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The International Journal of Logistics Management
Examining the inter-relationship among competitive market environments, green
supply chain practices, and firm performance
Seok-Beom Choi, Hokey Min, Hye-Young Joo,
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Seok-Beom Choi, Hokey Min, Hye-Young Joo, (2018) "Examining the inter-relationship among
competitive market environments, green supply chain practices, and firm performance", The
International Journal of Logistics Management, https://doi.org/10.1108/IJLM-02-2017-0050
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Examining the inter-relationship
among competitive market
environments, green supply chain
practices, and firm performance
Seok-Beom Choi
Department of Chinese Economics and Trade, Cheju Halla University, Jeju,
Republic of Korea
Hokey Min
Department of Management, Bowling Green State University, Bowling Green,
Ohio, USA, and
Hye-Young Joo
Chung-Ang University, Seoul, Republic of Korea
Abstract
Purpose –The purpose of this paper is to assess the impact of competitive market environments on the
firm’s decision to adopt green supply chain management (GSCM) practices, while checking to see if the firm’s
commitment to particular types of GSCM practices improves its performance.
Design/methodology/approach –To confirm a positive link between the firm’s GSCM practices to its
performance, the authors collected the data from 322 Korean firms via questionnaire surveys and then
analyzed these data using the structural equation model.
Findings –Among various types of GSCM practices, green purchasing has the greatest impact on both
manufacturing and marketing performances. Also, internal environmental management positively influenced
both manufacturing and marketing performances, whereas cooperation with customers and reverse logistics
had no significant impact on the firm’s manufacturing and marketing performances.
Originality/value –To provide a practical advice for firms which are hesitant to embrace green supply
chain practices due to skeptical views about their true managerial benefits, this paper discerned more
effective GSCM practices from less effective GSCM practices. In so doing, this paper is one of the few studies
which pinpointed what types of specific GSCM practices are most effective in enhancing firm performance.
Keywords Green supply chain management, Structural equation model, Firm performance
Paper type Research paper
1. Introduction
Over the last decade, the planet earth has suffered from an unusual cycle of unprecedented
heat waves, cold spells, droughts, floods and wildfires. For example, the summer of 2012
was the warmest summer on record, whereas the winter of 2014 was the coldest winter on
record for the USA. Similarly, Australia, Japan, Korea, China and Europe experienced the
record-setting heat waves in 2013 (Sheppard, 2014). The surface air temperatures on
the average so far this decade are about 0.9
o
C higher than they were in the 1880s
((The) Economist, 2015). Many suspect that this extreme weather pattern is a vital sign of
climate changes induced by human activities. Unless reversed, some scientists believe that
this pattern can eventually destroy the livelihood of all species on earth by disrupting the
biodiversity and causing serious food shortages (Pimm, 2009; Safont et al., 2012). A main
culprit for this concerning pattern is the emission of harmful carbon dioxide into air.
Actually, the level of atmospheric carbon dioxide reached 399 parts per million in 2014, The International Journal of
Logistics Management
© Emerald Publishing Limited
0957-4093
DOI 10.1108/IJLM-02-2017-0050
Received 26 February 2017
Revised 30 July 2017
27 November 2017
Accepted 10 April 2018
The current issue and full text archive of this journal is available on Emerald Insight at:
www.emeraldinsight.com/0957-4093.htm
This research was partly funded by the National Research Grant of the Korean Government
(NRF- 2014S1A2A2028564).
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which was the highest record for 650,000 years of this planet’s history ((The) Economist,
2015; NASA, 2015). This rapid rise of a carbon dioxide level in the air contributed to human
activities associated with transportation (especially, use of gasoline-powered vehicles),
inventory management, waste disposal, product manufacturing and energy creation (Piecyk
and McKinnon, 2010; Sundarakani et al., 2010; Hua et al., 2011). Recognizing the detrimental
impact of human activities (especially industrial activities) on environmental degradation, a
growing number of today’s customers have begun to appreciate the firm’s commitment to
environmental friendliness (greening initiatives) more than ever before. This changed
attitude of customers has prompted firms to consider leveraging their environmental
friendliness as the major selling point or the customer order-winning criterion. However, the
firm’s commitment to greening initiatives often create a dilemma in that the firm’s
environmental friendliness rarely comes free and its payoffs are not clearly known.
To handle this dilemma, a series of attempts have been made to assess the impact of
greening initiatives (e.g. green supply chain practices) on the firm’s performance. The focal
point of these attempts is to determine whether greening initiatives are worthy of investment
and managerial focus. For example, Klassen and McLaughlin (1996) discovered a strong link
between the firm’s greening initiatives and its financial performance, as measured by stock
market performance. Similarly, Melnyk et al. (2003) observed that the extent/maturity of the
firm’s environmental management system was directly correlated with the firm’s
performance, as expressed by perceived cost saving, lead time reduction, product quality
improvement, market position strength and corporate reputation enhancement. Later,
Montabon et al. (2007) confirmed that the firm’s environmental management practices such as
remanufacturing, environment-friendly product design and surveillance of the market for
environmental innovation were positively associated with the firm’s performance, as
expressed by sales growth and return on investment (ROI). A plethora of other studies
including the ones conducted by Florida (1996), Berry and Rondinelli (1998), Claver et al.
(2007), Yang et al. (2011), Schrettle et al. (2014) and Tseng et al. (2015) verified a causal
relationship between the firm’s environmental management practices and its performance.
While there is little doubt that the firm’s environmental management practices could
improve its performance, the aforementioned literature rarely explained which particular
environmental management practices or strategies were more effective in improving the
firm’s performance than their alternative options. More importantly, the aforementioned
literature did not examine how the collective environmental management efforts of multiple
firms belonging to the same supply chain network affected those firms’performances.
Recognizing the need for such examination, Zhu and Sarkis (2004) looked into the potential
relationship between the adoption of green supply chain management (GSCM) practices
such as internal and external environmental management, ISO 14000 certification,
investment recovery and eco-design and the firm’s operating performance such as cost
control. Generally speaking, GSCM is referred to as an incorporation of environment-
friendly initiatives into every aspect of supply chain activities encompassing sourcing,
product design and development, manufacturing, transportation, packaging, storage,
retrieval, disposal and post sales services including end-of-product life management
(Min and Kim, 2012). Based on the empirical study of Chinese manufacturers, Zhu and
Sarkis (2004) found that firms having higher levels (more mature stages) of GSCM practices
tended to reap the greater economic benefits in terms of some operational cost savings
(e.g. decrease in environmental compliance cost), while increasing other operating costs
(e.g. increase in costs of purchasing environment-friendly materials). This finding was
expected, but they also investigated the moderating effects of both quality management and
just-in-time manufacturing practices on the firm’s operational performance. They
discovered that in some instances firms adopting GSCM practices along with quality
management practices could benefit more due to the positive moderating effect of quality
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management practices, whereas JIT manufacturing practices could undermine the positive
influence of GSCM practices. Following suit, a number of other studies (e.g. Zhu et al., 2005;
Vachon and Klassen, 2006; Chien and Shih, 2007; Zhu and Sarkis, 2007; Youn et al., 2011;
Chan et al., 2012; Green et al., 2012; Zhu et al., 2012a,b, 2013; Graham and Potter, 2015;
Lee, 2015; Crifo et al., 2016; Jackson et al., 2016; Kirchoff et al., 2016) examined the
relationship between GSCM practices and their adopters’organizational performances
to confirm the influence of GSCM practices on organizational performance.
