benitez-Amado & Walczuch, 2011). We also find
support for the proposition that certain practices result in cost reductions, improved corporate
reputation, and/or Green innovation capabilities (Thambusamy & Salam, 2010; Corbett, 2010; Dao et
al., 2011). Specifically, our empirical results reveal a significantly positive relationship between Green
IT practices (IT sourcing, operations, and disposal) and cost reductions. Our results also demonstrate a
significant impact of Green IS practices (process re-engineering, environmental management systems,
and IS-enabled environmental technologies) on corporate reputation and environmental innovation
capabilities. These empirical insights offer meaningful contributions to research and executives, which
we discuss below.
DISCUSSION
The four key contributions from our work are (1) a new conceptual model of organizational benefits
accruing from Green IS initiatives; (2) a definition of the Green IS practices and Green IT practices
constructs, together with the development of a measurement instrument for these constructs; (3) an
empirical demonstration of the benefits that can be derived from Green IS initiatives as well as the
mechanisms that achieve these benefits; and (4) exemplary content and scope for the design of Green
IS strategies and the cultivation of environmentally sustainable organizational and technology
Green IT
Practices
Corporate
Reputation
R
2
= 0.354
Cost Reductions
R
2
= 0.215
0.412***
Green Innovation
Capabilities
R
2
= 0.239
Green IS
Practices 0.459***
0.572***
Significance level: not significant / * = 0.05 / ** = 0.01 / *** = 0.001
32
practices. We elaborate on each of these key contributions and discuss their implications for
researchers and practitioners.
A New Conceptual Model for Analyzing Organizational Benefits of Green IS
We set out to investigate the antecedents of Green IS initiatives and the benefits these initiatives might
provide to organizations. Although some frameworks, such as Melville’s (2010) BAO, are useful, they
don’t provide a specific or focused conceptual lens with which to explain both the antecedents and the
outcomes of Green IS investments. Our study provides a new conceptual model that builds a
nomological net of environmental orientation, Green IS strategy, Green IT practices, Green IS
practices, and organizational benefits. This model is important to the body of knowledge in IS
research, particularly to the relatively new area of Green IS research, as it provides the conceptual
foundation that defines the relationship between environmental actions in the form of Green IS
initiatives and the creation of organizational benefits as outcomes. Prior research has focused primarily
on the environmental benefits dimension of Green IS initiatives (e.g., Chen et al., 2008; Melville,
2010; Watson et al., 2010; Butler, 2011) but has devoted little study to t economic benefits. We add to
this a focus on economic dimensions of benefits, such as cost reductions and corporate reputation).
Thereby, our study offers a unique, logical chain from environmental orientation, which affects
executives’ beliefs and decision-making processes, to the formulation of Green IS strategies and the
implementation of Green IT/IS practices, through to both environmental and economic benefits. The
organizational-level environmental benefits, particularly those related to Green innovations,
demonstrate how organizational actions could lead to potential positive effects on the environment. It
also helps to address business concerns in achieving improved environmental sustainability through
product and process innovations that reduce negative environmental footprint. The addressed
economic benefit dimensions (cost reduction and corporate reputation) help clarify the relationship of
these benefit dimensions and how each is anchored in Green IS initatives. These findings are thus one
step further towards supporting organizational efforts to address environmental problems without
trading off cost reduction and brand reputation concerns of businesses.
Definition of the Green IT Practices and Green IS Practices Constructs and their Measurement
We reviewed the definitions of Green IT and Green IS prior to conceptualizing Green IS strategy,
Green IT practices, and Green IS practices. We also developed a measurement instrument that
operationalizes them. We conceptualized Green IS strategy through two sub-domains—organizational-
level and functional-level Green IS strategies—thus providing an important insight because each type
of strategy fosters the implementation of a unique set of Green IS practices and Green IT practices,
respectively. Such differences must be taken into consideration before making any empirical
generalization or abstraction. Further, we re-defined Green IT practices into the three-sub domains of
IT sourcing, operations, and disposal and divided Green IS practices into re-engineering of business
processes, environmental management systems, and environmental technologies. Thus, this study
33
developed and validated a measurement instrument for thirteen novel latent constructs that can be
applied in future research contexts, an important contribution to empirical research in Green IS, which
is dominated by conceptual work. Our work provides tools that can spur more systematic empirical
research in this field.
Mechanisms for Harvesting Benefit from Green IS
Our study demonstrates to researchers and executives who may be suspicious about the benefit of
Green IS initiatives that coherent Green IS investments not only contribute to environmental goals but
can also reduce costs, improve corporate reputations, and enhance Green innovation capabilities. This
evidence decreases the uncertainty about the economic impacts of Green IS initiatives and motivates
both business and IT executives to advance their environmental sustainability efforts.
In order to harvest the benefit of Green IS Investments, IT executives must make substantial changes
to their environmental orientation. The development of pro-environmental beliefs, values, and
standards of behavior are important precursors to the formulation of both organizational and/or
functional Green IS strategies, which can then be translated into Green IT practices and Green IS
practices, respectively. The successful implementation of Green IT practices can reduce costs, whereas
enterprise-wide Green IS practices enhance corporate reputations and strengthen Green innovation
capabilities. We demonstrate that an organization’s environmental orientation, which is formed by
executives’ beliefs concerning the importance of the environmental issues with which their firms are
confronted, has a substantial influence on the formulation of Green IS strategies. The firm’s
environmental orientation also has a substantial impact on the implementation of Green IT practices
and Green IS practices, although this effect is not pronounced because Green IS strategies mediate the
relationship. Our empirical insights illustrate a distinct path from environmental orientation through
Green IS strategy to the implementation of Green IT practices and Green IS practices. These findings
underscore the significance of formulating a Green IS strategy to translate executives’ environmental
beliefs into firm-specific implementations of Green IT practices and Green IS practices.
