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International Journal of Logistics Research and
Applications
A Leading Journal of Supply Chain Management
ISSN: 1367-5567 (Print) 1469-848X (Online) Journal homepage: http://www.tandfonline.com/loi/cjol20
Assessing the impact of green supply chain
practices on firm performance in the Korean
manufacturing industry
Seok-Beom Choi, Hokey Min, Hye-Young Joo & Han-Byul Choi
To cite this article: Seok-Beom Choi, Hokey Min, Hye-Young Joo & Han-Byul Choi (2017)
Assessing the impact of green supply chain practices on firm performance in the Korean
manufacturing industry, International Journal of Logistics Research and Applications, 20:2,
129-145, DOI: 10.1080/13675567.2016.1160041
To link to this article: http://dx.doi.org/10.1080/13675567.2016.1160041
Published online: 21 Mar 2016.
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Assessing the impact of green supply chain practices on firm
performance in the Korean manufacturing industry
Seok-Beom Choi
a
, Hokey Min
b
, Hye-Young Joo
c
and Han-Byul Choi
d
a
Department of Chinese Economics and Trade, ChejuHalla University, Jeju City, Korea;
b
James R. Good Chair in Global
Supply Chain Strategy, Department of Management, College of Business Administration, Bowling Green State
University, Bowling Green, OH, USA;
c
Korea e-Trade Research Institute, Chung-Ang University, Seoul, Korea;
d
College
of Business & Economics, Chung-Ang University, Seoul, Korea
ABSTRACT
As a growing number of customers tend to view corporate social
responsibility (CSR) as a key purchase decision criterion, demands for
CSR including environmental sustainability have accelerated in today’s
business world. To meet such demands, many firms consider embracing
environment-friendly business practices. However, many firms are still
hesitant to implement those practices due to sceptical views about their
real managerial benefits. Although the previous literature confirms the
positive link between a firm’s commitment to environmental
sustainability and its performance, the varying degree of impact of
different kinds of environment-friendly supply chain practices on the
firm’s operational performance is still unknown. To fill the void left by
prior research, this paper aims to classify various types of green supply
chain management (GSCM) practices and then assess the impact of each
of these distinct types on the firm’s operational performances (especially
manufacturing and marketing performance). Also, this paper examines
how the firm’s organisational profiles such as firm size affect the
particular firm’s choice of GSCM practices. Our experimental results
reveal that the chosen type of GSCM practices influences the firm’s
performance differently.
ARTICLE HISTORY
Received 2 July 2015
Accepted 25 February 2016
KEYWORDS
Green supply chain
management; practice types;
firm performance; Korean
manufacturing firms;
multivariate analysis
1. Introduction
Over the last decade, the planet Earth has suffered from a rare cycle of unprecedented heat waves,
cold spells, droughts, floods, and wildfires. For example, the summer of 2012 was the warmest sum-
mer on record, whereas the winter of 2014 was the coldest winter on record for the USA. Many sus-
pect that this extreme weather pattern is a vital sign of climate changes induced by human activities.
In particular, the emission of harmful carbon dioxide into the air is considered the main culprit for
these climate changes. As a matter of fact, the level of atmospheric carbon dioxide reached nearly 380
parts per million in 2014, which was the highest record for 650,000 years of this planet’s history
(NASA 2015). This rapid rise in the carbon dioxide level in the air is attributed to human activities
associated with travel (use of vehicles), waste disposal, product manufacturing, and energy creation.
Recognising a link between human activities (especially industrial activities) and environmental
degradation, a growing number of today’s customers have begun to pay more attention to the
firm’s environmental commitment than ever before and view it as the firm’s strength. This changed
attitude of customers has prompted firms to consider leveraging their environmental friendliness as
the major selling point. However, the dilemma of the firm’s environmental commitment lies in the
© 2016 Informa UK Limited, trading as Taylor & Francis Group
CONTACT Hokey Min hmin@bgsu.edu
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS, 2017
VOL. 20, NO. 2, 129–145
http://dx.doi.org/10.1080/13675567.2016.1160041
fact that the firm’s environmental friendliness rarely comes free and its payoffs are not clearly
known.
To deal with this dilemma, a series of attempts have been made to assess the impact of environ-
mental (environment-friendly) management on the firm’s performance. The focal point of these
attempts is to determine whether or not environmental management is worthy of investment and
a managerial focus. For example, Klassen and McLaughlin (1996) discovered a strong link between
the firm’s environmental management initiatives and its financial performance, as measured by stock
market performance. Similarly, Melnyk, Sroufe, and Calantone (2003) observed that the extent/
maturity of the firm’s environmental management system (EMS) was directly correlated with its per-
formance, as expressed by perceived cost saving, lead time reduction, product quality improvement,
market position improvement, and corporate reputation enhancement. Later, Montabon, Sroufe,
and Narasimhan (2007) confirmed that the firm’s environmental management practices such as
remanufacturing, environment-friendly product design, and surveillance of the market for environ-
mental innovation were positively associated with the firm’s performance, as expressed by sales
growth and return on investment. A plethora of other studies including the ones conducted by Flor-
ida (1996), Berry and Rondinelli (1998), Claver et al. (2007), Yang, Hong, and Modi (2011), Schrettle
et al. (2014), and Lannelongue, Gonzalez-Benito, and Gonzalez-Benito (2015) verified the link
between the firm’s environmental management practices and its performance.