Although a vast majority of the existing literature reported the positive influence of
GSCM practices on organizational performance, most of them did not clearly explain what
motived GSCM practices and did not specify which GSCM practices were most effective in
enhancing organizational performance. That is to say, the undisciplined adoption of GSCM
practices without formulating enterprise-wise implementation strategies cannot only
undermine their effectiveness, but also inflict damages to the firm’s competitiveness and
supply chain coordination. With this in mind, this paper aims to discern the most
appropriate GSCM strategy by classifying various types of GSCM practice options into
distinctive categories and then identifying the winning formula among them. Put simply,
this paper attempts to answer two fundamental research questions:
RQ1. Does the competitive market environment motivate the firm to adopt GSCM
practices? Which particular GSCM practices are significantly influenced by the
competitive market environment, if any?
RQ2. Do GSCM practices improve the firm performance? If so, is there any significant
difference in the extent of impact of the firm’s chosen types of GSCM practices on
the firm performance?
2. Theory building and hypotheses development
To examine which exogenous factor drives the GSCM practices and assess how much those
practices affect the firm performance, we employed two well-known theories in the strategic
management literature: a contingency theory and a resource-based view of the firm. To
elaborate, contingency theory, which was developed in the late 1960s, is one of the behavioral
theories that study how environmental variables influence the behaviors of organizations
(Lawrence and Lorsch, 1967; Chandra and Kumar, 2000). Contingency theory is predicated on
the premise that the firm’s strategy such as GSCM adoption strategy depends on its
endogenous and exogenous business environments (Donaldson, 2001). Contingency theory also
posits that the more organizational structures and processes can adapt to uncertain
environments, the more successful the firms will be (Miller, 1992). In other words, the firm’s
ability to embrace GSCM practices can be seen as a competitive differentiator in unstable and
uncertain business environments where the environment friendliness of the firm’s products and
services has gained more popularity and importance (e.g. favorable image) from today’s
customers. As such, the GSCM adoption/investment can be essential for the improvement of the
firm’s performance in highly turbulent business environments. On the other hand, a firm that is
resistant to any managerial changes such as GSCM practices, or reluctant to bear risk for
economic or technical reasons, may not be able to improve its competitive position in the market
and the subsequent organizational performance. Therefore, the contingency theory may help
the firm understand what truly drives the GSCM adoption and then identify a set of external
and internal environmental variables influencing the firm’s organizational performance.
A resource-based view of the firm theorizes that the firm, which possesses a bundle of
unique resources (e.g. assets, human capitals, capabilities, organizational process,
information, knowledge), can improve its performances and subsequently achieve
competitive advantages in the market (Wernerfelt, 1984; Barney, 1991). Put simply, the
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RBV theory is predicated on a premise that the firm competes on the basis of “unique”
corporate resources that are valuable, rare, difficult to imitate, and non-substitutable by
competitors (Barney, 1991). Considering that GSCM practices can be regarded as a unique
organizational capability (i.e. corporate resource), the RBV theory may be useful for
explaining how the adoption of GSCM practices improves the firm’s performance.
2.1 Defining the hypothetical model and constructs
Under the contingency and RBV theories described earlier, we develop a research
framework that is comprised of eight constructs: a competitive market environment,
internal environmental management, green purchasing, eco-design, cooperation with
customers, reverse logistics, manufacturing performance and marketing performance.
Herein, a competitive market environment is generally referred to as exogenous factors that
form the context for organizational actions and decision making (Li et al., 2006). These
factors may include institutional pressure (e.g. a government’s tougher environmental
standards, stricter emission control), peer pressure (e.g. industry norms for environmental
friendliness), the level of competition in the market and demand uncertainty (e.g. customer
preference for green products). Even though the firm has little or no control over its external
market environment, a greater awareness of its external market environment helps the firm
better adapt and execute appropriate GSCM practices. For example, Sony once lost 20bn
Yen and relinquished a chunk of the market share to its rival, Microsoft, in the gaming
industry after its PlayStation products failed to meet tougher environmental standards
set by the European Union (EU) countries. After such a loss, Sony decided to toughen up its
environmental standards including parts/components (e.g. coated wires) provided by its
suppliers (Lee and Choi, 2006). As this example illustrates, the failure to adopt or execute
environmental management practices could undermine the firm’s competitiveness in the
market. In other words, the higher level of competition in the market may motivate the firm
to take environmental management practices (e.g. GSCM practices) more seriously and
adopt such practices to gain competitive edges over its rivals.
Internal environment management is referred to as an organization’s endogenous
resources and capabilities dedicated to the environmental friendliness or sustainability of its
products and/or services. Since the successful implementation of GSCM practices requires
effective, committed and persistent leadership to achieve the goals of an entire firm, the firm
should consider its organizational readiness and resource capabilities defined by
endogenous factors such as top management support, cooperative organizational culture
and ISO 14000 certification. Indeed, Hoejmose et al. (2012) found that top management
support was a crucial driver for the firm’s engagement with GSCM practices. In addition,
cooperative organizational culture may have profound effects on the GSCM planning and
implementation process. The rationale being that cooperative organizational culture can
facilitate the communication between different departments and thus increase the firm’s
ability to share knowledge and perspectives of environmental management initiatives
across the different functional units of the firm.
Green purchasing is defined as a firm’s environmentally responsible buying practices
geared toward the conservation of natural resources, sustainment of eco-system quality, the
minimum use of water and energy sources, pollution prevention, a reduced disposal of
wasted materials to landfills and encouragement of suppliers for the development of
environment-friendly products. Green purchasing typically involves the cooperation
between the buying firm and its suppliers in reducing waste through recycling, reusing,
low-density packaging, and input material purification and substitution (Min and
Galle, 1997; Min and Galle, 2001).
Eco-design is defined as the firm’s ability to design and develop eco-friendly (green)
products which can be reused, recycled or packaged with bio-degradable materials
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(Donnelly et al., 2006; Knight and Jenkins, 2009). Its ability includes the use of materials and
parts/components, which are not harmful (hazardous) to human health or made with less
energy consumption. Such ability can be developed from the firm’s organization-specific
competencies such as cross-functional or cross-organizational coordination of green product
development, process improvement, green manufacturing and life cycle assessment.
Cooperation with customers is referred to as the firm’s effort to actively solicit and
consider customer feedback for developing green products, adopting eco-friendly
manufacturing process and using eco-friendly packaging materials (Zhu et al., 2010).
One example of such an effort includes the coordination of the firm’s production and
distribution operations based on the end-customer demand information provided by its
downstream supply chain partners (e.g. retailers). As such, the customer can participate in
green product development by passing on its feedback/suggestions about eco-friendly
products (e.g. elimination of sulfate from shampoos) to the manufacturer and its suppliers.