Although we proposed that the cultivation of both Green IS practices and Green IT practices could
result in similar organization-level benefits, the empirical data suggests that different value classes are
associated with different types of green practices. For example, Green IT practices, which target the
sourcing, operations, and disposal of IT equipment, not only decrease the need for hardware-specific
raw materials, electrical power, and e-waste, but also have economic benefits in the form of cost
reductions. However, because of their restricted focus on IT-related issues, these practices might not
contribute directly to enhancing the corporate reputation or encourage Green innovations in all
industries.
34
However, Green IS practices have a pronounced effect on corporate reputation since these practices
can reduce waste and emissions throughout the organization. IS-based environmental management
systems can facilitate the monitoring of and reporting about the corporate environmental footprint to
internal and external stakeholders, elevating the firm’s reputation. Green IS practices can also improve
the firm’s reputation by supporting the development of environmentally friendly products, thereby
adding to brand image and positive customer perceptions. The use of Green IS practices to transform
the company’s systems and processes can also strengthen the firm’s Green innovation capabilities,
probably because of the expertise that emerges from using IS to employ resources efficiently and to
quantify environmental impacts throughout the product lifecycle.
Exemplary Scope for Formulating Green IS Strategies and Cultivating Green IT/IS Practices
Our study suggests to IT executives that the scope of Green IS strategy can be formulated as an
organizational perspective and/or as a functional plan, each of which has its own effects. Functional
strategies foster the implementation of Green IT practices, whereas organizational strategies promote
the realization of cross-functional Green IS practices. For their part, CIOs should see the role of IS in a
broader business and corporate sustainability context than is currently typical. Because of their cross-
functional perspective, which results from delivering technical solutions to a wide range of business
units, CIOs are in a unique position to identify cross-functional synergies that can advance corporate
sustainability initiatives (Clark, 2010).
On the other hand, a strategy without implementation of supporting practices is as useless as the
uncoordinated implementation of activities without a unifying strategic focus. Our work suggests that
managers can choose from various Green practices, cultivating either Green IT practices or Green IS
practices, based on the goal they want to achieve. Many companies have implemented first Green IT
practices in their data centers and office environments (Park et al., 2012), but Green IS practices have
a more far-reaching potential that most companies have not fully exploited (Dao et al., 2011). Green
IS practices can facilitate sound corporate-sustainability management throughout the organization and
foster eco-innovations in products and services.
In order to assist in the scoping and cultivation exercises, existing literature can be used to identify
implementable Green IT practices and Green IS practices. Loeser (2013), for example, provides two
catalogues. One categorizes Green IT practices according to functional areas like IT sourcing, IT
operations in data centers and office environments, and IT disposal. The other describes Green IS
practices in areas such as governance, process optimization, innovative end projects and infrastructure.
Because the potential of Green IS initiatives to improve corporate environmentalism differs
substantially among companies and industries (Gartner Research, 2007), we recommend close
collaboration between IT and other business executives in order to identify the areas where Green IS
initiatives offer the greatest potential to contribute to the organization’s environmental goals.
35
LIMITATIONS
Our work has several limitations, conceptually, empirically and analytically. Conceptually, we
analyzed only the macro level of our research model in the context of Melville’s (2010) BAO
framework. We suggested several new concepts and examined them empirically with novel latent
constructs. However, micro-level concepts and constructs might also be important in clarifying the
relationships among environmental beliefs, actions, and outcomes. For example, environmental
orientation constitutes not only executives’ beliefs and experiences but also processes and culture,
which we did not examine. There is a potential for tension, “… due to conflicts between organizational
values (e.g., short-term profit motive) and personal values which are shaped by society (e.g., going
green to save the planet)” (Melville 2010, p.5). This potential tension might be relevant in instigating
action but was not covered in this study.
Moreover, for reasons of scope we operationalized Melville’s (2010) concept of outcomes as reported
organizational benefits only. The original framework describes outcomes as (a) both positive and
negative for (b) both business and the environment (Melville 2010). We did not examine negative
outcomes, nor did we examine the exact location of the benefits within an environmental context.
However, our operationalization of benefits as outcomes was grounded in the literature, and our
operationalization focused on both economic dimensions (e.g., cost reductions) and environmental
dimensions (e.g., green product innovations). Still, whether and how the creation of benefits across
these dimensions was shared or not (Porter and Kramer 2011) was not the focus of our study. This
limits our contribution because a business focus on achieving organizational benefits in isolation is
part of the broader sustainability problem (Shrivastava 1995).
We also note several empirical limitations. Although low response rates are not unusual in top-level
executive studies, the generalizability of our results might be limited because of the low response rate
to our survey. In hindsight, we could have chosen another strategy, such as contacting internal survey
champions (e.g., personal assistants), telephone calls, or other incentives. Still, our ambition was to
maximize the absolute rather than the relative size of the sample because senior-level IT executive
data on Green IS initiatives is notably scarce in the literature, and we wanted sufficient data to
maximize the validity of our statistical conclusions, possibly at the expense of external validity.
To assess the limitations to external validity, we performed three independent tests, none of which
indicated the presence of non-response bias. We also estimated the effect sizes that were discernible
with our dataset, which indicated that we could draw statistically valid conclusions for large and
medium effect sizes. Still, a larger sample would have made it possible to detect small effect sizes and
would have allowed us to test differences between companies of certain sizes, industries, or regions.
36
Another empirical limitation is that we examined large organizations in the US, Canada, Australia,
New Zealand, and Germany. The results might differ for small or medium-sized companies and/or
companies in other countries.