While there is little doubt that a firm’s commitment to environmental management practices
could lead to its performance improvement, the aforementioned literature rarely explained which
particular environmental management practices or strategies were more effective in improving
the firm’s performance. More importantly, the aforementioned prior literature did not examine
how the collected environmental management efforts of multiple firms belonging to the same supply
chain network affected those firms’performance. Recognising 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 environ-
ment-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 stage) of GSCM practices tended to reap the 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 find-
ing 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 management practices, whereas Just
In Time (JIT) manufacturing practices could hurt the positive influence of GSCM practices. Follow-
ing suit, a number of other studies (Zhu, Sarkis, and Geng 2005; Vachon and Klassen 2006; Chien
and Shih 2007; Zhu and Sarkis 2007; Shang, Lu, and Li 2010; Youn et al. 2011; Chan et al. 2012;
Green et al. 2012; Zhu, Sarkis, and Lai 2012a,2012b; Abareshi and Molla 2013; Zhu, Sarkis, and
Lai 2013; Choi and Hwang 2015; Dubey and Gunasekaran 2015; Laari et al. 2016) examined the
relationship between GSCM practices and their adopters’performance to confirm the influence of
GSCM practices on firm performance.
Although a vast majority of the existing literature reported the positive influence of GSCM prac-
tices on organisational performance as summarised in Table 1, we should not be blindsided by its
potential adverse impacts summarised in Table 1. That is to say, the undisciplined adoption of
GSCM practices without formulating wise implementation strategies can not only undermine
their effectiveness, but also inflict damages to the firm’s competitiveness and supply chain
130 S.-B. CHOI ET AL.
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 clusters and then identifying the
winning formula among them. This paper also checks to see whether or not the firm’s organisational
profile such as firm size can amplify the impact of chosen GSCM practices on the firm’s operational
performance. Put simply, this paper attempts to answer two fundamental research questions:
(1) Do firms have choices of distinctive GSCM practice types? What motivates those practices?
(2) Does the extent of the impact of GSCM practices on the firm’s operational performance differ
depending on the firm’s chosen types of GSCM practices?
2. Research design
Due in part to scarce natural resources, the Government of South Korea (Korea hereafter) has long
been known for its export-driven economic policy. To sustain such a policy, Korea encouraged its
multinational firms to comply with the clauses of the Free Trade Agreement with foreign countries
including the USA and Europe. One of those increasingly important clauses is strict adherence to
international environmental rules and standards. In fact, the Korea Ministry of Commerce, Industry,
and Energy (KMOCIE) launched the Development of and Support for Environmental Technology
Act of 1994 and enacted the Act on the Promotion of the Conversion into Environmentally-Friendly
Industrial Infrastructure in 1995 to promote cleaner production technology, foster environment-
friendly facilities, and transfer and disseminate the know-how of large firms’environmental manage-
ment initiatives to small- and medium-sized enterprises. In 2003, the Korean government
established the policy to expand environmental management initiatives throughout the entire supply
chain. Its main policy goal was to improve the Korean supplier’s environmental performance by soli-
difying a relationship between the focal company (i.e. buying firm) and its Korean supplier. For
instance, Finland-based Nokia required its Korean part suppliers to comply with its environmental
standards. Similarly, Sony demanded that its Korean component manufacturers adhere to Sony-
specific environmental standards that only allowed a limited amount of hazardous materials such
as cadmium and lead for its components. These examples illustrate that the Korean firm’s ability
Table 1. Pros and cons of GSCM.
Affected areas Potential benefits (+) Potential drawbacks (−)
Environmental
performance
.Reduction in greenhouse gases and waste
pollutants detrimental to the environment
.Increased resistance among suppliers and the
subsequent decrease in a pool of qualified
suppliers who can produce green materials,
components, and products
Technical
performance
.Greater motivation for the use of innovative
technology that is needed to develop
environment-friendly products
.More time to develop new products
Economic
performance
.Reduced material cost by using energy efficient
materials and/or reused/recycled materials
.Reduced disposal cost by decreasing the
sources of hazardous materials and wastes
.Greater revenue-generating opportunities with
environmentally conscious customers
.Reduced penalties for the violation of
environmental laws and regulations
.Greater initial investment in environmental
management initiatives
.Cost of environmental certifications (e.g. ISO
14000/14001)
.Higher cost of buying environment-friendly
materials, components, and products from a
smaller pool of green suppliers
Regulatory
compliance
.Compliances with stricter domestic and
international regulations will foster the positive
corporate image of the firm
.Different environmental standards and rules
across the different countries will put a strain on
the firm’s global supply chain activities
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 131
to meet stricter environmental standards has become the most important prerequisite for joining the
global supply chain network. The evidence of such ability includes certification by ISO 14001, the
government-induced Environment-Friendly Company Designation System, and the recent Low Car-
bon Product Certificate System which took effect in November 2011, as summarised in Table 2. This
government-induced certification led to a gradual increase in the number of Korean firms that met
ISO 14001 standards as shown in Figure 1.
Although environmental certification may have helped Korean firms meet order-qualifying cri-
teria for their buyers, it still would not give Korean firms a competitive advantage over their rivals
for securing the business contracts which mandate environmental compliance. Thus, Korean firms
are pressured to formulate more aggressive environmental strategies as their competitive differentia-
tor. In other words, the implementation of GSCM seems to be driven by the firm’s desire or long-
term strategic goal to stay competitive in the global market rather than the firm’s consciousness of
corporate social responsibility (CSR) (Park and Ghauri 2015). A strategic dilemma of such strategies
is created by the fact that those strategies necessitate a substantial investment in environment-
friendly manufacturing technology and equipment, not to mention facility upgrades and employee
training programmes. Particularly for small- or medium-sized Korean firms, this dilemma is known
to be one of the biggest stumbling blocks for embracing proactive environmental management
initiatives (BISD 2006). As such, we theorise that if a firm is convinced that benefits gained from
the firm’s environmental management such as GSCM can outweigh its investment expenditure, it
is highly likely that the firm will adopt environmental management initiatives such as GSCM prac-
tices. This theory is analogous to the well-known prospect theory introduced by Kahneman and
Table 2. The environmental management promotion policy of the Korean Government.