Reverse logistics is generally referred to as a series of distribution activities involved in
product returns, source reduction/conservation, recycling, substitution, reuse, disposal,
refurbishment, repair and remanufacturing (Stock, 1992; Min et al., 2006). In this paper,
reverse logistics is narrowly defined as the firm’s effort to reclaim and take back products
whose life cycles were ended rather than disposing those to landfills. Such an effort includes
the reuse and recycling of collected used products and their packages as well as the
utilization of information and communication technology (e.g. radio frequency
identification) to trace used products throughout their life cycles. Herein, we did not
include the environmental impact of forward logistics such as the impact of carbon
footprints caused by vehicle emission during the outbound movements, since a vast
majority of the company’s environmental initiative/policy focused on reverse logistics
activities (Murphy and Poist, 2000; Ahi and Searcy, 2013; Govindan et al., 2015; Fahimnia
et al., 2015). Another rationale being that, due to the nature of reverse logistics which
primarily aimed to recapture the value of end-of-life cycle or defective products through
collecting, recycling (to have more raw materials or raw parts), remanufacturing (to resell
them to secondary markets), repairing, and disposing them properly, the main focus of our
study will be reverse logistics.
Manufacturing performance is referred to as the extent of the firm’s ability to deliver
ordered products to its customers in accordance with each customer’s specific needs,
schedules and requirements (Kaplan, 1983; Klassen and Whybark, 1999). It also indicates
how much of the company’s total resources (total manufacturing output capacity) are fully
utilized at a given point in time (Gunasekaran et al., 2001; Gomes et al., 2004). Since
manufacturing performance reflects the manufacturing firm’s operational success in terms
of reduced product quality failures and order backlogs, its higher manufacturing
performance will lead to a smaller percentage of returned products and a lower level of
inventory, which, in turn, can contribute to production cost savings (e.g. reduction in
product recalls, safety stocks, and the volume of replaced products in exchange for defects).
For example, sound environmental practices can reduce material waste by recycling some of
the salvageable materials and thus help the firm better utilize its material resources. The
better utilization of the firm’s resources, in turn, can enhance manufacturing performance.
Marketing performance is referred to as the degree of benefits gained by the firm as a
result of offering eco-friendly products, which customers perceive as products of greater
value as compared to their alternative products (Sousa, 2004; O’Sullivan and Abela, 2007).
In other words, customers perceive the firm’s eco-friendly products as ones with higher
value and thus their degree of satisfaction with those products would be higher. Increased
customer satisfaction and more increased value for customers are expected to improve the
firm’s market position and subsequently enhance its brand image, market share and
sales revenue.
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2.2 Developing hypotheses
As discussed in the previous sub-section, a competitive market environment can motivate
the firm to adopt GSCM practices. Indeed, Kumar et al. indicated that firms, which initiated
strategic innovations, such as GSCM practices and then changed the rules of competition
tended to create greater market opportunities. In other words, GSCM practices can be
motivated by the firm’s conscious effort to stay competitive in the market under increased
government and industry peer pressures for adopting environmental management initiatives
(Bergh, 2002). Based on these premises, we theorize that the firm can take different forms of
GSCM practices depending on its strategic reaction to the competitive market environment
and different choices of GSCM practices may have influenced the firm’s performances to the
varying extent. To elaborate, our review of the existing literature (e.g. Carter and Ellram, 1998;
Tibben-Lembke and Rogers, 2002; Zhu and Sarkis, 2004; Zhu et al., 2005) suggests that five
constructs (internal environmental management, green purchasing, eco-design, cooperation
with customers and reverse logistics) can potentially influence the firm’s organizational
performance, as shown in Figure 1. To identify factors that are essential for the successful
implementation of GSCM practices and assess their impact on the firm’s manufacturing and
marketing performances, we developed a number of hypotheses and then tested their validity
using empirical data. In the following section, the rationale for these hypothesized
relationships is described in detail.
2.2.1 A competitive market environment and GSCM practices. An external environment
such as the presence of a high level of competition in the market is known to be one of the key
drivers for GSCM practices (Walker et al., 2008; Diabat and Govindan, 2011). The rationale
being that the increased competition would compel firms to learn more about the changing
demands and preferences of customers and thus increase the need for adopting innovative
management practices such as GSCM practices. Especially, given that GSCM practices are
known to improve the firm’s competitive edge, those practices will provide an important
leverage for a firm seeking a competitive differentiator in the market (Rao and Holt, 2005;
Cooperation
with customers
4
Reverse
logistics
5
Competitive
market
environment
1
Manufacturing
performance
6
Marketing
performance
7
Green
purchasing
2
Internal
environmental
management
1
Eco-design
3
1
2
3
5
4
6
7
Figure 1.
The hypothetical
model
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Uchida and Ferraro, 2007; Masoumik et al., 2014). In addition, the increased institutional
pressure such as stricter environmental guidelines, rules and regulations imposed by the
government forced some companies to adopt and implement GSCM practices (Seman et al.,
2012). For example, after the EU enacted a series of environmental regulations such as the
Waste Electrical and Electronic Equipment (WEEE), Restriction of Hazardous Substances
and Eco-design for Energy using Products (EUP) directives which forbade manufacturers
from selling products that contained hazardous materials onto the markets, many European
firms started to execute a number of corporate environmental management practices.
Similarly, after the Korean Government induced a certification by Environment-Friendly
Company Designation System, ISO 14001, and the Low Carbon Product Certificate System
which took effect in November of 2011, a growing number of Korean firms have begun to
recognize their ability to meet stricter environmental standards as the important prerequisite
for joining the global supply chain network and thus embraced GSCM practices (Jang and Jo,
2006). In fact, with a greater role of the government in the export-driven economy such as
Korea, coercive pressures mainly originated from the government can be a catalyst for the
firm’s adoption of environmental management practices (Rivera, 2004). To summarize, both
peer pressures from the competitive industry rivals and social normative pressures from
environment-conscious customers may force the firm to adopt environmental management
practices such as GSCM practices (Ball and Craig, 2010; Sarkis et al., 2011). In light of the
above discussions, we hypothesized that:
H1a. A firm, which operates in a highly competitive market environment, will have a
high level of commitments to and participation in internal environment
management practices.
For the same reason, we can add the following hypotheses based on the various different
types of GSCM practices:
H1b. A firm, which operates in a highly competitive market environment, will have a
high level of commitments to and participation in green purchasing practices.
H1c. A firm, which operates in a highly competitive market environment, will have a
high level of commitments to and participation in eco-design practices.
H1d. A firm, which operates in a highly competitive market environment, will have a
high level of commitments to and participation in environmental cooperation
initiatives with its customers.
H1e. A firm, which operates in a highly competitive market environment, will have a
high level of commitments to and participation in reverse logistics practices.
2.2.2 GSCM practices and manufacturing performance. These goals can be higher
productivity, customer service, employee morale, shareholder returns, competitiveness and
financial gains such as a greater ROI. In this view, a number of prior studies (e.g. Carter
et al., 2000; Bowen et al., 2001; Zhu and Sarkis, 2004; Rao and Holt, 2005; Zhu et al., 2005;
Chien and Shih, 2007; Koh et al., 2007; Vachon and Klassen, 2008; Youn et al., 2011; Green
et al., 2012; Laosirihongthong et al., 2013) found that GSCM practices had positive influences
on organizational performance. However, most of these studies (e.g. Carter et al., 2000; Rao
and Holt, 2005) focused on the assessment of impact of a particular GSCM practices such as
green purchasing and green manufacturing on financial (or economic) performance
measures including profitability, net income, and cost of goods sold. To take into account
both financial and non-financial performances (e.g. faster order fulfillment and
manufacturing speed), we would measure organizational performance at a disaggregated
manufacturing level.