Another empirical limitation is the use of single informants. A multiple-informant approach that
included both business and IT executives would have offered findings related to specific functional
areas and a more objective assessment of organizational benefits. Our key motivation was to construct
and analyze a data set that was global, cross-sectional, and from the senior executive level. However,
it is difficult to obtain multi-source data about every organization in a cross-sectional sample. Our
sample is comparable to other IT executive studies, which we determined by means of a ten-year
review of twenty-five articles published in the top-tier IS journals (Table 13).
A final empirical limitation is our choice to operationalize the outcome variable (organizational
benefits) through perceptual measures. Other ways of measuring organizational benefits could have
involved comparative data on organizations in relation to competitors, which could have delivered
more objective results, although this kind of data is also challenging to obtain.
Analytically, limitations may accrue from our application of PLS-SEM. We based this choice on
available guidelines, primarily the advantages that have been ascribed to PLS-SEM for complex,
hierarchical models (Wetzels et al., 2009; Gefen et al., 2011; Becker et al., 2012; Hair et al., 2013).
We are aware that a debate has ensued regarding the potential limitations and threats to validity
concerning PLS-SEM (Marcoulides et al., 2009; Goodhue et al., 2012), and our ambition is not to
contribute to this discussion or to make contributions to the methodological debate. We considered the
available methodological literature at that time, noting that our results meet the recommended criteria
for robustness, validity, and reliability (Ringle et al., 2012; Hair et al., 2013). Under the caveat that we
are not in a position to comment on or resolve methodological quarrels over our choice of data
analysis strategy, we posit that our results and interpretations are robust. We hope that we can leave
the methodological debate to colleagues more adept in resolving these issues than we are.
A second analytical limitation lies in the limited availability of control variables like organizational
culture, firm size, and institutional pressures. We had data only on firm size (number of employees,
annual IT budget), and our post-hoc analysis confirmed the robustness of our measures against
variations in firm size. Still, an organization’s culture may impact its environmental orientation (Molla
& Abareshi, 2012), and organizational Green IS initiatives are often triggered in response to
institutional pressures (Butler, 2011), so the impact of these variables on the theoretical model
advanced in this paper certainly deserves further empirical examination.
37
CONCLUSION
We examined how an organization’s environmental orientation and strategy influence Green IS
initiatives, and which organizational benefits accrue from these inititatives. We found that Green IS
strategies mediate the relationship between environmental orientation and the implementation of
Green IT/IS practices, which in turn lead to organizational benefits in the form of cost reductions,
corporate reputation enhancement, and Green innovation capabilities. . Through this research, we
reduced the economic uncertainty that is associated with far-reaching Green IS investments by
providing detailed empirical insights into the relationships between Green IT practices and Green IS
practices and organizational benefits. We make the case that Green IS practices, beyond IT-focused
Green IT measures that many organizations have already implemented, can add substantial corporate
benefits beyond cost reductions.
While our study provides unique theoretical and actionable contributions, we still regard its findings –
much like Green IS itself – as nascent. In moving forward, we hope that the pathways that flow from
our work will lead to extensions, challenges, and revisions of the knowledge on and around Green IS.
We have provided some pieces to the puzzle of environmental sustainability, but the puzzle is far from
solved.
38
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APPENDIX A: MEASUREMENT ITEMS
Table 10: Measurement items; (*) = item excluded from the analysis due to a low factor loading [λ<0.7].
Code Item Adopted from...
Environmental Orientation (EO)
EO1 Our company's executives and employee feel that the company has carved out a
significant position with respect to environmental protection.
Chen (2011)
EO2 Our company's executives and employees feel that the company has a set of
environmental goals worth striving for.
Chen (2011)
EO3 Our company's executives and employees feel that environmental preservation is a central
value of the company.
Banerjee (2002)
EO5 Our company's executives and employees identify strongly with the company’s actions with
respect to environmental matters.
Chen (2011)
Green IS Strategy (S) – Organizational (1) / Functional (2)
S11 Our company's top management recognizes the possibilities and strategic potential of
Green IT/IS.
Kearns & Sabherwal
(2007)
S12 Our company's top management emphasizes the role of Green IT/IS to drive
environmental sustainability within our company.
Paulraj (2011)
S13 Our company's top management demonstrates a high degree of involvement concerning
Green IT/IS initiatives.
Henriques & Sadorsky
(1999)
S15 Our company's top management considers Green IT/IS to be an essential enabler of our
corporate sustainability strategy.
Paulraj (2011)
S16 Our company's top management responds rapidly to early signals concerning areas of
opportunity for Green IT/IS.
Chen et al. (2010a)
S21 In our IT/IS planning processes, we have integrated environmental aspects. Banerjee (2002)
S23 In our IT/IS planning processes, we always give preference to IT projects and
infrastructure investments that are favorable from an environmental point of view.
New
S24 In our IT/IS planning processes, we have established performance indicators for assessing
the impact of Green IT/IS initiatives.
Molla et al. (2011)
S25 In our IT/IS planning processes, we have earmarked financial and other resources for
Green IT/IS initiatives.
Molla et al. (2011)
S26 In our IT/IS planning processes, we define concrete environmental targets for each Green
IT/IS initiative.
Molla et al. (2011)
Implementation of Green IT Practices (IT) – IT Sourcing (S) / Data Center Operations (C) / Office Environment IT Operations (O) /
IT Disposal (D)
ITS1 We monitor the environmental performance of our IT hardware and service suppliers. Molla et al. (2011)
ITS2 We always give preference to IT hardware and service suppliers which have a green track
record.