Regulations and policies governed by the
Korean Ministry of Knowledge Economy
Regulations and policies governed by the Korean
Ministry of Environment
Related acts Act on the promotion of the conversion into
environment-friendly industrial structure
Act on the development of and support for
environmental technology
Environmental
management promotion
policy
(1) ISO 14001 certificate system –EMS
(2) Development of clean production
technology and relocation promotion
projects
(3) Development of environmental
management techniques and
infrastructure projects
(1) Development of and support for
environmental technology projects –G-7
Project, Eco-Technopia 21
(2) Environment-friendly company designation
system and voluntary environment monitoring
system
(3) Environmental labelling system
(4) Low-carbon product certificate system
(5) Evaluation of environmental management
performance and information disclosure
system
Source: Jang and Jo (2006) and Kim (2011).
Figure 1. A cumulative number of ISO 14001 certified Korean firms.
Source: https://www.icin.or.kr/STAT/STAT_01_001_Cert_Year.aspx?ACCR_SCHM_NAME= KAB
132 S.-B. CHOI ET AL.
Tversky (1979). The prospect theory postulates that people tend to value outcomes that are obtained
with certainty more than the ones that are merely probable or risky (Kahneman and Tversky 1979;
Tversky and Kahneman 1992).
In addition, with a greater role of the government in an export-driven economy such as Korea,
coercive pressures mainly originating from the government can be a catalyst for the firm’s adop-
tion of environmental management practices (Rivera 2004). Likewise, both peer pressures from
industry rivals and social normative pressures from environment-conscious customers may
force the firm to adopt environmental management practices (Christmann and Taylor 2001;
Ball and Craig 2010; Sarkis, Zhu, and Lai 2011). Institutional theory describes how external press-
ures affect a firm to adopt an organisational practice. Put simply, the institutional theory under-
scores the dependence of a modern organisation on its social, political, and historical
environments which tend to shape the habit, culture, and custom of the individual organisation.
In other words, higher order factors such as institutional (external) pressure affect the organis-
ational behaviour in such a way that the organisation, faced with the new problem, tends to
use its accustomed old solution regardless of whether it actually works (Scott 1995; Dacin, Good-
stein, and Scott 2002). Therefore, institutional theory can be employed to examine how firms
resolve environmental issues under external pressures (Jennings and Zandbergen 1995). Since
the firm’s reaction to these external pressures and its subsequent motivation may vary depending
on the firm’s organisational culture, strategic orientation (e.g. marketing angles), and channel
power, we attempted to classify GSCM practices into four distinctive types: (1) customer-driven;
(2) opportunity-driven; (3) regulation-driven; and (4) competitor-driven GSCM practices. To
elaborate, customer-driven GSCM practices are based on the business strategy which employs
GSCM as a marketing tool to respond to increased customer demand for green (environment-
friendly) products and thus improve the firm’s performance with greater sales revenue (Fynes,
Burca, and Voss 2005; Chien and Shih 2007; Srivastava 2007; Zhu and Sarkis 2007). On the
other hand, opportunity-driven GSCM practices aim at changing the customer purchase behav-
iour by influencing the level of customer preferences for green products rather than passively
responding to the environmental need expressed by customers. According to Kumar, Scheer,
and Kotler (2000), firms that initiate strategic innovation and change the rules of competition
often create greater market opportunities. Unlike these two GSCM practices which are triggered
by internal organisational motives, both regulation-driven and competitor-driven GSCM practices
are heavily influenced by external entities such as government agencies and industry rivals. Regu-
lation-driven GSCM practices are intended to comply with international environmental rules
enacted by the Montreal Protocol, the Climatic Change Convention, the Basel Convention, the
Convention on Biological Diversity, and the European Union’s RoHS (Restriction of the use of
Hazardous Substances in electrical and electronic equipment) which often work as non-tariff
trade barriers. Finally, competitor-driven GSCM practices are motivated by the firm’s conscious
effort to stay competitive in the market under increased industry peer pressure for adopting
environmental management initiatives (Bergh 2002). Based on these classification schemes, we
theorise that the firm can take different forms of GSCM practices depending on their strategic
orientation and organisational characteristics, and different choices of GSCM practices may
have influenced the firm’s performance to a varying extent.
With this in mind, this paper attempts to identify the most appropriate GSCM practice options
for Korean firms of different sizes by assessing the impact of those options on the Korean firm’s oper-
ational performance such as manufacturing and marketing performance.
2.1. Research propositions
In light of the earlier discussions, we postulate that motivations behind the firm’s adoption of GSCM
practices are fourfold: (1) customer-driven; (2) opportunity-driven; (3) regulation-driven; and (4)
competitor driven. Also, we postulate that such motivations can dictate the outcome of GSCM
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 133
practices. In other words, we posit that the firm’s use of particular types of GSCM practices can sig-
nificantly influence the firm’s operational performance (especially manufacturing and marketing
performance). To verify these propositions, we carried out four steps of data analysis procedures
summarised in Figure 2. We started conducting exploratory factor analysis (EFA) to check the stat-
istical validity of four types of GSCM practices that were identified from prior literature. Then, we
conducted cluster analysis to identify any common denominators (characteristics) within each type
of GSCM practices. The cluster analysis is followed by a cross-tabulation of clusters and an extraction
of nuances from each cluster with respect to firm size. In the final step, we performed a one-way
analysis of variance (ANOVA) test to see if there is any difference in firm performance depending
upon the choice of a particular GSCM practice.