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From a financial standpoint, the firm’s ability to perform GSCM practices would help
reduce material waste and energy consumption needed to produce green (eco-friendly)
products and thus increase production cost savings. For example, Commonwealth
Edison saved $50m by managing its materials and equipment based on the eco-friendly life
cycle management approach. Also, the production and distribution of green products can
be translated into higher environmental quality. A decrease in quality failures will reduce
the need for rework, repair, and product recall and thus improve manufacturing efficiency
as a result of faster order fulfillment and shorter order cycle time. From the above
discussion, GSCM practices, which play a key performance driver role, can enhance
manufacturing performance. Therefore, we hypothesize that:
H2a. The greater the firm’s involvement in internal environmental management
practices, the higher the level of manufacturing performance.
H2b. The greater the firm’s involvement in green purchasing practices, the higher the
level of manufacturing performance.
H2c. The greater the firm’s involvement in eco-design practices, the higher the level of
manufacturing performance.
H2d. The greater the firm’s involvement in cooperation with its customers, the higher the
level of manufacturing performance.
H2e. The greater the firm’s involvement in reverse logistics practices, the higher the level
of manufacturing performance.
2.2.3 GSCM practices and marketing performance. The firm’s implementation of GSCM
practices reflects the firm’s flexibility to customize its products to meet the customer’s
specific environmental needs. In other word, the firm’s commitment to GSCM practices
would help the firm portray itself as a customer-friendly or customer-centric organization in
the customer’s eye. As such, the firm can strengthen its brand image (or reputation) and
subsequently increase its sales revenue and market share by attracting more customers who
favor green products over regular (environmentally harmful) products. In fact, Klassen and
McLaughlin (1996) observed that environmental management practices improved market
positions against competitors. Similarly, Zhu and Sarkis (2007) discovered that green
products helped the firm increase sales revenue and market share vis-a-vis less
environmentally committed competitors. Therefore, we hypothesize that:
H3a. The better the firm performs internal environmental management practices, the
higher the level of marketing performance.
H3b. The better the firm performs green purchasing practices, the higher the level of
marketing performance.
H3c. The better the firm performs eco-design practices, the higher the level of marketing
performance.
H3d. The higher the level of the firm’s cooperation with its customers, the higher the
level of marketing performance.
H3e. The better the firm performs reverse logistics practices, the higher the level of
marketing performance.
2.2.4 Differences in the extent of impact of GSCM practices on organizational performance.
Considering the diverse options of GSCM practices (e.g. green purchasing, eco-design, reverse
logistics) discussed earlier, it may be worth examining whether or not each option has a
different level of impact on organizational performance. For example, green purchasing may
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have the greater positive impact on manufacturing performance than eco-design, depending
on product characteristics, supplier capability and supplier network. Nevertheless, a vast
majority of the GSCM literature simply examined the causal relationship between GSCM
practices and firm performances without explaining the possibility that a different type of
GSCM practices will have the different level of impact on firm performance (Srivastava, 2007;
Min and Kim, 2012). The identification of particular GSCM practices which are more effective
in improving organizational performance than the others would help the firm develop the
more potent or best-in class GSCM practices. With that in mind, we postulate:
H4a. The extent of impact of GSCM practices on manufacturing performance differs
depending on the choice of a particular GSCM practice.
H4b. The extent of impact of GSCM practices on marketing performance differs
depending on the choice of a particular GSCM practice.
3. Research methodology
To test the hypotheses described in the previous section, we carried out the current study in
three phases: a pre-pilot, a pilot and a large-scale questionnaire survey. In the pre-pilot
phase, we generated potential survey items through theory development and a literature
review. In the pilot phase, we develop a structural equation model (SEM) along with the
identification of valid constructs. At the final stage, we conducted a large-scale survey via
mail questionnaires primarily targeting the Korean industry comprised of manufacturers
and their suppliers. Using the data obtained from this survey, we tested the validity of the
proposed SEM. Specific details of the current research methodology are described below.
3.1 Research sample
To validate above research propositions, we randomly targeted 330 multinational Korean
manufacturing firms listed on the KOSPI and KOSDAQ Stock Market that had been
engaged in export activities and adopted GSCM practices as a potential sample. To increase
a response rate from these firms, we hired a professional survey research organization
(The Statistics and Korea Information) in Korea and set up appointments with potential
survey respondents, followed by a personal visit from a group of surveyors. The surveys
were conducted between March 10, 2012 and May 15, 2012. Among 373 target survey
respondents, 322 survey responses were collected. Of these, 37 were considered invalid due
to missing or unreliable/unusual data. This produced 285 valid responses, with a response
rate of 76.4 percent, which far exceeded the targeted overall response rate of over 20 percent
for a valid assessment. Malhotra and Grover (1998) observed that a response rate over
20 percent was needed for a positive assessment of questionnaire survey results. To check
the presence of any non-response bias, which might favor certain outcomes due to
differences in opinions between survey participants and non-participants, we compared the
early responses (25 percent of the sample who responded the earliest) to late responses
(25 percent of the sample who responded the latest). As suggested by Armstrong and
Overton (1977) and Lambert and Harrington (1990), we conducted the t-test to see any
significant statistical differences in means between the first wave (early) responses and the
second wave (late) responses for all constructs. Our test results summarized in Table I
indicated that there was no presence of any non-response bias at α¼0.05.
As summarized in Table II, this sample consisted of firms from a wide array of
industries. These industries included pharmaceuticals (15.7 percent of the sample);
electronics and communications (15.0 percent); chemicals and plastics (11.7 percent);
electrical, mechanical, and appliances (10.0 percent); textiles and leather (9.3 percent); metals
(9.0 percent) and automotive (7.3 percent). With regard to firm size, firms with 500–799
employees were the most common (21.3 percent of the sample), followed by smaller firms
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with 50–99 employees (18.7 percent) and the ones with 1–49 employees (15.7 percent).
Large firms with employees more than 1,000 accounted for 8.0 percent of the sample. More
than one-tenth (13.4 percent) of the responding firms had annual sales revenue exceeding
$50m. About one-third (34.7 percent) of the responding firms reported the annual revenue of
$10–49.9m. More than one-fifth (27.6 percent) had sales revenue in the range of $5–9.9m;
while another one-fifth (24.3 percent) reported sales revenue below $4.9m. With respect to
the individual survey respondents, about one-third (34.3 percent) were general managers,
30.7 percent were directors and 15.0 percent of respondents were assistant managers.
Approximately two-fifths of the respondents represent general affairs departments
(40.0 percent), followed by management support departments, which encompass accounting,
human resource, and purchasing units (30.3 percent), production departments (12.0 percent),
and research and development (R&D) departments (10.7 percent). In this departmental
classification, we did not break down the general affairs department into smaller sub-units
with specific functions, because a growing number of Korean firms used the matrix
organizational structure where many managers were not clearly assigned to specific tasks
or roles as their daily work duties.
3.2 Questionnaire items
A survey questionnaire was comprised of 26 items divided into eight subsections. Among these
subsections, one of them was related a potential motivating factor for GSCM practices, five of
them deal with to different categories of GSCM practice types, while two of them were designed
to measure outcome-related variables including manufacturing and marketing performances
as shown in Table III. All items were scored on seven-point Likert-type scales measuring the
degree of agreement with item descriptions, ranging from 1 (not at all) to 7 (strongly).