Molla et al. (2009)
ITS5 We exclusively purchase energy-efficient IT hardware. Chen et al. (2010a)
ITC1* In our data center, we have consolidated and virtualized our servers. New
ITC3 In our data center, we have optimized the energy efficiency of our storage systems. New
ITC4 In our data center, we have optimized airflows and the entire cooling system. New
ITC6 In our data center, we thoroughly monitor IT energy consumption. New
ITO2 In our company's offices, we inform and educate users regarding the energy consumption
of IT.
New
ITO3 In our company's offices, we have installed power management software. New
ITO4 In our company's offices, we exclusively deploy energy efficient computers, such as
laptops and thin clients.
New
ITD1 To reduce e-waste, we dispose IT equipment in an environmentally friendly manner. Chen et al. (2010a)
ITD2 To reduce e-waste, we repair IT systems whenever possible. New
ITD3 To reduce e-waste, we always search for alternative uses of outdated IT systems. New
45
Table 10 (continued): Measurement items; (*) = item excluded from the analysis due to a low factor loading [λ<0.7].
Code Item Adopted from...
Implementation of Green IS Practices (IS) – Process Reengineering (R) / Environmental Management Systems (E) /
Environmental Technologies (T)
ISR1 Our company makes use of Green IT/IS to improve the efficiency of its production facilities. Karacaoglu & Özkanli
(2011)
ISR2 Our company makes use of Green IT/IS to streamline existing business processes. Tallon (2011)
ISR3 Our company makes use of Green IT/IS to develop new processes that are more
environmentally-friendly.
Christmann (2000)
ISR4 Our company makes use of Green IT/IS to transform the entire business towards long-term
sustainability.
Tallon & Pinsonneault
(2011)
ISR5* Our company makes use of Green IT/IS to reduce individual employee travel through
teleconferences.
New
ISR6 Our company makes use of Green IT/IS to optimize its supply chain processes. Wagner (2003)
ISE1 Our company makes use of information systems that provide important environmental
information for decision-making.
Venkatraman & Grant
(1986)
ISE2 Our company makes use of information systems to track resource and energy flows. New
ISE3 Our company makes use of information systems to control the effectiveness of
environmental programs.
Sharma (2000)
ISE4 Our company makes use of information systems to quantify company-wide carbon dioxide
emissions.
Molla et al. (2009)
IST1* Our company improves existing products with the help of information systems (e.g.,
tracking and analyzing the footprint of product lifecycles).
Christmann (2000)
IST2 Our company offers IT-enabled services with decreased environmental impact (e.g.,
dematerialization: e-commerce, online banking, digital music).
New
IST3 Our company enhances the environmental characteristics of its products or services by
embedding IT/IS in them (e.g., smart logistics, smart buildings, smart engines).
Tallon (2011)
IST4 Our company views IT/IS as enabler for developing new products and services that reduce
environmental impacts (e.g., traffic management systems, smart grids)
Banerjee (2002)
Organizational Benefits (OB) – Cost Reductions (C) / Corporate Reputation (R) / Green Innovation Capabilities (I)
OBC1 Our company has incurred lower costs for complying with environmental regulations than
our competitors.
Christmann (2000)
OBC2 Our company requires relatively less material and resources than our competitors. Christmann (2000)
OBC4 Our company has lower operational costs than our competitors. Kearns & Lederer (2000)
OBR1 Our company has a better corporate image than our competitors. Chang (2011)
OBR3 Our company is perceived as being environmentally responsible by our customers. Tallon & Pinsonneault
(2011)
OBR5* Our company is favored by shareholders due to our good reputation. New
OBI2 Our company is more capable of environmental R&D than our major competitors. Chen (2011)
OBI3 Our company is more capable of environmental management than our major competitors. Chen (2011)
OBI4 Our company is more capable of green innovations than our major competitors. Chen (2011)
46
APPENDIX B: ADDITIONAL SURVEY MATERIALS
Table 11: Comparison of expected and observed responses according to company size distributions.
Company size:
Number of
employees
Distribution in
original
population
(US Census1)
Distribution of
contact records in
database (random
sample)
Expected
observations
(according to
database)
Observations
(distribution of
empirical results)
Chi-Squared
test of
homogeneity
251 – 1,000 49% 45.6% 53.8 47 0.86
1,001 – 5,000 38% 31% 36.6 29 1.57
5,001 – 25,000 9% 18% 21.2 32 5.45
25,001 – 100,000 2% 3.5% 4.1 6 0.85
> 100,000 0.3% 1.9% 2.2 4 1.38
χ2 (critical value for f = 5: 11.07 for ɑ = 0.05) 10.11
Table 12: Nonresponse reasons (n = 100) [* = topic-specific reasons].
Reason for not participating in the survey # Percentage
I was too busy. 27 27%
Our company does not participate in any surveys. 19 19%
Our company security policies prevent us from sharing this kind of information. 10 10%
I did not receive your invitation. 9 9%
I never participate in surveys. 7 7%
Other reasons. 7 7%
I have a new position in the organization and thus cannot answer your questions. 6 6%
My time is too valuable to participate in research projects. 5 5%
I did not trust the confidentiality protection of your institution. 4 4%
Personally, I am not interested in the topic.* 2* 2%*
Our company does not address these issues.* 2* 2%*
I could not access your survey. 1 1%
I do not participate in un-solicited surveys from outside my country. 1 1%
1 https://www.census.gov/econ/esp/
47
Table 13: Summary of empirical IT executive studies on organizational benefits as reported in AIS-Top-8 journals.