2.2. Research sample
To validate the above research propositions, we randomly targeted 330 multinational Korean man-
ufacturing firms listed on the Korea Composite Stock Price Index (KOSPI) and Korea Securities
Dealers Automated Quotations (KOSDAQ) Stock Market that had been engaged in export activities
and adopted GSCM practices as a potential sample. To increase the response rate from these firms,
we hired a professional survey research organisation (The Statistics and Korea Information) in Korea
and arranged appointments with potential survey respondents, followed by a personal visit from a
group of surveyors. The surveys were conducted between 10 March 2012 and 15 May 2012. A
total of 322 survey responses were collected. Of these, 37 were considered invalid due to missing
or unreliable/unusual data. This produced a total of 285 valid responses, with a response rate of
86.4% which far exceeded the targeted overall response rate of 20% for a valid assessment. Malhotra
and Grover (1998) observed that a response rate over 20% was needed for a positive assessment of
questionnaire survey results. This sample consisted of firms from a wide array of industries. These
industries included pharmaceuticals (15.7% of the sample); electronics and communications
(15.0%); chemicals and plastics (11.7%); electrical, mechanical, and appliances (10.0%); textiles
and leather (9.3%); metals (9.0%); and automotives (7.3%). With regard to firm size, firms with
500–799 employees were the most common (21.3% of the sample), followed by smaller firms
with 50–99 employees (18.7%), and the ones with 1–49 employees (15.7%). Large firms with employ-
ees more than 1000 accounted for 8% of the sample. More than one-tenth (13.4%) of the responding
firms had annual sales revenue exceeding $50 million. About one-third (34.7%) of the responding
firms reported an annual revenue of $10–49.9 million. More than one-fifth (27.6%) had sales revenue
ranging from $5 to $9.9 million, while another one-fifth (24.3%) reported sales revenue below $4.9
million. With respect to individual survey respondents, about one-third (34.3%) were general man-
agers, 30.7% were directors, and 15.0% were assistant managers. Approximately two-fifths of the
respondents represent general affairs departments (40.0%), followed by management support
departments (30.3%), production departments (12.0%), and research and development (R&D)
departments (10.7%).
Figure 2. Data analysis procedures.
134 S.-B. CHOI ET AL.
2.3. Questionnaire items
The survey questionnaire comprised 19 items which are divided into 6 subsections. Among these
subsections, four of them were related to different categories of GSCM practice types with respect
to potential motivating factors, while two of them were designed to measure outcome-related con-
structs including manufacturing and marketing performance, as shown in Table 3. All but regu-
lation-driven constructs were measured by multi-items ranging from three to four items. It is
noted that the regulation-driven construct was measured by only two items due to difficulty in devel-
oping more than two substantially different items in terms of nuances, and thus its predictive validity
may be adversely affected (Bearden and Netemeyer 1999). All items were scored on seven-point
Likert-type scales which measured the degree of agreement with item descriptions, ranging from
1 (not at all) to 7 (highly so).
To measure the extent to which a responding firm performed either customer-driven or oppor-
tunity-driven GSCM practices, we developed questionnaire items similar to the ones used by Narver
and Slater (1990) and Yang (2007). To measure the degree to which a responding firm performed
either regulation-driven or competitor-driven GSCM practices, we developed the items proposed
by Zhu and Sarkis (2007). 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 are summarised in Table 3.
2.4. Data screening
Prior to conducting a multivariate data analysis, the data’s normality was evaluated in terms of skew-
ness and kurtosis. If an item’s skewness exceeds an absolute value of 3 or its kurtosis exceeds an
absolute value of 8, the item’s distribution is considered extreme, and thus violates statistical
Table 3. A summary of questionnaire items.
Constructs Items Key phrases Mean
Standard
deviation
Customer-driven GMN 1 We are fully committed to satisfying customers’environmental needs 4.1158 1.5935
GMN 2 We promote our environment commitments through open
communication across all business units
3.8702 1.3994
GMN 3 Our competitive business strategy is based on our understanding of
customers’environmental needs
4.5930 1.4176
GMN 4 We systematically attempted to solve customers’environmental
problems
4.3895 1.3657
Opportunity-driven GMG 1 We strive for the continuous improvement of environmental
friendliness of our products
3.9333 1.0238
GMG 2 We incorporate environmental solutions into our new product
development
3.9579 1.2066
GMG 3 We seek opportunities to meet environmental needs that customers
are not aware of
4.1930 1.3510
Regulation-driven REG 1 We attempted to comply with our government’s environmental
regulations
4.5684 1.7094
REG 2 We attempted to meet international environmental standards 4.3965 1.7035
Competitor-driven CDEN 1 We actively countered competitors’green strategies 4.6877 1.4379
CDEN 2 We produced green products of the higher quality than those of our
rivals
4.9649 1.4409
CDEN 3 We developed newer green products than those produced by industry
rivals
4.8807 1.3991
Manufacturing
performance
LOP 1 Our production cost has declined 4.2246 1.4112
LOP 2 Our order fulfilment speed has improved 4.0421 1.1770
LOP 3 The manufacturing cycle time has decreased 4.3088 1.2085
LOP 4 Our capability to meet delivery due dates has increased 4.2842 1.2160
Marketing
performance
BP 1 Our company’s brand image has improved 4.4456 1.3140
BP 2 Our sales revenue has grown 4.9158 1.4534
BP 3 Our market share has increased 4.1404 1.4122
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 135
assumptions regarding normality (Kline 2005). As shown in Table 4, these data do not violate nor-
mality, since no variable was found to exceed standard boundaries for excessive skewness or kurtosis.
In addition to normality testing, we checked to see if there were statistical outliers in the overall dis-
tribution. Outliers were identified by measuring the Mahalanobis Distance. The estimated Mahala-
nobis Distance indicates that p-value should be smaller than 0.005 or 0.001 to achieve statistical
significance which is much stricter than the commonly used level of significance (α) of .05 or .01
(Hair et al. 2006). Two cases of outliers were identified and excluded from the data analysis.