To measure the responding firm’s competitive strength in the market with its rivals, we
developed questionnaire items similar to the ones used by Chandler and Hanks (1994) and
O’Cass and Weerawardena (2010). To measure the extent to which a responding firm performed
specific type of GSCM practices, we developed the items proposed by Carter and Ellram (1998),
Ronald et al., Zhu and Sarkis (2004), Zhu and Sarkis (2007) and Ninlawan et al. (2010).
In addition, we developed four items gauging manufacturing performance and three items
gauging marketing performance based on performance indicators proposed by Vachon and
Klassen (2008) and Youn et al. (2011). All of these items were summarized in Table III.
4. Analysis and results
The preliminary statistical validity of all the hypotheses presented earlier was checked, using
the Pearson correlation. For each construct, a composite score was computed by taking the
average scores of all items. The results are presented in Table IV. All correlations but three
between eco-design and marketing performance (with a correlation coefficient of 0.086),
cooperation with customers and marketing performance (with a correlation coefficient of 0.030)
Construct Mean differences p-value
Competitive market environment −0.2622 0.154
Internal environment management −0.2533 0.129
Green purchasing −0.3422 0.051
Eco-design 0.2946 0.170
Cooperation with customers −0.1482 0.566
Reverse logistics −0.1333 0.442
Manufacturing performance −0.2488 0.147
Marketing performance 0.0533 0.763
Table I.
A summary of the
non-response bias test
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and manufacturing performance and marketing performance (with a correlation coefficient of
0.104) are statistically significant at the 0.01 level. It appears that there are high correlations
among some of the constructs. In particular, the correlation coefficients are 0.611 (competitive
market environment and green purchasing), 0.585 (internal environment management and
green purchasing), 0.568 (competitive market environment and manufacturing performance)
and 0.547 (eco-design and cooperation with customers). To further examine causal
relationships among the constructs, we tested the proposed hypotheses with valid and
reliable scales that measured some critical dimensions of the constructs.
Category Industry characteristics Number of respondents Percentage
Business type Chemical, fabric, plastic 35 11.7
Pharmaceutical 47 15.7
Fabric, leather, fur, shoes 28 9.3
Paper, pulp 16 5.3
Metal material 27 9.0
Assembled metal products 14 4.7
Electronics and communication equipment 45 15.0
Electric, mechanical, appliance 30 10.0
Automobile and automotive parts 22 7.3
Ship machinery equipment, precision machinery 17 5.7
Others 19 6.3
Sub-total 300 100.0
Firm size 1–49 employees 47 15.7
50–99 employees 56 18.7
100–299 employees 46 15.3
300–499 employees 35 11.7
500–799 employees 64 21.3
800–999 employees 28 9.3
1,000 or more employees 24 8.0
Sub-total 300 100.0
Sales revenue Less than $5m 73 24.3
$5–9.9m 83 27.6
$10–49.9m 104 34.7
$50–99.9m 32 10.7
$100 million or more 8 2.7
Sub-total 300 100.0
Years of experience Less than 10 years 15 5.0
10–29 years 108 36.0
30–49 years 135 45.0
50 years or longer 42 14.0
Sub-total 300 100.0
Managerial position Section chief 103 34.3
Sub-section chief 92 30.7
Assistant Dept. Manager 45 15.0
Staff 42 14.0
Department Manager 11 3.7
Executive 7 2.3
Sub-total 300 100.0
Department General Affairs 120 40.0
Management Support 91 30.3
Research and Development 32 10.7
Production 36 12.0
Overseas Marketing 9 3.0
Others 12 4.0
Sub-total 300 100.0
Table II.
Profile of the
respondents
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Items Mean SD
Competitive market environment (three items) 4.6589 1.1088
CME1. Many competitors in the product market 5.0567 1.3807
CME2. Much institutional and market pressure for eco-friendly initiatives 4.1500 1.4239
CME3. Big market uncertainty related to environmental problems 4.7700 1.4319
Internal environmental management ( four items) 4.3242 1.0958
IEM1. Active participation and involvement of CEOs inn GSCM 4.5533 1.6842
IEM2. Middle managers’support for GSCM 3.7367 1.3736
IEM3. Cooperation among departments to improve the eco-friendliness of products 4.7167 1.4340
IEM4. Obtaining ISO 14001 certificates 4.2900 1.3828
Green purchasing (three items) 5.2067 1.0478
GRP1. Cooperation with suppliers to enhance environmental quality 4.9867 1.3136
GRP2. Use ISO 14001 as an important factor for selecting suppliers 5.3933 1.1958
GRP3. Evaluate green practices of subcontractors (e.g. second-tier) suppliers 5.2400 1.2732
Eco-design (three items) 4.1833 1.2669
ED1. Design products to reduce material waste or energy consumption 4.1267 1.4156
ED2. Design products to increase the reuse and recycle of material and parts 4.2400 1.3864
ED3. Design to remove or reduce hazardous material throughout the manufacturing process 3.8700 1.3635
Cooperation with customers (three items) 4.0444 1.1862
CC1. Cooperate with customers for eco-design 3.9733 1.4280
CC2. Cooperate with customers for cleaner production (eco-friendly production) 3.8533 1.4348
CC3. Cooperate with customers for green packaging 4.3067 1.6416
Reverse logistics (three items) 4.0267 1.2360
RL1. Collect and take back end-of-life cycle products and parts 4.4467 1.4926
RL2. Collect and take back packaging materials and other used (non-virgin) materials 4.1233 1.4241
RL3. Use information systems to handle reverse logistics (e.g. waste collection) 3.9300 1.3507
Manufacturing performance ( four items) 4.1989 1.0345
POM1. Reduction in production cost as compared to competitors 4.2100 1.4257
POM2. Faster order fulfillment 4.0633 1.1989
POM3. Faster manufacturing process 4.3233 1.2344
POM4. Improved capacity to meet the manufacturing schedule and deadline 4.3000 1.2390
Marketing performance (three items) 4.5044 1.0644
MP1. Improved corporate brand image 4.4633 1.3118
MP2. Increased sales 4.9167 1.4618
MP3. Increased market share 4.1333 1.4197
Table III.
Description of items
Constructs Mean SD CME IEM GRP ED CC RL POM MP
1. CME 4.6589 1.1088 1
2. IEM 4.3242 1.0958 0.412** 1
3. GRP 5.2067 1.0478 0.611** 0.585** 1
4. ED 4.1833 1.2669 0.295** 0.211** 0.343** 1
5. CC 4.0444 1.1862 0.351** 0.344** 0.298** 0.547** 1
6. RL 4.0267 1.2360 0.356** 0.305** 0.373** 0.374** 0.405** 1
7. POM 4.1989 1.0345 0.568** 0.406** 0.409** 0.236** 0.102** 0.144* 1
8. MP 4.5044 1.0644 0.138* 0.280** 0.337** 0.086 0.030 0.145* 0.104 1
AVE (average variance extracted) 0.444 0.409 0.513 0.642 0.474 0.589 0.471 0.416
Notes: CME, competitive market environment; IEM, international environmental management; GRP,
green purchasing; ED, eco-design; CC, cooperation with customers; RL, reverse logistics; POM, manufacturing
performance; MP, marketing performance. *po0.1; **po0.01
Table IV.