Reference Types of Informants Respondent Role # of observations
Bharadwaj et al. (2007) Multiple Production and inventory managers 169
Bulchand-Gidumal & Melián-
González (2011)
Single IT experts 59
Chakravarty et al. (2013) Single Senior business manager (CEO or Founder
or Vice president)
109
Coltman et al. (2011) Multiple Two managers from the same business unit 86
Coltman et al. (2007) Single Senior business managers 293
Choi & Lee (2012) Single CIOs or management strategy executives 372
Keil et al. (2013) Single IT executives 63
Kettinger et al. (2013) Single Senior business manager (CEO or CFO or
Vice president)
103
Kim et al. (2011) Multiple CIOs and Finance managers 243
Leidner et al. (2011) Single Highest rating executives, CEO 283
Pavlou & El Sawy (2010) Single New product development manager 180
Quaadgras et al. (2014) Multiple IT and Non-IT managers 210
Rai et al. (2006) Single Supply chain and logistics managers 110
Ravichandran & Lertwongsatien
(2005)
Single CIOs, VPs, Assistant VPs and Directors of
Technology
119
Ray et al. (2005) Multiple IT and Customer Service managers 72
Rivard et al. (2006) Single CEO 96
Setia et al. (2013) Brunch managers, IS managers and actual
customers
170
Shah et al. (2007) Single Senior IT managers 114
Tanriverdi (2005) Senior IT and Business Executives 280
Wang et al. (2013) Single Senior purchasing managers 144
Wu & Hu (2012) Single Senior IT executives or managers 144
Wu et al. (2015) Single Business and IT executives, senior IT
managers
136
Yayla & Hu (2012) Multiple Executives, business managers 177
Zhang et al. (2008) Single Senior managers 180
Zhang et al. (2013) Single Senior managers 136
48
APPENDIX C: MEASUREMENT VALIDATION MATERIALS
Table 14: Item cross-loadings.
Second-order
construct
Organizational
Benefits
Green IT
Practices
Green IS
Practices
Green IS
Strategy
First-order
construct
Environmental
Orientation
Green Innovation
Capabilities
Corporate
Reputation
Cost
Reductions
IT Disposal
IT Operations
Data Center
IT Operations
Office Environment
IT Sourcing
Environmental
Management Systems
Process
Reengineering
Environmental
Technologies
Organizational
Green IS Strategy
Functional
Green IS Strategy
EO1 0.927 0.579 0.586 0.370 0.317 0.427 0.401 0.471 0.575 0.474 0.423 0.596 0.474
EO2 0.946 0.592 0.548 0.349 0.281 0.466 0.405 0.542 0.595 0.553 0.418 0.609 0.568
EO3 0.916 0.531 0.478 0.340 0.211 0.462 0.380 0.480 0.491 0.453 0.351 0.536 0.429
EO5 0.945 0.593 0.545 0.370 0.301 0.511 0.399 0.526 0.600 0.546 0.483 0.626 0.498
OBI2 0.585 0.948 0.369 0.290 0.188 0.263 0.210 0.396 0.424 0.389 0.352 0.349 0.442
OBI3 0.576 0.970 0.358 0.324 0.208 0.255 0.221 0.395 0.456 0.398 0.389 0.356 0.464
OBI4 0.605 0.946 0.403 0.351 0.201 0.254 0.230 0.390 0.429 0.359 0.346 0.358 0.473
OBR1 0.450 0.284 0.910 0.461 0.320 0.313 0.256 0.262 0.440 0.455 0.421 0.373 0.294
OBR3 0.605 0.432 0.918 0.342 0.303 0.380 0.345 0.369 0.528 0.492 0.399 0.473 0.353
OBC1 0.385 0.294 0.458 0.853 0.372 0.428 0.346 0.353 0.296 0.353 0.268 0.328 0.310
OBC2 0.324 0.332 0.350 0.911 0.212 0.325 0.305 0.358 0.149 0.284 0.311 0.329 0.282
OBC5 0.291 0.257 0.332 0.876 0.142 0.312 0.294 0.299 0.173 0.263 0.299 0.283 0.329
ITD1 0.276 0.126 0.330 0.299 0.765 0.361 0.236 0.290 0.233 0.234 0.216 0.157 0.228
ITD2 0.265 0.228 0.278 0.196 0.843 0.263 0.199 0.298 0.226 0.237 0.152 0.102 0.200
ITD3 0.139 0.138 0.169 0.157 0.768 0.213 0.183 0.253 0.246 0.260 0.207 0.107 0.247
ITC3 0.399 0.113 0.412 0.343 0.267 0.817 0.476 0.499 0.451 0.463 0.343 0.414 0.385
ITC4 0.374 0.279 0.210 0.278 0.396 0.777 0.277 0.431 0.243 0.253 0.289 0.284 0.189
ITC6 0.450 0.277 0.296 0.371 0.259 0.859 0.608 0.635 0.445 0.572 0.433 0.568 0.465
ITO2 0.396 0.241 0.291 0.248 0.149 0.436 0.850 0.578 0.523 0.489 0.419 0.493 0.632
ITO3 0.325 0.129 0.256 0.247 0.185 0.528 0.812 0.452 0.431 0.370 0.357 0.485 0.455
ITO4 0.301 0.179 0.249 0.388 0.318 0.442 0.761 0.590 0.282 0.452 0.299 0.358 0.425
ITS1 0.439 0.308 0.286 0.262 0.247 0.565 0.614 0.827 0.477 0.488 0.406 0.562 0.637
ITS2 0.476 0.405 0.326 0.347 0.322 0.528 0.548 0.902 0.518 0.616 0.363 0.519 0.682
ITS5 0.413 0.287 0.227 0.343 0.313 0.505 0.484 0.710 0.335 0.442 0.183 0.310 0.419
ISE1 0.457 0.319 0.368 0.159 0.231 0.317 0.422 0.464 0.829 0.515 0.387 0.498 0.552
ISE2 0.494 0.379 0.443 0.163 0.295 0.482 0.467 0.503 0.897 0.580 0.418 0.501 0.497