3. Analysis and results
To check to see if there really exist four distinctive types of GSCM practices, we conducted an EFA
which was designed to explore the possible underlying factor structure of a set of observed variables
without imposing a preconceived structure on the outcome (Child 1990). The EFA was preceded by
the Bartlett Test of Sphericity. The Bartlett Test (with a χ
2
value of 737.958, p< 0.001) showed that
some of the questionnaire items were significantly correlated among themselves. The Kaiser–Meyer–
Olkin (KMO) measure of sampling adequacy was also employed to measure the strength of the
relationship among these items. The EFA was further justified, since the KMO value of .688 was
in the acceptable range. The KMO value represents how well correlation between variable pairs
can be explained by other variables. As a general rule of thumb, if the KMO value is greater than
.8, the factor analysis use is considered meritorious, and if it is in the range .6 or .69, it is considered
middling (Hair et al. 1998). Considering the statistical significance of correlation among these items,
we conducted principal component analysis to determine the minimum number of common factors
needed to explain correlation among the items using the eigenvalue-greater-than-one rule. To obtain
a more meaningful representation of the factor structure, we used a Varimax rotation with Kaiser
normalisation. To elaborate, the Varimax rotation is an orthogonal rotation of the factor axes to
maximise the variance of the squared loadings of a factor (column) on all the variables (rows) in
a factor matrix where each factor tends to have either large (close to one) or small (close to zero)
loadings of any particular variable (Kaiser 1958). In particular, we chose the Varimax rotation
because it enables us to easily identify each variable with a single common factor. As expected, we
extracted four common factors: (1) customer-driven, (2) opportunity-driven, (3) regulation-driven,
and (4) competitor-driven GSCM practices. Table 5 shows that factor loadings for all items are
Table 4. Data screening results.
Variable
Assessment of normality
Case number
Mahalanobis distance
Skewness Critical ratio Kurtosis Critical ratio Mahalanobis d
2
p-Value 1 p-Value 2
GMN 1 .314 2.222 −1.262 −4.462 37 53.678 0.002 0.394
GMN 2 .532 3.761 −0.775 −2.741 152 48.023 0.008 0.668
GMN 3 −.150 −1.063 −1.159 −4.099 128 47.110 0.010 0.553
GMN 4 .033 0.231 −1.089 −3.850 144 46.865 0.010 0.370
GMG 1 .589 4.167 0.376 1.330 15 46.322 0.012 0.278
GMG 2 .650 4.598 0.089 0.315 280 45.226 0.015 0.317
GMG 3 .430 3.039 −0.660 −2.333 126 44.941 0.016 0.229
REG 1 −.335 −2.367 −1.028 −3.636 14 44.477 0.018 0.193
REG 2 −.164 −1.157 −1.101 −3.894 154 43.966 0.021 0.178
CDEN 1 −.176 −1.248 −1.031 −3.644 132 42.567 0.029 0.369
CDEN 2 −.604 −4.269 −0.508 −1.796 259 41.702 0.035 0.488
CDEN 3 −.473 −3.344 −1.097 −3.878 123 41.360 0.038 0.470
LOP 1 .237 1.678 −1.207 −4.269 260 40.971 0.041 0.475
LOP 2 .590 4.168 −0.395 −1.398 70 40.427 0.047 0.539
LOP 3 .479 3.386 −0.581 −2.053 80 40.342 0.048 0.459
LOP 4 .449 3.178 −0.703 −2.487
BP 1 .276 1.950 −0.755 −2.669
BP 2−.306 −2.162 −1.084 −3.832
BP 3 .417 2.949 −0.716 −2.530
136 S.-B. CHOI ET AL.
greater than .6, indicating internal validity for all items. Also, with an exception of one, all the com-
munality values are greater than .5, indicating that a majority of the factors can be explained by the
distribution of indicators within them. The eigenvalues of the factors were 2.114, 1.830, 1.606, and
1.996, respectively, and the items provided explanatory power rates of 17.618%, 16.630%, 15.246%,
and 13.382%, respectively. Cronbach’sαvalues for the factors ranged from .652 to .718, indicating
fair to good reliability for each of the items included in the respective factors. Although the accep-
table value of Cronbach’sαtypically ranges from .70 to .95, a lower value of Cronbach’sαcould be
due to a low number of items (no more than five questions for each construct in our study), poor
interrelatedness between items, or heterogeneous constructs. For exploratory analysis, the use of
measurement scales with a lower threshold such as Cronbach’sαvalue between .60 and .70 still
can be used (Nunnally 1978; Nunnally and Bernstein 1994).
Following the same procedure as described above, we conducted the EFA for the outcome vari-
ables related to firm performance. The KMO value of .670 was found to be acceptable, indicating no
problem with the factor analysis. The Bartlett Test of Sphericity was also found to be significant (with
aχ
2
value of 345.454, p< 0.001). This result indicates that the correlation matrix associated with the
population is not a unit matrix. From the EFA result, we confirmed that dependent variables have
two common factors: manufacturing and marketing performance. As summarised in Table 6, all fac-
tor loadings were above .7 and all communality values exceeded .5, indicating that all extracted items
are valid. Finally, the eigenvalues of the factors are 2.039 and 1.782, respectively, and the items pro-
vided explanatory power rates of 33.976%, and 29.696%, respectively. Cronbach’sαvalue for the
manufacturing performance factor was .752, while Cronbach’sαfor marketing performance was
.654. These results indicate that the factors achieve good reliability. Thus, the items used to measure
the factors that comprise independent and dependent variables are deemed valid and reliable.
After discovering the four dimensions of GSCM practices via EFA, we attempted to classify
responding firms into each category of the groups according to the similarity of their environmental
management practices. For this attempt, we employed a non-hierarchical k-means clustering
method, which is designed to classify a given data set through a certain predetermined number of
clusters (assume k clusters a priori) (Aldenderfer and Blashfield 1984). In a strict sense, factor analy-
sis is different from cluster analysis in that the former classifies variables into different dimensions
Table 5. EFA results on the types of GSCM practices.