The correlation matrix
of each construct
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4.1 The causal model
An SEM was used to test and estimate the causal relationships amongst various constructs
(Bollen and Long, 1993). In general, the SEM is comprised of two elements: the measurement
model and the structural model. The measurement model in SEM is used to measure and assess
the reliability and validity of latent variables, whereas the structural model is applied to
investigate the complex interrelations among latent variables ( Jöreskog and Sörbom, 1989).
After measurement scales were checked for reliability and validity, we conducted path analysis
as a testing procedure using the AMOS 17.0 program (Kim, 2008). Also, a covariance matrix
was used for structure analysis, while reviewing the goodness of fit index through the
confirmatory factor analysis. In SEM, the goodness of fit index establishes whether the model
is acceptable. If the model is acceptable, we can check to see whether specific paths are
significant. A result of the initial model showed the indices of χ
2
¼429.592 (df ¼271,
p-value ¼0.000), χ
2
/df ¼1.585, GFI ¼0.903, AGFI ¼0.875, CFI ¼0.920, TLI ¼0.904 and
RMSEA ¼0.044. This result indicates a relatively good fitness of the model (with the GFI
exceeding 0.90). However, three variables including ECDN3, RL1 and POL4 were excluded from
the model, since their factor loading was less significant and empirical loading was noticeably
lower than the cut-off value of 0.5 (Hair et al., 2006). A goodness of fit index of the model after
recalculation was χ
2
¼419.485 (df ¼202, p¼0.190), χ
2
/df ¼1.087, GFI ¼0.990, AGFI ¼0.920,
CFI ¼0.990, TLI ¼0.988 and RMSEA ¼0.017. In particular, we accepted a null hypothesis
(Σ¼Σ(θ)), since χ
2
value was insignificant. Other fitness indices were excellent and thus
strongly supported the theoretical model of this study, which matched well with the actual data.
After evaluating the fitness of the model, we verified the reliability and validity of the
measuring items. These results are recapitulated in Table V. First, the internal consistency
method based on the Cronbach’sαcoefficient was used to verify the reliability of the items
measuring each construct. The reliability test results showed that αcoefficients were in the
range of 0.636–0.777 which mostly exceeded an acceptable reliability criterion of 0.7
(Nunnally, 1978). Next, the convergent validity of measuring items was verified. The
convergent validity turned out to be significant for all path coefficients with the composite
reliability (C.R ⩾0.6~0.7) and the average variance extracted (AVE⩾0.5) value (Bagozzi and
Yi, 1988; Hair et al., 2006). Herein, it is noted that the Cronbach’sαvalue can be reduced if the
test length is either short or the number of items tested issmall (Nunnally, 1978; Streiner, 2003;
Hair et al., 2006). For example, the Cronbach’sαvalue (0.636) for the small (only three) number
of items considered for that construct adversely affected market performance. Table V shows,
all path coefficients are statistically significant. Also, the CR value exceeded 0.6 and AVE was
above or near 0.5 verifying the convergent validity of this study. The discriminant validity
was checked using the stringent method proposed by Fornell and Larcker (1981) which
examined if AVE was greater than the correlation squared values of all constructs
(AVEWΦ
2
). The results indicated a robust discriminant validity of the study unit, since
AVEs of the two latent factors were higher than the squares of the correlation coefficients of
the two latent factors as summarized in Table IV. For example, the correlation coefficients of
CME factor and GRP factor, which had the highest correlation with the correlation coefficient
of 0.611 and its square of 0.3733. Those two factors’square is smaller than both the AVE of
CME factor (Φ(CME and GRP)
2
¼0.3733 oAVE of CME ¼0.444) and the AVE of GRP
factor (Φ(CME and GRP)
2
¼0.3733 oAVE of GRP ¼0.513).
4.2 Results of hypotheses testing
The research hypotheses were tested using the SEM displayed in Figure 2. A goodness of fit
for the path analysis model was χ
2
¼330.039 (df ¼215, p¼0.000), χ
2
/df ¼1.535,
GFI ¼0.910, AGFI ¼0.885, CFI ¼0.936, TLI ¼0.924 and RMSEA ¼0.042. This result
indicates an excellent level of goodness of fit of the model. Accordingly, detailed hypotheses
tests were performed.
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Our test results confirmed that the competitive market environment was the major driving
force for GSCM practices adopted by Korean firms. To elaborate, the competitive market
environment has a positive impact on the execution of internal environmental
management (standardized β¼0.573, t-value ¼5.934, p-value ¼0.0001), green
purchasing (standardized β¼0.744, t-value ¼7.032, p-value ¼0.0001), eco-design
(standardized β¼0.430, t-value ¼4.295, p-value ¼0.0001), cooperation with customers
(standardized β¼0.484, t-value ¼5.350, p-value ¼0.0001), and reverse logistics
(standardized β¼0.489, t-value ¼5.190, p-value ¼0.0001). In particular, green
purchasing turned out to be most commonly used GSCM practices (Mean ¼5.2067,
SD ¼1.0478) as shown in Table III. Also, the ISO 14001 certification was the most
important factor for selecting the eco-friendly suppliers as part of green purchasing
practices (Mean ¼5.3933, SD ¼1.1958).
In the next phase, we tested to see which types of GSCM practices helped the
responding firms improve their manufacturing performance. Our test results revealed that
internal environmental management, green purchasing and econ-design have positive
effects on manufacturing performance. Indeed, we found a positive relationship
between the internal environment management and manufacturing performance
with the standardized βcoefficient of 0.298 and t-value of 3.237 ( p-value ¼0.001).
Constructs Items Standardized loading t-value Cronbach’sαCR AVE
Competitive market environment CME 1 0.596 –0.689 0.701 0.444
CME 2 0.587 7.603***
CME 3 0.796 8.643***
Internal environmental management IEM 1 0.690 –0.730 0.733 0.409
IEM 2 0.548 7.732***
IEM 3 0.648 8.800***
IEM 4 0.663 8.939***
Green purchasing GRP 1 0.713 –0.775 0.808 0.513
GRP 2 0.749 10.755***
GRP 3 0.738 10.657***
Eco-design ECDN 1 0.742 –0.777 0.781 0.642
ECDN 2 0.856 8.771***
Cooperation with customers CC 1 0.735 –0.695 0.723 0.474
CC 2 0.798 9.893***
CC 3 0.496 7.400***
Reverse logistics RL 2 0.797 –0.739 0.741 0.589
RL 3 0.737 7.073***
Manufacturing performance POL 1 0.649 –0.722 0.728 0.471
POL 2 0.722 8.682***
POL 3 0.687 8.541***
Marketing performance MP 1 0.470 –0.636 0.661 0.416
MP 2 0.883 5.289***
MP 3 0.497 5.986***
Fit statistics
χ
2
219.485 (df ¼202, p¼0.190)
Normed χ
2
1.087
Goodness-of-fit index (GFI) 0.990
Adjusted goodness of fit (AGFI) 0.920
Comparative fit index (CFI) 0.990
Tucker–Lewis index (TLI) 0.988
Root mean-square error of approximation (RMSEA) 0.017
Note: ***po0.001
Table V.