ISE3 0.561 0.458 0.539 0.289 0.293 0.440 0.482 0.503 0.937 0.648 0.546 0.565 0.576
ISE4 0.620 0.441 0.503 0.218 0.223 0.437 0.453 0.478 0.870 0.583 0.433 0.547 0.564
ISR1 0.370 0.298 0.428 0.338 0.224 0.541 0.497 0.575 0.538 0.823 0.404 0.492 0.470
ISR2 0.392 0.268 0.431 0.289 0.271 0.496 0.501 0.562 0.519 0.901 0.447 0.509 0.476
ISR3 0.500 0.336 0.439 0.291 0.276 0.475 0.477 0.531 0.572 0.880 0.442 0.557 0.590
ISR4 0.579 0.472 0.470 0.331 0.295 0.440 0.448 0.586 0.661 0.899 0.461 0.584 0.609
ISR6 0.480 0.325 0.466 0.231 0.241 0.410 0.431 0.490 0.543 0.806 0.425 0.511 0.393
IST2 0.300 0.292 0.259 0.157 0.182 0.269 0.164 0.220 0.356 0.322 0.819 0.378 0.378
IST3 0.406 0.331 0.443 0.349 0.247 0.444 0.445 0.355 0.454 0.434 0.917 0.581 0.469
IST4 0.457 0.371 0.441 0.327 0.199 0.418 0.497 0.433 0.508 0.543 0.896 0.585 0.488
49
Table 14 (continued): Confirmatory factor analysis with item-to-construct- and cross-loadings.
Table 15: Descriptive statistics for latent variable constructs.
Construct Mean Standard deviation Composite reliability
Corporate Reputation 4.635 1.049 0.910
Cost Reductions 4.112 1.030 0.912
IT Disposal 5.590 1.128 0.835
Environmental Management Systems 3.925 1.511 0.935
Environmental Orientation 4.744 1.455 0.964
Environmental Technologies 4.541 1.335 0.910
Organizational Green IS Strategy 4.140 1.498 0.965
Functional Green IS Strategy 3.505 1.358 0.927
Green Innovation Capabilities 4.070 1.139 0.969
IT Operations Data Center 5.178 1.279 0.859
IT Operations Office Environment 4.359 1.414 0.850
IT Sourcing 3.954 1.350 0.856
Process Reengineering 4.287 1.350 0.936
Second-order
construct
Organizational
Benefits
Green IT
Practices
Green IS
Practices
Green IS
Strategy
First-order
construct
Environmental
Orientation
Green Innovation
Capabilities
Corporate
Reputation
Cost
Reductions
IT Disposal
IT Operations
Data Center
IT Operations
Office Environment
IT Sourcing
Environmental
Management Systems
Process
Reengineering
Environmental
Technologies
Organizational
Green IS Strategy
Functional
Green IS Strategy
S11 0.618 0.346 0.438 0.365 0.154 0.458 0.496 0.511 0.525 0.511 0.545 0.885 0.609
S12 0.611 0.360 0.449 0.331 0.150 0.493 0.530 0.568 0.576 0.563 0.601 0.946 0.656
S13 0.589 0.290 0.435 0.302 0.107 0.467 0.521 0.522 0.545 0.557 0.535 0.949 0.621
S15 0.589 0.376 0.454 0.310 0.194 0.569 0.528 0.534 0.575 0.629 0.578 0.921 0.634
S16 0.516 0.330 0.356 0.343 0.113 0.460 0.466 0.521 0.535 0.580 0.504 0.899 0.648
S21 0.463 0.400 0.330 0.378 0.299 0.342 0.508 0.572 0.511 0.463 0.435 0.520 0.810
S23 0.523 0.535 0.337 0.413 0.264 0.395 0.529 0.634 0.526 0.527 0.431 0.557 0.818
S24 0.462 0.424 0.217 0.217 0.278 0.406 0.570 0.687 0.530 0.518 0.444 0.611 0.888
S25 0.359 0.337 0.283 0.255 0.195 0.374 0.526 0.534 0.500 0.499 0.453 0.631 0.857
S26 0.426 0.328 0.339 0.209 0.145 0.347 0.543 0.613 0.555 0.509 0.410 0.596 0.859
50
Table 16: Average variance extracted and correlation matrix of principal components (diagonal elements, highlighted
in bold, are the square root of AVE; off-diagonal elements are correlations between the constructs).
Corporate Reputation
Cost
Reductions
IT Disposal
Environmental Management Systems
Environmental Orientation
Environmental Technologies
Organizational Green IS Strategy
Functional Green IS Strategy
Green Innovation Capabilities
IT Operations Data Center
IT Operations Office Environment
IT Sourcing
Process Reengineering
Corporate Reputation 0.914
Cost
Reductions 0.438 0.880
IT Disposal 0.341 0.285 0.793
Environmental
Management Systems 0.531 0.240 0.294 0.884
Environmental
Orientation 0.579 0.383 0.299 0.608 0.933
Environmental
Technologies 0.448 0.332 0.240 0.509 0.451 0.878
Organizational Green IS
Strategy 0.464 0.358 0.157 0.599 0.636 0.602 0.921
Functional Green IS
Strategy 0.354 0.348 0.280 0.620 0.530 0.513 0.688 0.847
Green Innovation
Capabilities 0.394 0.337 0.209 0.457 0.616 0.380 0.371 0.481 0.955
IT Operations Data
Center 0.380 0.409 0.363 0.477 0.501 0.442 0.533 0.441 0.269 0.818
IT Operations Office
Environment 0.329 0.360 0.264 0.516 0.425 0.447 0.553 0.633 0.231 0.576 0.808
IT Sourcing 0.346 0.385 0.357 0.551 0.542 0.397 0.577 0.721 0.412 0.649 0.671 0.817
Process Reengineering 0.518 0.345 0.305 0.661 0.545 0.506 0.618 0.595 0.401 0.545 0.544 0.637 0.863
Table 17: Evaluation of higher-order constructs (*** = path between constructs significant at p<0.001).