Construct Phrases
Factor
loading Communality
Eigen
value
Cronbach’s
α
Customer-
driven
Understand the environmental demands of customers .790 .643 2.114 .686
Share environmental experiences across departments .704 .555
Systematically manage customer satisfaction .702 .519
Continuously monitor customer demand for
environmental friendliness
.648 .453
Opportunity-
driven
Identify additional environmental problems .805 .643 1.830 .652
Find new opportunities for improving living
environments
.771 .555
Seek new solutions for environmental problems
regardless of documentation
.702 .513
Regulation-
driven
Comply with the environmental regulations
stipulated by the domestic (Korean) Government
.869 .765 1.606 .658
Meet international environmental standards .785 .721
Competitor-
driven
Respond to the green strategy of competitor firms .800 .700 1.996 .718
Produce higher quality green products .791 .743
Provide distinctive green products and services .725 .627
Distribution Customer-driven: 17.618%
Opportunity-driven: 16.630%
Regulation-driven: 15.246%
Competitor-driven: 13.382%
Total distribution: 62.876%
Note: KMO Fit = .688, Bartlett’s Test of Sphericity: χ
2
value = 737.958 (0.000).
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 137
with respect to their homogeneity, while the latter classifies variables into different groups with
respect to their degree of homogeneity within groups and heterogeneity between groups. Despite
such a difference, cluster analysis can complement factor analysis for classification purposes (Lee
and Lim 2011). Thus, as shown in Table 7, we experimented with a different set of cluster numbers
ranging from two to five using the k-means clustering algorithm to determine how many clusters
would produce the best fit for the given data. These experiments also aimed at balancing the
group of responding firms and examining whether the four types of GSCM practices made any stat-
istical sense. When we experimented with two or three clusters, the case distribution was imbalanced
with the uneven (i.e. disproportionally large or small) number of firms belonging to one of the cat-
egories. On the other hand, when five clusters were formed, the case distribution turned out to be
relatively balanced. However, clusters 1 and 2 can be classified into the same type, since their two
largest centroid values belonged to the same column (type). Similarly, both clusters 3 and 5 can
be classified into the same type. This result indicates that five cluster categories would not produce
a meaningful classification scheme. Among these various classifications, it appears that four clusters
make most sense in terms of a case distribution balance and separation from one another. Also, four
clusters are sufficiently large enough to identify distinctive GSCM practices, while being small
enough to avoid redundancy and overlap among these clusters. Although discerning the distinctive
characteristics of these clusters (factors) is always challenging due to the potential presence of unob-
servable variables (e.g. management styles, business philosophy, and organisational culture) which
may influence GSCM practices, the interpretation of four clusters in terms of the firm’s GSCM dri-
vers (i.e. customer-driven, opportunity-driven, regulation-driven, and competitor-driven) best rep-
resents the responding firms’GSCM profiles.
Table 6. EFA results on firm performance.
Construct Phrase
Factor
loading Communality
Eigen
value
Cronbach’s
α
Manufacturing performance Prompt implementation of a green
manufacturing process
.818 .685 2.039 .752
Prompt order fulfilment .816 .667
Reduction in production cost .812 .660
Marketing performance Increased sales revenue .845 .715 1.782 .654
Increased market share .741 .549
Enhanced brand image .708 .544
Distribution Manufacturing performance:
33.976%
Marketing performance: 29.696%
Total distribution: 63.672%
Note: KMO Fit = .670, Bartlett’s Test of Sphericity: χ
2
value = 345.454 (0.000).
Table 7. The determination of cluster seed points.
Cluster
number
Centroid value of the factor
Case distributionOpportunity-driven Customer-driven Regulation-driven Competitor-driven
2 1 4.38 4.17 5.49 5.40 N = 192
24.08 3.87 3.33 4.21 N =93
3 1 3.94 3.96 5.86 5.02 N = 126
25.08 4.47 4.27 5.50 N =49
33.78 3.68 3.08 4.01 N = 110
4 1 3.51 3.61 2.60 3.60 N =40
2 2.58 3.71 4.96 4.13 N=82
3 4.69 4.22 5.95 5.64 N =82
44.82 4.37 3.44 5.38 N =81
5 1 4.98 4.82 5.32 5.77 N =65
2 3.67 3.61 5.95 5.22 N =65
33.35 3.25 3.20 3.41 N =42
4 4.34 4.42 2.05 5.27 N =33
54.54 3.98 4.29 4.36 N =80
138 S.-B. CHOI ET AL.
After verifying the presence of four clusters (i.e. four distinctive types of GSCM practices), we
attempted to discern the characteristics of each cluster of responding firms. Our test result of cluster
analysis summarised in Table 8 shows that three groups of Korean firms tended to use more than one
GSCM practice, while one group employed only the regulation-driven GSCM practice judging from
the centroid values. For example, the GSCM practice of cluster 3 seemed to be driven by both regu-
latory and competitive pressures as implied by the relatively high centroid values in the regulation-
driven type (5.95) and competitor-driven type (5.64). Also, notice that, with an exception of cluster 2,
the firm’s three other clusters of GSCM practices are driven by pressure from its competitors. This
implies that a majority of responding Korean firms are motivated to adopt GSCM practices due to
peer pressure. To go one step further, we checked to see if firm size had anything to do with the firm’s
choice of particular GSCM practices. Accordingly, a χ
2
test was performed after the cross-tabulation
of clusters as presented in Table 9. The result indicates that more than half (55%) of the responding
firms which performed opportunity- and competitor-driven GSCM practices turned out to be small
firms with fewer than 100 employees. Also, approximately one-third (31 out of 95) of small firms
with fewer than 100 employees tended to use the GSCM practice, due to the government regulatory
pressure. On the other hand, none of the large firms (i.e. those with greater than 500 employees) was
driven by market opportunities when they decided to adopt GSCM practices, while a majority of
large firms (70.5%, 79 out of 112) are sensitive to their peer pressure. To summarise, small firms
which have less than 100 employees and large ones with greater than 500 employees tended to
show more distinctive GSCM practices driven by certain factors.