Confirmatory factor
analysis of
overall constructs
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Similarly, there is a strong positive relationship between green purchasing and
manufacturing performance with the standardized βcoefficient of 0.305 and t-value of
3.142 ( p-value ¼0.002). Though not strong, we also found a positive relationship between
eco-design and manufacturing performance with the standardized βcoefficient of
0.127 and t-value of 1.727 ( p-value ¼0.084). On the contrary, we found that cooperation
with customers (standardized β¼0.120, t-value ¼1.481, p-value ¼0.139) and reverse
logistics (standardized β¼0.033, t-value ¼0.401, p-value ¼0.688) do not improve
manufacturing performance.
Our test result showed that not all of GSCM practices significantly contributed
to the improvement of marketing performance as shown in Figure 2. To elaborate, we
found that internal environmental management has a positive impact on the firm’s
marketing performance (standardized β¼0.161, t-value ¼1.753, p-value ¼0.080).
Also, we discovered that green purchasing has a positive impact on the firm’s
marketing performance (standardized β¼0.270, t-value ¼2.583, p-value ¼0.010). On the
other hand, eco-design has no significant impact on marketing performance (standardized
β¼0.007, t-value ¼0.097, p-value ¼0.923). Neither cooperation with customers
(standardized β¼0.117, t-value ¼1.398, p-value ¼0.162) nor reverse logistics
(standardized β¼0.026, t-value ¼0.316, p-value ¼0.752) has any significant impact on
marketing performance.
Internal environmental
management
(R2=0.328)
Supported
Not Supported
Competitive
Market
environment
1
Green
purchasing
(R2=0.555)
Eco-design
(R2=0.185)
Cooperation with
customers
(R2=0.234)
Reverse logistics
(R2=0.239)
Marketing
performance
(R2=0.122)
Manufacturing
performance
(R2=0.270)
11= 0.298
21= 0.161
12= 0.305
22= 0.270
13= 0.127
23= 0.007
14= 0.120
15= 0.033
24= 0.117
25= 0.026
51= 0.489
41= 0.484
31= 0.430
21= 0.744
11= 0.573
Notes: Goodness-of-fit indices: 2= 330.039 (df =215, p= 0.000); Normed 2= 1.535;
GFI = 0.910; AGFI = 0.885; CFI = 0.936; TLI = 0.924; RMSEA = 0.042
Figure 2.
Structural
model results
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Going a step further, we evaluated the relative impact of each type of GSCM practices on the
firm’s manufacturing and marketing performances by estimating the path coefficients in the
basic model and χ
2
changes in the constrained model. As shown in Table VI, internal
environmental management had a slightly different path coefficient from green purchasing.
Table VI showed that there was a distinctive difference in the extent of impact on manufacturing
performance among different types of GSCM practices. In particular, it should be noted that
green purchasing has the greater impact on manufacturing performance than eco-design
(Δχ
2
¼5.316Wχ
2
0.05(1) ¼3.84, Δdf ¼1), cooperation with customers (Δχ
2
¼11.959Wχ
2
0.05
(1) ¼3.84, Δdf ¼1) and reverse logistics (Δχ
2
¼9.054Wχ
2
0.05(1) ¼3.84, Δdf ¼1).
Similar results were obtained when we examined the statistical differences in the relative
magnitude of impact of each GSCM practice on marketing performance as summarized in
Table VII. Once again, green purchasing turned out to be more effective in improving the
firm’s marketing performance than eco-design, cooperation with customers and reverse
logistics. However, it should be noted that although internal environmental management
(β¼0.161) showed the lower absolute value of impact on marketing performance than
green purchase ( β¼0.270), their path difference was statistically insignificant.
To summarize, among various types of GSCM practices, green purchasing has the
greatest impact on both manufacturing and marketing performances. That is to say, green
purchasing turned out to be most effective in improving both manufacturing and marketing
performances. The possible explanation for this finding is that green purchasing precedes
the cooperative partnership with the buying firm’s suppliers (including lower-tier suppliers)
which ensures inter-organizational efforts and thus is more likely to practice eco-friendly
practices with the synergistic effect. In addition, green purchasing can be translated into
environmental quality at the source, which can be more effective in reducing quality failure
costs, which, in turn, lower overall production cost, while helping to build positive corporate
image for customers. In addition, internal environmental management positively influenced
both manufacturing and marketing performances, but not as much as green purchasing.
Path comparison Estimate Unconstrained χ
2
(df) Constrained χ
2
(df) Δχ
2
(df ¼1) Results
IEM 0.298 22.373 (32) 22.434 (33) 0.061 IEM ¼GRP
GRP 0.305
IEM 0.298 21.235 (24) 23.714 (25) 2.479 IEM ¼ED
ED 0.127
IEM 0.298 36.257 (32) 46.543 (33) 10.286 IEMWCC
CC 0.120
IEM 0.298 16.837 (24) 23.235 (25) 6.498 IEMWRL
RL 0.033
GRP 0.305 15.765 (17) 21.081 (18) 5.316 GRPWED
ED 0.127
GRP 0.305 33.999 (24) 45.958 (25) 11.959 GRPWCC
CC 0.120
GRP 0.305 11.967 (17) 21.021 (18) 9.054 GRPWRL
RL 0.033
ED 0.127 25.880 (17) 28.776 (18) 2.896 ED¼CC
CC 0.120
ED 0.127 10.673 (11) 12.077 (12) 1.404 ED¼RL
RL 0.033
CC 0.120 29.251 (17) 29.478 (18) 0.227 CC ¼RL
RL 0.033
Notes: IEM, international environmental management; GRP, green purchasing; ED, eco-design; CC,
cooperation with customers; RL, reverse logistics
Table VI.
Path differences of
GSCM practices for
manufacturing
performance
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On the other hand, cooperation with customers and reverse logistics had no impact on
manufacturing and marketing performances. In other words, neither the cooperation with
customers nor reverse logistics was effective in improving manufacturing and marketing
performances. The complete results of hypotheses testing are summarized in Table VIII.
Path comparison Estimate Unconstrained χ
2
(df) Constrained χ
2
(df) Δχ
2
(df ¼1) Results
IEM 0.161 25.322 (32) 26.128 (33) 0.806 IEM ¼GRP
GRP 0.270
IEM 0.161 17.126 (24) 21.241 (25) 4.115 IEMWED
ED 0.007
IEM 0.161 30.592 (32) 38.065 (33) 7.473 IEMWCC
CC 0.117
IEM 0.161 29.555 (24) 32.931 (25) 3.376 IEM ¼RL
RL 0.026
GRP 0.270 13.743 (17) 24.736 (18) 10.993 GRPWED
ED 0.007
GRP 0.270 29.630 (24) 42.230 (25) 12.600 GRPWCC
CC 0.117
GRP 0.270 24.351 (17) 30.437 (18) 6.086 GRPWRL
RL 0.026
ED 0.007 10.918 (17) 11.434 (18) 0.516 ED ¼CC
CC 0.117
ED 0.007 14.952 (11) 15.449 (12) 0.497 ED ¼RL
RL 0.026
CC 0.117 27.759 (17) 29.611 (18) 1.852 CC ¼RL
RL 0.026
Table VII.