Construct Sub-construct # items VIF Weights
Green IS Strategy Organizational Green IS Strategy 5 1.90 0.587***
Functional Green IS 5 1.90 0.499***
Green IT Practices IT Sourcing 3 2.30 0.369***
IT Operations Data Center 3 1.90 0.343***
IT Operations Office Environment 3 1.94 0.318***
IT Disposal 3 1.19 0.221***
Green IS Practices
Process Reengineering 5 1.91 0.504***
Environmental Management Systems 4 1.92 0.410***
Environmental Technologies 3 1.45 0.258***
Organizational Benefits Cost Reductions 3 1.29 0.437***
Corporate Reputation 2 1.35 0.293***
Green Innovation Capabilities 3 1.23 0.563***
51
APPENDIX D: SUPPLEMENTARY ANALYSES
Table 18: Path Coefficients and Standard Errors referring to Figure 2.
Path Path Coefficient Standard Error
P1: Environmental Orientation Green IS Strategy 0.6400 0.0538
P2: Environmental Orientation Green IT Practices 0.2024 0.0605
P3: Environmental Orientation Green IS Practices 0.2581 0.0474
P4: Green IS Strategy Green IT Practices 0.5786 0.0846
P5: Green IS Strategy Green IS Practices 0.5978 0.0627
P6: Green IT Practices Organizational Benefits 0.1823 0.1294
P7: Green IS Practices Organizational Benefits 0.4971 0.1113
Table 19: Path Coefficients and Standard Errors referring to the Detailed Analysis of Figure 3 and Figure 4.
Path Path Coefficient Standard Error
Environmental Orientation Organizational Green IS Strategy 0.6359 0.0556
Environmental Orientation Functional Green IS Strategy 0.5309 0.0624
Environmental Orientation Green IT Practices 0.2369 0.0622
Environmental Orientation Green IS Practices 0.2631 0.0505
Organizational Green IS Strategy Green IT Practices 0.1370 0.1200
Organizational Green IS Strategy Green IS Practices 0.3212 0.1023
Functional Green IS Strategy Green IT Practices 0.4679 0.0928
Functional Green IS Strategy Green IS Practices 0.3263 0.1005
Green IT Practices Cost Reductions 0.4124 0.1125
Green IT Practices Corporate Reputation 0.0320 0.1183
Green IT Practices Green Innovation Capabilities 0.0412 0.1547
Green IS Practices Cost Reductions 0.0684 0.1232
Green IS Practices Corporate Reputation 0.5723 0.0909
Green IS Practices Green Innovation Capabilities 0.4593 0.1315
Table 20: First-order Model Path Results (paths highlighted in bold are significant at the 0.05 level).
Relationship Path Sample Mean T Statistic
Beliefs
Actions
Environmental Orientation Organizational Green IS Strategy 0.63 11.08
Environmental Orientation Functional Green IS Strategy 0.53 8.58
Environmental Orientation IT Sourcing 0.54 6.16
Environmental Orientation IT Operations Data Center 0.50 7.89
Environmental Orientation IT Operations Office Environment 0.43 5.18
Environmental Orientation IT Disposal 0.31 3.28
Environmental Orientation Process Reengineering 0.55 9.02
Environmental Orientation Environmental Management Systems 0.61 9.76
Environmental Orientation Environmental Technologies 0.45 5.59
52
Table 22 (continued): First-order Model Path Results (paths highlighted in bold are significant at the 0.05 level).
Relationship Path Sample Mean T Statistic
Actions (Green IS Strategy) – Actions (Green IT Practices and Green IS Practices)
Organizational Green IS Strategy IT Sourcing 0.05 0.44
Organizational Green IS Strategy IT Operations Data Center 0.30 2.45
Organizational Green IS Strategy IT Operations Office Environment 0.20 1.37
Organizational Green IS Strategy IT Disposal -0.21 1.60
Organizational Green IS Strategy Process Reengineering 0.28 2.49
Organizational Green IS Strategy Environmental Management Systems 0.15 1.13
Organizational Green IS Strategy Environmental Technologies 0.43 3.61
Functional Green IS Strategy IT Sourcing 0.58 7.03
Functional Green IS Strategy IT Operations Data Center 0.10 0.97
Functional Green IS Strategy IT Operations Office Environment 0.47 3.76
Functional Green IS Strategy IT Disposal 0.28 2.74
Functional Green IS Strategy Process Reengineering 0.29 2.49
Functional Green IS Strategy Environmental Management Systems 0.35 2.76
Functional Green IS Strategy Environmental Technologies 0.17 1.55
Actions (Green IT Practices and Green IS Practices) – Outcomes
IT Sourcing Corporate Reputation -0.11 0.85
IT Sourcing Cost Reduction 0.12 0.78
IT Sourcing Green Innovation Capabilities 0.33 2.77
IT Operations Data Center Corporate Reputation 0.07 0.64
IT Operations Data Center Cost Reductions 0.18 1.74
IT Operations Data Center Green Innovation Capabilities -0.09 0.76
IT Operations Office Environment Corporate Reputation -0.04 0.29
IT Operations Office Environment Cost Reductions 0.10 0.91
IT Operations Office Environment Green Innovation Capabilities -0.21 1.56
IT Disposal Corporate Reputation 0.16 1.75
IT Disposal Cost Reductions 0.12 1.23
IT Disposal Green Innovation Capabilities 0.03 0.27
Process Reengineering Corporate Reputation 0.25 2.45
Process Reengineering Cost Reductions 0.10 0.84
Process Reengineering Green Innovation Capabilities 0.07 0.41
Environmental Management Systems Corporate Reputation 0.27 2.79
Environmental Management Systems Cost Reductions -0.14 1.32
Environmental Management Systems Green Innovation Capabilities 0.27 2.41
Environmental Technologies Corporate Reputation 0.17 1.85
Environmental Technologies Cost Reductions 0.16 1.62
Environmental Technologies Green Innovation Capabilities 0.19 1.72
53
Table 21: First-order Model R2 Results.