Finally, to validate the second research propositions we made earlier, we conducted a one-way
ANOVA to see if a choice of a particular type of GSCM practice can make a difference in the
firm’s manufacturing and marketing performance. In particular, to examine whether there is a stat-
istically significant difference across the four clusters, a multiple comparison method was used to
look for any specific differences between pairs of clusters. Both Scheffe and Bonferroni methods
were used to make multiple pairwise comparisons among the four clusters. Results shown in
Table 10 indicated that there was no significant difference in manufacturing performance between
clusters 1 and 2 at α= .05. However, cluster 1 was significantly different from both clusters 3 and 4 in
terms of its manufacturing performance. That is to say, the choice of a particular type of GSCM prac-
tice will matter to the firm’s manufacturing performance. Especially, as shown in Table 11, the firms
using opportunity/competitor-driven GSCM practices tended to do better than those using regu-
lation/competitor-driven or competitor/customer-driven GSCM practices in terms of their manu-
facturing performance. A possible explanation for this tendency is that opportunity-driven firms
were more proactive in leveraging GSCM practices than regulation/competitor-driven or competi-
tor/customer-driven firms, in that the former’s motivation came voluntarily from internal manage-
ment decisions, while the latter’s primary motives for adopting GSCM practices were originated
Table 8. Results of cluster analysis.
Factor
Cluster class
F-value
p-
ValueCluster 1 (n= 40)
Cluster 2
(n= 82) Cluster 3 (n= 82) Cluster 4 (n= 81)
Customer
driven
3.51 3.58 4.69 4.82 45.930 0.000
Opportunity
driven
3.61 3.71 4.22 4.37 11.991 0.000
Regulation
driven
2.60 4.96 5.95 3.44 205.501 0.000
Competitor
driven
3.60 4.13 5.64 5.38 88.593 0.000
Opportunity-driven/
competitor-driven
type
Regulation-
driven type
Regulation-driven/
competitor-driven
type
Competitor-driven/
customer-driven
type
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 139
from external pressure. On the other hand, the results summarised in Tables 10 and 11 showed that
the firm’s choice of particular GSCM practices had no bearing on its marketing performance.
4. Key findings and their managerial implications
This section summarises the key findings of our study and their managerial implications for firms
which are interested in leveraging GSCM practices as their competitive differentiator and then
improving their bottom line.
First, based on the institutional theory which relates corporate behaviour (including the commit-
ment to CSR) to institutional conditions such as government regulations, the presence of other enti-
ties (e.g. customers and competitors) monitoring corporate behaviour, and institutional norms, this
Table 9. A cross-tabulation of the clusters with respect to firm size.
Note: χ
2
test = 24.109 (df = 9), p= .004.
140 S.-B. CHOI ET AL.
study derived four types of GSCM practices with respect to factors influencing CSR (especially
environment-friendly supply chain practices). We found that the surveyed firms’GSCM practices
were motivated by four distinctive factors: (1) customer, (2) internal management, (3) government
regulations, and (4) industry peer (competitor) pressure. It is also noted that the surveyed firms were
affected by more than one motivating factor (e.g. a combination of regulatory and competitive
pressure) when adopting GSCM practices. Whatever the combination of these multiple factors
might be, a majority of the firms seemed to be affected by competitive peer pressure when employing
GSCM practices. Furthermore, we found that the firm’s motive for GSCM practices varied
Table 10. The results of post hoc ANOVA tests.
Dependent variable
(I) Case
cluster
no.
(J) Case
cluster no.
Average
difference
(I−J)
Standard
error
Significance
probability
95% confidence
interval
Min
value
Max
value
Manufacturing
performance
Scheffe 1 2 −.27114 0.19428 0.584 −0.8176 0.2753
3−.75081 0.19428 0.002** −1.2972 −0.2044
4−.63724 0.19467 0.015* −1.1848 −0.0897
2 1 .27114 0.19428 0.584 −0.2753 0.8176
3−.47967 0.15732 0.027* −0.9222 −0.0372
4−.36610 0.15781 0.148 −0.8099 0.0777
3 1 .75081 0.19428 0.002** 0.2044 1.2972
2 .47967 0.15732 0.027* 0.0372 0.9222
4 .11357 0.15781 0.915 −0.3303 0.5574
4 1 .63724 0.19467 0.015* 0.0897 1.1848
2 .36610 0.15781 0.148 −0.0777 0.8099
3−.11357 0.15781 0.915 −0.5574 0.3303
Bonferroni 1 2 −.27114 0.19428 0.984 −0.7874 0.2451
3−.75081 0.19428 0.001** −1.2670 −0.2346
4−.63724 0.19467 0.007** −1.1545 −0.1200
21 .27114 0.19428 0.984 −0.2451 0.7874
3−.47967 0.15732 0.015* −0.8977 −0.0617
4−.36610 0.15781 0.126 −0.7854 0.0532
3 1 .75081 0.19428 0.001** 0.2346 1.2670
2 .47967 0.15732 0.015* 0.0617 0.8977
4 .11357 0.15781 1.000 −0.3057 0.5329
4 1 .63724 0.19467 0.007** 0.1200 1.1545
2 .36610 0.15781 0.126 −0.0532 0.7854
3−.11357 0.15781 1.000 −0.5329 0.3057
Marketing
performance
Scheffe 1 2 −.09817 0.20620 0.973 −0.6781 0.4818
3−.19167 0.20620 0.834 −0.7716 0.3883
4−.38302 0.20661 0.331 −0.9641 0.1981
2 1 .09817 0.20620 0.973 −0.4818 0.6781
3−.09350 0.16697 0.957 −0.5631 0.3761
4−.28485 0.16749 0.410 −0.7559 0.1862
3 1 .19167 0.20620 0.834 −0.3883 0.7716
2 .09350 0.16697 0.957 −0.3761 0.5631
4−.19136 0.16749 0.728 −0.6624 0.2797
4 1 .38302 0.20620 0.331 −0.1981 0.9641
2 .28485 0.16697 0.410 −0.1862 0.7559
3 .19136 0.16749 0.728 −0.2797 0.6624
Bonferroni 1 2 −.09817 0.20620 1.000 −0.6461 0.4497
3−.19167 0.20620 1.000 −0.7395 0.3562
4−.38302 0.20661 0.389 −0.9320 0.1660
2 1 .09817 0.20620 1.000 −0.4497 0.6461
3−.09350 0.16697 1.000 −0.5372 0.3502
4−.28485 0.16749 0.541 −0.7299 0.1602
3 1 .19167 0.20620 1.000 −0.3562 0.7395
2 .09350 0.16697 1.000 −0.3502 0.5372
4−.19136 0.16749 1.000 −0.6364 0.2537
4 1 .38302 0.20661 0.389 −0.1660 0.9320
2 .28485 0.16749 0.541 −0.1602 0.7299
3 .19136 0.16749 1.000 −0.2537 0.6364
INTERNATIONAL JOURNAL OF LOGISTICS: RESEARCH AND APPLICATIONS 141
depending on its size which reflected its resource level. In particular, small firms tended to be more
sensitive to environmental regulations than the others. These findings implied that small firms could
be more vulnerable to penalties resulting from non-compliance with government regulations than
the others, due to their limited resources. On the other hand, it is somewhat surprising to find
that none of the large firms recognised GSCM practices as a key selling point. In other words,
these large firms tended to employ GSCM practices not because of their voluntary wills, but because
of external pressure or as part of their order-qualifying criteria.