Path difference
of GSCM practices
for marketing
performance
Hypothesis
sign Path Estimate t-value Results
H1 (+)Competitive market environment →GSCM practices
H1a Competitive market environment →Internal environment
management
0.573 5.934 ◌
H1b Competitive market environment →Green purchasing 0.744 7.032 ◌
H1c Competitive market environment →Eco-design 0.430 4.295 ◌
H1d Competitive market environment →Cooperation with customers 0.484 5.350 ◌
H1e Competitive market environment →Reverse logistics 0.489 5.190 ◌
H2 (+)GSCM practices →Manufacturing performance
H2a Internal environment management →Manufacturing performance 0.298 3.237 ◌
H2b Green purchasing →Manufacturing performance 0.305 3.142 ◌
H2c Eco-design →Manufacturing performance 0.127 1.727 ◌
H2d Cooperation with customers →Manufacturing performance 0.120 1.481 ×
H2e Reverse logistics →Manufacturing performance 0.033 0.401 ×
H3 (+)GSCM practices →Marketing performance
H3a Internal environment management →Marketing performance 0.161 1.753 ◌
H3b Green purchasing →Marketing performance 0.270 2.583 ◌
H3c Eco-design →Marketing performance 0.007 0.097 ×
H3d Cooperation with customers →Marketing performance 0.117 1.398 ×
H3e Reverse logistics →Marketing performance 0.026 0.316 ×
H4 Evaluation of GSCM practices for a path difference in manufacturing performance Δ
H5 Evaluation of GSCM practices for a path difference in marketing performance Δ
Notes: ◌¼supported; Δ¼partially supported; and ×¼not supported
Table VIII.
A summary
of hypotheses
test results
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5. Key findings and managerial implications
This section summarizes key findings of our GSCM study and their practical implications
for firms, which cope with the challenges of more volatile supply chain operations in an era
of environmental crisis. Noteworthy findings include.
First, a competitive market environment was found to have a direct positive impact on
GSCM practices encompassing internal environmental management, green purchasing,
eco-design, cooperation with customers and reverse logistics. This finding makes sense in
that more competitive market structures are associated with the firm’s higher productivity
and its subsequent performance improvement. This finding implies that competitive
pressure (especially in the export market) encourages firms to adopt GSCM practices
becausesuchpressureallowsthemtostaycompetitive with their rivals by offering
eco-friendly products and highlighting company-wide environment commitments.
For example, according to the Waste Electrical and Electronic Equipment (WEEE)
DirectiveandtheComplianceinAdvanceand Supporting System (COMPASS) mandated
by the EU, firms exporting their electronic products to the EU market are required to
specify the percentage of their recycled and/or reused parts/components. Thus, the
exporting firm that fails to comply with these directives cannot compete in the EU market.
Likewise, a number of other countries such as the United Arab Emirates (UAE), Sweden
and Taiwan required that the electronic manufacturers reveal the use of hazardous
materials for electronic devices. Thus, the firm’s adoption of GSCM practices can make a
difference in its competitive position in export markets with tougher environmental
standards. Another possible explanation for this finding is that tougher environmental
standards in the import country became the additional export barrier for Korean firms
competing in that country and thus those Korean firms tended to embrace GSCM practices
to overcome such a barrier. In fact, Ederington and Minier (2003) discovered that the
import country’s tough environmental standards could become the significant secondary
export barrier for exporting firms. In addition, while synthesizing the past decades of the
literature identifying various factors influencing export performance, Zou and Stan (1998)
observed that export barriers could have a significant adverse impact on the export
performance. As such, exporting Korean firms had to meet the import country’s
environmental standards by making commitments to GSCM practices.
However, this finding also indicates that the firms are more reactive to sustainability
causes than being proactive. The possible explanation for this tendency is that the firms
are often concerned about the upfront cost associated with a GSCM practices and a
difficulty in realizing hidden values (e.g. improved public relation, brand image, reduced
penalty for pollution) of the GSCM practices. In other words, viewing GSCM practices as a
cost center not as a profit center seems to be the hindrance to GSCM practices and the
firm’s inability to articulate the clear business value of GSCM practices forces its
management to take a less proactive stance for GSCM practices. To change this stance, the
firm needs to develop multi-dimensional performance metrics such as the balanced
scorecard that can help the firm evaluate the outcomes of GSCM practices from a number
of different angles and potential payoffs.
Second, among various GSCM practices, both internal environmental management and
green purchasing were found to influence both manufacturing and marketing performances
to the greater extent than the others did. To elaborate, eco-design has a weak positive effect
on manufacturing performance, while not affecting marketing performance at all. Neither of
cooperation with customers and reverse logistics has any significant impact on
manufacturing and marketing performances. This finding defies the conventional
wisdom that the efficient and effective deployment of the firm’s resources (e.g. returned
products) through reverse logistics can increase the firm’s competitive advantage and
subsequently enhance its performance (Daugherty et al., 2005). Somewhat incongruent with
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the finding of the earlier study by Green et al. (2008) indicating that customer satisfaction
can positively impact firm performance, this finding also implies that the firm’s
consideration of customer feedback for the improvement of GSCM practices and its efforts
to provide the customer with extended life cycle support did not pay any dividend for
enhancing manufacturing and marketing performances. Given this varying degree of
impact of GSCM practices on firm performance, the firm should prioritize the more effective
GSCM practices such as internal environmental management and green purchasing over the
less effective GSCM practices such as eco-design, cooperation with customers and reverse
logistics. That is to say, a greater amount of resource investment and managerial efforts
should be geared toward internal environmental management and green purchasing
practices to maximize the GSCM payback.
Finally, when pairwise comparisons of GSCM practices are made with respect to their
extent of impact on manufacturing and marketing performances, green purchasing was
found to dominate all but one GSCM practices, whereas internal environmental
management dominated two other GSCM practices. As such, we can conclude that
green purchasing is the most effective GSCM practice among five different options of GSCM
practices. Considering this finding, green purchasing should be at the forefront of GSCM
practices. As such, there is a need to develop more specific green purchasing strategies
amenable to company-wide environment guidelines and government rules/regulations.
These strategies may include:
•the development of clear green purchasing criteria such as the use of highly
reusable, recyclable, recoverable and salvageable product components and the
selection of suppliers with proven records of environmental compliances or
environmental certificates;
•constant communication with suppliers, raise of their awareness of environmental
issues, solicitation of their feedback on environmental practices and their early
involvement in new green product development; and
•continuous monitoring of green purchasing practices and the periodic assessment of
green purchasing efforts in terms of their impact on firm performance.
The investigation of the aforementioned strategies for their impact on firm performance can
be an intriguing future research agenda. In addition, the current study can be further
extended to consider the different categories of firm performance such as financial
performance or the firm’s public image and brand recognition. Another line of future
research that is worth pursuing includes the cross-cultural (national) comparison of the
impact of GSCM practices on firm performance. Speaking of the firm performance which
often relied on the self-assessment of survey respondents in the existing literature, it would
be intriguing to see if future research can develop a new yardstick for objectively assess the
firm performance in non-financial terms.
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About the authors
Dr Seok-Beom Choi is Full Professor of International Trade in the Department of Chinese Economics
and Trade at the Cheju Halla University.
Dr Hokey Min is James R. Good Chair in Global Supply Chain Strategy in the Department of
Management at the Bowling Green State University. He has published more than 185 articles in
numerous scholarly journals including International Journal of Physical Distribution and Logistics
Management. Dr Hokey Min is the corresponding author and can be contacted at: hmin@bgsu.edu
Dr Hye-Young Joo is Assistant Professor of International Trade at the Korea e-Trade Research
Institute of Chung-Ang University.
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