Green IS Strategy Green IT/IS Practices Organizational Benefits
Organizational Green IS
Strategy 0.40 IT Sourcing 0.56 Corporate Reputation 039
Functional Green IS
Strategy 0.28 IT Operations Data Center 0.33 Cost Reductions 0.24
IT Operations Office Environment 0.43 Green Innovation
Capabilities 0.30
IT Disposal 0.13
Process Reengineering 0.46
Environmental Management Systems 0.50
Environmental Technologies 0.39
Table 22: Power analysis results according to the methodology of Aguirre-Urreta and Rönkkö (2015).
Path Parameter R
Simulation Statistical power greater
than 0.8 (n = 118)?
Environmental Orientation Organizational Green IS Strategy 0.636 1.000 Yes
Environmental Orientation Functional Green IS Strategy 0.531 1.000 Yes
Environmental Orientation Green IT Practices 0.237 0.700 No
Environmental Orientation Green IS Practices 0.263 1.000 Yes
Organizational Green IS Strategy Green IT Practices 0.137 0.300 No
Organizational Green IS Strategy Green IS Practices 0.321 1.000 Yes
Functional Green IS Strategy Green IT Practices 0.468 1.000 Yes
Functional Green IS Strategy Green IS Practices 0.326 1.000 Yes
Green IT Practices Cost Reductions 0.412 0.800 Yes
Green IT Practices Corporate Reputation 0.032 0.090 No
Green IT Practices Green Innovation Capabilities 0.041 0.300 No
Green IS Practices Cost Reductions 0.068 0.100 No
Green IS Practices Corporate Reputation 0.572 1.000 Yes
Green IS Practices Green Innovation Capabilities 0.459 1.000 Yes
54
Table 23: MANOVA results: Latent variable scores by organizational size.
Latent variable Factor F (4, 118) P-value
Environmental Orientation
(Reflective first-order construct)
Number of employees1 0.91 0.46
Annual IT budget
2
0.72 0.58
Green IS Strategy
(Reflective-formative second-order construct)
Number of employees
1
1.37 0.25
Annual IT budget2 0.30 0.87
Organizational Green IS Strategy
(Reflective first-order construct)
Number of employees1 0.94 0.44
Annual IT budget
2
0.16 0.966
Functional Green IS Strategy
(Reflective first-order construct)
Number of employees1 1.60 0.18
Annual IT budget
2
0.59 0.67
Green IT Practices
(Reflective-formative second-order construct)
Number of employees
1
1.23 0.30
Annual IT budget
2
1.24 0.30
IT Disposal
(Reflective first-order construct)
Number of employees
1
0.43 0.78
Annual IT budget
2
1.48 0.21
IT Operations Data Center
(Reflective first-order construct)
Number of employees
1
0.64 0.63
Annual IT budget
2
1.14 0.34
IT Operations Office Environment
(Reflective first-order construct)
Number of employees
1
1.67 0.16
Annual IT budget
2
1.67 0.16
IT Sourcing
(Reflective first-order construct)
Number of employees1 1.75 0.15
Annual IT budget
2
1.30 0.27
Green IS Practices
(Reflective-formative second-order construct)
Number of employees
1
1.43 0.23
Annual IT budget
2
1.08 0.37
Process Reengineering
(Reflective first-order construct)
Number of employees
1
0.99 0.42
Annual IT budget
2
0.69 0.60
Environmental Management Systems
(Reflective first-order construct)
Number of employees
1
1.89 0.12
Annual IT budget
2
0.79 0.54
Environmental Technologies
(Reflective first-order construct)
Number of employees
1
1.25 0.29
Annual IT budget
2
2.11 0.08
Organizational Benefits
(Reflective-formative second-order construct)
Number of employees
1
0.13 0.97
Annual IT budget
2
0.98 0.42
Corporate Reputation
(Reflective first-order construct)
Number of employees
1
0.65 0.63
Annual IT budget
2
0.73 0.57
Cost Reductions
(Reflective first-order construct)
Number of employees
1
0.27 0.90
Annual IT budget
2
0.50 0.74
Green Innovation Capabilities
(Reflective first-order construct)
Number of employees
1
0.47 0.76
Annual IT budget
2
2.07 0.09
1 Ranking of variable (n): 251 to 1000 employees (47), 1001 to 5000 employees (29), 5001 to 25.000 employees (32), 25.001
to 100.000 employees (6), more than 100.000 employees (4).
2 Ranking of variable (n): less than USD 1 million (15), USD 1 million to USD 5 million (42), USD 5 million to USD 25
million (24), USD 25 million to USD 100 million (25), more than USD 100 million (12).
55
REFERENCES OF APPENDICES
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