Second, according to the prospect theory proposed by Kahneman and Tversky (1979), the firm
may choose a certain decision (or strategy) based on the potential value of losses and gains instead
of actual outcomes. By a similar analogy, we surmised that the firm’s decision to use GSCM practices
would be affected by its perceived positive impact on the firm’s performance. As such, we investi-
gated whether or not the firm’s GSCM practices led to improved manufacturing and/or marketing
performance. We discovered that the firm’s GSCM practices affected the firm’s manufacturing per-
formance in a positive way, but did not necessarily improve its marketing performance. For example,
thanks to the high level of environmental standards set by the Korean government as compared to
the neighbouring emerging economies (e.g. China, Malaysia, Indonesia, and Vietnam), Korean man-
ufacturing firms irrespective of their size are known to be more uniform in implementing environ-
ment-friendly practices and subsequently more competitive in the era of green growth (Heo 2013;
Kim et al. 2014; Kim and Thurbon 2015). In particular, we observed that firms using opportu-
nity/competitor-driven GSCM practices tended to do better than those using regulation/competi-
tor-driven or competitor/customer-driven GSCM practices in terms of their manufacturing
performance. That is to say, the firm which viewed GSCM practices as a key selling point and
thus was voluntarily motivated to use GSCM practices tended to reap a greater benefit of those prac-
tices. From a corporate strategic standpoint, we suggest that the firm should take the more proactive
stance than before to fully exploit the benefits of GSCM practices.
5. Concluding remarks and future research directions
GSCM has emerged as one of the increasingly important CSRs and corporate bottom lines. Although
its benefits are well documented in the literature, many firms are still hesitant to adopt GSCM prac-
tices. A lack of progress towards GSCM practices may have something to do with uncertainty about
their expected gains and losses. Thus, many firms may still wonder whether GSCM practices are
worthy of adoption, how those practices could be leveraged for a competitive advantage, and
what might be the critical incentives for implementing those practices. To ease such scepticism,
we identified four distinctive motivating factors (drivers) for GSCM practices and then assessed
the impact of GSCM practices on the firm’s operational (manufacturing and marketing) perform-
ance. The identification of those factors can help the firm select the most suitable GSCM strategy,
while aiding government policy-makers in introducing more workable environmental rules and
regulations. Also, the GSCM assessment tool that we proposed in this study would help the firm
Table 11. A summary of verification results for performance differences by clusters.
Dependent variables Cluster Average Standard error F-value/significance probability Post verification
Manufacturing performance Cluster 1 3.7167 0.9534 6.833/0.000** Cluster 1 > Cluster 3,
Cluster 1 > Cluster 4,
Cluster 2 > Cluster 3
Cluster 2 3.9878 0.9243
Cluster 3 4.4675 1.0552
Cluster 4 4.3539 1.0624
Marketing performance Cluster 1 4.3083 1.0138 1.502/0.214 Cluster 1 = Cluster 2 =
Cluster 3 = Cluster 4Cluster 2 4.4065 1.0974
Cluster 3 4.5000 1.0386
Cluster 4 4.6914 1.0964
*p< 0.05.
**p< 0.01.
142 S.-B. CHOI ET AL.
convince its stakeholders of the worthiness of GSCM practices. From a theoretical standpoint, this
paper attempted to make some sense out of institutional and prospect theories for CSRs and then to
explain what drove GSCM practices. As such, one of the important theoretical contributions of this
paper is the establishment of institutional and prospect theories for illuminating the firm’s motives
behind employing the GSCM practices and the development of research tools for assessing the
impact of GSCM drivers on firm performance. The theoretical foundation built by this study will
help the firm formulate a winning GSCM strategy and leverage it as a major competitive differen-
tiator in the global marketplace. Despite these novel attempts, this study should be further extended
to include the following aspects:
(1) The firm’s performance should be measured using actual performance outcomes in lieu of the
respondent’s perceived performance.
(2) The firm’s organisational profile may include other variables such as the firm’s industry sector
and channel power in the supply chain to better gauge the extent of its influence on the firm’s
motives behind GSCM practices.
(3) The sample should be increased by adding firms representing other countries such as the USA
and Japan.
(4) Based on current research propositions, more detailed hypotheses should be developed and
then tested using alternative data analysis such as confirmatory factor analysis.
Disclosure statement
No potential conflict of interest was reported by the authors.
Funding
This work was supported by the National Research Foundation Grant funded by the Korean Government. (NRF-
2014S1A2A2028564).
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